Firefighters Research Articles

Impact of astaxanthin supplementation on markers of cardiometabolic health and tactical performance among firefighters.

Firefighters are at risk for cardiovascular disease due to occupational-related inflammation, oxidative stress, and lifestyle practices. Astaxanthin (AX) possesses anti-inflammatory/antioxidant and purported ergogenic properties. This study examined the impact of supplementing the diet with 12 mg/d AX for four weeks on markers of inflammation, oxidative stress, cardiometabolic health, exercise capacity, and occupation-related performance in career firefighters. In a randomized, double-blinded, placebo-controlled, crossover fashion, 15 male career firefighters (34.5 ± 7.4 years; 177.7 ± 7.0 cm; 95.6 ± 12.0 kg; 30.1 ± 2.9 kg/m2; 11.03 ± 6.85 years of service) ingested 12 mg/d of AX (AstaReal®, AstaReal AB, Nacka, SWE) or placebo (PLA) for four weeks while following a standardized resistance training program. After each treatment, testing sessions were completed to assess inflammatory markers, oxidative stress markers, cardiopulmonary exercise capacity, and performance to a fire ground test (FGT) consisting of nine fire suppressive activities. Data were analyzed using general linear model (GLM) analysis with repeated measures. Clinical significance was assessed via mean changes from baseline with 95% confidence intervals. Analysis of mean percent changes from baseline revealed that AX supplementation lessened the inflammatory response to to performing an incremental maximal exercise test and attenuated increases in interleukin-1β, cortisol, and uric acid in response to performing fire suppressive activities compared to when they ingested PLA. However, most of these effects were within groups rather than between groups. Additionally, there was evidence that AX ingestion increased the ventilatory anaerobic threshold. Four weeks of AX supplementation did not significantly affect fasting markers of oxidative stress, blood lipids, performance during the FGT, general clinical chemistry panels, or self-reported side effects. Results provide some evidence that AX supplementation may help mediate occupation-related inflammation in response to high-intensity, short-duration exercise in firefighters. More research is warranted to determine if long-term supplementation can improve cardiometabolic risk in this population. ISRCTN10901752.

Increasing fire danger in the Netherlands due to climate change

Increasing fire danger in the Netherlands due to climate change

Field determination of trace concentrations of hazardous metals in waters by portable EDXRF.

A new effective method has been developed for field determination of trace concentrations of cadmium, arsenic, zinc, copper, nickel, chromium, and lead in waters (e.g., surface and wastewater) using an energy dispersive X-ray fluorescence (EDXRF) analyzer. Trace concentrations cannot be determined by EDXRF without preconcentration. Agar was used for the preconcentration of these potentially toxic elements (PTE). Metals are trapped in the three-dimensional network of agar gel. Subsequent swelling of the gel leads to more effective preconcentration. The swollen gels with PTE were analyzed by a portable energy dispersive X-ray fluorescence analyzer Delta Professional X (pXRF). The preconcentration factors for each PTE were determined. With pXRF, the detection limits were 7, 5, 6, 7, 9, 47, and 22 µg/L for Cr, Ni, Cu, Zn, As, Cd, and Pb, respectively. A reference material was used to assess the methodology performance, and it has a very good agreement. The optimized and verified methodology was tested in practice at the special chemical unit of the Fire Rescue Service of the Czech Republic. PTE were determined by pXRF in a mobile chemical laboratory in field analysis. The measured values were compared with those determined via inductively coupled plasma mass spectrometry (ICP-MS) and were in good agreement. Our method was applied to real wastewater samples collected during after a fire of accumulation batteries of domestic photovoltaic power plants, allowing accurate real-time estimation of PTE concentrations. Obtaining reliable data during an ecological accident has a very important role in minimizing the subsequent environmental impact of the accident.

Pervasive fire danger continued under a negative emission scenario

Enhanced fire-prone weather under greenhouse gas warming can significantly affect local and global carbon budgets from increased fire occurrence, influencing carbon-climate feedbacks. However, the extent to which changes in fire-prone weather and associated carbon emissions can be mitigated by negative emissions remains uncertain. Here, we analyze fire weather responses in CO2 removal climate model experiments and estimate their potential carbon emissions based on an observational relationship between fire weather and fire-induced CO2 emissions. The results highlight that enhanced fire danger under global warming cannot be restored instantaneously by CO2 reduction, mainly due to atmospheric dryness maintained by climatic inertia. The exacerbated fire danger is projected to contribute to extra CO2 emissions in 68% of global regions due to the hysteresis of climate responses to CO2 levels. These findings highlight that even under global cooling from negative emissions, increased fire activity may reinforce the fire-carbon-climate feedback loop and result in further socio-economic damage.

Work-related Fatigue: Relationship between Perceived and Performance Fatigability in Career Firefighters

Work-related Fatigue: Relationship between Perceived and Performance Fatigability in Career Firefighters

Research on the Construction of Grassroots Emergency Command System Under the Background of Informatization

Along with our economic development and public safety demand intensify., the task of fire rescue team is further expanded, and the construction of fire rescue command system becomes more and more important. This paper takes the information construction of the fire rescue team as the center, and studies the construction of the grass-roots emergency command system, aiming to promote the construction of the fire rescue system and improve the emergency rescue level of the fire rescue team. This paper takes the emergency rescue command system of the special service station of X Fire rescue detachment as the research object, and studies the construction of the basic emergency command system through literature research method and interview investigation methods.

Spatiotemporal analysis of weather-related fire danger associated with climate change in the Zagros Mountains, Iran

Spatiotemporal analysis of weather-related fire danger associated with climate change in the Zagros Mountains, Iran

Prediction of fire danger index using a new machine learning based method to enhance power system resiliency against wildfires

AbstractWildfires, which can cause significant damage to power systems, are mostly inevitable and unpredictable. Fire danger indexes, such as the Forest Fire Danger Index (FFDI) and the Canadian Fire Weather Index (FWI), measure the potential wildfire danger at a given time and location. Thus, by predicting these fire danger indexes in advance, power system operators can obtain valuable insight into the potential wildfire risks and can better be prepared to tackle the wildfires. However, due to dependency on weather conditions, these indexes usually have volatile time series, which make their prediction complex. Taking these facts into account, this paper, unlike previous approaches that predict fire danger indexes based on climatological models, develops a machine learning‐based forecast process to predict these indexes using the relevant weather data and past performance. To do this, first, a volatility analysis approach is presented to analyse the volatility level of the time series data of a fire danger index. Afterwards, an effective machine learning‐based forecast methodology using a new deep feature selection model is proposed to predict fire danger indexes. The developed forecast methodology is tested on the real‐world data of FFDI and FWI and is compared with several popular alternative methods reported in the literature.

Analysis of Traffic Accidents with the Deployment of the Fire Rescue Service in the Regions of the Czech Republic

Analysis of Traffic Accidents with the Deployment of the Fire Rescue Service in the Regions of the Czech Republic

The Global Forest Fire Emissions Prediction System version 1.0

Abstract. The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in near-real time for global air quality forecasting. The model uses a bottom-up approach, based on remotely sensed hotspot locations, and global databases linking burned area per hotspot to ecosystem-type classification at a 1 km resolution. Unlike other global fire emissions models, GFFEPS provides dynamic estimates of fuel consumption, fire behaviour and fire growth based on the Canadian Forest Fire Danger Rating System, plant phenology as calculated from daily global weather and burned-area estimates using near-real-time Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-detected hotspots and historical burned-area statistics. Combining forecasts of daily fire weather and hourly meteorological conditions with a global land classification, GFFEPS produces fuel consumption and emission predictions in 3 h time steps (in contrast to non-dynamic models that use fixed consumption rates and require a collection of burned area to make post-burn estimates of emissions). GFFEPS has been designed for use in operational forecasting applications as well as historical simulations for which data are available. A study was conducted showing GFFEPS predictions through a 6-year period (2015–2020). Regional annual total smoke emissions, burned area and total fuel consumption per unit area as predicted by GFFEPS were generated to assess model performance over multiple years and regions. The model's fuel consumption per unit area results clearly distinguished regions dominated by grassland (Africa) from those dominated by forests (boreal regions) and showed high variability in regions affected by El Niño and deforestation. GFFEPS carbon emissions and burned area were then compared to other global wildfire emissions models, including the Global Fire Assimilation System (GFAS), the Global Fire Emissions Database (GFED4.1s) and the Fire INventory from NCAR (FINN 1.5 and 2.5). GFFEPS estimated values lower than GFAS and GFED (80 % and 74 %) and had values similar to FINN 1.5 (97 %). This was largely due to the impact of fuel moisture on consumption rates as captured by the dynamic weather modelling. Model evaluation efforts to date are described – an ongoing effort is underway to further validate the model, with further developments and improvements expected in the future.

The Mathematical Model Based on the Parameters of Simulation Results Predicts the Fire Extinguishing Resource Demand of Naval Fires

In recent years, due to the diversity of fire scenes in ports and ships, the problem of fire command is complicated. In the case of power failure, the original command platform and fire extinguishing system will not be able to be used normally, or the fire extinguishing resources carried by the ship when it is on fire cannot be used. In the traditional firefighting ability research, there is no clear measure of firefighting ability, resulting in inaccurate calculation results. By corresponding to the quantity and composition of resources for the study of fire extinguishing capability, the combination of the fire dynamics simulation software PyroSim2020 and a calculation model that converts its resources into the total amount and flow of fire extinguishing agent can be provided. Based on the analytic hierarchy process (AHP), a firefighting demand grade calculation and judgment model was established, which included five factors: fire scale, combustion material characteristics, foam extinguishing agent performance, firefighting object characteristics, and external factors. It concluded that the demand assignment values of each index element were 2, 5, 6, 9, and 10, and further proposed the grade judgment criteria for the calculation results of comprehensive fire demand. Through the quantitative simulation of firefighting demand based on the fire scenario and calculation model test of the consumption prediction of cooperative firefighting equipment, it can also provide a strategic reference for related cooperative fire rescue.

An adaptable dead fuel moisture model for various fuel types and temporal scales tailored for wildfire danger assessment

Estimating the Dead Fuel Moisture Content (DFMC) is crucial in wildfire risk management, representing a key component in forest fire danger rating systems and wildfire simulation models. DFMC fluctuates sub-daily and spatially, influenced by local weather and fuel characteristics. This necessitates models that provide sub-daily fuel moisture conditions for improving wildfire risk management. Many forest fire danger rating systems typically rely on daily fuel moisture models that overlook local fuel characteristics, with consequent impact on wildfire management. The semi-empirical parametric DFMC model proposed addresses these issues by providing hourly dead fuel moisture dynamics, with specific parameters to consider local fuel characteristics. A calibration framework is proposed by adopting Particle Swarm Optimization-type algorithm. In the present study, the calibration framework has been tested by using hourly 10-h fuel sticks measurements. Implementing this model in forest fire danger rating systems would enhance detail in forest fire danger conditions, advancing wildfire risk management.

Observed Associations between Fire Danger and Climate Modes and Their Representation in ACCESS-S2

Abstract The increasing frequency and severity of wildfires in Australia, driven by climate change, pose a significant threat to ecosystems, lives, and property. This study examines the impact of climate drivers, specifically El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), Southern Annular Mode (SAM), Madden–Julian oscillation (MJO), and two modes of persistent high pressure in the Australia–Pacific region, on extreme fire danger. By analyzing observed and simulated fire danger in relation to these climate drivers, we aim to enhance our understanding of climate–fire mechanisms and contribute to Australia’s bushfire preparedness. Our findings indicate that all assessed drivers influence extreme fire danger, with key influences related to the drivers’ established relationships with rainfall and temperature. El Niño, positive IOD, and negative SAM events generally increase extreme fire danger across most of Australia and in most seasons. The two modes of Australian blocking exhibit similar effects, varying spatially. Specific phases of the MJO have significant seasonal relationships with fire danger. In some instances, increased fire danger is not directly linked to temperature or precipitation changes but rather driven by remote teleconnections, airflow, or pressure anomalies. Evaluating the accuracy of weather and climate forecasting systems in representing these relationships is crucial for the effective prediction and mitigation of fire hazards. The leading Australian climate simulation model effectively reproduces observed relationships but reveals biases in capturing certain aspects of climate variability. Advancements in this field would enhance fire weather forecasts, including those by the Australian Bureau of Meteorology with lead times of up to 4 months. Significance Statement This assessment of climate–fire relationships and modeling accuracy is pivotal in understanding the factors behind severe fire events and improving future predictions and mitigation. Prior research has already established links between elevated fire weather conditions and these climate drivers, highlighting their potential for predicting severe fire events. This study aims to deepen our comprehension of the factors driving severe fire events and facilitate improved bushfire risk management in Australia.

Assessing urban fire risk: An ensemble learning approach based on scenarios and cases

Urban fires represent a significant hazard to people’s lives and property, which makes it critical to estimate the risk adequately. Existing urban fire evaluation methods lack applicability because they do not take into account individual scene components and previous cases. As a result, this study offers the scenario- and case-based urban fire risk assessment approach (SCBUFRA), which seeks to achieve a more thorough and accurate urban fire risk assessment. First, the technique uses fire case and scenario data, as well as the recursive feature elimination method, to pick the elements utilized to assess urban fire risk. Second, the data-driven empowerment technique and stability analysis are utilized to determine the precise fire risk value and correctly quantify the fire danger level in each part of the city. Next, the Affinity Propagation (AP) technique is used to cluster scene elements. Ensemble learning is then used to create a risk prediction model by refining the weighting strategy of R2. Finally, Shapley additive explanations are used to investigate the elements causing urban fires. The findings show that SCBUFRA outperforms popular machine learning methods, that the number of crimes, gross population, and house price are the most important variables for fire prediction, and that the research is applicable to urban fire risk management and firefighting resource allocation.

Alcohol Misuse and Correlates with Mental Health Indicators among Firefighters

Background: Stress and repetitive exposures to potentially traumatizing events are inherent to first-responder professions. Firefighters and other crisis responders are often exposed to incidents in their work environments and are at risk of developing acute or posttraumatic symptomatology, depression, and anxiety, as well as harmful patterns of alcohol consumption. Most prior research and theory suggests that many firefighters use alcohol to cope with past and current stressors, and that increased drinking accompanies ongoing exposures to stressful occupational hazards. Objectives: The primary aim of this research was to examine alcohol consumption and mental health variables associated in the literature with alcohol misuse. Participants included 546 predominantly male career firefighters in the U.S. Using a cross-sectional design, we examined alcohol misuse in the context of self-reported depression, generalized anxiety, PTSD, and suicidal ideation using previously validated measures. Results: Based on the AUDIT’s recommended cut score of 8, 38.8% of our sample reported drinking at hazardous levels. Hazardous alcohol use was also significantly associated with several mental health indicators with correlations ranging from .28 to .34. Regression analyses revealed that over 14% of the variance in AUDIT scores could be explained by psychological symptom measures, and 29% of the variance in suicide risk was explained by a model using both alcohol and mental health measures. Conclusions: Findings suggest that psychological distress is associated with hazardous drinking, and that more consistent screening and referral for these issues, as well as multi-level interventions, are needed to effectively address these behavioral health concerns among firefighters.

MONITORING SISTEM PROTEKSI KEBAKARAN AKTIF PADA PLTU ANGGREK 2 X 25 MW

The Anggrek Unit Steam Power Plant (PLTU) is a power generation facility whose construction began in 2007 and began operating in 2019 with a capacity of 2 x 25 MW. Since its inauguration, PLTU Anggrek has become one of the main sources of electricity distribution in Gorontalo Province, so the frequency of power outages in the area has now decreased significantly. The water supply system for fire fighting uses BSP pipes. In this research, the aim is to determine the active fire protection system. This research was carried out with a qualitative descriptive design with data collection: observation and interviews. The assessment of the results of existing suitability will be assessed using descriptive percentage calculations. The research results show that of the 7 variables discussed, the average value for the level of fulfillment is 98.57%. Based on the level of fire audit assessment that meets the requirements of Indonesian national standards and international standards, the results are in the Good criteria or have met the requirements.

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.

Rancang Bangun Sistem Pendeteksi Kebakaran Hutan Menggunakan Drone Berbasis Computer Vision

The danger of forest and land fires in Indonesia is a serious threat that continues to recurevery year. According to U.S. data Fire Service, there are more than 700 forest fires everyyear burning more than 7 million hectares of land. These fires not only damage ecosystems,but also cause the loss of important biodiversity. Many of Indonesia's endemic flora andfauna have fallen victim, increasing the risk of extinction. The use of UAVs can also changedisaster management. This technology can be integrated with early warning systems basedon analytical data and artificial intelligence, increasing the accuracy of fire predictions andmore proactive responses. Thus, the application of this technology is not just a temporarysolution, but a long-term strategic step to maintain environmental sustainability andcommunity welfare. Technological solutions such as UAVs and cooperation betweenvarious parties are needed to overcome this challenge effectively. The aim of this researchis to create a computer vision-based system that uses deep learning algorithms, especiallyYou Only Look Once (YOLO), to detect forest fires quickly and accurately. In mitigating forest fire disasters, early detection of hotspots is an important step to prevent fires fromspreading to other areas and reduce the damage caused by fires to humans and theenvironment. To achieve the objectives of this study, several actions were taken. First,forest fire image data is collected and processed for use in training the YOLO model. Next,the model is trained using a dataset that covers various forest fire conditions, such as fireintensity, time of day, and weather conditions, to ensure that the model can find firehotspots in various conditions.

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.

Simulation Design and Research of Flame Generation in Virtual Fire Scene Based on Unity3D

Since the 21st century, with the continuous development and progress of science and technology, the possibility of fire danger is also rising, the requirements of public fire safety management have also been improved accordingly, and the problems in its fire safety management work make it difficult to meet the management requirements. The selection of firefighters has also become the top priority of management, good psychological quality of firefighters can keep calm in the face of fire explosions and various emergencies, and can make the most correct choice to complete the rescue and fire fighting work. In this paper, a building in a certain location is selected as the location to simulate the scene of fire. Through human-computer interaction to judge the psychological quality of firefighters. First use major modeling software such as 3ds Max, Adobe Fusecc, or find material in the Unity3D store to create models of fire scenes and people and import them into Unity3D. Using Unity3D's own particle system component to build flame models, adding collision body components to all models to detect their collision detection mechanism and the mechanism of flame spreading combustion. A Scenario-based Communication Ability Evaluation System Based on Traditional Cognitive Behavior Measurement and Advanced Eye Movement, EEG and NIR Acquisition Techniques.