Search Results (94)

This article discusses and compares approaches to the visualization of the danger zone formed as a result of spreading toxic substances during a fire at an industrial enterprise, to create predictive models and scenarios for evacuation and environmental protection measures. The purpose of this study is to analyze the features and conditions for the application of algorithms for predicting the spread of a danger zone, based on the Gauss equation and the probabilistic algorithm of a cellular automaton. The research is also aimed at the analysis of the consequences of a fire at an industrial enterprise, taking into account natural and climatic conditions, the development of the area, and the scale of the fire. The subject of this study is the development of software and algorithmic support for the visualization of the danger zone and analysis of the consequences of a fire, which can be confirmed by comparing a computational experiment and actual measurements of toxic substance concentrations. The main research methods include a Gaussian model and probabilistic, frontal, and empirical cellular automation. The results of the study represent the development of algorithms for a cellular automation model for the visual forecasting of a dangerous zone. They are characterized by taking into consideration the rules for filling the dispersion ellipse, as well as determining the effects of interaction with obstacles, which allows for a more accurate mathematical description of the spread of a cloud of toxic combustion products in densely built-up areas. Since the main problems of the cellular automation approach to modeling the dispersion of pollutants are the problems of speed and numerical diffusion, in this article the frontal cellular automation algorithm with a 16-point neighborhood pattern is used, which takes into account the features of the calculation scheme for finding the shortest path. Software and algorithmic support for an integrated system for the visualization and analysis of fire consequences at an industrial enterprise has been developed; the efficiency of the system has been confirmed by computational analysis and actual measurement. It has been shown that the future development of the visualization of dangerous zones during fires is associated with the integration of the Bayesian approach and stochastic forecasting algorithms based on Markov chains into the simulation model of a dangerous zone for the efficient assessment of uncertainties associated with complex atmospheric processes. Full article

Despite the commercial success of lithium-ion batteries (LIBs), the risk of thermal runaway, which can lead to dangerous fires, has become more concerning as LIB usage increases. Research has focused on understanding the causes of thermal runaway and how to prevent or detect it. Additionally, novel thermal runaway-resistant materials are being researched, as are different methods of constructing LIBs that better isolate thermal runaway and prevent it from propagating. However, field firefighters are using hundreds of thousands of liters of water to control large runaway thermal emergencies, highlighting the need to merge research with practical observations. To study battery fire, this study utilized a temperature abuse method to increase LIB temperature and investigated whether thermal runaway can be suppressed by applying external cooling during heating. The batteries used were pouch-type ones and subjected to high states of charge (SOC), which primed the thermal runaway during battery temperature increase. A water spray method was then devised and tested to reduce battery temperature. Results showed that, without cooling, a thermal runaway fire occurred every time during the thermal abuse. However, external cooling successfully prevented thermal runaway. This observation shows that using water as a temperature reducer is more effective than using it as a fire suppressant, which can substantially improve battery performance and increase public safety. Full article

Forest fires are one of the risks with the greatest socio-territorial impact in island areas with Mediterranean precipitation characteristics. Regions such as the Canary Islands, densely populated and where there is no clear differentiation between land uses, regularly suffer major forest fires that devastate a large part of their forest mass and endanger the lives and property of thousands of people. This paper characterizes the forest fire that occurred on the island of Tenerife during the month of August 2023—defined as the most severe in Spain during that year—from a double perspective: on the one hand, analysis of the exposure and vulnerability of the buildings located in the surroundings of the fire and, on the other hand, economic quantification of the assets salvaged thanks to the intervention of the means and resources deployed by the authorities. The conclusion is that the analyzed buildings exhibit moderate vulnerability and exposure, being high in aspects such as their poor accessibility, their proximity to forest areas and, specifically, to dangerous fuel models, as well as the slope of their surroundings and their proximity to ravines. In terms of the evaluation of what was salvaged, the losses avoided are estimated at EUR 187 million, which would have been added to the actual losses—EUR 164 million—especially thanks to the interventions carried out around the nearby buildings and agricultural areas. Full article

Safety zones protect firefighters from bodily injury and death caused by exposure to dangerous heat levels. These zones are defined by maintaining a safe distance from combustible fuels, a safe separation distance (SSD) derived from flame height. This study aimed to determine safety zones, integrating an existing automated identification-of-safety-zone model with vegetation height derived from a freely available high-resolution global canopy height dataset for Manavgat Forest Management Directorate (FMD) in Türkiye. Flame height, terrain slope, size of a safety zone, and distance to the closest road were also used as input in this model. The results indicated that vegetation height from high-resolution global canopy height offered promising results for determining potential safety zones (SZs) associated with SSD. Integrating the global canopy height dataset into the existing model could assist in determining the safety zone in the absence of lidar. Thus, this spatial model would provide a framework for decision-makers to develop fire prevention and suppression strategies for higher fire risk areas, especially before and during a fire. Full article

Understanding the characteristics of wildfires in North China is critical for advancing regional fire danger prediction and management strategies. This study employed satellite-based burned area products of the Global Fire Emissions Database (GFED) and reanalysis of climate datasets to investigate the spatiotemporal characteristics of wildfires, as well as their relationships with fire danger indices and climatic drivers. The results revealed distinct seasonal variability, with the maximum burned area extent and intensity occurring during the March–April period. Notably, the fine fuel moisture code (FFMC) demonstrated a stronger correlation with burned areas compared to other fire danger or climate indices, both in temporal series and spatial patterns. Further analysis through the self-organizing map (SOM) clustering of FFMC composites then revealed six distinct modes, with the SOM1 mode closely matching the spatial distribution of burned areas in North China. A trend analysis indicated a 7.75% 10a⁻¹ (p < 0.05) increase in SOM1 occurrence frequency, associated with persistent high-pressure systems that suppress convective activity through (1) inhibited meridional water vapor transport and (2) reduced cloud condensation nuclei formation. These synoptic conditions created favorable conditions for the occurrence of wildfires. Finally, we developed a prediction model for burned areas, leveraging the strong correlation between the FFMC and burned areas. Both the SSP245 and SSP585 scenarios suggest an accelerated, increasing trend of burned areas in the future. These findings emphasize the importance of understanding the spatiotemporal characteristics and underlying causes of wildfires, providing critical insights for developing adaptive wildfire management frameworks in North China. Full article

The increase in energy demand due to industrial development and urbanization has resulted in the development of large-scale energy facilities. Republic of Korea’s petrochemical industrial complexes serve as prime examples of this phenomenon. However, because of complex processes and aging facilities, many of which have been in operation for over a decade, these industrial complexes are prone to process-deviation-related accidents. Chemical accidents in energy facilities involving high-pressure liquids or gases are especially dangerous; therefore, proactive accident prevention is critical. This study is also relevant to corporate environment, social, and governance (ESG) management. Preventing chemical accidents to protect workers from injury is critical for business and preventing damage to surrounding areas from chemical accidents is a key component of ESG safety. In this study, we collected accident data, specifically injury-related incidents, from Republic of Korea’s petrochemical industrial complexes, which are the foundation of the energy industry. We analyzed the causes of accidents in a step-by-step manner. Furthermore, we conducted a risk analysis by categorizing accident data based on the level of risk associated with each analysis result; we identified the main causes of accidents and “high-risk process stages” that posed significant risk. The analysis reveals that the majority of accidents occur during general operations (50%, 167 cases) and process operations (39%, 128 cases). In terms of incident types, fire/explosion incidents accounted for the highest proportion (43%, 144 cases), followed by leakage incidents (24%, 78 cases). Furthermore, we propose a disaster safety artificial intelligence (AI) model to prevent major and fatal accidents during these high-risk process stages. A detailed analysis reveals that human factors such as accumulated worker fatigue, insufficient safety training, and non-compliance with operational procedures can significantly increase the likelihood of accidents in petrochemical facilities. This finding emphasizes the importance of introducing measurement sensors and AI convergence technologies to help humans predict and detect any issues. Therefore, we selected representative accident cases for implementing our disaster safety model. Full article

Recent research has used virtual environments (VEs), as presented via virtual reality (VR) headsets, to study human behavior in hypothetical fire scenarios. One goal of using VEs in fire scenarios is to elicit patterns of behavior which more closely align to how individuals would react to real fire emergency situations. The present study investigated whether elicited behaviors and perceived risk varied during fire scenarios presented as VEs via two viewing conditions. These included a VR condition, where the VE was rendered as 360-degree videos presented in a VR headset, and a screen condition, where VEs were rendered as fixed-view videos via a computer monitor screen. We predicted that the selection of actions during the scenario would vary between conditions, that participants would rate fires as more dangerous if they developed more quickly and when smoke was rendered as thicker, and that participants would report greater levels of immersion in the VR condition. A total of 159 participants completed a decision-making task where they viewed videos of an incipient fire in a residential building and judged what action to take. Initial action responses to the fire scenarios varied between both viewing and smoke conditions, with those assigned to the thicker smoke and screen conditions being more likely to take protective action. Risk ratings also varied by smoke condition, with evidence of higher perceived risk for thicker smoke. Several factors of self-reported immersion (namely ‘interest’, ‘emotional attachment’, ‘focus of attention’, and ‘flow’) were associated with risk ratings, with perceived presence associated with initial actions. The present study provides evidence that enhancing immersion and perceived risk in a VE contributes to a different pattern of behaviors during simulated fire decision-making tasks. While our investigation only addressed the ideas of presence in an environment, future research should investigate the relative contribution of interactivity and consequences within the environment to further identify how behaviors during simulated fire scenarios are affected by each of these factors. Full article

Departures from historical wildfire regimes due to climate change have significant implications for the structure and composition of forests, as well as for fire management and operations in the Alberta region of Canada. This study analyzed the relationship between climate and wildfire and used a random forest algorithm to predict future wildfire frequencies in Alberta, Canada. Key factors driving wildfires were identified as vapor pressure deficit (VPD), sea surface temperature (SST), maximum temperature (Tmax), and the self-calibrated Palmer drought severity index (scPDSI). Projections indicate an increase in wildfire frequencies from 918 per year during 1970–1999 to 1151 per year during 2040–2069 under a moderate greenhouse gas (GHG) emission scenario (RCP 4.5) and to 1258 per year under a high GHG emission scenario (RCP 8.5). By 2070–2099, wildfire frequencies are projected to increase to 1199 per year under RCP 4.5 and to 1555 per year under RCP 8.5. The peak number of wildfires is expected to shift from May to July. These findings suggest that projected GHG emissions will substantially increase wildfire danger in Alberta by 2099, posing increasing challenges for fire suppression efforts. Full article

The article analyzes the development of air temperatures and precipitation on two remote islands in the Adriatic Sea from 1961 to 2023, examining annual and monthly time scales. Lastovo Island is located in the southern Adriatic, and Lošinj Island is situated 277 km north, and both exhibit a sharp rise in air temperatures since 1998, though precipitation series show no significant trends of an increase or decrease. Using the New Drought Index (NDI) method, this study calculated drought intensities for the period 1961–2023. The analyses conducted in this study undoubtedly indicate a rising frequency and intensity of droughts, with severe droughts doubling and extreme droughts increasing fourfold in the recent period (1998–2023) compared to the previous one (1961–1997). The most pronounced increase in severe and extreme droughts occurs specifically from June to August. This trend is likely applicable to many small Mediterranean Islands, which number over 10,000 and have a permanent population of more than 1.6 million people, with numbers significantly rising during the tourist season. The increased water demand for agriculture and daily use, combined with increased drought risk, not only exacerbates the potential for forest fires but also threatens social structures and ecological conditions. This is particularly critical as the combination of drier conditions and increased fire risk poses a significant challenge, endangering natural landscapes and valuable historical sites that are integral to the islands’ identity and heritage. This study’s findings indicate a dangerous trend likely to persist and worsen with continued increases in air temperatures in the Mediterranean region. Full article

Due to the flammable and explosive nature of explosives, there are significant potential hazards and risks during transportation. During the operation of explosive transport vehicles, there are often situations where the vehicles around them approach or change lanes abnormally, resulting in insufficient avoidance and collision, leading to serious consequences such as explosions and fires. Therefore, in response to the above issues, this article has developed an explosive transport vehicle driving warning system based on object detection algorithms. Consumer-level cameras are flexibly arranged around the vehicle body to monitor surrounding vehicles. Using the YOLOv4 object detection algorithm to identify and distance surrounding vehicles, using a game theory-based cellular automaton model to simulate the actual operation of vehicles, simulating the driver’s decision-making behavior when encountering other vehicles approaching or changing lanes abnormally during actual driving. The cellular automaton model was used to simulate two scenarios of explosive transport vehicles equipped with and without warning systems. The results show that when explosive transport vehicles encounter the above-mentioned dangerous situations, the warning system can timely issue warnings, remind drivers to make decisions, avoid risks, ensure the safety of vehicle operation, and verify the effectiveness of the warning system. Full article

The frequency of extreme wildfire events (EWEs) is expected to increase due to climate change, leading to higher levels of atmospheric pollutants being released into the air, which could cause significant short-term impacts on human health (both for the population and firefighters) and on visibility. This study aims to gain a better understanding of the effects of EWEs’ smoke on air quality, its short-term impacts on human health, and how it reduces visibility by applying a modelling system to the Portuguese EWEs of October 2017. The Weather Research and Forecasting Model was combined with a semi-empirical fire spread algorithm (WRF-SFIRE) to simulate particulate matter smoke dispersion and assess its impacts based on up-to-date numerical approaches. Hourly simulated particulate matter values were compared to hourly monitored values, and the WRF-SFIRE system demonstrated accuracy consistent with previous studies, with a correlation coefficient ranging from 0.30 to 0.76 and an RMSE varying between 215 µg/m³ and 418 µg/m³. The estimated daily particle concentration levels exceeded the European air quality limit value, indicating a potential strong impact on human health. Health indicators related to exposure to particles were estimated, and their spatial distribution showed that the highest number of hospital admissions (>300) during the EWE, which occurred downwind of the fire perimeters, were due to the combined effect of high smoke pollution levels and population density. Visibility reached its worst level at night, when dispersion conditions were poorest, with the entire central and northern regions registering poor visibility levels (with a visual range of less than 2 km). This study emphasises the use of numerical models to predict, with high spatial and temporal resolutions, the population that may be exposed to dangerous levels of air pollution caused by ongoing wildfires. It offers valuable information to the public, civil protection agencies, and health organisations to assist in lessening the impact of wildfires on society. Full article

In this study, nine different types of essential facilities in the state of Iowa (such as hospitals, fire stations, schools, etc.) were analyzed on a county level in terms of flood depth, functionality and restoration time after flooding, and damage sustained during flooding. These essential facilities were also analyzed on the state level in terms of their location relative to the 100 y and 500 y flood zones. Results show that the number of essential facilities within the flood extent reached up to 39%, and during the 100 y flood scenario all but one of the six chosen counties lost functionality of 100% of their facilities. Most essential facilities were found to have a flood depth of 1 to 4 ft deep and a restoration time of 480 days. The purpose of this study is to bring awareness to decisionmakers regarding the risk that flooding events pose to essential facilities and to highlight the increasing dangers of flooding on a broader scale. This study will be beneficial to improve mitigation strategies, emergency response plans, and ensuring that emergency services and facilities are available in the event of future floods for the affected areas. Full article

The consequences of destructive earthquakes show that the problem of analyzing the response of reinforced concrete frames under seismic loads after a fire is relevant. The calculation models used for individual elements and buildings as a whole must take into account the nonlinear properties of concrete and reinforcement. In the spectral calculation method, the nonlinear properties of materials are taken into account by introducing a reduction coefficient to the elastic spectrum. When determining the reduction coefficient, a common deformation criterion is based on the use of the plasticity coefficient. The seismic resistance of a three-span, five-story reinforced concrete frame under four different fire exposure options is considered. The residual strength and stiffness of frame elements after a fire is assessed by performing a thermal engineering calculation in the SOLIDWORKS software for a standard fire. For the central sections of the elements, the highest temperatures were obtained after heating—during the cooling stage. The reduction coefficient is estimated by performing a nonlinear static analysis of reinforced concrete frames in OpenSees and constructing load-bearing capacity curves. Fracture patterns and damage levels in plastic hinges are analyzed. Based on the numerical modeling of reinforced concrete frames after exposure to fire, it was revealed that the most dangerous scenario is the occurrence of a fire on the first floor of the building. Based on the obtained plasticity coefficients, reduction coefficients were determined in the range of 2.62 to 2.44. The influence of fire on the permissible damage coefficient of a reinforced concrete frame is assessed using the coefficient φ_K—the coefficient of additional damage after a fire, which is equal to the ratio of the reduction coefficients for the control and fire-damaged frames. Depending on the percentage of damaged structures on the first floor, the following values were obtained: 50% or less—φ_K = 1.09; 100%—φ_K = 1.17. The obtained coefficients are recommended to be used when assessing the seismic resistance of a reinforced concrete frame after a local fire. Full article

The subject of this article is material research carried out on the ruins of a medieval castle located in west-central Poland. This facility was built at the beginning of the 15th century by the Order of St. John, and during its long life, it was subjected to many reconstructions. Unfortunately, in 1975, it was destroyed by fire. Since then, it has been left in a state of advanced ruin, exposed to climatic influences without any protection. The subject of the research was to assess the possibility of maintaining such buildings in a severely degraded condition while ensuring their technical efficiency. The article discusses a particular instance of “consolidation” applied to a structure in a state of historical, architectural, and structural ruin. After the diagnosis, it was determined that the structure should be safeguarded using a minimally invasive method. The purpose of these activities was to answer the question of whether the structure could be left to continue operating despite failing to meet the requirements of current standards and regulations while posing an additional danger to itself and the environment,. This goal was achieved by obtaining a considerable amount of data on the condition of the materials embedded in the masonry structure, thanks to which the initial parameters for conducting an assessment of the technical condition of the damaged masonry structure and evaluating the degree of its danger were developed. The results of the research and analysis carried out and described in this article can be used in other similar situations where saving national heritage objects through “artificial modern” strengthening will be unsafe and will lead to a loss of their authenticity. We still have a long way to go to develop a comprehensive method for “in situ” diagnosis of heterogeneous masonry structures, so we should use possible techniques and knowledge to conduct such assessments and propose rescue methods for historically valuable objects in a way that could minimize the damage and that can “easily” disappear from our surroundings. Each study should have a specific purpose, not only research but also a long-term perspective, making it possible to leave material for further research and analysis, including testing new research methods in real conditions of its installation. Full article

Proactive prevention and fighting fire at industrial facilities, often located in urbanized clusters, should include the use of modern methods for modeling danger zones that appear during the spread of the harmful combustion products of various chemicals. Simulation modeling is a method that allows predicting the parameters of a danger zone, taking into account a number of technological, landscape, and natural-climatic factors that have a certain variability. The purpose of this research is to develop a mathematical simulation model of the formation process of a danger zone during an emergency at an industrial facility, including an explosion of a container with chemicals and fire, with the spread of an aerosol and smoke cloud near residential areas. The subject of this study was the development of a simulation model of a danger zone of combustion gases and its graphical interpretation as a starting point for timely decision making on evacuation by an official. The mathematical model of the process of danger zone formation during an explosion and fire at an industrial facility presented in this article is based on the creation of a GSL library from data on the mass of explosion and combustion products, verification using the Wald test, and the use of algorithms for calculating the starting and ending points of the danger zone for various factor values’ variables, constructing ellipses of the boundaries of the distribution of pollution spots. The developed model makes it possible to calculate the linear dimensions and area of the danger zone under optimistic and pessimistic scenarios, constructing a graphical diagram of the zones of toxic doses from the source of explosion and combustion. The results obtained from the modeling can serve as the basis for making quick decisions about evacuating residents from nearby areas. Full article