Search Results (918)

More and more spaceborne infrared hyperspectral atmospheric observations are assimilated into data assimilation systems. The key to bias correction (BC) of these instruments depends on selecting predictors. However, it is difficult to find a set of predictors that are highly correlated with the O-B biases in all FY-3E/HIRAS-II channels, due to its multi-channel characteristics. A machine learning model XGBoost (EXtreme Gradient Boosting) BC scheme for FY-3E/HIRAS-II is established in this article. The selected predictors include model skin temperature, model total column water vapor, 1000–300 hPa thickness, 200–50 hPa thickness, scan position, observed brightness temperature (BT) and simulated BT. The method is also compared with the operational static BC and the variational BC, to validate its effect. The two-week data assimilation experiments show that the XGBoost BC is the most effective among the three BC schemes. The mean and standard deviation of O-B in all channels are the smallest after BC, and the effective observations through quality control are the largest, followed by the static BC. The static BC and variational BC are performed based on linear regression, which may lead to a small loss of valid observations in some channels that are weakly correlated with the predictor, whereas machine learning algorithms can search for the nonlinear correlation between biases and predictors. Compared with ERA5, both temperature- and humidity-analysis fields based on XGBoost BC are closest to ERA5 at all levels, and the root mean square errors do not change much over time. Full article

As a key technology for achieving building heating electrification and decarbonization, the air-source heat pump (ASHP) has long been constrained by outdoor heat exchanger frosting in cold and humid regions. Frosting leads to increased thermal resistance, a sharp rise in air-side pressure drop, and the attenuation of heating capacity, while traditional active defrosting methods, such as reverse-cycle defrosting, suffer from high energy consumption and heating interruption. This review aims to systematically present the recent research progress of superhydrophobic surfaces (SHSs) as a highly efficient passive anti-frosting strategy. First, the complex phase-change dynamics of frosting and key influencing factors such as environment and surface characteristics are deeply analyzed. Second, it elucidates how superhydrophobic surfaces achieve delayed frosting and sloughing off defrosting by delaying nucleation, promoting droplet self-removal, and reducing ice adhesion. Furthermore, fabrication processes suitable for complex fin structures are systematically reviewed from the perspectives of subtractive manufacturing, in situ growth, and additive coatings, and their industrialization prospects are compared. Finally, the practical effects of this technology in improving heat transfer coefficients, reducing fan energy consumption, and improving defrosting efficiency are evaluated. Although superhydrophobic technology has significant energy-saving potential, it still faces challenges such as poor long-term durability, wettability failure under extreme conditions, and residual micro-droplets. Future research should focus on the development of highly durable materials, the matching design of micro–nano structures with macro flow channels, and active–passive synergistic anti-frosting strategies. Full article

In the Yingjiang area of western Yunnan, precipitation is high throughout the year, making it one of the regions with the highest annual precipitation in mainland China. Extreme rainfall in this region often triggers severe flooding, yet the key mechanism of water vapor transport underlying abnormally heavy precipitation remains unclear. This study used automatic weather station observations of precipitation, the fifth-generation atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts, and Global Data Assimilation System (GDAS) data to analyze, for the first time, large-scale water vapor transport, precipitation mechanisms, and the primary water vapor sources and their contributions in this region. The results show the following: In the Yingjiang area, the water vapor sources at all height levels in summer are dominated by the southwest monsoon water vapor transport pathways, such as the Bay of Bengal and the Arabian Sea, with their total contributions to specific humidity and water vapor flux exceeding 70%. This indicates that low-latitude sea areas such as the Bay of Bengal and the Arabian Sea serve as key moisture source regions for Yingjiang in the global water vapor cycle. Water vapor transport over the windward slope causes strong low-level convergence and high-level divergence phenomena, and the suction effect leads to strong upward motion near the 850 hPa level. The pseudo-equivalent potential temperature isolines tilt along the mountain slope, maintaining an unstable stratification characterized by warm, humid lower layers and cold, dry upper layers, providing favorable thermal conditions for precipitation. In addition, in the summer of 2020, abnormally high southwest seasonal wind and air transport, combined with strong low-level convergence and high-level divergence of the vertical circulation structure, were key factors causing the abnormally high precipitation. This study provides an important reference for the prediction of extreme precipitation and the early warning of rainstorm disasters in the southwest monsoon region in the context of global climate change. Full article

Thermal stress in hot–humid urban environments constitutes a persistent sustainability challenge, driven by the interaction of extreme temperatures, high atmospheric moisture, and heat-retaining urban surfaces, which collectively intensify outdoor discomfort and increase cooling-energy demand. Within this context, soft landscape systems have gained recognition as nature-based solutions capable of moderating microclimates and enhancing residential livability; however, their systematic prioritization based on integrated sustainability performance remains insufficiently addressed, particularly in Gulf-region residential developments. This study proposes a sustainability-oriented decision-support framework that evaluates and prioritizes soft landscape strategies for thermal comfort enhancement using the Analytic Hierarchy Process (AHP) as the core analytical method. Expert judgments were elicited and structured across five sustainability-driven criteria—shading effectiveness, evapotranspiration potential, airflow facilitation, aesthetic–psychological comfort, and implementation and maintenance cost—and applied to five soft landscape alternatives. To verify the physical plausibility of the expert-derived prioritization, microclimate simulations were conducted using ENVI-met under extreme summer conditions, representing the hottest day of the year, at key diurnal intervals. The results reveal a clear dominance of shading-based mechanisms, with tree canopy systems emerging as the most effective and sustainable intervention due to their superior radiative control, ecological cooling capacity, and perceptual benefits. Simulation outputs confirm that canopy-driven strategies achieve the most substantial reductions in mean radiant temperature during peak thermal stress, while surface-based interventions provide secondary benefits primarily related to diurnal heat dissipation. At peak thermal stress (14:00), Scenario 2 reduced mean radiant temperature (MRT) from 71.69 °C to 54.23 °C (≈24% reduction) and PMV from 7.33 to 5.70 (≈22% reduction) relative to existing conditions. By integrating expert-based multi-criteria evaluation with simulation-based thermal verification, the study advances a robust and transferable framework for climate-responsive residential landscape planning. The findings reposition soft landscape systems as essential climatic infrastructure, offering actionable guidance for enhancing thermal resilience, reducing cooling-energy dependence, and supporting sustainable residential development in hot–humid regions. Full article

Quantifying the relative roles of climate change and human activities in vegetation change is essential for sustainable restoration planning, yet the impacts of extreme climate events and their time-lagged effects are often overlooked, biasing assessments of climatic controls. Here, we developed an integrated pattern–process–attribution framework to evaluate vegetation dynamics across China’s four major climatic zones using a long-term, high-resolution kernel normalized difference vegetation index (kNDVI) dataset for 2000–2024. Theil–Sen trend estimation and the coefficient of variation (CV) were used to characterize long-term changes and interannual stability. Partial correlation analysis was applied to isolate the independent associations between kNDVI and extreme climate indices while controlling for background mean temperature and precipitation, and lagged correlation analysis with 0–3-month lags was used to quantify delayed responses. A regression-based residual attribution was further used to decompose observed kNDVI changes into a climate-driven component and a human-activity-related component (approximated by the residual not explained by temperature and precipitation). Results show widespread greening with pronounced spatial heterogeneity, with the most extensive improvement in the Tropical and Subtropical Humid Region and the Temperate Humid and Semi-humid Region. Vegetation stability exhibits a southeast–northwest contrast, and the highest variability occurs in the Temperate Arid and Semi-arid Region and the western Qinghai–Tibet Plateau. Responses to climate extremes are region-dependent and generally short-lagged (mean 0.35–1.05 months), with drought constraints dominating in arid regions and thermal extremes (TXx) most relevant on the plateau. Nationally, human activities contribute 70.8% of vegetation change, exceeding the climate-driven contribution (29.2%). Full article

We investigated benzene variability in an urban environment using an interpretable, setting-based artificial intelligence framework. A seven-year dataset (2017–2023) of hourly pollutant concentrations (benzene, NO₂, SO₂, CO, O₃) measured in Zagreb (Croatia) was analyzed, as were meteorological variables. Multiple-ensemble decision tree models were developed, with hyperparameters optimized using metaheuristic algorithms. The best-performing model, Extra Trees optimized by the Sine Cosine Algorithm, achieved an R² of 0.87. Model interpretation employed Shapley additive explanations (SHAP), followed by PaCMAP embedding and HDBSCAN clustering to identify coherent environmental settings. Seven settings (C0–C6) and one residual group were identified, representing pollution-enhancing, suppressing, and transitional regimes. Two settings dominated benzene extremes. C6 reflected winter stagnation, characterized by strong combustion influence (CO contribution of 11.9%), shallow boundary layers (~290 m), weak winds, and high humidity. C4 represented a synoptic stability regime with enhanced heat fluxes and diminished after the COVID-19 period, consistent with altered anthropogenic activity. Low-benzene settings (C0, C1, C3) were associated with stronger mixing and higher oxidizing capacity, while transitional settings (C2, C5) reflected moderate conditions. Overall, the results show that a small number of environmental settings governed the benzene extremes, providing a transferable and interpretable framework for air quality assessment and policy support. Full article

Nowadays, air quality is a major issue, especially in large cities. Apart from air pollution, particulate matter (PM), especially PM2.5, poses serious health risks to individuals with respiratory conditions. Accurate forecasting of PM levels is crucial to warn vulnerable populations and reduce exposure. Machine learning models can effectively predict PM concentrations based on historical data and barometric conditions such as temperature and humidity. Such predictions can support timely public health interventions and environmental policy decisions. The selection of the optimal machine learning model for time series forecasting requires a careful balance between predictive accuracy and computational efficiency. This study evaluates a number of widely used models, such as Random Forest (RF), Long Short-Term Memory (LSTM), Convolutional Neural Network-LSTM (CNN–LSTM), Extreme Gradient Boosting (XGB/HistGradientBoosting), and hybrid approaches (LSTM embeddings + RF), in the context of time series forecasting for particulate matter (PM) concentrations. Performance is assessed using three key error metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Scaled Error (MASE). Additionally, the computational demands and development complexity of each model are analyzed. The overall results are of great interest for each application model, and in more detail, it is shown that the best compromise between accuracy and efficiency can be achieved, while a corresponding prediction model with satisfactory predictive performance can be implemented. The results show that CNN–LSTM and hybrid approaches provide high accuracy, while tree-based models are computationally efficient, offering practical options for real-time forecasting systems. Full article

Air pollution remains a major public-health concern, and exposure to particulate matter (PM), particularly PM₁₀ (with a diameter ≤ 10 µm), is associated with adverse respiratory and cardiovascular outcomes. Most research relies on a singular model for PM₁₀ surface estimation. This study is an assessment of a national-scale, daily PM₁₀ estimation framework for Lithuania (2019–2024), using a hybrid machine-learning method that combines Random Forest (RF) and extreme gradient boosting (XGBoost) algorithms. Hourly PM₁₀ observations were aggregated from 18 monitoring stations to obtain daily means and temporal means. The predictors integrated meteorological factors, such as temperature, wind, humidity, and precipitation, to determine satellite-based atmospheric composition from Sentinel-5P Tropospheric Monitoring Instruments (TROPOMI). Atmospheric components include nitrogen dioxide (NO₂), carbon monoxide (CO), sulfur dioxide (SO₂), ozone (O₃), formaldehyde (HCHO), and the absorbing aerosol index (AI). Moderate-Resolution Imaging Spectroradiometers (MODIS) were used to record land-surface temperature and static spatial descriptors, such as elevation, land cover, Normalized Difference Vegetation Index (NDVI), population, and road proximity. The dataset was partitioned temporally into training (70%), validation (20%), and testing (10%). The hybrid model achieved an improved accuracy, compared with single-model baselines, reaching a coefficient of determination (R²) of 0.739 in validation and R² = 0.75 in the tested dataset. Mean absolute error (MAE) was 3.15 µg/m³, and root mean square error (RMSE) was 3.98 µg/m³. The results indicate a slight tendency to overestimate PM₁₀ concentrations at lower concentration levels. Feature-importance analysis revealed that short-term temporal persistence is the key to daily PM₁₀ prediction, while meteorological variables provide secondary contributions. Temporal evaluation, using consecutive two-year windows, revealed a consistent improvement in predictive performance from 2019–2020 to 2023–2024, while station-level analysis showed moderate-to-strong agreement between the predicted and observed PM₁₀ concentrations across monitoring stations, with R² ranging from 0.455 to 0.760. This provides decision-support capabilities for air-quality management, the evaluation of mitigation measures, and integration of air-pollution considerations into sustainable urban planning strategies assessing public-health protection. Full article

High-altitude grazing animals are continuously exposed to strong wind and low temperature, which challenge physiological homeostasis and energy metabolism. Improving living conditions and nutritional supplementation are two commonly used strategies. In this study, sixty 7-month-old Gangba sheep (initial body weight (BW) 21.00 ± 1.90 kg) were allocated to a 42-day trial with four groups (open-air sheepfold, mobile sheepfold, open-air sheepfold + supplementary feeding, mobile sheepfold + supplementary feeding) to investigate their effects on growth performance, serum parameters and gut microbiota in naturally grazing Gangba sheep. Mobile sheepfolds increased the temperature–humidity index (THI) and reducing the wind chill index (WCI) (p < 0.05). The sheep with mobile sheepfold showed higher serum total antioxidant capacity and lower levels of heat shock proteins HSP70 and HSP90 (p < 0.05), indicating alleviated stress. Supplementary feeding markedly increased final BW and average daily gain (p < 0.05). The interaction between sheepfold type and feeding supplementation showed increasing IgA levels in the open-air sheepfold with supplementary feeding group and increasing IL-4 levels in the mobile sheepfold with supplementary feeding group, while TNF-α concentrations were reduced in all three treatment groups (p < 0.05). Meanwhile, KB and FFAs were increased in the open-air sheepfold with supplementary feeding group but decreased in the mobile sheepfold with supplementary feeding group (p < 0.05). The mobile sheepfold also increased the Bacillota-to-Bacteroidota ratio, suggesting improved microbial community structure. Functional predictions showed enrichment of reductive acetogenesis and reduction in aerobic chemoheterotrophy and sulfur-related respiration pathways (p < 0.05). Moreover, key microbial genera were significantly correlated with THI and WCI (p < 0.05). Collectively, these results demonstrated that mobile sheepfold together with feeding supplementation improve stress responses, serum immune and lipid metabolic indicators, and potentially altered gut microbial composition and function, providing insights into host–microbiota interaction in extreme high-altitude environments. Full article

The impact of ammonia (NH₃) emissions from animal agriculture on the secondary formation of inorganic fine particulate matter (i.e., iPM_2.5) has become of great public concern. The formation of iPM_2.5 from NH₃ is known as the gas–particle partitioning of gaseous NH₃ and aerosol ammonium (NH₄⁺), which is assumed to be in a thermodynamic equilibrium. This research aimed to gain an in-depth understanding of the impact of ambient NH₃ on secondary iPM_2.5 by analyzing the PM_2.5 mass closure, atmospheric chemical conditions, and the gas particle partitioning of NH₃-NH₄⁺ in the near field of a poultry production unit in North Carolina. Samples of precursor gases (i.e., NH₃, SO₂, NO₂) to iPM_2.5 and PM_2.5 were taken on this poultry production unit at four sampling stations in four wind directions through summer, autumn and winter seasons to determine gas concentrations and PM_2.5 chemical compositions. It was discovered that this rural site contained low ambient concentrations of iPM_2.5 precursor gases, and PM_2.5 composition was dominated by organic carbon (OC) (80% to 94%) while iPM_2.5 fraction was insignificant (0% to 2%). Low availability of H₂SO₄ and HNO₃ gases (from SO₂ and NO₂ conversions) limited NH₃ neutralization potential and iPM_2.5 formation; moreover, high OC fraction may inhibit NH₄⁺ formation. With the field measurements of ambient temperature, humidity, precursor gases and PM_2.5 chemical speciation data, the ISORROPIA-II thermodynamic equilibrium model was used to conduct the sensitivity analysis, and we found that iPM_2.5 was the most sensitive to increasing total HNO₃ (gas + aerosol) at low temperatures. The formation potential of iPM_2.5 at this rural site was at its highest during the wintertime when SO₂ was extremely low. Full article

Under extreme summer heat and humidity, the indoor thermal environment and occupants’ thermal adaptability in Lifen dwellings, a high-density residential typology in Wuhan, China, remain insufficiently documented. This study compares summer indoor thermal conditions and thermal comfort between traditional and newly built Lifen dwellings using combined field measurements and questionnaire surveys. Continuous monitoring was conducted in four representative dwellings from 16 to 21 July 2024, together with 192 valid questionnaires collected across the two Lifen communities. Results indicate clear differences in indoor thermal characteristics between the two dwelling types; old Lifen dwellings exhibited stronger perceived heat and more pronounced spatial thermal non-uniformity. Kitchens were identified as the most unfavorable spaces in dwellings. Regression analysis of thermal sensation reveals that residents in old dwellings had a higher neutral temperature than those in new dwellings (27.6 °C versus 26.0 °C) and demonstrated stronger overall thermal adaptability, with a relatively wider comfort range (0.8 °C). Combined evidence from field measurements and subjective voting suggests that long exposure, behavioral adjustment, and ventilation-driven cooling collectively enhance heat tolerance in old Lifen dwellings. These findings provide empirical support for thermal environment optimization and renovation strategies in naturally ventilated historic residential areas. Full article

Aridity represents a fundamental climatic constraint governing water resources, ecosystem functioning, and agricultural systems in transitional climate zones. This study examines the spatial organization and temporal variability of aridity and thermal continentality in North Macedonia using observational records from 13 meteorological stations distributed across contrasting altitudinal and physiographic settings. The analysis is based on homogenized monthly and annual air temperature and precipitation series covering the period 1991–2020. Aridity and continentality were quantified using the Johansson Continentality Index (JCI), the De Martonne Aridity Index (I_DM), and the Pinna Combinative Index (I_P). Temporal consistency and trend behavior were evaluated using Pettitt’s nonparametric change-point test, linear regression, the Mann–Kendall test, and Sen’s slope estimator. Links between aridity variability and large-scale atmospheric circulation were examined using correlations with the North Atlantic Oscillation (NAO) and the Southern Oscillation Index (SOI). The results show a spatially consistent and statistically significant increase in mean annual air temperature, with a common change point around 2006, while precipitation displays strong spatial variability and limited temporal coherence. Aridity patterns display a strong altitudinal control, with extremely humid to very humid conditions prevailing in mountainous western regions and semi-humid to semi-dry conditions dominating lowland and southeastern areas, particularly during summer. Trend analyses do not reveal statistically significant long-term changes in aridity or continentality over the study period, although low-elevation stations exhibit weak drying tendencies. A moderate positive association between I_DM and I_P (r = 0.66) confirms internal consistency among aridity indices, while summer aridity shows a statistically significant relationship with the NAO. These results provide a robust climatic reference for North Macedonia, establishing a first climatological baseline of aridity conditions based on multiple indices applied to homogenized observations, and contributing to regional assessments of hydroclimatic variability relevant to climate adaptation planning. Full article

We investigate the multi-layer atmospheric impacts of Typhoon Hagibis (2019), which formed on 6 October, tracked across 12–35° N and 135–155° E, and made landfall on 12 October over the Izu Peninsula, central Honshu, Japan. We present a multi-layer study that involves the troposphere, stratosphere and upper ionosphere to examine the thermodynamic and electromagnetic coupling between these layers due to such extreme weather conditions. Using ERA5 reanalysis, we identify pronounced stratospheric temperature perturbations, elevated atmospheric gravity wave (AGW) potential energy, substantial spatiotemporal variability in the zonal (U) and meridional (V) wind components, relative humidity, and specific rainwater content throughout the cyclone’s evolution. Quantitatively, AGW potential energy increased from background levels of <5 J kg⁻¹ to >40 J kg⁻¹ near the cyclone core, while tropospheric wind anomalies reached $\pm 30$–40 m s⁻¹, accompanied by relative humidity values exceeding 90% and specific rainwater content up to $1.5\times {10}^{-3}$ kg kg⁻¹, indicative of vigorous moist convection and strong vertical energy transport. The ionospheric response, derived from GPS-based Total Electron Content (TEC) at 10 Japanese IGS stations, reveals vertical TEC (VTEC) perturbations whose amplitudes and temporal evolution vary systematically with GPS-station-to-typhoon-eye distance, including clear enhancements and reductions around the closest-approach day. These signatures indicate a measurable ionospheric response to cyclone-driven atmospheric forcing under geomagnetically quiet conditions, confirming that Hagibis produced vertically coupled disturbances linking stratospheric AGW activity with ionospheric electron density variability. Full article

Agriculture in semi-arid regions faces increasing challenges from temperature extremes and moisture stress, necessitating climate-smart and resource-efficient production systems. This study examined maize–mushroom intercropping as a climate-smart strategy for semi-arid regions. Field experiments conducted at Tamil Nadu Agricultural University evaluated four maize planting geometries, with and without mulch, in 2022. Results showed that close-maize spacing (45 × 25 cm) with mulch moderated temperature, increased humidity, and improved mushroom yield and biological efficiency. The treatment achieved a land equivalent ratio above one, indicating superior land use efficiency. Optimal microclimatic conditions (26–33 °C; 80–98% RH) enhanced paddy straw mushroom growth, demonstrating that simple field-level modifications can stabilize microclimate and promote resilient farming in semi-arid ecosystems. Full article

African vegetation responses to extreme drought represent a key challenge for global change research and sustainable water–land resource management. Satellite remote sensing provides long-term observations of vegetation dynamics, yet conventional analyses focus on vegetation structural, greenness, or productivity changes, lacking of understanding on physiological adaptation. This study applies a multi-model framework integrating high-temporal-resolution (4-day) and multi-spectral satellite data with machine learning to disentangle structural and physiological responses across Central and Western Africa. Three key indicators were used: evapotranspiration (ET), relative solar-induced chlorophyll fluorescence (SIF_rel), and the ratio of midday to midnight vegetation optical depth (VOD_ratio), which respectively, represent water flux, photosynthetic activity, and water regulation. A random forest model, combined with SHapley Additive exPlanations (SHAP) analysis, was used to separate vegetation anomaly signals and identify key climatic controls. The results reveal pronounced differences in vegetation responses between arid and humid climatic regions. In arid regions, near-infrared reflectance of vegetation (NIRv) and solar-induced chlorophyll fluorescence (SIF) exhibited clear negative anomalies and significant pre-drought declines, accompanied by marked changes in vegetation optical depth (VOD), indicating canopy structural damage and reduced photosynthetic activity. In contrast, trend analysis revealed that although SIF and NIRv in humid regions showed relatively strong responses during the pre-drought phase, they did not exhibit significant trends after the drought peak, and changes in VOD were comparatively small, suggesting that higher water availability partially buffered the prolonged impacts of drought on vegetation structure and function. Process analysis showed that three months before and after drought peaks, physiological indicators exhibited strong anomalies that closely tracked drought duration. SIF_rel, ET signals peaked earlier than water-content anomalies (VOD_ratio), suggesting a two-phase regulation strategy: early stomatal closure followed by delayed deep-root water uptake. Physiological anomalies accounted for over 88% of total vegetation anomalies during drought peaks, highlighting their dominant role in early-stage drought response. Precipitation and temperature emerged as primary drivers, explaining 76.8% of photosynthetic variation, 60.3% of ET variation, and 53.9% of water-content variation in the development. The recovery is influenced by the duration of drought and the regrowth of vegetation. By explicitly decoupling physiological and structural vegetation responses, this study provides refined, process-based insights into African ecosystem adaptation to water stress. These findings contribute to more accurate drought monitoring, water availability assessment, and climate adaptation strategies, directly supporting sustainable water and land management goals. Full article