Search Results (1,041)

Forecasting fine particulate matter with a diameter of 2.5 μm (PM_2.5) is critically important due to its adverse effects on human health and environmental sustainability. Although ground-based monitoring stations provide accurate measurements, their limited spatial coverage restricts large-scale PM_2.5 assessment, especially in complex urban regions. Consequently, aerosol optical depth (AOD) derived from satellite imagery, combined with advanced deep learning (DL) techniques, has emerged as an effective alternative by offering wide spatial coverage and rich spatio-temporal information. This paper proposed an optimization-driven fuzzy transformer–deep belief network (ODFT-DBN) for accurate PM_2.5 air pollution prediction. The proposed framework integrates a fuzzy inference module to model uncertainty and nonlinear environmental relationships, a transformer encoder to capture long-range spatio-temporal dependencies, and a DBN to extract hierarchical features and improve prediction robustness. In addition, a novel multi-objective gray wolf optimizer (NMOGWO) is employed to jointly optimize the model hyper-parameters and fuzzy membership functions. The proposed approach is implemented for the city of Tehran, Iran, using meteorological variables, topographical features, ground-based PM_2.5 measurements, and satellite-derived AOD data. The ODFT-DBN model is compared with several benchmark methods, including bidirectional encoder representations from transformers (BERT), transformer, long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), DBN, and extreme gradient boosting (XGBoost). Experimental results demonstrate that the proposed framework achieves superior predictive performance, attaining an R² value of 0.94 and root mean square error (RMSE) of 0.8 μg/m³. Scatter plot analyses indicate a strong agreement between predicted and observed PM_2.5 values, while the proposed model exhibits low variance, stable convergence behavior, and acceptable computational time. Overall, the results confirm the effectiveness, robustness, and practical applicability of the proposed ODFT-DBN framework for spatio-temporal PM_2.5 forecasting. Full article

Fine particulate matter (PM_2.5) is studied for the first time at ground level in different sites of Greater Rosario (GR), an urban and industrial area of central-eastern Argentina. Twelve sites were selected according to land use, and 87 samples were analyzed during winter 2021 and summer 2022. The spatial and temporal distribution of PM_2.5 was examined, comparing results among sites and with global data. Ground-based data were complemented with satellite-derived Aerosol Optical Depth (AOD) and nitrogen dioxide vertical column density (NO₂ VCD). During winter, the highest PM_2.5 was obtained at an industrial site in northern GR, while in summer, maximum values were observed in the center of Rosario. Summer rain events could contribute to the wet deposition of suspended particles, resulting in lower PM_2.5 concentrations. Satellite data indicate higher average AOD in summer (attributable to forest fires in NE Argentina) and higher NO₂ VCD in winter, coinciding with burning events in the Paraná Delta islands and reflected in some PM_2.5 peaks. This analysis represents the first approach to assessing the air quality of Rosario and its surroundings, with on-site data collected in association with land use. Full article

Recently, numerous studies have exploited satellite Aerosol Optical Depth (AOD) to estimate near-surface particulate matter (PM) concentrations, with the aim of overcoming the limited spatial and temporal coverage of ground-based air quality monitoring networks. Despite significant progress, the relationship between AOD and PM remains highly uncertain, mainly due to the inadequate representation of local aerosol microphysical properties and of hygroscopic growth effects. In particular, satellite AOD is retrieved at ambient relative humidity, whereas standard PM measurements are performed under dry conditions. This study proposes a physics-informed, semi-empirical approach that overcomes these limitations by directly relating satellite AOD to PM measured at ambient humidity. Co-located measurements, from a Light Optical Aerosol Counter (LOAC) in the urban area of Bologna (Po Valley, Italy) during 2023, are used. This study is designed as a pilot application to evaluate the physical consistency of the proposed framework under well-characterised observational conditions, including spatial co-location, temporal matching to satellite overpasses, and exclusion of precipitation and desert dust events. The LOAC provides particle number size distribution and particle-type classification, which are used to estimate key aerosol properties controlling the AOD–PM theoretical relationship, including the Effective Radius, Extinction Efficiency, and aerosol Mass Density. These quantities, together with Mixing Layer Height, are combined within a theoretical framework linking PM and AOD, allowing for the derivation of a physically based scaling coefficient without relying on empirical hygroscopic growth corrections. The results show that using ambient PM_2.5 alone already yields a moderate linear correlation with AOD normalized by Mixing Layer Height (Pearson’s R = 0.56) whereas no meaningful correlation is found when using standard dry PM_2.5. When aerosol microphysical properties derived from LOAC measurements are incorporated, the correlation substantially improves (R = 0.76), with regression slopes close to unity and reduced errors, independently of the season. These results demonstrate that explicitly accounting for aerosol size and optical properties enhances the physical consistency and robustness of satellite-based PM estimates. The proposed framework also provides a pathway to indirectly derive aerosol hygroscopic growth factors by coupling ambient PM estimates from satellite observations with conventional dry PM measurements. This opens new perspectives for characterizing aerosol–humidity interactions from space and for improving air quality monitoring in regions lacking of dense in situ networks. Full article

Mie lidar has been profoundly applied in the profiling of aerosol optical coefficients in atmosphere. However, few studies further explore quantitative strategies for the retrieval of aerosol mass profiles from lidar observation. To address the growing need for spatial and temporal aerosol mass data, a synergic retrieval algorithm for simultaneously profiling the aerosol extinction coefficient and mass composition from spaceborne dual-channel lidar data is proposed. By constructing the relationship between mixed aerosol mass profiles and extinction coefficients at different wavelengths by a forward model, additional constraints are induced to improve the accuracy of lidar ratio, which is a critical parameter for the retrieval of aerosol extinction coefficients by solving the lidar equation. Meanwhile, aerosol composition profiles can also be deduced based on the a priori estimation of aerosol compositions and intrinsic optical features of the aerosols. This method is first applied in simulated data with wavelengths at 532 nm and 1064 nm. The simulations are based on the reanalysis data of aerosol mass concentration profiles in Inner Mongolia, China. Compared with the classic Fernald method using empirically estimated lidar ratio, the proposed method improves the accuracy of column-integrated aerosol extinction coefficients (also known as aerosol optical depth, AOD) by 19.58% at 532 nm and 3.57% at 1064 nm. The accuracy for column dust and sulfate aerosols is enhanced by 12.46% and 17.58%, respectively. Further validation with CALIOP observations suggests that the proposed method produces improved extinction results and reliable aerosol composition information. Full article

The rapid transition toward renewable energy requires transparent and spatially explicit methods for identifying suitable photovoltaic (PV) development areas. This study develops a geospatial artificial intelligence (GeoAI) decision-support framework for PV site suitability assessment in Rajasthan, India. Eleven harmonized predictors were used: global horizontal irradiance (GHI), photovoltaic power output (PVOUT), temperature, wind speed, aerosol optical depth (AOD), elevation, slope, albedo, land use/land cover (LULC), distance to roads, and distance to power lines. Reference labels were generated from an explicit rule-derived suitability index, class thresholds, and exclusion logic; therefore, the machine-learning task was to reproduce a transparent suitability framework rather than to predict observed PV yield or project-level performance. Extreme Gradient Boosting (XGBoost) was compared with simpler baseline models, evaluated using random and spatial-block validation, and interpreted using SHapley Additive exPlanations (SHAP). Independent overlays with known solar-installation records, presence-background robustness testing, and uncertainty/sensitivity analysis were used to examine spatial plausibility, spatial autocorrelation, deterministic label effects, and parameter uncertainty. The resulting outputs include pixel-level suitability zones, contiguous candidate polygons, district-level capacity-oriented summaries, and planning-priority classes. The framework is intended as a risk-aware regional screening tool: high model agreement indicates consistency with the constructed suitability labels, while final project decisions require parcel-scale land, grid, environmental, social, and economic assessment. Full article

This study explores the three-dimensional structure of dust aerosols over the Qaidam Basin using CALIPSO satellite observations from 2007 to 2022. The results show that polluted dust is the dominant aerosol type in this region. Dust activity peaks in spring, with its vertical extent reaching nearly 10 km. Dust Aerosol Optical Depth (DAOD) is relatively high in the northwest and central parts of the basin, with a spring peak of 0.25 and an autumn minimum of 0.12. DAOD has shown a notable decreasing trend over the past 16 years. In terms of vertical structure, dust aerosols are mainly concentrated below 4 km AGL, especially within the near-surface layer of 0–2 km, and their occurrence frequency declines as altitude increases. The dust layer thickness exhibits obvious seasonal variations, which are primarily controlled by changes in layer top height. The average thickness decreases from 1.53 km in spring to 0.61 km in winter, while the layer’s bottom height remains fairly stable. Analysis based on the LASSO-SHAP model indicates that potential evapotranspiration and friction velocity are the major factors affecting DAOD, highlighting the vital roles of surface dryness and near-surface dynamic forcing. Furthermore, investigation of typical dust events reveals distinct vertical stratification of dust transport. Low-level dust movement is restricted by basin terrain, whereas upper levels are governed by the westerlies. This study improves our understanding of the three-dimensional structure, seasonal evolution, and transport processes of dust aerosols in high-altitude arid basins. Full article

High-frequency FY-4B aerosol optical depth (AOD) observations provide useful spatiotemporal constraints for dust nowcasting, but their application over bright deserts is limited by retrieval gaps and high-AOD uncertainty. This study develops a physics-informed spatiotemporal learning framework for 15–60 min FY-4B AOD nowcasting over the Taklimakan Desert. Historical FY-4B AOD, valid masks, ERA5 dynamic fields, model-level diagnostics, and surface constraints are organized on a unified 48 × 64 grid. An LSTM–TCN–Transformer temporal backbone is combined with spatial-context encoding, mask-aware observation encoding, and structured source–transport prediction heads to represent both temporal evolution and spatial plume structures. A physics encoder represents boundary-layer mixing, vertical wind shear, source-region emission, upwind transport, and deposition loss. Mask-aware encoding and structured prediction heads are used to handle missing retrievals, source and transport increments, high-AOD tails, and low-confidence regions. Results show that FY-4B AOD constrains the main dust-belt position and spatial extent within 1 h, with skill decreasing from 15 to 60 min. High-coverage samples show more stable spatial structures, whereas low-coverage and extreme high-AOD cases have larger peak underestimation and boundary errors. The proposed framework improves high-AOD event detection and spatial-structure preservation compared with persistence, advective persistence, ConvLSTM, and ST-UNet baselines. An additional case-based comparison with MODIS MAIAC AOD and MERRA-2 dust optical depth shows partial spatial colocation between predicted high-value footprints and independent aerosol-enhancement references; however, the reported skill scores should still be interpreted mainly as spatiotemporal consistency with the FY-4B AOD product field rather than direct validation of true atmospheric dust loading. Full article

Rapid urbanization in semi-arid cities intensifies heat exposure, air pollution, and land-surface degradation, yet these stressors are often assessed separately. This study develops a scale-aware Urban Environmental Stress (UES) framework for Jaipur, India, using multi-sensor Earth observation data. The framework explicitly addresses indicator redundancy, weighting bias, short time-series interpretation, and temporal comparability. The final primary UES surface uses twelve retained stress-oriented indicators on a 500 m common analysis grid, excludes NDBI because it is algebraically redundant with NDMI when both are computed from the same NIR/SWIR bands, and applies equal weights so that built fraction does not dominate the composite. Entropy weighting is reported only as a sensitivity diagnostic. The resulting UES map identifies high relative stress in Jaipur’s dense urban core and transport-industrial corridors, with lower stress along the Aravalli flank and peri-urban green or water-adjacent areas. The framework is presented as a relative spatial prioritization tool rather than an absolute physical time series; temporal claims are limited to independently reported land-cover and individual-indicator trajectories unless fixed multi-year normalization and fixed weights are applied. Full article

Accurate surface downward shortwave radiation (SDSR) is essential for solar resource assessment, photovoltaic applications, and land–atmosphere studies. Although ERA5 is widely used in radiation-related research, its SDSR estimates over China still show considerable uncertainties under complex topographic and climatic conditions. Using hourly observations from the 162-station China Meteorological Administration (CMA) radiation network during April 2024–March 2025, of which 160 stations were retained after quality control, this study systematically evaluated ERA5 SDSR and developed a physics-guided Light Gradient Boosting Machine (LightGBM) correction framework. Raw ERA5 exhibits a strong systematic positive bias (PBIAS = 57.40%, ME = 124.2 W/m²) together with a pronounced nonlinear structural bias, characterized by overestimation under low-radiation conditions and underestimation under high-radiation conditions. The largest errors occur in the Southern Monsoon region in summer and the Northwest Arid region in spring, indicating the combined effects of cloud extinction, aerosol attenuation, and terrain-related representativeness differences. To address these mechanisms, the correction model incorporates physically relevant predictors from ERA5 and Copernicus Atmosphere Monitoring Service (CAMS), including cloud microphysical variables, aerosol optical depth, solar geometry, and elevation. SHapley Additive exPlanations (SHAP) analysis shows that the learned correction behavior is broadly consistent with known radiative-transfer processes. On the independent station hold-out test set, the correction increases the Pearson correlation coefficient from 0.8680 to 0.8967 and reduces RMSE from 173.1 to 100.8 W/m², while substantially suppressing the strong positive bias of raw ERA5. Additional robustness tests, including season-blocked validation, interpolation-sensitivity analysis, ablation experiments, and multi-model comparison, further support the stability of the framework. External benchmarking against FY-4B and Himawari also shows that the corrected ERA5 substantially narrows the gap relative to independent geostationary satellite products. Overall, the proposed framework provides an effective and physically interpretable approach for improving ERA5 SDSR over China. Full article

The Polarization and Directionality of the Earth’s Reflectances (POLDER)-3/GRASP (Generalized Retrieval of Aerosol and Surface Properties) aerosol products have been widely used in studies on radiative balance and climate change. However, the stability and consistency of the products have yet to be comprehensively evaluated, despite their critical importance for long-term studies. POLDER-3/GRASP products mainly consist of three variants: High-Precision (HP), Components, and Models. This study aims to evaluate the accuracy, stability, and consistency of these aerosol products at global and regional scales, and to provide usage recommendations. Compared with AERONET observations, the Components product shows the best performance for both aerosol optical depth (AOD) and Ångström Exponent (AE) retrievals, with Root Mean Square Error (RMSE) of 0.114 for AOD and 0.319 for AE. The Models AOD and HP AE also demonstrate relatively high validation accuracy, with RMSE of 0.138 for Models AOD and 0.366 for HP AE. Regionally, Components AOD and AE outperform those from the HP and Models products in 8 out of 10 regions. Stability evaluation shows that the stability metrics of the three AOD products range from 0.034 to 0.036 per decade, and none of them meet the Global Climate Observing System (GCOS) stability requirement (i.e., 0.02 per decade), which indicates that caution should be exercised when using POLDER-3/GRASP products for long-term analysis. In terms of consistency, Components AOD and Models AOD exhibit high agreement, while HP AOD is systematically higher than them. The AE retrieved by the three products shows considerable discrepancies, highlighting uncertainties in AE and spectral-AOD retrievals and pointing toward directions for future algorithmic improvements. In summary, considering global and regional accuracy, stability, and consistency, the Components AOD and AE products are generally recommended for use. For different regions, users can choose the appropriate product based on detailed validation and intercomparison results. Full article

Aerosol optical depth (AOD) and Ångström exponent (AE) are critical parameters for characterizing atmospheric aerosols, playing a pivotal role in atmospheric environmental monitoring and climate change studies. This study addressed the imperative need for a systematic evaluation of mainstream reanalysis products by conducting a comprehensive multi-scale assessment of the CAMS and MERRA-2 datasets (2003–2023), encompassing data quality verification, spatiotemporal pattern analysis, and trend evolution investigation. The following key findings emerge: (1) Both AOD data exhibited the best performance observed in low–mid latitudes. CAMS AOD (AODC) showed a slightly better correlation, while MERRA-2 AOD (AODM) demonstrated superior robustness. Both AE data performed similarly, and MERRA-2 AE (AEM) was superior. Both AE data performed better in low latitudes and near Europe. (2) CAMS and MERRA-2 showed good performance in annual and seasonal variations, with significant fluctuations and biases in the annual cycle. Both models achieved the highest AE performance in summer. MERRA-2 AOD demonstrated better hourly performance during daytime. The hourly stability of AE was slightly worse than AOD, with notably degraded performance during midday hours. (3) The distribution and trends of AOD over land showed spatial consistency. The distribution of AEM was generally lower than AEC’s. After ensemble empirical mode decomposition (EEMD), all datasets showed monotonically decreasing trends except for AEM. This study provides valuable insights into the strengths and limitations for CAMS and MERRA-2 and suggests possible areas for improvement in future data assimilation and parameterization. Full article

Estimating the joint probability density of seasonal geophysical variables like Aerosol Optical Depth (AOD) and Day of Year (DOY) presents three unresolved challenges. The first of these challenges is the periodic nature of the temporal axis. The second is the physically distinct scales of the two variables. The third is the marginal inconsistency introduced by smoothing operations. Existing methods for estimating probability density do not address each of these challenges simultaneously. Here we introduce CADS (the Circular-Adaptive Density Smoother), a computationally efficient algorithm for estimating the joint probability density of two seasonal geophysical variables that simultaneously addresses each of these three challenges. CADS is evaluated on two synthetic datasets and one real observational dataset of AERONET measurements from NASA Ames ($n=74,653$) using five-fold cross-validation. CADS achieves the highest mean log-likelihood relative to other methods for estimating probability density, and it is approximately 3000 times faster than kernel density estimation. An ablation study confirms that each component of CADS contributes independently to its high performance. Finally, a novel metric for evaluating the geometric correctness of the treatment of the circular boundary of the DOY variable is introduced. Full article

Atmospheric aerosols strongly regulate surface solar irradiance in tropical coastal environments through scattering and absorption. This study examines aerosol–irradiance interactions over Penang, Malaysia, using Aerosol Robotic Network (AERONET) observations of aerosol optical depth (AOD), single scattering albedo (SSA), and extinction Ångström exponent (AE); NASA’s Prediction of Worldwide Energy Resource (POWER) irradiance data; and Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) reanalysis for aerosol compositional context. Bottom-of-atmosphere radiative forcing efficiency (BOA RFE) was quantified for global, direct and diffuse irradiance (GHI, DNI and DHI) under clear- and all-sky conditions during 2014–2018. Results show persistent aerosol-induced attenuation of surface radiation, with GHI and DNI RFE predominantly negative, while DHI RFE remains consistently positive, indicating redistribution of solar energy from direct to diffuse components. Time resolved analysis reveals daily GHI RFE typically ranging from approximately −0.5 to −3.5 W m⁻² per unit AOD, with episodic excursions below −4 W m⁻² per AOD during high-aerosol events, whereas DNI RFE frequently reaches values below −0.8 W m⁻² per AOD, confirming its greater sensitivity to aerosol extinction. In contrast, DHI RFE commonly exceeds +5 W m⁻² per AOD and intermittently surpasses +10 W m⁻² per AOD, reflecting enhanced scattering and multiple-scattering effects. AOD-stratified analysis demonstrates a nonlinear weakening of forcing efficiency with increasing aerosol burden, with mean GHI RFE decreasing from approximately −1.6 to −0.4 W m⁻² per AOD between low- and high-AOD regimes, accompanied by corresponding reductions in DNI (−0.35 to −0.1 W m⁻² per AOD) and DHI (+3.3 to +0.8 W m⁻² per AOD). Overall, aerosol loading is identified as the dominant control on BOA radiative forcing efficiency in this tropical coastal environment, while SSA and AE act as secondary modulators. Full article

To comprehensively investigate the aerosol optical properties and vertical structures over the northeastern Tibetan Plateau (TP), a field campaign was conducted from January to August 2023 in the Hainan Tibetan Autonomous Prefecture. Ground-based sunphotometer measurements yielded a mean aerosol optical depth (AOD) of 0.18 and an Ångström exponent (AE) of 1.20 over the study period. The lowest AE, observed in April alongside the highest aerosol loading, suggests a predominance of dust aerosols during this period. This finding is further supported by the elevated vertical extinction profiles derived from LiDAR measurements, indicating long-range transboundary transport of dust aerosols from northern desert regions. Ground-based AOD measurements were used to validate satellite-derived MODIS retrievals and the assimilated MERRA-2 reanalysis product. Among the aerosol types examined, dust aerosols exhibited the highest accuracy in both AOD and AE validation. MERRA-2 was found to systematically underestimate AOD by 22% and AE by 35%. Nevertheless, due to its tighter expected error envelope, lower overall errors, and superior temporal continuity and spatial coverage, MERRA-2 remains a reliable data source for subsequent analyses. A long-term analysis spanning 2006 to 2025 identifies 2011 as a turning point, after which AOD declined at a rate of 0.0022 per year. This sustained reduction highlights the effectiveness of China’s air pollution prevention and control policies. Collectively, these findings provide essential insights for refining satellite retrieval algorithms and aerosol–climate models over the TP. Full article

Air pollution caused by fine particulate matter (PM_2.5) poses a serious public health threat in many South Asian megacities where monitoring networks remain limited. Lahore, Pakistan—frequently ranked among the world’s most polluted cities—still lacks reliable short-term PM_2.5 forecasting systems. This study develops a performance-weighted ensemble machine learning framework that integrates satellite observations, meteorological reanalysis data, and ground monitoring measurements to improve daily PM_2.5 prediction. Eleven predictor variables were processed using a unified Google Earth Engine pipeline, including MODIS aerosol optical depth, Sentinel-5P trace gases (CO, NO₂, SO₂), and ERA5 meteorological parameters. Four tree-based machine learning algorithms—Random Forest, XGBoost, LightGBM, and CatBoost—were trained using daily observations from 2019 to 2023. Model evaluation using an independent 2024 dataset showed strong predictive capability, with Random Forest achieving R² = 0.77 (RMSE = 24.75 µg m⁻³), XGBoost R² = 0.76 (RMSE = 26.32 µg m⁻³), CatBoost R² = 0.73 (RMSE = 30.39 µg m⁻³), and LightGBM R² = 0.70 (RMSE = 32.75 µg m⁻³). To further enhance performance, the best models were combined into a weighted ensemble (RF 0.5, XGBoost 0.3, and CatBoost 0.2), which produced the highest validation accuracy (R² = 0.77; RMSE = 23.37 µg m⁻³). Statistical testing using paired t-tests and Diebold–Mariano tests confirmed that the ensemble significantly reduced forecast errors compared with individual models. Feature importance analysis revealed that surface pressure, temperature, CO, and NO₂ were the most influential predictors of PM_2.5 variability. The proposed framework demonstrates that combining satellite data, reanalysis meteorology, and ground observations through ensemble learning can provide accurate and scalable air quality forecasting for data-limited urban environments. Full article