Atmospheric pollutants over the Himalayas and Tibetan Plateau (HTP) have
potential implications for accelerating the melting of glaciers, damaging
air quality, water sources and grasslands, and threatening climate on
regional and global scales. Improved knowledge of the variabilities,
sources, drivers and transport pathways of atmospheric pollutants over the HTP is significant for regulatory and control purposes. In this study, we
quantify the variability, source, and transport of CO in the urban areas
over the HTP by using in situ measurement, GEOS-Chem model tagged CO simulation, and the analysis of meteorological fields. Diurnal, seasonal, and interannual variabilities of CO over the HTP are investigated with
∼ 6 years (January 2015 to July 2020) of surface CO
measurements in eight cities over the HTP. Annual mean of surface CO volume
mixing ratio (VMR) over the HTP varied over 318.3 ± 71.6 to 901.6 ± 472.2 ppbv, and a large seasonal cycle was observed with high levels
of CO in the late autumn to spring and low levels of CO in summer to early
autumn. The diurnal cycle is characterized by a bimodal pattern with two
maximums in later morning and midnight, respectively. Surface CO VMR from
2015 to 2020 in most cities over the HTP showed negative trends. The IASI satellite observations are for the first time used to assess the performance
of the GEOS-Chem model for the specifics of the HTP. The GEOS-Chem simulations tend to underestimate the IASI observations but can capture the measured
seasonal cycle of CO total column over the HTP. Distinct dependencies of CO
on a short lifetime species of NO2 in almost all cities over the HTP
were observed, implying local emissions to be predominant. By turning off
the emission inventories within the HTP in GEOS-Chem tagged CO simulation, the relative contribution of long-range transport was evaluated. The results showed that transport ratios of primary anthropogenic source, primary
biomass burning (BB) source, and secondary oxidation source to the surface
CO VMR over the HTP varied over 35 % to 61 %, 5 % to 21 %, and 30 % to 56 %,
respectively. The anthropogenic contribution is dominated by the South Asia
and East Asia (SEAS) region throughout the year (58 % to 91 %). The BB
contribution is dominated by the SEAS region in spring (25 % to 80 %) and
the Africa (AF) region in July–February (30 %–70 %). This study
concluded that the main source of CO in urban areas over the HTP is due to local and SEAS anthropogenic and BB emissions and oxidation sources, which differ from the black carbon that is mainly attributed to the BB source from South-East Asia. The decreasing trends in surface CO VMR since 2015 in most cities over
the HTP are attributed to the reduction in local and transported CO
emissions in recent years.
Introduction
The Himalayas and Tibetan Plateau (HTP), also named the “Third Pole” (TP),
is an important region for climate change studies for several reasons. Due to its unique feature for interactions among the atmosphere, biosphere,
hydrosphere, and cryosphere, the HTP is referred to as an important
indicator of regional and global climate change (Pu et al., 2007; Yao et
al., 2012; Zhang et al., 2015). The HTP stores a large amount of ice masses
on the planet and provides the headwater of many Asian rivers which
contribute water resources to over 1.4 billion people, and it thus is referred to as the “Water Tower of Asia” (Xu et al., 2008; Immerzeel et al., 2010;
Gao et al., 2019; Kang et al., 2019). The glaciers and snowmelt over the HTP
can potentially modify the regional hydrology, contribute to global
sea-level rise, and trigger natural hazards which may threaten the health
and wealth of many populations (Singh and Bengtsson, 2004; Barnett et al., 2005; Immerzeel et al., 2010; Kaser et al., 2010; Bolch et al., 2012; Yao et
al., 2012; Gao et al., 2019). The HTP has an average altitude of about 4000 m above sea level (a.s.l.), which highly elevates topography of the earth
system and imposes profound effects on global atmospheric circulation and
climate change (Ye and Wu, 1998; Wu et al., 2012; Zhang et al., 2015;
Kang et al., 2019). The HTP is also of great interest for model validation,
since the extreme climate conditions and the variability between clean and
polluted conditions in the region are a challenge for current chemical
transport models (Ye et al., 2012; Kopacz et al., 2011; Zhang et al.,
2015).
Since the population level is very low, the HTP has long been regarded as an atmospheric background with negligible local anthropogenic emissions (Yao
et al., 2012; Kang et al., 2019). However, the HTP is surrounded by East
Asia and South Asia, which include many intensive anthropogenic and natural emission source regions (Zhang et al., 2015; Kang et al., 2019). The
transport of polluted air masses from the highly populated area in northern
India with its industry and agriculture can have a strong impact on the
composition of the atmosphere (Cong et al., 2009; Kang et al., 2019).
Furthermore, the Asian monsoon has a strong influence on the dynamics and
transport pathways in the HTP (Zhang et al., 2015; Kang et al., 2019).
Reanalysis results based on glacial ice cores and lake sediments have
revealed distinguishable anthropogenic disturbances from Asian emissions
since the 1950s (Wang et al., 2008; Cong et al., 2013; Zhang et al.,
2015; Kang et al., 2016, 2019). Convective transport around the
HTP areas has also been verified by satellite observations, chemical
transport model simulations, flask sampling analyses, and in situ
measurements of some key atmospheric constituents. These atmospheric
constituents include carbon monoxide (CO) (Park et al.,
2007, 2008), methane (CH4) (Xiong et al.,
2009), hydrogen cyanide (HCN) (Randel et al., 2010), polyacrylonitrile
(PAN) (Zhang et al., 2009; Ungermann et al., 2016; Xu et al., 2018),
ozone (O3) (Yin et al., 2017; Xu et al., 2018), and aerosol
(Cong et al., 2007, 2009, 2013; He
et al., 2014; Zhang et al., 2015; Zhu et al., 2019; Li et al., 2021; Gul et
al., 2021; Thind et al., 2021). Furthermore, urbanization,
industrialization, land use, and infrastructure construction over the HTP
have expanded rapidly in recent years, which could also emit air pollutants
into the atmosphere (Ran et al., 2014; X. Yin et
al., 2019).
The ecosystem over the HTP is sensitive and fragile under the extreme alpine
conditions. These exogenous and local atmospheric pollutants have potential
implications for accelerating the melting of glaciers, damaging air quality,
water sources, and grasslands, and threatening climate on regional and global scales (Pu et al., 2007; Xu et al., 2009; Yao et al., 2012; Kang et al.,
2016; H. Yin et al., 2019, 2020; X. Yin et al., 2019). Efforts have
been made to understand the variabilities of atmospheric pollutants over the
HTP. However, due to the logistic difficulties and poor accessibility of the
vast HTP, most studies are based on episodic measurements in specific
regions or at widely dispersed sites (Kang et al., 2019). An
inter-comparison of these data and deductions may show large inconsistencies
and uncertainties because the reported individual studies have often relied
on different instruments and techniques (Kang et al., 2019). In addition,
most previous studies have often concentrated on burdens, sources, and transport of carbonaceous aerosols, including organic carbon (OC) and black carbon (BC), over the HTP, but the studies on gaseous pollutants are limited
(Cong et al., 2007, 2009, 2013; He
et al., 2014; Zhang et al., 2015; Zhu et al., 2019; Li et al., 2021; Gul et
al., 2021; Thind et al., 2021). As a result, the variabilities, sources,
drivers, and transport pathways of atmospheric pollutants over the HTP are
still not fully understood.
CO is one of the most critical atmospheric pollutants and not only threatens human health, but also plays a vital role in atmospheric chemistry
(Zhang et al., 2019; Zheng et al., 2019). CO has a long atmospheric
residence time of a few months and is therefore established as a key tracer
for air pollution and transport in the atmosphere (Holloway et al., 2000;
Zheng et al., 2019). Natural sources such as biomass burning (BB) and
anthropogenic sources such as vehicle exhausts, industrial activities, and
coal combustions can emit CO directly into the atmosphere (Stremme et
al., 2013; Fisher et al., 2017). These CO emissions are mainly attributed to
incomplete combustion (Holloway et al., 2000; Stremme et al., 2013).
Furthermore, the atmospheric oxidation of methane (CH4) and numerous
non-methane VOCs (NMVOCs) provides additional important sources of atmospheric CO (Fisher et al., 2017). The major CO sink in the
troposphere is oxidation via reaction with hydroxyl radicals (OH). Since CO
is heavily involved in the relationship between atmospheric chemistry and
climate forcing, it is crucial to investigate its atmospheric burden,
variability, and potential driver over the HTP. CO over the HTP may
originate from various source regions and sectors; improved knowledge of their relative contributions to CO variability over the HTP is also
significant for regulatory and control purposes. Furthermore, an investigation of CO pollution can complement current atmospheric
investigation over the HTP since the chemical characteristic, climate
forcing, and deletion of CO are different from the well-established carbonaceous aerosols.
In this study, we quantify the variability, source, and transport of CO in
the urban areas over the HTP by using in situ measurement, GEOS-Chem model tagged CO simulation, and atmospheric circulation pattern techniques. Diurnal, seasonal, and interannual variabilities of CO over the HTP are
investigated with multiyear time series of surface CO measurements in eight
cities over the HTP. The performance of the GEOS-Chem full-chemistry model for the specifics of the HTP is first assessed with the concurrent satellite
observations. The GEOS-Chem model is then run in a tagged CO mode to
quantify the relative contribution of long-range transport to the observed CO variability over the HTP. The three-dimensional (3D) transport pathways of CO originating in various source regions and sectors to the HTP are finally
determined by the GEOS-Chem simulation, back-trajectory analysis, and atmospheric circulation pattern. Only a few studies have investigated the burden and variability of CO over the HTP (Ran et al., 2014; H. Yin et al., 2019). These studies uniformly focused on the most
developed regions in Lhasa and did not analyse interannual trends and transport of CO. This study not only expands the coverage of CO quantification over the HTP, but also provides insights into the interannual
trends, sources, and transport of CO in all urban areas over the HTP.
The next section gives a site description, the surface in situ CO data and auxiliary data, the methodology used to estimate the interannual trend of
surface CO, and the GEOS-Chem simulation used for source attribution.
Section 3 reports the results for surface CO variability over the HTP on
different timescales. Section 4 presents the results for GEOS-Chem model evaluation. Section 5 analyses the results for source attribution using GEOS-Chem tagged CO simulation and the analysis of meteorological fields. We conclude the study in Sect. 6.
Methods and dataSite description
Surface in situ CO measurements in eight cities over the HTP are used in
this study. The locations of these cities are shown in Fig. 1 and summarized
in Table 1. Ngari is located in the west, Diqing and Qamdo are located in the east, and the remaining cities are all in the central east of the HTP. Ngari, Shigatse, Lhasa, Shannan, and Nyinchi are adjacent to the Himalayas
region, and Naqu, Qamdo, and Diqing are relatively distant from the
Himalayas region. Generally, these cities represent the most developed and
populated areas over the HTP. The altitude of these cities ranges from 3.1
to 4.5 km a.s.l., and the population ranges from 110 000 to 770 000. The surface pressure of these cities is about 600 hPa or less throughout the
year (Table 1). Typically, all these cities are formed in flat valleys, with the surrounding mountains rising to more than 5.0 km a.s.l., and have continuous expansion and development over time. These cities are
characterized by a typical climate regime in high mountain regions and are dry and cold in most of the year. Due to the high altitude and thin air,
incident solar radiations over these cities are stronger than those over
other cities at the same latitude around the globe
(Ran et al., 2014).
Enlarged view for geolocations of all cities (yellow dots) over the Himalayas and Tibetan Plateau (HTP) (a). Geographical regions implemented in the standard GEOS-Chem tagged CO simulation (b). Latitude and longitude definitions are listed in Table 2. The base map is created by the Basemap package of Python.
Geolocations of measurement sites in eight cities over the HTP
region. All sites are organized as a function of increasing longitude. Population statistics are prescribed from the 2018 demographic data provided
by National Bureau of Statistics of China.
General atmospheric circulations over these cities are typically influenced by three synoptic systems: the warm and wet air masses during the monsoon
season in summer, the South Asian anticyclone that controls the upper
troposphere and above, and the subtropical mid-latitude westerlies in winter
(Yao et al., 2012; Ran et al., 2014; Yin et al., 2017). Inhibited by
surrounding mountains, local mountain peak–valley wind systems facilitate the accumulation of atmospheric pollutants near the ground under low wind
speed conditions (Kang et al., 2019).
Surface CO data and auxiliary data
Routine in situ measurement of surface air qualities over the HTP started in
2015, which are organized by the China National Environmental Monitoring
Center (CNEMC) network funded by the Chinese Ministry of Ecology and
Environment (http://www.cnemc.cn/en/, last access: 22 March 2020). The CNEMC network has monitored six surface air pollutants (including CO, O3,
NO2, SO2, PM10, and PM2.5) at 23 sites in eight cities
in Ngari, Lhasa, Naqu, Diqing, Shigatse, Shannan, Nyingchi, and Qamdo over
the HTP (Table 1). Each city has at least two measurement sites. Surface CO
volume mixing ratio (VMR) measurements at all sites are based on similar gas
correlation filter infrared analysers (http://www.cnemc.cn/en/). The hourly mean datasets have covered the period from January 2015 to present for all measurement sites in the eight cities (Table 1). We first applied filter criteria following that of Lu et al. (2019) to remove unreliable measurements. The resulting measurements at all
measurement sites in each city are then averaged to obtain a city-representative dataset.
The 3D back trajectories calculated using the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model
(http://ready.arl.noaa.gov/HYSPLIT.php, last access: 23 May 2020) are
used to determine the transport trajectories (Wang, 2014). The input gridded meteorological fields were from the Global Data
Assimilation System (GDAS-1) operated by the US National Oceanic and
Atmospheric Administration (NOAA) with a horizontal resolution of 1∘
latitude × 1∘ longitude and 23 vertical grids from 1000 to
20 hPa (https://ready.arl.noaa.gov/gdas1.php, last access: 23 May
2020). We verified that the wind fields provided by GDAS-1 are in good
agreement with those provided by the Goddard Earth Observing System-Forward Processing (GEOS-FP) meteorological fields used in GEOS-Chem (Fig. S1 in the Supplement). In
this study, calculation and analysis for all back trajectories are based on
the TrajStat module (Wang, 2014; http://meteothink.org/index.html, last
access: 1 July 2020).
The monthly IASI/Metop-A CO dataset version 6.5.0 is used to evaluate the
performance of the GEOS-Chem model for the specifics of the HTP. The IASI CO product is processed by the EUMETSAT Application Ground Segment using the Fast
Optimal Retrievals on Layers for IASI (FORLI) software (Hurtmans et al.,
2012). The IASI CO retrievals are performed in the 2143–2181.25 cm-1
spectral range using the optimal estimation method and tabulated absorption
cross sections at various pressures and temperatures to speed up the
radiative transfer calculation. A single a priori profile is used in the retrieval
scheme (Clerbaux et al., 2009). The temperature, pressure, humidity profiles, and cloud fractions used in FORLI are those from the EUMETSAT Level 2
processor. Only pixels associated with cloud fractions below 25 % are processed. The IASI CO product is a vertical profile given as partial
columns in moles per square metre in 18 layers between the surface and 18 km, with an extra layer from 18 km to the top of the atmosphere. The pressure levels associated with retrieval layers are provided with the CO
product. This IASI CO dataset also includes other relevant information such
as a general quality flag, the a priori profile, the total error profile, the air
partial column profile, and the averaging kernel (AK) matrix, on the same
vertical grid, and the total column and the associated total error. To
balance the accuracy and the number of valid data over the HTP, the IASI data within the ±1∘ latitude–longitude rectangular area around each city and with a total error of less than 15 % are selected.
Regression model for CO trend
We have used a bootstrap resampling model to determine the seasonality and
interannual variability of surface CO VMR over the HTP. The resampling
methodology follows that of Gardiner et al. (2008), where a third Fourier series plus a linear function was used to fit multiyear time series
of the surface CO VMR biweekly mean. All measurements are averaged by 2 weeks to lower the residual and improve the fitting correlation. The regression
model is expressed by Eqs. (1) and (2):
1Ymeas(t)=Ymod(t)+ε(t),2Ymod(t)=A0+A1t+A2cos2πt365+A3sin2πt365+A4cos4πt365+A5sin4πt365,3d%=Ymeas(t)-Ymod(t)Ymod(t)×100,
where Ymeas(t) and Ymod(t) represent
the measured and fitted surface CO VMR time series, respectively. A0 is
the intercept, A1 is the annual growth rate, and A1/A0 is the
interannual trend discussed below. In this study, we incorporated the errors
arising from the autocorrelation in the residuals into the uncertainties in
the trends following the procedure of Santer et al. (2008). The
A2–A5 parameters describe the seasonal cycle, t is the
measurement time elapsed since January 2015, and ε(t) represents
the residual between the measurements and the fitted results. Fractional
differences of measured CO VMR time series relative to their seasonal mean
values represented by Ymod(t) were referred to as
seasonal enhancements and were calculated as Eq. (3).
GEOS-Chem simulation
Two types of GEOS-Chem model simulations were involved in this study.
GEOS-Chem model version 12.2.1 (10.5281/zenodo.2580198, The International GEOS-Chem Community, 2019) was first run in a standard full-chemistry mode to be evaluated by the IASI CO product. The
GEOS-Chem model was then run in a standard tagged CO mode to quantify the relative contribution of long-range transport to the observed CO variability over the HTP (Bey et al., 2001)
(http://geos-chem.org, last access: 14 May 2020). The GEOS-Chem
full-chemistry simulations were also used to provide OH fields and secondary
CO production rates from CH4 and NMVOC oxidation for subsequent GEOS-Chem tagged CO simulation. Both types of simulations were driven by
GEOS-FP meteorological fields with a downgraded horizontal resolution of
2∘ latitude × 2.5∘ longitude and 72 vertical
grids from the surface to 0.01 hPa. Surface meteorological variables and planetary boundary layer height (PBLH) were implemented in a 1 h interval and other meteorological variables were in a 3 h interval. The time steps used in
the model are 10 min for transport and 20 min for chemistry and
emissions, as recommend for the GEOS-Chem full-chemistry simulation at 2 × 2.5 (Philip et al., 2016). The non-local schemes for the boundary-layer mixing process are described in Lin and McElroy (2010). The GEOS-Chem
simulation outputs 47 (tagged CO mode) or 72 (full-chemistry mode) vertical
layers of CO VMR concentration ranging from the surface to 0.01 hPa with a
horizontal resolution of 2∘× 2.5∘ and a
temporal resolution of 1 h (Sun et al., 2021). We spun up the model for
1 year (January 2014 to January 2015) to remove the influence of the initial conditions. We only considered CO simulations for the grid boxes
containing the eight cities over the HTP.
Global fossil fuel and biofuel emissions were from the Community Emissions
Data System (CEDS) inventory (Hoesly et al., 2018) and are replaced by regional emissions over the US by the National Emission Inventory (NEI), Canada by Canadian Criteria Air Contaminant, Mexico by Kuhns et al. (2005),
Europe by the European Monitoring and Evaluation Program (EMEP), East Asia and South Asia by the MIX inventory (Li et al., 2017; Zheng et al., 2018; Lu et
al., 2019), and Africa by the DICE-Africa inventory (Marais and Wiedinmyer, 2016). Global BB emissions are derived from the Global Fire Assimilation System
(GFAS) v1.2 (Kaiser et al., 2012; Di Giuseppe et al., 2018). The soil
NOx emissions were from Hudman et al. (2010, 2012). Biogenic emissions were from the Model of Emissions of Gases and Aerosols
from Nature (MEGAN version 2.1) inventory (Guenther et
al., 2012). Wet deposition followed that of Liu et al. (2001)
and dry deposition was calculated by the resistance-in-series algorithm
(Wesely, 1989; Zhang et al., 2001). The photolysis rates were obtained
from the FAST-JX v7.0 photolysis scheme (Bian and Prather, 2002). A
universal tropospheric–stratospheric chemistry (UCX) mechanism was implemented (Eastham et al., 2014).
Descriptions of all tracers implemented in the standard GEOS-Chem
tagged CO simulation and the geographical definitions of all source regions.
TypeTracerDescriptionRegionLocationAnthropogenicanthNAFossil fuel + biofuel CO emitted over the North America172.5–50.0∘ W; 24.0–88.0∘ NNAanthEUBAFossil fuel + biofuel CO emitted over Europe and boreal Asia17.5∘ W–72.5∘ E; 36.0–45.0∘ N and 17.5∘ W–172.5∘ E; 45.0–88.0∘ NEUBAanthSEASFossil fuel + biofuel CO emitted over South Asia and East Asia70.0–152.0∘ E; 8.0–45.0∘ NSEASanthROWFossil fuel + biofuel CO emitted South America, Africa, and Oceania112.5–32.5∘ W; 56∘ S–24∘ N and 17.5∘ W–70.0∘ E; 48.0∘ S–36.0∘ N and 70.0–170.0∘ E; 90.0∘ S–8.0∘ NSA + AF + OCEBiomass burningbbSABiomass burning CO emitted over South America112.5–32.5∘ W; 56∘ S–24∘ NSAbbAFBiomass burning CO emitted over Africa17.5∘ W–70.0∘ E; 48.0∘ S–36.0∘ NAFbbSEASBiomass burning CO emitted over South Asia and East Asia70.0–152.5∘ E; 8.0–45.0∘ NSEASbbOCBiomass burning CO emitted over Indonesia and Oceania70.0–170.0∘ E; 90.0∘ S–8.0∘ NOCbbEUBABiomass burning CO emitted over Europe and boreal Asia17.5∘ W–72.5∘ E; 36.0–45.0∘ N and 17.5∘ W–172.5∘ E; 45.0–88.0∘ NEUBAbbNABiomass burning CO emitted over North America173–50∘ W; 24.0–88.0∘ NNAOxidationoxCHCO chemically produced from CH4 oxidationGlobalGlobaloxNMVOCCO chemically produced from NMVOC oxidationGlobalGlobal
In GEOS-Chem tagged CO simulation, the improved secondary CO production
scheme of Fisher et al. (2017) was implemented, which adopts
secondary CO production rates from CH4 and NMVOC oxidation. The monthly mean OH fields and secondary CO production rates from CH4 and
NMVOC oxidation are archived from the full-chemistry simulation of this study. The GEOS-Chem tagged CO simulation includes the tracers of primary
anthropogenic (fossil fuel + biofuel) and BB sources and secondary oxidations from CH4 and NMVOCs. Descriptions of all these tracers are
summarized in Table 2, and the geographical definitions of all the source regions are shown in Fig. 1.
Surface CO variability over the HTPDiurnal cycle
Diurnal cycles of surface CO VMR over the HTP within the period of 2015–2020 are shown in Fig. 2. The surface CO magnitudes and the hour-to-hour
variations in Naqu, Qamdo, and Diqing are higher than those in other cities
in all seasons. Furthermore, the daily peak-to-trough contrasts in Naqu, Qamdo, and Diqing are also larger than those in other cities. The highest
surface CO hourly means are typically observed in Naqu in all seasons except in the second half-day (after 12:00 local time, LT) in autumn and winter
(September–October–November/December–January–February, SON/DJF), when the
highest surface CO values are observed in Qamdo.
Diurnal cycles (local time, LT) of surface CO VMR in four seasons
over the HTP. The vertical error bar is 1σ standard variation within that hour. Results are based on CO time series from 2015 to 2020 provided by the CNEMC network.
Statistical summary of surface CO VMR in eight cities over the HTP
region. All cities are organized as a function of increasing longitude.
Diurnal cycles of surface CO VMR in all cities generally show a bimodal
pattern in all seasons. For all cities, two diurnal maximums are generally
observed during 09:00 to 11:00 LT in the daytime and 21:00 to 23:00 LT in the
nighttime in all seasons. The timings of the daytime diurnal maximum in
spring and summer (March–April–May/June–July–August, MAM/JJA) in all cities
are 1 to 2 h earlier than those in SON/DJF (Table 3). However, the timings of the nighttime diurnal maximum in MAM/JJA in all cities are 1 to 2 h
later than those in SON/DJF. On average, the diurnal hour-to-hour variation
of surface CO VMR over the HTP spanned a large range of -47.7 % to
50.6 % depending on region, season, and measurement time. The diurnal
patterns of CO in all cities over the HTP were similar to those in other
cities in China (X. Yin et al., 2019; Zhao et al., 2016). Surface CO VMR
hourly means in Naqu, Qamdo, and Diqing varied over 455.8 ± 257.8 to 1485.1 ± 1104.7 ppbv, while other cities varied over 256.4 ± 177.1 to 650.0 ± 430.7 ppbv (Table 3). The Class-1 limit for the hourly mean CO concentration in China is 10 mg m-3 (8732.1 ppbv) and
all hourly mean CO VMRs from 2015 to 2020 over the HTP were below this limit (http://www.cnemc.cn/en/).
Seasonal cycle
Seasonal cycles of surface CO VMR over the HTP within the period of 2015 to 2020 are shown in Fig. 3. As generally observed in most cities over the HTP,
surface CO VMR showed clear seasonal features: (1) high levels of surface CO
VMR occur in the late autumn to spring and low levels of surface CO occur in
summer to early autumn; (2) the variations in the late autumn to spring are
larger than those in summer to early autumn; (3) seasonal cycles of surface
CO VMR in most cities show a bimodal pattern; i.e. a large seasonal peak occurs around November–December and a small seasonal peak occurs around
April–May.
Monthly mean of surface CO VMR over the HTP. Vertical error bar represents 1σ standard variation within that month. Results are based on CO VMR time series from 2015 to 2020
provided by the CNEMC network.
Surface CO VMR monthly mean and month-to-month variations in Naqu,
Qamdo, and Diqing are higher than those in other cities in all seasons.
Furthermore, the peak-to-trough contrasts in Naqu, Qamdo, and Diqing were also larger than those in other cities. Surface CO VMR monthly mean over the
HTP varied over a large range of 206.8 ± 93.5 to 1887.1 ± 1132.0 ppbv depending on season and region (Table 3), where Naqu, Qamdo, and Diqing
varied over 419.0 ± 221.2 to 1887.1 ± 1132.0 ppbv, and other
cities varied over 206.8 ± 93.5 to 759.4 ± 473.8 ppbv (Table 3).
Interannual variability
Biweekly mean time series of surface CO VMR over the HTP from 2015 to 2020
along with the fitted results by using the regression model Ymod(t) are shown in Fig. 4. Generally, the measured
and fitted surface CO VMRs over the HTP are in good agreement with a correlation coefficient (r) of 0.81–0.93. The measured features in terms
of seasonality and interannual variability can be reproduced by the
regression model. Seasonal enhancements calculated as Eq. (3) showed that large seasonal enhancements typically occur around November–December and April–May, which correspond to the timings of the seasonal
peaks for most cities. The trend in surface CO VMR from 2015 to 2020 over
the HTP spanned a large range of (-21.6 ± 4.5) % yr-1 to (11.9 ± 1.38) % yr-1, indicating a regional representation of each dataset.
Surface CO VMR in Ngari, Lhasa, Shannan, Naqu, Qamdo, and Diqing showed
negative trends. The largest decreasing trends were observed in Qamdo and
Naqu, which showed decreasing trends of (-16.98 ± 4.37) % yr-1 and
(-21.6 ± 4.5) % yr-1, respectively. Surface CO in Shigatse and
Nyingchi showed positive trends. A large increasing trend of (11.9 ± 1.38) % yr-1 was observed in Shigatse.
Interannual variabilities of surface CO from 2015 to 2020 over the
HTP. Green dots are the biweekly mean of in situ surface CO measurements. Vertical red error bar is 1σ standard variation within the respective 2 weeks. The seasonality and interannual trend in each city fitted by using a bootstrap resampling model with a third Fourier series (red dots) plus a linear function (black line) is also shown.
Surface CO VMR annual mean over the HTP varied over 318.3 ± 71.6 to
901.6 ± 472.2 ppbv depending on year and region (Table 3), where Naqu,
Qamdo, and Diqing varied over 531.4 ± 156.8 to 901.6 ± 472.2 ppbv, higher than those in other cities which varied over 318.3 ± 71.6
to 446.1 ± 138.5 ppbv (Table 3). The annual mean concentrations of
surface CO over the HTP were compared with those from other cities in China.
All cities over the HTP except Naqu and Qamdo can be ranked as a few of the
top-level cities with the best air quality. Naqu and Qamdo were ranked as
the middle-level cities with fair to poor air quality
(http://www.cnemc.cn/en/).
Model evaluation over the HTP
The performance of the GEOS-Chem model has been evaluated with available
observations over various regions in China and surroundings in previous
studies from different perspectives such as surface O3 concentration in
urban regions over China (Lu et al., 2019), tropospheric CO column over
eastern China (Chen et al., 2009; Sun et al., 2020) and the Pacific (Yan et al., 2014), tropospheric averaged HCHO concentration over
eastern China (Sun et al., 2021), and stratospheric NO2 partial column (H. Yin et al., 2019) and HCl partial
column over eastern China (Yin et al., 2020). Generally, GEOS-Chem is
able to reproduce the absolute values as well as seasonal cycles of trace
gases over the aforementioned regions. So far GEOS-Chem model evaluation over the complex topography and meteorology of the HTP has not been found in the
literature. Here we first use the IASI CO total column from 2015 to 2020 over the HTP to evaluate the model performance in the specifics of the HTP. As
the vertical resolution of GEOS-Chem is different from the IASI observation,
a smoothing correction was applied to the GEOS-Chem profiles. First, the
GEOS-Chem CO profiles were downgraded to the IASI altitude grid to ensure a
common altitude grid. Since the IASI overpass time is at about 09:30 LT in
the morning, only the GEOS-Chem simulations at 09:00 and 10:00 LT are
considered. The interpolated profiles were then smoothed by the monthly mean
IASI averaging kernels and a priori profiles (Rodgers, 2000; Rodgers and Connor,
2003). The GEOS-Chem CO total columns were calculated subsequently from the
smoothed profiles by using the corresponding regridded air density profiles
from the model. Finally, the GEOS-Chem total column time series were
averaged by month and compared with the IASI monthly mean data.
Correlation plots of the monthly mean of CO total column over the HTP for GEOS-Chem model simulation against IASI observation. The average for GEOS-Chem simulation is performed at 09:00 and
10:00 LT. The IASI dataset is selected within an ±1∘ latitude–longitude rectangular area around each city. The blue lines are linear fitted curves of the respective scatter points. The black dotted lines
denote one-to-one lines.
Correlation plots for the model-to-IASI data pairs in each region over
the HTP are shown in Fig. 5. Depending on regions, the GEOS-Chem simulations
over the HTP tend to underestimate the IASI observations by 9.2 % to
20.0 %. The largest GEOS-Chem vs. IASI differences occur in Qamdo and
Lhasa, with underestimations of 20.0 % and 18.5 %, respectively. The
lowest GEOS-Chem vs. IASI difference occurs in Nyingchi, with an underestimation of 9.2 %. These GEOS-Chem vs. IASI differences over the
HTP were mainly attributed to the underestimation of local emission
inventories and the coarse spatial resolution of the GEOS-Chem model grid
cells. The amount of residential energy use, including fossil fuels and
biofuels used for cooking and heating, is not recorded for Tibet in current energy statistics yearbooks; therefore, bottom-up inventories tend to underestimate anthropogenic emissions over the HTP (Zheng et al.,
2019). Meanwhile, emissions due to seasonal crop residue burning over the HTP are difficult to quantify accurately (Li et al., 2021). This can be shown by
the CO emission distribution over the HTP from the MEIC inventory in Fig. S2, which shows that both the spatial distribution and seasonality of CO
emission over the HTP are not in good agreement with the in situ
measurements. Besides, the coarse spatial resolution of the GEOS-Chem
simulations homogenizes CO concentrations within each 2∘× 2.5∘ model grid cell. The simulation results represent
the homogenized concentrations in the grid box at the grid-mean elevation,
which could cause significant bias near complex terrain (Yan et al., 2014). Especially the studied regions represent the most developed and
populated areas over the HTP, which are surrounded by large areas of rolling
mountains with sparsely interspersed farms, pasture, or residency. The horizontal transport and vertical mixing schemes simulated by the GEOS-Chem
model at coarse spatial resolutions are difficult to match IASI observation
with a ground pixel of a 12 km diameter footprint on the ground at nadir
(Clerbaux et al., 2009). Regional difference in CO levels could aggravate
the inhomogeneity within the selected GEOS-Chem model grid and thus aggravate the difference between modelled and measured CO concentrations. In addition, the difference between simulation and measurement could also be
associated with the uncertainties in meteorological fields, OH fields, and
stratosphere–troposphere exchange (STE) scheme over the HTP, which are known issues in the GEOS-Chem model (Bey et al., 2001; Kopacz et al., 2011).
Though not perfect in reproducing the absolute values of the IASI
observation, GEOS-Chem can capture the measured seasonal cycle of the CO total column over the HTP with a correlation coefficient (r) of 0.64 to 0.82
depending on regions. In subsequent study, the GEOS-Chem model is used for
investigating the influence of long-range transport. We turn off all emission inventories within the HTP in the GEOS-Chem tagged CO simulation
and assess the relative contribution of each source and geographical tracer.
The relative contribution of each tracer is calculated as the ratio of the
corresponding absolute contribution to the modelled total concentration
amount. Taking this ratio effectively minimizes the propagation of
systematic model errors that are common to all tracers, i.e. the uncertainties in meteorological fields, the vertical mixing and STE schemes,
and the mismatch in spatial resolution.
Source attributionLocal emission
The air quality in a city is influenced by local emission, which is spatially differentiated by energy consumption, economic development, industry
structure, and population. All the studied cities over the HTP have achieved rapid economic and population growth in recent years (Ran et al., 2014;
X. Yin et al., 2019). For example, Lhasa's gross domestic product (GDP) in
2018 was 29 times higher than that of 2001, and the population had increased
by more than 230 000 in 17 years (X. Yin et al., 2019). In order to
evaluate the influence of local emission, the relationship between in situ measurements
of NO2 and CO is investigated. Correlation plots of surface CO vs. NO2 daily mean VMR time series provided by the CNEMC network from 2015 to 2020 in eight cities over the HTP are shown in Fig. 6. The results show that NO2 and CO concentrations were correlated in all cities (r ranges
from 0.49 to 0.86) throughout the year. The overall good correlations between these two gas pollutants suggested common sources of ΔNO2 and ΔCO in these cities. As a short lifetime species (a
few hours), the emitted NO2 is heavily weighted toward the direct
vicinity of local emission regions. As a result, local emissions are
important sources of CO in all cities. However, the slope ΔNO2/ΔCO (ranges from 0.006 to 0.04) and the degree of the correlation in each city are different, indicating energy consumption and CO
emission rates in these cities are different, and additional sources of CO
could exist, e.g. from long-range transport or oxidation from CH4 and NMVOCs originating either nearby or in distant areas.
Correlation plots of surface CO vs. NO2 VMR daily mean over the HTP. The black line is a linear least-squares fit of the respective data. The linear equation of the fit and the resulting correlation coefficient (r) are shown. Both CO and NO2 time
series are provided by the CNEMC network. Vertical error bar is 1σ standard variation within that day.
The emission from coal burning for heating was thought to be the dominant source of primary gas pollutants in Lhasa in recent years (Ran et al.,
2014; X. Yin et al., 2019). A large portion of solely source results in the
highest correlation between NO2 and CO concentrations in Lhasa. In
contrast, Qamdo, Naqu, and Diqing are surrounded by alpine farmlands and
pastures. Historically, post-harvest crop residue (e.g. highland barley straws and withered grass) was often burned by local farmers to fertilize
the soil for the next planting season. As a fine fuel, post-harvest crop residue was often burned directly in the field in large piles and smoulders for weeks. These seasonal crop residue burning behaviours typically occur in the cold season, which could cause a high level of CO emission in this period. Furthermore, local residents extensively use dry yak dung as fuel for
cooking or heating throughout the year, which could elevate the background CO level in these regions. As a result, these higher local sources might be an
important factor explaining the higher CO magnitude in these regions.
Long-range transport
Monthly mean contributions of anthropogenic, BB, and oxidation from long-range transport to the surface CO VMR over the HTP are shown in Fig. 7. All
statistical results are based on GEOS-Chem tagged CO simulations by turning
off the emission inventories within the HTP. Due to the influence of
seasonally variable transport and magnitude of the regional emissions, the
anthropogenic, BB, and oxidation sources are all seasonal and regionally dependent. Generally, anthropogenic contributions in June–September and
DJF are higher than those in the rest of the year. In contrast, high levels
of oxidation contribution occur in JJA/SON and low levels of oxidation
contribution occur in MAM/DJF. For the BB source, contributions in MAM/DJF are larger than those in JJA/SON. Depending on season and region, relative
contributions of anthropogenic, BB, and oxidation transported to the surface
CO VMR over the HTP varied over 35 % to 61 %, 5 % to 21 %, and 30 % to 56 %,
respectively. The combination of anthropogenic and oxidation sources
dominated the contribution, which varied over 80 % to 95 % with an average of 89 % throughout the year.
Monthly mean contributions of anthropogenic, biomass burning (BB)
and oxidation transport to CO over the HTP. The vertical error bar represents 1σ standard variation within that
month. See Table 2 for a description of each tracer.
Monthly mean contributions of each geographical anthropogenic tracer
to the total anthropogenic-associated CO transport to the HTP. The vertical error bar is the 1σ standard variation within that month. See Table 2 for a description of each tracer.
Monthly mean contributions of each geographical BB tracer to the
total BB-associated CO transport to the HTP. The vertical error bar represents the 1σ standard variation within that month. See Table 2 for a description of each tracer.
Monthly mean contributions of CH4 and NMVOC oxidations to the
total oxidation-associated CO transport to the HTP. The vertical error bar represents the 1σ standard variation within that month. See Table 2 for a description of each tracer.
After normalizing each regional anthropogenic contribution to the total
anthropogenic contribution, the normalized relative (NR) contribution of
each anthropogenic region to the total anthropogenic associated transport is
obtained in Fig. 8. The results show that the anthropogenic associated
transport is mainly attributed to the influence of anthropogenic sources in
South Asia and East Asia (SEAS). The NR anthropogenic contribution in SEAS
ranges from 58 % in DJF to 91 % in SON. In addition, moderate
anthropogenic contributions from North America (NA) (10 % to 27 %), Europe
and boreal Asia (EUBA) (4 % to 12 %), and the rest of the world (ROW) (4 % to 10 %) are also observed in MAM/DJF. By using a similar normalized method, the NR
contributions of each BB tracer and oxidation tracer are obtained in Figs. 9
and 10, respectively. The results show that large BB contributions are
from the Africa (AF) region in July–February (30 %–70 %), the SEAS
region in MAM (25 % to 80 %), and the EUBA region in July–September (15 %
to 32 %). Additional moderate BB contributions are from the South America
(SA) region in May–June and September–December (9 % to 14 %), the
Oceania (OCE) region in the second half of the year (5 % to 15 %), and the NA region in September–December (8 % to 19 %). Depending on season and
region, 45 % to 67 % of oxidation contribution is attributed to CH4 oxidation, and 32 % to 55 % of oxidation contribution is attributed to
NMVOC oxidation. High-level NR contributions of CH4 oxidation occur in the cold season (November–March) and low-level NR contributions of
CH4 oxidation occur in the warm season (April–October). The NR
contributions of NMVOC oxidation varied over an opposite mode to that of CH4 oxidation; they maximize in the warm season and minimize in the
cold season. The JJA/SON meteorological conditions that show stronger solar
radiation, higher temperature, wetter atmospheric condition, and lower
pressure than those in DJF/MAM are more favourable for increasing VOC emissions from biogenic sources (BVOCs), which consolidates the fact that
contributions of NMVOC oxidation in the warm season are larger than those in the cold season.
By minimizing the propagation of model errors that are common to all tracers
(see Sect. 4), the major factors impacting the model interpretation are
the uncertainties in emission inventories and OH fields. The uncertainties
in CO emission inventories mainly impact primary anthropogenic and BB
sources and the uncertainties in CH4 and VOC emission inventories, and OH fields mainly impact secondary oxidation sources. Additional factors that affect the generation and deplete chemistry of CO or its precursors
(e.g. uncertainties in emission inventories of other atmospheric components, stratospheric intrusion of ozone and chemical mechanisms) could also contribute to the uncertainty of the interpretation. All these
factors may be seasonal and regionally dependent. A series of GEOS-Chem
sensitivity studies might be able to quantify these uncertainties, but this
is beyond the scope of the present work.
From Sect. 5.1 and the model interpretation here, we can conclude that the
main source of CO in urban areas over the HTP is due to local and SEAS anthropogenic and biomass burning emissions and oxidation sources. In contrast, black carbon in most of the HTP is largely attributed to South-East Asian biomass burning, and locally sourced carbonaceous matter from fossil
fuel and biomass combustion also substantially contributes to pollutants in urban cities and some remote regions, respectively (Cong et al., 2007, 2009, 2013; He et al., 2014; Zhang et al., 2015; Zhu et
al., 2019; Li et al., 2021; Gul et al., 2021; Thind et al., 2021). Our study
emphasized the different origins of diverse atmospheric pollutants in the
HTP.
Transport pathways
The 3D transport trajectories of CO originated in various source regions and
sectors to the HTP are identified as below. First, the GEOS-Chem tagged CO simulation is applied for determining the seasonal NR contribution of each tracer (Figs. 8 and 9). For the tracer with a NR contribution of larger than
30 % at a specific time (hereafter enhancement time), the global CO
distribution provided by the GEOS-Chem simulation is applied to search for
potential CO sources occurring before the enhancement time within 15 d.
Then, we generated a series of back trajectories with various travel times
to judge whether these CO emissions are capable of travelling to the
measurement region. For instance, with respect to each CO enhancement
measured at a specific time, we generated 10 back trajectories arriving at 100 m above the ground but with different travel times ranging from 3 to 15 d. If the back trajectories intersect a region where the GEOS-Chem simulation indicates an intensive CO source and the travel duration is
within ±2 h of the observed enhancement, then this specific CO
source could contribute to the observed enhancement over the HTP. The
transport trajectories for this CO source are finally determined. Meanwhile,
GEOS-Chem emission inventories are used to classify this CO source into an anthropogenic or BB source. This CO source is regarded as a BB source if the GEOS-Chem BB inventory indicates an intensive CO enhancement. Otherwise, it
is regarded as an anthropogenic source.
Travel trajectories of polluted air masses originating in AF (SON/DJF), SEAS and OCE (MAM/JJA), EUBA (SON/DJF), and NA (SON/DJF) that
reached Naqu (31.5∘ N) through long-range transport. For clarity, only a few trajectories are selected for demonstration. Travel times are 13, 7, 10, and 14 d, respectively. Global surface CO distributions based on
GEOS-Chem simulations are shown for 13, 7, 10, and 14 d prior to the
arrival time, respectively. The deeper the colour, the higher the CO concentration.
Spatial distribution of CO VMR in the GEOS-Chem tagged CO
simulations in different seasons, and the arrows represent the mean
horizontal wind vectors at 500 hPa. The HTP and the studied regions are
marked with a blue outline and yellow dots, respectively. Meteorological
fields are from the GDAS-1 data.
Figure 11 demonstrates travel trajectories of polluted air masses originating in the AF, SEAS + OCE, EUBA, and NA regions which arrived at Naqu
(31.5∘ N) over the HTP through long-range transport. As the GEOS-Chem BB inventory shows, CO emissions from southern Africa during July–September, central Africa during November–February, central Europe
during July–November, Siberia during June–September, and the South Asian peninsula during March–May are dominated by the BB source. Other potential CO sources are dominated by anthropogenic emissions. Figure 12 shows the spatial
distribution of CO VMR along with the mean horizontal wind vectors at 500 hPa in different seasons. Figure 13 illustrates the latitude–height and
longitude–height distributions of CO VMR along with the 3D atmospheric
circulation patterns in different seasons. The 3D transport pathways of CO
around the HTP are thus deduced as follows.
The first row shows the latitude–height distributions of CO VMR
averaged over 80–100∘ E in the tagged CO simulations in different
seasons (corresponding to different columns). The white contours at
intervals of 6 m s-1 represent the westerly (solid) and easterly
(dashed) mean meridional winds; the white area represents topography and the
arrows represent the wind vectors (vertical velocity in units of 10-4 hPa s-1 and zonal wind in m s-1); the studied regions are marked
with yellow dots. The second row is similar to the first row except that the
quantities are on the longitude–height perspective averaged over
28–33∘ N. Here the white contours represent the southerly (solid)
and northerly (dashed) mean zonal winds, and the horizontal component of the
wind vectors is meridional wind (m s-1). Meteorological fields are from
the GDAS-1 data.
As indicated by the arrows in Figs. 12 and 13, the strong surface cooling
in DJF over the HTP results in divergence and the formation of an enhanced
local circulation cell, while in JJA air masses converge toward the HTP from
the surroundings triggered by the ascent of strongly heated air masses over the HTP (Zhang et al., 2015). In DJF, the tropical
easterlies are weak, but the mid-latitude westerlies extend to the subtropics (∼ 20∘ N) near the surface and tropics
(∼ 10∘ N) over the middle troposphere (Fig. 13). In the summer monsoon season, the atmospheric circulation patterns around the HTP
change dramatically and are dominated by the reversal of the surface wind regime in the tropics, such as the South China Sea, Bay of Bengal, and Arabian Sea (Figs. 11 and 13). Meanwhile, the mid-latitude westerlies in JJA recede to
the North Temperate Zone (north of 30∘ N), and the westerly jet centre shifts to about 40∘ N (from about 30∘ N in DJF). In JJA, the tropical region in the south of the HTP is characterized by the
strong easterlies in the upper troposphere and by the south-westerly air flow in the lower troposphere (Fig. 13). The prevailing winds during the
transition seasons in MAM and SON are still westerlies (Fig. 13). These
above-seasonal atmospheric circulation patterns control the CO transport pathway around the HTP. Nevertheless, the transported CO scales to the HTP
are also influenced by source location and strength, travel trajectory, and elapsed time (Yao et al., 2012; Zhang et al., 2015; Kang et al., 2019).
In SON/DJF, a significant amount of CO from southern SEAS (anthropogenic source), northern AF (BB source), western EUBA, and northern NA
(anthropogenic source) can be transported to the HTP along the westerlies in
the dry winter monsoon conditions. CO originating in distant regions such as
western EUBA and NA reaches a high altitude (to 8 km) during the transport
(Fig. 11). However, CO from the densely populated and industrialized areas
in eastern China barely reaches the HTP because of strong removal along the
transport pathways to the HTP which circles around the Northern Hemisphere
along the westerlies during the winter monsoon season (Fig. 12). In MAM, CO
emissions from BB sources in the SEAS region can be transported to the HTP, which is mainly triggered by deep convection followed by northward transport into
the mid-latitude westerlies (Liu et al., 2003) (Fig. 13).
During the South Asian summer monsoon, the local abundant wet precipitation
can remove a large portion of SEAS-originated CO but can still affect the south-western HTP (Fig. 13). Along strong south-easterly air flow in the summer monsoon season, CO from eastern China can be uplifted higher and transported more to the north-eastern HTP than that in the DJF. In addition, large-scale
atmospheric deep convection can loft CO from upwind source regions (e.g. central SA and Indonesia, within the OCE region) into higher altitudes, where it can be transported to the HTP in SON or DJF. Generally, CO removals over
the HTP in all seasons are driven by atmospheric deep convection which lofts
CO into higher altitudes or by westerlies which transport local emissions far away (Fig. 13).
Factors driving surface CO variability over the HTP
Temporal CO burden is dependent on the difference between the CO source and
sink, which is determined by the accumulated influence of local emission,
transport, secondary generation, environmental capacity, and OH oxidation
capability. The environmental capacity is determined by atmospheric
self-clean capability, topography, deposition, and meteorological
condition (Hofzumahaus et al., 2009). Atmospheric self-clean capability
refers to the capability of the atmosphere in terms of depleting atmospheric
pollutants through physical and chemical processes (Rohrer et al., 2014).
Generally, the vertical self-clean capability is positively correlated
with the PBLH and the horizontal self-clean capability is positively
correlated with the wind speed (Rohrer et al., 2014). The OH oxidation
capability is positively correlated with temperature, radiation, and OH seasonality (Rohrer et al., 2014).
The bimodal pattern of diurnal cycles for surface CO VMR in urban areas over
the HTP is attributed to the following diurnal production and depletion
processes. The thin atmosphere over the HTP causes large temperature and
radiation differences between day and night (Yin et al., 2017; Kang et
al., 2019). The PBLH and OH oxidation capability in the nighttime is much lower than those in the daytime (Ran et al., 2014; Yin et al., 2017; X. Yin
et al., 2019). The CO emissions over the HTP start to generate after
sunrise and reach the daytime maximum during rush hours at 08:00 to 11:00 LT
in the morning. The CO concentration is then decreasing as a result of
depletion by reactions with OH to form O3 or transport far away (Ran
et al., 2014; X. Yin et al., 2019). Subsequently, CO emissions start to
generate again during rush hours at 16:00 to 19:00 LT in the afternoon and reach the nighttime maximum at 21:00 to 23:00 LT due to low PBLH and OH
oxidation capability in the nighttime (Ran et al., 2014; X. Yin et al.,
2019).
Similarly, the seasonal cycle of surface CO VMR in the urban areas over the
HTP is determined by the seasonal variability of CO source, environmental
capacity, and OH oxidation capability. High levels of surface CO VMR in the
late autumn to spring can be attributed to low PBLH and OH oxidation
capability but high local and transported CO in the period and vice versa for low levels of surface CO VMR in summer to early autumn (X. Yin et al.,
2019). Specifically, local anthropogenic CO sources (mainly heating
activities) and crop residue burning behaviours in urban regions over the HTP during the colder post-monsoon and winter months are higher than those in
other seasons. Meanwhile, the westerlies near the surface in SON/DJF are
weaker than those in MAM/JJA, which facilitates the accumulation of atmospheric pollutants (Fig. 13). Furthermore, high levels of CO are
observed in the late autumn to spring in neighbouring SEAS countries due to intensive anthropogenic emissions or BB practices (Kan et al., 2012;
Tiwari et al., 2014; Liu et al., 2018; Gani et al., 2019). These polluted
air masses can transport to the HTP region and elevate the local CO level
(Fig. 13). Thus, apart from local anthropogenic and BB emissions, these
transported sources might be an important factor explaining the high CO
pollution in winter.
Since the crop residue burning emissions result in poor air quality that
threatens local terrestrial ecosystems and human health, the Chinese government started to ban crop residue burning over China since 2015, and henceforth the crop residue burning events over the HTP decreased dramatically (Sun et
al., 2020) (http://www.chinalaw.gov.cn, last access: 19 June 2020).
Meanwhile, the major air pollutant emissions have decreased around the globe
in recent years as a consequence of active clean air policies for mitigating
severe air pollution problems in the major anthropogenic emission regions,
such as China, India, Europe, and North America (Zheng et al., 2018; Sun et al., 2021). Furthermore, elevated BB events in the AF, SEAS, and OCE regions were observed at the beginning of the studied years due to the El Niño–Southern Oscillation (ENSO) in 2015 (Sun et al., 2020). All these
factors probably drive a decreasing trend in surface CO VMR since 2015 in
most cities over the HTP. However, an overall increase in surface CO VMR in
Shigatse and Nyingchi since 2015 indicated that the decrease in transported
CO was overwhelmed by the increase in local CO emissions as a result of the
expansion of urbanization, industrialization, land use, and infrastructure
construction near the two cities.
Conclusions
In this study, we quantified the variability, source, and transport of CO in
the urban areas over the Himalayas and Tibetan Plateau (HTP) by using in
situ measurement, GEOS-Chem model tagged CO simulation, and the analysis of
meteorological fields. Diurnal, seasonal, and interannual variabilities of
CO over the HTP are investigated with ∼ 6 years (January 2015
to July 2020) of surface CO measurements in eight cities over the HTP.
Annual mean of surface CO volume mixing ratio (VMR) over the HTP varied over
318.3 ± 71.6 to 901.6 ± 472.2 ppbv, and a large seasonal cycle
was observed with high levels of CO VMR in the late autumn to spring and low
levels of VMR in summer to early autumn. Surface CO VMR burdens and
variations in Naqu, Qamdo, and Diqing are higher than those in other cities
in all seasons. The diurnal cycle is characterized by a bimodal pattern with
two maximums occurring around 09:00 to 11:00 LT in the daytime
and 21:00 to 23:00 LT in the nighttime. The trend in surface CO VMR from
2015 to 2020 over the HTP spanned a large range of (-21.6 ± 4.5) % yr-1
to (11.9 ± 1.38) % yr-1, indicating a regional representation of
each dataset. However, surface CO VMR from 2015 to 2020 in most cities over
the HTP showed negative trends.
The IASI satellite observations are for the first time used to assess the
performance of the GEOS-Chem full-chemistry model for the specifics of topography and meteorology over the HTP. Depending on the region, the
GEOS-Chem simulations over the HTP tend to underestimate the IASI
observations by 9.2 % to 20.0 %. Though not perfect in reproducing the
absolute values of the IASI observation, GEOS-Chem can capture the measured
seasonal cycle of the CO total column over the HTP with a correlation coefficient (r) of 0.64 to 0.82 depending on regions. Distinct dependencies
of CO on a short-lifetime species of NO2 in almost all cities over the HTP were observed, implying local emissions to be predominant. By turning
off the emission inventories within the HTP in GEOS-Chem tagged CO
simulation, the relative contribution of long-range transport was evaluated. The results showed that transport ratios of primary anthropogenic source, primary biomass burning (BB) source, and secondary oxidation source to the
surface CO VMR over the HTP varied over 35 % to 61 %, 5 % to 21 %, and 30 % to
56 %, respectively. The anthropogenic contribution is dominated by the
South Asia and East Asia (SEAS) region throughout the year (58 % to
91 %). The BB contribution is dominated by the SEAS region in spring (25 %
to 80 %) and the Africa (AF) region in July–February (30 %–70 %).
Additional important anthropogenic contributions from North America (NA) (10 %
to 27 %) and Europe and boreal Asia (EUBA) (4 % to 12 %) in spring and winter (MAM/DJF) are also observed. Additional important BB contributions
are from the EUBA region in July–September (15 % to 32 %), the South
America (SA) region in May–June and September–December (9 % to 14 %),
the Oceania (OCE) region in the second half of the year (5 % to 15 %), and
the NA region in September–December (8 % to 19 %). The decreasing trends
in surface CO VMR since 2015 in most cities over the HTP are attributed to
the reduction in local and transported CO emissions in recent years.
This study concluded that the main source of CO in urban areas over the HTP is due to local and SEAS anthropogenic and BB emissions and oxidation sources. In contrast, black carbon in most of the HTP is largely attributed to
South-East Asian biomass burning, and locally sourced carbonaceous matter from fossil fuel and biomass combustion also substantially contributes to pollutants in urban cities and some remote regions, respectively. This study
not only emphasized the different origins of diverse atmospheric pollutants
in the HTP, but also improved knowledge of the variabilities, sources,
drivers, and transport pathways of atmospheric pollutants over the HTP and
provided guidance for potential regulatory and control actions.
Code availability
The GEOS-Chem code is available from 10.5281/zenodo.2580198 (The International GEOS-Chem Community, 2019).
Data availability
Surface CO time series in all cities over the HTP and GEOS-Chem tagged CO simulations in this study are available on request from Youwen Sun (ywsun@aiofm.ac.cn). The IASI product used in this study is available from https://iasi.aeris-data.fr/CO_IASI_C_L3_data/ (IASI/Metop Community, 2021). The latest MEIC inventory is available from http://meicmodel.org/?page_id=560 (MEIC model Community, 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-9201-2021-supplement.
Author contributions
YS designed the study and prepared the paper with inputs from all the coauthors. HY carried out the GEOSChem CO simulations. YC, QZ, BZ, XL, and YT carried out the data analysis. JN, CL, and JL contributed to this work by providing constructive comments.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank the GEOS-Chem team for sharing the model and Tsinghua University,
China, for providing the latest MEIC inventory. We thank the IASI team for sharing the satellite CO product. We thank the NOAA for providing the HYSPLIT model and GEOS-FP meteorological files. We thank Yaqiang Wang for
providing the TrajStat module for air-mass back-trajectory calculation. We thank Martyn Chipperfield from the University of Leeds and Zhiyuan Cong from the Institute of Tibetan Plateau Research, CAS for their invaluable comments for improving this paper. Surface CO time series in all
cities over the Himalayas and the Tibetan Plateau are provided by the China
National Environmental Monitoring Center (CNEMC) network funded by the
Chinese Ministry of Ecology and Environment.
Financial support
This research has been supported by the National High Technology Research and Development Program of China (grant nos. 2019YFC0214802, 2017YFC0210002, 2018YFC0213201, and 2019YFC0214702), the National Science Foundation of China (grant nos. 41575021, 51778596, 41977184, and 41775025), the Major Projects of High Resolution Earth Observation Systems of National Science and Technology (grant no. 05-Y30B01-9001-19/20-3), the Sino-German Mobility programme
(grant no. M-0036), and the Anhui Province Natural Science Foundation of China (grant no. 2008085QD180).
Review statement
This paper was edited by Jason West and reviewed by two anonymous referees.
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