The tropospheric concentrations of nitrogen dioxide
(NO2) and formaldehyde (HCHO) have high spatio-temporal variability,
and in situ observations of these trace gases are still scarce, especially in
remote background areas. We made four similar circling journeys of mobile multi-axis differential optical
absorption spectroscopy
(MAX-DOAS) measurements in the Three Rivers' Source region over the Tibetan
Plateau in summer (18–30 July) 2021 for the first time. The differential
slant column densities (DSCDs) of NO2 and HCHO were retrieved from the
measured spectra, with very weak absorptions along the driving routes. The
tropospheric NO2 and HCHO vertical column densities (VCDs) were
calculated from their DSCDs by the geometric approximation method, and they
were further filtered to form reliable data sets by eliminating the
influences of sunlight shelters and the vehicle's vibration and bumpiness. The
observational data show that the tropospheric NO2 and HCHO VCDs
decreased with the increasing altitude of the driving route, whose
background levels ± standard deviations were 0.40 ±1.13×1015 molec. cm-2 for NO2 and 2.27±1.66×1015 molec. cm-2 for HCHO in
July 2021 over the Three Rivers' Source region. The NO2 VCDs show
similar geographical distribution patterns between the different circling
journeys, but the levels of the HCHO VCDs are different between the
different circling journeys. The elevated NO2 VCDs along the driving
routes usually corresponded to enhanced transport emissions from the
towns crossed. However, the spatial distributions of the HCHO VCDs depended
significantly on natural and meteorological conditions, such as surface
temperature. By comparing TROPOMI satellite products and mobile MAX-DOAS
results, we found that TROPOMI NO2 and HCHO VCDs have large positive
offsets in the background atmosphere over the main area of the Three Rivers'
Source. Our study provides valuable data sets and information of NO2
and HCHO over the Tibetan Plateau, benefitting the scientific community in
investigating the spatio-temporal evolution of atmospheric composition in
the background atmosphere at high altitudes, validating and improving the
satellite products over mountain terrains, and evaluating the model's
ability to simulate atmospheric chemistry over the Tibetan Plateau.
Chinese Academy of Meteorological Sciences2021Z013National Natural Science Foundation of China41875146Introduction
The Tibetan Plateau, also known as the Qinghai–Tibet Plateau in China, is
usually referred to as “the Third Pole” (or “the roof of the world”) with an
average surface altitude of 4000–5000 m, covering a vast
region located at 26–40∘ N latitude and 73–105∘ E longitude (Qiu, 2008). Due to thermal and
dynamic processes in conditions of high altitude and large terrain, the
Tibetan Plateau has an important influence on the atmospheric circulation
(such as the Asian Summer Monsoon), the Asian climate and even the global climate, and the
hydrological cycle (Bolin, 1950; Boos and Kuang, 2010; Dong et al., 2017;
Duan et al., 2007; Liu et al., 2007; Yanai et al., 1992; Zhou et al., 2009).
As the “Asian water towers”, there are many water resources in the forms
of glaciers, snowpack, lakes, and rivers over the Tibetan Plateau, which are
the headwaters of major rivers in Asia (such as the Indus River, Ganges River,
Yangtze River, Yellow River, and Lancang River) and influence economic
development and billions of peoples' survival in the downstream region (Xu et
al., 2008; Gao et al., 2019). Therefore, the area of “Three Rivers'
Source” (i.e. Yangtze River, Yellow River, and Lancang River) was
established as one of the first five national parks in China in 2021 to
better protect the ecological environment. However, we still know very
little about the ecological environment, including the atmospheric environment,
over this region at present. Almost no observations focus on the abundances
and variations of atmospheric composition over the Three Rivers' Source
region, limited by the extremely high altitude, topographical heterogeneity,
variable weather, and effective techniques and methods. As one of the remote
regions in Eurasia, the Tibetan Plateau, with low anthropogenic activities
and a low population density, can be considered to be a natural laboratory to
investigate the background atmospheric chemistry of the inner Eurasian
continent (Ma et al., 2021). With increasing emissions of air pollution over
the Tibetan Plateau and its surrounding areas (such as tourism in summer),
measurements of the background atmosphere with a high spatio-temporal
resolution are urgent to improve the understanding of the spatio-temporal
evolution of the atmospheric composition (Singh, 2021; Yang et al., 2019;
Kang et al., 2022).
Nitrogen dioxide (NO2) and formaldehyde (HCHO) are two important trace
gases in the troposphere, participating in the control of the strong
atmospheric oxidant of ozone (O3; Seinfeld and Pandis, 2016). Nitrogen
oxides (NOx), i.e. the sum of NO2 and nitric oxide (NO), can be not only released by various anthropogenic emission sources, such as the
burning of fossil fuels and biomass, but also emitted by natural
processes, including microbial activities in soils and lightning in the
atmosphere (Lee et al., 1997; Granier et al., 2011; Kurokawa et al., 2013).
HCHO is produced not only by primary sources (e.g. emissions of industry and
transportation in the city and biomass burning) but also by photochemical
oxidation of methane and non-methane volatile organic compounds (e.g.
isoprene emitted from natural vegetation) in the remote atmosphere
(Stavrakou et al., 2009). High-accuracy measurements of NO2 and HCHO
with high spatial and temporal resolution are beneficial to understanding their
various characteristics in the background atmosphere, which is quite useful for
validating the satellite products and is very valuable for exploring the processes of
atmospheric chemistry.
The ground-based observations of NO2 and HCHO concentrations in the
background atmosphere at high altitude are relatively scarce at present.
Under the frameworks of the Global Atmosphere Watch programme of the World
Meteorological Organization (WMO-GAW) and the Network for the Detection of
Atmospheric Composition Change (NDACC), long-term observations of
atmospheric composition have been carried out at some high mountain
stations, such as the Waliguan (WLG; 3816 m above sea level) global
atmosphere background observatory, located in the northeastern part of the
Tibetan Plateau (Xu et al., 2020; Ma et al., 2021). With respect to NO2
at WLG, previous studies found different levels (5–600 pmol mol-1) of NO2 during different periods, leading to
a positive or negative sign of net ozone production in the remote
troposphere (Xue et al., 2011; Meng et al., 2010; Ma et al., 2020, 2002). Short-term HCHO observations at WLG in 2005 indicated that the
possible sources for HCHO were photo-oxidation of biogenic emission of
isoprene, animal excrement, and long-distance transportation from polluted
air (Mu et al., 2007). The two stations of Qinghai Lake and Menyuan are
adjacent to WLG, but the diurnal variations of NOx (NO2) are
different and possibly influenced by traffic and residential emissions,
complex terrain, boundary layer processes, and transport from city air
masses (Wang et al., 2015; Zhao et al., 2020). According to the measurements
at the Qomolangma Atmospheric and Environmental Observation and Research
Station (QOMS; 4276 m above sea level) of the south-central Tibetan Plateau
from December 2017 to March 2019, the levels of NO2 and HCHO were
significantly higher than those at WLG station, related to local emissions
(e.g. tourism, biomass burning, vegetation) and air pollution transport from
South Asia (Xing et al., 2021; Ma et al., 2020). Increased
concentrations of tropospheric NO2 at QOMS are concentrated in the
lower layers, with obvious seasonal variations (peak of 1.28 nmol mol-1 in autumn) and diurnal variations (two peaks at
11:00–13:00 BJT and after 16:00 BJT; BJT denotes Beijing time
and is equal to coordinated universal time plus 8 h; Xing et al., 2021).
The tropospheric HCHO vertical distribution showed an exponential shape at
QOMS, with a seasonal peak of 5.20 nmol mol-1 in autumn, and
the peaks of HCHO appeared between 10:00–16:00 BJT in winter
and spring and after 16:00 BJT in summer and autumn, respectively (Xing et
al., 2021). In recent years, the China National Environmental Monitoring
Center (CNEMC) also established several atmospheric composition monitoring
stations over the Tibetan Plateau, but they mainly focused on the continuous
monitoring of the surface particulate matter with aerodynamic diameter less
than 2.5 and 10 µm (PM2.5 and PM10), NO2,
sulfur dioxide (SO2), O3, and carbon oxide (CO) in cities, such
as Lhasa and Xining (Chen et al., 2019; He et al., 2017; Yang et al., 2019).
As a whole, these station observations cannot meet the demand of detecting
the NO2 and HCHO variations with high spatial resolution over the
Tibetan Plateau, which are also crucial for the validation of satellite
products over areas with complex terrain. To the best of our knowledge,
there are no reports about mobile measurements of NO2 and HCHO in the
background atmosphere over the Tibetan Plateau.
The measurements of NO2 and HCHO with high spatial and temporal
resolution are challenging over the Tibetan Plateau. In the early days, some
studies on NO2 and HCHO were based on the time-consuming air-sampling
method (Mu et al., 2007; Meng et al., 2010; Ma et al., 2002). The air
samplers were analysed by ion chromatography or a spectrophotometer for
NO2 and by high-performance liquid chromatography and mass
spectrometry or gas chromatography for HCHO in the laboratory. With the
development of measurement techniques, in situ methods started to be applied
to measure surface concentrations of NOx (NO2) and volatile
organic compounds (VOCs) at a few stations (Wang et al., 2006; Xue et al.,
2011; Wang et al., 2015; Zhao et al., 2020; Ran et al., 2014; Chen et al.,
2019; Yang et al., 2019; Xue et al., 2013; Duo et al., 2018). These in situ
measurements at fixed stations were usually achieved by the
chemiluminescence analyser for NOx (NO2) and by the gas
chromatograph for VOCs, respectively. However, there are limitations in the
spatio-temporal representation for the sampling and in situ measurement
methods. As an alternative, satellite remote sensing can perform long-term
observations of NO2 and HCHO and can cover large areas with sparse
spatio-temporal resolution, but the uncertainties of satellite NO2 and
HCHO products are rather large owing to complex terrain and weather over the
Tibetan Plateau (Guo et al., 2016; Zhang et al., 2021). As a kind of
advanced ground-based remote-sensing technique, Multi-axis differential
optical absorption spectroscopy (MAX-DOAS) has been certified in the
measurement techniques of NDACC (De Mazière et al., 2018). The successful
observations of trace gases with very low abundances by MAX-DOAS depend on
multi-factors, such as long optical paths, a high signal-to-noise ratio of
the instrument, and characteristic spectral absorption features of the
target species. According to previous studies, MAX-DOAS has the potential to
measure tropospheric trace gases with very low level mixing ratios
(pmol mol-1 order for NO2 and sub-nmol mol-1 order for HCHO) in the background atmosphere at high-altitude
stations (Franco et al., 2015; Gil-Ojeda et al., 2015; Gomez et al., 2014;
Marais et al., 2021; Schreier et al., 2016). Also, this technique has been
used to measure the levels and monthly variations of NO2 and HCHO in
the global pristine atmosphere at WLG station (Ma et al., 2020).
Stratospheric O3 and its depleting substances (including NO2) have
been successfully retrieved from zenith DOAS spectra at a clean suburb
station in the northern Tibetan Plateau (Cheng et al., 2021). Moreover,
ground-based MAX-DOAS has been applied to monitor vertical distributions of
NO2 and HCHO in the southern Tibetan Plateau (Xing et al., 2021).
Compared with MAX-DOAS observations at a fixed site, mobile MAX-DOAS
measurements in the background atmosphere over the Tibetan Plateau are a
greater challenge for the following reasons: (1) the vehicle's violent vibration and bumpiness
reduce the stability of the signal acquisition and even introduce unknown
interference signals; (2) the measured signals can be strongly reduced by
shelters due to complex terrain, such as tunnels, bridges, signposts, and
mountains (usually such measurements have to be filtered out); and (3) the
observations in practice are also controlled by various factors, e.g.
variable weather, hypoxic environment in the plateau, geospatial signal loss,
and problems with the power supply. Although there are challenges in
measuring NO2 and HCHO concentrations by mobile MAX-DOAS over the
Tibetan Plateau, they are useful for studies on the spatio-temporal
evolution of the atmospheric composition in the background atmosphere,
validation and improvement of satellite products over mountain terrain, and
evaluation of the simulation results of atmospheric chemistry models over
the Tibetan Plateau.
We made the mobile MAX-DOAS measurements in July 2021 over the plateau
terrain for the first time. In this study, the primary objective is to
analyse the spectra of scattered sunlight collected in the Three Rivers'
Source region over the Tibetan Plateau, to obtain the data sets of tropospheric
NO2 and HCHO vertical column densities (VCDs) in the background
atmosphere at high altitudes, and to investigate the abundances and
spatio-temporal variations of tropospheric NO2 and HCHO VCDs during the
field campaign. Large effort was made on the spectral analysis and data
filtering to obtain reliable tropospheric NO2 and HCHO VCDs because of
the very weak spectral absorptions of the respective trace gases in the
background atmosphere at high altitude, as well as the influences of shelters
and the vehicle's vibration and bumpiness along the driving routes. In Sect. 2, we describe the field experiment in July 2021 over the Tibetan Plateau,
including the observation vehicle, MAX-DOAS instrument, experiment region,
and deployment strategies. Section 3 introduces the spectral analysis, as
well as the calculation and filtering of the NO2 and HCHO VCDs. In
Sect. 4, we present the abundances and the spatio-temporal variation of the
tropospheric NO2 and HCHO VCDs during the field campaign, as well as
the comparison with TROPOMI products. A summary and conclusions are given in
Sect. 5.
Field experimentDescription of vehicle and instrumentation
A mobile vehicle has been designed and assembled for measurements of
atmospheric composition over the Tibetan Plateau (Fig. 1a). The mobile
vehicle has been operated by the Chinese Academy of Meteorological Sciences
(CAMS) since February 2021. The outside parts of the instrumentation are fixed
on the roof of the vehicle, which is about 3.5 m above the ground. The
outside parts of the instrumentation contain the sensors for spatial position
(longitude, latitude, altitude) and attitude (yaw, pitch, and roll angles) of
the mobile vehicle. The units of the system control, data collection, screen
display, and uninterruptible power supply (UPS) are mounted in the interior
of the mobile vehicle. The UPS's battery pack, recharged after the mobile
vehicle reaches the destination of the observation route, can offer operation
time of around 16 h with a power of 2000 W. All instrumentation has been
specially reinforced to allow the mobile vehicle to travel over the
difficult road conditions of the Tibetan Plateau. The mobile vehicle usually
runs at a speed of ∼ 60 km h-1 for motorways and ∼ 40 km h-1 for ordinary roads, respectively, during our field experiment.
(a) Mobile observation vehicle for atmospheric composition and
meteorological parameters. Two parts of the Tube MAX-DOAS instrument are
installed (b) on the rear of the vehicle's roof and (c) inside the vehicle,
respectively.
For the field campaign of mobile observations of the atmospheric environment
over the Tibetan Plateau, the aforementioned vehicle was equipped with an
instrument called Tube MAX-DOAS (Donner, 2016; Cheng et al., 2021),
developed by the Max Planck Institute for Chemistry (MPIC), Mainz, Germany.
The Tube MAX-DOAS instrument contained two parts, one outside (Fig. 1b) and another
inside (Fig. 1c) the vehicle, respectively. (1) The outside part was fixed
on the rear of the vehicle's roof and is mainly composed of the telescope,
optical fibre, stepper motor, tubular shell, and protective cover. The
telescope, pointing to the back of the vehicle, rotated in the vertical
plane to achieve the measurement at seven different elevation angles
(3, 6, 10, 15, 20, 30, 90∘ relative to the mobile vehicle) driven by
the stepper motor. The scattered sunlight was collected by the telescope and
transferred to a spectrograph inside the vehicle via the optical fibre. (2)
The inside part was made up of the spectrograph, data collection unit,
temperature control unit, and a laptop which controls the instrument
operation and data collection. For each elevation angle, the Tube MAX-DOAS instrument
collected one spectrum at a stable detector temperature of 15 ± 0.1∘ with the integration time of ∼ 1 min. The
AvaSpec-ULS2048x64-USB2 spectrograph was built by the AVANTES company and
covered the wavelength range of 300–466 nm with
∼ 0.6 nm spectral resolution. The Tube MAX-DOAS instrument not only
automatically collected the scattered sunlight spectra for the cyclic
elevation angle sequences during daytime but also recorded spectra of dark
current (DC) and electronic offset (OS) at night for correcting the daytime
spectra of scattered sunlight. The laptop coordinated the operation of each
module during the measurement procedure. The MPIC Tube MAX-DOAS system has
been successfully applied to the ground-based observations of atmospheric
composition at the Golmud station over the northern Tibetan Plateau (Cheng
et al., 2021).
Description of the measurement location and deployment strategies
The mobile field observation campaign was performed over the Three Rivers'
Source region on the northeast of the Tibetan Plateau in western China (Fig. 2). The main vegetation types are alpine steppe and meadows in the region
along the observation route, belonging to a unique and typical alpine
ecosystem. The main landform is the mountain plain. The Three Rivers' Source
region has a typical plateau continental climate, characterised by a large
diurnal temperature difference, long sunshine time, and strong solar
radiation. There are also rapid spatial and temporal variations of the local
climate over the Three Rivers' Source region. Yak and sheep grazing in
summer is the main industry over the Three Rivers' Source region, isolated
from industrial and population centres.
Driving routes of the mobile observation vehicle. The driving
routes are added to (a) the terrain height map over the Tibetan Plateau
(red, blue and black lines) and (b) the street map (https://map.baidu.com/,
last access: 16 June 2022) in the experiment region as an overlay,
respectively. The altitudes along the driving routes are marked by dotted coloured curves in (b). Light blue lines and areas in (b)
indicate rivers and lakes.
In order to reveal the background abundance and spatio-temporal variation of
the atmospheric composition over the Three Rivers' Source region, we took
various factors into consideration during the design of the deployment
strategies, such as the regional representativeness of the driving routes,
the technical requirement of the passive MAX-DOAS measurement, the sunlight
shelter and the bumpy conditions along the driving route, the reliable
electric power safeguard, and first aid for sudden altitude sickness.
Finally, the mobile MAX-DOAS field experiment was carried out in the
southeast of Qinghai Province, China (Fig. 2b). The driving routes traverse
the Yangtze River and the Yellow River and are close to the Lancang River.
It took 3 d for one circling journey. Four circling journeys were
made during the mobile MAX-DOAS field experiment period in July 2021 (Table 1). We drove from the meteorological bureau of the city of Xining, the capital of
Qinghai Province, to the meteorological bureau of Dari County of the Guoluo
Tibetan Autonomous Prefecture, southeastern Qinghai Province, on the first
day of each circling journey (Fig. 2). We travelled from the meteorological
bureau of Dari County to the meteorological bureau of the Yushu Tibetan
Autonomous Prefecture on the middle day (Fig. 2). We returned to Xining city
from the Yushu Tibetan Autonomous Prefecture on the third day (Fig. 2).
Hereafter, the three segments of the closed-loop journey are referred to as
XD, DY, and YX, respectively. The durations were about 12, 8, and 13 h for
the XD, DY, and YX segments, respectively. Most of the driving routes are
motorways, except parts of the national roads in the YX segment. More
sunlight shelters occurred in the XD segment because of the tunnels,
bridges, signposts, and mountains. The observed MAX-DOAS data were saved on
the laptop and backed up when arriving at the terminus of each segment of
the journey. In addition to troubleshooting by field observers, our MAX-DOAS
team also provided the technical support via remote wireless network during
the campaign.
Observation periods and routes of the mobile MAX-DOAS field
experiment over the Three Rivers' Source region of the Tibetan Plateau in
July 2021.
CyclesXining to Dari (XD)Dari to Yushu (DY)Yushu to Xining (YX)12021-07-18, 09:00–22:49 BJT*2021-07-19, 09:05–17:40 BJT2021-07-20, 08:17–21:48 BJT22021-07-21, 08:09–21:40 BJT2021-07-22, 08:20–16:07 BJT2021-07-23, 08:18–21:38 BJT32021-07-25, 08:29–20:08 BJT2021-07-26, 08:08–15:20 BJT2021-07-27, 08:18–21:48 BJT42021-07-28, 08:27–18:56 BJT2021-07-29, 09:00–16:00 BJT2021-07-30, 08:21–22:35 BJT
* BJT denotes the Beijing time, corresponding to universal time coordinated (UTC) +8 h.
Spectral retrieval and data filteringSpectral analysis
Based on the Beer–Lambert law, the column densities of trace gases can be
retrieved from the scattered sunlight spectra by the widely used method of
differential optical absorption spectroscopy (DOAS; Platt and Stutz, 2008).
The basic idea of DOAS is to decompose the atmospheric spectral extinction
into two terms, i.e. terms with slow spectral variation (such as atmospheric
scattering) and fast variation (mainly trace-gas absorptions) with
wavelength. The slant column density (SCD) of a trace gas is defined as its
concentration integrated along the effective light path (Cheng et al.,
2019). The total (from the instrument to the top of atmosphere) SCD can be
split into two parts, i.e. so-called tropospheric SCD (SCDTrop) and
stratospheric SCD (SCDStra). For species concentrated in the
troposphere or for light traversing the same path in the stratosphere for
different elevation angles (α), the SCDStra can be neglected or
cancels out, respectively, which means SCDα,Stra≈SCD90,Stra (Ma et al., 2013). In the practice of the MAX-DOAS
spectral analysis, a Fraunhofer reference spectrum (FRS) needs to be
selected to correct the strong solar Fraunhofer lines. Thus, the result of
the spectral analysis is the so-called differential slant column density
(DSCD) of the target species (such as NO2 and HCHO in this study),
which represents the difference in trace-gas absorption between the measured
atmospheric spectrum and the FRS (Hönninger et al., 2004). There are two
schemes for the FRS selection from measured spectra (Wang et al., 2018): one
is using a fixed spectrum (hereafter named “fixed FRS”), usually at the
90∘ elevation angle during noon to minimise the tropospheric and
stratospheric contributions, for all measured spectra; the other is using
sequential spectra (hereafter named “sequential FRS”), which are defined
as the time-interpolated spectra between two zenith spectra measured before
and after the measurement time of the current off-zenith elevation angle.
Due to more similar atmospheric conditions and instrument properties between
a specific measured spectrum and the corresponding sequential FRS, higher
signal-to-noise ratios and smaller fitting errors are achieved by using a
sequential FRS rather than a fixed FRS. Figure 3 shows the root mean square (rms) of
the spectral-fitting residuals using a fixed FRS and a sequential FRS for
NO2 and HCHO, respectively. It is clear that the rms medians are
smaller for a sequential FRS than those for a fixed FRS. Thus, we prefer
to use the sequential FRS for the mobile MAX-DOAS measurements in this study.
For NO2, we can retrieve the DSCD not only in the ultraviolet (UV)
region (351–390 nm) but also in the visible region
(400–434 nm; Cheng et al., 2022, 2019). Figure 4
compares the NO2 DSCDs and the rms's of the spectral-fitting residuals
for using either the visible or UV spectral interval. The overall trends of
the NO2 DSCDs are consistent between both spectral intervals, with a
correlation coefficient of R=0.75, but the averaged rms's of the spectral-fitting residuals in the visible wavelength region, i.e. (6.26 ± 6.92) × 10-4, are smaller than those in the UV wavelength
interval, i.e. (7.62 ± 9.17) × 10-4. The final settings
of the NO2 and HCHO spectral retrieval parameters, such as cross
sections of the target and interference species, ring spectra, polynomial
degree, and intensity offset, are similar to those in previous studies (Cheng et al.,
2022, 2019) – see Table 2. The spectral analysis, including DC
and OS corrections of the measurement spectra and the spectral calibration
of the FRS, was implemented by the QDOAS software based on a non-linear
least-squares fitting method developed by the Royal Belgian Institute for
Space Aeronomy (BIRA-IASB; Danckaert et al., 2017). Figure 5 shows an example
of the spectral fitting for the NO2 and HCHO DSCDs from a spectrum
measured at the elevation angle of 15∘ at 11:02 BJT on 18 July
2021 (SZA = 34.11∘). In the post-processing of NO2 and
HCHO DSCDs, we applied the following filters: rms < 0.005; offset
(constant) should be between ±0.03; SZA < 80∘.
These filters were selected because they provide a good balance between quality
of the results and not skipping too many data. These filters almost filtered
out all “bad measurements”, which were caused by sunlight shelters and
bumpy conditions. Finally, relative to measurements with SZA < 90∘, the percentages of remaining DSCD data were 69 % for
NO2 and 74 % for HCHO, respectively. During DOAS measurements, the
instrument detection limit can be conveniently estimated by the spectral-fit
errors (Cheng et al., 2021; Coburn et al., 2011; Stutz and Platt, 1996). The
instrument detection limits of NO2 and HCHO DSCDs were defined as twice
the medians of the spectral-fit errors in this study, i.e. 0.68×1015 and 2.11×1015 molec. cm-2 at 15∘ elevation angle, respectively.
According to the DSCD detection limits divided by the differential air mass
factor (DAMF) for a 15∘ elevation angle, the VCD detection limits
were estimated to be about 0.24 × 1015 molec. cm-2 for NO2 and
0.74 × 1015 molec. cm-2 for HCHO, respectively. Note that, for individual
measurements, the VCD detection limits might be lower or higher by about
±30 % because of the uncertainties of the geometric approximation
(up to about 20 %) and the effect of varying ground slope (also up to
about 20 %). There are 17 % and 15 % of the retrieved NO2 and
HCHO DSCDs below the detection limits, respectively. Based on the spectral-fit errors, we can also calculate the relative errors for each NO2 and
HCHO DSCD. Then the mean relative errors of NO2 and HCHO DSCDs were
about 21 % and 12 % at a 15∘ elevation angle, respectively.
Fit settings for the NO2 and HCHO spectral analyses.
ParametersSetting for NO2Setting for HCHOFraunhofer reference spectrumSequential spectraSequential spectraFitting interval (nm)400–434324–359DOAS polynomialDegree: 5 Intensity offsetDegree: 2 (constant and order 1) Shift and stretchSpectrum Ring spectraOriginal and wavelength-dependent ring spectra NO2 cross sectionVandaele et al. (1998), 294 K, Io correction (1017 molec. cm-2) H2O cross sectionPolyansky et al. (2018), 293 K–O3 cross sectionSerdyuchenko et al. (2014), 223 K,Serdyuchenko et al. (2014), 223 K, 243 K,Io correction (1020 molec. cm-2)Io correction (1020 molec. cm-2)O4 cross sectionThalman and Volkamer (2013), 293 KThalman and Volkamer (2013), 293 KHCHO cross section–Meller and Moortgat (2000), 298 K
Statistics of the root mean square (rms) of the NO2 and HCHO
spectral-fitting residuals using a sequential FRS or fixed FRS (for
rms < 0.005 and SZA < 80∘) during the field
campaign. Lower (upper) error bars and boxes are the 10th (90th) and 25th
(75th) percentiles, respectively. Lines inside the boxes and dots denote the
medians and the mean values.
Comparison of NO2 spectral-fitting results using the visible
and UV wavelength intervals (for rms < 0.005 and SZA < 80∘) during the field campaign. (a) Linear fit of corresponding
NO2 DSCDs between visible and UV spectral intervals. (b) Corresponding
NO2 rms between visible and UV spectral intervals. The red lines denote
the results of the regression analyses, and the corresponding equations and
correlation coefficients are displayed in (a). The numbers in
(b) indicate the mean ± standard deviation (SD) in the visible
and UV spectral intervals.
Examples of DOAS spectral analyses for NO2 and HCHO. Black
curves and red curves with dots indicate the measured and fitted
differential optical depth for (a) NO2 and (b) HCHO, respectively. The
NO2 and HCHO DSCDs are 5.27 × 1015 and 9.36 × 1015 molec. cm-2,
respectively. The rms's of the spectral-fitting residuals between measured
and fitted spectra are 2.17 × 10-4 for (c) NO2 and 2.09 × 10-4 for (d) HCHO, respectively.
NO2 and HCHO VCDs
Based on the aforementioned filtered NO2 and HCHO DSCDs retrieved from
the spectra, we need to firstly obtain the tropospheric DSCDs at the
elevation angle α (i.e. DSCDα,Trop≡SCDα,TropSCD90,Trop), which are used to calculate the
NO2 and HCHO vertical column densities (VCDs) in the troposphere. In
the situation of fixed FRS, the DSCDTrop values are produced by the DSCDs of
off-zenith viewing direction minus those at a 90∘ elevation angle of
the same elevation sequence. In the case of sequential FRS in this study,
the DSCDs from spectral inversion can be regarded as DSCDTrop
(Hönninger et al., 2004).
The SCDs (or DSCDs) depend on the concentration profile of the target species,
effective light path length, measurement geometry, and solar position. Using
the air mass factor (AMF), the SCDs (or DSCDs) can be converted to the VCDs,
which are independent of the light path and the observation geometry and are
thus convenient for comparison between different measurements. The
tropospheric AMF at the elevation angle α (AMFα,Trop) is given by the ratio of the SCD to VCD in the
troposphere:
AMFα,Trop=SCDα,TropVCDTrop.
If α=90∘,
AMF90,Trop=SCD90,TropVCDTrop.
We define the DAMFα,Trop as the
tropospheric differential AMF, i.e.
DAMFα,Trop=AMFα,Trop-AMF90,Trop.
By Eq. (1) minus Eq. (2), VCDTrop can be
deduced:
VCDTrop=SCDα,Trop-SCD90,TropAMFα,Trop-AMF90,Trop=DSCDα,TropDAMFα,Trop,
where the AMF can be simulated by an atmospheric radiative transfer model or
estimated by the method of geometric approximation. The former method is
more accurate but requires information on various input parameters, such as
the profiles of trace gas and aerosol, which are usually not known. The
latter method is simpler and assumes trace gases are uniformly distributed
in the lower troposphere. Due to the lack of necessary data over the Tibetan
Plateau to simulate the correct NO2 and HCHO AMFs, we adopted the
geometric approximation method in this study. Here, it should be noted that
the errors caused by the geometric approximation method are much smaller for
measurements at high altitudes because the scattering probability is much
smaller compared to measurements at sea level. Thus, the direct-viewing-path
length becomes longer and is in better agreement with the assumptions of the
geometric approximation method. We explored the applicability of the
geometric approximation method by radiative transfer simulations with the
full spherical Monte Carlo radiative transfer model, MCARTIM (Deutschmann et
al., 2011; see Sect. S1 in the Supplement). The main findings are as follows: (1) the typical errors
of the geometric approximation are < 20 % for NO2 and HCHO
(Fig. S1). (2) The retrieved HCHO VCDs using the geometric approximation
will represent well the part of the HCHO profile located below 2 km, but the
use of the geometric approximation systematically underestimates
(∼ 60 %) the background HCHO above 2 km (Figs. S2, S3).
However, from model simulations over the Tibetan Plateau (Fig. S2; Ma et
al., 2019), we find that the corresponding vertical HCHO column density is
rather small: about 1.3 × 1015 molec. cm-2. Thus,
the retrieved HCHO VCDs underestimate the true total VCD by about
0.6 × 1.3 × 1015 molec. cm-2=8×1014 molec. cm-2. (3) In the presence of
clouds, the sensitivity for trace gases below the clouds is slightly
enhanced compared to the geometric approximation, while it is strongly
reduced for trace gases above the clouds (Fig. S4). The
AMFα,Trop in the condition of geometric
approximation in a polluted environment can be expressed as follows:
AMFα,Trop≈1sin(α)=sin-1(α).
Therefore, Eq. (4) becomes
VCDTrop=DSCDα,Tropsin-1(α)-1,(α≠90∘,AMF90,Trop=1).
Ideally, the elevation angles should be corrected by the attitude angles of
the mobile vehicle when applying the geometric approximation. However, the
partial system of the attitude angles of the mobile vehicle did not work
well, which may be connected with the special environment of the Tibetan Plateau
(such as low atmospheric pressure) and the bumpiness of the mobile observation
platform (leading to instabilities of the data collection). Thus, we use the
uncorrected elevation angles during the conversion of DSCD to VCD in
Eq. (6). Of course, the uncorrected elevation angles will cause some
errors if the mobile observation vehicle is not on a horizontal surface, but
these errors are typically small for the larger elevation angles (for
example, 15, 20, and 30∘). Based on the mobile platform attitude
angles, the elevation angle error is estimated to be about 2.3∘.
The corresponding error of an individual measurement will be up to about
21 %, but over the full loop, these errors will at least partially cancel.
To further judge how good the geometric approximation is, the resulting VCDs
derived for different elevation angles have been compared (Brinksma et al.,
2008). Table 3 shows the NO2 (HCHO) VCDs between the three elevation
angles (15, 20, and 30∘). The VCDs are rather consistent at the
three elevation angles, with correlation coefficients of R=0.91–0.95 for
NO2 and R=0.66–0.80 for HCHO, respectively (Table 3). This implies
that the geometric approximation method is self-consistent. The standard
deviation of the NO2 (HCHO) VCDs is small at
15∘ elevation angles (Table 3), implying the
high reliability of VCDs at a 15∘ elevation
angle (VCD15∘). Therefore, to compromise between accuracy of the
geometric approximation and signal to noise, the VCD15∘ were
treated as the reliable results based on a selection criterion (for NO2, the
absolute difference of VCDs between 15 and 20∘ is
<1× 1015 molec. cm-2, or the relative
difference is < 5 %; for HCHO, the absolute difference of VCDs
between 15 and 20∘ is < 2 × 1015 molec. cm-2, or the relative difference is < 5 %).
The filtered NO2 and HCHO VCD15∘ values during the mobile
measurement period were kept as the final results to explore the background
abundance and spatio-temporal variation of NO2 and HCHO over the Three
Rivers' Source region of the Tibetan Plateau. It took about 8 min to obtain measurements at two adjacent 15∘ elevation angles. Therefore, the
corresponding spatial resolution was approximately 8 km at a mobile-vehicle speed of
∼ 60 km h-1. Assuming that the trace gas
is located in the lowest 1000 m above the surface, we can also estimate the
horizontal extent of the line of sight through that layer. For measurements
at a 15∘ elevation angle, this extent is about 4 km.
Statistics for the NO2 and HCHO VCDs at the three elevation
angles (15, 20, and 30∘).
ParametersMean (median) ± standard deviation (1015 molec. cm-2) Correlation coefficient 15∘20∘30∘15∘ vs. 20∘15∘ vs. 30∘20∘ vs. 30∘NO21.40 (0.57) ± 2.611.42 (0.63) ± 2.521.59 (0.82) ± 2.700.950.910.94HCHO2.53 (2.35) ± 1.972.81 (2.69) ± 2.603.25 (3.20) ± 4.090.800.660.73
From our experience during the measurements, we also suggest that the
telescope scans at 15,
20, and 90∘ elevation angles in future mobile
MAX-DOAS measurements of the background atmosphere over mountain terrain.
There are at least two reasons: (1) the relatively large elevation angles
are less influenced by the road tilt and obstructions; (2) the measurements
at 15 and 20∘ elevation angles still have an enhanced
sensitivity to tropospheric trace gases (increase of sensitivity compared to
90∘ elevation angle is by about a factor of 3.8 and 2.9, respectively).
Interpretation of the resultsAbundance
The background levels of the filtered final NO2 and HCHO VCDs can be
estimated by the maximum-frequency method (Cheng et al., 2017). According to
the Lorentz fitted curves of the relative frequency distribution of the
NO2 and HCHO VCDs during the field campaign (Fig. 6a), the background
levels were 0.40 ± 1.13 × 1015 molec. cm-2 for NO2 and 2.27 ± 1.66 × 1015 molec. cm-2 for HCHO in summer on the northeast side of the
Tibetan Plateau, wherein the uncertainties of the background levels were
estimated by the standard deviations of NO2 and HCHO VCDs. The
background levels are smaller than those observed in summer 2018 at the
Qomolangma Atmospheric and Environmental Observation and Research Station of
the Chinese Academy of Sciences, located in the south-central Tibetan
Plateau (medians of 0.80 × 1015 molec. cm-2 for
NO2 and 3.13 × 1015 molec. cm-2 for HCHO,
respectively; Xing et al., 2021). To explore the dependence of the NO2
and HCHO VCDs on the route altitude (in the range of 2280–4830 m), we divided the mobile-route altitudes into vertical bins with
intervals of 500 m. Figure 6b shows the means, medians, and standard deviations
of the NO2 and HCHO VCDs in each vertical grid cell. There are
generally decreasing trends with increasing altitude. This is consistent
with our knowledge of the natural background atmosphere, i.e. the higher the
altitude, the lower the air density. Different from the nearly constant
decreasing rate of the HCHO VCDs with the route altitude, there are at least
two segments with significantly different decreasing rates above and below
2750 m altitude. The NO2 VCDs in the 2000–2500 m grid
cell (8.17 × 1015 molec. cm-2) were
substantially larger because the mobile route was close to the city of
Xining (about 2260 m altitude), where there are stronger anthropogenic
emission sources of air pollutants, such as increased urban transport
emissions leading to higher NO2 levels. The NO2 VCDs were quite
low at altitudes above 3500 m, partly related to almost no human
activities at this altitude. Due to very limited emissions of anthropogenic
VOCs over the Tibetan Plateau, the changes of the HCHO VCDs with altitude
were likely to be primarily connected with the natural process, such as the
oxidation of methane and non-methane volatile organic compounds (Stavrakou
et al., 2009). Combining the hourly surface air pressure and temperature at
2 m above the land surface with the 0.25∘× 0.25∘ resolution from ERA5, the profiles of NO2 and HCHO
mixing ratios were also derived from the corresponding mean and median VCDs
along driving routes, respectively (Fig. S5). As a whole, the measurements
(except close to the cities) at the higher altitudes in summer are able to
reflect the background atmosphere with rather low NO2 and HCHO levels
over the Three Rivers' Source region.
Overall characteristics of NO2 and HCHO VCDs during the field
campaign. (a) Frequency distributions of NO2 (blue column) and HCHO
(green column) VCDs, as well as their Lorentz distribution curves for
NO2 (red curve) and HCHO (magenta curve), respectively. (b) Dependence
of the NO2 and HCHO VCDs on mean altitude of driving route from 2000
to 5000 m at vertical intervals of 500 m. The black (red) lines with squares
(dots), stars (triangles), and error bars denote the means, medians, and
standard deviations of the NO2 (HCHO) VCDs for each altitude range.
Spatio-temporal variationNO2
The day-to-day variations of NO2 VCDs are similar between different
circling journeys, characterised by the larger means and 90th percentiles on
the first and the third days (i.e. on the days of the XD and YX driving
routes) and correspondingly lower values on the second day (i.e. on the day
of the DY driving route) of each circling journey (Fig. 7a). The NO2
means are always larger than the medians on each day, especially in the
situation of the XD driving route, partly because the driving route covers
small areas with very high NO2 abundances, such as Xining city, and
large background areas with relatively low NO2 abundances. For the same driving route of the four circling journeys, the
daily NO2 levels are close to each other, with the NO2 medians in
the range of 0.19–0.63 × 1015 molec. cm-2
during the field campaign.
Day-to-day variations of the daily averaged (a) NO2 and (b)
HCHO VCDs over the mobile observation routes (XD, DY, YX). Lower (upper)
error bars and boxes are the 10th (90th) and 25th (75th) percentiles of the
data. Lines inside the boxes and red dots denote the medians and the mean
values, respectively. The integrated sampling numbers for specific day are
labelled at the top axis. The blue curves with squares in (b) denote
the daily air temperature at 2 m above the land surface.
Figure 8 shows the spatial distributions of the tropospheric NO2 VCDs
along the XD, DY, and YX driving routes in July 2021. For the same segment
of four circling journeys (i.e. XD, DY, or YX), the tropospheric NO2
VCDs present a nearly consistent spatial distribution. It is also clear that
the tropospheric NO2 VCDs were elevated when the mobile observation
vehicle passed through counties or cities, such as Xining and Yushu. This
can be attributed to increased anthropogenic activities in cities or
counties, such as traffic and residential emissions. There are significantly
larger NO2 VCDs on the driving routes of southeastern Qinghai Lake,
which is a famous tourist destination. Moreover, as one of the arterial
roads to Tibet, there are many diesel vehicles passing through the basin of
Qinghai Lake via national motorways surrounding the lake. The touring buses
or cars, as well as the cargo transport vehicles, could lead to the higher
NO2 abundances in summer around Qinghai Lake. According to previous
studies at the northwest section of Qinghai Lake shore in October of
2010 and 2011, the emissions from diesel vehicles around Qinghai Lake were
likely the main source of nitrogen oxides (NOx; Wang et al., 2015).
The enhanced NO2 levels could even be found at the motorway junction
(such as the location of 35.20∘ N, 98.97∘ E) and the
tunnel exit (such as the location of 34.92∘ N, 99.40∘ E;
note that the telescope of the MAX-DOAS pointed to the reverse of the driving
direction; Fig. 8a1, d1). This situation would not appear once traffic flow
was lower at these special locations (Fig. 8b1, c1). The NO2 spatial
distributions over the main area of the Three Rivers' Source, such as around
the counties of Dari, Shiqu, Chenduo, and Maduo during the DY driving route
and the first half of the YX driving route, were relatively uniform with
very low levels (<1× 1015 molec. cm-2). Previous investigations of the tropospheric ozone chemical
budget, simulated and constrained by measured NO2 concentration at the
Waliguan background station located in the northeastern Tibetan Plateau,
showed that the NOx levels play a vital role in the net sign of ozone
production from formation and loss reaction for the tropospheric background
atmosphere (Ma et al., 2002, 2020; Xue et al., 2013). Therefore,
with more and more anthropogenic activities, the effects of increasing
NO2 levels on the photochemistry and oxidation capacity of the
background atmosphere should be paid more attention to better build an
ecological civilisation over the remote Three Rivers' Source region in the
future.
Spatial distributions of gridded NO2 VCDs with
0.25∘× 0.25∘ resolution. The observed NO2
VCDs in each spatial grid cell are averaged for three segments (1, 2, 3) of
four circling journeys (a, b, c, d). The main cities and counties on the
driving routes are marked by the black stars. On the background map, the
light-blue lines and areas represent rivers and lakes (such as Qinghai
Lake), the yellow lines denote the roads, and the grey lines indicate the
administrative boundaries.
The available time period, confined by the sunshine duration and the
distance of the driving routes, is the shortest for the DY driving route.
The diurnal cycle of the NO2 VCD means or medians presents high values
in the morning and evening and shows lower levels of ∼ 0.38 × 1015 molec. cm-2 from 12:00 to 17:00 BJT
(Fig. 9a). The means of the NO2 VCD are also significantly higher than
the corresponding medians before 11:00 BJT, with larger standard deviations.
The NO2 diurnal variation patterns of the XD, DY, and YX driving routes
are different, although the diurnal patterns are rather consistent for
different days of the same driving route (Fig. 9b–d). The NO2 VCDs
sharply decreased in the morning during the XD driving route, with larger
standard deviations around 16:00 BJT, when the mobile observation vehicle
was close to the toll station. For the DY driving route, the NO2 VCDs
stayed at the lower level and then slightly increased in the late afternoon.
In the situation of the YX driving route, the diurnal pattern of NO2
VCDs was a symmetric “U” shape. It should be noted that the mobile
observation vehicle reached the destination of the YX driving route around
22:00 BJT, and the lacking NO2 VCDs were due to SZA > 80∘ after 20:00 BJT. The amplitudes of the NO2 diurnal
variation and the maxima NO2 levels among different driving
routes decreased in the order of the segments XD, YX, and DY. Previous
studies at the background station of lower altitude showed that the NO2
diurnal variation could be affected by the higher photolysis rate owing to
stronger solar irradiance at noon and for a site location far away from
emission sources (Cheng et al., 2019). We also checked whether the enhanced
NO2 VCDs in the morning and evening might be an artefact caused by the
effect of stratospheric NO2 on the derived tropospheric NO2 VCD.
In our data analysis (see Sect. 3.1), it is assumed that the stratospheric
NO2 absorption is independent of the elevation angle. While this is not
exactly true, it is a valid assumption for typical measurement situations in
polluted or slightly polluted environments. If, however, the tropospheric
NO2 absorption is very weak, the remaining stratospheric influence
might be substantial. We tested this potential influence of the
stratospheric NO2 absorption on the retrieved tropospheric NO2 VCD
for our measurements by performing radiative transfer simulations using a
stratospheric NO2 profile with a stratospheric NO2 VCD of 4 × 1015 molec. cm-2. As a result, we found that,
for SZA < 80∘, the introduced NO2 DSCD for an
elevation angle of 15∘ is < 1 × 1015 molec. cm-2 (see Fig. S6), thus leading to a maximum
artificial NO2 VCD of 3.5 × 1014 molec. cm-2. Moreover, for SZA < 80∘, the artificial
NO2 VCD shows almost no SZA dependence. Thus, the potential influence of
the stratospheric NO2 absorption cannot explain the observed diurnal
cycle of the tropospheric NO2 VCD. From these findings, we conclude that
the NO2 diurnal variations were primarily caused by enhanced pollution
in the morning and evening, when the mobile observation vehicle was located
in or close to the cities or county town; i.e. the NO2 diurnal patterns
reflected the differences of the NO2 spatial distribution. An
additional effect on the diurnal variation is probably caused by the
enhanced NO2 photolysis around noon.
Diurnal variations of the NO2 VCDs over the mobile
observation routes. (a) Diurnal variations of the overall means (black curve
with squares), medians (red curves with dots), and standard deviations
(error bars) of the NO2 VCDs. (b) Diurnal variations of the mean
NO2 VCDs on selected days (18, 21, 25, and 28 July 2021), as well as the means
and standard deviations of the NO2 VCDs on the XD driving route. (c, d)
Same as (b) but for the DY and YX driving routes during the field campaign.
HCHO
The means and medians of the daily HCHO VCDs are basically consistent on all
days, with the maximum mean of 4.63 × 1015 molec. cm-2 on 21 July 2021 and the minimum mean of 1.15 × 1015 molec. cm-2 on 27 July 2021 (Fig. 7b). There are obvious
differences in the levels of HCHO VCDs between the different circling
journeys. The higher and lower HCHO VCDs appeared during the second circling
journey (i.e. 21–23 July 2021) and the third circling journey (i.e. 25–27
July 2021), respectively. HCHO has large natural vegetation sources, with
the emission strength depending strongly on weather conditions such as
temperature and solar radiation at the Earth's surface (Borovski et al.,
2014). We looked at air temperature at 2 m above the land surface and the
downward solar radiation at the surface (SSRD), which are derived from
hourly ERA5 reanalysis data with 0.25∘× 0.25∘
resolution. According to the ERA5 grid cell and hour to which each HCHO
measurement belongs, the air temperature and SSRD are extracted and then
averaged for each day (Fig. 10a). It is shown that the daily variations
between air temperature and HCHO VCDs are highly correlated, with the
correlation coefficient of R=0.95 (Figs. 7b, 10b). It is probable that higher
temperatures are connected with more VOCs emitted by vegetation, leading to
higher HCHO VCDs. The daily HCHO VCDs are also related to surface solar
radiation but with a smaller correlation coefficient of R=0.27 (Fig. 10b), which is probably caused by the higher variability of local solar
radiation over the Tibetan Plateau compared to the temperature. Therefore,
the remarkable HCHO daily variations are mainly connected with the variable
weather over the Tibetan Plateau, which affects the natural emissions of
HCHO precursors significantly.
Comparison of the HCHO VCDs with other data sets. (a) Day-to-day
variations of the mean air temperature at 2 m above the land surface (black
curves with squares) and the downward solar radiation at the surface (SSRD;
red curves with dots), as well as (b) linear fits between the two parameters
and the daily averaged HCHO VCDs over the mobile observation routes. The
error bars denote the standard deviations of the air temperature and SSRD in
(a). The lines denote the results of the regression analyses, and the
corresponding equations and correlation coefficients are displayed in (b).
Figure 11 shows the spatial distributions of the HCHO VCDs during the field
campaign in July 2021. For the specific driving routes (XD, DY, or YX), the
HCHO spatial distributions were similar on different days. Normally, the
HCHO VCDs were larger at the starting points and ending points of the driving
routes (if reaching the ending points under the condition of SZA < 80∘), which matched with the larger HCHO values in the morning
and evening (Fig. 12). However, the HCHO levels were significantly different
at the same location on different days. For example, the HCHO VCDs on the
second circling journey (Fig. 11b1–b3) were obviously larger than those on
the other three circling journeys, most probably due to higher surface
temperatures on the second circling journey (Fig. S7). From the northeast to
the southwest in the region of the mobile-observation field experiment, the
HCHO VCDs present a decreasing trend. These lower HCHO levels in the main
area of Three Rivers' Source reflect the overall conditions of atmospheric
HCHO background. The spatial distributions of the HCHO column observed by the
OMI satellite from 2009 to 2019 over the Tibetan Plateau also found that the
regions with sparse population and fewer human activities were frequently
affected by natural factors, such as air temperature and precipitation
(Zhang et al., 2021). The elevated HCHO VCDs around Maqin County of the XD
driving route were partly related to anthropogenic HCHO emissions, such as
biomass burning and fossil fuel combustion (Fig. 11a1, b1, c1, d1; Zhang et
al., 2021). Comparing the HCHO VCDs before and after Maduo County on the YX
driving route, the former were larger than the latter, corresponding to the
jump of the HCHO diurnal variation before and after 13:00 BJT (Fig. 12d).
Besides the differences in human activities, the spatial step changes in the
HCHO VCDs were also partly connected with the decreasing altitudes on the YX
driving route (Fig. 2b).
Same as Fig. 8 but for HCHO.
Same as Fig. 9 but for HCHO.
With respect to the total means and medians of the HCHO VCDs in the range of
1.92–4.36 × 1015 molec. cm-2 (Fig. 12a), their
diurnal variations are rather consistent throughout the whole day. They decrease slightly before 10:00 BJT and increase after 18:00 BJT, and they also have no
significant differences in their standard deviations. However, the diurnal
variations of the HCHO VCDs are obviously different, both for different days
of the same driving route and among different driving routes (Fig. 12b–d). On
average, the diurnal pattern of the HCHO VCDs during the XD driving route
presents a weak “U” shape, i.e. slightly higher HCHO levels in the morning
and evening. For the DY driving route, the total averaged HCHO VCDs almost
maintain the level around 2 × 1015 molec. cm-2
before 14:00 BJT and then gradually increase until the end of the DY
journey. The diurnal pattern of the HCHO VCDs for the YX driving route
presents a “W” shape; i.e. higher HCHO VCDs occur around 13:00 BJT, in the
morning, and in the evening. The variable diurnal cycles of HCHO VCDs were
also found by ship-based MAX-DOAS measurements over the middle and lower
Yangtze River in winter, where both primary sources and photochemical
secondary formation have large influences (Hong et al., 2018). Even at the
starting and ending points of the driving route, there were almost no strong
HCHO primary sources caused by anthropogenic activities over the Three
Rivers' Source region. Thus, we infer that the variable diurnal patterns of
HCHO were mainly connected with the secondary photochemical formation of
active VOCs emitted from vegetation (Mu et al., 2007). Meanwhile, due to the
varying local microclimates over the Tibetan Plateau, as well as the different
types and amounts of vegetation at different altitudes, the diurnal
variations of secondary HCHO production are quite changeable and closely
related to the specific property of a location. More comprehensive
observations are needed over the Tibetan Plateau in the future to deeply
understand the HCHO spatio-temporal evolution.
Comparison with TROPOMI observations
The TROPOspheric Monitoring Instrument (TROPOMI) is the sole payload on the
Copernicus Sentinel-5 Precursor (Sentinel-5P or S5P) satellite, which
provides measurements of multiple atmospheric trace species, including
NO2 and HCHO, at high spatial and temporal resolutions (Veefkind et al.,
2012). The S5P reference orbit is a near-polar sun-synchronous orbit with a
mean local solar time of 13:30 at the ascending node. TROPOMI covers the
wavelength ranges of ultraviolet–visible (270–495 nm), near
infrared (675–775 nm), and shortwave infrared
(2305–2385 nm) with a 108∘ field of view in nadir
view. TROPOMI achieves daily global coverage with a spatial resolution of
5.5 × 3.5 km2 at nadir since the along-track pixel size
reduction on 6 August 2019. The NO2 retrieval consists of a three-step
procedure: (1) the total NO2 SCDs are retrieved from the Level 1b
spectra measured by TROPOMI using the DOAS method; (2) the total NO2
SCDs are separated into stratospheric SCDs and tropospheric SCDs on the
basis of information coming from a data assimilation system; (3) the
tropospheric NO2 SCDs are converted into VCDs through a lookup table
of tropospheric AMFs. The first and third steps also apply to HCHO, but in
addition, a bias of the HCHO SCDs needs to be corrected before the
conversion of the HCHO SCDs to VCDs. In this study, we use the TROPOMI
Level 2 NO2 and HCHO products (i.e. S5P_L2__ NO2____HiR and S5P_L2__HCHO___HiR)
downloaded from the NASA Goddard Earth Sciences Data and Information
Services Center (GES-DISC; ESA and KNMI, 2021; ESA and DLR, 2020). For
comparison between the mobile MAX-DOAS and TROPOMI observations, their
NO2 and HCHO VCDs are gridded into 0.25∘× 0.25∘ cells (Figs. 13, 14). The reason for averaging two data sets
into a 0.25∘× 0.25∘ grid is to balance the
spatial resolution and the number of observed NO2 and HCHO VCDs at a
specific grid cell. The TROPOMI relative precisions in the “ΔT1.5” situation (explained below) are estimated to be 72 % and
113 % for tropospheric NO2 and HCHO VCDs, derived from the products
of S5P_L2__NO2____HiR and S5P_L2__HCHO___HiR, respectively.
Spatial distributions of the tropospheric NO2 VCDs observed
by TROPOMI on each day of the field campaign. The TROPOMI S5P_L2__NO2____HiR product has been gridded to
0.25∘× 0.25∘ cells. The main cities and
counties on the driving routes of the field campaign are marked by the black
stars. The black curves indicate the administrative boundaries. The white
circles and red plus symbols show the grid cell where the data of both
TROPOMI and MAX-DOAS are available on the same day or within a 1.5 h time
difference, respectively.
Same as Fig. 13 but for HCHO.
Figure 13 shows the spatial distributions of the tropospheric gridded
NO2 VCDs from TROPOMI on each day of the field campaign. The spatial
distributions of the tropospheric NO2 VCDs are basically consistent on
different days; i.e. higher values are found in the northeast, and lower
values are found in the southwest. Similarly as for the mobile MAX-DOAS, the TROPOMI
NO2 VCDs are larger around Xining city than in the main area of the Three
Rivers' Source region. However, the elevated trends of the tropospheric NO2
VCDs around the counties, which are clearly observed by the mobile MAX-DOAS,
are nearly not captured by TROPOMI. To validate the fine-scale
(0.25∘× 0.25∘) spatial variability in
tropospheric NO2 VCDs, we made a linear regression analysis between
both data sets (Fig. 15a). When using all tropospheric NO2 VCDs at the
same grid cell on the same day during the field campaign (referred to as “All”
in Fig. 15a, corresponding to the white circles in Fig. 13), the consistency
is good, with a correlation coefficient of R=0.67 between the two data
sets. However, the slope is much lower than unity, indicating that the
NO2 VCDs from TROPOMI are systematically lower than those from mobile
MAX-DOAS over the polluted areas. Besides the probable underestimation of
TROPOMI, the lower TROPOMI NO2 VCDs are also connected with the time
differences between the two observation methods at the same grid cell. In
contrast, there is almost no correlation of the two data sets if we only
use the tropospheric NO2 VCDs within the 1.5 h time difference between
mobile MAX-DOAS and TROPOMI at the same grid (referred to as “ΔT1.5” in Fig. 15a, corresponding to the red pluses in Fig. 13). The
weak correlation is understandable, because (1) the level and the
range variation of the NO2 VCDs are very small in the background
atmosphere over the Tibetan Plateau; and (2) the signal-to-noise ratio is
reduced due to the measurement errors for both MAX-DOAS and TROPOMI,
introduced by the spectral analysis, ground slope, and the applied
tropospheric AMF. Comparing the situations of All and ΔT1.5, significant differences in the correlation are connected with
the former, including the larger NO2 VCDs close to the cities, inferred
by the locations of the grid cell in Fig. 13. For the ΔT1.5 comparison, mostly the low background values are included. These results
indicate that the TROPOMI can distinguish the differences in tropospheric
NO2 VCDs between city and background atmosphere over the Tibetan
Plateau. Relative to the NO2 VCDs by mobile MAX-DOAS during the field
campaign, the relative (absolute) differences of the NO2 VCDs by
TROPOMI are -12 % (-9.47× 1013 molec. cm-2)
and 40 % (1.77 × 1014 molec. cm-2) for All
and ΔT1.5 on average, respectively. The positive bias for
ΔT1.5 is probably related to the horizontal NO2
inhomogeneity, caused by mountain terrains over the main area of the Three
Rivers' Source. However, without detailed knowledge about the true
three-dimensional NO2 distribution, this bias cannot be fully
understood in direction and magnitude. As a whole, in contrast to routine
TROPOMI validation based on site observations (Verhoelst et al., 2021), the
mobile MAX-DOAS observations can serve as a supplement to quantify the
impact of the fine-scale NO2 horizontal variability on satellite
products.
Linear fit between the tropospheric (a) NO2 and (b) HCHO
VCDs measured by the mobile MAX-DOAS and TROPOMI. The black squares and red
dots represent the available VCDs of both data sets at the same grid cell on
the same day or within a 1.5 h time difference, respectively. The black
(red) lines denote the results of the regression analyses, and the
corresponding equations and correlation coefficients are displayed in the
panels.
In contrast to NO2, the spatial distributions of the tropospheric
gridded HCHO VCDs from TROPOMI are not uniform among different days of the
field campaign (Fig. 14). The higher HCHO VCDs appear more in the second
circling journey, and the lower HCHO VCDs appear more in the third and fourth circling
journeys, consistent with the aforementioned results derived from mobile
MAX-DOAS. The HCHO levels around the city of Xining are also not
significantly enhanced and are even lower than those in the main area of the Three
Rivers' Source region on some days, such as 25 July 2021. We also perform a
linear regression analysis of tropospheric HCHO VCDs derived from mobile
MAX-DOAS and TROPOMI, respectively. Whether for the All (corresponding to the
white circles in Fig. 14) situation or for the ΔT1.5
(corresponding to the red pluses in Fig. 14) situation, the correlation
coefficients are the same (R=0.26 in Fig. 15b), indicating that there are
no strong anthropogenic HCHO sources along the driving routes, even in the
city of Xining. The rather small correlation coefficient between the two
data sets is also related to the rather small variability of the HCHO VCDs
and the relatively low signal-to-noise ratio of the TROPOMI satellite
product in the background atmosphere over the Tibetan Plateau. Comparing the
“ΔT1.5” situation between NO2 and HCHO, the correlation of
the tropospheric HCHO VCDs is higher than that of NO2, which is
probably related to the stronger HCHO daily variations in the background
atmosphere influenced by natural factors, such as air temperature and
precipitation (Zhang et al., 2021). Similar to the validations of TROPOMI at
remote sites by ground-based solar-absorption Fourier transform infrared
(FTIR) measurements (Vigouroux et al., 2020), an overestimation of the true
HCHO VCD by TROPOMI is also found during the field campaign, with
significantly larger relative (absolute) differences of 104 % (2.60 × 1015 molec. cm-2) and 87 % (2.16 × 1015 molec. cm-2) for “All” and “ΔT1.5” on
average, respectively (Fig. 15b). This large positive offset of the TROPOMI
HCHO VCDs is probably connected with the horizontal HCHO inhomogeneity
caused by mountain terrain and varying local microclimates over the Tibetan
Plateau. Therefore, although TROPOMI significantly improves the precision of
the HCHO observations at short temporal scales and for low HCHO columns (De
Smedt et al., 2021), it is still a challenge for satellite instruments to
detect the spatio-temporal variations of HCHO over the Tibetan Plateau.
Summary and conclusions
In this study, we performed mobile MAX-DOAS measurements over the Tibetan
Plateau in summer (18–30 July) 2021 for the first time. We analysed spectra
of scattered sunlight collected in the Three Rivers' Source region over the
Tibetan Plateau and obtained the data sets of tropospheric NO2 and
HCHO VCDs in the background atmosphere. We further investigated the
abundances and spatio-temporal variations of the tropospheric NO2 and
HCHO VCDs and validated the TROPOMI satellite products during the field
campaign.
We tested the influences of different Fraunhofer reference spectra (FRSs)
and different spectral intervals on the spectral retrieval and found that
the fitting residuals are smaller when using the sequential FRSs in the
NO2 visible-wavelength region for mobile MAX-DOAS measurements in the
background atmosphere over mountain terrain. After investigating the optimal
filters to eliminate the “bad measurements” caused by sunlight shelters
and the vehicle's vibration and bumpiness, the NO2 and HCHO DSCDs were
retained with the conditions of (1) rms < 0.005, (2) offset
(constant) between ±0.03, and (3) SZA < 80∘. The
qualified NO2 and HCHO DSCDs were converted to the corresponding VCDs
based on the air mass factor (AMF) estimated by the geometric approximation
method. Through comparing the resulting NO2 and HCHO VCDs at three
different elevation angles (15, 20, 30∘), the VCD15∘ values were further
filtered and kept as the final data sets of tropospheric NO2 and HCHO
VCDs when absolute and relative VCD differences (ΔVCD) between
15 and 20∘ are < 1015 molec. cm-2 or < 5 % for NO2 and < 2 × 1015 molec. cm-2 or < 5 % for HCHO,
respectively.
The background levels ± standard deviations of tropospheric NO2
and HCHO VCDs, estimated by the maximum frequency method, were 0.40 ± 1.13 × 1015 molec. cm-2 for NO2 and 2.27 ± 1.66 × 1015 molec. cm-2 for HCHO in
July 2021 over the Three Rivers' Source region. We also determined the
dependence of the tropospheric NO2 and HCHO VCDs on altitude, which
generally presents a decreasing trend with increasing altitude. This
characteristic for natural background atmosphere is probably mainly related
to the lower air density at higher altitudes. However, different from the
nearly constant decreasing rate of HCHO VCDs with increasing altitude, the
differences of decreasing rate above and below the 2750 m altitude for
NO2 VCDs are significant, which is highly connected with different
contributions of anthropogenic sources and natural sources for NO2 and
HCHO.
With respect to the spatio-temporal distributions, the day-to-day variations
of the NO2 VCDs between different circling journeys were similar; i.e.
similar geographical distributions of the NO2 VCDs were observed for
each circling journey. The tropospheric NO2 VCDs over the main area of the
Three Rivers' Source were relatively uniform with very low levels (<1× 1015 molec. cm-2), but they were usually
elevated in cities or counties, around Qinghai Lake, and even occasionally
at the motorway junction and the tunnel exit, where there were enhanced
transport emissions. The daytime diurnal patterns of NO2 VCDs, i.e.
higher values in the morning and evening, could also reflect the differences
of the NO2 spatial distribution. Based on radiative transfer
simulations, we can rule out that the stratospheric NO2 absorption can
explain the observed diurnal cycle of the tropospheric NO2 VCDs. Besides
the enhanced NO2 photolysis around noon, the enhanced NO2 VCDs in
the morning and evening were primarily caused by enhanced pollution levels
when the mobile observation vehicle was located in or close to the cities or
county towns. However, the day-to-day variations of the HCHO VCDs were
highly correlated to the air temperature and were significantly different between
different circling journeys. Overall, the HCHO VCDs presented a decreasing
trend from the northeast to the southwest in the region of the field
experiment. The HCHO VCDs were elevated at the starting points and ending
points of the driving routes, corresponding to larger HCHO VCDs in the
morning and evening. The levels of the HCHO VCDs were variable on different
days at the same location, implying that natural factors, such as air
temperature, significantly influenced the atmospheric HCHO photochemical
formation.
TROPOMI NO2 clearly presents the obvious influences of anthropogenic
sources on enhanced NO2 VCDs around Xining city; i.e. it can
distinguish the differences in tropospheric NO2 VCDs between the city
and the background atmosphere over the region of the field campaign. However, the
elevated trends of the tropospheric NO2 VCDs around the counties over
the main area of the Three Rivers' Source region, which are clearly observed
by the mobile MAX-DOAS, are nearly not captured by TROPOMI. In contrast, the
stronger influences of natural factors on HCHO lead to larger daily
variation of HCHO, which causes inconsistent and variable spatial
distributions of TROPOMI HCHO VCDs on different days but also a higher
correlation between mobile MAX-DOAS and TROPOMI than for NO2 for the
background atmosphere. The positive offsets of TROPOMI NO2 and HCHO
VCDs are 40 % and 87 % on average, respectively. This is probably caused
by mountain terrains and varying local microclimates over the main area of
the Three Rivers' Source region.
As a whole, we obtained valuable data sets and information of the
spatio-temporal variation of NO2 and HCHO over the Tibetan Plateau,
which have great potential for investigating the evolution of the
atmospheric composition in the background atmosphere at high altitude,
validating and improving the satellite products over mountain terrain, and
evaluating atmospheric chemistry model over the Tibetan Plateau.
Code and data availability
The filtered final NO2 and HCHO VCDs for
the field campaign by mobile MAX-DOAS observations on 18–30 July 2021 over
the Three Rivers' Source region of the Tibetan Plateau in China are
available upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-23-3655-2023-supplement.
Author contributions
XC and XX designed the field experiment.
SC and JM set up the mobile MAX-DOAS measurement platform under
discussions with XC, JL, SZ, SD, and TW. WZ, GB, BC, and SM contributed to the field
measurements. SC performed the spectra retrieval and data analysis
with contributions from TW, SZ, SD, and JM.
SC, JM, and TW prepared the paper with consent from
all co-authors.
Competing interests
At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “In-depth study of the atmospheric chemistry over the Tibetan Plateau: measurement, processing, and the impacts on climate and air quality (ACP/AMT inter-journal SI)”. It is not associated with a conference.
Acknowledgements
We thank the staff at the Qinghai Meteorological
Administration for supporting the measurements. We thank BIRA-IASB for QDOAS
spectral analysis software. We also thank ESA, KNMI, DLR, and NASA for the
TROPOMI satellite products.
Financial support
This research is supported by grants from the
Fundamental Research Funds for Central Public-interest Scientific Institution from the
Chinese Academy of Meteorological Sciences (grant no. 2021Z013), the
National Natural Science Foundation of China (grant no. 41875146), and the Fund of State Key Laboratory of Applied Optics (grant no. SKLAO2021001A02).
Review statement
This paper was edited by Steven Brown and reviewed by two anonymous referees.
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