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Asian Journal of Atmospheric Environment - Vol. 16 , No. 2

[ Research Article ]
Asian Journal of Atmospheric Environment - Vol. 16, No. 1
Abbreviation: Asian J. Atmos. Environ
ISSN: 1976-6912 (Print) 2287-1160 (Online)
Print publication date 31 Mar 2022
Received 14 Dec 2021 Revised 19 Feb 2022 Accepted 26 Feb 2022

Black Carbon Concentration during Spring Season at High Altitude Urban Center in Eastern Himalayan Region of India
Khushboo Sharma1) ; Rakesh Kumar Ranjan1), * ; Sargam Lohar1) ; Jayant Sharma1) ; Rajeev Rajak1) ; Aparna Gupta1) ; Amit Prakash2) ; Alok Kumar Pandey1), 3), 4), *
1)Department of Geology, Sikkim University, Gangtok, Sikkim, India
2)Department of Environmental Sciences, Tezpur University, Tezpur, Assam, India
3)School of GeoSciences, The University of Edinburgh, Edinburgh, EH9 3FF, UK
4)School of Physics and Astronomy, University of Leicester, Leicester, UK

Correspondence to : * Tel: +91-8100420032 (R.K. Ranjan) +91-9013743681 (A.K. Pandey) E-mail: (R.K. Ranjan) (A.K. Pandey)

Copyright © 2022 by Asian Association for Atmospheric Environment
This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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This study analyzed the BC associated with PM1 and the contribution of biomass burning to the BC using a portable seven-channel Dual spot Aethalometer in and around Gangtok, the capital city of Sikkim, India, during April 2021. Additionally, CO2 and meteorological parameters (Temperature, Pressure, and Relative Humidity) was measured. The minimum concentration of BC was found in rural areas where the contribution of biomass burning to the BC is highest. The observed spatial variability of BC over Gangtok Municipal Corporation (GMC) area is minimal. Five days back-trajectory analysis was done using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model to understand the regional influences of air masses at Gangtok. The air mass of the studied region is under influence of trans-regional transport from Indo-Gangetic Plains affecting the BC concentration over the studied region. The black carbon presence in the ambient air near the glacier heights in the Eastern Himalayan region may significantly cause localized warming, thereby enhancing glacier melts. The results have significant bearing for the policy-makers to take corrective steps in addressing the issue of rising BC concentration in high altitude regions. A further detailed study is needed to examine the effect of BC on radiative forcing and its large-scale effect on the East Asian summer monsoon using regional climate models.

Keywords: Black carbon, Aerosols, Eastern Himalaya, Biomass burning, Sikkim


Black Carbon (BC) is a strong light-absorbing component in the atmosphere (Bond et al., 2013) and has ability to alter the radiative forcing of the earth and thus contributing to climate change (Kang et al., 2020; Grivas et al., 2019). BC is a byproduct of incomplete combustion of biofuels, fossil fuels, and biomass burning and water insoluble agglomeration of carbon sphere (Khan et al., 2020; Sarkar et al., 2015). It is easily transportable both horizontally and vertically over a large region (Brooks et al., 2019; Dumka et al., 2010), hence considered to be one of the vital atmospheric constituents altering regional and global climate (Bansal et al., 2019). The high concentration of BC in the atmosphere can form smog, causing visibility depletion (Chatterjee et al., 2020; Zhou et al., 2012). BC affects the climate by heating the atmosphere, revamping the optical and physical properties of clouds (Prakash et al., 2020). BC deposition on glaciers reduces snow albedo that accelerates the melting of ice and snow (Dou and Xiao, 2016; Flanner et al., 2009). In addition, it causes atmospheric instability and affects regional circulation and precipitation patterns (Talukdar et al., 2019). BC associated with fine particulates have an adverse effect on public health. Also, fine particles, generally referred as PM1 and PM2.5, and PM10 are considered to be the primary reason for the degradation of visibility (Gong et al., 2015). Long-term exposure of BC can cause cardiovascular mortality, while the short-term exposure of BC causes pulmonary inflammation and other respiratory complications (Shehab and Pope, 2019; Lin et al., 2011).

The total global emission of BC is estimated as 4.8-7.2 Tg year-1 (Kant et al., 2020). Residential combustion of fuels contributes 60 percent of global anthropogenic BC emissions followed by 24 percent from the transport sector (Helin et al., 2018; Klimont et al., 2017). Such a huge amount of BC aerosols in the ambient atmosphere can trap heat subsidizing climate warming (Ramanathan and Carmichael, 2008). BC is the most significant human-induced emission after CO2 in terms of climate forcing. The total climate forcing of black carbon is larger than the direct force reported in the fourth IPCC report. The total climatic forcing estimate of +1.1 Wm-2 takes into account biofuel and open-biomass sources of black carbon, as well as cloud effects not specifically mentioned in the IPCC report (Bond et al., 2013). About one third of the global carbon burden is contributed by India and China (Lu et al., 2011). India’s standalone contribution of BC emission is found to be 0.41 Tg year-1 (Kant et al., 2020; Venkataraman et al., 2005). The majority of BC emissions in India is contributed by biofuel (42%) followed by open burning (33%) and fossil fuel (25%) (Venkataraman et al., 2005). The biomass burning in Indo-Gangetic Plains (IGP) has significantly increased the BC concentration in the atmosphere over the region (Singh et al., 2018; Latha et al., 2017). The transport of BC originating from IGP, and low-land foothills play a major role in aerosol loading and warming over the Eastern Himalaya region before the onset of summer monsoon (Ramanathan et al., 2007). Thus, the BC aerosols over the Eastern Himalaya is associated with the long-range transport from IGP (Kumar et al., 2011; Dumka et al., 2010) in addition to the local emissions from transport and domestic consumptions of fuels. The increasing concentration of BC over the Himalayan region has shown adverse impact on the health of glaciers in recent years (Zhao et al., 2017; Kulkarni et al., 2007). The importance of the Himalayas and its glaciers are evident as it is the source of three major river systems in the world viz. Indus, Ganges, and Brahmaputra that feed almost one-third of the world’s population. The natural sink for the ambient BC aerosols is the deposition over the glaciers, thereby creating micro heating zones enhancing the glacier melt. The present study indicates the dominant source contribution for BC in aerosol and its possible association with other variables revealing potential options for its control and regulations. The BC concentrations in the ambient air may further affect the delicate radiative balance in the sensitive glacier regions of the Himalayas that may have climatic consequences in the long run. Any increase in the early melting of glaciers due to BC deposition may have subsequent effects on water supplies downstream affecting large populations (Ramachandran et al., 2020; Kang et al., 2019). A significant amount of BC aerosol in the ambient atmosphere over the Himalayas is also linked with the mountain air circulations (Chatterjee et al., 2020; Decesari et al., 2010). Despite the severe impact of BC on Himalaya, the studies on monitoring of BC aerosols over Eastern Himalaya is very limited (Chatterjee et al., 2012). However, Chatterjee et al. (2012), Adak et al. (2014), Sarkar et al. (2015), and Roy et al. (2017) investigated BC concentration over the Darjeeling, a part of Eastern Himalaya. It has been observed that the total BC loading over Darjeeling was from local emissions, long-range transport, and mountain wind transport. Moreover, the concentration of BC in Darjeeling is noted to be higher than any other high-altitude stations in India and Nepal and even higher or comparable with some of the metro cities in India.

Sikkim in recent years has also experienced rapid urbanization, high vehicular pollution load, traffic congestion, and large-scale construction activities which in turn may have effects on the ambient air quality which has not been explored so far. Gangtok is the capital city of Sikkim and the largest urban centre in the region and hence considered in the present study as the representative urban area of the region. Being one of the favorite tourist destinations and the cleanest city of Northeast India with a good air quality index, this city has emerged as an important aspect of Sikkim tourism. The pristine ambient environment of Gangtok, in the lap of Himalaya, adds to its attraction among the tourists. Therefore, the ambient air quality has a significant bearing on the overall growth of the tourism sector in the state where tourism has the major share in the state GDP. The present study is carried out as an effort to fill the research gap that exists in air pollution research in Sikkim Himalaya. An attempt has been made to monitor the BC and its possible sources in this part of Eastern Himalaya.

2. 1 Study Area

Gangtok is the capital city of Sikkim in the Eastern Himalayan region at an altitude of approximately 1,650 m above mean sea level (amsl). It has a monsoon-influenced subtropical highland climate with a year-round mild temperate climate. The average minimum and maximum temperature for 30 years (1981-2010) in the city ranged between 5°C in winter to 22°C in summer (Hyvärinen et al., 2009). The weather in Gangtok during April is mostly warm and moist, with around 99 mm month-1 of rainfall. April marks the spring season, and the daytime maximum average temperature is around 24°C, and at night 14°C is normal.

Gangtok does not have major industries except some pharmaceutical manufacturing units in the suburbs and outskirts of the city. It is also one of the popular destinations for both domestic and international tourists. Around two million tourists have visited Sikkim in the year 2018, which is more than three times of the total population of Sikkim (Pradhan, 2018). As per the City Development Plan for Gangtok 2011, the capital has approximately 98% of the total combined share of vehicles in the state whereas public transport makes up only 1% of vehicle share. In addition to the state traffic load, there is also a high traffic influx from neighboring states (Pradhan, 2017).

2. 2 Experimental Design and Data

The measurement of BC associated with particulate matter having an aerodynamic diameter less than 1 μm (PM1) was carried out between 08:00:00 to 18:00:00 hour during the period from 04-04-2021 to 15-04-2021. In this period, the anthropogenic BC emission was very limited due to the COVID pandemic. The BC measurements were carried out across 13 locations in and around Gangtok. The characteristics of sampling sites (Table 1) vary from urban, sub urban to rural locations as detailed in the study area map (Fig. 1).

Table 1. 
Characteristics of sampling site.
S. No. Place Site name Site characteristics Latitude (°N) Longitude (°E) Altitude (m)
1 Marchak, Ranipool MCK Suburban 27.286 88.591 893
2 Adampool ADP Urban 27.311 88.586 919
3 Banjhakri Fall BJF Suburban 27.35 88.602 1,233
4 Lingding LIN Urban 27.322 88.603 1,265
5 Ranka Monastery RKM Sub urban 27.329 88.577 1,330
6 Rumtek Monastery RTM Suburban 27.297 88.574 1,398
7 Pakha PKH Rural 27.246 88.667 1,502
8 Namgyal Institute of Tibetology NIT Urban 27.316 88.605 1,529
9 Bakthang Fall BKF Urban 27.359 88.620 1,671
10 Ridge Park RDP Urban 27.329 88.616 1,726
11 Tashi Viewpoint TVP Suburban 27.372 88.617 1,878
12 Ganesh Tok GTM Urban 27.342 88.621 1,992
13 Hanuman Tok HTM Suburban 27.344 88.630 2,187

Fig. 1. 
Study area map showing sampling locations.

The BC measurement was done with a portable seven channel “Dual SpotAethalometer (Model: AE 33-7, Magee Scientific, USA), having a flow rate of five liters per minute with a base time of 60 seconds at 13 sites in and around Gangtok. The carbon dioxide (CO2) was measured using a CO2 sensor (Vaisala-GMP343) and meteorological parameters were measured using an ambient meteorological sensor (AMES-TRP159). The accuracy of meteorological sensors viz. temperature, humidity and pressure are ±0.15°C, ±2% and ±1 mbar respectively. Similarly, the measurement precision (excluding noise) at 25°C (77°F) and 1,013 hPa for CO2 sensor is ±0.5%. The inlet of the aethalometer was mounted at a height of 1.5 m above ground level. A minimum 20-minute run time was selected for the collection of BC particles at each site. A description of an aethalometer follows the study of Sandradewi et al. (2008) and its application details are provided by Drinovec et al. (2015). The aethalometer assesses the attenuation at seven different wavelengths ranging from near-ultraviolet to near-infrared (370, 470, 520, 590, 660, 880 and 950 nm), and the BC concentration was determined from the change in optical attenuation at 880 nm. Aerosols other than BC absorb very little at 880 nm therefore the absorption at this wavelength is mainly attributed to BC alone (Drinovec et al., 2015; Sandradewi et al., 2008a). The measurement uncertainty of an aethalometer due to filter loading and multiple scattering effects is reported to be about ±10% (Raju et al., 2020). The instrument also provides signatures of biomass burning to black carbon concentration in ambient air using the Sandradewi et al. (2008) model. It uses the different source contributions based on the differences in the spectral dependencies of absorption coefficients.

The data obtained from aethalometer, and other meteorological variables obtained from Indian Meteorological Department (IMD) were further subject to Multivariate Principal Component Analysis (PCA) using “Facto MineR” and “factoextra” packages in R. The data were subsequently segregated into 4 different altitude classes namely “500-1,000 m”, “1,000-1,500 m”, “1,500-2,000 m” and “>2,000 m”. This mixed data (Altitude class with other variables) was then used in Factor Analysis of Mixed Data (FAMD) that has the ability to integrate categorical values similar to multivariate correspondence analysis.

To ascertain the movement of the air mass over the studied region during the sample collection time i.e., April 2021, Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT - accessed on 06/08/2021) was used. HYSPLIT model has the feature for computing simple air parcel trajectories, complex air mass transport, dispersion, chemical transformation, and deposition which is widely used in air pollution dispersion studies and backward and forward trajectory analysis (Draxler and Rolph, 2012; Draxler, 2003, 1992). For HYSPLIT model configuration, National Centers for Environmental Prediction (NCEP) reanalysis data was obtained - from the National Oceanic and Atmospheric Administration (NOAA) website ( accessed on 06/08/2021). The HYSPLIT model control file has been configured to run the model at different heights for the backward and forward trajectory analysis. The relative differences in the trajectory were found to be non- significant (at 95% confidence limit) for 5 days. Therefore, five days backward trajectories were drawn from the sampling sites to investigate the movement of air mass to the sampling locations.


The results were compiled and aggregated for six different variables namely, Black Carbon (BC), contribution of biomass burning to BC in percentage (BB), Carbon Dioxide (CO2), Relative Humidity (RH), Pressure (PR) and Temperature (TM). The average values with standard deviation recorded at each sampling site for all the six variables are presented in supplementary Table 1 and the temporal variability is shown in supplementary Fig. 1. The average maximum BC concentration was observed at Linding area (LIN) (12.61 μg m-3), an urban site with high settlement and heavy traffic, and minimum was observed at Pakha (PKH) (5.11 μg m-3) which is a rural area (background) with low settlement and minimal traffic. Fig. 2 shows a graphical representation of the variation present in the dataset of an individual variable when compared to z-normalized values of Altitude (ALT), Biomass Burning (BB), Black Carbon (BC), Temperature (TM), Humidity (RH), Pressure (PR) and Carbon dioxide (CO2). Three outliers have been observed in the dataset that exceed the upper quartiles when represented in the box-whisker plot (Fig. 2).

Fig. 2. 
Box-whisker plot for Altitude (ALT), Black Carbon (BC), Contribution of biomass burning to BC (BB), Carbon Dioxide (CO2), Relative Humidity (RH), Pressure (PR) and Temperature (TM).

The BC concentrations, CO2 concentration and the humidity have outliers in the data set as evident from the values that exceed the maximum whisker. The results also indicate strong variance in these variables across the Gangtok city. The highest biomass burning contribution to the observed BC concentration in PM1 was found at rural site Pakha (PKH) (40.02%) where the total BC concentration is rather minimal in comparison to other sites. However, Ridge Park area (RDP) (34.19%) and Lingding (LIN) (24.44%) - a densely populated urban settlement has a relatively lower contribution from biomass burning. The reason may be attributed to the higher aerosol load due to enhanced traffic emissions thereby reducing the relative contribution of BB in total biomass. It is worth noting that the wood burning is the major source of household energy requirement viz. cooking and heating mostly in rural regions where wood is readily available as fuel. Although the contribution of BB to BC is greatest in rural regions such as PKH, the absolute BC concentration is rather low due to the region’s extremely low population density.

The maximum observed standard atmospheric CO2 concentration over the study area was found at ADP (534.84 ppm, altitude=919 m) followed by MCK (442.80 ppm, altitude=893 m) and RTM (418.81 ppm, altitude=1,398 m), whereas lowest CO2 concentration was observed at HTM (325.10 ppm, altitude=2,187 m). It is pertinent to mention that the CO2 concentration has strong altitudinal dependence as evident from the observed CO2 concentrations (Fig. 3).

Fig. 3. 
Altitudinal variation of Black Carbon and Carbon Dioxide over the Gangtok.

The comparative evaluation of observed BC concentration in Gangtok, Sikkim with other locations across India revealed rather uniformity in the BC concentration distributions irrespective of higher altitude and nature of sources. This points towards the influence of trans-boundary circulations of air masses in determining the concentration at high altitude regions. It is further verified through back trajectory model analysis which is carried out taking average wind field distribution during the days of sampling. The back trajectories simulation runs were done for the past five days i.e. 120 hours using HYSPLIT model. The back trajectories for 4 different days i.e. 5th, 8th, 11th, and 14th April 2021 at an interval of 3 days during the sampling episode were constructed (Fig. 4). It is evident from the results, that the most dominating contribution at high altitude came from high altitude westerlies passing over North Indian plains that are highly populated with large swaths of agricultural fields notorious for agricultural biomass burning (Jeelani et al., 2017; Dumka et al., 2014). At lower altitudes (red lines), wind turbulence was more prominent due to northeast - southwest valley alignment.

Fig. 4. 
Air Mass back trajectory plot (from HYSPLIT model) at different days during the sampling period.

In order to understand the nature of variability present in the dataset, normality test (supplementary Table 2) was carried out following KS test, Shapiro-Wilk test and Jarque Bera test. The test was conducted assuming the normal data as null hypothesis. As evident from the results, the data at most of the sites reject the null hypothesis indicating strong non-gaussian nature. Therefore, to analyse the statistical similarity and dissimilarity among the data across the sites, non-parametric Kruskal Wallis test was carried out (Table 2). All the variables reject the null hypothesis, indicating the presence of strong variance across the length and breadth of the city.

Table 2. 
Results of Kruskal Wallis test.
Statistics 1,288.4 94.1 1,475.3 2,173.2 1,386.7 1,435.7
P value 0 0 0 0 0 0

The Principal Component Analysis (PCA) and Factor Analysis of Mixed Data (FAMD) (Kassambara, 2017) were carried out to ascertain the degree of association among the variables at different locations in the city and at different altitudes using the “FactoMineR’’ package in R Programming language. The PCA analysis was carried out taking 9 variables with 84 average observations across the city, out of which two variables namely, altitude and sites are categorical in nature and rest are numeric. After PCA, 3 components (using the criteria Eigen-Value ≧1) were extracted that account for more than 76% of the data variations (supplementary Table 3). Fig. 5 shows an association between (a) BC concentration and ambient temperature along dimension 1 and 2, (b) BB contribution with altitude along dimension 1 and 2. It is pertinent to mention that dimension 1 and 2 represents principal component (PC) axis 1 and 2 respectively. The length of the arrow shows the contribution of individual variables to the aligned PCs and angle with the axis indicates the degree of association of individual variables to the PCs. As evident from Fig. 5, the BC concentrations and ambient temperature are strongly correlated with PC 2. BB contribution to BC concentration and altitude are aligned together along dimension 1 (PC1), however, the association between BB and altitude are rather weak. In this PCA analysis, BC from fossil fuel (BCff) and BC from biomass burning (BCbb) are taken in terms of percent of total that expresses the relative contribution of both in the local air. Since, PCA analysis was carried out using a scaling function, the distortion due to large value (affected due to large value of traffic sources) does not arise. This method of doing PCA with scaled values is widely used to negate the effect of large scaling differences in the data.

Fig. 5. 
Correlation biplot over principal component axis 1 and axis 2.

The concentration of different variables is evidently dependent on the elevation factor when sampling locations were classified into four different elevation groups. The elliptical circle denotes 95% significance line and the data points that exceed the corresponding elliptical line have a significant variance with the group mean (Fig. 6). The relative contribution of two categorical variables namely site and elevation to the total variance on a factorial biplot (are presented in Fig. 7), where Principal component 1 explains ~19% of total variance followed by PC 2 that explain ~14% inertia. The maximum contributions were made by the variables at elevation <1,500 m i.e. lower valley region and high-altitude regions of height >2,000 m. Mid altitude regions (1,500-2,000 m) are more aligned with high BB and BC concentrations in comparison to the other regions. The properties of BC and BB are strongly dependent on the elevation. With the exception of 1,500 to 2,000 m height region, the BC and BB properties differed significantly with elevations (Fig. 3). As evident from Fig. 7, sites ADP and MCK at height 500-1,000 m elevation range, have contrastingly opposite signatures from sites at 1,000 to 1,500 m elevation ranges such as BJF, RTM, RKM and LIN. Similarly, properties observed at HTM at more than 2,000 m height has significantly different from other elevations.

Fig. 6. 
Bivariate Factor Map over principal component axis 1 and axis 2.

Fig. 7. 
Factor analysis of Mixed data (FAMD) plot over bi-variate factorial map where Dim1 represents principal component 1 and Dim2 represents principal component 2.

The BC concentrations and BB contribution were further, interpolated using Inverse Distance Weighting (IDW) method in ArcGIS software (Fig. 8) and overlaid with different wards of Gangtok Municipal Corporation (GMC). The results point towards high BC concentration on mostly western and northern regions of the city with low contribution towards the south and eastern periphery of the city boundary. However, the contribution of the BB to the BC is lower in the urban area. In contrast the BC concentration in the rural area is relatively lower but the contribution of BB to BC is higher.

Fig. 8. 
Black carbon distribution over Gangtok (East Sikkim).

The results were in agreement with the findings of Sarkar et al. (2015) that showed strong seasonal variations in BC concentrations with the maximum concentration during pre-monsoon (5.0±1.1 μg m-3) followed by winter (3.9±2.2 μg m-3), post-monsoon (2.9±1.0 μg m-3) and minimum during monsoon (1.7±0.7 μg m-3). The total BC loading over Darjeeling was found to be from local emissions (56%), long-range transport (27%) and mountain wind transport (17%). When compared with the nearby mountainous region of Darjeeling, the biomass burning during winter and pre-monsoon dust transport (natural) processes contribute equally to the radiation effect over the Eastern Himalaya (Darjeeling), and their differential effects are also very similar.

It is evident that BC concentration over Gangtok during spring is found to be much higher than other high altitude stations like other parts of the Himalaya viz. Lachung (Arun et al., 2021) and Darjeeling (Sarkar et al., 2015) in Eastern Himalaya. Whereas hill stations in western Himalaya viz. Dehradun (Kant et al., 2020), Manora Peak (Babu et al., 2011) and Mukteshwar (Hyvärinen et al., 2009), Hanle (Kompalli et al., 2016) and in central Himalaya viz. Nagarkot and Langthang (Carrico, 2003) and NCOP Nepal (Marinoni et al., 2010) have reported lower concentration of BC than Gangtok.

Although present aerosol concentrations are low in the region, the relatively high BC concentration due to BB in the region that is very close to glacier height enhances the risk to the already fragile Eastern Himalayan ecosystem. The findings suggest that the black carbon concentration is quite comparable to other major urban centers in northern India. Any subsequent increase in the BC concentration may cause deterioration in air quality and put additional stress on the tourism sector thereby affecting the state’s overall economic well-being. It is therefore recommended to keep constantly monitoring the aerosol load at high altitude regions and their possible impact on nearby glaciers, as they are the major sources of freshwater for the ever increasing Indian population. BC effects on radiative forcing over the eastern Himalayas and its effects on the East Asian summer monsoon using regional climate models is further required to understand the BC impacts over the regions.


The black carbon aerosols have a known impact on environment. It is considered the second most important anthropogenic forcing agent for climate change. The black carbon concentration in PM1 was measured with an aethalometer during spring in the month of April 2021 when there was travel restrictions due to COVID pandemic and less traffic on the road. The observed BC concentration (8.02±1.82 μg m-3 to 12.61±4.29 μg m-3) in this part of the Himalaya during spring was found to be similar to the reported concentration from other parts of urban clusters of India, suggesting the strong influence of atmospheric circulation on BC concentrations in remote areas. The concentration of black carbon associated with PM1 shows significant correlation with altitude, humidity and temperature at Gangtok. The frequency of occurrence of maximum BC concentration is found to be high at the mid altitude region. The high BC associated with low biomass burning reveals the high probability of trans-boundary contribution. Therefore, the contribution of anthropogenic and biomass burning for BC mass concentrations depends on the altitude and transportation route. The absolute values of BC concentration are more likely to be influenced by the anthropogenic sources than the amount of biomass burning in Gangtok. The CO2 has also shown a strong altitudinal dependence with lower concentration with increasing height. The back trajectory analysis reveals a strong trans-boundary contribution of Black carbon from central and western Indo Gangetic plains. The study has significant implications for the regional and global climate models as the BC aerosol and its implications create strong uncertainty in the model estimates for future climate projections. The BC concentrations variations and the influence of long-range transport of biomass burning induced black carbon at high altitude glacier melts further enhances the rapidly receding glacier mass. It would be interesting to incorporate the effect of BC concentrations in glacier melts in global and regional model estimates.


This work was funded by the Department of Science and Technology, Government of India to the project DST’s Centre of Excellence (CoE), at Department of Geology, Sikkim University (DST/CCP/CoE/186/2019 (G)). The authors also acknowledge anonymous reviewers for constructive suggestions to improve the quality of the manuscript.


The authors declare that they have no conflict of interest.

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Table S1. 
Summary statistics of BC, BB, CO₂, RH, PR, and TM at different sampling sites during the sampling period.
Sampling site Altitude (m) BB (%) BC (μg m-3) TM (°C) RH (%) PR (hPa) CO2 (ppm)
MCK 893 28.88±11.92 9.73±3.30 21.77±3.54 64.14±22.75 910.72±2.80 442.8±130.84
ADP 919 28.88±12.74 10.20±6.20 23.06±3.50 61.57±23.03 906.5±2.56 534.84±306.72
BJF 1,233 26.24±12.00 11.12±8.81 26.02±1.10 40.48±6.82 874.22±1.34 366.07±14.58
LIN 1,265 24.44±9.36 12.60±6.27 19.76±1.27 60.73±13.79 874.86±1.71 370.7±15.83
RKM 1,330 30.33±13.61 8.09±3.41 24.41±1.71 43.69±6.50 864.03±3.56 357.43±11.12
RTM 1,398 32.22±12.07 8.17±1.78 23.59±3.31 50.35±15.51 859.68±11.96 418.81±106.43
PKH 1,502 40.02±28.11 4.10±6.53 16.37±3.08 72.56±8.50 849.19±1.34 353.36±9.82
NIT 1,529 29.79±10.60 9.88±3.51 19.76±1.12 54.7±6.87 848.97±1.80 357.11±14.27
BKF 1,671 27.16±17.25 12.06±8.06 23.00±1.26 45.43±5.98 831.64±0.74 355.38±21.57
RDP 1,726 34.19±13.58 9.39±2.66 20.29±1.22 52.48±6.61 827.22±0.98 344.57±33.04
TVP 1,878 28.48±11.82 9.81±7.67 20.97±1.73 51.26±10.62 811.39±0.88 330.98±12.05
GTM 1,992 25.13±10.13 11.35±12.29 21.19±1.72 49.72±7.72 801.26±1.20 332.16±33.25
HTM 2,187 29.04±11.49 8.02±4.57 20.09±1.81 47.34±8.68 785.59±3.96 325.1±23.41

Table S2. 
Results of normality test.
RDP 0.131 0.005 0.143 0.060 0.000 0.000 0.012 0.000 0.006 0.166 0.022 0.208 0.014 0.000 0.023 0.000 0.000 0.000
HTM 0.019 0.000 0.006 0.000 0.000 0.000 0.127 0.000 0.024 0.015 0.000 0.003 0.000 0.000 0.001 0.000 0.000 0.000
GTM 0.327 0.160 0.589 0.000 0.000 0.000 0.056 0.000 0.023 0.004 0.000 0.077 0.000 0.000 0.000 0.000 0.000 0.000
TVP 0.268 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.707 0.019 0.175 0.017 0.000 0.003 0.000 0.000 0.000
BKF 0.026 0.000 0.000 0.000 0.000 0.000 0.020 0.000 0.027 0.065 0.001 0.059 0.005 0.000 0.021 0.039 0.000 0.000
BJF 0.149 0.003 0.079 0.000 0.000 0.000 0.001 0.000 0.006 0.001 0.000 0.002 0.060 0.000 0.103 0.000 0.000 0.000
RKM 0.019 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.080 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
RTM 0.156 0.000 0.129 0.031 0.000 0.000 0.015 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.012 0.000 0.000 0.000
ADP 0.424 0.003 0.087 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.002 0.000 0.000 0.000 0.000 0.000 0.000
MCK 0.049 0.000 0.019 0.697 0.003 0.002 0.022 0.000 0.004 0.001 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000
LIN 0.009 0.000 0.014 0.082 0.000 0.000 0.030 0.000 0.014 0.004 0.000 0.002 0.003 0.000 0.202 0.000 0.000 0.000
NIT 0.204 0.001 0.042 0.000 0.000 0.001 0.000 0.000 0.007 0.002 0.000 0.007 0.000 0.000 0.104 0.026 0.000 0.000
PKH 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Table S3. 
Results showing PCA analysis
Principal components Eigen value Total inertia (%) Cumulative inertia
1 2.285 32.64 32.64
2 2.007 28.67 61.31
3 1.041 14.87 76.18
4 0.921 13.16 89.34
5 0.650 09.29 98.63
6 0.088 01.26 99.89
7 0.007 0.1 100.00
Total 7.000

Fig. S1. 
Time series plot showing interrelationship among different variables (BB%, BC, Temp, RH, PR, CO2).