Search Results (3,906)

Wildfires pose increasing challenges for Mediterranean landscapes, making rapid and reliable mapping of burn severity essential for management and recovery planning. This study applies an integrated geospatial workflow to wildfires that occurred in Greece during the 2024 summer season. Sentinel-2-derived dNBR and RBR indices were used to map burn severity, while CORINE Land Cover and Tree Cover Density datasets provided complementary context for interpreting how severity varied across different vegetation types and canopy-density conditions. A one-way ANOVA was used to summarize differences in burned area among severity classes. The results show that low and moderate-low severity levels dominated most fire perimeters, whereas high-severity patches were spatially limited and typically coincided with densely forested areas. Validation against Copernicus Emergency Management Service data yielded an overall agreement of approximately 94%, indicating that the applied multispectral workflow produced severity extents broadly consistent with independent operational products. By applying a consistent methodology across multiple fire events, this study demonstrates the value of combining spectral indices with land-cover information for interpreting severity patterns and supporting post-fire management. The findings highlight the usefulness of freely accessible remote sensing data for timely fire assessment in Mediterranean environments and provide a basis for future multi-regional and multi-year comparisons. Full article

Wildfire monitoring in tropical regions requires robust frameworks capable of transforming heterogeneous satellite detections into consistent, event-level information suitable for decision support. This study presents the DescrEVE Fogo (Descrição de Eventos de Fogo) framework, a relational and scalable system that models daily fire events in Brazil by integrating Advanced Very High Resolution Radiometer (AVHRR), Moderate-Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) active-fire detections within a unified Structured Query Language (SQL)/PostGIS environment. The framework formalizes a mathematical and computational model that defines and tracks fire fronts and multi-day fire events based on explicit spatio-temporal rules and geometry-based operations. Using database-native functions, DescrEVE Fogo aggregates daily fronts into events and computes intrinsic and environmental descriptors, including duration, incremental area, Fire Radiative Power (FRP), number of fronts, rainless days, and fire risk. Applied to the 2003–2025 archive of the Brazilian National Institute for Space Research (INPE) Queimadas Program, the framework reveals that the integration of VIIRS increases the fraction of multi-front events and enhances detectability of larger and longer-lived events, while the overall regime remains dominated by small, short-lived occurrences. A simple, prototype fire-type rule distinguishes new isolated fire events, possible incipient wildfires, and wildfires, indicating that fewer than 10% of events account for more than 40% of the area proxy and nearly 60% of maximum FRP. For the 2025 operational year, daily ignition counts show strong temporal coherence with the Global Fire Emissions Database version 5 (GFEDv5), albeit with a systematic positive bias reflecting differences in sensors and event definitions. A case study of the 2020 Pantanal wildfire illustrates how front-level metrics and environmental indicators can be combined to characterize persistence, spread, and climatic coupling. Overall, the database-native design provides a transparent and reproducible basis for large-scale, near-real-time wildfire analysis in Brazil, while current limitations in sensor homogeneity, typology, and validation point to clear avenues for future refinement and operational integration. 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

Intumescent fire-protective coatings based on epoxy binders are widely used to enhance the fire resistance of steel structures due to their high adhesion, mechanical strength, and durability. However, epoxy binders undergo exothermic thermo-oxidative degradation, which can adversely affect fire-protective performance. In this study, the effect of brominated epoxy resin content on the fire-retardant behavior of intumescent coatings was investigated using two systems: one initially supporting flame propagation and one inherently self-extinguishing. For the initially combustible coating, partial substitution of the epoxy diane resin with a brominated analogue at 12.5% resulted in complete self-extinguishing behavior according to UL-94, while higher substitution levels (≥50%) caused a 20–28% reduction in fire-protective efficacy as assessed by BS 476. For the initially non-combustible coating, a decrease in fire-protective performance of 15–20% was observed regardless of the substitution degree. Thermal analysis showed that coatings containing brominated resins exhibit an onset of thermal degradation approximately 80 °C lower than halogen-free analogues. FTIR and SEM analyses revealed that brominated resins alter the thermolysis mechanism, promoting the formation of oxygen-containing degradation products and a more heterogeneous, irregularly porous foamed char, thereby reducing its thermal insulation capacity. Overall, brominated epoxy resins exert a dual effect, improving self-extinguishing behavior while impairing fire-protective efficacy under prolonged thermal exposure. Brominated resin contents in the range of 10–50% represent a practical compromise, enabling self-extinguishing behavior while maintaining acceptable fire-protective performance. Full article

Most machine learning (ML)-based facility location studies utilize uniform grid partitioning, often overlooking spatial heterogeneity. This limitation can compromise the validity and practical applicability of the resulting site selections. In response to this issue, this paper uses fire stations as the research subject and proposes a location prediction method that considers the heterogeneous characteristics within cities. Firstly, the Gaussian Mixture Model (GMM) is adopted based on the Point of Interest (POI) data to determine the clustering centres of the study area. Secondly, a Voronoi diagram is constructed to divide the study area reasonably. Then, a comprehensive feature matrix is constructed by integrating multi-source spatial data and five machine learning models: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Logistic Regression (LR). These are then used for training and evaluation. Finally, the GBDT model with the best performance in terms of both the F1 score and the AUC value was selected to predict the location of fire stations in Chengguan District, Lanzhou City. The results demonstrate the GBDT model’s effectiveness in identifying the rationale behind existing fire station locations and predicting potential new locations. It predicts 12 suitable locations for new fire stations, and the suitability of these predicted locations is validated by comparing them with the existing fire station locations, 8 of which are in the same block as existing fire stations in Chengguan District. Adding micro fire stations at four new predicted locations would improve response efficiency. The results of the feature importance analysis show that road accessibility is the primary factor affecting fire station location selection. This study’s proposed method effectively enhances the reasonableness of fire station site selection and provides a basis for planning fire stations in new urban areas in the future. Full article

This study presents an analysis of selected approaches to transforming the Polish power system towards a net-zero greenhouse gas (GHG) emission economy by 2060. The generation-side system models primarily comprise renewable energy sources (RES), supported by nuclear power plants. Two system balancing scenarios were examined: Model G, based on biomethane-fired gas turbines and electrolysers utilising surplus energy; and Model H, which relies primarily on reversible fuel cells (RFCs) operating in a Power-to-Power configuration. Both models were considered under two demographic projections for Poland in 2060: maintaining the current population level (100%) and a decline to 71%. Simulations were performed with an hourly time step over a nine-year period, starting from 2060, using weather data from 2015 to 2023. The total electricity demand in the analysed scenarios ranges from 352 to 542 TWh/year, representing 2.1–3.2 times the current level. The proposed systems include 64 GW of onshore wind capacity, 33 GW of offshore wind, 136 GW of PV, 10 GW of nuclear generation, and extensive storage systems for electricity, heat, and gases (biomethane and hydrogen). In Model G, biomethane and hydrogen storage play a crucial role, requiring storage capacities of 5.8–7.5 billion Nm³ for biomethane and 6.2–7.0 billion Nm³ for hydrogen. In Model H, long-term storage relies on hydrogen reservoirs (approximately 12.5 billion Nm³) integrated with RFC units. The results demonstrate that the choice of architecture dictates the scale and technical requirements of the storage infrastructure. Notably, hydrogen serves as an effective energy storage medium, enabling the elimination of peak gas turbines from the system. Consequently, biomethane resources can be redirected to support the decarbonisation of other sectors of the economy. Full article

The acoustic technique emerges as a highly promising, cutting-edge solution that can be effectively employed for extinguishing flames in locations where the access to classical fire-protection measures is limited, the available extinguishing agent is severely restricted, or the burning materials are difficult to suppress using currently known methods. The results of the experimental attempts confirmed that low-frequency acoustic waves containing higher even harmonics from the tenth to the sixteenth order (inclusive) can successfully extinguish flames, demonstrating both the feasibility and the novelty of the acoustic technique for fire protection. Moreover, statistical analysis was applied to identify operational boundary values and assess their variability, supporting the optimal selection of system parameters required for rapid and effective flame extinguishing. By integrating an acoustic extinguisher with optional intelligent sensors, including artificial vision, it becomes possible to rapidly detect flames at much greater distances than with conventional smoke and temperature sensors, as well as to automatically extinguish them. In this context, an integrated solution combining acoustic waves with an artificial intelligence module (smart sensor) may be employed for comprehensive fire management, encompassing both fire detection and flame extinguishing. Full article

Arc fault is the dominant cause of fire in photovoltaic (PV) systems, making its accurate identification crucial for PV fire prevention. This study investigates the influence of voltage (200, 300, and 400 V) and current (3, 5, 7, 9, and 11 A) on the DC series arc fault characteristics in PV systems obtained through experimental analysis. The results show that voltage variation has a negligible impact on arc fault behavior, while higher current levels substantially increase noise in the arc fault signals. To effectively mitigate noise, this paper proposes a denoising method that combines an improved moss growth optimization algorithm (IMGO) with improved complete ensemble empirical mode decomposition featuring adaptive noise (ICEEMDAN). It is found that the IMGO-ICEEMDAN denoising algorithm can effectively diminish noise in current signals, broaden characteristic frequency bands, and ameliorate arc feature discernibility. Subsequently, an integrated multi-scale spatiotemporal model is developed to extract arc fault features from the denoised signals. The model employs wide deep convolutional neural networks (WDCNNs) and bidirectional long short-term memory (BiLSTM) networks for parallel feature extraction, supplemented by a cross-attention (CA) module to optimize feature integration. The proposed WDCNN-BiLSTM-CA model ultimately achieves a detection accuracy of 99.89%, demonstrating superior detection performance over conventional methods, such as CNN-GRU and 1DCNN-LSTM models. This work provides a reliable framework for arc fault detection and fire risk reduction in PV systems. Full article

Wood, which is flammable, is commonly used as a building material and can be improved using a suitable surface treatment. A promising coating solution is polyurea, featuring properties like flexibility, mechanical resistance, resistance against water, etc., but it is also easily flammable. Expandable graphite (EG) is effective as a flame retardant and environmentally suitable. In this study, we studied the suitability of polyurea improved with EG. Spruce wood samples with dimensions of 50 mm × 40 mm × 10 mm were divided into eight groups, each including five samples. Each group was subjected to two applications of polyurea and EG in various combinations to examine the best combination with the lowest mass loss. The second component of the experiments aimed to examine the effectiveness of EG, which was applied in different weights. During the experiments, samples were thermally loaded in an apparatus for 10 min, where a heat flux of 30 kW·m⁻² was applied to the sample surface and the mass loss was continuously recorded. Lastly, thermal analysis was performed. The best results were observed for the combination of NEOPROOF mixed with 0.3 g of EG covered with NEODUR. The thermal analysis results revealed substantial differences: NEOPROOF, a polyurea, had only one degradation step, while NEODUR, which also contained polyurethanes, decomposed in several steps. Full article

Polyimide aerogels (PIAs) possess enormous application potential in high-temperature thermal insulation scenarios. As high-efficiency thermal insulation materials, their thermal safety and thermal insulation performance are of crucial importance. Currently, poor dimensional stability, high-temperature pyrolysis, and severe shrinkage remain the key factors restricting their development and practical application. In this work, we employ an in situ co-gelation synthesis strategy, where methyltrimethoxysilane (MTMS) is introduced as the silica precursor to fabricate SiO₂/polyimide aerogels (Si@PIAs). This strategy enhances the interfacial bonding strength between the organic and inorganic phases, enabling their complementation of strengths. Experimental results demonstrate that the incorporation of the inorganic SiO₂ phase endows Si@PIAs with higher thermal safety, superior thermal insulation performance, lower density, and reduced shrinkage. Among them, Si₁₀@PIA performs best with a density of 85 mg/cm³, a thermal conductivity of 23.28 mW/(m·K), and a heat flow peak temperature of 720.7 °C. More importantly, pyrolysis analysis reveals that the pyrolysis process of Si@PIAs shifts to a randomized nucleation and growth model (n = 2/5) with the mechanism function g(α) = [−ln(1 − α)]^5/2. Compared with pure PIAs, Si@PIAs possess stronger resistance to pyrolysis, lower gross calorific value, and improved thermal safety. This study provides theoretical and practical guidance for the development of high-performance aerogel materials, promoting their application in lithium-ion battery separators, high-temperature insulation, and fire-resistant materials. Full article

Fire safety in high-rise talent apartments, which is vital for safeguarding strategic human resources, was investigated to enhance evacuation resilience. A coupled fire-evacuation model was established using PyroSim and Pathfinder. This study analyzed multi-factor management strategies, including occupant vertical distribution, evacuation speed, evacuation priority settings, panic psychology, and guide intervention. Additionally, an Artificial Neural Network (ANN) model was developed using simulation data obtained under non-panic conditions to predict evacuation time and explore intelligent algorithms. Results show that the evacuation stairwells are the primary bottlenecks. Panic psychology significantly reduces evacuation efficiency, with severe panic increasing total evacuation time by up to 71.1%. The combined strategy CS4, integrating Pyramidal Vertical Distribution (VD7) and Organized Segmented Speed Control (ES6), reduced evacuation time by 17.42%. Guide intervention effectively mitigates the negative impact of panic. The ANN model achieved a coefficient of determination (R²) of 0.8695, confirming its predictive capability. Visibility was identified as the key parameter determining the Available Safe Egress Time (ASET). This study demonstrates that an integrated “hard–soft combination” strategy, complemented by intelligent modeling, offers an effective approach to building a resilient evacuation system for high-rise talent apartments. Full article

Fires caused by electric vehicle (EV) batteries can pose hazardous situations during accidents and during the servicing of critically damaged vehicles. Managing and preventing such fires requires a thorough understanding of the underlying causes and processes. This article analyses lithium-ion (Li-ion) batteries in electric vehicles, demonstrating the effects of cell overheating, the production of runaway gases, and the resulting thermal catastrophe. We examine the composition of cell eruption gases (CEGs) and their implications for fire protection. Based on these findings, we assess the conditions for safe battery storage, safety guidelines for servicing electric and hybrid vehicles, fire suppression methods, and measures following fire suppression. Additionally, we analyze the unique characteristics of EV fire incidents and, based on these, outline the implementation requirements and safety technologies for repair bays designed to service critically damaged electric and hybrid vehicles. Finally, we propose implementing repair stations suitable for safe servicing operations and accrediting such repair bays, including a flowchart detailing the implementation process. Although this concept is still in its early stages, its implementation would significantly enhance the safety of servicing operations and the effective management of hazardous situations. Full article

This study analyzes the flow and dispersion characteristics of fire smoke within the complex spatial structure of a T-type subway interchange station to clarify the impact of geometric parameter variations on the smoke spread timeline and evacuation environment. A three-dimensional numerical model of a typical T-type interchange station was constructed based on field survey data, with key variables defined as the height difference ($H$) between the platform and concourse layers and the horizontal distance ($L$) from the fire source to the track intersection. Through the simulation of multiple fire scenarios, the relationship between the smoke front arrival time ($T$) and the critical danger time ($T_s$) at key evacuation nodes was quantified in relation to the structural parameters. The results demonstrated significant linear correlations between vertical smoke spread and horizontal diffusion to adjacent tracks with $H$ and $L$, respectively. Conversely, smoke intrusion at the transfer stairway exhibited nonlinear behavior driven by geometric constraints. The study notably highlights the dual effect of the height difference ($H$) on smoke spread. Significantly, the study highlights the dual effect of the height difference ($H$) on evacuation safety. While an increased height difference delays the initial vertical ascent and enlarges the smoke reservoir capacity, thereby extending the available safe egress time, it simultaneously elongates the physical evacuation path. Consequently, a trade-off emerges between the dispersion delay benefit and the increased evacuation distance. Strategies proposed based on the model analysis include the control of the vertical height difference to $H\le$ 11 m, the installation of smoke barriers, and the optimization of the smoke control system in the transfer corridors. These findings provide a theoretical basis and quantitative evidence for the optimization of smoke control systems and emergency evacuation design in T-type subway interchange stations. Full article

Fire hazards in closed industrial environments pose a significant threat to workers, infrastructure and production processes. Traditional detection systems, such as smoke and heat detectors, often have limitations in their settings, including delayed response time and a tendency for false alarms due to non-fire factors such as dust, humidity and vapors. This paper researches the applicability of gas sensors as an alternative or complementary method for early fire detection. This research presents an experimental evaluation of six gas sensors integrated with a microcontroller. Tests were conducted in a controlled environment, simulating industrial conditions by monitoring the combustion of different materials, such as wood, plastic and textile. Sensor responses were analyzed at horizontal distances of 2 m and 4 m from the fire source. Results show that all sensors detected combustion byproducts, with those at 2 m exhibiting a faster response and higher concentration readings. The findings confirm that a multi-sensor approach significantly increases detection reliability and enables an earlier response compared to conventional systems. Full article

Fire safety in heritage buildings is a major challenge. It is necessary to find effective solutions that minimise damage to the protected building and do not cause damage or diminish the aesthetic value of the building. This requires not only special equipment, but often also specific solutions. The easiest way to increase the fire safety level of a building is to retrofit it with active fire protection systems. The aim of this paper is to review fire detection solutions suitable for historic buildings, with particular emphasis on minimally invasive and visually unobtrusive systems. The study combines a structured review of point, linear, and aspirating smoke detection technologies with a demonstrative parametric sizing assessment of an aspirating smoke detection (ASD) system using a manufacturer-supported sizing software. The sizing analysis investigates how changes in sampling hole diameter and fan settings influence transport time, sensitivity distribution, and system balance under constrained routing conditions typical of heritage interiors. The results highlight key trade-offs between response time and system balance, providing practical guidance for designers and conservation professionals. The findings support the development of fire detection strategies that align with European recommendations for heritage protection while ensuring technical effectiveness. The paper also provides a guideline to professionals, architects, restorers, and heritage experts, who have key roles in the protection of heritage structures. Full article