Firefighters Research Articles

Perceptions Experiences and Challenges of Physical Activity among Firefighters with Coronary Heart Disease Risk Factors in the City of Cape Town Fire and Rescue Services

Background The development of atherosclerosis in firefighters is affected by various cardiovascular risk factors, such as smoking, poor dietary choices, and lack of physical activity. Together, these elements lead to obesity, high blood pressure, unhealthy cholesterol, and increased blood sugar levels. Continuous exposure to these risks raises the chances of worsening atherosclerosis, which can impede blood circulation to essential organs like the heart and brain. As a result, sudden cardiac death is the leading cause of fatalities while on duty in the fire service. Aim This study aimed to explore and describe the perceptions, experiences, and challenges associated with physical activity among firefighters with risk factors for coronary heart disease in the City of Cape Town Fire and Rescue Services. Methods A purposive sample of nine full-time active career firefighters from the City of Cape Town Fire and Rescue Services, representing three different ranks, was employed, using a descriptive qualitative study design. The semi-structured interview responses were recorded and transcribed verbatim. The participants were free to leave the study at any time and without penalty, and their rights to confidentiality were protected. The thematic analysis was then used to examine the transcripts. Results The results revealed that firefighters were not physically active enough to meet the suggested minimum levels of physical activity and were physically inactive. Sedentary lifestyles have numerous detrimental effects on the human body, such as elevated rates of dyslipidemia, hypertension, diabetes mellitus, cardiovascular disease mortality, and all-cause death. Therefore, it is essential to reduce sedentary behavior and increase physical exercise to improve the health and well-being of firefighters. Conclusion Policy strategies should be designed to introduce physical activities at low-moderate intensity levels and accommodate firefighters with coronary heart disease risk factors. Implementing team-based physical activity interventions has demonstrated positive impacts on individual behavior changes within the fire service.

Effect of Motivation on Employee Performance: A Case of Tanzania Fire and Rescue Force, Kigoma Region

This study sought to determine the effect of motivation on employee performance at the Tanzania Fire Rescue Force. The study was specifically designed to identify the effects of salary, fringe benefits, recognition, training & development on the employees' performance using a case of the Tanzania fire rescue force in Kigoma Region. The study employed a quantitative approach and cross-sectional research design. 50 structured questionnaires were distributed to 50 employees and the collected data were analyzed using descriptive statistics, principal component factors, correlation and simple linear regression analysis. The findings indicated the moderate effect of salary, Recognition & Appreciation on employee performance. The findings also revealed that personal commitments and job satisfaction are two fringe benefit factors influencing employee performance. Moreover, it was reported that Training & Development have contributed to 63% of the variation in employees' performance among workers. Additionally, the overall finding indicated that salary, fringe benefits, training, development, Recognition & Appreciation are seen as job-motivating factors that significantly impact employee performance. The study recommended that by nurturing a motivational work environment and supporting skill development and feedback mechanisms, the organization can further enhance employee performance and contribute to its overall success.

The problem of practical training of crew members of sea vessels for emergency situations: fire fighting

New methods of training crew members for fight a fire are being considered, based on the experience of chief officers and masters who have an experience of fire on a ship in reality. The collected analysis of incidents showed that today's methods of fire fighting characterized by scheduled duties between the crew are not effective, and are not implemented in an emergency situation. The proposed methods of practical training are aimed at reducing panic, rational use of crew forces and knowledge of special ship technical means of fire fighting.

Thermally robust hierarchical nanofiber triboelectric yarns for efficient energy harvesting in firefighting E-textiles

Textile-based triboelectric nanogenerators (TENGs) face challenges in achieving high performance and fulfilling functional requirement for special applications. This paper presents the development of a high-performance, thermally robust hierarchical polyimide (PI) nanofiber triboelectric yarns with a charge-trapping interlayer for application in firefighting E-textiles. The hierarchical nanofiber yarns, comprising a stainless-steel core electrode, a PI/MXene interlayer and PI outer tribo-layer, exhibit enhanced triboelectric performance due to the charge-trapping capabilities of MXene, which preventing the recombination of triboelectric and induced charges. When the MXene content in the charge-trapping interlayer reached 1 wt%, the corresponding TENG achieved its optimal output performance, with an output voltage of 153 V and a current of 13.13 μA and a peak power density of 0.84 W/m2 under optimal conditions. It also demonstrates exceptional thermal stability, maintaining stable output at temperatures up to 400 °C. The integration of the TENG into E-textiles enables real-time monitoring of firefighters’ physical activities and location tracking, significantly enhancing their safety and operational efficiency in fire scenarios. Additionally, the TENG powers electroluminescent yarns, improving visibility in dense smoke and dust environments. This research opens new avenues for the application of smart wearable systems in high-temperature environments, providing effective strategies for ensuring the safety of firefighters in fire rescue operations.

A methodology for assessing multiple hazards applied to Sweden

Despite extensive efforts through various international initiatives to reduce global warming, it has been determined that human induced climate change is here, that the present scale of disruption of the climate is unprecedented and will continue. Increasing weather volatility can be expected, which will most likely increase exposure to weather related hazards, e.g. wildfires, flooding. The aim of this paper is to present an index-based multi-hazard risk assessment method to assess wildfires and flooding hazard for two municipalities within Sweden. The method is designed to be used by the Fire and Rescue Service (FRS) for planning purposes and can be modified to take the local FRS’s capabilities and local conditions into account, thereby improving hazard preparedness at a local level. The analysis presented indicates that, while the frequency of multi-hazard overlap from wildfires and flooding is greatest in more northern parts of Sweden, the method provides important information even when applied to areas with limited overlap. A variation of the hazard assessment using a box kernel sliding window was studied to investigate the sensitivity of the model for rapid variations of an individual hazard level. Given that resource needs will typically spread over several days for large scale natural hazards, the box kernel approach is valuable in helping to identify a span of days when resources associated with incident response might be needed. In the future, the model should be expanded to include additional single hazards, the application to additional municipalities and extension to FRS planning exercises for natural hazards.

Injury Surveillance In Career Firefighters: A Retrospective Analysis Of Musculoskeletal Injury And Related Workers’ Compensation Costs

Injury Surveillance In Career Firefighters: A Retrospective Analysis Of Musculoskeletal Injury And Related Workers’ Compensation Costs

Differences In Career Firefighter Muscle Strength Outcomes Based On Self-reported Muscle Strengthening Behaviors

Differences In Career Firefighter Muscle Strength Outcomes Based On Self-reported Muscle Strengthening Behaviors

Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method

AbstractMore frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. In this study, we proposed an interpretable and accurate hybrid machine learning (ML) model, that explicitly represented the controls of fuel flammability, fuel availability, and human suppression effects on fires. The model demonstrated notable accuracy with a F1‐score of 0.846 ± 0.012, surpassing process‐driven fire danger indices and four commonly used ML models by up to 40% and 9%, respectively. More importantly, the ML model showed remarkably higher interpretability relative to other ML models. Specifically, by demystifying the “black box” of each ML model using the explainable AI techniques, we identified substantial structural differences across ML fire models, even among those with similar accuracy. The relationships between fires and their drivers, identified by our model, were aligned closer with established fire physical principles. The ML structural discrepancy led to diverse fire predictions and our model predictions exhibited greater consistency with actual fire occurrence. With the highly interpretable and accurate model, we revealed the strong compound effects from multiple climate variables related to evaporative demand, energy release component, temperature, and wind speed, on the dynamics of large fires and megafires in the western US. Our findings highlight the importance of assessing the structural integrity of models in addition to their accuracy. They also underscore the critical need to address the rise in compound climate extremes linked to large wildfires.

PAM material that instantly gives ordinary fabrics excellent flame retardant and thermal insulation properties for fire rescue

To effectively reduce the damage caused by flame burns or heat transfer to the human body during fire, we used PAM aqueous solution as the matrix, XG as the thickener, HPMC as the water-retaining agent to form the basic material system, and added different functional particles (APP, PTW, HCB) to prepare a fire-proof and heat-insulating PAM flame-retardant material for fire emergency rescue. Ordinary cotton fabrics were impregnated into PAM flame-retardant materials using a simple impregnation process. After the impregnation, the test was performed in a non-dropping state (simulating the thermal protection effect of PAM flame retardant materials directly acting on the outside of the human body at the fire scene). The results show that the PAM flame retardant material prepared by adding 4 wt% HCB has the best comprehensive performance. TPP test shows that spraying PAM flame retardant material on the outside of the fabric can instantly give the fabric a higher thermal protection performance. Under the total heat flux of 84 kW/m2, the thermal performance protection value of the fabric is 2641.8 kW·s/m2, and the second-degree burn time can reach 31.45 s. PAM flame retardant material does not damage the fabric. After soaping, the air permeability of the fabric decreases slightly, the moisture permeability and wettability are improved, and the breaking strength is almost unchanged. The CCT test showed that the thermal radiation flux was 50 KW/m2, the PHRR value of PAM flame retardant material was 10.64552 KW/m2, the THR was 6.9 MJ/m2, and the flame retardant performance was excellent. The PAM flame retardant material prepared in this project can be applied to the fire scene and directly sprayed on the outside of the clothing of rescuers and recipients, giving the fabric a better thermal protection effect. It can also be used to extinguish fires in the external environment. This material offers a novel solution for enhancing fire rescuers' and victims' safety protection levels.

Modification of the microclimate and fire danger by Berg winds in the midlands of KwaZulu-Natal

ABSTRACT Wildfires have the potential to result in significant loss of life and property damage. Mountain wave flow events (Berg winds) are integrally linked to periods of enhanced fire danger. The basis for enhanced fire danger during periods of mountain winds has long been established, supported by evidence from a number of case studies. A fuzzy logic model, applied to hourly meteorological data from four different sites varying in elevation, allowed for an in-depth study of the effects of Berg winds on the microclimate and fire danger. Results indicate that night-time Berg wind events occur less frequently than daytime events, also having a significantly reduced influence on the microclimate. Daytime Berg wind events were shown to result in a significant change in the microclimate and fire danger across all sites. Night-time Berg wind events did not result in significant change to air temperature and relative humidity but when combined with significant changes to wind speed did result in significant increases in fire danger. The sensitivity of the Lowveld Fire Danger Index was also tested. The study emphasizes the need for boundary layer processes such as Berg winds to be taken into account when forecasting and monitoring fire danger.

Spatial modelling and estimation of mammals' mortalities by Pantanal 2020 megafires

Abstract Extreme wildfire events, such as the Pantanal 2020 megafires, are expected to become more common. Assessing the impacts of such extreme events on wildlife is imperative for conservation planning. Direct observation of carcasses can provide valuable information on how these impacts relate to spatial heterogeneity. Here, we use double‐observer carcass surveys to assess the numbers and spatial patterns of direct mortalities of medium‐ to large‐sized mammals resulting from the Pantanal 2020 megafires in a large reserve (1080 km2) in the northern Pantanal wetland. Accounting for imperfect detection, we model the spatial variation in mortality occurrence and abundance, testing the effects of habitat‐related variables and wildfire severity using multi‐species N‐mixture models. We found that 26 out of 27 species of medium and large‐sized mammals died from the fires with a mean estimate of around 49 thousand individuals. The most affected species included capuchin monkeys, agoutis, peccaries, tapirs, brocket deer, tamanduas, coatis and capybaras. Direct mortality of mammals by wildfires was affected by landscape factors related to species habitats, species traits (probably related to escape or refuge strategies), and the intensity of the wildfires. Mortalities presented a general positive relationship with non‐flooded forests (a fire‐sensitive habitat in Pantanal) and with the severity of wildfires. Artificial water bodies, a common landscape structure in Pantanal, had 7.5 times more deaths than other areas. Synthesis and applications: With the approach used, we were able to: (i) identify mortality hotspots associated with landscape features; (ii) simultaneously identify the most affected species and assess the average relationships for all the assemblage of medium‐ and large‐sized mammals. We discuss conservation and management actions in two contexts: species prioritization for rescuing and monitoring; and territory prioritization for fire prevention and fighting.

Vegetation phenology as a key driver for fire occurrence in the UK and comparable humid temperate regions

Background Fire activity in the UK and comparable regions of northwest Europe is generally out of phase with peak fire weather conditions. Aims Here, we assess the potential effect of phenology on fire occurrence patterns for the UK. Methods We examined fire occurrence and vegetation phenology in the UK for 2012–2023, mapped onto the main fire-affected vegetation cover types within distinct precipitation regions, allowing the fire occurrence for fuels in different phenological phases to be explored across distinct ‘fuel’ types and regions. Key results The UK’s fire regime is characterised by burning in semi-natural grasslands and evergreen dwarf shrub ecosystems in early spring when vegetation is still dormant. During the high-greenness phase in late spring and summer, fire activity is reduced by a factor of 5–6 despite typically elevated fire weather conditions within that period. Conclusions and implications Semi-natural vegetation in the UK is very resistant to burning during the high-greenness phase. However, this ‘fire barrier’ is diminished during severe drought episodes, which are predicted to become more extreme in the coming decades. Incorporating phenology information into models therefore has great potential for improving future fire danger and behaviour predictions in the UK and comparable humid temperate regions.

Forest Fire Hazard Forecasting Based on Google Earth Engine Open Satellite Data

Introduction. Forest fires cause significant damage to both the natural fund and the national economy. In recent years, their harmful influence has increased due to global warming, and in Ukraine also due to the armed conflict. Thus, traditional methods of patrolling (ground and air) are not only costly, but also dangerous due to mines and possible shelling of border areas. Therefore, the role of surveillance using satellite systems is increasing. Space monitoring is more efficient and covers a larger area of ??the Earth's surface. Another important advantage is open access to information. The purpose of the paper is to build a mathematical model for determining fire danger based on climatic and biophysical satellite data for the forests of Ukraine, as well as a similar climatic zone with the possibility of further scaling to other climatic regions and types of vegetation cover. To adhere to the principles of open science, Google Earth Engine (GEE), a cloud-based platform that provides open access to dynamic collections of pre-processed Earth remote sensing results, was chosen. Results. Climatic and biophysical data for the forests of Ukraine for the years 2017-2020 were collected using the tools of the Python library for working with GEE data. Further, the obtained data were processed by two methods: linear (PCA) and non-linear (UMAP) in order to obtain statistically independent attributes. Both obtained datasets were subjected to statistical processing using the Bayesian method. Finally, for each point on the map for which information was collected, an indicator was calculated that predicted fire danger if the obtained value was greater than 1 and its absence in the opposite case. The resulting model showed its efficiency on training data. On the test dataset (data on Polish forests for the same period), the results turned out to be worse, in particular, the model using PCA did not predict absence of fire danger, and the model using UMAP generally showed lower performance. This can be due to both the imperfection of the model and the small size of the test dataset or factors unrelated to natural processes (in particular the human factor). Conclusions. An approach to forest fires forecasting based on satellite data is proposed. The obtained results indicate that the model is already effective at this stage, although machine learning methods have not yet been applied. However, it needs further improvement, so work on the model will be continued. Along with improving the quality of forecasts, attention will be paid to the geographic expansion and the creation of a web application. Keywords: fire danger in forests, satellite data, correlation, PCA, UMAP, hard-to-reach areas.

Development of national post-fire restoration system to assess net GHG impacts and salvage biomass availability

In light of the recent unprecedented wildfires in Canada and the potential for increasing burned areas in the future, there is a need to explore post-fire salvage harvest and restoration and the implications for greenhouse gas (GHG) emissions. Salvage logging and replanting initiatives offer a potential solution by regrowing forests more quickly while meeting societal demands for wood and bioenergy.This study presents a comprehensive modeling framework to estimate post-fire salvage biomass and net GHG emissions relative to a ‘do-nothing’ baseline for all of Canada's harvest-eligible forests. Forest ecosystem carbon emissions and removals were modeled at 1-ha spatial resolution for Canadian forests using the Generic Carbon Budget Model (GCBM) from 1990 to 2070 using several forest inventory data sources with future harvest and wildfires.Building upon previous research, our work integrated the Canadian Forest Fire Danger Rating System fire intensity to estimate fire severity of future wildfires. For 2024 to 2070, we assessed the changes in ecosystem carbon, emissions from harvested wood products, and substitution benefits from avoided emissions-intensive materials, relative to a forward-looking baseline. Our prototype system provides a comprehensive framework, configuration files, links to datasets to quantify the net GHG of post-fire restoration, and sample results for validation.•Developed spatially explicit forest carbon modeling system for all of Canada's forests.•Assessed the net GHG reduction from post-fire restoration.•Used system approach to consider forests, wood products and substitution benefits.

High-resolution projections of future FWI conditions for Portugal according to CMIP6 future climate scenarios

Wildfires are catastrophes of natural origin or initiated by human activities with high disruptive potential. "Portugal, located in western Iberia, has recently experienced several large fire events, including megafires, due to a combination of factors such as orography, vegetation, climate, and socio-demographic conditions that contribute to fuel accumulation.". One approach to studying fire danger is to use fire weather indices that are commonly used to quantify meteorological conditions that can lead to fire ignition and spread. This study aims to provide high-resolution (~ 6 km) future projections of the Fire Weather Index (FWI) for Portugal using the Weather Research and Forecasting (WRF) model, forced by the Max Planck Institute (MPI) model from the CMIP6 suite, under three emission scenarios (SSP2-4.5, SSP3-7.0, and SSP58.5) for the present period (1995–2014) and two future periods (2046–2065 and 2081–2100). The results show good agreement between FWI and its subcomponents from the WRF and reanalysis. The modelled FWI reproduced the climatological distribution of fire danger Projections indicate an increase in days with very high to extreme fire danger (FWI > 38) across all scenarios and time frames, with the southern and northeastern regions experiencing the most significant changes. The southern and northeastern parts of the territory experienced the largest changes, indicating significant changes between the scenarios and regions. This study suggests that FWI and its subcomponents should be investigated further. Our results highlight the importance of creating new adaptation measures, especially in the areas most at risk, prepared in advance by different players and authorities, so that the increasing risk of wildfires can be mitigated in the future.

Failure mechanism and thermal runaway behavior of lithium-ion battery induced by arc faults

As the widespread of lithium-ion battery systems such as electric vehicles and energy storage systems, the number of safety incidents due to electrical faults are increasing. Many accident reports have demonstrated that arc faults have become one of the main triggers of LIB system accidents, however, the related studies are inadequate. In this study, an arc imitation system is employed to investigate the influence of different arc energies on battery safety valve, as well as the electrochemical characteristics of faulty batteries. The results show that the minimum arc power to breach the safety valve ranges from 110 to 441 W. The maximum temperature rise rate on the battery surface can exceed 15 °C/s with arc power of around 1000 W. Further, the testing of in-situ and ex-situ indicate the faulty batteries undergo degradation and failure due to that moisture in the air enters the battery interior, resulting in increased internal resistance, loss of active materials and cyclable lithium. Finally, the faulty battery has no valve opening during thermal runaway, and the ignition time is four hundred seconds earlier than that of the normal battery, indicating more severe fire dangers. The results are valuable for safety design of battery systems in relation to arc faults, as well as the characteristic for fault detection and early warning.

A Multi-Scale Deep Learning Algorithm for Enhanced Forest Fire Danger Prediction Using Remote Sensing Images

Forest fire danger prediction models often face challenges due to spatial and temporal limitations, as well as a lack of universality caused by regional inconsistencies in fire danger features. To address these issues, we propose a novel algorithm, squeeze-excitation spatial multi-scale transformer learning (SESMTML), which is designed to extract multi-scale fire danger features from remote sensing images. SESMTML includes several key modules: the multi-scale deep feature extraction module (MSDFEM) captures global visual and multi-scale convolutional features, the multi-scale fire danger perception module (MFDPM) explores contextual relationships, the multi-scale information aggregation module (MIAM) aggregates correlations of multi-level fire danger features, and the fire danger level fusion module (FDLFM) integrates the contributions of global and multi-level features for predicting forest fire danger. Experimental results demonstrate the model’s significant superiority, achieving an accuracy of 83.18%, representing a 22.58% improvement over previous models and outperforming many widely used deep learning methods. Additionally, a detailed forest fire danger prediction map was generated using a test study area at the junction of the Miyun and Pinggu districts in Beijing, further confirming the model’s effectiveness. SESMTML shows strong potential for practical application in forest fire danger prediction and offers new insights for future research utilizing remote sensing images.

A brief descriptive analysis of a pilot study of peer support training and supervision for Texas firefighters

Background Peer support has been shown to reduce the incidence and severity of mental health disorders among firefighters. This pilot project had two aims: (1) to assess training satisfaction and knowledge, self-efficacy, and skills development of Texas firefighters participating in the International Association of Fire Fighters (IAFF) Peer Support Training, and (2) to determine if newly trained peer support specialists would use monthly phone supervision. Methods The study included 14 Texas firefighters who enrolled in the IAFF Peer Support Training. Pre- and post-tests were administered to evaluate training satisfaction, skills development, self-efficacy, and behavioral health knowledge of participants. Use of phone supervision over 6 months following the training was also recorded. Results Overall, participants reported high satisfaction with the training provided. While self-efficacy regarding detection of mental health issues and therapeutic empathy skills significantly improved following the training, participants did not show a significant improvement in knowledge regarding mental health. Attendance of participants to supervision calls was less than expected, although 71% attended at least one supervision call. Conclusions This project was successful in increasing firefighters’ self-efficacy and therapeutic empathy skills, and participants were highly satisfied with the training. However, further research on follow-up supervision of peer support trainees is needed.

A national-scale sampled temperate fuel moisture database

Fuel moisture content (FMC) is important for the ignitability, behaviour and severity of wildfires. Understanding the drivers of FMC and its spatial and temporal variability can help us develop fuel moisture models and inform assessments of wildfire behaviour and danger. Here we present the first United Kingdom (UK) national-scale temperate FMC dataset of 8,057 samples of eighteen different fuel constituents collected across 58 sampling sites between 2021–2023. We sampled fuels across emerging fire-prone ecosystems in the UK across three studies: (1) UK-wide longer-term sampling characterising the spatio-temporal drivers of FMC; (2) landscape-scale measurement through the North Yorkshire Moors to investigate landscape-driven variability in FMC; (3) plot-scale intensive sampling in the West Midlands to quantify diurnal patterns and among-sampler variability in fuel measurements. This database addresses a global fuel moisture measurement gap within traditionally non-fire prone regions. The database will advance our understanding of temperate fuel moisture dynamics and forms a fundamental contribution towards the development of a fire danger rating system for traditionally non-fire prone regions such as the UK.

Evaluating the Success Rate of Distal Femur Intraosseous Access Attempts in Pediatric Patients in the Prehospital Setting: A Retrospective Analysis

Objectives Although the proximal tibia is a common site for intraosseous (IO) line placement in pediatric patients, previously published data indicate high malposition rates in infants and children at this location. Although distal femur IO lines generally demonstrate higher flow rates than those at the proximal tibia, to date, there have been no published studies assessing distal femur IO access in pediatric patients. Thus, we aimed to compare the success rates of pediatric IO line insertion attempts between the proximal tibia and the distal femur in a prehospital setting. Methods We conducted a retrospective chart review of prehospital pediatric patients who underwent at least one IO line placement attempt by Palm Beach County Fire Rescue from May 2015 to January 2024. We excluded records lacking specific documentation of IO attempt location. We compared the unadjusted success rates of distal femur to proximal tibia, and we also compared success rates after propensity score matching and multivariable logistic regression. Secondarily, we assessed the prehospital complication rate of the IO lines at each anatomical site. Results We identified 163 pediatric patients who had an IO attempt and were eligible for analysis. Median age was 1.9 years (IQR: 0.46 to 4.2 years). Among those 163 patients, there were 234 vascular access attempts, including 82 IO attempts at the distal femur and 72 at the proximal tibia. The unadjusted success rate of distal femur attempts was 89.0%, compared to 84.7% for proximal tibia attempts, a difference of 4.3% (95% CI −6.4 to 15.0%). After propensity score matching, we found an adjusted odds ratio of 2.0 (95% CI 0.66 to 6.8), favoring the distal femur for successful placement. Prehospital complication rates were similar for distal femur (5.5%) and proximal tibia (4.9%). Conclusions This retrospective analysis of pediatric patients in a prehospital setting suggests that IO line placement at the distal femur might offer a marginally higher success rate compared to the proximal tibia. Despite not reaching statistical significance, these findings support the consideration of distal femur as a viable option for IO placement in the pediatric population.