Aerosol Science and Engineering

, Volume 1, Issue 4, pp 160–168 | Cite as

Seasonal Transport and Dry Deposition of Black Carbon Aerosol in the Southeastern Tibetan Plateau

  • Qiyuan Wang
  • Zhuzi Zhao
  • Jie Tian
  • Chongshu Zhu
  • Haiyan Ni
  • Yonggang Zhang
  • Ningning Zhang
  • Zhenxing Shen
  • Yongming Han
  • Junji Cao
Original Paper


To investigate the regional transport and dry deposition of black carbon (BC) aerosol in the southeastern Tibetan Plateau, continuous equivalent BC (eBC) mass concentrations were measured at a high-altitude remote site of Lulang from July 2008 to July 2009. The bivariate polar plots for eBC mass concentrations showed that large eBC values were often associated with low winds (< 2 m s−1) during the pre-monsoon, post-monsoon, and winter seasons. Moreover, strong winds (> 4 m s−1) from southeast or northeast also contribute to the large eBC loadings during the pre-monsoon, monsoon, and post-monsoon seasons. The concentration-weighted trajectory analysis showed that emissions from the eastern Kingdom of Bhutan, Assam of India, and southern Shannan Prefecture of Tibet had the most important contributions to the eBC pollution at Lulang during the pre-monsoon and monsoon seasons. In contrast, the eBC potential source region shifted to the east and southeast of Lulang during the post-monsoon and to the north India and northwest Nepal during the winter. The estimated eBC deposition rate was the highest for the pre-monsoon (6.3–62.6 μg eBC m−2 day−1), followed by the post-monsoon (4.6–45.9 μg eBC m−2 day−1), winter (4.3–43.1 μg eBC m−2 day−1), and monsoon (2.4–24.5 μg eBC m−2 day−1). Further calculations of eBC concentrations in the snow surface were 33.3–333.2, 61.5–614.7, 27.0–269.9, and 58.8–587.6 μg kg−1 during the pre-monsoon, monsoon, post-monsoon, and winter seasons, respectively, which resulted in the snow albedos being reduced by 2.6–25.3, 4.7–46.6, 2.1–20.5, and 4.5–44.5% accordingly.


Black carbon Regional transport Dry deposition Tibetan Plateau 

1 Introduction

Black carbon (BC) aerosol is a major particulate light-absorbing substance in the atmosphere (Bond et al. 2013). It is a by-product produced during incomplete combustion processes, including natural and anthropogenic sources (e.g., biomass burning and fossil fuel combustion) (Petzold et al. 2013). BC has substantial effect on the global climate due to its strong absorption of solar radiation, and it has been considered as the second strongest warming forcing agent after carbon dioxide (CO2) (Jacobson 2001; Ramanathan and Carmichael 2008; Bond et al. 2013). BC can further act as cloud condensation nuclei or enhance the evaporation rates in cloud layers, indirectly causing changes in climate (Nenes et al. 2002; Wang 2004). In addition, BC greatly damages air quality (Cao et al. 2012; Wang et al. 2013; Ding et al. 2016), and poses threat to human health (Jansen et al. 2005; Grahame et al. 2014). Since BC has a shorter lifetime compared to other greenhouse gases (e.g., CO2), its reduction can potentially provide a fast solution to mitigate near-term global warming, as well as to improve air pollution, and be beneficial to human health and food security simultaneously (Quinn et al. 2008; Grieshop et al. 2009; Jacobson 2010; Shindell et al. 2012; Bond et al. 2013).

The Tibetan Plateau (TP), covering an area of ~ 2,500,000 km2, is known as the “Third Pole” of the Earth. It holds the largest volume of ice at the low and middle altitudes (Qiu 2008). Serving as the “Water Tower of Asia”, the TP provides critical freshwater source for several major rivers including the Indus, Ganges, Brahmaputra, Yangtze, and Yellow River. These rivers are the lifelines to billions of people living in the downstream (Immerzeel et al. 2010). However, observations and modeling studies show that snowpack and glaciers in the TP have been undergoing rapid melting and shrinking (William et al. 2010; Lee et al. 2013; Loibl et al. 2014; Jacobi et al. 2015; Li et al. 2016; Xu et al. 2016; Ke et al. 2017). Re-analysis of the TP Landsat images and satellite altimetry data show that the glacier area in this region experienced notable shrinkage at a rate of − 0.31 ± 0.04 km2 year−1 during 1976–2013 (Ke et al. 2017). Li et al. (2016) employed multiple remote sensing data to investigate the changes of glaciers in the Dupuchangdake region of northwestern Tibet, and found that the glacier area in this region decreased from 409 to 393 km2 from 1991 to 2013.

BC is an important contributor to the observed rapid glacier retreat (Xu et al. 2009a). Once deposited on snow and ice in the TP, BC can significantly decrease the surface albedo, causing an aggravated surface melting (Ming et al. 2009, 2013; Xu et al. 2009b; Nair et al. 2013; Ménégoz et al. 2014; Qu et al. 2014). Ming et al. (2013) calculated that BC in the winter snowpack reduced the albedo by 11% in the Nam Co region in central Tibet. Qu et al. (2014) reported that the albedo of the Zhadang glacier decreased at a rate of − 0.003 a−1 from 2001 to 2012, and the contribution of BC to albedo reduction was 36% when the glacier was covered by aged snow.

Although anthropogenic activities in the TP are sparse, this region is surrounded by a number of strong BC source areas, including India and China (Wang et al. 2014). Simulated by GEOS-Chem, Kopacz et al. (2011) found that emissions from north India and central China contributed most of BC to the Himalayas, while the TP received a large number of BC from the western and central China, as well as from India, Nepal, the Middle East, and Pakistan. Lu et al. (2012) developed a novel back-trajectory approach to study the origin of BC reaching the Himalayas and the TP, and found that BC received by the Himalayas and the TP increased by 41% from 1996 to 2010. On an annual basis, South Asia was the largest contributor to the Himalayas and TP, accounting for 67%, followed by East Asia (17%), the former USSR region (~ 8%), Middle East (~ 4%), Europe (~ 2%), and Northern Africa (~ 1%). Although the high altitude of Himalayas can act as a “physical wall” that prevents transport of pollutants from South Asia to the TP (Zhao et al. 2017), the valley of Yarlung Tsangpo River created a “leaking wall”, where BC aerosol can be transported up onto the glaciers (Cao et al. 2010). This trans-Himalayas transport of BC is mostly controlled by the meteorological conditions over the Indo-Gangetic Plain. For example, convergent airflows produce a strong northeastward wind from the Bay of Bengal to the Himalayas, resulting in high BC concentrations in the southeastern TP (Zhao et al. 2017).

Currently, studies about BC sources and its impact on snow albedo in the TP based on the seasonal perspective are very limited. In this study, we discussed the impact of regional transport on BC aerosol in the southeastern TP using bivariate polar plot graphical technique (Sect. 3.1) and the concentration-weighted trajectory (CWT) analysis (Sect. 3.2). The BC concentration in the snow surface was calculated based on the assumption of minimal deposition velocity of BC (Sect. 3.3), and its effect on the snow albedo reduction is also assessed in Sect. 3.3.

2 Experimental Methods

2.1 Research Site

The sampling site was located in a remote area at Lulang (Fig. 1), which is situated on the west bank of the Yarlung Tsangpo River in Linzhi Prefecture, China. Continuous sampling was conducted from July 2008 to July 2009 at the Integrated Observation and Research Station for Alpine Environment in South-East Tibet, Chinese Academy of Sciences (94.44°E, 29.46°N, ~ 3300 m above sea level). The region is affected by the Indian monsoon, via the valley of Yarlung Tsangpo River (Yang et al. 2015). Lulang is located in this channel, where major anthropogenic sources nearby are absent, making the sampling site an ideal location for investigating the transport of pollutants from South Asia. Based on the seasonality of atmospheric circulation and meteorological conditions, we defined four seasons as follows: pre-monsoon (18 February 2009–27 April 2009), monsoon (16 July 2008–3 October 2008; 28 April 2009–26 July 2009), post-monsoon (4 October 2008–9 November 2008), and winter (10 November 2008–17 February 2009). More detailed description on the sampling site could be found in Zhao et al. (2013).
Fig. 1

Location of the sampling site. The map was downloaded from

2.2 Equivalent BC (eBC) Measurement

Five-minute averaged eBC mass concentrations were measured continuously with an aethalometer (Model AE-16, Magee Scientific Company, Berkeley, CA, USA). The term of eBC is specific to aethalometer measurement (Petzold et al. 2013). The detailed principle of aethalometer has been described previously (Hansen et al. 1984; Virkkula et al. 2007). Briefly, the instrument is based on the optical transmittance at a single wavelength of λ = 880 nm, and it measures the light attenuation (ATN) transmitted through a quartz-fiber filter. Sample air was drawn into the aethalometer at a flow rate of 4 L min−1 using a ~ 2 m length of conductive silicone tubing with a total suspended particle inlet. Before the atmospheric particles entered the instrument, they were dried with a silica gel dyer to avoid any effects of water condensation in the sampling line.

Based on the manufacturer’s default specific-attenuation cross section of 16.6 m2 g−1, the aethalometer converts the ATN to eBC concentration. However, a potential limitation of this instrument is that the decreasing transmission will lead to increasing attenuation with time; therefore, the relationship between ATN and eBC concentration will be non-linear (Weingartner et al. 2003). To compensate for the artifacts from the multiple scattering and shadowing effects, two empirical factors, that is, C and R(ATN), were used for correcting the absorption coefficient (b abs) (Weingartner et al. 2003):
$$b_{\text{abs}} = \frac{\text{ATN}}{{C \times R({\text{ATN}})}},$$
$$R({\text{ATN}}) = \left( {\frac{1}{f} - 1} \right) \times \frac{{\ln \left( {\text{ATN}} \right) - { \ln }(10\% )}}{{\ln \left( {50{\text{\% }}} \right) - { \ln }(10\% )}} + 1,$$
where C is 2.14 (Weingartner et al. 2003); f is 1.103 for wintertime and 1.114 for the rest of the seasons (Ram and Sarin 2009). The eBC concentrations were adjusted to the standard temperature (273.15 K) and pressure (1013.25 hPa).

3 Results and Discussion

3.1 Transport Direction

Fig. S1 shows a clear seasonal pattern of eBC concentrations, with the highest values in the pre-monsoon (0.98 µg m−3), followed by the post-monsoon (0.60 µg m−3), winter (0.57 µg m−3), and monsoon (0.32 µg m−3). Wind speed and wind direction are important for the dilution and diffusion of pollutants (Fast et al. 2007). Figure 2 presents the box plot of eBC mass concentrations as a function of wind speed increment during the four seasons. A slightly higher wind speed was found during the pre-monsoon (2.4 m s−1) rather than that during the other seasons, which showed similar average wind speed of 2.1 m s−1. As shown in Fig. 2, considerable difference in the mean and median eBC concentrations suggests that the eBC values varied largely at a given range of wind speed. Even so, the mean and median values still exhibited similar patterns for each season. During the post-monsoon, eBC concentrations showed an evident wind speed gradient with high eBC values related to the low wind speed. This can be explained by the strong wind being favorable to local horizontal diffusion, while the weak wind leads to accumulation of eBC aerosol. In contrast, eBC concentrations were relatively stable regardless of the wind speed during the pre-monsoon and monsoon. Although strong winds enhance the horizontal diffusion, it can also carry pollutants from upwind areas resulting in high eBC loadings (Gupta et al. 2017). For wintertime, eBC concentrations decreased with the increased wind speed at < 5 m s−1. However, the eBC concentrations increased with the enhanced wind speed at > 5 m s−1, indicating that regional transport may play an important role in accelerating eBC pollution.
Fig. 2

Box plot of eBC mass concentrations as a function of wind speed increment during the a pre-monsoon, b monsoon, c post-monsoon, and d winter. In each panel, the lower and upper edges of the boxes denote the 25 and 75% percentiles, respectively. The black line and red markers indicate the median and mean values, with vertical bars showing the 10th and 90th percentiles

Bivariate polar plot graphical technique was used to further investigate the relationship between eBC concentration and wind field, and to identify the transport directions of eBC aerosol. Figure 3 shows the bivariate polar plots for eBC mass concentrations at Lulang during the four seasons. For the pre- and post-monsoon, the prevailing wind directions were both northeasterly and southeasterly. For the monsoon season, the distribution of the wind direction was rather dispersed. During winter, the prevailing wind direction was northeasterly. The different patterns of the bivariate polar plots may reflect diverse directions of transport for eBC aerosol, which may be due to the varied seasonal eBC emissions and meteorological conditions. During the pre-monsoon, the average mass concentration of eBC was 0.72 µg m−3, and the large eBC values (> 0.65 µg m−3) were mainly associated with the low wind speed (< 2 m s−1) or with the high wind speed (> 4 m s−1) from the southeast. This indicates that both local and regional transport had important contributions to the eBC pollution occurring at Lulang. During the monsoon season, the average mass concentration of eBC was low with a value of 0.34 µg m−3. Occasionally, high eBC values (> 0.4 µg m−3) were observed at wind speed > 4 m s−1 from the southeast. During the post-monsoon, the average concentration of eBC was 0.53 µg m−3, with large eBC loadings (> 0.6 µg m−3) measured at low wind speed < 2 m s−1 or high wind speed > 6 m s−1 from the northeast and southeast. During winter, the average concentration of eBC was 0.51 µg m−3. The high eBC values (> 0.6 µg m−3) were concentrated at wind speed < 2 m s−1, indicating that eBC pollution may be mainly contributed by local emissions.
Fig. 3

Bivariate polar plots for the eBC mass concentrations based on the hourly data during the a pre-monsoon, b monsoon, c post-monsoon, and d winter

3.2 Identification of eBC Source Regions

The CWT analysis was performed for each season to investigate the spatial distribution of eBC potential source areas. It was based on the 5-day backward trajectories, and each trajectory was calculated when arriving at 500 m above ground level using the hybrid single-particle lagrangian integrated trajectory (HYSPLIT) model (Draxler and Rolph 2003). To calculate the CWT values, the entire geographic region covered by the trajectories is divided into an array of grid cells (defined by the cell indices i and j). In this study, ~ 12,616, ~ 17,052, ~ 19,200, and ~ 30,240 grid cells of 0.5° latitude × 0.5° longitude were retrieved for the pre-monsoon, monsoon, post-monsoon, and winter, respectively. Each grid cell is given a residence time-weighted concentration obtained by hourly averaged eBC concentration associated with the trajectories crossing that grid cell (Hsu et al. 2003):
$$C_{ij} = \frac{{\mathop \sum \nolimits_{l = 1}^{M} C_{l} \tau_{ijl} W_{i,j} }}{{\mathop \sum \nolimits_{l = 1}^{M} \tau_{ijl} W_{i,j} }},$$
where C ij is the average-weighted concentration in the ijth grid cell, C l the measured concentration of eBC on the arrival of trajectory l, τ ijl the number of trajectory end points in the ijth grid cell by trajectory l, and M the total number of trajectories. A high C ij value denotes that air parcels traveling over the ijth grid cell would contribute to eBC pollution. To reduce the effect of small values of τ ijl , the CWT values were multiplied by an arbitrary weight function W ij . The weighting function reduced the CWT values when the total number of end points per a particular cell was less than about three times the average values of the end points per cell (Wang et al. 2006).
$$W_{ij} = \left\{ \begin{aligned} 1.00\quad 120 < n_{ij} \hfill \\ 0.70\quad 40 < n_{ij} \le 120 \hfill \\ 0.42\quad 20 < n_{ij} \le 40 \hfill \\ 0.17\quad n_{ij} \le 20 \hfill \\ \end{aligned} \right..$$
The seasonal maps of CWT results are shown in Fig. 4a–d. For discussion purposes, we defined “eBC pollution” as the eBC concentration larger than the 75th percentile of all the data in each corresponding season. The 75th percentile values were 0.65, 0.35, 0.63, and 0.55 µg m−3 for the pre-monsoon, monsoon, post-monsoon, and winter, respectively. The CWT concentration gradients showed a similar source region for eBC pollution at Lulang during the pre-monsoon and monsoon. The source region was located to the southwest of Lulang (Region I), which includes the eastern Kingdom of Bhutan, Assam of India, and southern Shannan Prefecture of Tibet. However, the most important potential source region for the eBC pollution during the post-monsoon shifted to the east and southeast of Lulang (Region II), including Mêdog County of Tibet and northeast of Assam. The sources from Mêdog County indicated the pollution from the interior of the TP. Although the population is sparse in the TP, biofuels (e.g., yak dung and wood) are the main household energy for local residents (Ping et al. 2011), which produce noticeable amount of eBC aerosol, and hence in turn affects the atmosphere of the TP. It should be noted that sources from the northern part of Bangladesh (Region III) may also contribute to the high eBC loadings at Lulang. During the winter season, the largest possible source region for eBC pollution was located in north India and northwest Nepal (Regional IV). Moreover, high CWT values were occasionally found in central Tibet (Regional V), indicating sources from the interior of the TP. Further, time-averaged maps of BC column mass density, retrieved from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), were employed to describe the mean atmospheric eBC loadings around the TP (Fig. 5). As shown in Fig. 5, in general there were moderate or strong emissions observed in Region I, Region III and Region IV (see makers in Fig. 4), which are consistent with the distribution of large CWT values. This further supports that eBC pollution at Lulang was influenced by these regions.
Fig. 4

The maps of the concentration-weighted trajectory (µg m−3) for eBC concentrations during the a pre-monsoon, b monsoon, c post-monsoon, and d winter

Fig. 5

The maps of BC column mass density (kg m−2) around the Tibetan Plateau for the a pre-monsoon, b monsoon, c post-monsoon, and d winter. The green circles represent the sampling site

3.3 Estimated Impact of eBC Deposition

The total eBC mass dry deposition flux per 1 h was estimated through Eq. (5) (Santos et al. 2014):
$$F = C_{\text{eBC}} \times V_{\text{d}} \times 3600,$$
where F is the dry deposition flux (µg m−2), C eBC represents eBC concentration (µg m−3), and V d denotes the dry deposition velocity (m s−1). V d is a complex parameter related to the terminal velocity by gravitational settling and the resistances of atmospheric aerodynamic, quasi-laminar layer, and surface or canopy resistances. In this study, a constant minimal V d range of 1.0 × 10−4–1.0 × 10−3 m s−1 was used (Pryor et al. 2008). The total mass of eBC deposited on the surface was calculated as the sum of the eBC deposition integrated with all available data during the entire campaign. The estimated total eBC deposition amount was 1083.2–10,832.1 μg eBC m−2 during the whole sampling period, which corresponds to 3.6–36.1 μg eBC m−2 day−1. The eBC deposition rate showed clear seasonal patterns, with a maximum of 6.3–62.6 μg eBC m−2 day−1 for the pre-monsoon, followed by the post-monsoon (4.6–45.9 μg eBC m−2 day−1), winter (4.3–43.1 μg eBC m−2 day−1), and a minimum during the monsoon (2.4–24.5 μg eBC m−2 day−1). It should be noted that these values are representative of the lowest line of the actual eBC deposition due to our assumption of minimal range of deposition velocity.

We further estimated the eBC concentration in surface snow. Since the snow impurities are derived from dry deposition of atmospheric aerosols, the top layer of snow surface contains more impurities than the deeper part of the snow layer (Aoki et al. 2000, 2007). In this study, the concentration of eBC in the top layer of snow (e.g., 2-cm thickness) was estimated based on the assumption of uniform distribution for eBC in the top pure snow. Because eBC concentration is associated with snow water content, and snow density data in Lulang area are not available, the average snow density of 300 kg m−3 at the Parlong No. 4 Glacier was used (Yang et al. 2011). The glacier, located in the upper Yarlung Zangbu River Basin of southeast TP, has an area of ~ 11.7 km2 and a length of nearly 8 km. If the total eBC of 1083.2–10,832.1 μg eBC m−2 during the entire campaign was deposited on 2-cm thickness of pure snow, the eBC concentration in the snow surface would be 180.5–1805.4 μg kg−1. The seasonal variations for eBC concentrations in the snow surface showed that the highest value was obtained during the monsoon (61.5–614.7 μg kg−1), followed by the winter (58.8–587.6 μg kg−1) and pre-monsoon (33.3–333.2 μg kg−1), with the minimum value for post-monsoon (27.0–269.9 μg kg−1). Zhang et al. (2017) collected surface snow and snowpit samples from different glaciers in the southeastern TP in June 2015, and found that the eBC concentrations were 97.3, 318, and 125 μg kg−1 in the snow point samples collected from Dongga, Renlongba, and Demula glaciers, respectively. The amounts of eBC in the fresh snow and snowpits were generally lower than those in the aged snow and surface granular ice. Although direct comparison is not suitable for various studies due to their different analytical methodologies, sampling dates, and snow conditions, the high eBC concentrations in the southeastern glaciers indicate that the polluted air masses transported from South Asia may have a significant impact on these glacier melts.

Snow albedo is an important factor influencing glacier surface energy balance (Yang et al. 2011). To further evaluate the potential change in snow albedo caused by the deposited eBC, we used a simple linear regression equation derived from reduced albedo and BC concentration ([BC]) in snow (Ming et al. 2009):
$${\text{Reduced albedo}} = 0.0757 \times \left[ {\text{BC}} \right] + 0.0575,$$
where the units of reduced albedo and [BC] are % and µg kg−1, respectively. Based on the concentrations of eBC in the top layer of snow during the four seasons, the albedos reduced by 2.6–25.3, 4.7–46.6, 2.1–20.5, and 4.5–44.5% for the pre-monsoon, monsoon, post-monsoon, and winter, respectively. Due to the assumption of minimal range of deposition velocity, our estimation on snow albedo reductions may represent the lower boundary of the real condition. Moreover, the deposition of additional mineral dust onto snow surface also reduces the surface albedo, and thereby increases snow melting. For example, Zhang et al. (2017) used the SNICAR model to simulate the glacier albedo reduction in the southeastern TP and found that the albedo reduction for aged snow/granular ice caused by BC and dust ranged from ~ 10–19 and ~ 6–15%, respectively. Kaspari et al. (2014) indicates that BC concentrations in the winter–spring snow/ice horizons at Mera La (5400 m a.s.l., Nepal) reduced albedo by 6–10%, whereas dust can reduce albedo by 40–42%. Naturally, further field-based measurements combined with model studies for BC and mineral dust deposition onto TP regions and their impacts on snow albedo feedback are in urgent demand.

4 Conclusions

Surface eBC concentrations were measured with an aethalometer at Lulang, a high-altitude remote site in the southeastern TP, from July 2008 to July 2009. The bivariate polar plots for eBC mass concentrations showed obviously seasonal patterns. For the pre-monsoon season, the large eBC values (> 0.65 µg m−3) were mainly associated with the low wind speed (< 2 m s−1) or with the high wind speed (> 4 m s−1) from southeast, indicating the effects of local accumulation and regional transport. For the monsoon season, high eBC values (> 0.4 µg m−3) occasionally occurred at wind speed > 4 m s−1 from the southeast. For the post-monsoon season, large eBC loadings (> 0.6 µg m−3) were measured at low wind speed < 2 m s−1 or high wind speed > 6 m s−1 from the northeast or southeast. For the winter season, the high eBC values (> 0.6 µg m−3) were concentrated at wind speed < 2 m s−1, indicating that eBC pollution may be mainly caused by local emissions. The CWT analysis together with the distributions of the BC column mass density showed that sources located to the southwest of Lulang were the most important for eBC pollution during the pre-monsoon and monsoon. However, the possible source region for eBC pollution during the post-monsoon shifted to the east and southeast of Lulang, indicating the importance of biofuel burning emissions from the interior of the TP. During the winter, the largest potential source region for eBC pollution was located in north India and northwest Nepal.

The estimated total eBC deposition amount was 1083.2–10,832.1 μg eBC m−2, which corresponds to 3.6–36.1 μg eBC m−2 day−1. The eBC deposition rate showed clear seasonal variations, with a maximum of 6.3–62.6 μg eBC m−2 day−1 for the pre-monsoon, followed by the post-monsoon (4.6–45.9 μg eBC m−2 day−1) and winter (4.3–43.1 μg eBC m−2 day−1), and a minimum during the monsoon (2.4–24.5 μg eBC m−2 day−1). The average eBC concentration on 2-cm thickness of pure snow was estimated to be highest for the monsoon (61.5–614.7 μg kg−1), followed by the winter (58.8–587.6 μg kg−1) and pre-monsoon (33.3–333.2 μg kg−1), with the minimum value for the post-monsoon (27.0–269.9 μg kg−1). Based on these eBC concentrations in the top layer of snow, the albedos reduced by 2.6–25.3, 4.7–46.6, 2.1–20.5, and 4.5–44.5% for the pre-monsoon, monsoon, post-monsoon, and winter, respectively.



This work was supported by the National Natural Science Foundation of China (41230641, 41503118, and 41661144020). The authors are grateful to the Integrated Observation and Research Station for Alpine Environment in South-East Tibet, Chinese Academy of Sciences, for their assistance with field sampling.

Compliance with Ethical Standards

Conflict of interest

On behalf of all the authors, the corresponding author states that there is no conflict of interest.

Supplementary material

41810_2017_16_MOESM1_ESM.docx (22 kb)
Supplementary material 1 (DOCX 21 kb)


  1. Aoki T, Aoki T, Fukabori M, Hachikubo A, Tachibana Y, Nishio F (2000) Effects of snow physical parameters on spectral albedo and bidirectional reflectance of snow surface. J Geophys Res-Atmos 105:10219–10236. CrossRefGoogle Scholar
  2. Aoki T, Motoyoshi H, Kodama Y, Yasunari TJ, Sugiura K (2007) Variations of the snow physical parameters and their effects on albedo in Sapporo, Japan. Ann Glaciol 46:375–381. CrossRefGoogle Scholar
  3. Bond TC et al (2013) Bounding the role of black carbon in the climate system: a scientific assessment. J Geophys Res-Atmos 118:5380–5552. CrossRefGoogle Scholar
  4. Cao JJ, Tie XX, Xu BQ, Zhao ZZ, Zhu CS, Li GH, Liu SX (2010) Measuring and modeling black carbon (BC) contamination in the SE Tibetan Plateau. J Atmos Chem 67:45–60. CrossRefGoogle Scholar
  5. Cao JJ et al (2012) Impacts of aerosol compositions on visibility impairment in Xi’an, China. Atmos Environ 59:559–566. CrossRefGoogle Scholar
  6. Ding AJ et al (2016) Enhanced haze pollution by black carbon in megacities in China. Geophys Res Lett 43:2873–2879. CrossRefGoogle Scholar
  7. Draxler RR, Rolph GD (2003) HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) Model access via NOAA ARL READY Website http://www.arlnoaagov/ready/hysplit4htmlNOAA Air Resources Laboratory, Silver Spring, MD
  8. Fast JD et al (2007) A meteorological overview of the MILAGRO field campaigns. Atmos Chem Phys 7:2233–2257. CrossRefGoogle Scholar
  9. Grahame TJ, Klemm R, Schlesinger RB (2014) Public health and components of particulate matter: the changing assessment of black carbon. J Air Waste Manag Assoc 64:620–660. CrossRefGoogle Scholar
  10. Grieshop AP, Reynolds CCO, Kandlikar M, Dowlatabadi H (2009) A black-carbon mitigation wedge. Nature Geosci 2:533–534. CrossRefGoogle Scholar
  11. Gupta P, Singh SP, Jangid A, Kumar R (2017) Characterization of black carbon in the ambient air of Agra, India: seasonal variation and meteorological influence. Adv Atmos Sci 34:1082–1094. CrossRefGoogle Scholar
  12. Hansen ADA, Rosen H, Novakov T (1984) The aethalometer—An instrument for the real-time measurement of optical absorption by aerosol particles. Sci Total Environ 36:191–196. CrossRefGoogle Scholar
  13. Hsu Y-K, Holsen TM, Hopke PK (2003) Comparison of hybrid receptor models to locate PCB sources in Chicago. Atmos Environ 37:545–562. CrossRefGoogle Scholar
  14. Immerzeel WW, van Beek LPH, Bierkens MFP (2010) Climate change will affect the Asian water towers. Science 328:1382–1385. CrossRefGoogle Scholar
  15. Jacobi H-W et al (2015) Black carbon in snow in the upper Himalayan Khumbu Valley, Nepal: observations and modeling of the impact on snow albedo, melting, and radiative forcing. Cryosphere 9:1685–1699. CrossRefGoogle Scholar
  16. Jacobson MZ (2001) Strong radiative heating due to the mixing state of black carbon in atmospheric aerosols. Nature 409:695–697. CrossRefGoogle Scholar
  17. Jacobson MZ (2010) Short-term effects of controlling fossil-fuel soot, biofuel soot and gases, and methane on climate, Arctic ice, and air pollution health. J Geophys Res-Atmos 115:D14209. CrossRefGoogle Scholar
  18. Jansen KL, Larson TV, Koenig JQ, Mar TF, Fields C, Stewart J, Lippmann M (2005) Associations between health effects and particulate matter and black carbon in subjects with respiratory disease. Environ Health Perspect 113:1741–1746. CrossRefGoogle Scholar
  19. Kaspari S, Painter TH, Gysel M, Skiles SM, Schwikowski M (2014) Seasonal and elevational variations of black carbon and dust in snow and ice in the Solu-Khumbu, Nepal and estimated radiative forcings. Atmos Chem Phys 14:8089–8103. CrossRefGoogle Scholar
  20. Ke L, Ding X, Li W, Qiu B (2017) Remote sensing of glacier change in the central Qinghai-Tibet Plateau and the relationship with changing climate. Remote Sens 9:114. CrossRefGoogle Scholar
  21. Kopacz M, Mauzerall DL, Wang J, Leibensperger EM, Henze DK, Singh K (2011) Origin and radiative forcing of black carbon transported to the Himalayas and Tibetan Plateau. Atmos Chem Phys 11:2837–2852. CrossRefGoogle Scholar
  22. Lee W-S, Bhawar RL, Kim M-K, Sang J (2013) Study of aerosol effect on accelerated snow melting over the Tibetan Plateau during boreal spring. Atmos Environ 75:113–122. CrossRefGoogle Scholar
  23. Li Z, Tian L, Wu H, Wang W, Zhang S, Zhang J, Li X (2016) Changes in glacier extent and surface elevations in the Depuchangdake region of northwestern Tibet, China. Quat Res 85:25–33. CrossRefGoogle Scholar
  24. Loibl D, Lehmkuhl F, Grießinger J (2014) Reconstructing glacier retreat since the Little Ice Age in SE Tibet by glacier mapping and equilibrium line altitude calculation. Geomorphology 214:22–39. CrossRefGoogle Scholar
  25. Lu Z, Streets DG, Zhang Q, Wang S (2012) A novel back-trajectory analysis of the origin of black carbon transported to the Himalayas and Tibetan Plateau during 1996-2010. Geophys Res Lett 39:L01809. Google Scholar
  26. Ménégoz M et al (2014) Snow cover sensitivity to black carbon deposition in the Himalayas: from atmospheric and ice core measurements to regional climate simulations. Atmos Chem Phys 14:4237–4249. CrossRefGoogle Scholar
  27. Ming J, Xiao C, Cachier H, Qin D, Qin X, Li Z, Pu J (2009) Black carbon (BC) in the snow of glaciers in west China and its potential effects on albedos. Atmos Res 92:114–123. CrossRefGoogle Scholar
  28. Ming J, Wang P, Zhao S, Chen P (2013) Disturbance of light-absorbing aerosols on the albedo in a winter snowpack of Central Tibet. J Environ Sci 25:1601–1607. CrossRefGoogle Scholar
  29. Nair VS, Babu SS, Moorthy KK, Sharma AK, Marinoni A, Ajai (2013) Black carbon aerosols over the Himalayas: direct and surface albedo forcing. Tellus Ser B-Chem Phys Meteorol 65:19738. CrossRefGoogle Scholar
  30. Nenes A, Conant WC, Seinfeld JH (2002) Black carbon radiative heating effects on cloud microphysics and implications for the aerosol indirect effect—2. Cloud microphysics. J Geophys Res-Atmos 107:4605. CrossRefGoogle Scholar
  31. Petzold A et al (2013) Recommendations for reporting “black carbon” measurements. Atmos Chem Phys 13:8365–8379. CrossRefGoogle Scholar
  32. Ping X, Jiang Z, Li C (2011) Status and future perspectives of energy consumption and its ecological impacts in the Qinghai–Tibet region. Renew Sust Energ Rev 15:514–523. CrossRefGoogle Scholar
  33. Pryor SC et al (2008) A review of measurement and modelling results of particle atmosphere–surface exchange Tellus Ser. B-Chem Phys Meteorol 60:42–75. CrossRefGoogle Scholar
  34. Qiu J (2008) China: the third pole. Nature 454:393–396. CrossRefGoogle Scholar
  35. Qu B et al (2014) The decreasing albedo of the Zhadang glacier on western Nyainqentanglha and the role of light-absorbing impurities. Atmos Chem Phys 14:11117–11128. CrossRefGoogle Scholar
  36. Quinn PK et al (2008) Short-lived pollutants in the Arctic: their climate impact and possible mitigation strategies. Atmos Chem Phys 8:1723–1735. CrossRefGoogle Scholar
  37. Ram K, Sarin MM (2009) Absorption coefficient and site-specific mass absorption efficiency of elemental carbon in aerosols over urban, rural, and high-altitude sites in India. Environ Sci Technol 43:8233–8239. CrossRefGoogle Scholar
  38. Ramanathan V, Carmichael G (2008) Global and regional climate changes due to black carbon. Nat Geosci 1:221–227. CrossRefGoogle Scholar
  39. Santos F, Fraser MP, Bird JA (2014) Atmospheric black carbon deposition and characterization of biomass burning tracers in a northern temperate forest. Atmos Environ 95:383–390. CrossRefGoogle Scholar
  40. Shindell D et al (2012) Simultaneously mitigating near-term climate change and improving human health and food security. Science 335:183–189. CrossRefGoogle Scholar
  41. Virkkula A, Mäkelä T, Hillamo R, Yli-Tuomi T, Hirsikko A, Hämeri K, Koponen IK (2007) A simple procedure for correcting loading effects of aethalometer data. J Air Waste Manage Assoc 57:1214–1222. CrossRefGoogle Scholar
  42. Wang C (2004) A modeling study on the climate impacts of black carbon aerosols. J Geophys Res-Atmos 109:D03106. Google Scholar
  43. Wang YQ, Zhang XY, Arimoto R (2006) The contribution from distant dust sources to the atmospheric particulate matter loadings at Xi’an. China during spring Sci Total Environ 368:875–883. CrossRefGoogle Scholar
  44. Wang Q et al (2013) Long-term trends in visibility and at Chengdu, China. PLoS One 8:e68894. CrossRefGoogle Scholar
  45. Wang R et al (2014) Exposure to ambient black carbon derived from a unique inventory and high-resolution model. Proc Natl Acad Sci USA 111:2459–2463. CrossRefGoogle Scholar
  46. Weingartner E, Saathoff H, Schnaiter M, Streit N, Bitnar B, Baltensperger U (2003) Absorption of light by soot particles: determination of the absorption coefficient by means of aethalometers. J Aerosol Sci 34:1445–1463. CrossRefGoogle Scholar
  47. William KML, Maeng-Ki K, Kyu-Myong K, Woo-Seop L (2010) Enhanced surface warming and accelerated snow melt in the Himalayas and Tibetan Plateau induced by absorbing aerosols. Environ Res Lett 5:025204. CrossRefGoogle Scholar
  48. Xu BQ et al (2009a) Black soot and the survival of Tibetan glaciers. Proc Natl Acad Sci USA 106:22114–22118. CrossRefGoogle Scholar
  49. Xu BQ et al (2009b) Deposition of anthropogenic aerosols in a southeastern Tibetan glacier. J Geophys Res-Atmos 114:D17209. CrossRefGoogle Scholar
  50. Xu Y, Ramanathan V, Washington WM (2016) Observed high-altitude warming and snow cover retreat over Tibet and the Himalayas enhanced by black carbon aerosols. Atmos Chem Phys 16:1303–1315. CrossRefGoogle Scholar
  51. Yang W, Guo X, Yao T, Yang K, Zhao L, Li S, Zhu M (2011) Summertime surface energy budget and ablation modeling in the ablation zone of a maritime Tibetan glacier. J Geophys Res-Atmos 116:D14116. CrossRefGoogle Scholar
  52. Yang S, Xu B, Cao J, Zender CS, Wang M (2015) Climate effect of black carbon aerosol in a Tibetan Plateau glacier. Atmos Environ 111:71–78. CrossRefGoogle Scholar
  53. Zhang Y et al (2017) Light-absorbing impurities enhance glacier albedo reduction in the southeastern Tibetan plateau. J Geophys Res-Atmos. Google Scholar
  54. Zhao Z et al (2013) Aerosol particles at a high-altitude site on the Southeast Tibetan Plateau, China: implications for pollution transport from South Asia. J Geophys Res-Atmos 118:11360–11375. CrossRefGoogle Scholar
  55. Zhao S, Tie X, Long X, Cao J (2017) Impacts of Himalayas on black carbon over the Tibetan Plateau during summer monsoon. Sci Total Environ 598:307–318. CrossRefGoogle Scholar

Copyright information

© Institute of Earth Environment, Chinese Academy Sciences 2017

Authors and Affiliations

  • Qiyuan Wang
    • 1
  • Zhuzi Zhao
    • 1
  • Jie Tian
    • 2
  • Chongshu Zhu
    • 1
  • Haiyan Ni
    • 1
  • Yonggang Zhang
    • 1
  • Ningning Zhang
    • 1
  • Zhenxing Shen
    • 2
  • Yongming Han
    • 1
    • 3
  • Junji Cao
    • 1
    • 4
  1. 1.Key Laboratory of Aerosol Chemistry and Physics, State Key Laboratory of Loess and Quaternary GeologyInstitute of Earth Environment, Chinese Academy of SciencesXi’anChina
  2. 2.Department of Environmental Sciences and EngineeringXi’an Jiaotong UniversityXi’anChina
  3. 3.School of Human Settlements and Civil EngineeringXi’an Jiaotong UniversityXi’anChina
  4. 4.Institute of Global Environmental Change, Xi’an Jiaotong UniversityXi’anChina

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