Fire Type Research Articles

A Bayesian analysis of domestic fire response and fire injury

In this article domestic fire response and fire injury was examined using a Bayesian analysis approach. A Bayesian model was developed to estimate the probability of non-fatal fire injury associated with a given fire response (escape, return to fire, fight the fire) under given circumstances of non-fatal fire injury (age band, gender, smoke alarm presence, type of domestic fire). The Bayesian model was developed using non-fatal fire injury data recorded by Merseyside Fire and Rescue Service between 2011 to 2022. Overall, more domestic fire injuries relating to attempting to fight the fire occurred in properties with a smoke detector (82% of attempting to fight the fire injuries) compared to properties without a smoke detector (18% of attempting to fight the fire injuries). Similarly, fire injuries sustained returning to the fire mainly occurred in properties with a smoke detector (75% of returning to fire injuries) as opposed to properties without a smoke detector (25% of returning to fire injuries).

Effects of Large-Scale Wildfires on the Redistribution of Radionuclides in the Chornobyl River System.

Wildfires in radiologically contaminated areas raise significant concerns due to potential radionuclides redistribution and increased public radiation exposure. This study examined the impact of the 2020 Chornobyl wildfire on the redistribution of radionuclides, specifically 137Cs and 90Sr, in the Chornobyl River system. We determined the quantities and speciation of 137Cs and 90Sr in charred residues and soil after wildfires and analyzed the riverine concentrations of these radionuclides based on long-term monitoring data. Our findings indicate that the inventories of 137Cs and 90Sr in the charred residues and soil decreased with increasing distance from the nuclear power plant, which is consistent with the initial deposition patterns. However, the transfer of 137Cs and 90Sr from soil to charred residues did not correspond to the distance, type of contamination source, or fire type. Speciation analysis revealed that the water-soluble fractions of 137Cs and 90Sr in the charred residues were significantly higher than those in the soil, implying increased mobility. Following the wildfires, no significant increase in 137Cs concentration was observed in a river catchment in Chornobyl. However, 90Sr concentrations showed a significant increase, exceeding the permissible levels in drinking water (2 Bq/L) in Ukraine. This increase is attributed to hydrologically driven mobilization processes: (1) during snowmelt in spring and (2) the transport of soluble 90Sr from charred residues and surface soil into the river during high suspended solid concentration events. Collectively, our findings highlight the importance of continuous monitoring of radionuclide dynamics in postwildfire environments to better assess potential radionuclide redistribution and radiation exposure risks. These results provide valuable insights into the behavior of 137Cs and 90Sr in river systems affected by wildfires, contributing to a more accurate understanding of their environmental impacts and potential countermeasures.

A Forest Fire Prediction Model Based on Meteorological Factors and the Multi-Model Ensemble Method

More than half of South Korea’s land area is covered by forests, which significantly increases the potential for extensive damage in the event of a forest fire. The majority of forest fires in South Korea are caused by humans. Over the past decade, more than half of these types of fires occurred during the spring season. Although human activities are the primary cause of forest fires, the fact that they are concentrated in the spring underscores the strong association between forest fires and meteorological factors. When meteorological conditions favor the occurrence of forest fires, certain triggering factors can lead to their ignition more easily. The purpose of this study is to analyze the meteorological factors influencing forest fires and to develop a machine learning-based prediction model for forest fire occurrence, focusing on meteorological data. The study focuses on four regions within Gangwon province in South Korea, which have experienced substantial damage from forest fires. To construct the model, historical meteorological data were collected, surrogate variables were calculated, and a variable selection process was applied to identify relevant meteorological factors. Five machine learning models were then used to predict forest fire occurrence and ensemble techniques were employed to enhance the model’s performance. The performance of the developed forest fire prediction model was evaluated using evaluation metrics. The results indicate that the ensemble model outperformed the individual models, with a higher F1-score and a notable reduction in false positives compared to the individual models. This suggests that the model developed in this study, when combined with meteorological forecast data, can potentially predict forest fire occurrence and provide insights into the expected severity of fires. This information could support decision-making for forest fire management, aiding in the development of more effective fire response plans.

Electric cooktop fire incidents in Finland

Cooking related fires contribute to one-fourth of residential building fires in Finland. However, their prevalence is often underestimated in statistics due to underreporting when individuals manage situations without fire service assistance. Despite their significance, cooktop fires remain inadequately studied, impeding our understanding of residential fire safety and the advancement of effective prevention measures. This study aims to provide a comprehensive evaluation of electric cooktop safety in Finland. It explores factors undermining safety, analyses materials contributing to hazards, assesses financial damages, and scrutinizes typical cooktop fire scenarios and prevention methods. Utilizing data from statistics (2016–2022), Safety Investigation Authority reports (1998–2022), media reviews (1.7.2020–30.6.2023), and insurance data (2017–2020), supplemented by two survey studies, this research identifies consistent causative factors across multiple data sources. Common factors include excessive items left on cooktops, unattended cooking, and unintentional activation of cooktops. Approximately three-quarters of cooktop fires go unreported to fire services, suggesting a significantly higher prevalence than recorded in statistics. Moreover, minimal use of stove safety devices exacerbates the issue. According to insurance company data, the costs caused by cooktop fires are significantly higher than those reported in official statistics. Implementing existing technology is crucial for reducing the frequency and consequences of these fires.

NEIVAv1.0: Next-generation Emissions InVentory expansion of Akagi et al. (2011) version 1.0

Abstract. Accurate representation of fire emissions is critical for modeling the in-plume, near-source, and remote effects of biomass burning (BB) on atmospheric composition, air quality, and climate. In recent years application of advanced instrumentation has significantly improved knowledge of the compounds emitted from fires, which, coupled with a large number of recent laboratory and field campaigns, has facilitated the emergence of new emission factor (EF) compilations. The Next-generation Emissions InVentory expansion of Akagi (NEIVA) version 1.0 is one such compilation in which the EFs for 14 globally relevant fuel and fire types have been updated to include data from recent studies, with a focus on gaseous non-methane organic compounds (NMOC_g). The data are stored in a series of connected tables that facilitate flexible querying from the individual study level to recommended averages of all laboratory and field data by fire type. The querying features are enabled by assignment of unique identifiers to all compounds and constituents, including thousands of NMOC_g. NEIVA also includes chemical and physical property data and model surrogate assignments for three widely used chemical mechanisms for each NMOC_g. NEIVA EF datasets are compared with recent publications and other EF compilations at the individual compound level and in the context of overall volatility distributions and hydroxyl (OH) reactivity (OHR) estimates. The NMOC_g in NEIVA include ∼4–8 times more compounds with improved representation of intermediate volatility organic compounds, resulting in much lower overall volatility (lowest-volatility bin shifted by as much as 3 orders of magnitude) and significantly higher OHR (up to 90 %) than other compilations. These updates can strongly impact model predictions of the effects of BB on atmospheric composition and chemistry.

The number of seedlings from the soil seed bank of pine forests with differently dated fires near the Karabash copper smelter

We tested the hypothesis that the number of seedlings from the soil seed bank (SSB) in forests polluted by heavy metals and disturbed by recent fires decreases. It was also assumed that the consequences of pollution and fires for the soil seed bank are additive. We estimated the number of seedlings from the SSB of pine forests located near the Karabash copper smelter (KCS) (contaminated by Cu, Zn, Pb, and Cd) and from uncontaminated forests of the Ilmen State Reserve (ISR). In both areas, samples of the forest litter and humus horizon were taken from forests recently exposed to ground fires and long-term unburned forests. Samples were exhibited from June to September, conducting seven rounds of counting seedlings. Small peculiarities of the emergence of seedlings on the samples of the forest litter and the humus horizon were established. However, the regularities of the reaction of SSB to pollution and fire disturbances did not depend on the soil horizon. The number of seedlings on substrates from contaminated forests was 5–8 times lower than the number of seedlings on substrates from background forests. A decrease in the number of seedlings on polluted substrates was accompanied by an increase in the share of dicots in the total number of seedlings. The relationship between the number of seedlings and the age of fires was not found. The additivity of the consequences of pollution and fires has also not been established. Of the two types of damage, pollution and fires, the pollution factor is of leading importance for SSBs. The results indicate a low recovery capacity of the herb-shrub layer of polluted forests.

Determination of Toxicity at Different Trophic Levels of Aqueous Film-Forming Foams (AFFF) Used in Fire Fighting.

Aqueous film-forming foams (AFFF), containing perfluorinated surfactants, can reach the environment. The objective of this study was to determine the ecotoxicity of AFFF, according to the type of fire to be fought (A1: 1.05g.L- 1, A2: 3.15g.L- 1 and A3: 6.30g.L- 1), to bioindicators of different trophic levels. For Artemia salina a toxic effect was observed at sample A1 (at concentrations of 100%), A2 (at concentrations above 25%) and A3 (at concentrations above 12.5%). For Lactuca sativa all samples affected the number of germinated seeds, speed and percentage of germination and root length. To the Eisenia fetida earthworm, samples A2 and A3 were considered toxic due to the percent avoidance being 70% and 100%, respectively. In Macaca mullata renal cell culture test, none of the samples were toxic by the MTT test. Therefore, it is necessary to develop methods for the safe use of AFFF by professionals.

Estimation and Prediction of Carbon Emission from Typical Coalfield Fire in Xinjiang, China: Based on Field Detection and Historical Data

ABSTRACT Coalfield fires release large amounts of greenhouse gases like CO2 and methane. Quick and effective carbon emission estimates are vital for assessing fire control and enhancing carbon reduction. In this work, field detection was applied to estimate the coal loss of typical coalfield fires in Xinjiang, and a simplified model was used to estimate the carbon emission based on coal loss. This method was verified by comparing with literature data. Using this model, the carbon emissions of Jiangjunmiao, Sigonghe, and Changcaodong coalfield fire zones were estimated to be 1,245,187.5 t, 293,730 t, and 511,122 t, respectively. Besides the field test data, the historical data from government was also collected, in order to calculate the increase rate of carbon emission. In Xinjiang, substantial progress has been made in coal fire management. Our estimation indicates that from 1958 to 2019, Xinjiang’s efforts to control coalfield fires resulted in a reduction of 8.93 Gt of carbon emissions, roughly sevenfold the total carbon emissions of China in 2023. From 2019 to 2023, the annual rates of change in carbon emissions for coal fire areas in Xinjiang that were under active control and those that were left unregulated were −4% and + 13.3%, respectively. In order to achieve the carbon reduction targets in the coalfield fire zone, the three surveyed Xinjiang coalfields need to achieve an annual reduction rate of 58%-73% to achieve the 2030 plan, 20%-29% to achieve the 2050 plan, and 15%-22% to achieve the 2060 plan.

Using computer vision to classify, locate and segment fire behavior in UAS-captured images

The widely adaptable capabilities of artificial intelligence, in particular deep learning and computer vision have led to significant research output regarding flame and smoke detection. The composition of flame and smoke, also described as fire behavior, can be considerably different depending on factors like weather, fuels, and the specific landscape fire is being observed on. The ability to detect definable classes of fire behavior using computer vision has not been explored and could be helpful given it often dictates how firefighters respond to fire situations. To test whether types of fire behavior could be reliably classified, we collected and labeled a unique unmanned aerial system (UAS) image dataset of fire behavior classifications to be trained and validated using You Only Look Once (YOLO) detection models. Our 960 labeled images were sourced from over 21 h of UAS video collected during prescribed fire operations covering a large region of Texas and Louisiana, United States. National Wildfire Coordinating Group (NWCG) fire behavior observations and descriptions served as a reference for determining fire behavior classes during labeling. YOLOv8 models were trained on NWCG Rank 1–3 fire behavior descriptions in grassland, shrubland, forested, and combined fire regimes within our study area. Models were first trained and validated on classifying isolated image objects of fire behavior, and then separately trained to locate and segment fire behavior classifications in UAS images. Models trained to classify isolated image objects of fire behavior consistently performed at a mAP of 0.808 or higher, with combined fire regimes producing the best results (mAP = 0.897). Most segmentation models performed relatively poorly, except for the forest regime model at a box (locate) and mask (segment) mAP of 0.59 and 0.611, respectively. Our results indicate that classifying fire behavior with computer vision is possible in different fire regimes and fuel models, whereas locating and segmenting fire behavior types around background information is relatively difficult. However, it may be a manageable task with enough data, and when models are developed for a specific fire regime. With an increasing number of destructive wildfires and new challenges confronting fire managers, identifying how new technologies can quickly assess wildfire situations can assist wildfire responder awareness. Our conclusion is that levels of abstraction deeper than just detection of smoke or flame are possible using computer vision and could make even more detailed aerial fire monitoring possible using a UAS.

Fire enhances changes in phosphorus (P) dynamics determining potential post-fire soil recovery in Mediterranean woodlands

Soil phosphorus (P), which is essential for ecosystem functioning, undergoes notable changes after fire. However, the extent to which fire characteristics affect P dynamics remains largely unknown. This study investigated the impact of type of fire (prescribed burning and natural wildfires) of different levels of severity on P dynamics in Mediterranean soils. Soil P concentrations in the organic layers were strongly affected by fire severity but not fire type. Low severity fire did not have any observable effect, while moderate fire increased soil P levels by 62% and high severity decreased soil P concentration by 19%. After one year, the soil P concentration remained unchanged in the low severity fires, while rather complex recovery was observed after moderate and high severity fires. In the mineral layers, P concentration was reduced (by 25%) immediately after the fires and maintained for one year (at 42%). 31P-NMR spectroscopy revealed almost complete post-fire mineralization of organic P forms (mono- and diesters), large increases in inorganic orthophosphate and a decrease in the organic:inorganic P ratio (Po:Pi). After one year, di-esters and orthophosphate recovered to pre-fire levels at all sites, except those where parent material composition (high pH and Fe concentration) had an enduring effect on orthophosphate retention, and thus, on the total soil P. We showed that fire severity and soil pH (and hence, soil mineralogy) played an essential role in soil P dynamics. These findings are important for reliable assessment of the effects of fire on soil P conservation and for improving the understanding the impact of prescribed burning.

A COMPREHENSIVE REVIEW ON VATAJA GRAHANI ROGA

Grahani (duodenum) is regarded as the seat of Agni (digestive fire) and is sustained by its strength. Any factor that disrupts the normal function of Jatharagni (a type of digestive fire) can lead to Grahaniroga (Diarrhoea/Dysentry). Grahaniroga is categorised into different types based on the involvement of Doshas. Among all the kinds of Grahaniroga, Vataja Grahani Roga is more common and can be correlated with Irritable Bowel Syndrome (IBS). The causes of Vataja Grahani are primarily factors that aggravate Vata dosha. These include Katu, Tikta, Kashaya, Rooksha and Sheeta. The consumption of factors that aggravate Vata dosha leads to vitiated Vata enveloping the digestive fire (Agni), resulting in Manda Agni, or weak digestive fire. This condition causes delayed and difficult digestion, leading to Shukta paka forming, where the food becomes acidic. In Charaka Samhita, the treatment for Vataja Grahani is explained in detail. These include Aam Pachana, Deepaniya Ghrita, Anuvasana Basti,Virechana and Niruha Basti.

Seaport infrastructure risk assessment for hazardous cargo operations using Bayesian networks

Seaport infrastructure requires considerable resources and time for a full recovery from accidents caused by hazardous cargo. Despite their severity, the risk to seaport infrastructure from hazardous cargo operations has been insufficiently explored. This study aims to fill that gap by examining the risks to seaport infrastructure from the complex effects of hazardous cargo operations. It draws on literature, incident reports, and expert consultations to identify comprehensive risk factors and their interconnections. The study employs expert judgments alongside logistic regression to develop Conditional Probability Tables (CPTs) and conducts a risk analysis using Bayesian networks (BN). Our findings indicate that, under typical operating conditions, fire and explosion, corrosion, and improper handling are the most significant contributors to seaport infrastructure risk with probabilities of 8.73 %, 5.88 %, and 5.61 % respectively. Inverse propagation indicates that the contribution of improper handling and corrosion is enhanced by 153 % and 96 % respectively towards the increased risk. A sensitivity analysis was carried out to pinpoint critical risk factors. Based on these insights, the study suggests practical measures like the use of tracking and monitoring systems along with third-party audits for effective handling, augmented and virtual reality for advanced training, and automation technology for reduced human roles to subside risks to seaport infrastructure and promote uninterrupted operations.

The dynamic evolution characteristics of PM2.5 concentrations and health risk assessment during typical forest fires in China

In the context of climate change, increasingly frequent wildfire events are exacerbating the air quality crisis in China and have become a significant source of atmospheric PM2.5. This study selected 12 major forest wildfire events in China from 2018 to 2023. Using atmospheric pollutant and component concentration data, as well as meteorological data, the study employed MK trend analysis, forward trajectory simulation, potential source contribution function (PSCF), and health risk indices to investigate the evolution patterns of PM2.5 concentrations before and after each wildfire event. The study explored the driving role of meteorological factors in this evolution process and quantitatively analyzed the spatial distribution characteristics of health risks in surrounding cities post-wildfire. The results indicated that: 1) PM2.5 levels changed significantly before and after each wildfire event, although the proportion of components remained relatively stable. 2) PM2.5 concentrations exhibited varying characteristics within different buffer zones of the wildfire areas. In the forest wildfire events in Foshan, Guangdong Province, and Jinzhong, Shanxi Province, the average PM2.5 concentrations within the 50 km buffer zones reached approximately 90 μg/m3, about 4–5 times the multi-year average for these areas. 3) Wildfire emissions were mainly influenced by meteorological and topographical factors. Wind field dependence diagrams showed that PM2.5 tended to accumulate in all wind directions, particularly at lower wind speeds of 0–5 m/s 4) Potential source analysis revealed that PM2.5 emissions from wildfires posed significant risks to surrounding areas. Multiple events had high-risk areas (WPSCF ≥0.8), and among the cities in these high-risk areas, the health risk value (ΔM) for Ya'an City reached an extremely high 85.1. This study provides a theoretical basis for policymakers to develop locally tailored wildfire management policies, ensuring the protection of the public's right to breathe clean air.

Size scale effect on mass burning flux and flame behavior of solid fuels

The study investigates the horizontal fuel size effect on free-burning fires for PMMA plates and wood cribs. The fuel size effect on mass burning flux and flame behavior is mainly discussed and compared with typical pool fires. For the PMMA plate and liquid pool, when the fuel size is small (< 10 cm), either the 3D sidewall burning of PMMA or the container wall heated by flame can promote the burning flux at the horizontal projection area. As the fuel size increases, these side wall burning or heating effects decrease, causing the drop in burning flux with fuel scale for both the PMMA plate and liquid pool. For small-scale wood cribs, fire cannot self-sustain due to the large airflow cooling. With the increase in wood crib size, the burning rate first remains constant and then gradually increases, driven by the enhanced internal radiation. As the fuel size increases above 20–30 cm, the flame radiation dominates the burning flux for all fuel types. Fire dynamics simulator (FDS) was adopted to simulate the horizontal size effect by setting a varied fire source (horizontal projection) area. First, the flame geometry and heat release rate (HRR) of simulations were validated against experimental results. Subsequently, the validated fire model generates cases covering a broad range of fire scales. Finally, a new correlation of flame height with the fire heat release rate and fuel size is proposed, and its prediction capability is validated in the numerical fire modeling. This study quantifies the size effect on the burning rate for common solid fuels and provides valuable information for the numerical modeling of multi-scale fires.

Impact of Jet Fires on Steel Structures: Application of Passive Fire Protection Materials

Jet fires are the second most common fire scenario after spill fires. This type of fire is characteristic of gas and gas–oil fires occurring on oil platforms and gas production and processing plants. The consequences of such fires are characterized by high material damage; this is associated with extensive networks of technological communications, since there is a high density of technological facilities and installations in the territory where these fires occur. At such facilities, there is a large number of steel structures, which under the action of high temperature quickly lose their strength and deform. To protect steel structures in the oil and gas industry, fire protection is used, which consists of different types: boards in the form of flat plates, plasters, and epoxy paints. This paper compares three types of fire protection materials for steel structures under jet fire: board fireproofing, plaster composition, and epoxy coating. When comparing the efficiency in jet fire, cement boards were found to be the best. However, despite the better fire protection efficiency, their low application is expected due to their massiveness and the high cost of such protection and the difficulty of installation. Nevertheless, the development of fire depends on the place of its origin, the size of the initial fire zone, and the stability and massiveness of the metal elements of the vessel structure or the structure of the boards on which the equipment can be placed. Therefore, it is necessary to take these factors into account when selecting fire protection and to apply it depending on the required fire resistance limits of structures, which should be determined depending on the fire development scenarios.

Landcover-categorized fires respond distinctly to precipitation anomalies in the South-Central United States

Satellite detection of active fires has contributed to advance our understanding of fire ecology, fire and climate dynamics, fire emissions, and how to better manage the use of fires as a tool. In this study, we use active fire data of 12 years (2012–2023) combined with landcover information in the South-Central United States to derive a monthly, open-access dataset of categorized fires. This is done by calculating a fire predominance index used to rank fire-prone landcovers, which are then grouped into four main landscapes: grassland, forest, wildland, and crop fires. County-level aggregated analyses reveal spatial distributions, climatologies, and peak fire months that are particular to each fire type. Using the Standardized Precipitation Index (SPI), it was found that during the climatological fire peak-month, the SPI and fires exhibit an inverse relationship in forests and crops, whereas grassland and wildland fires show less consistent inverse or even direct relationship with the SPI. This varied behavior is discussed in the context of landscapes’ responses to anomalies in precipitation and fire management practices, such as prescribed fires and crop residue burning. In a case study of Osage County (OK), we find that large wildfires, known to be closely related to climate anomalies, occur where forest fires are located in the county and absent in areas of grassland fires. Weaker grassland fire response to precipitation anomalies can be attributed to the use of prescribed burning, which is normally planned under environmental conditions that facilitate control and thus avoided during droughts. Crop fires, on the other hand, are set to efficiently burn residue and are practiced more intensely in drier years than in wetter years, explaining the consistently strong inverse correlation between fires and precipitation anomalies. In our increasingly volatile climate, understanding how fires, vegetation, and precipitation interact has become imperative to prevent hazardous fire conflagrations and to better manage ecosystems.

Wildfire combustion emission inventory in Southwest China (2001–2020) based on MODIS fire radiative energy data

Wildfires, a persistent environmental menace, are a significant source of harmful gases and particulate emissions. This study leverages the fire radiative power (FRP) method to delineate a comprehensive wildfire emission inventory for Southwest China from 2001 to 2020. Daily fire radiative power data derived from 1 km MODIS Thermal Anomalies/Fire products (MOD14/MYD14) were used to calculate the FRE and combusted biomass. Available emission factors were assigned to three biomass burn types: forest, grass, and shrub fires. Over the span of two decades, we have compiled data and estimated the annual emissions of carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), sulfur dioxide (SO2), ammonia (NH3), nitrogen oxides (NOx), total particulate matter (TPM), black carbon (BC), organic carbon (OC), and non-methane volatile organic compounds (NMVOCs) to be 9809.13, 566.82, 25.79, 5.37, 12.25, 16.67, 133.53, 4.16, 41.81, and 97.23 Gg per year (Gg yr−1), respectively. In terms of fire type, forest fires accounted for the largest portion of total CO2 emissions (59.23%), with grass fires and shrub fires coming in second and third, accounting for 20.41% and 20.36%, respectively. Geographically, Yunnan Province were identified as the major contributor in Southwest China, accounting for 69.67% of the total emissions. Temporally, the maximum emission occurred in 2010 (24263.33 Gg), and the minimum emission occurred in 2017 (2917.66 Gg). And the emissions were mainly concentrated in February (23.33%), March (25.52%), and April (22.61%), which accounted for nearly three-fourths of the total emissions. The results of this study are much higher than those obtained by the burned area method, almost three times as high. In contrast, the results of this study are close to the fire emission data from the GFED4s and GFASv1.2 and QFEDv2.5r1 databases.

The effects of F-500 Encapsulator Agent on canine alerts and laboratory analysis using passive headspace concentration and gas chromatography-mass spectrometry

F-500 Encapsulator Agent (EA) is a fire suppression agent that is an alternative to traditional firefighting foams. It is marketed as having the capability to act on all four parts of the fire tetrahedron as well as being environmentally friendly and non-toxic. An internal survey of the use of F-500 EA by fire departments encountered by the Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF) ignitable liquid detection canine (ILDC) handlers in 2022 showed that this product is not yet in widespread use across the country, but where it has been implemented, it is frequently utilized on a variety of types of fires. Additional agencies are researching the product to determine if it should be adopted. As this product appears to be growing in popularity, it is important to understand whether the use of the product would affect a canine’s ability to detect ignitable liquids or a forensic laboratory’s ability to identify the presence of an ignitable liquid. Burned wood and burned carpet, two commonly encountered substrates, were spiked with gasoline or a heavy petroleum distillate (HPD) and F-500 EA was applied. At various time intervals, ILDC teams surveyed the samples and laboratory analysis was conducted. Results showed that the presence of F-500 EA can negatively affect canine alerts and the laboratory’s ability to identify ignitable liquids.

Multicategory fire damage detection of post‐fire reinforced concrete structural components

AbstractThis paper introduces an enhanced you only look once (YOLO) v5s‐D network customized for detecting various categories of damage to post‐fire reinforced concrete (RC) components. These damage types encompass surface soot, cracks, concrete spalling, and rebar exposure. A dataset containing 1536 images depicting damaged RC components was compiled. By integrating ShuffleNet, adaptive attention mechanisms, and a feature enhancement module, the capability of the network for multi‐scale feature extraction in complex backgrounds was improved, alongside a reduction in model parameters. Consequently, YOLOv5s‐D achieved a detection accuracy of 93%, marking an 11% enhancement over the baseline YOLOv5s network. Comparison and ablation tests conducted on different modules, varying dataset sizes, against other state‐of‐the‐art networks, and on public datasets validate the resilience, superiority, and generalization capability of YOLOv5s‐D. Finally, an application leveraging YOLOv5s‐D was developed and integrated into a mobile device to facilitate real‐time detection of post‐fire damaged RC components. This application can integrate diverse fire scenarios and data types, expanding its scope in future. The proposed detection method compensates for the subjective limitations of manual inspections, providing a reference for damage assessment.

Centering Amah Mutsun voices in the analysis of a culturally important, fire-managed coastal grassland.

Indigenous communities throughout California, USA, are increasingly advocating for and practicing cultural fire stewardship, leading to a host of social, cultural, and ecological benefits. Simultaneously, state agencies are recognizing the importance of controlled burning and cultural fire as a means of reducing the risk of severe wildfire while benefiting fire-adapted ecosystems. However, much of the current research on the impacts of controlled burning ignores the cultural importance of these ecosystems, and risks further marginalizing Indigenous knowledge systems. Our work adds a critical Indigenous perspective to the study of controlled burning in California's unique coastal grasslands, one of the most biodiverse and endangered ecosystems in the country. In this study, we partnered with the Amah Mutsun Tribal Band to investigate how the abundance and occurrence of shrubs, cultural plants, and invasive plants differed among three adjacent coastal grasslands with varying fire histories. These three sites are emblematic of the state's diverging approaches to grassland management: fire suppression, fire suppression followed by wildfire, and an exceedingly rare example of a grassland that has been repeatedly burned approximately every 2 years for more than 30 years. We found that Danthonia californica was significantly more abundant on the burned sites, whereas all included shrub species (Baccharis pilularis, Frangula californica, and Rubus ursinus) were significantly more abundant on the site with no recorded fire, results that have important implications for future cultural revitalization efforts and the loss of coastal grasslands to shrub encroachment. In addition to conducting a culturally relevant vegetation survey, we used Sentinel-2 satellite imagery to compare the relative severities of the two most recent fire events within the study area. Critically, we used interviews with Amah Mutsun tribal members to contextualize the results of our vegetation survey and remote sensing analysis, and to investigate how cultural burning contrasts from typical Western fire management approaches in this region. Our study is a novel example of how interviews, field data, and satellite imagery can be combined to gain a deeper ecological and cultural understanding of fire in California's endangered coastal grasslands.