Small Fires Research Articles

Enhanced CH4 emissions from global wildfires likely due to undetected small fires

Monitoring methane (CH4) emissions from terrestrial ecosystems is essential for assessing the relative contributions of natural and anthropogenic factors leading to climate change and shaping global climate goals. Fires are a significant source of atmospheric CH4, with the increasing frequency of megafires amplifying their impact. Global fire emissions exhibit large spatiotemporal variations, making the magnitude and dynamics difficult to characterize accurately. In this study, we reconstruct global fire CH4 emissions by integrating satellite carbon monoxide (CO)-based atmospheric inversion with well-constrained fire CH4 to CO emission ratio maps. Here we show that global fire CH4 emissions averaged 24.0 (17.7–30.4) Tg yr−1 from 2003 to 2020, approximately 27% higher (equivalent to 5.1 Tg yr−1) than average estimates from four widely used fire emission models. This discrepancy likely stems from undetected small fires and underrepresented emission intensities in coarse-resolution data. Our study highlights the value of atmospheric inversion based on fire tracers like CO to track fire-carbon-climate feedback.

Monthly mapping of Indonesia’s burned areas: implementation, history, techniques, and future directions

ABSTRACT Forest and land fires cause substantial economic, social, and environmental devastation. Interagency forest and land fire management has succeeded in decreasing the impact of these fires, particularly in Indonesia. Having comprehensive information on fire locations and frequencies will benefit national forest and land fire management programmes. This study describes nearly a decade of satellite-based burned area (BA) monitoring conducted by the Indonesian government. We discuss (1) the history of BA mapping in Indonesia, (2) the most recent techniques for producing monthly BA maps, (3) an evaluation of product accuracy, (4) advantages and disadvantages, and (5) recommendations for future research. The most recent approach combines manual and digital classification, primarily using Landsat images, but has been supplemented with Sentinel data since 2020. The digital analysis, named the normalized burn ratio difference index threshold, was used to distinguish between burned and unburned pixels, guiding the interpreter’s manual digitization. BA confidence levels were determined using active fire products and ground truth data. We engaged provincial and local agency stakeholders to verify the products and provide quality assurance. We also assessed product performance by examining high-resolution images captured at three different locations to ascertain the relative advantages and disadvantages, which varied depending on each region’s fire regime. Small fires and cloud cover reduced the accuracy of the national monthly BA product. However, the omission error decreased by 65% when all fires throughout the entire year were considered. The inclusion of all available Sentinel-2 (A and B) images yielded higher accuracy than using only Landsat 8 data. We conclude that Indonesia’s cloud coverage constraint requires additional observational data to obtain clear-sky imagery when using optical sensors, in addition to the adjusted technique. We urge the introduction of reliable digital classifications for all Indonesian fire regimes to simplify resource deployment and reduce manual labour operations.

Global expansion of wildland-urban interface intensifies human exposure to wildfire risk in the 21st century.

Rapidly increasing human-nature interactions exacerbate the risk of exposure to wildfires for human society. The wildland-urban interface (WUI) represents the nexus of human-nature interactions, where the risk of exposure to natural hazards such as wildfire is most pronounced. However, quantifying long-term global WUI change and the corresponding driving factors at fine resolution remain challenging. Here, we mapped and analyzed the global WUI at 30-meter resolution in 2000, 2010, and 2020. Our analysis revealed that the global WUI expanded by 35.6% since 2000, reaching 1.93 million square kilometer in 2020. Notably, 85% of this growth occurred between 2010 and 2020. The increase in WUI was primarily driven by the unprecedented expansion of global urbanization, contributing an additional 589,914 square kilometer of WUI. In addition, the number of small fires occurring in WUI areas has increased substantially since 2010. These findings underscore the rising wildfire risk to human society and highlight the urgency of implementing tailored fire management strategies in WUI areas.

Impact of Depopulation on Forest Fires in Spain: Primary School Distribution as a Potential Socioeconomic Indicator

Socioeconomic factors are increasingly considered in the study of forest fires. However, there is a gap in the literature on the possible relationship between basic services and infrastructures such as small rural schools and forest fires. Population decline in rural areas is leading to an increase in forest fire risk and social vulnerability to forest fires due to the abandonment of traditional agroforestry practices and the expansion of unmanaged forest canopy. In addition, rural schools are supposed to make rural municipalities livable and promote the people’s sense of community. In parallel, there is controversy over the closure of small local schools in sparsely populated rural areas worldwide. Our study identified that the forest area burned in the province of Avila (Central Spain), during the period 1996 to 2023, was higher in municipalities without rural primary schools. The presence of rural schools was as statistically significant as the influence of orographic variations of the territory, the number of incipient fires, and the reduction of population density during the same period. Our work contributes to highlighting the potential links between the decline of essential services in rural areas and the increase in forest fire risk, to urge policymakers to take a collaborative and holistic view.

Numerical Simulation of an Isolated N-Heptane Pool Fire

Refineries are industrial complexes of great economic importance which are located close to major cities. A pool fire accident that can occur from an oil leak combined with wind can result in disastrous consequences for such an industry. This study investigates the characteristics of an isolated n-heptane square pool fire of 36 m2 under the influence of a cross wind. The pool fire characteristics are numerically studied using open-source Computational Fluid Dynamics (CFD) software, such as FireFoam (v4.1) and Fire Dynamic Simulator (FDS) (version 6.9.0). The turbulent flow field and the fire characteristics were simulated with the LES Method. The crucial parameters of the pool fire, such as (a) the temperature and velocity fields, (b) the flame length and height, (c) the surface emissive power, and (d) the flame tilt angles, were computed. Comparisons against experimental data for both small and large-area pool fires from the literature were made successfully. The flame tilt angle is shown to correlate very well with the reciprocal of the Richardson number, which was approximated within a multiplication constant to the Froude number. Thus, both the reciprocal Richardson number and Froude number can be used for correlating the flame tilt angle. It is shown that both of these numbers are used to correlate the tilt angle of experimental pool fires with effective diameters from a fraction of a meter to approximately 16 m, and wind speeds up to 7 m/s. The goodness of a linear fit based on the sum of the residual squares is 0.91.

Wildfire CNN: An Enhanced Wildfire Detection Model Leveraging CNN and VIIRS in Indian Context

Introduction Wildfires are an unexpected global hazard that significantly impact environmental change. An accurate and affordable method of identifying and monitoring on wildfire areas is to use coarse spatial resolution sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). Compared to MODIS, wildfire observations from VIIRS sensor data are around three times as extensive. Objective The traditional contextual wildfire detection method using VIIRS data mainly depends on the threshold value for classifying the fire or no fire which provides less performance for detecting wildfire areas and also fails in detecting small fires. In this paper, a wildfire detection method using Wildfiredetect Convolution Neural Network model is proposed for an effective wildfire detection and monitoring system using VIIRS data. Methods The proposed method uses the Convolutional Neural Network model and the study area dataset containing fire and non-fire spots is tested. The performance metrics such as recall rate, precision rate, omission error, commission error, F-measure and accuracy rate are considered for the model evaluation. Results The experimental analysis of the study area shows a 99.69% recall rate, 99.79% precision rate, 0.3% omission error, 0.2% commission error, 99.73% F-measure and 99.7% accuracy values for training data. The proposed method also proves to detect small fires in Alaska forest dataset for the testing data with 100% recall rate, 99.2% precision rate, 0% omission error, 0.7% commission error, 99.69% F-measure and 99.3% accuracy values. The proposed model achieves a 26.17% higher accuracy rate than the improved contextual algorithm. Conclusion The experimental findings demonstrate that the proposed model identifies small fires and works well with VIIRS data for wildfire detection and monitoring systems.

On gravity-driven liquid nitrogen jets reach and horizontal spread for extinction of ground fires by aerial means

This paper describes experimental and numerical results on the reach and the spread of gravity-driven jets of liquid nitrogen (LN2) on the ground for applications to fire extinction by aerial means. A series of experiments released LN2 jets from different elevations in ambient air to measure their reach and spread distances upon the impingement. A numerical model was developed to simulate the behavior of such jets. Upon validation, the numerical model was used to further predict the LN2 pool mass and spreading distances under various release configurations. Results showed that the LN2 survivability is greatly affected by the release height of the cryogen, since the LN2 quantity reaching the ground decreases as the release height increases. Moreover, releasing larger initial LN2 quantities and, most importantly using larger nozzle diameters, both the LN2 pool mass and spreading diameter can be extended. Additional experiments were conducted where cryogen jets were released onto small (∼300 cm2) alcohol pool fires; results showed that only limited quantities of the LN2 evaporated in transit to the fire, and small amounts of the cryogen expediently snuffed the fires. A simplified model also suggested that in fire supression/extinction by LN2 the fuel cooling mechanism is of secondary importance compared to the mechanism of separating the fuel from oxygen.

Secrets Buried in the Pits: Ritual Activities in Western Anatolia in the First Half of the Second Millennium bce

Western Anatolian ritual pits provide valuable insights into socio-cultural, economic and symbolic practices during the Early to Middle Bronze Age. Findings in feasting pits, such as carbonized seeds and animal bones, indicate a strong link between ritual and food. Standing stones, altars and carefully arranged artefacts suggest a symbolic and sacred dimension beyond mere ceremonies. The pits from this period contain carbonized seeds and fragments of wood, indicating the presence of small fires during certain rituals. Changing features in ritual pits from the Early to Middle Bronze Age reveal a dynamic relationship between spatial arrangements and religious practices. The study shows that in the first half of the second millennium bce several ritual activities known from different regions reached western Anatolia for the first time. Interregional trade involved not only goods, but also the dissemination of rituals over a wide geographical area. This cultural interaction reveals western Anatolia as a dynamic and influential centre in this historical period. By exploring the ritual practices of second-millennium bce western Anatolia, this paper presents new perspectives on the rituals of the region.

LD-YOLO: A Lightweight Dynamic Forest Fire and Smoke Detection Model with Dysample and Spatial Context Awareness Module

The threat of forest fires to human life and property causes significant damage to human society. Early signs, such as small fires and smoke, are often difficult to detect. As a consequence, early detection of smoke and fires is crucial. Traditional forest fire detection models have shortcomings, including low detection accuracy and efficiency. The YOLOv8 model exhibits robust capabilities in detecting forest fires and smoke. However, it struggles to balance accuracy, model complexity, and detection speed. This paper proposes LD-YOLO, a lightweight dynamic model based on the YOLOv8, to detect forest fires and smoke. Firstly, GhostConv is introduced to generate more smoke feature maps in forest fires through low-cost linear transformations, while maintaining high accuracy and reducing model parameters. Secondly, we propose C2f-Ghost-DynamicConv as an effective tool for increasing feature extraction and representing smoke from forest fires. This method aims to optimize the use of computing resources. Thirdly, we introduce DySample to address the loss of fine-grained detail in initial forest fire images. A point-based sampling method is utilized to enhance the resolution of small-target fire images without imposing an additional computational burden. Fourthly, the Spatial Context Awareness Module (SCAM) is introduced to address insufficient feature representation and background interference. Also, a lightweight self-attention detection head (SADH) is designed to capture global forest fire and smoke features. Lastly, Shape-IoU, which emphasizes the importance of boundaries’ shape and scale, is used to improve smoke detection in forest fires. The experimental results show that LD-YOLO realizes an mAP0.5 of 86.3% on a custom forest fire dataset, which is 4.2% better than the original model, with 36.79% fewer parameters, 48.24% lower FLOPs, and 15.99% higher FPS. Therefore, LD-YOLO indicates forest fires and smoke with high accuracy, fast detection speed, and a low model complexity. This is crucial to the timely detection of forest fires.

Estimating fuel load for wildfire risk assessment at regional scales using earth observation data: A case study in Southwestern Australia

The risk of wildfires is increasing globally and models are critical to reducing this risk. Such models require information on fuel load, a crucial factor of fire behaviour, which is generally determined using a combination of fuel age and fuel accumulation models. Traditionally, estimating fuel load relies on manually compiled fire history data (MCFH). In this paper, we introduce an approach to estimate fuel load using readily available earth observation (EO) data, MODIS MCD64A1. The approach is applied to a wildfire-prone region in Southwestern Australia from 2001 to 2021. Results suggest that MODIS produces more accurate and reliable estimates of fuel load compared with MCFH. It is effective in maintaining spatially and temporally complete records of fires, as it reports 11,019 more hectares of burned areas associated with wildfires over the study period. MODIS performs better in capturing wildfires than prescribed burns, as the spatial overlapping ratio is higher for wildfires (0.63) than prescribed burns (0.42). The high agreement between the two datasets for fuel load estimation (weighted kappa of 0.91) results from grassland covering the majority of the landscape. However, the agreement is reduced for other vegetation types — 0.24 for pine, 0.36 for mallee heath, 0.39 for shrubland, and 0.58 for forest. MODIS has lower effectiveness in detecting small and under-canopy fires such as prescribed burns, suggesting the value in combining EO and manually compiled data to obtain improved estimates of fuel load. Due to the scope of objectives, the integration of EO and MCFH has not been fully explored in this study, which will be included in our future research. This study highlights the potential of earth observation data in assessing wildfire risk as the data are easily accessible and reliable, as well as efficient and cost-effective, and they provide the opportunity to develop mitigation strategies at regional scales.

Small scale pool fires: The case of toluene

Industrial applications adopting toluene as a solvent have been largely extended in recent years, including solutions within the framework of the energy transition and energy storage technologies. The potential use of this flammable compound in a different set of operative conditions and compositions requires a comprehensive and complete knowledge of its fire behaviour and combustion kinetic. To this scope, an innovative experimental procedure applicable to liquid reactive systems was developed for this scope and implemented at different boundary conditions. More specifically, the specimen was exposed to air and heat fluxes between 7 and 50 kW/m2, at a constant sample surface of 0.01 m2, an initial sample thickness of 0.01 m, and a distance between the sample and the horizontally oriented conical-shaped heater of 0.025 m. Measurements were compared with data from the current literature, when available, demonstrating the robustness and validity of the adopted procedure. Although an increase in the external flux leads to growing mass burning rates (i.e., from 0.47 g/s to 0.85 g/s), negligible effects on the ignitability were observed. Conversely, a peak in the heat release rate at 35 kW/m2 was measured. The observed reduction at higher external heat fluxes was attributed to less effective combustion, demonstrating that the maximum expected heat flux cannot be considered as an aprioristic worst-case scenario for the evaluation of pool fires. The collected data were, then, further utilized to obtain insights on the formation of the main products, including soot tendency. Based on the collected data a simplified kinetic model suitable for the computational fluid dynamics was proposed to reproduce the chemistry of the system.

분전반, 배전반 등에 사용하는 질산칼륨 기반의 소공간 소화장치의 성능에 관한 기초 연구 - 소화메커니즘 및 A급 소화성능을 중심으로

As there was no objective date on the fire extinguishing mechanism and performance of potassium nitrate-based small space fire extinguisher that can automatically extinguish fires in small spaces (less than 0.36 ㎡) such as distribution boards and distribution boards, we attemped to conduct basic research on this. We sought to confirm the importance of fires in switchboards and other devices through fire statistical analysis of electrical factors, and analyzed fire extinguishing mechanisms that react with heat based on potassium nitrate. We also sought to determine the interconnectivity of related variables through Class A fire extinguishing tests. Through analysis of fire statistics, the importance of electrical fires was confirmed and the cause of fire in the switchboard was investigated. By investigating its composition, it was found that it was a safe material that could be used for fire extinguishing, and that in the event of a fire, the thermal decomposition of KNO3 produced condensed aerosol particles, K2CO3, which could extinguish the fire. Through the Class A fire extinguishing test of the switchboard, it was confirmed that the fire could be reliably extinguished. The following conclusions were drawn through this study. 1. Analysis of fire statistics confirmed that electrical factors were the biggest cause of fire and that fires caused among this short circuit and insulation deterioration were the main causes. 2. The fire risk of switchboards, distribution boards, etc. was confirmed, and it was found that it was necessary to install simple fire extinguishers for small spaces that can quickly respond to and extinguish fires. 3. The fire extinguishing mechanism of potassium nitrate was confirmed to extinguish the fire by generating condensed aerosol particles, K2CO3, through thermal decomposition of KNO3 when a fire occurs. 4. Through the Class A fire extinguishing performance test, it was confirmed that there was a relationship between fire extinguishing delay time, weight, and volume, and the relationship could be expressed as , and it was found that a value of 1 or more is required for fire extinguishing.

Improving Fire Detection Accuracy through Enhanced Convolutional Neural Networks and Contour Techniques.

In this study, a novel method combining contour analysis with deep CNN is applied for fire detection. The method was made for fire detection using two main algorithms: one which detects the color properties of the fires, and another which analyzes the shape through contour detection. To overcome the disadvantages of previous methods, we generate a new labeled dataset, which consists of small fire instances and complex scenarios. We elaborated the dataset by selecting regions of interest (ROI) for enhanced fictional small fires and complex environment traits extracted through color characteristics and contour analysis, to better train our model regarding those more intricate features. Results of the experiment showed that our improved CNN model outperformed other networks. The accuracy, precision, recall and F1 score were 99.4%, 99.3%, 99.4% and 99.5%, respectively. The performance of our new approach is enhanced in all metrics compared to the previous CNN model with an accuracy of 99.4%. In addition, our approach beats many other state-of-the-art methods as well: Dilated CNNs (98.1% accuracy), Faster R-CNN (97.8% accuracy) and ResNet (94.3%). This result suggests that the approach can be beneficial for a variety of safety and security applications ranging from home, business to industrial and outdoor settings.

An Automated Cropland Burned-Area Detection Algorithm Based on Landsat Time Series Coupled with Optimized Outliers and Thresholds

Given the increasingly severe global fires, the accurate detection of small and fragmented cropland fires has been a significant challenge. The use of medium-resolution satellite data can enhance detection accuracy; however, key challenges in this approach include accurately capturing the annual and interannual variations of burning characteristics and identifying outliers within the time series of these changes. In this study, we focus on a typical crop-straw burning area in Henan Province, located on the North China Plain. We develop an automated burned-area detection algorithm based on near-infrared and short-wave infrared data from Landsat 5 imagery. Our method integrates time-series outlier analysis using filtering and automatic iterative algorithms to determine the optimal threshold for detecting burned areas. Our results demonstrate the effectiveness of using preceding time-series and seasonal time-series analysis to differentiate fire-related changes from seasonal and non-seasonal influences on vegetation. Optimal threshold validation results reveal that the automatic threshold method is efficient and feasible with an overall accuracy exceeding 93%. The resulting burned-area map achieves a total accuracy of 93.25%, far surpassing the 76.5% detection accuracy of the MCD64A1 fire product, thereby highlighting the efficacy of our algorithm. In conclusion, our algorithm is suitable for detecting burned areas in large-scale farmland settings and provides valuable information for the development of future detection algorithms.

Efficient forest fire detection based on an improved YOLO model

A forest fire is a natural disaster characterized by rapid spread, difficulty in extinguishing, and widespread destruction, which requires an efficient response. Existing detection methods fail to balance global and local fire features, resulting in the false detection of small or hidden fires. In this paper, we propose a novel detection technique based on an improved YOLO v5 model to enhance the visual representation of forest fires and retain more information about global interactions. We add a plug-and-play global attention mechanism to improve the efficiency of neck and backbone feature extraction of the YOLO v5 model. Then, a re-parameterized convolutional module is designed, and a decoupled detection head is used to accelerate the convergence speed. Finally, a weighted bi-directional feature pyramid network (BiFPN) is introduced to merge feature information for local information processing. In the evaluation, we use the complete intersection over union (CIoU) loss function to optimize the multi-task loss for different kinds of forest fires. Experiments show that the precision, recall, and mean average precision are increased by 4.2%, 3.8%, and 4.6%, respectively, compared with the classic YOLO v5 model. In particular, the mAP@0.5:0.95 is 2.2% higher than the other detection methods, while meeting the requirements of real-time detection.

Small Fires, Big Impact: Evaluating Fire Emission Estimates in Southern Africa Using New Satellite Imagery of Burned Area and Carbon Monoxide

AbstractVarious fire emission estimates for southern Africa during 2019, derived with multiple burned area data sets with resolutions ranging from 500 to 20 m, are evaluated using satellite carbon monoxide (CO) observations. Southern African emissions derived from burned area generated by 20 m Sentinel‐2 satellite imagery are up to 120% higher than other estimates because small fires are better detected with a higher‐resolution satellite instrument. A comprehensive comparison between simulated and observed atmospheric CO indicates that the Sentinel‐2 burned area data significantly improves emission estimates, with up to 15% reduction in CO concentration biases in comparison to emissions based on coarser resolution burned area data. We also found that the temporal lag between emissions and atmospheric CO concentrations during the peak fire month was related to atmospheric transport. These findings emphasize the importance of utilizing higher‐resolution satellite instruments in accurately estimating emissions and understanding the impact of small fires on global climate.

FSH-DETR: An Efficient End-to-End Fire Smoke and Human Detection Based on a Deformable DEtection TRansformer (DETR).

Fire is a significant security threat that can lead to casualties, property damage, and environmental damage. Despite the availability of object-detection algorithms, challenges persist in detecting fires, smoke, and humans. These challenges include poor performance in detecting small fires and smoke, as well as a high computational cost, which limits deployments. In this paper, we propose an end-to-end object detector for fire, smoke, and human detection based on Deformable DETR (DEtection TRansformer) called FSH-DETR. To effectively process multi-scale fire and smoke features, we propose a novel Mixed Encoder, which integrates SSFI (Separate Single-scale Feature Interaction Module) and CCFM (CNN-based Cross-scale Feature Fusion Module) for multi-scale fire, smoke, and human feature fusion. Furthermore, we enhance the convergence speed of FSH-DETR by incorporating a bounding box loss function called PIoUv2 (Powerful Intersection of Union), which improves the precision of fire, smoke, and human detection. Extensive experiments on the public dataset demonstrate that the proposed method surpasses state-of-the-art methods in terms of the mAP (mean Average Precision), with mAP and mAP50 reaching 66.7% and 84.2%, respectively.

Revisiting the 2023 wildfire season in Canada

The area burned by wildfires in Canada in 2023 is unprecedented in historical records. To help ensure the safety of communities and support the mobilization of firefighting resources, rapid detection of areas affected by wildfires is required. Satellite data are ideally suited to provide near real-time wildfire information over large areas. At the same time, clouds, smoke, and haze can obscure the collection of observations from sensors typically used for mapping purposes. Established methods using coarse spatial resolution satellites (e.g., MODIS, VIIRS) rely upon the combination of daily revisit to enable the rapid and reliable detection of large active fires, in full or in part, and the application of modeling (including spatial buffering) to infer additional, yet still obscured, areas. While timely, these initial maps of wildfire-impacted areas do not capture small fires (those smaller than 200 ha) and, importantly, are not intended to differentiate unburned areas within fire perimeters. To address these limitations, we used data from Sentinel-2A and -2B, and Landsat-8 and -9, which form a virtual constellation of four satellites to revisit and map burned area in Canada's forested ecosystems for the 2023 fire season. Availing upon the high temporal data density and using the Tracking Intra- and Inter-year Change algorithm (TIIC), an aggregate seasonal mapping of wildfires resulted in a total area affected by wildfires in 2023 of 12.74 Mha. Within this total area, 9.51 Mha of treed land cover was impacted. Shrubs and wetlands comprised most of the remaining non-treed area that was burned. Using a 2022 map of aboveground treed biomass (AGB), approximately 0.649 Pg of AGB was impacted by 2023 wildfires, representing an 11-fold increase in AGB impacts relative to a long-term annual average of treed AGB loss. Differences between the estimate of total burned area reported herein and the total burned area indicated by the Natural Resources Canada (NRCan) Fire M3 hotspot fire perimeters (18.64 Mha) were analyzed. Overall, estimates of burned area differed by 5.9 Mha, including over 1.13 Mha of water identified as burned within the NRCan perimeters. Differences in land cover and AGB impacts between the two products were also investigated and quantified. TIIC enables the near-continuous capture of areas impacted by fire through the fire season, allowing for within-year refinement of total burned area, rapid interrogation of land cover types impacted, and estimation of associated biomass consequences.

Causes of Ammonium Nitrate Explosions and Handling Mechanisms – Case Study of the 2020 Beirut Lebanon Explosion

Ammonium nitrate (AN) is a chemical that is widely used in industry, for example as fertilizer in agriculture, explosives in military and civilian applications (for example in mining) or as a basic ingredient in solid propellants. However, as a dangerous chemical, storing ammonium nitrate can cause problems if not properly. The characteristics of AN have been studied extensively that pure AN is stable at room temperature but can explode when mixed with impurities in a closed space or in the presence of a heat/flame source nearby. This was proven in the explosion disaster at the Port of Beirut, Lebanon in 2020. This article was written using a systematic literature review (SLR) approach, where the data used came from articles published on Google Scholar, DOAJ, Emerald, Springer, and Science Direct with relevant keywords according to the topic. This article aims to evaluate and find out the causes of the ammonium nitrate explosion disaster in Beirut, as well as provide an analysis of suggestions for handling mechanisms so that a similar incident does not happen again. Evaluation of the cause of the explosion at the Port of Beirut, with fire/heat burning down part of the warehouse. An that was stored in a port warehouse mixed with other goods caused a large explosion which was started by a small fire in the warehouse. The explosion caused the death of 220 people, injured 6,500 people, formed a crater 140 meters deep and an earthquake measuring 3.3 on the Richter scale. This explosion is classified as the third most destructive urban explosion of all time, after the atomic bombs on Hiroshima and Nagasaki at the end of World War II. As a precaution, proper handling and storage of AN is required in accordance with international regulations. To reduce the potential danger of explosions without causing significant risks, this is done through an appropriate disposal mechanism.

Autonomous firefighting using a quadruped robot

This paper presents the development of a proof-of-concept autonomous firefighting robot based on the Spot quadruped robot. Both the payload and software package for the autonomous detection, localization, and extinguishing of a small fire are discussed. A thermal camera is mounted to the front of Spot and a standard ABC fire extinguisher is mounted to the back. The thermal camera is used to autonomously detect fires and the fire extinguisher is used to put out a detected fire. The system was successfully tested at a local fire station in a controlled setting with actual fires. The results of this project have direct application in the area of autonomous first response in complex industrial environments and fire watch applications.