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Temporal and Spatial Variations of Satellite-Based Aerosol Optical Depths, Angstrom Exponent, Single Scattering Albedo, and Ultraviolet-Aerosol Index over Five Polluted and Less-Polluted Cities of Northern India: Impact of Urbanization and Climate Change

It is widely acknowledged that factors such as population growth, urbanization's quick speed, economic growth, and industrialization all have a role in the atmosphere's rising aerosol concentration. In the current work, we assessed and discussed the findings of a thorough analysis of the temporal and spatial variations of satellite-based aerosol optical parameters such as Aerosol Optical Depth (AOD), Angstrom Exponent (AE), Single Scattering Albedo (SSA), and Ultraviolet-Aerosol Index (UV-AI), and their concentration have been investigated in this study over five polluted and less-polluted cities of northern India during the last decade 2011–2020. The temporal variation of aerosol optical parameters for AOD ranging from 0.2 to 1.8 with decadal mean 0.86 ± 0.36 for Patna region shows high value with a decadal increasing trend over the study area due to rise in aerosols combustion of fossil fuels, huge vehicles traffic, and biomass over the past ten years. The temporal variation of AE ranging from 0.3 to 1.8 with decadal mean 1.72 ± 0.11 for Agra region shows high value as compared to other study areas, which indicates a comparatively higher level of fine-mode aerosols at Agra. The temporal variation of SSA ranging from 0.8 to 0.9 with decadal mean 0.92 ± 0.02 for SSA shows no discernible decadal pattern at any of the locations. The temporal variation of UV-AI ranging from -1.01 to 2.36 with decadal mean 0.59 ± 0.06 for UV-AI demonstrates a rising tendency, with a noticeable rise in Ludhiana, which suggests relative dominance of absorbing dust aerosols over Ludhiana. Further, to understand the impact of emerging activities, analyses were done in seasonality. For this aerosol climatology was derived for different seasons, i.e., Winter, Pre-Monsoon, Monsoon, and Post-Monsoon. High aerosol was observed in Winter for the study areas Patna, Delhi, and Agra which indicated the particles major dominance of burning aerosol from biomass; and the worst in Monsoon and Post-Monsoon for the Tehri Garhwal and Ludhiana study areas which indicated most of the aerosol concentration is removed by rainfall. After that, we analyzed the correlation among all the parameters to better understand the temporal and spatial distribution characteristics of aerosols over the selected region. The value of r for AOD (550 nm) for regions 2 and 1(0.80) shows a strong positive correlation and moderately positive for the regions 3 and 1 (0.64), mostly as a result of mineral dust carried from arid western regions. The value of r for AE (412/470 nm) for region 3 and (0.40) shows a moderately positive correlation, which is the resultant of the dominance of fine-mode aerosol and negative for the regions 5 and 1 (− 0.06). The value of r for SSA (500 nm) for regions 2 and 1 (0.63) shows a moderately positive correlation, which explains the rise in big aerosol particles, which scatters sun energy more efficiently, and the value of r for UV-AI for regions 1 and 2 shows a strong positive correlation (0.77) and moderately positive for the regions 3 and 1 (0.46) which indicates the absorbing aerosols present over the study region.


Introduction
In the atmosphere, there exist tiny suspended particles known as aerosols ranging from ~ 0.001 to ~ 100 μm in size (Mahowald et al. 2014) and subjected in the diverse types such as intermediate, fine and coarse mode depending on their dimension. Aerosols influencing the radiation from the sun is reduced by scattering and absorption, depending upon their chemical composition as well as size (Haywood and Ramaswamy 1998;Delene and Ogren 2002;Jacobson 2002;Gautam et al. 2021). Aerosols alter the Earth's radiation budget spatially and temporally by scattering, absorbing, and releasing radiation. This process, known as radiative forcing, changes the radiative balance of the Earth's climate (Charlson et al. 1992;Kaufman et al. 1998;Satheesh and Ramanathan 2000;Ramachandran et al. 2006;Ranjan et al. 2007). Their features are subject to large spatial and temporal changes as a result of their brief lifespans and uneven geographic distribution brought on by emissions, atmospheric chemical reactions, and weather patterns (Delene and Ogren 2002;Andrews et al. 2011;Boucher et al. 2013;Collaud Coen et al. 2013;Park et al. 2019;Kumar et al. 2022a, b). A potential role for submicron-sized aerosols is in the establishment of mist and fog at the location (Lillis et al. 1999).
Atmospheric aerosols from both natural and anthropogenic sources have noticeable effects on the ecological environment, urban air quality, climate change, and human health (Gautam et al. 2021;Bisht et al. 2022;Gupta et al. 2022). An increase in the concentration of suspended particles above cities has affected visibility in the majority of the main cities on the Indian subcontinent (Singh and Chauhan 2020;Gautam 2020a, b). It is essential to enhance our understanding of the characteristics of atmospheric aerosols on regional and global levels which provides a high level of temporal and spatial resolution because of their impact on climate, human health, and the environment. (Smirnov et al. 2002), particularly over the zone wherein density of population is sky-scraping (Dey and Di Girolamo 2010). Consequently, governments around the world and scientists are paying more attention to these issues (Cao et al. 2012;Guo et al. 2017;Pöschl, 2005).
Aerosol optical depth (AOD) is a crucial measure for determining the characteristics of atmospheric aerosols because it influences how direct solar light is reflected and absorbed (Ranjan et al. 2007). AOD spectral dependence is proven to be different for different aerosol species as a result of a change in their optical properties, physical properties, and chemical composition, defined by criteria like AOD, Single Scattering Albedo (SSA), and aerosol size distribution. The appropriate way to express this spectral dependence is through empirical formula proposed by Angstrom to get the parameters Angstrom Exponent (AE) and Angstrom turbidity coefficient (β) (Ångström 1964). To discriminate between or characterize various aerosol varieties, estimation of AE in various spectral intervals is a valuable technique (Kalapureddy et al. 2009;Kaskaoutis et al. 2010;Soni et al. 2011) and the principal causes of the dwindling of the ultraviolet, visible, and near-infrared radiation bands include molecular scattering by consistently mixed gases, scattering and absorption of aerosols, and penetration by unevenly mixed gases (Thomason et al. 1983).
Strong seasonal and aerosols variation from year to year over India is primarily caused by the regional monsoon system, atmospheric dynamics, seasonally shifting air-mass patterns, and the spatio-temporal distribution of emission sources/sinks (Arora and Reddy 2014;Ramachandran and Cherian 2008). Long-distance aerosol transportation has a tremendous impact on the Indian subcontinent (Ramanathan et al. 2001;Lawrence and Lelieveld 2010). The dynamical characteristics of atmospheric aerosols over the Indo-Gangetic (IG) region are principally dependent on the geographical settings and meteorological conditions . Other studies at the regional and global scales have been conducted utilizing a variety of satellite sensors and ground-based instruments, indicating a spike in aerosol loading, especially in certain parts of India (Beegum et al. 2008;Krishna Moorthy et al. 2013;Satheesh et al. 2008;Gupta et al. 2020). The role that aerosols play in modulating various atmospheric processes was acknowledged, despite the existence of numerous studies using satellite data at the international and national levels and the scarcity of studies of a similar nature. This study was conducted at the five North West Indian stations of Agra, Delhi, Ludhiana, Patna, and Tehri Garhwal during the period from 2011 to 2020. The objectives of this research are to highlight the longterm various atmospheric aerosol optical characteristics, wind pattern is observed and analyzed temporal and spatial aerosol distribution characteristics throughout the selected area using Moderate Resolution Imaging Spectrometer (MODIS), AOD (550 nm) and AE (412-470 nm) data and Ozone Monitoring Instrument (OMI), SSA (500 nm) and Ultraviolet-Aerosol Index (UV-AI) data, and also correlation between all the aerosol optical parameters over polluted and less-polluted environments.

Sites Description
Studies on long-term evaluation of various aerosol properties, derived from diverse satellite observations, are being offered to help understand the effects of recent human actions (Fig. 1).
The study was carried out between 2011 and 2020 in the five cities-Agra, Delhi, Ludhiana, Patna, and Tehri Garhwal-of India located in North West region to investigate the scenario of aerosol loading. Recently, the regions have experienced growth in terms of population, industrialization rate, urbanization, etc.

Agra
Agra region is bounded by the Latitude 27° N and by the Longitude coordinate 78° E. The average elevation of the city is 170 m above sea level. The city of Tajon is in the Yamuna river's banks in the Indian state of Uttar Pradesh, about 210 km south of the national capital New Delhi. The total area of Agra is 525.6 km and is the third most heavily populated city in Uttar Pradesh, which has the 24th-highest population in India. The dry plains of Rajasthan's Thar Desert encircle Agra by a distance of almost 695 km of its surrounding areas. The soils are a blend of loam and sand with surplus number of salts. The Mathura oil refinery is about 40 km to the (North-West) NW of the research site and is upwind because winds during the Winter are primarily from the NW sector. There are many industries, like ferrous and nonferrous, rubber processing, metal casting, brick kilns, tanneries, etc. Additionally, roughly 250 km to the NW are New Delhi's industrial zones. Also, the mainstream of foundries and Firozabad Glass industries and The National highway (NH-11) have greater traffic. The site is surrounded by a variety of deciduous trees and agricultural fields. Agriculture is the major activity and fields around the location are covered with a variety of crops in the Winter. In summer, uncultivated fields lie barren and dry (Safai et al. 2008).

Delhi
Delhi is a national capital and union territory of India and it is situated in north central region of India. It is bordered by the state of Haryana on three sides and by Uttar Pradesh to the east has an estimated population of approximately 17 million people (Census 2011). Latitude 28.4° North and Longitude 77.1° East characterize the region's boundaries, which are 216 m above mean sea level. Delhi is bordered by the Himalayas in the north, the central Indian plains in the south, and the Thar Desert in the west-southwest. The area is surrounded by a lot of small-and medium-sized businesses, and there are roughly 7 million people signed up vehicles as well, which are the main sources of air pollution there (Mishra and Goyal 2016;Kishore et al. 2019). This site can describe the combination of local and regional sources of air pollution. These are highly developed areas and are probably the major contributors to air pollution.

Ludhiana
The largest city in the Indian state of Punjab is Ludhiana, which ranks as India's 28th largest urban agglomeration (Tripathi and Mahey 2017). The city is positioned at Latitude 30.90° N and by the Longitude coordinate 75.85° E, with an area of approximately 310 km 2 and average elevation of 244 m above mean sea level (798 ft.) and predictable population of 1,618,879 as of the Census 2011. It is situated at the junction of numerous train lines on the Grand Trunk Road, which connects Delhi to Amritsar, and is a significant agricultural market and industrial center, which, as the primary causes of the variation in atmospheric aerosol loading brought on by frequent biomass burning events, to great extent correlated with the proportionate rise in the burning of biomass. A lot of agricultural waste, residue, and other substance are burned in this area. This might be the reason for the area's air quality significantly declining and increased risk to the human health. The city is located on the Sutlej River's former bank, which is now 13 km (8.1 miles) south of the river. The British Broadcasting Corporation (BBC) commonly refers to it as "Indian Manchester". It is a major industrial hub in northern India. The World Bank has named Ludhiana as the most business-friendly city in India, and it is one of the smart cities that the Indian government plans to build (Mehta and Rajan 2017).

Patna
The largest and capital of the Indian state of Bihar is Patna. The city has the mean elevation of 173 feet (53 m) above sea level and its region is bounded by the Latitude 25.6° N and by the Longitude coordinate 85.1° E. According to the United Nations, as of 2018, 1.8% increase from 2019. Patna had a population of 2.35 million, making it the 19th largest city in India. Northeastern India's Bihar state is home to the historic city of Patna, which stretches along the Ganges River's southern bank. In this region, AOD predominates and is associated with the irregular behavior of the summer monsoon, which has a significant impact on agriculture and the nighttime temperature throughout the Winter (Saikawa et al. 2019).

Tehri Garhwal
Tehri Garhwal is a district in the hill state of Uttarakhand, India. Its administrative headquarters is at New Tehri. It is bounded by the Latitude 30.38° N and by the Longitude coordinate 78.48° E and is a mountain range in the Garhwal Himalayas that is located 1981 m above mean sea level. The district's inhabitants' number 618, 931 (2011 census), a 2.35% intensify over the previous decade. It is encircled by the districts of Pauri Garhwal in the south, Uttarkashi in the north, Dehradun in the west, and Rudraprayag in the east. The Himalayas include the Tehri Garhwal region. According to the 2011 Census, Tehri Garhwal district has a population of 618,931. This gives it a ranking of 520th in India (out of a total of 688). The district has a population density of 169 inhabitants per square kilometer (440/sq mi) (Ramya and Devadas 2019) ( Table 1).   (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020). Contour color shows the geopotential height (m) of wind vectors pattern over our study locations Ludhiana, Delhi, Agra, Tehri Garhwal, and Patna during whole study period with the geopotential highest range 1480-1520 m. The wind is circulated starting from the direction of Pakistan and follows the path with Punjab, Haryana, Delhi, Uttar Pradesh, Uttarakhand, and Bihar. As the wind pattern is the same in the Indo-Gangetic Plain (IGP), we can understand the air-mass transportation over our study locations. This pattern reduces effects on aerosol concentrations over the Tehri Garhwal and Ludhiana region, and enhances precipitation which may further alter the land-sea thermal gradient, resulting in the weakening of circulation. Enhanced aerosol concentration over Patna, Agra, and Delhi regions is adjusting the precipitation distribution along with the weakening of low-level jet and moisture transport.

Wind Pattern
Aerosols come in a variety of forms, such as anthropogenic aerosols produced by urbanization and burning of harvest residue, industrialization, and seasonal forest fires (Jain et al. 2014;Pawar et al. 2015), Sand dust is generated in the Thar Desert or is imported from Saudi Arabia, the United Arab Emirates, and the Gulf Desert (Aher et al. 2014), as well as aquatic aerosols from nearby waters (Sinha et al. 2012;Goloub et al. 2001) Air mass trajectories play a crucial role in the movement of atmospheric aerosols over a broad range in both horizontal and upright directions (Badarinath et al. 2008;Kuniyal et al. 2009). The western Thar desert sends dry dust particles to the area during the Pre-Monsoon, which causes frequent dust storms and dry weather. Early in July, when monsoon winds transport moisture from the eastern half of the Ganga basin arriving in the area, the relative humidity rises to as high as 60-90%. The entire region is covered in anthropogenic aerosols throughout the Post-Monsoon and Winter seasons, which are carried by local and northerly winds. The region is heavily covered in thick fog and haze during the Winter by western disturbances. It is crucial to conduct a long-term analysis of satellite data because changes in aerosol concentration could have a significant impact on radiative forcing and climate (Boucher & Haywood 2001;Boucher and Tanré 2000;Charlson et al. 1992;d'Almeida 1987;Hansen et al. 1997;Harrison et al. 1990;Liao and Seinfeld 1998;Miller and Tegen 1998;Parungo et al. 1994;Ramanathan et al. 2001;Rotstayn 2000).

MODIS for AOD and AE Data
The Terra and Aqua satellites were launched by NASA's Earth Observing System (EOS) to track the properties of the Earth, the atmosphere, and the oceans (Savtchenko et al. 2004). The MODIS has been a crucial instrument on board the Terra satellites for making very accurate observations of aerosol and clouds, this began running in February of 2000 (Xiong et al. 2009;Fetzer et al. 2003). The MODIS, which is distinguished by excellent spatio-temporal resolution, monitors the radiation that is rising to the top of the atmosphere in 36 channels from 0.41 m to 14.2 m. It has an extensive swath of 2330 km, spatial resolutions of 250 m, 500 m, and 1000 m, and a temporal resolution of 1-2 days (Ichoku et al. 2004;Ali et al. 2019). (Alam et al. 2014) discovered that MODIS's ability to track various fluctuations and compile data allowed scientists to better understand the phenomenology of the atmosphere, earth, and oceans. Investigating aerosol characteristics and comprehending aerosol-cloud interactions benefit from these accumulated statistics.
Applying the Deep Blue (over the land) algorithms, the MODIS on board the Terra satellites offers complete worldwide coverage of aerosol optical property. The equator is crossed by the Terra satellites at approximately 10:30 and 1:30 p.m. (local time), correspondingly. The in-progress research used Level 3 MODIS Terra data (MYD08_D3v6) of Aerosol Optical Depth (AOD) and Angstrom exponent (AE) over the land of (i.e., at Agra, Delhi, Ludhiana, Patna, and Tehri Garhwal sites) during the period from 2011 to 2020 which is retrieved from Giovanni website (https:// giova nni. gsfc. nasa. gov/ giova nni/).

OMI for SSA and UV-AI Data
The National Aeronautics and Space Agency NASA Aura spacecraft is equipped with a visible and ultraviolet spectrometer called the OMI. OMI can monitor cloud pressure and coverage, which can provide information on tropospheric ozone, and can make a distinction between different types of aerosol, such as smoke, dust, and sulfates. It is a huge area imaging spectrometer with a viewing angle range of 114° across the track, which offers a broad expanse of 2600 km and allows measurements daily worldwide coverage (Levelt et al. 2006;Ahmad et al. 2003). OMI is a cooperative venture between the (NASA), the Finnish Meteorological Institute (FMI), and the Netherlands Agency for Aerospace Programmes (NAAP).The Level_3 OMI data (OMAERUVd_v003) of Single Scattering Albedo (SSA) at 500 nm and Ultraviolet-Aerosol Index was also used with a spatial resolution of 1° × 1° (https:// giova nni. gsfc. nasa. gov/ giova nni/) throughout the era of 2011-2020. Additional information about OMI retrievals is presented in Torres et al. (2018). Due to the seasons that are generally rainy or wet, when the sky is mostly cloudy and the air is humid, there were little opportunities for observations at the sites during the monsoon season. Because of this, the vast majority of the aerosol data are tainted by clouds, which means they may not accurately depict the atmosphere and must be excluded from the study. Due to irregularities in the monsoon's beginning, strength, and duration in various years, this could result in a significant annual fluctuation and greatly increase the uncertainty of the recorded aerosol characteristics (Gautam et al. 2009) ( Table 2).

Temporal Distribution of Decadal Mean of Aerosol Optical Parameters During 2011-2020
In this section, the outcome and corresponding discussions on Temporal and Spatial variability in AOD, AE, SSA, and UV-AI during the study period from 2011 to 2020 over five stations have discussed.  varies from 0.2 to 1.6 for the study location Agra. Similarly, the same variation of AOD has been shown for the study location Delhi and AOD varies from 0.2 to 1.6. The AOD variation for Ludhiana shows maximum nearby June month and vary from 0.2 to 1.4. For the study location Patna, the variation of AOD has been shown maximum to other locations and varies from 0.2 to 1.8. For the study location Tehri Garhwal, the AOD variation is the same as Agra and Delhi. The AOD is found to be less at Tehri Garhwal compared to other study locations and vary from 0.1 to 0.6. The variation of SSA is also shown in Fig. 3. For the study location Agra, the SSA variation is maximum in Pre-Monsoon and varies from 0.86 to 0.98. Similarly, for the study locations Delhi, Ludhiana, and Patna, SSA shows the same variation as the study location Agra. The SSA varies from 0.88 to 0.96, 0.86 to 0.98, and 0.88 to 0.98 for the study locations Delhi, Ludhiana, and Patna, respectively. The study location is slightly different to other location. It shows the value is maximum in the month of December and Pre-Monsoon and the SSA varies from 0.88 to 0.98.

Time Series Daily Mean Characteristics of Aerosol Optical Parameters
Also, Fig. 3 shows the variation of AE. The variation of AE is found to be minimum in Pre-Monsoon and Post-Monsoon for the study location Agra. Specially, the value of AE is found to be minimum in Post-Monsoon. This AE variation for Agra varies from 1.75 to 1.25. For the study location Delhi, the AE variation is found to be same for Delhi but large variation has been found in Pre-Monsoon (1.75-0) and less variation in Post-Monsoon (1.75-0.7). The AE variation is shown different for the study location Ludhiana since the AE variation in Pre-Monsoon and Post-Monsoon is the same as Delhi and Agra locations and same as variation has been found in June and Winter. At the study location Patna, the AE variation has been shown same as Delhi and Agra. The AE variation for Tehri Garhwal location is all different from the other locations. The AE variation is reduced in Winter and Pre-Monsoon, while increased in Post-Monsoon. Figure 3 also shows the variation of UV-AI. The maximum variation of UV-AI has been shown in Post-Monsoon for the study location Agra. Also, the maximum variation has been shown in Pre-Monsoon but specially around May and June and varies from − 0.5 to 2. The variation of UV-AI for Ludhiana has been shown minimum in March and maximum in Pre-Monsoon, especially May and June. The UV-AI varies from − 0.7 to 2.0 for the study location Ludhiana. For the study location Patna, the UV-AI has been minimum in December and maximum in Pre-Monsoon and Post-Monsoon. For the location Tehri Garhwal, we get maximum variation in Pre-Monsoon and Post-Monsoon. The UV-AI varies from − 0.7 to 1.
The time series plot of daily mean aerosol optical parameters (AOD, SSA, AE, and UV-AI) obtained from the satellite is presented in Fig. 3. The height of the atmospheric boundary layer has a substantial impact on the vertical mixing of aerosols, which has a significant impact on the columnar aerosol concentration (Ramachandran et al. 2012). Figure 3 demonstrates on a daily-basis AOD (550 nm) fluctuation over Agra, Delhi, Ludhiana, Patna, and Tehri Garhwal from 1 January 2011 to 31 December 2020. Figure 3a shows the nearly comparable trend of AOD (550 nm) for all the selected cities for every year using MODIS data. The entire annual mean on a daily-basis AOD (550 nm) concentrations for all five cities has been measured 0.68 ± 0.29, 0.68 ± 0.31, 0.66 ± 0.30, 0.67 ± 0.31, 0.70 ± 0.28, 0.75 ± 0.32, 0.71 ± 0.30, 0.69 ± 0.26,0.66 ± 0.32 and 0.68 ± 0.32 in AGRA; 0.72 ± 0.30, 0.69 ± 0.31, 0.68 ± 0.30, 0.70 ± 0.30, 0.73 ± 0.30, 0.76 ± 0.31, 0.69 ± 0.28, 0.75 ± 0.29,0.70 ± 0.30 and 0.68 ± 0.30 in Delhi; 0.56 ± 0.29, 0.59 ± 0.31, 0.56 ± 0.29, 0.58 ± 0.30, 0.60 ± 0.32, 0.63 ± 0.30, 0.58 ± 0.27, 0.62 ± 0.31,0.57 ± 0.32 and 0.53 ± 0.28 in Ludhiana; 0.79 ± 0.33, 0.71 ± 0.28, 0.76 ± 0.37, 0.73 ± 0.34, 0.83 ± 0.36, 0.85 ± 0.35, 0.78 ± 0.33, 0.86 ± 0.36,0.79 ± 0.35 and 0.73 ± 0.33 in Patna; and 0.23 ± 0.12, 0.24 ± 0.15, 0.21 ± 0.14, 0.22 ± 0.14, 0.25 ± 0.14, 0.26 ± 0.14, 0.23 ± 0.12, 0.23 ± 0.14, 0.21 ± 0.12 & 0.22 ± 0.11 in Tehri Garhwal, respectively, during the research period. The maximum concentration of AOD has been measured for Patna (0.86 ± 0.36) and minimum for Tehri Garhwal (0.22 ± 0.11) because AOD concentrations (> 0.39) have been identified over the Kedarnath sub-Himalayan region of Northern India since the Uttarakhand zone is typically calm and free from any form of dominating industrial areas and anthropogenic activity. The AOD values are determined to be significantly higher than the average nationwide AOD (0.126) and the AODs reported for the other continents between 2003 and 2012 were 0.168 for Africa, 0.182% for Asia, and 0.182% for Europe, and 0.098 for North America (Mao et al. 2014). The high AOD value in may be probably due to high wind speed and strong convection. The presence of fine-mode particle which is selective scatters, hence high AOD at a shorter wavelength (Singh et al. 2004). A rise in AODs relative to places where manmade aerosols are prevailing, such as New Delhi, suggests a rise in aerosols over the past 10 years due to the combustion of fossil fuels, huge vehicles traffic and biomass; while increasing trend in AODs over the Patna region indicates an increase in the amount of aerosols dominated by natural biogenic sources. An important source of atmospheric aerosols on the Indo-Gangetic Plains, particularly in the Indian state of Punjab, trash from agriculture has been burned. Every October, these actions continue to be fairly typical (Badarinath et al. 2009a, b;Sharma et al. 2010). The results show that AOD values are gradually increasing across all regions; however for 2020, we clearly observe a drop. This might be attributed to the stringent state stance about the COVID-19 pandemic.
Angstrom Exponent (AE) It provides comprehensive data on the predominant diameters of the particles in the aerosol size distribution and which has a secondary dependence on the aerosol composition and is negatively associated with particle size (Eck et al. 1999). The higher AE value (2 or more than 2) indicates the dominance of fine particles from the combustion sources and the lower AE value (close to 1 or less than 1) indicates the dominance of larger particles (Ångström 1964). Similarly, Fig. 3 demonstrates a dailybasis variation of AE over India's five stations Agra, Delhi, Ludhiana, Patna, and Tehri Garhwal from 1 January 2011 to 31 December 2020. Figure 3 shows the nearly same AE pattern for all cities over all years. The total annual mean of daily AE concentrations for all 5 cities has been measured 1.72 ± 0.11, 1.73 ± 0.11, 1.71 ± 0.12, 1.71 ± 0.13, 1.72 ± 0.13, 1.71 ± 0.13, 1.71 ± 0.13, 1.70 ± 0.13,1.71 ± 0.13 and 1.71 ± 0.13.in AGRA; and 1.51 ± 0.05, 1.50 ± 0.06, 1.50 ± 0.04, 1.50 ± 0.04, 1.50 ± 0.06, 1.48 ± 0.07, 1.49 ± 0.07, 1.49 ± 0.05, 1.48 ± 0.06 and 1.5 ± 0.05 in Tehri Garhwal, respectively, during the study period. Agra has the highest level of AE concentration that has been measured 1.72 ± 0.11 which is the resultant of the dominance of fine-mode aerosols, including nitrate and sulfate, two types of secondary inorganic aerosols (Hooda et al. 2016) and might be extremely absorbing aerosols, e.g., brown carbon, black carbon and mineral filth, when SSA is < 0.85 Xia et al. 2021). On days that may be less impacted by transported and locally raised filth, the maximum AE (up to 1.8) is observed. The lower influences on the IGB of dust transported from the western Indian desert and more elevated dust brought on by a relatively dry climate, which results in relatively low AE readings at both sites, may be to blame for the low daily values of AE Srivastava et al. 2014). Alternately, the reduced AE at a specific site may be brought on by locally elevated dust from numerous activities and little natural cover. These conditions are typically accompanied with chilly, dry meteorological conditions, which is compatible with a rise in dust loading under dry conditions (Lodhi et al. 2013) and minimum for Patna 0.85 ± 0.78. The Angstorm exponent expresses how the wavelength of incident light affects the spectral dependence of aerosol optical thickness (Sekiguchi et al. 2009). Aerosol loadings and particle size are evaluated using the link between AOD and AE .

Seasonal Average Aerosol Characteristics
Aerosol seasonal mean features of AOD, AE, SSA, and UV-AI at five the stations for the entire research period are revealed in Fig. 4. Demonstration of the seasonal variation of AOD, AE, SSA, and UV-AI during 2011-2020 is presented over India's five stations Agra, Delhi, Ludhiana, Patna, and Tehri Garhwal from 1 January 2011 to 31 December 2020. The total seasonal statistics of AOD, AE, SSA, and UV-AI concentrations for all five cities during Winter, Pre-Monsoon, Monsoon, and Post-Monsoon period has been shown in Table 3. Figure 4a shows the seasonal variation of AOD over India's five cities, i.e., Agra, Delhi, Ludhiana, Patna, and Tehri Garhwal during the study period. From Fig. 4a, the maximum variation of AOD was found to be in Winter and Monsoon for the cities (Agra, Delhi, and Patna) while minimum in Pre-Monsoon (for all cities). AODs were discovered to be considerably greater in Patna during all the seasons as compared to others, AOD exhibited the highest values see Fig. 2a in Winter and monsoon, among which Patna region (1.58), Delhi region (1.40), Agra region (1.46), and Patna city presented with the maximum value during the monsoon which almost similar manifestation emission sources; this finding indicated the particles major dominance of burning aerosol from biomass (Singh et al. 2004).It should be highlighted that during the Post-Monsoon period, agricultural biomass burning nearly controlled the aerosol climatology across the IGB (Ningombam et al. 2020). The enhancement in AOD is mostly due to transported mineral dust from western arid regions (Singh et al. 2004). The two regions presented with their lowest values in Monsoon and Post-Monsoon Tehri Garhwal region show (0.04), Ludhiana show (0.27) as most of the aerosol concentration is removed by rainfall (Yonget al. 2011). AODs decline in both areas during the monsoon season which is in line with the earlier outcomes discovered over Kanpur (Eck et al. 2010). The source of the air parcels (the Arabian Sea) has changed, resulting in wet deposition, which is significant for this downturn (Fig. 2). However, because the air parcels must travel via western India and the Thar desert before reach the study locations, the AOD reduction is not very significant. The classification of aerosol properties shows that the AOD accumulation mode can be attributed to fine particle coagulation or hygroscopic growth; during Pre-Monsoon and Monsoon seasons in Patna, it is due to coarse-mode particle accumulation or cloud contamination (Sharma et al. 2014). Figure 4(b) shows the seasonally variation of AE in India's five cities of Agra, Delhi, Ludhiana, Patna, and Tehri Garhwal for the duration of the study period. From Fig. 4b the maximum variation of AE was found to be in Winter for the cities (Agra and Patna) and Monsoon for the cities (Agra, Delhi, Patna, and Tehri Garhwal) while minimum in Pre-Monsoon for (Delhi and Ludhiana city). As opposed to that, readings of AE indicated comparatively higher at Agra, Delhi, and Patna (1.8) as compared to during all the seasons as shown in Fig. 4b which suggests relative dominance of fine-mode aerosols over Agra, Delhi, and Patna. The least values of AE arising in Pre-Monsoon (0.003) at Ludhiana sites is due to the fact that a significant factor in the MODIS data was the enlargement of particles brought on by hygroscopic development close to cloudy areas. (Kumar 2015;Gautam et al. 2022a, b). If the proportion of the undersized to the coarse-mode particles fluctuates, the Angstrom exponent might also change (Kumar 2014). It is also possible that a particle with a growth factor of 1.6 may have a particle with an Angstrom exponent that is 60% higher than a dry particle from human sources (Lyamani et al. 2008). Figure 4c shows the seasonally variation of SSA in India's five cities of Agra, Delhi, Ludhiana, Patna, and Tehri Garhwal during the study period. From Fig. 4c, the maximum and minimum variation of SSA was found to be in Monsoon and Post-Monsoon, respectively, for all the cities. Even still, all seasons show that the SSA is a little bit greater in Agra than at other study stations and highest during monsoon at for all the sites. All of the sites' SSA values are at their lowest during the Post-Monsoon, which is consistent with the time of year when more absorbent aerosol is present. A combination of higher emissions of absorbing aerosols like brown carbon and black carbon from burning biomass and height of the low-boundary layer that is not conducive to aerosol diffusion may be to blame for the low values of SSA during this season (Hooda et al. 2016;Bikkina et al. 2019;Srivastava et al. 2020). Figure 4d demonstrates the seasonal variation of UV-AI in India's five cities of Agra, Delhi, Ludhiana, Patna, and Tehri Garhwal during the study period. From Fig. 4d, the maximum variation of UV-AI was found to be in Post-Monsoon and Winter for the cities (Agra, Delhi, Ludhiana and Patna) and (Delhi) while minimum in Monsoon (for all cities). While it is discovered that the UV-AI is the highest during the Post-Monsoon Delhi (1.68) and Agra (1.54) as shown in (Fig. 2d) and weakest throughout the monsoon season at Patna and Tehri Garhwal both the stations, it is (− 0.23) Patna and (0.051) Tehri Garhwal. In the study, Kanawade et al. (2020) conducted research on the effects of a severe air pollution episode (SAPE) in the Post-Monsoon over Delhi in 2016 on aerosol properties AOD, UV-AI, and surface carbon monoxide concentrations which were found to have increased significantly by between 50 and 70% during the (SAPE). Outcomes are credited to the reduced air temperature and brisk breeze   that were seen over the station during the SAPE, together contemplating the high relative humidity, which caused the air to stagnate and, as a result, accumulated local and transported contaminants near to the surface. The higher levels are related to the photochemical oxidation of trace gases released during biomass burning, which produces O 3 . The results also pointed to a comparatively strong attenuation of UV irradiance from smoke particles compared to dust (around 6% higher) (Badarinath et al. 2008).

Spatial Distribution of Decadal Mean of Aerosol
Optical Parameters During 2011-2020 Figure 5 shows the spatial distribution of decadal mean of AOD, AE, SSA and UV-AI over (Agra, Delhi, Ludhiana, Patna, and Tehri Garhwal) cities of India obtained from MODIS during the era from 2011to 2020. In Fig. 5a Patna, Agra and Delhi reveal an enormous aerosol burden, with the AOD (> 0.6) across the entire year. The continuation of incredibly high AOD (> 0.8) was observed over the Patna region which is situated in the most populated low elevation locations, it has the mean elevation of 173 ft. (53 m) above sea level, due to enhanced dust impacts, primarily from fossil/biofuel consumption, agriculture burning with different aerosol emissions, burning of garbage and leftovers from agriculture (Banerjee et al. 2017).In comparison to Ludhiana and Delhi, Tehri Garhwal is distinguished by having a low aerosol loading (AOD < 0.18), with coarse particles dominating. It is interesting to note that these areas have low AOD levels and an increasing proportion of dust aerosols. seen in the size distribution from 0.60 to 1.68 as well as in the spectrum dependency of AOD. Figure 5c displays the indicating of SSA varying from 0.913 to 0.948; a higher SSA indicates the presence of scattering and hygroscopicity of aerosol, high value of SSA was ascribed to the rise of big particles that scatter solar radiation more effectively and trends in the absorption of brown carbon, scattering of secondary inorganic aerosol, and absorption of mineral dust all significantly contribute to the explanation of UV-AI. In Fig. 5d, UV-AI detects aerosol absorption variation from 0.079 to 0.993. The extent variation of AOD gives information on the particle dimension distribution of aerosol in the total atmospheric column. Over these areas, the strength of the winds from the west to the north is declining, which restricts the vertical mixing of airborne particulates and, as a result, permits the steady build-up of aerosols (Dey and Di Girolamo 2011). Although the over-segregated regions also experience this subsidence, the area with the greatest subsidence stretches from Delhi to Patna, where there is typically a notable increase in AOD. The significant amounts of anthropogenic fine particle aerosols, which are mainly concentrated in lowland and densely inhabited areas, made up a larger portion of the total aerosols in the other high AOD sites. Another significant source of enormous ejection of coarse-particle aerosols were emissions of filth and dust from civic coal fuel combustion and industrial pollutants (Cao et al. 2014;Luo et al. 2014).

Correlation Among the Aerosol Properties over the Selected Regions
To comprehend the temporal and spatial distribution characteristics of aerosols over the selected region, the correlation was investigated among the aerosol parameters during 2011-2020. Figure 6 depicts the plot matrix's several columns show five chosen regions together with their parameters exposures appear in the different rows that AOD (550 nm), AE (412/470 nm), SSA(500 nm), and UV-AI have a correlation as well as measurements made over five study stations to comprehend the effects of new anthropogenic activities, 1 for location (Agra); 2 for location (Delhi); 3 for location (Ludhiana); 4 for location (Patna) and 5 for location (Tehri Garhwal) from 2011 to 2020. These many aerosols optical parameters metrics were gathered from satellite. Correlation is perfect positive if r = 1. Correlation is strongly positive if r > 0.75. Correlation is moderately positive if 0.75 > r > 0.3. If r is negative, it signifies a negative relationship. The value of r for AOD (550 nm) for regions 2 and 1(0.80) shows a strong positive correlation and moderately positive for the regions 3 and 1(0.64), mostly as a result of mineral dust carried from arid western regions. The value of r for AE (412/470 nm) for region 3 and (0.40) shows a moderately positive correlation, which is the resultant of the dominance of fine-mode aerosol and negative for the regions 5 and 1 (− 0.06). The value of r for SSA (500 nm) for regions 2 and 1 (0.63) shows a moderately positive correlation, which explains the rise in big aerosol particles, which scatters sun energy more efficiently and the value of r for UV-AI for regions 1 and 2 show a strong positive correlation (0.77) and moderately positive for the regions 3 and 1(0.46) which indicates absorbing aerosols present over the study region. When the monsoon arrives, the negative correlation is characterized by the accumulation of aerosol within a water molecule and continuous precipitation washout, whereas the positive correlation is brought on by the season's remaining aerosols' hygroscopic properties (Nade et al. 2020;Prasad et al. 2007). Based on a validation technique employing daily level 3 MODIS Terra AOD and AERONET data for Kanpur, which is located in IGB, the correlation coefficients for the summer and Winter seasons were 0.29 and 0.47, respectively. They discovered that MODIS, with a slope of 0.51 and 0.48, respectively, overestimated the AOD levels during the summer and underestimated them during the Winter. Using AERONET data at 550 nm and MODIS Level 2 AOD data over Kanpur for 2004 (Tripathi et al. 2005a, b). Considering inclinations in the two cases 2.46 and 0.69, respectively, and R 2 values of 0.72 and 0.71 respectively, it was discovered that MODIS overestimates when the air is dusty and underestimates during non-dust seasons. It has been noted that R 2 values remained nearly same. (Jethvaet al. 2005) contrasted the monthly mean AOD at 550 nm computed from MODIS Level 3 daily gridded data well with AERONET-derived monthly average AOD measurements for Kanpur, India, for the time period of January 2001 to July 2003. They discovered that MODIS consistently overestimated summertime conditions while underestimating Wintertime conditions.

Conclusion
In this paper, we have selected five regions according to topography, and an enduring study of MODIS and OMI satellite-based aerosol data is conceded out. The present study estimated aerosol concentration and temporal and spatial variations of satellite-based optical characteristics of aerosols that are examined over five contrasting locations Agra, Delhi, Ludhiana, Patna, and Tehri Garhwal during 2011-2020. The present study estimated aerosol concentration according to wind patterns, temporal and spatial variations of satellite-based AOD (550 nm), AE (412/470 nm), SSA (500 nm), and UV-AI and their correlation among all parameters were analyzed and discussed in this study.
The average annual surface circulation of a wind-driven system of longshore current and wind stress exhibits similar patterns for all the study years. This pattern reduces effects on aerosol (pollution) concentrations over the Tehri Garhwal and Ludhiana region and enhance precipitation which may further alter the land-sea thermal gradient, resulting in the weakening of circulation. Enhanced aerosol (pollution) over Patna, Agra and Delhi region is adjusting the precipitation distribution along with the weakening of low-level jet and moisture transport. The maximum value of AODs (~ 0.86) with fluctuate similar trend for all the starting (2011-2019) years and reduced in 2020 due to strict pandemic guidelines, which indicate high aerosol loading across the IGB, which shows significant annual and seasonal variability. The high decadal value AE (> 1.0), with an increasing trend was also observed at all five stations. The increasing decadal trend in AE is found to be more pronounced at Agra compared to other study area which suggests relative increase in finemode aerosols at Agra. Further, SSA also shows nearly similar decadal mean value which does not show any considerable changes for all five stations. However, the decadal mean UV-AI is higher at Ludhiana (~ 2.36) with increasing trend for all study area, suggesting relative dominance of absorbing aerosols over Ludhiana. The trend is relatively higher at Ludhiana compared to other study area, which is likely due to local burning and a steady environment. An increasing trend of aerosol loading has been seen over IGP from west to east, which may be related to wind and a stable environment. The spatio-variation plots shows the similar trends as temporal plots above the given region.
Seasonally, the Winter and Monsoon showed greater AOD estimations than the Pre-Monsoon in the three regions. The thick, dense air during the Winter is the main cause of the high AOD. Over the whole research study area, seasonal AOD variation exhibited a significant annual cycle with maximums during the Winter and Monsoon and minimums during the Pre-Monsoon. High AOD values are associated with the hygroscopic growth of aerosols, secondary aerosol production, and contaminants due to agricultural combustion processes after crop harvesting in the neighboring districts, which causes pollutants to aggregate in this area. This resulted in the observation of the accumulation of more large particles of sand and dust in the bottom aerosol, this could potentially be influenced by the satellite's transit period.
Lastly, we saw a correlation between all the parameters is required to comprehend the characteristics of the temporal and geographical distribution of aerosols throughout the selected region. The value of r for AOD (550 nm) for regions 2 and 1(0.80) shows a strong positive correlation and moderately positive for the regions 3 and 1(0.64), mostly as a result of mineral dust carried from arid western regions. The value of r for AE (412/470 nm) for regions 3 and (0.40) show a moderately positive correlation, which resultant the dominance of fine-mode aerosol and negative for the regions 5 and 1 (− 0.06). The value of r for SSA (500 nm) for regions 2 and 1 (0.63) shows a moderately positive correlation, which explain the rise in big aerosol particles, which scatter sun energy more efficiently and the value of r for UV-AI for regions 1 and 2 shows a strong positive correlation (0.77) and moderately positive for the regions 3 and 1 (0.46) which indicates absorbing aerosols present over the study region.
This study improves the perceptive of the importance of aerosol heating rate over the selected region because it can alter the temperature and modify the monsoon over the Indian region. Considering how it affects the ecosystem, human health, and the climate, it becomes crucial to enhance knowledge of atmospheric aerosol properties with high spatial and temporal precision on regional and international levels, especially across the area with a high population density (Arora and Reddy 2015).