Introduction
The Tibetan Plateau (TP) and the surrounding Hindu Kush Himalayan mountains
are known as the “third pole” of the Earth (Qiu, 2008) due to their
immense area and high elevation (Yao et al., 2012). Because
of the contrast of thermal heating between continent and ocean, the TP plays
a fundamental role in the formation of the Asian monsoon system and Northern
Hemispheric climatology (Wu and Zhang, 1998). The TP and Himalayas,
with more than 100 000 km2 of glaciers, contain the largest ice mass
outside the polar region (Xu et al., 2009; Yao et al., 2012). Over the
past decades, climate change impacts have been revealed due to a marked air
temperature rise and dramatic glacier shrinkage across this area (Kang et al., 2010).
Due to sparse population and minimal industrial activities, the TP is
considered one of the most pristine terrestrial regions, alongside the
Arctic and Antarctic. However, growing evidence has demonstrated that
widespread atmospheric brown clouds (ABCs) over south Asia may affect this
region (Bonasoni et al., 2010; Kaspari et al., 2011; Lu et al., 2012; Xia et
al., 2011; Wang et al., 2010). Research has attempted to reveal a link
between climate change over the TP (e.g., air temperature rising, glacier
melting) and the distribution of anthropogenic pollutants (mainly absorbing
carbonaceous materials) (Qian et al., 2015; Wang et al.,
2014b). Ramanathan and Carmichael (2008) reported that in the high
Himalayan region, solar heating caused by black carbon (BC) could be
approximately equivalent to the warming by CO2 in terms of the melting
of snowpack and glaciers.
Could we quantitatively differentiate the various factors that contribute to
glacier melting, including aerosols, greenhouse gas and BC deposition on
the snow surface? Clearly, to answer this question and reduce the
uncertainties, adequate knowledge of the aerosol properties is urgently
needed. Some scientists have used different models to reveal the importance
of carbonaceous aerosol in this region (Menon et al., 2010; Qian et al.,
2011; Yasunari et al., 2010). So far, most works on aerosol composition have
been carried out on the south slope of the Himalayas, such as in Langtang,
Nepal (Carrico et al., 2003), Godavari (Stone et al., 2010), Nepal Climate Observatory at Pyramid (NCO-P)
(Decesari et al., 2010) and Manora Peak, India (Ram et al., 2010). Long-term aerosol
chemistry measurements from the TP are extremely scarce mainly due to its
remoteness and challenging weather conditions, with measurements limited to
Lulang (Zhao et al., 2013), Waliguan (Ma et al., 2003), Namco (Ming et al., 2010) and Qinghai Lake (Li et
al., 2013). As we know, no systematic data on carbonaceous aerosols from the
south edge of the TP (i.e., the north slope of Himalayas) have been reported.
From the spatial distribution of aerosols observed by satellites (e.g.,
MODIS, Fig. S1 in the Supplement), there was a clear difference between south Asia and Tibetan
Plateau. Therefore, as the boundary area this region merits special
attention.
In this paper, we present results from 1-year measurements of organic
carbon (OC), elemental carbon (EC), water-soluble organic carbon (WSOC) and
major ions in the aerosols at Mt. Everest, the south edge of the TP. Our aim
is to provide baseline levels of aerosols for this region, reduce the
assessment uncertainties of aerosol radiative forcing and provide more
information on their transport mechanism.
Location of the sampling site (QOMS, 4276 m a.s.l.) at the
south rim of the Tibetan Plateau, with the NCO-P (5079 m a.s.l.) and the
summit of Mt. Everest (8844 m a.s.l.).
Methodology
Description of research site
In 2005, Qomolangma (Mt. Everest) Station for Atmospheric and Environmental
Observation and Research (QOMS; 28.36∘ N, 86.95∘ E, 4276 m a.s.l.) (Fig. 1) was established to begin continuous monitoring of
the environment (Ma et al., 2011). A solar-electricity system
generates the power to maintain the instrumentation. According to the
observations achieved so far, the Mt. Everest region (QOMS) is a typical
representative of the middle Himalayas in terms of climate, air circulation
systems and environmental characteristics (Chen et al., 2012; Li et al.,
2012; Ma et al., 2011). Sandy soil with sparse grass and small rocks cover
the land surface around the QOMS. Due to its harsh environment, QOMS is
relatively isolated from industrial zones and cities, with a very limited
local population (Ma et al., 2011).
Aerosol sampling
From August 2009 to July 2010, total suspended aerosol particle (TSP)
samples were collected weekly at QOMS using medium-volume samplers (KC-120H,
Laoshan Co.). During the sampling, the flow rate was automatically adjusted
to 100 L min-1 at standard condition. The sampling duration of each
sample was 24 h. Aerosols were collected using 90 mm diameter quartz
filters (QM/A, Whatman, UK), which were pre-combusted at 450 ∘C
for 6 h. Field blanks were collected every month by placing filters into
the filter holder for a few minutes with no air flowing. After sampling, the
filters were wrapped with aluminum foil and frozen until analysis.
Eventually, 50 samples were successfully obtained.
OC and EC analysis
The quartz filters were analyzed for OC and EC using a carbon analyzer (DRI
model 2001). Briefly stated, a filter aliquot (0.5 cm2) was analyzed for eight
carbon fractions following the IMPROVE-A thermal/optical reflectance (TOR)
protocol (Cao et al., 2007; Chow et al., 2007). Four OC fractions (OC1,
OC2, OC3 and OC4) were determined at 140, 280, 480 and 580 ∘C in
pure He atmosphere, which was subsequently switched to 2 % O2 / 98 %
He atmosphere to determine EC1, EC2 and EC3 at 580, 740 and 840 ∘C, respectively. The residence time of each heating step was defined by the
flattening of the carbon signal. The pyrolyzed carbon fraction (OPC) is
determined when reflected laser light returns to its initial value after
oxygen is introduced. In general, OC is defined as OC1 + OC2 + OC3 + OC4 + OPC and EC is defined as EC1 + EC2 + EC3 - OPC. The detection
limit for the carbon analyzer was 0.05 µg C cm-2 for OC and 0.05 µg C cm-2 for EC.
Water-soluble ions and WSOC
An aliquot of filter (2.54 cm2) was extracted with 10 mL ultrapure
water with sonication for 30 min. The extracted solutions were filtrated
with syringe-driven filters (MillexGV PVDF, 0.22 µm; Millipore,
Ireland) to remove the quartz fiber debris and other insoluble impurities.
Then the water-soluble ionic species (Cl-, SO42-,
NO3-, Ca2+, Na+, K+, Mg2+ and
NH4+) were analyzed using an ion chromatograph (761 Compact IC,
Metrohm). Anions were measured with a suppressor on a Shodex SI-90 4E column
using an eluent mixture of 1.8 mM Na2CO3, 1.7 mM NaHCO3 and
40 mM H2SO4 at a flow rate of 1.2 mL min-1. Cations were
determined on a Metrohm C2-150 column with tartaric acid (4 mM) and
dipicolinic acid (1 mM) as an eluent. The overall uncertainty in determining
ionic species is less than 4 % (Miyazaki et al., 2010). The
detection limit for all cations and anions was 0.01 µg m-3, which
was calculated according to the air volume of actual samples.
To quantify WSOC, a portion of filter (19.1 cm2) was extracted and
filtrated using the same procedure for major ions described above. Then the
extract was injected into a total carbon analyzer (TOC-V, Shimadzu). The
method detection limit used was 4 µg L-1 with a precision of
±5 %. All the concentrations of carbonaceous and ionic components
in this study are field-blank corrected. It should be noted that there are
possible sampling artifacts by the adsorption/evaporation of gaseous organic
materials on/from the quartz membrane. However, no quantitative information
on such positive/negative artifact is available in this study; therefore, no
correction was made for the data of carbonaceous components.
Determination of levoglucosan
Levoglucosan was determined by GC/MS after the extraction of the samples
with a methanol/methylene chloride mixture followed by BSTFA derivatization.
Details of the analytical procedure are presented elsewhere (Fu et al., 2008).
Meteorology and backward air-mass trajectories
At the QOMS station, various meteorological parameters (Fig. 2) were
recorded by a 40 m atmospheric boundary layer tower that measures wind
speeds, wind direction (014A-L, Met One), relative humidity, air
temperature, air pressure (HMP45C, Vaisala) and rain intensity (TE525MM-L,
Young) (Chen et al., 2012; Li et al., 2012).
Monthly mean air temperature reaches a maximum of 12.3 ∘C in July and a minimum in January of -3.2 ∘C. Humidity is highest in
August and lowest in December. Precipitation was unevenly distributed
throughout the year, with more than 90 % of annual precipitation occurring
from June to September. According to the meteorological parameters at QOMS
(Fig. 2), the climatology is roughly divided into four seasons, i.e., pre-monsoon, monsoon, post-monsoon and winter (the definition of different
seasons was shown in Table S1). These seasons are generally in agreement
with the seasonal definition made in a previous study in this region
(Bonasoni et al., 2010). In general, this region is controlled by the Indian
monsoon system in summer (June–August), characterized by relatively high
temperature and humid weather with prevailing southerly winds. In the
remaining period, westerlies dominate the large-scale atmospheric
circulation patterns with limited precipitation.
Time series of ambient temperature, atmospheric pressure,
relative humidity and wind speed at QOMS from August 2009 to July 2010.
To reveal the transport pathway of air masses that arrive at QOMS, 7-day
backward trajectories were computed using the HYSPLIT model (Draxler and Rolph, 2012) and Global Data
Assimilation System (GDAS) data for each sampling day. Given the typical height of
the planetary boundary layer in this region (Chen et al., 2012), the arrival height of air mass in these modeling was set to 500 m above ground level.
Results and discussion
Characteristics and temporal variations of OC and EC
The statistical summaries of carbonaceous components in the aerosols from
QOMS are presented in Table 1. The average concentrations of OC and EC in
the aerosols from QOMS were 1.43 ± 1.16 and 0.25 ± 0.22 µg m-3, respectively. The concentration levels of OC and EC at QOMS are
about 3 times higher than those of Muztagh Ata, northwest TP
(Cao et al., 2009), while they are comparable to those reported from
the central and northeastern TP (Li et al., 2013; Ming et al., 2010)
(Table 2). In contrast, OC and EC concentrations from the southeastern TP
(Tengchong and Lulang) are significantly higher than those at QOMS, possibly
due to the higher contribution of biomass burning (Engling et al.,
2011; Zhao et al., 2013). When compared with sites on the south slopes of the
Himalayas, QOMS data present the same order of OC and EC as NCO-P
(Decesari et al., 2010) and Langtang (Carrico et al., 2003) but 3 to 6-fold lower than
Manora Peak, India (Ram et al., 2010), and Godavari, Nepal (Stone et al., 2010). The latter two sites are at lower
altitudes and are closer to the populated areas of south Asia, heavily
influenced by anthropogenic emissions. Generally, the high-altitude sites on
both sides of the Himalayas (i.e., Langtang, NCO-P and QOMS) exhibit similar
OC and EC abundance, which could be considered as a regional baseline level
to be used in the regional climate model as input parameters.
Seasonal average abundances (along with standard deviation) of OC,
EC, WSOC and water soluble ionic species (µg m-3), as well as the
ratios of OC / EC and WSOC / OC.
Annual
Pre-monsoon
Monsoon
Post-monsoon
Winter
Number
50
13
11
13
13
Carbonaceous components
OC
1.43 ± 1.16
2.61 ± 1.58
0.81 ± 0.14
1.06 ± 0.53
1.14 ± 0.50
EC
0.25 ± 0.22
0.44 ± 0.31
0.10 ± 0.06
0.19 ± 0.07
0.26 ± 0.12
OC / EC
6.69 ± 6.33
6.63 ± 4.05
10.58 ± 11.95
5.56 ± 2.03
5.18 ± 3.58
WSOC
0.77 ± 0.60
1.28 ± 0.87
0.49 ± 0.25
0.71 ± 0.26
0.54 ± 0.29
WSOC / OC
0.58 ± 0.24
0.47 ± 0.09
0.59 ± 0.28
0.62 ± 0.23
0.57 ± 0.27
Levoglucosan
0.019 ± 0.037
0.047 ± 0.064
0.004 ± 0.003
0.007 ± 0.005
0.014 ± 0.008
Water-soluble inorganic ions
Cl-
0.02 ± 0.03
0.04 ± 0.04
0.01 ± 0.01
0.02 ± 0.02
0.02 ± 0.04
NO3-
0.20 ± 0.27
0.51 ± 0.37
0.06 ± 0.04
0.08 ± 0.04
0.12 ± 0.07
SO42-
0.43 ± 0.54
1.06 ± 0.66
0.09 ± 0.09
0.18 ± 0.07
0.32 ± 0.24
Na+
0.07 ± 0.06
0.13 ± 0.06
0.04 ± 0.04
0.04 ± 0.03
0.06 ± 0.05
NH4+
0.03 ± 0.09
0.10 ± 0.16
BDL
BDL
0.00 ± 0.01
K+
0.02 ± 0.05
0.06 ± 0.07
BDL
BDL
0.00 ± 0.02
Ca2+
0.88 ± 0.56
1.19 ± 0.48
0.50 ± 0.18
1.01 ± 0.75
0.79 ± 0.36
Mg2+
0.04 ± 0.02
0.06 ± 0.02
0.02 ± 0.01
0.05 ± 0.01
0.04 ± 0.01
BDL: below detection limits (0.01 µg m-3 for
cations and anions).
Comparison of OC and EC concentrations (µg m-3)
and OC / EC ratios of aerosols from QOMS with other sites in the Himalayas and
on the Tibetan Plateau.
Location
Description
Sample
Sampling period
OC
EC
OC / EC
Method
Reference
QOMS
Southern TP(4276 m)
TSP
Aug 2009–Jul 2010
1.43 ± 1.16
0.25 ± 0.22
6.7 (1.91–43.8)
TOR
This study
Namco
Central TP (4730 m)
TSP
Jul 2006–Jan 2007
1.66 ± 0.79
0.082 ± 0.07
31.9 ± 31.1
TOR
Ming et al. (2010)
Muztagh Ata
Northwest TP (4500 m)
TSP
Dec 2003–Feb 2005
0.48
0.055
10 (2.9–32.1)
TOR
Cao et al. (2009)
Qinghai Lake
Northeast TP (3200 m)
PM2.5
Jul–Aug 2010
1.58 ± 0.59
0.37 ± 0.24
5.9 (1.85–21.8)
TOR
Li et al. (2013)
Lulang
Southeast TP(3360 m)
TSP
Jul 2008–July 2009
4.28 ± 2.05
0.52 ± 0.35
1.7–58.4
TOR
Zhao et al. (2013)
Tengchong
Southeast TP (1640 m)
PM10
Apr–May 2004
5.8 ± 4.4
1.5 ± 1.0
2.63
TOR
Engling et al. (2011)
Manora Peak, India
Himalayas (1950 m)
TSP
Feb 2005–Jul 2008
8.2 ± 5.2
1.3 ± 1.2
7.3 ± 3.4
TOT
Ram et al. (2010)
NCO-P, Nepal
Himalayas(5079 m)
PM10
Pre-monsoon 2006–2008
2.4
0.5
4.8
TOT
Decesari et al. (2010)
Monsoon
0.9
0.1
9
Post-monsoon
1.4
0.1
14
Dry season
1.2
0.1
12
Langtang, Nepal
Himalayas (3920 m)
PM2.5
Jun–Sep 1999
0.75 ± 0.69
0.15 ± 0.16
5.0
TOT
Carrico et al. (2003)
Oct 1999–Jan 2000
1.81 ± 1.25
0.52 ± 0.48
3.48
Feb–May 2000
3.44 ± 4.19
0.48 ± 0.38
7.17
Godavari, Nepal
S. Himalayas (1600 m)
PM2.5
2006
4.8 ± 4.4
1.0 ± 0.8
4.8
TOT
Stone et al. (2010)
In a previous study, Ming et al. (2008) estimated
atmospheric EC concentration in the region based on the analysis of an ice
core from the East Rongbuk Glacier, Mt. Everest. Apparently, there is a big
discrepancy between our EC data (annual average of 0.25 ± 0.22 µg m-3)
and the EC data estimated by ice cores (average of 0.077 ± 0.045 µg m-3 during 1951–2001). One potential reason is that
several parameters (e.g., scavenging ratio of EC) need to be assumed to
convert the EC in the ice core to atmospheric concentration, which may
introduce some uncertainty. Moreover, dramatically increasing trends of EC
in the Himalayas and the TP ice cores have been reported
(Cong et al., 2013; Kaspari et al., 2011), i.e., a 2.5 to 3-fold rise in recent decades compared to background
conditions. Therefore, our EC data for 2009–2010, which are higher than the
average EC concentration for 1951–2001, are reasonable.
The OC / EC ratios at QOMS range from 1.91 to 43.8 with an average of 6.69. Such
high ratios are commonly found in different areas of the TP (Table 2). There
are two potential reasons for those high OC / EC ratios. One reason may be a
strong solar radiation (exceeding 7500 MJ m-2) over the TP, because
substantial secondary organic carbon (SOC) could be formed through
photochemical reaction (Wan et al., 2015). The other potential
reason is the influence of biomass burning. Usually, the aerosols emitted
from biomass burning have higher OC / EC ratios. For example, Watson et
al. (2001) have reported an OC / EC ratio of 14.5 for forest
fires. Considering the specific condition of this study (QOMS), the second
reason is more likely, i.e., the strong influence of biomass-burning
emissions. The higher abundance of OC than EC on the TP emphasizes that OC
should not be ignored in the quantification of total radiative forcing of
aerosol by climate models (Kopacz et al., 2011).
Although some organic carbon has light-absorbing capability (i.e., brown
carbon), the net effect of organic carbon on climate is negative (cooling)
(Stocker et al., 2013), which may attenuate the positive radiative
forcing caused by EC.
Temporal variations (weekly) of OC, EC and WSOC at the QOMS
site from August 2009 to July 2010.
The temporal variations of the aerosol OC, EC and WSOC are illustrated in
Fig. 3. Clearly, the OC, EC and WSOC share a significant seasonal
pattern, i.e., a maximum in the pre-monsoon period and a minimum in the
monsoon season. Higher abundance of OC and EC implies that the contributions
from anthropogenic activities are larger in pre-monsoon than other seasons.
Similar seasonal trends of aerosol composition were also reported previously
on the south slopes of the Himalayas, such as in Langtang
(Carrico et al., 2003) and NCO-P (Decesari et al., 2010). This
phenomenon indicates that these regions (Mt. Everest), both slopes of the
Himalayas, have a common atmospheric environmental regime, although the high
altitude of the Himalayas was once considered a good barrier for the
spreading of atmospheric pollutants in south Asia. This point will be
further discussed in Sect. 3.5.
Relationship between OC and EC
Examining the relationship between OC and EC can provide meaningful insights
into the origin and possible reaction process during the transport (Turpin and Huntzicker, 1995). At QOMS, a
strong correlation (R2=0.81) was observed between OC and EC during
the pre-monsoon season (Fig. 4a), indicating common emission sources and
transport processes. The correlation coefficients between OC and EC in the
other three seasons were lower than that of the pre-monsoon season (Fig. 4b,
c, d), with the lowest correlation observed in the summer monsoon season
(R2=0.08), suggesting that there are other influences. In addition
to the common emission sources (e.g., fossil fuel and biomass burning), OC
could also be produced by biogenic sources and the formation of secondary OC. The relative importance of different sources and/or formation
processes
merits a further study.
SOC has often been calculated from the primary OC / EC ratio (EC-tracer
method) (OCpri= EC × (OC / EC)min, OCsec= OCtot –
OCpri), which is assumed to be relatively constant for a given site
(Turpin and Huntzicker, 1995). The lowest OC / EC ratio in the aerosol
was suggested for use as the primary source to calculate the SOC abundance
(Castro et al., 1999), when the secondary production of OC is
expected to be minimal. However, for the samples from QOMS, we found that
calculating SOC formation using this method was not reliable. The minimum
OC / EC ratios differ greatly among various seasons (3.40, 3.78, 1.91 and 2.67
for pre-monsoon, monsoon, post-monsoon and winter, respectively). Even for
each season (11–13 samples for each seasons), the lowest three values of
OC / EC ratios also varied substantially. Therefore, the SOC formation
estimated by the conventional EC-tracer method is not presented here.
Relationship between OC and EC in aerosols of different seasons at QOMS.
Water-soluble organic carbon
The WSOC in aerosols, a major proportion of total organic carbon, could
affect the hygroscopic property of the particles and their ability to act as
cloud condensation nuclei (Psichoudaki and Pandis, 2013). The
abundance of WSOC relative to OC could be employed as an indictor to
decipher whether organic aerosol is primary or secondary, because SOC
usually tends to be more water soluble than primary organic matter
(Psichoudaki and Pandis, 2013). The concentration of WSOC at QOMS
varied from 0.07 to 3.22 µg m-3, with an average of 0.77 µg m-3 (Table 1). The average WSOC / OC ratios at QOMS were 0.47, 0.59, 0.62
and 0.57 for pre-monsoon, monsoon, post-monsoon and winter, respectively.
The lowest WSOC / OC in pre-monsoon indicated the dominant contribution from
primary emission sources with poor aging and less SOA (secondary organic aerosol) formation.
Furthermore, in the pre-monsoon season, the WSOC concentration exhibited a
significant positive correlation with OC (y=0.54x-0.12, R2=0.94), which could be ascribed to the influence of biomass combustion.
Previous studies have revealed that organic matters emitted from biomass
burning were substantially composed of water-soluble polar organic
compounds, including dicarboxylic acids, sugars, aromatic acids, etc.
(Claeys et al., 2010; Fu et al., 2012; Kundu et al., 2010). No evident
correlation was found between WSOC and OC in other seasons when OC
concentrations were low (Fig. 5).
Water-soluble ionic species
Sulfate was the most abundant anion species followed by nitrate, accounting
for 25 and 12 % of total ionic mass, respectively (Table 1). Ca2+
was the most abundant cation species with an annual average of 0.88 µg m-3. Cl- and Na+ only consisted of a very minor portion of
total ions, indicating that at QOMS the influence of sea salt is negligible.
Water-soluble Ca2+ is a typical tracer of crustal material (dust)
(Ram et al., 2010). At QOMS, the time series of
Ca2+ was somewhat uniform throughout the years (Fig. 6),
implying that the mineral dust loading at QOMS is relatively constant. This
pattern was obviously in contrast to other ionic species (NH4+,
K+, NO3- and SO42-). The temporal variation
patterns of Ca2+ and SO42- are different (Fig. 6), and thus
the correlation is not strong (R2=0.27), which excludes the
possibility that they predominantly co-occurred in some minerals (e.g.,
gypsum).
Relationship between WSOC and OC in aerosols from QOMS.
Temporal variations (weekly) of water-soluble ionic species
(Ca2+, K+, NH4+, SO42- and NO3-) in
aerosols collected at QOMS (units: µg m-3).
Soluble potassium (K+) is a good tracer of biomass burning
(Andreae and Merlet, 2001; Cachier et al., 1995). In our study,
the K+ concentrations were below detection limit in most samples, but
K+ concentrations did show peaks in the pre-monsoon season (Fig. 6).
Furthermore, K+ and EC demonstrated a good relationship (R2=0.66, n=9) during that period, indicating that they were both derived
from biomass burning (Fig. 7c). A significant correlation between
NO3- and SO42- was not surprising (Fig. 7a), because
they generally form from the oxidation of NOx and SO2, which are
closely related to fossil-fuel combustion. In the pre-monsoon season with a
high abundance of NH4+ (Fig. 6), NH4+ and
NO3- exhibited a good correlation (R2=0.80, n=9),
implying that they are present as NH4NO3 in the aerosol particles.
Correlations between various chemical components:
(a) SO42- and NO3-, (b) NH4+ and NO3-,
(c) K+ and EC, (d) NO3- and EC.
The seasonal variation of biomass burning (K+) coincided with that of
ions associated with the fossil-fuel combustion (NH4+,
NO3-, and SO42-), suggesting that in the pre-monsoon
season, QOMS might have received mixed anthropogenic pollution. But another
explanation is more plausible. According to earlier observation by
transmission electron microscopy (Li et al., 2003), large
amounts of K2SO4 and KNO3 were present in aged smoke aerosols
from biomass burning. Andreae et al. (1988) pointed out that haze
aerosol from biomass burning is comprised of abundant NH4+,
K+, NO3- and SO42-. Similarly, NH4+,
K+, NO3- and SO42- are also reported as major
water-soluble inorganic ions in aerosols from biomass burning on the
southeastern Tibetan Plateau (Engling et al., 2011). In addition
to K+, levoglucosan is also used as a specific marker for biomass
burning, which is formed by the pyrolysis of cellulose but not formed by
fossil-fuel combustions (Simoneit et al., 1999). In the pre-monsoon
season, EC, OC and K+ show good correlations with levoglucosan (Fig. 8), which further indicates that carbonaceous components in QOMS aerosols
were predominantly from biomass burning.
The relationship between EC, OC, K+ and levoglucosan
in aerosols at QOMS during the pre-monsoon season, 2010.
Transport mechanism of aerosols
Seven-day backward air-mass trajectories corresponding to each sampling date
were calculated using the HYSPLIT model (Draxler and Rolph, 2012). Seven days were chosen because of the typical residence time of
carbonaceous aerosols in the atmosphere. The trajectories were generally
consistent with other descriptions of air-circulation patterns in previous
studies (Cong et al., 2009) that correspond to the
south Asian monsoon regime (Fig. 9). In the summer monsoon season, air
masses are derived from Bangladesh and northeast India and bring moisture
that originates in the Bay of Bengal. In the non-monsoon season, strong
westerlies pass through western Nepal, northwest India and Pakistan (i.e., southern Himalayas). Although the transport pathways of air masses arriving
at QOMS during pre-monsoon, post-monsoon and winter are similar (Fig. 9), a
distinctly higher carbonaceous aerosol level was found only in the
pre-monsoon season (Fig. 3), which emphasizes the importance of source
strength changes.
According to the previous ABC research (Ramanathan et al., 2005) and the emission
inventory (Wang et al., 2014a), a high loading of atmospheric
pollutants exists over the southern slopes of the Himalayas and was
pronounced in the pre-monsoon season. We further checked the biomass-burning
emissions from different seasons using the active fire product from MODIS
(MODerate-resolution Imaging Spectroradiometer, both Terra and Aqua
data set), which was provided by Fire Information for Resource Management
System (FIRMS, https://earthdata.nasa.gov/firms). Figure 10 clearly shows
that the active fire counts (representing the agricultural burning and
forest fires) peaked in pre-monsoon (April). This finding is in agreement
with the vegetation fire study on the southern slopes of the Himalayas by
Vadrevu et al. (2012). In general, the seasonal pattern of
carbonaceous components (OC, EC and WSOC), their strong correlation with
K+ and levoglucosan and the air-mass trajectories and active
fire spots distribution all suggest that the higher loadings of
carbonaceous aerosols in the pre-monsoon season at QOMS were most likely
affected by the biomass burning (agricultural and forest fires) in northern
India and Nepal.
Seven-day backward trajectories at QOMS on each sampling
day during different seasons.
In addition to the large-scale atmospheric circulation, the local orographic
effect on air-pollutant transport should also be taken into account
(Hindman and Upadhyay, 2002). In mountainous areas, because of
the temperature difference between mountaintop and lowland, a diurnal valley
wind system occurs that blows upward during the day and reverses
downward during the night. As shown by Bonasoni et al. (2010), the wind regime at
NCO-P (southern slope of the Himalayas) was characterized by an evident
daily circle of mountain/valley breeze. During the daytime, the valley winds
(southerly) were predominant with maximum wind speed in the afternoon.
Therefore, the daytime up-valley breeze delivered the air pollutants from
the foothills (south Asia ABC) to higher altitudes (> 5000 m a.s.l.). Aerosol mass concentration, BC and ozone at NCO-P exhibit strong
diurnal cycles, with minima during the night and maxima during the afternoon,
especially in the pre-monsoon season (Decesari et al., 2010; Marinoni et
al., 2010). However, distinct mountain/valley breeze circulation was
observed on the northern slopes of the Himalayas (QOMS). A dominating
down-valley wind occurs on the north side of Mt. Everest in the daytime,
especially in the afternoon. Furthermore, the driving force of the vast snow
cover at high altitude could form a “glacier wind”, and the up-valley air
flow produced by intense ground surface heating is overcome by down-valley
air flow “glacier wind” and “mountain wind” (Chen et al., 2012; Zou et al., 2008). Therefore,
daytime intense valley wind circulation could make the valleys efficient
channels for the transport of air pollutants crossing over the Himalayas
(Fig. S2), i.e., from the low altitude of south Asia to the Tibetan Plateau.
The spatial distribution of fire spots observed by MODIS in different seasons
(August 2009 to July 2010) (https://firms.modaps.eosdis.nasa.gov/firemap/).
Because both QOMS and NCO-P have sun photometers and participated in the
AERONET project, the same instrument (Cimel 318), the same data processing
method and simultaneous observation between QOMS and NCO-P make it possible
to compare aerosol optical depth (AOD) data directly between the two slopes of the Himalayas (Xu et al., 2014; Gobbi et al., 2010).
As shown in Fig. 11, the daily AOD (500 nm) of QOMS and NCO-P varied in a highly similar
pattern (the correlation significant at p < 0.001), which
suggests
that the observation at QOMS can also capture the pollution signals as
NCO-P. Recently, Lüthi et al. (2014)
investigated the transport mechanisms of pollutants across the Himalayas using a
high-resolution model. They found some trajectories with low altitudes
originate from the TP and then flow down through valleys to the foothills
of the Himalayas during nighttime, where they can mix with air pollutants, and
are then blown onto the TP again during daytime. For the vertical
distribution of aerosols, two examples of such transport episodes revealed by
CALIOP satellite, now provided in the Supplementary Information (Fig. S3), clearly showed that the pollution plumes from south Asia could
transport across the Himalayas during the pre-monsoon season.
The temporal variations of the daily aerosol optical depth
(AOD, 500 nm) at QOMS and NCO-P during the pre-monsoon season, 2010 (n=70).
We roughly estimated the timescale for air masses transported from the
southern slope of Mt. Everest (NCO-P) to QOMS. The straight distance between
the two sites is about 40 km, and along the valley the real distance is
about 50 km if we consider the terrain effect (Fig. 1). The average wind
speed in pre-monsoon season is 7.86 m s-1 (Table S1). This means that the air
mass could travel from the southern slope of Mt. Everest and reach QOMS in
less than 2 h, even at the average wind speed. These results
demonstrate that at QOMS we can capture the air-pollution signal from the
southern Himalayas. This air-mass transport of pollutants caused by mountain
terrain along the valley was also supported by WRF modeling, i.e., at the
upper valley there is a pronounced southerly flow onto the Tibetan Plateau
(Bonasoni et al., 2010). In this study, a similar seasonal trend of aerosol composition was revealed
between the southern and northern slopes of the Himalayas. The most probable
explanation is that the local mountain/valley breeze circulation (south-to-north air flow) acts as the connection for the air pollutants crossing the
Himalayas.