Introduction
Atmospheric aerosols undergo changes in their microphysical, chemical and
optical properties, especially in high-altitude mountainous regions. These
changes primarily determine their roles in modifying regional climate,
cryosphere and hydrology. This is particularly true for the Himalayan and
Tibetan Plateau (HTP) region, which is surrounded by Asian dust and strong
anthropogenic emissions. These natural and manmade airborne chemicals, such
as light-absorbing materials, reactive nitrogen and heavy metals, can impact
regional monsoon rainfall (e.g., Ramanathan et al., 2005; Lau et al., 2006),
snow–ice albedo (e.g., Ming et al., 2008; Xu et al., 2009; Qu et al., 2014),
nitrogen deposition (Liu et al., 2013, 2015) and meltwater composition (e.g.,
Zhang et al., 2015). Although these effects remain poorly understood, it is
of first-order importance to characterize these remote atmospheric aerosols.
In the HTP, aerosol optical properties and chemical compositions have been
observed almost entirely at a few specific sites. Ground-based measurements
have focused on the relatively small concentrations of fine particles and
total suspended particles (TSPs) in the HTP atmospheric surface layer (Zhao
et al., 2013; Xu et al., 2014). Satellite and ground-based remote sensing
have also been employed and have pointed to a low aerosol optical depth (AOD)
in this region (Xia et al., 2008, 2011; Yan et al., 2015). Mineral dust has
been identified as one of the main aerosol components in the central
Himalayas (Decesari et al., 2010) and the central TP (Zhang et al., 2001;
Cong et al., 2007; Kang et al., 2016). Analysis of dust plumes from the
surrounding deserts (the Taklimakan, Gobi and southwest Asian deserts) has indicated some potential source areas of atmospheric particulates
(Huang et al., 2007; Liu et al., 2008; Xia et al., 2008). However, these
results have revealed only the somewhat pristine characteristics of HTP
aerosols, dependent largely upon a significant understanding of mineral dust.
Much uncertainty remains over the correct evaluation of aerosol sources,
transportation and deposition, especially in relation to a much wider variety
of aerosol species. Furthermore, the mountains produce extensive
mountain-valley breezes, alpine glacier and snow, and stratosphere–troposphere
exchanges. These conditions could in turn affect aerosol properties via
transportation and chemical processes by facilitating the upward diffusion of
aerosol matters (Decesari et al., 2010; Cong et al., 2015) and by changing
the oxidizing capacity of the troposphere (Lin et al., 2008). Hence, there are
additional obstacles in understanding HTP atmospheric aerosols.
Anthropogenic emissions into this region occur occasionally and are dependent
on local and large-scale atmospheric dynamics. During the pre-monsoon period,
“atmospheric brown cloud” stacks up in the southern foothills of the
Himalayas (Ramanathan et al., 2001). Mountain-valley breeze circulations
allow these aerosols to spread upslope and then enhance the
concentrations of carbonaceous and inorganic matters in fine aerosols over
the Himalayas (Decesari et al., 2010; Babu et al., 2011; Cong et al., 2015;
Lüthi et al., 2015). The Westerlies transport of air pollutants is
dominant in the western TP (Cao et al., 2009). Additionally, the South Asian
monsoon system is one of the important atmospheric dynamics in the transport
of pollutants to the southeastern TP from southern and southeastern Asia (Liu
et al., 2013; Sheng et al., 2013). Consequently, light-absorbing substances
(such as black carbon) have received special attention. Studies have raised
different hypotheses concerning aerosol impacts, including a suppression of the Southern Asian
monsoon through a weakening of the meridional surface temperature gradient
(Ramanathan et al., 2005) and an increase of regional monsoonal rainfall in
northern India, the Himalayas and the southern Tibetan Plateau (TP) through
the “elevated-heat-pump” effect (Lau et al., 2006). Furthermore, the
post-depositional effect of decreasing snow–ice albedo is likely to lead to
reductions in the HTP glaciers (Xu et al., 2009; Ming et al., 2012; Qu et
al., 2014). However, the validity of these hypotheses strongly depends on
the characteristics and spatiotemporal variations in these particles
(principally in mass loadings, chemical compositions, size distributions and
optical properties) and their related atmospheric processes.
In general, the HTP, as a unique upland region where the relatively pristine
tropospheric environment is juxtaposed with Asian anthropogenic emissions, is
highly suitable for the study of background atmospheric aerosols and the
interactions between natural and anthropogenic emissions, processes which may
have far-reaching environmental and climatic consequences (Lawrence, 2011;
Vernier et al., 2011).
It was imperative, therefore, that the first comprehensive observation of HTP
atmospheric background aerosols was conducted during the 2011–2013 period,
based on four stations located in different ecosystems. Accordingly, we
present in this study online PM2.5 (particles with aerodynamic diameters
≤ 2.5 µm) concentrations and filter-sampled particles, as well
as the size distributions of these size-segregated particles (Sect. 3.1). The
diurnal variations in fine-aerosol masses are also discussed with particular
reference to local geomorphology, source emissions and meteorological
settings (Sect. 3.2). As part of our research, we attempted to integrate
these in situ observations with aerosol optical properties derived from both
ground-based and satellite remote sensing, aiming to construct a
topographical view of their spatial and seasonal patterns (Sect. 3.3).
Geographical conditions and aerosol observations at HTP background
sites (Ngari, QOMS, Nam Co and SET stations).
Station
Location
Altitude (a.s.l.)
Description
Research content
Observation
Instrumentation
Ngari station
79∘42′ E 33∘23′ N
4264 m
Semi-arid area,western TP
1. Online and size distribution ofaerosol masses
1. PM2.5 (5 min) and nine-stage aerosol mass (weekly)
TEOM RP1400 and nine-stage Anderson samplers
QOMS station
86∘57′ E 28∘21′ N
4300 m
North slope of the central Himalayas
2. Chemical compositionand matter closureof size-segregatedaerosols
Soluble salts,heavy metals, OCand EC (biweekly)
IC, ICP-MS and thermaloptical carbon analyzer
Nam Co station
90∘57′ E 30∘46′ N
4746 m
Alpine grassland, central TP
3. Aerosol optical properties
3. Aerosol optical depth and Ångström exponent (hourly)
Microtops II sun photometer at the Ngari and SET stations, *CIMEL sun photometerat the QOMS and Nam Co stations
SET station
94∘44′ E 29∘46′ N
3326 m
Alpine forest,southeastern TP
* The QOMS and Nam Co stations are also
Aerosol Robotic Network (AERONET) sites
(http://aeronet.gsfc.nasa.gov/). The abbreviations IC, ICP-MS and TEOM
stand for ion chromatography, inductively coupled plasma mass spectroscopy
and tapered element oscillating microbalance, respectively.
Materials and methods
Monitoring sites and the regional environment
The HTP is the greatest upland region of the Eurasian continent in the
Northern Hemisphere's middle–low latitudes and composes landscapes covered
mainly by alpine forest, grassland and/or meadow, barren areas, and patchy
glacier and/or snow cover. We take “upland” in the HTP region to be land above
2800 m a.s.l.; thus, this region has an upland area of
∼ 5 000 000 km2 (Fig. 1a). Four comprehensive observation
platforms were established within different landscapes, including the Ngari
station (79∘42′ E, 33∘23′ N, 4264 m a.s.l.), the
Qomolangma–Everest (QOMS) station (86∘57′ E, 28∘21′ N,
4300 m a.s.l.), the Nam Co station (90∘57′ E,
30∘46′ N, 4,746 m a.s.l.) and the Southeastern Tibet (SET)
station (94∘44′ E, 29∘46′ N, 3,326 m a.s.l.) (Figs. 1
and S1 in the Supplement). The high-altitude, inland topography produces a
generally cold, arid and windy climate across most of the HTP. Additionally,
the atmospheric circulation systems (including the South Asian monsoon, the
East Asian monsoon, and the Westerlies) control the seasonal and spatial
variations in precipitation patterns, i.e., winter–spring precipitation in
the western HTP (Pamir area), monsoonal rainfall in the southeastern and
eastern TP and Himalayas, and sparse precipitation in the northern regions
(Fig. S2 in the Supplement).
The main landscapes
(1∘ × 1∘ pixel resolution) (a) and the
aerosol observation sites in the HTP (b–e). The highland HTP region
is taken as land above 2800 m a.s.l., but the thresholds are
1500 m a.s.l. for areas 92–97∘ E and 26–34∘ N, and
2000 m a.s.l. for areas 98–104∘ E and 24–34∘ N,
accounting for the regional deviations caused by the extremely steep
topography. The classification of landscapes, according to MODIS land cover
classification (Broxton et al., 2014), suggests different land covers at
these stations (here the forest areas comprise evergreen, mixed, and
deciduous forests).
Time series for hourly air temperature (T), relative humidity
(RH), pressure (P), precipitation amount (PA), wind speed (WS) and wind
direction (WD) in the HTP during 2011–2013 at the Ngari station (black),
the QOMS station (red), the Nam Co station (blue) and the SET station
(cyan), respectively.
Records of daily air pressure (P), temperature (T), relative humidity
(RH), precipitation amount (PA), horizontal wind speed (WS) and wind
direction (WD) observed at these stations displayed regional variability and
seasonality of meteorology in the HTP during 2011–2013 (Fig. 2). Generally,
the levels of P were clearly different and decreased with ascending
altitude, showing values of 605.4 ± 3.7 hPa at the Ngari station,
604.6 ± 3.2 hPa at the QOMS station, 570.7 ± 4.4 hPa at the Nam
Co station and 679.5 ± 2.9 hPa at the SET station (±1.0
standard error). The average horizontal WS values were 2.7 ± 1.1,
4.3 ± 1.6, 3.4 ± 1.4 and 1.1 ± 0.7 m s-1 for the
Ngari, QOMS, Nam Co and SET stations, respectively. The PA was controlled by
Asian monsoon systems within annual ranges of 173.3–243.8, 444.2–488.2 and
436.6–905.8 mm at the QOMS, Nam Co and SET stations, respectively. The
lowest annual PA (40.9–125.3 mm) and mean RH (29.2 ± 14.7 %) were
observed at the Ngari station. Compared to other stations, the greater
seasonal variability in T at the Ngari station, i.e., from the lowest value
(-10.6 ± 4.8 ∘C) in December–February to the highest value
(14.0 ± 3.1 ∘C) in June–August, can be explained by its position far
inland and its attendant climate.
Concentrations (µg m-3) of hourly, daily and baseline
PM2.5 and differences (µg m-3) between online and
baseline PM2.5 at four HTP stations for 2011–2013. No.
and SD stand for
number and standard deviation, respectively.
No.
Range
Mean ± SD
No.
Range
Mean ± SD
No.
Range
Mean SD
No.
Range
Mean ± S.D.
PM2.5
Ngari station
QOMS station
Nam Co station
SET station
Hourly
1963
0.2–267.4
18.5 ± 24.3
4049
0.1–99.7
13.8 ± 12.3
11 067
0.2–98.8
11.8 ± 8.1
6871
0.1–78.5
11.7 ± 10.0
Daily
88
7.1–77.3
18.2 ± 8.9
236
2.6–48.1
14.5 ± 7.4
480
3.9–43.9
11.9 ± 4.9
351
2.8–28.7
11.7 ± 4.7
Baseline
1594
4.9–24.4
11.2 ± 3.2
1658
2.6–18.4
9.8 ± 3.1
8880
3.3–28.4
9.8 ± 3.6
4032
2.7–20.3
9.2 ± 3.0
Online minus baseline
1477
-16.3–109.7
4.2 ± 14.0
1431
-15.7–53.7
2.1 ± 2.0
8590
-17.7–33.0
0.8 ± 5.3
3554
-13.9–49.4
1.2 ± 6.6
Observation protocols for HTP atmospheric aerosols
Detailed information of HTP aerosol measurements is presented in Table 1
and includes the physical, chemical and optical properties of atmospheric
aerosols at the Ngari, QOMS, Nam Co and SET stations.
RP 1400 series tapered element oscillating microbalance (TEOM) machines were
installed and operated at each station to collect PM2.5 data from
autumn 2011 onwards. PM2.5 mass was weighed and quantified based on
the oscillation frequency of the tapered tube (Patashnick and Rupprecht,
1991). Their values were recorded at 5 min intervals. Values ranged from
0 to 5 g m-3, with a resolution of 0.1 µg m-3 and a
precision of ±0.5 µg m-3 over a 24 h average (Xin et
al., 2015). At each station, size-segregated airborne particles (with the
diameters of < 0.43, 0.43–0.65, 0.65–1.1, 1.1–2.1, 2.1–3.3,
3.3–4.7, 4.7–5.8, 5.8–9.0 and > 9.0 µm) were
collected weekly using airborne particle nine-stage samplers (Andersen Series
20-800, USA) at a flow rate of 28.3 L min-1. Quartz filters and
cellulose membranes (with diameters of 81 mm) were applied alternately for
measuring different chemical species, with a collection time of 72 h per
week (always from Monday to Wednesday). Before and after sampling,
the filters were weighed using a microbalance (sensitivity ±0.01 mg)
after drying for 48 h at 25 ∘C and 50 % humidity (Xin et al.,
2015). Mass concentrations of these filtered samples were in turn obtained
according to the standard sampling volume.
Methods of data analysis
The baseline properties of atmospheric aerosol mass revealed a relatively
stable and low aerosol loading, excluding possible perturbations (Kaufman
et al., 2001; Xia et al., 2011). Following Kaufman et al. (2001), we
calculated the median of 50 consecutive hourly average values of online
PM2.5 masses over 2–3 days and removed data sequences with standard
deviations higher than those of the whole time series by repeatedly shifting
the running medians by one measurement point. The standard deviation
thresholds were 24 µg m-3 at the Ngari station,
13 µg m-3 at the QOMS station, 9 µg m-3 at
the Nam Co station and 11.7 µg m-3 at the SET station.
Consequently, the remaining datasets were considered the time series of
baseline PM2.5 masses.
We applied monthly Level 3 datasets of Multi-angle Imaging Spectroradiometer
(MISR) to characterize atmospheric column AOD (at 550 nm) over the HTP for
2011–2013. Level 2.0 Aerosol Robotic Network (AERONET) datasets at the
QOMS station and Level 1.5 datasets at the Nam Co station were also used to
address fine-mode AOD (at 500 nm). A time-average map of the aerosol
fine-mode fraction (at 550 nm) for 2011–2013 was also constructed using
monthly Moderate Resolution Imaging Spectrometer (MODIS) Terra (version 5.1)
Level 3 values. Additionally, a global 0.5 km land cover climatology that
derived from MODIS (Broxton et al., 2014) was converted to a 1∘ × 1∘ pixel resolution using ArcGIS software, which provided the HTP's land
cover datasets.
Hourly and daily mean concentrations (µg m-3) of
PM2.5 at the Ngari station (October 2011–October 2012), the QOMS
station (March 2012–December 2013), the Nam Co station
(October 2011–December 2013) and the SET station (May 2011–December 2013)
in the HTP. Periods with no data were due to power supply problems or
equipment breakdown. A daily mean was calculated only when at least eight
hourly means were available during that day.
Frequency distributions of daily PM2.5 concentrations over the
HTP observed during the 2011–2013 period. High-concentration peaks around
the range of 12.5–20 µg m-3 occurred in the frequency curves
of the Ngari and QOMS stations, as indicated by the grey shading. The maximum
PM2.5 bin concentration was set to 50 µg m-3, although a
small fraction existed at higher concentrations.
Results and discussion
Mass concentrations of online PM2.5 and segregated particles
Figure 3 and Table 2 show the time series and statistics for online
PM2.5 measurements monitored at four HTP stations during 2011–2013. The
daily mean concentrations were 18.2 ± 8.9 µg m-3 at the
Ngari station, 14.5 ± 7.4 µg m-3 at the QOMS station,
11.9 ± 4.9 µg m-3 at the Nam Co station and
11.7 ± 4.7 µg m-3 at the SET station. Fine-aerosol
masses were therefore generally low but variable against various background
atmospheres. These results were comparable with the monitored values of
11.7 ± 15.5 µg m-3 at a station in the Qilian Shan in the northeastern TP (Xu et al., 2014) and
26.6 ± 19.3 µg m-3 at a background Himalayan site
(Panwar et al., 2013).
Baseline levels of hourly PM2.5 mass were estimated to be
11.2 ± 3.2 µg m-3 at the Ngari station,
9.8 ± 3.1 µg m-3 at the QOMS station,
9.8 ± 3.6 µg m-3 at the Nam Co station and
9.2 ± 3.0 µg m-3 at the SET station (Table 2). The
discrepancies between online PM2.5 and their baselines were also
calculated. Consequently, average percentages and concentration levels were
∼ 22.7 % and 4.2 ± 14.0 µg m-3 at the Ngari
station, ∼ 16.6 % and 2.1 ± 2.0 µg m-3 at the
QOMS station, ∼ 6.8 % and 0.8 ± 5.3 µg m-3 at
the Nam Co station, and ∼ 10.3 % and
1.2 ± 6.6 µg m-3 at the SET station (Table 2).
Therefore, relatively great distinctions were found at the Ngari and QOMS
stations. Significant variations, indicated by their daily frequency curves,
also occurred at the Ngari and QOMS stations and were associated with
episodes of high-concentration events (Fig. 4). These results implied a
disturbance in the high-concentration aerosol masses of inland Asia
associated with possible dust impact and dependent upon proximity to local
arid and barren areas (for their typical landscapes, see Fig. S1 in the
Supplement).
We further assessed mineral matter content in fine particles by analyzing
elements in PM2.1 samples with inductively coupled plasma mass
spectroscopy (ICP-MS). Mineral matter content was assumed to be a mixture of
mainly crustal oxides, i.e., SiO2, Al2O3, CaO, Fe2O3,
K2O, Na2O and MgO. A detailed description of this approach can be
found in Xin et al. (2015) (also see Table S1 in the Supplement). Mineral
content was 26 % at the Ngari station and 29 % at the QOMS station.
Our measurements revealed the impact of regional dust emissions, even for
fine particles, over the HTP's barren areas. Proportions were 2–3 times
those of PM2.1 (mean content 10.8 %) measured at a suburban site
impacted by heavy air pollutants in North China (Xin et al., 2015) and
PM2.0 (content of 14 ± 4 %) sampled at a human-influenced site
in Hungary (Maenhaut et al., 2005).
Average mass concentrations ± standard deviations
(µg m-3) for size-segregated particles (at various
µm) and PM1.1, PM2.1, PM9 and TSP sampled from the
HTP surface atmosphere during 2011–2013. No. stands for number of
samples.
Species
Ngari
QOMS
Nam Co
SET
station
station
station
station
No.
54
89
65
66
< 0.43
7.7 ± 5.1
5.8 ± 6.0
4.1 ± 4.3
2.6 ± 2.6
0.43–0.65
8.8 ± 5.8
6.6 ± 6.2
3.7 ± 3.4
2.4 ± 2.1
0.65–1.1
7.5 ± 3.6
6.5 ± 5.0
3.6 ± 4.5
2.6 ± 2.2
1.1–2.1
7.0 ± 3.8
7.6 ± 6.8
3.2 ± 3.1
2.3 ± 2.0
2.1–3.3
8.1 ± 5.5
7.1 ± 5.5
3.3 ± 3.5
2.3 ± 2.1
3.3–4.7
7.2 ± 3.6
8.3 ± 11.1
3.4 ± 3.8
2.5 ± 2.0
4.7–5.8
7.2 ± 3.4
8.3 ± 10.5
3.4 ± 4.0
2.4 ± 2.2
5.8–9
7.8 ± 7.1
7.7 ± 5.9
3.7 ± 4.9
2.0 ± 1.6
> 9
5.6 ± 8.2
7.8 ± 6.6
3.5 ± 4.1
2.3 ± 3.5
PM1.1
24 ± 14.5
18.9 ± 17.3
11.3 ± 12.2
7.7 ± 6.8
PM2.1
30.6 ± 14.2
26.3 ± 20.6
14.5 ± 12.9
10.0 ± 8.2
PM9
60.9 ± 27.5
57.5 ± 45.4
28.4 ± 25.9
19.2 ± 15.0
TSP
66.4 ± 29.6
65.1 ± 50.9
31.9 ± 29.0
21.5 ± 18.0
Size distributions of mass aerosol particles in the background
surface atmosphere of the HTP (a: Ngari station, b: QOMS
station, c: Nam Co station, d: SET station) as observed
over the 2011–2013 period. Boxes show the percentile values (25, 50, 75) and
whisker plots show maximum and minimum of non-outlier numbers. The small
blue circles behind the boxes are the distribution points.
Wind rose plots for afternoon (12:00–16:00 LT) and nighttime
(00:00–04:00 LT) in July and August at the QOMS station (a, b) and
the SET station (c, d). An hourly horizontal wind direction (WD) was
used, with its radii values expressed as percentages for wind blowing from
particular directions.
Seasonal diurnal variations in PM2.5 concentrations, air T
and RH over the 2011–2013 period at four background HTP sites (the Ngari
station, the QOMS station, the Nam Co station and the SET station). The local
time (LT) was used at each site according to longitudinal position.
Diurnal variations in PM2.5 masses and related environmental
factors for 6–10 April 2012 at the Ngari station (located in a typical
barren and arid area of inland Asia). SR is downward shortwave radiation and
soil T is the surface soil temperature at 0 cm. The local time (LT) and a
30 min mean were used.
Correlations between SR and soil T (a), soil T and
PM2.5 mass (b), and WS and PM2.5 mass (c) in
the morning (08:30–10:30 LT) at the Ngari station for 6–10 April 2012.
The smaller inserts show all recorded points within the measured time frame.
Note that the fit line of Fig. 9a is for 6–9 April 2012 because there was a
rainfall event (∼ 08:00–11:00 LT) on 10 April 2012, as indicated in
Fig. 8. However, even if the dataset for 10 April 2012 is included, the fit
line remains more or less consistent, with R2=0.61 and
P < 0.001. The local time (LT) and a 30 min mean were used.
Diurnal variations in PM2.5 concentrations and related
environmental factors for 8–12 October 2012 at the Nam Co station. Based on local time (LT), 30 min
mean datasets were used.
Table 3 shows the statistical results of segregated-particle mass loadings
according to weekly filters. These particles exhibited a general tendency of
the Ngari and QOMS stations (barren sites) > Nam Co station
(grassland site) > SET station (forest site) in their mass
levels, suggesting a potential effect associated with the HTP land cover.
Furthermore, bimodal size distributions of surface-atmospheric particle
masses occurred in these upland regions with an average pattern of a
relatively small peak in accumulation mode and a more notable peak in coarse
mode (Fig. 5). This represents an aerosol mass distribution pattern typical
of continental background air (Willeke and Whitby, 1975). In previous
studies, accumulation mode particles were observed at Mt. Waliguan
observatory (Kivekäs et al., 2009), possibly formed by the coagulation or
condensation of smaller aerosol particles. The effects of dust emission on
coarser particles have been reported in the TP atmosphere (Zhang et al.,
2001).
Diurnal variations in mass concentrations of fine aerosols
In these background atmospheres, the intensity of diurnal variabilities in
PM2.5 masses was roughly characterized by their daytime
(06:00–18:00 local time, LT) to nighttime (18:00–06:00 LT) ratios. Their
average ratios were ∼ 2.5 at the Ngari station, ∼ 1.1 at the QOMS
station, ∼ 0.9 at the Nam Co station and ∼ 1.8 at the SET
station, based on hourly observations during the 2011–2013 period.
Spatial patterns in AOD and TSP mass (a) and aerosol
fine-mode fraction and the ratio of PM2.5 to TSP (b) over the
HTP. (a) Mean MISR AOD (at 550 nm) for 2011–2013 as derived
from monthly Level 3 datasets. (b) A time-average map of the
MODIS fine-mode fraction (at 550 nm) for 2011–2013, according to monthly
Terra (version 5.1) Level 3 values. Ground-based observations are average
values sampled in 2011–2013.
Mean MISR AOD (at 550 nm) for two cross-sections during various
months in the 2011–2013 period. Missing datasets are plotted in white. The
longitudinal section a is from the southeast (100∘ E,
25.5∘ N–102∘ E, 31.5∘ N) to the northwest
(79.5∘ E, 32.5∘ N–81.5∘ E, 38.5∘ N); the
latitudinal section b is from the south (95∘ E,
28∘ N–101∘ E, 28∘ N) to the north
(95∘ E, 39∘ N–101∘ E, 39∘ N).
J–D stands for the months of January–December.
Seasonal characteristics of landscape-classified aerosol masses in
the HTP based on in situ observations and remote sensing datasets. Boxes
show the percentile values (25, 50, 75) and whisker plots show maximum and
minimum of non-outlier numbers, and the small point within each box is the
mean value. The abbreviations are March–May: MAM; June–August: JJA;
September–November: SON and December–February: DJF. Please also see the
expanded information about Fig. 13 in the Supplement.
Higher ratios were found in valleys around the QOMS and SET stations,
suggesting a negative impact of mountainous valleys on the diffusion of local
aerosol masses. The local geomorphology around these sites is displayed in
Fig. S3 in the Supplement. Conversely, these topographical settings also produced
mountain-valley wind circulations aligned with valley orientation, as
identified in July and August (Fig. 6). We analyzed the hourly datasets for
the summer monsoon period (July and August) since the mid-latitude westerlies
are more prevalent during the other periods and thus constrain the influence
of synoptic-scale wind. Horizontal WD at the QOMS station was consequently
stronger and clearly inverse compared to that at the SET station. Such a
topographically forced circulation can facilitate the spread of aerosols
upslope (Decesari et al., 2010; Babu et al., 2011; Cong et al., 2015). This
would explain the ratio being lower at the QOMS station than at the SET
station. The Ngari station is located in a relatively open geomorphological
setting, but experiences marked diurnal variations. This phenomenon can be
attributed to the dust lift from the barren land surface in the daytime, as
will be discussed below.
The overall patterns of diurnal variability in fine-aerosol mass, atmospheric
T and RH, as well as in horizontal WD, are shown in Fig. 7. These fine-particle masses begin to arise during 06:00–08:00 LT, accompanied by an
increase in T and a decrease in RH. Around noon
(10:00–14:00 LT), concentrations decreased again, shown by the trough in
their diurnal curves, coinciding with the highest T and horizontal WD
values, and the lowest RH. Consequently, bi-peak patterns in diurnal
variations were especially marked for the Nam Co station (whole year), and
for the QOMS and SET stations (autumn and winter). In contrast, the Ngari
station, in the arid Asian interior, evinced a single-peak pattern in diurnal
variations. Such variations are typically found in dust provenances (Mbourou
et al., 1997; Stout, 2010), resulting from the atmospheric and land surface
conditions prevalent during the daytime.
In the cases of 6–10 April 2012, solar radiation (SR) imposed dramatic
changes on the soil and atmospheric T, RH and WS in the morning
(Figs. 8 and 9). Here, SR was taken as the downward shortwave radiation and
the soil T was the surface soil temperature at 0 cm, measured using an
automated weather system at the Ngari station. Increases in atmospheric T
and soil T, and a decline in RH, were synchronous from 06:00 to 07:00 LT in
response to solar heating. SR and soil T values rose increasingly in
tandem during the 08:30–10:30 LT period, forming a very close relation
(R2=0.96, P < 0.001), apparently in response to the arid and
barren setting and cloud-free air at that time (Figs. 8 and 9a). Hence, soil
T rose from ∼ -2.5 to ∼ 20 ∘C beyond the dew point
temperature, and gradually dried out the surface moisture and uppermost layer
of land (Fig. 9b). This in turn implied a reduction in the critical dust
burst threshold for barren conditions (Stout, 2010). Furthermore, the rise in
morning WS created an atmospheric dynamic suited to dust suspension in the
late morning when fine materials were transported up from the land surface
into the atmosphere (Fig. 8c). The combination of a declining critical dust
burst threshold and favorable atmospheric fluctuation induced the increase in
fine particles in the atmosphere with a peak near noontime. During the
14:00–18:00 LT period WS was strongest with a range of
4–10 m s-1. Dependent upon its intensity, WS can dilute fine-particle
masses, rather than affect fine-particle fluctuations between sandy surfaces
and the air. In addition, a decrease in saltation activity prior to the WS
drop has frequently been observed in barren and arid continental
interiors, possibly resulting from a reduction in turbulent wind fluctuations
in the late afternoon (Stout, 2010). This effect can also restrict dust burst
and thus its contribution to ambient fine-particle content. These in situ
observations established that land surface and low-layer atmosphere are the
key physical controls of the diurnal PM2.5 mass cycle at the Ngari
station. They also confirmed that local dust emissions contributed to the
chemical composition of fine aerosols.
Bi-peak-like diurnal variations in the PM2.5 masses at the Nam Co
station, located in a grassland site near the great Nam Co Lake (Fig. S3 in
the Supplement), are shown in Fig. 10. The planetary boundary layer height
(PBLH) was derived from National Center for Environmental Prediction
reanalysis data, which used a 1∘ × 1∘ pixel and a 3 h temporal
resolution (http://www.arl.noaa.gov/gdas1.php). In response to
increasing T, the PBLH rose during the daytime from < 100 to
> 2500 m, associated with a rise in WS (Fig. 10). This
combination of factors resulted in a marked diffusion of fine particles,
shown by the trough in PM2.5 concentrations between 10:00 and 16:00 LT.
This also accounted for the < 1 daytime-to-nighttime ratio.
Spatial and seasonal patterns in atmospheric aerosol masses
The monthly mean MISR-AOD values for 2011–2013 suggested that HTP atmospheric
aerosol masses were generally isolated from surrounding emissions (Fig. 11a).
The integrated results of surface-atmospheric aerosol parameters and
atmospheric-column aerosol optical properties yielded spatial distributions
that suggested that TSP concentrations and MISR-AOD values decreased as land
cover varied from barren land, through grassland, to forest (Fig. 11a).
The mean fraction of PM2.5 to TSP was 27.4 ± 6.65,
22.3 ± 10.9, 37.3 ± 11.1 and 54.4 ± 6.72 % for the
Ngari station, QOMS station, Nam Co station, and SET station, respectively
(Fig. 11b). These values increased from barren to forest areas, inversely to
TSP masses. The spatial distribution of the aerosol fine-mode fraction (at
550 nm) in the time-average map (derived from MODIS) was clearly consistent
with the ground-based results recorded at various sites (marked by circles
with various colors in Fig. 11b).
Figure 12 shows how MISR-AOD values varied along two cross-sections in
different months. These results further confirmed a general decline in AOD
from northwest to southeast crossing typical plateau landscapes
(section A), and from north to south
in the eastern TP (section B).
Furthermore, such a spatial pattern was more notable for April–August,
coinciding with the appearance of the reported Asian tropospheric aerosol
layer during this period (Vernier et al., 2011). This may imply the
significance of the development of the Asian tropospheric aerosol layer in
modulating the AOD level over this plateau.
TSP mass and MISR-AOD values over HTP forest (SET station) and grassland (Nam
Co station) sites shared a common seasonal pattern, with relatively higher
values in spring and summer, followed by relatively lower values in autumn
and winter (Fig. 13a). At the barren site (QOMS station) there were
inconsistent seasonal patterns between surface-atmospheric TSP (PM2.5)
and atmospheric column AOD (fine-mode AOD) (Fig. 13a, b). Furthermore, there
was no correlation between hourly surface PM2.5 mass and fine-mode AOD
(at 500 nm) at this site (Fig. S4 in the Supplement). Using the
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO),
Huang et al. (2007) detected frequent dust plumes in the lower atmosphere
(∼ 4–7 km a.s.l.) of the western HTP. These dust plumes possibly
impacted the vertical distribution of aerosol masses over these barren areas.
PM2.5 concentrations and fine-mode AOD values were higher in spring and
summer than in autumn and winter at HTP forest and grassland sites but not
at the barren site (Fig. 13b). Ratios of PM2.1 to TSP were apparently
higher at the SET and Nam Co stations compared to the QOMS station, with a more
marked difference in summer and autumn (Fig. 13c). In a background
continental atmosphere, fine aerosols mainly originate from biogenic or
wildfire emissions. Wildfires were extremely rare in the HTP region, and
fire-related emissions from the Asian Brown Cloud occurred only during the
winter and spring, as measured in the Himalayan region (Cong et al., 2015;
Decesari et al., 2010). Therefore, biogenic emissions and related products
may be essential sources of fine aerosols over HTP forest and grassland
areas. In the southeastern TP, strong monoterpene emissions were reported
since there are a great number of alpine forest species (Wang et al., 2007);
biogenic emissions were identified as the main precursors of atmospheric
low-weight organic acids (Liu et al., 2014). In the central TP, biogenic
contributions to secondary organic carbon were estimated to be
∼ 75 %; biogenic aerosol tracer concentrations were also higher in
summer than in winter (Shen et al., 2015).
Summary and conclusions
We studied aerosol mass loadings for the period 2011–2013 over the highland
region of the HTP on both local and regional scales by integrating
multistation measurements with satellite and ground-based remote sensing.
We found that mass concentrations of these surface atmospheric aerosols were
relatively low and varied with land cover, with the general tendency of Ngari
and QOMS (barren sites) > Nam Co (grassland site) > SET
(forest site). PM2.5 concentrations at these sites were
18.2 ± 8.9, 14.5 ± 7.4, 11.9 ± 4.9 and
11.7 ± 4.7 µg m-3, respectively. Correspondingly, their
fractions to TSP were 27.4 ± 6.65, 22.3 ± 10.9,
37.3 ± 11.1 and 54.4 ± 6.72 %. Bimodal mass distributions of
size-segregated particles were found at all sites, with a relatively small
peak in accumulation mode and a more marked peak in coarse mode. Diurnal
variations in fine aerosol masses generally displayed a bi-peak pattern at
the QOMS, Nam Co and SET stations and a single-peak pattern at the Ngari
station, controlled by the effects of local geomorphology, mountain-valley
breeze circulations and aerosol emissions. Mineral matter content in
PM2.1 samples was 26 % at the Ngari station and 29 % at QOMS, or
∼ 2–3 times that of reported results at human-influenced sites.
Furthermore, our observations confirmed that land surface and boundary layer
settings create a dynamic for these fine particles to be lifted from the
barren land surface into the atmosphere.
Combining surface aerosol and atmospheric-column aerosol optical property
data, we found that TSP masses and MISR-AOD values generally decreased as
land cover varied from barren to forest, inversely to PM2.5 ratios. The
seasonality of aerosol mass parameters was land-cover dependent. Over forest
and grassland areas, TSP mass, PM2.5 mass, MISR-AOD and fine-mode AOD
values were higher in spring and summer and relatively lower in autumn and
winter. Such spatial and seasonal patterns were possibly associated with
regional biogenic emissions and related aerosol products. At QOMS, there
were inconsistent seasonal patterns between surface TSP mass (PM2.5
mass) and atmospheric column AOD (fine-mode AOD).
This study provides new insights on understanding the mass properties of HTP
atmospheric aerosols. HTP aerosol masses (especially their regional
characteristics and fine-particle emissions) need to be treated sensitively
in relation to assessments of their climatic effect and potential role as
cloud condensation nuclei and ice nuclei.