ABSTRACT Coal spontaneous combustion (CSC) occurring within the composite goaf of close-distance coal seams remains one of the most challenging issues in mine fire prevention and suppression efforts. To address the high-temperature hazards within the close-distance composite goaf areas at a mining site operated by Pingmei Group, a combined approach of numerical simulation, field testing, and theoretical analysis was employed. This study investigated the fracture development characteristics, air leakage patterns, and measures for preventing CSC in the region. Research indicates that advancing the working face of the underlying coal seam beyond the stopping line of the overlying coal seam creates numerous air leakage pathways through disturbance-induced inter-seam fracture structures. This process also caused the collapse of the overlying unmined seam, leading to significant residual coal distributed three-dimensionally between the overlying coal seam and underlying coal seam. The composite goaf’s airflow typically shifts from the underlying coal seam to the underlying coal seam, influenced by the variance in pressure inside it. Residual coal in the composite goaf results in a differential oxygen concentration distribution. Using the mine’s geological conditions and composite goaf air leakage patterns, the precise locations of high-temperature CSC hazard areas were determined. Effective mitigation measures, including cement-based material filling and sealing, inerting and cooling with liquid nitrogen and carbon dioxide, and gel foam coverage for oxygen isolation, were successfully implemented to address the high-temperature hazards of composite goafs. These findings provide valuable technical references for preventing and combating CSC in close-distance composite goaf areas.

Aims: The present study aimed to assess employment generation as a direct-use ecosystem service of the Amrabad Tiger Reserve, Telangana, India, and to examine its role in supporting local livelihoods and strengthening conservation outcomes across forest divisions. Study Design: Descriptive, cross-sectional study based on records analysis and field verification. Place and Duration of Study: The study was conducted in the Achampet, Amrabad, and Nagarjuna Sagar forest divisions of the Amrabad Tiger Reserve, Telangana, India, during 2023–2025. Methodology: Employment data were obtained through systematic analysis of Forest Department administrative records, supplemented by field-level verification and structured interactions with divisional officials. Employment was classified into functional categories including forest protection and patrolling, fire prevention and control, wildlife rescue and monitoring, administrative support, vehicle operations, and ecotourism services. Division-wise and category-wise employment distribution was analyzed to assess spatial variation and livelihood significance. Results: The Amrabad Tiger Reserve generated direct employment for 381 individuals, with the Amrabad Division contributing the highest share (234 individuals; 61.4%), followed by Achampet (113; 29.7%) and Nagarjuna Sagar (34; 8.9%). Labour-intensive roles dominated the employment structure, notably base camp helpers (140; 36.7%), Tiger Conservation Force personnel (89; 23.4%), fire watchers (50; 13.1%), and drivers (24; 6.3%). Ecotourism-related employment accounted for 24 positions (6.3%), comprising nature guides and ecotourism drivers. The majority of jobs were accessible to local and forest-dependent communities, indicating strong livelihood linkage. Conclusion: Employment generation represents a significant socio-economic benefit of the Amrabad Tiger Reserve, contributing to livelihood security, reducing reliance on extractive forest use, and fostering community participation in conservation. A key limitation of the study is its reliance on officially recorded employment data, which may underestimate informal or seasonal livelihood opportunities linked to conservation activities.

ABSTRACT As one of the crucial processes for maintaining ecosystem balance, natural fires are often characterized by extensive coverage and unpredictability. However, uncontrolled wildfires pose inestimable threats to ecosystems, the atmospheric environment, and human health. Therefore, scientifically predicting and monitoring the temporal and spatial distribution of wildfires is of great significance for fire prevention and control. Based on remote sensing data from Himawari-8, a new-generation geostationary satellite, this study integrates contextual information and dynamic threshold detection methods. The method employs slope deviations of infrared channels to achieve near-real-time detection of fire points. Experimental results demonstrate that the AHI_IGFDA forest fire detection algorithm proposed in this study can extract fire point information efficiently and rapidly. Quantitative comparisons with the FY-3C/VIRR fire detection algorithm and the official Himawari-8 wildfire product (WLF) confirm its outstanding performance: AHI_IGFDA achieves a precision of 0.61, which is significantly superior to WLF (0.18) and FY-3C/VIRR (0.08). Meanwhile, it maintains a low omission rate of 0.36 compared to WLF (0.42) and FY-3C/VIRR (0.84). The optimization of the infrared gradient in this novel fire detection algorithm endows it with higher detection accuracy and lower miss detection rate, and provides new insights and methods for achieving near-real-time fire detection.

Objective. The objective of this study is to assess a large warehouse complex's compliance with fire safety requirements and analyze its fire hazard level. Method . A method for calculating quantitative indicators of fire hazard levels for large warehouse buildings is presented. The developed method is designed to reliably assess the potential threat of a fire breaking out and developing in a large warehouse complex. Fire hazard generation parameters are examined in detail, including architectural and planning features, fire load, fire safety equipment, and more. Result . Fire risk determination is based on calculating values reflecting the fire hazard for the facility in question. The resulting data are compared with the limit values defined in regulatory documents. The calculated fire risk parameters quantify the likelihood of a fire occurring at a facility and demonstrate the potential consequences for building occupants and the safety of material resources. Conclusion . The individual fire risk calculation method can be used to implement fire prevention measures, ensure personnel safety, and safeguard material resources at warehouse facilities. The proposed approach enables optimization of facility fire safety costs by selecting the most appropriate and well-reasoned solutions. The author proposes additional measures to ensure fire safety in warehouse facilities.

Biological firebreaks are essential for forest fire prevention, with Schima superba serving as a dominant species in southern China. However, the dynamics of soil quality across stand ages in these firebreaks and their capacity to sustain long-term fertility and ecological functions that support forest stability remain poorly understood. In this study, a space-for-time substitution approach was employed to investigate three Schima superba firebreaks of different ages located on ridge sites, integrating soil quality assessment and constraint diagnosis indices. The results revealed that most soil indicators increased significantly with stand age. The minimum dataset (MDS) included available nitrogen (AN), soil organic carbon (SOC), sand, silt, pH, electrical conductivity (EC), bulk density (BD), microbial biomass phosphorus (MBP), and soil water content (SWC). The retained indicators accounted for 47.06% of the original total set. The soil quality index (SQI) values ranged from 0.29 to 0.64 and increased significantly with stand age. Despite these improvements, the overall SQI predominantly fell within Levels III and IV, reflecting generally poor soil quality. The primary contributors to SQI were AN, SOC, SWC, MBP, BD, and pH. Constraint diagnosis across stand ages identified SOC, SWC, silt, and MBP as major limiting factors. Nutrient-related constraints (SOC, AN, and MBP) accounted for 35.62%-40.59% of the total limitations, highlighting nutrient deficiency as the principal restrictive factor. However, physical constraints (sand, silt, and BD) and EC may pose increasing risks to the long-term sustainability of Schima superba firebreaks. These findings provide a robust scientific foundation for assessing the ecological function of Schima superba firebreaks and for guiding soil management and improvement strategies.

Educação ambiental para a prevenção de incêndios no mundo: uma revisão da literatura Environmental education fosters environmentally literate citizens capable of addressing sustainability challenges. This article presents a literature review on environmental education and how fire-related topics are approached in primary education worldwide. The review combined quantitative bibliometric analysis and a qualitative systematic review supported by Artificial Intelligence, using Scopus and Web of Science databases. A total of 16,421 articles were identified since 1968, with 4,065 duplicates removed. The annual growth rate of publications was 11.43%. Female researchers ranked highest in productivity and impact (H-index). Core themes in environmental education include climate change, biodiversity, waste management, and ecology. However, only 0.152% of all documents addressed fire education in schools and universities. Most fire-related studies focused on prevention, safety, and forest fire suppression.

ABSTRACT This study systematically examines the effect of a novel multi-dimensional synergistic inhibitor inspired by the combined acid-resistant mechanism of the gastric mucosa and gastric mucus barrier on suppressing coal spontaneous combustion, in order to evaluate its practical performance in addressing the shortcomings of traditional inhibitors in terms of long-term effectiveness and synergy. Through coal spontaneous combustion tendency tests and programmed temperature rise experiments, the macroscopic inhibitory effects of the anti-seepage and deoxidation inhibitor imitating gastric mucus and 15 wt% MgCl2 solution were compared and analyzed. The results show that this inhibitor forms a synergistic oxygen shielding structure of “anti-permeation – isolation – oxygen consumption – inertization” through a multi-dimensional synergistic mechanism composed of sodium alginate gel physical coverage, glucose oxidase catalytic oxygen consumption and magnesium chloride inertization and oxygen isolation, significantly increasing the intersection point temperature of the coal sample, delaying the release of characteristic gases, reducing the total release, and achieving an average inhibition rate of 76.2%. Its comprehensive performance is significantly superior to that of traditional magnesium chloride solution. Therefore, this biomimetic multi-dimensional synergistic inhibitor can achieve efficient and sustained suppression of coal spontaneous combustion, providing a promising new approach for the prevention and control of coal mine fires.

When forest fires occur, timely detection and initial attack are critical for fire prevention. This study focuses on optimizing the cruise path of Unmanned Aerial Vehicles (UAVs) from the perspective of initial attack. It aims to maximize coverage of regions where initial attack success rates are low, shorten the time taken to detect fires, and, in turn, boost detection effectiveness and the initial attack success. In this paper, a path planning strategy, Improved Multi-Objective Crested Porcupine Optimizer (IMOCPO), is proposed. This strategy employs a weighted sum approach to formulate a composite objective function that balances global search and local optimization capabilities, considering practical requirements such as UAV endurance and uneven distribution of risk areas, thus enhancing adaptability in complex forest environments. The weight selection is justified through systematic grid search and validated by sensitivity analysis. The proposed strategy was compared and evaluated with a related strategy using four metrics: high-risk coverage rate, grid coverage rate, Average Distance Risk (ADR), and Average Grid Risk (AGR). Results show that the proposed path planning strategy performs better in these metrics. This study provides an effective solution for optimizing UAV cruise strategies in forest fire monitoring and has practical significance for improving the intelligence of forest fire prevention.

In this study, to reveal the changes in explosion pressure and flame propagation characteristic, a 12 L cylindrical explosion device was used to conduct experiments on the explosions of two-phase mixtures of paper powder and volatile organic compounds (VOCs) at varying concentrations. The findings indicate that, at a constant paper powder concentration, increasing the VOCs concentration initially causes minor fluctuations in the maximum explosion pressure (Pmax), followed by an increase. At a constant VOCs concentration, as the paper powder concentration rises, the Pmax also increases, while the time to reach peak explosion pressure initially decreases before increasing. Additionally, under the two-phase concentration range produced in the production process, higher concentrations of paper powder and VOCs significantly enhance flame brightness, combustion intensity, heat release rate, and flame duration. These insights provide data support for determining the alarm limit values of VOCs concentration detection, provide a scientific basis for evaluating and predicting explosion risks associated with paper powder and VOCs, offering significant practical implications for fire and explosion prevention in the printing industry.

Spontaneous coal combustion accounts for more than 90% of mine fires, and at the same time, the ‘dual carbon’ strategy requires fire prevention and extinguishing materials to have both low-carbon and environmentally friendly functions. To meet on-site application needs, a composite gel with fast injection, flame retardant, and CO2 adsorption functions was developed. PVA-PEI-PAC materials were selected as the gel raw materials, and an orthogonal test with three factors and three levels was used to optimize the gelation time parameters to identify the optimal formulation. The microstructure of the gel, CO2 adsorption performance, as well as its inhibition rate of CO, a marker gas of coal spontaneous combustion, and its effect on activation energy were systematically characterized through SEM, isothermal/temperature-programmed/cyclic adsorption experiments, and temperature-programmed gas chromatography. The results show that the optimal gel formulation is 14% PVA, 7% PEI, and 5.5% PAC. The gel microstructure is continuous, dense, and rich in pores, with a CO2 adsorption capacity at 30 °C and atmospheric pressure of 0.86 cm3/g, maintaining over 76% efficiency after five cycles. Compared with raw coal, a 10% gel addition reduces CO release at 170 °C by 25.97%, and the temperature-programmed experiment shows an average CO inhibition rate of 25% throughout, with apparent activation energy increased by 14.96%. The gel prepared exhibited controllable gelation time, can deeply encapsulate coal, and can efficiently adsorb CO2, significantly raising the coal–oxygen reaction energy barrier, providing an integrated technical solution for mine fire prevention and extinguishing with both safety and carbon reduction functions.

Forest ecosystems, as vital natural resources, are increasingly endangered by wildfires. Effective forest fire management relies on the accurate and early detection of small–scale flames and smoke. However, the complex and dynamic forest environment, along with the small size and irregular shape of early fire indicators, poses significant challenges to reliable early warning systems. To address these issues, this paper introduces SER–YOLOv8, an enhanced detection model based on the YOLOv8 architecture. The model incorporates the RepNCSPELAN4 module and an SPPELAN structure to strengthen multi-scale feature representation. Furthermore, to improve small target localization, the Normalized Wasserstein Distance (NWD) loss is adopted, providing a more robust similarity measure than traditional IoU–based losses. The newly designed SERDet module deeply integrates a multi–scale feature extraction mechanism with a multi-path fused attention mechanism, significantly enhancing the recognition capability for flame targets under complex backgrounds. Depthwise separable convolution (DWConv) is utilized to reduce parameters and boost inference efficiency. Experiments on the M4SFWD dataset show that the proposed method improves mAP50 by 1.2% for flames and 2.4% for smoke, with a 1.5% overall gain in mAP50–95 over the baseline YOLOv8, outperforming existing mainstream models and offering a reliable solution for forest fire prevention.

Fire spreading characteristics and prevention and control of courtyard-type ancient buildings

Enhanced fire prevention and fire extinguishing performances of silica gel foam containing ammonium polyphosphate

Ferroconcrete-inspired design of a robust and efficient flame-retardant cellulose composite for fire prevention

This study identifies the key climatic and topographic factors influencing large wildfires in South Korea and develops a spatial methodology for risk mapping. Using wildfire records (2017-2023) and meteorological datasets, the Classification and Regression Tree (CART) model was applied to extract the significant variables. Maximum surface sensible heat flux (<i>sshf_max</i>), maximum shortwave radiation (<i>sshf_max</i>), average slope, and maximum evapotranspiration were the most influential variables, with <i>sshf_max</i> being the primary split variable. These variables were normalized and weighted using a WLC approach to produce a wildfire risk map for Gyeongsangbuk-do. This province has been most affected by wildfires in the past decade. The southeastern region was classified as having the highest risk (level 1), whereas the northwestern regions had lower risk (levels 2-3). Despite the limited number of large-fire cases, this study offers a baseline framework to prioritize fire prevention and resource allocation. Future improvements are expected through the integration of global datasets and advanced machine learning.

In the context of green transition and digital transformation, forestry is becoming a strategic area of application of current modern technologies. The Internet of Things (IoT), artificial intelligence (AI), big data analysis (Big Data) and Digital Twins define the basic infrastructure of smart forestry. By connecting sensors, drones and satellites, IoT allows for continuous monitoring of forest ecosystems, risk anticipation and decision optimization in real-time. The purpose of this study is to perform a comprehensive narrative analysis of the relevant scientific literature from the recent period (2020–2025) regarding the application of IoT in forestry, highlighting the conceptual, technological and institutional developments. Based on a selection of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (29 full-text articles), four major axes are analyzed: (A) forest fire detection and prevention; (B) climate-smart forestry and carbon accounting; (C) forest digitalization through the concepts of Forest 4.0, Forest 5.0 and Digital Twins; (D) sustainability and digital forest policies. The results show that IoT is a catalyst for the sustainable transformation of the forest sector, supporting carbon accounting, climate-risk reduction and data-driven governance. The analysis highlights four major developments: the consolidation of IoT–AI architectures, the integration of IoT and remote sensing, the emergence of Forest 4.0/5.0 and Digital Twins and the growing role of governance and data standards. These findings align with the objectives of the EU Forest Strategy 2030 and the European Green Deal.

ABSTRACT Coal remains a critical component of global energy supply, maintaining a central role in many national energy structures. However, large-scale coal mining has led to increasing economic losses and environmental concerns caused by mine fires. Understanding the current research landscape in mine fire prevention and control is vital for improving mine safety management, optimizing fire prevention strategies, and promoting environmental and energy efficiency. In this context, Using CiteSpace and the Web of Science Core Collection, this paper employs CiteSpace and the Web of Science Core Collection to perform a bibliometric analysis of publications from 2000 to 2023. The results reveal a sharp increase in the number of publications since 2014, with Fuel, Process Safety and Environmental Protection, and Energy emerging as leading journals. China, the United States, and Australia are the primary contributors, with China University of Mining and Technology ranking first among institutions. Keyword timeline and burst analyses indicate dynamic research trends, with “spontaneous combustion,” “oxidation,” and “fire” being the most frequent terms, and “pyrolysis,” “agent,” and “parameters” representing current research hotspots. This study provides a comprehensive overview of the field’s development and offers valuable insights for future research directions.

The frequent occurrence of fires has prompted China to accelerate the development of community fire prevention and emergency management systems. Language, serving both communicative and affective functions by facilitating the flow of information and fostering mutual understanding, runs through the entire process of community fire emergency management. In response to the early-stage nature of this field and the lack of a systematic framework, this study constructs a dynamic capability evaluation system for urban community fire-related emergency language services (FELS) by integrating multi-source and heterogeneous data. First, by adopting a hybrid approach combining dynamic capability theory and text mining, a three-level indicator system is established. Second, based on domain knowledge, quantitative methods and scoring rules are designed for the third-level qualitative indicators to provide standardized input for the model. Third, a weighting and integration framework is developed that simultaneously considers the internal mechanism characteristics and statistical properties of indicators. Specifically, a knowledge-driven weighting approach combining FAHP and fuzzy DEMATEL is employed to characterize indicator importance and interrelationships, while the CRITIC method is used to extract Data-Driven weights based on data dispersion and information content. These knowledge-driven and Data-Driven weights are then integrated through a multi-feature fusion weighting approach. Finally, a linear weighting model is applied to combine the normalized indicator values with the integrated weights, enabling a systematic evaluation of the dynamic capabilities of community FELS. To validate the proposed framework,, application tests were conducted in four representative types of urban communities, including internationally developed, aging and vulnerable, newly developed, and economically diverse communities, using fire emergency scenarios as the entry point. The external validity and internal robustness of the proposed model were verified through these tests. The results indicate that the evaluation system provides accurate, objective, and adaptive assessments of dynamic capabilities in FELS across different community contexts, offering a governance-oriented quantitative tool to support grassroots fire prevention and to enhance community resilience.

This study aims to analyze Greenpeace's advocacy campaign on forest fire prevention and peatland conservation as a solution to overcome the climate crisis. Greenpeace, as an international environmental organization, has been running advocacy campaigns to reduce land damage and support peatland conservation. This study employs a qualitative research method with qualitative data analysis (QDA) and adopts a descriptive approach. Data was obtained from online media sources, websites, and previous studies related to Greenpeace campaigns. Data processing was carried out using NVivo 12 Plus software, utilizing the Crosstab Query feature to visualize the relationship between variables and a Word Cloud to identify the dominant words in the data. The results of the analysis indicate that Greenpeace's advocacy campaigns play a significant role in driving change, both by raising public awareness about the importance of preserving peat ecosystems and by advocating for more sustainable forest management. Through these campaigns, the understanding of the need for more environmentally friendly management will continue to grow. The novelty of this research lies in the use of Nvivo-based digital analysis to map the interrelationships between campaign elements, including advocacy messages, communication approaches, and public responses, thereby producing a more systematic understanding of the effectiveness of Greenpeace's campaign strategies on the issues of forest fire prevention and peatland conservation

This study analyzes long-term climate change trends in the municipality of Autlán de Navarro, Jalisco, Mexico, focusing on temperature and precipitation over the period 1961–2023. Using ERA5-Land reanalysis data with a 9 km spatial resolution, validated and adjusted with records from nine CONAGUA meteorological stations, the research examines changes between two climatological periods (1961–1990 and 1991–2023). Descriptive statistics, trend analysis, and climate classification following García’s adaptation of the Köppen system were applied to identify climatic transformations and their potential environmental and socio-economic implications. Results indicate a consistent increase in both temperature and precipitation. Average annual temperature shows a positive trend of approximately 0.017 °C per year, while average annual precipitation increased between 69 and 146 mm over the last 30 years, with a long-term trend of 3.27 mm per year. Rainfall remains concentrated in summer months, but recent decades exhibit higher accumulated precipitation, particularly from June to October, and drier winters in some localities. Temperature increases are moderate (generally below 1 °C per month) but persistent across all months. These changes have led to shifts in local climate classifications, generally toward warmer and more sub-humid conditions, with reduced winter rainfall in urban Autlán and Corcovado, and increased summer and winter precipitation in areas such as El Chante and the Sierra de Manantlán. The study discusses implications for agriculture, water availability, forest fire risk, and ecosystem vulnerability. The conclusion is that localized climate analyses are essential for effective adaptation planning. They recommend improved water management, adoption of efficient irrigation technologies, agroforestry and polyculture practices, reforestation, fire prevention strategies, and environmental education to mitigate climate change impacts and strengthen regional resilience.