Search Results (168)

Aerosols play a critical role in modulating the land–atmosphere energy balance, influencing regional climate dynamics, and affecting air quality. Xinjiang, a typical arid and semi-arid region in China, frequently experiences dust events and complex aerosol transport processes. This study provides a comprehensive analysis of the spatiotemporal evolution and potential source regions of aerosols in Xinjiang from 2005 to 2023, based on Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products (MCD19A2), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical profiles, ground-based PM_2.5 and PM₁₀ concentrations, MERRA-2 and ERA5 reanalysis datasets, and HYSPLIT backward trajectory simulations. The results reveal pronounced spatial and temporal heterogeneity in aerosol optical depth (AOD). In Northern Xinjiang (NXJ), AOD exhibits relatively small seasonal variation with a wintertime peak, while Southern Xinjiang (SXJ) shows significant seasonal and interannual variability, characterized by high AOD in spring and a minimum in winter, without a clear long-term trend. Dust is the dominant aerosol type, accounting for 96.74% of total aerosol content, and AOD levels are consistently higher in SXJ than in NXJ. During winter, aerosols are primarily deposited in the near-surface layer as a result of local and short-range transport processes, whereas in spring, long-range transport at higher altitudes becomes more prominent. In NXJ, air masses are primarily sourced from local regions and Central Asia, with stronger pollution levels observed in winter. In contrast, springtime pollution in Kashgar is mainly influenced by dust emissions from the Taklamakan Desert, exceeding winter levels. These findings provide important scientific insights for atmospheric environment management and the development of targeted dust mitigation strategies in arid regions. Full article

Aerosols play a crucial role in air quality, climate regulation, and public health; their timely monitoring is hence fundamental. The aerosol optical depth (AOD) is the parameter used to investigate the spatial–temporal distribution of aerosols from space. Specifically, the AOD retrieved from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm applied to a Moderate Resolution Imaging Spectroradiometer (MODIS) is suitable for aerosol investigation at a local scale by exploiting its high spatial resolution (1 km × 1 km). In this study, the MAIAC AOD retrieval over Rome (Italy) was validated for the first time, using ground-based data provided by an AERONET station operating in a semi-rural environment close to the city, over a time series from January 2001 to December 2022. Moreover, AOD trends were evaluated in a study area encompassing Rome and its surroundings, characterized by a transition zone between urban and rural environments. The results show a general underestimation of the MAIAC AOD; specifically, the validation process highlighted the less accurate performance of the algorithm under higher aerosol loading and with predominantly coarse mode aerosol. Interesting results were obtained concerning the influence of the geometrical configuration of satellite acquisition on the accuracy of the MAIAC product. In particular, the solar zenith angle, the relative azimuth and the scattering angle between the principal plane of the sun and satellite synergistically influence retrievals. Finally, the spatial distribution of the AOD shows a decreasing trend over the 2001–2022 period and a strong influence of the city of Rome over the whole study area. Full article

The Directional Polarimetric Camera (DPC) aboard the Chinese GaoFen-5 02 satellite is designed to monitor aerosols and particulate matter (PM). In this study, we retrieved the aerosol optical depth (AOD) over the Jing–Jin–Ji (JJJ) region using multi-angle data from the DPC, employing a combination of dark dense vegetation (DDV) and multi-angle retrieval methods. The added value of our method included novel hybrid methodology and good practical performance. The retrieval process involves three main steps: (1) deriving AOD from DPC data collected at the nadir angle using linear parameters of land surface reflectance between the blue and red bands from the MOD09 surface product; (2) after performing atmospheric correction with the retrieved AOD, calculating the variance of the normalized reflectance at all observation angles; and (3) leveraging the calculated variance to obtain the final AOD values. AOD images over the JJJ region were successfully retrieved from DPC data collected between January and June 2022. To validate the retrieval method, we compared our results with aerosol products from the AErosol RObotic NETwork (AERONET) Beijing-RADI site, as well as aerosol data from MODerate-resolution Imaging Spectroradiometer (MODIS) and the generalized retrieval of atmosphere and surface properties (GRASP)/models over the same site. In terms of validation metrics, the correlation coefficient (R²) and root mean square error (RMSE) indicated that our method achieved high accuracy, with an R² value greater than 0.9 and an RMSE below 0.1, closely aligning with the performance of GRASP. Full article

The Wide-Swath Imaging (WSI) sensor is a Chinese satellite launched in 2022, capable of providing data at resolutions ranging from 75 to 600 m for monitoring aerosols, fire points, and dust, among other uses. In this study, we developed a Dark Dense Vegetation method to retrieve the Aerosol Optical Depth (AOD) quickly from WSI 600 m data. First, after splitting into three types according to the Normalized Difference Vegetation Index (NDVI), we calculated the empirical parameters of land reflectance between the red (0.65 μm) and blue (0.47 μm) channels using Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance products over the Beijing area. Second, the decrease in the NDVI was simulated and analyzed under different AODs and solar zenith angles, and we introduced an iterative inversion approach to account for it. The simulation retrievals demonstrated that the iterative inversion produced accurate results after less than four iterations. Thirdly, we utilized the atmospherically corrected NDVI for dark target identification and output the AOD result. Finally, retrieval experiments were conducted using WSI 600 m data collected over Beijing in 2023. The retrieved AOD images highlighted two air pollution events occurring during 3–8 March and 27–31 October 2023. The inversion results in 2023 showed a strong correlation with Aerosol Robotic Network station data (the correlation coefficient was greater than 0.9). Our method exhibited greater accuracy than the MODIS aerosol product, though it was less accurate than the Multi-Angle Implementation of Atmospheric Correction product. Full article

Investigating the spatiotemporal variations in Aerosol Optical Depth (AOD) in terrestrial ecosystems and their driving factors is significant for deepening our understanding of the relationship between ecosystem types and aerosols. This study utilized 1 km resolution AOD data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Mann–Kendall (M-K) trend test to analyze the spatiotemporal variations in AOD in seven ecosystems in Northern Xinjiang from 2001 to 2023. The geographic detector model was employed to investigate the effects of driving factors, including gross domestic product, population density, specific humidity, precipitation, temperature, wind speed, soil moisture, and elevation, on the distribution of AOD in the ecosystems. The results indicate that over the past 23 years, wetlands had the highest annual average AOD values, followed by settlements, farmlands, deserts, grasslands, others, and forests, respectively. Furthermore, the AOD values decrease with increasing ecosystem elevation. The annual mean of AOD in Northern Xinjiang generally shows a fluctuating upward trend. The M-K test shows that the proportion of area with an increasing trend in AOD in the settlement ecosystems is the highest (92.17%), while the proportion of area with a decreasing trend in the forest ecosystem is the highest (21.78%). On a seasonal scale, grassland, settlement, farmland, forest, and wetland ecosystems exhibit peak values in spring and winter, whereas desert and other ecosystems only show peaks in spring. Different types of ecosystems show different sensitivities to driving factors. Grassland and forest ecosystems are primarily influenced by temperature and altitude, while desert and settlement ecosystems are most affected by wind speed and humidity. Farmlands are mainly influenced by wind speed and altitude, wetlands are significantly impacted by population density and humidity, and other ecosystems are predominantly affected by humidity and altitude. This paper serves as a reference for targeted air pollution prevention and regional ecological environmental protection. Full article

This study investigates the impacts of summer monsoon activity on aerosols over the Indian region. We analyze the variability of aerosols during active and break monsoon phases, as well as strong and weak monsoon years, using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Our findings show a clear distinction in aerosol distribution between active and break phases. During active phases, the Aerosol Optical Depth (AOD) and aerosol extinction are lower across the Indian region, while break phases are associated with higher AOD and extinction. Furthermore, we observed a significant increase in AOD over Central India during strong monsoon years, compared to weak monsoon years. Utilizing the vertical feature mask (VFM) data from CALIPSO, we identified polluted dust and dusty marine aerosols as the dominant types during both active/break phases and strong/weak monsoon years. Notably, the contributions of these pollutants are significantly higher during break phases compared to during active phases. Our analysis also reveals a shift in the origin of these aerosol masses. During active phases, the majority originate from the Arabian Sea; in contrast, break phases are associated with a higher contribution from the African region. Full article

Nowadays, dried lake beds constitute the largest source of saline dust storms, with serious environmental and health issues in the surrounding areas. In this study, we examined the spatial–temporal distribution of monthly and annual dust events of varying intensity (dust in suspension, blowing dust, dust storms) in the vicinity of the desiccated Urmia Lake in northwestern (NW) Iran, based on horizontal visibility data during 2009–2022. Dust in suspension, blowing dust and dust storm events exhibited different monthly patterns, with higher frequencies between March and October, especially in the southern and eastern parts of the Urmia Basin. Furthermore, the intra-annual variations in aerosol optical depth at 500 nm (AOD₅₅₀) and Ångström exponent at 412/470 nm (AE) were investigated using Terra/Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) data over the Urmia Lake Basin (36–39°N, 44–47°E). Monthly distributions of potential coarse aerosol (AE < 1) sources affecting the lower troposphere over the Urmia Basin were reconstructed, synergizing Terra/Aqua MODIS AOD₅₅₀ for AE < 1 values and HYSPLIT_4 backward trajectories. The reconstructed monthly patterns of the potential sources were compared with the monthly spatial distribution of Terra MODIS AOD₅₅₀ in the Middle East and Central Asia (20–70°E, 20–50°N). The results showed that deserts in the Middle East and the Aral–Caspian arid region (ACAR) mostly contribute to dust aerosol load over the Urmia Lake region, exhibiting higher frequency in spring and early summer. Local dust sources from dried lake beds further contribute to the dust AOD, especially in the western part of the Urmia Basin during March and April. The modeling (DREAM8-NMME-MACC) results revealed high concentrations of near-surface dust concentrations, which may have health effects on the local population, while distant sources from the Middle East are the main controlling factors to aerosol loading over the Urmia Basin. Full article

Fengyun-4B (FY-4B) is the latest Chinese next-generation geostationary meteorological satellite. The Advanced Geostationary Radiation Imager (AGRI) aboard FY-4B is equipped with 15 spectral bands, from visible to infrared, suitable for aerosol optical depth (AOD) retrieval. In this study, an overland AOD retrieval algorithm was developed for the FY-4B AGRI. Considering the large directional variation in the FY-4B AGRI reflectances, a bidirectional reflectance distribution function (BRDF) database was built, through which to estimate land surface reflectance/albedo. Seasonal aerosol models, based on four geographical regions in China, were developed between 2016 and 2022 using AERONET aerosol products, to improve their applicability to regional distribution differences and seasonal variations in aerosol types. AGRI AODs were retrieved using this new method over China from September 2022 to August 2023 and validated against ground-based measurements. The AGRI, Advanced Himawari Imager (AHI), and Moderate-Resolution Imaging Spectroradiometer (MODIS) official land aerosol products were also evaluated for comparison purposes. The results showed that the AGRI AOD retrievals were highly consistent with the AERONET AOD measurements, with a correlation coefficient (R) of 0.88, root mean square error (RMSE) of 0.14, and proportion that met an expected error (EE) of 65.04%. Intercomparisons between the AGRI AOD and other operational AOD products showed that the AGRI AOD retrievals achieved better performance results than the AGRI, AHI, and MODIS official AOD products. Moreover, the AGRI AOD retrievals showed high spatial integrity and stable performance at different times and regions, as well as under different aerosol loadings and characteristics. These results demonstrate the robustness of the new aerosol retrieval method and the potential of FY-4B AGRI measurements for the monitoring of aerosols with high accuracy and temporal resolutions. Full article

The advantages of performing aerosol retrieval with multi-angle, multi-spectral photopolarimetric measurements over intensity-only measurements come from this technique’s sensitivity to aerosols’ microphysical properties, such as their particle size, shape, and complex refraction index. In this study, an extended LUT (Look Up Table) algorithm inherited from a previous work based on the assumption of surface reflectance spectral shape invariance is proposed and applied to PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) measurements to retrieve aerosols’ optical properties including aerosol optical depth (AOD) and aerosol fine-mode fraction (FMF). Case studies conducted over East China for different aerosol scenes are investigated. A comparison between the retrieved AOD regional distribution and the corresponding MODIS (Moderate-resolution Imaging Spectroradiometer) C6 AOD products shows similar spatial distributions in the Jing-Jin-Ji (Beijing–Tianjin–Hebei, China’s mega city cluster) region. The PARASOL AOD retrievals were compared against the AOD measurements of seven AERONET (Aerosol Robotic Network) stations in China to evaluate the performance of the retrieval algorithm. In the fine-particle-dominated regions, lower RMSEs were found at Beijing and Hefei urban stations (0.16 and 0.18, respectively) compared to those at other fine-particle-dominated AERONET stations, which can be attributed to the assumption of surface reflectance spectral shape invariance that has significant advantages in separating the contribution of surface and aerosol scattering in urban areas. For the FMF validation, an RMSE of 0.23, a correlation of 0.57, and a bias of −0.01 were found. These results show that the algorithm performs reasonably in distinguishing the contribution of fine and coarse particles. Full article

Top-down observational constraints on emissions flux estimates from satellite observations of chemical composition are subject to biases and errors stemming from transport, chemistry and prior emissions estimates. In this context, we developed an ensemble data assimilation system to optimize the initial conditions for carbon monoxide (CO) and aerosols, while also quantifying the respective emission fluxes with a distinct attribution of anthropogenic and wildfire sources. We present the separate assimilation of CO profile v9 retrievals from the Measurements of Pollution in the Troposphere (MOPITT) instrument and Aerosol Optical Depth (AOD), collection 6.1, from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. This assimilation system is built on the Data Assimilation Research Testbed (DART) and includes a meteorological ensemble to assimilate weather observations within the online Community Atmosphere Model with Chemistry (CAM-chem). Inversions indicate an underestimation of CO emissions in CAMS-GLOB-ANT_v5.1 in China for 2015 and an overestimation of CO emissions in the Fire INventory from NCAR (FINN) version 2.2, especially in the tropics. These emissions increments are consistent between the MODIS AOD and the MOPITT CO-based inversions. Additional simulations and comparison with in situ observations from the NASA Atmospheric Tomography Mission (ATom) show that biases in hydroxyl radical (OH) chemistry dominate the CO errors. Full article

A sequence of dust intrusions occurred from the Sahara Desert to the central Mediterranean in the second half of June 2021. This event was simulated by means of the Weather Research and Forecasting coupled with chemistry (WRF-Chem) regional chemical transport model (CTM). The population exposure to the dust surface PM2.5 was evaluated with the open-source quantum geographical information system (QGIS) by combining the output of the CTM with the resident population map of Italy. WRF-Chem analyses were compared with spaceborne aerosol observations derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and, for the PM2.5 surface dust concentration, with the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. Considering the full-period (17–24 June) and area-averaged statistics, the WRF-Chem simulations showed a general underestimation for both the aerosol optical depth (AOD) and the PM2.5 surface dust concentration. The comparison of exposure classes calculated for Italy and its macro-regions showed that the dust sequence exposure varies with the location and entity of the resident population amount. The lowest exposure class (up to 5 µg m⁻³) had the highest percentage (38%) of the population of Italy and most of the population of north Italy, whereas more than a half of the population of central, south and insular Italy had been exposed to dust PM2.5 in the range of 15–25 µg m⁻³. The coupling of the WRF-Chem model with QGIS is a promising tool for the management of risks posed by extreme pollution and/or severe meteorological events. Specifically, the present methodology can also be applied for operational dust forecasting purposes, to deliver safety alarm messages to areas with the most exposed population. Full article

The latest epidemiological studies have revealed that the adverse health effects of PM_2.5 have impacts beyond respiratory and cardio-vascular diseases and also affect the development of the brain and metabolic diseases. The need for accurate and spatio-temporally resolved PM_2.5 data has thus been substantiated. While the selective information provided by station measurements is mostly insufficient for area-wide monitoring, satellite data have been increasingly applied to comprehensively monitor PM_2.5 distributions. Although the accuracy and reliability of satellite-based PM_2.5 estimations have increased, most studies still rely on a single sensor. However, several datasets have become available in the meantime, which raises the need for a systematic analysis. This study presents the first systematic evaluation of four satellite-based AOD datasets obtained from different sensors and retrieval methodologies to derive ground-level PM_2.5 concentrations. We apply a random forest approach and analyze the effect of the resolution and coverage of the satellite data and the impact of proxy data on the performance. We examine AOD data from the Moderate resolution Imaging spectroradiometer (MODIS) onboard Terra and Aqua satellites, including Dark Target (DT) algorithm products and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) product. Additionally, we explore more recent datasets from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3a and from the Tropospheric Monitoring Instrument (TROPOMI) operating on the Sentinel-5 precursor (S5p). The method is demonstrated for Germany and the year 2018, where a dense in situ measurement network and relevant proxy data are available. Overall, the model performance is satisfactory for all four datasets with cross-validated R² values ranging from 0.68 to 0.77 and excellent for MODIS AOD reaching correlations of almost 0.9. We find a strong dependency of the model performance on the coverage and resolution of the AOD training data. Feature importance rankings show that AOD has less weight compared to proxy data for SLSTR and TROPOMI. Full article

We present a supervised machine learning (ML) approach to improve the accuracy of the regional horizontal distribution of the aerosol optical depth (AOD) simulated by the CHIMERE chemistry transport model over North Africa and the Arabian Peninsula using Moderate Resolution Imaging Spectroradiometer (MODIS) AOD satellite observations. Our method produces daily AOD maps with enhanced precision and full spatial domain coverage, which is particularly relevant for regions with a high aerosol abundance, such as the Sahara Desert, where there is a dramatic lack of ground-based measurements for validating chemistry transport simulations. We use satellite observations and some geophysical variables to train four popular regression models, namely multiple linear regression (MLR), random forests (RF), gradient boosting (XGB), and artificial neural networks (NN). We evaluate their performances against satellite and independent ground-based AOD observations. The results indicate that all models perform similarly, with RF exhibiting fewer spatial artifacts. While the regression slightly overcorrects extreme AODs, it remarkably reduces biases and absolute errors and significantly improves linear correlations with respect to the independent observations. We analyze a case study to illustrate the importance of the geophysical input variables and demonstrate the regional significance of some of them. Full article

The Saudi government targets building eight solar plants across the country by 2030, which are expected to produce more than 3600 MW, enough to power more than 500,000 homes. However, the vast desert environment in Saudi Arabia increases dust and aerosol loading in the atmosphere, which affect the performance of photovoltaic systems due to scattering and absorption of the solar radiation by dust particles. In this work, ground-based data from weather stations located in six Saudi cities, Dammam, Hafar Al Batin, Riyadh, Jeddah, Najran, and Arar, along with data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to examine the effects of dust loading on aerosol optical parameters, air temperature, and solar irradiance. The effects of three major dust storms that blew over different regions in Saudi Arabia on 20 March 2017, 23 April 2018, and 15 April 2021 have been investigated. It is found that there is a strong correlation between dust loading and aerosol optical parameters. The maximum Aerosol Optical Depth (AOD) was recorded over Jeddah on 19 March 2017 (about 2), over Riyadh on 20 March 2017 (about 2.3), over Riyadh on 24 April 2018 (about 1.5), and over Najran on 15 April 2021 (about 0.9). Strong dust events are found to reduce air temperature by a few degrees in high dust loading regions. The study found that such large dust loading decreases the direct and global solar irradiance components, while it increases the diffuse component over the cities of Jeddah, Riyadh, and Najran. This could be an indication that scattering from dust particles can play a significant role in the solar irradiance intensity. Full article

High aerosol levels pose severe air pollution and climate change challenges in Iran. Although regional aerosol optical depth (AOD) trends have been analyzed during the dusty season over Iran, the specific factors that are driving the spatio-temporal variations in winter AOD and the influence of meteorological dynamics on winter AOD trends remain unclear. This study analyzes the long-term AOD trends over Iran in winter during the period 2000–2020 using the updated Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) and the Moderate Resolution Imaging Spectroradiometer (MODIS) datasets. Our results showed that the winter AOD exhibited a significant upward trend during the period 2000–2010 followed by a significant decrease during the period 2010–2018. We found that the winter AOD trends are important over this arid region due to multiple meteorological mechanisms which also affect the following spring/summer dusty period. Ground-based observations from Aerosol Robotic Network data (AERONET) in the Middle East region display trends comparable to those of both MERRA-2 and MODIS and indicated that aeolian dust and the meteorological dynamics associated with it play a central role in winter AOD changes. Furthermore, this study indicated that a significant downward trend in winter sea level pressure (SLP) during the early period (2000–2010) induced hot and dry winds which originated in the desert regions in Iraq and Arabia and blew toward Iran, reducing relative humidity (RH) and raising the temperature and thus promoting soil drying and dust AOD accumulation. In contrast, a significant increase in winter SLP during the late period (2010–2018) induced cold and wet winds from northwestern regions which increased RH and lowered the temperature, thus reducing dust AOD. This suggests that the changes in AOD over Iran are highly influenced by seasonal meteorological variabilities. These results also highlight the importance of examining wintertime climatic variations and their effects on the dust aerosol changes over the Middle East. Full article