Safety at the building is a safe condition physically, socially, spiritually, psychologically and other threats that endanger humans. Building Safety not only focus on active fire protection, but also need to concern about passive protection. Regarding from that safety aspect, the aspect of building life saving is a prevention for building users so that they have time for evacuation process until they leave the building. The aspect of saving lives is reviewed from the fulfillment of criteria such as distance between buildings, fire fighting access routes, pavement layers or hardstanding, fire fighting access inside buildings, access to exit facilities (corridors), access to exit facilities (doors), lighting on evacuation routes. , Signage, Placement of assembly point. So that an evacuation plan is created along with the fulfillment of protect users in saving lives at the building.

Background: Peatland ecosystems play an important role in the hydrological cycle and carbon cycling. In Indonesia, peatlands store about 28.6 gigatonnes of carbon which is equivalent to 10 years of global fossil fuel emissions. Peatlands act as a water storage during wet seasons and slowly release water during dry seasons to maintain river discharges and hydrological balance. However, climate change induced prolong drought has increased peatland dryness in recent decades which elevate the risks of unwanted peatland fires. During El Nino-induced drought in 2015, over 2.6 million hectares of forest and land burned, emitting 0.81–1.4 gigatonnes of greenhouse gases. The extreme fires damaged biodiversity, degraded water quality and displaced thousands of locals. This study aimed to analyze peatland wetness as an indicator of fire occurrences in forest and land fires (FLFs) in Riau, Indonesia by examining the relationship between degree of peatland wetness derived from satellite imagery and hotspots data. Methods: Peatland wetness was estimated from microwave backscattering coefficients at several RadarSat synthetic aperture radar (SAR) wavelengths and cross validated with water table depth measurements from 120 monitoring wells. Hotspots data between 2015-2020 were obtained from NASA's MODIS active fire product. Finding: Preliminary results showed significant negative correlations between peatland wetness and numbers of hotspots in peatlands, with more hotspots occurring in drier peatlands compared to wetter ones. This implies that maintaining peatland hydrological functions through continuoussaturation is pivotal to prevent severe peatland wildfires under future climate change. Conclusion: Conservation efforts to restore hydrological balance in degraded peatlands through re-wetting strategies are recommended. Further research utilizing machine learning algorithms to produce high-resolution peatland wetness maps can improve fire risk monitoring in peatlands. Novelty/Originality of this Study: This study introduces the novel concept of utilizing peatland wetness as a key indicator for predicting and mitigating forest and land fires in Indonesia, particularly in Riau Province. By combining peatland moisture and temperature data, the research establishes threshold values to better predict fire risks and guide timely mitigation efforts, thereby enhancing the efficiency and effectiveness of FLF response activities.

Assessment of the effectiveness of coarse resolution fire products in monitoring long-term changes in fire regime within protected areas in South Africa

Central Chile (CC, latitudes 32–40°S) experienced very active fires in 2017 and 2023. These fires burned large areas and killed many people. These unprecedented fires for CC presented a need for more defined fire weather conditions on the synoptic scale. In this paper, fire weather conditions were analyzed using various satellite-derived fire data (hotspots, HSs), wind streamlines, distribution maps of wind flow and temperature, and various synoptic-scale weather maps. Results showed that local winds, known as Puelche, blew on the peak fire days (26 January 2017 and 3 February 2023). The number of HSs on these days was 2676 and 2746, respectively, about 90 times the average (30). The occurrence of Puelche winds was confirmed by streamlines from high-pressure systems offshore of Argentina to the study area in CC. The formation of strong winds and high-temperature areas associated with Puelche winds were identified on the Earth survey satellite maps. Strong winds of about 38 km h−1 and high temperatures above 32 °C with low relative humidity below 33% were actually observed at the weather station near the fire-prone areas. Lastly, some indications for Puelche winds outbreaks are summarized. This paper’s results will be used to prevent future active fire occurrences in the CC.

Algeria, the main fire hotspot on the southern rim of the Mediterranean Basin, lacks a complete fire dataset with official fire perimeters, and the existing one contains inconsistencies. Preprocessed global and regional burned area (BA) products provide valuable insights into fire patterns, characteristics, and dynamics over time and space, and into their impact on climate change. Nevertheless, they exhibit certain limitations linked with their inherent spatio-temporal resolutions as well as temporal and geographical coverage. To address the need for reliable BA information in Algeria, we systematically reconstructed, validated, and analyzed a 40-year (1984–2023) BA product (NEALGEBA; North Eastern ALGeria Burned Area) at 30 m spatial resolution in the typical Mediterranean ecosystems of this region, following international standards. We used Landsat data and the BA Mapping Tools (BAMTs) in the Google Earth Engine (GEE) to map BAs. The spatial validation of NEALGEBA, performed for 2017 and 2021 using independent 10 m spatial resolution Sentinel-2 reference data, showed overall accuracies > 98.10%; commission and omission errors < 8.20%; Dice coefficients > 91.90%; and relative biases < 3.44%. The temporal validation, however, using MODIS and VIIRS active fire hotspots, emphasized the limitation of Landsat-based BA products in temporal fire reporting accuracy terms. The intercomparison with five readily available BA products for 2017, by using the same validation process, demonstrated the overall outperformance of NEALGEBA. Furthermore, our BA product exhibited the highest correspondence with the ground-based BA estimates. NEALGEBA currently represents the most continuous and reliable time series of BA history at fine spatial resolution for NE Algeria, offering a significant contribution to further national and international fire hazard and impact assessments and acts as a reference dataset for contextualizing future weather extremes, such as the 2023 exceptional heat wave, which we show not to have led to the most extreme fire year over the last four decades.

The most significant football stadium riot tragedy occurred at the National Stadium (Estadio Nacional) in Lima, Peru, resulting in 362 fatalities. The second largest tragedy transpired at the Kanjuruhan Stadium, Malang Regency, Indonesia, leading to 135 deaths. This research aims to evaluate the conformity of evacuation routes and fire protection systems at the one of Football Stadium in Greater Malang, in accordance with the standards and regulations applicable in Indonesia. This analytical descriptive study employs a quantitative research design. The research process involved examining the factual conditions on the ground and subsequently comparing the suitability of evacuation routes and fire protection systems against the standard benchmarks and regulations in place in Indonesia. The results of this study indicate that the conformity level of the evacuation routes, comprising emergency stairs, evacuation routes, safety signs, assembly points, and emergency exits, is at 61.9 percent, denoting adequate compliance. Conversely, the active fire protection systems, encompassing portable fire extinguishers (APAR), hydrants, fire alarms, fire detectors, and sprinklers, show 0 percent compliance, meaning no conformity exists. This study is anticipated to serve as a reference for enhancing evacuation routes and fire protection systems at Football Stadium X: stadium in Greater Malang.

Abstract Increases in wildfire activity and the resulting impacts have prompted the development of high-resolution wildfire behavior models for forecasting fire spread. Recent progress in using satellites to detect fire locations further provides the opportunity to use measurements toward improving fire spread forecasts from numerical models through data assimilation. This work develops a physics-informed approach for inferring the history of a wildfire from satellite measurements, providing the necessary information to initialize coupled atmosphere–wildfire models from a measured wildfire state. The fire arrival time, which is the time the fire reaches a given spatial location, acts as a succinct representation of the history of a wildfire. In this work, a conditional Wasserstein generative adversarial network (cWGAN), trained with WRF–SFIRE simulations, is used to infer the fire arrival time from satellite active fire data. The cWGAN is used to produce samples of likely fire arrival times from the conditional distribution of arrival times given satellite active fire detections. Samples produced by the cWGAN are further used to assess the uncertainty of predictions. The cWGAN is tested on four California wildfires occurring between 2020 and 2022, and predictions for fire extent are compared against high-resolution airborne infrared measurements. Further, the predicted ignition times are compared with reported ignition times. An average Sørensen’s coefficient of 0.81 for the fire perimeters and an average ignition time difference of 32 min suggest that the method is highly accurate. Significance Statement To initialize coupled atmosphere–wildfire simulations in a physically consistent way based on satellite measurements of active fire locations, it is critical to ensure the state of the fire and atmosphere aligns at the start of the forecast. If known, the history of a wildfire may be used to develop an atmospheric state matching the wildfire state determined from satellite data in a process known as spinup. In this paper, we present a novel method for inferring the early stage history of a wildfire based on satellite active fire measurements. Here, inference of the fire history is performed in a probabilistic sense and physics is further incorporated through the use of training data derived from a coupled atmosphere–wildfire model.

In the operational conduct of a company, especially those relating to the storage of materials and goods in warehouses, the occupational health and safety OHS) aspects are critical factors that cannot be neglected. Fire was one of the consequences of a non-standard OHS application. The purpose of this research was to provide a proposal for the design of fire protection systems in the Archives and Documents Company, which are light fire extinguishers, sprinkler systems, hydrants, and reservoirs. This research method is observational-descriptive. In conceptual planning, layout, and coordination with the company, the researchers directly perform observations on the PT Archives and Documents to analyze and identify the deficiencies of fire protection equipment owned by the associated companies. ADC requires a total of 9 light fire extinguishers, 192 sprinkler points, 6 hydrants, and a reservoir size of 343,000 liters as a fire protection system according to the National Fire Protection Association standards (NFPA). All buildings that are occupied by humans and have a risk of fire must be equipped with fire protection. for future research in the field of fire safety research to design an emergency response plan for a building by simulating it with advanced technology. By implementing the proposed fire protection, the Company can prevent fire and spread of fire more widely

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

Revisiting the 2023 wildfire season in Canada

Air pollution has become an increasing concern in the Mekong region due to seasonal vegetative burning triggered by related anthropogenic activities and climate change. While the assumption of a correlation between agriculture burning and air pollution is a common postulation, little evidence exists on the association between fire incidents and air pollution concentrations. The current study explores the relationship between satellite-derived fire occurrence, land surface characteristics, and particulate matter 2.5 (PM2.5) concentrations for the five Lower Mekong countries, namely Cambodia, Laos, Myanmar, Thailand, and Vietnam, in an effort to gain new insights into fire distributions related to air quality. Publicly available daily active fire hotspots from the VIIRS satellite instrument, annual land cover products from the MODIS satellite, and mean monthly ground-level PM2.5 estimates from the V5.GL.04 database were analyzed in two relational assessments; first, the distribution of VIIRS active fire counts and fire radiative power (FRP) temporally and spatially and secondly, the correlations between the monthly VIIRS active fire counts, cumulative monthly FRP and mean monthly PM2.5 estimates per country and land cover type. The results suggest a statistically significant positive correlation between monthly fire counts, cumulative FRP, and PM2.5 estimates for each country, which differ based on land cover. The strongest correlation between monthly fire incidences and PM2.5 estimates was found in the case of Myanmar. For all countries combined, fires detected in forests displayed the highest correlation with monthly PM2.5 estimates. This study demonstrates the use of the VIIRS active fire product and provides important insights into temporal and spatial fire distributions as baseline information for fire prevention and mitigation strategies in the Mekong region.

Building safety is a form of building reliability against disasters, one of which is fire hazard. In Indonesia, there are still many buildings that do not apply this security. In this research, the case study taken is the Gebang Raya block A flat, Tangerang City. This research is aimed at identifying the completeness of evacuation facilities based on standardization and also knowing the equipment and placement of active fire protection equipment in Gebang Raya block A flats. The research method used is descriptive qualitative by conducting field observations, interviews, documentation and literature review to obtain information about fire evacuation facilities, standardization of fire evacuation facilities, and equipment and placement of active fire protection systems in the flats. From the results of the discussion, it can be stated that the Gebang Raya block A flats in terms of completeness are not in accordance with existing standards and regulations. In terms of standardization that Gebang Raya block A flats still have fire evacuation facilities, especially emergency stairs that do not meet the standards both based on theory and existing regulations. In terms of completeness, the active fire protection system in this flat does not meet. There is only a standpipe and slang pipe system and a light fire extinguisher.

Multi-resolution monitoring of the 2023 maui wildfires, implications and needs for satellite-based wildfire disaster monitoring

Fire is a serious threat that can cause significant material loss and loss of life. To overcome this threat, implementing an active fire protection system is very important in efforts to prevent and control fires. This research aims to analyze the application of active fire protection systems through literature studies. This study also evaluates the effectiveness of the protection system in responding to fire incidents. The research results show that there are two fire protection systems, namely an active protection system and a passive protection system. An active fire protection system can provide early warning and start the extinguishing process quickly, thereby reducing the negative impact of fires. With a deeper understanding of these aspects, it is hoped that more effective solutions can be obtained in protecting assets and human lives from the dangers of fire.

Lithium-ion batteries (LIBs) have emerged as the most commercialized rechargeable battery technology. However, their inherent property, called thermal runaway, poses a high risk of fire. This article introduces the “Battery Immersed in Fire Prevention Material (BIF)”, the immersion-type battery in which all of the LIB cells are surrounded by a liquid agent. This structure and the agent enable active battery fire suppression under abusive conditions while facilitating improved thermal management during normal operation. Abuse tests involving a battery revealed that the LIB module experienced fire, explosions, and burnouts with the target cell reaching temperatures of 1405 °C and the side reaching 796 °C. Conversely, the BIF module exhibited a complete lack of fire propagation, with temperatures lower than those of LIBs, particularly 285 and 17 °C, respectively. Under normal operating conditions, the BIF module exhibited an average temperature rise ~8.6 times lower than that of a normal LIB. Furthermore, it reduced the uneven thermal deviation between the cells by ~5.3 times more than LIB. This study provides a detailed exploration of the BIF and covers everything from components to practical applications. With further improvements, this technology can significantly enhance fire safety and prevent the thermal degradation of batteries in the real world.

Wildland fires cause economic and ecological damage with devastating consequences, including loss of life. To reduce these risks, numerous fire detection and recognition systems using deep learning techniques have been developed. However, the limited availability of annotated datasets has decelerated the development of reliable deep learning techniques for detecting and monitoring fires. For such, a novel dataset, namely, SWIFT, is presented in this paper for detecting and recognizing wildland smoke and fires. SWIFT includes a large number of synthetic images and videos of smoke and wildfire with their corresponding annotations, as well as environmental data, including temperature, humidity, wind direction, and speed. It represents various wildland fire scenarios collected from multiple viewpoints, covering forest interior views, views near active fires, ground views, and aerial views. In addition, three deep learning models, namely, BoucaNet, DC-Fire, and CT-Fire, are adopted to recognize forest fires and address their related challenges. These models are trained using the SWIFT dataset and tested using real fire images. BoucaNet performed well in recognizing wildland fires and overcoming challenging limitations, including the complexity of the background, the variation in smoke and wildfire features, and the detection of small wildland fire areas. This shows the potential of sim-to-real deep learning in wildland fires.

Abstract Fires were historically rare in tropical forests of West and Central Africa, where dense vegetation, rapid decomposition, and high moisture limit available fuels. However, increasing heat and drought combined with forest degradation and fragmentation are making these areas more susceptible to wildfires. We evaluated historical patterns of Moderate Resolution Imaging Spectroradiometer active fires in African tropical forests from 2003 to 2021. Trends were mostly positive, particularly in the northeastern and southern Congo Basin, and were concentrated in areas with high deforestation. Year‐to‐year variation of fires was synchronized with increasing temperature and vapor pressure deficit. There was anomalously high fire activity across the region during the 2015–2016 El Niño. These results contrast with the drier African woodlands and savannas, where fire has been decreasing. Further attention to fires in African tropical forests is needed to understand their global impacts on carbon dynamics and their local implications for biodiversity and human livelihoods.

Wildfire smoke (WFS) is a mixture of respirable particulate matter, environmental gases, and other hazardous pollutants that originate from the unplanned burning of arid vegetation during wildfires. The increasing size and frequency of recent wildfires has escalated public and occupational health concerns regarding WFS inhalation, by either individuals living nearby and downstream an active fire or wildland firefighters and other workers that face unavoidable exposure because of their profession. In this review, we first synthesize current evidence from environmental, controlled, and interventional human exposure studies, to highlight positive associations between WFS inhalation and cardiovascular morbidity and mortality. Motivated by these findings, we discuss preventative measures and suggest interventions to mitigate the cardiovascular impact of wildfires. We then review animal and cell exposure studies to call attention on the pathophysiological processes that support the deterioration of cardiovascular tissues and organs in response to WFS inhalation. Acknowledging the challenges of integrating evidence across independent sources, we contextualize laboratory-scale exposure approaches according to the biological processes that they model and offer suggestions for ensuring relevance to the human condition. Noting that wildfires are significant contributors to ambient air pollution, we compare the biological responses triggered by WFS to those of other harmful pollutants. We also review evidence for how WFS inhalation may trigger mechanisms that have been proposed as mediators of adverse cardiovascular effects upon exposure to air pollution. We finally conclude by highlighting research areas that demand further consideration. Overall, we aspire for this work to serve as a catalyst for regulatory initiatives to mitigate the adverse cardiovascular effects of WFS inhalation in the community and alleviate the occupational risk in wildland firefighters.

Wildfires significantly threaten ecosystems and human lives, necessitating effective prediction models for the management of this destructive phenomenon. This study integrates Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) modules to develop a novel deep learning model called CNN-BiLSTM for near-real-time wildfire spread prediction to capture spatial and temporal patterns. This study uses the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire product and a wide range of environmental variables, including topography, land cover, temperature, NDVI, wind informaiton, precipitation, soil moisture, and runoff to train the CNN-BiLSTM model. A comprehensive exploration of parameter configurations and settings was conducted to optimize the model’s performance. The evaluation results and their comparison with benchmark models, such as a Long Short-Term Memory (LSTM) and CNN-LSTM models, demonstrate the effectiveness of the CNN-BiLSTM model with IoU of F1 Score of 0.58 and 0.73 for validation and training sets, respectively. This innovative approach offers a promising avenue for enhancing wildfire management efforts through its capacity for near-real-time prediction, marking a significant step forward in mitigating the impact of wildfires.

Accelerated reduction in China's cropland fires against the background of policy enhancement