Search Results (3,214)

This study evaluates the potential of L-band passive microwave data for monitoring soil moisture (SM) in boreal and temperate forests using SMAP and SMOS AM and PM overpasses. SMAP and SMOS Level 3 SM products were first assessed for spring and summer seasons. SMOS showed lower accuracy (r² = 0.04–0.24, ubRMSE = 0.09–0.13 m³/m³), while SMAP performed better (r² = 0.18–0.62, ubRMSE = 0.05–0.07 m³/m³) across sites and overpasses. Given the larger number of SMAP TB observations at a fixed incidence angle and greater temporal coverage over the study area, SMAP was selected for SM estimation using ML models. Feature importance analysis identified brightness temperature (TB) as the most influential variable, followed by vegetation water content (VWC), air and soil temperatures, and the microwave polarization difference index (MPDI). Soil and air temperatures were interchangeable during AM overpasses, whereas PM overpasses showed distinct differences, likely due to thermal absorption by dense vegetation. Using optimal features, SM was estimated with CatBoost, Gradient Boosting (GB), Random Forest (RF), and Principal Component Regression (PCR), using stratified shuffle split (SSS) and leave-one-year-out cross-validation (LOYOCV). In SSS, CatBoost achieved slightly higher accuracy than the other ensemble models (AM: r² = 0.73; PM: R² = 0.74), while PCR yielded substantially lower accuracy across both overpasses. LOYOCV showed closer rankings among models, with CatBoost ranking highest overall (r² = 0.58 for AM and 0.54 for PM). Results highlight the feasibility of improved SM estimation in forests using L-band TB, VWC, temperature variables, and MPDI. Full article

Xizang is characterized by high altitude, low air pressure, strong atmospheric transparency, and complex terrain, while sparse ground stations coexist with continuously available remotely sensed data, and systematic studies on SSR bias correction and meteorological influences under plateau conditions remain limited. This study focuses on a short-term spring case at one urban observation site in Lhasa, using observations collected from 4 to 30 April 2025 to investigate the bias correction of remotely sensed surface solar radiation (SSR) and the influence of meteorological factors. Ground observations and Himawari-8 remotely sensed data were first spatially and temporally matched and preprocessed. Spearman correlation analysis was then used to select key input variables. Support vector regression, random forest, XGBoost, and multiple linear regression models were subsequently developed, followed by a Stacking ensemble model for bias correction. Finally, local sensitivity analysis was conducted to examine the local response of the correction model to selected meteorological variables at a representative baseline point. The results showed that the correlation coefficient between remotely sensed SSR and ground-observed SSR was 0.88 ($p<0.001$). The Stacking ensemble model achieved the best performance, with a test set $R^2$ of 0.8796, an MAE of 118.54 W/m², and an RMSE of 152.41 W/m². Local sensitivity analysis showed that a +10 hPa perturbation in air pressure increased the model output by 173.45 W/m², while a +10 °C perturbation in air temperature increased the output by 23.76 W/m². This study provides a reference for improving the accuracy of remotely sensed SSR and for solar resource assessment in plateau regions. Full article

Accurate air temperature observation requires minimizing solar radiation-induced deviations, which are strongly influenced by radiation shield performance. However, conventional shields often produce significant errors under strong solar radiation or weak ventilation. In this study, an air temperature observation system integrating a radiation shield and a backpropagation (BP) neural network-based correction method is proposed. Computational fluid dynamics (CFD) simulations were conducted to quantify radiation-induced temperature deviations under representative meteorological conditions, and the simulated dataset was used to train and test the neural network model. Initial field comparison experiments were performed using a 076B forced-ventilation system as a reference, where measured differences were treated as experimental deviations and model outputs as predicted deviations. The results show that, before correction, the proposed system exhibited a maximum deviation of 1.05 °C and a mean deviation of 0.26 °C, while the root mean square error and mean absolute error between experimental and predicted deviations were 0.30 °C and 0.23 °C, respectively. The correction significantly reduced temperature deviations, demonstrating the effectiveness of the proposed system in improving measurement accuracy. Full article

Poor indoor air quality, inadequate ventilation, and unnoticed local disturbances can reduce occupant well-being and compromise practical safety in smart-home and small-building environments. Although low-cost Internet-of-Things (IoT) sensing technologies are widely available, many monitoring systems remain focused on single-modality sensing and do not jointly evaluate environmental conditions, vibration activity, communication reliability, and gateway-side interpretation within one framework. This study presents the design, implementation, and proof-of-concept evaluation of a low-cost, privacy-conscious, non-imaging IoT-based indoor environment and safety-awareness monitoring framework built with ESP32/Arduino sensor nodes and a Raspberry Pi gateway. The system integrates carbon dioxide, temperature, humidity, gas-resistance/VOC-trend indication, and vibration sensing with MQTT-based communication and edge-side analytics. Controlled subsystem experiments showed that CO₂ concentration differentiated ventilation conditions, increasing from 395.47 ppm in the valid empty/open-door baseline to 1083.16 ppm in the closed occupied condition. Vibration states were distinguished using root-mean-square acceleration features across calm, surface-disturbance, footstep, play, and jump conditions. MQTT evaluation using 1000-message batches showed no observed message loss or duplicates across the tested QoS/network combinations, although latency and throughput varied by network configuration and QoS level. QoS 1 provided a practical balance between low latency and protocol-level delivery assurance in the tested local/Wi-Fi setting. A final integrated validation run further demonstrated synchronized acquisition from indoor environmental, vibration, and outdoor CO₂ reference publishers through the same Raspberry Pi gateway, with zero missing or duplicate sequence flags across the three streams. Overall, the findings indicate that lightweight open-source IoT hardware can support a reproducible building-level sensing and edge-analytics prototype for indoor environment and safety-awareness monitoring. Broader deployment in standard-sized rooms, multi-room buildings, and smart-city infrastructure remains future work. Full article

This study investigates the simultaneous elimination of ethyl acetate (EA), a representative volatile organic compound (VOC), and Escherichia coli aerosols from indoor air using a continuous-flow dielectric barrier discharge (DBD) plasma reactor coupled with a photocatalytic luminous textile system (Cu/TiO₂-coated fibers). The effects of applied voltage, relative humidity, and air-flow rate on pollutant removal and disinfection performance were systematically evaluated. Optimal DBD operation at 18 kV, 1 m³ h⁻¹ airflow, and 70% relative humidity achieved single-process removal efficiencies of 77% for EA and 2 log reduction (CFU mL⁻¹) for E. coli. When photocatalysis was coupled with DBD plasma, a significant combined effect was observed, increasing EA degradation to 87% and bacterial inactivation to 3.8 log (CFU mL⁻¹). The coupling enhanced active-species generation, improved CO₂ selectivity (up to 53%), and reduced residual ozone concentration. Humidity positively affected microbial inactivation due to °OH radical formation but slightly decreased VOC degradation by limiting ozone regeneration. Results demonstrate the efficiency and scalability of the DBD–photocatalysis hybrid system for multi-pollutant indoor air purification, offering rapid, low-temperature treatment suitable for industrial-scale applications. Full article

Mountain ecosystems are sensitive response units and critical ecological barriers to global climate change. Located in the mid-latitude climate transition zone, these ecosystems feature high ecological sensitivity and complex driving mechanisms, creating an urgent need to conduct long-sequence, high-precision dynamic assessments in order to support ecological conservation and climate adaptation decision-making. However, three key research gaps remain in the field: first, traditional assessments are dominated by static observation, lacking the capacity for long-sequence dynamic analysis and future projection; second, the coupled interaction mechanism among multiple ecological factors remains unclear, with insufficient quantitative and physical mechanism characterization; third, existing ecological zoning has not been validated for robustness, rendering it incapable of addressing climate disturbances and extreme scenarios. In order to study the regional atmospheric ecosystem, this study takes Shangluo in the eastern Qinling Mountains as the study area and constructs an integrated assessment framework integrating multi-dimensional diagnosis, simulation and projection, dynamic zoning and robustness validation based on long-sequence multi-factor data covering the years 1965–2024. The study aims to reveal the long-sequence evolution patterns and four-dimensional coupling mechanism of the Qinling Mountains atmospheric ecosystem, developing a reproducible and transferable dynamic assessment model. The results show that the study area exhibits the characteristic of elevation-dependent warming, and the correlation coefficients between elevation and air temperature, and between vegetation coverage and air quality reach −0.89 and −0.76, respectively.; ecological quality presents a spatial pattern of being high in the southwest and low in the northeast, with a coefficient of variation across the whole study area lower than 0.03. The results of 1000 Monte Carlo random disturbance validation runs show that even under intensified climate stress, the zoning pattern still maintains extremely strong disturbance resistance. This study reveals the steady-state multi-factor interaction mechanism in mountainous regions, addressing the defects of traditional static assessments that ignore ecosystem evolution and lag effects. The dynamic projection model constructed in this study can be transferred to similar mid-latitude mountainous regions worldwide, providing theoretical and technical support for regional ecological governance. Full article

This study presents a dataset of meteorological and soil-hydrological instrumental observations collected at three agrometeorological stations in the East Kazakhstan Region during the growing seasons of 2022–2025. The dataset includes time series from automatic weather stations: WS “OCES-1” (Solnechnoe village) provides hourly data over four years (2022–2025; 14,614 records; 65 variables), while WS “OCES-2” (Lugovoe village; 203,279 records) and WS “Altyn Kazan” (Sulusary village; 207,115 records) provide minute-resolution data for 2025 (49 variables each). Measured parameters at 200 cm height include air temperature and humidity, atmospheric pressure, precipitation, wind speed and direction; soil measurements down to 100 cm depth include temperature and moisture. Also, field-based express measurements of volumetric soil moisture within a 1 m profile (every 10 cm) were collected during three campaigns (May–August 2025), resulting in a total of 253 measurements. The stations are located across steppe and forest-steppe landscapes of the transboundary Altai–Sayan mountain region on active agricultural lands under diverse soil–climatic conditions. Climate types correspond to Dfb and Dfa per the Köppen–Geiger classification. Soils are classified under WRB as Chernozems and Calcic Chernozems. The dataset is published in CSV format on Zenodo under a CC-BY 4.0 license. Full article

High-resolution estimation of global near-surface air temperature (Ta) is essential for investigating microclimates, ecosystem processes, and agricultural suitability. However, sparse in situ observations do not capture local heterogeneity, whereas existing datasets lack fine-scale detail because of their coarse spatial resolution. To address this limitation, we developed an ensemble machine-learning framework using Landsat 8/9 data. Predictions from LightGBM, XGBoost, and CatBoost were combined through Bayesian model averaging (BMA), which assigns probabilistic weights to individual models to improve robustness. The models were trained using a globally distributed spatiotemporal matchup dataset that paired HadISD in situ Ta observations with MODIS/VIIRS products to support subsequent Landsat-based application. Key inputs included land surface temperature (LST), vegetation indices, elevation, solar zenith angle, and spatiotemporal features. The BMA ensemble achieved strong validation performance, with an RMSE of ~3 K, near-zero bias, and an R² of 0.92. Feature-importance analysis identified LST as the dominant predictor, underscoring the primary role of surface thermal state in estimating Ta. The proposed method can generate robust global Ta fields at 90 m resolution, revealing fine-scale thermal patterns that have previously been difficult to resolve at the global scale. Unlike many regional models calibrated for single study area or dependent on dynamic external auxiliary fields, our Landsat-predominant application framework supports operational mapping of clear-sky and overpass-time Ta. Such detailed instantaneous data can advance climate research, improve assessments of ecological responses and climate impacts, and support applications such as urban heat island monitoring and precision agriculture. Full article

Earth–Air Heat Exchangers (EAHEs) are passive systems that use the thermal interaction between air and soil along buried ducts to moderate supply air temperature, thereby lowering building energy consumption and improving indoor comfort conditions. This device has been employed in several countries and under diverse climatic characteristics. The integration of EAHE systems with bioclimatic design strategies contributes to improved building energy performance and more efficient use of thermal resources. This study aims to computationally investigate the thermoenergetic performance of EAHE system, for both cooling and heating purposes, installed in Social Housing (SH) across different Brazilian bioclimatic zones, and to propose strategies that improve the energy efficiency of these built environments. The study involves the validation and verification of a computational model and the thermoenergetic assessments of an SH unit, investigating different solar orientations and the installation of EAHE. These evaluations are performed via dynamic simulations conducted with the EnergyPlus software. The results show that the installation of the EAHE system coupled to the SH improves the thermoenergetic performance of the indoor environment, mainly by enhancing thermal comfort across different Brazilian bioclimatic zones (BZ). In BZ2R, the EAHE increased the annual PHFT by 4.5%, corresponding to seventeen additional days per year within the acceptable operative temperature range. The highest monthly improvement was observed in BZ1M, where the PHFT increased by 14.3% in January, equivalent to more than four additional days of thermal comfort in that month. The system proved to be more effective in zones 1M, 2R, 3B, and 4B, particularly in climates with lower annual average dry-bulb temperatures. Regarding energy performance, the EAHE showed benefits in specific months and conditions, indicating that its feasibility should be assessed through monthly thermoenergetic analyses rather than only annual indicators. This work provides validated and verified references and parameters for future projects and contributes to the state of the art in this field, as there are still few studies evaluating EAHE systems integrated into buildings using this software, despite its widespread use in building performance analysis. Full article

This study investigated the effects of construction design and seasonal climatic conditions on bedding dynamics in bedded-pack dairy systems with contrasting composting functionality. The study intentionally included systems representing both composting bedded-pack barns (CBP), characterized by active management (regular turning and ventilation), and non-composting bedded-pack barns (BPB), which lacked aeration and did not promote active composting, resulting in limited or absent composting activity. Nine farms were divided into three groups: CONV (large, full-time CBP), ADAP (adapted, full-time CBP), and PART (partially used BPB). Evaluations were conducted during both cold and hot seasons. Composting dynamics were assessed over 24 h by measuring bedding temperature and moisture at eight points. During daytime, additional measurements at twenty points allowed for spatial distribution analysis using the inverse distance weighting method. Bedding attributes—including pH, density, depth, and particle size—were also measured in eight points. A 2 × 3 factorial design (two seasons, three barn types) was applied, and data were analyzed using Tukey’s test and Pearson correlation. Microclimate conditions were monitored through air temperature and humidity. Bedding temperature was significantly higher in the hot season (36.55 °C) compared to the cold season (32.12 °C), and was highest in the ADAP group (40.01 °C), followed by CONV (37.39 °C) and PART (26.18 °C) (p < 0.05). The 24 h temperature curve indicated favorable composting conditions only in the CONV and ADAP groups. Spatial temperature distribution varied significantly across locations in most barns (p < 0.05). Moisture content was lower in the hot season (46.91% and 41.41%) than in the cold season (57.03% and 51.97%) for CONV and ADAP, respectively. Moisture and temperature were significantly correlated with key bedding characteristics (p ≤ 0.05). Overall, a greater combination of characteristics associated with more favorable composting conditions was observed in ADAP barns, particularly during the hot season, whereas PART systems showed conditions incompatible with active composting. Full article

Drought and related climate features (aridity, water balance) in Romania since 1961 are well documented, but studies spanning longer periods are limited and typically rely on modelled or sparse observational data. This study presents an analysis of drought, water balance and aridity in Romania over 123 years (1901–2023), using monthly data from 156 weather stations included in the RoCliHom dataset. Drought evolution is analyzed using the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Aridity is examined with the De Martonne Aridity Index. The non-parametric Mann–Kendall test is used for trend detection, which allows a fair comparison with previous studies on drought and aridity in Romania. Trend magnitude is calculated with Sen’s slope estimator. Our results show a clear increase in evapotranspiration as a sign of climate warming over the country since the beginning of the 20th century. Annual precipitation amount presents no major changes. Water balance has decreased in July and August at 40% and 85% of the locations, respectively. During the growing season, drought has intensified within the last seven, six and five decades, but there are no significant changes over the full period of study in this respect. We found strong negative correlations between SPEI and North Atlantic Oscillation, Northern Annular Mode and Arctic Oscillation teleconnection indices. The evolution over the 123-year period shows that the drought episodes that occurred in recent decades are not without precedent in the long-term climatic context. Full article

Accurate, high-resolution gridded air temperature forecasts are essential, particularly in mountainous areas with complex terrain. This study proposes a two-stage processing framework DOWN + BC to downscale Global Forecast System (GFS) temperature forecasts and correct their bias. The approach first employs a random forest (RF) model to geographically downscale 3-hourly 0.25° GFS forecasts to a 30 m resolution (DOWN), followed by bias correction (BC) using a first-order adaptive Kalman filter (AKF). The accuracy of the DOWN + BC-processed forecasts was evaluated against both automatic weather station (AWS) observations and high-resolution air temperature fields derived from an extreme gradient boosting model (XGB-derived). The results indicate that (1) the DOWN step effectively refines the spatial detail of temperature distribution, though it yields limited improvement in accuracy compared to the raw GFS forecasts; (2) the combined DOWN + BC method substantially enhances forecast accuracy. At AWS locations, the root mean square error (RMSE) of GFS forecasts decreased by 37.84% in January 2020 and 41.16% in July 2023. Relative to the XGB-derived temperature distribution, RMSE was reduced by 47.27% and 33.79% for the respective periods. Full article

Hydroxyapatite (HA) serves as an ideal in vitro substitute model for calcified plaques. At present, the influence mechanisms of processing parameters and operating environments on the machining morphology and thermal evolution of HA during picosecond laser processing remain unclear, and there is a lack of systematic analyses combining experiments and simulations. In this study, the effects of laser parameters and operating environments on structural parameters were systematically investigated from both experimental and simulation perspectives. The results demonstrate that within the laser energy range of 30–70 μJ, the groove depth and width are 12.1–47.8 μm and 15.6–32.1 μm in air, respectively, while they reach 15.4–48.6 μm and 22.4–47.3 μm underwater. Within the repetition frequency range of 100–140 kHz, the groove depth and width are 27.3–36.1 μm and 21.3–27.7 μm in air, respectively, compared with 34.6–45.4 μm and 33.3–53.3 μm underwater. The underwater-processed grooves exhibit larger dimensions and higher temperature-field values than those processed in air. Morphological observations further show that the groove bottoms formed in air exhibit bamboo-joint-like and granular features, whereas the underwater-processed grooves present a more uniformly distributed granular morphology. The simulation results agree well with the experimental data, with errors controlled within 12%, verifying the reliability of the established model. This study elucidates the morphological and thermal mechanisms of HA picosecond laser processing, supporting biomedical HA machining and paving the way for calcified plaque ablation and bone repair. Full article

This study examines the meteorological and gaseous pollutant drivers of PM_2.5 under mild, moderate, and severe pollution conditions in the Beijing–Tianjin–Hebei region, with the aim of supporting sustainable air quality management. Daily observations from approximately 65 monitoring stations from 1 November 2021 to 31 October 2024 were used, including PM_2.5, four gaseous pollutants (SO₂, NO₂, CO, and O₃), and five meteorological variables: temperature, pressure, relative humidity, precipitation, and wind speed. A CatBoost–SHAP framework was adopted, with CatBoost used for station-level spatial prediction of PM_2.5 and SHAP applied to interpret variable contributions. Based on predefined PM_2.5 thresholds, 425 pollution days were classified into those three pollution-level scenarios. These pollution days occurred mainly in winter and spring, with higher frequencies in Handan, Baoding, and Shijiazhuang, followed by Tianjin and Beijing. The model performed well across the three pollution-level scenarios. The severe-pollution scenario achieved the highest R², indicating a clearer spatial structure under high-PM_2.5 conditions. Although absolute RMSE and MAE increased with pollution severity, their normalized values changed little, suggesting that larger errors mainly reflected stronger spatial heterogeneity at higher PM_2.5 concentrations. SHAP results showed that CO, precipitation, wind speed, and temperature dominated the prediction structure. CO was the most stable and influential predictor, but its importance should be interpreted as an indicator of combustion-related pollution accumulation rather than direct causality. Precipitation represented event-dependent wet scavenging, wind speed reflected dispersion conditions, and temperature captured seasonal and thermal background effects. SHAP dependence analysis further indicated that CO had the clearest direct dependence, whereas wind speed and temperature were more background-dependent, and precipitation acted as an episodic nonlinear regulator. Full article

Outdoor thermal comfort models are generally developed for healthy populations and may not be directly applicable to patients with altered thermoregulatory capacity. This study examined summer outdoor thermal responses of lung cancer patients in Shenyang, China, focusing on differences by treatment modality and disease stage. Field microclimatic measurements and questionnaire surveys were conducted in four typical outdoor microenvironments: waterfront place, tree-shaded space, open square, and enclosed porch. A total of 706 lung cancer patients were surveyed and stratified by treatment modality and disease stage. Physiologically equivalent temperature (PET) was calculated using RayMan Pro based on measured air temperature, mean radiant temperature, air velocity, relative humidity, clothing insulation, and activity-based metabolic rate. Subgroup differences were observed in neutral PET and thermal comfort ranges. Chemotherapy patients had the highest neutral PET at 26.0 °C, while immunotherapy patients had the lowest at 22.6 °C. Radiotherapy, surgery, and targeted therapy groups showed neutral PET values of 23.3 °C, 23.7 °C, and 24.5 °C, respectively. Early-stage patients had a neutral PET of 23.8 °C, whereas late-stage patients showed a higher value of 25.8 °C and a narrower neutral range of 23.1–28.5 °C. The surgery group had a broad acceptable PET range of 20.5–28.6 °C, while the late-stage group had a narrower range of 24.7–26.8 °C. Preferred temperature was also higher in the chemotherapy and late-stage groups. These findings indicate heterogeneous summer outdoor thermal responses among lung cancer patients and provide empirical evidence for subgroup-sensitive thermal assessment and outdoor space design near healthcare facilities. Full article