Search Results (965)

Climate change in Antarctica has profound global implications, influencing sea level rise, atmospheric circulation, and the Earth’s energy balance. This study presents a data-driven baseline analysis of meteorological observations from a British Antarctic Survey automatic weather station (2020–2024). Temporal and seasonal analyses reveal strong insolation-driven variability in temperature, snow depth, and solar radiation, reflecting the extreme polar day–night cycle. Correlation analysis highlights solar radiation, upwelling longwave flux, and snow depth as the most reliable predictors of near-surface temperature, while humidity, pressure, and wind speed contribute minimally. A linear regression baseline and a Random Forest model are evaluated for temperature prediction, with the ensemble approach demonstrating superior accuracy. Although the short data span limits long-term trend attribution, the findings underscore the potential of lightweight, reproducible pipelines for site-specific climate monitoring. All analysis codes are openly available in github, enabling transparency and future methodological extensions to advanced, non-linear models and multi-site datasets. Full article

Evaporation Duct Height (EDH) is a crucial parameter in evaporation duct modeling, as it directly influences the strength of the waveguide trapping effect and significantly impacts the over-the-horizon detection performance of maritime radars. To address the limitations of low prediction accuracy and limited interpretability in existing deep learning models under complex marine meteorological conditions, this study proposes a surrogate model, BLA-EDH, designed to emulate the output of the Naval Postgraduate School (NPS) model for real-time EDH estimation. Experimental results demonstrate that BLA-EDH can effectively replace the traditional NPS model for real-time EDH prediction, achieving higher accuracy than Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) models. Random Forest analysis identifies relative humidity (0.2966), wind speed (0.2786), and 2-m air temperature (0.2409) as the most influential environmental variables, with importance scores exceeding those of other factors. Validation using the parabolic equation shows that BLA-EDH attains excellent fitting performance, with coefficients of determination reaching 0.9999 and 0.9997 in the vertical and horizontal dimensions, respectively. This research provides a robust foundation for modeling radio wave propagation in the Yellow Sea and Bohai Sea regions and offers valuable insights for the development of marine communication and radar detection systems. Full article

This study evaluated the seasonal variability of soil–atmosphere greenhouse gas (GHG) fluxes—carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)—across a land-use gradient in the Andean–Amazon transition zone of Colombia. The gradient included five land-use types incorporating at least one innovative climate-smart practice—improved pasture (IP), cacao agroforestry system (CaAS), copoazu agroforestry system (CoAS), secondary forest with agroforestry enrichment (SFAE), and moriche palm swamp ecosystem (MPSE)—alongside the dominant regional land uses, old-growth forest (OF) and degraded pasture (DP). Soil GHG fluxes varied markedly among land-use types and between seasons. CO₂ fluxes were consistently higher during the dry season, whereas CH₄ and N₂O fluxes peaked in the rainy season. Agroecological and restoration systems exhibited substantially lower CO₂ emissions (7.34–9.74 Mg CO₂-C ha⁻¹ yr⁻¹) compared with DP (18.85 Mg CO₂-C ha⁻¹ yr⁻¹) during the rainy season, and lower N₂O fluxes (0.21–1.04 Mg CO₂-C ha⁻¹ yr⁻¹) during the dry season. In contrast, the MPSE presented high CH₄ emissions in the rainy season (300.45 kg CH₄-C ha⁻¹ yr⁻¹). Across all land uses, CO₂ was the dominant contributor to the total GWP (>95% of emissions). The highest global warming potential (GWP) occurred in DP, whereas CaAS, CoAS and MPSE exhibited the lowest values. Soil temperature, pH, exchangeable acidity, texture, and bulk density play a decisive role in regulating GHG fluxes, whereas climatic factors, such as air temperature and relative humidity, influence fluxes indirectly by modulating soil conditions. These findings underscore the role of diversified agroforestry and restoration systems in mitigating GHG emissions and the need to integrate soil and climate drivers into regional climate models. Full article

Hakka traditional vernacular dwellings embody regionally specific climatic adaptation strategies. This study takes the Meizhou Guangludi enclosed house as a case study to evaluate its climate adaptability with longevity and passive survivability factors of the Hakka three-hall enclosed house under subtropical climatic conditions. A mixed research method is employed, integrating visualized parametric modeling analysis and on-site measurement comparisons to quantify wind, temperature, solar radiation/illuminance, and humidity, along with human comfort zone limits and building environment. The results reveal that nature erosion in the Guangludi enclosed house is the most pronounced during winter and spring, particularly on exterior walls below 2.8 m. Key issues include bulging, spalling, molding, and fractured purlins caused by wind-driven rain, exacerbated by low wind speeds and limited solar exposure, especially at test spots like the E8–E10 and N1–N16 southeast and southern walls below 1.5 m. Fungal growth and plant intrusion are severe where surrounding trees and fengshui forests restrict wind flow and lighting. In terms of passive survivability, the Guangludi enclosed house has strong thermal insulation and buffering, aided by the Huatai mound; however, humidity and day illuminance deficiencies persist in the interstitial spaces between lateral rooms and the central hall. To address these issues, this study proposes strategies such as adding ventilation shafts and flexible partitions, optimizing patio dimensions and window-to-wall ratios, retaining the spatial layout and Fengshui pond to enhance wind airflow, and reinforcing the identified easily eroded spots with waterproofing, antimicrobial coatings, and extended eaves. Through parametric simulation and empirical validation, this study presents a climate-responsive retrofit framework that supports the sustainability and conservation of the subtropical Hakka enclosed house. Full article

The Bełchatów Lignite Mine (BLM) in central Poland, one of Europe’s largest Neogene lignite deposits, provides key insights into palaeofloral evolution. Located in the Kleszczów Graben, the BLM consists of four distinct lithological units: subcoal, coal, clayey-coal, and clayey-sandy units. The study presents a palynological investigation of 31 samples from all units, identifying 78 sporomorph taxa, including 10 plant spores, 15 gymnosperm pollen, and 53 angiosperm pollen taxa. Pollen grains from angiosperms and gymnosperms were consistently observed in all samples, while plant spores were scarce. The analysis reveals three distinct palynological zones, reflecting shifts in vegetation. The first zone is characterized by swamp, riparian, and mixed mesophilous forests, dominated by Taxodium/Glyptostrobus, Ulmus, Carya, Engelhardia, Pterocarya, and Quercus. In the second zone, slightly cooler climatic conditions led to the decline of Taxodium/Glyptostrobus and Alnus, indicating a deterioration of swamp forests. The third zone marks a subsequent recovery of these forests. Palaeoclimatic interpretations indicate three phases: a subtropical-humid climate during the Early Miocene, fluctuating humidity in the late Early Miocene, and a transition to a warm-temperate and humid climate in the Late Miocene. Full article

This study presents an integrated assessment of four decades (1985–2023) of environmental and climate alterations in the principal metropolitan conurbation of the eastern Brazilian Amazon, encompassing Belém and its adjacent municipalities. By combining high-resolution land use/land cover (LULC) dynamics with in situ meteorological data, including understudied elements, such as relative humidity (RH) and wind speed, and satellite-derived precipitation estimates (CHIRPS v3), we advance the scientific understanding of regional climate trends. Our results document significant climate shifts, including pronounced dry-season warming (+1.5 °C), atmospheric drying (−4% in RH), attenuated wind patterns (−0.4 m s⁻¹), and altered precipitation regimes, which exhibit strong spatiotemporal coupling with extensive forest loss (−20%) and rapid urban expansion (+84%) between 1985 and 2023. Multivariate analyses reveal that these land–climate interactions are strongest during the dry regime, underscoring the role of surface–atmosphere feedbacks in amplifying regional changes. Comparative analysis of past (1980–1999) and present (2005–2024) decades demonstrates a marked intensification in the frequency and magnitude of extreme seasonal climate events. These findings elucidate a critical feedback mechanism that exacerbates climate risks in tropical urban areas. Consequently, we argue that mitigation public policies must prioritize the strict conservation of peri-urban forest fragments (vital for moisture recycling and local climate regulation) and the strategic implementation of green infrastructure aligned with prevailing wind patterns to enhance thermal comfort and resilience to hydrological extremes. Full article

Shaded resting zones in rotational grazing systems are prone to thermal stress due to limited ventilation and the congregation of animals during peak heat periods. Addressing these challenges requires sensing solutions that are not only accurate but also capable of adapting to dynamic environmental conditions and energy constraints. In this context, we present the development and simulation-based validation of a self-configurable IoT protocol for adaptive environmental monitoring. The approach integrates embedded machine learning, specifically a Random Forest classifier, to detect critical conditions using synthetic data of temperature, humidity, and CO₂. The model achieved an accuracy of 98%, with a precision of 98%, recall of 85%, and F1-score of 91% in identifying critical states. These results demonstrate the feasibility of embedding adaptive intelligence into IoT-based monitoring solutions. The protocol is conceived as a foundation for integration into physical devices and subsequent evaluation in farm environments such as rotational grazing systems. Full article

Soil organic carbon (SOC) constitutes the largest terrestrial carbon pool and plays a crucial role in climate regulation, soil fertility, and ecosystem functioning. Understanding its spatiotemporal dynamics is particularly important in semi-arid regions, where fragile environments and extensive ecological restoration may alter carbon cycling. The Loess Plateau, the world’s largest loess accumulation area with a history of severe erosion and large-scale vegetation restoration, provides a natural laboratory for examining how environmental gradients influence SOC storage over time. This study used a random forest model with multi-source environmental data to quantify soil organic carbon density (SOCD) dynamics in the 0–100 cm soil layer of the Loess Plateau from 2005 to 2020. SOCD showed strong spatial heterogeneity, decreasing from the humid southeast to the arid northwest. Over the 15-year period, total SOC storage increased from 4.84 to 5.23 Pg C (a 7.9% rise), while the annual sequestration rate declined from 0.046 to 0.020 kg·m⁻²·yr⁻¹, indicating that the regional carbon sink may be approaching saturation after two decades of restoration. Among soil types, Cambisols were the largest carbon pool, accounting for over 44% of total SOC storage. Vegetation productivity emerged as the dominant driver of SOC variability, with clay content as a secondary factor. These results indicate that although ecological restoration has substantially enhanced SOC storage, its marginal benefits are diminishing. Understanding the spatial and temporal patterns of SOC and their environmental drivers provides essential insights for evaluating long-term carbon sequestration potential and informing future land management strategies. Broader generalization requires multi-regional comparisons, long-term monitoring, and deeper soil investigations to capture ecosystem-scale carbon dynamics fully. Full article

Indoor environmental factors such as CO₂ concentration, temperature, and humidity can significantly influence individuals’ emotional states and productivity. This study continuously collected environmental data using wireless sensors and emotional data from wearable devices in an office-like remote-work setting. Machine learning models, including Random Forest and Gradient Boosting Decision Tree, were developed and interpreted using SHAP (Shapley Additive Explanations). The proposed models achieved estimation accuracies above 85%. SHAP analysis revealed that CO₂ concentration, temperature, and humidity were influential factors in predicting pleasant or unpleasant states. These findings demonstrate the feasibility of real-time, data-driven emotion estimation and provide insights into the design of indoor environments that foster comfort and mental well-being. Full article

Air quality significantly affects human health, productivity, and overall well-being. This study applies machine learning techniques to analyse and predict air quality in Hamilton, New Zealand, focusing on particulate matter (PM2.5 and PM10) and environmental factors such as temperature, humidity, wind speed, and wind direction. Data were collected from two monitoring sites (Claudelands and Rotokauri) to explore relationships between variables and evaluate the performance of different predictive models. First, the unsupervised k-means clustering algorithm was used to categorise air quality levels based on data from one or both locations. These cluster labels were then used as target variables in supervised learning models, including random forests, decision trees, support vector machines, and k-nearest neighbours. Model performance was assessed by comparing prediction accuracy for air quality at either Claudelands or Rotokauri. Results show that the random forest (93.6%) and decision tree (91.8%) models outperformed k-nearest neighbours (KNN, 83%) and support vector machine (SVM, 61%) in predicting air quality clusters derived from k-means analysis. The three clusters (very good, good, and moderate) reflected seasonal and urban–semi-urban gradients, while cross-location validation confirmed that models trained at Claudelands generalised effectively to Rotokauri, demonstrating scalability for regional air quality forecasting. These findings highlight the potential of combining clustering with supervised learning to improve air quality predictions. Such methods could support environmental monitoring and inform strategies for mitigating pollution-related health risks in New Zealand cities and beyond. Full article

Accurate estimation of daily reference evapotranspiration (ET_o) is crucial for sustainable water resource management and irrigation scheduling, especially in water-scarce regions like Arizona. The standardized Penman–Monteith (PM) method is costly and requires specialized instruments and expertise, making it generally impractical for commercial growers. This study developed 35 ET_o models to predict daily ET_o across Coolidge, Maricopa, and Queen Creek in Pinal County, Arizona. Seven input combinations of daily meteorological variables were used for training and testing five machine learning (ML) models: Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Support Vector Machine (SVM). Four statistical indicators, coefficient of determination (R²), the normalized root-mean-squared error (RMSE_n), mean absolute error (MAE), and simulation error (S_e), were used to evaluate the ML models’ performance in comparison with the FAO-56 PM standardized method. The SHapley Additive exPlanations (SHAP) method was used to interpret each meteorological variable’s contribution to the model predictions. Overall, the 35 ET_o-developed models showed an excellent to fair performance in predicting daily ET_o over the three weather stations. Employing ANN10, RF10, XGBoost10, CatBoost10, and SVM10, incorporating all ten meteorological variables, yielded the highest accuracies during training and testing periods (0.994 ≤ R² ≤ 1.0, 0.729 ≤ RMSE_n ≤ 3.662, 0.030 ≤ MAE ≤ 0.181 mm·day⁻¹, and 0.833 ≤ S_e ≤ 2.295). Excluding meteorological variables caused a gradual decline in ET-developed models’ performance across the stations. However, 3-variable models using only maximum, minimum, and average temperatures (T_max, T_min, and T_ave) predicted ET_o well across the three stations during testing (17.655 ≤ RMSE_n ≤ 13.469 and S_e ≤ 15.45%). Results highlighted that T_max, solar radiation (R_s), and wind speed at 2 m height (U₂) are the most influential factors affecting ET_o at the central Arizona sites, followed by extraterrestrial solar radiation (R_a) and T_ave. In contrast, humidity-related variables (RH_min, RH_max, and RH_ave), along with T_min and precipitation (P_r), had minimal impact on the model’s predictions. The results are informative for assisting growers and policymakers in developing effective water management strategies, especially for arid regions like central Arizona. Full article

The subtropical transitional zone of China exhibits highly complex climatic conditions and diverse forest ecosystems, making it a critical region for understanding vegetation–water interactions. This study employed the Thermal Dissipation Probe (TDP) method to monitor sap flow in three typical forest types—evergreen broad-leaved forest, bamboo forest (Dendrocalamus latiflorus), and Chinese fir (Cunninghamia lanceolata)—in a subtropical transitional watershed in southern China. The aims were to quantify seasonal and annual variations in sap flow, to examine the effects of environmental drivers, and to analyze the hysteretic responses between sap flow and the drivers. The main findings were as follows: (1) bamboo forests exhibited significantly higher sap flow density than evergreen broad-leaved and fir forests at both annual and seasonal scales, though the overall transpiration of bamboo forests was lower than the others due to its limited sapwood area; (2) sap flow was positively correlated with potential evapotranspiration, solar radiation (Ra), vapor pressure deficit (VPD), air temperature, and soil temperature, while it was negatively correlated with relative humidity, atmospheric pressure, soil moisture, and precipitation; (3) Ra and VPD were identified as the dominant drivers of sap flow variations, with nonlinear increases that leveled off once thresholds were reached; (4) clear hysteresis patterns were observed, with sap flow peaks consistently lagging behind Ra but occurring earlier than VPD. These results advance our understanding of forest water-use strategies in the subtropical transitional zone and provide a scientific basis for improving water resource management and ecosystem sustainability in this region. Full article

Wildfire activity is intensifying globally as climate change amplifies heat waves, droughts and wind extremes, threatening biodiversity. South Korea (63% forested) has experienced a sharp rise in large fires. We analysed 905 wildfires ≥ 5 ha from 1980–2024, linking burned area to maximum wind speed, relative humidity, temperature and forest structure (conifer, broadleaf and mature–stand ratios, forest cover). Pearson correlations, HC3-corrected regression, a 1000-tree Random Forest and five-fold validated XGBoost interpreted with SHAP captured linear and nonlinear effects; WUI influences were examined qualitatively. Each 1 m s⁻¹ increase in peak wind expanded burned area by ~8.5 ha, whereas a 1% rise in humidity reduced area by ~3 ha (p < 0.01). Broadleaf prevalence restrained spread, while high conifer and mature–stand proportions enlarged it. Machine learning raised explanatory power from R² = 0.62 to 0.66 and showed that very dry air, strong winds and conifer cover above half the landscape coincided with the largest events. Burned area during 2020–2024 reached 29,905 ha—sevenfold that of 2015–2019. These results imply that extreme fire weather, flammable pine fuels and expanding WUI settlements jointly elevate risk; implementing real-time meteorological thresholds, targeted fuel treatments and stricter WUI zoning can help mitigate this risk. Full article

Laboratory buildings represent some of the highest energy-consuming infrastructure due to stringent environmental requirements and the continuous operation of specialized equipment. Ensuring both energy efficiency and indoor air quality (IAQ) in such spaces remains a central challenge for sustainable building design and operation. Recent advances in Internet of Things (IoT) systems allow for real-time monitoring of multivariate environmental parameters, including CO₂, total volatile organic compounds (TVOC), PM_2.5, temperature, humidity, and noise. However, these datasets are often noisy or incomplete, complicating conventional monitoring approaches. Supervised anomaly detection methods are ill-suited to such contexts due to the lack of labeled data. In contrast, unsupervised machine learning (ML) techniques can autonomously detect patterns and deviations without annotations, offering a scalable alternative. The challenge of identifying anomalous environmental conditions and latent operational states in laboratory environments is addressed through the application of unsupervised models to 1808 hourly observations collected over four months. Anomaly detection was conducted using Isolation Forest (300 trees, contamination = 0.05) and One-Class Support Vector Machine (One-Class SVM) (RBF kernel, ν = 0.05, γ auto-scaled). Standardized six-dimensional feature vectors captured key environmental and energy-related variables. K-means clustering (k = 3) revealed three persistent operational states: Empty/Cool (42.6%), Experiment (37.6%), and Crowded (19.8%). Detected anomalies included CO₂ surges above 1800 ppm, TVOC concentrations exceeding 4000 ppb, and compound deviations in noise and temperature. The models demonstrated sensitivity to both abrupt and structural anomalies. Latent states were shown to correspond with occupancy patterns, experimental activities, and inactive system operation, offering interpretable environmental profiles. The methodology supports integration into adaptive heating, ventilation, and air conditioning (HVAC) frameworks, enabling real-time, label-free environmental management. Findings contribute to intelligent infrastructure development, particularly in resource-constrained laboratories, and advance progress toward sustainability targets in energy, health, and automation. Full article

Heat stress has a major impact on dairy cow health and productivity, especially during early lactation. Conventional heat stress monitoring methods frequently rely on single indicators, such as the temperature–humidity index (THI), which may miss subtle physiological and metabolic responses. This study presents a novel threshold-based classification framework that integrates biologically meaningful combinations of environmental, behavioural, and physiological variables to detect early-stage heat stress responses in dairy cows. Six composite heat stress conditions (C1–C6) were developed using real-time THI, milk temperature, reticulorumen pH, rumination time, milk lactose, and milk fat-to-protein ratio. The study applied and assessed five supervised machine learning models (Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Random Forest (RF0, Neural Network (NN), and an Ensemble approach) trained on daily datasets gathered from early-lactation dairy cows fitted with intraruminal boluses and monitored through milking parlour sensor systems. The dataset comprised approximately 36,000 matched records from 200 cows monitored over 60 days. The highest classification performance was observed for RF and NN models, particularly under C1 (THI > 73 and milk temperature > 38.6 °C) and C6 (THI > 74 and milk temperature > 38.7 °C), with AUC values exceeding 0.90. SHAP analysis revealed that milk temperature, THI, rumination time, and milk lactose were the most informative features across conditions. This integrative approach enhances precision livestock monitoring by enabling individualised heat stress risk classification well before clinical or production-level consequences emerge. Full article