Search Results (1,894)

High accuracy and time synchronous aerosol optical depth (AOD) is essential for atmospheric correction (AC) of medium and high spatial resolution (MHSR) remote sensing data. However, existing high-resolution AOD retrieval methods often rely on sparsely distributed ground-based measurements, which limits their capacity to resolve fine-scale spatial heterogeneity and consequently constrains retrieval performance. To address this limitation, we propose a framework that takes GF-1 top-of-atmosphere (TOA) reflectance as input, where the model is first pre-trained using MCD19A2 as Pseudo-labels, with high-confidence samples weighted according to their spatial consistency and temporal stability, and then fine-tuned using Aerosol Robotic Network (AERONET) observations. This approach enables improved retrieval accuracy while better capturing surface variability. Validation across multiple regions demonstrates strong agreement with AOD measurements, achieving the correlation coefficient (R) of 0.941 and RMSE of 0.113. Compared to models without pretraining, the proportion of AOD retrievals within EE improves by 13%. While applied to AC, the corrected surface reflectance also shows strong consistency with in situ observations (R > 0.93, RMSE < 0.04). The proposed Trans-AODnet significantly enhances the accuracy and reliability of AOD inputs for AC of high-resolution wide-field sensors (e.g., GF-WFV), offering robust support for regional environmental monitoring and exhibiting strong potential for broader remote sensing applications. Full article

Understanding the evolution of the atmospheric boundary layer height (BLH) is essential for characterizing air–surface exchange and air pollution processes. This study investigates the consistency and applicability of three BLH retrieval methods based on multi-source remote sensing observations at Beijing Southern Suburb station during autumn–winter 2023. Using Doppler wind lidar (DWL) and microwave radiometer (MWR) data, the Haar wavelet covariance transform (HWCT), vertical velocity variance (Var), and parcel methods were applied, and 10 min averages were used to suppress short-term fluctuations. Statistical analysis shows good overall consistency among the methods, with the strongest correlation between HWCT and Var method (R = 0.62) and average systematic positive bias of 0.4–0.6 km for the parcel method. Case studies under clear-sky, cloudy, and hazy conditions reveal distinct responses: HWCT effectively captures aerosol gradients but fails under cloud contamination, the Var method reflects turbulent dynamics and requires adaptive thresholds, and the Parcel method robustly describes thermodynamic evolution. The results demonstrate that the three methods are complementary in capturing the material, dynamic, and thermodynamic characteristics of the boundary layer, providing a comprehensive framework for evaluating BLH variability and improving multi-sensor retrievals under diverse meteorological conditions. Full article

Air pollution is a major but often under-integrated driver of forest dynamics at the global scale. This review combines a bibliometric analysis of 258 peer-reviewed studies with a synthesis of ecological, physiological, and biogeochemical evidence to clarify how multiple air pollutants influence forest structure, function, and regeneration. Research output is dominated by Europe, East Asia, and North America, with ozone, nitrogen deposition, particulate matter, and acidic precipitation receiving the greatest attention. Across forest biomes, air pollution affects growth, wood anatomy, nutrient cycling, photosynthesis, species composition, litter decomposition, and soil chemistry through interacting pathways. Regional patterns reveal strong context dependency, with heightened sensitivity in mountain and boreal forests, pronounced ozone exposure in Mediterranean and peri-urban systems, episodic oxidative stress in tropical forests, and long-term heavy-metal accumulation in industrial regions. Beyond being impacted, forests actively modify atmospheric chemistry through pollutant filtration, aerosol interactions, and deposition processes. The novelty of this review lies in explicitly framing air pollution as a dynamic driver of forest change, with direct implications for afforestation and restoration on degraded lands. Key knowledge gaps remain regarding combined pollution–climate effects, understudied forest biomes, and the scaling of physiological responses to ecosystem and regional levels, which must be addressed to support effective forest management under global change. Full article

The absolute radiometric calibration of a satellite sensor is an essential process that determines the coefficients required to convert the radiometric quantities of satellite images. This procedure is crucial for ensuring the applicability and enhancing the reliability of optical sensors onboard satellites. This study performs the absolute radiometric calibration of the Compact Advanced Satellite 500-1 (CAS500-1) Advanced Earth Imaging Sensor System-C (AEISS-C), a low Earth orbit satellite developed independently by Republic of Korea for precise ground observation. Field campaign using a tarp, an Analytical Spectral Devices FieldSpecIII spectroradiometer, and a MicrotopsII sunphotometer was conducted. Additionally, reflectance-based vicarious calibration was performed using observational data and the MODerate resolution atmospheric TRANsmission model (version 6) radiative transfer model (RTM). Cross-calibration was also performed using data from the Sentinel-2 MultiSpectral Instrument, RadCalNet observations, and MODIS Bidirectional nReflectance Distribution Function (BRDF) products (MCD43A1) to account for differences in spectral response functions, viewing/solar geometry, and atmospheric conditions between the two satellites. From these datasets, two correction factors were derived: the Spectral Band Adjustment Factor and the BRDF Correction Factor. CAS500-1/AEISS-C acquires satellite imagery using two Time Delay Integration (TDI) modes, and the absolute radiometric calibration coefficients were derived considering these TDI modes. The coefficient of determination (R²) ranged from 0.70 to 0.97 for the reflectance-based vicarious calibration and from 0.90 to 0.99 for the cross-calibration. For reflectance-based vicarious calibration, aerosol optical depth was identified as the primary source of uncertainty among atmospheric factors. For cross-calibration, the reference satellite and RTMs were the primary sources of uncertainty. The results of this study will support the monitoring of CAS500-1/AEISS-C, which produces high-resolution imagery with a spatial resolution of 2 m, and can serve as foundational material for absolute radiometric calibration procedures for other CAS500 satellites. Full article

The Fengyun-4 microwave satellite provides high-temporal-frequency observations at the 183 GHz band, providing unprecedented data for all-weather, three-dimensional measurements of atmospheric parameters. It is of importance to establish a simulated brightness temperature (BT) dataset for this band prior to launch, which can support the relevant quantitative applications significantly. Compared with clear-sky conditions, the accuracy of BT simulations under cloudy ones is considerably lower, primarily due to the influence of the adopted ice particle models. Up until now, few studies have systematically investigated ice particle model selection for different cloud types at the 183 GHz frequency band. In this paper, multi-sensor observations from Cloud Profiling Radar (CPR), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and Visible Infrared Imaging Radiometer Suite (VIIRS) were used as realistic atmospheric profiles. Using the high-precision radiative transfer model Atmospheric Radiative Transfer Simulator (ARTS), BT simulations at 183 GHz were performed to explore the optimal ice particle models for seven classical cloud types. The main conclusions are given as follows: (1) The sensitivity of simulated cloud radiances to ice particle habits differs with respect to different cloud phases. For altocumulus (Ac), stratocumulus (Sc), and cumulus (Cu) clouds, the different choices of ice particle model have little impacts on the simulated brightness temperatures (<1 K), with RMSEs below 3 K across multiple models, indicating that various models can be applied directly for such simulations. (2) For some mixed-phase clouds, including altostratus (As), nimbostratus (Ns), and deep convective (Dc) clouds, the Small Block Aggregate (SBA) and Small Plate Aggregate (SPA) models demonstrate good performance for As clouds, with RMSEs below 2.5 K, while the SBA, SPA, and Large Column Aggregate (LCA) models exhibit similarly good performance for Ns clouds, also achieving RMSEs below 2.5 K. For Dc clouds, although the SBA model yields RMSEs of approximately 10 K, it still provides a substantial improvement over the spherical model, whereas for cirrus (Ci) clouds, any non-spherical ice particle models are applicable, with RMSEs below 2 K. (3) Within the 183 GHz frequency band, channels with the higher weighting-function peaks are less sensitive to variable adoptions of ice particle models. These results offer valuable references for accurate radiative transfer simulations on 183 GHz frequency. Full article

This study investigated the site-specific ionic composition and wet deposition loads of rainwater collected from eight actively cultivated agricultural regions across South Korea, with the aim of quantifying spatial and seasonal variability and interpreting how regional agricultural characteristics and surrounding site conditions influence major ion concentrations and deposition patterns. Rainfall samples were obtained using automated samplers and analyzed via high-performance ion chromatography for major cations (Na⁺, $\mathrm{N}{\mathrm{H}}_4^{+}$, K⁺, Ca²⁺, Mg²⁺) and anions (Cl⁻, $\mathrm{N}{\mathrm{O}}_3^{-}$, $\mathrm{S}{\mathrm{O}}_4^{2-}$, $\mathrm{N}{\mathrm{O}}_2^{-}$). The results revealed significant seasonal fluctuations in ion loads, with $\mathrm{N}{\mathrm{H}}_4^{+}$ (peak 1.13 kg/ha) and K⁺ (peak 0.25 kg/ha) reaching their highest levels during summer due to increased fertilizer use and crop activity. Conversely, Cl⁻ peaked in winter (2.11 kg/ha in December), particularly in coastal regions, likely influenced by de-icing salts and sea-salt aerosols. Correlation analysis showed a strong positive association among $\mathrm{N}{\mathrm{H}}_4^{+}$, $\mathrm{N}{\mathrm{O}}_3^{-}$, and $\mathrm{S}{\mathrm{O}}_4^{2-}$ (r = 0.89 and r = 0.84, respectively), indicating shared atmospheric transformation pathways from agricultural emissions. Ternary diagram analysis further revealed regional distinctions: coastal regions such as Gimhae and Muan exhibited Na⁺ and Cl⁻ dominance, while inland areas like Danyang and Hongcheon showed higher proportions of Ca²⁺ and Mg²⁺, reflecting differences in aerosol sources, land use, and local meteorological conditions. These findings underscore the complex interactions between agricultural practices, atmospheric processes, and local geography in shaping rainwater chemistry. The study provides quantitative baseline data for evaluating non-point source pollution and developing region-specific nutrient and soil management strategies in agricultural ecosystems. Full article

Methoxy radicals (CH₃O•), formed as intermediates during methane oxidation, may play an underexplored but locally significant role in the atmospheric oxidation of dimethyl sulfide (DMS), a key sulfur-containing compound emitted primarily by marine phytoplankton. This study presents a comprehensive computational investigation of the reaction mechanisms and kinetics of DMS oxidation initiated by CH₃O•, using density functional theory B3LYP-D3(BJ)/6-311++G(3df,3pd), CCSD(T)/6-311++G(3df,3pd), and UCBS-QB3 methods. Our calculations show that DMS reacts with CH₃O• via hydrogen atom abstraction to form the methyl-thiomethylene radical (CH₃SCH₂•), with a rate constant of 3.05 × 10⁻¹⁶ cm³/molecule/s and a Gibbs free energy barrier of 14.2 kcal/mol, which is higher than the corresponding barrier for reaction with hydroxyl radicals (9.1 kcal/mol). Although less favorable kinetically, the presence of CH₃O• in localized, methane-rich environments may still allow it to contribute meaningfully to DMS oxidation under specific atmospheric conditions. While the short atmospheric lifetime of CH₃O• limits its global impact on large-scale atmospheric sulfur cycling, in marine layers where methane and DMS emissions overlap, CH₃O• may play a meaningful role in forming sulfur dioxide and downstream sulfate aerosols. These secondary organic aerosols lead to cloud condensation nuclei (CCN) formation, subsequent changes in cloud properties, and can thereby influence local radiative forcing. The study’s findings underscore the importance of incorporating CH₃O• driven oxidation pathways into atmospheric models to enhance our understanding of regional sulfur cycling and its impacts on local air quality, cloud properties and radiative forcing. These findings provide mechanistic insights that improve data interpretation for atmospheric models and extend predictions of localized variations in sulfur oxidation, aerosol formation, and radiative forcing in methane-rich environments. Full article

Wet deposition is a major sink for atmospheric aerosols, but its representation in conventional global climate models (GCMs) remains highly uncertain, partly as a result of the partitioning between convective and stratiform precipitation. Using the Super-parameterized Community Atmosphere Model (SPCAM) as a benchmark, we evaluate the performance of the conventional CAM5 model in simulating precipitation and aerosol wet deposition. SPCAM explicitly resolves convection and provides a more physical representation of cloud and precipitation processes. Compared to SPCAM, CAM5 overestimates the frequency of light convective rainfall by up to 50% at rain rates from 1 to 20 mm day⁻¹ and underestimates heavy convective precipitation, leading to a more than 90% contribution from convective precipitation to total rainfall in the tropics, far exceeding that in satellite observations. Accordingly, this bias results in an overestimation of aerosol wet removal by convective precipitation (74.2% in CAM5 versus 47.6% in SPCAM) and an underestimation by large-scale precipitation, as well as an overestimation of aerosol wet removal by light rain (84.0% in CAM5 versus 65.5% in SPCAM). As a result, CAM5 shows systematic biased wet deposition fluxes simulations across aerosol types and sizes compared to SPCAM, particularly in tropical regions. The misrepresentation of convective-stratiform rainfall partitioning in conventional GCMs like CAM5 significantly distorts aerosol lifetime and distribution. Improving convective parameterizations to better capture precipitation frequency distribution and partitioning is essential for credible aerosol-climate projections. Full article

Currently, brake particle emissions from traffic are considered one of the dominant sources of particulate matter in the atmosphere. A recent question concerns the contribution to brake particles of Battery Electric Vehicles (BEVs). The present work assesses brake particle emissions by measurements of particle number (PN) and mass (PM) of three light-duty BEVs. One front disc brake of each vehicle has been enclosed in a customized casing with appropriate ventilation for forming the aerosol. All three BEVs have been measured on a two-axis chassis dynamometer. The BEV relying more on electric braking (some 68% of the braking energy was covered by electric braking) had the lowest brake PN emissions over the (emissions) WLTC at 6.4 × 10⁹ km⁻¹ per front brake. This was less than half with respect to the other BEV (where only 52% of the braking energy was electric). PM emissions of the two vehicles were similar at 0.93 mg/km for PM < 12 μm and 0.65 mg/km for PM < 2.5 μm, both for one front brake. However, one of the measured BEVs had extraordinarily high PN emissions, some 23 times higher than the lowest-emitting BEV. The difference in PM was not as high, but was some four times higher. Full article

The multiple sources and concomitant negative environmental and health impacts of volatile organic compounds (VOCs) in the atmosphere demonstrate their importance in air pollution control. This study employed environment- and health risk-oriented source apportionment methods to quantitatively estimate VOCs’ contribution to air pollution and health risks, using offline VOC measurements from the Harbin urban region from 2021 to 2022. Total volatile organic compounds (TVOCs) averaged 25.6 ± 8.2 ppb, except for alkanes (34.4%), and aromatics (24.2%) were found to be a major contributor, with the highest L_OH (38.0%), ozone formation potential (OFP) (43.0%), and secondary organic aerosol formation potential (SOAFP) (95.0%) and exerting a directly toxic effect (46.0%). Positive matrix factorization (PMF) source apportionment revealed that vehicle exhausts, combustion sources, solvent and coating usage, solvent and fuel evaporation, and petrochemical industry sources were key VOC sources. A health risk assessment showed that there was an integrated carcinogenic risk of 5.8 × 10⁻⁴, with respiratory (1.5 × 10⁻⁴) and hematologic systems (1.5 × 10⁻⁴) representing higher carcinogenic risks. Both benzene and naphthalene exhibited carcinogenic risks of 1.5 × 10⁻⁴, implying an excess of higher cancer risk levels (1.0 × 10⁻⁴). Significant joint environmental and health benefits could be obtained by reducing benzene and naphthalene concentrations by about 50.0%, along with the abatement of vehicle exhausts (82.6%), combustion sources (40.7%), and solvent and coating usage (50.7%). This study can serve as useful guidance for the quantitative mitigation of hazardous VOCs and their key sources. Full article

In this study, the authors wish to introduce an unsupervised procedure designed for real-time generation of maps depicting advected aerosols, specifically focusing on desert dust and smoke originating from biomass combustion. This innovative approach leverages the high-resolution capabilities provided by Sentinel-2 imagery, operating at a 10 m scale, which is particularly advantageous for urban settings. Concurrently, it takes advantage of the near-daily revisit frequency afforded by Sentinel-3. The methodology involves generating aerosol maps at a 10 m resolution using bands 2, 3, 4, and 5 of Sentinel-2, available in L1C and L2A formats, conducted every five days, contingent upon the absence of cloud cover. Subsequently, this map is enhanced every two days through spatial modulation, utilizing a similar map derived from the visible and near-infrared observations (VNIR) captured by the OLCI instrument aboard Sentinel-3, which is accessible at a 300 m scale. Data from the two satellites undergo independent processing, with integration at the feature level. This process combines Sentinel-3 and Sentinel-2 maps to update aerosol concentrations in each 300 m × 300 m grid every two days or more frequently. For the dates when Sentinel-2 data is unavailable, the spatial texture or the aerosol distribution within these grid cells is extrapolated. This spatial index represents an advancement over prior studies that focused on differentiating between dust and smoke based on their scattering and absorption characteristics. The entire process is rigorously validated by comparing it with point measurements of fine- and coarse-mode Aerosol Optical Depth (AOD) obtained from AERONET stations situated at the test sites, ensuring the reliability and accuracy of the generated maps. Full article

Aerosol optical depth (AOD) is essential for air quality monitoring and climate research. However, satellite-based retrievals suffer from cloud-related data gaps, and reanalysis products are limited by coarse spatial resolution and substantial production latency. This study develops a real-time, gap-free, high-resolution (1.5 km) AOD retrieval system for South Korea. The system integrates Copernicus Atmosphere Monitoring Service (CAMS) forecasts, high-resolution meteorological fields, and ground-based air quality observations within a machine learning framework. Three models with varying training periods were systematically evaluated using cross-validation and independent validation with 2024 Aerosol Robotic Network (AERONET) data. The optimal model, trained on 2015–2023 data, achieved a mean absolute error (MAE) of 0.075 and a correlation coefficient (R) of 0.841 during the 2024 independent validation, significantly outperforming the original CAMS forecast. The system demonstrated robust and consistent performance across varying land cover types, seasons, and AOD conditions, from clean to highly polluted. Empirical orthogonal function (EOF) analysis confirmed that the product successfully captures physically meaningful spatiotemporal patterns, including transboundary pollution transport, regional emission gradients, and topographic effects. Providing real-time, gap-free, 3-hourly daytime AOD, the proposed model overcomes the limitations of cloud-induced gaps in satellite data and the latency and coarseness of reanalysis products. This enables robust operational monitoring and aerosol research across the Korean Peninsula. Full article

Water-soluble inorganic ions (WSIIs) are major components of PM_2.5 and play a prominent role in atmospheric acidification. Previous studies have mainly focused on urban areas, whereas research pertaining to county-level cities remains comparatively limited. To fill this gap, PM_2.5 samples were collected from March 2018 to February 2019 in Botou, a county-level city in the Jing–Jin–Ji region. Seasonal variation of WSII were studied, and their sources was apportioned by Positive Matrix Factorization (PMF) model. Annual PM_2.5 concentrations were 79.15 ± 48.44 mg/m³, which is 2.26 times of the Level II standard limit specified the National Ambient Air Quality Standard. Nitrate (NO₃⁻) was the most abundant ion, followed by ammonium (NH₄⁺) and sulfate (SO₄²⁻). The secondary inorganic aerosols (SIA, i.e., SO₄²⁻, NO₃⁻, and NH₄⁺) constituted 35.1± 4.7% of PM_2.5 mass. PM_2.5 mass, SO₄²⁻, NO₃⁻, NH₄⁺, K⁺, and Cl⁻ showed highest concentrations in winter. Ammonium salts were existed as ammonium sulfate ((NH₄)₂SO₄) and ammonium nitrate (NH₄NO₃) in spring, summer, and autumn, while it also can be existed as ammonium chloride (NH₄Cl) in winter. PMF analysis shows that the sources of WSIIs dominated by secondary source and followed by biomass burning. These results highlight the need for improved controls on gaseous precursors (NH₃, NO₂ and SO₂) and biomass burning to effectively reduce PM_2.5. Full article

An accurate estimate of sun irradiance is important for solar energy management in urban areas with complicated atmospheric conditions. The urban solar irradiance (USI) can be predictively researched with a variety of models; however, basing this entirely on one model often leads to other important conditions being omitted. A hybrid framework is suggested in this study, integrating the Perez–Ineichen PI model with a Deep Neural Network (DNN) model for predicting USI in Wuhan, China. The PI model predicts clear-sky irradiance labels based on atmospheric parameters normalized against the National Solar Radiation Database for greater accuracy. The model is trained on the Clear Sky Index with real-time atmospheric parameters gained from ground station measurements and satellite images. Following correlation analysis using bands from Sentinel-2 to find suitable bands for the model, the algorithm was prepared for atmospheric parameters, including cloud cover, aerosol concentration, and surface reflectance, all of which impact solar radiation. The architecture incorporates attention methods for important atmospheric parameters and skip connections for greater training stability. Results from the Deep Neural Network-Selected bands (DNN-S) and Deep Neural Network-All bands (DNN-A) models gave different performances, with the DNN-S model yielding better accuracy with a RMSE of 69.49 W/m² clear-sky, 87.60 W/m² cloudy-sky, and 72.57 W/m² all-sky. The results were validated using hyperspectral imagery, along with cloud mask, solar area, and surface albedo-derived products, confirming that the USI estimates are supported by the high precision and consistency of Sentinel-2-derived irradiance estimates. Full article

Air pollution is an important environmental issue that affects social development and human life. Atmospheric fine particulate matter (PM_2.5) is the primary pollutant affecting the air quality of most cities in the authors’ country. It can cause severe haze, reduce air visibility and cleanliness, and affect people’s daily lives and health. Therefore, it has become a primary research object. Ground monitoring and satellite remote sensing are currently the main ways to obtain PM_2.5 data. Satellite remote sensing technology has the advantages of macro-scale, dynamic, and real-time functioning, which can make up for the limitations of the uneven distribution and high cost of ground monitoring stations. Therefore, it provides an effective means to establish a mathematical model—based on atmospheric aerosol optical thickness data obtained through satellite remote sensing and PM_2.5 concentration data measured by ground monitoring stations—in order to estimate the PM_2.5 concentration and temporal and spatial distribution. This study takes the Yangtze River Delta region as the research area. Based on the measured PM_2.5 concentration data obtained from 184 ground monitoring stations in 2023, the newly released sixth version of the MODIS aerosol optical depth product obtained via the US Terra and Aqua satellites is used as the main prediction factor. Dark-pixel AOD data with a 3 km resolution and dark-blue AOD data with a 10 km resolution are combined with the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis meteorological, land use, road network, and population density data and other auxiliary prediction factors, and XGBoost and LSTM models are used to achieve high-precision estimation of the spatiotemporal changes in PM_2.5 concentration in the Yangtze River Delta region. Full article