Intelligent fire‐warning materials and sensors have gained considerable attention due to their excellent passive flame resistance and sensitive active fire‐alarm behaviors. However, current nanofiller‐based fire‐warning composites (e.g., MXene, graphene) still face limitations, including intrinsic dark feature, poor structural reliability, and unstable fire‐warning response, hindering their broad use in decorative applications. Herein, a novel MXene derivative‐based bilayered composite nanocoating on wooden substrates with translucent features, exceptional flame resistance, and sensitive fire‐warning response is reported. MXene oxide porous nanoparticles are synthesized by a facile and simple oxidation of MXene and show unexpected network structure and semitransparent feature. Utilizing double‐layer structure and designed cross‐linked interface, the final nanocoatings applied onto the wooden substrates display good mechanical property and tunable optical transparency. Further, such double‐layer design guarantees excellent fire resistance performance through the formation of a compact C/N/P‐dopped TiO2 network during combustion. More interestingly, the resulting composite nanocoatings also display a rapid fire‐responsiveness (≈2.9 s), extended alarm duration (>300 s), and high repeated fire‐alarm capacity (>30 cycles) even after 1 year outdoors. In this work, a novel strategy is provided for designing intelligent semitransparent, fire‐retardant, and fire‐warning coatings for fire safety of wooden architecture.

Forests worldwide play a critical role in biodiversity conservation and climate regulation, yet they face unprecedented challenges, particularly from wildfires. Early wildfire detection is essential for preventing rapid spread, protecting lives, ecosystems, and economies, and mitigating climate change impacts. Traditional wildfire detection methods relying on human surveillance are limited in scope and efficiency. However, advancements in remote sensing technologies offer new opportunities for more efficient and comprehensive detection. This study highlights the integration of satellite sensors, capable of detecting thermal anomalies, smoke plumes, and vegetation health changes, with machine learning, particularly Support Vector Machines (SVMs), to enhance detection efficiency and accuracy. These algorithms analyse satellite data to identify fire patterns and provide near real-time alerts. SVMs’ adaptability over time improves performance, making them suitable for evolving fire regimes influenced by climate change. Focusing on the Wolgan Valley in Eastern Australia, the study utilised Landsat-8 imagery and SVMs to detect active fires and classify burned areas. Results demonstrated that combining various spectral bands, such as the Shortwave Infrared (SWIR) and Near-Infrared (NIR), enhances the identification of active fires and smoke. The introduction of the Normalized Difference Fire Index (NDFI) further refines detection capabilities by leveraging distinct spectral characteristics from Landsat 8 imagery. Despite the promise of these technologies, challenges such as data availability and model interpretability remain. Future research should focus on integrating diverse data sources, advancing machine learning techniques, developing real-time monitoring systems, addressing model interpretability, integrating unmanned aerial vehicles, and considering climate change impacts. This study underscores the potential of machine learning algorithms and innovative indices like NDFI to improve wildfire detection and management strategies, ultimately enhancing our ability to protect lives and ecosystems in fire-prone regions.

The neglected disproportionate contributions of active fires in greenhouse gas emissions globally

This study analyzes the implementation of firefighting procedures and evacuation methods in a hospital environment, with a focus on ensuring rapid rescue operations and evacuation methods in a real fire in 2024. This research emphasizes the integration of firefighting technologies, including fire detection systems, real-time communication networks, and specialized evacuation strategies for immobile patients. This work further examines the optimization of the emergency response through the coordinated efforts of an integrated rescue system, emphasizing tactical decision making and resource allocation. The findings demonstrate the effectiveness of evacuation methods in the event of needing to evacuate a larger number of people, as well as meeting the need to ensure that active fire protection systems are in an operational state. This research provides key recommendations for improving fire protection measures in healthcare facilities, ensuring faster response times and increased patient protection. Subsequently, after evaluating and reviewing all the options, conclusions were drawn from the on-site results, and recommendations were defined for future fires in similar facilities.

This paper presents a development of a “smart center” for studying particulate matter (PM) and other gaseous emissions pollution in a surface coal mine located in the Jharia coalfield of Jharkhand, India, utilizing internet of things (IOT)-based capabilities and low-cost sensors data. These pollutants in the air in and around surface mines present significant environmental challenges that require comprehensive assessment and management. This study undertook four winter months from January to April 2023 of real-time, low-cost IoT-based monitoring of PM1, PM2.5, PM10, SO2, NO2, O3, CO, CO2, TVOC, and noise pollution, along with meteorological parameters, such as humidity, rainfall, and temperature. Data were collected at three locations: within a mine site area, in a buffer zone located 1.5 km from the mine, and at a non-impacted residential area within the university campus. Mining operations and active mine fires are primary contributors to pollution. Peak PM concentrations around the mining site were observed during morning and evening hours (5–7 a.m. and 4–6 p.m.). This study further identified that air pollutant concentrations were positively correlated with humidity but negatively correlated with the atmospheric temperature at the mine site, but not at the university campus of the IIT–ISM.

Forest fires have a profound influence on the economy, ecology, and environment. Realizing the potential of remote sensing in forest fire management, a study was taken up to investigate the spatiotemporal behavior of active forest fires in a mountainous terrain of Uttarakhand State, north India, using 15 years' time-series historical MODIS (C6) active fire point products. Results indicate an over-all fire incidence detection accuracy of 62.3% with a KHAT value of 0.59. Moreover, a regular trend in intra-annual behavior in fire incidences with peaks during the hot and dry period of the year was observed and a large year-to-year variability in fire regimes with no significant trends over time could be noticed. The approach and results are discussed in detail along with the future perspective.

Forest fire emission estimates over South Asia using Suomi-NPP VIIRS-based thermal anomalies and emission inventory

Background In response to increasing risk of extreme wildfire across western North America, forest managers are proactively implementing fuel treatments. Aims We assessed the efficacy of alternative combinations of thinning, pruning and residue fuel management to mitigate potential fire behaviour and effects in seasonally dry forests of interior British Columbia, Canada. Methods Across five community forests, we measured stand attributes before and after fuel treatments in 2021 and 2022, then modelled fire behaviour and effects using the Fire and Fuels Extension to the Forest Vegetation Simulator. Key results For our study area, field measurements combined with fire behaviour modelling indicated: (1) low-intensity thinning from below reduced potential of passive crown fire, whereas high-intensity thinning reduced potential of passive and active crown fire; (2) pruning after thinning from below did not further reduce potential of passive crown fire; and (3) chipping or pile burning of residue fuel mitigated potential of passive crown fire, but fire effects associated with chipping remain a concern. Conclusions and implications There is limited prior research on the impacts of fuel treatments in western Canada. This research contributes to better understanding the potential impacts of fuel treatments in the fire-prone forests of interior British Columbia.

Landscape fires in subtropical southern Africa (2–20°S) are a prominent regional source of nitrogen oxides (NOx) and ammonia (NH3), affecting climate and air quality as precursors of tropospheric ozone and aerosols. Here we evaluate GEOS-Chem model skill at reproducing satellite observations of vertical column densities of NO2 from TROPOMI and NH3 from IASI driven with three distinct and widely used biomass burning inventories (FINNv2.5, GFEDv4s, GFASv1.2). We identify that GFASv1.2 use of fire radiative power and a NOx emission factor that is almost half that used by the other two inventories is most consistent with TROPOMI and that FINNv2.5 use of active fires and landscape-specific fuel loads and biomass consumed is most consistent with IASI. We use a simple mass-balance inversion to calculate top-down NOx emissions of 1.9 ± 0.6 Tg NO for June–October and NH3 emissions of 1.2 ± 0.4 Tg for July–October. All inventories collocate NOx and NH3 emissions, whereas most of the pronounced emissions of NOx and NH3 are separate and have distinct seasonality in the top-down estimate. We infer with GEOS-Chem more efficient ozone production (13 Tg ozone per Tg NO) with the top-down informed NOx emissions than the inventory emissions, as GFASv1.2 NOx is almost 20% less than top-down NOx and the 2.3- to 2.5-times greater FINNv2.5 and GFEDv4s NOx reduces sensitivity of ozone formation to NOx. Both NOx and NH3 top-down emissions are unaffected by use of plume injection heights, limited to GFASv1.2 in GEOS-Chem, and NH3 is insensitive to acidic sulfate and nitrate aerosol emissions absent in all inventories. The top-down emissions estimates and comparison to satellite observations suggest a hybrid bottom-up approach could be adopted to discern byproducts of smouldering and flaming fires.

Forest fires have important ecological, social and economic consequences causing loss of life and property. In order to prevent these consequences, it is very important to intervene in active fires in a timely manner and to determine the extent of burnt areas as soon as possible. In such studies, remote sensing methods provide great benefits in terms of speed and cost. In recent years, various methods have been developed to segment active fires and burnt areas with satellite images. Deep learning methods successfully perform segmentation processes in many areas such as disease detection in the field of health, crop type determination in the field of agriculture, land use and building detection in the field of urbanization. In this study, a method has been developed that automatically detects both active fires and burned areas that need to be re-enacted in terms of location and area size by using the same Sentinel 2 scene in a single time using deep learning methods. In particular, a new training and validation data set was created to train the U-Net+InceptionResNetV2 (CNN) model. By combining the powerful features of U-Net with InceptionResNet V2, a convolutional neural network trained over more than one million images on the ImageNet very base, we aim to examine its capabilities in burned area and active fire detection. The model applied on the test data has been shown to give successful results with an overall accuracy of 0.97 and an IoU (Intersection over union) value of 0.88 in the detection of burnt areas, and an overall accuracy of 0.99 and an IoU value of 0.82 in the detection of active fires. Finally, when the test images that were not used in the training dataset were evaluated with the trained model, it was revealed that the results were quite consistent in the detection of active fires and burnt areas and their geographical locations.

The array of wildfire activities instigated by human endeavors has emerged as a significant source of atmospheric pollution, posing considerable risks to both public health and property safety. This study harnesses Sentinel-2 satellite data, employing a variety of methods including spectral index methods, thresholding, and the Random Forest (RF) model for active fire spot detection. The research encompasses a wide range of land cover types across various Chinese regions. Utilizing the Gini coefficient, the study assesses the importance of spectral and texture features in the RF, culminating in the selection of an optimal feature combination for the construction of a bespoke RF model tailored for active fire detection. The research utilized texture features based on the Grey Level Co-occurrence Matrix (GLCM), demonstrating their significant contribution to enhancing the accuracy of fire detection using the RF model. Our analysis reveals that GLCM-based texture features, which form 40% of the model’s final feature set, are crucial for improving detection accuracy. The optimized RF model demonstrates a marked superiority in identifying active fires, achieving an overall accuracy of 86.1%. The study results demonstrate that the bespoke RF model is suitable for detecting active fire across various land cover environments in China.

Conventional firefighting tools and methods can strain water sources, require toxic foams, or rely on pre-installed countermeasures. A low-cost, non-toxic, and portable option was previously overlooked in portable devices: electrically assisted “ionic wind” fire suppression. Conductive aerosols, carried by vortex rings, can potentially extend the length of an electric arc and suppress fires. After the simulation, two prototype vortex ring launchers were compared, one using compressed air and another using an elastic diaphragm. The efficiency of each test case was assessed with a purpose-built automated image analysis system. The compressed air vortex launcher had a significantly higher efficiency than the elastic diaphragm prototype, with a p-value of 0.0006. Regardless of the prototype or the use of conductive aerosols, the device had an effective range of up to 1.98 m. The highest reliability of 90 ± 4.1% was achieved at 1.52 m from the launcher. The observations with compressed air launcher results saw no significant difference regarding the use of the conductive aerosol. Further investigation of the concept requires a systematic examination of other types of fires, electronic optimization, permutations of chemicals and concentrations, other types of vortex generation, and human factors. The computer vision system could also be used to further detect and target active fires. Beyond firefighting, the device can be adapted to applications ranging from manufacturing to aerospace. Regardless of the use of conductive aerosols, handheld vortex ring generators are a versatile, potential firefighting tool.

The open burning of agricultural residues is a widespread practice with significant environmental implications. This study explores the potential of satellite remote sensing to detect and analyze small-scale agricultural fires in Portugal, focusing on their spatial and temporal characteristics. Using active fire detection products from various satellite platforms, including VIIRS, MODIS, SLSTR, and SEVIRI, we conducted a detailed analysis across two local case studies and a national-scale assessment. This study evaluates both active fire detections and post-fire burned area estimations, using high-resolution satellite imagery to overcome the limitations associated with the small size and low intensity of these fires. The results indicate that while active fire detections are feasible for larger-scale burning, challenges remain for smaller fires due to resolution constraints. A systematic comparison with an agricultural burning request database further highlights the need for the enhancement of temporal and spatial precision in data to improve detection reliability. Despite these limitations, this work underscores the importance of remote sensing tools in monitoring agricultural burning practices and enhancing environmental management efforts.

Frequent forest fires, driven by hotter and drier climates, threaten biodiversity and human health, causing significant economic losses, air pollution, soil erosion, and degeneration. Current active and passive fire protection methods often suffer from environmental pollution, poor flexibility, and limited availability in remote areas. However, fast-acting surface flame retardants for passive forest fire protection, particularly for foliage, are rare. Herein, we report an easily obtainable gelatin-based fire spray, which resulted in 1.8 and 16.3-fold extension in ignition time, 34% and 39% reductions in total heat release, 78% and 92% reductions in fire growth index for dead and fresh leaves, respectively. After the fire warning is suppressed, for instance by rain, the sprayed substances can decompose and provide nitrogen and phosphorus as leaf and soil fertilizers without affecting soil microbial function, which increase plant net photosynthesis by 84% and effective nitrogen and phosphorus by 664% and 140%, respectively. Our green flame retardant and fertilizer material allows for simultaneous tree fire protection and growth.

Introduction: Hospital fire can result in greater casualties, injuries to patients or staff, and loss of property and equipment compared to fires in other types of building. This is attributed to the presence of a large number of vulnerable individuals, including those who are ill, disabled, pregnant, children, elderly, immunocompromised, on life support, or incapable of moving independently. This study aims to assess the implementation of the fire protection system, life-saving facilities, and fire management in Hospital X. Methods: This was a quantitative study on active fire protection system facilities, passive protection system facilities, live-saving facilities, and fire management as the subjects. Data were collected through observation, interviews, and document review, as well as a checklist and then analyzed by comparing the actual conditions with applicable standards and regulations. The final result was presented as the percentage compliance level and categorization according to the criteria established by the Research and Development Agency of the Public Works Department. Results: The active fire protection system presented a standard fulfillment rate of 53%, categorizing it as poor. The passive fire protection systems similarly demonstrated a poor fulfillment rate at 42%, while the life-saving facilities achieved a 66.7% fulfillment rate, placing them in the quite good category. Additionally, fire management attained an 81% fulfillment level, which falls under the good category. Conclusion: Hospital X has a good fire prevention approach with a standard fulfillment level of 60%.

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.

Background Reducing the delay between the detection of a fire and the arrival of the initial attack (IA) crew can have a significant impact on the likelihood of the IA’s success. Aims The objective of this study was to identify factors influencing same-day getaway time, next-day getaway time and travel time of helitack IA crews in the province of Quebec, Canada. Methods Using generalised linear modelling and model selection, we analysed the impact of multiple factors on these three distinct times. Key results Our results show that factors such as the distance between the departure base and the fire, the number of flight legs to reach a fire, dispatch hour, departure base location, the fire’s rate of spread, Julian date, the number of active fires, fuel type and the fire’s size at detection all influenced getaway time and travel time with varying degrees of influence. Conclusions The factors with the highest influence were distance for travel time and dispatch hour for both same-day and next-day getaway times. Implications Addressing these high-impact factors through the modification of deployment policies and the positioning of helitack crews could help reduce response times.

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.

Open biomass burning has significant adverse effects on regional air quality, climate change, and human health. Extensive open biomass burning is detected in most regions of China, and capturing the characteristics of open biomass burning and understanding its influencing factors are important prerequisites for regulating open biomass burning. The characteristics of open biomass burning have been widely investigated at the national scale, with regional studies often focusing on northeast China, but few studies have examined regional discrepancies in spatiotemporal variations over a long timescale in Guangxi province. In this study, we used the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG), combined with land cover data and high-resolution remote sensing images, to extract open biomass burning (crop residue burning and forest fire) fire points in Guangxi province from 2012 to 2023. We explored the spatial density distribution and temporal variation of open biomass burning using spatial analysis methods and statistical methods, respectively. Furthermore, we analyzed the driving forces of open biomass burning in Guangxi province from natural (topography, climate, and plant schedule), policy, and social (crop production and cultural customs) perspectives. The results show that open biomass burning is concentrated in the central, eastern, and southern parts of the study area, where there are frequent agricultural activities and abundant forests. At the city level, the highest numbers of fire points were found in Baise, Yulin, Wuzhou, and Nanning. The open biomass burning fire points exhibited large annual variation, with high levels from 2013 to 2015 and a remarkable decrease from 2016 to 2020 under strict control measures; however, inconsistent enforcement led to a significant rebound in fire points from 2021 to 2023. Forest fires are the predominant type of open biomass burning in the region, with forest fires and crop residue burning accounting for 76.82% and 23.18% of the total, respectively. The peak period for crop residue burning occurs in the winter, influenced mainly by topography, planting schedules, crop production, and policies, while forest fires predominantly occur in the winter and spring, primarily influenced by topography, climate, and cultural customs. The results indicate that identifying the driving forces behind spatiotemporal variations is essential for the effective management of open biomass burning.

Smoke, flame, and heat detectors are included in the active fire protection system that must be present on board. The function of these detectors is to detect fires early. This research aims to optimize the maintenance of smoke, flame, and heat detectors on board ships. This research was conducted using a qualitative method with primary data sources obtained through notes from interviews, field observations, documentation, and secondary data obtained from various literacies. This research was conducted at MV Pan Mutiara. In this study, it can be concluded that the optimization of smoke, flame, and heat detector maintenance needs to be done on MV Pan Mutiara so that the detector can work properly because if the detector is damaged, this equipment loses its function and it can be a leading cause of fire that cannot be controlled. Then the maintenance optimization is carried out by making a special schedule, as evidenced by the results of the interview, where according to 3RD Officer the maintenance schedule is made by dividing the total number of detectors on board over 6 months, this period is taken into consideration of the workload of 3RD Officer at POS SM company which is more compared to other companies.