Fire doi: 10.3390/fire9050205

Authors: Nursel Kütük Mustafa Özakça

Reinforced concrete (RC) slabs with internal voids are increasingly used to improve material efficiency; however, their residual structural performance after fire exposure remains insufficiently understood. This study presents a numerical investigation of RC slabs with different void geometries using a three-dimensional nonlinear Finite Element (FE) model. A sequential thermal–structural approach was adopted, in which fire exposure was simulated through transient thermal analysis, and the resulting spatial distribution of maximum temperatures was used to assign residual material properties to each FE based on its local peak temperature, followed by structural analysis under ambient conditions. A parametric study was conducted on seven slab configurations, including two solid slabs and five voided slabs with spherical, elliptical, ellipsoidal, capsule, and biaxial capsule geometries. To ensure a consistent evaluation, two reference solid slabs were considered: a 230 mm thick slab to enable comparison under identical geometric conditions, and a 160 mm thick slab representing equivalent concrete volume to assess material efficiency. Fire exposure was applied according to the ISO 834 standard fire curve for durations of 30, 60, and 90 min. The results indicate that voided slabs exhibit higher deflections than the solid slab of identical thickness due to reduced stiffness, while achieving comparable performance relative to the solid slab with equivalent concrete volume. These findings highlight the trade-off between structural stiffness and material efficiency under increasing fire exposure time.

Fire doi: 10.3390/fire9050204

Authors: Kevin Manuel Galván Lara Yosune Miquelajauregui Luis Fernando Enriquez Ocaña Alf Enrique Meling-López Christoph Neger John Abatzoglou Leopoldo Galicia César Hinojo Graciela Jiménez-Guzmán Edelmira Rodríguez Alcantar

Wildfire prediction using machine learning (ML) and deep learning (DL) has expanded rapidly, yet synthesis regarding algorithmic configurations, data practices, and transparency remains limited. This systematic review characterizes ML/DL applications in wildfire prediction (2020–2025) using a PRISMA-EcoEvo framework across 341 peer-reviewed studies, with detailed analysis of 110 articles from 2024. Publication output increased steadily, concentrated geographically in China and the United States. Methodologically, ensemble tree-based methods (26.7%) and deep learning architectures (59.4%) coexist, reflecting adaptation to diverse data modalities. Input data are dominated by vegetation/fuel characteristics (44.7%) and historical fire labels (41.2%), while socioeconomic variables remain marginal (1.2%). Evaluation practices distinguish classification and regression tasks, yet metric heterogeneity constrains cross-study comparability. Critically, only 7.7% of studies provided publicly accessible code, with a significant association between algorithm family and code availability (χ2 = 78, p = 0.0012). Collectively, wildfire ML/DL research demonstrates technical advancement but remains geographically concentrated and constrained by limited transparency. Strengthening reporting standards, metric-task alignment, dataset documentation, and open-code practices is essential to translate computational innovation into globally robust, reproducible wildfire decision-support systems.

Fire doi: 10.3390/fire9050203

Authors: Xi Zhang Boning Li Li Wang Chunyu Yu Xiaoxu Li

Aiming at the inherent bottlenecks of traditional smoke detection technologies in high and large-volume building scenarios, this paper conducts research on an early fire smoke detection method for high and large-volume spaces based on Light Detection and Ranging (LiDAR). A special experimental platform was independently designed to obtain the physical characteristics of smoke particles from standard smoldering fires. Combined with the optical scattering and reflection interaction mechanism between laser and particulate matter, the theoretical feasibility of LiDAR for smoke detection was systematically verified. Smoke irradiation experiments were carried out in the full detection distance, and the LiDAR point cloud characterization characteristics of smoldering smoke were clarified. A special smoke detection algorithm based on point cloud features was designed, a LiDAR smoke detection system was built, and multi-condition comparative experiments with traditional photoelectric smoke detection methods were carried out in a full-scale laboratory. The experimental results show that the LiDAR-based smoke detection method proposed in this paper has significant advantages over traditional detection methods in terms of alarm response speed, detection coverage, and height adaptability. This research provides a brand-new technical path and reference for the theoretical research and engineering application of early fire warning technology for high and large-volume buildings.

Fire doi: 10.3390/fire9050202

Authors: Nuri Özgür Aydın Mehmet Kopaç

Accurate and efficient modeling of laminar premixed flames is essential for chemical mechanism validation and parametric studies in combustion science. For this purpose, CombF was developed—a semi-analytical computational framework for one-dimensional (1D) laminar premixed flames—offering flexible control over nodal distributions and optional incorporation of experimental temperature data. Unlike conventional fully coupled solvers, CombF explicitly separates the initialization and solution stages, enabling structured control over intermediate structure and temperature constraints while preserving physical consistency. The methodology employs linear interpolation between pre- and post-reaction equilibrium states, adaptive grid refinement, and finite-difference solutions of species and energy conservation equations, with radiation heat transfer optionally included. CombF was validated for ethylene–air premixed flames by comparison with experimental data under varying equivalence ratios and inlet velocities using the YARC-AF kinetic mechanism, and for methane–air premixed flames by additional benchmark comparisons with Cantera, employing the DRM22 mechanism. CombF predictions were further validated against methane and propane–air flames under varying inlet compositions and velocities using the Diego mechanism and evaluated using the curve matching (CM) score, L2 norms, and phase shift alignment via a nonparametric bootstrap approach. The results demonstrate strong agreement for major species (CO2, H2O), while intermediate species (CO, CH2O) show higher sensitivity to temperature profile choice and nodal resolution, providing a more discriminating assessment of model fidelity. Incorporating experimental temperature fields substantially improves species distribution accuracy and structural alignment. Thus, CombF provides a reliable, flexible, and experimentally adaptive framework that is capable of accurately capturing flame structures, offering a practical tool for preliminary analyses, parametric exploration, and instructional applications in combustion research.

Fire doi: 10.3390/fire9050201

Authors: Xiaoxiao Li Kaida Yan Shiyuan Zhang Liqing Si Lifu Shu Mingyu Wang Weike Li Fengjun Zhao Qiuhua Wang

Near-surface wind field simulation in complex mountainous terrain is essential for predicting wildfire behavior and supporting fire risk management. WindNinja, a widely used diagnostic wind downscaling model, is strongly dependent on its initial input data; however, systematic evaluations of its input sensitivity and simulation accuracy remain limited. In this study, a representative canyon area was selected as the study site. WindNinja was driven by three types of input data: local meteorological station observations, national meteorological station observations, and ERA5-Land reanalysis data. Two indices—the Wind Forcing Intensity (WFI) index and the Thermal Forcing Intensity (TFI) index—were constructed to classify weather-forcing scenarios and evaluate simulation accuracy under different conditions. The results show that differences in the statistical characteristics of the initial wind sources produce pronounced sensitivity in WindNinja simulations. Simulations driven by local meteorological observations generally overestimate wind speed, whereas ERA5-Land-driven simulations systematically underestimate wind speed, with national-station results falling between these two cases. Simulation accuracy varies with terrain position: wind direction errors dominate in valleys, whereas wind speed errors dominate on ridges and hilltops. Weather background conditions significantly influence simulation accuracy. Wind forcing intensity dominates the magnitude and dispersion of simulation errors, while strong thermal forcing leads to an overall decline in simulation accuracy and stability. These findings highlight the sensitivity of WindNinja to initial wind sources and weather background conditions in complex terrain and provide guidance for its application and uncertainty control in wildfire behavior modeling.

Fire doi: 10.3390/fire9050200

Authors: Devan Allen McGranahan

Wildland fire scientists have made substantial advances in measuring fire behavior, but properly collecting data is often beyond the capacity of prescribed fire managers and by definition all but impossible for wildfire events. While a method for the immediate assessment of burn severity has been developed around multispectral imagery from space-based Earth observation systems, there has been little comparison of these post hoc metrics to actual fire behavior. Meanwhile, the application of research results from experimental prescribed burns to rangeland affected by wildfire can be impeded by a lack of understanding of how immediate burn severity differs between wildfires and prescribed burns, especially in rangelands. Overall, much of what is known about wildland fire behavior, severity, and effects comes from forests, whereas rangelands are characterized by having lower fuel loads comprised of fine vegetation that promotes high rates of spread and brief residence time. This paper provides rangeland-specific information on the relationships between direct field-based fire behavior measurements and a space-based index of burn severity (differenced Normalized Burn Ratio, ΔNBR, from Sentinel-2 imagery), and uses those data to compare burn severity across 54 prescribed burns in North Dakota, USA, and 28 nearby wildfires in the US Northern Great Plains. In prescribed burns, remotely sensed burn severity increased with rate of spread and flame temperature 15 cm above the ground, but had no statistically significant relationship with soil surface temperature. In the semi-arid western zone of the Northern Great Plains, wildfires and prescribed burns had similar, low–moderate severity; wildfires in the eastern zone tended to be of moderately high severity and thus greater than the low severity of the experimental prescribed burns. By describing meaningful gradients in surface fire behavior in rangelands with ΔNBR, even those without the capacity to measure fire behavior in the field can monitor prescribed fire effectiveness and incorporate burn severity in adaptive management plans. Understanding the relationship between burn severity across wildfires and prescribed burns is a critical step in applying knowledge gained from research on prescribed fires to areas impacted by wildfire. Resistance to prescribed burning might be overcome by increasing livestock managers’ experience with post-fire forage resources through grazing areas burned in unintentional wildfires, but current practice and policy discourage or outright prevent ranchers from doing so. Future research ought to connect burn severity with ecosystem recovery metrics to ensure post-fire grazing does not impair rangeland sustainability.

Fire doi: 10.3390/fire9050199

Authors: Jeevan Rawal Liangbo Hu

Wildfires may significantly alter the mineralogical and microstructural characteristics of geological materials, leading to increased susceptibility to landslides, debris flows, and other related hazards. These processes may involve considerable post-fire hydrological changes that affect the infiltration rate and the surface runoff in the burned soils. In the present study, a laboratory experimental investigation is carried out focusing on the water infiltration in burned soils which were produced in a muffle furnace at accurately controlled temperatures within 400 °C∼800 °C. The original and burned soils were first subjected to a number of geotechnical tests, including grain size distribution, consistency, and hydraulic conductivity. Subsequently, their water infiltration rates were measured in a laboratory setup. Finally, numerical simulations are performed to assess the infiltration process based on the Green–Ampt model. The experimental results reveal significant differences in the hydrological behavior between burned and unburned soils. Overall, burned soils experienced quicker ponding and slower infiltration. However, as the burning temperature increased from moderate to high, the infiltration rate also rose considerably, along with delayed ponding time. This trend may be related to the microstructural change in the grain size distribution explored experimentally in the present study. The numerical results are highly consistent with the experimental data. The hydraulic conductivity is identified as the predominant parameter in the infiltration process examined and simulated in the present study. Its evolution with varied burning temperatures can also be traced to the fire-induced alteration in the grain size distribution, and primarily accounts for the differences in the infiltration of different soil specimens. The present study demonstrates the potential of laboratory experiments complemented with a quantitative modeling approach in improving our understanding of soil’s post-fire hydrological responses.

Fire doi: 10.3390/fire9050198

Authors: Yunhao Li Song Lin

In a tank farm, even if the separation distance meets the codes and standards, a pool fire in one tank may spread quickly to another tank. Most destructive and uncontrollable fire accidents are induced with multiple pool fires. In current work, the thermal buckling behaviors of a fixed-roof tank subjected to one (two) neighboring pool fire(s) (burning tanks) are numerically studied. The effects of the number of the pool fires, the separation distance between two pool fires, and the distance between the adjacent tank and pool fires are analyzed. The results indicate that the thermal buckling zone of the target tank subjected to two pool fires is larger than that subjected to one pool fire, and the maximum displacement for two pool fires is almost equal to that for one pool fire. The target tank subjected to one pool fire loses stability and reaches a new stable state faster than that subjected to two pool fires. The thermal buckling zone expands as the distance between the two pool fires increases but decreases with increasing separation distance between the pool fire and the target tank. The findings provide useful guidance for the structural optimization of steel storage tanks against pool fire exposure and offer theoretical support for emergency response and fire rescue in tank farms.

Fire doi: 10.3390/fire9050197

Authors: Fangjiao Ma Thomas A. Jones

Accurate smoke forecasting during wildfires is essential for hazard assessment and public health protection. Current operational models have limitations in representing dynamic fire-atmosphere interactions. This study aimed to assess the performance of the fire-atmosphere coupled version of the Warn-on-Forecast System (WoFS) and compare it with the classic WoFS in simulating wildfire smoke distribution and structure. Two Oklahoma wildfire events were simulated, and model outputs were compared against radar reflectivity observations for plume-top height, horizontal dispersion, and vertical structure. Both models showed comparable agreement with observations. WoFS-Smoke performed similarly or better in the early forecast period (0–1 h) due to direct smoke injection, whereas WRF-SFIRE, using a WoFS environment, required ~1 h spin-up before producing more realistic, continuous plume structures through fire-atmosphere coupling. SFIRE tended to overestimate plume height in one case and underestimate it in another. Coupling WoFS to SFIRE generally produced more realistic forecast smoke plume characteristics resulting from the dynamical coupling between the forecast environment and wildfire properties. The combination of WoFS and WRF-SFIRE opens up new possibilities in short-term wildfire smoke forecasting, setting the foundation for future operational models.

Fire doi: 10.3390/fire9050196

Authors: Jinrui Deng Wencheng Miao Jipei Sun Haiping Bai Yaqiang Su Jinyou Wang Bingcai Sun Yinghua Jing Xin Xu

Passive Fourier transform infrared (FTIR) spectral imaging technology is easily affected by complex background radiation for leakage monitoring at natural gas stations, leading to low gas identification sensitivity, poor detection accuracy and a high false alarm rate. To address these issues, the spectral characteristics of typical background interference sources and their impact mechanisms were first analyzed in this work. Subsequently, a targeted background denoising method was developed and then on-site gas release experiments were conducted in a typical natural gas station. The results demonstrated that the proposed background denoising method can effectively suppress complex environmental background interference and reduce the false alarm rate. This study provides a solution for enhancing the reliability and practicality of passive FTIR spectral imaging technology in remote gas leakage monitoring at industrial sites.

Fire doi: 10.3390/fire9050195

Authors: Marc Padilla Ruben Ramo Jose Luis Gomez-Dans Sergio Sierra Bernardo Mota Roselyne Lacaze Kevin Tansey

Despite the well-known strong influence of spatial resolution on the quality of burned area mapping and the need for timely environmental information, global wildfire monitoring services are commonly based on coarse spatial resolution (300–500 m) reflectance imagery and deliver products months or years after the present date. The paper presents, for the first time, an algorithm that provides highly accurate near-real-time medium spatial resolution burned area, from 20 m Sentinel-2 imagery. The paper exploits a pioneering sensor-independent potential of a mapping method, based on land surface reflectance modelling and machine learning, originally optimised for Sentinel-3 imagery. The mapping method uses predictions of time series of burned area from a neural network, which are combined with the spatio-temporal density of active fire detections. The mapping method was calibrated and validated using reference datasets for the years 2020 and 2019, respectively. The novelty of this method lies in its high accuracy and multi-latency flexibility: it achieves a Dice coefficient (DC) of 82.7% with zero-day latency, already surpassing the 81.8% accuracy of current state-of-the-art non-time critical methods. As reflectance data availability increases, accuracy scales to DC 84.7% and 85.4% with 5 and 10 days of latency, respectively, and to DC 87.2% for monthly composites with 45 days of latency.

Fire doi: 10.3390/fire9050194

Authors: Tiantian Yang Wanxing Ren Qing Guo Shuo Yang Ligang Lu Yin Chang Qi Wang

Immersive entertainment venues use spatial complexity to enhance visitor experience, but these design features may impair emergency evacuation, particularly when lighting fails. We conducted a 2 × 2 factorial experiment with 264 participants to quantify how spatial complexity and lighting conditions interact to affect evacuation performance. Ultra-wideband positioning provided centimeter-level tracking. Results showed very large main effects for spatial complexity (ηp2 = 0.976) and lighting (ηp2 = 0.863), but critically, a significant interaction (ηp2 = 0.799) revealed asymmetric patterns: darkness barely affected simple spaces (6.5% increase) but severely impaired complex spaces (36.9% increase), with a nine-fold amplification. The worst-case scenario (high complexity + darkness) increased evacuation time by 115% compared to optimal conditions. Findings demonstrate that spatial complexity and lighting combine synergistically, creating multiplicative rather than additive risk, with the worst-case combination increasing evacuation time by 115% relative to optimal conditions. Findings support prioritizing spatial simplification and emergency lighting redundancy in the design of complex immersive venues.

Fire doi: 10.3390/fire9050193

Authors: Xuguang Li Lei Zheng Qiaotong Zhang Jinbo Zhang Qiuju Ma Chenghui Li

Obstacles can significantly affect the thermal radiation distribution of oil storage tank fires; however, this issue has received relatively limited attention in previous studies. Taking aviation kerosene fires as an example, this study employed a cylindrical flame radiation model combined with the Monte Carlo method to investigate the variation in the radiative flux incident on the target and the flame-target view factor under different obstacle widths (W), heights (H) and target distances (d). The results indicate that obstacles block the flame radiation path, thereby reducing the radiative flux in the region behind the obstacle compared with the unobstructed condition. The view factor first decreases with increasing W and then approaches a stable value. The critical width (Wcr) is independent of H but increases with d. A similar relationship is observed between H and the critical height (Hcr). Based on geometric analysis, analytical expressions for Wcr and Hcr were derived. In addition, a predictive model for the view factor shielding ratio (φ) was established using three dimensionless geometric parameters, achieving a coefficient of determination of R2 = 0.976, which demonstrates good predictive accuracy. These findings provide theoretical guidance for fire risk assessment in tank farm areas.

Fire doi: 10.3390/fire9050192

Authors: Chao Ding Chenjin Zhang Yuhang Han Qianwen Han Han Wang Jinlong Zheng Mingming He Hong Zhu

This study establishes an experimental platform consisting of an adjustable inclined surface and a cross-slope wind system. Turbulent diffusion flames are investigated by examining the variation characteristics of flame morphology under slope angles of 10–40°, cross-slope wind velocities of 0.8–2.0 m/s, and heat release rates of 15.38–61.50 kW. The results show that variations in slope angle change the components of buoyancy in the normal and tangential directions. The normal component influences the lifting of the flame perpendicularly to the slope, while the tangential component, together with differences in air entrainment on both sides of the flame, promotes flame inclination and spreading along the slope surface. The cross-slope wind enhances the horizontal stretching and attachment tendency of the flame through inertial shear, while simultaneously suppressing flame height and its development along the slope. The coupled effects of these factors cause the flame morphology to gradually transition from a nearly vertical state to an attached state. Based on dimensionless analysis, empirical correlations of flame morphology parameters are established by introducing the cross-slope wind Froude number, dimensionless heat release rate, the density ratio of propane to air, and a slope function. Within the experimental range of this study, the data under various conditions show good collapse and correlation under the selected dimensionless parameters.

Fire doi: 10.3390/fire9050191

Authors: Alice Fletcher Holle Jiemin Zhang Imrana I. Kabir

Polyurethane (PU) is a highly versatile class of polymer utilised in many industries, including the aerospace sector. In conjunction with its superior mechanical properties, chemical resistance, and durability, it can be highly flammable depending on its form. This poses a risk aboard aircraft, which contain numerous fire hazards and cramped cabin spaces, proving an obstacle for the evacuation of passengers in an emergency. Flame-retardant additives have proven to enhance the thermal properties of polyurethane, but their toxicity and tendency to degrade mechanical performance make them unappealing. This review addresses three main topics: (1) the basic synthesis and structure of PU and modification through additives; (2) types of PU, their properties, and applications in the aerospace industry; and (3) evaluation methodologies for characterising PU performance, studying mechanical properties and thermal degradation. Several key challenges remain, including understanding the long-term durability of modified PU, optimising between fire performance and mechanical properties, improving the sustainability of PU throughout its lifetime, and validating numerical simulation as a viable testing method. This review aims to guide future research on modified PU technology to achieve safer, high-performing, and sustainable solutions for the aerospace industry and beyond.

Fire doi: 10.3390/fire9050190

Authors: Xucheng (Fred) Huang Ke Xu Jeremy A. Sarnat Yang Liu

Wildfire smoke contributes substantially to episodic PM2.5 exposure, yet outdoor measurements may not represent indoor conditions. We analyzed indoor PurpleAir sensors and nearby outdoor monitors from U.S. residences (2019–2024) to estimate smoke-day changes in the outdoor–indoor PM2.5 difference and characterize heterogeneity across regions. After data quality control and the application of completeness criteria, 509 monitor pairs contributed 250,873 monitor-days. Smoke days were assigned using the NOAA Hazard Mapping System smoke-plume polygons. Pair-specific time-series models estimated smoke-day changes in the outdoor–indoor PM2.5 difference, which were pooled using random-effects meta-analysis; heterogeneity was summarized by clustering indoor and outdoor smoke–non-smoke contrasts. In the unadjusted summary, the mean outdoor PM2.5 was 8.61 vs. 5.63 µg/m3 on smoke vs. non-smoke days and the mean indoor PM2.5 was 6.33 vs. 5.09 µg/m3, reflecting an increase in the mean outdoor–indoor difference from 0.54 to 2.27 µg/m3 (p < 0.001). The pooled smoke-day effect on the outdoor–indoor difference was 0.88 µg/m3 (95% CI: 0.80, 0.96). Clustering identified four distinct response patterns, most commonly outdoor increases exceeding indoor increases, with smaller subsets showing extreme outdoor amplification or net indoor reductions under modest outdoor increases. These results indicate that indoor protection during smoke episodes is common but variable and support exposure characterization beyond outdoor concentrations alone.

Fire doi: 10.3390/fire9050189

Authors: Sara Mostofi Fatih Yesevi Okur Ahmet Can Altunişik Ertugrul Taciroğlu

This study proposes a sensor-free approach for indoor fire detection that leverages existing Wi-Fi infrastructure as a passive sensing modality. By extracting Channel State Information (CSI) from prevalent 802.11n Wi-Fi signals and applying an unsupervised deep anomaly detection model, the approach conceptualizes the wireless environment as a sensorless detection field capable of identifying combustion-induced perturbations without requiring any physical sensors. CSI data were collected in both normal and flame-induced states under three combustion conditions (gasoline, wood, plastic), each introducing unique signal perturbations. These data, which exhibit diverse signal perturbations, were used as input to four unsupervised deep anomaly detection architectures: a variational autoencoder (VAE), a 1D convolutional autoencoder (CNN-AE), a long short-term memory autoencoder (LSTM-AE), and a hybrid CNN-LSTM autoencoder. Each architecture was trained exclusively on baseline data to learn compact latent representations of normal signal patterns. Among the evaluated architectures, CNN-AE achieved perfect detection across all scenarios, showing high responsiveness to signal disruptions. LSTM-AE tracks prolonged combustion but struggles with fast-onset anomalies. VAE maintains low error during baseline but misses sharp deviations. These findings validate that Wi-Fi CSI encodes latent combustion features. The method requires no additional sensors and operates on existing signals, enabling scalable smart building integration via lightweight software updates.

Fire doi: 10.3390/fire9050188

Authors: Yong Jin Yufei Gu Hongjiang Zhu Yang Xu Chuan Jiang Jianping Zhu Yuejin Zhu

In view of the practical engineering demand and performance optimization of compressed air-driven fire-extinguishing projectile launchers, a two-dimensional axisymmetric compressible flow numerical model is established based on ANSYS Fluent 2023. Numerical verification is conducted by comparing with classical zero-dimensional theoretical results and reference data from the published literature to guarantee simulation accuracy. Combined with the internal ballistic motion characteristics, the present study systematically investigates the effects of initial pressure, flow passage structure, loading position and projectile mass on launch dynamic behavior and the energy utilization mechanism. The results reveal that the initial high-pressure chamber pressure dominates the total energy output of the system. Appropriately increasing the valve gap and nozzle diameter can improve flow characteristics and energy transfer efficiency. Adjusting the loading position and barrel length effectively balances the internal ballistic response, while larger projectile mass brings higher inertial resistance and obvious efficiency attenuation. This work clarifies the quantitative influence of key structural and operating parameters, and provides theoretical support and engineering reference for the design, parameter matching and performance improvement of similar fire-extinguishing launching equipment.

Fire doi: 10.3390/fire9050187

Authors: Yeshvant Matey Raymond de Callafon Ilkay Altintas

Prescribed fires can reduce hazardous fuel loads, but planning remains challenging in landscapes with complex terrain, mixed vegetation, and nearby infrastructure. Predicting and controlling where a prescribed fire may breach its containment lines can be carried out by integrating fire-behavior simulations with practical treatment planning. This paper presents a two-stage framework using QUIC-Fire to identify high-risk escape zones and allocate containment treatments under cost and resource constraints. Stage 1 identifies high-risk boundary segments and assigns adjacent zones to fuel removal or moisture treatment to limit simulated fire spread beyond the control line. Stage 2 refines these assignments by incorporating treatment costs and penalty values near infrastructure to evaluate resource-constrained alternatives. Applied to a 14.2-ha prescribed-fire unit in Mount Laguna, California, the optimized Stage 2 configuration maintained containment under the simulated conditions while reducing total implementation cost from USD 97,319 to USD 95,266 (approximately 2.1%) and reducing fire-engine demand from six to three. These results illustrate how cost-aware treatment reallocation can improve resource efficiency for prescribed-fire performance.

Fire doi: 10.3390/fire9050186

Authors: Jialu Cao Yibao Wang Chong Li

Urban fires are highly destructive with high casualty rates, often causing significant casualties and property losses. The obstruction of the Community Emergency Corridor System is a critical factor exacerbating fire casualties, directly related to residents’ life safety and public security governance effectiveness. Currently, community emergency corridors face severe systemic bottlenecks in the coordinated development of triadic space (physical, social, and information spaces), and the lag of information space has become a fatal shortcoming restricting emergency response efficiency, highlighting the urgent need for a comprehensive evaluation framework. However, existing studies mostly focus on a single spatial dimension, lacking a systematic framework for the coordinated patency of triadic space. Based on this, this study adopts the triadic space perspective, takes 77 typical communities in China as research objects, and uses the Entropy Weighted TOPSIS method to construct an evaluation index system for the accessibility of the Community Emergency Corridor System and systematically measure its level. The results show that the patency of triadic space is unbalanced overall; social space outperforms physical and information spaces (with the latter being the lowest), reflecting deficiencies in emergency information release and acquisition. Regionally, accessibility in Northeast China is significantly higher than in other regions (Northeast > West > Central > East), and eastern China has the lowest scores in physical and information spaces due to high urbanization, dense buildings, and land scarcity. Corresponding countermeasures are proposed to address regional disparities. The triadic space evaluation framework and methodological path provide a replicable analytical tool for urban fire-oriented community emergency management and references for fire resilience governance in other countries or high-density communities.

Fire doi: 10.3390/fire9050185

Authors: Langlang Liu Zhilong Wei Haisheng Zhen Wenwen Wang Yang Wu

This paper conducts an experimental study to develop a response strategy for lithium-ion battery fires. Guided by the principle of “first control, then extinguish”, the strategy integrates a lithium-ion battery-specific fire-controlling blanket with castable fire-extinguishing agents. Both fire tests of e-bikes and lithium-ion batteries are conducted. From e-bike fire tests, the feasibility of rescuers conducting close-range disposal of LIB (lithium-ion battery) fires is analyzed from three perspectives, i.e., fire evolution stage, battery splashing and high temperature. The results indicate a high risk of fire spread, as well as a strong likelihood of human injury caused by flying LIB debris and extremely hot gases. Subsequently, the fire-controlling capability of the fire blanket is validated. It not only blocks splashing batteries and jet flames, reducing combustion intensity, but also offers a safe way for personnel to operate the portable fire extinguishers. Through two castable extinguishing agents tested, the perfluorohexanone-based agent outperforms the water-based alternative. The reasons are as follows. First, perfluorohexanone evaporates easily in the low-temperature, confined environment created by the fire blanket. Second, it possesses both physical and chemical fire-extinguishing capabilities, ultimately delivering a more potent combustion suppression effect.

Fire doi: 10.3390/fire9050184

Authors: Zhijie Huang Xiansong Fu Shouxiang Lu Wenbin Yao

Accurate measurement of the 2D soot spatial distribution is vital for optimizing combustion efficiency and reducing pollutant emissions. While 1D laser extinction (LE) is robust and cost-effective, it provides only line-of-sight integrated information, lacking the spatial resolution required to resolve complex soot topologies. We propose Soot-MixFormer, a hybrid deep learning model designed for the high-fidelity inversion of 2D soot distributions from 1D extinction data. The architecture integrates CNN-based local feature extraction with Transformer-based global dependency modeling. Key innovations include a dynamic decoupled generation head and a Dual-Axial Gated Refinement (DAGR) module coupled with a physical hard constraint layer to ensure mass conservation and physical consistency. Experimental results demonstrate that Soot-MixFormer significantly outperforms baseline MLP and CNN models, achieving a Structural Similarity Index (SSIM) of 0.800 and a Pearson Correlation Coefficient (PCC) of 0.915, and a highly suppressed Root Mean Square Error (RMSE) representing less than 10% relative error in high-concentration zones. Furthermore, the model exhibits exceptional robustness, maintaining a cosine similarity above 0.72 even under 10% simulated measurement noise. The model is highly efficient, with only 0.97 M parameters and a real-time inference speed of ~246 FPS. This study provides a novel, low-cost diagnostic paradigm for real-time, high-accuracy monitoring of soot fields in industrial combustion environments, effectively bridging the gap between simple 1D sensing and complex 2D spatial reconstruction.

Fire doi: 10.3390/fire9050183

Authors: Lvchao Qiu Zheng Zhou Wenjun Ou Yutong Zhou Jingrui Hu Zhoufeng Zhao Huimin Liu Kuangda Lu Shouwei Jian

Transformer fires within indoor substations constitute severe hydrocarbon fire scenarios characterized by rapid heat release rates and extreme peak temperatures, posing a critical threat to the structural integrity of steel frameworks and power grid stability. To rigorously assess structural safety under such conditions, this study employs a sequential thermal-mechanical coupled numerical methodology combining Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA). Focusing on a 110 kV indoor substation, the research simulates the transient, non-uniform temperature fields induced by transformer oil combustion and analyzes the thermo-mechanical response of key steel components. Furthermore, the protective efficacy of two non-intumescent coatings (Material A and Material B) with distinct thermal conductivities is systematically evaluated. Computational results elucidate significant thermal stratification, with upper-level structures sustaining exposure to temperatures exceeding 1500 K. Unprotected steel components subjected to direct flame impingement exhibit severe stress concentrations and plastic deformation, reaching their load-bearing limit within 4825 s. The application of fire-retardant coatings markedly enhances fire resistance; a 5 mm layer of Material A (λ = 0.20 W/(m·K)) extends the time to failure to approximately 9390 s. Notably, increasing the thickness of Material A to 20 mm, or alternatively employing a 10 mm layer of Material B (λ = 0.10 W/(m·K)), effectively mitigates thermal stress concentrations. This ensures structural deformation remains within safe limits throughout a 3 h (10,800 s) fire duration. This study provides a theoretical basis and quantitative engineering references for the optimal fire protection design of substation steel structures.

Fire doi: 10.3390/fire9050182

Authors: Chengyao Hou Pingshan Liu

Reliable detection of personnel is critical for situational awareness and life-saving interventions during indoor fire rescue operations, where dense smoke rapidly obscures visibility and compromises conventional vision systems. Visible-light cameras fail under such conditions due to severe Mie scattering, while thermal infrared (TIR) imaging—though capable of penetrating smoke—often lacks the fine-grained texture needed to distinguish human forms from background clutter. Furthermore, practical deployment of multimodal sensors is hindered by spatial misalignment between modalities, which degrades fusion efficacy and detection accuracy. To address these challenges, this paper proposes GLAFC-YOLO (Global-Local Alignment and Frequency-aware Cross-attention Fusion), a dual-stream multimodal detection framework specifically designed for personnel localization in smoke-obscured indoor fires. GLAFC-YOLO fuses near-infrared (NIR) and TIR imagery through three novel components: (1) a Global-Local Feature Alignment Subnet (GL-FAS) that rectifies geometric misalignment across modalities; (2) a Modality-Adaptive Frequency Channel Attention (MA-FCA) module that enhances complementary smoke-penetrating thermal signatures and NIR texture cues in the frequency domain; and (3) a Confidence-Aware Transposed Cross-Attention (CA-TCA) mechanism that suppresses smoke-induced artifacts and restores weakened human-centric features. Evaluated on a newly collected multimodal dataset of indoor fire scenarios with annotated personnel, GLAFC-YOLO achieves substantial improvements over the baseline YOLOv11 architecture. Specifically, it achieves Recall improvements of 43.2% and 0.5% compared to unimodal NIR and TIR baselines, respectively. In addition, it achieves improvements of 37.4% and 3.9% in mAP50 and 17.3% and 17.0% in mAP50–95. Experimental results indicate that GLAFC-YOLO outperforms competitive models and reduces personnel miss rates, demonstrating its robustness and readiness for real-world fire-rescue assistance.

Fire doi: 10.3390/fire9050181

Authors: Scott D. Brau Benjamin J. Mendelson Rudi A. Marciniak David J. Cornell Kyle T. Ebersole

Recruitment of firefighters is, in part, hindered due to attrition from fire academies. This study explored initial fitness differences between those who graduated (GRAD) or were released (REL) from the academy. During the first week of the academy, recruits (N = 407; GRAD = 354, REL = 53; 26.6 ± 7.2 yrs; 177.6 ± 8.6 cm; 87.9 ± 17.2 kg) completed an assessment battery including: body composition using skinfold calipers to estimate percent body fat (BF) and fat-free mass (FFM); shoulder mobility via Apley’s scratch test (APLEY); aerobic fitness (VO2peak) and heart rate recovery (HRR1min) estimated from the five-minute Forestry step test; muscular strength via the sum of right and left handgrip (SHG); and muscular endurance via a paced two-minute push-up test (PU). A t-test identified age differences between GRAD and REL, followed by separate ANCOVAs for each fitness measure, and logistic regression to identify the ability of fitness measures to predict academy outcome. GRAD had a lower age and BF and a higher FFM, VO2peak, SHG, and PU, but did not differ in APLEY or HRR1min. The full model predicting release was significant; age, BF, and FFM were significant predictors. These results provide pre-fire academy preparation guidance for optimizing the potential for successful academy completion.

Fire doi: 10.3390/fire9050180

Authors: Maria Prodan Emilian Ghicioi George Artur Gaman Daniel Pupazan Marius Cornel Suvar Nicolae Vlasin Florin Manea Irina Nalboc Andrei Szollosi-Mota Gheorghe Daniel Florea Robert Laszlo

While polyurethane acoustic foam is widely used in entertainment settings for sound absorption, it poses a considerable fire risk when exposed to sparks from pyrotechnic devices. Even though fountain-type pyrotechnic devices are often perceived as producing “cold sparks”, the ignition potential of a single incandescent particle remains insufficiently quantified. This study experimentally investigates the ignition capacity of a fountain-type pyrotechnic article on pyramidal polyurethane acoustic foam under controlled conditions. Three dedicated experimental configurations were developed: (i) ignition probability tests at various distances, (ii) scaled configuration tests reproducing realistic installation geometry, and (iii) high-speed visualization of single incandescent particle interaction with the foam surface. For the first two configurations, ignition probabilities of 20% and 22.2% were obtained. High-speed recordings showed two distinct interaction mechanisms: particle fragmentation and ricochet, which did not result in ignition; partial penetration with localized melting, volatile release, and gas-phase ignition when residual thermal energy (about 0.5–1 J) was retained. The results demonstrate that even isolated single incandescent particles generated under realistic conditions can initiate the combustion of polyurethane acoustic foam. These findings challenge the “cold spark” safety perception and provide quantitative evidence that particle–induced ignition represents a significant fire hazard in enclosed environments where combustible acoustic materials and pyrotechnic effects coexist. The findings in this paper have direct implications for safety regulations in entertainment venues.

Fire doi: 10.3390/fire9050179

Authors: Isaac Forrest Ali Tohidi Angel Farguell Aurélien Costes Leda N. Kobziar Phinehas Lampman Eric Rowell Adam Kochanski

Bioaerosols released during wildland and prescribed fires may influence ecosystems, air quality, and microbial dispersal, yet their transport and deposition remain poorly understood. This study combined infrared uncrewed aircraft system (UAS) observations of a prescribed burn with the coupled fire–atmosphere model WRF-SFIRE and a Lagrangian particle model in order to evaluate how uncertainties in simulated fire behavior affect predicted bioaerosol (bacterial cell) transport and deposition. A reconstruction of the observed spatiotemporal evolution of the fire was derived from thermal UAS measurements acquired during the burn and incorporated into a WRF-SFIRE simulation, in which the modeled fire spread was constrained to follow this reconstructed progression. This benchmark run was compared with two unconstrained, fully coupled simulations that used a low and a high estimate of fuel moisture content (FMC) to represent typical uncertainty in fire rate of spread (ROS) prediction. Despite substantial differences in fire intensity and plume dynamics among the simulations, the resulting bioaerosol transport pathways and deposition patterns were broadly consistent across cases. The horizontal transport of the bioaerosols was dominated by the ambient Easterly wind and the bioaerosols were lofted by fire-affected updrafts—some exceeding 10 m/s—within the buoyant plume structure resolved in WRF-SFIRE. Deposition hot-spots appeared in consistent locations in the three simulations, especially regions where topography forced up-slope transport. Although the most intense fire produced slightly greater local deposition—likely due to a combination of stronger fire-induced downdrafts and overturning from penetration into strong vertical wind shear above the boundary layer—differences were small relative to the overall deposition footprint. These results suggested that, for burns of this scale, bioaerosol transport and deposition predictions are relatively robust to realistic uncertainties in fire-behavior modeling. This finding indicates that coupled fire–atmosphere and particle-transport modeling frameworks could be employed to quantitatively forecast microbial transport and deposition during future controlled burn experiments.

Fire doi: 10.3390/fire9050178

Authors: Jikun Liu Chenghu Wang Guiyun Gao Yiyu Wang

The lack of scientific methods for selecting monitoring indicators and equipment undermines the efficiency of forest fire emergency response. To address this gap, we developed a novel evaluation model for emergency monitoring indicators based on the spatiotemporal evolution of forest fires. The model, comprising four primary and eight secondary factors, leverages a hybrid TriFAHP and DBN approach to objectively determine factor weights based on survey data from 20 domain experts. The results indicate that the primary factor weights rank as follows: Monitorability (0.3807) > Timeliness (0.3353) > Sensitivity (0.1874) > Feasibility (0.0966). Four indicators (wind speed, temperature, flame, and gas) were identified as the most suitable for core monitoring. Furthermore, stage-specific monitoring strategies were proposed, prioritizing different core indicators across the ignition, spread, and fully developed fire stages. An indicator and equipment association was established, recommending optimal configurations such as UAV-mounted thermal imagers and lidar anemometers. The practical applicability of the proposed framework was successfully validated through real-world case studies, including the 2019 to 2020 Australia bushfires. This study provides a standardized framework aligning indicators, equipment, and scenarios, offering theoretical and practical guidance for optimizing emergency monitoring systems.

Fire doi: 10.3390/fire9050177

Authors: Stephen A. Whitmore Ryan J. Thibaudeau Ava T. Wilkey

Hybrid rocket technologies are gaining recognition as eco-friendly alternatives to traditional propulsion systems. Utah State University’s Propulsion Research Laboratory has developed a High-Performance Green Hybrid Propulsion (HPGHP) technology, leveraging 3D-printed ABS fuel for reliable, low-energy ignition. Among tested materials, only ABS shows suitable electrical-breakdown properties for arc ignition. Unfortunately, due to the proprietary formulations in commercial ABS blends, and its limited use as a rocket-propellant, related composition and combustion data are limited. This study uses spectroscopic evaluation and bomb calorimetry to estimate material compositions, enthalpies of formation, and combustion energies for multiple commercially available 3-D print feed stock ABS types, finding minimal differences amongst the samples tested. Based on these test results, “representative” ABS properties including chemical formula, mean molecular weight, enthalpy of formation, and Higher Heating Value, is recommended. Follow-on tests with 5 alternative, commonly used, 3D-printable thermoplastic feed stocks demonstrate that ABS has significantly higher energy content. This result supports ABS’s advantages and utility as a conveniently fabricated hybrid rocket fuel.

Fire doi: 10.3390/fire9040176

Authors: Roghayeh Jahdi Liliana Del Giudice Michele Salis

We analyzed wildfire hazard profiles across the Hyrcanian temperate forests of northern Iran (Guilan Province) by simulating a large set of wildfires with FlamMap MTT. We first derived geospatial data on terrain, fuel models, weather conditions, and historical wildfire occurrence (1992–2022) for the study area. We stratified fire weather conditions and fuel moisture based on the bioclimatic classification of the study area, considering observed extreme fire weather, as well as observed and random fire ignition locations for the simulations. The wildfire simulations were used to estimate burn probability (BP), conditional flame length (CFL), fire size (FS), and crown fire probability (CFP). BP ranged from 0 to 5.0 × 10−2, with mean values of 1.3 × 10−3 and 1.1 × 10−3 for observed and random scenarios, respectively. The mean value of CFL from random ignition simulations (0.78 m) was substantially higher than that obtained in the observed ignition simulations (0.54 m), ranging from 0 to 6.75 m. We evidenced significant differences between observed and random ignition simulations for all wildfire hazard metrics. The highest wildfire hazard profiles were observed in the Cold-Mountainous bioclimatic zone under the random ignition simulations. On average, the annual number of anthropic structures threatened by wildfires ranged from 97 (observed scenario) to 123 (random scenario). This research provides detailed and spatially explicit fire hazard and exposure maps to inform fire modeling, land management, and policy actions.

Fire doi: 10.3390/fire9040175

Authors: Damir Krstinić Jakov Bejo Toma Sikora Marin Bugarić

Early detection is a key tool for mitigating the devastating effects of wildfires. Single-frame detection methods that do not consider inter-frame dependencies often fail to detect smoke plumes at the earliest stage and at greater distances, or produce excessive false alarms. Biological vision is particularly sensitive to motion cues, and this translates well to automated systems. Recent temporal-memory approaches have demonstrated improved performance over purely spatial methods, but typically rely on complex, computationally heavy multi-stage architectures. This study investigates the possibility of encoding temporal and contextual information into additional image channels as a basis for compiling data models with increased information content. Seven distinct data models were proposed, and corresponding datasets were generated to train standard YOLO architectures without modifications to the network structure. The datasets were compiled from real wildfire footage collected from an operational wildfire surveillance system in Croatia, comprising 333 annotated sequences of real fires recorded between 2018 and 2024. Experimental evaluation compared the performance of YOLO models trained on the information-enriched datasets with those trained on standard RGB images. Based on the results, the best data model for early wildfire smoke detection, combining original RGB channels with short-term and long-term temporal memory, was selected. Comparative evaluation demonstrated improved detection accuracy, achieving up to 5 percent higher true-positive detection rate for models trained on spatio-temporal data compared to standard RGB images, while maintaining low inference latency. The proposed approach shifts the focus to the structure and information content of the data while preserving the efficiency of standard convolutional neural network architectures. This approach could be applied to other problems requiring high efficiency and real-time operation, where temporal and contextual information can improve detection performance.

Fire doi: 10.3390/fire9040174

Authors: Haoshi Sun Jing Luo Pincong Wu Jizhe Wang Yuxian Bing Mengqi Yuan Xijing Li Yuanzhi Li Xinming Qian Qi Zhang

This study experimentally investigates the combustion behavior of RP-3 aviation kerosene pool fires (300~800 mm) within a confined space, specifically focusing on the complex interaction between buoyancy-driven plumes and mechanical negative pressure ventilation. By integrating high-precision mass loss measurements with multiple characteristic parameters, this research uniquely characterizes the transition of energy feedback mechanisms under confined suction flow. Results show that ventilation enhances combustion intensity and compresses the fire cycle. For an 800 mm pool, the peak mass loss rate rose by 57.1%, from 16.71 g/s to 26.25 g/s. This enhancement stems from boundary layer thinning, which transitions the combustion from diffusion-controlled to kinetics-controlled. Ventilation also induces severe flame tilt with a non-monotonic trend. The tilt angle peaks at 84° for 600 mm pools but drops to 64° at 800 mm as buoyancy momentum increases. Additionally, an energy contrast of vertical cooling and horizontal heating was observed. Axial peak temperatures decreased by 20%, while downwind thermal radiation flux increased by up to 125%. The ventilation system essentially acts as a directional energy projector, shifting heat loads toward the downwind region. These findings support the optimization of fire safety and detection designs for industrial ventilation systems. This study experimentally investigates the combustion behavior of RP-3 aviation kerosene pool fires (300–800 mm) within a confined space, specifically focusing on the complex interaction between buoyancy-driven plumes and mechanical negative pressure ventilation. By integrating high-precision mass loss measurements with multi-point thermal and imaging diagnostics, this research uniquely characterizes the transition of energy feedback mechanisms under confined suction flow.

Fire doi: 10.3390/fire9040173

Authors: Karam Abu El Haija Rafael M. Santos

Enhanced rock weathering (ERW) is a promising carbon dioxide removal strategy that accelerates silicate mineral dissolution to generate alkalinity and sequester carbon in soils and aquatic systems. The frequency and severity of fires are increasing globally, and fire-prone regions such as agricultural lands, forests, and grasslands overlap substantially with potential ERW deployment areas. However, fire–ERW interactions remain unexamined. This perspective synthesizes the literature on fire effects on soil properties to develop a conceptual framework for predicting fire impacts on ERW performance. An assessment of the available literature reveals that the effects of fire on soil pH and inorganic carbon are nonlinear with respect to severity, complicating both dissolution kinetics and carbon verification. Base cation pulses from ash are temporary and subject to rapid export. Fire-induced soil water repellency and erosion may dominate chemical effects in controlling ERW material fate, particularly during the first year post-fire. Pyrogenic carbon and thermally altered minerals create novel soil–rock interactions with unknown consequences for weathering rates. The authors concluded that fire history must be incorporated as a covariate in ERW deployment planning and monitoring, reporting, and verification design.

Fire doi: 10.3390/fire9040172

Authors: Xiang Chen Tianyi Ma Xiangyu Liu Qianle Tang Chang Xu Wenjun Xie Shilong Feng Ying Zhou Sainan Yin Yanlong Shan

The Changbai Mountain Forest Region contains one of the best-preserved mountain forest ecosystems in eastern Asia and serves as a critical ecological barrier in China. Using the Baihe Forestry Bureau as the study area, this research quantified forest surface fire behavior, and based on the historical wildfire occurrence data and the forest fire spread trends, proposed targeted strategies for fire prevention and emergency resource allocation. Forest fires in the coniferous and broad-leaved mixed near-mature forest pose the greatest threat to the region. The establishment of five supply storages in five strategic locations and the construction of new firebreak roads are essential for effective fire management in the Baihe Forestry Bureau.

Fire doi: 10.3390/fire9040171

Authors: Zihan Lu Jiachen Zhu Zi Wang Lu Liu Benjamin Tawiah Long Yan Bin Yu

The development of high-performance flame-retardant (FR) polypropylene (PP) with high mechanical integrity remains a challenge. Herein, we demonstrate a synergistic flame retardancy system for PP achieved via partial substitution of piperazine pyrophosphate (PAPP) with 1 wt.% magnesium borate whiskers (MBW) for improved flame retardancy, and thermal and mechanical properties. The optimized PP/24PAPP/1MBW exhibits exceptional FR performance, driven by the formation of a highly ordered, continuous phosphorus–boron hybrid char in the condensed phase. Cone calorimetry test results reveal an 80% reduction in peak heat release rate, a 54% reduction in total heat release, and a 33% reduction in total smoke production compared to neat PP, while the UL-94 test confirms a V-0 rating with complete suppression of flaming drips. Morphological study of the char residue using Raman spectroscopy and SEM attributes this performance to enhanced char graphitization and structural coherence enabled by boron-mediated cross-linking. More importantly, this transformative flame retardancy performance is achieved without severe compromise to mechanical properties, retaining over 89% of the original tensile strength. This work confirms the PAPP/MBW system as a highly efficient, low-additive approach to creating advanced fire-safe polymer composites for engineering applications.

Fire doi: 10.3390/fire9040170

Authors: Fuqing Han Shouzhong Feng Guozhi Wang Weili Wang Yani Zhang

Traditional road tunnel ventilation systems suffer from high energy consumption and limited effectiveness in fire smoke control. Thus, there is a pressing need to develop advanced air purification technologies that integrate low energy demand with efficient smoke mitigation capabilities. In this study, a self-developed negative ion purification system was implemented, and systematic full-scale experimental investigations were conducted in both a test tunnel and an operational road tunnel to evaluate its performance in air purification and smoke suppression under normal operation and fire conditions. Key parameters, including negative ion concentration, particulate matter concentration, carbon monoxide (CO) concentration, and smoke distribution characteristics, were measured to elucidate smoke evolution behavior and the underlying mechanisms influenced by negative ions. The results show that the negative ion purification system can rapidly establish a high-concentration negative ion field within the tunnel space. Under normal operating conditions, negative ions markedly reduce particulate matter concentrations and their fluctuations, thereby effectively improving tunnel air quality. Under fire conditions, the system maintains high purification efficiency, with significant reductions in particulate matter concentration observed in the test tunnel and clear suppression of longitudinal particulate transport in the real tunnel. In particular, PM10 exhibits a higher removal efficiency. In addition, negative ions promote particle agglomeration and gravitational settling, accelerate CO dilution and dispersion, and significantly improve tunnel visibility. The results demonstrate that the negative ion purification system exhibits strong applicability and considerable engineering potential across different spatial scales and fire scenarios.

Fire doi: 10.3390/fire9040169

Authors: Yang Zhou Yixing Liu Fengge Yang Zhengyang Wang

This study focuses on flame spread and merging in discrete fuel arrays composed of birch rods under dual fire source conditions. Tests were performed with five fuel spacings (nL/W = 1, 2, 3, 4, single source) and eight array spacings (S = 2 mm to 9 mm) to quantitatively evaluate the influence of these parameters on the flame merging behavior and key spread characteristics. The results indicate that the probability of flame merging decreases with increasing fuel spacing and is strongly affected by array spacing. Both the inter-fire temperature and dimensionless temperature rise were found to follow distinct power-law relationships with spacing. Flame height is governed by both spacing parameters. In contrast, the flame spread rate responded to array spacing but exhibited minimal sensitivity to fuel spacing. In this study, heat flux between the two arrays is demonstrated to be dominated by thermal radiation. A predictive model was formulated for the merged flame height, demonstrating close agreement with the experimental results.

Fire doi: 10.3390/fire9040168

Authors: Clara Esteban Eva Luna Lara Javier Madrigal María Verdum Patricia Jové Mariola Sánchez-González

Quercus suber bark, known as cork, is an important fire-adaptive trait of this Mediterranean species. However, the increased frequency of wildfires and poor forest management practices can be significant challenges in managing the sustainable exploitation of cork oak stands. This study evaluates cork’s thermal behavior and organoleptic quality for commercial applications under three experimental fire scenarios: prescribed burn, low-intensity wildfire, and high-intensity wildfire. Bench-scale tests were conducted using a vertical mass loss calorimeter to simulate heat exposure levels, measuring temperature changes at four cork depths and quantifying heat-induced damage. Morphological traits—cork thickness, corkback thickness, and relative humidity—were recorded as predictor variables. Additionally, organoleptic and aromatic analyses were performed to assess the suitability of fire-exposed cork for wine stopper production. Results were consistent with the available literature, confirming that cork thickness significantly reduces the maximum temperature at the phellogen level. Specifically, mean cork thickness showed a significant negative effect on Tmax4 (β = −0.02, p < 0.001), indicating a consistent decrease in internal temperatures with increasing thickness across all heat flux levels. By contrast, cork consumption (mass loss) was primarily driven by heat flux intensity rather than cork structural traits. Aromatic profiling and organoleptic analysis revealed the presence of smoke-related compounds in cork samples exhibiting external carbonization. This effect was observed under higher heat flux exposure (particularly at 25 and 50 kW m−2), where visible charring occurred. Under these conditions, commercial quality may be partially compromised, whereas samples without external carbonization did not show comparable aromatic alteration. Further field validation is recommended.

Fire doi: 10.3390/fire9040167

Authors: Helena van den Berg Sesma Edgar Lorenzo-Sáez Victoria Lerma-Arce Jose-Vicente Oliver-Villanueva Mauricio Acuna

Estimating wildfire greenhouse gas (GHG) emissions in Mediterranean landscapes is challenging due to heterogeneous fuel mosaics and limited scalability of field-based approaches. This study presents a Geographic Information System (GIS) based framework that integrates land-cover data, pre-fire biomass estimates, fire severity mapping, and established emission factors to produce spatially explicit estimates of biomass consumption and GHG emissions. Fire severity was derived from multitemporal Sentinel-2 imagery using the differenced Normalized Burn Ratio (ΔNBR) and combined with land-cover information to define vegetation–severity classes for emission estimation. A key innovation is the identification of co-occurring vegetation types within the same spatial units, allowing emissions to be quantified across vegetation mixtures rather than single classes, providing a more realistic representation of Mediterranean forests. Applied to the 2022 Bejis wildfire, pre-fire biomass within the burned area was 673,601 tons. Coniferous forests dominated, but co-occurrence with shrubland and herbaceous layers produced the highest emission contributions, highlighting the role of vegetation interactions. Total emissions were estimated at 625,938 tons of equivalent CO2, and comparison with large-scale datasets (CAMS Global Fire Assimilation System, Global Fire Emissions Database) shows general coherence. This severity-driven, vegetation-explicit framework demonstrates robust potential for quantifying wildfire emissions across heterogeneous Mediterranean landscapes, though uncertainties remain due to pre-defined biomass, burning efficiency, emission factors, assumptions in fire severity mapping, and limited field validation. The approach can support improved regional GHG inventories and wildfire management strategies.

Fire doi: 10.3390/fire9040166

Authors: Ji Wu Qiuze An Jiang Huang Wanki Chow Yuanhua He

Aerial forest firefighting is a critical technology for wildfire suppression. Recent studies have examined suppression agent drop dynamics, deposition patterns, and optimization strategies. This review synthesizes advances from three perspectives: (i) in-flight suppression agent jet dynamics, (ii) ground deposition patterns, and (iii) suppression effectiveness, while outlining future research directions. Flight altitude, velocity, and momentum ratio govern jet behavior—affecting penetration, expansion, and breakup. Momentum ratio, shaped by drop velocity and aircraft speed, is pivotal in penetration depth and fragmentation. Deposition patterns vary with delivery systems and flight parameters: low-altitude/low-speed drops yield higher coverage density over smaller areas, whereas high-altitude/high-speed drops cover larger areas but less densely. Suppression efficacy depends on fire intensity–vegetation interactions, droplet size–coverage requirements, and operational parameters such as response time, aircraft capacity, and real-time intelligence. Large droplets excel in cooling high-intensity flames, while fine droplets provide efficient area coverage. Adequate resources and integrated data enhance outcomes. Future work should couple multi-physics models of terrain, meteorology, and fire plume dynamics, and develop integrated deposition models including wind, thermodynamics, terrain, and fire behavior to optimize aerial dispersion in diverse wildfire scenarios.

Fire doi: 10.3390/fire9040165

Authors: Robert Marian Popa Adrian Clenci Julien Berquez Rodica Niculescu Cătălin Magheru

As part of efforts to support the transition toward a zero-carbon future, this research evaluates how the use of natural gas and liquefied petroleum gas under lean burn conditions affects the energy efficiency and environmental outcomes of a diesel engine that has been retrofitted to operate with spark ignition. The assessment of the ecological potential of these low-carbon gaseous fuels was performed at the engine test bed at optimum spark advance set from the condition of achieving maximum brake thermal efficiency (i.e., lowest carbon dioxide emission, CO2). The results found with lean mixtures are compared to those obtained under stoichiometric conditions, as well as to those from a commercial gasoline engine of comparable size, equally operated at stoichiometry. With lean burning, a clear improvement is observed for all operating points in terms of brake thermal efficiency with respect to the stoichiometric operation. The results highlight a slightly greater improvement when operating with natural gas lean mixtures: between (1.35 and 2.35) percentage points gained in this case, compared to (1.15–2.10) percentage points gained in the case of liquefied petroleum gas. As for CO2, a maximum 28% reduction when using natural gas is achieved with lean operation with respect to the commercial gasoline engine. Using lean mixtures also brings an important reduction in the engine-out pollutants (carbon monoxide, nitric oxides and particulate number). However, with respect to stoichiometric operation, cyclic variability of the prototype degrades with lean burning but remains lower than one of the baseline commercial gasoline engines.

Fire doi: 10.3390/fire9040163

Authors: Fouad Fatoom Răzvan Calotă Ilinca Năstase Florin Bode

Photovoltaic (PV) systems are important for sustainable energy infrastructure, but their rapid deployment introduces complex fire dynamics that current regulations fail to address adequately. While existing standards focus on the electrical safety of individual components, they often neglect the risks arising from the interaction between the PV array and the building envelope. This review synthesizes current research on ignition mechanisms, thermal behavior, and the aerodynamic propagation of smoke to evaluate these overlooked hazards. A primary finding is that the interstitial space between the panel and the roof functions as a “heat trap,” significantly altering airflow patterns and accelerating flame spread even across fire-rated materials. The analysis further highlights that standard testing protocols do not sufficiently account for the urban dispersion of toxic combustion byproducts, such as hydrogen fluoride and volatile organic compounds. By evaluating recent advancements in Computational Fluid Dynamics (CFD) and helium-based surrogate testing, this paper demonstrates that accurate prediction of pollutant transport requires coupled modeling of wind effects and thermal buoyancy. The study concludes that ensuring urban fire resilience demands an evolution from component certification to integrated system assessments that include installation geometry, ventilation strategies, and environmental impact.

Fire doi: 10.3390/fire9040164

Authors: Janice L. Coen

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.

Fire doi: 10.3390/fire9040162

Authors: MuYuan Hsu RyhNan Pan LiYu Tseng ShiuanCheng Wang PoWen Huang ChiJi Lin ChungHwei Su

Air curtain systems have been proposed as a supplementary smoke control strategy for vehicle tunnels, particularly where structural constraints limit the installation or upgrading of conventional ventilation systems. However, most previous studies rely on numerical simulations or fixed experimental facilities, while flexible experimental platforms and the influence of vehicle obstruction on smoke behavior remain less explored. This study experimentally investigates the smoke confinement performance of an air curtain using a 1:18 modular detachable scaled vehicle tunnel model. The modular configuration enables flexible assembly and adjustment of the experimental setup for different test conditions. A series of laboratory experiments was conducted using a liquefied petroleum gas (LPG) burner to simulate a vehicle fire. Temperature measurements and smoke visualization were performed under different air curtain jet velocities and vehicle obstruction conditions to analyze the interaction between the air curtain jet and buoyancy-driven smoke flow. The results show that the air curtain significantly restricts the upstream propagation of hot smoke and modifies the thermal field inside the tunnel. When the jet velocity reached approximately 5 m/s, the temperature in the protected region decreased by about 25–35% compared with the case without an air curtain. In addition, the presence of vehicle models altered the airflow structure and increased heat accumulation in the middle region of the tunnel cross-section. These results demonstrate that the proposed modular tunnel model provides a reliable experimental platform for tunnel fire research and highlights the importance of considering vehicle obstruction effects in tunnel smoke control studies.

Fire doi: 10.3390/fire9040161

Authors: Yuchen Du Daniel Jacome Jianghao Wang

Identifying small-scale burn scars is critical for global carbon accounting, yet remains computationally challenging due to spectral complexity and ground truth scarcity in heterogeneous landscapes. Conventional deep learning models often fail to generalize in such environments, lacking both domain-specific priors and representative training distributions required for precise segmentation. Here, we show that optimizing the fine-tuning of the Prithvi Earth Foundation Model (EFM) via Multidimensional Latin Hypercube Sampling (LHS) establishes a robust framework for this task. Our comparative analysis reveals that the domain-adapted Prithvi model achieves a Mean Intersection over Union (mIoU) of 0.91, outperforming standard Vision Transformers (ViT) by 31.9% and significantly surpassing reconstruction-based architectures, such as Scale-MAE. We demonstrate that LHS is superior to Simple Random Sampling (SRS) for optimizing foundation models, as it ensures statistical fidelity with a Kolmogorov–Smirnov (KS) statistic below 0.1 and effectively captures the tail distributions of fire weather indices. Furthermore, our framework exhibited exceptional data efficiency, retaining 94.5% of its peak accuracy with only 100 training samples. These findings provide a scalable solution for monitoring small-scale disasters in data-constrained regions and validate the synergy between rigorous sampling strategies and EFMs.

Fire doi: 10.3390/fire9040160

Authors: Yumin Dong Peifeng Li Xiqing Guo Ziyang Li

Rapid and balanced situational awareness of fire fronts is critical for effective initial response to forest fires, yet suboptimal task planning for Unmanned Aerial Vehicle (UAV) swarms can delay intelligence delivery. This paper presents a cloud–edge collaborative approach that integrates edge-driven rapid task partitioning with cloud-based global workload balancing, explicitly addressing the NP-hard multiple traveling salesman problem underlying multi-UAV reconnaissance. At the edge, a fire-spread-informed line clustering algorithm quickly assigns monitoring points to UAVs, exploiting low-latency processing for initial sectorization. The cloud then refines this allocation through a novel cooperative–competitive task transfer mechanism that minimizes the makespan. Extensive simulations and a real-world case study based on the 2020 Liangshan wildfire show that the proposed method reduces makespan by up to 24.5% compared to conventional centralized and distributed baselines, while remaining robust under severe communication constraints.

Fire doi: 10.3390/fire9040159

Authors: Maximilian Pache Michaela D. Detsi Ioannis D. Mandilaras Dimos A. Kontogeorgos Maria A. Founti

Gypsum-based fire protection relies on thermally activated dehydration, where chemically bound water is released and evaporated, thereby providing an endothermic heat sink that delays heat penetration through assemblies. In parallel, inorganic hydrated salts are increasingly used as flame-retardant additives in gypsum-based systems to enhance heat absorption over specific temperature ranges. Fire simulation tools and performance-based fire engineering approaches require reliable kinetic data and reaction enthalpies that can be implemented as coupled thermal–chemical source terms. However, additive-specific kinetic datasets remain limited, particularly under restricted vapor exchange conditions representative of porous construction materials. This work investigates the thermal decomposition behavior and dehydration kinetics of Aluminum Trihydrate (Al(OH)3, ATH), Magnesium Hydroxide (Mg(OH)2, MDH), Calcium Aluminate Sulfate (3CaO·Al2O3·3CaSO4·32H2O, CAS), and Magnesium Sulfate Heptahydrate (MgSO4·7H2O, ESM) with emphasis on vapor-restricted conditions representative of confined porous systems. Differential scanning calorimetry (DSC) experiments were conducted at three heating rates (2, 10, and 20 K/min for MDH, CAS and ESM and 20, 40 and 60 K/min for GB-ATH) up to 600 °C using pinhole crucibles to simulate autogenous vapor pressure. The thermal analysis indicates that ATH and MDH exhibit predominantly single-step dehydration behavior, while ESM shows a complex multi-step mechanism. Although CAS presents a single dominant thermal peak in the DSC signal, the isoconversional analysis reveals a multi-stage reaction behavior, demonstrating that peak-based interpretation alone may be insufficient for such systems. Kinetic parameters were determined using both model-free (Starink) and model-fitting approaches in accordance with the recommendations of the Kinetics Committee of the International Confederation for Thermal Analysis and Calorimetry (ICTAC). All reactions were consistently described using the Avrami–Erofeev model as an effective phenomenological representation of the conversion behavior. The extracted kinetic triplets were validated through numerical simulations, showing good agreement with experimental conversion and reaction rate data. The resulting kinetic parameters and dehydration enthalpies provide a physically consistent dataset for the description of dehydration processes under restricted vapor exchange. These results support the development of thermochemical models for gypsum-based systems; however, their transferability to full-scale assemblies remains subject to validation under coupled heat- and mass-transfer conditions.

Fire doi: 10.3390/fire9040158

Authors: Andrea Majlingova Erik Piater Radovan Hilbert Tibor-Sándor Kádár

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.

Fire doi: 10.3390/fire9040157

Authors: Iulian-Cristian Ene Vlad Iordache Dan-Adrian Ionescu Florin Bode Ilinca Năstase Ion Anghel

This work presents an evaluation of the effectiveness of active ventilation methods compared to passive ventilation methods in a typical B + GF + 9 building, focusing on the impact of burner height location on smoke control performance. The numerical model was validated using a full-scale room fire experiment involving a 4350 kJ/s wood crib load, where the HRR was calibrated via the mass loss method, achieving an RMSE of 210 kW and MRE of 5.04%. FDS simulations were conducted across six scenarios involving burners on the ground, fifth, and ninth floors. The findings demonstrate that, while natural ventilation allows the stairwell to reach lethal conditions with temperatures exceeding 180 °C and CO concentrations above 0.24%, the implementation of top-level mechanical pressurization maintains temperatures below the 60 °C tenability threshold. The mechanical ventilation system extended the Available Safe Egress Time (ASET) by 75% to 110%, with effectiveness increasing as the burner elevation approached the fan location. Overall, the study provides a validated approach for transforming stairwells into protected refuge zones in existing mid-rise buildings. Overall, merging empirical with computational methods is a proven basis for simulating scaled-up, complicated layouts. This guarantees accurate initial conditions when analyzing urban fire emergencies.

Fire doi: 10.3390/fire9040156

Authors: Zeeshan Ur Rehman Hamid Hassan Jung Hoon Han Jin Doo Yoon Seung Woo Park Ji Hyeon Park Dong Geon Ha Bon Heun Koo

In this work, poly(acrylic acid)-based layers were injected to form a sandwich layer between the cationic and anionic species for a compact and effective fire-retardant coating on cotton fabric using the layer-by-layer coating technique. From the SEM analysis, as the number of tri-layers increases, the attachment intensity increases, as can be seen for poly(acrylic acid) chitosan and bentonite clay PCB-5TL (the highest tri-layers), while in the case of halloysite-based coatings, as the number of tri-layers increases, instead of attachment, the agglomeration increases due to the high surface area of halloysite nanoclay tubes. FTIR and UV confirmed the finding from the new peak entry and an increase in thickness. The highest thermal residue, ~18%, was obtained for poly(acrylic acid) chitosan and halloysite nanoclay PCH-5TL with a maximum degradation peak intensity at ~389 °C. From the flammability and after-burning SEM investigation test, it was observed that the halloysite-based coating with a higher number of layers offered higher resistance against the flame spread and ignition and, thus, produced a higher amount of char.

Fire doi: 10.3390/fire9040155

Authors: Tengteng Long Yun Liu Xiaohui Pu Zhi Li Shun Li Qiuhua Wang Li Han Ning Lu Leiguang Wang Weiheng Xu

Prescribed burning is important for reducing wildfire risk and regulating fuel loads, but its implementation in mountainous forests is strongly influenced by the coupled effects of the wind field and topography, making it difficult to control. This study focuses on Yunnan pine (Pinus yunnanensis) forests in southwestern China. A three-dimensional Fire Dynamics Simulator (FDS) combined with measured fuel characteristics was used to simulate 21 slope (0–35°) and wind speed (0–2 m s−1) combinations to quantitatively analyze the fire spread, flame structure, and gaseous emission characteristics during downslope prescribed burning. The local fire spread rate (ROS), evaluated along three lateral lines (Y = 2.5, 5.0, and 7.5 m), exhibits a non-monotonic dependence on slope over the tested range, with a minimum near 30° and a modest rebound at 35°. A downslope wind of 1 m s−1 promotes near-surface heating and accelerates spread, whereas a stronger wind of 2 m s−1 lifts flames away from the fuel bed and suppresses combustion. Thermal field analysis reveals that peak temperature decreases with increasing slope and that a late-stage secondary heating episode occurs at 35°. CO2 emissions are significantly positively correlated with fuel consumption, reaching a peak of 717.5 kg under a 35° slope and no-wind conditions. CO emissions, as an indicator of combustion efficiency, reach their highest value of 2.23 kg at a 35° slope and a wind speed of 1 m s−1, indicating that their trend is not entirely consistent with the ROS and temperature and that there is a certain degree of decoupling. The interaction between slope and wind speed transforms fire behavior from a cooperative to a competitive mechanism, and the topography–wind field coupling provides differentiated control over the combustion intensity and completeness. This study provides a scientific basis for the safe implementation of mountain burning programs and for regional carbon emission assessments.

Fire doi: 10.3390/fire9040154

Authors: Amit Chandra Anjan K. Bhowmick Ashutosh Bagchi

This article investigates the response of an unprotected three-storey steel moment-resisting frame subjected to a suite of horizontally traveling fire scenarios. A series of multi-step finite-element simulations was conducted to analyze the impact of traveling fires on both the global and local responses of a low-rise building frame. The research considers a range of fire types, both uniform and spatially varying, as well as different locations, and sizes to capture a diverse array of fire scenarios. Non-uniform compartment fires are modeled using the improved traveling fire method (iTFM), while uniform fires are simulated using the Eurocode parametric (EC) fire model. Four traveling fire scenarios with floor area coverage ranging from 5% to 48% are examined. The resulting deformation patterns, along with bending moment and axial force distributions in critical beam and column sections within the fire compartments, are thoroughly evaluated. The findings reveal that, within the case study frame and the range of parametric analyses, a uniform compartment fire does not necessarily yield the worst-case scenario commonly assumed in design codes. Instead, global and local structural responses are primarily influenced by traveling fire scenarios.

Fire doi: 10.3390/fire9040153

Authors: Daoyong Zhu Long Wang Bo Liu Yuyuan Zhang

In this study, a composite powder explosion suppressant (MVTS–NaHCO3) was prepared via the wet coating method of the solution–crystallization (WCSC) process, using modified vanadium–titanium slag (VTS) as the carrier and NaHCO3 as the active suppressive component. A 20 L spherical explosion apparatus and a transparent pipeline explosion propagation test system were employed to investigate the effects of the composite powder explosion suppressant with different mass fractions (0%, 10%, 20%, 30%, 40%, 50%) on the explosion pressure and micro-mechanism of polyacrylonitrile (PAN) dust. The experimental results indicated that the MVTS–NaHCO3 composite powder exhibited a significant suppression effect on PAN dust explosions. In the confined 20 L vessel, complete suppression was achieved when the mass fraction of the composite powder explosion suppressant exceeded 30%, with a maximum explosion pressure reduction of 53.2%. In the semi-open pipeline, 40% composite powder explosion suppressant reduced the maximum explosion pressure to 0.08 MPa (a reduction rate of 82.6%), and complete suppression was achieved at a mass fraction of 50%. Microstructural analysis revealed that the suppression performance of the composite powder explosion suppressant is attributed to the synergetic effects of physical and chemical mechanisms. Physically, NaHCO3 decomposes endothermically (100 kJ/mol), releasing CO2 and H2O and thereby diluting the oxygen concentration, while the porous structure of MVTS enhances dispersibility. Chemically, the hydroxyl groups on the surface of MVTS bond with NaHCO3, delaying its decomposition, while metal hydroxides (e.g., Al(OH)3) decompose thermally to form Al2O3, which adsorbs and quenches free radicals (e.g., ·OH, ·H), thereby inhibiting chain reactions. This study provides new insights for the resource utilization of VTS and the prevention and control of industrial dust explosions. The findings have important reference value for optimizing explosion suppressant formulations and improving the intrinsic safety.

Fire doi: 10.3390/fire9040152

Authors: Alexandra D. Solomou Nikolaos Proutsos Panagiotis Michopoulos Athanassios Bourletsikas

Wooden, nature-based barrier structures are widely implemented after wildfire in Mediterranean forests to reduce runoff connectivity and trap sediment, yet their ecological footprint on early plant recovery remains poorly quantified in Greece. We assessed two-year vascular plant recovery in forest landscapes burned during the 2021 wildfire season (Parnitha, Attica; Mavrolimni, Corinthia/Peloponnese) using repeated field surveys in 2022 and 2023. Sixteen permanent plots were established within operational rehabilitation works and assigned to the dominant structure types: wattles (brush/branch piles), contour-oriented hillslope log barriers, and channel log dams. In each year, vascular plant composition and recovery endpoints (species richness and diversity indices, density, cover, and aboveground biomass) were quantified using standardized quadrat sampling. Vegetation cover and biomass increased strongly from 2022 to 2023 at both sites, indicating rapid early reassembly. Against this dominant year effect, structure type was associated with pronounced biodiversity and compositional differences, most clearly in Parnitha where log barriers exhibited markedly reduced diversity in 2022 and community turnover patterns differed among structures. Plot-level PERMANOVA on Bray–Curtis dissimilarities calculated from log(x + 1)-transformed abundances did not detect a statistically significant structure type effect in either year (p > 0.05), whereas descriptive Bray–Curtis heatmaps suggested compositional contrasts among structure type × year combinations. Indicator–species analysis further identified a limited set of taxa associated with specific structures, suggesting provisional structure-linked microsite filtering during early assembly. By quantifying community composition and indicator taxa alongside structural recovery, this study provides operational-scale evidence that common wooden post-fire measures may be associated with early biodiversity signals in the first two years after fire, although these patterns should be regarded as provisional given the short monitoring period and limited replication. Incorporating these signals into post-fire land management can improve intervention design and placement, aligning risk reduction with biodiversity recovery in Mediterranean landscapes.

Fire doi: 10.3390/fire9040151

Authors: Riza Hadafi Punari Kadir Arifin Mohamad Xazaquan Mansor Ali Kadaruddin Ayub Azlan Abas Ahmad Jailani Mansor

Data centers are critical digital infrastructure supporting cloud computing, artificial intelligence, and global information services. Despite their high-reliability design, they remain vulnerable to fire incidents due to continuous operation, high electrical loads, dense power systems, and the increasing use of lithium-ion batteries. Although such events are rare, their consequences can be severe, including service disruption, equipment damage, financial loss, and risks to data integrity. This study presents a systematic literature review of fire safety risk management frameworks in data centers, following PRISMA guidelines. Peer-reviewed studies published between 2020 and 2025 were retrieved from Scopus and Web of Science, screened, and appraised using structured quality criteria. Twelve empirical studies were synthesized and benchmarked against NFPA 75 and NFPA 76 standards. The findings are organized into three domains: Strategic Management, Fire Risk, and Fire Preparedness. The results show a strong focus on technical prevention and electrical hazards, while organizational readiness, emergency response, and recovery remain underexplored. Benchmarking indicates that industry standards adopt a more comprehensive lifecycle approach than the academic literature. This study reframes data center fire safety as a socio-technical reliability system and highlights critical gaps, providing a foundation for future research and improved fire safety governance and resilience.

Fire doi: 10.3390/fire9040150

Authors: Jin-chan Park Jong-chan Yun Min-ho Baek

Data on wildfires (burned area ≥ 100 ha) in South Korea were compiled for 2000–2025 and analyzed together with the national geospatial inventories of hazardous fuel facilities to characterize wildfire-triggered Natech exposure and potential consequence distances. In total, 47 large wildfire events were identified, burning approximately 139,800 ha, with all events occurring during the late winter–spring window (February–May). The spatial overlays of wildfire footprints with facility locations identified 805 gasoline/diesel stations and 227 LPG filling stations located within wildfire-affected districts, corresponding to 14.1% of gas stations and 11.5% of LPG stations in the nationwide facility dataset. Facility exposure was geographically clustered, with the highest concentrations occurring in the eastern and southeastern wildfire hotspots. To quantify potential technological impact extents under wildfire escalation, ALOHA simulations were conducted for a wildfire-induced BLEVE/fireball scenario involving a 10,000 L mobile tank with representative fuels (propane for LPG, n-octane for gasoline, and n-dodecane for diesel). The modeled thermal radiation threat zone radii (10, 5, and 2 kW·m−2) were 228/322/502 m for propane, 250/353/550 m for n-octane, and 254/358/559 m for n-dodecane. Together, the event-based wildfire dataset, facility overlay results, and scenario-based impact distances provide an integrated, quantitative basis for assessing wildfire-triggered Natech conditions at the wildland–urban/industrial interface in South Korea.

Fire doi: 10.3390/fire9040149

Authors: Giovanny S. Oliveira Antônio M. L. Bezerra Domingos F. S. Souza Carlos E. A. Padilha Juan A. C. Ruiz

Greenhouse gas (GHG) emissions are one of the leading environmental concerns faced nowadays. The chemical looping combustion (CLC) process is one of the main processes that aim for carbon capture, utilization, and storage (CCUS), allowing the generation of a high-purity CO2 stream that can be easily captured. Brazil has a wide variety of biomasses that could be applied to CLC, and the behavior of these biomasses can be predicted using machine learning algorithms. An artificial neural network (ANN) was created considering the biomass characteristics (proximate and ultimate analysis) and fuel reactor temperature as input data to assess their influence on CLC performance parameters (carbon capture efficiency, ηCC, and total oxygen demand, ΩT) and gas compositions. The characteristics of five Brazilian biomasses were considered in the constructed ANN to predict their behavior on CLC performance. The ANN presented a good data fit, with R2 achieving values higher than 0.973. Volatile matter played a crucial role in predicting the CLC performance parameters. Rice husks presented the smoothest results for ηCC and ΩT, while the CO2 composition was most affected by the eucalyptus characteristics. Experimental tests with all the biomasses should be carried out to provide a higher prediction capability of the algorithm.

Fire doi: 10.3390/fire9040148

Authors: Yuanjing Huang Chen Zeng Weijun Pan Rundong Wang Zirui Yin Yangyang Li Shiyi Huang

With the increasing complexity of wildfire emergency response, the aerial emergency response system is imposing increasing demands on both safety and decision rationality of takeoff and landing site selection. Site selection decisions are influenced by multi-dimensional factors, including geographical location, meteorological factors, and operational safety considerations, resulting in a pronounced coupling of multiple factors in the decision-making process. However, existing studies primarily focus on spatial suitability evaluation or technical implementation, often relying on predefined indicator systems and independence assumptions, while lacking a systematic characterization of the influencing factor system and its interrelationships in takeoff and landing site selection. To address this gap, this study proposes a novel structured decision-making framework to systematically analyze and optimize the selection of takeoff and landing sites for bucket firefighting helicopters in wildfire aerial emergency response scenarios. First, a procedural grounded theory approach is employed to systematically identify the influencing factors associated with site selection, thereby constructing a traceable decision-making factor system. Second, fuzzy DEMATEL is applied to model the causal relationships and structural interdependencies among these factors. Finally, a cumulative contribution rate based on centrality is introduced to screen and optimize the decision indicators, resulting in a refined set of key decision indicators. The results reveal the structural roles of different influencing factors in site selection, reduce the reliance on experience-driven judgment, and reconceptualize the problem from traditional indicator weighting and ranking into a structured decision-making process involving multi-factor coupling. This provides systematic decision support for takeoff and landing site selection in wildfire aerial emergency response and establishes a foundation for subsequent spatial suitability analysis and case-based validation. Furthermore, the results are consistent with expert experience and practical operational constraints, indicating the potential applicability of the proposed method in real-world decision-making.

Fire doi: 10.3390/fire9040147

Authors: Chan Jin Lim Heemun Chae

This study examines the effects of meteorological fire danger and human activity on wildfire ignition patterns in South Korea using records from 2004 to 2023. A percentile-based Fire Weather Index (FWI) classification, derived from negative binomial regression, identified critical daily fire frequency thresholds at FWI 4.39 (μ ≥ 1 fire/day) and FWI 6.84 (μ ≥ 2 fires/day). Bivariate LISA analysis revealed a spatial mismatch between resident population density and wildfire frequency: High–High (HH) clusters were concentrated in the Seoul metropolitan fringe, while Low–High (LH) clusters appeared in mountainous provinces where forest visitor ignitions and agricultural burning are the primary causes. In HH clusters, cigarette-related ignitions and structure-to-forest transitions were comparatively more frequent. Wildfire events were concentrated in age class 4–5 coniferous and broadleaf stands, and mean ignition-to-building distances in metropolitan areas frequently fell below 150 m. These findings suggest that prevention strategies should shift from uniform resident-oriented approaches toward spatially differentiated management targeting transient populations in LH areas and Wildland-Urban Interface (WUI) exposure in HH areas.

Fire doi: 10.3390/fire9040146

Authors: Nikola Mitrović Vladica Stojanović Mihailo Jovanović Željko Grujčić Dragan Mladjan

This manuscript is a continuation of the research published in Fire 2025, 8(8), 302, i.e., it deals with the examination of the cause-and-effect relationships of fires in the Republic of Serbia from the aspect of human safety. Among others, variables related to gender, age, and severity of injuries caused by fires are introduced, on which various methods of statistical analysis and stochastic modeling are first applied. Continuous age variables are modelled using the flexible Generalized Additive Models for Location, Scale, and Shape (GAMLSS) framework, where the Generalized Normal Distribution (GND) is identified as the optimal generative model for injuries, while a Reflected Log-Normal Distribution with positive support (RefLOGND+) provides the best fit for fatalities. The quality of such modeling is formally verified, and the probabilities of injury and death of individuals in certain age categories are predicted, revealing a pronounced concentration of injuries in the working-age population and a markedly higher relative risk of fatal outcomes among elderly individuals. Thereafter, by applying certain Data Mining (DM) techniques, primarily the Apriori algorithm, the most frequently occurring association rules are found, which indicate typical patterns and demographic structure of injuries and deaths in fires in Serbia. Finally, using the CART (Classification and Regression Trees) algorithm, several decision trees are formed that describe the impact and relationship of different causes of fires on injury and death in fires. In this way, some important and frequent patterns are observed that indicate key fire risk factors that significantly affect the demographic structure of human casualties. The results thus obtained provide a basis for developing targeted strategies for fire prevention and improving emergency response planning.

Fire doi: 10.3390/fire9040145

Authors: Zhixu Chen Nan Liu Chang Guo Xiaoyu Liang Chuanjie Zhu

Powder coatings, as a widely used green surface treatment material, face significant combustion and explosion risks due to the simultaneous presence of high-concentration combustible dust clouds and electrostatic ignition sources in their application environments. With the advancement of new materials and emerging industrial sectors, research on powder coating explosions has become increasingly interdisciplinary, resulting in a somewhat fragmented knowledge base. To systematically reveal the knowledge structure, research hotspots, and development trends in this field, this study employs bibliometric methods based on 857 relevant publications retrieved from the Web of Science (WOS) Core Collection database between 2015 and September 2025. Using VOSviewer (Version 1.6.20) and CiteSpace (Version 6.4), the analysis examines institutional collaboration, journal distribution, author collaboration patterns, regional differences, co-citation relationships, knowledge foundations, and research frontiers. The results indicate that powder coating explosion research has gradually developed an integrated knowledge system centered on materials science, chemical engineering, and combustion science. Institutions from China, Russia, and India represent some of the most productive contributors in this field. Current research hotspots focus on the explosion mechanisms of powder coatings, explosion-proof materials, risk assessment, numerical simulation, and protective measures for emerging industrial applications. Future trends are expected to focus increasingly on intelligent explosion suppression systems, multi-scale coupling mechanisms, and international collaborative governance. This study provides a comprehensive knowledge map to support scientific planning and safety strategy development in powder coating explosion research.

Fire doi: 10.3390/fire9040144

Authors: Grigory Kuksin Ilia Sekerin Linda See Dmitry Schepaschenko

Overwintering peat fires are increasingly reported in the boreal regions, where they persist underground through winter and reignite in spring, intensifying greenhouse gas emissions and landscape degradation. This study investigates the conditions that enable peat fires to survive freezing and snow cover, and presents practical methods for their winter detection and suppression. We combined satellite data, UAV-based thermal imaging, time-lapse photography, and ground measurements of temperature, groundwater depth, and peat moisture to identify active overwintering hotspots. Our results show that these fires persist primarily where groundwater levels remain below 60 cm, particularly under tree roots, compacted soil, or elevated terrain that limits moisture recharge. UAV thermal imaging proved the most reliable detection tool, identifying 98% of hotspots. We developed and successfully applied a winter extinguishing method that involves mechanical disruption and dispersion of smoldering peat over frozen ground, allowing rapid cooling without re-ignition. These findings clarify the mechanisms sustaining overwintering fires and provide an effective approach for their mitigation, contributing to reduced emissions and improved management of boreal peatlands vulnerable to climate change.

Fire doi: 10.3390/fire9040143

Authors: Yuheng Cheng Yuchen Lin Yanwei Wu Lida Huang Tao Chen Wenguo Weng Xiaole Zhang

The Analytic Hierarchy Process (AHP) is widely used in Multi-Criteria Decision Analysis (MCDA), yet its strong reliance on expert judgment constrains its scalability and may introduce variability in weighting outcomes, particularly in high-stakes applications such as wildfire risk assessment. In this study, we investigate how Large Language Models (LLMs) can function as decision-support agents in an AHP-style hierarchical evaluation task derived from validated wildfire literature. Based on this structure, four representative LLM-assisted strategies are examined: Direct LLM Scoring (DLS), Multi-Model Debate Scoring (MDS), Full-Document Prompting (FDP), and Indicator-Guided Prompting (IGP). To evaluate their effectiveness, we benchmark LLM-generated rankings against expert-defined ground truth across 16 sub-criteria. Using the mean correlation coefficient R as the key evaluation metric, with reported values expressed as mean ± standard deviation across models: DLS shows no correlation with expert rankings (R = 0.009 ± 0.070), MDS yields marginal gains (R = 0.181), and FDP remains unstable (R = 0.081 ± 0.189). By contrast, IGP, which incorporates retrieval-informed structured prompting, shows the highest agreement with the expert reference among the four compared strategies (R = 0.598 ± 0.065), suggesting that structured contextual guidance may improve the performance of LLM-assisted weighting within the evaluated benchmark. This study suggests that, within the evaluated wildfire benchmark and the tested set of hosted LLMs, LLMs may serve as useful decision-support tools in MCDA tasks when guided by structured inputs or coordinated through multi-agent mechanisms. The proposed framework provides an interpretable basis for exploring LLM-assisted risk evaluation in the present wildfire benchmark, while further validation is needed before extending it to other environmental or safety-critical contexts.

Fire doi: 10.3390/fire9040142

Authors: Kin Ip Chu Majid Aleyaasin

In this paper, an Artificial Neural Network (ANN) is built to predict the performance of intumescent coatings subjected to the ISO 384 fire curve. The performance metric is called the Retention Loss Onset Time (RLOT) in the structural steel. The network receives the steel and coating thicknesses as input and provides RLOT as the performance of any intumescent coating in a fire accident with substantial accuracy. The required data for obtaining the model is provided by revisiting the recent attempts in this field, which include hybrid numerical and experimental methods. It is found that the trapped gas fraction parameter and empirical expansion ratio substantially affect the accuracy of predictive modelling. Therefore, a new, comprehensive dynamic model that numerically simulates the bubble expansion process has been developed. This novel method directly determines the expansion ratio of the thermal conductivity model. The Eurocode is then used with multi-layer models to predict the steel temperature profile for a 1 h duration ISO fire. The accuracy is improved by modelling the temperatures and thermal resistances at the centre of each divided layer. The effects of different coatings and steel thicknesses are also investigated to provide the required data. The results are verified and validated by comparing them with the recent numerical and empirical results available in the literature.

Fire doi: 10.3390/fire9040141

Authors: Dan K. Thompson Giovanni Fusina Patrick Jackson

In Canada, early fire detection is an important component of wildfire management, and it utilizes a combined effort approach including public reports, aviation patrols, and satellite observations. The role of ground-based continuous smoke sensors has not been formally assessed in Canadian wildfire management detection systems. Dense networks of ground-based, internet-enabled continuous smoke sensors were deployed at three locations across southern Canada during 2023 and 2024, in concert with planned prescribed fire in grass fuels as well as incidental wildfire ignitions. Smoke sensor detection of fires was compared to polar orbiting and geostationary fire detection. Large fire events (50–600 ha) with a ground smoke detector distance of 1–2 km were observed on most occasions (n = 7), but the detection rate dropped to 30% for fires 1 ha or smaller. Follow-up smoke monitoring after the initial detection offered valuable information on smoke production and dispersion across multiple sensors. This typically nighttime smoldering smoke production fell below the threshold for geostationary satellite fire observation and is otherwise only captured sparingly by polar orbiting satellites. Thus, ground-based smoke detection systems likely fit an important niche for monitoring low-energy (i.e., smoldering) smoke events from fully contained fires or to monitor fires considered recently extinguished.

Fire doi: 10.3390/fire9040140

Authors: Shenghao Li Mingshan Wu Jiangxia Ye Xun Zhao Sophia Xiaoxia Duan Mengting Xue Wenlong Yang Zhichao Huang Bingjie Han Shuai He Fangrong Zhou

Wildfires at the wildland–urban interface (WUI) have increased in frequency and severity under global warming and intensified human activities. As a representative high-altitude mountainous region in southwestern China, Yunnan features complex topography, steep climatic gradients, and dispersed settlements interwoven with wildlands, making it a fire-prone area where wildfire management is particularly challenging. However, a fine-scale WUI dataset is currently lacking for this region. To address this gap, we refined WUI classification thresholds using a one-factor-at-a-time (OFAT) method and generated the first fine-resolution WUI map of Yunnan. Seasonal wildfire driving factors from 2004 to 2023 were quantified, and machine learning models were applied to produce seasonal susceptibility maps. SHapley Additive exPlanations (SHAP) were employed to interpret the dominant contributing factors. The resulting WUI covers 25,730.67 km2, accounting for 6.5% of Yunnan’s land area. Random forest models effectively captured seasonal wildfire susceptibility patterns, with AUC values exceeding 0.83 across all seasons. High susceptibility zones (>0.5) comprised 30.09% of the WUI in spring, 25.74% in winter, 22.61% in autumn, and 13.74% in summer. SHAP analysis revealed that anthropogenic factors consistently drive wildfire occurrence, while climatic conditions in the preceding season influence vegetation status and subsequently affect wildfire likelihood in the current season. By integrating static “where” mapping with dynamic “when” susceptibility analysis, this study establishes a comprehensive “When–Where” framework that supports both long-term WUI planning and short-term seasonal early warning. The integration of fine scale WUI mapping with seasonal susceptibility modeling enhances wildfire risk management in complex high-altitude regions. These findings provide a scientific basis for location-specific, time-sensitive, and full-chain wildfire management in mountainous landscapes and contribute to cross-border ecological security governance in the Indo-China Peninsula.

Fire doi: 10.3390/fire9040139

Authors: Shuai Tang Jie Xu Li Zhang

The increasing frequency of global forest fires necessitates rapid and accurate detection methods. This study proposes a forest fire detection and segmentation framework based on the MST++ hyperspectral reconstruction model to improve the accuracy and robustness of wildfire monitoring under complex environmental conditions. The proposed method first reconstructs hyperspectral images from RGB inputs using an MST++ model trained on the NTIRE 2022 RGB-to-hyperspectral dataset (950 paired samples), followed by fire and smoke segmentation based on spectrally sensitive bands. For segmentation experiments, 118 flame images from the BoWFire dataset and 100 manually annotated smoke images from public datasets (D-Fire and DFS) were used. Quantitative results demonstrate that the proposed MST++-based method significantly outperforms the conventional U-Net baseline. In flame segmentation, MST++ achieved an IoU of 76.90%, an F1 score of 86.81%, and a Kappa coefficient of 0.8603, compared to 44.42%, 58.15%, and 0.5625 for U-Net, respectively. For smoke segmentation, MST++ achieved an IoU of 91.76% and an F1 score of 95.66%, surpassing U-Net by 17.08% and 10.32%, respectively. In fire–smoke overlapping scenarios, MST++ maintained strong robustness, achieving an IoU of 89.64% for smoke detection. These results indicate that hyperspectral reconstruction enhances discrimination capability among flame, smoke, and complex backgrounds, particularly under low-light and overlapping conditions. The proposed framework provides a reliable and efficient solution for early forest fire detection and demonstrates the potential of hyperspectral reconstruction approaches in disaster monitoring applications.

Fire doi: 10.3390/fire9040138

Authors: Clara Ochoa Magí Franquesa Marcos Rodrigues Emilio Chuvieco

A substantial proportion of wildfires in Mediterranean regions continue to be recorded without information about the cause or source of ignition, limiting our ability to understand ignition drivers and design effective prevention strategies. In this study, we develop a spatially harmonised wildfire database for mainland Spain by integrating ignition records from the Spanish General Fire Statistics (EGIF) with fire perimeters generated from satellite images. We then apply a Random Forest classifier to infer ignition causes for events lacking cause attribution. To interpret model behaviour, we use Shapley Additive Explanation (SHAP) values at both global and local scales. Results indicate that human-caused ignitions are dominant, with intentional and negligence-related fires accounting for 52.13% of all known events, although they are associated with contrasting climatic and land-use settings. Negligence-related fires tend to occur under hot, dry and windy conditions, often in agricultural interfaces, whereas intentional fires are more frequent under cooler and wetter conditions and in areas with higher population density and land-use change. Lightning-caused fires represent a small fraction of total ignitions (3%) but exhibit a distinct climatic signature, occurring primarily in sparsely populated areas, under intermediate moisture conditions, and often leading to larger burned areas. Despite strong overall model performance (F1-score = 0.82), minority classes (e.g., lightning and fire rekindling, 0.17%) remain challenging to classify, reflecting both data imbalance and uncertainty in causal attribution. Overall, the combined use of machine learning and explainable AI provides a coherent spatial characterisation of wildfire ignition drivers across mainland Spain, highlights systematic differences among ignition causes, and identifies key limitations in existing fire cause records. This framework represents a practical step towards improving fire cause information by integrating remote sensing products with field-based fire reports, thereby supporting more targeted and evidence-based fire risk management.

Fire doi: 10.3390/fire9030137

Authors: Yoonseok Kim Stephen Cha Jaehwan Oh Deokhui Lee Taesoon Kwon Seokwoo Hong Jonghoon Kim Kyohyuk Lee

During a tunnel fire, the foremost priority is the safe evacuation of passengers. Extreme temperatures and toxic combustion products can quickly lead to mass casualties, so evacuation support systems require fast forecasts of how hazardous conditions will evolve in space and time. This study investigates whether sparse sensor measurements can be used to reconstruct future tunnel-wide fire conditions on two-dimensional sections that are directly relevant to structural assessment and human exposure. To this end, we develop 2D ST-FAM, a data-driven forecasting model that maps time-resolved measurements from 75 tunnel sensors to future temperature, soot, and carbon monoxide (CO) fields derived from 108 computational fluid dynamics (CFD) fire simulations. The study is organized around three questions: whether the model can accurately reconstruct future tunnel fields from sparse measurements, whether this performance is maintained on both the vertical center plane and the horizontal breathing plane, and which physical quantities remain most challenging to predict. Results show high structural agreement with the CFD reference fields over the full 1800 s prediction horizon, with average structural similarity index (SSIM) values of 0.964 for temperature, 0.984 for CO, and 0.937 for soot. These findings indicate that sparse-sensor forecasting is feasible for tunnel-scale temperature and toxic-gas field prediction, while soot prediction remains comparatively more difficult because of its sharper spatial structures.

Fire doi: 10.3390/fire9030136

Authors: Doru Coșofreț Florin Postolache Adrian Popa Octavian Narcis Volintiru Daniel Mărășescu

This paper proposes a physics-informed hybrid digital CO2 emission-control technology for maritime transport, designed for adaptive ship speed optimization along a predefined geographical route between two ports, discretized into quasi-stationary segments and evaluated under forecasted metocean conditions, subject to economic and regulatory constraints associated with maritime decarbonization. The framework integrates two exact optimization methods, Backtracking (BT) and Dynamic Programming (DP), with a reinforcement learning approach based on Proximal Policy Optimization (PPO), operating on a unified physical, economic, and regulatory modeling core. By reducing propulsion fuel demand, the system acts as an upstream CO2 emission-control mechanism for ship propulsion. This operational stabilization of the engine load creates favourable boundary conditions for advanced combustion processes and reduces the volumetric flow of exhaust gas, thereby lowering the technical burden on potential post-combustion carbon capture systems. Segment-wise speed profiles are optimized subject to propulsion limits, Estimated Time of Arrival (ETA) feasibility, and regulatory constraints, including the Carbon Intensity Indicator (CII), the European Union Emissions Trading System (EU ETS) and FuelEU Maritime. The physics-based propulsion and energy model is validated using full-scale operational data from four real voyages of an oil/chemical tanker. A detailed case study on the Milazzo–Motril route demonstrates that adaptive speed optimization consistently outperforms conventional cruise operation. Exact optimization methods achieve voyage time reductions of approximately 10% and fuel and CO2 emission reductions of about 9–10%. The reinforcement learning approach provides the best overall performance, reducing voyage time by approximately 15% and achieving fuel savings and CO2 emission reductions of about 13%. At the route level, the Carbon Intensity Indicator is reduced by approximately 10% for the exact methods and by about 13% for PPO. Backtracking and Dynamic Programming converge to nearly identical globally optimal solutions within the discretized decision space, while PPO identifies solutions located on the most favourable region of the cost–time Pareto front. By benchmarking reinforcement learning against exact discrete solvers within a shared physics-informed structure, the proposed digital platform provides transparent validation of learning-based optimization and offers a scalable decision-support technology for pre-fixture evaluation of fixed-route voyages. The system enables quantitative assessment of CO2 emissions, ETA feasibility, and regulatory exposure (CII, EU ETS, FuelEU Maritime penalties) prior to transport contracting, thereby supporting economically and environmentally informed operational decisions.

Fire doi: 10.3390/fire9030135

Authors: Ioannis Zarikos Nadia Politi Effrosyni Karakitsou Εirini Barianaki Nikolaos Gounaris Diamando Vlachogiannis Athanasios Sfetsos

This study presents a comprehensive wildfire risk assessment framework for Rhodes Island, Greece, aimed at quantifying the impacts of climate change on hazard levels and vulnerability in a typical Mediterranean environment. The approach integrates Fire Weather Index (FWI) data, detailed fuel-type mapping, and multiple vulnerability indicators covering ecological, socioeconomic, and population factors, enabling spatially explicit estimates of current and future wildfire risk. Historically, Rhodes mostly faces moderate wildfire risk, mainly in central and northeastern regions, with localised areas of higher risk near settlements and key economic sites. Climate forecasts for 2025–2049 predict a notable increase in hazard, with areas experiencing extreme fire weather (FWI > 50) increasing from 15.19% to 66–72%, across all emission scenarios. Ecological vulnerability is particularly alarming, as 93% of the island is already highly susceptible; fire-prone forest and agricultural zones are expected to move into the highest ecological risk categories, especially in the central mountain areas. The devastating 2023 wildfire, which burned over 17,600 hectares, caused more than €5.8 million in direct damages and led to the largest evacuation in the island’s history, closely aligning with high-risk zones modelled in the framework. An important insight is the limited spatial variation in near-future risk between RCP 4.5 and RCP 8.5, indicating that significant wildfire intensification is largely unavoidable by mid-century, emphasising the urgent need for quick adaptation and risk mitigation efforts for Mediterranean critical infrastructure and communities.

Fire doi: 10.3390/fire9030134

Authors: Honggang Wang Yoon Ko

Building materials’ fire properties directly affect the fire risk of buildings. Ignition, the initiating event of any building fire, occurs when a heat source ignites surrounding combustible materials. Although several parameters—such as the Thermal Response Parameter (TRP), thermal inertia, ignition temperature, ignition time, critical heat flux (CHF), and heat of combustion—have been used to characterize ignition behavior, a unified metric capable of representing overall ignitability under diverse and often unknown and unintended heat source (UHS) patterns is generally lacking. To address this gap, we propose a new method to evaluate material ignitability by generalizing UHS patterns and linking them to known or readily obtainable material properties, including ignition temperature and thermal inertia. The UHS patterns are represented using lognormal distributions for both exposure duration and incident heat flux (IHF), reflecting conditions that may occur in real buildings. Monte Carlo simulations are employed to generate a large number of heat exposure events from these UHS patterns, enabling statistical determination of material ignitability. The method applies to both thermally thick and thermally thin materials, with a simple expression provided to determine the critical thickness separating these behaviors. Sensitivity analysis demonstrates that the ignitability metric is robust with respect to variations in the lognormal distribution parameters. The proposed ignitability metric provides a general measure of a material’s susceptibility to ignition under typical building fire scenarios and enables relative comparison of fire risk for buildings differing only in the materials adopted.

Fire doi: 10.3390/fire9030133

Authors: Hui Zhu Xinglong Fang Xufeng Shen

To analyze the fire resistance performance of the substation framework protected by fire-retardant coating, herringbone column structure substation frameworks under heating curve conditions specified by the ISO 834 standard were simulated using ABAQUS software. Moreover, this study investigated the temperature field, stress field, and displacement characteristics of the substation structure under typical fire scene conditions. The research results indicate the following: (1) Without fire-retardant coating, the surface temperature of the bare substation framework reaches 500 °C within a short period, and a large temperature difference between the interior and exterior of the steel pipe is caused, which may induce brittle cracking within the steel. Within the 1000 s period from the start of heating, the strength of the steel structure decreases with the increase in temperature. Stress is gradually concentrated on the steel structure, and the heated part of the bare steel truss undergoes a deformation displacement of more than 0.1 m, making it susceptible to brittle fractures in the steel. The maximum deflection of the steel structures exceeds the critical value of 0.07 m. (2) With fire-retardant coating, the surface temperature of the steel can be maintained below 310 °C, and the stress in most areas of the substation framework remains below 170 Mpa. The displacement and deformation of the transformer frame are significantly reduced, and the deformation can be maintained below 0.02 m. All positions of the substation framework are in the upward expansion stage, and the deflection does not exceed 0.02 m.

Fire doi: 10.3390/fire9030132

Authors: Xinjie He Dewei Yang Qiting Huang Cunsui Liang Yingpin Yang Guoxue Xie Zelin Qin Runxi Pan Yuning Xie

Crop residue open burning is a major source of atmospheric pollutants that degrade regional air quality, enhance climate forcing, and threaten public health through emissions of particulate matter, greenhouse gases, and toxic species. In southern China, satellite-based emission estimates are often underestimated because frequent cloud cover and limited spatiotemporal resolution hinder the detection of agricultural fires. In this study, crop residue open burning emissions in Guangxi province from 2017 to 2023 were quantified using a statistical approach. The open burning proportion (OBP) was updated on an annual basis using the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG), and recently reported emission factors (EFS) were adopted to enhance estimation accuracy. Annual emissions of pollutants were then spatially distributed to 0.05° × 0.05° grid cells based on satellite-detected fire counts and land cover information. The results indicated the total emissions of black carbon (BC), organic carbon (OC), sulfur dioxide (SO2), nitric oxide (NOX), carbon monoxide (CO), carbon dioxide (CO2), fine particles (PM2.5), coarse particles (PM10), ammonia (NH3), methane (CH4) and non-methane volatile organic compound (NMVOC) in Guangxi province during 2017–2023 were 58.90, 230.48, 37.90, 213.95, 4234.41, 108,775.48, 583.09, 667.70, 46.36, 322.74 and 710.20 Gg, respectively. Sugarcane residue burning was identified as the dominant contributor, accounting for 41.26–64.38% of total emissions, followed by rice (20.66–43.06%), corn (5.11–17.25%), and cassava (4.33–6.45%). Emissions exhibited clear interannual variability, declining from 2017 to 2020 under strict control measures and increasing again from 2021 to 2023 as enforcement weakened. Incorporating annually updated VIIRS-derived OBPS into the statistical inventory improves the temporal representation and reliability of multi-year emission estimates for agricultural burning.

Fire doi: 10.3390/fire9030131

Authors: João Costa Domingos Martinho

Wildfires are increasingly complex and geographically dynamic phenomena that require timely and context-sensitive decision support across the management cycle. Artificial intelligence (AI) has been widely applied to wildfire detection, prediction, and remote sensing; however, a systemic understanding of how AI methods are structurally integrated into decision-support architectures remains limited. The present configurational mapping review, reported in alignment with PRISMA-ScR guidance, examines AI applications in rural wildfire management between 2020 and 2024. Using a configurational framework, explicit scope–algorithm–vector relations are mapped, identifying how specific AI paradigms are operationalised through technological infrastructures to support decision-relevant functions. A total of 27 articles were included, from which 168 scope–algorithm–vector triplets were extracted and analysed descriptively. The results reveal a concentration of applications in detection and evolution prediction tasks, predominantly supported by machine learning methods and remote sensing platforms. Explicitly linked configurations to action-oriented or prescriptive decision functions are less frequently documented. The findings contribute to a structured mapping of AI deployment patterns in wildfire management and provide a conceptual basis for future research addressing integrative and action-oriented system design.

Fire doi: 10.3390/fire9030130

Authors: Pere Pons Roger Puig-Gironès Josep M. Bas Carles Tobella

The background modification of ecosystems affected by fire can cause black or dark colours in animals to become adaptive, providing better protection against visually oriented predators. We surveyed fire-prone Mediterranean woodlands to describe the behaviour, position and background characteristics of the black cicada Tibicina quadrisignata Hagen, 1855 found in recently burnt and unburnt trees. A human detectability test, using cicada pictures in natural backgrounds taken during the fieldwork, was used to assess detection risk. Most cicadas found were solitary males uttering courtship song. Many cicadas flew when approached, with 82% of flight initiation distances being less than 3 m and half of the flights being less than 30 m. Cicadas favoured sunny locations in early morning, and shady sites as the temperature increased. Fire altered fine-scale microhabitat use by cicadas, since cicadas were found in 71% thicker stems and at 14% lower height on the tree, in burnt trees, in relation to unburnt trees. Generalised Linear Mixed Models (GLMMs) revealed a negative fire effect on cicada detection by human test participants. The probability of detection fell from 0.62 in unburnt backgrounds to 0.48 in burnt backgrounds, while the time needed for detection did not change between burnt and unburnt sites. Overall, these results show that T. quadrisignata cicadas adjust their substrate use after fire and are less detectable on burnt backgrounds. Real predation risk, however, also depends on thermoregulation-associated exposure, courtship song activity and predator densities.

Fire doi: 10.3390/fire9030129

Authors: Shuzhen Shao Yi Lu Shiliang Shi Yubo Wang Tao Wang

To address the challenges of rapid water loss and insufficient long-term inhibition efficiency of conventional inhibitors in the high-temperature environments of deep goafs, a novel, environmentally friendly Deep Eutectic Inhibitor (DEI) was synthesized. This DEI utilizes citric acid (Ca) and proline (Pr) as the hydrogen bond donor and acceptor, respectively, with ascorbic acid (VC) and propyl gallate (PG) serving as antioxidants. A moisture retention evaluation model based on Fick’s law of diffusion was established to systematically investigate the liquid-domain stability of the DEI across a temperature range of 30 °C to 120 °C. The results demonstrate that the DEI exhibits superior moisture retention capabilities under high-temperature conditions, with the relative moisture retention peaking in the 80–110 °C range. Mechanistically, the formation of a robust hydrogen bond network effectively counteracts moisture evaporation driven by thermal kinetic energy. Furthermore, the DEI demonstrated significant inhibition effects on four coal samples with varying degrees of metamorphism. Tests on oxidative heat release characteristics revealed that DEI treatment delayed the initial oxidation temperature of the coal. Kinetic analysis further indicated that during the critical oxidation stage (200–300 °C), the apparent activation energy of the treated coal samples increased by 10.28–18.9 kJ/mol, effectively suppressing the spontaneous combustion process. This study contributes to the development of high-efficiency and eco-friendly fire prevention materials for coal mines.

Fire doi: 10.3390/fire9030128

Authors: Tingren Wang Guangwei Liu Kai Yu Baolin Yao

Accurate identification of fire smoke is a key link in realizing early fire prevention and control. Traditional intelligent video and image processing technologies are significantly restricted by environmental factors, with weak anti-interference capabilities and limitations in distinguishing fire smoke, leading to a high false alarm rate of fires. To address this problem, this paper proposes an unconventional visual field smoke detection method based on image enhancement. The method innovatively improves the Retinex algorithm by integrating improved guided filtering, adaptive brightness correction, and CLAHE-WWGIF joint processing, which realizes targeted optimization for the unique interference factors of under-construction nuclear power plants such as water mist, low illumination, and equipment occlusion. First, an improved Retinex algorithm is used to process the image to improve the image brightness and contrast, retain edge details while avoiding halo artifacts, reduce the impact of noise, and optimize visual features. Then, the sample data set is integrated, and the YOLOv11 target detection algorithm is used to achieve accurate identification and positioning of smoke targets. Experimental data shows that the fire identification method achieves an accuracy rate of 93.6% and 92.3% for fire smoke identification in interference-prone scenarios such as dark nights and water mist, respectively, and the response time to fire smoke is only 1.8 s and 2.1 s. In practical on-site applications at nuclear power plant construction sites, the method is integrated into an “edge computing + distributed deployment” hardware system, which realizes real-time smoke detection in core areas such as nuclear islands and conventional islands with a false alarm rate of less than 5% and a detection delay of ≤300 ms, meeting the ultra-strict safety monitoring requirements of nuclear power projects. Experiments show that this method can be effectively applied to smoke detection scenarios under unconventional visual fields, accurately identify smoke, provide reliable technical support for fire smoke identification under unconventional visual fields, significantly reduce the false alarm rate of fire detection, and provide technical support for the safety of under-construction nuclear power plants.

Fire doi: 10.3390/fire9030127

Authors: Gökhan Öztürk Müjdat Fırat

This study explores the viability of condensate petroleum, an ultra-light hydrocarbon derived from natural gas production, as an alternative diesel engine fuel. The researchers tested six different fuel blends, increasing the condensate volume by 10% increments, in a compression ignition engine under three distinct load conditions (25%, 50%, and 75%) to evaluate both combustion characteristics and emission performance. The results demonstrate that condensate blends significantly enhance key combustion parameters. The heat release rate, in-cylinder pressure, and in-cylinder temperature all increased, with the highest heat release rate improvement of 35.6% observed at a 75% load using a 60% condensate petroleum blend. However, increasing the condensate ratio also extended ignition delay times and raised the ringing intensity, which peaked with a 34.7% increase at a 25% load. Brake thermal efficiency improved at lower and medium loads—achieving a maximum 11.2% increase with the 50% condensate petroleum blend at 50% load—but decreased when the engine reached 75% load. In terms of environmental impact, the condensate blends proved largely beneficial. Carbon monoxide emissions dropped by 57.9% (at 75% load, 60% condensate petroleum), smoke opacity decreased by 72.6% (at 25% load, 40% condensate petroleum), and hydrocarbons fell by 34.4% (at 50% load, 60% condensate petroleum). The primary drawback was that nitrogen oxide emissions worsened, increasing by 20.4% at 75% load with the 50% condensate petroleum blend. Overall, the study concludes that the effects of condensate petroleum are highly acceptable, making it a promising alternative fuel and additive for diesel engines.

Fire doi: 10.3390/fire9030126

Authors: Mingyun Cho Chan Park

Anthropogenic wildfires account for the majority of wildfire ignitions in human-dominated landscapes, yet their spatial drivers remain insufficiently understood at national scales. This study aims to identify key factors influencing anthropogenic wildfire occurrence and to develop a robust and interpretable prediction framework using nationwide data from South Korea. Wildfire occurrence records from 2011–2021 were integrated with daily meteorological, environmental, and socio-economic variables at a 1 km grid resolution. A stacking ensemble model combining Random Forest, XGBoost, LightGBM, Extra Trees, and logistic regression was implemented to improve predictive robustness under rare-event conditions. Model performance was evaluated using ROC–AUC, PR–AUC, and threshold-optimized F1-scores, and variable contributions were interpreted using feature importance and SHAP analyses. The ensemble model achieved a PR–AUC of 0.934 and an ROC–AUC of 0.941. Relative humidity and maximum temperature were identified as influential meteorological variables, while human-accessibility-related variables, particularly distance to roads and agricultural land, showed consistently high contributions to spatial ignition probability. These findings indicate that anthropogenic wildfire occurrence is shaped by interactions between fire-weather conditions and spatial patterns of human accessibility. The proposed framework provides a scalable approach for understanding anthropogenic wildfire mechanisms and supporting prevention strategies in forested landscapes.

Fire doi: 10.3390/fire9030125

Authors: Mohammad I. Alzghoul Suhaib M. Hayajneh Jamal Nasar

The adoption of electric vehicles (EVs) has posed new challenges to fire safety, especially when multiple EVs are transported on electric trailers, as limited studies exist on heavy electric vehicle transportation and little research has been conducted on fire development during EV tunnel transport. The aim of this study is to investigate the temperature, smoke, and tenability conditions produced by an electric trailer transporting eight EVs, where a fire initiates and spreads to all eight EVs, under two scenarios: natural ventilation and longitudinal tunnel ventilation. The Fire Dynamics Simulator (FDS) was used, and the combined peak heat release rate (HRR) of the vehicles was found to exceed 76 MW. Air temperatures around the fire source exceeded 1100 °C, while temperatures above 950 °C were recorded at the tunnel ceiling. The simulations captured thermal behaviour, smoke propagation, and the accumulation of carbon dioxide (CO2) and carbon monoxide (CO). Longitudinal ventilation was shown to reduce upstream smoke spread and help maintain tenable conditions for evacuation and emergency response. These findings raise critical safety concerns regarding EV transportation in tunnels and support improved decision-making for tunnel infrastructure design and emergency responders.

Fire doi: 10.3390/fire9030124

Authors: Xueming Shu Cheng Li Yixin Xu Jingwu Wang Yinuo Huo Juanxia He

With the widespread application of automatic fire alarm systems in various types of buildings, the problem of fire false alarms has gradually become prominent, which not only causes resource waste, but also may reduce users’ trust in the alarm system, thereby affecting the efficiency of emergency response in actual fires. According to data from a certain fire cloud platform, 99.85% of the suspected fires predicted by its system are false alarms. Although existing models can recognize most fire accidents, the accuracy of fire alarm recognition is only 0.15%, due to loose judgment logic, which still requires a large amount of manpower to verify alarms. This article analyzes a large amount of false alarm data and explores the main causes of false alarms, including environmental interference, equipment failure, and improper human operation. By using a fire dynamics simulator (FDS) to establish fire simulation models under different data settings, horizontal and vertical multi-scene fire simulation data are obtained. The study combines simulation and platform data to form a fire and false alarm dataset using a one-dimensional convolutional neural network (1D-CNN) and deep neural network (DNN) deep learning techniques to learn the deductive rules of the fire scene, establish a two-stage judgment model, and gradually, accurately, judge the results. By quantifying the precision, recall, and F1 score of the model, a deep learning model designed to accurately identify genuine fire alarms while filtering out false ones is proposed that can significantly reduce the false alarm rate. The results indicate that the model can identify 1705 false alarms out of 2255 highly suspected true alarms identified by existing systems in multiple practical scenarios and eliminate 75.61% of false positive alarms. On the premise of ensuring an authenticity recognition rate greater than 98%, the accuracy of fire alarm recognition increased from 0.15% to 28.85%, which will significantly reduce the workload of staff verifying alerts, and has good practical value.

Fire doi: 10.3390/fire9030123

Authors: Ju-Yeol Park Sun-Yeo Mun Jae-Min Kim Cheol-Hong Hwang

An experimental study was conducted to quantitatively assess the separate effects of smoke-layer height and temperature on fire spread along a cable tray in a compartment. Smoke-layer height was controlled by varying the opening height (h) using side-wall configurations (SW0%, SW25%, and SW50%), while smoke-layer temperature was adjusted by changing the heat release rate (HRR) of an LPG burner (10, 14, and 18 kW). Fire spread was quantified using flame imaging and measurements of HRR, fire growth and spread rates, incident heat flux at tray height, and gas temperature and O2 concentration above and below the tray. At 10 kW, self-extinction occurred before the flame reached the tray end for all side-wall configurations. At 14 and 18 kW, fire spread to the tray end occurred under SW25% and SW50%. For a given HRR, SW50% produced higher heat flux and temperature near the tray but lower oxygen concentration, especially below the tray. These findings indicate that cable tray fire spread is governed by the combined effects of smoke-layer height and temperature through thermal feedback and local oxygen availability. Fire spread was promoted by stronger thermal feedback, but could be limited under a deeper smoke layer when oxygen availability near the tray was reduced.

Fire doi: 10.3390/fire9030122

Authors: Manish K. Sah Pratik Bhatt Vasant A. Matsagar Heesun Kim Venkatesh K. R. Kodur

In this study, the comparative residual performance of fire-exposed reinforced concrete (RC) beams with rectangular and T-shaped cross-sections is investigated. Two concrete beams, one with a T-section and the other with a rectangular section, were tested under the combined effects of fire exposure and structural loading. Data generated in the tests during and following fire exposure is utilized to compare the thermal and structural response of the beams. The results indicate a notable difference in the temperature evolution, mid-span deflection, and the residual capacity of the beams. The T-beam experienced greater deflection and stiffness degradation due to its larger exposed surface area (approximately 17% higher than the rectangular beam) and flange geometry, despite comparable peak rebar temperatures. A simplified approach, based on the maximum concrete and rebar temperatures and corresponding strength reductions, is proposed to evaluate the residual capacity of fire-exposed RC beams. For equal cover depth to reinforcement, peak rebar temperature is unaffected by cross-section shape as long as the web of the T-beam is not slender. T-shaped beams with similar overall depth exhibit greater post-fire strength retention than rectangular beams when the neutral axis lies within the flange. A 20% reduction in the web thickness and a combined reduction of 20% in web and 37% in flange thickness result in a comparable decrease in the flexural capacity to that of the rectangular beams of similar depth, indicating that the flange plays a key role in maintaining post-fire performance.

Fire doi: 10.3390/fire9030121

Authors: Musa Al-Yaman Miral AlMashayeikh Majd AlFedailat Ahmad M. A. Malkawi Majid Al-Taee

Fire safety plays a vital role in protecting lives, property, and the environment, and it keeps communities and organizations running safely. Many existing fire pump control systems fall short in educational and small-to-medium industrial settings: they often control only one pump at a time, rely heavily on manual monitoring, and come with high costs that limit accessibility. To address these gaps, we developed an affordable, hands-on educational kit that brings real-world fire safety systems into the classroom using modern automation technology. The system is built around a Delta DVP12SA211R PLC chosen for its built-in real-time clock, integrated RS-232/RS-485 ports for reliable communication, and expanded with DVP16SP11R digital I/O and DVP04AD-S2 analog input modules to interface with simulated sensors mimicking smoke detection and water pressure. Students interact with the system through a Delta DOP-110IS HMI, which features Ethernet connectivity for remote observation, electrical isolation for safe operation, and a 200 ms screen update rate to ensure responsive, realistic feedback. The kit enables learners to explore critical emergency scenarios, including automatic switching between jockey and main pumps, low-pressure alerts, and system failover, transforming theoretical concepts into tangible skills. In user evaluations, 57.1% of students with no prior experience reported that the simulations closely mirrored real-world systems, while 80% of those with a fire safety background found the kit reinforced their existing knowledge; notably, 57.1% of instructors rated it as highly effective for teaching core fire safety principles across diverse learner profiles. By integrating industrial-grade hardware with scenario-based learning, this tool not only deepens understanding of fire protection systems but also better prepares future engineers for the practical demands of fire safety and industrial automation careers.

Fire doi: 10.3390/fire9030120

Authors: Qing Ma Chang Liu Xiaobin Li Dawei Li Xinzhe Li Yixuan Wu

This study investigates how outlet pressure influences the fire suppression performance of a compressed air foam system (CAFS), with the aim of supporting system optimization and engineering applications. An experimental apparatus for foam performance testing is used to measure changes in foam flow rate, expansion, initial velocity, initial momentum, and drainage time at different outlet pressures. On the basis of relevant theoretical models, the factors causing discrepancies between model predictions and experimental results are examined, and the models are then refined. How the outlet pressure of CAFS affects foam performance is thereby clarified. The results show that foam flow rate increases as outlet pressure increases. At higher pressures, shear-thinning and intensified gas–liquid mixing affect the foam. As a result, the growth of flow rate in the range of 0.01–0.03 MPa is significantly higher than that in the range of 0.06–0.10 MPa. Both initial velocity and initial momentum increase significantly with increasing pressure, whereas the expansion decreases. Within the outlet pressure range of 0.01–0.10 MPa, the initial velocity increases from 1.23 m/s to 6.65 m/s, the initial momentum rises from 4.6 kg·m/s to 34.1 kg·m/s, and the expansion decreases from 9.2 to 5.4, indicating reduced foam stability. Drainage time and drained mass vary non-monotonically with outlet pressure. The longest drainage time and the smallest drained mass occur at 0.06 MPa. Fire suppression performance improves as outlet pressure increases. A higher outlet pressure enables the foam solution to penetrate the flame zone more effectively and to cover the surface of the burning material. In addition, changes in foam properties enhance the thermal insulation and smothering effects of the foam layer, as well as its heat absorption and cooling capacity. These effects together improve the efficiency of fire source cooling.

Fire doi: 10.3390/fire9030119

Authors: Mahmoud S. Waly Guan Heng Yeoh Maryam Ghodrat

Recently, numerous countries have experienced devastating wildfires, leading to significant destruction and loss of life. These catastrophic events highlight the shortcomings in current building regulations and testing methods. There is a pressing need for a more profound understanding of the characteristics and behaviour of large outdoor fires to address these inadequacies effectively. Wildfires can spread to structures located at the wildland–urban interface, leading to further fire propagation from one building to another. In this study, the Fire Dynamics Simulator (FDS) model was validated using experimental data from the National Institute of Standards and Technology (NIST). The experiment consisted of a target wall and a small wooden shed containing six wooden cribs as fuel, with a separation distance of 3 m. Both FDS and the experiment proved that 3 m is the safe separation distance. Different shed materials, such as steel, were used, which reduced the total heat release rate by 40% and the flame height by 20%. The effects of wind speed and direction were investigated using two wooden sheds in FDS to observe fire spread between them. The safe separation distance was 3 m for both wind speeds (2 and 5 m/s) in all directions, where the critical temperature was not reached to cause self-ignition of the second shed, except in the north direction (inward) at a speed of 5 m/s. When the separation distance increased to 3.5 m, the average heat flux at the other shed reduced to 3.18 kW/m2, which did not cause self-ignition. Therefore, the safe separation distance between two structures for a wind speed of 5 m/s should be 3.5 m to mitigate the spread of fire based on the shed dimensions and the fire source load.

Fire doi: 10.3390/fire9030118

Authors: Jiajie Qin Zhangfeng Huang Xin Liu Jingjing Li Wenbin Zhang

The prevalence of high-rack warehouses and large-space facilities with high ceilings poses significant challenges to traditional automatic sprinkler systems, which often exhibit activation delays and limited suppression efficacy. This study investigates the spatio-temporal evolution and distribution characteristics of fire-induced thermal smoke flow through a hybrid approach combining full-scale fire experiments and numerical simulations. A physical hypothesis is proposed: the ceiling temperature field approximately follows a two-dimensional Gaussian distribution. Through parametric numerical simulations under varied ambient temperatures, fire identification criteria were calibrated, encompassing a sustained increase in the average temperature rise within high-temperature zones, the attainment of a predefined threshold, and the spatial stabilization of the Gaussian distribution center. Subsequently, a precise algorithm for rapid fire identification and source localization was developed. Experimental validation demonstrates that the proposed algorithm significantly outperforms traditional passive-activation closed sprinklers, advancing fire detection by 46–67 s. Furthermore, the fire source localization error is maintained within half of the sprinkler spacing. The algorithm also exhibits robust environmental adaptability and generalizability across a wide ambient temperature range, providing a technical foundation for active-actuation fire suppression.

Fire doi: 10.3390/fire9030117

Authors: Yulin Wu Rui Li Mengying Zhang Jiaxin Shi Fan Zhou Mazyar Etemadzadeh Md Jakir Hossain Md Jalal Uddin Rumi Guowen Song

Fire smoke, rich in toxic ultrafine particles and polycyclic aromatic hydrocarbons (PAHs), poses significant health risks to first responders and vulnerable populations. In this study, a reproducible combustion–smoke simulation platform was developed to mechanistically quantify fire behavior, particle emissions, and PAH toxicity under controlled heat flux and oxygen conditions. Consistent combustion and smoke emissions were achieved by measuring heat release rate, particle mass, particle number concentration, and PAH concentration, with an overall average coefficient of variation below 15%. Systematic experiments with representative biomass (pine, oak) and plastics (PVC, polystyrene) demonstrate that fuel composition, heat flux, and oxygen availability jointly govern particle formation and PAH partitioning. Regardless of the combustion factors, ultrafine particles dominated the particle number concentration (55.5–86.2%). Plastic combustion generated 7 to 59 times particle mass, up to 260 times higher PAH emissions, and up to 58,500 times greater PAH toxic equivalent quotient (PAH-TEQ) than wood. Oxygen-deficient and smoldering regimes shifted emissions toward fine and ultrafine particles enriched in high-molecular-weight PAHs, revealing a coupled physical–chemical hazard not captured by bulk PM metrics alone. These results establish a quantitative framework linking combustion regime, particle size, and PAH toxicity, providing critical insight for exposure assessment, PPE design, and mitigation strategies in ventilation-limited and mixed-fuel fire scenarios.

Fire doi: 10.3390/fire9030116

Authors: Pei Wang Jiaming Liu Yuyao Cao Hui Zhang

Person Re-identification (Re-ID) plays an important role in dynamic evacuation path planning and safety monitoring. However, rapid appearance changes and limited long-term surveillance data significantly degrade model robustness in emergency scenarios. To address this issue, a virtual try-on-based data augmentation framework is proposed for person Re-ID. A prompt-based automatic clothing mask generation (PACMG) module integrating Grounding DINO and the Segment Anything Model (SAM) is developed to improve clothing mask accuracy under low-resolution, occlusion, and complex background conditions. A tiered augmentation strategy is further designed to alleviate identity-level imbalance. Experimental results demonstrate that the proposed method increases the clothing replacement validity rate from 52% to 73.61% while preserving identity consistency and distribution stability, as verified through multi-level analyses. When the augmented data are incorporated into the training set, consistent improvements in Rank-1 accuracy and mAP are observed on a ResNet-50-based person Re-ID benchmark. These results indicate that the augmented data enhance robustness to appearance variation, providing practical support for robust person tracking in evacuation scenarios.

Fire doi: 10.3390/fire9030115

Authors: Zhendong Li Guangwei Liu Kai Yu Shijie Du

In night-time construction scenarios of under-construction nuclear power plants, some yellow lights and open flames exhibit highly similar visual characteristics, resulting in frequent false alarms of fire sources. Such false alarm information tends to drown out real fire alarm signals, which not only severely disrupts construction operations but also endangers fire safety. To address this problem, this paper proposes an intelligent fire risk identification method based on an enhanced YOLOv8n (named YOLO-Fire). Specifically, shallow convolutional layers embedded with a coordinate attention mechanism are integrated into the Backbone of YOLOv8n; the Neck is optimised to improve the efficiency of multi-scale feature fusion; and the Head is enhanced to strengthen the localization and classification branches. Additionally, a composite loss function combining classification loss, regression loss, and similarity loss is designed, coupled with night-scene-specific data augmentation techniques and a two-stage progressive training strategy. Experimental results show that YOLO-Fire reduces the false alarm rate by 14.3%, increases the mean average precision (mAP@0.5) for open flames by 11.3% to 75.2%, and maintains an inference speed of over 85 frames per second (FPS). This study achieves an optimal balance between false alarm control, small object detection accuracy, and real-time processing efficiency, effectively resolving the misclassification issue between open flames and lights in night-time construction scenarios, and providing precise and efficient intelligent technical support for fire risk prevention and control during the construction phase of nuclear power plants.

Fire doi: 10.3390/fire9030114

Authors: Adeniyi P. Asiyanbi

In Canada and elsewhere, there is an ascendancy of a whole-of-society approach that centres shared responsibility for wildfire management. This article engages the debates on the rise of shared responsibility for wildfire management to argue that this context demands a renewed research focus on understanding how the public allocates responsibility for wildfire management. We illustrate this argument through a case study of public engagement with wildfire risk and shared responsibility in Whistler, British Columbia, western Canada. Our case study draws on evidence from a quantitative survey administered to 1311 participants in the spring and summer of 2024. The study reveals a near-universal concern about wildfires among the participants and a high level of risk perception. This is consistent with community climate and wildfire reports and plans. This level of concern is driving a high level of mitigation activity completion among participants, even though the level of preparedness is mixed. Our study found a marked pattern of responsibility allocation across the phases of wildfire management. Participants put the municipal government at the forefront of mitigation, preparedness, and response. The provincial government was ranked as most responsible for recovery. Homeowner responsibility declined as one moves from mitigation and preparedness through to response and recovery. Private actors, such as insurance, have greater responsibility in the recovery phase. Multivariate General Linear Models (GLMs) show that how respondents allocate responsibility for various aspects of wildfire management is influenced by home ownership, prior wildfire experience, perceived preparedness, and commitment to bearing the costs of FireSmart assessment. We conclude that a sustained research commitment is needed to further elucidate the dynamics of public expectations and attitudes in the context of shared responsibility for wildfire management.

Fire doi: 10.3390/fire9030113

Authors: Aušra Stankiuvienė Ritoldas Šukys

The fire performance of ventilated facade systems incorporating combustible insulation remains a critical issue in contemporary building design. This study presents a full-scale natural-fire test of a ventilated, rendered facade system containing 150 mm expanded polystyrene (EPS) insulation, conducted in accordance with the DIN 4102-20 methodology. Temperature measurements were recorded at key facade locations via K-type thermocouples, and flame spread, materials melting, and degradation were documented through visual observations. The combustion chamber reached a peak temperature of 912 °C, while the thermocouple located above the opening recorded a maximum temperature of 786 °C. No sustained flaming or debris above the 3.5 m height limit was observed, yet significant internal EPS melting occurred throughout the cavity. These findings underscore the potency of the “chimney effect” in ventilated cavities, highlight the limitations of the current acceptance criteria, and provide evidence relevant to ongoing efforts to develop more coherent approaches to facade fire-safety assessment.

Fire doi: 10.3390/fire9030112

Authors: Mohamed Sharaf Duncan Sutherland Rahul Wadhwani Khalid Moinuddin

Wildland fires, including surface and crown fires, present significant challenges for ecosystems and forest management. Accurate fire modelling is crucial for risk assessment and mitigation strategies. The Fire Dynamics Simulator (FDS) v6.8.0, developed by the National Institute of Standards and Technology (NIST), is a physics-based model that simulates fire behaviour by incorporating advanced physics and chemistry. However, its reliability requires thorough validation. This study validates FDS 6.8.0’s performance in modelling both surface fires and single tree burning. Two separate simulation sets were conducted. For surface fires, pine needle fuel beds were used at a laboratory scale to examine fire behaviour on slopes of 0°, 10°, and 20°. The results were validated against experimental data. A burning Douglas fir tree was simulated, and the results were compared with experimental measurements. The surface fire simulations at 0° and 10° slopes showed strong agreement with experimental data. In single-tree burning, both experimental and simulated results exhibited similar trends, with a rapid increase to a peak mass-loss rate (MLR) followed by a gradual decline. Validating FDS 6.8.0 forms an essential first step toward supporting the investigation of complex wildland fire behaviour, such as surface-to-crown fire transition, canyon fire, and dynamic escalation, using the same FDS version.

Fire doi: 10.3390/fire9030111

Authors: Flavio Tiago Couto Cátia Campos Federico Javier Beron de la Puente Paulo Vítor de Albuquerque Mendes Hugo Nunes Andrade Katyelle Ferreira da Silva Bezerra Nuno Andrade Filippe Lemos Maia Santos Natalia Verónica Revollo André Becker Nunes Rui Salgado

The 2025 fire season in Portugal was marked by large fires, underscoring the vulnerability of the forested areas to fire. The study analyzes the main meteorological conditions during a critical period of fire activity and addresses the following question: Why can the northeast (NE) weather pattern be so critical for fire danger in Portugal? Fire severity in the Arouca wildfire, the largest fire of the period, was estimated using a methodology that integrates foundation vision models with computer vision algorithms. ECMWF analyses and convection-permitting Meso-NH simulations are used to examine large-scale circulation and the mesoscale environment, respectively. Synoptic-scale analysis revealed the Azores anticyclone centered slightly northwest of the Iberian Peninsula (IP), with its eastern sector directly affecting the northern IP under north/northeast winds. The hectometric-scale simulation demonstrated that orographically enhanced wind gusts over the northern Portuguese mountains substantially intensified near-surface fire-weather conditions when the winds were nearly easterly. Furthermore, strong low-level winds and atmospheric stability constrained vertical plume growth, favoring horizontal smoke transport. In addition, the study highlights that Arouca’s fire had 88% of its area affected with moderate to high severity. Overall, the results demonstrate that the interaction between large-scale NE circulation and local orography plays a decisive role in amplifying fire danger in northern Portugal, emphasizing the need for high-resolution atmospheric modeling to identify fire-prone regions under specific synoptic patterns.

Fire doi: 10.3390/fire9030109

Authors: Beyda Taşar Ahmet Burak Tatar Alper Kadir Tanyildizi Oğuz Yakut

In this study, a deep learning-based multimodal framework is presented for forest fire detection using RGB images, which synthetically generates night-vision-like, white-hot, and green-hot pseudo-thermal representations. The synthetic modalities are derived directly from RGB data and integrated into a hardware-independent multimodal learning pipeline to increase visual diversity without relying on additional sensing hardware. Each modality is processed using an ImageNet-pretrained convolutional backbone, and modality-specific feature vectors are combined through feature-level concatenation before classification. The proposed framework was evaluated using multiple backbone architectures, including ResNet18, EfficientNet-B0, and DenseNet121, which were assessed independently under a unified experimental protocol. Experiments were conducted on two datasets with substantially different scales and characteristics: the FLAME dataset (39,375 images, binary classification) and the FireStage dataset (791 images, three-class classification). For both datasets, stratified 80–20% training–validation splits were employed, and online stochastic data augmentation was applied exclusively to the training sets. On the FLAME dataset, the proposed framework achieved consistently high performance across different backbone and modality configurations. The best-performing models reached an accuracy of 99.66%, precision of 99.80%, recall of 99.66%, F1-score of 99.73%, and ROC AUC value of 0.9998. On the more challenging FireStage dataset, the framework demonstrated stable performance despite limited data availability, achieving an accuracy of 93.71% for RGB-only configurations and up to 93.08% for selected multimodal combinations, while macro-averaged F1-scores exceeded 0.92, and ROC AUC values reached up to 0.9919. Per-class analysis further indicates that early-stage fire (Start Fire) patterns can be discriminated, achieving ROC AUC values above 0.96, depending on the backbone and modality combination. Overall, the results suggest that synthetic-modality-based multimodal learning can provide competitive performance for both large-scale and data-limited fire detection scenarios, offering a flexible and hardware-independent alternative for forest fire monitoring applications.

Fire doi: 10.3390/fire9030110

Authors: Haining Zhu Shuwen Liu Huimin Jia Sanping Li Liangkuan Zhu Xingdong Li

This study focuses on enhancing wind speed prediction for wildfire spread simulation by proposing an integrated forecasting approach. The original wind speed series is first processed via variational mode decomposition (VMD), with its parameters [K, α] optimized via particle swarm optimization (PSO). Every intrinsic mode function (IMF) resulting from this decomposition is predicted using a bidirectional long short-term memory model incorporating an attention mechanism (AM-BiLSTM), and the final wind series is reconstructed from these predictions. Model training and validation were conducted using data from controlled burning experiments in the Mao’er Mountain area of Heilongjiang Province, China. Predictive performance is evaluated through multiple statistical metrics, error distribution analysis, and Taylor diagrams. To assess practical utility, the predicted wind field is further applied in FARSITE to drive wildfire spread simulations. Results demonstrate that the PSO-VMD-AM-BiLSTM model provides reliable wind forecasts and contributes to improved fire spread prediction accuracy, indicating its potential for decision support in wildfire management. To achieve accurate forest fire spread prediction, we construct the MCNN model, which is based on early perception of understory wind fields using predicted wind speed data and adopts a multi-branch convolutional neural network architecture to extract fire spread features. FARSITE is employed to simulate forest fire spread in the Mao’er Mountain region, generating a dataset for model training and testing. After 50 training epochs, the loss value of the MCNN model converges, achieving optimal prediction performance when the combustion threshold is set to 0.7. Compared to models such as CNN, DCIGN, and DNN, MCNN shows improvements in evaluation metrics including precision, recall, Sørensen coefficient, and Kappa coefficient. To validate the model’s predictive performance in real fire scenarios, four field ignition experiments were conducted at the Liutiao Village test site: homogeneous fuel combustion, long fire line combustion, alternating fuel combustion, and multiple ignition source merging combustion. Comprehensive evaluation across the four experiments indicates that the model achieves precision, recall, Sørensen coefficient, and Kappa coefficient values of 0.940, 0.965, 0.953, and 0.940, respectively, with stable prediction errors below 6%. These results represent improvements over the comparative models DCIGN and DNN. The proposed MCNN model can adapt to forest fire spread prediction under different scenarios, offering a novel approach for accurate forest fire prediction and prevention.

Fire doi: 10.3390/fire9030108

Authors: Ioannis Karakonstantis George Xylomenos

Two-dimensional cellular automata (CA) models are widely used for wildfire simulation due to their clean representation of environment and fire mechanics and their computational efficiency. In this review we describe the mechanisms through which forestry fuel characteristics, topographic features, firefighting suppression strategies, fire spotting behavior and meteorological conditions are represented and integrated within these models. While these models are effective for large scale simulations, in which high precision is not critical, their reliance on discrete representations of space and time, along with simplified local state transition rules, introduces additional challenges and limitations. This review presents key methodologies, hybrid implementations, and model extensions of CA-based wildfire simulation models, highlighting their inherent strengths, limitations, and practical challenges. In addition, it provides a classification of the computational and simulation techniques applied to wildfire spread and behavior.

Fire doi: 10.3390/fire9030107

Authors: Chenyu Ma Siquan Yang Jing Cui Qiang Li Qichao Yao De Zhang Jiachang Guo Xinqian Wang Chong Qu

Assessing wildfire susceptibility requires integrating environmental and anthropogenic factors to quantify the probability and vulnerability of fires in a given area. Many existing machine-learning models offer high predictive power but limited interpretability, restricting their utility for operational decision-making. This study is the first to apply the intrinsically interpretable deep network TabNet to wildfire susceptibility modeling. By fusing multi-source data and leveraging TabNet’s feature-mask matrix, we achieve accurate prediction and built-in explanation without relying on auxiliary tools. On a dataset of 133,811 samples, the proposed model achieves an Area Under the Curve (AUC) of 0.760, recall of 0.883, precision of 0.395, and an F1.5 score of 0.640, outperforming XGBoost (version 1.5.0) and other baseline models. The importance rankings derived from the feature-mask matrix align with the Shapley Additive Explanations (SHAP) results, confirming the reliability of the explanations. This approach combines predictive accuracy with transparency, providing a deployable framework for wildfire early warning, risk management, and ecosystem conservation.

Fire doi: 10.3390/fire9030106

Authors: Yanglong Wu Diping Yuan Dingli Liu Weijun Liu Zhe Cheng Guohua Wu Kang Liu Lei Zou

The effective delineation of fire station response zones is critical for urban public safety planning, yet traditional methods often fail to account for dynamic traffic conditions, leading to suboptimal resource allocation. This study proposes a novel block-unit-based method that incorporates real-time traffic data to delineate fire station response zones, improving the scientificity of response time estimation. The method was validated using data from Daxiang District, China, a typical urban–rural mixed region, encompassing 2230 block units, 4 fire stations, and 13,097 demand points. Analysis of 1,225,047 data samples revealed an average travel time of 960.7 s, highlighting significant accessibility challenges. The re-delineated response zones cover areas ranging from 1.07 to 156.24 km2, with significant variations. It is attributed to the concentration of fire stations in urban areas, insufficient coverage of vast rural regions, and the proximity of one station to a river and regional boundary. These findings underscore the spatial inequities in fire service provision and the need for a more balanced resource allocation strategy. Recommendations include establishing rural fire stations, improving urban traffic conditions, and relocating certain fire stations. This approach can enhance regional accessibility and provides a scientific basis for fire service planning.