Ignition Point Research Articles

A Systematic Approach to Map and Evaluate the Wildfire Behavior at a Territorial Scale in the Northwestern Iberian Peninsula

In the current context of extreme wildfires, understanding fire behavior at a territorial level has proven crucial for territory planning. This type of analysis is usually conducted by analyzing past wildfire statistics. In this study, we forego the past information related to wildfires and analyze, instead, the behavior of the entire territory in the face of wildfires. This allows for the distribution of ignition points to be systematized and for typical and atypical weather scenarios to be considered. This analysis relies on the use of wildfire simulation software. Ignition points used for the simulations were distributed using a systematic 1 × 1 km grid throughout the whole study area. Wildfires were simulated for each ignition point using eight different weather scenarios representing both typical and atypical weather conditions. The fire behavior on the territory was analyzed using rate of spread and intensity parameters for each simulated wildfire. It was observed that this territory is extremely prone to large wildfires both in typical and atypical weather conditions and that there is a tendency for extreme behaviors to develop. Some features were identified as prevention issues that ought to be addressed. This study develops a strategy to evaluate, in a systematic manner, the response of the territory to the threat of wildfires.

Study on the plume flow behavior and air entrainment characteristics of co-burning fires

In most of the fire accidents, there is large oil layer leaking into the fire dike and multiple fire points burning simultaneously. A series of numerical simulations for co-burning of a dike and double tanks (co-burning) with different spacing S have been conducted to study the plume flow behavior and air entrainment characteristics. The simulation results show that there is a conical fuel-rich region on the upper rim of the tank which results in the entrained air to flow circularly along the surface of the conical region. With the increase of S, the restriction effect of tank sidewall on air entrainment from environment enhances, while the restriction degree of air entrainment in the middle area of the double tanks decreases, affecting the distribution of plume velocity field and temperature field. And under the coupling effect of them, the tilt degree of tank flame decreases with the increase of S (from 0.3 m to 0.7 m). The air entrainment restriction coefficient αB, αS are introduced to characterize the restriction effect of air entrainment between the external dike fire and the double tank fires. Based on this, a co-burning plume entrainment model has been established, which can be applicable to different spacing S.

Modeling of CI engine performance and emission parameters using artificial neural network powered by catalytic co-pyrolytic renewable fuel

Emission and performance parameters of a 4-stroke CI engine operated on a blend of catalytic co-pyrolysis oil with pure diesel, produced through Azadirachta indica seed, waste LDPE (low-density polyethylene), and aluminium oxide (Al2O3) as a catalyst, are modelled in the current work using an Artificial Neural Network (ANN). At 500°C temperature, the highest oil output obtained was 93.91 wt%. The produced liquid fuel possessed similar physical features to that of pure diesel, including density (794 kg/m3) and heating value (44.42 MJ/kg), but lower flash and fire points that would assist in a better and complete combustion of the fuel blend resulting in a better performance and combustion characteristics. Using inputs including brake mean effective pressure, load, brake power, and torque, a developed ANN model was applied to forecast the performance (Brake Thermal Efficiency and Brake Specific Fuel Consumption) along with emission characteristics (Smoke and NOx). The Levenberg-Marquardt back-propagation training technique was applied for emissions and performance characteristics prediction having the best accuracy. Regression coefficients (R2) for predicting BTE, BSFC, NOx, and smoke were all very near to 1: 0.99801, 0.9983, 0.95753, and 0.97467. The study determines that the proposed alternative fuel could be utilized in blend with the pure diesel to in an unmodified diesel engine. It has also been found that artificial neural networks (ANN) could prove to be useful to model and forecast the performance or emissions of renewable fuels in diesel engines, with the potential for these fuels to be employed in transportation.

A dataset of high energy-consuming industrial products in the ASEAN during 2012–2021

The uneven energy distribution and economic development levels of the countries in the ASEAN region have led to the uncoordinated distribution of high-energy-consuming industries in the region. It is extremely important to timely obtain the spatial distribution pattern of high energy-intensive industries. In this paper, on the basis of the 375m high spatial and temporal resolution NPP-VIIRS active fire point data and vector data density segmentation algorithms, we produced a dataset of high energy-consuming industrial products in the ASEAN during 2012–2021. This dataset covers valuable information, such as latitude and longitude of industrial heat sources data, administrative region to which they belong, and the year of operation start and end, etc. It can provide scientific data support for energy distribution and economic development in the region, as well as decision-making support for its cooperation with China for further development.

An Integrated Hydrometallurgical Treatment and Combustion Process for Sustainable Production of Sm2O3 Nanoparticles from Waste SmCo Magnets

Samarium (Sm), as one of the rare earth elements (REEs), has gained significant attention in the production of SmCo magnets due to their high corrosion and oxidation resistance, as well as their high-temperature stability. SmCo magnets find applications in various industries, including but not limited to national defense, aerospace, military, and medical equipment. Sm and Co have been classified as a critical metal due to its economic importance and supply risk. Recovering Sm from SmCo magnets is an effective method to ensure a stable supply. The present study investigates an integrated hydrometallurgical treatment and combustion process for the preparation of rare earth oxide (Sm2O3) powders from SmCo. Initially, SmCo powders is exposed to nitric acid, and the resulting slurry is selectively oxidized at 250 °C to obtain Sm(NO3)3, Co2O3, and Fe2O3. Subsequently, the selectively oxidized powders are leached with water to extract Sm. Sm2O3 powders are produced from the obtained leaching solution using an energy- and time-efficient solution combustion process. In this process, once the ignition point of the leaching solution-citric acid complex is reached, combustion occurs and concludes within a short time. The combusted powders are then calcined at different temperatures to produce crystalline Sm2O3 powders. Finally, the optimal conditions for the production of Sm2O3 are identified, and the produced powder is characterized through XRD and FESEM analysis.

Fire-RPG: An Urban Fire Detection Network Providing Warnings in Advance

Urban fires are characterized by concealed ignition points and rapid escalation, making the traditional methods of detecting early stage fire accidents inefficient. Thus, we focused on the features of early stage fire accidents, such as faint flames and thin smoke, and established a dataset. We found that these features are mostly medium-sized and small-sized objects. We proposed a model based on YOLOv8s, Fire-RPG. Firstly, we introduced an extra very small object detection layer to enhance the detection performance for early fire features. Next, we optimized the model structure with the bottleneck in GhostV2Net, which reduced the computational time and the parameters. The Wise-IoUv3 loss function was utilized to decrease the harmful effects of low-quality data in the dataset. Finally, we integrated the low-cost yet high-performance RepVGG block and the CBAM attention mechanism to enhance learning capabilities. The RepVGG block enhances the extraction ability of the backbone and neck structures, while CBAM focuses the attention of the model on specific size objects. Our experiments showed that Fire-RPG achieved an mAP of 81.3%, an improvement of 2.2%. In addition, Fire-RPG maintained high detection performance across various fire scenarios. Therefore, our model can provide timely warnings and accurate detection services.

Research on an Edge Detection Method Applied to Fire Localization on Storage Racks in a Warehouse

When a fire occurs on storage racks in a warehouse, it is not advisable to find the location of the fire point accurately, because there are a large number of goods on the storage rack, and many interference factors such as light will disturb the precise location of the fire. In response to the above problems, and thanks to the high-speed growth of deep learning technology, we propose an edge detection method and apply it in fire locations successfully. We adopt VGG-16 as our backbone and introduce an attention module to suppress background information and eliminate interference. We test the proposed method on our collected dataset, and the results show that our proposed model can extract the shelf edges more completely and locate the fire point accurately. In terms of detection speed, our method can achieve 0.188 s per image, which meets the requirements of real-time detection. Our approach lays a good foundation for the precise extinguishing of fire that occurs on storage racks.

Sentinel 2 based burn severity mapping and assessing post-fire impacts on forests and buildings in the Mizoram, a north-eastern Himalayan region

The Increasing frequency and severity of forest fires worldwide highlights the need for more effective Burnt area mapping. Finding the effects of fire on vegetation and putting mitigation methods in place, depends on post-fire evaluation. In this study, the location of the burned regions and the severity of the fire were determined using high-resolution multi-spectral images from Sentinel 2 on Google Earth Engine (GEE) platform. Three widely used fire severity indices—differenced Normalized Burn Ratio (dNBR), Relativized Burn Ratio (RBR), and Relativized dNBR (RdNBR)—based on pre-fire Normalized Burn Ratio (NBR) and post-fire NBR—were computed and compared based on their accuracy using very high-resolution planet imagery fire points and equal number of random non fire points. Maps also validated with active fires, ground based photos and crowdsourced images. The accuracy (AUC) of the RdNBR map was 85%, RBR - 84% and dNBR −82%. The RdNBR index demonstrated highest level of accuracy. Then the loss to vegetation using pre-fire and post-fire NDVI was analysed. The analysis of pre-fire and post-fire NDVI provided insights into the extent of vegetation loss. The analysis of vegetation loss offered valuable information regarding the impact of fire on the affected areas. Google building dataset was used to monitor the percent of buildings under threat due to these fires. Around 8.77% of buildings were found in high severity region. Accurate mapping aids post-fire evaluation, guided mitigation strategies, and enhanced forest management and ecological restoration.

Effect of oxy-fuel combustion with different O2/CO2 fractions on the pulverized coal combustion mechanism and heat transfer characteristics in rotary kilns: A numerical simulation study

Oxy-fuel combustion combined with carbon capture and storage is the most effective and direct technology to achieve carbon neutrality in cement industry. In this work, numerical simulation was employed to investigate the effects of air combustion and oxy-fuel combustion with different O2/CO2 fractions on the airflow field, temperature field, pulverized coal combustion mechanism, heat transfer characteristics and NOx distribution in rotary kiln. The results show that the airflow field under different conditions has no significant change. When transitioning from the air condition to the oxy-fuel condition, the ignition point of pulverized coal extends backward by approximately 0.2 m. The maximum temperature decreases from 2306 K to 2052 K, and the temperature becomes more uniform. As the oxygen concentration in the primary air increases, both the maximum and average temperatures gradually rise, and the flame length shortens from 25.4 m to 24.16 m. In the rotary kiln, the locations of char oxidation and gasification reactions differ, but these reaction locations remain consistent under different conditions. Under oxy-fuel conditions, the oxidation rate and oxidation proportion of char decrease, while the gasification rate and gasification proportion of char increase, resulting in a shortening of the burnout distance of coal from 34.5 m to 31.3 m. With increasing oxygen concentration in the primary air, the rates of char oxidation and gasification gradually rise, further shortening the burnout distance. The decrease in temperature at the front end of the rotary kiln under oxy-fuel conditions reduces the total heat transfer in the burning zone by 15 %. When the oxygen content of the primary air is 70 %, it ensures stable calcination of clinker. Additionally, under oxy-fuel conditions, the NOx generation is significantly reduced. With the increase of oxygen concentration in the primary air, the NOx concentration at the outlet decreases slightly from 451 ppm to 406 ppm, due to the increase in kiln temperature that favors the reduction of fuel NOx.

Influence of surface activated nanophase Pr6O11 particles on the physio-chemical and tribological characteristics of SAE20W40 automotive lubricant

In this study, the formulation of nano lubricants is undertaken through the utilization of SAE20W40 engine oil and surface-activated praseodymium oxide (Pr6O11). An analysis is conducted on the impact of concentration profile (0.1, 0.3, 0.5 wt.%) on various physio-chemical properties, including stability, viscosity, fire and flash point, calorific value, acid value, and iodine value of the nano lubricants. The wear rate and coefficient of friction are determined through the utilization of a pin-on-disc tribometer for all the synthesized nano lubricants. A comprehensive examination on the attributes of the synthesized nano lubricants is presented by means of informative images and graphs obtained from SEM, EDS, TEM, FT-IR, UV-Vis, and DSC. It is revealed that the lubricant oil containing 0.3 wt.% of Pr6O11 demonstrates superior performance when compared to the base lubricant.

Investigation on the Tea Polyphenol/Swelling Gels to Inhibit Coal Spontaneous Combustion: An Experimental Study

ABSTRACT Developing new fire-fighting material is most significant for effective prevention and control of coal spontaneous combustion. To enhance the flame-retardant performance of the gel, the tea polyphenol/swelling gels are developed using a water-soluble polymerization method. Thermogravimetric analysis, programmed temperature experiments, and Fourier-transform infrared spectroscopy are used to reveal the structural characteristics, microscopic morphology, and thermal stability of the gel. Experimental testing the tea polyphenol/swelling gels’ water absorption, water retention, and reusability as well as their inhibitory effect on coal spontaneous combustion. The results showed that the prepared tea polyphenol/swelling gels exhibited a porous three-dimensional network structure, with a maximum swelling ratio of 607.2 g/g and a salt absorption rate of 121.4 g/g. After the testing coal sample is treating by the tea polyphenol/swelling gels, its CO production rate is sharply decreased, but the corresponding ignition point temperature and flame-retardant rate are significantly increased. The activation energy in the first stage is increased by 32.2 kJ·mol−1, in the second stage by 4.0 kJ·mol−1, and in the third stage by 23.2 kJ·mol−1. Additionally, the active functional groups such as hydroxyl and methyl in the coal are also reduced by the treatment of tea polyphenol/swelling gels. Correlation analysis indicate that the inhibition effect of coal spontaneous combustion can be achieved by reducing -OH, CH3/CH2 groups, while the increasing of C = C, C- O, C- H functional groups in the coal. Moreover, the tea polyphenol/swelling gels performing the characteristics of excellent water absorption, inhibiting coal spontaneous combustion by absorbing heat, covering the coal microstructure, and inhibiting the oxidative activity of coal molecules. This study can provide theoretical guidance for the application of swelling gels in inhibiting coal spontaneous combustion.

Spray combustion characteristics and soot formation potential of Nano-Al2O3 diesel at low ambient temperatures

Nanoparticle additives have the potential to enhance combustion efficiency and reduce emissions in liquid combustion processes. This study thoroughly investigates the spray evaporation, ignition, and combustion characteristics of nanoparticle/fuel blends, especially under challenging environmental conditions, to improve combustion efficiency and reduce emissions. Employing diffused background illumination, shadowgraph, Mie scattering, high-speed natural luminosity, and two-color method, the spray combustion dynamics of thermally stable nano-Al2O3/diesel (NAD) blends were examined within a constant volume combustion chamber. Diesel, a prevalent primary or auxiliary fuel, represents other highly volatile renewable fuels, while Al2O3, a significant component in combustion fly ash, is noted for its specific catalytic properties and ability to re-burn, eliminating organic toxic elements. The results indicated that NAD blends exhibit a faster evolution rate for both liquid and vapor sprays compared to pure diesel, attributed to the increased kinetic energy of breakup droplets. Under most experimental conditions, incorporation of nano-Al2O3 slightly reduced the ignition delay time due to enhanced heat transfer. Notably, nano-Al2O3 altered the ignition mechanism of the diesel spray, transitioning it from partially premixed to diffusion combustion and relocating the ignition point nearer to the injector. The combustion flame of NAD blends demonstrated increased luminosity, a shorter flame lift-off length, and variations in start-of-injection and total-injection-nozzle-lift-off. However, due to a high equivalence ratio mixture between the liquid phase spray and combustion region, the soot concentration in NAD blends was higher than that in diesel. Additionally, the elevated flame temperature in NAD blends significantly increased the soot concentration. Overall, nano-Al2O3 markedly enhances diesel combustion, especially in low ambient temperatures, suggesting its impressive potential for the high-efficiency and low-emission utilization of renewable fuels or the combustion of recycled waste solvents.

Properties of PS-modified asphaltic concrete containing waste RHA and de-silted sand

This research evaluated the impact of incorporating waste polystyrene (PS) in bitumen-binder modification for sustainable asphalt, with replacement levels ranging from 5% to 50% by weight. Rice husk ash (RHA) was used as a filler, and de-silted sand served as the fine aggregate, while crushed granite was employed as the coarse aggregate. Various tests, including penetration, viscosity, flash point, fire point, specific gravity, ductility, and the Marshall stability test, were conducted on the asphalt binder. The modified binder's penetration, ductility, viscosity, and specific gravity decreased with increasing PS content, whereas the flash and fire points increased. Stability testing showed that at 10% PS content, the asphalt had properties suitable for heavy traffic (wearing course), with stability, flow, density, air voids, voids in mineral aggregates (VMA), and voids filled with bitumen (VFB) values of 6825 N, 3.33 mm, 2.362 g cm−3, 4.52%, 18.21%, and 75.18%, respectively. Within the 15%–45% PS content range, the modified asphalt met the criteria for medium traffic (binder course). FTIR and SEM analyses confirmed the successful synthesis of PS-modified asphalt, revealing the encapsulation of PS microspheres on the binder’s surface, forming a composite network structure in the asphalt. The study concluded that this modified asphalt, derived from waste materials, could be effectively utilized in constructing asphalt pavement courses. These findings indicate the potential for the developed modified asphalt to serve as an economical road surfacing material while addressing pollution and environmental concerns associated with waste.

Research on the Quantitative Assessment Method of HVDC Transmission Line Failure Risk during Wildfire Disaster

It is increasingly important to effectively predict the failure of HVDC transmission lines caused by wildfire disasters. On the basis of comprehensively considering the distribution of fire points, the characteristics of wildfire propagation, and the failure factors of the transmission line, a method for calculating the probability of failure in HVDC transmission lines during wildfire disasters is proposed to quantify the risk of HVDC transmission line failures caused by wildfire disasters. Using the ArcGIS 10.7. platform, the study examined the quantity of fire points within the buffer zone of each HVDC transmission line from 2001 to 2022. The results indicate significant variations in the number of fire incidents in the buffer zones of various transmission lines. Notably, there has been a noticeable increase in the number of fire incidents along several HVDC transmission lines, including Xizhe, Baihetan-Jiangsu, Baihetan-Zhejiang, and Fufeng, in recent years. Based on the number of fire points in the buffer zone obtained through ArcGIS processing and the proposed failure probability calculation model, six HVDC hydropower transmission channels in the Sichuan Province were analyzed. At the same time, the proposed probability calculation model was simplified, and a corresponding linear evaluation index was introduced. The regression analysis results indicate that the proposed index can effectively assess the failure risk of HVDC transmission lines during wildfire disasters.

Biomass Burning in Northeast China over Two Decades: Temporal Trends and Geographic Patterns

Despite the significant impacts of biomass burning (BB) on global climate change and regional air pollution, there is a relative lack of research on the temporal trends and geographic patterns of BB in Northeast China (NEC). This study investigates the spatial–temporal distribution of BB and its impact on the atmospheric environment in the NEC region during 2004 to 2023 based on remote sensing satellite data and reanalyzed data, using the Siegel’s Repeated Median Estimator and Mann–Kendall test for trend analysis, HDBSCAN to identify significant BB change regions, and Moran’s Index to examine the spatial autocorrelation of BB. The obtained results indicate a fluctuating yet overall increasing BB trend, characterized by annual increases of 759 for fire point counts (FPC) and 12,000 MW for fire radiated power (FRP). BB predominantly occurs in the Songnen Plain (SNP), Sanjiang Plain (SJP), Liaohe Plain (LHP), and the transitional area between SNP and the adjacent Greater Khingan Mountains (GKM) and Lesser Khingan Mountains (LKM). Cropland and urban areas exhibit the highest growth in BB trends, each surpassing 60% (p < 0.05), with the most significant growth cluster spanning 68,634.9 km2. Seasonal analysis shows that BB peaks in spring and autumn, with spring experiencing the highest severity. The most critical periods for BB are March–April and October–November, during which FPC and FRP contribute to over 80% of the annual total. This trend correlates with spring planting and autumn harvesting, where cropland FPC constitutes 71% of all land-cover types involved in BB. Comparative analysis of the aerosol extinction coefficient (AEC) between areas with increasing and decreasing BB indicates higher AEC in BB increasing regions, especially in spring, with the vertical transport of BB reaching up to 1.5 km. County-level spatial autocorrelation analysis indicates high–high clustering in the SNP and SJP, with a notable resurgence of autocorrelation in the SNP, suggesting the need for coordinated provincial prevention and control efforts. Finally, our analysis of the impact of BB on atmospheric pollutants shows that there is a correlation between FRP and pollutants, with correlations for PM2.5, PM10, and CO of 0.4, 0.4, and 0.5, respectively. In addition, the impacts of BB vary by region and season, with the most significant impacts occurring in the spring, especially in the SNP, which requires more attention. In summary, considering the escalating BB trend in NEC and its significant effect on air quality, this study highlights the urgent necessity for improved monitoring and strategic interventions.

Unmanned Aerial Vehicle Fire Detection Platform Based on Semantic Yolov5 and Autonomous Recognition

In recent years, as fire image detection has become a research hotspot. One class of methods is color-based methods, which are very sensitive to brightness and shadows. As a result, the number of false alarms generated by these methods is high. Aiming at the task requirements of airborne binocular vision obstacle avoidance and target tracking, this paper establishes the verification platform architecture of UAV (Unmanned Aerial Vehicle) binocular vision obstacle avoidance and target tracking. For the update and maintenance of boundary regions, we can also continuously extract richer information from the boundary, make more elaborate plans, and develop an incremental method to detect locally updated maps within the boundary. The fire point can be independently and quickly identified through deep learning to extinguish the fire accurately. Assuming that the system incorrectly identifies 2 out of 80 non-fire sources as fire sources, so the results indicate a precision of about 88%, a recall of 90%. However, the traditional fire detection is around 80%.

FPS-U2Net: Combining U2Net and multi-level aggregation architecture for fire point segmentation in remote sensing images

Traditional methods for fire point segmentation (FPS) in satellite remote sensing images (RSIs) overly rely on threshold judgment, which are greatly affected by factors such as regional time and show poor generalization. Besides, due to the difference between natural scene images (NSIs) and RSIs, directly apply NSIs-based deep learning methods to forest fire RSIs without any modification fails to achieve satisfactory results. To address these issues, first, we construct a Landsat8 RSI-FPS dataset covering different years, seasons and regions. Then, for the first time, we apply salient object detection (SOD) to FPS in forest fire monitoring and propose a novel network FPS-U2Net to improve the performance of FPS. FPS-U2Net is based on U2Netp (a lightweight U2Net), to make better use of the multi-level features from adjacent encoders, we propose multi-level aggregation module (MAM), which is placed between the encoder and decoder at the same stage to aggregate the adjacent multi-scale features and capture richer contextual information. To make up for the weakness of BCE loss, we employ the hybrid loss, BCE + IoU, for the training of the network, which can guide the network learn the salient information from pixel and map levels. Extensive experiments on three datasets demonstrate that our FPS-U2Net significantly outperforms the state-of-the-art semantic segmentation and SOD methods. FPS-U2Net can accurately segment fire regions and predict clear local details.

Geospatial forest fire risk assessment and zoning by integrating MaxEnt in Gorkha District, Nepal

Forest fires are an imminent danger to natural forest ecosystems, and carrying out zoning studies and forest fire risk assessments are of great practical significance in steering fire prevention, minimizing fire incidents, and limiting the environmental consequences of fire. Using the Gorkha district of Nepal as a case study, this study used remotely sensed high-temperature fire data as the forest fire sample. Nine parameters related to topography, climatic conditions, vegetation, and human intervention were used as environmental variables affecting fire occurrence. Next, a MaxEnt forest fire risk assessment model was generated with GIS and R, which analysed the contribution, significance, and responses of environmental variables to the forest fire in Gorkha District. The findings demonstrate that (1) following a test of sample locations for forest fires, the MaxEnt model has excellent relevance and practicality when applied to fire risk assessment; (2) Out of 2747 fire points in the forest, only 110 Spatio-temporally independent fire points were used for the model building having high and normal confidence level. Regarding Area Under Curve (AUC) values, the training data yielded results of 0.875, while the test data produced acceptable results of 0.861 with a standard deviation of 0.0322; (3) the importance of climatic and Land Use Land Cover (LULC) variables to forest fire are 56.2 % and 32.9 %, respectively, and their contribution to forest fire are 32 % and 47.6 %, respectively. (4) There are numerous and intricate ways that environmental factors influence forest fires. The forest fire response curves to the nine chosen environmental variables are complex and nonlinear rather than linear; Maximum temperature of the warmest month (bio_5), Isothermality (bio_3), Precipitation of Driest Quarter (bio_17) and mean Diurnal Range (bio_2) bear a nonlinear positive link with the possibility of forest fires. In contrast, elevation, slope, temperature seasonality (bio_4), distance from the settlement, and LULC have a favorable stimulating response to the possibility of forest fires within an appropriate interval. (5) In Gorkha, there are geographical differences in the risk of forest fires. Only 12.83 % of the whole area is made up of areas at significantly high risk or above, compared to 87.17 % for high-risk and below.

Surface modification of nitrocellulose by interfacial self-assembly of metal-phenolic network for enhanced thermal stability and eco-friendly propulsion energy

Metal-phenolic networks (MPNs) have received widespread interest owing to their ability to incorporate metal ions and phenolic ligands in a modular manner. However, the effect of MPNs on nitrocellulose (NC), an energetic material for propulsion, has not been extensively investigated. Herein, we fabricated MPN-coated NC fibers (NC@MPN) and confirmed the successful coating and homogeneous distribution of MPN on the surface of NC through Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), UV–visible spectroscopy, scanning electron microscopy (SEM) and Energy Dispersive spectroscopy (EDS). The thermal stability of NC was enhanced as evidenced by differential scanning calorimetry (DSC), accelerating rate calorimetry (ARC) and the ignition point test. The thermal decomposition activation energy of NC@MPN can reach 200.73 kJ•mol−1, 7.50 % higher than that of NC primitive fiber (186.72 kJ•mol−1). The ARC tests demonstrate that the implementation of MPN coating prolonged the time of entering the adiabatic section by 5.4 °C and the forecast temperatures for TD8 and TD24 have increased by 4.6 and 4.8 °C, respectively. Additionally, the ignition point was also raised by 2 °C. The aforementioned three experimental results provide evidence for the enhanced thermal stability conferred by MPNs. Besides, thermogravimetric analysis (TGA) results demonstrate a reduction in the residue from thermal decomposition. The present study presents a simple and robust coating strategy for the modular surface engineering of energetic materials, which significantly improves thermal stability and reduces the residues of thermal decomposition.

Hierarchical dynamic estimation of fire service accessibility based on POI big data

Failure to obtain timely fire services will lead to thermal runaway phenomenon, which will seriously threaten the lives of people and property safety. In this study, a dynamic method was proposed to estimate fire service accessibility based on real-time traffic. To validate the method, a total of 110,055 POls extracted from online maps and existing 12 fire stations in Yiyang were used as fire service demand points and supply points. Travel times of fire trucks from supply points to demand points were calculated based on real-time traffic, and a total of 550,275 valid samples were obtained. The results showed that the ≥10 min fire service coverage rate in Yiyang is only 71.60 %. Based on the distribution area of demand points ≥10 min, the locations of three optimization solutions of new fire stations are proposed. By calculating the total average time (TAT) of the three optimization solutions of proposed new fire stations, it is concluded that TAT decreases by 743.99 s, 99.72 s, 92.59 s, respectively. From perspectives of safety and economy, the construction of the 10 fire stations of optimized solution I will effectively improve the regional fire service level.