High Aerosol Optical Depth Research Articles

Investigations on the Global Spread of the Hunga Tonga-Hunga Ha’apai Volcanic Eruption Using Space-Based Observations and Lagrangian Transport Simulations

On 15 January 2022, the Hunga Tonga-Hunga Ha’apai (HTHH) (175.38° W, 20.54° S) volcano erupted explosively. It is considered the most explosive volcanic eruption during the past 140 years. The HTHH volcanic eruption caused intense ripples, Lamb waves, and gravity waves in the atmosphere which encircled the globe several times, as reported by different studies. In this study, using OMI, SAGE-III/ISS, and CALIPSO satellite observations, we investigated the spread of the volcanic SO2 cloud due to the HTHH eruption and subsequent formation of sulfuric acid clouds in the stratosphere. It took about 19–21 days for the stratospheric SO2 injections of the HTHH to encircle the globe longitudinally due to a dominant westward jet with wind speeds of ~2500 km/day, and it slowly dispersed over the whole globe within several months due to poleward spread. The formation of sulfuric acid clouds intensified after about a month, causing the more frequent occurrence of high aerosol optical depth elevated layers in the stratosphere at an altitude of about 20–26 km. This study deals with the dynamics of volcanic plume spread in the stratosphere, knowledge of which is essential in estimating the accurate radiative effects caused by perturbations in the earth–atmosphere system due to a volcanic eruption. In addition, this knowledge provides important input for studies related to the geo-engineering of the earth’s atmosphere by injecting particulates and gases into the stratosphere.

Climatology and interannual variability of type-dependent aerosol optical depth and vertical distribution over southwest China and northern India from multiple satellite and aerosol reanalysis datasets

Aerosol loading and properties have an important influence on the radiative effect of aerosols and regional climate change. Based on the long-term datasets of aerosol optical products retrieved from multiple satellite observations (MODIS/Terra, MODIS/Aqua, MISR, and CALIOP/CALIPSO) and a reanalysis product (CAMS) from 2007 to 2019, the climatology and interannual variability in total aerosol optical depth (AOD) and type-dependent AODs related to aerosol particle properties and components over southwest China (SWC) and northern India (NI) are investigated, and the vertical distribution of type-dependent aerosol extinctions are also examined. Overall, consistent spatial distribution characteristics of the AOD climatology are captured across the different datasets, albeit with slight differences in magnitude. Multi-year mean high AODs are mainly concentrated in areas such as the Sichuan Basin (SCB) (∼0.5) and NI (∼0.6), dominated by anthropogenic aerosols, and the Tarim Basin (∼0.5), dominated by mineral dust aerosols. In contrast, the central Tibetan Plateau (CTP) area, with a relatively clean background, has a lower AOD (∼0.1) on a multi-year average basis. When the total AOD is separated by chemical components, the CAMS reanalysis demonstrates that sulfate and carbonaceous aerosols (CAs) contribute nearly equally to AOD in SCB, while CAs is the largest contributor to the high aerosol loading in NI. An enhanced vertical aerosol extinction coefficient (AEC) is mainly located below 1 km in both SCB and NI, reaching 0.35 km−1 and 0.55 km−1. In CTP, the AEC has two peaks (∼0.02 km−1) at 2.5 km and 3.5 km. By partitioning the column AOD by different altitude ranges, we find that about 73.8% (80.6%) of the AOD in SCB (NI) is located in the lower troposphere (0–2 km). The resulting trend analyses show that a significant decreasing (increasing) trend (P < 0.05) in AOD in SCB (NI) is accurately captured by both the satellite products and the CAMS reanalysis. In contrast, a significant decline (P < 0.1) in the CTP region is observed in CAMS only, while a non-significant decline is detected in the satellite products. The results of this study have potential implications for understanding the vertical distribution and interannual variability of regional aerosol loading.

The new MISR research aerosol retrieval algorithm: a multi-angle, multi-spectral, bounded-variable least squares retrieval of aerosol particle properties over both land and water

Abstract. Launched in December 1999, NASA's Multi-angle Imaging SpectroRadiometer (MISR) has given researchers the ability to observe the Earth from nine different views for the last 22 years. Among the many advancements that have since resulted from the launch of MISR is progress in the retrieval of aerosols from passive space-based remote sensing. The MISR operational standard aerosol (SA) retrieval algorithm has been refined several times over the last 20 years, resulting in significant improvements to spatial resolution (now 4.4 km) and aerosol particle properties. However, the MISR SA still suffers from large biases in retrieved aerosol optical depth (AOD) as aerosol loading increases. Here, we present a new MISR research aerosol (RA) retrieval algorithm that utilizes over-land surface reflectance data from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) to address these biases. This new over-land and over-water algorithm produces a self-consistent aerosol and surface retrieval when aerosol loading is low (AOD &lt;0.75); this is combined with a prescribed surface algorithm using a bounded-variable least squares solver when aerosol loading is elevated (AOD &gt;1.5). The two algorithms (prescribed + retrieved surface) are then merged as part of our combined surface retrieval algorithm. Results are compared with AErosol RObotic NETwork (AERONET) validation sun-photometer direct-sun + almucantar inversion retrievals. Over land, with AERONET AOD (550 nm) direct-sun observations as the standard, the root mean squared error (RMSE) of the MISR RA combined retrieval (n=11563) is 0.084, with a correlation coefficient (r) of 0.935 and expected error of ±(0.20×[MISRAOD]+0.02). For MISR RA retrieved AOD &gt;0.5 (n=664), we report an Ångström exponent (ANG) RMSE of ∼0.35, with a correlation coefficient of 0.844. Retrievals of ANG, fine-mode fraction (FMF), and single-scattering albedo (SSA) improve as retrieved AOD increases. For AOD &gt;1.5 (n=66), FMF RMSE is &lt;0.09 with correlation &gt;0.95, and SSA RMSE is 0.015 with a correlation coefficient of ∼0.75. Over water, comparing AERONET AOD to the MISR RA combined retrieval (n=4596), MISR RA RMSE is 0.063 and r is 0.935, with an expected error of ±(0.15×[MISRAOD]+0.02). ANG sensitivity is excellent when MISR RA reported AOD &gt;0.5 (n=188), with an RMSE of 0.27 and r=0.89. Due to a lack of coincidences with AOD &gt;1 (n=21), our conclusions about MISR RA high-AOD particle property retrievals over water are less robust (FMF RMSE =0.155 and r=0.94, whereas SSA RMSE =0.010 and r=0.50). In general, better aerosol particle property constraints can be made at lower AOD over water compared to our over-land retrievals. It is clear from the results presented that the new MISR RA has quantitative sensitivity to FMF and SSA (and qualitative sensitivity to non-sphericity) when retrieved AOD exceeds 1, with qualitative sensitivity to aerosol type at lower AOD, while also eliminating the AOD bias found in the MISR SA at higher AODs. These results also demonstrate the advantage of using a prescribed surface when aerosol loading is elevated.

Aerosol optical properties over an urban industrial area, Raipur, Chhattisgarh, India

Optical properties of aerosols including Aerosol optical depth (AOD), Angstrom exponent (AE) and Single scattering albedo (SSA)) were derived and analyzed for classifying the aerosols and identification of possible sources with percentage contribution over an urban-industrial city Raipur, India, during the year 2019 to 2021. The mean annual values of AOD and AE during 2019–2021 were 0.53 ± 0.2 and 1.20 ± 0.33 respectively, with the highest AOD (AE) values in summer:(0.67 ± 0.21 (0.98 ± 0.13)), spring: (0.52 ± 0.17 (0.93 ± 0.23)), autumn: (0.59 ± 0.21 (1.36 ± 0.26)) and winter: (0.44 ± 0.15 (1.49 ± 0.15)) respectively. The results revealed the dominance of finer aerosols with higher (lower) AE value during the winter (summer) season. Whereas, lower (higher) SSA values during the winter (summer) indicated the presence of absorbing types of aerosols. Based on the aerosol optical characteristics (AOD versus AE), three distinct aerosol types were discovered throughout the study period, namely mixed aerosols (60%), biomass-urban (37%), and dust aerosols (3%). To discriminate between urban and biomass burning aerosols, optical characteristics (AE versus SSA) were examined and found three main sources: mixed aerosols (71%) urban-industrial (21%) and biomass burning aerosols (8%). These findings will be useful for identifying potential sources as well as serve as an input for estimating the radiative forcing caused by regional aerosols.

Diverse cloud and aerosol impacts on solar photovoltaic potential in southern China and northern India

Cloud and aerosol are two important modulators that influence the solar radiation reaching the earth’s surface. It is intriguing to find diverse impacts of clouds and aerosols over Southern China (SC) and Northern India (NI) which result in remarkable differences in the plane-of-array irradiance (POAI) that signifies the maximum available solar photovoltaic potential by combining the latest satellite retrieval results and modeling tools. By separating the impacts of cloud and aerosol on the POAI, it is found that clouds are responsible for the most reduction of POAI in the SC, while aerosols and clouds are equally important for the NI region. The frequent occurrences of low and middle level clouds with high optical depth in the SC, as compared with the much lower occurrences of all levels of clouds with lower optical depth in the NI, is regarded as the major reason for the differences in the POAI. The differences in the main compositions of aerosols in the SC (sulfate) and the NI (dust) could be essential to answer the question of why higher aerosol optical depth in the SC whereas leads to weaker reduction in the POAI than that in the NI. The mitigation measures targeting on the controls of different types of aerosols should be considered for different regions.

Validation and Analysis of MAIAC AOD Aerosol Products in East Asia from 2011 to 2020

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 &gt; spring &gt; autumn &gt; 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.

Classification of MODIS fire emission data based on aerosol absorption Angstrom exponent retrieved from AERONET data

Biomass burning emits a large quantity of gaseous pollutants and aerosols into the atmosphere, which perturbs the regional and global climate and has significant impacts on air quality and human health. In order to understand the temporal and spatial distributions of biomass burning and its contribution to aerosol optical and radiative impacts, we examined fire emission data and its contribution to aerosol optical and radiative impacts over six major hot-spot continents/sub-continents across the globe, namely North-Central (NC) Africa, South America, US-Hawaii, South Asia, South East Asia, and Australia-New Zealand, using long-term satellites, ground-based and re-analysis data during 2000–2021. The selected six sites contributed ∼70% of total global fire data. The classification of biomass burning, such as pre, active, and post burning phases, was performed based on the Absorption Angstrom Exponent (AAE) estimated from 55 AERONET (AErosol RObotic NETwork) stations. The study found the highest contribution of fire count (55 %) during the active burning phase followed by post (36 %) and pre (8 %) burning phases. Such high fire counts were associated with high absorption aerosol optical depth (AAOD) during the active fire event. Strong dominance of fine and coarse mode mixed aerosols were also observed during active and post fire regimes. High AAOD and low Extinction Angstrom Exponent (EAE) over NC Africa during the fire events suggested presence of mineral dust mixed with biomass burning aerosols. Brightness temperature, fire radiative power and fire count were also dominated by the active burning followed by post and pre burning phases. The maximum heating rate of 3.15 K day−1 was observed during the active fire events. The heating rate profile shows clear variations for three different fire regimes with the highest value of 1.80 K day−1 at ∼750 hPa altitude during the active fire event.

Characterization of columnar aerosol over a background site in Central Asia

Ground-based observational characterization of atmosphere aerosols over Central Asia is very limited. This study investigated the columnar aerosol characteristics over Issyk-Kul, Kyrgyzstan, a background site in Central Asia using the long-term (∼14 years: August 2007–November 2021) data acquired with the Cimel sunphotometer. The mean aerosol optical depth (AOD) and Ångström exponent (AE) during the observation period were 0.14 ± 0.10 and 1.19 ± 0.41, respectively. Both AOD and AE varied across seasons, with highest AOD in spring (0.17 ± 0.17). Regarding the aerosol types, clean continental aerosols were dominant type (65%), followed by mixed aerosols (∼19%), clean marine aerosols (∼14%), dust (0.8%), and urban/industrial and biomass burning aerosol (0.7%). The aerosol volume size distribution was bimodal indicating the influence of both anthropogenic and natural aerosols with clear dominance of coarse mode during the spring season. Mainly dust and mixed aerosols were present during high aerosol episodes while the coarse mode aerosol volume concentration was 7.5 (strong episodes) and ∼19 (extreme episodes) times higher than the whole period average. Aerosol over this background sites were from local and regional sources with some contribution of long-range transport.

The Spatial-Temporal Differentiation of Aerosol Optical Properties and Types in the Beijing–Tianjin–Hebei Region Based on the Ecological Functional Zones

Atmospheric aerosol pollution has seriously affected the ecological environment and human health in recent years. There are great differences in aerosol optical properties and types due to the influence of environmental conditions, meteorology, industrial and agricultural activities, and other factors of each ecological functional zones. Using MODIS aerosol products (including MCD19A2 and MOD04_3K), this study discussed the temporal and spatial distribution of aerosol optical depth (AOD), Ångström wavelength index (AE) and aerosol types in the Beijing–Tianjin–Hebei region (BTH region) based on the ecological functional zones from 2015 to 2020. The results showed as follows: (1) The AOD in BTH region showed an obviously spatial pattern of low in the north and high in the south, while the spatial pattern of AE was opposite to that of AOD. In addition, the dominant aerosol type of the north part was clean aerosol, the dominant aerosol type of the middle part was biomass burning or urban-industrial aerosol, while that of the other part was mixed aerosols. (2) The seasonal changes of AOD and AE indexes in each ecological functional area had obvious seasonal changes, and the AOD and AE values were highest in summer. At the same time, the proportion of biomass combustion or urban industrial aerosol was the highest in summer. (3) The ecological functional areas with fewer human activities were dominated by clean aerosols, with lower AOD and higher AE value. The ecological functional areas dominated by cities were dominated by mixed aerosols, with higher AOD. The ecological functional areas dominated by agriculture and heavy industry were dominated by biomass combustion or urban industrial aerosols, with the largest AOD. (4) Compared with 2015, the average AOD of each ecological functional area decreased significantly to 2020, and biomass combustion or urban industrial aerosols changed to mixed aerosols.

Accuracy assessment and climatology of MODIS aerosol optical properties over North Africa.

In this study, the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) product was compared with ground-based measurements at five sites of the Aerosol Robotic Network (AERONET) in North Africa. The MODIS AOD showed a good correlation coefficient of ~0.78, a very small mean bias error of 0.009, and a root mean square error of 0.126 with AERONET. The Dark Target/Deep Blue (DT/DB) algorithm showed better performance at low aerosol loading while underestimating AOD at higher aerosol loading, mainly for coarse-dominated aerosol types. This work also showed the benefits of using MODIS retrievals as a reliable data source for aerosols and providing a long-term aerosol type classification. The primary aerosol type is dust emitted from the Sahara Desert, and the dusty atmosphere becomes gradually mixed with pollution aerosols approaching the coastal region. The annual mean MODIS AOD at 550 nm and Ångström exponent at 412-650 nm (AE) ranged from 0.17 to 0.45 and from 0.13 to 1.25, respectively, in Algeria between 2001 and 2019. Lower AOD (< 0.22) and higher AE (> 1) were found in the northern region, while the highest AOD (0.35 to 0.45) and the lowest AE (< 0.25) were observed over the Tanezrouft desert in southern Algeria. The seasonal mean AOD was highest in summer, while the lowest was in winter due to very high easterly and northeasterly Harmattan surface wind over Zone of Chotts and the Tidikelt Depression, respectively. The negative AOD trends observed over Algeria could be partially connected to the decline (increase) in surface (850 hPa) winds over potential dust source areas in southern Algeria.

Land Use and Land Cover Influence on Sentinel-2 Aerosol Optical Depth below City Scales over Beijing

Atmospheric aerosols can impact human health, necessitating the understanding of their distribution determinants, especially in urban areas. The study discusses the relationships between five major land cover types and aerosol optical depth (AOD) within a city combining the high-resolution satellite-derived AOD products (derived from Sentinel-2) and land cover products (60 m and 100 m, respectively) for Beijing and its surroundings from 2017 to 2019. Contribution analysis is performed to quantitatively evaluate the influences of land cover on regional AOD over the study area. Patterns of aerosol distribution remarkably vary in time and space. Statistics of seasonal average AOD peak in spring and then progressively decline from summer through autumn to winter. High AOD values coincide with a low normalized difference vegetation index (NDVI) and a high normalized difference built-up index (NDBI). Urban and built-up land is a major contributor to regional AOD in the study area, especially in spring; forest and grassland always reduce AOD. Anthropogenic activities have a non-negligible influence on AOD and can even reverse the contribution of a land cover type to aerosols. Insights of the study promote the comprehension of the impacts of land cover on aerosols and air pollution and contribute to the planning of land use within a city.

Infusing satellite data into aerosol forecast for near real-time episode detection and diagnosis in East Asia

A near-real-time (NRT) aerosol forecast and diagnostic approach is developed based on the system of Infusing satellite Data into Environmental Applications for East Asia, herein denoted as IDEA-EA. The design incorporates a 0.5-degree Global Forecast System (GFS) and Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol and cloud retrievals for meteorological and remote sensing inputs. The primary output of IDEA-EA includes aerosol forward and backward air mass trajectory forecasts, migration visualization, and data synthesis purposed for NRT aerosol detection, monitoring, and source tracing in East Asia. Two aerosol episodes of Southeast Asia (SEA) biomass burning and Chinese haze infusion with Gobi dust are illustrated by IDEA-EA to demonstrate its forecast and source tracing capabilities. In the case of SEA biomass burning (late March 2021), forward trajectories of IDEA-EA forecasted air masses with high aerosol optical depth (AOD) from SEA affecting Taiwan. The IDEA-EA forecasts were verified by increased AOD and surface PM2.5 observations at a mountain site. In the case of the Chinese haze (October 30, 2019), backward trajectories from the northern tip of Taiwan traced air masses back to the east coast of mainland China and possibly further to the Gobi Desert. Compared with conventional numerical model simulations, the combination of the state-of-the-art aerosol remote sensing and trajectory modeling in IDEA-EA provides a cost-effective alternative for air quality management.

Spatiotemporal distribution of aerosols over the Tibet Plateau and Tarim Basin (1980–2020)

Aerosol Optical Depth (AOD) represents the optical attenuation of aerosol, which, to a certain extent, can reflect regional air pollution levels with obvious spatiotemporal characteristics. However, the currently limited monitoring technology cannot provide long-time sequential and detailed AOD information. Therefore, in this paper, Modern Era Retrospective-Analysis for Research and Applications, Version 2 (MERRA-2) data, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data from 1980 to 2020 were used to analyze the characteristics of both the spatiotemporal distribution of aerosols and dust transportation in the Tibet Plateau and Tarim Basin. Moreover, Random Forest (RF) Model was employed for ranking the importance of multiple natural factors (evaporation, mean surface temperature, daily precipitation, mean air pressure, mean relative humidity, mean temperature and mean wind speed) on AOD, Wavelet Analysis Method was utilized for the scale analysis between key natural factors and AOD, and Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) Model was applied to the simulation of the transportation trajectories of dust aerosols. The results show that high AOD values in the Tibet Plateau and Tarim Basin appeared in spring. Dust aerosols in the northern slope of the Tibet Plateau dispersed and decreased from the north to the middle of Plateau. Dust Aerosol Optical Depth (DAOD) reached its peak in spring and summer with the maximum transportation range of 34° N or so. Besides, Tarim Basin transported dust to the northern slope of the Tibet Plateau significantly, which was then transported to the main body of the Plateau along its northern slope (37°N) with a dust particle proportion of 40%–80%. Moreover, precipitation was the most significant factor (32.18%) influencing the spatiotemporal distribution of AOD. The former lagged behind the latter by 5/12 periods at most and showed a negative correlation with it.

Decoupling between PM2.5 concentrations and aerosol optical depth at ground stations in China

Surface PM2.5 concentrations and aerosol optical depth (AOD) are two air pollution metrics tightly connected. Many studies have used AOD to derive PM2.5 concentrations without investigating their inconsistencies. Here, we explored the associations between surface PM2.5 and AOD using ground-level data from 19 stations in China during 2017–2019. Unexpectedly, we found low correlation coefficients of 0.03–0.60 between daily PM2.5 and AOD for most sites. Such decoupling between PM2.5 and AOD is further compared to simultaneous meteorological factors such as air temperature, specific humidity, sea level pressure, and wind speed. We found that specific humidity dominates the correlations with normalized PM2.5-AOD differences at 14 out of 19 sites. On average, specific humidity increases from 2.83 g kg−1 for the cases with low AOD but high PM2.5–11.89 g kg−1 for those with high AOD but low PM2.5, indicating that hygroscopic growth of aerosols may play an important role in decoupling the associations between PM2.5 and AOD. Random forest (RF) models using AOD as the only input yield a low R of 0.49 between the predicted and observed PM2.5 concentrations. The inclusion of specific humidity in the RF model increases the R to 0.74, close to the R of 0.81 with three additional meteorological factors. Our study revealed a strong decoupling between PM2.5 and AOD and suggested including specific humidity as a key parameter in the retrieval of long-term PM2.5 using AOD data in China.

Bayesian Aerosol Retrieval-Based PM2.5 Estimation through Hierarchical Gaussian Process Models

Satellite-based aerosol optical depth (AOD) data are widely used to estimate land surface PM2.5 concentrations in areas not covered by ground PM2.5 monitoring stations. However, AOD data obtained from satellites are typically at coarse spatial resolutions, limiting their applications on small or medium scales. In this paper, we propose a new two-step approach to estimate 1-km-resolution PM2.5 concentrations in Shanghai using high spatial resolution AOD retrievals from MODIS. In the first step, AOD data are refined to a 1×1km2 resolution via a Bayesian AOD retrieval method. In the second step, a hierarchical Gaussian process model is used to estimate PM2.5 concentrations. We evaluate our approach by model fitting and out-of-sample cross-validation. Our results show that the proposed approach enjoys accurate predictive performance in estimating PM2.5 concentrations.

Long‐term trends in aerosol optical properties and their relationship with cloud properties over southern India and Sri Lanka

AbstractAsia is one of the continents where aerosol levels are comparatively higher across the world. India and Sri Lanka are some of the regions in the South Asian continent where pollution is increasing rapidly due to the rise in industrialization. The present study investigated the interactions between atmospheric aerosol and cloud microphysical properties and their spatial, temporal, and seasonal variation at local and regional scales during 2000–2020 using remotely sensed data sets in south India and Sri Lanka. High values (&gt;0.5) of annual mean aerosol optical depth (AOD) were detected over southern India. On the other hand, lower values (0.2) were detected over Sri Lanka. In terms of seasonality, a high level of AOD was registered in both southern India and Sri Lanka during the premonsoon and monsoon periods. The angstrom exponent (AE412‐470) revealed the dominance of fine‐mode particles during winter and the postmonsoon, generally from biomass burning and industrial activities. The long‐term analysis exhibited an increasing trend of atmospheric aerosol concentration over southern India and Sri Lanka. Interestingly, there was a decrease in AOD during the year 2020; the reduction in anthropogenic activities in the region was attributed to the COVID‐19 lockdown, hence less accumulation of pollutants in the atmosphere. AOD showed a positive correlation with cloud effective radius over the western areas along with the Indian Ocean, north, southeast, and southern end of India, while negatively correlated with high AOD areas such as northeast of the study domain. The AOD and cloud optical depth were positively correlated over continental areas, while negative correlations were notable over the Indian Ocean around Sri Lanka, implying heterogeneities of aerosol's effect on cloud microphysical properties over the study area. Finally, the results from wind circulation and backward air mass trajectories reveal higher concentrations of fine‐mode particles associated with the continent, whereas coarse‐mode particles originate from the oceans.

Spatiotemporal Variation and Influencing Factors of AOD in the North China Plain

A better knowledge of the spatial and temporal variation in atmospheric aerosol and its influencing factors is of great significance to controlling atmospheric pollution and improving the atmospheric environment. First, the visible infrared imaging radiometer suite (VIIRS) intermediate product (IP) aerosol optical depth (AOD) data from 2013 to 2019 were used to analyze the temporal and spatial variation in AOD in the North China Plain. Secondly, SO2, NO2, PM2.5, meteorological data, NDVI, DEM, GDP, and POPU were selected as influencing factors, and the linkage models between AOD and its influencing factors were established based on the XGBoost model for each of the five representative cities in the North China Plain to quantitatively estimate and reveal the contribution of various influencing factors behind the temporal and spatial distribution in AOD. The results showed that in terms of spatial distribution, the AOD of the North China Plain was bounded by the Taihang Mountains, showing a pattern of high AOD in the southeast and low AOD in the northwest. In terms of temporal changes, the annual average value of AOD in the five cities showed an overall decreasing trend, and the monthly average value of AOD first increased and then decreased, with the highest value appearing in July and the lowest value in December. In addition, the AOD estimation model established in this paper for the five cities in North China had high accuracy, with R2 ranging from 0.60 to 0.67. Among the factors influencing AOD in the North China Plain, NO2 and SO2 were the most influential factors contributing to AOD in the five cities. In addition, PM2.5 was another important pollutant emission factor. In terms of meteorological factors, temperature (T), relative humidity (RH), wind speed (WS), and wind direction (WD) were the other four important influencing factors. There were both commonalities and differences in the rankings of the contribution and importance of AOD influencing factors in the five representative cities in North China.

Performance of MODIS Deep Blue Collection 6.1 Aerosol Optical Depth Products Over Indonesia: Spatiotemporal Variations and Aerosol Types

This study aims to evaluate the performance of the long-term Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) Collection 6.1 (C6.1) in determining the spatiotemporal variation of aerosol optical depth (AOD) and aerosol types over Indonesia. For this purpose, monthly MODIS DB AOD datasets are directly compared with Aerosol Robotic Network (AERONET) Version 3 Level 2.0 (cloud-screened and quality-assured) monthly measurements at 8 sites throughout Indonesia. The results indicate that MODIS DB AOD retrievals and AERONET AOD measurements have a high correlation in Sumatra Island (i.e., Kototabang (r = 0.88) and Jambi (r = 0.9)) and Kalimantan Island (i.e., Palangkaraya (r = 0.89) and Pontianak (r = 0.92)). However, the correlations are low in Bandung, Palu, and Sorong. In general, MODIS DB AOD tends to overestimate AERONET AOD at all sites by 16 to 61% and can detect extreme fire events in Sumatra and Kalimantan Islands quite well. Aerosol types in Indonesia mostly consist of clean continental, followed by biomass burning/urban industrial and mixed aerosols. Palu and Sorong had the highest clean continental aerosol contribution (90%), while Bandung had the highest biomass burning/urban-industrial aerosol contribution to atmospheric composition (93.7%). For mixed aerosols, the highest contribution was found in Pontianak, with a proportion of 48.4%. Spatially, the annual mean AOD in the western part of Indonesia is higher than in the eastern part. Seasonally, the highest AOD is observed during the period of September–November, which is associated with the emergence of fire events.

Comparative Analysis of Aerosol Optical Properties over High Altitude Region of Western Ghats in Southern India

Observations of aerosols and Black Carbon (BC) were carried out at Ooty, high altitude region in Western Ghats using Multi wavelength solar radiometer (MWR) and Aethalometer. For the years 2018 and 2021, the Optical Characteristics of Aerosols and Clouds (OPAC) model was used to estimate monthly, seasonal, and spectral variations of aerosol optical properties such as Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA), and Asymmetry Parameter (ASY). The dominance of fine anthropogenic aerosols was shown by higher AOD during the pre-monsoon period. The maximum and minimum seasonal variation of AOD occurred during pre-monsoon (1.1 ± 0.02) and winter (0.21 ± 0.001) respectively. The significant spectral variation of AOD occurred during March to May as it decreases with the increase in wavelength .The SSA increases as the wavelength increases, ranged between 0.83 ± 0.02 and 0.77 ± 0.01.The variability of SSA is significant during January and February which is a characteristic of coarse type aerosols. Asymmetry Parameter with the monthly mean of 0.75 ± 0.01 indicated the forward scattering of aerosols and there is no significant difference in them over the years.

Spatiotemporal Analysis of MODIS Aerosol Optical Depth Data in the Philippines from 2010 to 2020

Satellite remote sensing for air quality assessment provides information over a large spatial coverage and time period that shows the trends and effects of anthropogenic activities. Using data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite from the years 2010 to 2020, the spatiotemporal variations to aerosol optical depth (AOD) in Koronadal City and Quezon City were studied. Validation showed a strong relationship between the MODIS AOD values and the Aerosol Robotic Network (AERONET) AOD values (R2 = 0.83) and a low root mean square error (RMSE) of 0.26. Annual variation in the AOD of the two study areas showed a peak AOD value in 2015 due to an immense biomass burning in Indonesia and a low AOD value in 2020 due to the COVID-19 lockdown. Koronadal City experienced a high AOD value during the fall season due to aerosols from biomass burning in Indonesia that were carried by the southwest monsoon. Quezon City experienced a high AOD value during spring from increased local sources, as well as long-range transport pollutants from East Asia that were carried by northeasterly winds. Overall, this study provides an understanding of the spatiotemporal variations in aerosols in the Philippines, which could be used in environmental management, air quality regulations, and health assessment studies. This shows the urgency of monitoring and mitigating poor air quality in the Philippines.