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This study focuses on atmospheric aerosols, especially aerosol optical depth (AOD), over Dire Dawa, Ethiopia, from 2009 to 2020. At first, a correlation between the four satellite sensors and AERONET was made for validation purposes and to determine the sensor that best represents Dire Dawa. Intercomparisons were also made among the four satellite sensors. After all statistical tests, annual, seasonal, and decadal trend analyses were made. The validation results indicated that the AOD of MODIS-terra showed the best correlation with AERONET with R2 (0.78), RMSE (0.03), and MBE of 0.02 and represented the area better than the rest. The inter-comparison of AOD retrieved from multi-spectral satellite sensors showed a positive and satisfactory correlation between MODIS-Terra and OMI. Only MODIS-Aqua showed a linearly increasing mean annual AOD with R2 = 0.43. In three seasons (summer, autumn, and spring), AOD showed linear increments over the 12 years, with R2 ranging between 0.3 and 0.5. The three seasons also had nearly identical AODs of 0.23-0.28. However, winter had the lowest value of 0.2. MODIS-terra, out of the four sensors, exhibited increasing decadal tendency over the 2009-2020 period. Monthly analysis revealed that August had the highest AOD (0.265), and January had the lowest (0.14). The value of AOD obtained from this study over Dire Dawa shows a higher value during all seasons except during winter. Thus, this study gives a glimpse into the use of multi-spectral satellite sensors to monitor air quality over a semi-arid urban region.

This study presents the spectral monthly and seasonal variation of aerosol optical depth (τAOD), single scattering albedo (SSA), and aerosol absorption optical depth (AAOD) between 2010 and 2018 obtained from the Aerosol Robotic Network (AERONET) over Pokhara, Nepal. The analysis of these column-integrated aerosol optical data suggests significant monthly and seasonal variability of aerosol physical and optical properties. The pre-monsoon season (March to May) has the highest observed τAOD(0.75 ± 0.15), followed by winter (December to February, 0.47 ± 0.12), post-monsoon (October and November, 0.39 ± 0.08), and monsoon seasons (June to September, 0.27 ± 0.13), indicating seasonal aerosol loading over Pokhara. The variability of Ångström parameters, α, and β, were computed from the linear fit line in the logarithmic scale of spectral τAOD, and used to analyze the aerosol physical characteristics such as particle size and aerosol loading. The curvature of spectral τAOD, α’, computed from the second-order polynomial fit, reveals the domination by fine mode aerosol particles in the post-monsoon and winter seasons, with coarse mode dominating in monsoon, and both modes contributing in the pre-monsoon. Analysis of air mass back trajectories and observation of fire spots along with aerosol optical data and aerosol size spectra suggest the presence of mixed types of transboundary aerosols, such as biomass, urban-industrial, and dust aerosols in the atmospheric column over Pokhara.

Stratospheric Aerosol Geoengineering (SAG) is one of the solar geoengineering approaches that have been proposed to offset some of the impacts of anthropogenic climate change. Past studies have shown that SAG may have adverse impacts on the global hydrological cycle. Using a climate model, we quantify the sensitivity of the tropical monsoon precipitation to the meridional distribution of volcanic sulfate aerosols prescribed in the stratosphere in terms of the changes in aerosol optical depth (AOD). In our experiments, large changes in summer monsoon precipitation in the tropical monsoon regions are simulated, especially over the Indian region, in association with meridional shifts in the location of the intertropical convergence zone (ITCZ) caused by changes in interhemispheric AOD differences. Based on our simulations, we estimate a sensitivity of − 1.8° ± 0.0° meridional shift in global mean ITCZ and a 6.9 ± 0.4% reduction in northern hemisphere (NH) monsoon index (NHMI; summer monsoon precipitation over NH monsoon regions) per 0.1 interhemispheric AOD difference (NH minus southern hemisphere). We also quantify this sensitivity in terms of interhemispheric differences in effective radiative forcing and interhemispheric temperature differences: 3.5 ± 0.3% change in NHMI per unit (Wm −2 ) interhemispheric radiative forcing difference and 5.9 ± 0.4% change per unit (°C) interhemispheric temperature difference. Similar sensitivity estimates are also made for the Indian monsoon precipitation. The establishment of the relationship between interhemispheric AOD (or radiative forcing) differences and ITCZ shift as discussed in this paper will further facilitate and simplify our understanding of the effects of SAG on tropical monsoon rainfall.

Aerosol optical depth (AOD), fine-mode fraction (FMF), and absorption aerosol optical depth (AAOD) are essential for quantifying aerosol extinction and related climate and air-quality effects. Yet, most satellite retrievals target a single wavelength or parameter. In this study, a deep neural network (DNN) framework was developed to synergistically retrieve AOD, FMF, and AAOD from Sentinel-5P/TROPOMI at seven wavelengths across 380–772 nm. Parameter-specific feature engineering was designed by incorporating physical linkages among aerosol optical properties. Bayesian optimization was employed to tune hyperparameters, and SHAP (Shapley additive explanations) was used to interpret feature contributions. The proposed model demonstrated high accuracy and robustness on an independent test set. The retrieved AOD showed excellent agreement with AERONET (R = 0.960, MAE = 0.034, RMSE = 0.070), and similarly strong performance was achieved for FMF (R = 0.955, MAE = 0.027, RMSE = 0.039). For AAOD, an overall correlation of 0.86 was obtained (MAE = 0.005, RMSE = 0.008). Comparisons with existing satellite products indicated globally consistent spatial patterns and improved spatial continuity under high aerosol loading. Overall, the proposed data-driven approach enhances the efficiency and coverage of multi-parameter aerosol retrieval while maintaining high accuracy, supporting absorbing aerosol monitoring, aerosol-type discrimination, and climate-effect assessment.

The purpose of this study is to validate the daily Terra-MODIS level 2 combined dark target (DT) and deep blue (DB) aerosol optical depth (AOD) retrievals with a spatial resolution of 10 km against the ground-based AERONET AOD data to be used in evaluating the air pollution and impact of meteorological variables over Qena, Egypt, in 2019. The regression analysis demonstrated an accepted agreement between the MODIS and AERONET AOD data with a correlation coefficient ( R ) of 0.7118 and 74.22% of the collocated points fall within the expected error (EE) limits. Quality flag filtering and spatial and temporal collocation were found to have a significant impact on the regression results. Quality flag filtering increased R by 0.2091 and % within EE by 17.97, spatial collocation increased R by 0.0143 and % within EE by 1.13, and temporal collocation increased R by 0.0089 and % within EE by 4.43. By validating the MODIS AOD data seasonally and analyzing the temporal distribution of the seasonal AOD data to show the retrieval accuracy variations between seasons, it was found that the MODIS AOD observations overestimated the AERONET AOD values in all seasons, and this may be because of underestimating the surface reflectance. Perhaps the main reason for the highest overestimation in summer and autumn is the transportation of aerosols from other regions, which changes the aerosol model in Qena, making accurate aerosol-type assumptions more difficult. Therefore, this study recommends necessary improvements regarding the aerosol model selection and the surface reflectance calculations. Temperature and relative humidity were found to have a strong negative relationship with a correlation of − 0.735, and both have a moderate association with AOD with a correlation of 0.451 and − 0.356, respectively. Because Qena is not a rainy city, precipitation was found to have no correlation with the other variables.

The current study investigates the relationship among Aerosol Optical Depth (AOD), rain rate in aerosol types (fine-mode and coarse-mode), and meteorological conditions (such as relative humidity and vertical velocity) during Indian Summer Monsoon (ISM) over regions of the Gangetic plain (GP) and the Modified Core Monsoon Region (MCMR) of India. The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products, including AOD and Angstrom Exponent (AE), the observed rainfall gridded data of India Meteorological Department (IMD) and reanalyzed data of relative humidity (RH), vertical velocity (VV) and wind speed and direction as meteorological variables from European Center for Medium Range Weather Forecasting (ECMWF) Reanalysis v5 (ERA5) are used for the period of 2003–2021. It is noticed that regions of GP and MCMR experienced significant rainfall suppression with increasing aerosol loading in moderately polluted condition and beyond moderately polluted condition, the different rain rate responses to aerosol loading is observed. Different aerosol types and meteorological conditions may have different influences on the AOD-rain rate relationship. It is found that an increase in AOD may suppress or enhance the rain rate in a region like GP where high RH condition prevails, as well as the presence of fine-mode aerosol can enhance the rain rate in a highly polluted condition (AOD ≥ 0.4). An increase in AOD may enhance the rain rate in conditions of high and low RH over the regions. In high and low updraft conditions, an increase in AOD suppresses the rain rate. Additionally, high (low) RH and high (low) updraft promote high (low) rain rate over the regions.

Central Asia has become a key node of the belt and road corridor. It is located in arid and semi-arid climate regions, and it is a region where the contribution of global aerosols of sand and dust is continuous. However, few studies have been conducted on the Central Asian aerosol optical depth. Therefore, this paper relied on the belt and road sustainable development dataset to analyze the spatial-temporal variations in the AOD in Central Asia and provide spatial-temporal characteristics of the AOD for environmental services. We analyzed the spatial and temporal variation in the aerosol optical depth (AOD) in Central Asia by using MODIS/AQUA C6 MYD08_M3 images from 2008 to 2017. The results showed that (1) the annual average AOD in Central Asia in the past decade varied from 0.183 to 0.232, which indicated a slow decline starting in 2014. The percentage of average annual decline was approximately 0.18%, and the regular distinct revealed the distribution characteristics of AOD. In different years, the Central Asian region exhibited the similar monthly change characteristics: from July to December, the AOD decreased, and from December to February, it increased. In different seasons, the Central Asian region exhibited the different seasonal change characteristics: the AOD value was higher in the spring and summer. The mean values in the spring, summer, autumn, and winter were 0.273, 0.240, 0.155, and 0.183, respectively, and the standard deviations were 0.036, 0.038, 0.025, and 0.048, respectively. (3) Based on spatial distribution characteristics, the Tarim Basin, Aral Sea region, and Ebinur Lake area were high value areas, and Kazakhstan was a low value area. The AOD of the surrounding area of the Aral Sea had increased in the last 5 years, while that of Kazakhstan, Uzbekistan, and Turkmenistan had decreased. The AOD of the Taklamakan area exhibited an inter-annual change. Sand dust aerosols were the largest contributors to the AOD in the Taklamakan area. The rising trend of the AOD in the Aral Sea area was clear, with an average annual increase of 0.234%, and the contribution of salt dust aerosols to the AOD increased. The average annual AOD in the Ebinur Lake area remained stable.

In the present study, we have made an effort to examine the influence of dust aerosols on the precipitation over India, and six homogeneous rainfall zones of India during pre-monsoon (March to May) and monsoon (June to September) seasons. In this regard, a set of sensitivity experiments are performed using a regional climate model, namely, RegCM-4.7.0, for 11 years. Extensive analysis has been carried out to evaluate the model’s performance in simulating the mean precipitation, its variability (spatial and temporal), dust aerosol optical depth, and monsoonal wind. It is observed that the model has the potential to capture the distinct features like prevailing north westerlies with less precipitation during pre-monsoon and southwesterlies with more precipitation during monsoon season, with systematic differences in magnitude while compared against observation. High dust aerosol optical depth is found during pre-monsoon season over central northeast, west central and northeast parts of India. Dust significantly increases precipitation parts over southern peninsula, west central and northwest during the monsoon season due to increases in wind at 850 hPa facilitating moisture transport from Arabian Sea. Interestingly, it is also noticed that dust aerosols modulate the magnitude of extreme precipitation indices over India implying importance in including dust aerosols effects in the climate models.

Biomass burning in the Amazon region, especially during the dry season, generates aerosol dispersion events across the southern part of the continent, with impacts observable thousands of kilometers from the emission source. This study presents a long-range aerosol transport case from September 2024, in which smoke aerosols from forest fires in the central Amazon reached southeastern and southern Brazil, affecting the air quality in distant areas such as São Paulo and São Martinho. The event was simulated using the Weather Research and Forecasting model with Chemistry (WRF-Chem), configured with the MOZCART chemical mechanism, combined with MERRA-2 reanalysis data and by using the 3BEM biomass burning emission inventory. Satellite datasets from MODIS and MERRA-2 reanalysis were used to evaluate the model’s performance. The results indicate that the South American Low-Level Jet (SALLJ) played a key role in transporting carbonaceous aerosols over long distances. The model successfully captured the spatial and temporal evolution of the aerosol plume and its impacts, although it tended to underestimate aerosol optical depth (AOD) values compared with satellite observations. This study highlights the WRF-Chem’s capability to simulate extreme smoke transport events in South America and supports its potential application in forecasting and air quality assessments.

Fossil-fuel combustion related winter heating has become a major air quality and public health concern in northern China recently. We analyzed the impact of winter heating on aerosol loadings over China using the MODIS-Aqua Collection 6 aerosol product from 2004-2012. Absolute humidity (AH) and planetary boundary layer height (PBL) -adjusted aerosol optical depth (AOD*) was constructed to reflect ground-level PM2.5 concentrations. GIS analysis, standard statistical tests, and statistical modeling indicate that winter heating is an important factor causing increased PM2.5 levels in more than three-quarters of central and eastern China. The heating season AOD* was more than five times higher as the non-heating season AOD*, and the increase in AOD* in the heating areas was greater than in the non-heating areas. Finally, central heating tend to contribute less to air pollution relative to other means of household heating.