Introduction
The Tibetan Plateau and Himalayas (TPH), generally called the “third
pole”, is the highest (average altitude of more than 4000 m a.s.l.) and
largest (∼ 2 500 000 km2) plateau in the world. This region has
been recognized as one of the most pristine in the world due to its
high altitude, sparse population, and the minor influence of anthropogenic
activities (Yao et al., 2012a). Considering the intense dynamical and
thermal forcing effects in this region, the TPH not only plays a key role in the formation
of Asian monsoon systems, but also impacts large-scale atmospheric
circulation, the hydrological cycle, as well as global climate (Duan and Wu,
2005; Wu et al., 2007). Over the past few decades more attention has been
paid to the environment and climate change in the TPH, owing to the fact that this region is
very susceptible to global climate change as evidenced by rapid air
temperature increases and dramatic glacier shrinkage (Xu et al., 2009; Kang et
al., 2010; Yao et al., 2012b; Yang et al., 2014). The atmospheric environment in
the TPH, although it is one of the most pristine regions in the world, is thought to be significantly influenced by air pollution from surrounding countries
(Hou et al., 2003; Lau et al., 2008). For example,
polluted air masses, particularly from South and Southeast Asia regions, have
frequently been observed being transported to the Himalayas (Bonasoni et al.,
2010; Cong et al., 2015), heating the aloft air masses over the TPH (Lau et
al., 2006; Ramanathan and Carmichael, 2008) and declining the surface albedo
after their deposition onto snow and glaciers
(Xu et al., 2009). As a
consequence, characterizing the aerosol physicochemical properties in the
TPH, including mass loading, chemical composition, size distribution, and
source, are of great importance to better understanding the aerosol chemistry,
estimating the aerosol radiative forcing, and evaluating the effect of
polluted air masses on the ecology and environment in the TPH region.
Numerous aerosol measurements have been conducted in the TPH region in past
decades to characterize the physicochemical properties, sources, and
transport pathways of ambient aerosol (Liu et al., 2008; Decesari et al.,
2010; Marcq et al., 2010; Marinoni et al., 2013; Putero et al., 2014; Xu et
al., 2018; R. Zhang et al., 2017). South and Southeast Asia are two major
polluted regions due to their intense biomass burning (BB) activities from
natural forest fires and traditional human burning activities for
residential heating and cooking (Engling et al., 2011; Yadav et al., 2017).
The average pollutant distribution
of South and Southeast Asia during the 12 April to 12 May 2016 period
can be further revealed by the distribution of average aerosol optical
depth in Fig. 1. During the pre-monsoon period, atmospheric pollutants
associated with BB emissions in South Asia are generally advected by
regional and long-range transport (e.g., westerlies and South Asian monsoon
system) to the Himalayas and build up in the southern foothills, where they are then
lifted up to high altitude by the Himalayan topography and typical
valley wind circulation (Zhao et al., 2013; Cong et al., 2015; Liu et al.,
2017). However, the chemical properties of aerosol particles are still not
well understood
in the Himalayas region due to its remote and
harsh environments, challenging weather conditions, and logistical
difficulties. In addition, most available studies are based mainly on off-line filter sampling of ambient aerosol or snow/ice samples
followed by laboratory analyses (Decesari et al., 2010; Ram et al., 2010;
Li et al., 2016; Wan et al., 2017). These studies have usually had a relatively
low temporal
resolution (days to weeks); therefore, real-time consecutive field
measurement, especially focusing on the high-resolution size-resolved
chemical characteristics of aerosol particles, is of great importance and
necessary to give insight into the sources and the dynamic chemical
evolution of ambient aerosol.
Online real-time instrument such as the Aerodyne aerosol mass spectrometer (AMS),
which can be used to characterize the chemical properties and sources
of submicron aerosol particles with high time resolution and sensitivity,
has been greatly developed and widely implemented worldwide (Canagaratna et
al., 2007; Zhang et al., 2007a; Jimenez et al., 2009; Li et al., 2017).
Although the deployments of the AMS in China started in 2006, most
of the studies in China have been conducted in urban areas, including
Beijing–Tianjin–Hebei (Sun et al., 2013, 2016), Yangtze
River Delta (Wang et al., 2016a, b), Pearl River Delta
regions (Huang et al., 2011), and Lanzhou (Xu et al., 2014, 2016;
X. Zhang et al., 2017) as shown in Fig. S1 in the Supplement. However only a few studies have been deployed
in remote sites thus far, such as Menyuan (Du et al., 2015), Mt. Yulong (Zheng
et al., 2017), and Nam Co (Xu et al., 2018; Wang et al., 2017). In this
paper, an Aerodyne high-resolution time-of-flight mass spectrometer (HR-ToF-AMS)
was deployed at the Qomolangma Station for Atmospheric and
Environmental Observation and Research (QOMS) on the northern slope of the
Himalayas to fill the vacancy of real-time mass spectrometer measurement at
high elevation sites and to evaluate the significant impacts of BBs from
polluted areas in southern Asia on TPH aerosol properties during the
pre-monsoon season. Here, we report an overview of the 5 min real-time
chemical and physical characteristics of submicron aerosols (PM1),
including mass loading, composition, size distribution, and acidity as well as
temporal and diurnal variations. The sources of organic aerosols (OA) are
also investigated using positive matrix factorization analysis on the
high-resolution OA mass spectrum. BB influence and chemical evolution of
aerosols in polluted plumes are examined by combining back trajectory
analysis of air masses and fire hotspots information.
Experimental methods
Sampling site
The QOMS (28.36∘ N, 86.95∘ E, 4276 m a.s.l.; Fig. 1),
which is located on the northern slope of Mt. Everest (∼ 30 km
away), was established for atmospheric and environmental observation in 2005
(Ma et al., 2008). The geomorphic and climate features around the QOMS
are typically alpine, comprising cold and arid areas covered by sandy soil with sparse
vegetation. The QOMS is located in a long river valley and isolated from
residential areas due to its harsh environment, with a small village (with a
population of ∼ 300) to the south (∼ 10 km). The
closest larger settlement, Dingri County, is ∼ 100 km south from the QOMS.
A freeway is located at the front of the QOMS for tourism with increased
tourist during summer. The measurements were conducted from 12 April to
12 May 2016. Since this period was within the typical pre-monsoon season of the
TPH, the large-scale atmospheric circulation pattern was dominated by
westerly or southwesterly winds and limited precipitation. Owing to
distinct thermal forcing from the southern mountains and glaciers, the QOMS
was locally dominated by strongly mountain–valley circulation with
down-slope wind prevailing during the daytime, especially in the afternoon
(Figs. 1c and S2) (Zou et al., 2008), which made the valley an
efficient channel for the downward transport of air masses from the high-altitude troposphere.
Instrumentation
A suite of real-time instruments were collocated to measure the
physiochemical properties of fine particles at the QOMS, including an
Aerodyne HR-ToF-AMS (Aerodyne Research Inc., Billerica, MA, USA) for 5 min
size-resolved chemical compositions (organics, sulfate, nitrate, ammonium,
and chloride) of non-refractory submicron particulate matter (NR-PM1);
a scanning mobility particle sizer (SMPS, model 3936, TSI Inc., Shoreview,
MN, USA) for 5 min particle number concentration and size distribution
between 14.6 and 661.2 nm in mobility diameter (Dm); and a photoacoustic
extinctiometer (PAX, DMT Inc., Boulder, CO, USA) for particle light
absorption and scattering coefficient (babs and bscat) at 405 nm and
further deriving black carbon (BC) mass concentration. All instruments were
placed in an air-conditioned room and the temperature was maintained at
∼ 20 ∘C. Ambient aerosol particles were introduced
through a 0.5 inch copper tube which stemmed about 1.5 m out of the rooftop.
A PM2.5 cyclone (model URG-2000-30EH, URG Corp., Chapel Hill, NC,
USA) was used in front of the sampling inlet for removing coarse particles
with size cutoffs of 2.5 µm in aerodynamic diameter (Dva). A
diffusion dryer was placed behind
the cyclone to dry the ambient air and
eliminate the potential effect of humidity on particles. The total length of the
sampling line was about 5 m and the retention time of particles was less
than 2.5 s in the whole inlet. The total air flow rate from the sampling
inlet was about 10 L min-1, with part of the flow shared by the HR-ToF-AMS
and the SMPS while the remaining flow exhausted via an external pump. The
meteorology data including wind speed (WS), wind direction (WD), relative
humidity (RH), temperature (T), and solar radiation (SR) during this study
were obtained from a Vantage Pro2 weather station (Davis Instruments Corp.,
Hayward, CA, USA). Note that all references to date and time in this study are
reported in local time (LT), i.e., Beijing time (BJT: UTC+8 h).
HR-ToF-AMS operation and data analysis
HR-ToF-AMS operation
A detailed instrumental description of the Aerodyne HR-ToF-AMS can be found
elsewhere (DeCarlo et al., 2006), therefore only a brief summary is provided here.
The HR-ToF-AMS consists of three main parts: an aerosol sampling
inlet, a particle sizing vacuum chamber, and a particle composition
detection section (Jimenez et al., 2003). Ambient particles are sampled into
the instrument through a critical orifice (130 µm in this study for
enhancing the transmission efficiency at the high-altitude area) and focused
into a concentrated and narrow beam through an aerodynamic lens.
Particles are then accelerated into the sizing vacuum chamber and obtain
different velocities for particles with different sizes due to the
supersonic expansion induced by a pressure difference between the two chambers.
Meanwhile, a mechanical chopper with two radial slits located 180∘
apart is used to intercept the focused particle, and then the time of flight (P-ToF)
from the chopper to the vaporizer is measured to obtain the
aerodynamic size of particles. After passing through the sizing chamber,
particles are directed onto a resistively heated surface (∼ 600 ∘C)
under a high vacuum and ionized by a 70 eV electron
impact, before finally being detect by the high-resolution time-of-flight mass
spectrometer. In this study, the HR-ToF-AMS was only toggled under the high
sensitive V-mode (detection limits ∼ 10 ng m-3). Under V-mode
operation, the instrument also switched between the mass spectrum (MS) mode
and the particle P-ToF mode every 15 s, spending 6 and 9 s on each, to
obtain the mass concentrations and size distributions of the non-refractory
species, respectively.
The HR-ToF-AMS was calibrated for ionization efficiency (IE) and particle
sizing at the beginning, in the middle, and at the end of this study
according to the standard protocols (Jayne et al., 2000). Both the
calibrations of IE and particle sizing were performed using mono-dispersed
ammonium nitrate particles with nominal diameters of 70–300 nm. Default
relative ionization efficiency (RIE) values were assumed in this study as
1.1 for nitrate, 1.3 for chloride, and 1.4 for organics. The RIE values of 3.9
and 4.2 were used for ammonium based on the results of two IE
calibrations at the beginning and in the middle of this study, while RIE
values of 1.6 and 1.4 were determined similarly for sulfate by using
mono-dispersed ammonium sulfate particles, respectively.
HR-ToF-AMS data analysis
The mass concentrations and size distributions of NR-PM1 species and
the ion-speciated mass spectra, composition and elemental composition of
organics were determined from the HR-ToF-AMS data using the standard
ToF-AMS analysis toolkit SQUIRREL (v1.56) and PIKA (v1.15c) modules written
in Igor Pro (Wavemetrics Inc., Lake Oswego, OR, USA). An empirical particle
collection efficiency (CE) of 0.5 was used to compensate for the incomplete
transmission and detection of particles due to particle bouncing at the
vaporizer and partial transmission through the aerodynamic lens, which has
been widely used in field studies employing AMS with a dryer installed in
front of the inlet (Xu et al., 2014, 2016). The elemental ratios
of oxygen-to-carbon (O / C), hydrogen-to-carbon (H / C), nitrogen-to-carbon
(N / C), and organic mass-to-organic carbon (OM / OC) for this study were
determined using the “improved-ambient” method (referred as I-A method)
(Canagaratna et al., 2015), which increased O / C on average by 34 %, H / C on
average by 15 %, and OM / OC on average by 17 % (Fig. S3) compared with
those determined from the “Aiken ambient” method (referred as A-A method)
(Aiken et al., 2008).
Positive matrix factorization (PMF) analysis using the PMF2.exe algorithm (v4.2)
(Paatero and Tapper, 1994) in robust mode was conducted on the high
resolution mass spectra (HRMS) to determine distinct OA components in this
study. The analysis was performed using an Igor Pro-based PMF Evaluation
Tool (PET, v2.03) (Ulbrich et al., 2009), downloaded from
(http://cires.colorado.edu/jimenez-group/wiki/index.php/PMF-AMS_Analysis_Guide). The data and error matrices input into the
PMF analysis were generated from analyzing the V-mode data via PIKA fitting.
Detailed PMF analysis was thoroughly evaluated following the procedures
summarized in Table 1 of Zhang et al. (2011). Isotopic ions were generally
excluded and the four ions of O+, HO+, H2O+, and
CO+ were down-weighted in the PMF analysis, because they were determined
according to the relationship with CO2+ signal (Ulbrich et al.,
2009). The “bad” ions with S / N less than 0.2 were removed from the HRMS data
and error matrices before PMF analysis, and “weak” ions with S / N between 0.2
and 2 were down-weighted by increasing their errors. In addition, some runs
with huge mass loading spikes were also removed from the data and error
matrices. The detailed matrix preparation and data pretreatment can also
found in Xu et al. (2014).
A summary of key diagnostic plots of the PMF results for this study is
presented in Fig. S4. Overall, the PMF solutions were investigated for one to
eight factors and for the rotational parameter (fPeak) varying from -1 to 1
with a step of 0.1. Besides examining the model residuals, scaled residuals,
and the Q/Qexp contributions for each m/z and time following procedures
detailed in Table 1 of Zhang et al. (2011), the optimum solution can also be
evaluated by comparing the mass spectra of individual factors with
reference spectra from specific sources or other ambient AMS measurements;
comparing the time series of individual factors with the known external
tracers; and analyzing the diurnal variations of individual factors.
Finally, the three-factor solution with fPeak = 0 was chosen in this work. The
direct comparisons of the mass spectra, time series, and diurnal variations
for two-factor and four-factor solutions were also shown in Figs. S5 and S6,
respectively. The two-factor solution does not resolve the small, yet distinct
nitrogen-containing OA, while the four-factor solution shows a splitting factor
from the BB OA resolved in the three-factor solution and seems just like a
simple separation of the two BB polluted episodes.
Other relevant data
The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT4) model
developed by the National Oceanic and Atmospheric Administration (NOAA)
(Draxler and Rolph, 2003) was used to investigate the origins of air masses
in this study, using the meteorological data from the NOAA Global Data
Assimilation System (GDAS). The back trajectories were calculated every 6 h
at an ending height of 500 m a.g.l. (above ground level) at the QOMS during the
entire campaign, and then clustered according to similarity in
spatial distribution. Finally, a four-cluster solution was adopted according
to its small total spatial variance.
Summary of meteorological and HR-ToF-AMS data. The 5 min time series
of (a) ambient temperature (T), relative humidity (RH), and solar
radiation, (b) wind speed (WS) colored by wind direction (WD),
(c) mass concentrations of PM1 species, (d) mass
concentrations of organic components, (e) mass contributions of PM1
species to total PM1 as well as total PM1 mass concentrations, and
(f) mass contributions of organic components to organics. The time
series of hourly average f60 (= C2H4O2+/OA) values
for the entire period is also shown. The markers of PP1 and PP2 represent the
two polluted periods while CP1 and CP2 are clear periods.
![]()
Aerosol optical depth (AOD) at 550 nm was derived from the observations made
by National Aeronautics and Space Administration (NASA) Moderate Resolution
Imaging Spectroradiometer (MODIS) onboard the Terra satellite. The
distribution of average aerosol optical depth (AOD) in a large area
(20–45∘ N, 60–110∘ E) around
the TPH during the entire period of this study is given in Fig. 1d.
Various active fire hotspots were detected over South and Southeast Asia by
the Fire Information for Resource Management System (FIRMS) provided by the
MODIS satellite (https://firms.modaps.eosdis.nasa.gov), demonstrating the
possibility that active wildfires or BBs from South and Southeast Asia may
have significant impacts on the air conditions in the TPH region.
The aerosol liquid water content (ALWC) was estimated with the Extended AIM (E-AIM) Aerosol Thermodynamics Model
(http://www.aim.env.uea.ac.uk/aim/aim.php). The input data included the
concentrations of sulfate, nitrate, ammonium, and chloride measured by the
HR-ToF-AMS as well as the relative humidity (RH) and temperature of ambient air.
Results and discussion
Overview of the study
Meteorological conditions
The measurement period of our study was within the typical pre-monsoon
season of the TPH. The meteorological conditions were therefore
characterized by relatively cold, dry, and windy weather, and the
westerlies dominated large-scale atmospheric circulation patterns with
little precipitation, as displayed in Fig. 2. During the study, the averaged
diurnal air temperature ranged from -2.0 to 12.5 ∘C with an
average (±1σ) of 5.7 (±5.0) ∘C, and the RH
ranged from 15.3 to 67.5 % with an average of 39.8 ± 18.8 %. Only
two light precipitation events (1 and 0.5 mm day-1) occurred on 1 and
8 May, respectively. The WDs at QOMS were predominantly southwesterly,
which were mainly associated with the thermally driven mountain–valley winds
and glacier winds (Zou et al., 2008). For the diurnal variation of wind
conditions, a nearly calm wind period (hourly average WS less than 2 m s-1)
was observed in the early morning; after sunrise until noon, there was a weak up-slope wind period (from the north); the diurnal
wind cycles were otherwise dominated by down-slope wind (from the
southwest) with a maximum hourly average WS value of up to 7 m s-1
(Figs. 2b and S2).
Inter-comparisons between different instruments
An inter-comparison of the total PM1 (NR-PM1 + BC) mass
concentrations measured by the HR-ToF-AMS (CE = 0.5) and the PAX with
particle volumes (assuming spherical particles) determined from the SMPS is
shown in Fig. S7. Overall, the PM1 mass is closely correlated
(R2 = 0.97) with that of SMPS particle volume during the entire
campaign, with a linear regression slope of 2.86. This slope is
significantly higher than the estimated average PM1 density of 1.44 g cm-3,
which is calculated based on the measured particle compositions
in this study and the assumed particle densities of 1.2 for organics,
1.78 for (NH4)2SO4, 1.72 for NH4NO3, 1.52 for NH4Cl,
and 1.8 g cm-3 for BC (Zhang et al., 2005b; Xu et al., 2016). This
discrepancy is likely introduced by various factors, including different
transmission sizes between HR-ToF-AMS and SMPS (up to ∼ 1.0 µm
in Dva for AMS vs. limited size range of 14.6–661.2 nm
in Dm for SMPS), rough calculation of PM1 density using assumed
composition densities and spherical shape without considering the particle
porosity, as well as the use of the empirical and constant CE value of 0.5 in
this study. This phenomenon was also observed at other sites in previous
studies (Ge et al., 2012; Huang et al., 2012; Xu et al., 2014; Du et al., 2015).
The average mass contributions of PM1 (calculated by NR-PM1 + BC)
species (a) during the entire sampling period and (b) as a
function of the total PM1 mass concentrations. The white solid line in (b)
shows the percentage of the data number in each mass bin
to the total data number.
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Mass concentration and chemical composition of PM<inline-formula><mml:math id="M106" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">1</mml:mn></mml:msub></mml:math></inline-formula>
As shown in Fig. 2, the mass concentrations of PM1 and all PM1
species, as well as their mass fractions in PM1 varied dynamically
throughout this study. Two polluted periods (PP1 and PP2) were identified
according to their high PM1 mass concentrations (daily average PM1
mass is larger than 5 µg m-3), high contributions from BBOA and
unique back trajectories. The rest periods characterized by low PM1
mass concentrations were considered as clear periods (CP1 and CP2). The
5 min total PM1 mass concentration ranged from 0.18 to 27.97 µg m-3
for the study, with an average (±1σ) value of 4.44 ± 4.54 µg m-3.
This average value was more than two times
lower than most of the PM1 mass concentrations measured with Aerodyne
AMS or aerosol chemical speciation monitor (ACSM) instruments at various
urban, suburban, rural, or background sites in China (10.9–138.8 µg m-3)
(Fig. S1); however only slightly lower than that at Mt. Yulong (5.7 µg m-3)
located at the southeastern edge of the TPH, and much
higher than that at Nam Co Station (2.0 µg m-3) located in the
central of the TPH. Moreover, as shown in Table S1 in the Supplement, the PM1 mass
concentration in this study was also lower than those measured at three
remote island sites in Asia which were frequently influenced by outflow from
China, Korea, and Japan (i.e., 7.9 µg m-3 for Okinawa island,
12.0 µg m-3 for Fukue island in Japan, and 10.7 µg m-3
for Jeju island in Korea) (Takami et al., 2005; Jimenez et al.,
2009), as well as the PM1 mass concentration (15.1 µg m-3)
obtained at the Bachelor Mountain in US which was heavily
impacted by wildfire smoke plumes (Zhou et al., 2017). However, it was
higher than those reported at other coastal, high elevation, forest, or
remote background sites in North America and Europe (0.55–2.91 µg m-3)
(Zhang et al., 2007a; Sun et al., 2009; Fröhlich et al.,
2015). Although the measurements mentioned above were conducted at various
sites worldwide during different seasons, these comparisons further
demonstrate that QOMS is a typical high elevation, remote, background site in Asia.
Overall, organics and BC were the two dominant PM1 species (
contributing 54.3 and 25.0 % on average to the total PM1 mass, respectively)
followed by sulfate (9.3 %), ammonium (5.8 %), nitrate (5.1 %), and
chloride (0.4 %) (Fig. 3a). The high contributions of organics and BC at
QOMS were significantly associated with the active BB emissions by
long-range transport from polluted areas in South Asia. Organic compounds
and BC have been revealed as two dominant components of BB aerosols and have
generally been used to identify BB events in previous studies (Bond et al., 2004;
Bougiatioti et al., 2014). In addition, biomass burning at high elevation
regions of the Himalayas and southern Asia was more incomplete burning and had the potential to
emit
high amounts of BC. This conclusion can be further revealed by their enhanced
mass concentrations and contributions, especially for organics, during the
two distinct polluted episodes influenced by active BB plumes. Figure 3b
showed the mass contributions of PM1 species as a function of total
PM1 mass concentrations. The PM1 mass loadings in this study were
mostly below 6 µg m-3 (accounted for ∼ 77 %);
The mass contribution of organics increased significantly with the increase
of total PM1 mass loading whereas the rest species showed relatively
stable or decrease trends, suggesting dominant contributions of organics
in the polluted episodes at QOMS.
Acidity and size distributions of submicron aerosols
To evaluate the bulk acidity of NR-PM1 in this study, we calculated the
NH4+ concentration (NH4calc+) based on the mass
concentrations of sulfate, nitrate, and chloride measured by the HR-ToF-AMS
and assumed full neutralization of these anions by ammonium (Zhang et al.,
2007b). The scatter plot of the measured
NH4+ (NH4meas+) concentration vs. the NH4calc+
concentration for the entire campaign was shown in Fig. S7. A tight correlation (R2 = 0.97) existed between
NH4meas+ and NH4calc+ with a linear regression
slope of 1.2, indicating that there were excesses
of ammonium in the submicron
particle. This slightly high NH4meas+ / NH4calc+
ratio was quite different to results from various urban and rural
sites in China, where bulk aerosols were neutralized overall or acidic due
to the enriched gaseous precursors of SO2 and NOx which could be
further oxidized to sulfate and nitrate (Sun et al., 2013; Xu et al., 2014;
Du et al., 2015; X. Zhang et al., 2017). The excess ammonium at QOMS might
relate to the important contributions of organic acids in this area (Cong et
al., 2015), which could underestimate the NH4calc+ due to the neglect of organic acids in the ion-balance
calculation, and the non-negligible contributions of nitrogen-containing
organic compounds to NHx+ which finally
overestimated the NH4meas+ (Sun et
al., 2009; Ge et al., 2012). As mentioned above, atmospheric aerosols in the
TPH region were significantly influenced by BB emissions from South Asia
during the sampling periods. BBs would emit large amounts of
nitrogen-containing organic compounds (Fleming et al., 2018; Zhou et al.,
2017) and as discussed in Sect. 3.2.
The average size distributions of (a) mass concentrations and
(b) mass contributions of NR-PM1 species for the entire study.
![]()
Figure 4 shows the average size distributions of NR-PM1 species and
their mass contributions as the function of size distribution. Overall, all
chemical species showed a nearly consistent but narrow accumulation mode
peaking at ∼ 500 nm in Dva, indicating the well
internal-mixed and aged aerosol particles at QOMS. Ultrafine particles
(particles with diameter less than 100 nm) were dominated by organics (more
than 70 %), while the mass contributions of chemical species at the major
peak (∼ 500 nm) were organics (∼ 65 %),
sulfate (∼ 13 %), nitrate (∼ 11 %), ammonium
(∼ 10 %), and chloride (∼ 1 %). The
contribution of organics decreased with the increase of size mode, while the
contributions of three major inorganic species (sulfate, nitrate, and
ammonium) slightly increased with the increasing sizes (Fig. 4b).
Diurnal variations of chemical species
The average diurnal cycles of meteorological parameters as well as the
PM1 species and their mass fractions for the entire campaign are shown
in Fig. 5. All PM1 species presented a similar diurnal pattern with
lower concentrations in the daytime and higher concentrations at
nighttime. The mass concentrations reached their minimum values at around
15:00. This pattern was accompanied by enhanced wind speed and
increased air temperature in the afternoon, which could related to the
dynamics of the planetary boundary layer (PBL). After that, the mass
concentrations began to build up and reached high levels at
nighttime. Note that the mass concentrations of chloride and BC also displayed
a slight peak during the early morning, which corresponded with calm
wind conditions and the lowest air temperature of the day; this could also be
associated with the enhanced local emissions at QOMS in the morning. The
diurnal cycles of mass contributions of each PM1 species were
relatively stable for the entire campaign, besides the slight increase of BC
from 24 % at ∼ 08:00 LT to 30 % at ∼ 10:00 LT.
Overall, organics dominated PM1 throughout the day (49–57 %),
followed by BC (23–30 %), sulfate (9–10 %), ammonium (5–6 %),
nitrate (4–6 %), and chloride (0.3–0.8 %).
The diurnal cycles of (a) meteorological parameters (temperature,
RH, wind speed, and solar radiation), (b) mass concentrations and
(c) mass contributions of PM1 chemical species for the entire study.
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Bulk characteristics of OA
Figure 6a and b showed the average mass contributions of the four elements
and the six ion categories to total organics, respectively. The organic mass
was on average composed of 36.8 % carbon, 57.9 % oxygen, 4.0 %
hydrogen, and 1.3 % nitrogen. For ionic categories, CxHyO1+
ions dominated the total OA accounting for 41.3 %, followed by
CxHyO2+ (24.9 %), CxHy+ (23.9 %),
HyO1+ (6.1 %), CxHyNp+ (2.9 %) and
CxHyOzNp+ (0.9 %). The contributions of oxygen and
the two major oxygenated ion fragments (CxHyOz+ = CxHyO1+ + CxHyO2+)
at QOMS were quite a bit higher than those obtained at other urban or rural sites
in China, whereas carbon and CxHy+ ions had
relative lower contributions, e.g., 38 % of CxHyOz+
and 21 % of oxygen vs. 56 % of CxHy+ and 70 % of
carbon in urban Lanzhou (Xu et al., 2014), and 37.4 % of CxHyOz+
vs. 51.2 % of CxHy+ in urban Nanjing (Wang et al., 2016a), suggesting that OA at QOMS were highly aged.
Correspondingly, the average high-resolution OA mass spectrum (Fig. 6c) also
showed significantly high contribution (∼ 25 %) at m/z 44
signal (one of the most reliable marker of oxygenated OA) compared with
other ion fragments, e.g., 5 % at m/z 43 (indicator for less oxidized
compounds), 1.7 % at m/z 55 (important COA fragment), and 0.4 % at m/z 57
(tracer for traffic-related emission) (Alfarra et al., 2004; Zhang et al.,
2005a). The average O / C ratio was 1.07 during this study, which was much
higher than those observed at various urban and rural sites in China using
the I-A method, e.g., 0.37 in Beijing (Sun et al., 2016), 0.36 in Lanzhou
(Xu et al., 2016), 0.35 in Nanjing (Wang et al., 2016a), and 0.65 in Ziyang
(Hu et al., 2016). Moreover, the average O / C ratio was even higher than
that of 0.98 from the background site of Mt. Wuzhi in southern China (Zhu et
al., 2016), indicating that OA at QOMS was more oxidized and aged during
long-range transport. The average H / C, N / C and OM / OC ratios were, on
average, 1.29, 0.026, and 2.55 in this study, determined by a nominal chemical formula of
OA as C1H1.29O1.07N0.026.
For the diurnal cycles, the O / C ratio had two peaks one in the early morning and one in the
late afternoon, likely related to the production of secondary organic
aerosol (SOA) via aqueous-phase reactions or photochemical oxidation
processes during these two periods. H / C and N / C ratios, however, showed inverse
diurnal cycles compared with O / C, peaking at around 08:00–10:00 LT in
the morning. The Van Krevelen diagram (H / C vs. O / C), which has been widely used to probe the oxidation reaction mechanisms for bulk OA (Heald et al.,
2010), showed an apparent inverse correlation
(R2 = 0.57) with a slope
of -0.48 at QOMS (Fig. S8). Ng et al. (2011b) have suggested that a slope
of -0.5 indicates a net change in chemical composition from the addition of
both acid and alcohol/peroxide functional groups without fragmentation,
and/or carboxylic acid groups with fragmentation.
Organic aerosol source apportionment
Source apportionment via PMF analysis on the high-resolution OA mass
spectrum identified three distinct factors in this campaign according to
their unique temporary variations, mass spectrum (MS) profiles, element
ratios, correlations with tracers, and diurnal patterns, i.e., a BB-related
OA (BBOA), a nitrogen-containing OA (NOA) and a more-oxidized oxygenated OA (MO-OOA).
Detailed discussion on each factor is given in the following subsections.
The average contributions of (a) four elements (C, O, H, and N)
and (b) six ion categories (colors as in c) to OA for the
entire study; (c) the average high-resolution mass spectrum of OA
(colors show six ion categories); (d) the diurnal variations of O / C,
H / C, and N / C ratios.
![]()
The PMF results of (a)–(c) the time series of three
OA factors and corresponding tracer species, (d)–(f) the
diurnal variations of the mass concentrations of the three OA factors (the
whiskers above and below the boxes indicate the 90th and 10th percentiles, the
upper and lower boundaries respectively indicate the 75th and 25th percentiles,
the lines in the boxes indicate the median values, and the cross symbols indicate
the mean values), (g)–(i) high-resolution mass spectra of the
three OA factors colored by six ion families at m/z < 120, and
(g')–(i') the unit resolution mass spectra at m/z > 120
for each OA factor.
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BBOA
Although significant high contribution at m/z 44 (mostly CO2+) was
found in all of the three OA components, the BBOA MS was also characterized by
contributions at m/z 60 (mainly C2H4O2+) and
m/z 73 (mainly
C3H5O2+) (Fig. 7g), which were generally regarded as well-known tracers for BB
emissions (Alfarra et al., 2007). The average fraction of the signal at
m/z 60 (referred as f60) in the BBOA mass spectrum was 0.61 %, which was
higher than the typical value of ∼ 0.3 % in the absence of BB
impacts (Cubison et al., 2011). The time series of BBOA correlated tightly
with those of C2H4O2+ (R2 = 0.91) and
C3H5O2+ (R2 = 0.87) as well as BC (R2 = 0.72) and nitrate
(R2 = 0.75) (Fig. 7a and Table S2). When the influence of high contribution
at m/z 44 was ignored, the BBOA mass spectrum in this study correlated well
(R2 = 0.5–0.9) with BBOA mass spectrum identified at other sites worldwide
(Ng et al., 2011a; Mohr et al., 2012; Saarikoski et al., 2012; Crippa et
al., 2013, 2014; Xu et al., 2016), as shown in Fig. S9. The
average mass concentration of BBOA was 1.05 µg m-3 for the
entire study and contributed a large fraction (43.7 %) of the total OA
mass on average (Fig. 8a), indicating that BBOA was an important component
of OA during the pre-monsoon season at the QOMS. The diurnal cycle of BBOA
showed high concentrations during nighttime whereas relatively low
concentrations were found during daytime (Fig. 7d). Correspondingly, the mass
contributions of BBOA to total OA mass decreased distinctly from
∼ 55 % at 00:00 LT to 28 % at 15:00 LT (Fig. 8b). In addition,
higher mass concentrations and contributions of BBOA were found during the
two polluted episodes (PP1 and PP2) than those recorded during the clear periods,
further indicating the important contribution of BBOA to OA in this region.
Figure 8c showed the mass fractions of the three OA components as a function
of total OA mass during the entire campaign. A continuously increased trend
was found for the BBOA contributions with the increasing OA mass, which
contributed ∼ 15 % when the total OA mass was less than
0.3 µg m-3, whereas it reached up to more than 75 % with the OA
mass increased to 9 µg m-3. This dominant contribution of BBOA
during the polluted periods was consistent with results from previous
studies which showed that BB emission were an important source of aerosol to the southern
TPH (Engling et al., 2011; Xia et al., 2011; Putero et al., 2014; Cong et
al., 2015). The O / C ratio (0.85) of BBOA in this study was quite a bit higher than BBOA factors identified at other urban/rural sites in previous studies
(Aiken et al., 2009; Huang et al., 2011; Mohr et al., 2012; Sun et al.,
2016; Xu et al., 2016), suggesting its long-range transport feature. This
aged BBOA feature was similar with those obtained at other remote sites
worldwide, such as a remote forest site in Finland (Raatikainen et al.,
2010), a remote background site in Greece (Bougiatioti et al., 2014), and a
national air quality background sites in southern China
(Zhu et al., 2016),
where OA were generally highly oxidized.
(a) The average mass concentration of OA and mass contributions
of three OA factors to total OA; (b) the diurnal variations of mass
contributions of three OA factors to total OA and the total OA mass concentration; and
(c) the average mass contributions of three OA factors as a function
of total OA mass concentrations. The black solid line in (c) shows
the percentage of the data number in each OA mass bin to the total data number.
![]()
The average back trajectory clusters during the entire study and the
corresponding mass contributions of PM1 species and OA factors to the total
PM1 mass. The areas of each pie chart are scaled by the corresponding
average PM1 mass concentrations. The average PM1 mass concentrations,
number of fire hotspots as well as the average and maximum fire radiative
powers (FRP) belonging to each of the clusters during the entire measurement period are also given.
![]()
NOA
Besides the two highest signals at m/z 43.99 (CO2+) and 27.995
(CO+) which together contributed half of the total
NOA signal due to the highly aged OA nature at QOMS, the NOA MS was also
characterized by some nitrogen-containing fragments, such as m/z 27.011
(CHN+), 41.027 (C2H3N+),
and 43.006 (CHON+). In total, these three fragments
were thought to comprise nearly half of the nitrogen-containing signals in the NOA
factor and finally contributed 5 % of the total NOA signal. The average
O / C ratio of NOA for the entire campaign was 1.10 with the highest N / C
ratio (0.068) among the three OA components. This high N / C ratio at QOMS was
comparable with nitrogen-containing OA factors identified in previous
studies, such as 0.06 in Mexico City (Aiken et al., 2009), 0.078 in Po
Valley, Italy (Saarikoski et al., 2012), and 0.053 in New York (Sun et al.,
2012). The time series of NOA showed tightly correlation (R2 = 0.62)
with that of estimated organic nitrates, whereas relatively weak
correlations with PM1 species and OA ions (Table S2). In addition, the
f60 value (∼ 0.37 %) was also slightly higher than the
background f60 (0.3 %) of BB aerosols (Fig. 7h). These results
together suggested that this oxygenated OA factor was likely a
nitrogen-containing OA and might be related to the aged BB emissions,
consistent with results from previous studies stating that large amounts of
nitrogen-containing organic compounds were found from BB aerosols (Laskin et
al., 2009; Gautam et al., 2016; Wang et al., 2017). Recently, Fleming et
al. (2018) found dung burning, a very popular activity in Himalayas and
India for residential cooking and heating, could emit much more
nitrogen-containing OA than wood burning. Our filter samples during high
BBOA period analyzed by Fourier Transform Ion Cyclotron Resonance Mass
Spectrometry (FTICR-MS) also found
amount of ON molecular (in preparation).
As shown in Figs. 7e and 8b, both the diurnal cycles of mass concentrations
and fractions of NOA displayed a distinct increase in the morning, similar to the
diurnal patterns of chloride, element ratios of H / C and N / C, and the
estimated organic nitrates. This diurnal feature of NOA at QOMS was quite
consistent with NOA factors identified in Po Valley, Italy (Saarikoski
et al., 2012) and in Mexico City (Aiken et al., 2009), or less-oxidized
oxygenated OA (LO-OOA) in the southeastern USA (Xu et al., 2015) where have
active BB emissions. NOA contributed ∼ 14 % of the total OA
mass on average, with an average mass concentration of 0.34 µg m-3
for the entire study (Fig. 8a).
MO-OOA
An obvious more oxygenated OA factor was also identified in this study
according to its significant high signal at m/z 44 (∼ 25 %)
and the high average O / C ratio of 1.34 (Fig. 7i). The time series of MO-OOA
correlated closely (R2 = 0.7) with sulfate and nitrate (Fig. 7c and
Table S2). Moreover, the mass spectrum of MO-OOA in this study tightly resembled more aged and low-volatility oxygenated OA (LV-OOA)
observed using AMS instruments at various sites worldwide (Fig. S9), e.g.,
an R2 of 0.89 and 0.97 to those in Lanzhou, China (Xu et al., 2014;
X. Zhang et al., 2017), 0.96 to that in Paris, France (Crippa et al., 2013),
0.95 to that in Barcelona, Spain (Mohr et al., 2012), in addition to 0.70 and
0.71 to the standard LV-OOA mass spectrums obtained from abundant AMS data
sets by Ng et al. (2011a) and Crippa et al. (2014). The diurnal variation of
MO-OOA was mainly driven by the dynamics of the PBL height, with high
concentrations during the nighttime yet relatively low concentrations during daytime (Fig. 7f). This pattern was quite different to those observed
in previous studies in that LV-OOA generally showed elevated concentrations
during the afternoon in accordance with strong photochemical activities,
suggesting that SOA at QOMS were mainly oxidized and aged during
long-range transport. On average, MO-OOA contributed 42.4 % of the
total OA mass, with an average mass concentration of 1.02 µg m-3
for the entire study (Fig. 8a). As shown in Figs. 2f and 8c, MO-OOA also
displayed enhanced mass contributions during the clear periods, especially
for the period after 2 May when the average mass fraction of MO-OOA increased
to ∼ 68 % of the total OA mass.
Impact of BB emissions on aerosol characteristics
Sources of BB aerosols
In order to understand the transport pathways and the potential source areas
of aerosol, three-day back trajectories of air mass were calculated at an ending
height of 500 m a.g.l. every 6 h at the QOMS from 12 April to
12 May 2016. A four-cluster solution and the wildfire hotspots around the
QOMS during the entire measurement period are presented in Fig. 9. Cluster 1
and 2 (C1 and C2), which originated from the west of the QOMS and passed
over many hotspot areas (e.g., Indo-Gangetic Plain and Nepal), represented
two polluted clusters. On the contrary, C3 and C4, which accounted for half
of the total back trajectories, were identified as clear clusters. C3
traveled a short distance from the southwest of the QOMS, whereas C4 was
from the north of the QOMS and passed over the inland of the TPH. The
average PM1 mass concentrations for C1 and C2 were 5.17 and 6.61 µg m-3,
respectively, which were two to three times higher than those
for the two clear clusters (2.74 and 2.21 µg m-3). The mass
contributions of OA and BBOA during C1 and C2 were up to more than
55 and 25 %
of the total PM1 mass on average, whereas weak contributions
were found for the clear clusters (C3 and C4), indicating the significant
impacts of BB emissions from South Asia on aerosol loadings at QOMS.
Comparison of aerosol characteristics and air mass origins during different episodes
As shown in Fig. 2, the mass concentrations and compositions of PM1
varied dynamically during the entire sampling period. Two polluted periods
(PP1 and PP2) and two clear periods (CP1 and CP2) were identified. The
comparisons of average mass concentrations and other indicators for the four
different episodes were presented in box plots in Fig. 10, whereas the
corresponding back trajectories of air masses and MODIS fire hotspots belong
to each episode period were given in Fig. 11, respectively.
Box plots of mass concentrations of total PM1 and its species,
f60 and f44 values, mass contribution of three OA components to
organics, element ratios (H / C and O / C), and carbon oxidation states
(OSc) among the four polluted and clear periods. The whiskers
indicate the 90th and 10th percentiles, the upper and lower boundaries of boxes
indicate the 75th and 25th percentiles, the lines in the boxes indicate the
median values, and the markers indicate the mean values.
![]()
The 72 h back trajectories (grey solid lines) calculated every 6 h
for the different episodes. Pie charts show the average mass contributions of
PM1 species and OA factors to the total PM1 mass for each of the episodes
(scaled by the corresponding average PM1 mass concentrations). The average
PM1 mass concentrations, number of fire hotspots as well as the average
and maximum fire radiative powers (FRP) belonging to all trajectories for the
different episodes are also given.
![]()
During the two polluted periods, PM1 mass concentrations were much
higher than those in clear periods (8.06 and 7.87 µg m-3 for PP1
and PP2 vs. 2.76 and 1.82 µg m-3 for CP1 and CP2; similarly
hereinafter), with higher contributions from OA (60.1 and 57.5 %
vs. 48.1 and 43.9 %) and BBOA (38.3 and 36.6 % vs. 14.3 and
7.5 %) (Figs. 10 and 11). In addition, f60 were also higher during
polluted periods than those for clear periods (0.34 and 0.34 % vs. 0.26 and
0.22 % on average) (Fig. 10). Air masses during PP1 and PP2
generally originated from long-range transport to the west of the QOMS,
which would pass through intense wildfires areas in South Asia (e.g.,
Indo-Gangetic Plain and Nepal where high AOD values are shown in Fig. 1d and
active fire hotspots are displayed in Figs. 9 and 11). The number of fire hotspots around the
air mass trajectories during PP2 was more than three times higher than
during other periods. Although the hotspot number around the air mass
trajectories during PP1 was not as abundant as that during PP2 and even
slightly lower than that during CP1, it was only collected within three days for
PP1 whereas 8–10 days for the other periods. Hence, the BB activities were
also more frequent and intense during the short PP1 and finally resulted in
the highest average PM1 mass concentration among these periods. Back
trajectories in CP1 also originated from the west of QOMS and passed over northern India and Nepal; however, both the intensity of
the number of fire hotspots
(1089 hotspots in ∼ eight days) and average FRP (19.6) were
obvious lower than that in PP2. CP2 was the most clear period, during which the
average PM1 mass concentration was more than four times lower than
those in polluted periods. Back trajectories during CP2 period were from
either the north of QOMS which passed over inland areas of the TPH or the
south of QOMS with quite short distances and low WSs. These results together
suggested the significant roles of air mass sources and BB emissions to
aerosol characteristics at QOMS.
Case study on the chemical evolution of BB emission aerosols
In order to examine how atmospheric aging affects the aerosol chemistry
characteristics at QOMS, the typical evolution process of a BB aerosol plume
(referred as BB evolution case) was analyzed from 30 April at 15:00 LT when a
fresh BB plume occurred to 1 May at 18:00 LT when the BB plume was highly aged
after undergoing various atmospheric oxidation processes. The temporal
variations of meteorological parameters, mass concentrations, and mass
contributions of each PM1 species and OA components as well as other
chemistry parameters before and during this BB evolution case were all shown in Fig. 12.
Before the BB evolution case, all the mass concentrations decreased slowly
and synchronously from 00:00 to 10:00 LT on 30 April. This was consistent
with the nearly stable trends of mass contributions and other chemistry
parameters, indicating the relatively unified air mass sources and stable
atmospheric conditions. After that, the wind circulation changed from
thermally-driven down-slope winds (mostly southwest) to weak up-slope
winds (northeast). In this period, BBOA and f60 values kept relatively
stable in contrast to other species likely due to the weakness of air dilution
and local sources. All the species reached their minimum at around 15:00 LT due
to the lift of PBL.
The temporal variations of meteorological parameters, mass concentrations,
and mass contributions of each PM1 species and OA components as well as
the N / C ratio, f60 values, carbon oxidation states (OSc),
aerosol liquid water content (ALWC), and single scattering albedo (SSA) for the
case study period from 30 April at 00:00 LT to 1 May at 18:00 LT.
![]()
Case study of chemical evolution of BB plume from 30 April at 15:00 LT
to 1 May at 18:00 LT. The periods marked with arrows are three distinct
evolution processes. Pie charts in (a) are the mass contributions of three
OA factors to total OA at the beginning and end of each process,
respectively. The scatter plots in (b) are the aerosol chemistry
parameters as a function of BBOA mass concentration, logarithmic values of
cumulative aerosol liquid water content (CALWC), and cumulative solar radiation
for the corresponding process.
![]()
The BB evolution case in this study was further divided into three different
situations (as marked with arrows in Figs. 12 and 13), including the arrival
of the fresh BBOA plume (from 15:00 to 24:00 LT on 30 April), followed by
aqueous-phase oxidation at nighttime (from 02:30 to 07:10 LT on 1 May), and
photochemical oxidation during the daytime (from 10:00 to 18:00 LT on 1 May). All
the mass concentrations began to increase from 15:00 LT and finally reached
maximum PM1 mass loading of 18.4 µg m-3 at 24:00 LT, which
was about four times higher than the average PM1 mass during the entire
campaign. Thus continuous increase was mainly dominated by the dramatic
increase of BBOA, which reached up to 10.8 µg m-3 and
contributed 88 % of the total OA mass and 50 % of the total PM1 at
24:00 LT (Fig. 13a), suggesting a distinct presence of BB emissions during this
period. In contrast, the total OA mass was comprised of 78 % of MO-OOA and
12 % of BBOA at 15:00 LT. Similar continuous increasing trends could also be
found for the mass concentration of calculated organic nitrate in this
stage. In addition, nine aerosol chemistry parameters were presented as a
function of BBOA mass concentrations during this period (Fig. 13b). The mass
contributions of OA to PM1 (fOrg) and BBOA to total OA (fBBOA),
f60, and H / C ratio were all increased with the increasing
BBOA mass, whereas the mass contribution of MO-OOA to total OA (fMO-OOA),
O / C ratio, carbon oxidation state (OSc = 2 × O / C - H / C)
of OA, and aerosol single scattering albedo (SSA) were
decreased, obviously indicating the fresh nature of this BB plume. The
significant impacts of the fresh BB plume during this period were mainly
associated with the unique wind circulation and the long-range transport of
air masses. As displayed in Fig. 12b, the wind circulation changed from the
weak up-slope winds to the strong down-slope glacier winds on 30 April at
15:00 LT, with a WS increase from ∼ 2 to 8 m s-1.
Meanwhile, the back trajectories during this period also showed that the air
masses passed over northern India and Nepal,
where active wildfires occurred. The air masses then accumulated and uplifted
to the Himalayas and finally moved downward to the QOMS with the strong glacier winds.
A distinct aqueous-phase oxidation process was found in the nighttime from
02:30 to 07:10 LT on 1 May. Although the total PM1 and its species showed
nearly stable mass concentrations during this period, the BBOA mass
decreased gradually (from 82 to 70 %) whereas MO-OOA increased
constantly (from 14 to 20 %) with the significant increase of RH (up
to 91 }) and aerosol liquid water content (ALWC) (Fig. 12). The scatter
plots of the aerosol chemistry parameters vs. the logarithmic values of
cumulative ALWC, which could be used for the aqueous-phase oxidation during
transport, also showed apparent increase trends for fMO-OOA, O / C ratio,
OSc, and SSA that generally indicated the aerosol aging extent. All of
these together suggested a distinct aqueous-phase oxidation of BBOA in the nighttime.
From sunrise, all mass concentrations decreased gradually, which was mainly
related to the increasing PBL height and clear air mass dilution. The
back trajectories indicated that air masses during this period firstly moved inland north of QOMS where rare wildfires took place. Moreover, the
BB plume further underwent strong photochemical oxidation during the daytime
due to the strong solar radiation. MO-OOA only contributed 26 % of the
total OA mass at 10:00 LT, but it could increase to 74 % at 18:00 LT after
long-time photochemical oxidation. In contrast, BBOA mass contribution
decreased from 49 to 20 %. The cumulative solar radiation, which
denoted the total amount of solar radiation that the plumes were exposed to
during transport, could be used as an indicator of the extent of
photochemical aging in the daytime (Zhou et al., 2017). Clear increasing
trends were found for fMO-OOA, O / C ratio, OSc, and SSA values with
increasing cumulative solar radiation, whereas decreasing trends in
fBBOA, fNOA, H / C ratio, and f60 values were observed, suggesting a possible
oxidation mechanism that oxidized the relatively fresh BBOA and NOA to aged
MO-OOA during the daytime. Another interesting phenomenon was the continuous
increase of SSA during both the aqueous-phase and photochemical oxidation
periods on 1 May (Figs. 12e and 13b), indicating the potential influence of
atmospheric aging to aerosol optical property at QOMS.