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Study area map including four major cities of India: Delhi (28.7° N, 77.1° E), Jaipur (26.9° N, 75.8° E), Kolkata (22.6° N, 88.4° E), and Chennai (13.1° N, 80.3° E)
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The paper evaluates long-term (2007–2018) temporal and spatial variations in aerosol optical depth (AOD) over four major cities of India, i.e., Delhi, Kolkata, Chennai, and Jaipur, by using Collection 6, Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua Level-3, 1°×1° gridded dataset. Annual analysis reveals a significant increas...
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... different parts of India, i.e., Chennai (13.1° N, 80.3° E) from the southern part, Jaipur (26.9° N, 75.8° E) from the western part, Kolkata (22.6° N, 88.4° E) from the eastern part, and Delhi (28.7° N, 77.1° E) from northern part of India. The geographical locations of the four Indian megacities (Chennai, Delhi, Jaipur, and Kolkata) are shown in Fig. 1. Chennai, the capital of Tamil Nadu, experiences tropical wet and dry climate, with average annual rainfall of 1300 mm (Dhiman et al. 2019), roughly covers an area of 426 km 2 , and accounts approx. 10.3 million populations. Delhi the capital of India holds 28.9 million populations with only 1484 km 2 area. Delhi experiences a dry-hot ...
Citations
... The IGP has shown significant seasonal and interannual fluctuation in aerosols because of local climatology, atmospheric dynamics, and emissions of both natural and man-made aerosols (Kaskaoutis et al., 2013;Nair et al., 2007). According to Payra et al. (2021), Kolkata witnessed the most notable rise in AOD percentage from 2007 to 2018 at 39%, with Delhi showing a 27.34% increase, followed by Chennai and Jaipur, both with a 26.30% increase. Gupta (2008) found that due to the large scale of industrial activity and transportation fleet, the level of PM10 is five to six times greater than the safe level in Kanpur's central areas. ...
Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30 m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of − 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP.
... The IGP has shown signi cant seasonal and interannual uctuation in aerosols because of local climatology, atmospheric dynamics, and emissions of both natural and man-made aerosols (Kaskaoutis et al., 2013;Nair et al., 2007). According to Payra et al. (2021), Kolkata witnessed the most notable rise in AOD percentage from 2007 to 2018 at 39%, with Delhi showing a 27.34% increase, followed by Chennai and Jaipur, both with a 26.30% increase. Gupta (2008) found that due to the large scale of industrial activity and transportation eet, the level of PM10 is ve to six times greater than the safe level in Kanpur's central areas. ...
Aerosol Optical Depth (AOD) serves as a crucial indicator for assessing regional air quality by quantifying aerosol levels in the atmosphere. While various satellite methods exist for estimating AOD, the spatial resolution of established AOD products is often limited. However, obtaining higher-resolution AOD data is essential for gaining a deeper understanding of regional and urban air pollution issues. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499° N, 80.3319° E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries. We have used Landsat 8 imagery and the SEMARA algorithm, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30 meters. This methodology was applied over the period from 2014 to 2022 and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error (RMSE) of 0.035, and root mean bias (RMB) of -4.91%. Furthermore, we conducted a comprehensive comparison with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle with different land use and land cover classes. The SEMARA approach proved to be more effective for AOD retrieval on brighter surfaces within the barren and built-up land categories for harvested period. This methodology holds great potential for monitoring aerosols over bright urban areas.
... We combined Level-3 daily aerosol optical depth (AOD; 550 nm) retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua (MYD07_D3 v6.1) and Terra (MOD08_D3 v6.1) with a resolution of 1 • × 1 • from 2003 to 2019 (Payra et al., 2021;Pu and Jin, 2021) to examine the westward transport of African dust. Level-3 monthly cloud-free dust extinction coefficients at 532 nm between 2006 and 2019 from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite were also used to analyze the vertical profiles of trans-Atlantic dust plumes. ...
Drought is an extreme hydroclimate event that has been shown to cause an increase in surface fine dust near source regions, yet the drought–dust relationship in regions predominantly influenced by long-range-transported dust such as the southeastern USA (SEUS) has received less attention. Using long-term surface fine-dust observations, the weekly US Drought Monitor (USDM), and the monthly standardized precipitation–evapotranspiration index (SPEI), this study unmasks spatial disparity in drought–dust relationships in the contiguous USA (CONUS) where the SEUS shows a decrease in surface dust concentrations during drought in contrast to the expected increase in dust found in other CONUS regions. Surface fine dust was found to decrease by ∼ 0.23 µg m−3 with a unit decrease in SPEI in the SEUS, as opposed to an increase of ∼ 0.12 µg m−3 in the west. The anomalies of dust elemental ratios, satellite aerosol optical depth (AOD), and dust extinction coefficients suggest that both the emissions and trans-Atlantic transport of African dust are weakened when the SEUS is under droughts. Through the teleconnection patterns of the negative North Atlantic Oscillation (NAO), a lower-than-normal and more northeastward displacement of the Bermuda High (BH) is present during SEUS droughts, which results in less dust being transported into the SEUS. At the same time, enhanced precipitation in the Sahel associated with the northward shift of the Intertropical Convergence Zone (ITCZ) leads to lower dust emissions therein. Of the 10 selected models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6), GISS-E2-1-G was found to perform the best in capturing the drought–dust sensitivity in the SEUS. This study reveals the mechanism of how droughts influence aerosol abundance through changing long-range transport of dust.
... In view of the above importance, numerous researchers have reported their investigations on long term changes in few aerosol parameters, i.e., AOD and COT only specifically on regional basis over some parts of Indian regions ( Payra et al., 2021). They showed their main findings about primarily an increasing trend in aerosol loading or enhancement in AOD 550nm values in the densely populated and industrialized region of hot aerosol regimes such as Indo-Gangetic Plain (IGP) region and Southern Indian regime along most heavily polluted, industrialized, highly populated of major urban major cities, such as Delhi, Kanpur,Varanasi, Lucknow, Bangalore, Mumbai, Chennai, Ahmadabad, etc., (Kumar, et al., 2018 ;Kumar, et al., 2021;Payra, et al., 2021, more references also there in). ...
... In view of the above importance, numerous researchers have reported their investigations on long term changes in few aerosol parameters, i.e., AOD and COT only specifically on regional basis over some parts of Indian regions ( Payra et al., 2021). They showed their main findings about primarily an increasing trend in aerosol loading or enhancement in AOD 550nm values in the densely populated and industrialized region of hot aerosol regimes such as Indo-Gangetic Plain (IGP) region and Southern Indian regime along most heavily polluted, industrialized, highly populated of major urban major cities, such as Delhi, Kanpur,Varanasi, Lucknow, Bangalore, Mumbai, Chennai, Ahmadabad, etc., (Kumar, et al., 2018 ;Kumar, et al., 2021;Payra, et al., 2021, more references also there in). Although the remarkable advancement in space technology over present decade has been made and gave an opportunity in aerosols understanding in depth due to availability of long term available aerosol data retrieved from space based multi satellites on board observations, there is still a lack of understanding about the long term aerosols loading variability of their optical and physical properties specifically over least explored Western Indian region, which is highly influenced by natural mineral dust aerosols activities along with in nowadays by anthropogenic activities too (Sharma & Kulshrestha, 2017). ...
Long term satellite observations over more than one decade of several aerosols parameters, i.e., AOD550 nm, AE, COT, UV-AI and ASA have been analyzed to describe their overall monthly and seasonally climatology over least explored region of Western Indian sites. It has been found that maximum aerosols loading characteristics of coarse aerosols of dust mineral origin in May and minimum aerosols values in December month at selected arid sites and semi-arid site. Aerosol variables in noon hours seem to their two time higher values than their fore-noon magnitude at all selected places. Observed findings may be interpreted in view of mixed effect of increasing accumulation of regional and local aerosols emission activities. An significant long term trend in aerosols variable of positive values of more 47% in AE and 25% in AOD 550 nm itself would be indicated due to the extra-enhancement in human made activities of more than 10% in term of population growth, population density, transportation vehicles, industries as the enhancement in local anthropogenic aerosols production sources specially over western arid sites. Thus, the abundance of fine size of anthropogenic aerosols is found to be systematically enhanced in the last decade, which is serious concern to both climate and air pollution change aspect over western Indian region also in similar to other Indian regions.
The aerosol particles present in the atmospheric region mainly affect the climate radiative forcing directly by scattering & absorbing the sunlight. Also, it indirectly influences the formation of clouds, precipitation and acts as a considerable uncertainty in assessing Earth's radiation budget. Determination of aerosol type is significant in characterizing the aerosol role in the atmospheric processes, feedback, and climate models. This paper proposes two aerosol classification models, one based on the source and another based on the composition, to classify the aerosols using aerosol optical properties. The source based aerosol classification method helps to identify the sources which cause pollution in a particular region. Based on the results, proper control measures can be taken to reduce pollution. The composition based aerosol classification helps to identify the nature of aerosol types, such as absorbing or non-absorbing. This classification helps to study the climate of the Kanpur region. The aerosol data is taken from AERONET (AErosol RObotic NETwork) for the period 2002-2018 for the Kanpur region. The composition based aerosol classification model uses Single Scattering Albedo (SSA), Angstrom Exponent (AE), and Fine Mode Fraction (FMF) parameters to categorize aerosols based on their composition. The source based aerosol classification model classifies the aerosols based on values of AE and Aerosol Optical Depth (AOD) and describes the source of the aerosol particles. Knowledge of aerosol sources and compositions helps execute policies or controls to reduce aerosol concentrations. Machine learning algorithms, Naïve Bayes, K Nearest Neighbor, Decision Tree, Support Vector Machine, and Random Forest are used to validate classification schemes. The performance analysis of machine learning algorithms is compared using ten different metrics, and the results are also compared with the existing aerosol classification models. The results of the classification show that the source based aerosols of the desert and arid background and the composition based aerosols of types, Mixture Absorbing, Coarse absorbing (Dust), and Black Carbon are dominant over the Kanpur region during the study period considered. The Number of non-absorbing (scattering) type aerosols are least in the study region considered during the study period at all the seasons. It is found that the Random Forest and Decision Tree models outperform the other machine learning models considered and the existing classification models in terms of accuracy (99.55 %) and other performance metrics considered.
Investigating the spatiotemporal variation characteristics of aerosol optical depth (AOD) and its driving factors is essential for assessing atmospheric environmental quality and alleviating air pollution. Based on a 22-year high-resolution AOD dataset, the spatiotemporal variations of AOD in mainland China and ten national urban agglomerations were explored based on the Mann–Kendall trend test and Theil–Sen median method. Random forest (RF) and multiscale geographically weighted regression (MGWR) were combined to identify the main driving factors of AOD in urban agglomerations and to reveal the spatial heterogeneity of influencing factors. The results showed that areas with high annual average AOD concentrations were mainly concentrated in the Chengdu–Chongqing, Central Plains, Shandong Peninsula, and Middle Yangtze River urban agglomerations. Southern Beijing–Tianjin–Hebei and its surrounding areas revealed the highest AOD pollution during summer, whereas the worst pollution during the remaining three seasons occurred in the Chengdu–Chongqing urban agglomeration. Temporally, except for the Ha-Chang and Mid-Southern Liaoning urban agglomerations, where the average annual AOD increased, the other urban agglomerations showed a decreasing trend. Among them, the Central Plains, Middle Yangtze River, Guanzhong Plain, and Yangtze River Delta urban agglomerations all exhibited a decline greater than 20%. According to the spatial trends, most urban agglomerations encompassed much larger areas of decreasing AOD values than areas of increasing AOD values, indicating that the air quality in most areas has recently improved. RF analysis revealed that PM2.5 was the dominant factor in most urban clusters, followed by meteorological factors. MGWR results show that the influencing factors have different spatial scale effects on AOD in urban agglomerations. The socioeconomic factors and PM2.5 showed strong spatial non-stationarity with regard to the spatial distribution of AOD. This study can provide a comprehensive understanding of AOD differences among urban agglomerations, and it has important theoretical and practical implications for improving the ecological environment and promoting sustainable development.
Atmospheric corrections (AC) factors in adverse atmospheric effects while determining surface reflectance (SR) from remote sensed satellite images. Deep learning (DL)-based methods using only remote-sensed images are capable of learning nonlinear relations between the top of the atmosphere (TOA) and the bottom of the atmosphere (BOA) to provide consistent SR estimates. The changes over time in atmospheric conditions, geographical terrains and sensing processes has caused significant changes in the statistical properties of satellite images affecting the nature of the relationship between TOA and BOA, commonly referred to as a dataset shift. If this phenomenon is significant then over time the statistical properties of the data used to train DL models are vastly different from the test data for which we are performing AC. This distributional mismatch can cause DL models to perform poorly due to its inability to extend temporally. In this paper, we aim to ascertain the presence of data set shift using Landsat 8 images from 2013 and 2020. We create a joint distribution between the TOA and BOA for temporally separated training and testing sets, and study data set shift from three perspectives; 1. Use the Kolmogorov-Smirnov test to measure the distribution changes. 2. Apply Wasserstein distance over differences in TOA and BOA to measure the numerical drift directly. 3. Using the DL model to identify the drift and study model generalization. All three approaches indicate a dataset shift and provide strong evidence for its presence.
