Search Results (27)

Based on previous studies and the ecological characteristics of Portunus trituberculatus, we hypothesized that climate change could substantially reshape its suitable habitat in Zhoushan fishing ground. Under present-day climate conditions (2010–2020), P. trituberculatus exhibits a distinct seasonal distribution pattern in this region. However, its potential spatial response to future climate change, and whether suitable habitat will remain available, remains poorly understood. To address this gap, we combined species occurrence records with environmental variables from the Bio-ORACLE v3.0 database, including benthic temperature, benthic salinity, benthic current velocity, primary productivity, bathymetry, topographic slope, and topographic aspect, to develop a maximum entropy (MaxEnt) model and predict the potential distribution of suitable habitat for P. trituberculatus under present-day conditions and future SSP1-2.6 and SSP2-4.5 scenarios for 2030–2040, 2040–2050, and 2090–2100. Model performance was high across all seasons, with area under the curve values exceeding 0.80. Primary productivity and benthic temperature were the dominant environmental predictors, highlighting the joint influence of trophic conditions and thermal constraints on habitat suitability. Future projections revealed pronounced seasonal reorganization of suitable habitat rather than a uniform range shift. Spring suitable habitat expanded consistently under both scenarios, with the magnitude of expansion increasing toward the end of the century and reaching 46.9% by 2100 under SSP2-4.5, likely because warming relaxed low-temperature limitation during the early seasonal transition. In contrast, suitable habitat in autumn and winter generally contracted. Autumn losses were moderate but persistent, ranging from 5.4% to 16.4%, whereas the strongest declines occurred in winter, particularly under SSP2-4.5, where habitat reductions exceeded 30% after mid-century. These contractions were likely associated with cumulative thermal stress and related environmental changes under continued warming. Summer responses were scenario-dependent, showing weak gains or net declines under SSP1-2.6 but substantial expansion under SSP2-4.5 after mid-century, reaching up to 23.6% by 2050, suggesting that habitat suitability in this season is shaped by interactions among thermal conditions, trophic support, and habitat characteristics. Overall, these findings reveal strong seasonal asymmetry in habitat responses to climate change and provide a scientific basis for seasonally adaptive management of P. trituberculatus resources in Zhoushan fishing ground. Full article

Recent work demonstrated that Cicindelidia hemorrhagica (LeConte) inhabiting geothermal springs in Yellowstone National Park (YNP) possess morphological traits that reduce internal heat load when exposed to bottom-up thermal stress. To investigate whether this pattern extends to other tiger beetle species occupying diverse environments, we quantified the internal abdominal temperatures of six species differing in habitat preference and putative thermal adaptation. Using a water-bath system that simulated surface heating, we compared the temperature differential (ΔT) between beetle-loaded and bare thermocouples across multiple temperatures. Linear mixed-effects models were used to evaluate the influence of location and species on internal temperature. Across all experimental temperatures, C. hemorrhagica exhibited the greatest ΔT values, indicating the lowest internal temperatures relative to the thermal environment, regardless of whether individuals originated from YNP or non-thermal Idaho habitats. In contrast, the warm-resilient Cicindela repanda (Dejean) and non-warm-adapted C. longilabris (Say) showed the smallest ΔT values and therefore the highest internal temperatures. Ventral abdominal coloration—ranging from bright red (C. sedecimpunctata (Klug)) to dark blue-green (C. oregona (Dejean))—did not correlate with internal temperatures, suggesting that it is a poor predictor of heat absorbance or reflectance under bottom-up heat exposure. These results indicate that C. hemorrhagica is uniquely effective at limiting internal heat gain from surface heating, and that it may possess a preadaptive morphological mechanism facilitating thermal resistance in geothermal habitats. Full article

Heat stress is a major challenge for broiler production worldwide and is expected to intensify with more frequent heatwaves. This study focuses on mechanically ventilated broiler houses in South Korea, where heatwaves have become increasingly frequent. Three regression meta-models were developed to predict the indoor temperature–humidity index (THI) directly from weather forecast data, using simulated results from a validated building energy simulation (BES) model. A TRNSYS-based BES model was validated against field measurements from four rearing cycles in a commercial broiler house (RMSE 1.31–2.16; MAPE < 2.00%). Using 3072 simulation cases that combined multiple sites, thermal-transmittance levels, cooling conditions, building sizes, and broiler body weights, three regression meta-model approaches were evaluated: a condition-specific regression meta-model for each condition set, a unified regression meta-model with categorical predictors, and a single variable meta-model using only external THI as a predictor. All three showed strong predictive performance, and the unified regression meta-model achieved R² = 0.978, RMSE = 0.817, and MAPE = 0.829, providing the best balance between accuracy and simplicity. This unified model offers a practical tool to link weather forecasts with broiler-house design and environmental-control decisions for heat-stress risk management. Full article

As global temperatures rise, cities struggle with heat stress and the limitations of traditional cooling strategies. This study introduces “seasonal urban exodus”—temporarily relocating urban residents to cooler areas during summer—as a behavioral climate adaptation strategy driven by the need for thermal comfort. To assess social feasibility, a survey was conducted among 163 urban residents in Romania. The dataset was analyzed using linear regression and machine learning algorithms (Random Forest and K-Means clustering). The results show that 77.9% of respondents would relocate for 1–2 months if they had adequate destination infrastructure, while a 2 °C temperature increase would cause 46% to migrate temporarily. Predictive modeling identified barriers related to heat (p = 0.009) and transportation (p = 0.016) as the most significant predictors of relocation intention. These results suggest that seasonal mobility is a viable social response to urban heat islands. However, while this adaptation strategy improves individual thermal comfort, further interdisciplinary research—including life-cycle assessments, travel emission calculations, and the evaluation of rural energy systems—is absolutely necessary to determine the net carbon balance and environmental viability of these relocation patterns. Full article

Rapid urbanization, environmental degradation and climate variability are intensifying the exposure of urban populations to multiple, interacting hazards in megacities. In India’s capital, Delhi, extreme heat, worsening air quality and flood-related stress overlap in impacted areas, exacerbated by high population density in low-lying zones and extensive built-up cover. This study develops an integrated spatial framework for assessing relative multi-hazard risk potential in Delhi by combining remote sensing, climate reanalysis, land use and demographic datasets into a predictive modeling system to support urban resilience planning. A comprehensive suite of twenty-two predictors representing thermal stress, air quality, surface indices, topography, hydrology, land use land cover (LULC), and demographic data was derived from diverse Earth observation sources. A cloud-native workflow leveraging Google Earth Engine (GEE) and Python 3 harmonized these predictors to train a Light Gradient Boosting Machine (LightGBM) model with five-fold spatial cross-validation. Quantile regression was used to estimate lower (P10) and upper (P90) predictive bounds, which are interpreted here as empirical predictive intervals around the modeled risk surface rather than as a strict separation of different uncertainty types, while SHapley Additive exPlanations (SHAP) decomposed the non-linear contributions of individual features. The model achieved predictive accuracy (R² = 0.98, MAE = 0.01), with residuals centered near zero and consistent performance across spatial folds, demonstrating strong generalizability. Road density (63.4%) and population density (25.9%) emerged as the primary predictors of the modeled risk surface, followed by building density and NO₂ concentration. Conversely, vegetation cover (NDVI) functioned as a critical mitigating buffer. Spatial risk maps identified persistent high-risk clusters in eastern and northeastern Delhi, coinciding with dense transport networks and industrial zones. The integrated P90 mapping framework provides spatially explicit and uncertainty-aware information on relative multi-hazard risk potential to guide targeted interventions, such as transport corridor mitigation and urban greening in Delhi and other rapidly urbanizing cities. Full article

This study investigates whether long-term global warming is associated with fertility decline across 195 countries from 1960 to 2023, and whether this relationship varies by economic development and adaptive capacity. We analyze Total Fertility Rate (TFR) data from the World Bank alongside temperature anomaly measures from NOAA and NASA using Pearson correlations and ordinary least squares (OLS) regression models. Regional analyses include Africa, Asia, Europe, the Middle East, and the Arctic, with GDP per capita serving as a proxy for economic development and adaptive capacity. Globally, temperature anomalies and fertility exhibit a strong negative correlation ($r\approx -0.90$, $p<0.001$). However, substantial regional heterogeneity emerges after controlling for GDP. In Africa ($r=-0.89$) and the Middle East, temperature anomalies remain statistically significant predictors of fertility decline even after GDP adjustment ($\beta =-0.99$, $p<0.001$; $\beta =-1.27$, $p<0.001$, respectively). In contrast, temperature effects become statistically insignificant in South Asia, East Asia, Europe, and the Arctic once GDP is controlled, indicating that fertility decline in these regions is driven primarily by socioeconomic modernization rather than climatic stress. These findings suggest that global warming functions as a conditional demographic stressor whose impact depends critically on adaptive capacity. In regions with limited infrastructure, including constrained access to air conditioning, healthcare, and occupational heat protection, rising temperatures remain significant predictors of fertility decline, potentially mediated through heat-sensitive biological mechanisms such as impaired spermatogenesis. By contrast, in higher-income regions, high adaptive capacity appears to buffer reproductive systems from thermal stress, allowing socioeconomic factors to dominate fertility dynamics. Full article

To address the challenge of obtaining high-spatiotemporal-resolution and high-precision temperature grids for agricultural meteorological monitoring, this research focuses on rice heat stress monitoring with the China Meteorological Administration Land Data Assimilation System (CLDAS) and develops a temperature correction model that synergizes physical mechanisms with a data-driven strategy by introducing a GeoAI framework. Ensemble learning methods (XGBoost, LightGBM, and Random Forest) were utilized to process a comprehensive set of predictors, integrating dynamic surface features derived from FY-4 satellite’s high-frequency observation data. The data comprised surface thermal regime metrics, specifically the daily maximum land surface temperature (LST_max) and its diurnal range (LST_{max_min}), along with vegetation indices including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Further, topographic attributes derived from a digital elevation model (DEM) were incorporated, such as slope, aspect, the terrain ruggedness index (TRI), and the topographic position index (TPI). The approach uniquely capitalized on the temporal resolution of geostationary data to capture the diurnal land surface dynamics crucial for bias correction. The proposed models not only enhanced temperature data quality but also achieved impressive accuracy. Across China, the root mean square error (RMSE) was reduced to 1.04 °C, mean absolute error (MAE) to 0.53 °C, and accuracy (ACC) to 0.97. Additionally, the most notable improvement was that the RMSE decreased by nearly 50% (from 2.17 °C to 1.11 °C), MAE dropped from 1.48 °C to 0.80 °C, and ACC increased from 0.72 to 0.96 in the southwestern region of China. The corrected rice heat stress data (2020–2023) indicated that significant negative correlations exist between yield loss and various heat stress metrics in the severely affected middle and lower Yangtze River region. The research confirms that embedding geostationary meteorological satellites within a GeoAI framework can effectively enhance the precision of agricultural weather monitoring and related impact assessments. Full article

The present study aimed to apply machine learning algorithms to estimate respiratory rate (RR, breaths min⁻¹) and rectal temperature (RT, °C) as indicators of thermal stress in Dorper breeding rams, based on environmental and thermal variables obtained through infrared thermography. The algorithms Random Forest (RF) and Support Vector Regression (SVR) with radial kernel were employed, using ocular globe temperature (OGT), air temperature (AT), relative humidity (RH), and coat surface temperature (CST) as predictor variables, and rectal temperature (RT) and respiratory rate (RR) as response variables. Data were collected on a property located in Garanhuns, Pernambuco State, Brazil, under two environmental conditions (with and without climate control), totaling 20 monitored animals and 120 paired observations. Model performance was evaluated using the coefficient of determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), complemented by cross-validation (k-fold = 10), and model interpretability was assessed using SHapley Additive exPlanations (SHAP) to quantify the contribution of each predictor variable to model predictions. The results indicated that the RF model showed superior performance in predicting the physiological variables RR and RT, with higher coefficients (RR: R² = 0.858; RT: R² = 0.687) and lower error values. For RR, the RF model achieved RMSE = 16.38 and MAE = 13.33; while for RT, the errors were RMSE = 0.217 and MAE = 0.154. In contrast, the radial kernel SVR model showed lower performance, with R² values of 0.742 (RR) and 0.533 (RT), and RMSE and MAE values of 21.05 and 17.38 for RR, and 0.262 and 0.196 for RT, respectively. The application of machine learning-based models proved to be a viable and accurate alternative for estimating physiological indicators of thermal stress, contributing to the development of automated thermal management strategies for sheep in the Brazilian semi-arid region. The proposed data-driven approach demonstrates that low-cost thermal sensors combined with explainable artificial intelligence can support automatic decision-making for climate adaptation and animal welfare in semi-arid sheep production systems. Full article

University campuses feature spatially diverse environments where thermal performance varies seasonally and spatially. In this study, we integrate field measurements with ENVI-met simulations to evaluate how sky view factor (SVF) influences microclimate and outdoor thermal comfort-quantified via air temperature (Ta), mean radiant temperature (Tmrt), wind speed (WS), relative humidity (RH), physiologically equivalent temperature (PET), and the Universal Thermal Climate Index (UTCI)-within urban street and urban building spaces on a temperate Chinese campus. The results reveal contrasting thermal responses: in summer, low-SVF urban street spaces (SVF_avg 0.075) exhibit moderate heat stress (PET_avg 34.5–39.5 °C) due to radiative trapping and limited ventilation, whereas high-SVF urban building spaces (SVF_avg 0.159) face greater heat load and stronger thermal stress, with peak PET exceeding 49.9 °C. In winter, high-SVF urban building spaces benefit from solar gain, improving thermal comfort. Statistical analyses indicate non-linear threshold effects of SVF on comfort indices, with summer comfort positively correlated at SVF > 0.2, and winter comfort negatively associated at SVF ≤ 0.4. These findings identify SVF as a key geometric predictor of seasonal thermal comfort in distinct campus spatial types, provide quantitative thresholds to guide climate-resilient campus planning in warm temperate zone. Full article

In addition to air temperature and personal factors, other weather quantities govern the outdoor human thermal perception. This study provides a new targeted approach for the evaluation of extreme events based on a specific multivariable bioclimate index. Heat waves (HWs) and outdoor human thermal stress (OHTS) events that occurred in downtown Rome (Italy) over the years 2018–2023 are identified, characterized, and compared through appropriate indices based on the air temperature for HWs and the Mediterranean Outdoor Comfort Index (MOCI) for OHTS events. The overlap between the two types of events is evaluated for each year through the hit (HR) and false alarm rates. The outcomes reveal severe traits for HWs and OHTS events and higher values of HR (minimum of 66%) with OHTS as a predictor of extreme conditions. This pilot investigation confirms that the use of air temperature threshold underestimates human physiological stress, revealing the importance of including multiple parameters, such as weather variables (temperature, wind speed, humidity, and solar radiation) and personal factors, in the assessment of hazards for the population living in a specific geographical region. This type of approach reveals increasingly critical facets and can provide key strategies to establish safe outdoor conditions for occupational and leisure activities. Full article

Infrared thermography (IRT) is explored as a non-invasive method for indirectly measuring parameters related to animal performance and welfare. This study investigates a feature extraction method termed the “thermal signature” (TS), a descriptor vector derived from the temperature matrix of an animal’s body surface, representing the percentage distribution of temperatures within predefined ranges. The TS, combined with environmental data, serves as a predictor attribute for machine learning-based classifier models to assess heat stress levels. The methodology was applied to a dataset collected from two groups of five dairy calves housed in a climate-controlled chamber and exposed to two artificial heat waves over 13 days. Data, including IRT measurements, respiratory rate (RR), rectal temperature (RT), and environmental variables, were collected five times daily (from 6 a.m. to 10 p.m., every four hours). Classifier models were developed using random forest (RF), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN) algorithms. The RF models based on RR achieved the highest accuracies, 94.1% for two heat stress levels and 80.3% for three heat stress levels, using TS configurations with six temperature ranges. The integration of TS with machine learning-based models demonstrates promising results for developing or enhancing classifiers of heat stress levels in dairy calves. Full article

This study concerns the application of statistical learning (SL) in thermal stress assessment compared to the results accomplished by an international expert group when developing the Universal Thermal Climate Index (UTCI). The performance of diverse SL algorithms in predicting UTCI equivalent temperatures and in thermal stress assessment was assessed by root mean squared errors (RMSE) and Cohen’s kappa. A total of 48 predictors formed by 12 variables at four consecutive 30 min intervals were obtained as the output of an advanced human thermoregulation model, calculated for 105,642 conditions from extreme cold to extreme heat. Random forests and k-nearest neighbors closely predicted UTCI equivalent temperatures with an RMSE about 3 °C. However, clustering applied after dimension reduction (principal component analysis and t-distributed stochastic neighbor embedding) was inadequate for thermal stress assessment, showing low to fair agreement with the UTCI stress categories (Cohen’s kappa < 0.4). The findings of this study will inform the purposeful application of SL in thermal stress assessment, where they will support the biometeorological expert. Full article

The study focused on the prediction of the Temperature Vegetation Dryness Index (TVDI), an agricultural drought index, for a Mango orchard in Tamale, Ghana. It investigated the temporal relationship between the meteorological drought index, Standardized Precipitation Index (SPI), and TVDI. The SPI was calculated based on utilizing precipitation data from the World Meteorological Organization (WMO) database (2010–2022) and CMIP6 projected precipitation data (2023–2050) from 35 climate models representing various Shared Socioeconomic Pathway (SSP) climate change scenarios. Concurrently, TVDI was derived from Landsat 8/9 satellite imagery, validated using thermal data obtained from unmanned aerial vehicle (UAV) surveys. A comprehensive cross-correlation analysis between TVDI and SPI was conducted to identify lag times between these indices. Building on this temporal relationship, the TVDI was modeled as a function of SPI, with varying lag times as inputs to the Wavelet-Adaptive Neuro-Fuzzy Inference System (Wavelet-ANFIS). This innovative approach facilitated robust predictions of TVDI as an agricultural drought index, specifically relying on SPI as a predictor of meteorological drought occurrences for the years 2023–2050. The research outcome provides practical insights into the dynamic nature of drought conditions in the Tamale mango orchard region. The results indicate significant water stress projected for different time frames: 186 months for SSP126, 183 months for SSP245, and 179 months for both SSP370 and SSP585. This corresponds to a range of 55–57% of the projected months. These insights are crucial for formulating proactive and sustainable strategies for agricultural practices. For instance, implementing supplemental irrigation systems or crop adaptations can be effective measures. The anticipated outcomes contribute to a nuanced understanding of drought impacts, facilitating informed decision-making for agricultural planning and resource allocation. Full article

Monitoring and mapping crop water stress and variability at a farm scale for cereals such as maize, one of the most common crops in developing countries with 200 million people around the world, is an important objective within precision agriculture. In this regard, unmanned aerial vehicle-obtained multispectral and thermal imagery has been adopted to estimate the crop water stress proxy (i.e., Crop Water Stress Index) in conjunction with algorithm machine learning techniques, namely, partial least squares (PLS), support vector machines (SVM), and random forest (RF), on a typical smallholder farm in southern Africa. This study addresses this objective by determining the change between foliar and ambient temperature (Tc-Ta) and vapor pressure deficit to determine the non-water stressed baseline for computing the maize Crop Water Stress Index. The findings revealed a significant relationship between vapor pressure deficit and Tc-Ta (R² = 0.84) during the vegetative stage between 10:00 and 14:00 (South Africa Standard Time). Also, the findings revealed that the best model for predicting the Crop Water Stress Index was obtained using the random forest algorithm (R² = 0.85, RMSE = 0.05, MAE = 0.04) using NDRE, MTCI, CCCI, GNDVI, TIR, Cl_Red Edge, MTVI2, Red, Blue, and Cl_Green as optimal variables, in order of importance. The results indicated that NIR, Red, Red Edge derivatives, and thermal band were some of the optimal predictor variables for the Crop Water Stress Index. Finally, using unmanned aerial vehicle data to predict maize crop water stress index on a southern African smallholder farm has shown encouraging results when evaluating its usefulness regarding the use of machine learning techniques. This underscores the urgent need for such technology to improve crop monitoring and water stress assessment, providing valuable insights for sustainable agricultural practices in food-insecure regions. Full article

This work investigates real-time monitoring of extrusion-induced degradation in different grades of PLA across a range of process conditions and machine set-ups. Data on machine settings together with in-process sensor data, including temperature, pressure, and near-infrared (NIR) spectra, are used as inputs to predict the molecular weight and mechanical properties of the product. Many soft sensor approaches based on complex spectral data are essentially ‘black-box’ in nature, which can limit industrial acceptability. Hence, the focus here is on identifying an optimal approach to developing interpretable models while achieving high predictive accuracy and robustness across different process settings. The performance of a Recursive Feature Elimination (RFE) approach was compared to more common dimension reduction and regression approaches including Partial Least Squares (PLS), iterative PLS (i-PLS), Principal Component Regression (PCR), ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). It is shown that for medical-grade PLA processed under moisture-controlled conditions, accurate prediction of molecular weight is possible over a wide range of process conditions and different machine settings (different nozzle types for downstream fibre spinning) with an RFE-RF algorithm. Similarly, for the prediction of yield stress, RFE-RF achieved excellent predictive performance, outperforming the other approaches in terms of simplicity, interpretability, and accuracy. The features selected by the RFE model provide important insights to the process. It was found that change in molecular weight was not an important factor affecting the mechanical properties of the PLA, which is primarily related to the pressure and temperature at the latter stages of the extrusion process. The temperature at the extruder exit was also the most important predictor of degradation of the polymer molecular weight, highlighting the importance of accurate melt temperature control in the process. RFE not only outperforms more established methods as a soft sensor method, but also has significant advantages in terms of computational efficiency, simplicity, and interpretability. RFE-based soft sensors are promising for better quality control in processing thermally sensitive polymers such as PLA, in particular demonstrating for the first time the ability to monitor molecular weight degradation during processing across various machine settings. Full article