Black carbon (BC), water-insoluble organic carbon (OC), and mineral dust are
important particles in snow and ice which significantly reduce albedo and
accelerate melting. Surface snow and ice samples were collected from the
Karakoram–Himalayan region of northern Pakistan during 2015 and 2016 in
summer (six glaciers), autumn (two glaciers), and winter (six mountain
valleys). The average BC concentration overall was
2130
Carbon is an essential component of atmospheric aerosols, where it appears
in the form of black carbon (BC, also known as elemental carbon, EC), and
organic carbon (OC). BC is emitted into the atmosphere from the incomplete
combustion of carbon-based fuels (mainly fossil fuels and biomass)
(Jacobson, 2004), while OC can be directly emitted into or formed in the
atmosphere. After deposition on snow and ice surfaces, BC particles
significantly reduce the snow albedo (hemispheric reflectance) in the
visible part of the electromagnetic spectrum, cause snow albedo feedback
(Doherty et al., 2013), enhance solar radiation absorption (Warren and
Wiscombe, 1980), and accelerate snow melting (Hansen and Nazarenko, 2004).
BC, both in air and deposited on snow, is important in net positive forcing
of the climate. Clean snow is one of the most reflective natural surfaces on
Earth at the ultraviolet and visible wavelengths, while BC is the most
efficient light-absorbing species in the visible spectral range (Horvarth,
1993). With regard to BC, 1 ng g
Increased BC mass concentration and deposition on the Tibetan glaciers over
the last 20 years (Xu et al., 2009) have played a significant role in rapid
glacier melting in the region (Xu et al., 2012; Yao et al., 2012). A high
concentration of aerosol has deposited on the snow surface and increased the
BC content in snow over the southern edge of the Tibetan Plateau to the
north of the Himalayas (Gertler et al., 2016). The southern slope of the
Himalayas is relatively even more exposed to BC due to emissions from India
and transport through southwesterly and westerly winds (Xu et al., 2009;
Yasunari et al., 2010). BC deposited on snow in the Himalayan region induces
an increase in net shortwave radiation at the snow surface with an annual
mean of about 1 to 3 W m
At present, south and east Asia are considered to be the two largest BC emission regions in the world and are likely to remain so (Menon et al., 2010). BC transported from east Asia can be lifted high and moved towards the northeast during the summer monsoon season (Zhang et al., 2015; Cong et al., 2015; Lüthi et al., 2015), affecting the life of glaciers and snow-covered areas.
Research into the glaciers of the extended Himalayan region and Tibetan Plateau has prime importance because these glaciers act as a water storage tower for south and east Asia, and shrinking could affect the water resources for up to 1 billion people (Immerzeel et al., 2010). The glaciated area in northern Pakistan may be more exposed to BC effects than that in other regions because it can potentially receive emissions generated from both south and central Asia as well as from the Middle East. Meltwater coming from these glaciers flows into the river Indus, which has major economic importance for the people of Pakistan.
A number of authors have described the concentration and impacts of light-absorbing particles in the Tibetan glaciers (for example Qian et al., 2015; Wang et al., 2015; Zhang et al., 2017; Li et al., 2017; Niu et al., 2017). However, until now, no studies have been published relating to the concentration of light-absorbing aerosols in the surface snow and ice of northern Pakistan, and although several authors have investigated transport pathways over the Himalayan region (e.g., Babu et al., 2011 for the western trans-Himalayas; Lu et al., 2012 for the Tibetan Plateau and Himalayas), little is known about the potential sources and transport pathways of pollutants affecting the Pakistan area.
In this study, we looked at the concentration of light-absorbing particles (BC, OC, dust) in snow and ice in northern Pakistan, their impact on snow albedo and radiative forcing, and the likely source regions. Albedo was estimated from the BC and dust concentrations identified in collected samples of snow and ice using the online snow albedo simulation Snow, Ice, and Aerosol Radiation (SNICAR) model (Flanner et al., 2009). Radiative forcing was calculated from the albedo reduction obtained from the SNICAR model together with the incident shortwave solar radiation obtained from the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model. The frequency distribution of aerosol subtypes (smoke, polluted continental, dust, and others) in the atmosphere over the study area was calculated for the snow and ice sampling periods using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite data from 2006 to 2014 as a further indication of the types of aerosol contributing to the observed deposition. The potential source regions of pollutants were identified using spatial variance in wind vector maps prepared using Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data, calculation of back air trajectories using the HYSPLIT-4 (Hybrid Single Particle Lagrangian Integrated Trajectory) model, and a simple region-tagged chemical transport model (Weather Research and Forecast, WRF-STEM). The back air trajectories approach has been used in many studies to identify possible source regions for atmospheric and deposited BC (Zhang et al., 2013). Pollutant source regions identified using the different approaches were compared and the most likely source regions of the pollutants identified.
The study area and sampling sites:
The study area was located around 35.40
A total of 50 surface ice and 49 snow samples were collected from the glaciers in summer 2015 and 2016 (Passu – 15; Gulkin – 31; Barpu – 6; Mear – 8; Sachin – 35; Henarche – 4) and 13 (total of ice and snow samples combined) in autumn 2016 (Gulkin – 7; Sachin – 6) at elevations ranging from 2569 to 3895 m a.s.l. (Fig. 1). Eighteen snow samples were collected in winter 2015 and 2016 from nearby mountain valleys at elevations of 1958 to 2698 m a.s.l.; the winter sampling region was divided into six sites (S1 to S6) based on geographical location and elevation (Fig. 1). Samples were collected using the “clean hands – dirty hands” principle (Fitzgerald, 1999). Ice samples were collected from the surface (5 cm depth) at different points on the glaciers. The elevation difference between collection points on the same glacier ranged from 30 to 100 m.
The samples were preserved in ultraclean plastic bags, allowed to melt in a temporary laboratory near the sampling location, and filtered through quartz filters immediately after melting. An electric vacuum pump was used to accelerate filtration. The melted snow/ice volume of the samples was measured using a graduated cylinder. Sampled filters were carefully packed inside petri slides marked with a unique code representing the sample.
The snow density of winter snow samples was measured using a balance,
snow/ice grain sizes were observed with an accuracy of 0.02 mm using a hand
lens (25
Before analysis, sampled filters were allowed to dry in an oven for 24 h and then weighed using a microbalance. The dust mass on the filters was calculated from the mass difference in weight before and after sampling (Kaspari et al., 2014; Li et al., 2017).
There are many methods available for analyzing BC and OC. The three methods
considered the most effective for measuring BC and water-insoluble OC
concentrations in snow are thermal optical analysis, filter-based analysis,
and single-particle soot photometer analysis (Ming et al., 2008). The
thermal optical (filter-based) analysis method has been used by many
researchers (e.g., Li et al., 2017) and was chosen for the study. This is an
indirect method for measuring BC and OC on sampled filters; it follows
Beer's law and uses the stepwise combustion of the particles deposited on quartz
filters (Boparai et al., 2008), followed by the measurement of light
transmission and/or reflectance of the filters. The BC and OC content in the
collected samples was measured using a thermal optical Desert Research Institute (DRI) carbon analyzer,
similar to the Interagency Monitoring of PROtected Visual Environments (IMPROVE) protocol (Cao et al., 2003). The temperature
threshold applied to separate the two species is described in M. Wang et al. (2012). A few (
The CALIPSO models also define multiple aerosol subtypes – clean
continental, clean marine, dust, polluted continental, polluted dust, smoke,
and other – using the 532 nm (1064 nm) extinction-to-backscatter ratio. The
frequency of these different aerosol subtypes in the atmosphere over the
study region was investigated using CALIPSO data for the same months in
which ice and snow samples were collected, i.e., January, May, June, and
December, over the period June 2006 to December 2014. The CALIPSO Level 2
lidar vertical feature mask data product describes the vertical and
horizontal distribution of clouds and aerosol layers (downloaded from
The frequencies of different subtypes were calculated along the specific paths followed by CALIPSO over the study region.
Snow albedo was estimated for each of the 18 winter samples and the average
calculated for samples at each of the sites (1 to 6). Albedo from two sites
– S1 (Sost), which had the highest average concentration of BC and dust,
and S6 (Kalam), which had the lowest average concentration of BC and dust –
was further explored using the SNICAR model (Flanner et al., 2007). The aim
was to quantify the effect of BC, dust, and mass absorption cross section
(MAC) on albedo reduction. Sensitivity model experiments were carried out
using various combinations of BC, dust, and MAC values, while other
parameters were kept constant (parameters for sites 1 and 6 shown in the
Supplement, Table S1). Snow albedo was simulated for different
daylight times, with the solar zenith angle (SZA) set in the range 57.0–88.9
Three methods were used to identify the potential source regions of pollutants found at the study site: wind maps, emissions inventory coupled with back trajectories, and a region-tagged chemical transport modeling analysis.
Wind vector maps were prepared using MERRA-2 reanalysis data (available from
the National Aeronautics and Space Administration (NASA):
Air trajectories were calculated backwards from the sampling sites (S1:
36.40
The WRF-STEM model was used as a third approach for identifying the origin
(source regions) of air masses carrying pollutants. The WRF-STEM model uses
region-tagged carbon monoxide (CO) tracers for many regions in the world to
identify geographical areas contributing to observed pollutants (Adhikary et
al., 2010). Region-tagged CO tracers are used as a standard air quality
modeling tool in various regional and global chemical transport models to
identify pollution source regions (Chen et al., 2009; Park et al., 2009; Lamarque and Hess, 2003). The
WRF-STEM model domain was centered on 50.377
Concentration of black carbon, organic carbon, and dust in summer, autumn, and winter samples in 2015 and 2016.
The minimum, maximum, and average concentrations of BC, OC, and dust in the ice and snow samples are given in Table 1.
The OC and BC concentration values were blank corrected by subtracting the
average value of the field blanks. Blank concentrations were used to
calculate detection limits as mean
The lowest BC (82 ng g
The highest average concentration of BC was found in autumn samples from the Sachin glacier and the highest average concentration of OC in summer samples from the same glacier. The average concentration of BC was much greater in autumn than in summer on the Sachin glacier but somewhat greater in summer than in autumn on the Gulkin glacier, indicating highly spatiotemporal patterns in the deposition of particles. The marked difference on the Sachin glacier may have reflected the difference in the direction of air, which comes from Iran and Afghanistan in summer and the Bay of Bengal via India in autumn, with the generally lower deposition on the Gulkin glacier more affected by other factors (such as slope aspect of the glacier and status of local emissions near the glacier). There was no clear correlation between the average BC concentration in glacier samples and glacier elevation. However, winter snow samples showed a weak increasing trend in average BC with site elevation (Table 1, Fig. S3).
Most summer samples were collected from surface ice (Fig. S2a), but a few samples for Gulkin and Sachin were collected from aged snow on the glacier surface (Fig. S2b, c). Dust was visible on the relatively aged snow, and the BC and OC concentrations in these snow samples were much higher than those in ice. The highest average BC values in winter were also observed in aged snow (from Sost) and the lowest in fresh snow (from Kalam) (Table 1). Generally, snow samples collected within 24 h of a snowfall event were considered to be fresh snow.
We analyzed the ratios of OC to BC in the different samples as in
atmospheric fractions; this can be used as an indicator of the emission
source, although apportionment is not simple and only indicative. The BC
fraction is emitted during the combustion of fossil fuels, especially biomass
burning in rural areas in winter, and urban emissions from road transport.
The OC fraction can be directly emitted to the atmosphere as particulate
matter (primary OC) from fossil fuel emissions, from biomass burning, or in the
form of biological particles or plant debris; it can also be generated in
the atmosphere as gases are converted to particles (secondary OC). In
general, lower OC
Frequency distribution of aerosol subtypes in the atmosphere over the study region calculated from CALIPSO data; average for the study months in 2006 to 2014.
However, these results should be considered with caution. There are a number of
factors that can affect the OC
A wide range of values has been reported by different authors for BC concentrations in snow and ice samples from different regions (Table S2). The concentrations of BC in our samples were higher than those reported by many authors (Table S2) but were comparable with the results reported by Xu et al. (2012) in the Tien Shan, by Li et al. (2016) in the northeast of the Tibetan Plateau, by Wang et al. (2017) in northern China, and by Zhang et al. (2017) in western Tien Shan, central Asia. High concentrations indicate high deposition rates on the snow and ice surface, but there are several possible reasons for a wide variation in values apart from differences in deposition rates, including differences in sampling protocols, geographical/sampling location and elevation of sampling site (Qu et al., 2014), and year/season of sampling. The majority of samples were from the ablation zone of the glaciers. Strong melting of surface snow and ice in the ablation zone could lead to BC enrichment and high-BC concentrations, as observed by Li et al. (2017) for glaciers on the southern Tibetan Plateau. The sampling season (May to September in our study) is an important factor because rapid enrichment occurs as snowmelts during the melting season. The peak melting period is May to August/September; thus, the concentration of BC, OC, and dust in our samples would have been increased as melting progressed due to the enrichment in melting snow and scavenging by the melting water. In most cases, snow and ice samples were collected quite a long time after snow fall, and the concentration of pollutants would also have increased in the surface snow and ice due to dry deposition. It seems likely that the pollutants in surface samples would be affected by sublimation and deposition until the next melt season (Yang et al., 2015). In some of the cases in our study, the average concentration of BC, OC, and/or dust for a particular glacier/site was increased as a result of a single highly concentrated sample, reflecting the wide variation that results from the interplay of many factors.
Enrichment is more marked at lower elevations as the temperatures are higher, which enhances melting and ageing of surface snow, while deposition also tends to be higher because the pollutant concentrations in the air are higher (J. Wang et al., 2012; Nair et al., 2013). Previous studies have tended to focus on the accumulation area of glaciers (e.g., ice cores and snow pits), where enrichment influences are less marked, and on high-elevation areas, where deposition is expected to be lower, in both cases leading to lower values. In our study, the majority of samples collected in summer and autumn were collected from the ablation area of debris-covered glaciers where enrichment influences are marked due to the relatively high temperature, and this is reflected in the relatively high values of BC, OC, and dust. Li et al. (2017) showed a strong negative relationship between the elevation of glacier sampling locations and the concentration of light-absorbing particles. Stronger melt at lower elevations leads to higher pollutant concentrations in the exposed snow. Equally, BC may be enriched in the lower-elevation areas of glaciers as a result of the proximity to source areas as well as by the higher temperatures causing greater melting. Thus, the main reason for the high concentrations of BC, OC, and dust in our samples may have been that the samples were taken from relatively low-elevation sites. Human activities near the sampling sites in association with the summer pilgrimage season probably also contributed to an increase in pollutant concentrations. Our results do not necessarily indicate that all the glaciers in the Karakoram region are substantially darkened by BC. The ablation zones of debris-covered glaciers which are at relatively low elevations and near pollution sources may be more polluted than other glacier areas.
The analysis of aerosol types using the CALIPSO data identified smoke as the most frequent aerosol type over the study region in both summer and winter, indicating that biomass burning may be the dominant source of emissions. Figure 2 shows the average frequency of different aerosol types in May–June (summer) and December–January (winter) over the period 2006 to 2014 in the form of a box plot. The frequency of different aerosol subtypes in June from 2006 to 2014 is shown in Fig. S4; smoke had the highest frequency (39 %), followed by dust (21 %), polluted dust (12 %), and other (20 %). This type of aerosol measurement in the atmosphere was useful for our current study because it provides observation-based data over the study region, whereas the other approaches used (such as modeling) were based on interpolation not observation. Pollutant deposition depends on the concentration of pollutants in the atmosphere, and the results are consistent with the high concentration of BC (from smoke) and dust particles in the glacier and snow surface samples.
Spectral variation in albedo for winter sampling sites and selected
mass absorption cross-section (MAC) values:
The albedo of individual winter snow samples was calculated using the SNICAR
model and then averaged for each site (S1 to S6). Figure 3a shows the average
for each site across the visible and infrared spectrum. Two sites were chosen
for further analysis: S1 (Sost), which had the highest average concentration
of BC, and S6 (Kalam), which had the lowest average concentration of BC. The
albedo was simulated for selected MAC values and SZA for samples at the two
sites, as described in Sect. 2.4 (“Methods”). The values for the
average albedo of samples from the two sites simulated for MAC values of 7.5,
11, and 15 m
The percentage change in albedo was calculated in absolute terms as the
change between albedo values with a pollutant (BC or dust or both) and a
reference albedo value with zero pollutants (zero BC and dust
concentration). Table 2 shows the calculated percentage reductions in daily
minimum, maximum, and mean broadband snow albedo at different MAC values
(7.5, 11, 15 m
Both the snow albedo and the impact of light-absorbing particles depend on a range of factors including the SZA, snow depth, snow grain size, and snow density. For example, the snow albedo reduction due to BC is known to be less in the presence of other light-absorbing particles as these will absorb some of the available solar radiation (Kaspari et al., 2011). The snow albedo calculated for our samples was strongly dependent on the SZA with albedo increasing with decreasing SZA, especially at near-infrared wavelengths (Table S3).
The impact of snow ageing was also investigated. The winter samples from S1 (Sost) were aged snow, whereas those from S6 (Kalam) were fresh snow (Table 1, Fig. S5b, c). Not only was dust clearly visible on the surface of the aged snow, the grain size was large and the snow was dense. The aged snow had a much higher concentration of BC and dust, which reduced the albedo, but the extent of reduction is also affected by other factors. Albedo reduction by BC and dust particles is known to be greater for aged snow than for fresh snow (Warren and Wiscombe, 1985). In our samples, the calculated reduction in snow albedo for high MAC values (15) compared to low MAC values (7.5) was greater in aged snow than in fresh snow (Fig. 3b). The effective grain size of snow increases with time as water surrounds the grains. Snow with a larger grain size absorbs more radiation because the light can penetrate deeper into the snowpack, thus decreasing surface albedo (Flanner and Charles, 2006) . In the melting season, the snowpack becomes optically thin and more particles are concentrated near the surface layer, which further increases the effect on albedo.
Snow albedo reduction (%) by black carbon, dust, and black carbon plus dust at the site with the lowest average pollutant concentration (S6) and the site with the highest average pollutant concentration (S1) under different mass absorption cross-section (MAC) values.
The estimated reduction in snow albedo by dust and BC (up to 29 % of the daytime maximum value, Table 2) was higher than that reported by others for high Asia based on farmers' recordings (e.g., 1.5 to 4.6 % reported by Nair et al., 2013) and in the Himalayas (Ming et al., 2008; Kaspari et al., 2014; Gertler et al., 2016). However, although the values were relatively high, they were at the same level or lower than the estimates for an albedo reduction of 28 % by BC and 56 % by dust in clean ice samples and of 36 % by BC and 29 % by dust in aged snow samples, reported by Qu et al. (2014) for surface samples from the Zhadang glacier, China. Simulation results by Ming et al. (2013) showed BC, dust, and grain growth to reduce the broadband albedo by 11, 28, and 61 %, respectively, in a snowpack in central Tibet. Dust was the most significant contributor to albedo reduction when mixed inside the snow and ice or when the glacier was covered in bare ice. In our case BC was a more influential factor than dust during a similar study period to that reported by Li et al. (2017), indicating that BC plays a major role in albedo reduction.
The possible reasons for the relatively high values for albedo reduction in our samples include the lower elevation of the sampling locations, relatively high concentrations of BC and dust, high MAC values, low snow thickness, underlying ground quality, the presence of small and large towns near the sampling sites, and the predominance of aged snow samples. Most of the samples collected in winter were from places with a snow depth of less than 50 cm (Fig. S5a); thus, mud, stones, and clay below the snow layer would be expected to increase the absorption of solar radiation and reduce the albedo.
The high albedo reduction in the visible range of the electromagnetic spectrum could be due to the relatively high concentration of surface snow impurities. The total amount of deposited particles in the surface layer of aged snow was relatively high, indicating a high deposition rate of atmospheric pollutants.
Flanner et al. (2007) reported that BC emission and snow ageing are the two
largest sources of uncertainty in albedo estimates. The uncertainties in our
estimated albedo reduction include the BC type (uncoated or sulfate coated),
the size distribution of dust concentration, the accuracy of snow grain
size, snow texture, snow density, and the albedo of the underlying ground.
Sulfate-coated particles have an absorbing sulfate shell surrounding the
carbon; recent studies confirm that coated BC has a larger absorbing power
than non-coated BC (Naoe et al., 2009). We used uncoated black carbon
concentration in the SNICAR model, but the pollutants at the remote site are
presumed to be mainly from long-range transport; thus, the BC may have gained
some coating. The albedo reduction for sulfate-coated black carbon was
calculated to be 3–8.5 % higher, depending on the MAC and SZA values,
than for uncoated black carbon at the low-concentration site S6 (Fig. S6).
The snow grain size (snow aging) and snow texture are also large sources of
uncertainty. The effect of snow grain size is generally larger than the
uncertainty in light-absorbing particles and varies with the snow type
(Schmale et al., 2017). The albedo reduction caused by 100 ng g
RF is a measure of the capacity of a forcing agent to affect the energy balance in the atmosphere – the difference between sunlight absorbed by the Earth and energy radiated back to space – thereby contributing to climate change. Changes in albedo contribute directly to radiative forcing: a decrease in albedo means that more radiation will be absorbed and the temperature will rise. In snow and ice, the additional energy absorbed by any pollutants present also increases and accelerates the melting rate.
Various authors have described the impact of albedo change in snow and ice
on radiative forcing. Zhang et al. (2017) reported that a reduction in
albedo by 9 to 64 % can increase the instantaneous radiative forcing
by as much as 24.05–323.18 W m
We calculated the radiative forcing in the samples assessed for daytime
albedo and daily (24 h) mean albedo. The radiative forcing at different
daylight times caused by BC deposition varied from 3.93 to 43.44 W m
Daily mean radiative forcing reduction and albedo reduction (%)
caused by black carbon and dust for different mass absorption cross-section
values (MAC) in
Both radiative forcing and albedo reduction increased with decreasing daytime SZA, indicating higher melting at midday compared to morning and evening. Figure 4 shows the daily mean albedo reduction and corresponding radiative forcing caused by BC for fresh (low-BC) and aged (high-BC) snow with different MAC values. Snow aging (snow grain size) plays an important role in albedo reduction and radiative forcing. According to Schmale et al. (2017) the effect of snow grain size is generally larger than the uncertainty in light-absorbing particles, which varies with snow type. Snow aging reduces snow albedo and accelerates snowmelt, but the impact of snow aging on BC in snow and the induced forcing is complex and includes spatial and seasonal variation (Qian et al., 2014).
An increase in MAC value from 7.5 to 15 led to an increase in radiative
forcing by 1.48 W m
Monthly average horizontal wind patterns at 850 hPa during
Figure 5 shows the spatial variance of wind vector maps (
Trajectory analysis using the HYSPLIT model showed that in May and June 2015, air parcels reached the study site along three different pathways: one from north Asia (Russia) via central Asia (Kazakhstan); one from western Asia (Cyprus and Syria) via central and southern Asia (Georgia),; and one via India, which was more local (Fig. 6). The trajectories in summer had distinct pathways, while those in winter were dispersed in all directions, partially covering west, east, and south Asia and completely covering central Asia. Figure 6 shows the product of extinction and emission calculated along the pathways of trajectories calculated using the vertical profile for aerosol extinction over the study region obtained from the monthly CALIPSO satellite-based extinction data. Scattering and absorption decreased exponentially with increasing elevation (Fig. S1) but were still visible at elevations above 5 km in summer.
Source contribution regions of pollutants identified using an
emissions inventory (Representative Concentration Pathways) coupled with back
trajectories:
The RCP emission data combined with back trajectories and extinction data showed that the hot spot regions of pollution that affected the study sites during winter were mainly to the southwest rather than very distant (Fig. 6b). Iran, Turkmenistan, Azerbaijan, Georgia, the eastern part of Turkey, and the southwestern part of Russia all showed comparatively high-pollutant emissions in winter which moved towards northern Pakistan. The western part of Kazakhstan, Uzbekistan, and northeastern Turkey emitted particularly high concentrations of pollutants.
The combination of the back-trajectory results and surface-wind direction analysis indicated that during the sampling months, aerosols were significantly influenced by the long-range transport of pollutants coming from central and south Asia, with a small contribution from west and east Asia. This differs somewhat from previous reports which suggested that the Tibetan Plateau and Himalayan region are mainly affected by pollutants from east and south Asia (Zhang et al., 2015). An increasing trend has been reported for black carbon emissions in central and south Asia over the past 150 years (Bond et al., 2007), and a significant increase has been found in black carbon concentrations in glacier snow in west China in the last 20 years, especially during the summer and monsoon seasons (Ming et al., 2008). In south Asia, the largest source of atmospheric black carbon is emission from biomass and biofuels used for cooking and heating (dung, crop residues, wood) (Venkataraman et al., 2005).
The results indicate that only a low level of pollutants (minor contribution) reached the study area from northwest China. BC particles emitted from distant low-latitude source regions such as tropical Africa barely reach the Tibetan Plateau and Himalayan regions because their emissions are removed along the transport pathways during the summer monsoon season (Zhang et al., 2015).
The contribution of pollutants from potential source regions was also investigated using the WRF-STEM model with tagged carbon monoxide tracers and source regions of east Asia, south Asia, central Asia, the Middle East, Europe, the Russian Federation, and west Asia. (The individual countries in the regions are listed in Table S5.)
Figure 7 shows the results of the model simulations for summer (1 June to 4 July 2015) and winter (15 December 2015 to 17 January 2016) at two glacier sites (Sachin and Shangla), where the model terrain elevation was close to the observation terrain elevation. The model simulations showed Pakistan to be the major contributor of pollutants in summer (77 % at Shangla and 43 % at Sachin) followed by the south Asian countries. The south Asian countries were the major contributor in winter (47 % at Shangla and 71 % at Sachin) followed by Pakistan, which is in line with the findings by Lu et al. (2012) that south Asia contributed 67 % black carbon in the Himalayas. There were minor contributions of 2–7 % of pollutants from Afghanistan, Iran, central Asia, and the Middle East and extremely small amounts from east Asia, Europe, Africa, west Asia, and China. The contribution from Iran, the Middle East, and Europe was greater in winter than in summer, while the contribution from central Asia and China was greater in summer than in winter. The proportion of daily contributions fluctuated considerably, with higher contributions from Iran, the Middle East, and Europe on individual days in winter, ranging, for example, from 2 to 30 % for the Middle East.
The concentration of hydrophobic BC (BC1), hydrophilic BC (BC2), and total
black carbon (BC
The high-BC concentration in the atmosphere over the study region was attributed to long-range transport from urban source regions. Potential source regions of the pollutants deposited on glaciers and snow were identified using wind vector mapping with MERRA-2 reanalyzed data, calculation of back air trajectories using the HYSPLIT-4 model, and chemical transport pathways using the WRF-STEM tagged chemical transport model. The back-trajectory results indicated that the majority of pollutants in summer were from central and south Asia, and those in winter were from Iran, Pakistan, Iraq, Turkmenistan, Azerbaijan, Georgia, Jordan, Syria, Tunisia, Ukraine, Libya, and Egypt. The WRF-STEM model indicated that most anthropogenic pollutants were from Pakistan and south Asia during both summer and winter. However, both approaches showed a reasonable contribution from central Asian countries and a limited contribution from east Asian countries in summer. The wind vector maps also indicated that the study site was mostly affected by westerly winds. All three approaches showed a reasonable contribution from neighboring countries such as Afghanistan, Pakistan, Iran, and India in specific months. Overall, the results indicate that south, central, and west Asia were the major sources of the pollutants detected at the sampling sites.
There was some mismatching in source regions among the three approaches. The WRF-STEM model and wind vector maps both identified a small contribution from east Asia, but this was not identified in the back trajectories approach. Similarly, the wind vector maps and back air trajectories showed a dominant contribution from the west, while the WRF-STEM model showed a major contribution from Pakistan and south Asia. The differences in the results obtained by the different methods may be due in part to the complex topography of the region and the different altitudes used in the methods, the coarse resolution of the WRF-STEM model, and differences in the emission source inventories and meteorological parameters used by the WRF-STEM and HYSPLIT-4 models. The limitations of using back trajectories to identify source regions is discussed further in a paper by Jaffe et al. (1999).
Furthermore, the atmospheric BC concentration over the Himalayas has significant temporal variations associated with synoptic and mesoscale changes in the advection pattern (Babu et al., 2011) which can affect pollutant transport and deposition. The large uncertainty among different emission inventories can also affect the results, especially in the Himalayan region.
Source contribution regions of carbon monoxide for selected sites
identified by WRF-STEM during
BC and OC concentrations were measured using
thermal optical analysis of snow and ice surface samples collected from
glacier and mountain valleys in northern Pakistan in summer, autumn, and
winter. The samples contained high concentrations of BC, OC, and dust in low-elevation glaciers and surface snow in mountain valleys. The samples from
Sost contained the highest average concentration of BC in mountain valley
snow (winter) and those from Kalam the lowest, probably due to the impact of
snow age and an increased concentration of black carbon and dust (the Sost
samples were aged snow, and Kalam samples were fresh snow). The average
concentration of BC in surface samples from the Sachin glacier was higher in
autumn than in summer; the BC values in summer snow samples collected from
the Sachin and Gulkin glaciers (aged snow from the glacier surface) were
much higher than those in ice. The average BC concentration in summer
samples collected from glaciers was 2130
Snow albedo was calculated for winter samples using the SNICAR model with
various combinations of BC and dust concentrations, three values for MAC,
and a range of values for SZA (57–88.89
The overall uncertainty of the BC and OC concentrations was estimated, taking into account the analytical precision of concentration measurements
and the mass contribution from field blanks. The uncertainty in the BC and
OC mass concentrations was calculated from the standard deviation of the
field blanks, the experimentally determined analytical uncertainty, and the
projected uncertainty associated with filter extraction. The major source of
uncertainty was the effect of dust on the OC
The albedo reduction from OC was not quantified. The contribution of OC to
total visible absorption in the top snow layer is relatively small compared
to that of BC and dust but has been shown to be significant (
The possible uncertainties on the modeling side relate to the use of CO as a tracer for light-absorbing particles to identify the source region. Uncertainties are also attributed to errors in the emission inventories, simulated meteorology, and removal processes built into the model. The physics and chemistry of removal of BC and CO differ, especially in the wet season. However, we analyzed the model during pre-monsoon and relatively dry periods when there should be a relatively good correlation in the transport of CO and BC. The global emission inventories used are unable to capture emissions at a local scale, and the contribution of local sources may also be underestimated by coarse-resolution models. High-resolution models and emission inventories at a local scale are required to capture local emissions.
Better constrained measurements will be required to obtain more robust results. High-resolution satellite imagery, high-resolution models, and continuous monitoring will help to reduce the present uncertainty.
The data can be accessed at
The authors declare that they have no conflict of interest.
This study was supported by the National Natural Science Foundation of China (41630754, 41671067, 41721091), the Chinese Academy of Sciences (QYZDJ-SSW-DQC039), the State Key Laboratory of Cryosphere Science (SKLCS-ZZ-2017), program funding to ICIMOD from the governments of Sweden and Norway, and ICIMOD core funds contributed by the governments of Afghanistan, Australia, Austria, Bangladesh, Bhutan, China, India, Myanmar, Nepal, Norway, Pakistan, Switzerland, and the United Kingdom. Acknowledgement is also due to A. Beatrice Murray for English editing of the manuscript. The authors would like to thank both the anonymous reviewers, whose reviews were extremely helpful in enhancing the quality of the manuscript. We would also like to convey our gratitude to the editor for the smooth handling of the manuscript.Edited by: Robert McLaren Reviewed by: two anonymous referees