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Dinosaur goes to the fire station
\"The fire station is full of fun in this exciting title. Trying on a fire helmet, sliding down the fire pole, and checking out a big red fire truck are all on tap when this dino visits his fireman brother at the station. Beginning readers will love watching their dinosaur friend try on a fireman's outfit and watching him learn what dinos do at a fire station when they're on the clock. But what happens at the station when a call comes in? For these dinos, it's time for action!\"--Publisher's website.
Book
Models to Simulate Effective Coverage of Fire Station Based on Real-Time Travel Times
In recent years, frequent fire disasters have led to enormous damage in China. Effective firefighting rescues can minimize the losses caused by fires. During the rescue processes, the travel time of fire trucks can be severely affected by traffic conditions, changing the effective coverage of fire stations. However, it is still challenging to determine the effective coverage of fire stations considering dynamic traffic conditions. This paper addresses this issue by combining the traveling time calculation model with the effective coverage simulation model. In addition, it proposes a new index of total effective coverage area (TECA) based on the time-weighted average of the effective coverage area (ECA) to evaluate the urban fire services. It also selects China as the case study to validate the feasibility of the models, a fire station (FS-JX) in Changsha. FS-JX station and its surrounding 9,117 fire risk points are selected as the fire service supply and demand points, respectively. A total of 196 simulation scenarios throughout a consecutive week are analyzed. Eventually, 1,933,815 sets of valid sample data are obtained. The results showed that the TECA of FS-JX is 3.27 km2, which is far below the standard requirement of 7.00 km2 due to the traffic conditions. The visualization results showed that three rivers around FS-JX interrupt the continuity of its effective coverage. The proposed method can provide data support to optimize the locations of fire stations by accurately and dynamically determining the effective coverage of fire stations.
Journal Article
Good night fire engines
Take a tour of the fire station and learn about the equipment that fire fighters use and how they do their job.
Book
Size-resolved particulate matter inside selected fire stations and preliminary evaluation of the effectiveness of washing machines in reducing its concentrations
The study aimed to determine and compare the mass concentration and size distribution of particulate matter (PM) at two Polish fire stations, one equipped with a washing machine intended for the decontamination of uniforms (FSN) and the other not equipped with this type of device (FSC), to assess the effectiveness of washing machines in reducing PM concentrations inside fire stations and estimate PM doses inhaled by firefighters while performing activities in truck bays and changing rooms during one work shift. The average PM concentrations at the FSN were 18.2–28.9 µg/m
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and 27.5–37.3 µg/m
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, while at FSC they were 27.4–37.9 µg/m
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and 24.6–32.8 µg/m
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in the truck bays and changing rooms, respectively. At each measurement point, most of the PM mass (65–75%) was accumulated as fine particles. The dominance of fine particles in the total mass of PM results in high values of PM deposition coefficients (0.59–0.61) in three sections of the respiratory tract at each monitoring site. This study initially indicates the effectiveness of washing machines in reducing the concentration of fine particles and demonstrates the necessity, as well as directions for further research in this area.
Journal Article
Advanced GIS and fuzzy logic integration for strategic fire station placement in Yanbu industrial city, Saudi Arabia
In the dynamic landscape of urban safety and emergency management, the strategic siting of fire stations emerges as a critical factor for enhancing operational efficiency and public safety. This study introduces a groundbreaking approach based on the Evaluation-Reallocation-Planning (E-R-P) method, tailored for Yanbu Industrial City in Saudi Arabia. This method transforms the decision-making process for fire station placement, offering a blend of current site evaluations, strategic station reallocation, and forward-thinking service planning. At the heart of this research lies the integration of fuzzy logic with Geographic Information System technology and location-allocation models, crafting a robust evaluation framework. The spectrum of algorithms was delved into to dissect the insights from fuzzy logic operations, focusing on three pivotal criteria: socioeconomic dynamics, infrastructure, and urban planning. The findings unveil a stark reality: Yanbu's current fire station network covers 24.8% of the city's structures. However, applying the E-R-P model boosts this coverage by an impressive 18.6%, reshaping the city's emergency response landscape. The fuzzy SUM operator emerged as a critical tool in pinpointing prime locations for new fire stations, which marked a significant stride in disaster readiness. This study redefines emergency service deployment in Yanbu and sets a precedent for other burgeoning cities worldwide. It offers a scalable, data-centric framework for optimizing fire station locations, enhancing urban safety, and streamlining city management.
Journal Article
Exploring Spatiotemporal Accessibility of Urban Fire Services Using Real-Time Travel Time
The accessibility of urban fire services is a critical indicator in evaluating fire services and optimizing fire resource allocation. However, previous studies have mainly concentrated on measuring the spatial accessibility of fire services, and little, if any, consideration has been paid to exploring the spatiotemporal dynamics of the accessibility of urban fire services. Therefore, we used real-time travel time to extend an existing spatial accessibility method to measure the spatiotemporal accessibility of fire services in a case study of Nanjing, China. The results show that (1) the overall accessibility of fire incidents and fire stations in Nanjing, China, is uneven, with relatively high accessibility in the southwest and northeast of the city center; (2) the number of fire incidents with low-level accessibility apparently increases in rush hours (i.e., 07:00–09:00 and 17:00–19:00 h) in the southeast and north of the city center, and the fire incidents with medium-level and high-level accessibility easily change to lower levels under the influence of traffic congestion, with fire incidents with medium-level accessibility being affected the most; (3) the accessibility of fire stations changes over time with an obvious W pattern, with lower accessibility during rush hours than at other times, and several fire stations in the city center present an asymmetric W pattern; (4) the accessibility decline ratio for fire stations in rush hours is greater in the city center than in urban suburbs, and the decline ratios are strongly related to the travel time increase and the percent increase in uncovered fire incidents during rush hours. The results and findings demonstrate that our method can be used to explore the spatiotemporal dynamics of the accessibility of fire services, and so can guide policymakers in improving fire services.
Journal Article
Dear dragon goes to the firehouse
A boy and his pet dragon go on a class trip to the local fire house and learn about firefighting equipment and fire safety.
Book
Location Optimization of Urban Fire Stations Considering the Backup Coverage
Urban fires threaten the economic stability and safety of urban residents. Therefore, the limited number of fire stations should cover as many places as possible. Moreover, places with high fire risk should be covered by more fire stations. To optimize the location of urban fire stations, we construct a multi-objective optimization model for fire station planning based on the backup coverage model. The improved value of environment and ecosystem (SAVEE) model is introduced to quantify the spatial heterogeneity of urban fires. The main city zone of Wuhan is used as the study area to validate the proposed method. The results show that, considering the existing fire stations (85 facilities), the proposed model achieves a significant 38.56% in high-risk areas that can be covered by more than one fire station. If the existing fire stations are not considered when building 95 fire stations, the proposed model can achieve coverage of 50.07% in high-risk areas by utilizing more than one fire station. As a result, the proposed backup coverage model would perform better if the protection of high-risk areas is improved with as few fire stations as possible to guarantee more places covered.
Journal Article