Recent Spatial and Temporal Analysis of Aerosol Optical Depth From MERRA-2 Over Borneo Island

Abstract. Satellites provide information on the temporal and spatial distributions of aerosols on regional and global scales. With the same method applied to a single sensor all over the world, a consistent data set is to be expected. However, the application of different retrieval algorithms to the same sensor and the use of a series of different sensors may lead to substantial differences, and no single sensor or algorithm is better than any other everywhere and at all times. For the production of long-term climate data records, the use of multiple sensors cannot be avoided. The Along Track Scanning Radiometer (ATSR-2) and the Advanced ATSR (AATSR) aerosol optical depth (AOD) data sets have been used to provide a global AOD data record over land and ocean of 17 years (1995–2012), which is planned to be extended with AOD retrieved from a similar sensor. To investigate the possibility of extending the ATSR data record to earlier years, the use of an AOD data set from the Advanced Very High Resolution Radiometer (AVHRR) is investigated. AOD data sets used in this study were retrieved from the ATSR sensors using the ATSR Dual View algorithm ADV version 2.31, developed by Finnish Meteorological Institute (FMI), and from the AVHRR sensors using the aerosol optical depth over land (ADL) algorithm developed by RADI/CAS. Together, these data sets cover a multi-decadal period (1987–2012). The study area includes two contrasting areas, both in regards to aerosol content and composition and surface properties, i.e. a region over north-eastern China, encompassing a highly populated urban/industrialized area (Beijing–Tianjin–Hebei) and a sparsely populated mountainous area. Ground-based AOD observations available from ground-based sun photometer AOD data in AERONET and CARSNET are used as a reference, together with broadband extinction method (BEM) data at Beijing to cover the time before sun photometer observations became available in the early 2000s. In addition, MODIS-Terra C6.1 AOD data are used as a reference data set over the wide area where no ground-based data are available. All satellite data over the study area were validated against the reference data, showing the qualification of MODIS for comparison with ATSR and AVHRR. The comparison with MODIS shows that AVHRR performs better than ATSR in the north of the study area (40∘ N), whereas further south ATSR provides better results. The validation against sun photometer AOD shows that both AVHRR and ATSR underestimate the AOD, with ATSR failing to provide reliable results in the wintertime. This is likely due to the highly reflecting surface in the dry season, when AVHRR-retrieved AOD traces both MODIS and reference AOD data well. However, AVHRR does not provide AOD larger than about 0.6 and hence is not reliable when high AOD values have been observed over the last decade. In these cases, ATSR performs much better for AOD up to about 1.3. AVHRR-retrieved AOD compares favourably with BEM AOD, except for AOD higher than about 0.6. These comparisons lead to the conclusion that AVHRR and ATSR AOD data records each have their strengths and weaknesses that need to be accounted for when combining them in a single multi-decadal climate data record.

The COVID-19 pandemic caused lockdowns worldwide throughout many cities, implementing numerous restrictions on human activity. Previous studies have shown that various anthropogenic pollutants have decreased in many regions from these lockdowns, which should translate into aerosol levels, measured by aerosol optical depth (AOD), also having a large reduction. However, AOD levels over metropolitan areas during the lockdown periods have not been sufficiently documented. Using NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) AOD data, we analyzed AOD level variances during the pandemic over 13 selected metropolitan areas in Europe, India, and China. We hypothesized that reduced human activities from the pandemic lockdowns would also result in a large decrease in AOD levels in metropolitan areas. So, we compared the average AOD levels during the lockdown to the previous five-year average during the city’s corresponding lockdown dates in order to investigate the statistical significance of variations. For European cities, there were few changes in AOD levels observed during the pandemic lockdown periods. As for the Indian cities, three showed slight decreases in AOD levels (~0.5%–7.4%) and one showed an 11% increase. However, compared to Indian and European cities, all of the cities in China showed noticeable reductions in AOD levels during the lockdown (~11%–30%). Statistical analysis did not show statistical significance for most of the cities due to the large variation of AOD, suggesting that the AOD level decrease attributed to the reduction of human activities does not exceed the range of natural AOD level variation.

A long-term historical aerosol optical depth (AOD) data set from 1982 to 2011 over China (15–45° N; 75–135° E) with 0.1 spatial resolution has been produced from Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres—Extended level-2B data. The spatial distribution pattern shows that high AOD values are found in central and eastern China over the entire period with AODs larger in summer and spring than in autumn and winter. As the high-quality products from AERONET were absent for this period over mainland China, AOD data obtained using the broadband extinction method from solar radiation stations have been used to verify the quality of the AVHRR AOD data set over China. The intercomparison results show that the interannual variation of AOD has been well captured in the variation curve of the AOD monthly mean and the variation trend is also consistent over the whole period. The correlation coefficient of the monthly mean is mostly larger than 0.55, the agreement index is larger than 0.57, and the relative error is less than 21%. Both AVHRR and visibility data sets show high values in regions with rapid economic development. Using Moderate Resolution Imaging Spectroradiometer AOD data as references, it is found that AVHRR AOD from this paper has better accuracy in general than that from Deep Blue (DB) algorithm over China, especially over eastern and southern China, while DB provides more coverage especially over bright surface such as northwest China. This long-term historic AOD data set can be used together with other AOD data sets to study the climate and environmental changes, especially in the 1980s and 1990s.

Dust storms, intensified by climate change, pose a significant environmental challenge in Iraq and the Middle East. This study evaluates remote sensing-derived Aerosol Optical Depth (AOD) data and investigates its correlation with rainfall to explore the spatiotemporal co-variation between the two variables. The analysis covers the period 2000-2021, with high-spatial-resolution long-term Google Earth Engine (GEE) data to investigate AOD over Iraq and rainfall data from IMOS to study the association of aerosol with rainfall over 38 stations covering all of Iraq. Time series components have been decomposed into (trend, seasonal, and random) of the monthly mean rainfall and AID index using R Studio. The results demonstrate that rainfall and aerosol index AOD distributions vary significantly monthly and seasonally between Iraq’s northern and southern regions. There is no rainfall during the summer; even so, AOD data show the highest values in the summer and minimum values in the coldest winter months across the study locations. Maximum AOD levels occur over the southern area with less rainfall, progressively decreasing towards the north, while rainfall values are at their highest. Temporal and regional examination of AOD changes in Iraq reveals notable variances, with high dust levels having the most significant impact between 2008 and 2012 and a relative decrease after 2013. Dust concentrations in the southern and central regions are higher than in the north, particularly during the summer and spring. The study found a good inverse correlation (r = −0.7) between AOD levels and rainfall. The results demonstrate that rainfall reduces aerosol concentrations, especially in arid areas, whereas other regions are affected by different factors that require more examination. These findings underscore the critical role of rainfall in mitigating dust levels and emphasize the urgent need for sustainable environmental management to combat increasing desertification under ongoing climate change.

Satellite-based measurements of aerosols are one of the most effective ways to understand the role of aerosols in climate in terms of spatial and temporal variability. In the present study, we attempted to analyse spatial and temporal variations of satellite derived aerosol optical depth (AOD) over Indian region using moderate resolution imaging spectrometer over a period of 2001–2011. Due to its vast spatial extent, Indian region and adjacent oceanic regions are divided into different zones for analysis. The land mass is sub divided into five different zones such as Indo Gangetic Plain (IGP), Indian mainland, North Eastern India (NE), South India-1 (SI-1), South India-2 (SI-2). Oceanic areas are divided into Arabian Sea and Bay of Bengal. Arabian Sea is further divided as three zones viz. Northern AS (NAS), Central AS (CAS) and Eastern AS (EAS) zones. Bay of Bengal is divided as North BoB (NBoB), West BoB (WBoB), Central BoB (CBoB), and East BoB (EBoB). The study revealed that among all the land regions, IGP showed the highest peak AOD value (0.52 ± 0.17) while SI-2 showed the lower values of AOD in all the months compared to all India average. The maximum AOD is observed during premonsoon season for all regions. During the winter, average AOD levels were substantially lower than the summer averages. Peak of aerosol loading (0.35 ± 0.159) is observed in March over NE region, whereas in all other regions, peak is observed during May. Frequency distribution of long term AOD ( 0.5) shows a shift of frequency distribution of AOD from 0.5. Oceanic areas also shows seasonal variation of AOD. Over Arabian Sea, high AOD values with greater variations were observed in summer monsoon season while in Bay of Bengal it is observed during winter monsoon. This is due to the high wind speed prevailing in Arabian Sea during monsoon season which results in production of more sea salt aerosol. Highest AOD values are observed over NAS during monsoon season and over NBOB during winter season. Lowest AOD values with its lower variations observed in both the central region of Arabian Sea and Bay of Bengal.

Satellite-based aerosol optical depth (AOD) retrieval products are essential in air pollution and climate change research. Unfortunately, cloud contaminations and unfavorable surface conditions result in a considerable proportion of missing AOD data. Numerous studies have been conducted to reconstruct large-scale AOD data gaps by utilizing adjacent spatiotemporal information or modeling AOD data via various external geographical data. However, the erratic variation of AOD and the inconvenience of external data weaken the accuracy and efficiency of reconstruction. To address these issues, a novel big data based iterative variation mining framework (IVMF), utilizing multi-spatiotemporal information on AOD variations, is proposed to reconstruct large-scale AOD data over China from 2000 to 2020. Simulated and real-data experiments are carried out to validate the effectiveness and robustness of the IVMF. Results show that the spatial patterns of the reconstructed AOD are consistent with those of the original AOD in the simulated experiments. The final reconstructed AOD data strongly correlate with in-situ AOD measurements in the real-data experiments (correlation coefficient of 0.91). After reconstruction, the average daily AOD coverage increases from 30.42% to 96.69% (a 218% increase). Besides, results reveal that central China exhibits severe AOD levels, while northwest China presents low AOD levels. Overall, the proposed IVMF can largely resolve the missing AOD data problem with outstanding accuracy and efficiency, and has great potential to be generalized to other regions and remote sensing products.

Agricultural biomass burning (ABB) in central and east China occurs every year from May to October and peaks in June. During the period from 26 May to 16 June 2007, one strong ABB procedure happened mainly in Anhui, Henan, Jiangsu and Shandong provinces. This article focuses on analysis of this ABB procedure using a comprehensive set of aerosol optical depth (AOD) data merged by using the optimal interpolation method from the Moderate Resolution Imaging Spectroradiometer, the Multi-angle Imaging Spectroradiometer (MIRS) as well as Sea-viewing Wide Field-of-view Sensor (SeaWiFS)-derived AOD products. In addition, the following additional data are used: fire data from the National Satellite Meteorological Centre of China Meteorological Administration, the mass trajectory analyses from hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model and ground-based AOD and Ångström data derived from the Aerosol Robotic Network and China Aerosol Remote Sensing Network. The results show that merged satellite AOD values can expand the spatial coverage of agricultural biomass aerosol distributions with good accuracy (R = 0.93, root mean square error = 0.37). Based on the merged AOD images, the highest AOD values were found concentrated in central China as well as in eastern China before 6 June and further extended to northeast China after 12 June. AODs from ground measurement show that eastern China always keeps high AOD values (>1.0), with a maximum exceeding 3.0 and extending as high as nearly 5.0 during this ABB event. With the help of the HYSPLIT model, we analysed the ABB sources and examined how transport paths affect the concentrations of air pollutants in some sites. The results show that Henan, Jiangsu and Anhui provinces are the three main sources in this ABB.

Monthly aerosol optical depth (AOD) data over central India during 2001-2010 obtained from Moderate Resolution Imaging Spectroradiometer are analyzed for trend and periodicity. For this purpose, spectral analysis and linear trend analysis are performed. High AOD during monsoon followed by summer months are observed. Spatial analysis did not show any significant spatial variations in AOD levels. Spectral analysis suggested two dominant periods; 12 months and 6 months consistent with the annual and seasonal patterns. Trend analysis showed an insignificant trend at all the locations. Decadal change in AOD is the highest in Nagpur, which is an urban agglomeration station. Less developed and nonurban areas, however show decreasing or insignificant trend in AOD levels. Correlation with change in population over the last decade at different locations showed significant positive relationship with percentage change in AOD levels suggesting the effect of urban agglomeration on AOD in central India.

This study examines the long term trend of the radiatively active atmospheric aerosols which can influence the Earth’s energy budget directly by scattering and absorbing radiation and indirectly by acting as cloud condensation nuclei. MODIS sensor on board the NASA Earth Observing System Terra and Aqua satellite based Aerosol Optical Depth (AOD) data are used for long term analysis of aerosols over Bongaigaon, Assam for the period August, 2002 to March, 2017. Highest AOD values are observed in pre-monsoon (March-May) season due to long range transportation as well as intense biomass burning activities especially as a part of Jhum cultivation. In general, AOD values are low in post-monsoon (October-November) season which may be due to wash out of aerosols by rain in the preceding months without enough replacement. The monthly AOD values vary from its highest value 0.949 in April, 2016 to its lowest value 0.107 in November, 2002 for the study period. From the comparison of MODIS Terra and Aqua AOD at 550 nm, it is clearly seen that generally Terra AOD at 10:30 hr is higher than the Aqua AOD at 13:30hr. A slowly increasing trend of both Aqua and Terra AOD at 550 nm is observed over the study location. The observed Ångström exponent value varies from its minimum value in monsoon season to its maximum value in winter season. With increasing AOD values, horizontal visibility decreases over Bongaigaon.

Aerosols are a critical component of the Earth's atmosphere and can affect the climate of the Earth through their interactions with solar radiation and clouds. Cloud fraction (CF) and aerosol optical depth (AOD) at 550 nm from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used with analogous cloud and aerosol properties from Historical Phase 5 of the Coupled Model Intercomparison Project (CMIP5) model runs that explicitly include anthropogenic aerosols and parameterized cloud‐aerosol interactions. The models underestimate AOD by approximately 15% and underestimate CF by approximately 10% overall on a global scale. A regional analysis is then used to evaluate model performance in two regions with known biomass burning activity and absorbing aerosol (South America (SAM) and South Africa (SAF)). In SAM, the models overestimate AOD by 4.8% and underestimate CF by 14%. In SAF, the models underestimate AOD by 35% and overestimate CF by 13.4%. Average annual cycles show that the monthly timing of AOD peaks closely match satellite data in both SAM and SAF for all except the Community Atmosphere Model 5 and Geophysical Fluid Dynamics Laboratory (GFDL) models. Monthly timing of CF peaks closely match for all models (except GFDL) for SAM and SAF. Sorting monthly averaged 2° × 2.5° model or MODIS CF as a function of AOD does not result in the previously observed “boomerang”‐shaped CF versus AOD relationship characteristic of regions with absorbing aerosols from biomass burning. Cloud‐aerosol interactions, as observed using daily (or higher) temporal resolution data, are not reproducible at the spatial or temporal resolution provided by the CMIP5 models.

East Asia is one of the most important sources of aerosols in the world. The distribution of aerosols varies across time and space. Accurate aerosol data is crucial to identify its spatiotemporal dynamics; thus, it is of great significance to obtain and verify new aerosol data for this region. Based on the Aerosol Optical Depth (AOD) data of the Aerosol Robotic Network (AERONET) program for 17 stations from 2011 to 2020, this study comprehensively verified the accuracy and applicability of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD 1 km products among different seasons, elevations, and climate zones over entire East Asia. The results showed that: (1) The overall accuracy of MAIAC AOD was high in East Asia, and the accuracy of Terra was slightly better than that of Aqua. MAIAC AOD showed significant heterogeneity among sites. MAIAC AOD performed well in areas with high vegetation cover and flat terrain, while the inversion accuracy was relatively low in areas with low vegetation cover and high terrain. (2) In general, MAIAC AOD and AERONET AOD showed good agreement in different seasons, presenting as winter > spring > autumn > summer. Yet the accuracy and consistency of Terra AOD product were better than Aqua product. (3) MAIAC AOD showed different accuracy at different elevations and climate zones. It had a high correlation and best inversion accuracy with AERONET AOD at low and medium elevations. MAIAC AOD had better inversion accuracy in the arid and warm temperate zones than that in the equatorial and cold temperate zones. (4) AOD distribution and its trend showed significant spatial differences in East Asia. The high AOD values were dominant in the Sichuan basin and the eastern plains of China, as well as in India and Bangladesh, while the relatively low AOD values were distributed in southwestern China and the areas north of 40°N. AOD in most parts of East Asia showed a negative trend, indicating a great improvement in air quality in these regions.

Air pollution index (API) is an index commonly used in Malaysia to determine the air quality level. It is a ground truth data measurement which is unable to unambiguously quantify air quality level at higher atmosphere. On the other hand, aerosol optical depth (AOD) from AERONET data obtained using sun photometer provides reading of the air quality for a column of atmosphere from ground surface. We first determine the quantitative correlation between the API and AOD data collected in Penang, Malaysia, between January – September, 2012, using two independent methods, one based on regression analysis and the other interpolation. Our purpose is to establish a systematic numerical procedure to determine whether aerosol transported in high altitude from other location has occurred. Two independent methods for establishing the quantitative relationship between the API and AOD data were used as a way to facilitate the verification of our approach. In our method, data from southwest monsoon period (August to September) were used as "calibration dataset" to establish the quantitative correlation between the AOD and API data. The established calibrated coefficients is then used to predict the AOD of other months, which are then compared against the data actually measured. Discrepancy between the predicted and measured AOD data can then be interpreted as an indication of whether the atmosphere at high altitude is polluted by aerosol transported from other location. If the predicted AOD is much larger than that measured, back trajectory analysis was applied to identify the aerosol transported source. This procedure is very helpful to investigate the aerosol transportation and distribution patterns during monsoon and non monsoon periods.

Chapter16 - Study of the aerosol parameters and radiative forcing during COVID-19 pandemic over Srinagar Garhwal, Uttarakhand

Spatio-temporal variations in aerosol optical and cloud parameters over Southern India retrieved from MODIS satellite data

Aerosol optical characteristics have been investigated to explore regional and seasonal inconsistencies of aerosols and to define the dominant type throughout South Asia from 2001 to 2021. MODIS aerosol products from collection 6.1 have been used in present study, that comprise daily values of Angstrom exponent (AE) and aerosol optical depth (AOD) data. MODIS-derived AODs are validated by using nine ground-based AERONET station data. Overall, an adequate correlation is found among the two datasets. However, an overestimation of the MODIS retrievals is found in one site named Jaipur and underestimations are found at two sites named as Gandhi-college and Karachi. The seasonal evaluation shows that aerosol distribution found between 0 and 1.05, depending on the change in geographical location. The highest AOD value originates over the Indo-Gangetic plain (IGP), mostly throughout warm season. The second maximum AOD value covers a large area of South Asia during spring, summer and autumn. The lowest values of AOD are found in winter season excluding the IGP. A region with high aerosol optical depth (AOD) values support a low value of angstrom exponent (AE) indicating the coarse aerosol during warm seasons (spring and summer) over IGP. The region with high AOD and high AE values is showing fine aerosol during the mild to cold seasons (autumn and winter). The threshold values for AOD and AE have been used to classify aerosols. The results demonstrate that urban/industrial aerosols prominent in every season across the region dominate in spring and summer due to frequent occurrence of dust events. The mixed type aerosol is second largest contributor in aerosol formation in all seasons. The Biomass burning/smoke aerosol is dominant over IGP due to open forest and crop burning in autumn. Clean and maritime aerosol has small unnoticeable involvement in the studied region.