Atmospheric aerosols influence the global and regional climates directly through scattering and absorption of solar radiation, indirectly, through modifying the physical, optical and radiative properties of clouds and semi-directly by evaporating the clouds [1-5]. Both natural as well as anthropogenic aerosols affect the atmosphere by disturbing the radiative balance of the earth-atmosphere, and also have the potential to affect the human’s health and visibility reduction by degrading the environment [4,6-8]. Their effect on climate is expressed by Aerosol Radiative Forcing (ARF), which can be defined as the effect of aerosols on solar radiative fluxes at the surface, within the atmosphere and at the top of the atmosphere [9,10]. The global average radiative forcing by aerosols is approximately -1.2 W/m2 [11], which is controlled by optical and physico-chemical and dynamical properties of aerosols [12,13].

Aerosol observations over high-altitude remote terrain provide valuable information on the background aerosol properties. For better understanding the magnitude of aerosol effects on our geo-biosphere system, it is very important to understand the physical, chemical, dynamical, radiative and optical properties of aerosols in diverse locations [14,15]. Further, aerosol and precursor gases (ozone and water vapour) observations at the high-altitude locations of Himalaya (above 4000 meter above mean seal level, AMSL) are crucial for various climatic implications, as these sites are the reservoir of fresh water in the form of glaciers and snow cover. The accumulation and melting of Himalayan glaciers are influenced by the long-range transport of absorbing aerosols from the different sources (natural as well as anthropogenic) [16,17]. Menon et al. [18], suggested that an increase in the concentration of black carbon aerosol and its deposition over snow and glacier ice can decrease the snow/ice albedo leading to an enhancement in the melting of the glaciers. This will affect the hydrology of North Indian rivers originating from glaciers, which in turn can cause water stress in the near future. A recent study also revealed a signature of increasing dust-induced snow darkening with surface elevations above 4000 m AMSL [19].

Numerous studies suggested the importance of aerosol observations from remote, sparsely inhabited, high-altitude regions in India [20-31,9,10, and references therein]. Aerosol observations from the Manora Park located at an altitude of less than 2000 m AMSL represent central Himalayan regions are reported by Pant et al. [21] and Sagar et al. [20]. Other authors also reported high-altitude aerosol observations representing the northwestern Himalayan region from Kullu and Mohal, [32,33] during different time periods. Characterization of aerosol optical properties over the high-altitude station Hanle (4500 m AMSL), Leh for a period of more than three years was reported by Ningombam et al. [9]. Prior to this, Singh, and Singh [34] and Ghude et al. [35] also studied aerosol properties in the same region.

Since most of the aerosol sources are of terrestrial origin, the variability in their properties will be very large close to the surface. However, aerosol characteristics have a more synoptic perspective over high-altitude stations which lie above the mixing region and in the free troposphere [20]. Such high-altitude stations would be indicative of background levels of aerosols concentration against which the urban/industrial impacts can be compared [5,21,36,37]. Also, aerosols in high-altitude remote regions are quite away from potential sources and are more representative of free tropospheric conditions [21]. The high-altitude stations lie in the boundary-layer during daytime and in the free troposphere during the nighttime, thus providing a valuable opportunity to investigate the transport/mixing of aerosols and gases from/between the boundary-layer to/and the free troposphere [5]. The concentration of aerosols present in the boundary layer is produced by both natural as well as anthropogenic processes. However, aerosols found in the troposphere and layers above are largely due to gas-to-particle conversion processes [5]. Therefore, vertical (altitude-resolved) distributions of columnar aerosol and precursor gas parameters in the high-altitude regions, encompassing boundary-layer and free troposphere, play an important role in the transport and transformation of these constituents from source regions due to processes such as entrainment, mixing etc. [5, 38]. Considering the importance of high-altitude observations, the present paper aims to obtain vertical distribution and characterization of aerosols and precursor gases (ozone and precipitable water vapor) from hot, arid-to-semi-arid plains of Haryana to remote high-altitude regions of western Himalaya to the arid cold desert of Ladakh.

The ground-based observations were made by a multi-filter radiometer (Solar Light Company-make MICROTOPS-II, Sun-photometer Model 540, and Ozonometer Model 521) during the 10 days field campaign from 1 to 10 August 2019. Columnar Aerosol Optical Depth (AOD) at five different wavelengths (380, 500, 675, 870 and 1020 nm) have been evaluated. Total Columnar Ozone (TCO) is measured by recording differential absorption of solar light intensity at wavelengths in the UV region (305.5 nm and 320 nm). The measurement at the third wavelength (312.5 nm) is used to correct for particulate scattering and stray light. The columnar PWV was obtained from the irradiance measured at wavelengths of 940 nm and 1020 nm. The estimation of PWV was made by following the differential optical absorption method applied the irradiance data archived at 940 nm (maximum absorption for water vapour) and at 1020 nm (less absorption or almost transmission for water vapour) [39].  The variations in AOD for spectral bands centred at 380,500, 675, 870 and 1020 nm have been utilized to retrieve columnar aerosol size distribution/wavelength exponent (Alpha) by applying the constrained liner inversion method King et al. [40]. Observations were carried out from selected experimental sites during ascending/descending routes. The name of the location, altitude, latitude, longitude, location codes, weather conditions along with observed values of the studied parameters are compiled in Table 1. At each location, more than 5 sets of observations were recorded. For each measurement, latitude, longitude, and altitude values have been fed to the sun-photometer/ozonometer as per the instrument’s requirement. The ground-based observations were also compared with space-borne products.

The Moderate Resolution Imaging Spectroradiometer (MODIS) is a multipurpose satellite and has been extensively used for the temporal and spatial variability of aerosols, columnar ozone and precipitable water vapour content in order to characterize and understand their potential effects [40-45]. MODIS is a passive imaging radiometer and measures reflected solar and emitted thermal radiation in 36 bands, across a 2330 km swath with global coverage [40, 46]. Satellites move along polar orbit with an inclination 98.5°, with a period 99 min and height of 705 km, cross the line from north to south at about 10:30 a.m. (Terra) and from south to north around 1:30 p.m. (Aqua) local time.

The physical properties of atmospheric aerosols are strongly dependent on their source of origin. The sources of aerosol are widely distributed and vary significantly from one region to the other [9]. The meteorological parameters were obtained from the second Modern-Era Retrospective analysis for Research and Application (MERRA-2) data product (inst1_asm_Nx) [47]. The temperature at surface, 2-meter air temperature, 2-meter specific humidity, surface pressure, 2-meter eastward wind and 2-meter northward wind. The 2-meter relative humidity is computed using temperature, specific humidity, and pressure. The air mass history arriving at the two sampling sites was obtained by running the National Oceanic and Atmospheric Administration (NOAA) Hybrid Single Particle Lagrangian 5-days backward for every hour (UTC) using Global Data Assimilation System GDAS 10*10 datasets [48, 49]. A trajectory density map was prepared using Transat software [50]. A grid layer with 0.25×0.25 grid resolution was created up to the extent of the trajectories and the number of endpoints (hours) falling in each grid cell were calculated.

Experimental Sites

Observations were recorded from different geographical regions varying from plains of Haryana to higher mountains of Himalaya to adjacent Ladakh mountainous regions during 01-10 August 2019. A sum of total of 26 experimental sites was chosen for the present work. These regions differ in terms of altitude, latitude, weather, and climate conditions from north to south and east to west. Experimental sites cover many States including Haryana, Punjab, Jammu and Kashmir, Ladakh, and Himachal Pradesh. Ladakh is one of the driest, coldest, and remotest places in the world. Experimental sites lie in the Jammu and Kashmir regions that cover one of the highest roads of India (Chang La mountain pass, 5369 m ASL. Tanglang La mountain pass, 5328 m ASL). The Panchgaon village of the Haryana district lies at an altitude of 250 m AMSL representing the lowest altitude characterized by relatively high average annual temperature (25C°) and rainfall (540 mm). The highest altitude site lies at Chang La mountain pass (>5300 m AMSL) characterized by very low temperature (~5°C) and average annual precipitation (~100 mm).

Aerosol Optical Depth (AOD) Distributions

Aerosol Optical Depth (AOD) is a key parameter to determine the aerosol content (load) in the atmosphere and indirectly air pollution level [51-54]. Location-wise spatial variation in average AOD values at three different wavelengths (380, 500, 1020 mm) measured during the campaign (ascending and descending routes) were plotted over the Indian map (Fig.1a, b & c). The route followed during the campaign was highlighted in two different colors. The altitude-wise (height profiles) of AOD at three different wavelengths (380, 500 and 1020 mm) are plotted and is shown in Fig. 2. Significant variability in the AOD values was observed from low to high-altitude regions (Figure 1 & 2). The observed AOD values varied from 1.98 to 0.12 (380 mm), 1.80 to 0.12 (500 mm) and 1.27 to 0.09 (1020 mm) wavelengths. The highest AOD at all three wavelengths was observed at L-319 (see Table 1). The lowest AOD value at 380 mm wavelength was observed at L-3100, and the lowest at 500 and 1020 mm were observed at the highest altitude region (L-5360). It is evident from figure 2 that AOD at all the three characteristic wavelengths decreases with an increase in altitude. Moreover, the wavelength dependency (lower AOD at longer wavelength) in these variations has also been seen at most of the experimental sites, particularly in the relatively lower altitudes, which is consistent with the Mie theory. The coarse-mode aerosol particles (AODs at 1020 nm) are found to deviate from those observed at 380 nm and 500 nm, indicating different aerosol chemical and growth processes. The ground-level variation among the three wavelengths (380, 500 and 1020 mm) was relatively larger over lower-altitude regions up to the height of 1 km compared to higher-altitude regions. This trend was possibly due to high wind shear activities (Intensity of turbulence). AOD values showed a steep decrease with an increase in altitude up to about 1 km and thereafter with a slow rate up to 5.5 km, which is attributed to local atmospheric boundary layer height variations.

Figure 1: Map showing the location-wise variations in AOD values at (a) 380 nm, (b) 500 nm and (c) 1020 nm wavelengths. Colored circles represent the experimental sites, yellow line represents the ascending route and pink line represents the descending route followed during the expedition.

Figure 2: Altitude variations in the sunphotometer-derived AOD at three characteristics wavelengths of 380 nm, 500 nm, and 1020 nm.

Aerosol Size Index (ASI) Distributions

The understanding of aerosol size distribution is essential as the number of cloud condensation nuclei per mass of aerosol depends on the chemical composition of aerosols as a function of size [4]. The Angstrom exponent or wavelength exponent (α) is a commonly used parameter to assess the relative dominance of fine-sized atmospheric aerosols over coarse atmospheric aerosols. Angstrom exponent yields information on the predominant size of suspended particles and is inversely proportional to the average size of the particle [54,55]. The values of α <0.7 suggest the dominance of coarse mode atmospheric aerosols that are associated with dust and sea salts, and the values of α >1.8 indicate the dominance of fine mode atmospheric aerosols that are associated with anthropogenic activities (pollution, smoke particles etc). The values of α lie between these two thresholds (0.7 to 1.8) are the mixed type aerosols (natural and anthropogenic aerosols) [56]. The AODs measured at the wavelengths of 380, 500, 675, 870 and 1020 nm were utilized to measure Alpha (α) by applying the constrained liner inversion method [57]. The altitude-wise (height profiles) of computed Alpha (α) values during the field campaign are plotted and is shown in (Figure 3).

Figure 3: Vertical height profile of Alpha (aerosol size distribution) during the study period.

Significant variability in the α values was observed during the field campaign, and it varied from 0.02 to 1.1. The highest value of α (1.14) was observed at the lowest altitude experimental site (L-250), and the lowest value of α (0.01) was observed L-1525, Manali, Himachal Pradesh). The value of α at the highest experimental site was 0.08 (L-5360, Changla Mountain pass). Interestingly, the two lowest values of α were observed at L-1500 (0.02) and L-1105 (0.04). These two experimental sites lie in the Manali and adjacent regions of Himachal Pradesh. The lowest values observed in these regions is possibly due to intermittent rainfall. Relatively, lower α values (less than 1 or close to 1) observed during the field campaign from almost all the experimental sites (lower to higher altitude) indicated the dominance of coarse mode and mixed-mode aerosols, suggesting the influence of natural sources (81%) as compared to anthropogenic (19%) sources. This feature suggests that the environment around the experimental sites is pristine.

Total Column Ozone (TCO)

The total column ozone (TCO, expressed in DU), which is equivalent to the thickness of the pure ozone layer at standard temperature and pressure, was measured at different altitudes from the selected locations during the field campaign. Location-wise average TCO values measured by mobile portable Ozonometer during the campaign (ascending and descending routes) were plotted over the Indian map (Figure 4) and are also compiled in table 1. Relatively higher TCO values were observed over lower-altitude regions and lower TCO values were observed over higher-altitude regions. Ground-based observations are compared by satellite retrieved TCO products.

Figure 4: Map displaying the location-wise variations in total column ozone (TCO). Colored circles represent the experimental sites, the yellow line represents the ascending route, and the pink line shows the descending route followed during the expedition.

(Figure 5) displays the altitude-wise comparison between ground-based TCO values and satellite retrieved OMI-TOMS product. Satellite retrieved TCO (OMI-TOMS) values show underestimation compared to ground-based observations (Ozonometer). The overall decreasing trend in TCO values with increasing altitude was observed in both ground based as well as satellite products, except at a few locations (Figure 5). Ground-based measurement shows significant variability in TCO measurements compared to satellite retrieved products. Ground-based TCO observations varied from 349.4 to 267.3 DU, however, satellite retrieved TCO values ranged from 264.9 to 290.7 DU. Ground-based results show the highest TCO value at relatively lower altitude locations (L-252), and lowest TCO values at the highest altitude experimental site (L-5360). Surface ozone is considered as ‘bad ozone’ created by chemical reactions between air pollutants created by several sources (vehicular, industries etc.). Air pollution level is generally low over high-altitude regions due to less anthropogenic activities, which in turn do not produce much surface ozone. This could be the possible reason for high TCO over lower-altitude regions and lower TCO over high-altitude pristine experimental sites. The results also show a steep decrease in TCO with the increase in altitude up to about 1 km and thereafter with a slow rate up to 5.5 km.

Figure 5: Plot depicting the distribution of ground-based and satellite-retrieved total column ozone (TCO) at different altitudes.

Precipitable Water Vapor (PWV)

The columnar precipitable water vapour (PWV, expressed in cm) is obtained from the radiance measurements made at 940 and 1020 nm, which were measured at different altitudes from the selected locations during the field campaign. Location-wise average PWV values measured by mobile portable Ozonometer during the campaign (ascending and descending routes) were plotted over the Indian map (Figure 6) and are also compiled in(table 1).

Table 1: Name of the experimental stations, their code, altitude, latitude, longitude, weather conditions and their corresponding average values of AOD, Alpha, TCO and PWV.

Figure 6: Location-wise plot of precipitable water vapor (PWV). Colored circles representing the experimental sites, yellow line represents the ascending route and pink line showing the descending route followed during the expedition.

Relatively higher PWV content was observed over lower-altitude humid regions and lower values were noticed over high-altitude arid regions. Ground-based observations are compared by satellite retrieved MODIS (Terra) PWV products. A very good correlation (^R2 = 0.91) was observed between ground-based, and MODIS (Terra) retrieved PWV content. Significant variability in PWV content was seen from low to higher-altitude regions and it varies from 4.47 to 0.26 cm (Ground-based observations) and (6.3 to 0.16 cm MODIS -Terra). Altitude-wise comparison of ground-based and satellite retrieved MODIS products of PWV are shown in (Figure 7). Ground-based observations show the highest PWV content over L-250 and the lowest value over L-4514 (Table 1). The ground-based and satellite retrieved PWV plotted values suggested a significant decreasing trend with an increase in altitude (Figure 7). The results also show a steep decrease with an increase in altitude up to about 1 km and thereafter with a slow rate up to 5.5 km. The highest and lowest PWV values were found at the lowest and higher-altitude experimental sites, respectively. The lower PWV in Ladakh and adjacent locations coincide well with the known aridity of the region.

Figure 7: Vertical distributions of ground-based and satellite-retrieved precipitable water vapor (PWV) during the period of expedition.

Air Mass Trajectory Analysis

To examine the probable air mass sources, a HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used at each experimental site to generate backward trajectories. This model was developed by the Air Resources Laboratory (ARL), National Oceanic and Atmospheric Administration (NOAA), and has been widely used for tracing the origin of the air mass [9,10,58-59]. Backward trajectories of 120 hours (5 days) were generated from the different selected low to high-altitude regions at a different elevation above ground level (m AGL), in order to estimate the probable air mass sources. The maps of air mass trajectories at different locations are compiled and shown in Fig.8. The long route of air masses was observed at lower altitude locations suggesting distant source of air masses. Local sources of air masses were observed at higher altitudes. As the study period was the month of August, which is a monsoon season in India, there are two prominent ocean sources (Arabian Sea and Bay of Bengal) were also observed particularly at lower to mid-altitude experimental sites. In Ladakh and adjacent regions, located at higher altitude, lies in the rain shadow regions, the influence of ocean moisture sources were absent. In these locations, air mass is local terrestrial in origin and arrives from the south (China).

Figure 8: HYSPLIT analysis of backward air mass trajectories for the selected experimental sites, (a): L-256, (b): L-251, (c): L-252, (d): L-319, (e): L-970, (f): L-1525, (g): 1597, (h): L-3100, (i): L-3500, (j): L-4110, (k): L-4274, (l): L-4400, (m): L-4514, (n): L-5015, (o): L-5360. See table 1 for an explanation of each location considered in the study.

The trajectory density map was also prepared at two high-altitude selected experimental sites (Site-1: L-3500 and Site 2: L-4110) (Figure 9). These two sites were chosen because of clear sky conditions and hence more observations were obtained at these sites. Fig. 9 suggests that sampling site 1 and site 2 receive air masses from the South as well as from the North regions. The higher number of hours in a grid cell or region indicates the slow air mass speed and also the increased residence time of air masses. (Figure 9a) shows that air masses arriving at the site at three heights had slow air mass speeds which indicates the potential source regions are very near and present in the South of the site. (Figure 9b) shows that air masses are having higher wind speeds at all three heights (500, 1000, 1500 m AGL) and were distributed widely in the North region (China). When compared to site 2, site 1 has slow air mass speeds coming from the South direction, which indicates that site 1 is highly influenced by the slow air masses coming from the south than site 2 (Figure 10b).

Figure 9: Trajectory density maps showing the number of hours spent by air masses arriving at the two sampling locations (top-bottom) a) 34⁰ 15`,77<sup>0</sup>57` and b) 34⁰ 02`, 78<sup>0</sup> 28` from 1^st to 9^th August 2019, at three heights (left-right) 500 m, 1000 m and 1500 m above ground level. The color bar denotes the number of hours that fall in a grid cell.

Local Meteorology over Observation Sites

The meteorological parameters (temperature, surface pressure, relative humidity wind direction and speed) obtained from the second Modern-Era Retrospective analysis for Research and Application (MERRA-2) are shown in (Figure 10a). Considerable variability in wind speed and direction from low to high-altitude experimental sites were noticed (Figure 10a). It is seen from the Figure that wind speed is the highest over the lower-altitude experimental sites and the wind direction is south-west coming from the Bay of Bengal (moist wind). The experimental sites which are located over higher-altitude regions (Himalaya and south of it, Ladakh regions) show slow wind speed and north-east wind direction (mostly dry winds). The higher temperature was observed over lower-altitude sites and the lower temperature was seen over high-altitude regions as expected (temperature decrease with altitude). Considerable differences in terms of relative humidity from north to south can be seen in (Figure 10). As the measurements were made during the south-west monsoon season, higher relative humidity is expected in monsoon-influenced regions. The highest relative humidity was observed along the east-west extension of the Himalayan mountains. Lower relative humidity was observed along the north-south extension of the Himalayan belt. However, a significant reduction in relative humidity can be seen north of the Himalayan mountains (Ladakh and adjacent regions). The lower relative humidity over Ladakh is due to well-known aridity and the rain-shadow nature of the region. A good relationship between the altitude, relative humidity, wind speed and direction, AOD, TCO and PWV was observed. The AOD and TCO values were observed low in higher altitude regions with low relative humidity, low wind speed and N-E wind direction, and higher AOD and TCO values were observed over low-altitude sites with high relative humidity, high wind speed and S-W wind direction. The higher PWV values were observed in high relative humidity regions (source of air mass is ocean) and lower PWV values were noticed in lower humidity regions (high altitude Ladakh and adjacent regions).

Figure 10: Map showing the meteorological parameters, relative humidity in color, the surface temperature in contours, wind vectors (arrows) show the magnitude and direction of the wind, averaged for the period from 1^st to 9^th August 2019. The points on the map show the site location and the connecting line represents the track of the sampling dates starting from 1^st August (South) to 9^th August (North). b) Map showing the 7-day backward air mass trajectories at 00, 03, 06, 09, 12, 15, 18 and 21 UTC at 500m above ground level arriving at the two sampling site locations 34⁰ 15`,77<sup>0</sup>57` on 5^th August (solid line) and 34⁰ 02`, 78<sup>0</sup> 28` on 7^th August 2019 (dotted line).

In the present study, spatiotemporal variations of aerosols and precursor gases from different geographical regions varying from plains of Haryana to higher mountains of Himalaya to adjacent Ladakh mountainous regions during 01-10 August 2019 have been investigated. Such studies are very sparse over the globe and more so in India. Significant variations in the loading of aerosols and precursor gasses were observed over the selected experimental sites. The observed AOD values varied from 1.98 to 0.12 at 380 mm, 1.80 to 0.12 at 500 mm and 1.27 to 0.09 at 1020 mm wavelengths. The highest AODs at all three wavelengths were observed at L-319. The lowest AOD value at 380 mm wavelength was observed at L-3100. The lowest AODs at 500 and 1020 mm were observed at the greater altitude region (L-5360). The ground-based and satellite retrieved PWV plotted values suggested a significant decreasing trend with an increase in altitude. Ground-based observations show the highest PWV content over L-250 and lowest value over L-4514. The loading of aerosol and precursor gases showed a steep decrease with an increase in altitude up to about 1 km and thereafter with a slow rate up to 5.5 km, which is attributed to local atmospheric boundary layer height variations. Relatively, lower α values in almost all the experimental sites indicated the dominance of coarse mode and mixed-mode aerosols, which in turn suggest the dominance of naturally generated aerosols (>80%).

The 5-day backward air mass trajectories at different locations revealed a longer route of air masses at lower altitude locations suggested distant source and local source of air masses at higher altitude. Two prominent ocean sources (Arabian sea and Bay of Bengal) were observed particularly at lower to mid-altitude experimental sites. Ladakh and adjacent regions lies in the rain-shadow regions, the influence of ocean moisture sources were absent. A good relationship among altitudinal variations of relative humidity, wind speed/direction, loading of aerosol and precursor gases were observed. Lower AOD and TCO values were observed in higher altitude regions with low relative humidity, low wind speed and N-E direction, and higher AOD and TCO values were observed over low-altitude sites with high relative humidity, high wind speed and   S-W direction. The higher PWV values were observed in monsoon-influenced regions with high relative humidity, and lower PWV values were seen in high altitude regions with low relative humidity.

The work reported in this paper was possible due to the vision, kind support and encouragement from Dr. Aseem Chauhan, Hon’ble Chancellor of the Amity University Haryana (AUH), Manesar-Gurugram. The authors are thankful to Hon’ble Vice-Chancellor, Deputy Vice-Chancellor, and Pro-Vice-Chancellor and all other authorities of AUH, Manesar-Gurugram for their continued encouragement and support. Authors are also thankful to (i) the Director of Indian Institute of Tropical Meteorology, Delhi Unit (IITM-DU), New Delhi for infrastructure support and (ii) Vijay Kanawade of University of Hyderabad, Hyderabad for helpful suggestions. The authors would like to express their gratitude to NASA’s Giovanni for providing the aerosol optical depth, ozone and precipitable water vapour data for the study period, and NOAA-ARL for the support with the HYSPLIT Model airmass trajectory analysis.

The corresponding author (Panuganti C.S. Devara) is responsible for conceptualization, methodology, project administration and supervision, and original manuscript preparation.  The principal author (Abul Amir Khan) has contributed to project administration, data curation and analysis, draft-writing, and plotting. Naresh Chandra Pant is responsible for data validation and visualization of the paper. Atul K. Srivastava has contributed to the formal analysis of data, plotting and review of the manuscript. Varap Varaprasad has contributed to the formal analysis of data and validation.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

The authors declare that they have no conflict of interest.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

1. Albrecht BA. Aerosols, cloud microphysics, and fractional cloudiness. Science. 1989; 245: 1227-1230.
2. Charlson RJ, Schwartz SE., Hales JM et al. Climate forcing by anthropogenic aerosols. Science. 1992; 255: 423-430.
3. Ackerman AS, Toon OB, Taylor JP, Johnson DW, Hobbs PV, Ferek RJ. Effects of aerosols on cloud albedo: Evaluation of Twomey’s parameterization of cloud susceptibility using measurements of ship tracks. J Atmospheric Sciences. 2000; 57: 2684-2695.
4. Penner JE, Andreae MO, Annegarn H . Aerosols, their direct and indirect effects. In Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. 2001; 289-348.
5. Devara PCS, Sonbawne SM, Saha SK. Novel technique for profiling of aerosol, ozone and water vapor during winter using mobile radiometers over a hilltop station. Aerosol and Air Quality Research. 2014; 14: 1443-54.
6. Hrabcak P. Comparison of the optical depth of total ozone and atmospheric aerosols in Poprad-Gánovce, Slovakia. Atmospheric Chemistry and Physics. 2018; 18: 7739-7755.
7. Filonchyk M, Yan H, Yang S, Lu X. Detection of aerosol pollution sources during sandstorms in Northwestern China using remote sensed and model simulated data. Advances in Space Research. 2018; 61: 1035-46.
8. Devara PCS, Kumar A, Sharma P B. Multi-sensor study of the impact of air pollution on COVID-19. J. Infect. Dis. Res. 2021; 3: 22.
9. Ningombam SS, Bagare SP, Sinha N, Singh RB, Srivastava AK, Larson E et al. Characterization of aerosol optical properties over the high-altitude station Hanle, in the trans-Himalayan region. Atmospheric research. 2014; 138: 308-23.
10. Ningombam SS, Bagare SP, Srivastava AK, Sohn BJ, Song HJ, Larson E. Aerosol radiative forcing over a high-altitude station Merak, in the trans-Himalayan region during advection of anthropogenic events from the Indo-Gangetic Plain. Atmospheric Environment. 2014; 98: 253-9.
11. Haywood J, Schulz M. Causes of the reduction in uncertainty in the anthropogenic radiative forcing of climate between IPCC (2001) and IPCC (2007). Geophysical Research Letters. 2007; 34.
12. Cheng YF, Wiedensohler A, Eichler H, Heintzenberg J, Tesche M, Ansmann A et al. Althausen D, Herrmann H, Gnauk T. Relative humidity dependence of aerosol optical properties and direct radiative forcing in the surface boundary layer at Xinken in Pearl River Delta of China: An observation based numerical study. Atmospheric Environment. 2008; 42: 6373-6397.
13. Wang L, Gong W, Xia X, Zhu J, Li J, Zhu Z. Long-term observations of aerosol optical properties at Wuhan, an urban site in Central China. Atmospheric Environment. 2015; 101: 94-102.
14. Moorthy KK, Niranjan K, Narasimhamurthy B, Agashe VV, Murthy BK. Aerosol Climatology over India; 1-ISRO GBP MWR Network and Database. ISRO GBP SR-03. 1999; 99.
15. Satheesh SK, Srinivasan J. Enhanced aerosol loading over Arabian Sea during the pre?monsoon season: Natural or anthropogenic? Geophysical Research Letters. 2002; 29: 21-31.
16. Vadrevu KP, Ellicott E, Giglio L, Badarinath KV, Vermote E et al. Vegetation fires in the himalayan region–Aerosol load, black carbon emissions and smoke plume heights. Atmospheric Environment. 2012; 47: 241-251.
17. Thind PS, Kumar D, John S. Source apportionment of the light absorbing impurities present in surface snow of the India Western Himalayan glaciers. Atmospheric Environment. 2021;246..
18. Menon S, Koch D, Beig G, Sahu S, Fasullo J, Orlikowski D. Black carbon aerosols and the third polar ice cap. Atmospheric Chemistry and Physics. 2010; 10: 4559-4571.
19. Sarangi C, Qian Y, Rittger K, Leung LR, Chand D, Bormann KJ et al. Dust dominates high-altitude snow darkening and melt over high-mountain Asia. Nature Climate Change. 2020; 10: 1045-1051.
20. Sagar R, Kumar B, Dumka UC, Moorthy KK, Pant P. Characteristics of aerosol spectral optical depths over Manora Peak: A high?altitude station in the central Himalayas. J Geophysical Research: Atmospheres. 2004; 109.
21. Pant PA, Hegde P, Dumka UC, Sagar R, Satheesh SK, Moorthy KK, Saha A, Srivastava MK. Aerosol characteristics at a high?altitude location in central Himalayas: Optical properties and radiative forcing. J Geophysical Research: Atmospheres. 2006 Sep 16; 111.
22. Dumka UC, Moorthy KK, Pant P, Hegde P, Sagar R, Pandey K. Physical and optical characteristics of atmospheric aerosols during ICARB at Manora Peak, Nainital: A sparsely inhabited, high-altitude location in the Himalayas. Journal of earth system science. 2008; 117: 399-405.
23. Hegde P, Pant P, Naja M, Dumka UC, Sagar R. South Asian dust episode in June 2006: Aerosol observations in the central Himalayas. Geophysical Research Letters. 2007; 34.
24. Hegde P, Pant P, Bhavani Kumar Y. An integrated analysis of lidar observations in association with optical properties of aerosols from a high-altitude location in central Himalayas. Atmospheric Science Letters. 2009: 48-57.
25. Hyvärinen AP, Lihavainen H, Komppula M, Sharma VP, Kerminen VM et al. Continuous measurements of optical properties of atmospheric aerosols in Mukteshwar, northern India. Journal of Geophysical Research: Atmospheres. 2009; 114.
26. Sellegri K, Laj P, Venzac H, Boulon J, Picard D, Villani P et al. Seasonal variations of aerosol size distributions based on long-term measurements at the high-altitude Himalayan site of Nepal Climate Observatory-Pyramid (5079 m), Nepal. Atmospheric Chemistry and Physics. 2010; 10: 10679-1090.
27. Ram K, Sarin MM, Hegde P. Long-term record of aerosol optical properties and chemical composition from a high-altitude site (Manora Peak) in Central Himalaya. Atmospheric Chemistry and Physics. 2010; 10: 11791-11803.
28. Marinoni A, Cristofanelli P, Laj P, Duchi R, Calzolari F, Decesari S et al. Aerosol mass and black carbon concentrations, a two-year record at NCO-P (5079 m, Southern Himalayas). Atmospheric Chemistry and Physics. 2010; 10: 8551-8562.
29. Nair VS, Babu SS, Moorthy KK, Sharma AK, Marinoni A. Black carbon aerosols over the Himalayas: direct and surface albedo forcing. Tellus B: Chemical and Physical Meteorology. 2013; 65: 19738.
30. Sarkar C, Chatterjee A, Singh AK, Ghosh SK, Raha S. Characterization of black carbon aerosols over Darjeeling-A high altitude Himalayan station in eastern India. Aerosol and Air Quality Research. 2015;15: 465-78.
31. Srivastava P, Naja M. Characteristics of carbonaceous aerosols derived from long-term high-resolution measurements at a high-altitude site in the central Himalayas: radiative forcing estimates and role of meteorology and biomass burning. Environmental Science and Pollution Research. 2021; 28: 14654-14670.
32. Kuniyal JC, Thakur A, Thakur HK, Sharma S, Pant P, Rawat PS et al. Aerosol optical depths at Mohal-Kullu in the northwestern Indian Himalayan high altitude station during ICARB. J Earth System Science. 2009; 118: 41-48.
33. Guleria RP, Kuniyal JC, Rawat PS, Thakur HK, Sharma M et al. Aerosols optical properties in dynamic atmosphere in the northwestern part of the Indian Himalaya: a comparative study from ground and satellite based observations. Atmospheric Research. 2011; 101: 726-738.
34. Singh S, Singh R. High?altitude clear?sky direct solar ultraviolet irradiance at Leh and Hanle in the western Himalayas: Observations and model calculations. Journal of Geophysical Research: Atmospheres. 2004; 109.
35. Ghude SD, Jain SL, Arya BC, Bajaj MM. Comparison of total column water vapour measured at New Delhi, Maitri (Antarctica), Leh and Hanle. 2005.
36. Arya BC, Jain SL. The measurement of water vapour at Maitri, Antarctica. Indian J. Radio Space Phys. 1997; 26: 117-1120.
37. Srivastava AK, Dey S, Tripathi SN. Aerosol characteristics over the Indo-Gangetic Basin: implications to regional climate. Atmospheric Aerosols-Regional Characteristics-Chemistry and Physics. 2012; 10.
38. Devara PCS, Pandithurai G, Raj PE, Sharma S. Investigations of aerosol optical depth variations using spectroradiometer at an urban station, Pune, India. J Aerosol Science. 1996; 27: 621-632.
39. Devara PCS, Maheskumar RS, Raj PE, Dani KK, Pandithurai G, Rao YJ. Correlative measurements of aerosol optical depth and size distribution around INDOEX–FFP 98 from multi-spectral solar radiometry. Current Science. 1999: 977-980.
40. King MD, Menzel WP, Kaufman YJ, Tanre D, Gao BC et al. Cloud, and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS. IEEE Transactions on Geoscience and Remote Sensing. 2003; 41: 442-458.
41. Kaufman YJ, Koren I, Remer LA, Tanre D, Ginoux P, Fan S. Dust transport and deposition observed from the Terra?Moderate Resolution Imaging Spectroradiometer (MODIS) spacecraft over the Atlantic Ocean. Journal of Geophysical Research: Atmospheres. 2005; 110.
42. Levy RC, Remer LA, Kleidman RG, Mattoo S, Ichoku C et al. Global evaluation of the Collection 5 MODIS dark-target aerosol products over land. Atmospheric Chemistry and Physics. 2010; 10: 10399-10420.
43. Ramachandran S, Kedia S, Srivastava R. Aerosol optical depth trends over different regions of India. Atmospheric Environment. 2012; 49: 338-347.
44. He L, Chen JM, Pisek J, Schaaf CB, Strahler AH. Global clumping index map derived from the MODIS BRDF product. Remote Sensing of Environment. 2012; 119:118-30.
45. Wei J, Li Z, Peng Y, Sun L. MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison. Atmospheric Environment. 2019; 201: 428-40.
46. Sayer AM, Munchak LA, Hsu NC, Levy RC, Bettenhausen C, Jeong MJ. MODIS Collection 6 aerosol products: Comparison between Aqua's e?Deep Blue, Dark Target, and merged data sets, and usage recommendations. Journal of Geophysical Research: Atmospheres. 2014; 119: 913-965.
47. Gelaro R, McCarty W, Suárez MJ, Todling R, Molod A, Takacs L. et al. The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Journal of climate. 2017; 30: 5419-5454.
48. Draxler RR. HYSPLIT4 user’s guide. NOAA Tech. Memo. ERL ARL-230, NOAA Air Resources Laboratory, Silver Spring, MD.1999.
49. Stein AF, Draxler RR, Rolph GD, Stunder BJ, Cohen MD, Ngan F. NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bulletin of the American Meteorological Society. 2015; 96: 2059-2077.
50. Wang YQ, Zhang XY, Draxler RR. TrajStat: GIS-based software that uses various trajectory statistical analysis methods to identify potential sources from long-term air pollution measurement data. Environmental Modelling & Software. 2009; 24: 938-939.
51. Dickerson RR, Kondragunta S, Stenchikov G, Civerolo KL, Doddridge BG, Holben BN. The impact of aerosols on solar ultraviolet radiation and photochemical smog. science. 1997; 278: 827-30.
52. Sayer AM, Hsu NC, Bettenhausen C. Implications of MODIS bow-tie distortion on aerosol optical depth retrievals, and techniques for mitigation. Atmospheric Measurement Techniques. 2015; 8: 5277-5288.
53. Filonchyk M, Yan H, Zhang Z. Analysis of spatial and temporal variability of aerosol optical depth over China using MODIS combined Dark Target and Deep Blue product. Theo App Climatology. 2019; 137: 2271-2288.
54. Ghan SJ, Rissman TA, Elleman R, Ferrare RA, Turner D, Flynn C et al. Use of in situ cloud condensation nuclei, extinction, and aerosol size distribution measurements to test a method for retrieving cloud condensation nuclei profiles from surface measurements. J Geo Research: Atmospheres. 2006; 111.
55. Devara PCS, Maheskumar RS, Raj PE, Dani KK, Sonbawne SM. Some features of columnar aerosol optical depth, ozone and precipitable water content observed over land during the INDOEX-IFP99. Meteorologische Zeitschrift. 2001; 10: 123-130.
56. Kaufman YJ, Holben BN, Tanré D, Slutsker I, Smirnov A, Eck TF. Will aerosol measurements from Terra and Aqua polar orbiting satellites represent the daily aerosol abundance and properties? Geophysical Research Letters. 2000; 27: 3861-3864.
57. King MD, Byrne DM, Herman BM, Reagan JA. Aerosol size distributions obtained by inversions of spectral optical depth measurements. J Atmos Sci. 1978; 35: 2153-2167.
58. Draxler RR, Rolph GD. HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) model access via NOAA ARL READY NOAA Air Resources Laboratory, Silver Spring. 2003
59. Bailey HL, Kaufman DS, Henderson AC, Leng MJ. Synoptic scale controls δ18O in precipitation across Beringia. Geophysical Research Letters. 2015; 42: 4608-16.