We present comprehensive long-term ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS)
measurements of aerosols, nitrogen dioxide (NO2), and formaldehyde
(HCHO) from Mohali (30.667∘ N, 76.739∘ E; ∼310 m above
mean sea level), located in the densely populated Indo-Gangetic Plain (IGP)
of India. We investigate the temporal variation in tropospheric columns,
surface volume mixing ratio (VMR), and vertical profiles of aerosols,
NO2, and HCHO and identify factors driving their ambient levels and
distributions for the period from January 2013 to June 2017. We observed
mean aerosol optical depth (AOD) at 360 nm, tropospheric NO2 vertical
column density (VCD), and tropospheric HCHO VCD for the measurement period to
be 0.63 ± 0.51, (6.7 ± 4.1) × 1015, and (12.1 ± 7.5) × 1015 molecules cm-2,
respectively. Concerning the tropospheric NO2 VCDs, Mohali was found to
be less polluted than urban and suburban locations of China and western
countries, but comparable HCHO VCDs were observed. For the more than 4 years of measurements during which the region around the measurement
location underwent significant urban development, we did not observe obvious
annual trends in AOD, NO2, and HCHO. High tropospheric NO2 VCDs
were observed in periods with enhanced biomass and biofuel combustion (e.g.
agricultural residue burning and domestic burning for heating). Highest
tropospheric HCHO VCDs were observed in agricultural residue burning periods
with favourable meteorological conditions for photochemical formation, which
in previous studies have shown an implication for high ambient ozone also
over the IGP. Highest AOD is observed in the monsoon season, indicating
possible hygroscopic growth of the aerosol particles. Most of the NO2
is located close to the surface, whereas significant HCHO is present at
higher altitudes up to 600 m during summer indicating active
photochemistry at high altitudes. The vertical distribution of aerosol,
NO2, and HCHO follows the change in boundary layer height (BLH), from the ERA5 dataset of European Centre for Medium-Range Weather Forecasts, between summer and winter.
However, deep convection during the monsoon transports the pollutants at high
altitudes similar to summer despite a shallow ERA5 BLH. Strong gradients in
the vertical profiles of HCHO are observed during the months when primary
anthropogenic sources dominate the formaldehyde production. High-resolution
MODIS AOD measurements correlate well but were systematically higher than
MAX-DOAS AODs. The ground-based MAX-DOAS measurements were used to evaluate
three NO2 data products and two HCHO data products of the Ozone
Monitoring Instrument (OMI) for the first time over India and the IGP.
NO2 VCDs from OMI correlate reasonably with MAX-DOAS VCDs but are
lower by ∼30 %–50 % due to the difference in vertical
sensitivities and the rather large OMI footprint. OMI HCHO VCDs exceed the
MAX-DOAS VCDs by up to 30 %. We show that there is significant scope for
improvement in the a priori vertical profiles of trace gases, which are used
in OMI retrievals. The difference in vertical representativeness was found
to be crucial for the observed biases in NO2 and HCHO surface VMR
intercomparisons. Using the ratio of NO2 and HCHO VCDs measured from
MAX-DOAS, we have found that the peak daytime ozone production regime is
sensitive to both NOx and VOCs in winter but strongly sensitive to
NOx in other seasons.
Introduction
Air pollution is a serious issue in south Asia with the Indo-Gangetic Plain
(IGP) being one of the hotspots of both present and future forecasts
(Giles, 2005). For example, over the IGP, the ambient air quality
standards of several criteria air pollutants (e.g. ozone, PM10, and
PM2.5) are violated for more than 60 % of the days in a year
(Pawar et al., 2015; Kumar et al., 2016). NOx (sum of NO and
NO2) and volatile organic compounds (VOCs) are the precursors of ozone
and secondary organic aerosols. Formaldehyde, the most abundant carbonyl
compound in the atmosphere, is the primary source of HO2 radicals in
the troposphere, which in the presence of NOx can ramp up ozone
production (Wolfe et al., 2016; Fortems-Cheiney et al., 2012). While
NOx is a major anthropogenic primary pollutant, formaldehyde has
both biogenic and anthropogenic sources and is mainly formed during the
atmospheric oxidation of methane and VOCs (e.g. alkenes), a process that is
related to the production of ozone in the troposphere. An increase in both
NO2 and HCHO over India with an average annual rate of 2.2 % and
1.5 % per year, respectively, was documented using more than 15 years of
dataset from multiple satellite instruments (Mahajan et al.,
2015). Significant spatial and seasonal variabilities in NO2 and HCHO
were shown in these studies, but their maximum tropospheric columns were
observed over the IGP.
Differential optical absorption spectroscopy (DOAS) (Platt,
1994), a technique based on the Beer–Lambert's law, has found its versatile
application in the last 2 decades for remote sensing of tropospheric
pollutants including aerosol, NO2, and HCHO from both ground-based and
space-borne platforms. Ground-based multi-axis (MAX-) DOAS instruments
provide continuous measurements of trace gases and aerosol including their
vertical profile by observing scattered sunlight at different, mostly slant,
elevation angles (Hönninger et al., 2004; Wagner et al., 2011, 2004; Sinreich et al., 2005). One of the significant advantages of
this technique is that, from one spectrum, many atmospheric constituents
(e.g. aerosol, NO2, HCHO, BrO, glyoxal, HONO, oxygen dimer (O4),
SO2, and water vapour) can be quantified. Another advantage of the MAX-DOAS
technique is that it does not require a radiometric calibration and can be
operated autonomously even in very remote locations. Due to their simple
design, the vast applicability for the detection of multiple atmospheric
constituents, low power demand, minimal maintenance, possible automation, and
remote access, MAX-DOAS instruments have been extensively employed both for
long-term monitoring (Ma et al., 2013; Chan et al., 2019; Wang et al.,
2017a, b) and extensive fields campaigns (Li et al.,
2013; Heckel et al., 2005; Schreier et al., 2020; Halla et al., 2011) over the
last decade. These measurements have been used for characterization of
pollution and its source attribution (Wang et al.,
2014), emission strength (Shaiganfar et al., 2017, 2011), chemistry, and transport (MacDonald et
al., 2012) and for the validation of satellite observations (Wang et al.,
2017a; Drosoglou et al., 2017; Mendolia et al., 2013). Over India,
ground-based measurements of trace gases are limited primarily to in situ
measurements (e.g. Gaur et al., 2014; Sinha
et al., 2014; Kumar et al., 2016), whereas MAX-DOAS
measurement of trace gases (e.g. NO2, HCHO) and aerosol have rarely
been reported. The few studies are limited only to 4 d of mobile
measurement around Delhi (Shaiganfar et al., 2011) for an estimation of NOx emission from Delhi and satellite validation and more
recently to a suburban site Pantnagar (29.03∘ N, 79.47∘ E) (Hoque et al., 2018) and a rural site Barkachha (25.06∘ N, 82.59∘ E) in the Indo-Gangetic Plain (Biswas et
al., 2019). Though in situ techniques provide crucial continuous measurements of
targeted atmospheric pollutants (e.g. NOx, O3, aerosol, and VOCs),
logistical constraints in their setup and maintenance limit their spatial
and temporal coverage. Unless specifically designed inlets are used to
alternate between different altitudes or mounted on aircraft or balloons, these
measurements also lack the information about vertical profiles.
The DOAS principle has also been applied to the backscattered signal
measured in the UV and visible wavelengths by several sun-synchronous
satellite instruments to provide almost daily global coverage of the spatial
distribution of aerosol (Torres et al., 2007; Levy et al., 2013), NO2
(Boersma et al., 2011), formaldehyde (González Abad et al., 2015; Zara et al., 2018), and several other
trace gases (Gonzalez Abad et al., 2019) for more than 2 decades. Satellite observations of NO2 and HCHO have been extensively
used for a variety of applications ranging from (but not limited to)
validating chemistry transport models in various atmospheric environments,
the assessment of bottom–up emission inventories, assessing seasonal and long-term trends, constraining emission strength on NOx sources, the lifetime of
NOx (Beirle et al., 2011; Huijnen et al., 2010; Ma et al., 2013; Chan
et al., 2019), and VOC emissions trends, characterization, and their source
contribution (Kaiser et al., 2018; Zhu et al., 2017; Fu et al., 2007).
Satellite observations over India have been employed to study long-term
trends and the spatial distribution of NO2 and HCHO and trends in NOx
emissions (Ghude et al., 2008, 2013; Mahajan et al.,
2015; Hilboll et al., 2013) and to investigate important processes
contributing to HCHO formation and constraining the VOC emissions (Chaliyakunnel et al., 2019; Surl et al., 2018).
Despite their attractive spatial coverage, satellite observations have their
inherent uncertainties, arising from the retrieval algorithm, the presence of
clouds, the underlying assumption for calculations of a priori profiles,
air mass factors, and background corrections. For example, satellite
measurements of trace gases located close to the surface usually
underestimate the actual values around megacities due to the so-called
aerosol shielding and gradient smoothing effect (Ma
et al., 2013). Ground-based remote-sensing techniques, e.g. MAX-DOAS, have
proved instrumental for the validation of satellite measurements (Wang et
al., 2017a; Jin et al., 2016; Ma et al., 2013; Schreier et al., 2020; Mendolia et al., 2013; Irie et al., 2008; Brinksma et al., 2008). In addition to validation, MAX-DOAS measurements complement satellite observations by
providing information about the diurnal and vertical profiles of trace gases
and aerosol. Additionally, the MAX-DOAS observations also have the potential
to bridge the gap between the scales of in situ and satellite observations as the former are more sensitive to concentration close to their inlet, whereas the
latter are representative of a larger area up to few hundred square kilometres.
Over India, the application of ground-based remote-sensing techniques for
the validation of atmospheric chemistry and composition observations is majorly limited for aerosol measurements, except for the study by
Shaiganfar et al. (2011) using 4 d of mobile
measurements. Over polluted regions, the Ozone
Monitoring Instrument (OMI) was found to underestimate the
NO2 vertical
column densities (VCDs), while the inverse was observed for clean regions. Sun
photometers have been used in the past to validate MODIS aerosol optical depth (AOD) measurements
(Tripathi et al., 2005; Mhawish et al., 2019). Considering the spatial and
temporal variation in emission sources over all of India, there is an urgent need
for the validation of satellite observations of trace gases with ground-based
remote-sensing measurements. Even though several in situ measurements of NO2
have been reported over India, the fundamental difference in the retrieved
information for satellite and in situ measurements (VCD and surface concentration)
also precludes a direct intercomparison. The two stationary MAX-DOAS
measurements so far over India focussed primarily on surface volume mixing
ratios (VMRs) and have not reported the VCDs of trace gases, and hence they lack
intercomparison with the satellite observation. To the best of our
knowledge, so far there have not been any measurements probing the vertical
distribution of NO2 and formaldehyde over India, which limits the
understanding of vertical transport of pollutants at various temporal scales
(diurnal or seasonal). Moreover, the retrieved profile results close to the
surface can also be compared to in situ measurements.
In this paper, we present more than 4 years (January 2013–June 2017)
of MAX-DOAS measurements of AOD, NO2, and HCHO vertical column
densities, vertical profiles, and surface concentration (extinction) from
Mohali in the north-west IGP and investigate the factors driving these
parameters. We perform a detailed comparison of several NO2, HCHO, and
AOD data products of OMI and MAIAC (Multi-Angle Implementation of Atmospheric Correction) AOD data product of MODIS with MAX-DOAS measurements and discuss the discrepancies. The ratio of NO2 and
formaldehyde was employed to investigate whether NOx or VOC drives
ozone production in different seasons. The volume mixing ratios of NO2
and HCHO close to the surface were evaluated with in situ measurements to understand
the spatial representativeness of both the measurements.
Experimental and data analysesSite description
Here, we report the measurements and satellite observations from a suburban
site, Mohali, located in the north-west Indo-Gangetic Plain. Figure 1 shows the
location of Mohali, major cities, and a terrain map around North India. We also
show the spatial distribution of mean NO2 tropospheric vertical column
densities probed by the TROPOMI satellite (van Geffen et al., 2017; Veefkind
et al., 2012) for the period December 2017–October 2018 in a 25 km × 25 km box around Mohali. The Himalayan mountain range starts at
∼35 km in the north. The in situ and ground-based remote-sensing
measurements were performed at the IISER (Sinha et al., 2014) Mohali atmospheric chemistry facility
(30.667∘ N, 76.739∘ E; 310 m a.m.s.l.). A detailed
description of the site including the seasonal characteristic of
meteorological parameters, characteristic wind sectors, and major emissions can be found elsewhere (Kumar et al., 2016; Pawar et al., 2015; Sinha et
al., 2014). The urban city
Chandigarh is located in the wind sector spanning from north to east, while the west and south are comprised mostly of
agricultural lands and small cities or towns. Two major power plants are
located within 50 km of the measurement site. At ∼45 km to the
north-west (∼342∘) is the 1260 MW Guru Gobind
Singh super thermal power plant (PP1), Rupnagar, which was operational with
90 % of its capacity until 2014. Since February 2014, a 1400 MW power
plant (PP2) has been functional in Rajpura, which is ∼18 km
south-west (∼230∘) of the measurement site.
Left: terrain map showing the location of Mohali (red triangle) in
North India, along with major cities (crosses) and two major thermal power
plants near Mohali (blue circles: PP1 – Guru Gobind Singh super thermal power
plant; PP2 – Larsen & Toubro super thermal power plant (NPL), Rajpura)
and province boundaries. Right: mean tropospheric NO2 VCD measured by
TROPOMI overlaid on the terrain map of a 0.5∘× 0.5∘ box around Mohali (shown in the left panel) for the period
December 2017–October 2018. The black arrow indicates the viewing direction
of the MAX-DOAS instrument. Terrain maps are adapted from Stamen
(http://maps.stamen.com/terrain, last access: 17 November 2020).
For the climate of India, the year can be divided into the following four
seasons: winter (November to February), summer (March–June), monsoon
(July–August), and post-monsoon (September–October). Large-scale crop residue
burning events occur in the late summer and late post-monsoon, which
strongly perturb the atmospheric chemistry and composition (Kumar et al., 2018; Sarkar et al., 2013). In order to
account for these perturbations, Kumar et al. (2016) have
recommended a further classification of summer and post-monsoon months into clean and polluted periods. The primary fetch region of the air masses is
north-west throughout the year except in the monsoon season, when the wind direction is
primarily south-east. Figure F1 shows the wind rose plots indicating the wind
speed and wind direction frequencies around Mohali in the four major seasons
over the measurement period. Over the years 2012–2017, rapid urbanization has
happened in Mohali and nearby regions (e.g. the commissioning of new
international airport terminal and highways, extended construction
activities for residential (e.g. Aero city, ECO city) and institutional
(Knowledge City, Medicity) purposes). According to the census of 2011, the
district of Mohali holds the top rank in urban population growth among all
districts in the state of Punjab at a rate of 90.2 % for the period
between the years 2001 and 2011 (Tripathi and Mahey, 2017).
MAX-DOAS measurement setup and spectral analysis
A MAX-DOAS instrument (Hoffmann Messtechnik GmbH) was installed at ca. 20 m above
ground level with an azimuth viewing direction of 10∘
anticlockwise from the north. The instrument primarily consists of a Czerny
Turner spectrometer (Ocean Optics USB 2000+), an optical assembly
consisting of a quartz lens that collects the scattered sunlight, quartz
optical fibre that transmits the light to the spectrometer, and electronics
in a sealed metal box. The box is mounted on a stepper motor which can be
programmed to set the elevation viewing angle of the instrument. The
spectral resolution of the spectrometer was ∼0.7 nm in the
spectral range of 318–465 nm with a field of view (FOV) of
0.7∘. In order to avoid light outside the telescope's FOV being scattered onto the fibre, a black tube (ca. 6 cm long) was mounted
in front of the lens. The scattered sunlight spectra were recorded for
elevation viewing angles 1, 2, 4, 6, 8, 10, 15, 30, and 90∘ at a total integration time (number of scans × acquisition time for one scan) of 60 s each. Since the complete
MAX-DOAS instrument was mounted outside, it was important to adjust the
detector temperature so that the following two conditions are met:
The detector temperature is lower than the ambient temperature.
The difference between the ambient temperature and detector temperature is
not more than 20∘ C. This ensures that the workload on the
Peltier cooler is manageable and the detector temperature is stable.
Hence, depending on the seasons, the detector temperature was adjusted. Figure F2 shows the nominal detector temperatures (Tset) and actual detector
temperature (Tcold) for the various periods during the measurement
period. The dark current and offset spectra were recorded every night, and
while performing the spectral analysis, these were subtracted from the
measured spectra recorded at a similar detector temperature. Additional offset
was corrected from the measured spectrum accounting for the mean intensity
recorded by the dark pixels (pixel nos. 1–6 among the 2048 pixels) of the
spectrometer. Wavelength-to-pixel calibrations were performed in the QDOAS
software (http://uv-vis.aeronomie.be/software/QDOAS/, last access: 20 November 2020) (Danckaert et al., 2012) every time the detector temperature
was changed, by matching the structures in a measured spectrum in the zenith
direction at around noontime with those in a highly resolved solar spectrum.
Horizon scans were performed every day at around 12:00 local time (LT).
Figure F3 shows a typical variation in measured intensity and its derivatives
at three different wavelengths over an elevation angle range from
-3 to 3∘. A steep increase was observed in the
measured intensity, which was centred between 0 and
0.3∘ for various wavelengths chosen for analysing the horizon
scan. During the period of measurement, the horizon in the viewing direction
was determined by a residential building with a height of about 40 m at a
distance of 3 km. The viewing angle of this visible horizon would be about
0.38∘ in good agreement with the results from the elevation scan.
Thus we did not need to perform any correction for the true horizon in
further analyses (Donner et al., 2020). We also see from Fig. F3 that the FOV of the
instrument is rather large (>0.7∘), and typically the
root mean square (rms) of the spectral analysis for the measurements at 1∘ elevation
is substantially larger than those for the higher-elevation angles. Hence,
we excluded the measurements at 1∘ elevation angle from further
analyses.
The measured spectra of the scattered sunlight were analysed for NO2,
HCHO, and the oxygen dimer (O4) using the QDOAS software. Table 1 lists
the wavelength intervals, included cross sections and other relevant details
pertaining to the different retrievals, and Fig. 2 shows example DOAS fits
and residuals for these retrievals. The typical values (peak of the
frequency distribution) of the rms of the DOAS fit
residuals are around 5 × 10-4, 7 × 10-4,
6 × 10-4, and 6 × 10-4 for O4, NO2 (UV), NO2 (VIS), and HCHO, respectively. In order to retain analysis results
corresponding to good-quality fits, we have excluded the O4, NO2, and HCHO differential slant column
densities (dSCDs) corresponding to an rms greater than 2 × 10-3
and solar zenith angles higher than 85∘ (Wang et al., 2019). The rms threshold
removes 1.1 %, 1.4 %, 0.7 %, and 1.3 % of the O4, NO2 (UV),
NO2 (VIS) and HCHO dSCDs, respectively, of all the measured dSCDs at
solar zenith angles less than 85∘.
Spectral analysis settings and considered cross sections in QDOAS
for the retrieval of dSCDs of NO2 (in UV and Vis), HCHO, and O4.
SpeciesNO2 (UV)NO2 (Vis)HCHOO4Fit window338–370 nm400–460 nm336.5–359 nm352–387 nmFitted absorption cross sectionsNO2 (298 K with Io correction corresponding to an SCD of 1017 molecules cm-2)1, NO2 (220 K pre-orthogonalized to NO2 cross section at 298 K)1, HCHO (297 K)2, O3 (223 K with Io correction corresponding to an SCD of 1020 molecules cm-2)3, BrO (223 K)4, O4 (293 K)5, RingNO2 (298 K with Io correction corresponding to an SCD of 1017 molecules cm-2)1, H2O (296 K)6, O3 (223 K with Io correction corresponding to an SCD of 1020 molecules cm-2)3, O4 (293 K)5, RingHCHO (297 K)2, BrO (223 K)4, O3 (223 K with Io correction corresponding to an SCD of 1020 molecules cm-2)3, NO2 (298 K with Io correction corresponding to an SCD of 1017 molecules cm-2)1, O4 (293 K)5, RingO4 (293 K)5, NO2 (298 K with Io correction corresponding to an SCD of 1017 molecules cm-2)1, HCHO (297 K)2, O3 (223 K with Io correction corresponding to an SCD of 1020 molecules cm-2)3, BrO (223 K)4, RingPolynomial order5555Intensity offsetConstant and first orderConstant and first orderConstant and first orderConstant, first, and second orderFraunhofer reference selectionSequentialSequentialSequentialSequentialShift and stretchSpectrumSpectrumSpectrumSpectrum
1 Vandaele et al. (1998). 2 Meller
and Moortgat (2000). 3 Serdyuchenko et al. (2014).
4 Fleischmann et al. (2004). 5 Thalman and
Volkamer (2013). 6 Rothman et al. (2010).
Example DOAS fits and residuals for NO2 in the visible (dSCD =2.91×1016±2.89×1014 molecules
cm-2) and UV (dSCD =2.09×1016±8.17×1014 molecules cm-2), HCHO (dSCD =7.23×1016±4.32×1015 molecules cm-2), and O4 (dSCD =2.06×1043±4.25×1041 molecules2
cm-5) for a typical spectrum measured on 26 July 2015 at a solar zenith
angle of 20∘ and 2∘ elevation angle.
The spectral analysis is performed with respect to a Fraunhofer reference
spectrum (FRS) measured in the zenith direction of each complete elevation
angle measurement sequence in order to account for the Fraunhofer lines and
the stratospheric contribution of the absorbers
(Hönninger et al., 2004). For analysing the off-axis
spectra measured at time “t”, we calculate the FRS at the time of the
measurement by interpolating the zenith spectra measured before and after
the complete measurement sequence. Thus, the primary retrieved quantity from
MAX-DOAS spectral analysis is the so-called dSCD. dSCD of a trace gas (absorber) at an elevation angle
α can be regarded as the difference between the absorber
concentration integrated along the photon path at elevation angle α
(SCDα) and zenith direction (SCD90).
dSCDα=SCDα-SCD90
dSCD is related to the tropospheric VCD through
differential air mass factors (dAMF or AMFα-AMF90):
VCD=dSCDdAMF.
Unless specifically mentioned, we will use VCD to refer to tropospheric VCDs in the
paper hereafter. The air mass factors are related to the light path of the
photons reaching the telescope of the instrument and depend on several
parameters, e.g. elevation angle, solar zenith angle, relative solar azimuth
angle with respect to the instrument, surface reflectance, aerosol and trace
gas vertical profile, and aerosol optical properties. In Sect. 2.4, we
provide the details of the calculation of air mass factors and subsequent
profile inversion to retrieve the VCDs and vertical profiles.
DOAS retrievals of NO2 can be performed both in the UV and visible
wavelength windows but have their respective advantages and limitations.
NO2 has a stronger absorption in the visible as compared to UV. Hence,
the DOAS fit in the visible results in smaller fit uncertainties as compared
to that in the UV (Fig. A1). This is an important aspect, especially for
instruments with rather low quantum efficiencies like the detector of the
MAX-DOAS instrument used in this study. However, the profile inversion
methods (please refer to Sect. 2.4) to retrieve NO2 vertical profiles
and VCDs also require information about aerosol extinction profiles at the
same wavelength as that used for NO2 retrieval. The aerosol extinction
profile retrieval for DOAS relies on the O4 measurements, which have a
rather weaker absorption at the visible wavelengths (in the spectral range
of our instrument). Hence an alternative approach is to use an
Ångström exponent to scale the aerosol extinction profiles derived
at UV wavelengths to visible wavelengths. The aerosol extinction profiles
calculated for the visible wavelength are subsequently used as an input
parameter for the NO2 profile inversion in the visible window. In
Appendix A, we compare the performance and internal consistency of the
NO2 profiles and VCDs retrieved in the UV and visible by the profile
inversion algorithm and by the geometric approximation under various sky
conditions. Briefly, we found very good agreement between the NO2 VCDs
retrieved in the UV and visible under clear-sky conditions with a low aerosol
load (slope=0.95 and r=0.9). Even in the clear-sky case with high
aerosol load and cloudy-sky conditions, a reasonable agreement for NO2
VCDs between the retrieval in UV and visible was observed (slope=0.75 and
0.78 for high aerosol and cloudy cases, respectively; r=0.82 for both). The
NO2 dSCDs from the retrieval in the UV wavelength window were found to be in good
agreement with those retrieved in the visible, but they were systematically lower
(r=0.9, slope=0.95 and a negative offset of 1.1 × 1014
molecules cm-2).
Cloud classification
Clouds have a strong impact on MAX-DOAS measurements and subsequent profile
inversion as they alter the light path and intensity (Wagner et al.,
2014, 2011). Clouds are generally not included within the
radiative transfer models for profile inversion. The cloud classification
scheme is based on the measured radiances at 360 nm, the colour index (ratio of
measured radiances at 330 and 390 nm), and the measured O4 air mass
factors (O4 slant
column density (SCD) / O4 VCD) (for details see Wagner et al., 2016). Besides the absolute values of
these quantities, their temporal variation and their elevation
dependencies are also considered. The threshold for these quantities (the spread
of O4, the normalized CI, the spread of the CI, and the temporal
variation in the CI) are parameterized as polynomials of the SZA as provided
in Wagner et al. (2016). We can classify the sky
conditions into the following seven categories: (1) clear sky with low
aerosol load, (2) clear sky with high aerosol load (AOD >0.85 at 330 nm), (3) broken clouds, (4) cloud holes, (5) continuous clouds, (6) fog, and (7) optically thick clouds (Wagner et al., 2014, 2016). In
the first step, using the colour index (CI), its variation across the zenith
spectrum for an adjacent elevation sequence, and its variation within an
elevation sequence, the primary cloud classification is performed to
retrieve information about primary conditions (types 1–5). The identification of
fog and thick clouds is performed in the second step using the O4 air mass factor (AMF) and
measured radiance at 360 nm. Fog is identified if there is very little
variation in O4 dSCD for different elevation angles within the
measurement sequence. The details pertaining to the calculation of
thresholds for radiance for the identification of thick clouds are provided in
Appendix B. While the classification of aerosols might be slightly affected
by the specific properties of the local aerosol, the cloud classification is
robust to the variability of aerosol properties. However, this is not
critical here because the main aim – the cloud classification – is hardly
affected by these specific aerosol properties.
Figure 3 shows the percentage of the mean monthly sky condition for the
complete measurement period. The most prominent sky condition in all the
seasons is “clear sky with high aerosol load”, comprising ∼48 % of the total. March and April are marked by the maximum occurrence
of clear-sky conditions with low aerosol load at ∼18 %.
Continuous clouds and optically thick clouds are most abundant in
July–August, which is marked by the monsoon season. Please note that due to
the widespread crop residue burning and suppressed meteorological
conditions, the months of October and November witness severe smog events in the
north-west Indo-Gangetic Plain leading to very poor air quality and low
visibility. The poor air quality conditions extend until December due to
emission from domestic burning for heating and similar meteorological
conditions. The prevalent high aerosol load conditions are also marked by
the cloud classification algorithm as seen by occurrences greater than
55 % in October, November, and December. Fog is observed in December and
January, when winter is at its peak.
Relative frequencies of occurrence of various sky conditions in
different months of the year over Mohali as derived from 4.5 years of
MAX-DOAS observations. Note that the secondary cloud classifications of fog
and optically thick clouds are not mutually independent and exclusive of the
primary classification (clear sky, clear sky with high aerosol load, broken
clouds, cloud holes, and continuous clouds). Hence, these are shown
separately above the 100 % mark.
Profile inversion to retrieve vertical profiles and vertical column
densities from slant column densities
In order to account for the complex dependence of air mass factors on viewing
geometry and measurement conditions, radiative transfer models (e.g. McARTIM; Deutschmann et al., 2011) are employed. dAMFs are calculated
for various combinations (nodes) of viewing geometry and profiles (of trace
gases and aerosol), which are stored offline as multi-dimensional lookup
tables (LUTs) for various wavelengths (e.g. separate LUTs for 343, 360
and 430 nm). Profile inversion techniques use these LUTs to determine the
scenarios which best match the measured dSCDs. From these scenarios, the
aerosol and trace gas profiles, VCDs, and AOD are derived.
We have used MAPA (Mainz Profile algorithm) (Beirle et
al., 2019) version 0.98 for this purpose. The vertical profiles of trace gas
concentrations (or aerosol extinction) can be parameterized using three
profile parameters namely column parameters (c) (VCD for trace gases and AOD
for aerosol), height parameter (h), and shape parameter (s) (Beirle et
al., 2019; Wagner et al., 2011). In the first step, the aerosol profiles are
retrieved using the measured O4 dSCDs. A Monte Carlo approach is
utilized to identify the best ensemble of the forward model parameters (h, s
and c) which fit the measured O4 dSCDs for the sequence of elevation
angles. Generally, a scaling factor (0.8 in most of the cases) is applied to
the measured O4 dSCDs before they are used in the profile retrieval (see Wagner et al., 2019, and
references therein). The reason for this scaling factor is still not
understood, and in Sect. 3.3, we also investigate the effect of the
different scaling factors on the intercomparison of the retrieved AOD with
satellite observation from MODIS. In the second step, the aerosol profiles
retrieved from the O4 inversion are used as input to retrieve similar model parameters (h, s, and c) for the trace gases (e.g. NO2 and
HCHO). To assess the quality of the retrievals, MAPA also provides
“valid”, “warning”, or “error” flags for each measurement sequence,
which are calculated based on pre-defined thresholds for various fit
parameters (Beirle et al., 2019). We have used the
lookup tables calculated at 360 nm for the inversion of O4 and NO2
in the UV and 343 nm for HCHO and 430 nm for NO2 in the visible window.
For the HCHO profile inversion, we observed unrealistic h and s at high
solar zenith angles (SZA >60∘), which are probably
related to spectral interferences with the ozone absorption within the DOAS
analysis. Therefore, we only consider HCHO profile results for measurements
with an SZA of less than 60∘. For the retrieval of NO2 in the
visible wavelength window and HCHO, the aerosol extinction profiles
retrieved at 360 nm were scaled to those at 430 and 343 nm using an
Ångström exponent of 1.54. This value was derived as the mean of the
Ångström exponent (AE) between 470 and 550 nm measured by MODIS for the
measurement period, where we do not observe a strong intra-annual variation
(Fig. F4). We have calculated the AE using the
measured AOD at 470 and 550 nm according to
AE=-logAOD470/AOD550log(470/550).
We also investigated the effect of the choice of Ångström exponent
on the profile inversion for a smaller subset of our data spanning 15 d.
We found that AE values of 1.25 and 1.75 (minimum 5th percentile and
maximum 95th percentile in Fig. F4) resulted in the same retrievals and the difference in the mean NO2 VCD was less than
0.1 %. The surface NO2 concentrations were slightly higher (4 %)
for an AE value of 1.25 and were 3 % lower for an AE value of 1.75 as compared
to those for an AE value of 1.54.
For the profile inversion using MAPA, we compare the number of valid and those retrievals flagged as warning and error in Table 2. We note
that the sky conditions associated with thick clouds and fog are mostly
flagged as errors in the profile inversion. For the analysis results shown
in the paper hereafter, we only retained the DOAS measurements corresponding
to sky conditions without thick clouds and fog.
Frequency of MAPA retrievals flagged as “valid”, “warning”, or
“error” for various species and different sky conditions.
All-sky conditions (n=63107) Condition except for thick clouds and fog (n=58209) O4NO2 (UV)*NO2 (VIS)HCHOO4NO2 (UV)*NO2 (VIS)HCHOValid2753024287249812167127096238992463721462Warning102811289312634154919699123041198114758Error2529625927254922594521414220062159121989
* The number of valid retrievals for NO2 (UV) is calculated using
relaxed rms flag criteria (Appendix A).
Satellite dataOMI
OMI aboard the AURA satellite crosses the
Equator at 13:42 local solar time in the ascending orbit. OMI has an
effective ground pixel size of 13 × 24 km2 at nadir view, which
broadens up to 13 × 150 km2 at the swath edges (Levelt et
al., 2006; Schenkeveld et al., 2017). The intensity of backscattered solar
radiation from the Earth's atmosphere is measured by two spectrometers in
three bands in the spectral ranges of 260–311, 307–383, and 349–503 nm.
With an across-track swath width of ∼2600 km, OMI provides
daily coverage of the Earth in 14 orbits. Several data products of AOD, NO2, and HCHO retrieved from OMI measurements are
available, which we briefly describe in Appendix C and compare against
MAX-DOAS observation in the subsequent sections. From the level 2 data
corresponding to individual orbits every day, we have retained the data
pertaining to the centre of the ground pixel within 0.25∘× 0.25∘ of the measurement site. For sensitivity
analysis, we have also separately selected the collocated pixels, i.e. OMI
pixels whose corner points contained the exact location of the measurement
location. Ground pixels with an effective cloud fraction >0.3 or
measurements affected by the so-called row anomaly and problems in the DOAS
retrieval were filtered out for the analysis. In order to minimize the
effect of the diurnal variation, we have only chosen the MAX-DOAS
measurements between 07:00 and 09:00 UTC (between 12:30 and 14:30 LT) for comparison with OMI observations. We have used the OMAERUV data
product for AOD, the DOMINO V2, OMNO2 v3.0, and the QA4ECV data products for
NO2, and the OMHCHO and QA4ECV data products for HCHO for the evaluation
with the corresponding MAX-DOAS retrieved quantities (see Appendix C for the details of the satellite data products). The retrieval and
quality control details pertaining to these data products are provided in
Appendix C.
MODIS
Satellite measurements of the AOD were also obtained
from the MODIS instruments on-board the TERRA and AQUA satellites having
Equator overpasses at local solar times 10:30 and 13:30, respectively. We
have used the MAIAC data product available at 1 × 1 km2 spatial
resolution (Lyapustin et al., 2018). Since MAIAC is a
combined product of MODIS TERRA and AQUA, we have chosen the daily means of
the DOAS measurements between 9:30 and 11:30 and between 12:30 and 14:30 LT for the intercomparison.
Ancillary measurements
In situ measurements of NOx (NO and NO2) were performed using a model 42i
trace level analyser (Thermo Fischer Scientific) based on the
chemiluminescence technique. Ambient NO2 is first converted into NO
using a heated molybdenum converter, which further reacts with excess ozone
generated inside the instrument to produce a chemiluminescence signal
proportional to the available NO. Checks for zero drifts were performed
every week, and five point calibrations were performed every month.
Meteorological parameters (e.g. ambient temperature, rainfall, wind speed,
wind direction, and relative humidity) were measured using collocated met
sensors (Met One Instruments Inc.). The details pertaining to the
measurements' principle and calibration protocol can be found elsewhere
(Sinha et al., 2014; Kumar et al., 2016).
In situ measurements of formaldehyde were performed at Mohali using a high-sensitivity proton transfer reaction mass spectrometer (PTR-MS)
(Lindinger et al., 1998). Inside the PTR-MS, HCHO (proton
affinity = 170.4 kcal mol-1) is chemically ionized by hydronium ions
(H3O+) because of its higher proton affinity than that of water
vapour (proton affinity = 165.2 kcal mol-1), prior to its detection
using a quadrupole mass analyser. Using a PTR-MS, HCHO is detected at a
protonated mass-to-charge ratio (m/z) of 31. The measured signal (counts
s-1) is converted to VMRs using the m/z dependent sensitivity factors,
which are usually determined using calibration experiments. Due to the
degradation of the detector, the sensitivity of PTR-MS might also change
with time, which is evaluated using routine calibration performed using a
gas standard of known VMR (Chandra and Sinha, 2016; Sinha et al., 2014).
However, such a calibration for HCHO could not be performed due to the
unavailability of a calibration standard. Hence, we have calculated
theoretical sensitivity factors for HCHO, similar to the method incorporated
by Kumar et al. (2018). The major sources of uncertainty in the HCHO
measurements, when a theoretical sensitivity factor is used, are from (1)
uncertainty in the proton transfer reaction rate constant of the reaction
between HCHO and H3O+ (∼15 %) and (2) the ratio of
transmission efficiencies of HCHOH+ and H3O+ (∼25 %) (Zhao and Zhang, 2004; de Gouw and Warneke, 2007). Systematic
uncertainties due to the degradation of the detector would increase with time.
Ambient humidity is also known to interfere with the formaldehyde
measurements with a PTR-MS, and we have performed an absolute humidity-based
correction, according to Cui et al. (2016). HCHO VMRs increase by
∼30 % on average after the application of the absolute
humidity-based correction.
Results and discussionSeasonal and annual trends of AOD, NO2, and HCHO
Figure 4 shows the time series of monthly mean AOD, NO2 VCD, and HCHO VCD
for the complete measurement period from January 2013 until June 2017. The
vertical error bars show the monthly variability as the interquartile range.
The gaps in the time series are due to instrument malfunction (primarily due
to the stepper motor and connection to the measurement computer). The mean AOD for
the complete measurement period was 0.63 ± 0.51, with the monthly
means varying between 0.30 in March 2013 and 1.25 in August 2014. We
quantify the seasonal variability of the measured AOD to be 187 %, which is
calculated as the difference between the maximum and minimum of the 30 d
running mean divided by the mean over the measurement period. For the same
months, very small inter-annual variability in the monthly means (<0.2) was observed for all the months except April–July. We observe the
maximum AOD (0.8–1.2) during the monsoon months (June–August) at Mohali,
which is most probably caused by the hygroscopic growth of the aerosol
particles (Altaratz et al., 2013). Previous studies comparing
various MODIS AOD data products and AERONET measurements over the
Indo-Gangetic Plain have also found the maximum AOD in the monsoon months
but also significant differences among the different data products
(Mhawish et al., 2019; Tripathi et al., 2005). The retrieval of the
aerosol size distribution from AERONET measurements has shown that during the monsoon, the coarse-mode fraction increases by more than 50 % of the
annual average over the Indo-Gangetic Plain (Tripathi et
al., 2005). In order to further confirm that the high AOD during the monsoon
is not an artefact caused by the persistent cloud cover over the IGP, we
have investigated the seasonality of the AOD under cloud-free conditions
measured at different AERONET sites in the IGP nearest to Mohali (New Delhi
∼250 km south and Lahore ∼250 km west). Both
at Lahore and New Delhi, high AOD values are observed in the monsoon months
(June–August) (Fig. F5). Relatively high monthly mean AOD (0.6–0.9) values
are also observed in May and October, which are characterized by crop
residue fire emissions. We have also compared the MODIS AOD (converted to
440 nm using the AE derived from Eq. (3) to the AERONET AOD at these two
stations and found very good agreement in the daily measured values with
Pearson correlation coefficients (r) >0.84 (Fig. F5) and an
overall bias <10 % for both sites.
Monthly and annual variation in (a) AOD, (b) NO2, and (c) HCHO
vertical column densities derived from MAX-DOAS measurements. The lower and
upper vertical error bars represent the 25th and 75th percentiles,
respectively.
The mean NO2 VCD for the complete measurement period was (6.7 ± 4.1) × 1015 molecules cm-2 with a variability in the
monthly means between 4.7 × 1015 molecules cm-2 in July 2015 and 8.9 × 1015 molecules cm-2 in November 2013. The
observed NO2 VCDs are comparable to those observed in long-term
measurements in rural and suburban environments (Kramer et al., 2008; Drosoglou et al., 2017) and satellite observations in the Indian
metropolitan city Mumbai (Hilboll et al., 2013). These are
much smaller than those observed in the urban areas worldwide (Mendolia
et al., 2013; Drosoglou et al., 2017) or rural, suburban, and urban locations
of China (Ma et al., 2013; Wang et al., 2017a; Chan et al., 2019; Jin et
al., 2016; Vlemmix et al., 2015), where the mean monthly levels are generally
higher than 1 × 1016 molecules cm-2. The observed seasonal
variability of 145 % in the 30 d running means can be explained by the
seasonality of emissions and the changing lifetimes.
For NO2, we see that the monsoon months (July and August) are cleanest
followed by early pre-monsoon months (March–April). The primary emission
sources active throughout the year in and around the region are vehicular
emissions, garbage burning, and biomass burning for cooking and construction
activities. In the suburban and rural regions around the measurement site,
biomass (e.g. wood, domestic, and agricultural residue) and biofuel (coal)
burning serve as the primary source of heating. Increased emissions from
biomass burning for domestic heating in winter when the atmospheric lifetime
of NO2 is at a maximum, marks the highest observed NO2 VCDs in the
year. Also, during the crop residue burning active periods of summer
(May–June) and post-monsoon (October–November), we observe enhanced NO2
VCDs. Notwithstanding the strong urbanization trends, a significant annual
trend is not observed in either of the three parameters (AOD, NO2, and
HCHO) measured by MAX-DOAS, indicating the dominance of non-organized
emission sources, e.g. domestic heating, garbage burning, and crop residue
burning for the ∼4.5-year measurement period. This is
further ascertained by the fact that we do not see any noticeable
weekday–weekend dependence in either of NO2, HCHO, and AOD (Fig. F6).
The mean HCHO VCD for the complete measurement period was (12.1 ± 7.5) × 1015 molecules cm-2, with a strong seasonality with
monthly means ranging from 5.3 × 1015 molecules cm-2 in
March 2017 to 17.7 × 1015 molecules cm-2 in October 2015. The seasonal variability was found to be strongest at 284 % for HCHO
among the three parameters measured by MAX-DOAS. The mean and monthly
variability is comparable to those observed in the urban areas of China (Vlemmix et al., 2015; Wang et al., 2017a). In contrast to these
long-term measurements of formaldehyde in China, the minimum VCDs are not
observed in the winter but rather observed in March. Globally photochemical
production from biogenic and anthropogenic hydrocarbons dominates the
formaldehyde sources while having a minor fraction being directly emitted from
biomass burning and vegetation (Fortems-Cheiney et al., 2012). At complex suburban environments, e.g. Mohali, different sources
can dominate the formaldehyde production in different periods of the year.
At an urban site Kolkata in the IGP, the contribution of primary sources to
ambient formaldehyde was observed to be 71 % and 32 % during summer and
winter, respectively (Dutta et al., 2010).
At Mohali two distinct formaldehyde enhancement periods are observed; first
in May–June and second in October, both of which are the periods when crop
residue burning is practised around the region. Several identified and
unidentified chemical compounds are formed in the atmosphere due to crop
residue fires, which readily react with OH radicals and form second- and
higher-generation oxidation products and potentially form formaldehyde as a
by-product (Kumar et al., 2018; Sarkar et al., 2013). The
pre-monsoon period of May and June provides favourable conditions (e.g. long
daytime hours, uninterrupted solar radiation, and high temperature) for the
photochemical production of secondary pollutants from the precursors emitted
from wheat residue fires (Kumar et al., 2016; Sinha et al., 2014). This is
reflected in very high formaldehyde VCDs observed in May and June in the
range of 16.9 × 1015–17.4 × 1015 molecules
cm-2. Similar levels of HCHO VCDs are also observed in October, when
paddy residue fires are active in the region. Also, the photochemical
production of ozone is attributed to the emission from crop residue fires by
Kumar et al. (2016). The observed maximum NO2 VCDs
in the winter months indicates very high primary emissions, which could
eventually lead to formaldehyde production from its co-emitted precursors.
However, low ambient temperatures and lack of ample sunlight hours result in HCHO VCDs that are
not so high compared to May, June, and October.
The agricultural lands in the north-west fetch region of Mohali practice
agroforestry, where poplar (most common) and eucalyptus trees are planted in
the periphery of fields (Sinha et al., 2014; Pathak et al., 2014; Mishra et
al., 2020). Over the Indo-Gangetic Plain, biogenic sources contribute to
∼40 % of the total VOC emission flux annually (Chaliyakunnel et al., 2019). In the early post-monsoon season,
soil moisture availability and daytime temperatures between 30 and 35 ∘C provide a favourable condition for isoprene emissions
(Guenther et al., 1991). Generally, a strong variability is
observed in the number of rainfall events and the total rainfall over the
IGP between different years (Fukushima et al., 2019), which also
affects the soil moisture availability and in turn the biogenic emissions
from plants. For the period discussed in this study with available MAX-DOAS
HCHO measurements, the years 2014 and 2015 were quite different with respect
to the monsoon rainfall. During the monsoon months, 2014 witnessed 18 rainy
days (total rainfall = 378 mm), while 2015 witnessed 32 rainy days (total
rainfall = 435 mm) in Mohali. Following the number of rainfall events, the
early post-monsoon months (August–September) of 2015 witnessed higher HCHO VCDs, which
can be attributed to the photo-oxidation of stronger biogenic emissions of
isoprene (Mishra and Sinha, 2020). Poplar trees in the Indian
subcontinent show little emissions in the months from December to March
(Singh et al., 2007) due to loss of leaves. Minimal biogenic and
anthropogenic emissions of formaldehyde and its precursors in March resulted
in the minimum VCDs during the year. From these observations, we conclude
that anthropogenic emissions (primarily due to biomass burning) and their
oxidation dominate the formaldehyde seasonality in most of the year except
early post-monsoon where biogenic emissions have a major contribution to the
measured formaldehyde.
Diurnal variation and vertical profiles of AOD, NO2, and HCHO
Figure 5 shows the diurnal variation in the mean vertical profiles of aerosol
extinction, NO2 concentration and HCHO concentration for different
months of the year retrieved using MAX-DOAS measurements at Mohali. The
corresponding diurnal variations in the VMR of NO2 and HCHO and aerosol
extinction close to the surface are shown as Figs. F7–F9.
Hourly mean vertical profiles of aerosol extinction (top three rows,
shades of olive), NO2 concentrations (middle three panels, shades of
blue) and HCHO concentrations (bottom three panels, shades of red) in
different months retrieved from 4.5 years of MAX-DOAS measurements over
Mohali.
The vertical profile of aerosol extinction is expected to be primarily
driven by the boundary layer height (BLH) and to some extent, the
photochemistry, which eventually drives secondary aerosol formation (Wang et al., 2019). At Mohali, the
diurnal evolution of the aerosol extinction profile heights reaches its
maximum during afternoon hours. In Fig. 6a, we show the typical diurnal
evolution of BLH from the ERA5 reanalysis data for the four major seasons.
We observe a growth of the BLH from morning until noon with a maximum at
14:00 LT and a subsequent decline. The maximum BLH up to 3 km is observed
in summer. Shallow daytime BLH up to 1.2 km are observed in the monsoon
period due to overcast sky conditions, stronger wind, and high surface
moisture and in winter due to low surface temperature and low surface heat
flux (Sathyanadh et al., 2017). We observe that the aerosol is
trapped in the bottom layers (within 400 m) in winter, whereas during the
afternoon hours in summer, monsoon, and early post-monsoon months, aerosol
extinction up to 0.2 km-1 is observed even at around 1.5 km altitude.
Though the ERA5 BLH is shallow in monsoon, yet we observe similar aerosol
profiles during that period as during summer, which indicates that at
Mohali, the vertical distribution of aerosol does not follow ERA5 BLH
transition from summer to monsoon. Over India, the monsoon months are
characterized by strong convective activity, which can bring the surface air
aloft to several kilometres despite a shallow ERA5 BLH (Lawrence and
Lelieveld, 2010). The convection is rather strong in the Himalayan foothill
region (which also includes Mohali) and even pumps the surface pollutants into the UTLS (upper troposphere–lower stratosphere) (Fadnavis et al., 2015). The evidence of pollutant
transport associated with deep convection is crucial for PAN (peroxyacetyl nitrate) formation in
the UTLS, which is observed by the modelling studies over the IGP and
Himalayan region. Long-lived non-methane VOCs (e.g. ethane) can be
transported to the UTLS where both convective NOx transported from the
surface and exchanged from the stratosphere serve as fuel for PAN formation.
High aerosol extinction (>1.5 km-1) is observed in the
surface layer in the winter months, with the maximum in December. In the
winter months, biomass burning contributes to primary aerosol, the formation
of secondary aerosol and particle growth as a result of coating on existing
aerosol particles. Moreover, the high ambient relative humidity in winter (Kumar et al., 2016) further contributes to the growth of
the existing aerosol particles, which further increases the aerosol
extinction and in the extreme cases can lead to intense fog.
(a) Diurnal evolution of the hourly means ERA5 boundary layer
height (BLH) at Mohali for the four major seasons of the year. (b) Mean
afternoon time (between 12:00 and 15:00 LT) profile height (with
75 % of the total amount below) for aerosols, NO2, and HCHO and the
ERA5 BLH for different months. The upper and lower vertical error bars
represent the monthly variability as 75th and 25th percentiles,
respectively.
In order to quantitatively describe the mixing altitude, we define a
characteristic profile height H75, as the height below which 75 % of
the trace gas column (or AOD) is located (Vlemmix et
al., 2015). The profile parameter h used in the MAPA inversion
algorithm represents the height below which the concentration (or
extinction) of the trace gas (or aerosol) remains constant. For elevated
profiles (for details, please refer to Wagner et al., 2011), h refers to the height above the trace gas (or aerosol) layer
where the concentration (or extinction) becomes zero. Using h and shape
parameter s, we calculate a profile height (H75) by employing
Eqs. (2–5) of Beirle et al. (2019).
We show the diurnal evolution of characteristic profile heights (H75)
in Fig. F10 for the four major seasons. Figure 6b shows the mean afternoon
time characteristic profile heights (H75) for aerosol, NO2, and
HCHO for different months, together with the mean ERA5 BLH. Due to their
short atmospheric lifetime (<6 h) during the daytime, H75 for
NO2 and HCHO is lower than that for aerosol. H75 for the
measured species is observed to be smaller than the typical boundary layer
heights. In the monsoon season, we observe H75 comparable to that in
summer, even though the boundary layer height is shallow and comparable to
that in winter. Trace gases and aerosol from the surface are lofted up due
to deep convection in the monsoon leading to high H75. This indicates
that the vertical mixing of aerosol during the monsoon is not driven by the
parameters used to calculate the ERA5 BLH but rather follows the trend of
ambient daytime temperature, which does not show such a large difference
between summer and monsoon (e.g. Fig. S2 of Kumar et al., 2016). The profile heights for aerosols and HCHO in summer months are
similar to those observed in Beijing (Vlemmix et al., 2015), but we observe a much stronger seasonal dependence. H75 up to
1.1 km for aerosols are observed for the summer (except March) and monsoon
months, while in winter H75 is usually less than 500 m. In all
months, we observe the minimum H75 among the three measured species for
NO2 (∼200 to 450 m), due to its short lifetime and
production close to emission sources near the surface. H75 for NO2
is generally much smaller than for Beijing (urban) and Xingtai (suburban) in
China (Wang et al., 2019; Vlemmix et al., 2015).
In all the months and hours of the day, the major fraction of the NO2
column is located in the bottom-most layer extending from the surface until 200 m. Until 11:00 LT, more than 60 % of the NO2
column is located in the bottom-most layer in all the months. In winter
months (November–February), the same is true for all hours of the day, but in the
morning time (until 11:00 LT) the fraction in the bottom layer is even
>80 %. During the late afternoon of the summertime, monsoon, and early post-monsoon, when the BLH grows deeper due to the heating of the surface, we observe a considerable fraction (∼20 %–30 %) of NO2
in the layer extending from 200 to 400 m. There is a very small fraction
(<5 %) of NO2 column in the layers above 600 m.
We observe the maximum NO2 columns in the morning hours between
08:00 and 11:00 LT, subsequently decreasing during the day. The major
factors driving the NO2 columns are emissions and lifetime with respect
to OH radicals. For the surface concentration, boundary layer dynamics also
play an important role. Emissions from traffic and biomass burning for
heating and cooking peak in the morning and evening hours. The lifetime of
NO2 is at a minimum in the afternoon when OH radial concentration peaks,
which explains the decrease during the day. The amplitude of the diurnal
profiles of the NO2 surface VMR (Fig. F7) is at a maximum during winter
months when there is little diurnal variability in the BLH (dilution effect)
and NO2 lifetime (sink effect) between morning and late afternoon hours
and the shape is driven primarily by the emissions (source). The amplitude
is at a minimum during the monsoon when biomass burning ceases (Mishra
and Sinha, 2020). In Sect. 3.7, we will discuss the diurnal variation in
the NO2 surface VMR retrieved from MAX-DOAS and in situ measurements for
different months.
For formaldehyde, we observe a comparable distribution among the 0–200 and
200–400 m layers for all seasons except winter. In winter months, the
bottom-most layer contains up to 50 % of the total column, which is
smaller as compared to aerosols and NO2. During the late afternoon
hours of summer, monsoon, and the post-monsoon period, we also observe a
larger fraction of the HCHO column in the 200–400 m layer as compared to the
0–200 m layer. In the presence of high NOx close to the surface, OH
concentration is depleted, which might result in slowing down of the
formaldehyde production from precursor VOCs. The higher characteristic
profile heights of HCHO as compared to those of NO2 can be attributed
to the secondary photochemical formation from primary precursor emissions. A
considerable fraction of primary emissions is transported to intermediate
layers (similar to NO2) during summer, monsoon, and early post-monsoon,
where secondary products (e.g. formaldehyde) are formed due to active
photochemistry. The months in which primary anthropogenic emissions of
formaldehyde and its precursors are stronger (e.g. months except for March,
April, July, August, and September), the gradients of vertical profiles of
HCHO are stronger in the layers from the surface to 1 km altitude. For the
surface VMR, we observe maxima in the morning hours in all seasons except
winter and late post-monsoon (Fig. F8). Even though formaldehyde is formed
photochemically, which should increase during the day, the VMR close to the
surface is reduced due to the vertical mixing in the afternoon hours. We
observe the highest daytime HCHO surface VMR in winter months since the
major fraction of HCHO is trapped in the bottom-most layer.
Intercomparison and temporal trends of aerosol optical depth
Figure 7a shows the time series of the AOD at 360 nm
retrieved from MAX-DOAS measurements of O4 (scaled by 0.8) and the AOD
at 360 nm calculated from MODIS MAIAC data product over Mohali. The
corresponding scatter plot is shown in Fig. 7b. Similar plots for the OMAERUV
AOD product at 354 nm are shown in Figs. 7c and d. The solid line
represents the monthly mean values, while the individual dots in the
background represent the daily measurements. The vertical error bars
represent the standard error of the mean (σ/N), where
σ represents the standard deviation and N represents the number of
daily measurements for the month.
Intercomparison of daily (dots) and monthly mean (lines and
markers) AOD at 360 nm retrieved from ground-based MAX-DOAS measurements and
from the MODIS MAIAC data product (a, b) and OMI AERUV data product
(c, d). The monthly mean of the MAX-DOAS and satellite data products
were calculated by considering only the days of the month when both the
measurements were available, causing different MAX-DOAS monthly means in (a) and (c).
The mean MAX-DOAS AOD at 360 nm for the measurement period if averaged
around the MODIS overpass time (between 9:30 and 11:30 and between 12:30 and
14:30 LT) was 0.59 ± 0.39 as compared to the MODIS AOD of 0.81 ± 0.53. The correlation coefficients (r) for the linear
regressions of daily and monthly mean data between the MAX-DOAS and MODIS
AOD are found to be 0.78 and 0.85, respectively. For the monthly mean, we
also performed the orthogonal distance regression (ODR) weighted by σ-2 (where σ is the monthly standard deviation) between
MAX-DOAS and MODIS AOD, for which the slope and offset were 1.13 and 0.12,
respectively.
Initially, we performed a comparison of AOD retrieved from MAX-DOAS
measurements without applying any scaling factor to the measured O4
dSCD. While there was a general agreement between the trends in the AOD
retrieved from MAX-DOAS and MODIS, the MAX-DOAS AOD showed a strong
underestimation (Fig. F11). Several MAX-DOAS measurement studies and
comparison with independent datasets (e.g. sun photometer) found that a
scaling factor (less than 1) was necessary to bring MAX-DOAS results and
independent measurements into agreement (Wagner et al., 2009, 2019; Beirle et al., 2019). However, a similar number of studies did not
find the need to apply such a scaling factor (Wang et al., 2017a; Franco
et al., 2015). Currently, the reason for the scaling factors is not
understood (Wagner et al., 2019, and references therein). In most cases, when scaling factors are
used, values between about 0.8 and 0.9 are found best. In our case, the
application of a scaling factor was found to be necessary to bring MAX-DOAS
and satellite measurements into an agreement. In order to further confirm
the choice of the scaling factor, the profile inversion was performed with a
variable scaling. Figure F12 shows the distribution of the scaling factor
which concludes that an O4 scaling factor of 0.8 fits best for our
measurements.
The NASA OMAERUV data product provides the AOD at 354 nm, but the spatial
resolution and temporal coverage are not as good as for the MODIS MAIAC
product. From Fig. 7c and d, we observe that OMAERUV generally
underestimates the AOD over Mohali, and the level of agreement is also worse
both for daily and monthly mean values. For OMAERUV, the mean AOD was 0.53 ± 0.26. Over central and east Asia, independent comparison of OMI with
AERONET measurements also found a ∼50 % underestimation by
OMAERUV and a poor agreement for a 10-year period (Zhang et al.,
2016). We think that the much coarser spatial resolution of OMAERUV as
compared to the MAIAC data product is among the probable reasons for the
worse agreement. In order to evaluate this hypothesis, we created a time
series of the MODIS MAIAC data product, spatially averaged over 5 and 25 km around Mohali.
MAX-DOAS measurements are spatially representative of a few kilometres in
the field of view, depending on the ambient aerosol load and elevation
angle, whereas the ground footprints of individual OMI pixels are
13 × 24 km2 in the best case. We have calculated the horizontal
sensitivity distance (HSD) of MAX-DOAS for low-elevation angles as the
e-folding distance of O4 dAMF from the instrument location (Wagner
and Beirle, 2016). Figure F13 shows that the mean afternoon time (between
12:00 and 15:00 LT) HSD ranges between 5 and 7 km for clear-sky
condition with low aerosol load and between 3 and 6 km for high aerosol
conditions. Here it is important to note that this estimate is mainly
representative of the near-surface layers. While for the trace gas
inversions, the VCD is constrained by all elevation angles, the
determination of the AOD is mostly constrained by the high-elevation angles.
For high-elevation angles, the sensitivity range is much closer to the
instrument (at distances up to 1 and 2 km for layer height of 0.5 and 1 km,
respectively) (see Fig. 6a). Comparing the spatially degraded time series
with MAX-DOAS AOD resulted in a worse agreement (r=0.75 and 0.79 for the
daily and monthly means, respectively) for 25 km but did not change
significantly for 5 km (Fig. F14) as compared to the original comparison
when only a 2 km area around Mohali was considered for spatial averaging. In
addition to the effect of horizontal gradients, the poor agreement of the
OMAERUV product might also be caused by residual cloud contamination.
Further, non-representative assumptions about the aerosol types between
smoke, dust, and non-absorbing aerosols used for the inversion of the
measured reflectances might play a role, as highlighted by Zhang et al., 2016.
Intercomparison and temporal trends of NO2 vertical columns
Figure 8 shows the time series of the NO2 vertical column densities
measured by MAX-DOAS and by OMI for the three different data products.
Please note that for calculating the monthly means, we only considered the
days of the month when both cloud-screened and quality-controlled satellite
and MAX-DOAS data were available. Within the complete study period, the
DOMINO, QA4ECV, and OMNO2 data products had 60 %, 47 %, and 46 % days of
cloud- and quality-screened data, respectively. We observe a general agreement in
the trends of the NO2 VCDs between the MAX-DOAS and OMI datasets.
However, all OMI data products generally underestimate the NO2 VCDs.
The mean MAX-DOAS NO2 VCD for the measurement period if averaged
between 12:30 and 14:30 LT (around the OMI overpass) was (5.4 ± 3.0) × 1015 molecules cm-2. The mean VCD for OMI
was (3.7 ± 2.4) × 1015 molecules cm-2, (3.7 ± 1.3) × 1015 molecules cm-2, and (3.7 ± 1.7) × 1015 molecules cm-2, respectively, for the DOMINO,
QA4ECV, and OMNO2 data products. The reasons for the systematic difference
between the MAX-DOAS and OMI measurements can be attributed to several
factors including (1) difference in spatial representation and (2)
differences in vertical sensitivity of MAX-DOAS and OMI. Previous validation
studies over China also found systematic underestimation of ∼60 % over Nanjing by the OMNO2 and ∼30 % over Beijing and
Nanjing by the DOMINO product (Chan et al., 2019; Ma et al., 2013). The
observed discrepancies were attributed to differences in spatial
representativeness, which introduces smoothing of the measured NO2 VCD
over a large satellite ground pixel and to the shielding effect of aerosols.
At Mohali, the maximum disagreement of the OMI products is observed during
the late post-monsoon and winter months, where all satellite data products
significantly underestimate the NO2 VCDs. Note that a large amount of
aerosol and trace gases are emitted from the crop residue and domestic fires
in these months, a major fraction of which is trapped close to the surface
due to suppressed ventilation.
Intercomparison of time series of daily (dots) and monthly mean
(lines and markers) NO2 VCDs retrieved from ground-based MAX-DOAS
measurements and the OMI DOMINO data product (a), the OMI QA4ECV data
product (b), and the OMI OMNO2 data product (c). The vertical error bars
represent the monthly variability as the standard error of the mean. The
monthly mean of the MAX-DOAS and satellite data products were calculated by
considering only the days of the month when both the measurements were
available, causing different MAX-DOAS means in (a), (c), and (e). Scatter plots
(panels b, d, and f for DOMINO, QA4ECV, and OMNO2, respectively) using the
daily and monthly mean values are shown adjacent to the time series.
Figure 8 also shows the linear regression fits of the three OMI data products
versus the MAX-DOAS NO2 VCDs. We observe smaller scatter in the QA4ECV
and OMNO2 products for both daily and monthly values, as compared to DOMINO
product. An ODR fit was performed between
the monthly means of MAX-DOAS and OMI NO2 VCDs weighted by σ-2, where σ is the monthly standard deviation. The slopes of
the ODR fit between the MAX-DOAS and OMI monthly mean NO2 VCDs were
0.94, 0.59, and 0.78, respectively, for DOMINO, QA4ECV, and OMNO2. The offsets
of the ODR fits were -8.1× 1014, 8.4 × 1014, and
-1.7× 1014, respectively, for DOMINO, QA4ECV, and OMNO2,
respectively. Over Mohali, we observe excellent consistency between the
QA4ECV and OMNO2 products with a slope and correlation coefficient (r) of
0.94 and 0.72, respectively, between both datasets.
Since we have retained the OMI pixels whose centre points lie within
0.25∘× 0.25∘ of the MAX-DOAS measurement site,
differences can arise due to the smoothing effect across the OMI ground
pixels. With the pristine regions of the Himalayan mountain range only
∼35 km from Mohali, smaller NO2 VCD from OMI
measurements are expected due to systematic gradients towards the mountain
range. The effect of smoothing over a large area can be minimized if only
collocated pixels are retained for the intercomparison. However, this
significantly reduced the number of available days for intercomparison. If
only collocated pixels were considered, we were left with only 35 %,
25 %, and 22 % of the measurement days for DOMINO, QA4ECV, and OMNO2,
respectively. Due to the poor statistics, we did not observe improvements in
the correlation coefficient (r) of the linear regression of the daily data
and these changed from 0.38, 0.50 and 0.43 to 0.38, 0.56, 0.43,
respectively, for DOMINO, QA4ECV, and OMNO2. In the absence of higher-resolution NO2 data for the study period, we could not quantify the
effect of the different spatial representativeness of the MAX-DOAS and OMI
measurements.
One of the major reasons for the disagreement between satellite and MAX-DOAS
measurement is the difference in vertical sensitivity of the two
measurements. Satellite instruments have limited sensitivity close to the
ground. In contrast, MAX-DOAS measurements have the highest sensitivity
close to the ground, while it becomes virtually zero above 3–4 km. The
limited sensitivity of the satellite instruments is addressed using the box
air mass factors (bAMFs) and the a priori profiles of the trace gases to be
retrieved (Eskes and Boersma, 2003). In Appendix D we
compare the bAMFs used in the DOMINO and QA4ECV retrievals with those
calculated by employing the radiative transfer model (RTM) McARTIM over
Mohali using the mean aerosol extinction profiles retrieved from the
MAX-DOAS measurements. A discrepancy is found between the calculated bAMFs
and those used for OMI retrievals (DOMINO and QA4ECV) (Fig. D1), such that
the calculated bAMFs show systematically higher values close to the surface.
In such a case, attribution of a smaller fraction of NO2 in layers
close to the surface in the a priori profiles cause a systematic
underestimation of the VCDs (please see Appendix D for details). We found
that the a priori NO2 profiles used in the DOMINO v2 retrievals
strongly differ from those retrieved using the MAX-DOAS measurements for
winter and polluted post-monsoon when a large fraction of NO2 is
present in layers close to the surface (Figs. 5 and D2).
In order to eliminate the difference caused by the non-representative a
priori NO2 profiles, we calculated the “modified MAX DOAS VCDs”
(called VCDmod hereafter), which represent the MAX-DOAS NO2 VCDs
as observed by OMI. The application of the OMI tropospheric averaging
kernels and a priori profiles also to the MAX-DOAS profiles makes the
comparison independent of the a priori profiles used for the OMI retrieval.
In order to do so, we apply the tropospheric averaging kernels of OMI DOMINO
(AKtrop) data product to the NO2 vertical profiles (xdoas)
retrieved from MAX-DOAS in layers (i) from ground (i=0) to h=4 km (i=20), according to the following equation (Rodgers and Connor,
2003):
VCDmod=∑i=0hAKtrop,i(xdoas,i-xap,i)+xap,i.
Here, xap,i represents the DOMINO a priori NO2 profiles. Please
note that the total averaging kernels (AKtot) provided in the DOMINO
level 2 data product are converted to tropospheric averaging kernels using
the ratio of total AMF (AMFtot) and tropospheric AMF
(AMFtrop):
AKtrop,i=AKtot×AMFtotAMFtrop.
Figure 9 shows the time series of original MAX-DOAS VCDs (red), OMI DOMINO
VCDs (blue), and modified MAX-DOAS VCDs (black). We observe that the bias
between the OMI and MAX-DOAS measurements is smaller if the averaging
kernels and a priori profiles are applied to the MAX-DOAS NO2 profiles.
However, in contrast to the MAX DOAS VCDs, the VCDMOD are
systematically lower than the DOMINO NO2 VCDs.
Time series of daily (dots) and monthly means (lines and markers)
of MAX-DOAS NO2 VCDs, OMI DOMINO NO2 VCDs, and modified MAX-DOAS
VCDs modified by using the DOMINO averaging kernels and a priori profiles.
While the application of the DOMINO averaging kernels and a priori NO2
profiles to the MAX-DOAS profiles according to Eq. (4) accounts for the
reduced OMI sensitivity close to the surface, it does not account for the
limited sensitivity of MAX-DOAS at higher altitudes (above 3–4 km).
VCDMOD hence represents the MAX-DOAS NO2 VCD from the ground to
up to ∼4 km altitude, whereas the DOMINO NO2 VCDs
represents the NO2 VCDs from the ground until the tropopause. For a
qualitative estimate of the NO2 column at high altitudes, we have
calculated the fraction of the NO2 column between 4 km altitude and the
tropopause by only considering the NO2 partial VCDs of the TM4 a priori
profiles in various layers. The NO2 partial columns in the 4 km to tropopause altitude range account for 7 %–18 % (interquartile range) of the
total NO2 a priori VCDs. Hence, due to the limited sensitivity of
MAX-DOAS at higher altitudes, the VCDMOD is systematically smaller than
DOMINO NO2 VCDs.
Please note that a similar comparison with OMI QA4ECV and OMNO2 products is
not possible as a priori profiles and averaging kernels, respectively, are
not available for these data products. For qualitative evaluation, we have
used the TM4 NO2 a priori profiles with QA4ECV averaging kernels and
calculated VCDMOD. This also results in an improvement of the bias
between the MAX-DOAS and QA4ECV NO2 VCDs (Fig. F15). A different
approach for improved agreement between MAX-DOAS and satellite VCDs is by
using the MAX-DOAS NO2 profiles as a priori profiles for the
calculation of air mass factors for the satellite retrieval (Chan et al., 2019; De Smedt et al., 2015). We discuss this approach and its
limitations in Appendix D.
Intercomparison and temporal trends of HCHO vertical columns
Figure 10 shows the time series of HCHO VCDs measured by MAX-DOAS and by OMI
for the QA4ECV (panel a) and OMHCHO products (panel c) with the respective
scatter plots (panels b and d). Within the chosen quality and cloud filters,
the QA4ECV and OMHCHO datasets have 42 % and 67 % days, respectively,
out of the complete study period. The mean MAX-DOAS HCHO VCD for the
measurement period if averaged between 12:30 and 14:30 LT (around
OMI overpass) was (11.3 ± 6.9) × 1015 molecules
cm-2. The mean VCDs from OMI observations were (14.9 ± 11.3) × 1015 molecules cm-2 and (11.3 ± 11.7) × 1015 molecules cm-2 for the QA4ECV and OMHCHO data products,
respectively. A small negative bias was expected in the MAX-DOAS HCHO VCDs,
as its sensitivity is limited at higher altitudes where a background HCHO
may be present due to the oxidation of long-lived hydrocarbon (mainly
methane). Though the non-methane VOCs dominate the formaldehyde production
over land, methane oxidation is a ubiquitous source of formaldehyde
across the globe. At high altitudes (between 3.6 and 8 km) in a pristine
location (Jungfraujoch), the background HCHO VCDs have been observed between
0.75 × 1015 and 1.43 × 1015 molecules cm-2 (Franco et al., 2015) which is equivalent to ∼10 % of the total column as measured over Mohali.
Intercomparison of time series of daily (dots) and monthly mean
(lines and markers) HCHO VCDs retrieved from ground-based MAX-DOAS
measurements and the OMI QA4ECV data product (a) and the OMI OMHCHO data
product (c). The respective vertical error bars represent the 1σ monthly variability as the standard error of the mean. The monthly means of
the MAX-DOAS and satellite data products were calculated by considering only
the days of the month when both the measurements were available, causing
different MAX-DOAS means in (a) and (c). Scatter plots (b, d) using the
daily and monthly mean values are shown adjacent to the time series.
In general, we see slightly higher VCDs by the OMI QA4ECV product compared
to MAX-DOAS, except for the post-monsoon months, when we observe a better
agreement between the two datasets. For the OMHCHO product, the monthly mean
HCHO VCDs agree well with MAX-DOAS except for the post-monsoon of 2015,
where MAX-DOAS VCDs were higher. The generally good agreement of the OMHCHO
VCDs with MAX-DOAS is in line with previous works (Chan et al.,
2019; Wolfe et al., 2019). The OMHCHO product was also shown to have a good
agreement with airborne measurements in the remote troposphere (Wolfe et al., 2019). However, the accountability in
the range of the monthly mean HCHO VCDs was much better for the QA4ECV
product (slope=0.77) as compared to OMHCHO (slope=0.41). In comparison
to the QA4ECV NO2 dataset, we observe a higher noise in both spatial
and temporal patterns of the HCHO VCDs, which arises due to the relatively
small atmospheric absorption. The larger uncertainty in the QA4ECV HCHO
dataset is also evident from the scatter of daily measurements where
sometimes VCDs close to zero are observed. For some months (e.g. January 2015,
January 2016, March 2017, April 2017, and June 2017) when less than 6 d of QA4ECV
data is retained for calculating the monthly means for intercomparison with
MAX-DOAS measurements, the values should be considered carefully. If only
collocated pixels are considered for the QA4ECV HCHO product, the statistics
get poorer (only 17 % of valid observations) and we observe a worse
coefficient of correlation (r=0.19 and 0.17 for daily and monthly means)
with MAX-DOAS measurements.
The finding that in contrast to NO2, no general underestimation is
observed for the comparison of the satellite HCHO VCDs to the MAX-DOAS HCHO
VCDs can be attributed to the different vertical profiles and the different
vertical sensitivities of MAX-DOAS and OMI observations. Since in general,
the HCHO profiles reach higher altitudes than NO2, the satellite
observations capture a larger fraction of the total HCHO column. However, we
cannot perform a comparison of the modified MAX-DOAS VCDs for HCHO similar
to that calculated for DOMINO NO2, as the total AMFs and averaging
kernels (needed for Eq. 5) are not available for the QA4ECV and OMHCHO data
products, respectively.
Discerning the sensitivity of ozone production on NOx and VOCs
Martin et al. (2004) recommended the use of the ratio of the
formaldehyde and NO2 columns from satellite observations as an
indicator for the ozone production regime. HCHO/NO2 ratios of less than 1
represent a VOC sensitive regime, whereas values greater than 2 indicate a
NOx sensitive regime. Intermediate values of the HCHO/NO2 ratio
indicate a strong sensitivity towards both NOx and VOCs. The threshold
for this indicator was initially calculated for afternoon time (between
13:00 and 17:00 LT) but was later extended to also include morning period
by Schroeder et al. (2017). However, Schroeder et al. (2017) also indicated that the upper limit of the
intermediate regime might vary spatio-temporally. Nonetheless, higher
HCHO/NO2 indicates that a reduction in NOx emissions would be more
effective for ozone reduction. While the in situ measurements of the total OH
reactivity are a more robust method to evaluate the ozone production regime,
due to the experimental constraints, these measurements are reported only
rarely and for short time periods (e.g. a few weeks or months) (Kumar et
al., 2018; Kumar and Sinha, 2014). Mahajan et al. (2015)
evaluated the ozone production regime over India using the ratio of HCHO and
NO2 VCDs observed from SCIAMACHY for the mean of the years 2002–2012. Over
the north-west IGP, the HCHO/NO2 was observed to be less than 1 in the
winter months and between 1 and 2 in all other months. From our
intercomparisons in the previous sections, we note that while the OMI
NO2 VCDs are generally underestimated, the HCHO VCDs are generally well
accounted for. Hence the true HCHO/NO2 will be smaller than those
indicated by satellite observations, which indicates that the estimated
sensitivity of the ozone production regime towards NOx should be
smaller and shifted towards VOCs. Using the WRF-CMAQ model simulations at
36 × 36 km2 resolution over India for 2010, Sharma
et al. (2016) have evaluated the ozone production to be strongly sensitive
to NOx emissions throughout the year and recommended reduction in
transport emissions which account for 42 % of the total NOx
emissions. However, with an increase in transport and power plant emissions
(strong NOx sources) over India, the regimes are susceptible to shift
away from being NOx limited and need to be re-evaluated. Figure 11 shows the
monthly mean HCHO/NO2 ratio calculated using the MAX-DOAS measurements
for the morning (09:30–11:30 LT), noontime around the OMI overpass
(12:30-14:30 LT), and late afternoon (15:30–17:30 LT). We observe a
stronger (smaller) sensitivity towards NOx during the late afternoon
(morning) as compared to noontime similar to other urban locations in the
USA (Schroeder et al., 2017). VOCs contribute to ozone production
via their oxidation by OH radicals and subsequent formation of peroxy
radicals. During the build-up hours of ozone (between sunrise until
noontime) at Mohali, the radicals' abundance is expected to be limited. Hence,
the ozone production is more sensitive to VOC (or “radicals”) during
morning, which shifts towards NOx later during the day. In winter
months, mean daytime HCHO/NO2 ratios between 1 and 2 are observed,
which represent sensitivity towards both NOx and VOCs. The sensitivity
of the ozone production regime changes towards NOx with the onset of
summer and stays like that until the end of the post-monsoon season. Over
the Indo-Gangetic Plain, the strongest ozone pollution episodes are observed
in the summer and post-monsoon months during the afternoon hours between
12:00 and 16:00 LT (Kumar et al., 2016; Sinha et al., 2015). Surface
ozone measurements from Mohali have shown enhancement in its ambient
concentrations during the late post-monsoon as compared to the early post-monsoon even though the daytime temperature drops by 6 ∘C. During
summer, enhanced precursor emission from fires lead to an increase in
∼19 ppb ozone under similar meteorological conditions.
Considering the stronger sensitivity of daytime ozone production towards
NOx, the ozone mitigation strategies should focus on NOx emission
reductions.
Monthly mean HCHO VCD/NO2 VCD ratios (triangles) calculated
from MAX-DOAS measurements for the morning (09:30–11:30 LT, red), noon
around the OMI overpass time (12:30–14:30 LT, black), and late afternoon
(15:30–17:30 LT, blue) over Mohali. The lines at the centres of the boxes
represent the median; the boxes show the interquartile ranges whereas the
whiskers show the 5th and 95th percentile values.
Surface volume mixing ratios of NO2 and HCHO
The surface VMRs of NO2 and HCHO can be derived
from the MAX-DOAS measurements using the retrieved profiles. For MAPA, the
profiles are saved using output grids of a uniform thickness of 200 m.
The mean concentrations in the bottom-most layer have been used to calculate
VMR by considering the measured ambient temperature.
Figure 12 shows the time series of the NO2 VMRs measured with the in situ chemiluminescence analyser and that in the lowest 200 m
grid obtained from the profile inversion from the MAX-DOAS measurements. The
measurement frequency of the in situ measurements was 1 min, whereas, from
MAX-DOAS, the mean concentrations are retrieved from individual complete
sequences (∼10 min). Hence, we averaged the in situ
measurements to coincide with the exact MAX-DOAS measurement time. We
observe a reasonable agreement between the two measurements in terms of
temporal variability, but the MAX-DOAS surface VMRs are systematically
higher until the end of 2014. The differences between the two can probably
be explained by the difference in the horizontal and vertical
representativeness and resolution and have been discussed in detail in
Appendix E. Briefly, the surface VMR from MAX-DOAS represents the mean in
the bottom-most 200 m grid of MAPA output, while those from in situ measurements are
more sensitive to air mass sampled closed to the inlet. Hence MAX-DOAS VMRs
are representative of a larger area around the measurement site, and we
infer from Fig. 1 that the measurement location is relatively cleaner compared to the surroundings in terms of NO2 levels. The mean NO2 surface VMR from
MAX-DOAS measurements was 8.2 ± 6.7 ppb, whereas that from the
concurrent in situ measurements was 6.0 ± 5.0 ppb. If we consider all in situ
observations (which also include the night-time observation and periods
when MAX-DOAS measurements were unavailable), the mean NO2 VMR was 8.6±6.9 ppb. As compared to previous MAX-DOAS measurements from India
(Biswas et al., 2019), at a rural site (0.8 ± 0.2 ppb),
we observe much higher NO2 VMR in Mohali. However, these are comparable
to previous in situ NO2 VMR measured for a period of more than 1 year at
urban and suburban locations (distant from traffic) of India (e.g. Mohali:
8.9 ppb; Pune: ∼9.5/8.7 ppb; Kanpur: 5.7 ppb) (Gaur et
al., 2014; Kumar et al., 2016; Beig et al., 2007; Debaje and Kakade, 2009) but
smaller than near traffic urban measurement (e.g. New Delhi: 12.5/18.6 ppb; Agra: 15–35 ppb) (Saraswati et al., 2018; Tiwari et al., 2015; Singla
et al., 2011). Please note that we have used an NO2/NOx ratio of
0.9 to estimate NO2 VMR for comparison with the previous measurements
which reported NOx VMR and hence have a larger uncertainty
(Kunhikrishnan et al., 2006). The inset of Fig 12a shows the
monthly variability of surface NO2 VMR as a box-and-whiskers plot.
Similar to the VCDs, we observe maximum NO2 VMR in winter followed by
the crop residue burning active periods of post-monsoon and summer.
(a) Daily mean NO2 mixing ratios measured using an in situ
chemiluminescence analyser (black line) and average mixing ratio in the
lowest layer (0–200 m) retrieved by the profile inversion of MAX-DOAS
measurements (red line). The green and orange dots in the background show
the mixing ratios corresponding to the individual MAX-DOAS elevation
sequences flagged as valid and warning, respectively. The inset of (a)
shows the monthly variability of the MAX-DOAS surface NO2 VMR as a box-and-whiskers plot; (b) shows the frequency distribution of the BIAS
(MAX-DOAS – in situ) for the individual surface VMR measurements; (c) shows
the scatter plot between the daily mean surface NO2 VMR from MAX-DOAS
and an in situ analyser colour-coded according to the profile height (H75). The
individual measurements are shown as black dots in the background of (c).
Figure 13 shows the time series of the HCHO VMR measured
with the PTR-MS and those derived for the lowest 200 m layer obtained from
profile inversion of the MAX-DOAS measurements. The mean HCHO surface VMR
from MAX-DOAS measurements was 8.7 ± 7.5 ppb, whereas that from
concurrent in situ PTR-MS measurements was 3.3 ± 1.7 ppb. For HCHO, higher
VMR from MAX-DOAS measurements can be explained by its photochemical formation
at altitudes higher than the inlet of PTR-MS. This has been further
discussed in Appendix E. The measured HCHO VMRs are comparable to previous
MAX-DOAS measurements from India (Pantnagar: 2–6 ppb) but much lower than
those measured previously in India using offline techniques (e.g. north
Kolkata: 16 ppb; south Kolkata: 11.5 ppb) (Dutta et al., 2010; Hoque et al.,
2018). From the monthly variation in HCHO VMRs shown in the inset of Fig. 13a, we observe that the maximum is observed in late post-monsoon and winter
followed by late summer (crop residue burning active period). This monthly
variability is slightly different from that of the HCHO VCDs (discussed in
Sect. 3.1). A shallower profile (Fig. 5) in the late post-monsoon and
winter leads to high surface VMR even though the VCD is smaller than that in
late summer.
(a) Daily mean HCHO mixing ratios measured using an in situ
chemiluminescence analyser (black line) and average mixing ratio in the
lowest layer (0–200 m) retrieved by the profile inversion of MAX-DOAS
measurements (red line). The green and orange dots in the background show
the mixing ratios corresponding to the individual MAX-DOAS elevation
sequences flagged as valid and warning, respectively. The inset of (a)
shows the monthly variability of the MAX-DOAS surface HCHO VMR as a box-and-whiskers plot; (b) shows the frequency distribution of the BIAS
(MAX-DOAS – in situ) for the individual surface VMR measurements; (c) shows
the scatter plot between the daily mean surface HCHO VMR from MAX-DOAS and
PTR-MS colour-coded according to the profile height (H75). The
individual measurements are shown as black dots in the background of (c).
Figure F7 shows the mean diurnal profiles of the surface NO2 VMRs for
different months using MAX-DOAS and in situ measurements. A typical diurnal feature
representative of a suburban location was observed from the in situ measurements,
which is explained in detail elsewhere for Mohali for different seasons
(Kumar et al., 2016). For the daytime hours, when
MAX-DOAS observations are also available, we observe a general agreement in
the absolute values and temporal evolution between the two measurements in
all the seasons except winter. The occurrence of a morning peak for the in situ
measurements between 07:00 and 09:00 LT in all months is
primarily driven by emissions (traffic and biofuel combustion). For the
MAX-DOAS observations, a similar maximum occurs between 08:00 and 10:00 LT and between 09:00 and 11:00 LT in winter months. The time shift
compared to the maximum in the in situ measurements can be explained by the
accumulation of the surface emission in the boundary layer and breaking of
the nocturnal boundary layer. The latter causes the NOx present at high
altitudes (e.g. from power plants >200 m, Fig. 5) to mix in with
the surface layers. A modelling study performed using the CMAQ model has
shown that the peak in the diurnal profiles of NO2 columns occurs 2–3 h later than that for surface concentrations (Fishman et al., 2008). Since MAX-DOAS surface VMR
represents the mean over a few hundred metres, we expect similar behaviour
in its comparison with in situ measurements. In winter months, though the diurnal
trends are similar in both observations, a general overestimation by
MAX-DOAS is found, which is caused by a shallower boundary layer and the
presence of high NO2 mixing ratios above the inlet of the in situ analyser.
Conclusions
We have presented long-term (from January 2013 until June 2017) MAX-DOAS
measurements of NO2, HCHO, and aerosols from a regionally representative
suburban site, Mohali, in the densely populated north-west Indo-Gangetic Plain. MAX-DOAS radiance measurements at 360 nm and a colour index (ratio of
measured radiances at 330 and 390 nm) were employed to quantitatively
determine the prevalent sky conditions. Clear sky with high aerosol load
conditions (AOD at 360 nm >0.85) was observed for about half of
the measurement period. The profile inversion algorithm MAPA was used to
derive the aerosol optical depth, vertical column density of NO2 and
HCHO, and vertical profiles of aerosol extinction, NO2, and HCHO.
A mean AOD at 360 nm, tropospheric NO2 VCD, and HCHO VCD for the
measurement period were observed to be 0.63 ± 0.51, (6.7 ± 4.1) × 1015, and (12.1 ± 7.5) × 1015 molecules cm-2, respectively, with substantial seasonal
variations in all the measured parameters. While the NO2 VCDs are
generally lower than those observed in the suburban and urban location of
China and western countries, the HCHO VCDs are comparable to previously
reported values. Despite the rapid urbanization, no evident annual trends
were observed in AOD, NO2, and HCHO VCDs for the measurement period. The
seasonal trends are rather driven by non-organized anthropogenic sources of
emissions, e.g. agricultural residue burning, waste burning, and biofuel
burning for heating and cooking. Early summer (March) and monsoon (July and
August) months are the cleanest with respect to the measured NO2 and
HCHO VCDs, but high AOD was observed during the monsoon likely due to
hygroscopic growth of the aerosol particles. Maximum NO2 VCDs were
observed in winter months, followed by the periods of post-monsoon and summer
when extensive crop residue burning is practised in the agricultural regions
near the measurement site. Maximum formaldehyde VCDs were observed in the
summer and post-monsoon seasons when photochemical production from
precursors emitted from agricultural residue fires is favoured by
meteorological conditions (high temperature and strong solar radiation).
Biogenic sources were also found to be crucial for formaldehyde during the
monsoon and early post-monsoon periods. The vertical profiles retrieved from
MAX-DOAS measurements show that the major fraction of the NO2 column is
located close to the surface (0–200 m) in all the seasons, while the
same is true for HCHO only in winter. In other seasons, comparable HCHO
mixing ratios are also observed in higher layers until 600 m indicating
active photochemistry at higher altitudes. Interestingly, the seasonal
variation in the vertical profiles of aerosol extinction did not depend on
the significant change in the ERA5 boundary layer heights between summer and
monsoon. In addition to serving as an input for the retrieval of VCDs from the
preliminary satellite data analysis, vertical profiles retrieved from
MAX-DOAS measurements can also be used to validate the regional atmospheric
chemistry models around the measured location.
We observed a very good agreement in the temporal trend, and a slight
overestimation by the high-resolution MODIS MAIAC data product around Mohali
with respect to the AOD retrieved from MAX-DOAS O4 measurements. The
OMI aerosol data product “OMAERUV” generally underestimated the AOD at
Mohali compared to MAX-DOAS, and the accountability is also not as good as
for the MAIAC product. In an ideal case, sun photometer measurements, which
provide AOD at 360 nm, are best suited for an intercomparison with the
MAX-DOAS measurements. However, such measurements were not available at our
measurement location, and for future studies, sun photometer
measurements are recommended for a direct comparison of the AOD at 360 nm. We
use the MAX-DOAS measurements of NO2 and HCHO to evaluate the three
widely used NO2 data products (DOMINO v2, QA4ECV, and OMNO2) and two
HCHO data products (QA4ECV and OMHCHO) of OMI for the first time over India
and the Indo-Gangetic Plain. Among the three OMI NO2 data products, we
observe reasonable agreement between MAX-DOAS and OMI for the latter
two. However, all three OMI data products underestimate the MAX-DOAS
NO2 VCDs by 30 %–50 %. The maximum discrepancy is observed in late post-monsoon and in the winter months when a large amount of trace gases and
aerosol is trapped in the air close to the surface. A major reason behind
the discrepancy between the MAX-DOAS and OMI observations is the inaccurate
representation of the a priori NO2 profiles and aerosol profiles used
for the calculation of air mass factors in satellite retrievals. If we account
for the decreased satellite sensitivity close to the ground, a smaller bias
is observed between the MAX-DOAS and OMI NO2 VCDs. Because of their large
ground footprint, OMI measurements are representative of a larger area
around the MAX-DOAS measurement site, which is also responsible for part of
the discrepancy. Two OMI data products of HCHO are also compared against
MAX-DOAS observations. The QA4ECV HCHO product was found to exceed the
MAX-DOAS HCHO VCDs by ∼30 % overall, whereas a generally
good agreement was found with the OMHCHO data product. Using the ratio of
HCHO and NO2 VCDs measured from MAX-DOAS, we found that the daytime
ozone production is sensitive towards NOx in summer, monsoon, and post-monsoon, whereas a strong sensitivity towards both NOx and VOCs was
observed in winter. We observed a transition from stronger VOC sensitivity
to stronger NOx sensitivity on ozone production from morning to late
afternoon.
The mean surface VMRs of NO2 and HCHO retrieved
from MAX-DOAS observations for the measurement period were 8.2 ± 6.7 ppb and 8.7 ± 7.5 ppb, respectively. We have compared the surface
volume mixing ratios retrieved from MAX-DOAS measurements with those
measured using in situ analysers. The temporal intraday and the day-to-day
variations in the NO2 surface VMR agree well for both measurements, but
the MAX-DOAS measurements were generally higher than those measured using
the in situ analysers, with a bias of 0.67 ± 2.85 ppb. For HCHO, however, a
poorer agreement in the temporal trend and a large bias of 2.29 ± 4.47 ppb was observed between MAX-DOAS and in situ measurements. The observed
differences can be mainly attributed to the differences in vertical and
horizontal representativeness of both the measurements. The MAX-DOAS surface
VMRs are representative of trace gases (e.g. HCHO or NO2) located from the surface to altitudes of up to a few hundred metres. We found evidence of
vertical gradients of photochemically formed compounds and hence stronger
differences were observed when a significant fraction of trace gases is
present at altitudes higher than the inlet of in situ analysers.
MAX-DOAS instruments can provide stand-alone routine measurements of trace
gases and aerosol from remote locations, which can be further employed for
air quality assessment, similar to the AERONET global network. (e.g. PANDORA,
NDACC: https://www.ndaccdemo.org/instruments/uv-visible-spectrometer, last access: 17 November 2020). A plethora of studies related to satellite observations
of tropospheric pollutants from China and western countries have sensitized
the authorities to reduce emissions to curb the pollution (de Foy et al., 2016). MAX-DOAS measurements were of substantial importance to validate
and complement the satellite observations in these studies. With the Indian
subcontinent being projected as the new hotspot of anthropogenic emissions (Li et al., 2017; De Smedt et al., 2018), similar (or new generation, e.g.
TROPOMI, GEMS) satellite and corresponding MAX-DOAS observation will be of
great importance for future studies.
A performance of the NO2 retrieval in the UV and visible spectral range
and comparison of the NO2 VCD obtained from the profile retrievals to
the results from the geometric approximation
The DOAS spectral analysis and profile inversion have been performed both in
the UV and visible spectral ranges. As mentioned in Sect. 2.2 of the main
text and also shown in Fig. A1 for a smaller subset of data from 2015, the
analysis in the UV results in larger fit uncertainties, which eventually
leads to a poorer performance of the profile inversion.
For an elevation angle sequence, MAPA finds the best fit of the model dSCDs by minimizing the difference compared to the measured dSCDs. The difference
is quantified by the rms (R), which is defined as
R=(Sfm-Sms)2Neas.
Here Sfm and Sms represent dSCDs sequences from model and
measurements and Neas represents the number of elevation angles in the
measurement sequence. Warning and error flags are raised for the retrieved
profile if the following two conditions are fulfilled:
Rbm (rms for the best matching model dSCD) exceeds the sequence median
dSCD uncertainty.
Rn (the ratio of Rbm and maximum dSCD of a sequence) exceeds the
predefined thresholds (Beirle et al., 2019).
The larger fit uncertainties of the NO2 analysis in the UV directly
result in larger errors in the NO2 dSCDS. These larger errors lead to
larger residuals and hence larger Rbm, leading to more flagged
sequences. Here we check if the threshold for Rn can be relaxed from
that (0.05) recommended by Beirle et al. (2019) for the
NO2 retrieval in the UV while still retaining reasonable retrievals. We
have calculated the total retrieval quality flag as described in Sect. 2.8.6 of Beirle et al. (2019) by varying Rn from
0.05 to 0.15. A larger Rn leads to a larger number of retrievals being
flagged as valid. The retrieved VCDs and concentrations in the layer closest
to the surface derived in the UV for different Rn values are compared
to those in the visible as reference. We find that increasing Rn from 0.05
to 0.15 for the UV retrieval almost doubles the number of valid retrievals
while keeping similar statistical agreement as compared to Vis retrievals.
From Fig. A2, we see that the slope of the linear regression of the NO2
VCDs between UV and Vis remains close to 0.95, while the correlation
coefficient (r) only changes from 0.91 to 0.89. Similar results are obtained
for the surface concentration, where the slope of the linear regression and
correlation coefficient (r) only slightly change from 0.87 and 0.94 to 0.85
and 0.93, respectively.
For several years, the geometric approximation has been used for the
calculation of VCDs from the dSCDs retrieved from DOAS spectral analysis. If
only the photon light path is taken into account with the assumption that
the last scattering event before the photon reached the telescope of the
instrument has happened above the trace gas layer, the geometric air mass
factors for an elevation angle α can be calculated
trigonometrically:
AMFα,Geo=1sinα.
Since the geometric approximation relies on the single scattering
approximation above the trace gas layer, it is expected to work well under
clear-sky conditions with low aerosol loads and for trace gases confined to
layers close to the surface. Generally, there is a trade-off between the
sensitivity and validity of geometric approximation for the choice of
elevation angle. At low-elevation angles, though the measurements are more
sensitive, the probability of scattering within the trace gas layer is
rather high. We have chosen an elevation angle of 15∘ for the
calculation of geometric VCDs. Several studies (Bösch, 2018; Wagner et
al., 2011; Jin et al., 2016) have shown that the geometric VCDs usually agree
within 20 % to the VCDs that are retrieved using radiative transfer
simulations if the trace gas is not located at higher altitudes
(>1000 m) and trace gas is confined within the aerosol layer. In
Fig. A3, we compare the VCDs calculated from the geometric approximation and
from valid MAPA retrievals for various sky conditions, both for NO2 in
the UV and visible and for HCHO (in the UV).
As expected, we see an excellent agreement between the NO2 geometric
VCDs and those retrieved from the profile inversion in clear-sky conditions
with low aerosol load both for the UV and visible retrievals. The level of
agreement is still reasonable in high aerosol conditions and cloudy cases;
however, for such conditions, the geometric VCDs are systematically lower.
This finding is in line with that observed by Wagner et al. (2011). It indicates the effect of
photon scattering within the trace gas layer. We also observe that the
agreement of NO2 VCDs between geometric approximation and profile
inversion is better for the UV than for the visible retrieval. If we chose a 30∘ elevation angle for the calculation of the geometric VCDs,
reasonable agreement (r>0.9) with the VCDs retrieved from
profile inversion is observed only for the visible retrievals. For the
NO2 retrievals in the UV, reasonable agreement (r=0.86) was observed
for clear sky with low aerosol load, but a larger scatter (r<0.8)
is found for clear-sky conditions with high aerosol load and for cloudy-sky
conditions.
Similar to NO2, we also observe the best agreement for the HCHO VCDs
retrieved using the geometric approximation and those using the profile
inversion for clear-sky conditions with low aerosol load. For high aerosol
loads and cloudy-sky conditions, the VCDs from the geometric approximation
are significantly lower. In contrast to NO2, a significant fraction of
formaldehyde is usually found in higher layers, which limits the validity of
geometric approximation. The bias gets higher for HCHO VCDs greater than
∼1.5× 1016 molecules cm-2. If we chose
observations at a 30∘ elevation angle for the calculation of the
geometric VCD, lower correlation with the VCDs retrieval using the profile
inversion was observed (r<0.8) for all sky conditions.
We also checked the internal consistency of the NO2 dSCDS retrieved
from the DOAS analysis in the UV and visible. For the comparison, even for a geometric approximation, we only consider those sequences for which the
profile retrieval was flagged valid. From Fig. A3 (panels j–l), we see that
in clear-sky cases, NO2 VCDs from the geometric approximation in the
visible are systematically higher than those retrieved in the UV. The
Rayleigh scattering probability is inversely proportional to the fourth
power of the wavelength, and therefore in the UV, the higher scattering
probability results in shorter light paths within the trace gas layer. Since
the AMFs from the geometric approximation are independent of the wavelength,
the shorter dSCDs results in smaller VCDs from the geometric approximation
in the UV. The radiative transfer models, however, account for different
path lengths in UV and the visible. Hence, the VCDs retrieved using MAPA do not show
any systematic bias in UV or the visible (Fig. A2). It is interesting to note that
the difference in the NO2 VCDs retrieved from the geometric
approximation in the UV and visible is much smaller for cloudy-sky
conditions. For such conditions, the differences in the light path lengths
in the UV and visible are usually much smaller than for clear-sky
conditions.
DOAS fit errors of the NO2 retrievals in the UV and
visible wavelength windows.
Comparison of NO2 VCDs (a, c) and surface concentrations
(b, d) retrieved in the UV against those retrieved in the visible. Please note
that the number of valid data points shown in this figure corresponds to the
number of valid overlapping retrievals in UV and the visible. The number of valid
retrievals for NO2 in UV shown in Table 2 corresponds to the
Rn flag with a threshold <0.15.
Comparison of VCDs calculated using the geometric approximation
at 15∘ elevation angle and that calculated using MAPA for NO2
in the UV (top row), NO2 in the visible (second row), and HCHO (third
row) for various sky conditions: clear sky with low aerosol load (left),
clear sky with high aerosol load (middle), and cloudy-sky conditions except for thick clouds and fog (right). The bottom row shows the comparison of
NO2 VCD calculated using the geometric approximation for spectral
analyses in the UV and visible for the three different sky conditions.
Identification of thick clouds
For the identification of thick clouds, Wagner et al.
(2016) proposed the use of an absolute calibration method for the O4
absorption in the Fraunhofer reference spectrum. However, this method can
only be applied for relatively short time periods, over which the spectral
properties of the MAX-DOAS instrument stay almost the same. For the
measurements used in this study, this method cannot easily be applied
because the spectral properties of the Mini-MAX-DOAS instruments are known
to vary rather strongly even within rather short periods of time (a few days
to weeks). In addition, for the measurements used in this study, the
detector temperature had to be changed frequently according to the seasonal
variation in the ambient temperature. Therefore, it was impossible to create
a consistent time series of absolutely calibrated measured O4
absorption over the complete measurement period. Therefore, the
identification of thick clouds was performed based on the measured radiances (Wagner et al., 2014; Wang et al., 2015)
While optically thin clouds often lead to an increase in the measured
radiance, optically thick clouds lead to a strong decrease (compared to
clear-sky radiance). However, such an approach can only be applied if there
is no significant degradation in the measured radiance over the whole
measurement period. Figure B1 shows the time series of the measured radiances
(at 360 nm) for an SZA interval between 55 and 60∘. A
significant decrease is not observed in the measured radiance during measurement period of more than 4 years, except for the intra-annual seasonal
variation. Hence, we can use the measured radiance for the identification of
thick clouds. Please note that the strongly increased values of the
radiances are caused by optically thin clouds. Note that the linear trend
was fitted only for the period from 1 January 2013 to 31 December 2016 to minimize the
impact of the seasonal variation.
In order to identify the optically thick clouds, the measured radiances in
the zenith direction have to be compared to an SZA-dependent reference value
(see Wagner et al., 2014 and 2016), which is obtained from RTM simulations for a well-defined
atmospheric scenario. However, as these instruments are usually not
radiometrically calibrated, the measured radiances cannot be directly
compared to the reference radiances derived from the RTMs. Hence, we first
have to perform a calibration of measured radiances.
For the calibration, we first calculated the radiance in the zenith
direction at 360 nm for an SZA range between 7 and 95∘
using the RTM McARTIM (Deutschmann et al., 2011). The simulations
are done for an AOD = 0.3, aerosol particles properties as proposed by
Wagner et al. (2014) and a surface albedo of 5 %. In
the next step, 2 clear days in summer (such that the measurements are
available even at solar zenith angles less than 10∘) with AOD (at
360 nm) close to 0.3 (as measured by MODIS) are selected. By comparing the
SZA dependence of the measured radiances with the simulated radiances (Fig. B2), we determined the calibration factor to be 1.48 × 10-8 counts-1 for our measurements. Here it should be noted that from the
similar relative SZA dependences of the measured and simulated radiances at
larger SZA, we can conclude that the AOD at 360 nm was similar to 0.3.
Figure B2a shows the variation in the calibrated measured
radiances on the 2 selected days and the polynomial fit of the simulated
radiances as a function of the SZA. It should be noted that in contrast to
Wang et al. (2015), we have observed good agreement
between the measured and simulated radiances even for solar zenith angles
less than 40∘. In the study by Wang et
al. (2015) in Wuxi, the disagreement was attributed to a possible deviation
from the parameterized Henyey–Greenstein aerosol phase function. For Mohali,
a better agreement is probably related to the presence of a different
aerosol type compared to that in Wuxi. A single fifth-order polynomial was
fitted to the calibrated measured radiances in the complete SZA range to
derive an SZA-dependent function of the clear-sky normalized radiance, which
is subsequently used as an SZA-dependent reference.
For the identification of thick clouds, we have used a threshold of 0.94
times the SZA-dependent threshold radiance similar to
Wang et al. (2015). In order to check the
consistency of the thick-cloud identification using the normalized
radiances, we have also performed thick-cloud identification based on the
O4 AMFs as described by Wagner et al. (2016) for a
small period in July 2014. Figure B3 shows an excellent consistency of the
thick-cloud identification using the two methods. In panel a, the
periods having smaller radiances than the threshold show larger O4 AMFs
than the respective threshold in the bottom panel.
Time series of the measured radiances at 360 nm derived from the
MAX-DOAS measurements in the zenith viewing direction for SZA between
55 and 60∘. Please note that the trend is calculated
only for the measurements from 1 January 2013 to 31 December 2016 to minimize the
effect of seasonal variation. The dark black line indicates the linear
regression of the measured radiance as a function of day number.
Dependence of the measured and simulated radiances in zenith
direction (at 360 nm) on the solar zenith angle for the 2 selected days
(a). Panel (b) shows all measured normalized radiances (after
calibration) in zenith direction, colour-coded for three sky conditions:
black – cloudy sky; orange – clear sky with low aerosol load; blue – clear sky with high aerosol load. All measurements corresponding to thick clouds lie
below the threshold (shown as green curve) in (b).
(a) Measured calibrated radiances in the zenith direction and (b) O4 AMFs derived for zenith direction for a selected short period of the
measurements, over which the instrument properties were almost constant.
Also shown are the simulation results for the SZA-dependent thresholds. On
some days greatly enhanced O4 AMFs are found, indicating the presence of
optically thick clouds. On these days, greatly decreased radiances are also measured.
Satellite data products used for comparison against MAX-DOAS measurementsOMAERUV
The OMAERUV algorithm uses the Lambert equivalent reflectivity (LER) calculated from the measured radiance at 388 nm to first
yield the AODs and aerosol absorption optical depth (AAOD) at 388 nm.
Subsequently, using an inherent aerosol model, the AOD at 354 and 500 nm
are calculated (Torres et al., 2007). The OMAERUV V1.8.9.1 product
used in this study also provides an indicator for the data quality named
“FinalAlgorithmFlags” corresponding to every measurement. For the
intercomparison in this study, we have only retained the data corresponding
to a “FinalAlgorithmFlags” value of 0, which represent the most reliable
retrievals of AOD.
DOMINO V2
The DOMINO version 2.0 NO2 product (Boersma et al., 2011) makes use of the
measurements in the 405–465 nm wavelength interval. The NO2 SCD retrieved by employing the DOAS technique in the first
step is separated for stratospheric and tropospheric composition. The DOAS
fit includes the absorptions due to NO2, O3, O4, H2O
(l), H2O (g), and the Ring effect. Subsequently, the tropospheric SCD is
converted into the tropospheric VCD using the tropospheric air mass factors.
Both the calculation of the air mass factor using a radiative transfer model
and the estimation of the stratospheric NO2 uses the NO2 fields
from a 3∘× 2∘ spatial resolution global
chemistry transport model TM4.
OMNO2 v 3.0
The OMNO2 algorithm developed by NASA (Marchenko et al., 2015) uses the 402–465 nm fit window
for the NO2 retrieval. In the first step, the wavelength calibration
and Ring correction are performed in seven sub-windows within the spectral
range. The DOAS fit involves an iterative process, in which first a
preliminary estimate of NO2, H2O, and glyoxal is made using fits in
smaller sub-windows of the full spectral range of 402–465 nm. The absorption
due to this initial estimate is used for the determination of instrumental
noise in the original spectrum. The noise-corrected spectrum in the next
stage is again subjected to a DOAS fit including the absorptions from
NO2, H2O (g), glyoxal, and the Ring effect in the 402–465 nm spectral
range to retrieve the NO2 SCDs. A stratospheric correction is applied,
and air mass factors are applied to retrieve the tropospheric NO2 VCDs.
The NO2 a priori vertical profiles used for the calculation of the AMF are
taken from the climatology of the 4-year-long global model initiative (GMI)
2∘× 2.5∘ spatial resolution chemistry
transport model simulation. The cloud information is incorporated from the
external OMCLDO2 product, which calculates the cloud fraction using the
contrast in measured radiances between clear and cloudy pixels. The cloud
pressure (an indicator of cloud height) is calculated using the O4
absorption at 477 nm.
QA4ECV
In this work, we also use the OMI QA4ECV (quality assurance
for essential climate variables) data product for NO2 (Boersma et al., 2018) and HCHO (De Smedt et al., 2018) for comparison with
respective MAX-DOAS measurements. The DOAS retrieval of NO2 SCDs uses a
similar wavelength window and absorbers as DOMINO v2 but includes an
additional intensity offset. Also, an optical depth fit is performed in
place of an intensity fit. While for DOMINO the wavelength calibration was
performed prior to the fit in the 409–428 nm window, for QA4ECV, it is
performed along with the DOAS fit in the 405–465 nm wavelength window. The
most significant improvement in the QA4ECV NO2 retrieval concerns the
AMF calculation and the stratospheric background correction. QA4ECV uses the
1∘× 1∘ spatial resolution TM5 model for the
calculation of a priori NO2 profiles. In several studies, QA4ECV
NO2 products are shown to have a better agreement with ground-based
measurements and a smaller uncertainty than the other OMI NO2 data
products (Boersma et al., 2018; Chan et al., 2019; Zara et al., 2018).
The QA4ECV HCHO algorithm performs a DOAS optical depth fit including HCHO,
O3, BrO, NO2, O4, and the Ring effect in the wavelength
interval 328.5–359 nm to obtain the HCHO SCDs (Zara et al., 2018). The across-track stripes
observed in the retrieved dSCDs are corrected by subtracting an OMI detector
row-dependent mean equatorial pacific HCHO SCD. The a priori vertical
profiles of HCHO are also calculated using the 1∘× 1∘ spatial resolution TM5 model.
OMHCHO
The OMHCHO v003 (González Abad et
al., 2015) is a level 2 formaldehyde data product from NASA. The HCHO slant
column densities are retrieved by employing a DOAS intensity fit in the
328.5–356.5 nm wavelength window, which includes the absorptions from HCHO,
O3, NO2, BrO, O4, and the Ring effect. Similar to OMNO2, the
air mass factor for the conversion of the SCD into the VCD is calculated by
considering climatological HCHO vertical profiles. The cloud information is
also taken from the OMCLDO2 product. In order to account for the observed
across-track stripes in the VCD, a normalization is performed with respect
to the GEOS-Chem model calculated monthly climatological means over the remote Pacific.
MAIAC
The collection 6 MCD19A2 level 2 gridded product from MODIS
provides the 1 × 1 km2 spatially resolved AOD at 470 and 550 nm based on the MAIAC (Multi-Angle Implementation of Atmospheric Correction)
algorithm. In contrast to previous swath-based retrievals for individual
ground pixels, the MAIAC algorithm grids the L1B top-of-atmosphere
reflectance in 1 × 1 km2 predefined sinusoidal grids prior to
further processing. For each grid, data up to 16 d are accumulated, which
include measurements at various viewing geometries from different orbits.
The AOD retrieval relies on the ratio of measured spectral regression
coefficients (SRC) at Band3 (459–479 nm)/Band7 (2105–2155 nm) and
Band3/Band4 (545–565 nm). The analysis of time series of SRC enables the
separation of the relatively static surface properties and fast varying
atmospheric properties (e.g. AOD).
To generate a time series of AOD for comparison with MAX-DOAS, we have
extracted the MODIS data spatially averaged within 2 km of the measurement
location in Mohali. We have assumed a linear dependence between the
logarithms of the wavelength and the AOD in the wavelength range 360 and
550 nm to convert the MODIS AOD measured at 470 nm using the Ångström
exponent calculated according to Eq. (3). We have only retained the highest-quality MODIS AOD measurements corresponding to a QA value of “0000” in
bits 8–11 provided in the MAC19A2 dataset (Lyapustin et
al., 2018). This criterion removes all the data contaminated by clouds and
those adjacent to cloudy pixels.
Evaluation of the air mass factors and a priori profiles in the OMI
retrievals
The sensitivity of satellite instruments is usually characterized by the
so-called bAMF profiles (Eskes and
Boersma, 2003). bAMFs can be regarded as the air mass factors for discrete
atmospheric layers. They can be integrated from the surface until the tropopause
(Trop), weighted by the trace gas profile, according to the following
equation to get tropospheric air mass factors.
AMFTrop=∑i=0TropbAMFiVCDiVCDTrop
The vertical profiles of trace gases and aerosol extinction are a piece of
important information needed to derive VCDs from the satellite measurements.
Due to the absence of such measured information, global chemistry, usually
transport, models (e.g. a coarse 2.5∘× 2.5∘
spatial resolution TM4 for DOMINO and finer 1∘× 1∘ spatial resolution TM5 for QA4ECV) are employed. From Eq. (D1) it
is evident that if a relatively small fraction of an absorber (for e.g.
NO2) is located close to the ground in the a priori profiles, the
resulting AMFs become positively biased and, finally, the VCDs become
negatively biased. The finer horizontal resolution of the a priori NO2
profiles in the QA4ECV product probably results in a more accurate
representation of the NO2 vertical profiles, especially close to strong
emission sources like Mohali, and thus improves the retrieved NO2 VCD.
In order to further investigate the underestimation (in particular late post-monsoon and winter months), we first calculate the box air mass factors (with
the RTM McARTIM) at 30∘ SZA over Mohali using mean aerosol
extinction profiles (Fig. F16) retrieved from MAX-DOAS measurements and
compare them with bAMFs used for the DOMINO and QA4ECV NO2 retrievals.
We perform this comparison for September (representative of early post-monsoon or clean conditions), October, and November (representative of late
post-monsoon and winter, respectively) (Fig. D1). Two striking
features are observed:
The overall vertical variability of the bAMFs is different in the satellite
products compared to the bAMF calculated for the MAX-DOAS aerosol profiles.
The calculated bAMFs indicate a rather sharp increase with altitude until
the first 1000 m.
The calculated bAMFs show systematically higher values close to the surface
than those used in the satellite retrievals. The largest underestimation is
found for the DOMINO bAMF, which uses the rather coarse TM4 model input.
The smaller bAMF of the satellite product in the lower layers indicates a
smaller weight of these layers where a major fraction of NO2 is
present. Hence, the AMFs will become smaller and the VCDs become higher if
the relative a priori NO2 profiles for satellite data retrieval are
be adjusted to the observed profiles. However, if the a priori profiles
assume a smaller fraction of NO2 in layers close to the surface, higher
layers will get a larger weight in Eq. (D1), resulting in larger AMF and
smaller VCD. Hence, in the next step, we compare the a priori NO2
profiles of the satellite data product with those retrieved from MAX-DOAS
measurements. Unfortunately, our comparison is limited to the DOMINO
product, as the a priori NO2 profiles are not available for the other
products.
Figure D2 shows monthly mean relative vertical profiles of NO2 retrieved
by MAX-DOAS and the corresponding TM4 profiles. We can clearly notice two
differences:
The vertical gradient in the relative profile shape is stronger in the
MAX-DOAS profiles than in the TM4 product.
The TM4 relative a priori shape is somewhat similar in all months, whereas
the profile shape retrieved from DOAS changes strongly with season. More
than half of the NO2 is located in the bottom-most layer in the winter
months.
The consequence of the first observation is that the total air mass factors
will generally be higher if the TM4 profiles are used as a priori, which
results in smaller VCDs as also observed in Fig. 8. The consequence of the
second observation is that in the winter months, the discrepancy due to the a
priori profile is even stronger, resulting in a larger disagreement with the
measured MAX-DOAS VCDs.
Accurate satellite AMFs can be recalculated using MAX-DOAS vertical profiles
as a priori, which could improve the agreement with MAX-DOAS observations as
also shown by De Smedt et al. (2015). The satellite AMFs corresponding to the MAX-DOAS a priori profiles
(xm) can be recalculated according to the following equation:
AMFtrop(xm)=AMFtot(xa)∑l=1LAlxm,l∑l=1Lxm,l.
Here, xa is the original satellite (TM5) a priori trace gas profile, L is the tropopause level index, A represents the satellite averaging kernels, and AMFtot is the total air mass factor.
Total AMF (AMFtot) and satellite averaging kernels used for OMI
retrievals are crucial information required to recalculate the satellite
(OMI) AMF. For NO2, only the DOMINO product provides both AMFtot
and satellite averaging kernels in the data product. We attempted to
recalculate the AMFtrop using the MAX-DOAS profiles, but this
resulted in very small air mass factors (and very large recalculated OMI
VCDs). The small AMFtrop is due to the fact that the MAX-DOAS
profiles do not account for the background NO2 in the free troposphere,
where the satellite averaging kernels are large. In the next step, we used
hybrid profiles such that we only replaced the profiles in the lowest 5
and 2.5 km of the TM5 profile with those retrieved from MAX-DOAS
measurements. The observations are summarized in Fig. D3. We note that even
with the hybrid approach, there is an overestimation of VCDs for many
months. This is probably caused by the incorrect aerosol profiles used for
the calculation of the averaging kernels in the satellite analyses.
For formaldehyde, AMFtot is not provided in the QA4ECV products,
while averaging kernels are not available for OMHCHO products. However, we
approximated AMFtot to be close to AMFtrop because of the
negligible amount of HCHO present in the stratosphere. Using this
approximation, we have recalculated the modified AMFtrop using
MAX-DOAS profiles as a priori. Similar to NO2 we have replaced the
profile in the lowest 2.5 km of the TM5 profile with that retrieved from
MAX-DOAS measurements. For HCHO, the modified VCDs are largely positively
biased. Like for NO2, this overestimation might be caused by incorrect
aerosol profiles used for the calculation of the averaging kernels in the
satellite analyses.
Monthly mean box air mass factors used in the DOMINO (a) and
QA4ECV (b) NO2 data products under clear-sky conditions (cloud fraction <0.1) over Mohali for September (when the aerosol load is small)
and October and November (when the aerosol load is high close to the
surface). Panel (c) shows the corresponding box air mass factors
calculated using MAX-DOAS aerosol extinction profiles.
Monthly mean relative a priori NO2 profiles over Mohali
retrieved from MAX-DOAS measurements around the OMI overpass time (between
12:30 and 14:30 LT) (a) and the TM4 a priori profiles used for the
DOMINO retrieval (b). The a priori profiles are not available for the QA4ECV
NO2 product.
Time series of the MAX-DOAS and OMI DOMINO NO2 tropospheric
VCDs for (a) when no modifications in the a priori profiles are applied, (b)
when the a priori profiles of DOMINO are replaced by the MAX-DOAS profiles,
(c) when the lowest 5 km of the profiles of the a priori are replaced by the
corresponding MAX-DOAS profiles, and (d) when the lowest 2.5 km of the
profiles of the a priori are replaced by the corresponding MAX-DOAS
profiles.
Time series of the MAX-DOAS and OMI QA4ECV HCHO tropospheric VCDs
for (a) when no modifications in the a priori profiles are applied, (b) when
the a priori profiles of QA4ECV are replaced by the MAX-DOAS profiles, and (c)
when the lowest 2.5 km of the a priori profiles are replaced by
corresponding MAX-DOAS profiles.
Comparison of surface concentration of NO2 and HCHO from MAX-DOAS and
in situ measurements
In addition to the comparison with the NO2 and HCHO VCDs from satellite
data products, the surface concentration derived from MAX-DOAS is also compared with in situ measurements. From this comparison, a first-order assessment
of the quality of the profile retrieval is obtained (Wang et al.,
2019; Vlemmix et al., 2015). Often systematic differences of up to 30 % are
found between MAX-DOAS and in situ measurements which are mainly related to the
limited vertical (and horizontal) resolution of the MAX-DOAS profiles
(Vlemmix et al., 2015; Wang et al., 2019; Li et al., 2013). The vertical
resolution of the profiles retrieved from MAX-DOAS measurements depends
strongly on altitude.
Taking the standard deviation of the daily means into account for an ODR fit
further improves the agreement between the MAX-DOAS and in situ measurements
of the NO2 surface VMRs. The frequency distribution of the bias between
the two measurements shows a normal distribution which peaks at
∼0.7 ppb. Please note that in Fig. 12, we have used all
profiles which were flagged as valid and warning by MAPA. The linear
correlation coefficient (r) changes slightly from 0.62 to 0.60 if only valid
retrieval results were considered, while for the ODR fit, the slope and
intercept change from 0.83 and 1.78 to 0.90 and 1.39, respectively. MAX-DOAS
is sensitive towards air masses in the viewing direction of the instrument, whereas in situ analysers are sensitive for air directly sampled by the inlet.
NO2 is primarily emitted (or converted very fast from NO near the
source) close to surface. So, if the measurements are performed in the
vicinity of emission sources, we expect higher NO2 from in situ measurements
than MAX-DOAS, which provides a mean concentration in the 0–200 m output
grid. This was also observed in previous intercomparison studies (Wang et
al., 2019; Li et al., 2013; Piters et al., 2012). To a surprising extent, we
observe that until the end of 2014, most surface VMRs from MAX-DOAS are
systematically higher than the in situ measurements, while afterwards, the
differences become smaller. A plausible reason for the positive bias is the
plumes from the Rupnagar power plant (PP1, Fig. 1), ∼45 km far
from Mohali in the north-west direction, which also is the viewing direction
of the MAX-DOAS instrument. Pawar et al. (2015)
have previously shown back trajectories of air mass arriving at Mohali for a
period of 2 years (2011–2013). Except during monsoon, more than 80 % of the
back trajectories were among the clusters “westerlies”, “local”, or “calm”,
all of which include the location of PP1. In monsoon, these clusters
accounted for more than 50 % of the total. From the wind rose plot of Fig. F1, we also observe that in all the seasons except monsoon, the major fetch
region includes PP1. The Rupnagar power plant was active with 90 % of its
capacity until October 2014 and operated only with 20 % of its capacity
until the ceasing of its operation in 2018
(https://timesofindia.indiatimes.com/city/chandigarh/Punjab-shuts-10-of-14-thermal-power-plants/articleshow/44730937.cms, last access: 19 November 2020). The power plant plume is emitted directly at an altitude (∼100 m), much higher than the inlet height of the
in situ measurements (∼15 m). Due to their coarser vertical resolution,
the MAX-DOAS surface VMRs are also influenced by the NO2 at higher
altitudes (e.g. from the power plant plume). During stagnant conditions, the
vertical mixing is suppressed, and we expect a larger bias between the two
measurements. From the MAX-DOAS NO2 profiles, we can also estimate the
extent of the vertical mixing of NO2 in terms of the characteristic
profile height (H75). Figure 12c shows the scatter plot between the
MAX-DOAS and in situ surface VMRs of NO2 colour-coded according to H75.
We observe that for profile heights of less than 200 m, the MAX-DOAS
surface VMRs are more positively biased than for higher H75. During
the summer months (March–June), due to the radiative heating of the surface,
vertical mixing is enhanced and leads to a higher H75 (Fig. 6). Also,
the down-mixing of the power plant plume to the surface is more efficient
during such conditions. Hence, during summer 2013, even though the power
plant was operational at high capacity, we see a smaller bias between the
two datasets. For some applications, the limited vertical resolution of
MAX-DOAS instrument can be regarded as an advantage in terms of robustness
against stratification in stable meteorological conditions and yield a more
spatially representative value.
The horizontal heterogeneity of the NO2 VMR and differences in the
spatial representativeness of the measurements can also add to the observed
bias in the overall measurement period. The measurements were performed
within an educational institute campus, located in the south-east corner of
the tri-city Panchkula, Chandigarh, and Mohali. From the high-resolution
TROPOMI NO2 maps for the year 2018 (Fig. 1), we can observe that the
measurement location is relatively clean (with respect to the NO2 VCD)
compared to the surrounding regions. The viewing direction of the MAX-DOAS
instrument is towards the city, and the horizontal sensitivity along the
range of sight is typically a few kilometres. Thus, the MAX-DOAS
measurements are sensitive for an urban air mixture consisting of higher
NO2 than at the measurement location. Post-2014, the bias is within
20 %, similar to those observed in previous studies, which can be
attributed to these factors.
The frequency distribution of the bias (MAX-DOAS – PTR-MS) in the
individual measurements of the HCHO surface VMRs shows a distribution
similar to lognormal with a maximum at ∼1.1 ppb and skewed
towards positive values. The large bias can also be inferred from the large
offset (3.18 ppb) and slope (1.14) in the linear regression of HCHO VMRs
measured by MAX-DOAS and by PTR-MS. We observe that until May 2015, there
was a general agreement between the two measurements regarding their
temporal variability, but the in situ VMRs were generally lower. Post-May 2015, the
bias between the two measurements became larger. The reason for the larger
bias is not well understood. We also observe a large variability in the
MAX-DOAS HCHO VMRs, which possibly arises due to a larger uncertainty in the
MAX-DOAS HCHO measurements as compared to the random uncertainty of
∼30 % in the PTR-MS HCHO measurements. The major
contribution to the error budget is from fitting errors in the DOAS fit in
addition to the uncertainties in the profile inversion algorithm and cross
sections. The HCHO surface VMRs retrieved using the MAX-DOAS measurements
have an uncertainty of ∼50 % (as compared to only
∼20 % for NO2 surface VMR) (Wang et
al., 2017b).
Secondary photochemical production is the major source of atmospheric
formaldehyde. The photo-oxidation of primarily emitted VOCs occurs in the course of their mixing in the boundary layer, and hence, a
significant amount of formaldehyde is observed at altitudes of up to 600 m or
even higher in some cases. The surface VMRs from MAX-DOAS shown in Fig. 13
represent the mean in the lowest 200 m layer of the MAPA output; which might
also be influenced by higher altitudes due to a limited vertical resolution of
MAX-DOAS. Surface VMRs from the PTR-MS measurements are sensitive to the
inlet height (∼15 m). Hence, a higher VMR from the MAX-DOAS
measurement was expected. This is further supported by our observations in
Fig. F8, where we observe that for the periods when the emissions of
precursors of HCHO are higher (e.g. from crop residue fires in May, June,
October, and November and from burning for domestic heating in December and
January), the bias between the MAX-DOAS and in situ VMRs is also higher. Nevertheless,
keeping in mind the systematic uncertainty of the in situ measurements (which could
not be quantified within the scope of this study due to the unavailability of
calibration standards) and the high uncertainty of MAX-DOAS measurements, we
cannot further interpret the comparison results.
Additional figures
Wind rose plots showing the major fetch region of air mass
arriving at Mohali for the four major seasons of the year.
The time series of the nominal (Tset) and the actual
temperature (Tcold) of the detector within the MAX-DOAS instrument.
Example plots showing the measured radiance (a) and the
derivative of the radiance (b) during a horizon scan spanning elevation
viewing angles from -3 to 3∘. The figures correspond
to the horizon scan performed on 17 September 2014.
Monthly variation in Ångström exponent for the wavelength
pair 470 and 550 nm derived from MODIS measurements over Mohali. The green
triangles represent the monthly means. The centre line of each box
represents the median values, whereas the box represents the interquartile
range. The whiskers represent the 5th and 95th percentiles.
Monthly variation in the AOD (at 440 nm) as observed by AERONET
sun photometers (red boxes) and MODIS (black boxes) at two sites (a, Lahore
and, b, New Delhi), which are the nearest stations to Mohali in the
Indo-Gangetic Plain. Panels (c) and (d) show the corresponding scatter plots
indicating the agreement in the daily MODIS and AERONET measurements.
Mean weekly variation in (a) AOD, (b) NO2 VCD, and (c) HCHO VCD. The bottom row shows the mean weekly variation in (d)
aerosol extinction, (e) NO2 concentration, and (f) HCHO concentration in
the bottom-most layer (0–200 m) retrieved from the profile inversion of the
MAX-DOAS measurements.
Mean diurnal profiles of surface NO2 mixing ratios measured
by an in situ analyser (black) and retrieved from the MAX-DOAS measurements (red)
for different months of the year. The dark line represents the mean value, while the shaded region above and below the dark lines represent the
75th and 25th percentiles, respectively.
Mean diurnal profiles of surface HCHO mixing ratios measured by
the PTR-MS (black) and retrieved from the MAX-DOAS measurements (red) for
different months of the year. The dark line represents the mean value, while
the shaded region above and below the dark lines represent the 75th and
25th percentiles, respectively.
Mean diurnal profiles of the aerosol extinction in the
bottom-most layer (0–200 m) retrieved from the MAX-DOAS measurements for
different months of the year. The dark line represents the mean value, while
the shaded region above and below the dark lines represent the 75th and
25th percentiles, respectively.
Diurnal variation in characteristic profile heights of
aerosol (a), NO2(b), and HCHO
(c) for the four major seasons.
Intercomparison of daily (dots) and monthly mean (lines and
markers) AOD at 360 nm retrieved from ground-based MAX-DOAS O4
measurements and from the MODIS MAIAC data product when no scaling factors
were applied for the O4 dSCDs.
Frequency distribution of O4 scaling factors derived from
profile inversions, which allowed us to vary the O4 scaling factors in
order to achieve an agreement between measurements and forward model. The
green bars show retrievals which are flagged as valid, while the orange and red
bars indicate retrievals with warning and error flags.
Box-and-whiskers plot showing the horizontal sensitivity
distance of MAX-DOAS measurements during afternoon hours (between 12:00 and
15:00 LT) for 2∘ elevation angle. The blue boxes represent
clear-sky conditions with low aerosol load, and the orange boxes indicate
clear-sky conditions with high aerosol load.
Intercomparison of daily (dots) and monthly mean (lines and
markers) AOD at 360 nm retrieved from ground-based MAX-DOAS O4
measurements and from the MODIS MAIAC data product when spatially averaged
over 5 km (a, b) and 25 km (c, d) around Mohali. O4 dSCD
were scaled by a factor of 0.8, as discussed in the main text.
Time series of daily (dots) and monthly means (lines and
markers) of MAX-DOAS NO2 VCDs, OMI QA4ECV NO2 VCDs, and MAX-DOAS
VCDs modified using the QA4ECV averaging kernels and the TM4 a priori
profiles.
Monthly mean aerosol extinction profiles (a) retrieved from
MAX-DOAS O4 measurement and HCHO profiles (b) over Mohali at around
the OMI overpass time (12:30–14:30 LT).
Data availability
The MODIS MAIAC (Lyapustin et al., 2018), OMI OMAERUV (Torres et al., 2007), OMNO2 (Marchenko et al., 2015), and OMHCHO (González Abad et al., 2015) data can be downloaded
from the LAADS website (https://ladsweb.modaps.eosdis.nasa.gov/, last access: 19 November 2020). The OMI DOMINO (Boersma et al., 2011)
and QA4ECV (Boersma et al., 2018; De Smedt et al., 2018) data are available at http://www.temis.nl (last access: 19 November 2020). The MAX-DOAS measurement
data, spectral analysis, and profile inversion results can be obtained from
the corresponding author. The in situ measurement data can be obtained from IISER
Mohali atmospheric chemistry facility by contacting Vinayak Sinha
(vsinha@iisermohali.ac.in).
Author contributions
VK and TW prepared the paper with contributions from all
co-authors. VK, StD, AKM, and SeD operated the MAX-DOAS
instrument, and VK performed spectral analyses and profile inversion with
help from SB, StD, SeD, and TW. YW helped VK with cloud classification.
VK, AKM, and VS operated the in situ analysers and contributed to the
analyses. All co-authors contributed to modifications and discussions in
preparing the paper.
Competing interests
Thomas Wagner is a member of the editorial board of the journal.
Acknowledgements
We acknowledge the IISER Mohali atmospheric chemistry facility for the support
with logistics related to the operation of the MAX-DOAS instrument and
the sharing of meteorological and in situ measurement data. Vinod Kumar acknowledges the
Alexander von Humboldt Foundation and Max Planck Society for a supporting
postdoctoral stipend. Abhishek Kumar Mishra acknowledges MHRD, India, for support regarding a PhD fellowship. Vinayak Sinha thanks the Max Planck Society and Max Planck Institute for
Chemistry and the Department of Science and Technology, India, for funding a
Max Planck India partner group at IISER Mohali through which this long-term
collaboration could be accomplished. We acknowledge the free use of DOMINO
NO2 and QA4ECV NO2 and HCHO level 2 data products from OMI from
http://www.temis.nl (last access: 17 November 2020). We thank the Level-1 and Atmosphere Archive & Distribution
System (LAADS) Distributed Active Archive Center (DAAC) for providing free
access to the OMAERUV, OMNO2, OMHCHO, and MAIAC data products. We thank
Philippe Goloub and Brent N. Holben for establishing and maintaining
the AERONET sites New Delhi and Lahore, respectively, whose data have been
used in this study. We thank the two anonymous reviewers for their
constructive feedback to the paper.
Financial support
The article processing charges for this open-access publication were covered by the Max Planck Society.
Review statement
This paper was edited by Yugo Kanaya and reviewed by two anonymous referees.
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