Fire doi: 10.3390/fire9080352

Authors: Hao Tang Deli Zhu Xuefeng Han

Electric vehicle (EV) fires can rapidly affect smoke and thermal conditions in underground parking garages. In this study, fifteen PyroSim/FDS cases were screened, but quantitative analysis was restricted to four prescribed-extraction cases and one baseline-matched two-device spray pair. The 0.30 m production mesh was selected using characteristic-fire-diameter, geometric-resolution, and computational-cost criteria. A matched 0.20/0.30/0.50 m check yielded non-monotonic fixed-point responses; mesh independence was not established. Extraction cases were compared using 270–300 s means and the first downward crossing of a 10 m visibility reference. At the same nominal outflow of 10 m3/s, two 5 m/s surfaces produced lower M1 gas temperature and CO and higher visibility than one 10 m/s surface. Relocating the second spray device beneath the vehicle reduced the τ = 120–150 s mean M4 underside-region gas temperature from 776.5 to 103.4 °C, while M1 visibility remained about 0.22 m. Because the model lacks a physical make-up-air path and corresponding experiments were not reproduced, these findings are limited to local prescribed-boundary comparisons. Relevant experiments support the represented mechanisms but the results do not validate the absolute point values. The simulations do not demonstrate battery extinguishment, maintained tenability, or code compliance.

Fire doi: 10.3390/fire9080351

Authors: Shuangjie Mei Yang Cao Xuefeng Han

Battery electric vehicle (BEV) fires in road tunnels can intensify thermal, smoke transport, visibility, and CO exposure hazards under confined ventilation. This study evaluated the combined mitigation performance of water mist and semi-transverse mechanical ventilation. A three-dimensional PyroSim/FDS model of a 200 m × 10 m × 5 m tunnel was established with a 7 MW BEV design fire at the midpoint. The prescribed-source model was assessed against a reduced-scale lithium-ion battery tunnel experiment; at the representative monitoring location, the simulated temperature history reproduced the main trend, with deviations of approximately 7% and 10% at the first and second peaks. Thirty-six coupled cases examined ventilation mode, nominal opening velocity, nozzle arrangement and spacing, flow rate input, droplet diameter, and spray cone angle. Supply ventilation improved hot-smoke-layer cooling and visibility, whereas exhaust ventilation more effectively reduced the local CO volume fraction. Under the baseline weighting scheme, the highest-ranked case reduced the peak local ceiling-region and near-fire gas temperatures by 77.8% and 82.2%, increased average visibility during 200–500 s by 42.9%, and achieved a comprehensive relative mitigation index (CRMI) of 56.6%. Two supplementary nominal 10 MW simulations showed that this case retained substantial thermal control, reducing the two peak temperatures by 65.7% and 74.1%, but did not improve local visibility or CO. Thus, the thermal-mitigation trend persisted at the higher nominal input, whereas the full multi-hazard ranking was not transferable across fire sizes.

Fire doi: 10.3390/fire9080350

Authors: Yunli Xu Guangshuai Han Jie Geng

With the rapid development of recycling and secondary utilization of end-of-life battery materials, it is crucial to clarify the impact of full-lifecycle degradation on the thermal safety limits of lithium-ion batteries. This study focuses on a 16 Ah NCM613|graphite pouch battery. First, it analyzes the evolution of capacity decay, thickness expansion, and internal resistance during cycling at room temperature (25 °C) and high temperature (45 °C). Furthermore, an adiabatic accelerated calorimeter (ARC) is employed to investigate the influence of different states of health (SOH) levels (95% and 85%) on the battery’s thermal runaway characteristics. The findings indicate that, macroscopically, batteries in all states follow similar voltage–temperature failure pathways, with mass loss rates confined to a narrow range of approximately 16%, emphasizing the low catastrophic potential of mid-nickel chemistry. However, the microscopic kinetic mechanisms exhibit significant anisotropy: although thickness and internal resistance display no apparent abrupt increase during the late stage of room temperature aging, the capacity exhibits a highly nonlinear plunge behavior. The severe internal lithium plating side reaction triggered by this phenomenon causes the self-heating onset temperature to drop rapidly from 130.0 °C in the fresh state to 79.7 °C. Concurrently, the activation energy of the exothermic side reaction, fitted using a simplified Arrhenius equation, exhibits a non-monotonic variation with aging progress. In the early stages of aging at 95% SOH, due to high temperatures promoting more significant growth of the interfacial film or moderate film formation at room temperature enhancing interfacial thermal stability, the activation energies for both aged batteries increase, and the energy barrier at high temperatures is slightly higher than at room temperature; however, during the deep aging stage at 85% SOH, due to the degradation of active material components and the emergence of lithium plating characteristics, the energy barrier significantly decreases, with high-temperature-aged batteries exhibiting a greater reduction, highlighting the cumulative negative impact of prolonged high-temperature exposure on thermal safety. The research provides a core scientific basis for establishing a battery safety early warning and dynamic health management system covering the entire lifecycle.

Fire doi: 10.3390/fire9080349

Authors: David Novák Kateřina Hájková Vlastimil Borůvka Tomáš Kytka

Potassium silicate is used as an inorganic fire-retardant treatment for wood, but its effect on the short-term thermal response of different species under combined temperature–moisture conditions remains insufficiently described. This study investigated spruce (Picea abies (L.) H. Karst) and beech (Fagus sylvatica L.) wood impregnated with potassium silicate and exposed to temperatures representing drying, mild thermal loading and the onset of thermal degradation. The evaluation included impregnation uptake, moisture content, mass changes, heat-transfer behavior, differential scanning calorimetry (DSC), chemical composition, Fourier-transform infrared spectroscopy (FTIR) of isolated cellulose and color measurements. Spruce showed higher uptake than beech, with an average weight percentage gain (WPG) of 10.5% compared with 3.6%. The treatment increased equilibrium moisture content by 2.6 percentage points in spruce and 1.1 percentage points in beech. Heat-transfer measurements showed that temperature and moisture governed heating: higher target temperatures were reached faster, whereas air-conditioned samples heated more slowly due to water evaporation. At lower temperatures, the direct effect of impregnation on heating time was limited, whereas at higher temperatures the treatment more clearly affected the subsequent degradation response. DSC revealed lower thermal resistance of beech and increased endothermic heat absorption in impregnated samples, particularly spruce. Higher-temperature exposure caused mass loss, hemicellulose degradation, moderate cellulose structure modification and visible color changes, with ΔE* exceeding 52 in impregnated spruce after 210 °C. The elevated-temperature response was governed by wood species, uptake, moisture content and thermal exposure level.

Fire doi: 10.3390/fire9080348

Authors: Bin Sun

This study addresses the critical need for accurate and efficient large-scale urban fire spread path prediction in dense urban areas by proposing a new gravitational framework-based theory. Its core innovation is the “characteristic attractive force” model, which mechanistically quantifies fire spread as a dynamic interaction between buildings, integrating factors like spacing, height, area and density effects to predict trajectories from the initially ignited building. This study adopts a GIS-based rapid prediction framework that circumvents the dependence on complex physical parameters. It utilizes high-precision spatial data and optimized algorithms to streamline prediction processes while retaining favorable prediction accuracy. Validated on two real-world clusters, the proposed approach enables effective visualization of dynamic propagation trajectories and pathway spectra that characterize the detailed propagation routes and ignition sequences. Notably, the framework achieves exceptional efficiency, completing predictions for large clusters in tens of seconds per scenario, making it suitable for real-time risk assessment. Overall, this work advances urban fire modeling with an innovative, efficient, and practical tool to support fire safety engineering and emergency management decision-making.

Fire doi: 10.3390/fire9080347

Authors: Jady D. Smith Anthony Power Francis E. Putz Sam Van Holsbeeck

Climate change, altered ecosystems, and expanding development in fire-prone landscapes are increasing fire risk in the wildland–urban interface (WUI). This study uses Noosa, southeast Queensland, Australia, as a case study for a preliminary modeling assessment of irrigated green firebreaks (iGFBs). Using the AMICUS Vesta Mk2 fire-behavior model, fire spread rates and fireline intensity were compared across dry eucalypt control scenarios, non-irrigated green firebreak scenarios, and irrigated green firebreak scenarios receiving 1 and 2 mm m−2 day−1 of water. In line with future climate predictions, these scenarios were compared under progressively worsening fire-weather conditions. The drought-affected dry eucalypt control produced the highest predicted fire spread rates and fireline intensity, and although non-irrigated green firebreak scenarios reduced fire behavior, they may still exceed typical suppression thresholds under catastrophic conditions. In contrast, iGFB scenarios consistently reduced both fire spread rates and fireline intensity across all fire-weather classes. Sensitivity analysis indicated that the model outputs were most responsive to drought- and moisture-related assumptions, supporting the importance of fuel moisture in the performance of the iGFB concept. Although iGFBs are not a stand-alone solution suitable for all settings, the findings provide a preliminary region-specific proof of concept for iGFBs and support the need for further applied research.

Fire doi: 10.3390/fire9080346

Authors: Umar Daraz Štefan Bojnec

Wildfires are increasingly recognized as environmental disturbances associated with socio-economic transformations in agrarian systems. This study examines the associations between reported wildfire exposure, land-market outcomes, and agrarian inequality in the Malakand Division of northern Pakistan, a region characterized by forest–agriculture interfaces and livelihood dependence on land. The study aims to analyze how different levels of wildfire exposure are associated with land values, ownership patterns, market transactions, inequality, and coping strategies among farming households. A quantitative cross-sectional design was employed using a sample of 400 households selected through multistage sampling. Data were collected through structured questionnaires and analyzed using ANOVA, chi-square tests, multiple and logistic regression, hierarchical regression, and principal component analysis. Results show that reported land values differed significantly across wildfire-exposure categories (F = 48.72, p < 0.001), with directly exposed households reporting the lowest values. Regression analysis identified direct wildfire exposure as the strongest negative statistical predictor of reported land value (β = −0.468, p < 0.001), while directly exposed households had substantially higher odds of reporting land sales (Exp(B) = 6.35). Chi-square results indicate a significant association between wildfire exposure and land transactions (χ2 = 64.82, p < 0.001). Retrospectively reported landholding data show an increase in the Gini coefficient from 0.41 before the reported fire period to 0.53 afterward. The addition of land-transaction variables increased the explained variance in agrarian inequality to 72%, which is consistent with a potential land-market pathway but does not constitute evidence of causal mediation. Coping strategies such as land sale, migration, and borrowing emerged as dominant reported responses among affected households. The study concludes that wildfire exposure is strongly associated with land devaluation, land sales, and greater agrarian inequality. Because the study is cross-sectional and lacks an independently observed pre-fire baseline or causal identification strategy, these findings should not be interpreted as definitive causal effects.

Fire doi: 10.3390/fire9080345

Authors: Penghui Jia Haihui Han Xiaojuan Yan Chendi Gao Chuntao Yin Xiaoyan Chen

Identifying coal fire risk areas is essential for safe production in coal mines. Land Surface Temperature (LST) retrieval and high-temperature anomaly extraction are core techniques for coal fire risk detection. To address the insufficient evaluation of the accuracy of relevant algorithms for arid open-pit mines, this study takes the Xingsheng Open-Pit Coal Mine in Yiwu County, Xinjiang as the research object. Based on Landsat imagery and UAV thermal infrared data, we systematically compared five mainstream LST retrieval algorithms and six high-temperature anomaly extraction algorithms and determined the optimal combination for long-term monitoring. The results indicate that all five algorithms can effectively depict LST spatial distribution under normal temperature conditions. The Jiménez-Muñoz split-window algorithm performs best for small-scale coal fire identification, with a mean absolute error of 3.25 °C and a relative error of 5.53%, and its fitting slope of 0.82 proves superior stability. For high-temperature anomaly extraction methods, the gradient threshold method achieves a 100% overlap rate with actual anomalies and no omission, which is ideal for large-scale surveys; the cluster analysis method balances detection accuracy and economic benefits for pit-scale investigations. Using 52 valid Landsat images from 2013 to 2025, long-term monitoring reveals that high-temperature anomalies are most active in summer, with an average patch area of 5.65 × 105 m2, and weaken sharply in winter. According to the observed spatiotemporal evolution patterns, the dynamic changes in thermal anomalies are inferred to be mainly associated with human mining activities, with coal seam conditions as the secondary influencing factor. This study provides reliable technical references for coal mine safety management and coal fire disaster prevention.

Fire doi: 10.3390/fire9080344

Authors: Milorad Giljača Vladan Radonjić Oto Iker Ivana Rašović Sonja Pravilović

Accurate short-term wildfire forecasting is essential for effective early warning, operational planning, and resource allocation. This study proposes the Dynamic Spatio-Temporal Fire Pressure Model (DST-FPM), a leakage-controlled forecasting framework that integrates wildfire memory, spatial connectivity, cumulative fire pressure, and seasonal variability using historical satellite-derived active fire detections. The framework combines an Active Cell Framework (ACF), Dynamic Fire Pressure (DFP), the Fire Connectivity Index (FCI), Five-Day Fire Pressure (FFP), and the Operational Fire Risk Pressure (OFRP) index within an Extreme Gradient Boosting (XGBoost) model to predict wildfire occurrence over three-day and five-day forecasting horizons, with the five-day horizon adopted as the primary operational scenario. The methodology was evaluated across Bosnia and Herzegovina, Croatia, and Montenegro using 3,591,054 grid-cell-day observations collected between January 2020 and December 2025. Independent chronological training, validation, and testing datasets were combined with temporal, spatial, and spatio-temporal validation procedures to assess model robustness. For the primary five-day forecasting horizon, the proposed framework achieved a ROC AUC of 0.773, a PR AUC of 0.147, a balanced accuracy of 0.678, and a Matthews correlation coefficient of 0.135 on the independent testing dataset, while maintaining stable performance across all validation procedures. The fitted XGBoost model consistently assigned high predictive importance to the proposed fire pressure indicators, while Top-K analysis showed that 13.5% of future wildfire occurrences were identified within only 1% of the highest-priority grid-cell-day observations. These findings indicate that integrating wildfire memory, spatial connectivity, cumulative fire pressure, and seasonal variability provide complementary predictive information for short-term wildfire forecasting while preserving interpretability, robustness, and operational applicability.

Fire doi: 10.3390/fire9080343

Authors: Yiyu Wang Guiyun Gao Aibin Wang Ao Wang Jikun Liu

The increasing frequency and severity of wildfires pose growing challenges to ecological security in remote forest regions. In road-sparse primeval forests, wildfire prevention and ground emergency response are constrained not only by fire-prone environmental conditions, but also by limited tactical access routes. Existing wildfire susceptibility studies can identify areas with higher fire occurrence potential, whereas route planning studies often optimize access without explicitly considering where fires are more likely to occur. This study developed a GIS-based decision-support framework linking wildfire susceptibility modelling with firefighting access route planning in the northern primeval forest region of the Greater Khingan Mountains, China, to improve the efficiency of wildfire prevention and response in areas with sparse road networks. Using 887 historical fire points and nine environmental and anthropogenic predictors, Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models were compared to identify relatively wildfire-prone areas. High-susceptibility locations were grouped into operational management zones using K-means clustering. A generalized forest traversal cost surface was constructed by integrating terrain, vegetation, land cover, water constraints, and existing-road accessibility, and a hybrid simulated annealing and 2-opt algorithm was used to design candidate access corridors. Results show that the RF model achieved the best internal-validation performance (AUC = 0.948; overall accuracy = 0.873), and feature-importance comparison showed that land surface temperature, proximity to roads, and NDVI were the most influential predictors. In total, 386 target points extracted from the high- and extreme-susceptibility classes were grouped into 12 spatial clusters. The optimized network identified 1008.46 km of candidate corridors and reduced the mean nearest-access distance for 13 historical wildfire events by 53.7% after the planned network was incorporated. After incorporating the planned corridors into the existing road system, the road-network density increased from 0.96 to 2.015 m/hm2. These findings demonstrate that susceptibility-driven route planning can translate predicted fire-prone areas into prioritized management units and candidate access corridors, thereby reducing spatial accessibility gaps and supporting phased patrol deployment and emergency-resource allocation in road-sparse primeval forests.

Fire doi: 10.3390/fire9080342

Authors: Yu-Tsung Ho Chung-Chyi Chou Yi-Lin Chen

Indoor positioning identifies location but does not directly indicate whether a route remains passable, how hazard exposure changes, or which alternative should be considered under deteriorating fire conditions. As a result, a geometrically shorter route may still be selected despite greater hazard exposure, blockage, or positioning uncertainty. This study proposes a dynamic hazard-aware route-risk assessment model based on Fire Positioning Infrastructure Theory (FPIT) for fireground decision support in building fires. The model converts BIM/IFC spatial semantics into a computable graph, maps normalized hazard scenario data onto graph edges, excludes edges exceeding scenario-specific hazard or blockage criteria, and evaluates the remaining feasible routes using an integrated route-risk score, hazard exposure, travel time, and positioning uncertainty. A normalized illustrative computational demonstration showed that the conventional shortest route had the lowest travel time but higher route-risk score, hazard exposure, and positioning uncertainty. The FPIT-based lower-route-risk-score alternative had lower values for these indicators but required longer travel time, while the intermediate detour provided a compromise. Pareto comparison retained the three feasible routes as non-dominated alternatives with different score–time–uncertainty characteristics. The computational demonstration illustrates the model’s internal calculability, traceability, comparability, and ability to represent route trade-offs; it does not constitute empirical validation or evidence of operational effectiveness in actual fireground environments.

Fire doi: 10.3390/fire9080341

Authors: Weiwei Fan Mingyang Ma Fan Li Wu Wei

Based on the decoupled n-dodecane skeletal mechanism and the computational fluid dynamics (CFD) numerical framework, a multilayer feedforward neural network surrogate model was developed to predict ignition delay in a constant-volume combustion vessel. The Levenberg–Marquardt optimizer with adaptive damping coefficients was used for model training, with mean squared error as the loss function and an inherent early stopping mechanism to prevent overfitting without additional weight decay regularization. To eliminate random interference from initial parameter settings, the surrogate model underwent 1000 repeated training trials, each with random weight re-initialization. The effects of hidden neurons, data partition strategy, normalization scheme, and sample size on predictive performance were systematically examined. The optimal configuration—three hidden neurons, a 70:15:15 data split, and a 105-sample training set—showed low sensitivity to data normalization. The resulting surrogate model is concise and sample-efficient, maintaining satisfactory prediction accuracy at 800 K and 1100 K while substantially reducing computational overhead. It provides a practical and reliable tool for subsequent combustion prediction and uncertainty quantification of hydrocarbon fuels. The feedforward neural network surrogate model substantially cuts the computational overhead for fuel combustion prediction to merely 15–20 min for every batch of 60 samples.

Fire doi: 10.3390/fire9080340

Authors: Dongyin Lai Lin Liu Juanxiu Liu Jing Zhang Xiaohui Du Ruqian Hao Xudong Wang

Early aerial smoke detection is vital for wildfire response, but deploying accurate two-stage deep detectors on resource-limited Unmanned Aerial Vehicles (UAVs) remains computationally prohibitive. Moreover, under uniform supervision, standard knowledge distillation struggles on aerial smoke data, where foreground–background imbalance is severe and smoke boundaries are visually ambiguous. To resolve this, we propose the Decoupled Topology Distance Distillation (DeTD) framework to compress two-stage smoke detectors for real-time edge inference. DeTD features three key innovations. First, a decoupling module uses ground-truth-derived binary masks to isolate smoke and background features, mitigating distillation class imbalance. Second, a topology distance distillation module projects these decoupled features onto a unit hypersphere, employing a novel Symmetric Triplet Loss. This jointly optimizes the intra-class compactness and inter-class separability of both the foreground and background relational geometry between the teacher and student networks. Third, prediction-head soft-label distillation transfers class-conditional knowledge, synergistically complementing the intermediate-feature distillation. Comprehensive experiments on the D-Fire benchmark and a custom aerial UAV dataset yield mAP50 scores of 67.4% and 70.6%, respectively. DeTD consistently outperforms thirteen recent distillation baselines, and the lightweight student attains real-time-compatible inference, narrowing the accuracy–efficiency gap and indicating feasibility for deployment on resource-constrained UAV edge hardware.

Fire doi: 10.3390/fire9080339

Authors: Aliaksei Patsekha Robert Galler Mario Weitzer

Longitudinal ventilation is a primary smoke-control strategy in road tunnels, yet its effect on structural thermal exposure remains insufficiently quantified under full-scale conditions. This study examined whether increased airflow mitigates lining heating by lowering peak temperatures or instead redistributes thermal loading in time and space. Full-scale gasoline–diesel pool-fire experiments were conducted at the Research Centre “Zentrum am Berg” under two ventilation regimes and two fire-source elevations, while surface temperatures of protected tunnel linings were recorded continuously. Higher ventilation generally delayed peak temperatures and produced broader high-temperature plateaus, despite similar or moderately lower peak values at the lower source elevation. When the fire source was positioned closer to the tunnel ceiling, lower ventilation produced higher but shorter-lived temperature peaks, whereas stronger ventilation reduced maxima but prolonged heating. Overall, cumulative thermal exposure, quantified by a temperature–time integral, was greater under higher-airflow conditions. These results show that, within the tested range of ventilation and source-elevation conditions, peak temperature alone does not adequately represent structural fire severity and that duration-dependent exposure metrics should be included in performance-based tunnel fire design.

Fire doi: 10.3390/fire9080338

Authors: Bo Song Bo Li Zhiyong Zhang Yun Chen Qingyang Wang Xing Zhang Zhen Cao Tao Yue Jianwu Jiang

To address the problems of small fire-spot scale, blurred boundaries, complex backgrounds, and insufficient feature representation of weak targets in Unmanned Aerial Vehicle (UAV)-based early forest fire detection, a YOLOv8n-based forest fire detection model, termed TriRHC-YOLO, is proposed. The model first introduces Reparameterized VGG (RepVGG)Block into the backbone network to enhance the extraction capability of shallow local features. Subsequently, a Hierarchical Feature Attention (HFA) module is designed to collaboratively model fire-spot features from three levels, namely directional structures, local textures, and global semantics, thereby enhancing the network’s capability to discriminate fire targets and suppressing interference from complex forest backgrounds. Finally, a Cross Stage Partial Feature Fusion with Cascade Star Block (C2f-CStar) module is designed to improve the representation capability of the model for local structural information and weak salient fire-spot features under complex backgrounds through cascaded spatial feature reconstruction and a star-shaped multiplicative gating mechanism. In addition, a UAV-specific early forest fire detection dataset is constructed based on the FLAME and FLAME_VISION datasets, and experimental validation is conducted on this dataset. The experimental results show that the proposed TriRHC-YOLO outperforms several classical YOLO algorithms, including YOLO11n, YOLO12, and YOLO26, as well as six advanced YOLO-based improved models. The Recall, mean Average Precision (mAP)@0.5, and mAP@0.5:0.95 reach 0.769, 0.848, and 0.608, respectively. The results of the ablation experiments further verify the effectiveness of the three designed modules. Moreover, the proposed model contains only 3.181 M parameters and achieves 168.251 Frames Per Second (FPS), demonstrating favorable real-time detection capability. Overall, the proposed method can effectively improve the detection accuracy of early weak fire targets and the background suppression capability under complex forest backgrounds, making it suitable for real-time UAV-based forest fire inspection tasks.

Fire doi: 10.3390/fire9080337

Authors: Shusmita Saha Jeanette Cobian-Iñiguez

Wind gusts are known to significantly influence wildfire behavior, yet their direct role in ignition dynamics remains underexplored in laboratory settings. This study investigates how controlled wind gusts affect ignition behavior, combustion transitions, and heat re-lease characteristics of wildland fuels using a bench-scale wind tunnel. Three fuel types, Excelsior, wild oat (Avena), and Wheatgrass were exposed to heated stainless-steel par-ticles under varying wind speeds (1.0 and 2.0 m/s) and gust frequencies (0.03, 0.05, and 0.07 Hz). Key ignition parameters, including ignition temperature, ignition delay, smol-dering-to-flaming (StF) transition, burnout time, and heat release rate (HRR), were measured and analyzed. The results show that increasing gust frequency consistently impacted ignition behavior which reduces ignition and transition times across all fuels while raising ignition temperatures and HRR. For instance, StF transition times in Avena dropped from 58 to 42 s and flaming ignition temperatures rose from ~415 °C to ~498 °C as gust frequency increased from 0.03 Hz to 0.07 Hz at 2.0 m/s wind speed. Also, for the same set of experiments, HRR rose from 1674 J/s to 2372 J/s with increasing gusts. These findings indicate that gusty winds enhance convective heat transfer and oxygen availability, accelerating fire initiation and intensifying combustion. The results offer valuable insights for improving predictive fire spread models, ignition risk assessments, and wildfire mitigation strategies under transient wind conditions.

Fire doi: 10.3390/fire9080336

Authors: Delei Kong Dong Ma Yongning Yu Yixuan Yang Fucheng Zhang Huogen Luo

Coal spontaneous combustion is a severe hazard in the goafs of shallow-buried coal seams, particularly under the condition of continuous surface air leakage. This study conducted an integrated experimental and 3D multi-field coupled numerical investigation based on the Huojitu Coal Mine. Experimental kinetic analyses revealed that the upper seam coal exhibits a significantly higher oxygen consumption rate and CO generation capacity than the lower seam coal, characterized by an earlier initial CO generation temperature of 40 °C compared to 60 °C. Subsequent simulations indicated that the flow field and oxygen distribution within the overlying goaf exhibit a distinct “U-shaped” profile governed by surface air leakage. The sequential extraction of the lower coal seam significantly expands the oxidation zone on the return side of the overlying goaf, leading to the formation of a critical high-temperature zone exceeding 100 °C near the return side of the setup entry. Guided by these findings, a three-phase foam technology was implemented in the field, effectively encapsulating the residual coal and drastically reducing the CO concentration at the upper corner from a peak of 221 ppm to a stable 5 ppm. The findings highlight the role of surface air leakage in coal mining and provide corresponding strategies to mitigate spontaneous combustion risks in shallow-buried coal seams.

Fire doi: 10.3390/fire9080335

Authors: Shu-Chen Tsai Meng-Xin Chi Wei-Min Luo

The aim of this study is to explore the fire resilience of traditional ancient villages in Huizhou, China, and to reveal “traditional environmental planning knowledge” as a spatial survival strategy for high-density settlements. This study adopts a qualitative interpretive paradigm, combining historical geography with a literature review, field surveys, and overlay analysis. The study found that these villages, during site selection, utilized basin topography to construct a multi-level disaster mitigation system encompassing “macro-level water systems, meso-level alleyways, micro-level firewalls, and sandwich fire-extinguishing floors.” This endogenous physical technology, based on defensive awareness and community agreements, achieves a dynamic balance of resilience between humans and the environment. The cultural interpretation based on the indicators in this study primarily reflects the disaster resilience potential of traditional planning. The conclusions should be carefully interpreted within the framework of traditional environmental design. Furthermore, commercial intervention, infrastructure renovation, and population loss are leading to the neglect of this defensive space. This lack of a holistic perspective will trigger the “resilience degradation” of ancient villages. Future research urgently needs to establish a “resilience decay model” to quantitatively assess the disaster resistance capabilities remaining after damage to the surrounding buffer space, based on traditional environmental planning knowledge.

Fire doi: 10.3390/fire9080334

Authors: Boning Li Rui Guo Zhen Cao Li Wang Qixing Zhang Xi Zhang

Concealed fires within the insulation layers of buildings, such as cold storage facilities and cinemas, present a serious fire hazard because heat generated by electrical faults can accumulate behind protective panels before ignition and then spread rapidly once combustion begins. Conventional fire detection methods have limited capability to identify these hidden thermal abnormalities at the pre-ignition stage. To address this problem, this paper proposes a deep learning method, called the Multi-Scale Cross-Modal Fusion Network (MSCMFNet), that uses multispectral images to identify abnormal heat sources beneath insulation layers before visible combustion occurs. A standardized experimental platform was developed to accurately simulate subsurface heat sources within the pre-ignition temperature range of insulation materials. Instead of relying on fixed temperature thresholds, the proposed method learns the characteristic spectral patterns produced by hidden heating. It extracts information from different spectral bands, combines these complementary features, and verifies the persistence of detected heat sources over time to reduce false alarms caused by non-fire disturbances. Experimental results demonstrate that the proposed method can effectively detect concealed thermal anomalies before ignition, providing reliable early warning and offering a promising approach to improving fire safety in buildings that make extensive use of insulation materials.

Fire doi: 10.3390/fire9080333

Authors: Katarína Dúbravská Miroslava Mamoňová Viera Kučerová

This study investigated microstructural, optical, and thermal changes in spruce wood (Picea abies) exposed to controlled laboratory heating to identify indicators associated with the transition from progressive thermal degradation to the initial char formation. Cubic specimens measuring 20 × 20 × 20 mm were exposed to selected temperatures between 240 and 300 °C under atmospheric conditions, with a 15 min isothermal exposure period. Microstructural changes were evaluated by scanning electron microscopy (SEM) and quantitative tracheid double cell wall measurements, supported by simultaneous thermal analysis (STA) and color and reflectance analyses. Simultaneous thermal analysis (TG/DTG/DSC) was performed on separate specimens from the same wood material to provide complementary thermal evidence. The most pronounced microstructural changes were observed between 250 and 260 °C, including substantial thinning of tracheid cell walls, degradation of bordered pits, increased brittleness, and localized structural collapse. Quantitative measurements showed reductions in double cell wall thickness exceeding 50% at 260 °C. TG/DTG analysis indicated the onset of intensive thermal degradation at 254.2 ± 1.48 °C, while optical measurements showed pronounced darkening and reduced differentiation of reflectance spectra above approximately 260 °C. The combined evaluation of complementary analytical methods indicates that the 250–260 °C interval represents a condition-dependent microstructural transition associated with accelerated thermal degradation and the early development of a charred structure under the applied experimental conditions. These findings provide complementary experimental evidence for interpreting the early stages of wood charring and may support the interpretation and future refinement of heat transfer and pyrolysis models. They complement, rather than replace, the conventional 300 °C engineering char line criterion used in structural fire design.

Fire doi: 10.3390/fire9080332

Authors: Senlin Yao Shian Jin Shaokun Ge Ya Ni Gaoming Du Yingjian Hu Yin Liang

This study evaluates high-silica fiber/silica aerogel composites (HSFACs) for the passive fire protection of bridge cables. The primary objective is to reveal the high-temperature degradation mechanism of HSFAC and quantitatively determine a reliable thickness scheme for long-term hydrocarbon-fire protection of bridge cables. HSFAC specimens were heat-treated and characterized by thermal conductivity, tensile testing, SEM/TEM, FTIR, and TG analysis. A self-built furnace was used to assess an HSFAC-based cable protection system under hydrocarbon-fire exposure. Increasing heat-treatment temperature enlarged the pore and particle sizes of HSFAC and reduced its thermal-insulation performance. During 120 min of fire exposure, the cable protected by a single 5 mm HSFAC layer reached 300 °C within 45 min, whereas the cable protected by a double-layer 5 + 5 mm HSFAC system remained below 300 °C throughout the test. Finite element simulations validated against the experimental results confirmed that increasing HSFAC thickness improved thermal protection. After 90 min, the predicted cable-surface temperatures were 556 °C and 314 °C for HSFAC thicknesses of 5 mm and 10 mm, respectively. By integrating high-temperature material characterization, HC-fire testing, and thickness-dependent numerical analysis, this study links material degradation to system-level fire performance and provides a quantitative basis for HSFAC thickness design.

Fire doi: 10.3390/fire9080331

Authors: Eric Scheepbouwer

AI-based decision support is moving into fire practice and governance, where it is used to prioritise inspections, analyse building and community risk, examine station coverage, support evacuation planning, interpret warnings, and explore fire scenarios. These tools can extend analytical capacity, but they also create a decision role migration problem: an output developed for prediction, prioritisation, warning, simulation, or planning may later be treated as clearance, justification, or authority. Existing fire model evaluation guidance recognises that validation is use-specific; AI systems add a further challenge because outputs can migrate across dashboards, reusable software components, interfaces, and institutional procedures. This article develops a role-sensitive framework for bounded deployment of AI decision support in urban fire risk management. The framework classifies AI outputs by epistemic role, decision proximity, validation basis, temporal coupling, consequence asymmetry, and governance explicitness. Its central synthesis is that evidence sufficient to warn may be insufficient to clear. Probabilistic outputs can support screening, investigation, prioritisation, and scenario analysis; permissive or safety-proximate claims require stronger assurance, uncertainty communication, fallback rules, and explicit authority allocation. The contribution is a governance logic for keeping exploratory, advisory, policy-shaping, and safety-proximate AI roles separate in urban fire management and policy formulation.

Fire doi: 10.3390/fire9080330

Authors: Jiangao Zhang Jing Yang Pei Zhu Zhi Sun Quan Shao

High-rise building fires pose substantial challenges to conventional firefighting operations due to restricted rescue space and the difficulty of delivering suppression resources rapidly. To improve response efficiency, this study proposes a two-stage mission planning framework for multi-station UAV-based firefighting. The proposed methodology simultaneously accounts for environmental wind, building obstacles, fire evolution, and UAV payload constraints. In the first stage, an improved particle swarm optimization (PSO) algorithm is employed to generate time-optimal flight paths satisfying both spatial obstacle-avoidance and wind-field constraints. In the second stage, based on the actual flight times derived from the first stage, the multi-UAV resource scheduling problem is formulated as a mixed-integer linear programming (MILP) model to minimize the total fire suppression mission duration. Additionally, an isochrone-based firefighting coverage circle is introduced to optimize the layout of additional fire stations. Simulation results indicate that while optimized paths remain geometrically similar under varying wind conditions, wind-induced flight time variations significantly affect UAV arrival sequences and flight times. In the scheduling stage, differences in station layouts and fire scales alter projectile release timing; under unfavorable conditions, such temporal differences can increase the total mission duration by more than 28%. Notably, the optimized addition of fire stations effectively enhances response redundancy in high-rise clusters, reducing fire suppression time in adjacent scenarios by approximately 50%. The proposed method provides theoretical support and methodological guidance for cooperative UAV firefighting and emergency resource optimization in urban environments.

Fire doi: 10.3390/fire9080329

Authors: Izuchukwu Oscar Okafor Zifei Liu Mayowa Boluwatife George

Accurate quantification of wildfire risk is essential for balancing wildfire mitigation and prescribed fire management in grassland ecosystems, yet existing fire danger indices do not explicitly distinguish seasonal fuel dynamics from daily weather variability. This study presents a hierarchical framework for quantifying wildland fire risk by explicitly separating seasonal wildfire potential from daily weather-driven fire activity. The framework introduces the Daily Burned Area Ratio (DBAR) as a quantitative measure of realized wildfire risk and decomposes it into the Seasonal Burned Area Ratio (SBAR) and the Daily Burn Activity Index (DBAI). Wildfire records from the U.S. Forest Service Fire Program Analysis Fire-Occurrence Database, Oklahoma Mesonet weather observations, and remotely sensed vegetation data collected between 1995 and 2020 were used to develop and evaluate the framework in the Flint Hills of Kansas and Oklahoma. SBAR was modeled using grass curing and air temperature to characterize the seasonal baseline of wildfire activity, whereas DBAI was modeled using dead fuel moisture content (DFMC) and wind speed to quantify day-to-day departures from that seasonal baseline. The SBAR model accurately reproduced the characteristic bimodal wildfire regime of the Great Plains, whereas the DBAI model identified DFMC as the dominant control on daily wildfire activity, with wind speed providing an important secondary influence. Compared with the Burning Index (BI) and the Grassland Fire Danger Index (GFDI), the hierarchical framework achieved superior performance in discriminating fire days from non-fire days. Global sensitivity analysis further demonstrated that the framework provides a more balanced representation of the influences of grass curing, relative humidity, air temperature, and wind speed than the conventional indices. By explicitly separating seasonal fuel dynamics from short-term weather variability, the proposed framework provides an ecologically interpretable, locally calibratable, and operationally practical approach to wildfire risk assessment. Because the seasonal and daily components can be calibrated independently, the framework is readily transferable to other grassland ecosystems and provides a flexible foundation for adaptive wildfire and prescribed fire management under changing climatic conditions.

Fire doi: 10.3390/fire9080328

Authors: Jingwei Liang Qingnian Deng Liyan Niu Shihui Zhou Jiahai Liang Zekai Guo Liang Zheng Yile Chen

Traditional fire detection technologies for buildings are no longer adequate for the fire prevention and control needs of complex structures, while emerging artificial intelligence technologies have become the core path to break through the bottlenecks in this industry. Existing reviews suffer from non-standardized paradigms, one-sided scopes, insufficient methodological evaluation, incomplete time coverage, and a lack of engineering orientation, failing to meet the evidence-based research needs of the field. This study strictly followed the PRISMA 2020 systematic review guidelines, selected relevant SCI papers from the Web of Science Core Collections from 1 January 2016 to 30 March 2026, in JCR Q1 and Q2, and finally included 221 valid papers; it systematically carried out bibliometric analysis and technical system sorting. The results showed that (1) the number of publications in this field showed a significant exponential upward trend, with China accounting for 52% of the research output, ranking first in the world, and (2) convolutional neural networks and YOLO series algorithms are the mainstream application technologies in the field. The study clarified the performance differences, advantages, and disadvantages, and applicable scenarios of various artificial intelligence algorithms. This study identified the existing technical and methodological limitations in the field and explored the core research directions for the future, and provided evidence-based support for the academic research and engineering implementation of artificial intelligence in the field of building fire detection and prevention.

Fire doi: 10.3390/fire9080327

Authors: Chao Liu Ziheng Pu Wei Guo Shuai Wang Zhigang Ren

To address the discrepancy between the constant-power fire sources currently used in cable fire-related research and cable fire protection product testing and actual cable fires, this paper proposes a cable equivalent combustion simulation method based on electrical fault fires. The cable tunnel experiment platform was built and, based on energy equivalence, used an igniter to simulate a fault arc’s thermal effect and ignite the cable, obtaining the temperature rise characteristics at multiple points in the fire source area. Based on the experimental data, a simulation model for the mixed combustion of multiple cable materials was established and revised, and the heat release rate (HRR) under different fire scenarios was calculated. Then, an equivalent fire source device capable of simulating the aforementioned HRR curve was designed. The results indicate that under ignition conditions with an igniter power of 400 kW and duration of 90 s, the cable fire development exhibits nonlinear dynamic evolution, with a flame height of 0.63 m. The peak temperature rise rate and peak temperature at the measurement point reach 3.27 °C/s and 926 °C, respectively. When 39.4% of the insulation layer material of the cable participates in combustion, and the fuel molecular formula is C2.28H5.70O1.42N0.08Si0.65, the relative error between simulated and experimental temperatures during stable combustion is 3.0%. Heat release rates for mild, moderate, and severe fires stabilize near 350 kW, 420 kW, and 530 kW under this calibrated cable model. The relative error between the temperature curve from the fire source device during the stable combustion stage and that from the actual combustion experiment is 3.4%, indicating favorable equivalence.

Fire doi: 10.3390/fire9080326

Authors: Daniel Rosales-Giron Chad M. Hoffman Rodman R. Linn Scott M. Ritter Justin P. Ziegler

Coupled fire–atmosphere models (CFAMs) are increasingly proposed as an important tool for investigating a range of scientific and management questions, including the design of fuel management strategies. Uncertainty in CFAM outputs arises from environmental and fuel inputs, and a host of user-defined simulation choices such as fire approach angle and ignition timing. In this study, we used the Wildland–Urban Interface Fire Dynamics Simulator (WFDS) to quantify uncertainty in rate of spread and canopy consumption across pre- and post-restoration ponderosa pine stands. Two ensembles were conducted: (1) varying fire approach angles across 12 rotations and (2) varying ignition time in 80 simulations (five per plot, with 0 s delay, 250 s delay, and three random intervals in between). Metrics evaluated were rate of spread and percent canopy consumption. Variability was quantified using coefficients of variation (CVs). Approach-angle variation produced a mean CV of 5.46% for rate of spread, with treated stands exhibiting reduced variability relative to untreated stands. Canopy consumption showed a mean CV of 6.16%, with treatment having no effect. Ignition-time variation produced smaller CVs (rate of spread: 2.47%; canopy consumption: 3.60%) with no differences between management conditions. These results indicate that user-defined configuration choices contribute measurable but modest uncertainty, and that structural modifications from management can reduce sensitivity of fire spread. Incorporating these sources of uncertainty into formal frameworks will improve interpretation of CFAM outputs for operational and research applications.

Fire doi: 10.3390/fire9080325

Authors: Rubén Jiménez-Panadero Fernando Alacid Rodrigo Yáñez-Sepúlveda Vicente Javier Clemente-Suárez

Professional wildland firefighters are exposed to substantial physical, thermal, respiratory, and psychosocial demands. This cross-sectional study examined whether cardiometabolic and work-related health indicators differed among the five provinces of Castilla-La Mancha after accounting for age and sex. The analytical sample comprised 757 salaried GEACAM wildland firefighters (663 men and 94 women). Outcomes were visceral fat level, device-derived metabolic age, metabolic age gap, the Work Ability Index (WAI), the 14-item Perceived Stress Scale (PSS-14), self-rated health, and anxiety problems. Province effects were tested using heteroscedasticity-robust linear models or logistic regression, with age and sex as covariates. Adjusted pairwise provincial contrasts were corrected using Holm’s method. Province was associated with visceral fat level (F(4, 687) = 3.21, p = 0.013, partial R2 = 0.021) and PSS-14 score (F(4, 685) = 4.64, p = 0.001, partial R2 = 0.029). No adjusted provincial association was found for metabolic age, metabolic age gap, WAI, self-rated health, or anxiety problems. After Holm correction, PSS-14 scores were lower in Albacete than in Guadalajara (adjusted difference = −3.17, 95% CI −5.18 to −1.16; pHolm = 0.018) and Toledo (−3.54, 95% CI −5.36 to −1.72; pHolm = 0.001). Although the global province effect for visceral fat was significant, no individual pairwise contrast remained significant after Holm correction. Provincial heterogeneity was therefore modest and concentrated in perceived stress and, less conclusively, visceral fat. These findings support age- and sex-adjusted surveillance while precluding causal explanations based on terrain, fire exposure, workload, or recovery, none of which were measured.

Fire doi: 10.3390/fire9080324

Authors: Rui Liu Jia Xie Hanbing Hao Taiyun Zhu Yi Guo Xiang Liu Yang He Jiaqing Zhang Tianchang Meng

Early detection of incipient fire signs, such as dilute smoke emerging at valve hall penetration seals, remains a critical challenge for fire safety in ultra-high-voltage (UHV) converter stations. This study addresses the limitation through controlled valve hall sealing simulations that systematically reproduce the complete fire evolution process—from initial dilute smoke leakage, through dense smoke accumulation, to eventual flame overflow—thereby constructing a high-fidelity dataset tailored to complex converter station environments with low-contrast and varying illumination conditions. Building on this dataset, an attention-enhanced YOLOv5s model integrating multiple attention mechanisms (GAM, CBAM, CA, and ECA) is proposed as the core of an end-to-end visual detection framework. Rigorous experiments and ablation studies validate the framework’s superiority: dilute smoke detection precision improves substantially from a baseline of 57% to 81%, with recall increasing from 61% to 84%; dense smoke and flame detection achieve accuracies of 92% and 98%, respectively. Compared with the original model, false and missed alarm rates are significantly reduced, demonstrating strong robustness against background interference and lighting variations. The proposed method enables reliable, high-precision monitoring across all fire stages, providing a novel technical pathway for early fire warning in UHV converter stations and offering extensibility to other large-scale industrial fire monitoring scenarios.

Fire doi: 10.3390/fire9080323

Authors: Liudas Sukevicius Mindaugas Grigonis Ramune Zurauskiene

This study investigates the reaction of charred wood cladding systems to fire, focusing on the influence of wood species, charred surface treatment, flame retardant treatment, surface coating and board orientation. Nine different configurations of larch and spruce cladding samples with 20 mm thickness were investigated, which were installed into the system according to standard requirements. The samples differed by wood type, charred surface treatment, board orientation, covering with flame retardant agents and additional surface coverings. Two commercial flame retardant treatments and three commercial surface coatings were included in the experimental matrix. Fire behaviour was evaluated according to the EN 13823 Single Burning Item (SBI) method, analysing the ignition time, fire growth rate index, total heat release from the specimen in the first 600 s of exposure to the main burner flames, smoke growth rate index and total smoke production from the specimen in the first 600 s of exposure to the main burner flames as indicators. Although it was determined that the ignition time fluctuated in a relatively narrow range (from 5 min 12 s to 5 min 36 s), analyses of the additional SBI indicators revealed much clearer differences between the systems. The best results were obtained for the charred notched spruce system treated with the phosphate-, urea- and biocide-based flame retardant, which had the longest ignition time and lowest values for the investigated indicators. Meanwhile, the least favourable behaviour was observed in the notched untreated spruce system and in the horizontally oriented system, marked by higher fire spreading and smoke formation values. The obtained results show that the reaction of a wood cladding system to fire is not determined only by the type or application rate of flame retardant, but also the whole system’s structure, including the wood type, surface processing, covering combination and installation configuration.

Fire doi: 10.3390/fire9080322

Authors: Morey J. Kolber James N. Ippolito William J. Hanney

Introduction: Approximately 50% of firefighter injuries occur in the musculoskeletal system and poor movement quality increases injury risk. Previous research suggests that corrective exercise interventions with integrated movements improve movement literacy; however, the long-term benefit of corrective exercise programming has not been established among firefighters. Purpose: To investigate, as a continuation of a previously published short-term (mean—267 days) outcome pilot study, the long-term effectiveness of auto-generated corrective exercise programming on movement literacy scores among firefighters with lower baseline scores. Methods: Nine male firefighters (mean age +/− standard deviation 40 (12)) with baseline Functional Movement System (FMS™) scores less than 14/21 were initially recruited from October 2021 to September 2022. Baseline FMS™ assessments included detailed explanations of the seven movement screens as well as five clearing procedures and scoring criteria. A certified FMS™ professional performed each test first, prior to scoring, to demonstrate what was expected and firefighters were permitted to attempt each test for a total of three times with the highest score retained. Scores ranged from 0 to 3 for each of the seven movement screens with a maximum composite score of 21. Upon completing the screenings, test scores were reviewed and a detailed report was provided to each firefighter through the FMS™PRO APP. Additionally, each participant received an auto-generated corrective exercise program from the FMS™PRO APP with exercise figures, descriptions, and videos to be performed prior to routine conditioning programs. Participants were re-evaluated an average of 813 days after baseline for the long-term follow-up (F2). Pairwise comparisons from baseline to F2, as well as from a previously reported short-term follow-up (F1) to F2, were evaluated using the Friedman and Wilcoxon signed-rank tests. A Bonferroni correction resulted in an adjusted alpha of p = 0.025. Results: Composite FMS™ scores improved from a baseline mean of 11.3/21 to 14.7/21 at F2. When F2 was compared to the F1 scores from the previously published data set, scores declined by a mean of 1.4 points. Compared with baseline, scores at F2 were significantly improved (p = 0.013) with a large effect size (r = 0.83). In contrast, score declines (r = −0.73) from F1 to F2 were not statistically significant after Bonferroni correction (p = 0.028). Conclusions: An auto-generated corrective exercise program combined with a detailed explanation of baseline performance was effective in improving overall movement literacy in a standardized movement assessment tool in a long-term follow-up exceeding 2 years. The composite score change exceeded the threshold of error based on a previously established minimal detectable change (MDC) of 2.5/21, indicating true change occurred from baseline to F2. The decline from F1 to F2 did not exceed the threshold of error based on the MDC and may represent a regression to the mean and participant acknowledgement of marginal adherence after F1. Long-term adherence to programming is necessary to prevent the tapering of improvements seen in this study. The absence of a comparison or control group is a study limitation.

Fire doi: 10.3390/fire9080321

Authors: Yibo Zhang Weijun Pan Yanqiang Jiang Lang Lei Qinyue He

In mountainous wildfire response, unmanned aerial vehicle (UAV) emergency services depend on rapidly deployable and spatially reliable vertiports. However, existing site-selection methods insufficiently support continuous UAV operations in topographically complex terrain. This study develops a geographic information system (GIS)-based framework integrating Multi-Criteria Decision-Making (MCDM) and k-medoids clustering to shift UAV vertiport planning from individual site suitability ranking to networked deployment. The framework organizes feasible vertiport candidates into sector-based support staging nodes for coordinated multi-node wildfire response. Applied to the Xichang wildfire, the selected vertiport candidates reduced the mean first-arrival time by up to 71.4% compared with existing fire stations. Under differentiated reload-time assumptions, the staging node support mode further reduced the mean sortie cycle time by up to 56.85% and increased the mean delivery capacity from 17 to 42 fire-extinguishing bombs per UAV per hour. These results indicate that combining vertiport suitability evaluation with staging node-based logistical coordination improves the spatial organization and operational efficiency of UAV-enabled wildfire suppression. The framework offers decision support for networked UAV vertiport and staging node deployment in mountainous wildfire response.

Fire doi: 10.3390/fire9080320

Authors: Peng Lei Yunqiang Wang Yajin Fan Jie Chen

Underground deep vertical spaces are a new form of architectural structure for the efficient use of land resources. However, their slender geometry can intensify smoke transport and thermal hazards during fires. Sealing is a potential emergency strategy, but its influence on smoke dynamics and flame extinction in deep shafts remains insufficiently quantified. This study investigates smoke transportation characteristics and flame self-extinction in a sealed deep vertical space using 1:26 reduced-scale experiments combined with CFD simulations. Across the tested conditions, sealing consistently increased the characteristic upper-shaft centerline temperature rise, with enhancements ranging from 43.2% to 374.9%. The vertical centerline temperature above the fire exhibits a segmented decay behavior: in the plume-rise region it follows a power-law trend, while the decay coefficient deviates from the ideal-plume expectation, consistent with the thermal shielding effect associated with the confined upper hot-gas layer. Under sealed conditions, a distinct “ghosting” flame behavior and eventual self-extinction were observed. The combined flame, thermal, and simulated flow-field evidence is consistent with an oxygen-limited interpretation, although this mechanism was not directly verified by gas-species measurements. Based on the reduced-scale dataset, the self-extinction time was normalized by the characteristic oxygen-consumption timescale to2, yielding texttO2=2.074h1 m−0.394Q1 kW−0.071, which provides a quantitative description of flame self-extinction under the tested sealed conditions.

Fire doi: 10.3390/fire9080319

Authors: Samson Tan Teik Toe Teoh Paul Joseph Khalid Moinuddin

Battery Energy Storage Systems (BESS), utilising chemistries based on Nickel Manganese Cobalt (NMC) containing lithium-ion devices, often present fire safety hazards that existing qualitative risk frameworks, including NFPA 855’s 5 × 5 consequence-likelihood matrix, are insufficiently granular to quantify. This paper presents an original probabilistic risk assessment (PRA) of fire hazards associated with BESS for a 485.52 kWh NMC installation at the Equinix SG4-4A data centre in Singapore, using Monte Carlo simulation (N = 10,000 iterations) to characterise uncertainty in hydrogen fluoride (HF) gas dose, time to Immediately Dangerous to Life or Health (IDLH) concentration, cabinet-to-cabinet propagation probability, and suppression effectiveness. The HF yield is modelled as a triangular distribution (0.3–0.8 g/kWh, mode 0.5 g/kWh), ventilation activation delay as log-normal (median 90 s), and suppression effectiveness as a piecewise function of water application delay. The results demonstrated that HF dose exceeded the National Institute for Occupational Safety and Health (NIOSH) IDLH of 25 mg/m3 in 100% of simulated scenarios for both single- and two-compartment designs, thus confirming that threshold HF toxicity was essentially unavoidable for any occupant present during a full thermal runaway event, and that ventilation alone cannot achieve adequate risk reduction. The single-stage suppression effectiveness was found to be only 37.9% (mean), providing quantitative confirmation that two-stage (clean agent + water) suppression is warranted for NMC chemistry. The two-compartment design was found to reduce the peak HF dose by 50%, and also reduced the mean IDLH clearance time from 599 to 301 min, thus shifting residual risk from As Low As Reasonably Practicable (ALARP)-tolerable to broadly acceptable under UK Health and Safety Executive (HSE) criteria. The paper proposes a quantitative PRA framework as a complement to NFPA 855 Chapter 5’s qualitative Hazard Mitigation Analysis, enabling more informed engineering decisions for BESS fire safety. To the best of our knowledge, this is the first study to apply Monte Carlo simulation to HF dose modelling in a tropical data-centre BESS context and thereby address a documented gap in the literature.

Fire doi: 10.3390/fire9080318

Authors: Zhuqing Zhang Simin Zhu

Explosive detonation is a high-speed and high-energy chemical-physical transformation process that rapidly generates high-temperature and high-pressure gases as well as shock waves. These energies are released intensely in a short time, exhibiting extremely strong destructive power. When these high-temperature and high-pressure gases and shock waves act on the surface of combustibles, they can instantly peel off the hot core on the surface, disrupting the conditions necessary for sustaining the combustion reaction and thereby achieving a fire-extinguishing effect. However, to attain this application goal, it is essential to select explosive materials with both high energy density and low sensitivity. In this study, DFTB-MD (Density Functional Tight-Binding Molecular Dynamics) and DFT (Density Functional Theory) methods were employed to systematically investigate the thermal decomposition process of benzotrifuroxan (BTF)/1,3,5-trinitrobenzene (TNB) cocrystal nanoparticles under high-temperature conditions. Our simulations reveal, for the first time, that the thermal decomposition mechanism of BTF/TNB cocrystal nanoparticles is strongly size-dependent: the 1.8 nm particles exhibit earlier ring-opening of BTF due to the higher surface-to-volume ratio, while the 2.2 nm particles show superior structural stability and lower molecular diffusivity. Meanwhile, increasing temperature from 2100 K to 2400 K shifts the dominant initial decomposition pathway from C–NO2 cleavage in TNB to ring rupture in BTF. These findings provide atomic-scale theoretical insights into the design and application of BTF/TNB cocrystal nanoparticles for explosion-based fire suppression.

Fire doi: 10.3390/fire9080317

Authors: Grazia Pellizzaro Carla Scarpa Marcello Casula Annalisa Canu Bachisio Arca Michele Salis Valentina Bacciu

Forest fires are a recurring disturbance in Mediterranean ecosystems, but they also impact air quality and public health, particularly given recent trends towards increasingly widespread and extreme fires. This study analyzed six large fires that occurred in Sardinia, Italy, between 2009 and 2021, in order to evaluate their impact on ground-level PM10 concentrations and to investigate the influence of fire size, fuel type, and meteorological conditions. The analysis included data on fire perimeters and land cover, meteorological conditions, smoke plume trajectory simulations using HYSPLIT, satellite imagery, and PM10 concentration measurements from the regional air quality monitoring network. The six case studies differed markedly in terms of burned area, vegetation composition, duration, and weather context. The results showed that the extent of the fire is likely not the most significant factor influencing the increase in PM10 observed in the days following the fires. The most pronounced increases in PM10 concentrations were recorded during the Isili and Montiferru fires, which differed in burned area but were similar in terms of fuel composition, dominated by forest and shrubland vegetation. These factors, together with favorable atmospheric conditions for plume transport and particulate matter deposition, likely contributed to the observed increases in PM10, including exceedances of WHO and national daily limit values. By contrast, Bonorva, Ittiri, and Borore showed limited or no clear accumulation of PM10, despite large burned areas in some cases. These findings suggest that the effects of wildfires on air quality in the Mediterranean region can be influenced by several features, such as meteorological conditions, biomass burned, area burned, severity and intensity of fires. Furthermore, the observed exceedance of WHO thresholds highlights the need to integrate public health considerations into wildfire risk management in the Mediterranean basin.

Fire doi: 10.3390/fire9080316

Authors: Lihong Zhao Ping Fang Songtao Liu Shiyao Liu Xu Zhang Xiaolin Yang Rongkun Pan Yonghao Mao

As rooftop photovoltaic (PV) systems are increasingly deployed on taller buildings and across a wider range of building applications, localized overheating or initial fires caused by electrical faults, combustible roof-covering materials, or maintenance-related ignition sources may affect PV modules and contribute to subsequent fire spread over the rooftop system. In this study, a full-scale fire experiment was conducted on a flat-laid rooftop PV system using a nominal 100 kW n-heptane pan fire as a controlled localized external fire source to investigate the fire development and escalation mechanism of the system. The results show that the fire hazard was first and primarily concentrated in the confined under-panel space: the average cavity peak temperature of the ignited array reached 684.0 °C, with a local maximum of 853.4 °C, both significantly higher than the maximum upper-surface center temperature of 370.7 °C. The involvement of the waterproofing membrane in combustion was the key amplifying mechanism driving the transition from localized heating to a sustained high-temperature event; the average cavity temperature exceeded 500 °C after 234 s and remained above this threshold for approximately 201 s, with an average cavity heat accumulation index of 178.1 × 103 °C·s. Compared with the lower upper-surface center measuring points, hazardous temperatures beneath the modules were reached earlier by 149, 193 and 247 s at the thresholds of 50, 100 and 200 °C, respectively. Under the tested configuration, these findings provide engineering insights for fire-risk identification, early monitoring, and fire-safe design of flat-laid rooftop PV systems in high-rise building applications.

Fire doi: 10.3390/fire9080315

Authors: Yang Liu Shunbing Zhu Yue Sun Jianlong Zhang Zhengxiang Han

This experiment investigated changes in the key parameters of explosion pressure peak (Pmax) and pressure rising rate peak ((dP/dt)max) and flame propagation characteristics of wood dust explosion (pine, cypress and poplar dusts) under different wood dust diameters and concentrations using a 20 L spherical explosion apparatus. Combined with thermogravimetric analysis and Fourier transform infrared spectroscopy, the pyrolysis behavior of different wood dusts and the generation mechanism of gas-phase flammable products were elucidated. The results indicate that the type, wood dust diameter and concentration of dust had a great impact on the severity of explosion. As the dust diameter decreased, Pmax and (dP/dt)max both showed a trend of first increasing and then decreasing. Among them, the explosion pressure and Kst value of 300-mesh poplar wood were the highest, reaching 0.72 MPa and 11.6 MPa·m/s. The flame propagation characteristics were comprehensively influenced by dust morphology, volatile matter content and concentration. Among them, the peak height of flame propagation and its instantaneous velocity were obviously higher for poplar and pine wood dusts than those of cypress wood due to the high carbon and volatile matter contents. The overall quality loss rate of poplar dust in the thermogravimetric experiment was the highest, while its pyrolysis reaction rate was also the highest. The mass loss rate of 140-mesh poplar wood reached 90.5%, and the thermal decomposition reaction rate reached 19.48%/min. The large number of alkanes, aldehydes and ketones, as well as gases such as CO and CO2 generated during the pyrolysis, provided the material basis for the chain reaction of a dust explosion. This study systematically elucidated the differences in explosion parameters, flame propagation behavior, and pyrolysis processes of different wood dust, thus providing theoretical support for their explosion risk assessment and safety protection.

Fire doi: 10.3390/fire9080314

Authors: Long Chen Xiaoxing Zhong Zhenlu Shao Tao Zhou Guofu Zhang Fei Cao Zichao Jia

Accurate localization of concealed fire sources is the fundamental prerequisite for effective coal fire control and mitigation. The self-potential method, as a passive, cost-effective geophysical technique for large-area surveys, exhibits unique advantages in coal fire detection, yet 3D inversion of self-potential data for high-precision coal fire source localization remains insufficiently studied. In this paper, a 3D iterative compact inversion algorithm based on the minimum support functional is adopted to invert self-potential data for 3D localization of underground concealed coal fire sources. The algorithm’s reliability and robustness are verified systematically via numerical simulations at both laboratory and field scales, followed by sandbox experiments with artificial battery sources and burning coal specimens, and finally validated its engineering applicability in the Sandaoba Coal Fire Area in Xinjiang. Numerical results show that the algorithm achieves high-precision inversion of source current density across multiple orders of magnitude, with a relative error below 10%. Sandbox experiments confirm that coal combustion generates a typical dipole self-potential field with distinct negative surface anomalies, whose amplitude is positively correlated with combustion intensity. Field inversion results successfully delineate the 3D boundary of the concealed fire source (buried at 40–140 m), which is highly consistent with borehole temperature measurement data. This study provides a robust technical framework for 3D detection of concealed coal fires, offering strong technical support for precise coal fire governance and hazard mitigation.

Fire doi: 10.3390/fire9070313

Authors: Junwei Shi Ziyan Zhang Mengyao Zhang

Thermal runaway in electric vehicle power battery packs is a key risk in fire prevention and control for electric transportation. Its triggering, propagation, and failure modes are jointly affected by external thermal abuse, material insulation performance, and side reactions inside cells. To identify the temperature response and fire risk of power battery packs under different thermal abuse intensities, this study established a three-dimensional multiphysics thermal runaway simulation model in COMSOL Multiphysics 6.1, coupling solid heat transfer, electrochemical heat generation, and side-reaction heat release. A semi-quantitative risk ranking was then performed using failure mode, effects, and criticality analysis (FMECA). The model considered the low-temperature safe conditions, 120 °C, 140 °C, and 170 °C, as the main ambient temperature conditions, while also analyzing the effects of the heat transfer coefficient on trigger time and peak temperature. The results show that, under the low-temperature safe condition and the 120 °C condition, the battery module mainly exhibits slow heating and does not undergo thermal runaway. Based on the side-reaction characteristics, the temperature near 125 °C can be used as a risk warning threshold for thermal runaway. At 140 °C, the side-reaction heat source increases markedly, and the system enters the thermal runaway risk region. Because the trigger time is strongly affected by the heat transfer coefficient and monitoring position, this condition is interpreted only as a risk-acceleration stage under critical thermal abuse. Approximately 167 °C can be regarded as the critical threshold for irreversible thermal runaway. Under severe thermal abuse at 170 °C, rapid intensification of internal side reactions increases the peak module temperature to 375–385 °C. Temperature field evolution shows that heat is transferred mainly from the exterior to the interior before thermal runaway, forming an outside-high- and inside-low-temperature distribution. After the runaway stage begins, heat release from internal cell side reactions becomes dominant, and the high-temperature region concentrates inside the module, producing a gradient reversal with a higher internal temperature. The FMECA results show that the positive electrode–electrolyte reaction has the highest RPN, with a value of 405. Accelerated SEI decomposition and the negative electrode–electrolyte reaction also form key risk links in the chain heat-release pathway. This study provides a reference for thermal management, fire barrier design, and fire risk classification of power battery packs.

Fire doi: 10.3390/fire9070312

Authors: Youhao Xie Wenjie Wei Liangyuan Qi Weiyi Xing Yuan Hu

The issue of thermal runaway in power batteries of new-energy vehicles occurs frequently, posing a serious threat to life and property safety. This study aims to develop a high-performance fire-proof coating to address this problem. Specifically, the research focused on constructing an organic-inorganic composite intumescent fire-resistant coating, with modified halloysites (Ti-HNTs) serving as the key component. In this coating system, the intumescent flame-retardant (IFR) system and Ti-HNTs were employed as the organic and inorganic components, respectively, while water-based epoxy resin emulsion was selected as the matrix material. Through the utilization of XPS, FTIR, and SEM techniques, it was verified that the Ti-HNTs were successfully modified and integrated well with the coating matrix. Following further optimization of the Ti-HNTs proportion and coating thickness, it was determined that the coating containing 4% Ti-HNTs with a designed thickness of 1.5 mm exhibited the optimal fire-proofing performance. In the fire-resistance experiment, after 10 min of testing, the temperature of this coating could reach a minimum of 215.9 °C. Compared to the control group, its heat-insulation effect was enhanced by 49.4%, with an expansion multiplier of 37.7 and a maximum smoke density of 22.55. These results were significantly superior to those of the control group without the addition of Ti-HNTs. SEM analysis indicated that the coating could form a uniform and dense carbon layer, with an inner surface featuring a honeycomb-bubble structure. This SEM-analyzed Ti-HNTs-modified fire-proof coating demonstrated excellent fire resistance and thermal-isolation effects in new-energy vehicle batteries, thus providing reliable fire protection for the batteries. Additionally, impact-resistance tests revealed that the coating could withstand a simulated battery pressure-relief impact without penetration, maintaining its structural integrity and thermal-barrier function. This further validated its reliability for battery fire protection.

Fire doi: 10.3390/fire9070311

Authors: Katrin Kuhnen Mariana S. Andrade Mortimer M. Müller Harald Vacik

Wildfires are an increasing threat in Central Europe and pose challenges for protective forests and areas at the wildland–urban interface (WUI). Understanding, describing and predicting fire behaviour is therefore becoming more relevant for fire management. This work aims to reconstruct the fire spread behaviour of past fire events occurred under differing environmental conditions with selected fire spread models. The three fire spread models Farsite, SimtableTM and Prometheus were selected according to a list of predefined properties they were expected to fulfil. Subsequently, they were tested under different environmental conditions and evaluated against documented perimeter of past fire events. The focus of the analysis was on the spatial perimeter to quantify metrices such as over- and underestimated areas in percent, Sørensen–Dice coefficient and the Jaccard similarity coefficient. Farsite showed the best overall results in both regions. Simtable performed well in steep and complex terrain but produced underestimations in flat terrain. Prometheus lagged, likely due to inadequate parametrization of fuel data, which is a key input parameter in fire spread modelling. As Farsite is readily accessible, it has the greatest potential for further application and more in-depth research. For higher reliability, additional empirical data on fire behaviour are needed to develop custom fuel models or refine current adjustments used to simulate fire spread.

Fire doi: 10.3390/fire9070310

Authors: Jaeun Choi Wonseok Yang Seokju Kim Ahyeon Jeong Jiwoo Baek Nanggyun Ko Chumni Jeon Eun Sang Jung

Accurate wildfire prediction increasingly determines whether emergency resources arrive before a disaster becomes uncontrollable, yet the dominant paradigm reduces the problem to binary occurrence, offering no estimate of the severity that drives suppression planning. This study develops a machine-learning framework for four-class wildfire severity prediction, conditional on ignition, from weather-station observations and calendar terms alone. We construct a composite severity index (CSI) by applying principal component analysis to five damage dimensions (burned area, suppression equipment, personnel, duration, and property loss) recorded for 868 wildfires in Gangwon Province, South Korea (2011–2022) and pair standard observations with effective humidity and six indices of the Canadian Forest Fire Weather Index (FWI) System. Under a leakage-safe protocol, the strongest tree ensembles reach a macro F1 of 0.46 to 0.50 (recommended configuration: 0.41 ± 0.03 across 20 repeated splits) against a four-class chance level of 0.25, and the recommended Random Forest attains an extreme-class recall of 0.474; the CSI target outperforms burned area by 5.5 macro-F1 points under identical inputs. A weather-only screen separates extreme from non-extreme events with an ROC AUC of 0.758, capturing 47% of extreme events at a 20% alert budget. We also quantify how oversampling misplaced before the train-test split inflates the macro F1 to 0.65–0.83, a cause for caution for the severity-prediction literature.

Fire doi: 10.3390/fire9070309

Authors: Wisdom M. D. Dlamini

Burned-area totals are useful for national monitoring, but they do not reveal how, when or under what social and ecological conditions a landscape burns. We developed an event-based fire-regime and stewardship framework for Eswatini, a topographically compressed southern African country where protected areas, communal rangelands, cropland margins, plantation landscapes and peri-urban interfaces occur in close proximity. Global Fire Atlas event histories for 2001–2025 were organised by fire year and intersected with approximately 10 km2 hexagonal units. The burned-area rate, event frequency, recurrence, seasonality, large-fire dominance, pyrodiversity and trend were used to classify fire-regime types independently of socio-ecological predictors. An XGBoost regression model, evaluated on a 20% held-out test set, was interpreted using exact TreeSHAP diagnostics. Fire activity was strongly seasonal: July–September accounted for 78.2% of the burned area, with August alone accounting for 34.2%. Eight fire-regime types were identified, ranging from low-information and episodic units to frequent small-fire mosaics, large-fire-dominated areas and emerging burned-area intensification regimes. The burned-area-rate model performed well on held-out data (R2 = 0.71; Spearman rho = 0.75). Human modification, goat density, elevation, forest probability, fuelwood dependence and precipitation seasonality ranked among the most influential predictors, but their fitted effects were non-linear and often bidirectional. The combined diagnostics supported six adaptive management zones covering protected-area stewardship, conservation-sensitive management, settlement–livelihood interfaces, late-season risk reduction, monitoring and integrated landscape management. Although the Eswatini results are context-specific, the workflow offers a transferable way to connect fire histories, socio-ecological contexts and place-based stewardship in African mosaic landscapes.

Fire doi: 10.3390/fire9070308

Authors: Jinrui Deng Jianfeng Li Yang Cao Bingcai Sun Yinghua Jing Shengli Chu

For gas leakage monitoring in obstacle environments such as oil and gas stations, the layout of fixed sensors directly affects the validity of monitoring data and the accuracy of subsequent leakage source localization. To achieve effective coverage of high-risk areas with a limited number of sensors and reduce deployment costs, this paper proposes a multi-objective optimization method for sensor layout based on the non-dominated sorting genetic algorithm-II (NSGA-II). Based on multi-scenario computational fluid dynamics simulation data, the peak concentration, hazardous concentration duration, and leakage probability at each monitoring point are extracted as risk characteristic indicators. The NSGA-II analytic hierarchy process is employed to determine the weight of each indicator, and a comprehensive risk classification model for the monitored area is established. This is adopted for solution seeking. Through non-dominated sorting and crowding distance calculation, the Pareto optimal front is searched in the solution space. The optimized layout scheme is applied to the leakage source term estimation based on particle filter, and the performance of different layout schemes is compared and analyzed with the source localization error as the evaluation index. Case studies show that the sensor layout optimized by the non-dominated sorting genetic algorithm achieves effective coverage of high-risk areas. With the same number of sensors, its high-risk area coverage rate outperforms that of the multi-objective particle swarm optimization algorithm (MPSOA). Following the application of the optimized layout, the localization accuracy of leakage source term estimation is significantly improved. Compared with the traditional grid and circular layouts, the source localization error is reduced by approximately 44%. Compared with the layouts optimized by the MPSOA and genetic algorithm (GA), the error is decreased by 30.4% and 33.3%, respectively. The proposed sensor layout optimization method based on the NSGA-II can effectively balance monitoring coverage and economic cost, and significantly improve the localization accuracy of gas leakage sources. This study provides a theoretical basis and technical support for the optimal deployment of fixed gas sensor networks in complex scenarios.

Fire doi: 10.3390/fire9070307

Authors: María Alicia Arcos Ángel Balaguer-Beser Luis Á. Ruiz José L. Soriano-Sancho

Live fuel moisture content (LFMC) is a key determinant of fuel flammability and forest fire danger; however, its operational monitoring remains challenging due to the limited spatial and temporal coverage of field measurements. This study aims to assess the operational suitability of a Random Forest-based methodology for LFMC estimation by extending a previously validated local-scale approach to a regional and multi-year context. Weighted average LFMC was modeled across 67 shrubland plots in the Valencian Region (eastern Spain) from 2017 to 2025 using Sentinel-2 spectral indices and aggregated meteorological variables consistent with prior research. Model performance was evaluated under spatially independent and combined spatio-temporal training–testing scenarios designed to approximate real-world wildfire monitoring conditions. Results show that the model exhibits good spatial transferability when applied to shrubland plots not used during training within the same temporal domain, while temporal extrapolation is more limited and dependent on the stability of climatic conditions represented in the training data, with a marked decline in performance under changing temperature and precipitation regimes. These findings highlight key drivers of LFMC prediction, identify validation strategies under operational constraints, and contribute to the development of scalable monitoring approaches for wildfire danger assessment and fuel management in Mediterranean shrublands.

Fire doi: 10.3390/fire9070306

Authors: A. Maleah Winkler Andrew R. Moore William R. Kinnaird

Firefighters often perform multiple consecutive bouts of high-intensity work in hot environments, which can lead to physiological fatigue and increased health risks. This study examined changes in core temperature, skin temperature, and heart rate during recovery periods following repeated bouts of work in recruit firefighters (n = 10) across a full day of outdoor training. Participants completed four work–rest cycles consisting of firefighting drills performed in full personal protective equipment (PPE), followed by passive recovery after PPE removal. Physiological measurements were recorded one minute prior to recovery and then at 5, 10, and 20 min throughout recovery. Skin temperature and heart rate decreased significantly (p < 0.05) within 5–10 min of recovery in most rounds. However, core temperature required at least 20 min to significantly decline (p < 0.05). These findings suggest that shorter recovery periods may be sufficient for heart rate and skin temperature to return to baseline, whereas longer recovery periods are needed for heat to dissipate from the core, especially as work intensity increases. Thus, standard rehabilitation protocols may be insufficient under more extreme working conditions and should be adjusted accordingly to ensure firefighter safety.

Fire doi: 10.3390/fire9070305

Authors: Duckha Jeon Woodam Chung Geonho Kim Byung-Doo Lee Chun Geun Kwon Hee-Young Ahn Ye-Eun Lee Hee Han

The Republic of Korea experiences over 500 forest fires annually, consuming more than 4000 ha. Helicopters are the primary resource for initial attack, but effectively dispatching these limited resources during multiple simultaneous fires poses a significant challenge, as these incidents compete for the same pool of helicopter resources. To support real-time, operational-level helicopter dispatch decisions, an interactive decision support framework was developed that integrates information gathering, fire prioritization, and dispatch optimization. This framework employs an integer linear programming (ILP) approach to minimize the weighted sum of suppression costs and resulting burn perimeters, while allowing for uncontained fires when fire spread rates exceed the cumulative suppression capacity of available helicopters. The framework was applied to two test cases: (1) five hypothetical simultaneous fire incidents, and (2) four actual simultaneous fire incidents recorded on 22 March 2025, with the resulting solutions compared against manual dispatch decisions made by the Korea Forest Service (KFS). The results demonstrate the framework’s capability to analyze diverse fire suppression scenarios and generate a range of effective dispatch options. By integrating real-time fire behavior simulation and optimization, incorporating fire damage potential, and replicating the Republic of Korea’s unique suppression practices, this framework aims to enhance real-time helicopter dispatch decision-making, contributing to the KFS’s ongoing efforts to integrate scientific knowledge into forest fire suppression and management.

Fire doi: 10.3390/fire9070304

Authors: Sai Yao Zilong Liang Zixuan Zhang Suqin Chen Lijing Wang Mingchao Wang Haijun Zhang

Monnex has become the most efficient dry powder extinguishing agent due to its unique fire extinguishing mechanism—the “burning fragmentation” phenomenon. To study the fire extinguishing mechanism of Monnex in detail and elucidate the process of its “burning fragmentation” phenomenon, we have examined the microstructure changes and compositions of Monnex powder during its thermal decomposition process. The results indicate that Monnex undergoes complex iterative reactions and produces explosive intermediates (NH4NO3, KCN, and KN3) when entering the fire. Upon reaching the temperature of 240 °C, the explosive substance is completely pyrolyzed and undergoes a mini- burning fragmentation, resulting in the decomposition of Monnex powder into particles and the release of a large amount of inert gases and free radicals. This is the reason why Monnex has become an optimal dry powder. Toxic substances KCN and KOCN were found during the whole pyrolysis process, so personal protection should be paid attention to in practical applications. Our research not only improves the understanding of the Monnex fire extinguisher, but also provides important scientific evidence for the development of fire extinguishing technologies and environmentally friendly fire protection materials.

Fire doi: 10.3390/fire9070303

Authors: Jiaxu Pei Ruihuan Zhang Hualong Yan Yulu Hao Yu Huang Jin Xiao

Accurate detection in the early stages of a fire is a crucial prerequisite for the efficient implementation of fire suppression and emergency rescue operations. Its accuracy and timeliness directly affect the control of disaster loss severity. Traditional fire detection methods mainly include three categories, which are manual inspection, sensor detection, and visual recognition. However, manual inspection is restricted by labor costs and time efficiency, making it difficult to achieve large-scale, high-frequency and real-time fire monitoring. Sensor detection is easily interfered by environmental factors such as temperature, humidity, and dust, leading to frequent false alarms and missed alarms. Visual recognition technology has shortcomings in aspects such as detailed feature perception, dynamic scene modeling, and reasoning robustness in complex environments, making it difficult to meet the requirements of high-precision detection. To address these issues, this study innovatively proposes a lightweight fire and smoke detection model based on semantic enhancement and frequency domain perception modeling, which is named the SemaFire you only look once (SemaFire-YOLO) model. The model constructs a large language and vision assistant (LLaVA) semantic guidance module, which uses a large language model to understand and guide the semantic features of images, thereby enhancing the saliency representation intensity of small and weak target regions. Then, a Haar wavelet-based downsampling module is adopted, which compresses spatial information while preserving high-frequency features such as flame edges and smoke textures, improving the accuracy of target recognition. Next, the convolution modulation mechanism is introduced to replace the traditional attention mechanism, enhancing the overall modeling efficiency and reducing computational overhead. Finally, a Dynamic Tanh normalization module is adopted to replace the batch normalization module in the traditional YOLO algorithm, strengthening the model’s representation stability and reasoning robustness under unstable input distributions. Experimental results show that the SemaFire-YOLO model achieves a mean average precision (mAP@0.5) of 64.30% on the fire image dataset, which is 0.8, 2.0, 0.6, and 3.8 percentage points higher than that of mainstream models such as YOLOv5n, YOLOv8n, YOLOv11n, and YOLOv12n, respectively. It exhibits better boundary detection capability and practical deployment potential. Through visual analysis, the results indicate that the improved SemaFire-YOLO model achieves more accurate detection and higher confidence in actual complex scenarios, further verifying the model’s robustness and accuracy in complex scenarios such as low contrast and dynamic fire conditions.

Fire doi: 10.3390/fire9070302

Authors: Daniel D. B. Perrakis Nicholas J. R. Hebda S. W. Taylor

Surface fire frequently occurs beneath the canopy of North American forests under moderate wind speed and moisture-deficit conditions. Surface rate of spread (sROS) models can provide guidance for suppression operations and can be incorporated into fire growth modelling systems and other tools. We used a database of primarily Canadian experimental surface fires in conifer and deciduous stands from multiple sites to fit empirical sROS models for operational use and compare with pre-existing models. Various predictor combinations represented fires in boreal conifer (BOCON), deciduous, and Ponderosa pine-dominated stands, the latter analyzed to estimate grass-curing influence. The main predictors were wind speed (WS10), estimated fuel moisture, and Canadian Fire Weather Index (FWI) System components (original and stand-adjusted). The ensuing fitted models (N = 51–93) were evaluated using standard metrics and tested using an independent conifer dataset (N = 26). The simplest model finds BOCON sROS to be equal to 1.2% of the WS10, 1/7th the speed of crown fire spread under similar conditions; it is easily calculated as 20% of WS10 using a common unit conversion (WS10 in km h−1, sROS in m min−1). The best-performing sROS models displayed nonlinear-sigmoidal responses to wind and litter moisture variables, including the Initial Spread Index (ISI), and improved upon pre-existing models. Estimated accuracy was mostly +/− 2–4 m min−1 within the range of the data in both training and validation datasets. These models reflect a dataset gathered from multiple sites using varying experimental methods. While imprecise, they are suitable for many applications, including operational forecasting and designing hazard reduction treatments.

Fire doi: 10.3390/fire9070301

Authors: Se Yeon Jung Su-Young Son

This study investigated how fatigue induced in firefighter boots (FBs) versus sport shoes (SSs) affects ankle joint position sense (JPS), range of motion (ROM), and subjective responses. Twelve healthy males participated in a mixed-design study, performing a calf-raise fatigue protocol in either FB or SS randomly. Ankle JPS, ROM, subjective ankle movement scores (SAMSs), and ratings of perceived exertion (RPEs) were assessed barefoot pre- and post-fatigue. A significant fatigue × footwear × ankle position interaction was observed for JPS constant error (CE) (p = 0.017, ηp2 = 0.183). Follow-up analyses revealed a significant fatigue × ankle position interaction for CE in the SS condition (p = 0.032, ηp2 = 0.299), whereas no significant fatigue-related effects were found in the FB condition. No significant footwear × fatigue × movement direction interaction was observed for ankle ROM (p = 0.561, ηp2 = 0.065), and fatigue-related ROM changes did not differ between footwear conditions. Subjective outcomes differed between footwear conditions after fatigue, with higher SAMS scores in the SS condition (p = 0.016, d = 1.67) and higher RPE scores in the FB condition (p = 0.020, d = 1.66). These findings indicate a dissociation between objective and subjective responses to fatigue, with objective changes limited to a CE interaction pattern in JPS, whereas subjective responses were clearly differentiated by footwear condition.

Fire doi: 10.3390/fire9070300

Authors: Zhixin Wu Dianji Wang Xuefang Li Huan Liu Shishuai Nie Peirong Chen Wenfeng Zhan

Safety concerns regarding hydrogen dispersion, fire, and explosion hinder the commercialization of hydrogen fuel cell vehicles (HFCVs). This study developed and validated a numerical model for hydrogen leakage, dispersion, and explosion in representative accident scenarios using real-vehicle experimental data from a manufacturer-provided HFCV. The analysis examined the effects of leakage orifice diameter, leakage orientation, vehicle motion, and ignition timing on hazard evolution. Large-orifice leakage accelerates flammable cloud formation and expands the hazard range, whereas small-orifice leakage prolongs cloud persistence. Vehicle motion enhances turbulent mixing and reduces near-field accumulation. Immediate ignition produces a jet flame with a maximum radiative heat-flux impact distance of 44.1 m, whereas delayed ignition increases explosion severity and generates a peak overpressure of 0.14 bar. These findings support the risk assessment and safety design of HFCVs.

Fire doi: 10.3390/fire9070299

Authors: Nagesh Babu Vemula Farooq Shaik Gopinath Dhamodaran Radha Krishna Gopidesi

This work examines the performance, combustion, and emission characteristics of a diesel engine coated with a ceramic thermal barrier coating and fueled with emulsified Jatropha biodiesel blended with water and butanol. A low heat rejection (LHR) engine was prepared by depositing a 100 µm NiCrAlY bond coat and a 200 µm of 8YSZ ceramic top coat via air plasma spraying. B20W10Bu5, B20W10Bu10, and B20W10Bu15 ternary emulsions were successfully produced using ultrasonic homogenization. The experimental outcomes indicate that the ceramic-coated engine exhibited higher thermal efficiency than that of the conventional engine. The highest performance was achieved with B20W10Bu10 fuel, which resulted in a 7.4% increase in the brake thermal efficiency and a 7.8% decrease in the brake-specific fuel consumption relative to the results for the conventional coated diesel engine. Hydrocarbons, carbon monoxide, and smoke emissions decreased considerably due to the combined impacts of oxygenated fuel composition, micro-explosions, and thermal insulation capability. It can be seen from the discussion above that the utilization of a ceramic thermal barrier coating and the Jatropha-based ternary emulsion fuel, especially B20W10Bu10, shows great promise for enhancing engine performance while lowering exhaust emissions.

Fire doi: 10.3390/fire9070298

Authors: Anahi Méndez Lozano Brittany Barreto Martinez Dalston J. Karto Alicia M. Kinoshita

Riparian zones provide vital ecosystem services, including water purification, soil aeration, and recreation. Anthropogenic activities and invasive plant species threaten native vegetation and alter fire patterns. This study investigates the impact of invasive vegetation cover (IVC) on riparian fire patterns in Otay Valley Regional Park, San Diego, California, using Sentinel-2 imagery to analyze 13 fires that occurred in 2019. The impact of IVC on fire patterns was assessed using high-resolution Normalized Difference Vegetation Index (NDVI) and Differenced Normalized Burn Ratio (dNBR) from 2019 to 2023. We found nuanced fire dynamics relationship driven by species-specific traits. Results showed that post-fire NDVI was consistently highest in areas with <25% IVC, suggesting more stable vegetation recovery in native areas. In contrast, areas with >75% IVC had high NDVI variability and greater canopy loss, particularly where species such as Melilotus albus and mixed annual forbs dominated. IVC was evaluated descriptively rather than as an inferential predictor due to the small number of fire counts. Descriptive patterns indicate that post-fire vegetation response varied by dominant invasive species, with resilient taxa such as Arundo donax, Tamarix ramosissima, and Eucalyptus spp. showing evidence of rapid or sustained recovery. These findings highlight the complexity of fire dynamics in invaded riparian systems and the importance of species-specific monitoring. We recommend integrating remote sensing with targeted invasive vegetation species management to improve fire resilience and ecological integrity in urban riparian corridors.

Fire doi: 10.3390/fire9070297

Authors: Chuanfu Kou Xigan Chen Shiqi Zeng Jiaqiang E Yinjie Ma

Background: As global environmental issues and the energy crisis continue to intensify, diesel–natural gas dual-fuel engines have been extensively studied due to their stable combustion, low emissions, abundant natural gas reserves, and relatively low cost. Methods: Based on a modified YCK15 six-cylinder heavy-duty diesel engine, the experiments and GT-SUITE v2016 simulation were used to study the effects of NG substitution rate (NGSR) and diesel injection timing (DIT) on the combustion characteristics, power and emission performance of a diesel–NG dual-fuel engine running at 1800 rpm, with NGSR ranging from 0 to 50% and DIT ranging from 5 °CA BTDC to 17 °CA BTDC under four engine load conditions: 100%, 75%, 50% and 25%. Significant Findings: The results showed that the NGSR and DIT have considerable impact on performance enhancement and emission reduction. As NGSR increased, cylinder pressure decreased under high load and increased under low load. Under four loads, the temperature inside the cylinder revealed a downward trend, and the power and indicated thermal efficiency (ITE) decreased slightly, with power and ITE declining by less than 5% and 2%, but the fuel economy and emissions were well improved. Compared to 50% NGSR and pure diesel condition, brake-specific fuel consumption (BSFC) decreased by 5.63%, 4.60%, 2.98%, and 1.83%, respectively, and NOx emissions decreased by 32.68%, 36.41%, 37.90%, and 38.99%, respectively. As DIT increased, cylinder pressure and temperature both increased under all four load conditions, and the power and ITE improved significantly, but this caused an increase in NOx emissions. Compared to DIT of 17 °CA BTDC with 5 °CA BTDC, power increased by 8.29%, 9.76%, 13.38%, and 16.51%, respectively, and ITE increased by 7.69%, 8.77%, 11.46%, and 12.77%, respectively. The response surface was established and performance optimized using the design of experiments (DOE) module in GT-SUITE v2016. At an NGSR of 50% and 100% loads, the optimized power was 0.431% higher than the pure diesel mode, ITE was 0.396% higher, brake-specific fuel consumption was reduced by 7.397%, and NOx emissions were reduced by 27.027%.

Fire doi: 10.3390/fire9070296

Authors: Jan Hora Petr Kučera Adéla Snohová Martin Trčka Tereza Česelská

This study examines whether a train fire near an evacuation interface in a railway tunnel can create an adverse configuration relevant to evacuation design and collective risk assessment. It focuses on twin single-track tunnels, in which the parallel tunnel serves as a safe area, and evacuation is carried out through cross-passages and boundary portals. If a fire impairs such an interface, evacuees may be forced to continue to a more distant exit. The problem is formulated as a geometric-probabilistic screening task. The model calculates the probability that, after the train has stopped, the fire lies within a tolerance zone around an evacuation interface. The probability is derived analytically using deterministic convolution and verified via Monte Carlo simulation for trains with lengths of 200 m and 400 m. This verification concerns mathematical calculation only, not the physical, smoke, operational, or evacuation assumptions. The geometric probability is linked to collective risk through representative train fire frequencies, external consequence indicators, and selected F/N criteria. With ε = 37 m and portals included as boundary evacuation interfaces of the finite tunnel domain, the adverse-configuration probabilities are similar: approximately 14.0% for the 200 m train and 14.6% for the 400 m train. The difference becomes decisive only after considering the magnitude of the consequences and the traffic intensity. Under the reference assumptions, the 400 m high-occupancy case reaches the selected Dutch criterion at about four train passages per day. A fire near an evacuation interface, therefore, cannot be treated as marginal solely because the tunnel meets the 500 m cross-passage spacing requirement. Acceptability depends on geometry, occupancy, fire frequency, the definition of consequences, traffic intensity, and the selected risk framework. The homogeneous fire-origin distribution is used only as a neutral first-order assumption; more refined spatial fire-origin models and broader comparisons across safety criteria are needed.

Fire doi: 10.3390/fire9070295

Authors: Yoo Youl Choi Kyu Nam Jeon A Young Choi Ha Young Kwon Chang Hoon Song

Flame-retardant coatings and films are widely used to delay flame spread on interior finishing and wood-based materials; however, their fire effluent toxicity has not been sufficiently characterized, and direct comparisons between these product types remain scarce. This study evaluated three commercial flame-retardant coatings and three flame-retardant films using the KS F 2271 gas toxicity test, NES 713 toxicity index test, and Py-GC/MS and HS-GC/MS analyses. Representative coating and film products were also applied to medium-density fiberboard (MDF) to assess average incapacitation time, total smoke release (TSR), and total heat release (THR). All tested specimens, including the 1 coat/layer, increased-loading, and MDF-applied conditions, satisfied the Korean gas toxicity criterion of 9 min. However, increased loading affected the two product groups differently; the intumescent coating showed a marked reduction in average incapacitation time, whereas the films remained relatively stable. The coatings produced higher toxicity indices and more diverse detected gases and pyrolysis products than the films. In MDF-based specimens, flame-retardant treatment increased average incapacitation time and reduced TSR and THR. These findings show that fire effluent toxicity differs between coatings and films and should be considered together with flame-retardant performance.

Fire doi: 10.3390/fire9070294

Authors: Kelsey Glover Rohit Mittal Steven A. Kahn

Firefighting is a high-risk occupation involving intense physical exertion and hazardous environments. Occupational exposures, including combustion byproducts, contribute to long-term cancer risk and acute burn injuries. While line-of-duty fatalities have declined, substantial morbidity remains, particularly among female and wildland firefighters who have historically been underrepresented in research. A narrative review was conducted using PubMed, Scopus, and Google Scholar to identify relevant studies published after 2010. Search terms included firefighter, fatality, injury, cardiovascular disease, occupational exposure, cancer, wildland, and female. Articles were synthesized using the NIOSH Hierarchy of Controls framework. Cardiovascular events and overexertion remain leading contributors to line-of-duty deaths, while non-fatal injuries are commonly musculoskeletal. Occupational exposures, related to dermal absorption of toxins and improper use of protective equipment, contribute to burn injuries and may increase long-term cancer risk. Wildland firefighters face risks in the expanding wildland-urban interface, such as prolonged smoke exposure and extended exertion, which may elevate cardiopulmonary and cancer risks. Female firefighters face challenges related to ergonomic mismatch with protective equipment primarily designed for male body dimensions. Firefighters face health risks from a variety of environmental and operational factors. Risk mitigation must transition from a reliance on PPE to higher-level engineering and administrative controls. Future research should prioritize longitudinal health tracking and standardized equipment for diverse fire service populations.

Fire doi: 10.3390/fire9070293

Authors: Yilang Zhou Chao Li Ruihang Yang Tiejun Zhou Jiayi Chen Haobin Li

Due to enclosed space, long evacuation distances, and complex path structures, key nodes in deep underground spaces are prone to forming bottlenecks during fire evacuation. To collect evacuation behavior data at potential bottlenecks, an interactive video-based hypothetical choice (HC) experiment was conducted with 104 valid samples. Exit distance, sub-safe zone setting, congestion, pedestrian flow guidance, and smoke were systematically examined. The results showed that: (a) exit distance, sub-safe zone setting, congestion at the nearest exit, and smoke significantly affected evacuation decisions, with clear avoidance of near-exit congestion and smoke; (b) congestion on paths to non-nearest exits had a relatively weak effect, and pedestrian flow guidance did not produce significant herding; and (c) gender, age, professional background, and evacuation experience influenced path choice differences under certain conditions. Notably, evacuees prioritized smoke avoidance over all other cues, while congestion triggered non-compensatory route switching rather than herding behavior. These findings enrich the empirical database on pedestrian evacuation dynamics in deep underground spaces and provide a quantitative basis for evacuation simulation, spatial optimization, and safety management.

Fire doi: 10.3390/fire9070292

Authors: Ioannis Mitsopoulos Irene Chrysafis Konstantinos Lagouvardos Giorgos Mallinis

The spatial relationships between lightning-induced fire ignition and topography, vegetation, climate, and weather were analyzed in the region of East Macedonia and Thrace, northeastern Greece. The study was based on reported lightning-induced ignitions during the 2009 fire period. Lightning data for the same period was provided by the ZEUS lightning detection network operated by the National Observatory of Athens, while fire statistics were obtained from the official records of the Greek Fire Service. A total of 198 lightning strike events (66 fire ignitions and 132 non-fire events) were used for model development. Statistical models based on Logistic Regression (LR) and random forests (RF) were developed to estimate the probability of lightning-induced fire using topography, climate, weather, and vegetation indices as predictor variables. According to the analysis results, the probability of an area being affected by lightning-induced fire is primarily determined by the Normalized Difference Vegetation Index (NDVI) and the accumulated precipitation in 24 h equal to or less than 2.5 mm expressed by Dry Thunderstorm (DT) day occurrence in this dataset. The logistic regression model achieved an area under the ROC curve of 0.94 and an overall classification accuracy of 91.9%, while the random forest model produced an Out-Of-Bag (OOB) error rate of 3.0%. Although the models have not been subjected to independent validation and include a single year’s data, the results demonstrate high internal classification performance and provide valuable insights into the primary drivers of fire ignition following lightning strikes in the study region. The outcomes of the present study will be useful in assessing spatially explicit fire risk, the planning and coordination of efforts to identify high-fire-risk areas, and designing long-term fire management and climate change adaptation strategies.

Fire doi: 10.3390/fire9070291

Authors: Jiaqing Zhang Hanlin Zhou Binbin Zhang Zhuo Song Yuning Guo Weiguo Song

Forest fire susceptibility mapping is an important component of disaster risk reduction, particularly in transitional climatic zones such as Anhui Province, China. Traditional approaches often rely on expert weighting (AHP) or linear assumptions, which may be insufficient for capturing the complex, non-linear interactions of fire drivers. This study develops a data-driven framework integrating 816 field-surveyed fuel plots with MODIS active fire data (2000–2025). We applied a systematic preprocessing pipeline, including 1–99% Winsorization to reduce the influence of sensor outliers, Non-Linear Gamma Curvature Normalization to represent asymmetrical risk responses, and a spatial buffer-based pseudo-absence protocol combined with semantic land-cover masking to reduce label ambiguity and macro-environmental bias. Benchmarking against seven machine learning algorithms on a naturally balanced dataset showed that the Random Forest (RF) model achieved the highest test-set performance among the evaluated models (Test AUC = 0.831). Youden’s J statistic was used to define a data-driven risk threshold. The results suggest that topographic configuration and forest stand density act as important baseline constraints and interact with physiological moisture stress indicators to influence fire susceptibility. The species-level risk analysis was broadly consistent with ecological expectations: coniferous forests showed the highest predicted high-risk proportion (79.10%), whereas soft broadleaves showed a substantially lower predicted high-risk proportion (4.29%). Spatial mapping indicated a “South-High, North-Low” pattern associated with topographic forcing and fuel continuity, which may provide useful information for regional fire management and the planning of green firebreaks.

Fire doi: 10.3390/fire9070290

Authors: Zhuozhi Cheng Jinyang Dai Qiuyang Cao Xiaoning Song Qixing Zhang

Fires pose a serious threat to life and property, making early flame detection critical for reducing fire losses. However, existing single-band flame detection methods cannot fully exploit complementary spectral information and are prone to false alarms in complex environments. To address this issue, we propose a Dual-Band Flame Attention Network (DBFANet), which consists of a visible-light channel, a near-infrared channel, and a fusion channel. The visible-light and near-infrared channels employ DAB-DETR for flame detection, while the fusion channel adopts a multi-level feature fusion structure with spatial and channel attention mechanisms to enhance effective fusion information. In addition, a Dual-Band Flame Deep Context Fusion Module and a Flame Texture Information Aggregation Module are designed to improve cross-band feature representation and multi-scale flame perception. A Dual-Band Comprehensive Decision Module is further introduced to integrate the detection results from all three channels and suppress false positives under complex illumination conditions. Experimental results on a self-built dual-band flame dataset show that DBFANet achieves average precisions of 95.0% and 93.1% in the visible-light and near-infrared bands, respectively, with false alarm rates as low as 0.013 and 0.025. These results demonstrate the effectiveness and robustness of the proposed method for flame detection in challenging environments.

Fire doi: 10.3390/fire9070289

Authors: John Rogath John Hui Huang Haifeng Gao Xiaoying Han Faris Jamal Mohamedi Abbas Khurram Xiangxuan Zeng Zhan Shu

Wildfires threaten natural ecosystems and human livelihoods in the Tanzanian Miombo woodlands. This study presents the first locally calibrated, high-resolution wildfire susceptibility map for the Iringa region, developed using a robust machine learning framework. Multi-decadal remote sensing data (MODIS fire occurrences, 2001–2024) were integrated with climatic, topographic, vegetation, and anthropogenic variables to train four classifiers: Random Forest, XGBoost, support vector machine with RBF kernel, and Logistic Regression. A balanced dataset of 9096 fire points and an equal number of randomly sampled non-fire points was used. The data were split into 70% for training and 30% for testing. Model performance was evaluated using accuracy, area under the ROC curve (AUC), accuracy, precision, and F1-score. Random Forest achieved the highest overall performance (AUC = 0.845, accuracy = 0.759, precision = 0.789 and F1 = 0.771), followed by XGBoost (AUC = 0.828, accuracy = 0.736, precision = 0.700 and F1 = 0.757), SVM (AUC = 0.755, accuracy = 0.679, precision = 0.648 and F1 = 0.709), and Logistic Regression (AUC = 0.740, accuracy = 0.661, precision = 0.631 and F1 = 0.696). Feature importance analysis identified altitude as the most influential variable, followed by wind speed, distance to road, and NDVI. Kernel Density Estimation revealed spatially distinct fire clusters concentrated in central and southern hotspots. Temporal analysis showed that 94% of fires occur during the dry season (June–November), peaking sharply in October. These findings provide an evidence-based framework for fire prevention and sustainable management of Iringa’s Miombo woodlands.

Fire doi: 10.3390/fire9070288

Authors: Jorge Yanez Mike Kuznetsov Leonid Kagan Gregory Sivashinsky

In a Hele-Shaw cell, unconventional fragmented flame propagation occurs for Peclet numbers less than 15. Until now, the regimes arising were organized in a simple taxonomy. Here, we endeavor to classify our experiments in view of the Theory of Solitons, a part of Synergetics discipline. This approach allows us to recognize new general patterns previously unidentified. Furthermore, this permits us to identify a much richer variety of topologies and typologies of regimes than initially thought.

Fire doi: 10.3390/fire9070287

Authors: Yu Wang Baoshan Jia Zikun Pi Rui Li Tianzhi Yang Zhanpeng He Hui Zhuo Tongren Li

To address the major safety hazard that heat released from mining polyurethane (PU) reinforcement materials may induce spontaneous combustion of residual coal in goaf, this study selected No. 3 coal from Wangzhuang Coal Mine, Shanxi Lu’an, as the research object. A self-developed large-capacity, large-scale experimental system was used to conduct programmed heating experiments on 2.0 kg multi-particle-size coal-PU mixed samples. The effects of PU content on characteristic gas release, crossing point temperature (CPT), residue morphology, and TGA-DSC characteristic temperatures were systematically investigated, and the reaction-kinetic evolution was further analyzed using the distributed activation energy model (DAEM). The results show that coal and PU exhibit a significant synergistic enhancement effect during co-heating. As the PU content increased, the release concentrations of CO, C2H4, and C2H6 increased markedly, and their initial release temperatures decreased, whereas CH4 generation was inhibited by hydrogen-radical competition; no C2H2 was produced below 400 °C. The CPT decreased linearly with an increasing PU content, with an average decrease of approximately 8.5 °C for every 10% increase in PU content. Residue morphology showed clear critical features: glassy agglomerates appeared when the PU content exceeded 16.67%, and dense bulk coking occurred when the PU/coal mass ratio was greater than 1:10. TGA-DSC analysis showed that when the PU/coal ratio was lower than 1:10, the ignition temperature of the mixed sample was higher than that of pure coal, indicating an inhibitory synergistic effect. When the ratio exceeded 1:10, the ignition temperature decreased significantly, and the synergy shifted to promotion; increasing the heating rate shifted the characteristic temperatures to higher values and increased the reaction intensity. DAEM analysis further confirmed that when the PU ratio exceeded 1:10, the apparent activation energy of the mixed samples was lower than that of pure coal. Coal powder also acted as a physical skeleton that effectively dispersed molten PU, eliminated the activation-energy peaks of pure PU in the conversion ranges of 30–50% and 70–90%, and substantially improved combustion stability. Mechanistically, low-temperature PU melting and coating optimized heat and mass transfer, medium-temperature pyrolysis released active radicals and combustible gases that altered coal pyrolysis pathways and the radical reaction environment, and high-temperature hydrogen-radical competition reshaped the gas-product distribution. Together, these processes form a complete chain of synergistic spontaneous combustion. This study identifies key safety threshold parameters for PU reinforcement materials, recommends a PU content of ≤9.10%, and identifies CO and C2H4 as priority early-warning gases, providing direct experimental evidence for characteristic-gas-based early warning and mine fire prevention.

Fire doi: 10.3390/fire9070286

Authors: Mansoor Alghamdi Ahmad Abadleh Sami Mnasri Malek Alrashidi Ibrahim S. Alkhazi Majed Abdullah Alrowaily Charles Z. Liu

Indoor evacuation under emergency conditions remains a challenging problem due to dynamic hazards, uncertain infrastructure availability, and variability in human behavior. Traditional evacuation systems rely heavily on centralized architectures, making them vulnerable to communication failures and delayed global decision making. To address these limitations, this paper proposes a novel framework termed Federated Edge-Semantic Learning for Decentralized Resilient Evacuation (FESL-DRE). The proposed framework distributes evacuation intelligence across edge nodes, enabling autonomous decision making without dependence on a central controller. It integrates semantic reasoning to transform raw sensor data into interpretable environmental states, federated learning to model behavioral patterns in a privacy-preserving manner, and a gossip-based coordination mechanism to propagate hazard information across neighboring nodes. An adaptive routing strategy is developed to account for hazard levels, crowd density, and human behavioral variability. The framework is evaluated using a simulation-based environment under dynamic hazard conditions and varying levels of node failure. Experimental results demonstrate that FESL-DRE achieves superior performance compared to classical and centralized adaptive methods, with improvements in evacuation success rate, reduced blocked movement attempts, and enhanced resilience under moderate infrastructure degradation. Furthermore, the proposed approach maintains low communication overhead and demonstrates promising scalability characteristics within the evaluated simulation environment. The results highlight the potential of decentralized intelligence for evacuation support and provide a foundation for future validation in realistic smart building and IoT-enabled environments.

Fire doi: 10.3390/fire9070285

Authors: Peng Cai Rui Liu Zhi Zhang Zhilei Wang Shishuai Nie Huan Liu Yi Liu Anfeng Yu

To investigate the hydrogen leakage dispersion and explosion characteristics of fuel cell vehicles in an underground parking garage, experimental and numerical simulation studies were conducted. The results show that the hydrogen leakage concentration exhibits an evolutionary pattern of a rising stage followed by a plateau stage, with a stratified distribution characterized by higher concentration at the top and lower concentration at the bottom. Higher leakage flow rate leads to a faster concentration growth rate, while the two are not in a direct proportional relationship. The hydrogen concentration near the leakage outlet was relatively low. The maximum explosion overpressure reached 194 kPa at a hydrogen concentration of 20%, with higher overpressure observed on the walls. Flame propagation followed a four-stage law, and a Laval nozzle effect appeared at the leakage outlet. Ventilation can rapidly suppress hydrogen accumulation, and the ventilation effect approached optimality at a wind speed of 8 m/s. The explosion venting area exerted the most significant influence: when the venting area increased from 0.36 m2 to 1.44 m2, the overpressure decreased by 76%. The explosion venting position was the second most influential factor, while the vent shape had negligible effects. This study provides a scientific basis for the safety prevention and control of hydrogen energy applications in underground spaces.

Fire doi: 10.3390/fire9070284

Authors: Luan Zang Mingzhou Liu Yangyi Wu Hongyan Zhu Yueqi Han Wei Gao Jingrui Li Haifeng Liu

This study presented a comprehensive optimization of a piezoelectric injector specifically designed for pure methanol compression-ignition engines. As a fuel for compression-ignition engines, methanol exhibits broad application prospects. To overcome the challenges posed by methanol’s low cetane number and energy density, a co-optimization strategy was implemented, targeting the actuator, drive waveform, and internal flow geometry. The redesigned injector exhibited superior dynamic performance, featuring significantly faster response times and enhanced operational stability, which were critical for precise fuel delivery control. Furthermore, the optimized internal flow path increased the effective flow rate, ensuring sufficient fuel supply across all engine operating conditions. The upgraded injector was rigorously tested on an engine bench, demonstrating substantial performance gains. Brake thermal efficiency improved from 38.9% to 40.4% at low load and from 43.68% to 46.07% at high load. Emissions of CO, formaldehyde, acetaldehyde, and unburned methanol were consistently reduced, with the maximum reduction reaching 23.1%, confirming markedly enhanced combustion completeness. This improvement was directly attributed to the injector’s refined spray characteristics and precise control, although it led to a slight increase in NOx emissions due to higher peak combustion temperatures.

Fire doi: 10.3390/fire9070283

Authors: Rida Kanwal Song Weiguo

Wind is a fundamental driver of wildfire behavior, yet wind–fire relationships remain poorly characterized in the mountainous regions of South Asia, where ground-based observations are scarce. This study examines the wildfire in the Suleiman Range of western Pakistan for May 2022, integrating Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detection, Landsat-derived burn severity, ECMWF Reanalysis v5 (ERA5) meteorological data, and Shuttle Radar Topography Mission (SRTM) topography data. Twenty-nine wildfire-classified detections (Fire Radiative Power, FRP range 6.0–52.1 megawatts (MW)) were analyzed across the Sherani, Musakhel, and Dera Ismail Khan (D.I. Khan) districts between 18 and 29 May 2022. The ERA5 wind speed at the fire points was moderately positively correlated with the FRP, although strong collinearity with temperature prevented the separation of the effects of wind and temperature. The wind direction was highly consistent throughout the event. Spread events were defined as consecutive detection pairs; among pairs separated by more than 2 km, four were aligned with the ERA5 downwind direction. These findings are consistent with synoptic winds broadly contributing to eastward fire progression, whereas local-scale spread was likely modulated by the terrain-channeled winds that ERA5 cannot resolve at its ~27 km grid scale. Elevation was strongly negatively correlated with the FRP. The burn severity analysis indicated that approximately 86 km2 of burn occurred, predominantly at low-to-moderate severity. This integrated workflow offers a transferable template for characterizing wildfire behavior in data-sparse mountainous regions.

Fire doi: 10.3390/fire9070282

Authors: Maya Zegel Anka A. Vujanovic Matthew W. Gallagher

Extant research has documented elevated rates of alcohol use among the fire service. While some studies have sought to examine the role of drinking motives in firefighter alcohol use, findings are limited by a lack of exploration into the validity of established alcohol use measures among this population. The present study explored the factor structure of the Alcohol Use Disorders Identification Test (AUDIT) and the revised, short-form Drinking Motives Questionnaire (DMQ-R-SF) among a large sample of career firefighters in the southern U.S. (N = 679). Participants were included in this secondary analysis if they reported any lifetime alcohol use and completed the measures of interest. Confirmatory factor analyses supported the established three-factor AUDIT and four-factor DMQ-R-SF. SEM results indicated that coping-motivated alcohol use was statistically significantly positively associated with each AUDIT subscale (i.e., hazardous consumption, dependence symptoms, and harmful consequences). Notably, conformity-motivated alcohol use was inversely associated with hazardous consumption. Findings underscore the importance of understanding and addressing alcohol use among firefighters, particularly drinking to cope with negative affect.

Fire doi: 10.3390/fire9070281

Authors: Jiuling Zhang Jinghan Zhang Ying Liu Jiuyuan Fan Huiyong Niu Ruijiang Zhang

Severe air leakage in the goaf of gob-side entry retaining panels can intensify oxygen supply to residual coal and consequently increase the probability of coal spontaneous combustion. Taking the 3451S working face of a coal mine in Hebei Province as the engineering case, this study integrated in situ beam-tube monitoring with Fluent-based numerical simulation to characterize the evolution of the spontaneous-combustion three zones and to optimize prevention and control measures. The results demonstrate that the oxidation zone is characterized by an inclined, continuous band-like distribution penetrating the goaf. The simulated oxygen distribution is consistent with the field measurements, demonstrating the reliability of the established numerical model. The ventilation pattern markedly affects the air-leakage flow field and oxygen concentration distribution, and the Y-type ventilation mode exhibits a higher spontaneous-combustion risk. When the air-volume ratio between the 3451S haulage roadway and the gob-side retained entry is adjusted to 3:1, the oxidation-zone area decreases by approximately 11%. A combined control strategy involving cement-blanket and polymer-spraying leakage sealing, together with precise nitrogen injection, is then proposed to improve the goaf oxygen environment. At a nitrogen-injection rate of 600 m3/h, the oxidation-zone area is reduced by 11,160 m2 and the CO concentration remains stable at approximately 4.9 ppm, providing field evidence for improved fire-prevention performance. These results support the design of targeted spontaneous-combustion control strategies for gob-side entry retaining goafs.

Fire doi: 10.3390/fire9070280

Authors: Jiaqi Liu Fan Deng Hui Li Yinmei Zeng Xiaopeng Guo Jiajia Guo

Fire occurrence in Southwest China is jointly shaped by meteorological conditions, topography, vegetation status, and human activities. To improve the interpretability and validation rigor of regional fire susceptibility assessment, this study developed a grid-day-based susceptibility assessment framework for Yunnan, Sichuan, Guizhou, and Chongqing using MODIS active-fire detections and multi-source environmental data from 2015 to 2024 at a 5 km grid resolution. A sensitivity analysis was conducted to determine the training sample configuration, and a 1:2 positive-to-negative sampling ratio was adopted. Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) were compared, and SHapley Additive exPlanations (SHAP), together with partial dependence plots (PDP), were used to interpret key drivers and their interactions. Data from 2015 to 2018 were used for model training, while data from 2019 to 2024 were used to evaluate the model’s cross-year transferability within the same study domain, rather than full spatiotemporal independence. The results show that the 1:2 sampling ratio achieved a favorable balance between fire detection and false-alarm control. In five-fold stratified cross-validation, RF outperformed LR and SVM (AUC = 0.9167; F1-score = 76.70%). In the cross-year transferability test, areas classified as high and very high susceptibility captured 62.04–68.95% of the observed fire points while accounting for less than 32% of the total area. Soil moisture and maximum temperature contributed most strongly to the model output, and their interaction revealed a pronounced dry-hot statistical response pattern associated with elevated susceptibility. Fire susceptibility also exhibited stable positive spatial autocorrelation, with hotspot areas concentrated in the dry-hot valleys near the Sichuan-Yunnan border and in central-southern Yunnan. Because the model was built with under-sampled negatives and same-day environmental matching, the output should be interpreted as a relative fire susceptibility index for spatial assessment and statistical attribution rather than as a calibrated occurrence probability or a forward-looking daily forecast.

Fire doi: 10.3390/fire9070279

Authors: Rundong Wang Lanxi Xu Yuanjing Huang Weijun Pan Zirui Yin

Wildfires in plateau and mountainous regions are increasingly destructive, often disrupting ground transportation networks and severely constraining emergency logistics, while unmanned aerial vehicles (UAVs) remain limited by payload capacity. To address this challenge, this study proposes an air–ground collaborative emergency material dispatch method for dynamic, time-varying wildfire environments. A multi-layer spatiotemporal network model is developed by incorporating key uncertainties, including fire spread and meteorological fluctuations, into dynamic parameters, and a multi-objective mixed-integer programming framework is established to jointly optimize emergency response time, total dispatch cost, and rescue fairness. To solve the resulting high-dimensional dynamic rescheduling problem, a Fast Ant Colony Optimization-Genetic Algorithm (FACO-GA) integrated with a rolling horizon mechanism is designed. Simulation results under Level 1–10 dynamic perturbations show that, compared with conventional standalone algorithms (GA and ACO), the proposed method demonstrates markedly better robustness and computational efficiency, reducing the extreme average rescheduling response time to 6.80 s, while maintaining a Hypervolume (Hv) retention rate of 96.30% and limiting the Spacing (Sp) change rate to 4.15%. These findings indicate that the proposed approach can effectively overcome computational bottlenecks and provide an adaptive decision-support framework for emergency logistics dispatch in complex wildfire scenarios. Furthermore, comprehensive ablation studies and sensitivity analyses validate the structural necessity of the rolling horizon and ACO modules, ensuring the algorithm’s parameter robustness under extreme stochastic perturbations.

Fire doi: 10.3390/fire9070278

Authors: Zhengshen Huang Rui Wang Xin Li Weili Kou Qinyan Gu Zengxing Li Jiangxia Ye Qiuhua Wang

In Unmanned aerial vehicle (UAV) forest fire detection, flames and smoke exhibit dramatic scale variations. Existing methods often struggle with multi-scale feature extraction, fusion quality, and localization reliability, resulting in limited accuracy improvements. To address this issue, this study optimizes the backbone, neck, and head of YOLOv11n to propose a novel multi-scale forest fire detector (MFFDet), which consists of three key modules: (1) the Multi-Scale Feature Calibration Module (MFCM) is designed to improve multi-scale feature representation by context aggregation and detail calibration; (2) the Cross-Scale Semantic Alignment Module (CSAM) is proposed to enhance fusion quality by applying channel reorganization and local spatial refinement; and (3) the Location Quality Estimator Head (LQEH) is presented for reliable localization by mapping the statistical information of regression distributions into a localization quality score, which systematically boosts the accuracy and stability of multi-scale object detection. In addition, to alleviate the scarcity of UAV forest fire detection data, this study constructs a UAV Forest Fire Dataset (UF2D), providing important data support for UAV-based fire detection. Experiments on UF2D show that MFFDet achieves an mAP@0.5 of 70.1%, the best among all compared models, representing a 4.4% improvement over the baseline. Moreover, it attains the top performance on small, medium, and large objects, with APs of 20.3%, APm of 31.5%, and APl of 44.8%, highlighting MFFDet’s robustness and accuracy for multi-scale flame and smoke detection in a complex forest fire environment, which bears important practical significance for the intelligent upgrade of forest fire prevention and control.

Fire doi: 10.3390/fire9070277

Authors: Yutao Zhang Linjia Wang Rui Liu Yuanbo Zhang Hang Song Qiang Guo Jing Bian Haochen Li

To systematically examine the effects of ambient wind speed on the fire behavior of inclined tunnel cables, this paper determines the combustion characteristics of ZR-RVV cable combustion parameters using synchronous thermal analysis and cone calorimetry. A 1:20 scaled tunnel platform was established based on Froude similarity criterion to conduct combustion experiments under varying wind speeds (0–0.7 m/s) and inclination angles (−30°–30°). Results indicate the ignition time of the cable decreases gradually with increasing external heating radiation intensity (25–50 kW/m2), with ignition at 295.1 °C. A modified Richardson number (Ri*) is introduced to quantitatively identify the dominant flow regime. It is confirmed that when |θ| ≈ 20°, Ri* ≈ 1, and the fire behavior transitions from “domination” (Ri* < 0.5) to “buoyancy-driven stack effect domination” (Ri* > 2). This critical inclination angle provides decisive guidance for fire source localization, smoke control, and exhaust design. Increasing ambient wind speed significantly reduces the fire temperature and dilutes the smoke; at a wind speed of 0.7 m/s, the maximum temperature drop at the ceiling monitoring point reaches 67%, while CO/CO2 concentrations decrease correspondingly. The findings provide a theoretical basis for smoke exhaust design and fire monitoring in tunnel fire protection.

Fire doi: 10.3390/fire9070276

Authors: Nida Kati Ferhat Ucar

Rapid electric-vehicle adoption is reshaping urban energy and mobility systems, especially in multi-unit dwellings (MUDs), where concentrated charging in shared parking areas simultaneously stresses distribution transformers and amplifies the consequences of charger faults, battery thermal events, smoke spread, and emergency-access constraints. The central argument of this paper is that grid stress, resident-facing service quality, lifecycle cost, and fire-risk exposure in enclosed residential parking should be governed jointly rather than as four separate problems. To make that argument concrete, we develop an integrated framework that couples stochastic EV adoption, residential charging-behavior simulation, XGBoost demand forecasting, and linear-programming-based optimization for coordinated control, and we evaluate it through 1000 Monte Carlo trials on representative Turkish MUDs. Unmanaged charging triggers transformer overload at about 30% EV penetration, whereas coordinated control reduces peak demand by 44.7% (405 kW to 224 kW) and raises load factor from 0.40 to 0.68. Strict capacity protection exposes a sharp service–quality trade-off, with only 8.9% of users reaching 80% state of charge (SOC) by departure. Smart charging lowers upfront cost by about 55% ($200 vs. $439 per dwelling unit) and yields roughly $306 net present value per unit over ten years. Building on these results, we propose a five-pillar fire-risk-aware governance architecture—coordinated control, interoperability standards, time-of-use pricing, building–utility coordination, and monitoring—that turns coordinated charging into a preventive governance layer for reducing hazardous congestion in enclosed residential charging environments.

Fire doi: 10.3390/fire9070275

Authors: Antoine Lebeaut Samuel J. Leonard Maya Zegel Gauri Doshetty Brian J. Albanese Anka A. Vujanovic

Firefighters experience elevated suicide risk; however, factors influencing this risk remain understudied among this frontline population. Distress overtolerance (DO), or enduring high emotional distress, may represent a novel risk factor in the fire service, where occupational norms reinforce persistence under stress. The present study examined whether DO subfactors (Capacity for Harm [CH]: persisting through distress despite harm to one’s well-being; Fear of Negative Evaluation [FNE]: persisting through distress to avoid negative judgment) predicted variance in suicidal ideation and suicide risk. Firefighters (N = 79) were recruited from a U.S.-based national first responder service agency and completed self-report measures at baseline and follow-up as part of a larger study of mental health among first responders during the COVID-19 pandemic. Hierarchical regression models using baseline data indicated that CH accounted for significant variance in suicidal ideation (ΔR2 = 0.13) and suicide risk (ΔR2 = 0.16), while FNE accounted for variance only in suicide risk (ΔR2 = 0.07). Longitudinal models indicated that both CH and FNE were significantly and preliminarily associated with suicide risk, but only CH was significantly associated with suicidal ideation. Findings suggest that DO may represent a clinically meaningful suicide risk factor in firefighters, with implications for assessment and prevention efforts.

Fire doi: 10.3390/fire9070274

Authors: Wangeun Ji Sugi Choi Heejun Kwon Haiyoung Jung

Vision-based fire detection models are often limited by the insufficient diversity of annotated fire and smoke images, particularly in terms of fire location, flame scale, smoke density, ignition cause, and indoor scene context. This study investigates whether generative AI-based synthetic images can expand fire-image diversity and improve object detection-based fire detection performance. Real fire images were combined with conventional augmented images and synthetic images generated using ChatGPT-4.o and ChatGPT-5.5. The generated images were constructed using multivariable prompts considering fire location, scale, and cause, and unsuitable samples were screened using a pretrained fire detection model. YOLOv8n, YOLOv11n, and RT-DETR were trained under 48 dataset–detector conditions and evaluated using fixed validation and test datasets. The results showed that generated-image-based training generally maintained or improved detection performance compared with the original and conventional augmentation conditions. In particular, selected ChatGPT-4.o-based YOLOv11 conditions showed statistically supported improvements over matched augmentation conditions, with increases of +0.052 in Precision, +0.031 in Recall, +0.065 in mAP@0.5, and +0.038 in mAP@0.5:0.95. LPIPS and t-SNE analyses indicated that the generated images formed structured perceptual and feature-space distributions relative to real fire images. Scenario-based inference using location-specific video frames also showed stable model responses in several complex indoor fire environments. These findings suggest that validated generative AI-based images can supplement the limited visual diversity of real fire datasets and improve the robustness of vision-based fire detection models.

Fire doi: 10.3390/fire9070272

Authors: Mei Zhao Guoqiang Dong Xu Lu Yajie Zhao Jingjing Gao Xinxin Wei Chenhui Xu Yu Liu Lianqiang Li Yachao Wang

A novel material, chitosan oligosaccharide@melamine polyphosphate (CMP), with enhanced flame-retardant and hydrophobic properties, was synthesized by cross-linking melamine polyphosphate (MPP) with chitosan oligosaccharide. Compared with MPP, the CMP overcomes its inherent drawbacks when used as a flame retardant in polylactic acid (PLA) composites, namely the high loading demand and unsatisfactory interfacial compatibility with the polymer matrix. The results demonstrated that the peak heat release rate (p-HRR) dropped significantly in comparison to pure PLA, from 304.69 kW·m−2 to 210.39 kW·m−2, while the fire performance index (FPI) increased from 0.1 to 0.48 s·m−2·kW−1. Furthermore, the fire growth index (FGI) decreased from 1.51 kW·m−2·s−1 to 1.03 kW·m−2·s−1. Additionally, the CMP demonstrated enhanced thermal stability, making the pyrolysis activation energy Eα increase from 135.04 to 191.97 kJ/mol during 308~416 °C by pyrolysis kinetics. Compared to composite PLA incorporating pristine MPP, the CMP-modified counterpart exhibits superior mechanical properties and significantly enhanced hydrophobicity, evidenced by a maximum water contact angle reaching 93.96°. It provides a strategy for adapting phosphorus-based flame retardants for PLA, thereby broadening their applicability across diverse scenarios.

Fire doi: 10.3390/fire9070273

Authors: Robert Lepadatu Felicia Michis Parikshit N. Mahalle Luminita Moraru

Spatial chromatic instability is currently one of the most robust methods for improving solutions proposed for image-based fire detection systems. Real flames exhibit erratic, turbulent local color variations, providing a more reliable discriminative signal than global color information alone, especially in visually ambiguous non-fire situations. This study proposes a generalizable feature representation based on the Spatial Chromatic Instability Index (ICCS) to measure local RGB variations (ICCSR, ICCSG, ICCSB, and ICCST). Two public datasets comprising both fire image files and non-fire imagery were used. The Hilbert–Schmidt Independence Criterion (HSIC) and Silhouette coefficient analysis were used to quantify the statistical dependence between feature sets and the resulting cluster separation. To evaluate the practical discriminatory performance of spatial chromatic instability, three classifiers, i.e., Logistic Regression, Linear SVM, and Random Forest, were employed. To verify the proposed approach’s effectiveness, three deep learning models, Swin Transformer, MobileViT, and ViT-Base-16, were also employed for cross-checking. Performance metrics demonstrated that integrating ICCS features into global color features improved classification. Logistic Regression performed best overall on the Kaggle dataset when local ICCS features were included, achieving an accuracy of 0.935 and an F1-score of 0.958. For the Mendeley dataset, Linear SVM achieved an accuracy of 0.862 and an F1-score of 0.881. The ICCS is a robust, easy-to-understand, and fast approach for identifying fires. It has real potential in early warning systems, mainly due to its limited requirements for computing power.

Fire doi: 10.3390/fire9070271

Authors: Jana Jaďuďová Stanislava Gašpercová Linda Makovická Osvaldová Lukáš Valla

Wood-processing industrial facilities in which Norway spruce wood (Picea abies) is processed and where products such as sawdust, wood chips or wood are generated are considered high-risk operations from the perspective of fire safety and explosion hazards. This is due to the combination of combustible material, fine particulate matter, ignition sources, and the potential for dust explosions. In this article, we focused on three different fractions of spruce wood (Picea abies) commonly present in the wood-processing industry: sawdust, wood chips, and compact wood. Experimental measurements were carried out under laboratory conditions in accordance with ISO 871. Ignition temperature, flash-ignition temperature, and activation energy are key parameters that determine the susceptibility of spruce wood, sawdust, and wood dust to ignition. Fine wood fractions exhibit lower activation energy and lower ignition temperatures, which increases the probability of combustion initiation. The activation energy for spontaneous ignition of sawdust was 45.1 kJ·mol−1, compared with 66.5 kJ·mol−1 for compact wood and 31.9 kJ·mol−1 for wood chips. The activation energy for the flash point of sawdust was 48.5 kJ.mol−1, for wood chips was 36.8 kJ.mol−1 and for compact wood was 44.9 kJ.mol−1. In combination with airborne wood dust, these conditions create a significant potential for fire development and dust explosions in wood-processing industrial facilities.

Fire doi: 10.3390/fire9070270

Authors: Jingnan Wu Zhenhua Wang Qinghai Liu Juncheng Jiang Liang Ma Mingguang Zhang Yong Pan Ru Zhou Lei Ni Meng Li Kaifeng Wang

Growing interest in transporting hydrogen via natural gas pipelines highlights the need to understand flame characteristics during accidental leakage. However, limited literature is available on addressing the flame horizontal projection length of hydrogen-blended natural gas jet fires under inclined conditions. Therefore, a series of experiments was conducted to investigate inclined H2/CH4 jet fires, with methane used as a surrogate for natural gas. Experiments with hydrogen content ranging from 0% to 20% were performed to examine the effects of inclination angle (0°, 30°, 45°, 60°, and 90°), nozzle diameter (2, 3, and 4 mm), and gas flow rate (4–25 L/min) on the flame morphological characteristics. It was found that the flame color evolves from a transparent blue base to a yellow luminous tip with increasing hydrogen content or fuel exit velocity, accompanied by soot enrichment in the luminous region. The flame horizontal projection length was quantified under different conditions. Results show it is only slightly affected when the hydrogen content is below 20%, whereas it increases with fuel exit velocity and nozzle diameter, and decreases with inclination angle. An explicit model was proposed by introducing the dimensionless heat release rate (Q˙*), which predicts the flame horizontal projection length with good agreement with experimental data. The findings provide a basis for the safety design and risk assessment of hydrogen-blended natural gas pipelines.

Fire doi: 10.3390/fire9070269

Authors: Michael B. Tiller Brian P. Oswald Alyx S. Frantzen I-Kuai Hung Yuhui Weng

Ash-free net heat content (AF-NHC) represents the combustible heat content of plant biomass and is an important parameter in fire behavior and fire effects modeling. Despite its widespread use, little information exists regarding seasonal and regional variation in AF-NHC among common woody fuels of the southeastern US. This study quantified seasonal and regional variation in AF-NHC among five common woody species in eastern Texas: yaupon (Ilex vomitoria), greenbrier (Smilax spp.), eastern red cedar (Juniperus virginiana), Chinese privet (Ligustrum sinense), and escarpment live oak (Quercus fusiformis). Foliage samples were collected during the dormant and growing seasons across the Pineywoods, Post Oak Savannah, and Blackland Prairie ecoregions and were analyzed using oxygen bomb calorimetry. Linear mixed-effects models evaluated species, season, and species × season effects while accounting for regional variation. AF-NHC ranged from 17.35 to 19.92 MJ kg−1 and differed significantly among species and seasons, with distinct species-specific seasonal trajectories (p < 0.05). Regional variation accounted for approximately 41% of total model variance, indicating that environmental conditions influence fuel thermal properties. AF-NHC was greatest in yaupon and red cedar, intermediate in privet and greenbrier, and lowest in live oak. Although AF-NHC likely exerts less influence on fire behavior than fuel consumption and the rate of spread, species-specific differences in combustible heat content may contribute to variation in potential heat release and fuel combustibility. These findings provide baseline AF-NHC values for common eastern Texas woody fuels and improve the understanding of spatial and temporal variation in fuel thermal properties relevant to fire effects and wildfire hazard assessment.

Fire doi: 10.3390/fire9070268

Authors: Gian Marco Ludovici Paola Amelia Tassi Alba Iannotti Colomba Russo Francesco Gargallo di Castel Lentini Mostafa Mohammed Atiyah Sijo Asokan Simona Maiello Irene Stilo Federica Orazzo Vito Graziano Saeed Bin Hadher JohnBaptist Galiwango Andrea Malizia

Wildfires are increasingly complex emergencies driven by climate variability, the expansion of wildland–urban interfaces, and the interaction between fire events and hazardous environments. These factors pose significant challenges for emergency management, particularly in the presence of cascading effects and multi-hazard interactions. This review examines the potential contribution of Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) frameworks to wildfire emergency management, focusing on preparedness and operational response. A narrative analysis of interdisciplinary literature was conducted to identify conceptual and operational overlaps between fire science and CBRNE-based approaches, with particular attention to command structures, hazard assessment, and response coordination. The analysis indicates that wildfire management systems often remain fragmented, with variability in procedures, training, and the integration of monitoring technologies. Evidence from CBRNE operational models suggests that structured command systems, field-based analytical capabilities, and interoperable procedures support improved situational awareness and decision-making. The review highlights how selected CBRNE principles, including structured command systems, zoning strategies, hazard characterization, and interoperability mechanisms, may address persistent gaps in complex wildfire emergency management, providing a basis for improved coordination, operational effectiveness, and system resilience.

Fire doi: 10.3390/fire9070267

Authors: Yongle Yan Yichao Zhao Jiuwu Hui

Accurate multi-channel gas concentration prediction is very important for coal mine safety. However, the dynamic reconstruction of the sensor network often interferes with the input sequence. Existing models face a critical trade-off: channel-independent models are robust to sequence changes but ignore spatial coupling, while channel-dependent models overfit fixed sequences, leading to performance collapse during rearrangements. This paper presents a gas concentration prediction framework based on channel permutation-invariant interaction (CPiRi) to reconcile these limitations. CPiRi employs a spatio-temporal decoupling architecture where a frozen univariate pre-trained encoder independently extracts temporal features to ensure sequence robustness. Subsequently, a permutation-equivariant spatial module utilizes self-attention to model inter-channel gas emission relationships based on data content rather than positional indices. To achieve true permutation invariance, we introduce channel-shuffling regularization during training, forcing the model to learn content-driven relational reasoning. Evaluations on 15 real-world Chinese coal mine datasets demonstrate that CPiRi achieves highly competitive accuracy and consistently outperforms mainstream baselines in both prediction precision and structural adaptability. This study offers a robust technical pathway for gas monitoring in dynamic environments, substantially improving the reliability of intelligent mine safety systems.

Fire doi: 10.3390/fire9070266

Authors: Evgenii I. Ponomarev Evgeny G. Shvetsov

A quantitative analysis of larch-dominated Siberian forest regions was conducted to evaluate wildfire characteristics in relation to Fire Radiative Power (FRP), long-term meteorological dynamics, and FRP range ratios. The results were validated against empirical stand mortality data spanning the period 2001–2024, obtained from the Global Forest Change dataset. Spatiotemporal burn characteristics were derived from the standard MODIS burned area product, while FRP data were extracted from the corresponding thermal anomalies product. Increasing trends in extreme FRP values were observed (4.5–17.9% of annual fire pixels), indicating that high-intensity fires progressively drive tree stand mortality statistics (R2 = 0.58, p < 0.01). Seasonal anomalies of the Duff Moisture Code (DMC), surface soil and litter moisture, and the Standardized Precipitation Evapotranspiration Index (SPEI) were the primary predictors of both wildfire intensity and tree cover mortality. Spatiotemporal analysis of FRP and tree cover mortality revealed that the most pronounced positive trends were concentrated in the central and northeastern forest regions of Siberia, which also exhibit high mean FRP values. These regions also experienced intensifying drought, as evidenced by the analysis of meteorological data. Consequently, under projected regional climate change, an escalating prevalence of high-intensity forest fires is anticipated to induce severe, potentially irreversible degradation of these forest stands and ecosystems.

Fire doi: 10.3390/fire9060265

Authors: Qingnian Deng Jingwei Liang Shihui Zhou Zekai Guo Liyan Niu Yuhao Huang Liang Zheng Yile Chen

Fire is the core safety threat to the survival and development of timber-framed buildings, and passive fire prevention intervention is the core foundation of fire protection systems for timber-framed buildings. Existing reviews suffer from limitations such as incomplete scenario coverage, insufficient breakdown of intervention mechanisms, and a lack of methodological standardization. This study strictly followed the PRISMA 2020 systematic review guidelines, searching the relevant literature from January 2016 to April 2026 on the Web of Science, Scopus, and Science Direct databases. After standardized screening, 89 valid articles were finally included and a systematic study was conducted through bibliometric analysis, keyword visualization, and multi-dimensional classification coding. The results show that the number of publications in this field has been continuously increasing from 2016 to 2025, with China accounting for 31.46% of the total, ranking first globally. The study constructed a core intervention mechanism system for passive fire prevention in timber-framed buildings, covering four categories: intrinsic flame-retardant modification, isolation protection, structural optimization, and spatial control. The working principles, application effects, advantages and disadvantages, and engineering application scenarios of each mechanism were clarified. This study systematically sorts out the core intervention mechanisms of passive fire prevention in timber-framed buildings, clarifies the research status and development trends in this field, and can provide evidence-based support for the design optimization, technology development, and engineering practice of passive fire protection for timber buildings.

Fire doi: 10.3390/fire9060264

Authors: Valentin Waeselynck David Saah

Background: Some widely used wildland fire behavior models, like the Fire Area Simulator (FARSITE), propagate fire fronts by computing the front-normal velocity (spread rate) as a function of local inputs and the front-normal direction. Such models are sometimes observed to cause the collapse of crown fires into sharp wedge shapes that eliminate heading fire behavior. Aims: We set out to document this phenomenon and, more generally, understand the relationships between fire shapes and spread rate functions. Methods: The phenomenon is studied both mathematically and through simulation experiments. Non-smooth fire fronts are theorized mathematically by an Eikonal partial differential equation (H(x,τ,Dτ)=1), where the unknown τ(x) is the time-of-arrival function and the Hamiltonian H(x,t,p) is positively homogeneous and possibly non-convex in p; convex analysis is used to study viscosity solutions in constant conditions. Results: We show that a fire spread model preserves the smoothness of fire fronts if and only if it is equivalent to using the Huygens principle. Nontrivially, this is equivalent to a convexity criterion on the inverse spread rate profile, which is then the polar dual of the Huygens wavelet; this corresponds to Hamiltonian–Lagrangian duality. The relevance of smoothness-destroying models to crown fire is debated. Exact analytical formulas are derived for fire growth in constant conditions. Conclusions: Our understanding of fire spread models is improved by solving the spread equations in more general ways than previously known. In particular, the collapse of heading crown fires into sharp shapes is now explained. Smoothness-destroying spread models cannot be simulated by algorithms based on travel time like cellular automata; their general well-definedness remains an open question. Fire modelers can use these findings to guide their search for improved crown fire models, and more generally to verify the accuracy of numerical implementations.

Fire doi: 10.3390/fire9060263

Authors: Qirui Wang Qinpei Chen Xiaoying Zhang Zhuoer Sun

In recent years, the rapid expansion of low-temperature facilities—such as cold storage and indoor ice and snow venues—has underscored their pronounced vulnerability to fire, as evidenced by multiple severe incidents. Due to their distinct environmental conditions, existing theoretical frameworks, technical approaches, and standards exhibit limited applicability. Consequently, the fire risk characteristics of such facilities remain insufficiently defined, and systematic methods for hazard identification and assessment are lacking. This study conducts a detailed analysis of fire incident data from representative low-temperature facilities to identify the fire risk characteristics across all lifecycle stages, including construction, renovation and expansion, operation, maintenance, and demolition. An integrated framework combining the WBS/RBS (Work Breakdown Structure/Risk Breakdown Structure) matrix and complex network (CN) methods is then proposed to establish a structured methodology for full lifecycle fire hazard identification and classification. The results address critical gaps, including the absence of clearly defined lifecycle fire risk profiles and a robust scientific basis for hazard identification, and provide a technical foundation for lifecycle fire risk management in low-temperature facilities.

Fire doi: 10.3390/fire9060262

Authors: Yu Sun Zi-Hao Deng Yao-Quan Yang Xiao-Chao Pu Li-Wei Li Rong She Xiao-Yan Yang

Disentangling the combined effects of heat and ash in natural forest fires is challenging, hindering understanding of soil microbial post-fire responses. A 90-day simulated fire experiment with 16S rRNA sequencing monitored bacterial communities and functional potential in topsoil (0–10 cm) and subsoil (10–20 cm) under seven treatments: blank control/BC, dry ash/DA, wet ash/WA, low-intensity heating/LH, high-intensity heating/HH, charcoal smoldering combustion/CSC, and Fire, with samples collected every ten days. Results: (1) α diversity declined mainly in the topsoil, with reductions of 12.04–19.82% for Shannon, 1.23–2.86% for Simpson, and 16.03–31.34% for the Chao index. Subsoil only declined under CSC. (2) Both heating and ash treatments increased the relative abundance of low-abundance and endemic taxa. Heating significantly enriched thermotolerant, xerotolerant, and oligotrophic taxa, such as Ramlibacter. (3) Topsoil heating treatments separated from BC (p ≤ 0.01), ash clustered with BC; pH and water content drove differentiation (p ≤ 0.05). (4) Topsoil predicted function potential showed early suppression (0–20 d), mid recovery (30–60 d), and late enhancement (70–90 d) for most treatments, except WA with sustained suppression. Heat determines disturbance depth and initial bacterial loss, while ash reshapes soil properties to influence community reassembly, acting as sequential but distinct environmental filters, providing a framework for post-fire bacterial community reorganization.

Fire doi: 10.3390/fire9060261

Authors: Izabella Downar Bakalarczyk Mário Augusto Pires Vaz Ygor Freitas de Almeida

Mapping wildfire patterns in Wildland–Urban Interface (WUI) areas is a fundamental tool for fire management and prevention, particularly in regions where urban expansion occurs in close proximity to natural vegetation. This mapping approach makes it possible to identify critical zones and to support more effective interventions adapted to the specific conditions of each territory. This work analyzed wildfires in the state of Tocantins, Brazil, using detailed geospatial data and advanced analysis techniques and statistics to characterize the dynamics of burned areas. Data used for the project were retrieved from MapBiomas and the Geoprocessing Laboratory of the Public Ministry of Tocantins (LABGEO), applying logistic regression models to explore the relationship between the distance of WUIs and the frequency of wildfires. The methodology covered the spatial distribution of fires and the different dynamics observed by type and size of burned area, allowing for a more detailed analysis. The results indicated significant variations in the proportion of burned areas inside and outside the WUIs, suggesting that proximity to these interfaces plays a critical role in the occurrence pattern of fires. Notably, Palmas, the state capital, stood out as one of the municipalities with the highest concentration of impacts in WUI areas, highlighting the relevance of these zones in environmental risk management. The study emphasizes the importance of adopting regional approaches that consider local specificities in the management and prevention of wildfires. The integration of geospatial data with robust statistical methodologies can guide more effective management strategies, assisting in the planning of public policies adapted to the socio-environmental dynamics of Tocantins.

Fire doi: 10.3390/fire9060260

Authors: Roland El Hage Abdoulay Sadou Ahmadou Roufaou Uriche Michael Nzouotoup Placide Uwizeyimana Rodolphe Sonnier

There is growing interest in the development of sustainable thermal insulating materials from renewable resources, a strategy which can stand as an alternative to conventional petroleum-based insulating materials. In this study, bio-based porous insulating materials derived from partially hydrolyzed collagen (rabbit-skin) and containing ammonium polyphosphate (APP) as flame retardant and miscanthus fibers as reinforcement are prepared. Four freeze-dried formulations were prepared: pure partially hydrolyzed collagen (COL), partially hydrolyzed collagen with APP (COL-APP), partially hydrolyzed collagen with miscanthus particles (COL-M) and a ternary formulation that included both additives (Col-APP-M). The density, porosity, thermal conductivity, specific heat capacity, compressive mechanical properties and fire behavior were evaluated. The neat collagen foam had the lowest density (122 kg·m−3), highest porosity (91%), and lowest thermal conductivity (0.045 W·m−1·K−1). The addition of APP and/or miscanthus increased density and showed limited change in thermal conductivity, which remains comparable with insulating materials (0.0445–0.0510 W·m−1·K−1). Specific heat capacities of partially hydrolyzed collagen foams were also relatively high (1319–1390 J·kg−1·K−1) as compared to some other typical insulating materials. Mechanical experiments demonstrated that APP had considerably improved the compression stiffness and strength through the physical crosslinking and densification effects in the partially hydrolyzed collagen network. Analysis of fire behavior with both Pyrolysis Combustion Flow Calorimetry (PCFC) and cone calorimetry further indicated that the addition of APP yielded improved flame retardancy with a very low heat release. These results showed that partially hydrolyzed collagen-based foams reinforced by APP and lignocellulosic particles are sustainable thermal insulation materials with desired thermal performances, improved mechanical stability, and enhanced flame retardancy.

Fire doi: 10.3390/fire9060259

Authors: Hongpeng Qiu Eric Wai Ming Lee Lingling Hu Xiangping Xian

This study develops an integrated computational framework to assess the passage efficiency of a dam crest serving as a critical inter-regional corridor following a severe explosion event. The framework combines a physics-based damage model with an agent-based cellular automata (CA) approach that incorporates pedestrian behavioral heterogeneity. The damage model conceptualizes three concentric zones: a complete fragmentation zone (0–1.5 m) with total material disintegration, a primary damage zone (1.5–5 m) following an exponential decay in structural integrity, and a secondary damage zone (5–20 m) governed by a power-law attenuation of fragmentation effects. Pedestrian behavior is parameterized by the Allowable Conflict Coefficient (ACC), the inverse of interpersonal friction, and the Emergency Level (EL), which scales the desired velocity. Extensive simulations under stochastic and targeted impact scenarios reveal a consistent evacuation performance hierarchy: Center (C) > Bottom-Left (BL) > Top-Left (TL) > Bottom-Right (BR) ≈ Top-Right (TR). Exit-proximal damage (TR, BR) increased evacuation time by up to 85% compared with central impacts. Results demonstrate a strong coupling between physical friction and urgency: the “faster-is-faster” effect is maximized under low friction (high ACC), while high friction not only suppresses the benefits of elevated EL but can also induce “faster-is-slower” phenomena under extreme conditions. These findings underscore that optimal evacuation strategies depend critically on both impact location and crowd behavior management, providing actionable insights for emergency planning and highlighting the importance of conflict mitigation in enhancing infrastructure resilience. The proposed framework thus offers a versatile and validated simulation tool for emergency planners to proactively assess and optimize evacuation strategies under various damage scenarios.

Fire doi: 10.3390/fire9060258

Authors: Qinyan Gu Chao Xi Weili Kou Zhengshen Huang Jiangxia Ye Qiuhua Wang

Understanding post-fire visible greenness change is important for assessing spatial heterogeneity in mountainous burned landscapes, but satellite observations often cannot capture local variation. This study developed a workflow using Unmanned Aerial Vehicle (UAV) Red–Green–Blue (RGB) imagery for RGB-interpreted burn severity classification and Green Leaf Index (GLI)-derived visible greenness change analysis three years after fire. The workflow integrated object-based Random Forest (RF) classification, bi-temporal GLI difference (ΔGLI) detection, and terrain-stratified analysis under RGB-only conditions. Object-based multi-feature representation, including a 41-dimensional (41D) feature set of color, texture, and gradient metrics, supported local burn severity mapping, although performance gain over the 23-dimensional (23D) set was modest and not statistically significant. The burned area was dominated by high and moderate severity classes. GLI-derived analysis showed limited visible greenness increase (mean ΔGLI = 0.0058), with slightly more than half of pixels being positive; high severity areas had higher ΔGLI, while low severity areas showed limited or negative values. ΔGLI also varied across terrain, being higher on steeper slopes, mid-to-upper elevations, and east-facing aspects. The workflow provides a practical local-scale approach for post-fire analysis using high-resolution UAV RGB imagery, with results interpreted as case-specific visible greenness patterns rather than comprehensive ecological recovery.

Fire doi: 10.3390/fire9060257

Authors: Hui Zhang Lifu Shu Qifei Wang Mingyu Wang Wanzhou Chen

This study, based on multi-source data fusion and risk index models, has developed a comprehensive methodological system for evaluating the risk of forest fires caused by human factors. The system starts with four dimensions, i.e., exposure, hazard factors, vulnerability, and prevention and control capabilities, and constructs an evaluation framework with 19 secondary indicators. It also establishes single-category risk index models for four types of dominant fire sources: agricultural activities, religious ceremonies, tourism, and power distribution lines. Through weighted synthesis and exponential smoothing algorithms, it achieves daily dynamic risk forecasting. The research took the typical forest areas in the Mentougou, Changping, and Yanqing districts of Beijing as the application demonstration areas, collecting meteorological data, geographic information data, risk census ledgers, online hiking trajectories, and 2530 social survey questionnaires to complete the local parameter calibration and validation of the model. The retrospective analysis of 22 typical human-caused fire cases from 2018 to 2025 shows that the risk percentile of the ignition points in all cases was above 87.8%, indicating that the model has a good risk identification capability. Based on the evaluation results, differentiated control measures for different types of fire sources were proposed. The research results have been integrated into Beijing’s forest fire risk monitoring and early warning system, providing a scientific tool for the refined management of human-caused fire sources.

Fire doi: 10.3390/fire9060256

Authors: Heng Li Jialong Zhang Jingwen Yang Chenkai Teng Kai Luo Kaiping Sun

The Greater Khingan, a key cold-temperate coniferous forest region in northern China, is frequently affected by forest fires with severe ecological and economic impacts. The study investigates the influence of key environmental and anthropogenic drivers on forest fire susceptibility and evaluates multiple machine-learning approaches for regional fire assessment. Using 2001–2018 fire point data and multi-source remote sensing data, we integrated 13 driving factors across four dimensions: meteorology, topography, vegetation, and human activities. Collinear variables were screened using the Variance Inflation Factor (VIF). Three machine learning models—Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM)—were constructed to assess the long-term potential risk of forest fire occurrence. Driving mechanisms were analyzed using standardized regression coefficients and the SHapley Additive exPlanations (SHAP) interpretable algorithm, and spatial distribution maps of regional forest fire risk were generated based on the optimal model. Among the three models, RF achieved the highest predictive accuracy, with an accuracy of 0.919 and an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.966, significantly outperforming LR and SVM. SHAP analysis reveals that forest fires are primarily driven by climatic factors (Pres and Prec as core drivers), regulated by topographic factors, and weakly affected by human factors. The proposed framework provides an effective tool for long-term forest fire susceptibility assessment by combining robust predictive performance with interpretable model outputs. The findings provide scientific support for long-term strategic forest fire risk zoning, regional firefighting resource allocation, and the formulation of differentiated prevention and control strategies, and also offer methodological references for forest fire prediction in other cold-temperate forest regions in China.

Fire doi: 10.3390/fire9060255

Authors: Wei Zhang Kai Wang Xiaosong Song

Aircraft cargo compartment fires constitute a significant type of aviation fire, posing a grave threat to aviation safety. To guard against and respond to such fires, existing aircraft cargo compartments are equipped with smoke detection fire detectors, which rely on perceiving changes in smoke transmittance to determine the onset of a fire. However, these detectors offer relatively low recognition accuracy and cannot provide a direct visual representation of the fire. In this work, we introduce a fire recognition method built on image sensors and a deep learning model. In light of the irregular shapes of flames and smoke, an improved interactive triplet attention mechanism (ITAM) is integrated into the You Only Look Once version 5 (YOLOv5) model, enhancing the model’s recognition accuracy. Furthermore, the original Neck structure is replaced with an Asymptotic Feature Pyramid Network (AFPN), improving the model’s ability to recognize small targets, which is particularly useful for detecting flames and smoke early in a fire. This paper further improves the model’s recognition accuracy by introducing the Focaler-IoU loss function, which balances the feature learning of hard and easy samples. Therefore, the network model in this paper is named IATF-YOLO. Ablation experiments demonstrate that our algorithm improves accuracy by 2%, while comparative experiments with several mainstream baseline models show that our algorithm achieves a 0.7% accuracy improvement, with a final peak accuracy of 93.6%.

Fire doi: 10.3390/fire9060254

Authors: Haiwen Xu Daochun Huang Peng Li Xincheng Quan Tianhao Peng

Wildfires can threaten the safety of transmission towers by degrading galvanized coatings and reducing the load-bearing capacity of steel members exposed to elevated temperatures. This study investigates the thermal damage evolution and structural response of transmission tower legs under localized wood-crib fire exposure through a combined experimental and numerical approach. A 1:4 scale tower-leg model was subjected to a single wood-crib fire exposure for approximately 20 min, during which temperature histories, surface damage patterns, and deformation of the fire-exposed members were recorded. The results show that the maximum measured temperature reached 803 °C and decreased approximately linearly with height, leading to distinct damage zones along the tower leg. The galvanized coating exhibited progressive degradation, including oxidation, melting, cracking, and local peeling, while the surface appearance changed from bright silver to black and finally to gray-white with reddish-brown areas in severely heated regions. A temperature-informed elastic–plastic finite element model was then used to interpret the global structural response. The analysis indicates that elevated temperature reduced the stiffness and load-bearing capacity of the fire-exposed side, causing deformation concentration and torsional distortion in diagonal members. The proposed framework provides a practical basis for post-fire damage identification and rapid structural assessment of transmission towers in wildfire-prone regions.

Fire doi: 10.3390/fire9060253

Authors: Tianyi Yang Xiaobo Wang Wenhao Deng Sichen Liu Hanzhong Deng Yafei Shan

Coal spontaneous combustion (CSC) remains a major hazard in coal mining. Research on CSC has largely focused on macromolecular structures, while the behavior of low-molecular-weight compounds remains unclear. Using B3LYP/6-311G density functional theory, this study systematically reveals thirteen microscopic reaction pathways, active sites, and the energy barrier order of pentanol during coal spontaneous combustion. The oxidation proceeds via thirteen multi-step pathways involving bond breaking and formation, with the dominant reaction being oxygen attack on the -CH2OH group to produce pentanal (CH3CH2CH2CH2CHO) and water as the main products. The priority order of thirteen reaction pathways between pentanol and oxygen was established as: Path 6 > Path 3 > Path 8 > Path 5 > Path 4 > Path 1 > Path 11 > Path 10 > Path 9 > Path 12 > Path 7 > Path 2. The results reveal the multi-step bond-breaking and formation mechanism at the molecular level, providing a fundamental theoretical framework for understanding the radical chain oxidation mechanism of low molecular weight compounds in CSC.