Search Results (90)

Developing sustainable and fire-resistant infrastructure is a critical technological, economic, and environmental challenge for modern construction stakeholders. Traditional cementitious composites experience severe microstructural degradation under extreme temperatures and their high carbon footprint exacerbates global environmental concerns. While the individual high-temperature behaviors of supplementary cementitious materials and fibers have been widely studied, the long-term synergistic mechanisms of high-volume fly ash combined with steel fibers under extreme thermal shock remain critically underinvestigated. To address this urgent need and bridge this scientific gap, hybrid mortars incorporating high-volume fly ash (FA) and steel fibers (SF) were tested under prolonged curing (150 days) and extreme heat (up to 600 °C). In terms of engineering and construction effects, the optimal CFA50-F hybrid composite delivered the highest residual compressive and flexural capacities (retaining nearly 60% of its late-age compressive strength at 32.00 MPa), preserved acoustic continuity (restricting UPV loss to 41.4%), and severely restricted high-temperature capillary permeability (limiting the water absorption increase to 49.7%) compared to traditional plain matrices. Scientifically, this superior resistance is governed by a two-step protective mechanism. High-volume FA chemically stabilizes the matrix by consuming vulnerable portlandite and preventing the formation of expansive calcium oxide. Simultaneously, ultra-fine FA particles physically densify the interfacial transition zones, securely anchoring the steel fibers and preventing premature high-temperature pull-out, while enabling the fibers to bridge thermally induced macro-cracks successfully. Environmentally and economically, an annualized service-life Life Cycle Assessment (LCA) revealed that substituting 50% of the cement with FA completely subsidizes the production-stage carbon penalty of the metallic reinforcement. By extending the operational lifespan to 40 years, the CFA50-F composite achieves a net 27% reduction in annualized global warming potential, providing a highly sustainable and cost-effective material solution. Full article

Annual area burned correlates with temperature and fuel aridity, yet extreme wildfire outcomes arise from a small fraction of fires and rapid-growth days. This asymmetry indicates that thermodynamic favorability sets background susceptibility but does not determine when extreme growth occurs. This Perspective proposes a cross-scale framework that distinguishes susceptibility from regime-conditioned event-scale realization. At seasonal and regional scales, temperature and humidity influence fuel dryness, ignition likelihood, and fire-season length, explaining substantial interannual variability in area burned. These variables vary smoothly in space and retain signal under aggregation. By contrast, extreme fire growth occurs during short-lived synoptic configurations that organize winds, pressure gradients, and stability into discrete opportunity windows that permit sustained spread. The strongest winds governing rapid spread are intermittent, terrain-structured, and often unresolved in coarse datasets or aggregated indices. Within these windows, terrain interactions, organized flow, and fire–atmosphere feedbacks can amplify growth until circulation patterns shift. Extreme wildfire behavior therefore operates as a gated joint-probability process requiring the coincidence of susceptibility (S), dynamical weather opportunity (W), and ignition (I). Separating susceptibility from realization reconciles strong climate–fire correlations with the dynamical control of event-scale extremes. Full article

Wildfire occurrence in temperate Europe is increasingly shaped by the interaction of human activities and short-term climatic anomalies rather than by natural ignition processes alone. This study analyses national wildfire statistics from Slovakia covering the period 2010–2025 to investigate temporal trends in wildfire occurrence, ignition causes, and fire-related impacts, including economic damages and human casualties. Official fire records provided by the Fire Research Institute of the Ministry of the Interior of the Slovak Republic were analyzed using descriptive and exploratory statistical methods. The dataset includes annual information on wildfire frequency, detailed ignition cause classifications, direct economic losses, fatalities, and injuries. European-scale wildfire patterns were considered for contextual comparison using data from the European Forest Fire Information System (EFFIS). Results show that wildfire occurrence in Slovakia is overwhelmingly dominated by human-caused ignitions, with negligence-related activities forming a persistent baseline of ignition pressure throughout the study period. The extreme wildfire year 2012, during which more than 11,000 wildfire events were recorded, illustrates how routine human behaviors can be strongly amplified under climatically favorable conditions without altering the underlying cause structure. Importantly, wildfire impacts were found to be weakly correlated with fire frequency, as years with moderate numbers of fires occasionally generated disproportionately high economic damages and casualties. These findings demonstrate that wildfire risk in Slovakia is primarily driven by behavioral ignition patterns modulated by short-term climatic variability. The results support a shift towards prevention-oriented and impact-focused wildfire risk management strategies, consistent with current European policies emphasizing integrated risk assessment, early warning, and targeted prevention in temperate regions. Full article

As climate-driven disasters intensify globally, this study investigates how environmental volatility influences the pro-environmental initiatives of micro-entrepreneurs in Chile. While Chile possesses world-class seismic resilience, the 2020–2025 period marked a dramatic shift toward hydro-climatological extremes, including mega-fires and catastrophic flooding. Integrating construal level theory, protection motivation theory, and the concept of focusing events, this research examines the psychological and structural drivers of business adaptation. Results indicate that residing in disaster-prone regions is insufficient to trigger proactivity; instead, a stark distinction exists between abstract geographic proximity and the behavior triggered by personal exposure. Furthermore, mediation analysis provides mixed support for the role of business profit; while profit loss negatively mediated equipment efficiency and recycling, the magnitude was marginal. This coping gap suggests that resource-constrained actors favor low-cost survivalist tactics over systemic shifts due to depleted organizational slack. Ultimately, the study highlights that disasters are powerful but inefficient teachers; without addressing technical and financial barriers to mitigation, global supply chains remain fragile despite localized disaster experiences. Full article

Titanium aluminide intermetallics have gained considerable attention as high-temperature structural materials for aerospace applications, but are susceptible to “titanium fire” under extreme service conditions. The role of Al elements on the combustion behavior of titanium aluminide intermetallics remains not fully understood. Herein, the influence of Al content on the ignition critical condition and burning rate of Ti_1−xAl_x alloys was investigated by using promoted ignition combustion (PIC) tests under oxygen-enriched atmosphere. Results indicated that the critical oxygen pressure of Ti_1−xAl_x alloys increases from 0.11 MPa to 0.23 MPa, and the ignition temperature under oxygen pressure of 0.41 MPa increases from 1059.5 ± 4.8 K to 1120.4 ± 2.5 K as Al content increases from 20 at% to 70 at%. However, the combustion rate increases from 11.85 ± 0.13 mm·s⁻¹ to 14.05 ± 0.09 mm·s⁻¹ as Al content increases from 20 at% to 70 at%. Moreover, the activation energy for ignition increases from 105.44 kJ·mol⁻¹ to 153.04 kJ·mol⁻¹ as Al content increases from 20 at% to 70 at%. According to the microstructure analysis after combustion, the influence of Al content on the ignition activation energy and burning rate is attributed to multiple factors involving bonding energy, melting temperature, and heat release. Full article

Twin wildfires burned over 9500 ha in Seferihisar, İzmir, western Türkiye, on 29—30 June 2025 under extreme fire weather conditions. This study reconstructs the spatiotemporal progression of the fires and examines the drivers of contrasting behaviors and burn severity. Multi-source datasets—Sentinel-2 imagery, VIIRS/MODIS thermal detections, MTG images and thermal detections, aerial photos, and ground data—were integrated to delineate progression polygons and compute rate of spread (ROS), fuel consumption (FC), and fire-line intensity (FI). Kuyucak fire showed rapid early growth, burning 3554 ha in 2.5 h (mean ROS of 5.0 km h⁻¹; mean FI of 37,789 kW m⁻¹), driven by strong northeasterly winds of 40–50 km h⁻¹, steep terrain, dense Pinus brutia fuels, and very low dead fine-fuel moisture (<6%). Kavakdere fire advanced more slowly (mean ROS of 1.6 km h⁻¹) across open grassland and cropland, yielding lower FC and FI. Synoptic analysis revealed a strong pressure-gradient-induced northeasterly wind regime linked to a mid-tropospheric geopotential height dipole between Central Europe and the Eastern Mediterranean, while WRF simulations indicated a dry boundary layer and enhanced low-level winds during peak spread. Sentinel-2 dNBR burn severity mapping showed substantial spatial variability tied to fuel and topography contrasts. Findings demonstrate how twin ignitions under similar weather conditions can produce divergent outcomes, underscoring the need for terrain- and fuel-aware strategies during extreme Mediterranean fire outbreaks. Full article

Floods and wildfires are two extreme environmental events with significant yet different impacts on soil health and on two particularly important soil pollutants, heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs), which are directly associated with ishytoxic properties and their ability to enter the food chain. The present study includes a methodological approach that was based on a literature review of published studies conducted worldwide regarding these two phenomena. The main forms of both pollutants, their possible sources and inevitable deposition onto the soil surface, along with their behavior–transport–mobility, and their residence time in soil were investigated. Furthermore, the changes that both HMs and PAHs induce in the physicochemical properties of post-flood and post-fire soils (in soil pH, Cation Exchange Capacity (CEC), organic matter content, porosity, mineralogical alterations, etc.), are investigated after a literature review of various case studies. Wildfires, in contrast to floods, can more easily remove large quantities of heavy metals into the soil ecosystem, most likely due to the intense erosion they cause. At the same time, floods appear to significantly burden soils with PAHs. In wildfires, the largest mean increases were observed for Mn (386%), Zn (300%), and Cu (202%). In floods, Pb showed the highest mean increase (534%), with Cd also rising substantially (236%). Regarding total PAHs, mean post-event concentrations reached 482.3 μg/kg after wildfires, compared to 4384 μg/kg after floods. Changes in the structure and chemical composition of flooded and burned soils may also affect the mobility and bioavailability of the pollutants under study. Overall, these two phenomena significantly alter soil quality, affecting both ecological processes and potential health impacts. Full article

Advancing rapidly in modern bridge engineering technology, through concrete-filled steel tubular (CFST) arch bridges have achieved widespread application in transportation infrastructure development. Nevertheless, vehicle fires occurring in complicated operational settings may rapidly escalate into major disasters. Fires in oil tankers are particularly dangerous for the safety of bridges. This study examines the fire resistance of through concrete-filled steel tubular (CFST) arch bridges exposed to tanker truck fires. The study formulates a detailed model utilizing Fire Dynamics Simulator (FDS) to simulate fire scenarios, elucidating the spatial temperature distribution characteristics within arch bridge structures. A three-dimensional finite element model established in ABAQUS (Abaqus 2024, Dassault Systèmes Simulia Corp, Providence, RI, USA) is employed to simulate structural responses by analyzing the mechanical behavior of key components under different fire conditions. Practical fire resistance design recommendations for extreme tanker truck fire scenarios are ultimately proposed. Numerical results demonstrate that structural components near the fire source (such as transverse bracings, hangers, and fire-exposed arch surfaces) experience significantly higher temperatures than other regions. Notable temperature gradients developing along hangers and arch ribs in fire-affected zones are observed, while substantial cross-sectional temperature gradients occurring in these components under tanker truck fires reveal their damage evolution mechanisms. The fire exposure scenario at the quarter-point of the midspan is identified as the most critical fire exposure scenario for through CFST arch bridges under tanker truck fires. Under this extreme scenario, the deflection on the fire-exposed side of the global structure exhibits a significant three-stage distribution characteristic: an initial ascending phase around 0–800 s, followed by a sharp descending phase during 800–1100 s, and then a stabilization trend. A fire resistance limit criterion based on component failure ($t_{f3}$ = 853.43 s) is established, and a global fire resistance limit assessment methodology for through CFST arch bridges under extreme tanker truck scenarios is proposed. Full article

Smoking in oilfield operation areas poses a severe risk of fire and explosion accidents, threatening production safety, workers’ lives, and the surrounding ecological environment. Such behavior represents a typical preventable unsafe human action. Detecting smoking behaviors among oilfield workers can fundamentally prevent such safety incidents. To address the challenges of low detection accuracy for small objects and frequent missed or false detections under extreme industrial environments, this paper proposes a GCEA-YOLO network based on YOLOv11 for smoking behavior detection. First, a CSP-EDLAN module is introduced to enhance fine-grained feature learning. Second, to reduce model complexity while preserving critical spatial information, an ADown module is incorporated. Third, an enhanced feature fusion module is integrated to achieve effective multiscale feature aggregation. Finally, an EfficientHead module is employed to generate high-precision and lightweight detection results. The experimental results demonstrate that, compared with YOLOv11n, GCEA-YOLO achieves improvements of 20.8% in precision, 6.9% in recall, and 15.1% in mean average precision (mAP). Overall, GCEA-YOLO significantly outperforms YOLOv11n. Full article

The increasing frequency of extreme wildfire behavior globally, particularly under the influence of anthropogenic climate change, poses unprecedented challenges to traditional fire management paradigms. This study presents a comprehensive, multi-dimensional case study of the catastrophic eruptive fire event that occurred in Xintian County, Hunan Province, China, on 17 October 2022. By integrating data on long-term ecological drought, critical synoptic-scale weather conditions, and real-time emission profiles of combustion products, we delineate the mechanistic chain leading to eruptive fire development in a subtropical evergreen broad-leaved forest region, historically considered a low-to-moderate fire risk zone. Our results demonstrate that the eruptive fire was a consequence of a critical convergence of factors: a protracted pre-conditioning drought that significantly reduced live and dead fuel moisture, a specific meteorological window characterized by extremely low relative humidity (<60%) and initially high wind speeds (peak at 16.5–17.9 m/s), and the abundant production and accumulation of flammable pyrolysis gases (e.g., CO, CH₄) from the dominant Masson pine forests. The emission data pinpointed October 19th as the key tipping point, marking the transition to high-intensity combustion. This study underscores the vulnerability of subtropical forest ecosystems to eruptive fires during compound extreme events. Our findings provide a critical scientific basis for updating fire danger rating systems and early warning strategies in similar ecological regions under a changing climate. Full article

Santa Ana winds are extreme meteorological events that strongly affect the U.S.–Mexico border region, often associated with droughts, high fire risk, and hydrological imbalance. Understanding the temporal behavior of key atmospheric variables during these events is crucial for integrated water resource management in semi-arid regions such as the Guadalupe Basin in northern Baja California. In this study, we explored the predictive capability of several hybrid deep learning architectures—Long Short-Term Memory (LSTM), Convolutional Neural Network combined with LSTM (CNN–LSTM), and Bidirectional LSTM with Attention (BiLSTM–Attention)—to model the temporal evolution of wind speed, wind direction, temperature, relative humidity, and atmospheric pressure using Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data from 1980 to 2020. Model performance was evaluated using RMSE, MAE, and R² metrics and compared against persistence and climatology baselines. The BiLSTM–Attention model achieved the best overall performance, showing particularly high accuracy for temperature (R² = 0.95) and relative humidity (R² = 0.76), while maintaining angular errors below 35° for wind direction. The results demonstrate the potential of hybrid deep learning models to capture nonlinear temporal dependencies in meteorological time series and provide a methodological framework to enhance hydrometeorological understanding and water resource management in the Guadalupe Basin under Santa Ana wind conditions. Full article

The use of artificial oxygenation to counteract the effects of hypoxia and improve living standards in high-altitude, low-oxygen settings is widespread. A recognized consequence of this intervention is that it elevates the risk of fire occurrence. In this study, we simulated a real fire environment with low-pressure oxygen enrichment in a plateau area. A new multi-measuring apparatus was constructed by integrating an electronic control cone heater and a low-pressure oxygen enrichment combustion platform to enable the simultaneous measurement of multiple parameters. The combined effects of varying oxygen concentrations and thermal irradiance on the combustion behavior of wood plastic composites (WPCs) under specific low-pressure conditions were investigated, and alterations in crucial combustion parameters were examined and evaluated. Increasing the oxygen concentration and heat flux significantly reduced the ignition and combustion times. For instance, at 50 kW/m², the ignition time decreased from 75 s to 16 s as the oxygen concentration increased from 21% to 35%. This effect was suppressed by higher heat fluxes. Compared with low oxygen concentrations and low thermal radiation environments, the ignition time of the material under high oxygen concentrations and high thermal radiation conditions was shortened by more than 78%, indicating that its flammability is enhanced under extreme conditions. Higher oxygen concentrations enhanced the heat feedback to the fuel surface, which accelerated pyrolysis and yielded a more compact flame with reduced dimensions and a color transition from blue-yellow to bright yellow. This intensified combustion was further manifested by an increased mass loss rate (MLR), elevated flame temperature, and a decline in residual mass percentage. The combustion of WPCs displayed distinct stage characteristics, exhibiting “double peak” features in both the MLR and flame temperature, which were attributed to the staged pyrolysis of its wood fiber and plastic components. Full article

The precise forecasting of renewable energy production and usage is essential for the stability, efficiency, and sustainability of contemporary power systems. This requirement is especially urgent in South Africa, a nation currently grappling with considerable energy issues, such as recurrent load shedding, outdated coal-fired power plants, and an increasing electricity demand. As the country moves towards a more renewable-focused energy portfolio, the capacity to anticipate future energy requirements is crucial for effective planning, operational stability, and grid resilience. This study introduces a hybrid approach that combines deep learning and machine learning techniques, specifically integrating long short-term memory (LSTM) neural networks with extreme gradient boosting (XGBoost) to provide more accurate and detailed forecasts of energy demand. LSTM networks are particularly effective in capturing long-term temporal dependencies in sequential data, such as patterns of energy usage. At the same time, XGBoost delivers high-performance gradient-boosted decision trees that can manage non-linear relationships and noise present in extensive datasets. The proposed hybrid LSTM-XGBoost model was trained and assessed using high-resolution data on energy consumption and weather conditions gathered from a coastal municipality in KwaZulu-Natal, South Africa, a country that exemplifies the convergence of renewable energy potential and challenges related to energy reliability. The preprocessing steps, including normalization, feature selection, and sequence modeling, were implemented to enhance the input data for both models. The performance of the model was thoroughly evaluated using standard statistical metrics, specifically the mean absolute error (MAE), the root mean squared error (RMSE), and the coefficient of determination (R²). The hybrid model achieved an MAE of merely 192.59 kWh and an R² of approximately 0.71, significantly surpassing the performance of the individual LSTM and XGBoost models. These findings highlight the enhanced predictive capabilities of the hybrid model in capturing both temporal trends and feature interactions in energy consumption behavior. Full article

Wildfires have become increasingly frequent and destructive environmental hazards, especially in boreal ecosystems facing prolonged droughts and temperature extremes. This study presents an integrated spatio-temporal framework that combines Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN), Fire Radiative Power (FRP), and the differenced Normalized Burn Ratio (ΔNBR) to characterize the dynamics and ecological impacts of large-scale wildfires, using the extreme 2023 Quebec fire season as a case study. The analysis of 80,228 VIIRS fire detections resulted in 19 distinct clusters across four fire zones. Validation against the National Burned Area Composite (NBAC) showed high spatial agreement in densely burned areas, with Intersection over Union (IoU) scores reaching 62.6%. Gaussian Process Regression (GPR) revealed significant non-linear relationships between FRP and key fire behavior metrics. Higher mean FRP was associated with both longer durations and greater burn severity. While FRP was also linked to faster spread rates, this relationship varied by zone. Notably, Fire Zone 2 exhibited the most severe ecological impact, with 83.8% of the area classified as high-severity burn. These findings demonstrate the value of integrating spatial clustering, radiative intensity, and post-fire vegetation damage into a unified analytical framework. Unlike traditional methods, this approach enables scalable, hypothesis-driven assessment of fire behavior, supporting improved fire management, ecosystem recovery planning, and climate resilience efforts in fire-prone regions. Full article

This study assessed the effectiveness of stress-based cognitive-behavioral training compared to standard training in firefighters, emphasizing their ability to distribute extinguishing water and cool environments evenly during enclosure fires. Experiments took place at the Zbiroh training facility with two firefighter teams (Team A with stress-based training and Team B with standard training) under realistic conditions. Using 58 thermocouples and 4 radiometers, temperature distribution and radiant heat flux were measured to evaluate water distribution efficiency and cooling performance during interventions. Team A consistently achieved temperature reductions of approximately 320 °C in the upper layers and 250–400 °C in the middle layers, maintaining stable conditions, whereas Team B only achieved partial cooling, with upper-layer temperatures remaining at 750–800 °C. Additionally, Team A recorded lower radiant heat flux densities (e.g., 20.74 kW/m² at 0°) compared to Team B (21.81 kW/m²), indicating more effective water application and adaptability. The findings confirm that stress-based training enhances firefighters’ operational readiness and their ability to distribute water effectively during interventions. This skill is essential for safer and effective management of indoor fires under extreme conditions. This study supports the inclusion of stress-based and scenario-based training in firefighter education to enhance safety and operational performance. Full article