Fire

This study focuses on firefighters’ relationship with different types of change in their profession and what barriers and facilitators might contribute to how they respond. Informed by the Force Field analysis of change, interviews were conducted to better understand what specific barriers and facilitators contribute to their views on types of change and the level of influence they carried. Twenty-five interviews were conducted with firefighters from a variety of backgrounds, including different ages, genders, ranks, and experience levels for both career and volunteer firefighters. Thematic analysis identified different responses to four common rationales that helped to explain the acceptance or dismissal of changes. These were as follows: (1) openness or apprehension towards change; (2) the results of a cost–benefit analysis that considered financial and manpower limits, perceived legitimacy of the problem, and efficacy of the solution; (3) reference to past experiences with changes that had failed or succeeded; and (4) trusted messengers that respected the chain of command were preferred. These themes are applicable across multiple types of changes, including technological and cultural adaptation. However, they also reveal challenges that may emerge due to friction with firefighters’ professional identities and traditional masculine norms. The patterns identified here can help to inform future efforts to implement changes and to anticipate likely points of friction or motivation that can be leveraged. Full article

Crown fire hazard assessment and behavior prediction in dwarf pine (Pinus pumila) forests are dictated by the amount of canopy fuel available, topography, and weather. In this study, we collected data on CFL (available canopy fuel load), CBD (canopy bulk density), and CBH (canopy base height) through the destructive sampling of dwarf pine trees in the Greater Khingan Mountains of Northeast China. Allometric equations were developed for estimating the canopy’s available biomass, CFL, and CBD to support the assessment of canopy fuel. Three burning scenarios were designed to investigate the impact of various environmental parameters on fire behavior. Our findings indicated that the average CFL of a dwarf pine was 0.36 kg·m⁻², while the average CBD was measured at 0.17 kg·m⁻³. The vertical variation trends of both CFL and CBD exhibited consistency, with values increasing progressively from the bottom to the top of the tree crown. Fire behavior simulations indicated that the low CBH of dwarf pine trees increased the likelihood of crown fires. Various factors, including wind speed, slope, and CBH, exerted considerable influence on fire behavior, with wind speed emerging as the most critical determinant. Silvicultural treatments, such as thinning and pruning, may effectively reduce fuel loads and elevate the canopy base height, thereby decreasing both the probability and intensity of crown fires. Full article

Despite increased focus on resilient planning and construction design in areas prone to wildfire impacts, recent research has found inconsistent approaches, a lack of evidence-based performance criteria, and limited suitable code-based verification methods for use in wildfire contexts. These limitations serve to reduce the potential effectiveness of measures intended to improve wildfire community and build resilience. The lack of suitable verification methods is particularly problematic in Australia, where complex building code requirements associated with enhanced wildfire resilience have been extended to hospitals, child care facilities, schools, and other assembly buildings. To address this issue, this paper proposes the Wildfire Expected Risk to Life and Property (WERLP) verification method. As a holistic absolute probabilistic verification method, WERLP can be applied to both building and urban design contexts within the Australian jurisdiction. The application of WERLP is demonstrated using the case study of a new hospital development. Full article

The disintegration of near-limit flames propagating through the gap of Hele–Shaw cells has recently become a subject of active research. In this paper, the flamelets resulting from the disintegration of the continuous front are interpreted in terms of the Zeldovich flame balls stabilized by volumetric heat losses. A complicated free-boundary problem for 2D self-drifting near circular flamelets is reduced to a quasi-1D model. The quasi-1D formulation is then utilized to obtain the locus of the flamelet velocity, size, heat losses, and Lewis numbers at which the self-drifting flamelets may exist. Full article

Forest fires pose severe ecological, climatic, and socio-economic threats, destroying habitats and emitting greenhouse gases. Early and timely warning is particularly challenging because fires often originate from small-scale, low-temperature ignition sources. Traditional monitoring approaches primarily rely on single-source satellite imagery and empirical threshold algorithms, and most forest fire monitoring tasks remain human-driven. Existing frameworks have yet to effectively integrate multiple data sources and detection algorithms, lacking the capability to provide continuous, automated, and generalizable fire monitoring across diverse fire scenarios. To address these challenges, this study first improves multiple monitoring algorithms for forest fire detection, including a statistically enhanced automatic thresholding method; data augmentation to expand the U-Net deep learning dataset; and the application of a freeze–unfreeze transfer learning strategy to the U-Net transfer model. Multiple algorithms are systematically evaluated across varying fire scales, showing that the improved automatic threshold method achieves the best performance on GF-4 imagery with an F-score of 0.915 (95% CI: 0.8725–0.9524), while the U-Net deep learning algorithm yields the highest F-score of 0.921 (95% CI: 0.8537–0.9739) on Landsat 8 imagery. All methods demonstrate robust performance and generalizability across diverse scenarios. Second, data-driven scheduling technology is developed to automatically initiate preprocessing and fire detection tasks, significantly reducing fire discovery time. Finally, an integrated framework of multi-source remote sensing data, advanced detection algorithms, and a user-friendly visualization interface is proposed. This framework enables all-weather, fully automated forest fire monitoring and early warning, facilitating dynamic tracking of fire evolution and precise fire line localization through the cross-application of heterogeneous data sources. The framework’s effectiveness and practicality are validated through wildfire cases in two regions of Yunnan Province, offering scalable technical support for improving early detection of and rapid response to forest fires. Full article

In response to the prevalent issues of short inhibition cycles and poor environmental compatibility in traditional inhibitors, this study prepared a new sepiolite-based composite inhibitor by loading imidazolium ionic liquid onto sepiolite. Through TG-DTG analysis, cone calorimeter experiments, and FTIR spectroscopy, we comparatively investigated the combustion characteristics of the composite inhibitor and its effects on the oxidation properties, inhibition performance, and active functional groups of coal samples. The results demonstrate that appropriate loading optimizes the thermal stability of sepiolite. Compared with conventional inhibitors, the composite exhibited the minimum weight loss rate at characteristic temperatures and achieved greater delays in critical temperature points of coal samples. The composite inhibitor delayed ignition time by 27–44 s compared to conventionally inhibited coal. The 3% [BMIM][BF₄]/sepiolite formulation showed CO emission peak intensity 3.02 times that of raw coal within 0–200 s, while reducing CO₂ production rate by 10.56% compared to MgCl₂-treated samples at 1000 s. The P_PFI exhibited maximum enhancement. Post-inhibition analysis revealed a 22–51% reduction in peak areas of active functional groups, indicating that the sepiolite-based composite achieves inhibition through synergistic physical and chemical interactions. Ultimately, a sepiolite-based composite inhibitor with environmental benignity was developed, whose inhibition performance is significantly enhanced compared to the traditional inhibitor MgCl₂. This research provides theoretical foundations for developing advanced inhibitor materials in coal mine applications. Full article

Pinus nigra Arnold, which is naturally widespread in mountainous and Mediterranean ecosystems, is a key species for reforestation due to its ecological and economic value. As climate change and changing fire regimes increase the wildfire risk, understanding its fire resilience has become critical. In this study, the morpho-physiological traits (thickness, roughness, moisture content) and flammability characteristics (ignition, heat release, mass loss, as determined in laboratory flammability tests) of the bark of P. nigra were investigated. The trees were selected based on their age (young vs. old) and fire exposure (burned vs. unburned). The bark thickness was significantly greater in older trees, while the bark moisture content was significantly lower in previously burned trees (p ≤ 0.05). The bark thickness correlated strongly with the ignition time, heat release, and mass loss. These results indicate that the age of the tree primarily affects the bark thickness and time to cambium death, while fire exposure primarily affects the bark moisture content, regardless of age. Understanding that the bark thickness and flammability play a key role in tree survival may aid in the selection of individuals or stand structures better suited to survive in fire-prone conditions and in the strategic planning of burns to reduce fuel loads without exceeding the mortality risk of younger or thinner-barked individuals. Full article

Given the critical importance of timely forest fire detection to mitigate environmental and socio-economic consequences, this research aims to achieve high detection accuracy while maintaining real-time operational efficiency, with a particular focus on minimizing computational complexity. We propose a novel framework that systematically integrates normalization, feature selection, adaptive oversampling, and classifier optimization to enhance detection performance while minimizing computational overhead. The evaluation is conducted using three distinct Canadian forest fire datasets: Alberta Forest Fire (AFF), British Columbia Forest Fire (BCFF), and Saskatchewan Forest Fire (SFF). Initial classifier benchmarking identified the best-performing tree-based model, followed by normalization and feature selection optimization. Next, four oversampling methods were evaluated to address class imbalance. An ablation study quantified the contribution of each module to overall performance. Our targeted, stepwise strategy eliminated the need for exhaustive model searches, reducing computational cost by 97.75% without compromising accuracy. Experimental results demonstrate substantial improvements in F1-score, AFF (from 69.12% to 82.75%), BCFF (61.95% to 77.91%), and SFF (90.03% to 96.18%) alongside notable reductions in False Negative Rates compared to baseline models. Full article

This study investigates the certification outcomes of intermediate-level ICs in The National Fire Service Academy in South Korea through a comprehensive quantitative analysis of their evaluated competencies. Using assessment data from 141 candidates collected from 2022 to 2024, we examine how scores on six higher-order competencies—comprising 35 sub-competencies—influence pass or fail results. Descriptive statistics, correlation analysis, logistic regression (a statistical model for binary outcomes), random forest modeling (an ensemble decision-tree machine-learning method), and principal component analysis (PCA; a dimensionality reduction technique) were applied to identify significant predictors of certification success. Visualization techniques, including heatmaps, box plots, and importance bar charts, were used to illustrate performance gaps between successful and unsuccessful candidates. Results indicate that competencies related to decision-making under pressure and crisis leadership most strongly correlate with positive outcomes. Furthermore, unsupervised clustering analysis (a data-driven grouping method) revealed distinctive performance patterns among candidates. These findings suggest that current evaluation frameworks effectively differentiate command readiness but also highlight specific skill domains that may require enhanced instructional focus. The study offers practical implications for fire training academies, policymakers, and certification bodies, particularly in refining curriculum design, competency benchmarks, and evaluation criteria to improve fireground leadership training and assessment standards. Full article

To investigate the distribution law of gas migration in the gob area of a fully mechanized mining face, the similarity principle was employed, combined with Darcy’s law for porous media seepage, to derive the similarity criteria for simulating gas migration in the gob. An experimental platform for a similar model of the gob area in a fully mechanized mining face was designed and constructed, enabling the regulation of ventilation modes, working face airflow velocity, and gas release in the gob. By adjusting the layout of the tailgate, airflow velocity of the working face, and gas release rate, experimental studies were conducted on the gas flow, gas migration, and variation of gas concentration at the upper corner under different airflow velocities in “U,” “U + I,” and “U + I” type ventilation modes. The results indicate that the ventilation mode determines the spatial variation law of airflow and gas migration in the gob; the airflow velocity of the working face governs the fluctuation degree and influence range of airflow and gas migration in the gob; and both the ventilation mode and airflow velocity affect gas accumulation at the upper corner. The “U + I” type ventilation mode is most effective in reducing gas concentration at the upper corner. Airflow velocities that are too low or too high are not conducive to gas emission at the upper corner, with the optimal control of gas concentration being achieved when the airflow velocity ranges from 1.5 to 2.5 m/s. The experimental results validate the distribution law of airflow and gas migration in the gob of a fully mechanized mining face, providing a basis for selecting ventilation process parameters for such mining operations. Full article

Fire is a disaster that seriously threatens people’s lives. Because fires occur suddenly and spread quickly, especially in densely populated places or areas where it is difficult to evacuate quickly, it often causes major property damage and seriously endangers personal safety. Therefore, it is necessary to detect the occurrence of fires accurately and promptly and issue early warnings. This study introduces YOLOv11-CHBG, a novel detection model designed to identify flames and smoke. On the basis of YOLOv11, the C3K2-HFERB module is used in the backbone part, the BiAdaGLSA module is proposed in the neck, the SEAM attention mechanism is added to the model detection head, and the proposed model is more lightweight, offering potential support for fire rescue efforts. The model developed in this study is shown by the experimental results to achieve an average precision (mAP@0.5) of 78.4% on the Dfire datasets, with a 30.8% reduction in parameters compared to YOLOv11. The model achieves a lightweight design, enhancing its significance for real-time fire and smoke detection, and it provides a research basis for detecting fires earlier, preventing the spread of fires and reducing the harm caused by fires. Full article

Burned area (BA) mapping and fire severity assessment are essential for understanding fire occurrence patterns, formulating post-fire restoration strategies and evaluating vegetation recovery processes. However, existing BA datasets are primarily derived from coarse-resolution satellite imagery and often lack sufficient consideration of fire severity. To address these limitations, this study utilized dense time-series Landsat imagery available on the Google Earth Engine, applying the qualityMosaic method to generate annual composites of minimum normalized burn ratio values. These composites imagery enabled the rapid identification of fire sample points, which were subsequently used to train a random forest classifier for estimating per-pixel burn probability. Pixels with a burned probability greater than 0.9 were selected as the core of the BA, and used as candidate seeds for region growing to further expand the core and extract complete BA. This two-stage extraction method effectively balances omission and commission errors. To avoid the repeated detection of unrecovered BA, this study developed distinct correction rules based on the differing post-fire recovery characteristics of forests and grasslands. The extracted BA were further categorized into four fire severity levels using the delta normalized burn ratio. In addition, we conducted a quantitative validation of the BA mapping accuracy based on Sentinel-2 data between 2015 and 2023. The results indicated that the BA mapping achieved an overall accuracy of 93.90%, with a Dice coefficient of 82.04%, and omission and commission error rates of 26.32% and 5.25%, respectively. The BA dataset generated in this study exhibited good spatiotemporal consistency with existing products, including MCD64A1, FireCCI51, and GABAM. The BA fluctuated significantly between 1985 and 2010, with the highest value recorded in 1987 (13,315 km²). The overall trend of BA showed a decline, with annual burned areas remaining below 2000 km² after 2010 and reaching a minimum of 92.8 km² in 2020. There was no significant temporal variation across different fire severity levels. The area of high-severity burns showed a positive correlation with the annual total BA. High-severity fire-prone zones were primarily concentrated in the northeastern, southeastern, and western parts of the study area, predominantly within grasslands and forest–grassland ecotone regions. Full article

Dust explosion prevention and mitigation of the consequences thereof require measurement of dust explosion parameters. Testing methods are defined by European and American standards, producing results in explosion chambers of a 1 m³ standard volume and, alternatively, 20 L. However, the results are influenced by some processes that are neglected by the standards, perhaps because it is believed that their effect is small in a 1 m³ chamber. But their effect becomes significant in a smaller 20 L chamber. Preconditioning of the system caused by dust dispersion itself, as well as the ignitor flame, is one such problem. The aim of this work is to further investigate the physical and chemical processes caused by dust preheating after an ignitor’s action. Analytical methods, such as STA, GC/MS and FTIR, were used to analyse the composition of the atmosphere after exposure of lycopodium dust, a natural material, to certain temperatures up to 550 °C in air and nitrogen. In the second step, gas samples were taken from the 20 L chamber after dispersion of lycopodium and ignition by two 5 kJ pyrotechnical ignitors. Depending on the temperature and atmosphere, various concentrations of CO, CO₂, H₂O, NO_x and organic compounds were measured. It was observed that the dispersed dust decomposed into mostly CO and CO₂ in the area near the ignitors, even in an atmosphere in which the oxygen concentration was lower than 2% by volume. The concentrations of other organic compounds were very low and included mostly methane, ethylene and acetaldehyde. However, when incorporating CO, the overall concentration of flammables was high enough to generate a hybrid mixture. Full article

Landsat time series data, which have become freely available in recent years, are commonly used to detect changes in land cover and monitor ecosystem disturbances. Thyme habitats are areas under protection due to their high ecological value. However, human activity leading to land use competition, mainly from overgrazing, poses an increased threat to these habitats. The impact of these disturbances is underreported, and their detection remains essential for thyme conservation. The island of Lemnos was chosen as the study area, because of the significant areas of thyme habitats, which are currently under pressure due to rural abandonment, desertification, overgrazing, and systematic fires in recent decades. A long-term Landsat time series was generated, and the Normalized Difference Vegetation Index (NDVI) was calculated. The change detection algorithm (BFAST) was used to detect and characterize significant changes (breakpoints) within the time series and compare them to local fire events. The analysis showed that Lemnos thyme habitats have been significantly reduced in size due to fires and their conversion to new grazing areas for livestock production. Measures should be taken to conserve thyme habitats with the participation of local stakeholders, including livestock farmers and beekeepers. Satellite monitoring techniques are important tools that could facilitate this conservation process. Full article

The imperative for high-performance protective materials has catalyzed the rapid evolution of polyurea (PUA) coatings, widely recognized for their mechanical robustness, chemical resistance, and rapid-curing properties. However, their inherent flammability and harmful combustion byproducts pose significant challenges for safe use in applications where fire safety is a critical concern. In response, significant efforts focus on improving the fire resistance of PUA materials through chemical modifications and the use of functional additives. The review highlights progress in developing flame-retardant approaches for PUA coatings, placing particular emphasis on the underlying combustion mechanisms and the combined action of condensed-phase, gas-phase, and interrupted heat feedback pathways. Particular emphasis is placed on phosphorus-based, intumescent, and nano-enabled flame retardants, as well as hybrid systems incorporating two-dimensional nanomaterials and metal–organic frameworks, with a focus on exploring their synergistic effects in enhancing thermal stability, reducing smoke production, and maintaining mechanical integrity. By evaluating current strategies and recent progress, this work identifies key challenges and outlines future directions for the development of high-performance and fire-safe PUA coatings. These insights aim to guide the design of next-generation protective materials that meet the growing demand for safety and sustainability in advanced engineering applications. Full article

Forests play a vital role in the global carbon cycle; however, the carbon sink capacity of African forests is increasingly threatened by wildfires, rising temperatures, and ecological degradation. This study analyzes the spatiotemporal dynamics of forest carbon fluxes across Africa from 2001 to 2023, based on multi-source remote sensing and climate datasets. The results show that wildfires have significantly disrupted Africa’s carbon balance over the past two decades. From 2001 to 2023, fire activity was most intense in the woodland–savanna transition zones of Central and Southern Africa. In countries such as the Democratic Republic of the Congo, Angola, Mozambique, and Zambia, each recorded burned areas exceeding 500,000 km², along with high recurrence rates (e.g., up to 0.7584 fires per year in South Sudan). These fire-affected regions often exhibited high ecological sensitivity and carbon density, which led to pronounced disturbances in carbon fluxes. Nevertheless, the Democratic Republic of the Congo maintained an average annual net carbon sink of 74.2 MtC, indicating a high potential for ecological recovery. In contrast, Liberia and Eswatini exhibited net carbon emissions in fire-affected areas, suggesting weaker ecosystem resilience. These findings underscore the urgent need to incorporate wildfire disturbances into forest carbon management and climate mitigation strategies. In addition, climate variables such as temperature and soil moisture also influence carbon fluxes, although their effects display substantial spatial heterogeneity. On average, a 1 °C increase in temperature leads to an additional 0.347 (±1.243) Mt CO₂ in emissions, while a 1% increase in soil moisture enhances CO₂ removal by 1.417 (±8.789) Mt. However, compared to wildfires, the impacts of these climate drivers are slower and more spatially variable. Full article

To enhance the understanding and management of fire risks in subway stations, this study aims to identify critical fire risk transmission paths using an integrated PSR–DEMATEL–ISM approach. A comprehensive evaluation framework is first constructed based on the Pressure–State–Response (PSR) model, systematically categorizing 22 influencing factors into three dimensions: pressure, state, and response. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is then employed to analyze the causal relationships and centrality among these factors, distinguishing between cause and effect groups. Subsequently, Interpretive Structural Modeling (ISM) is applied to organize the factors into a multi-level hierarchical structure, enabling the identification of risk propagation pathways. The analysis reveals five high-centrality and high-causality factors: fire safety education and training, completeness of fire management rules and regulations, fire smoke detection and firefighting capability, operational status of monitoring equipment, and effectiveness of emergency response plans. Based on these key drivers, six major transmission paths are derived, reflecting the internal logic of fire risk evolution in subway environments. Among them, chains originating from Fire Safety Education and Training (S6), Architectural Fire Protection Design (S7), and Completeness of Fire Management Rules and Regulations (S16) exhibit the most significant influence on system-wide safety performance. This study provides theoretical support and practical guidance for proactive fire prevention and emergency planning in urban rail transit systems, offering a structured and data-driven approach to identifying vulnerabilities and improving system resilience. Full article

Fire smoke significantly affects human health and air quality. The HYSPLIT dispersion model estimates the area impacted by smoke downwind, but the results are sensitive to input data. This study investigates the impact of different fire emission inputs on dispersion modeling results, focusing on three versions of the Wildland Fire Emissions Inventory System (WFEIS) used to initialize HYSPLIT. The three input datasets include MODIS (FEI_BASE), a combination of MODIS and MTBS (FEI_COMBO), and a version incorporating a cloud cover regression (FEI_COMBO+CC). Dispersion modeling results are compared across the western U.S. for 2013, 2016, and 2018, showing a variation of up to 200% in results depending on the emissions input. Model results are evaluated with ground-based PM_2.5 data and visible satellite imagery. The cloud cover regression improves the identification of fire days missed by FEI_BASE potentially impacting health effect studies. Correlations between modeled PM_2.5 and EPA data improve with FEI_COMBO+CC, particularly in 2013 and 2016, making it a stronger candidate for use in research on health effects. Despite some variability in RMSE, the higher correlation observed with FEI_COMBO+CC supports its use as a more accurate representation of fire-related PM_2.5 transport. Full article

Wildfires are increasing in frequency and severity, with Australia’s 2019–2020 Black Summer burning over 18 million hectares. Accurate prediction of wildfire behavior is essential for effective risk assessment and emergency response. This study presents a machine learning framework for predicting wildfire dynamics across Australia’s seven regions using the IBM wildfire dataset. Various Machine Learning (ML) models were evaluated to forecast three key indicators: Fire Area (km²), Fire Brightness Temperature (K), and Fire Radiative Power (MW). Lasso Regression consistently outperformed the other models, achieving an average RMSE of 0.04201 and R² of 0.29355. Performance varied across regions, with stronger results in areas like New South Wales and Queensland, likely influenced by differences in topography, microclimate, and vegetation. However, limitations include the exclusion of ignition sources such as lightning and human activity, which are critical for capturing the environment accurately and improving predictive accuracy. Future work will integrate these factors alongside more detailed weather and vegetation data. Practical implementation may face challenges related to real-time data availability, system integration, and response coordination, but this approach offers promising potential for operational wildfire decision support. Full article