Fire, Volume 6, Issue 6 (June 2023) – 30 articles

Fires in tunnels are a major concern due to the casualties they may cause. Therefore, forced ventilation is mandatory in long tunnels, despite the significant associated costs. In shorter tunnels, however, natural ventilation may be sufficient to comply with safety regulations. Accordingly, the analysis of natural fire smoke flow is relevant for tunnels shorter than 1000 m. This paper presents a computational investigation of the influence of the tunnel slope on the contamination of the cold lower layer with smoke and discusses how it impairs the user’s egress. Large-eddy simulations of the smoke propagation show three different regimes, namely, a quasi-horizontal tunnel behavior for a slope of 0.5%, a transitional behavior for slopes in the range of 1% to 5% and a quasi-forced ventilation behavior for a slope of 7%. The computational results are compared with the application of 1D equations to predict the upper layer temperature, the average mass flow rate, the upper layer mass flow rate, the upper layer velocity and the lower layer velocity. The distance from the fire to the location where the lower layer contamination with smoke starts is accurately predicted by the one-dimensional model for slopes of 2% and 3.5%. However, in the case of lower or higher slopes, the one-dimensional model performs poorly and needs further improvement. Full article

This research analyzes the spatiotemporal patterns of wildfire regime attributes (e.g., seasonality, size, frequency, and burn rate) across the Golestan National Park (GNP), northeast Iran over the last two decades. We used a variety of data, including existing vegetation data, current vegetation survey, and historical wildfire data, and then data were processed through ArcMap. We also predicted fire exposure profiles (burn probability (BP), conditional flame length (CFL (m)), and fire size (FS (ha)) by the application of the minimum travel time (MTT) fire spread algorithm. The kernel density estimation (KDE) method was used to estimate wildfire likelihood, based on recent wildfires (2000–2020) that occurred in the GNP. Finally, we developed a logistic regression model to investigate how independent variables such as weather, fuel, and topographic data influence wildfires in the park. Wildfires in the landscape have not been constant in either space or time. Their extent, seasonality, frequency, and other wildfire regime characters varied considerably across the landscape. Our results highlighted that shrublands in the southern part of the park showed, in general, the highest values in terms of the wildfire regime attributes. Large fires (10–100 ha, 51%) and very large fires (>100 ha, 24%), fire intervals greater than 10 years (90%), and high burn rates (>1% y⁻¹, 35%) are all characteristics that contribute to high wildfire activity in shrublands. Similarly, areas predicted to have high wildfire exposure levels (average BP = 0.004; average CFL = 1.60 m; average FS = 840 ha) are found in the fuel models of high-load grass and medium-load shrub. Finally, the regression model results revealed that weather and fuel were the most influential parameters (R² ≥ 0.2), while topography had comparatively less influence in the study area. In light of these results, we suggest proactively incorporating this information into fire and fuel management which can help develop a fire prevention plan, predict fire ignition probability and frequency, and finally address altered fire regimes threatening the park. Full article

This is an exploratory study aiming to assess the fire risk perception by operators of the Vale do Guadiana Natural Park (PNVG), southern Portugal. To maximize the sample size, a questionnaire survey was distributed among 35 entities with activities in tourism, hunting, and agriculture, as well as among members of PNVG’s co-management commission. For data analysis and interpretation, quantitative and qualitative analyses were used. Survey responses revealed that the entities expressed concern about and made efforts toward the search for and improvement in mitigation strategies in the occurrence of fires. A total of 69.6% of the respondents have knowledge of the occurrence of fires in the region. The qualitative analysis highlights the concern with biodiversity, as well as with the maintenance and cleaning of the PNVG. This study verifies the degree of importance that the tourism sector should give to the impacts caused by fires. The impact of climate change favoring fires was recognized by the entities, as well as the fact that the loss of biodiversity due to fires may have a direct impact on the attractiveness of this tourist destination, indicating the importance of environmental conservation strategies for the region. Full article

Recent studies have argued that changes in fire regimes in the 21st century are posing a major threat to global biodiversity. In this scenario, incorporating species’ physiological, ecological, and evolutionary traits with their local fire exposure might facilitate accurate identification of species most at risk from fire. Here, we developed a framework for identifying the animal species most vulnerable to extinction from fire-induced stress in the Brazilian savanna. The proposed framework addresses vulnerability from two components: (1) exposure, which refers to the frequency, extent, and magnitude to which a system or species experiences fire, and (2) sensitivity, which reflects how much species are affected by fire. Sensitivity is based on biological, physiological, and behavioral traits that can influence animals’ mortality “during” and “after” fire. We generated a Fire Vulnerability Index (FVI) that can be used to group species into four categories, ranging from extremely vulnerable (highly sensible species in highly exposed areas), to least vulnerable (low-sensitivity species in less exposed areas). We highlight the urgent need to broaden fire vulnerability assessment methods and introduce a new approach considering biological traits that contribute significantly to a species’ sensitivity alongside regional/local fire exposure. Full article

Safety-specific passive leadership has been negatively linked to diminished safety outcomes, including safety behaviors. However, this relationship is not fully understood. Research has not fully examined mediating factors that may be influenced by passive leadership, which then influence safety behaviors. Research among firefighters in this context is particularly absent. As such, this study aimed to examine relationships between safety-specific passive leadership, stress, anxiety, and compliance-oriented safety behavior outcomes among 708 professional firefighters. A path analysis was completed. The hypothesized model fit was very good and hypothesized relationships were confirmed. Safety-specific passive leadership was positively, significantly associated with increased firefighter stress perceptions and stress was positively, significantly associated with anxiety. Anxiety was negatively, significantly associated with both safety compliance and personal protective equipment behavior. This study has implications for researchers and practitioners. The findings emphasize the importance of active leaders in the fire service as passive leadership in the context of safety is distressing, which results in anxiety and ultimately diminished safety behavior outcomes, which could place firefighters at risk for injuries, illness, or death. Full article

Geovisualization tools can supplement the statistical analyses of landscape-level wildfire behavior by enabling the discovery of nuanced information regarding the relationships between fire spread, topography, fuels, and weather. The objectives of this study were to develop and evaluate the effectiveness of geovisualization tools for analyzing wildfire behavior and specifically to apply those tools to study portions of the Thomas and Detwiler wildfire events that occurred in California in 2017. Fire features such as active fire fronts and rate of spread (ROS) vectors derived from repetitive airborne thermal infrared (ATIR) imagery sequences were incorporated into geovisualization tools hosted in a web geographic information systems application. This geovisualization application included ATIR imagery, fire features derived from ATIR imagery (rate of spread vectors and fire front delineations), growth form maps derived from NAIP imagery, and enhanced topographic rasters for visualizing changes in local topography. These tools aided in visualizing and analyzing landscape-level wildfire behavior for study portions of the Thomas and Detwiler fires. The primary components or processes of fire behavior analyzed in this study were ROS, spotting, fire spread impedance, and fire spread over multidirectional slopes. Professionals and researchers specializing in wildfire-related topics provided feedback on the effectiveness and utility of the geovisualization tools. The geovisualization tools were generally effective for visualizing and analyzing (1) fire spread over multidirectional slopes; (2) differences in spread magnitudes within and between sequences over time; and (3) the relative contributions of fuels, slope, and weather at any given point within the sequences. Survey respondents found the tools to be moderately effective, with an average effectiveness score of 6.6 (n = 5) for the visualization tools on a scale of 1 (ineffective) to 10 (effective) for postfire spread analysis and visualizing fire spread over multidirectional slopes. The results of the descriptive analysis indicate that medium- and fine-scale topographic features, roads, and riparian fuels coincided with cases of fire spread impedance and exerted control over fire behavior. Major topographic features such as ridges and valleys slowed, or halted, fire spread consistently between study areas. The relationships between spotting, fuels, and topography were inconclusive. Full article

The paper is devoted to the analysis of two different approaches for the numerical simulation of gaseous combustion. The first one is based on a full system of Navier-Stokes equations describing the dynamics of the compressible reactive medium, while the second one utilizes low-Mach number approximation. The compressible model is realized by the traditional low-order numerical scheme and the contemporary CABARET method. The low-Mach approach is implemented on the base of a widely known FDS numerical scheme. The benefits and disadvantages of compressible and low-Mach approaches are discussed and demonstrated on a specially developed set of problem setups, applicable for validation and verification of the numerical methods for combustion analysis. In particular, the laminar flame velocity test, spherical bomb test, and multidimensional modeling of combustion development inside the rectangular closed vessel are performed via both techniques that allowed to determine the applicability limits of the low-Mach number approximation. Full article

Fire accidents due to oil-immersed transformers seriously threaten the safe operation of power systems. In this paper, the similarity principle was used to design a high-pressure water mist fire-extinguishing test platform for a small-scale transformer fire, and the design method achieved a good fire extinguishing effect. The results indicate that a deflagration phenomenon, lasting about 2–4 s, could be observed after activating the high-pressure water mist system; the flame temperature rose rapidly at first, then dropped sharply, and finally cooled to the indoor temperature. The nozzle’s flow rate in this system has a significant impact on the fire extinguishing time. Meanwhile, the adjustment of the upper nozzle height also influenced the fire suppression effectiveness of the system, where a height of 1800 mm achieved the best performance compared to the others. In addition, the ambient wind speed is a very unfavorable factor for transformer fire suppression, where the fire extinguishing efficiency decreases rapidly with the increase in wind speed. Therefore, under low wind speed conditions, the high-pressure water mist system has great advantages in the fire suppression of outdoor oil-immersed transformers, and the above research results can provide a reference for the optimization design of this system. Full article

Fire spread prediction is a crucial technology for fighting forest fires. Most existing fire spread models focus on making predictions after a specific time, and their predicted performance decreases rapidly in continuous prediction due to error accumulation when using the recursive method. Given that fire spread is a dynamic spatiotemporal process, this study proposes an expanded neural network of long short-term memory based on self-attention (SA-EX-LSTM) to address this issue. The proposed model predicted the combustion image sequence based on wind characteristics. It had two detailed feature transfer paths, temporal memory flow and spatiotemporal memory flow, which assisted the model in learning complete historical fire features as well as possible. Furthermore, self-attention mechanisms were integrated into the model’s forgetting gates, enabling the model to select the important features associated with the increase in fire spread from massive historical fire features. Datasets for model training and testing were derived from nine experimental ground fires. Compared with the state-of-the-art spatiotemporal prediction models, SA-EX-LSTM consistently exhibited the highest predicted performance and stability throughout the continuous prediction process. The experimental results in this paper have the potential to positively impact the application of spatiotemporal prediction models and UAV-based methods in the field of fire spread prediction. Full article

Mapping fire risk in South Florida depends on spatially varying water levels, fuel characteristics, and topography. When surface water levels recede below the lowest topographic features (cypress strands, marshes, etc.), the ecosystem loses its natural, wetted fire breaks, and landscape-level fire risk increases. We developed a geospatial method to generate daily, categorical fire risk maps; the maps visualize low-to-high risk areas using a newly developed 100 m DEM, modeled water levels, fuel types, and fire management units. We assigned fire risk by creating a water level distribution for each unique combination of fuel type and fire management unit; fire risk was then assigned for each pixel based on risk percentiles commonly used by fire management agencies. Assigning risk based on unique fuel types and management units helped avoid over- or under-assigning fire risk that may occur when applying landscape-level “average” risk relationships. Daily maps also incorporated (1) energy release component data to better estimate fuel moisture and (2) historical burn footprints to reduce risk in recently burned areas. Our data-driven approach generated at management-relevant spatial scales may enable more informed prescribed burn planning and may increase the efficiency of staff and resource allocation across the landscape on high-wildfire-risk days. Full article

An improved multiple imputation based on R language is proposed to deal with the miss of data in a fire prediction model, which can affect the accuracy of the prediction results. Hazard and operability (HAZOP) is used to accurately find the data related to the research purpose, and exclude data with a missing rate greater than 80% and small differences in characteristics. Then, by changing the m value in the mice package under the R language (R-mice), the relevant parameters of the complete filling factor set under different m values are obtained. The value of m is determined after observing and comparing the parameters. The proposed method fully considers the randomness of filling and the difference between the generated dataset. Taking Hubei Province as an example, the data processed by this method are used as the input of the Bayesian network, and the fire trend is used as the output. The results show that the improved multiple imputation based on R-mice can solve the problem of missing data very well, and have a high prediction effect (AUC = 94.0800). In addition, the results of the predictive reasoning and sensitivity analysis show that the government’s supervision has a vital influence on the trend of fires in Hubei Province. Full article

Amazonian biodiversity has been used for generations by human populations, especially by Indigenous peoples and traditional communities in their cultural, social and economic practices. However, forest degradation, driven by forest fires, has threatened the maintenance of these resources. This study examined the effects of recent forest fires on species with timber, non-timber and multiple-use potential in Brazil’s state of Acre. Forest inventories in five forest types were analyzed, identifying species with timber, non-timber and multiple-use potential based on a review of existing scientific articles, books and studies in the technical literature. The indicators of the effect of forest fires on species density were based on the mean and standard deviation of tree density and absolute abundance. We found that 25% of the tree individuals have potential for use by humans, 12.6% for timber, 10.7% non-timber and 1.4% have multiple-use potential. With the negative impact of fire, the reduction in timber, non-timber and multiple-use potential can range from 2 to 100%, depending on the vegetation type and especially on the recurrence of fire. However, even in forests that are degraded by fire, species that are useful to humans can be maintained to a certain degree and contribute to other environmental services, thus they must be preserved. Full article

Leaders are crucial to ensuring the well-being of their subordinates. This study aims to understand the effects of two leadership styles (empowering vs. directive) on subordinates’ well-being in an emergency situation (i.e., rural fire). A simulation study was conducted with two experimental conditions (empowering vs. directive leadership), and the subordinates’ stress levels were measured before and after the simulated episode. Contrary to expectations, empowering leadership had no significant effect on subordinates’ stress levels, while directive leadership contributed to reducing them. As expected, this effect was stronger for the subordinates with higher levels of stress prior to the simulated episode. Full article

The cause of large areas of treeless Sedgeland and Scrub communities in western Tasmania, one of the wettest regions of Australia, has long puzzled ecologists, given the climatic suitability for temperate Eucalyptus and rainforests. A pervasive theory, known as the ecological drift model, is that landscape fires have created a dynamic mosaic of fire-adapted and fire-sensitive vegetation. A contrary view, known as the fire cycle model, asserts that fire patterns are a consequence, not a cause, of the mosaics, which are edaphically determined. We leveraged the opportunity presented by a large wildfire that occurred in a Sedgeland tract surrounded by Eucalyptus forest in the Huon Valley in 2019 to help discriminate between these competing models. Specifically, we sought to determine whether there was any evidence that the Sedgeland was becoming infilled with Scrub prior to the 2019 fire, and whether the fire caused the Scrub community to convert to Sedgeland. A field survey was used to assess the mortality of shrubs and their regeneration following the 2019 fire, and we used dendrochronology to determine the age of the fire-killed shrubs. We also used historical aerial photography since the 1980s to map fire scars and the distribution of Sedgeland and Scrub. We found that fire killed most shrubs in the Sedgeland and Scrub communities and initiated a cohort of shrub regeneration. Dendrochronological analysis of the fire-killed shrubs revealed that most were established approximately 40 years ago, following a fire that is apparent from aerial photography and most likely occurred around 1983. An analysis of aerial photography revealed that since 1980, the distribution of the Scrub community has remained stable, although the density of shrubs declined following the 1983 fire. The recovery of the burned Scrub areas in 1983 and the rapid regeneration of the shrubs following the 2019 fire is more consistent with the fire cycle model than the ecological drift model. These findings concord with the demonstrated stability of the Eucalyptus forest boundary at this site revealed by a separate study. The slow growth of the shrubs cautions against frequently burning Sedgelands, because it could cause the collapse of shrub populations by killing the immature cohort initiated by fire. Full article

This study investigates the kinetics data of glass wool (GW) and extruded polystyrene (XPS) insulation materials used in cladding systems using a systematic framework. The determination of appropriate kinetic properties, such as pre-exponential factors, activation energy and reaction orders, is crucial for accurately modelling the full-scale fire performance of insulation materials. The primary objective of this research is to extract thermal and kinetics data of XPS and GW insulation materials employed in high-rise buildings. To obtain these properties, thermogravimetric analysis (TGA) is conducted at four different heating rates: 5, 10, 15 and 20 K/min. The TGA results serve as the basis for determining the kinetic properties using a combination of model-free and model-based methods. The outcomes of this study are expected to be highly beneficial in defining the pyrolysis reaction steps and extracting kinetics data for fire modelling of such insulation materials. This information will enhance the understanding of the fire behaviour and performance of these materials during fire incidents, aiding in developing more accurate fire models and improving fire safety strategies for cladding systems in high-rise buildings. Full article

Rice is the most important cereal in Asia. However, it also results in the generation of large quantities of rice-derived residues (i.e., rice straw and rice husk). Due to the residues richness in lignocellulosic components, they potentially have considerable value in material and/or energy production without illegal burning in open fields. This work focused on investigating the thermochemical properties and inorganic/metal element contents of rice straw and rice husk. The former included proximate analysis, calorific value, thermogravimetric analysis (TGA) and energy dispersive X-ray spectroscopy (EDS). The latter covered the ten elements most relevant to their slagging/fouling indices. The results showed that they are suitable for energy use as biomass fuels, but rice husk was superior to rice straw because of the high silica content in the rice husk and the significant contents of potassium, sulfur and phosphorus in the rice straw. Using several slagging and fouling indices, the evaluation results were also consistent with their contents of inorganic elements or oxides. To increase the fuel properties of rice-derived residues, they could be pretreated with alkaline leaching, thus causing lower emissions of particulates and reduced slagging tendency when co-firing them with coal in industrial boilers. Full article

Research has recently transitioned from the study of fossil-based materials to bio-sourced ones, following the quest to achieve sustainability. However, fire presents a unique hazard to bio-composite materials, which limits their applicability in various sectors. This necessitates an in-depth assessment of the fire behaviour of biobased composites used for specific applications. Improving the fire properties of bio-composites with flame retardants tends to reduce mechanical strength. Therefore, this review focused on biobased composite materials for packaging, structural, automotive, and aeronautical applications that are both mechanically strong and fire safe. It was noticed that the interfacial bonding between the matrix and the reinforcement should be optimized. In addition, optimum amounts of flame retardants are required for better fire performance. This article covers flame retardants for biobased composites, the optimum amount required, and the extent of improvement to the thermal stability and flammability of the materials. This research will help material scientists and the like in their selection of biomass feedstock, flame retardants, and general materials for different types of applications. Full article

Wildfire is a sudden and highly destructive natural disaster that poses significant challenges in terms of response and rescue efforts. Influenced by factors such as climate, combustible materials, and ignition sources, wildfires have been increasingly occurring worldwide on an annual basis. In recent years, researchers have shown growing interest in studying wildfires, leading to a substantial body of related research. These studies encompass various topics, including wildfire prediction and forecasting, the analysis of spatial and temporal patterns, the assessment of ecological impacts, the simulation of wildfire behavior, the identification of influencing factors, the development of risk assessment models, techniques for managing combustible materials, decision-making technologies for firefighting, and fire-retardant methods. Understanding the factors that affect wildfire spread behavior, employing simulation methods, and conducting risk assessments are vital for effective wildfire prevention, disaster mitigation, and emergency response. Consequently, it is imperative to comprehensively review and explore further research in this field. This article primarily focuses on elucidating and discussing wildfire spread behavior as a key aspect. It summarizes the driving factors of wildfire spread behavior and introduces a wildfire spread behavior simulation software and its main applications based on these factors. Furthermore, it presents the research progress in wildfire risk assessment based on wildfire spread behavior factors and simulation, and provides an overview of various methods used for wildfire risk assessment. Finally, the article proposes several prospects for future research on wildfire spread: strengthening the dynamic monitoring of wildfires and utilizing comprehensive data from multiple sources, further exploring the differential effects of key factors on wildfire spread, investigating differences in driving factors, improving wildfire models in China, developing applicable software, and conducting accurate and scientific assessments of wildfire risks to protect ecological resources. Full article

Wildfires can cause serious imbalances in ecosystems, primarily at the soil level, making it vulnerable to degradation processes such as erosion. During and after a fire, changes occur in soil properties, including pH, which affects the solubility and availability of nutrients. Currently, there is a great diversity of protocols, some involving normalized standards, to determine soil pH, but there is no consensual or universal analytical method for this parameter, especially in burned soils, in which mineral and organic fractions could have been modified. Therefore, the objective of the present work is to evaluate the effect that variations in these analytical protocols may have on pH results. For this, five methods commonly found in the international bibliography for the analysis of pH of soil in water (pH_H2O) were selected and compared to propose the most precise procedure. The analytical methods were applied to 43 soil samples, collected in a plot subjected to prescribed burning in the Parque Natural de Montesinho (Northern Portugal). The studied methods differ in the following protocol items: water suspension ratio (1:2.5 or 1:5), mechanical stirring time in the suspension (10 min or 1 h), and in the resting time for the solid particles to settle (15 min or 8 h). The obtained results point to the suitability of the five methods used for soil pH analysis, indicating that there are no statistically significant differences. However, results also allow suggesting a more appropriate method concerning practical reasons, such as labor in a lab. Thus, to make the analysis process more profitable, M2 is a good option because it uses a small amount of sample (5 g), short agitation (10 min) and settling time (15 min). In turn, M1 and M5, which use a lower proportion of soil (1:2.5) show lower pH variation in the measurements. This fact may be explained by a smaller dilution effect. Considering that these two methods differ in the settling time, it is suggested to apply M1, because only 15 min are required. Therefore, the main conclusion reached with this work is that the measurement of soil pH using M1, i.e., a soil:water ratio of 1:2.5, with agitation of 10 min and settling time of 15 min, is a robust and more expeditious protocol to be applied to soil samples after a fire. Full article

Dust explosions are a major concern in many industrial facilities and particularly in storage areas of biomass materials. Although venting standards (EN 14491 and NFPA 68) provide satisfactory safety levels for most industrial applications, they present some limitations and there exist situations that they do not contemplate. Vented dust explosions in a 4500 m³ silo for the storage of wood pellets were simulated by computational fluid dynamics. Maximum overpressures were registered and compared. The influence of several parameters including initial turbulence level, dust concentration, ignition location, and vent area was studied. A factorial analysis was carried out to determine the importance of each of the four parameters, along with possible interactions between them. The results showed great variations in the overpressures between the different scenarios simulated. Vent area, ignition location, and dust concentration showed similar effects on the overpressure (around 25%), while initial turbulence had half this effect (13%). One interaction effect out of the eleven possible interactions was identified as relevant for this specific industrial scenario: the combination of the ignition location and the initial turbulence, with an additional effect of 5% on the overpressure. The factorial analysis applied in this study could be of interest to the risk assessment of industrial facilities. Full article

Fire perception based on machine vision is essential for improving social safety. Object recognition based on deep learning has become the mainstream smoke and flame recognition method. However, the existing anchor-based smoke and flame recognition algorithms are not accurate enough for localization due to the irregular shapes, unclear contours, and large-scale changes in smoke and flames. For this problem, we propose a new anchor-free smoke and flame recognition algorithm, which improves the object detection network in two dimensions. First, we propose a channel attention path aggregation network (CAPAN), which forces the network to focus on the channel features with foreground information. Second, we propose a multi-loss function. The classification loss, the regression loss, the distribution focal loss (DFL), and the loss for the centerness branch are fused to enable the network to learn a more accurate distribution for the locations of the bounding boxes. Our method attains a promising performance compared with the state-of-the-art object detectors; the recognition accuracy improves by 5% for the mAP, 8.3% for the flame AP50, and 2.1% for the smoke AP50 compared with the baseline model. Overall, the algorithm proposed in this paper significantly improves the accuracy of the object detection network in the smoke and flame recognition scenario and can provide real-time fire recognition. Full article

The unfixed flame propagation velocity of a gas explosion and the fixed response time of explosion suppression devices are the important reasons for the poor protective effect of active explosion suppression. A flexible explosion suppression method based on buffer energy absorption is detailed in this study. The explosion suppression system consists of an explosive characteristic monitoring system, an explosion suppression agent system, and an explosion suppression airbag. An empty pipe experiment and an explosion suppression experiment with a flexible-airbag gas-explosion suppression device were conducted in a 20.5 m-long pipe with an inner diameter of 180 mm. The flame propagation velocity and maximum overpressure values were compared between the two groups of experiments. The experimental results show that the flame wave propagation can be completely suppressed by the explosion suppression device under certain pressure. The occurrence time of maximum overpressure at each pressure measuring point is also analyzed. P3 is generally later than P4, which verifies the existence of energy absorption and explosion suppression effect of airbag. Finally, the energy absorption effect of the airbag is analyzed theoretically. The shock wave overpressure calculated in the sealing limit state of the airbag is 0.3432 MPa, and the maximum error is 7.8%, which provides reliable guidance and prediction for the experimental process in the future. Full article

Understanding the impact of drought on fire dynamics is crucial for assessing the potential effects of climate change on wildfire activity in China. In this study, we present a series of multiple linear regression (MLR) models linking burned area (BA) during mainland China’s fire season from 2001 to 2019, across seven regions, to concurrent drought, antecedent drought, and time trend. We estimated burned area using Collection 6 Moderate Resolution Imaging Spectradiometer (MODIS) and drought indicators using either the Standardized Precipitation Evapotranspiration Index (SPEI) or the self-calibrated Palmer Drought Severity Index (sc-PDSI). Our findings indicate that the wildfire season displays a spatial variation pattern that increases with latitude, with the Northeast China (NEC), North China (NC), and Central China (CC) regions identified as the primary areas of wildfire occurrence. Concurrent and antecedent drought conditions were found to have varying effects across regions, with concurrent drought as the dominant predictor for NEC and Southeast China (SEC) regions and antecedent drought as the key predictor for most regions. We also found that the Northwest China (NWC) and CC regions exhibit a gradual decrease in burned area over time, while the NEC region showed a slight increase. Our multiple linear regression models exhibited a notable level of predictive power, as evidenced by the average correlation coefficient of 0.63 between the leave-one-out cross-validation predictions and observed values. In particular, the NEC, NWC, and CC regions demonstrated strong correlations of 0.88, 0.80, and 0.76, respectively. This indicates the potential of our models to contribute to the prediction of future wildfire occurrences and the development of effective wildfire management and prevention strategies. Nevertheless, the intricate relationship among fire, climate change, human activities, and vegetation distribution may limit the generalizability of these findings to other conditions. Consequently, future research should consider a broad range of factors to develop more comprehensive models. Full article

Fire accidents threaten public safety. One of the greatest challenges during fire rescue is that firefighters need to find objects as quickly as possible in an environment with strong flame luminosity and dense smoke. This paper reports an optical method, called violet illumination, coupled with deep learning, to significantly increase the effectiveness in searching for and identifying rescue targets during a fire. With a relatively simple optical system, broadband flame luminosity can be spectrally filtered out from the scattering signal of the object. The application of deep learning algorithms can further and significantly enhance the effectiveness of object search and identification. The work shows that this novel optics–deep learning combined method can improve the object identification accuracy from 7.0% with the naked eye to 83.1%. A processing speed of 10 frames per second can also be achieved on a single CPU. These results indicate that the optical method coupled with machine learning algorithms can potentially be a very useful technique for object searching in fire rescue, especially considering the emergence of low-cost, powerful, compact violet light sources and the rapid development of machine learning methods. Potential designs for practical systems are also discussed. Full article

Using lightweight fire-rated board (LFRB) presents cost-effective opportunities for various passive fire protection measures. The aim of the project is to develop an LFRB with enhanced fire resistance, acoustic properties, and mechanical properties. These properties were determined using a Bunsen burner, furnace, energy-dispersive X-ray, impedance tube instrument, and Instron universal testing machine. To fabricate the LFRBs, vermiculite and perlite were blended with flame-retardant binders, and four types of LFRBs were produced. A fire test was conducted to compare the fire-resistance performance of the LFRBs with a commercially available flame-retardant board. The B2 prototype showed exceptional fire-resistant properties, with a temperature reduction of up to 73.0 °C, as compared to the commercially available fire-rated magnesium board. Incorporating nano chicken eggshell into the specially formulated flame-retardant binder preserved the LFRBs’ structural integrity, enabling them to withstand fire for up to 120 min with an equilibrium temperature of 92.6 °C. This approach also provided an absorption coefficient of α = 2.0, a high flexural strength of 3.54 MPa, and effective flame-retardancy properties with a low oxygen/carbon ratio of 2.60. These results make the LFRBs valuable for passive fire protection applications in the construction and building materials industry. Full article

Accurate representation of fire emissions and smoke transport is crucial for current and future wildfire-smoke projections. We present a flexible modeling framework for emissions sourced from the First Street Foundation Wildfire Model (FSF-WFM) to provide a national map for near-surface smoke conditions exceeding the threshold for unhealthy concentrations of particulate matter at or less than 2.5 µm, or PM_2.5. Smoke yield from simulated fires is converted to emissions transported by the National Oceanic and Atmospheric Administration’s HYSPLIT model. We present a strategy for sampling from a simulation of ~65 million individual fires, to depict the occurrence of “unhealthy smoke days” defined as 24-h average PM_2.5 concentration greater than 35.4 µg/m³ from HYSPLIT. The comparison with historical smoke simulations finds reasonable agreement using only a small subset of simulated fires. The total amount of PM_2.5 mass-released threshold of 10¹⁵ µg was found to be effective for simulating the occurrence of unhealthy days without significant computational burden. Full article

Fire spread behavior models are used to estimate fire behavior metrics, fire hazard, exposure, and risk across the landscape. One of the most widely used fire spread models is the minimum travel time (MTT), which requires a very time-consuming, interactive, trial-and-error calibration process to reproduce observed fire regimens. This study presents the MTTfireCAL package for R, a tool that enables fast calibration of the MTT fire spread models by testing and combining multiple settings and then ranking them based on the model’s capacity to reproduce historical fire patterns, such as fire size distribution and fire frequency. Here, we explain the main methodological steps and validate the package by comparing it against the typical calibration procedures in two study areas. In addition, we estimate the minimum number of fire runs required to ensure a reliable calibration. Overall, the use of MTTfireCAL R package and the optimization of the number of ignitions used allowed for a faster calibration of the MTT modeling system than the typical trial-and-error calibration. The MTT modeling system calibrated using MTTfireCAL was also able to better reproduce the historical fire patterns. This tool has the potential to support the academic and operational community working with MTT. Full article

In order to select the location of fire stations more scientifically and improve the efficiency of emergency management in chemical industry parks (CIPs), an improved risk calculation model for hazardous chemicals has been proposed by taking the domino effect and the identification of major hazardous installations for hazardous chemicals into account. In the analysis of the domino effect, the Monte Carlo simulation was used. Then, a location model of the fire stations was established with the optimization objectives of minimizing total cost and maximizing total risk coverage. The solving procedure of the location model is based on the augmented $\epsilon$-constraint method combined with the TOPSIS method. Finally, a green chemical industry park was used as a case study for the validation and analysis of the location model. The results showed that the improved model could protect the high-risk areas, which is beneficial for the location decisions of fire stations. Full article

The real-time monitoring and analysis system based on video images has been implemented to detect fire accidents on site. While most segmentation methods can accurately segment smoke areas in bright and clear images, it becomes challenging to obtain high performance due to the low brightness and contrast of low-light smoke images. An image enhancement model cascaded with a semantic segmentation model was proposed to enhance the segmentation effect of low-light smoke images. The modified Cycle-Consistent Generative Adversarial Network (CycleGAN) was used to enhance the low-light images, making smoke features apparent and improving the detection ability of the subsequent segmentation model. The smoke segmentation model was based on Transformers and HRNet, where semantic features at different scales were fused in a dense form. The addition of attention modules of spatial dimension and channel dimension to the feature extraction units established the relationship mappings between pixels and features in the two-dimensional spatial directions, which improved the segmentation ability. Through the Foreground Feature Localization Module (FFLM), the discrimination between foreground and background features was increased, and the ability of the model to distinguish the thinner positions of smoke edges was improved. The enhanced segmentation method achieved a segmentation accuracy of 91.68% on the self-built dataset with synthetic low-light images and an overall detection time of 120.1 ms. This method can successfully meet the fire detection demands in low-light environments at night and lay a foundation for expanding the all-weather application of initial fire detection technology based on image analysis. Full article

Patterns of spatial heterogeneity in forests and other fire-prone ecosystems are increasingly recognized as critical for predicting fire behavior and subsequent fire effects. Given the difficulty in sampling continuous spatial patterns across scales, statistical approaches are common to scale from plot to landscapes. This study compared the performance of four spatial interpolation methods (SIM) for mapping fine-scale fuel loads: classification (CL), multiple linear regression (LR), ordinary kriging (OK), and regression kriging (RK). These methods represent commonly used SIMs and demonstrate a diversity of non-geostatistical, geostatistical, and hybrid approaches. Models were developed for a 17.6-hectare site using a combination of metrics derived from spatially mapped trees, surface fuels sampled with an intensive network of photoload plots, and topographic variables. The results of this comparison indicate that all estimates produced unbiased spatial predictions. Regression kriging outperformed the other approaches that either relied solely on interpolation from point observations or regression-based approaches using auxiliary information for developing fine-scale surface fuel maps. While our analysis found that surface fuel loading was correlated with species composition, forest structure, and topography, the relationships were relatively weak, indicating that other variables and spatial interactions could significantly improve surface fuel mapping. Full article