Search Results (2,709)

Accurate evapotranspiration (ET) estimation is critical for sustainable water management in arid environments. This study estimates actual ET over the Al-Hofuf region, Al-Ahsa Oasis, Saudi Arabia, during 2024 using a cloud-based remote sensing approach. Landsat 9 Level-2 imagery was combined with ERA5-Land meteorological data to quantify spatial and temporal ET variations across a 25 km buffer. Vegetation dynamics were characterized using the Normalized Difference Vegetation Index (NDVI) to derive crop coefficients (Kc) within a Kc–ET₀ framework, where reference ET (ET₀) was obtained from ERA5-Land potential evaporation. All processing utilized Python (Version 3.14) on Google Colab and Google Earth Engine for scalable computation. Eighty-eight cloud-free Landsat 9 scenes were processed following cloud and shadow masking. Mean NDVI, Kc, and daily ET values were compiled into a comprehensive time-series dataset. Model performance was evaluated through cross-validation with MODIS MOD16A2 and internal consistency checks, demonstrating strong statistical agreement (R² = 0.82, NSE = 0.71, PBIAS = +8.3%). Results revealed pronounced seasonal variability closely linked to vegetation activity and atmospheric demand, with peak ET occurring during summer months (June–July: 7.2–7.5 mm day⁻¹) and minima in winter (January–February: 2.0–2.6 mm day⁻¹). Findings demonstrate that cloud-based techniques provide reliable, cost-effective ET monitoring in data-scarce, groundwater-dependent regions. Validation confirms Kc-ET₀ estimates reliably capture spatial and temporal patterns, supporting practical irrigation management applications. This approach aids precision irrigation and long-term water sustainability planning in Al-Hofuf, contributing significantly to national water conservation objectives under Saudi Arabia’s Vision 2030 and National Water Strategy. Full article

Urban areas significantly influence atmospheric flow fields and momentum exchange processes, which are relevant for wind energy applications and meso-scale atmospheric modeling. However, meso-scale simulations typically represent urban effects using surface roughness parameterizations that neglect volumetric momentum losses within the urban canopy layer. In this study, a methodology is presented to derive a volumetric urban canopy parameterization directly from building-resolved computational fluid dynamics (CFD) simulations. A detailed micro-scale CFD simulation of a real urban region is used to evaluate the momentum balance within a control volume surrounding the urban region. Based on this analysis, two key parameters are derived: the vertical distribution of the House Area Density (HAD), representing the geometric characteristics of the urban morphology, and an effective drag coefficient describing the momentum loss induced by the built environment. These parameters are subsequently implemented as volumetric source terms in a urban canopy model formulated analogously to plant canopy parameterizations. The resulting urban canopy model is validated by comparison with the fully resolved CFD simulation. The results show good agreement in the streamwise momentum balance and pressure loss distribution, while computational cost is significantly reduced. The proposed urban canopy model provides a physically consistent framework for representing urban momentum sinks in meso-scale flow simulations. Full article

The identification of the chemical species responsible for the anomalous near-ultraviolet (UV) opacity in the Venusian cloud for “unknown absorber” remains a paramount challenge in planetary science. This study presents a comprehensive quantum chemical investigation into a broad suite of candidate molecules, including isomers of thiosulfeno (S₂O₂), the hydroxysulfonyl radical (HSO₃), disulfur monoxide (S₂O), disulfur dichloride (S₂Cl₂), iron(III) chloride (FeCl₃), phosphine (PH₃), and structural isomers of polysulfur oxides (S₃O). Utilizing Time-Dependent Density Functional Theory (TD-DFT) at the CAM-B3LYP/def2-TZVPP level of theory, we systematically mapped electronic transitions across three distinct environmental phases: gas-phase (without solvent), supercritical CO₂, and concentrated H₂SO₄ aerosols. To establish confidence in the predicted results, our TD-DFT approach was rigorously benchmarked against high-level theoretical methods (CCSD(T), EOM-CCSD, and MRCI+Q) from recent literature. All these electronic transitions were modeled via the Solvation Model based on Density (SMD). Our results demonstrate a profound topological and environmental dependence on spectral signatures. Among the candidates, trans-OSSO (t-OSSO) emerged as the most viable near-UV absorber candidate, exhibiting a highly allowed π → π* transition at 379.37 nm (f = 0.1140) in H₂SO₄, providing a near-perfect alignment with the observed 365 nm planetary albedo drop. Conversely, the polysulfur oxide cis-S₃O was acknowledged as a primary visible-light chromophore, with an intense absorption at 436.31 nm (f = 0.1280) responsible for the characteristic yellow tint of the planet. Additionally, the photochemically maintained SSCl₂ isomer was identified as a critical broadband near-UV absorber. Species such as S₂O and planar S₃O were found to function as critical mid-UV shields (270–300 nm). This work establishes a multi-chromophore model of the Venusian atmosphere, where a chemically stratified network of sulfur-oxygen chains and chlorine-sulfur reservoirs, tuned by the acidic aerosol matrix, collectively governs radiative balance and atmospheric super-rotation of the planet. Furthermore, to account for massive continuum tailing into the visible region (>400 nm), we employed a semi-classical Reflection Principle approach to model 1D vibronic broadening. This analysis revealed that while standard solvent effects induce minor solvatochromic shifts, ground-state structural fluxionality in the OSSO isomers drives intense, symmetry-allowed transitions deep into the visible spectrum, an effect absent in structurally constrained or rigid control species. Full article

Even under a warming climate, winter sea ice in the Bohai Sea continues to threaten ships and offshore/coastal infrastructure. Reliable pre-season prediction of the overall wintertime sea ice condition in the Bohai Sea, as represented by the Bohai Sea Ice Grade (BSIG), is therefore important for disaster preparedness and mitigation. Based on the 1979–2024 BSIG record, this study compares seven statistical and AI-based seasonal prediction methods: analog year analysis, multiple linear regression, stepwise regression, Principal Component Regression, a cross-correlation-based regression model, support vector regression, and the Bayesian Ensemble Bohai Ice Grade Net (BE-BIGNet). As potential precursors, we considered sea ice extent in 14 Arctic regions together with 114 large-scale atmospheric and oceanic circulation indices. The results suggest substantial differences in predictive skill among the methods. Among the tested approaches, BE-BIGNet, which combines Bayesian regularization with bootstrap median ensembling, achieves strong full-period performance and stable skill during the independent test period, suggesting that it may provide a useful framework for operational BSIG forecasting in the Bohai Sea. Full article

Biomass burning is a major source of atmospheric pollution. However, rapid and quantitative assessment of particulate matter in smoke plumes remains challenging, owing to the physical uncertainties, limited coverage, and labor-intensive quality control of conventional monitoring approaches. Existing image-based deep learning methods typically address either smoke detection or air quality assessment separately. To address this gap, we develop a Unified Smoke Detection and Aerosol Estimation Framework (SDAF), a three-stage deep learning approach evaluated using a smoke-rich airborne dataset. The framework integrates smoke localization with PM₁ estimation by combining a YOLOv11-based detector with an optimized convolutional neural network. The model achieves high accuracy under in-plume conditions (R² of 0.985). However, its performance degrades under out-of-plume conditions due to substantial differences in visual features between the two domains. Consequently, direct across-domain transfer performs poorly, whereas region of interest (ROI)-level fine-tuning substantially improves performance for out-of-plume images (R² of 0.621). Despite these promising results, fundamental limitations remain. Image-based PM₁ estimation is intrinsically ill-posed due to the non-unique mapping between visual observations and particle mass. Overall, the framework enables an integrated workflow from smoke localization to quantitative PM₁ estimation using image data alone, offering a scalable solution for biomass burning monitoring and air quality assessment while highlighting the fundamentally indirect nature of image-based PM₁ inference relative to spatially resolved retrievals. Full article

This study tackles the middle atmosphere phenomenon known as Mesospheric Inversion Layers (MILs). Reinterpreting Envisat’s GOMOS instrument limb-sounding temperature profiles which we compared to the MSIS-2.0 climatological model, we studied 340,000 resolute temperature profiles, detecting 44,000 (13%) MILs in this dataset. We have shown that MILs are a worldwide phenomenon, concentrated around the tropics and in the Winter Hemisphere’s mid-latitude region (between $30\%$ and $50\%$ of profiles are MILs in those areas). MILs follow a correlation law ($R^2=0.5$ on pure data, $R^2=0.97$ on binned-mean data) between the log-amplitude of its peak and its altitude. Median altitudes are about 70 km worldwide, but the median amplitude reached by equatorial MILs is typically higher (14.5 K compared to the others at 12.5 K). Lastly, equatorial MILs (but not mid-latitude MILs) are correlated with high-difference estimated tide temperature gradient contributions. Results suggest that the MIL is a common phenomenon with statistically consistent characteristics. Seasonal occurrence hinted that there is probably a class of MILs favoured by planetary waves at the edge of the polar vortex, while the equatorial type of inversions seems to occur when the atmospheric tide model flattens the temperature gradient around 70 km. Full article

In this study, we present a machine-learning approach—a land use random forest (LURF) model—to produce daily PM₁₀ concentration maps at a 1 km resolution across the Campania region for the year 2022. The model combines daily measurements from 13 ARPA Campania monitoring stations with a wide set of spatial and atmospheric information. The predictors include population, land cover, road network, ERA5 meteorological data, satellite aerosol observations from MODIS, output from the CHIMERE chemistry transport model, and a flag identifying days affected by Saharan dust transport. The model is trained and validated using a station-based cross-validation scheme that accounts for spatial correlation between sites. Under this scheme, the LURF reproduces observed concentrations with substantially smaller errors than the raw CHIMERE output (RMSE of 11.0 vs. 23.6 $\mathsf{\mu }$g m⁻³). CHIMERE concentrations and ERA5 meteorology emerge as the most informative predictors, while the dust flag specifically improves the representation of episodic high-PM₁₀ events. The resulting 1-km maps reveal clear urban–rural contrasts. They identify pollution hotspots in the Naples metropolitan area and along major motorways that are not visible in coarser model outputs. Probabilistic exceedance maps further show that meeting the future 2030 EU limit value of 20 $\mathsf{\mu }$g m⁻³ will be challenging across much of the metropolitan area. Overall, the proposed framework provides a low-cost, practical tool for high-resolution PM₁₀ exposure assessment, supporting epidemiological studies, environmental justice analyses, and air quality management in regions with complex terrain and limited monitoring coverage. Full article

The Antarctic stratospheric ozone plays a crucial role in the polar climate system and is strongly influenced by energetic particle precipitation. Among these processes, medium-energy electron (MEE) precipitation enhances the production of odd nitrogen (NOx) in the polar mesosphere and stratosphere, thereby driving ozone depletion through catalytic reactions. However, quantifying its atmospheric impact remains challenging, largely because the spatial and temporal variability of MEE is poorly constrained, and most current global chemistry–climate models lack a realistic MEE forcing. This study employs the Whole Atmosphere Community Climate Model coupled with Sodankylä Ion Chemistry (WACCM–SIC) to investigate the influence of MEE precipitation during 2013–2014, when moderate geomagnetic storms were more frequent in the winter of 2013. A control simulation (Case1) and two sensitivity experiments (Case 2 and Case 3) were conducted to isolate MEE-driven effects. Model-simulated NO_x (NO + NO₂) and ozone concentrations agree well with satellite observations, indicating that WACCM–SIC captures the key photochemical and dynamical processes. The results further suggest that the direct impact of MEE precipitation on the middle and lower atmosphere during winter is relatively weak. Nevertheless, MEE-generated NO_x can be efficiently transported downward within the polar vortex, reaching altitudes below 15 km. In these regions, MEE-related NO_x enhancement can reach up to 5%, with values during the winter of 2013 approximately twice those in 2014. Sensitivity experiments further reveal that enhanced NO_x leads to pronounced ozone depletion in the lower stratosphere, with ozone losses reaching up to 25%. A clear negative relationship between NO_x and ozone is therefore evident, highlighting the importance of accurately representing MEE precipitation in chemistry–climate models. Full article

When the global energy transition is analyzed through economic lenses, the constraints imposed by the laws of thermodynamics are often overlooked. This study addresses the Latecomer’s Dilemma—the predicament of semi-peripheral nations compelled to decarbonize without the capital stock accumulated following the example of the countries of the Global North during their more than two hundred years of industrial development associated with the saturation of the atmosphere with carbon dioxide. A novel phase space model of the Anthropocene is constructed, synthesizing the political concept of ecological debt with the biophysical reality of entropy debt. The application of the laws of systems ecology and non-equilibrium thermodynamics enables the mapping of national development trajectories against the saturated “atmospheric bathtub”. The analysis identifies a critical Injustice Gap—a region of phase space physically foreclosed by historical emissions. Moreover, it has been demonstrated that a circular economy powered by low-density renewables functions as an entropy trap, converting material debt into radiative debt without achieving a closed loop. Consequently, the Polish correction vector is proposed as a stabilization mechanism. This study’s findings indicate that addressing the emerging phenomenon of adaptation apartheid necessitates the implementation of a high-density energy flux, namely Generation IV nuclear reactors, which would be funded by a retroactive ETS3 mechanism. This approach fulfills the thermodynamic condition for material closure, thereby substantiating the notion that energy justice constitutes a physical necessity for planetary stability. This study quantifies the historical radiative debt of a single early-industrialized hub (Manchester) at approximately 142.8 billion EUR. The novelty lies in the synthesis of biophysical laws and the Latecomer’s Dilemma through the proposed ETS3 mechanism. Full article

Remote sensing (RS) imagery often suffers from non-uniform atmospheric scattering, resulting in severe contrast degradation, detail blurring, and spectral distortion. While recent advanced State Space Models (SSMs) offer efficient long-range modeling, they frequently struggle with spectral–spatial coupling interference and lack explicit physical constraints, leading to over-smoothed textures and color biases in high-reflectance regions. In this paper, we propose PhysWave-SSN, a Physics-Inspired Frequency-Decoupled Network specifically designed for high-fidelity RS image dehazing. The architecture employs a task-adaptive frequency-specific screening strategy to effectively isolate structural details from atmospheric interference. Specifically, we first introduce a Frequency-Aware Selection Gate (FASG) that unifies adaptive channel screening with physical transmission estimation, enabling precise recalibration of frequency components. To bridge the gap between physical scattering principles and state space representation learning, we develop a Physics-Informed SSM (PI-SSM), where the discretization step size of Mamba is dynamically modulated by the estimated haze density. This mechanism allows the model to adaptively adjust its spatial receptive field according to local degradation levels, enhancing physical interpretability. Furthermore, a Luminance-Adaptive Fusion Module (LAFM) is presented to protect high-reflectance land covers and maintain spectral consistency. Extensive experiments on multiple RS datasets demonstrate that PhysWave-SSN achieves superior performance, notably attaining a maximum PSNR gain of 2.49 dB while ensuring high structural and spectral fidelity. Full article

During wet deposition, particulate matter and gaseous species in the atmosphere are ultimately transported to the Earth’s surface via precipitation and subsequently incorporated into terrestrial ecosystems. Therefore, investigating the fluxes, chemical compositions, and source apportionment of regional wet deposition is of great scientific importance. An analysis of the concentrations, deposition fluxes, spatiotemporal variations, and source apportionment of water-soluble ions in wet deposition can further enhance our understanding of the water-soluble ion characteristics, atmospheric pollution profiles, and potential ecosystem impacts of wet deposition in the Yangtze River Delta and Pearl River Delta regions. Coastal cities in China are most developed regions, and also areas suffering from severe air pollution. This study investigates the chemical characteristics, sources and wet deposition fluxes of water-soluble inorganic ions in precipitation in two typical coastal urban agglomerations of China: Ningbo in the Yangtze River Delta and Guangzhou in the Pearl River Delta. Precipitation samples were collected and analyzed to determine the concentrations of major ions. The results revealed distinct ionic compositions between the two regions. In Ningbo, NO₃⁻ and SO₄²⁻ were the predominant ions accounting for 16.98% to 23.22% of the total, reflecting the influence of anthropogenic emissions from fossil fuel combustion and mobile sources with the NO₃⁻/SO₄²⁻ ratio of 0.90 and 0.70. In Guangzhou, precipitation was characterized by high contributions of SO₄²⁻, NO₃⁻, NH₄⁺, and Ca²⁺, accounting for 17.22% to 23.29% of the total, indicating a mixed influence of industrial emissions, agricultural activities, and construction dust with the NO₃⁻/SO₄²⁻ ratio of 0.92 and 0.87. A clear inverse relationship between rainfall amount and ion concentration was observed at all sites (p < 0.05), demonstrating a significant dilution effect. Seasonality played a crucial role in deposition fluxes. In Ningbo, fluxes peaked during summer from 4667 to 5156 mg·m⁻², while in Guangzhou, distinct dry and rainy season patterns influenced the scavenging efficiency of different ion species. Urban sites exhibited enhanced scavenging of crustal and anthropogenic ions (e.g., Ca²⁺, NH₄⁺) during the rainy season, whereas the coastal site showed elevated fluxes of marine-derived ions (Na⁺, Cl⁻, Mg²⁺, SO₄²⁻) during the same period. The observed trends in ion fluxes suggest a gradual improvement in regional air quality over the study period. These findings elucidate the complex interactions between anthropogenic activities, natural sources, and meteorological factors in shaping the wet deposition chemistry in coastal urban environments, providing essential data for developing regional deposition models and assessing the ecological impacts of atmospheric pollution. Full article

Helium-rich unconventional natural gas resources have attracted increasing attention from both academia and industry. A pronounced local enrichment of helium has recently been identified in coal-derived unconventional natural gas in the Sanjiaobei block on the eastern margin of the Ordos Basin. To clarify the main controls on helium enrichment in unconventional natural gas in this area and to guide the exploration of helium-rich resources, this study systematically examines the source of helium, its transport carrier, multiphase fractionation processes, and enrichment and accumulation pattern in natural gas. The analysis is based on conventional gas composition, helium volumetric content, carbon isotopes, and noble gas isotopes (He, Ne, and Ar) measured from wellhead gas samples collected from 11 production wells in the block, together with the regional deep structural evolution and hydrogeological conditions. The results show that: (1) the helium volumetric content of natural gas in the study area ranges from 0.0175% to 0.214%, with an average of 0.108%, and most wells fall within the high-helium grade category; (2) the helium isotope ratios ³He/⁴He (R/Ra) of the samples range from 0.0148 to 0.0824, indicating a typical crustal helium source; the good positive correlation between helium and nitrogen volumetric contents suggests that the two components share a highly consistent source affinity or common migration and accumulation behavior during fluid evolution; and the extremely high He/Ne ratios, on the order of 10⁴, together with excess Ar isotopes, indicate that the gases experienced little dilution by shallow atmospheric water or modern atmospheric fluids during migration and accumulation. The formation of helium-rich unconventional gas reservoirs on the eastern margin of the Ordos Basin is interpreted to be characterized by basement-derived helium supply, activation by tectonothermal events, groundwater transport, efficient fault-controlled migration, reservoir capture along migration pathways, and sealing by stagnant groundwater and lithologic barriers. On this basis, a helium enrichment model is established. This model depicts the geochemical evolution pathway of trace noble gases in a natural gas system and may provide a useful reference for helium resource evaluation in analogous areas. Full article

Based on leaf area index (LAI) and enhanced vegetation index (EVI) datasets, this study systematically analyzes the spatial distribution and temporal variation characteristics of vegetation index trends at the global scale, clarifying the overall pattern of global greening and the seasonal differences in vegetation greening between eastern China and India. Regions with significant greening in China and India were selected as sensitivity zones, and a coupled land–atmosphere model was used to simulate seasonal differences in the climate response to greening. The findings reveal that: (1) Vegetation greening in eastern China is most pronounced in summer, whereas in India, the greening effect is most prominent in autumn; (2) The synergistic greening of both regions induces a year-round cooling effect in southeastern China, whereas northeastern China experiences summer warming and cooling in the other seasons. Furthermore, spring greening in China and India leads to a pronounced and widespread cooling across the mid-to-high latitudes of Eurasia. (3) In terms of precipitation, southwestern China shows an increasing trend in summer rainfall, while southeastern China shows a decreasing trend. In India, synergistic greening leads to spring and summer warming and autumn and winter cooling, with the cooling and increased precipitation effects being most significant in autumn. Full article

Farmland particulate pollution seriously affects regional atmospheric quality, and exploring efficient field dust control strategies is an urgent need for agricultural ecological protection. This study employed a wind tunnel and online dust monitoring system to investigate the dust reduction effect of straw return in conservation tillage in Beijing farmland under varying wind speeds and precipitation levels, providing theoretical and technical support for straw coverage configuration and dust pollution control. Given the insufficient understanding of the combined impacts of straw coverage, wind speed and precipitation on farmland particulate emissions, this study examined how these key factors jointly affect fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), and total suspended particulate (TSP) emissions. A three-factor, three-level response surface experiment modeled these relationships and identified optimal conditions for suppressing PM emissions—51.35% straw coverage, 3.96 m·s⁻¹ wind speed, and 32.36 mm precipitation—yielding average PM_2.5, PM₁₀, and TSP concentrations of 26.31, 31.71, and 42.43 μg·m⁻³, respectively. Field data showed that the mean absolute errors (MAEs) between predicted and measured concentrations were 0.52–5.80, 0.46–3.93, and 1.83–5.68 μg·m⁻³ for PM_2.5, PM₁₀, and TSP, respectively, corresponding to relative prediction accuracies of 90.42–97.95%, 95.03–98.52%, and 93.10–97.21%—indicating strong model accuracy. This approach enhances dynamic monitoring of straw return practices and guides rational field management. By integrating meteorological conditions and particulate emission characteristics, the model can quantitatively assess regional straw coverage and screen optimal straw mulching rates. It provides a clear data reference for decision-makers to formulate targeted dust prevention policies, standardize straw return regulation, and advance eco-friendly and sustainable agricultural production. Full article

Tropical coastal regions exhibit pronounced spatial and temporal variability in rainfall driven by seasonal atmospheric circulation and coastal–orographic interactions. Accurate representation of the temporal distribution of rainfall is essential for hydrologic modeling and infrastructure design. This study develops regionalized Huff curves for the Department of Magdalena, Colombia, addressing a critical gap in the characterization of rainfall temporal patterns in tropical coastal regions. A total of 270 short-duration (5–6 h) rainfall events from automatic stations were converted into normalized cumulative mass curves. The resulting curves were grouped into homogeneous temporal patterns using clustering algorithms. Three dominant storm types were identified: early-peak (Curve 1), intermediate (Curve 2), and uniform (Curve 3), reflecting the region’s coastal, lowland, and orographic influences. Probability envelopes and representative design hyetographs were derived to quantify intra-event variability. Rainfall–runoff simulations for a 100-km² watershed showed peak-flow differences of up to 132% between storm types, highlighting the sensitivity of hydrologic response to rainfall temporal distributions. The resulting regionalized Huff curves provide a practical and transferable framework for hydrologic modeling, flood-risk assessment, and infrastructure planning in tropical regions with limited high-resolution rainfall data. Full article