Search Results (224)

Accurate fire source location in an aircraft cargo compartment cannot be determined by common design practices. This study proposes an advanced fire location inversion framework based on a Convolutional Long-Short-Term Memory (ConvLSTM) network. A self-designed interpolation preprocessing module is introduced to realize the integration of spatial and temporal sensor data. The model was trained and validated using a comprehensive database generated from large-scale fire dynamics simulations. Hyperparameter optimization, including a learning rate of 0.001 and a 5 × 5 convolution kernel size, can effectively avoid the systematic errors introduced by interpolation preprocessing, further enhancing model robustness. Validation in simplified scenarios demonstrated a mean squared error of 0.0042 m and a mean positional deviation of 0.095 m for the fire source location. Moreover, the present study assessed the model’s timeliness and reliability in full-scale cabin complex scenarios. The model maintained high performance across varying heights within cargo compartments, achieving a correlation coefficient of 0.99 and a mean absolute relative error of 1.9%. Noteworthily, reasonable location accuracy can be achieved with a minimum of three detectors, even in obstructed environments. These findings offer a robust tool for enhancing fire safety systems in aviation and other similar complex scenarios. Full article

The spontaneous combustion disaster of coal not only causes a waste of resources but also affects the safe production of coal mines. In order to accurately detect the range and location of the spontaneous combustion source of coal, this paper studies and summarizes previous research results, and based on the principles and research and development progress of existing detection technologies such as the surface temperature measurement method, ground temperature measurement method, wellbore temperature measurement method, and infrared remote sensing detection method, it briefly reviews the application of various detection technologies in engineering practice at this stage and briefly explains the advantages and disadvantages of each application. Research shows that the existing technologies are generally limited by the interference of complex environmental conditions (such as temperature measurement deviations caused by atmospheric turbulence and the influence of rock layer structure on ground temperature conduction) and the implementation difficulties of geophysical methods in mining applications (such as the interference of stray currents in the ground by electromagnetic methods and the fast attenuation speed of waves detected by geological radar methods), resulting in the insufficient accuracy of fire source location and difficulties in identifying concealed fire sources. In response to the above bottlenecks, the ”air–ground integrated” fire source location determination technology that breaks through environmental constraints and the location determination method of a CSC fire source based on a multi-physics coupling mechanism are proposed. By significantly weakening the deficiency in obtaining parameters through a single detection method, a new direction is provided for the detection of coal spontaneous combustion fire sources in the future. Full article

In this study, a large portion of data on the chemical composition of precipitation falling in the South Baikal region shows the main factors determining their formation in 2017–2024. Taking into account the high variability of meteorological conditions in the region, both in time and in space, a method of observing the chemical composition of atmospheric precipitation has been developed, which makes it possible to determine its composition depending on the conditions of air mass formation. Using statistical analysis, marker substances characterizing the main groups of sources influencing the composition of atmospheric precipitation were identified. Joint analysis of air mass trajectories and data on chemical composition of precipitation allowed for establishing the areas of location of potential sources of precipitation pollution. All precipitation events were categorized based on the similarity of air mass formation conditions and chemical composition. Precipitation composition data collected on the shores of Lake Baikal reflect the influence of different types of pollutants such as industrial emissions, motor vehicles, dust storms, and forest fires. The results of the study are relevant for air quality assessment in the region and demonstrate the potential of using precipitation chemistry data to understand the long-range transport of pollutants, which contributes to sustainable development by increasing the availability of air quality data in ecologically significant regions such as Lake Baikal. Full article

Tropical peatlands store approximately 105 gigatons of carbon (GtC), serving as vital long-term carbon sinks, yet remain critically underrepresented in climate policy. Indonesia peatlands contain 57GtC—the largest tropical peatland carbon stock in the Asia–Pacific. However, decades of drainage, fires, and lax enforcement practices have degraded vast peatland areas, turning them from carbon sinks into emission sources—as evidenced by the 1997 and 2015 peatland fires which emitted 2.57 Gt CO₂eq and 1.75 Gt CO₂eq, respectively. Using system theory validated against historical data (1997–2023), we develop a causal loop model revealing three interconnected feedback loops driving irreversible collapse: (1) drainage–desiccation–oxidation, where water table below −40 cm triggers peat oxidation (2–5 cm subsistence) and fires; (2) fire–climate–permafrost, wherein emissions intensify radiative forcing, destabilizing monsoons and accelerating Arctic permafrost thaw (+15% since 2000); and (2) economy–governance failure, perpetuated by palm oil’s economic dominance and slack regulatory oversight. To break these vicious cycles, we propose a precautionary framework featuring IoT-enforced water table (≤40 cm), reducing emissions by 34%, legally protected “Global Climate Stabilization Zones” for peat domes (>3 m depth), safeguarding 57 GtC, and ASEAN transboundary enforcement funded by a 1–3% palm oil levy. Without intervention, annual emissions may reach 2.869 GtCO₂e by 2030 (Nationally Determined Contribution’s business-as-usual scenario). Conversely, rewetting 590 km²/year aligns with Indonesia’s FOLU Net Sink 2030 target (−140 Mt CO₂e) and mitigates 1.4–1.6 MtCO₂ annually. We conclude that integrating peatlands as irreplaceable climate infrastructure into global policy is essential for achieving Paris Agreement goals and SDGs 13–15. Full article

Electrical cabinet fire scenarios constitute a significant risk within nuclear facilities, emphasizing the need to mitigate uncertainties in risk evaluations. Owing to the disparate nature of electrical cabinet parameters, only a few factors have been experimentally explored and statistically analyzed to assess their impact on peak HRR. In this study, we conducted both a cabinet parameter study and a combustible configuration study to systematically evaluate their influence on peak HRR and time-to-peak HRR. A series of 51 simulation matrices were created using statistical experiment design (SED) and ANOVA to quantify the influence of cabinet volume, combustible surface area, vent area, ignition characteristics, and burning behavior (e.g., HRRPUA and duration). A computational fluid dynamics (CFD) model, specifically a Fire Dynamics Simulator (FDS), was used to model the ignition source and flame spread inside of the electrical cabinet that influence peak HRR. The most impactful parameters influencing peak HRR and time-to-peak HRR were identified. The findings revealed that the configuration of combustibles and the placement of the ignition source play a pivotal role in determining the peak HRR. A partition screening analysis was conducted to identify the conditions under which the ventilation area becomes a more significant parameter. Additionally, a comparison between experimental results and numerical simulations demonstrated good agreement, further validating the predictive capability of the model. Full article

The scaling up of carbon capture, utilization, and storage (CCUS) deployment is constrained by multiple factors, including technological immaturity, high capital expenditures, and extended investment return periods. The existing research on CCUS investment decisions predominantly centers on coal-fired power plants, with the utilization pathways placing a primary emphasis on storage or enhanced oil recovery (EOR). There is limited research available regarding the chemical utilization of carbon dioxide (CO₂). This study develops an options-based analytical model, employing geometric Brownian motion to characterize carbon and oil price uncertainties while incorporating the learning curve effect in carbon capture infrastructure costs. Additionally, revenues from chemical utilization and EOR are integrated into the return model. A case study is conducted on a process producing 100,000 tons of methanol annually via CO₂ hydrogenation. Based on numerical simulations, we determine the optimal investment conditions for the “CO₂-to-methanol + EOR” collaborative scheme. Parameter sensitivity analyses further evaluate how key variables—carbon pricing, oil market dynamics, targeted subsidies, and the cost of renewable electricity—influence investment timing and feasibility. The results reveal that the following: (1) Carbon pricing plays a pivotal role in influencing investment decisions related to CCUS. A stable and sufficiently high carbon price improves the economic feasibility of CCUS projects. When the initial carbon price reaches 125 CNY/t or higher, refining–chemical integrated plants are incentivized to make immediate investments. (2) Increases in oil prices also encourage CCUS investment decisions by refining–chemical integrated plants, but the effect is weaker than that of carbon prices. The model reveals that when oil prices exceed USD 134 per barrel, the investment trigger is activated, leading to earlier project implementation. (3) EOR subsidy and the initial equipment investment subsidy can promote investment and bring forward the expected exercise time of the option. Immediate investment conditions will be triggered when EOR subsidy reaches CNY 75 per barrel or more, or the subsidy coefficient reaches 0.2 or higher. (4) The levelized cost of electricity (LCOE) from photovoltaic sources is identified as a key determinant of hydrogen production economics. A sustained decline in LCOE—from CNY 0.30/kWh to 0.22/kWh, and further to 0.12/kWh or below—significantly advances the optimal investment window. When LCOE reaches CNY 0.12/kWh, the project achieves economic viability, enabling investment potentially as early as 2025. This study provides guidance and reference cases for CCUS investment decisions integrating EOR and chemical utilization in China’s refining–chemical integrated plants. Full article

This study investigates the spread patterns of tunnel fires and examines issues related to emergency response. It focuses on the temperature characteristics, spread patterns, conditions leading to multi-source fires, and the efficacy of water mist suppression methods in heavy-haul railway tunnel fires. The research employs theoretical derivations and numerical simulations to achieve its objectives. It was discovered that, during a fire in a heavy-haul railway tunnel, the temperature inside the tunnel can exceed 500 °C. Furthermore, depending on the nature of the goods transported by the train and under specific wind speed conditions, the fire source has the potential to spread to other carriages, resulting in a multi-source fire. Using the numerical simulation software Pyrosim 2022, various wind speed conditions were simulated. The results revealed that at lower wind speeds, the smoke demonstrates a reverse flow phenomenon. Concurrently, when the adjacent carriage on the leeward side of the fire is ignited, the high-temperature reverse flow smoke, along with the thermal radiation from the flames, ignites combustible materials in the adjacent carriage on the windward side of the burning carriage. Through theoretical derivation and numerical simulation, the critical wind speed for the working conditions was determined to be 2.14 m/s. It was found that while a higher wind speed can lead to a decrease in temperature, it also increases the flame deflection angle. When the wind speed exceeds 2.4 m/s, although the temperature significantly drops in a short period, the proximity of combustible materials on the leeward side of the carriage becomes a concern. At this wind speed, the flame deflection angle causes heat radiation on the leeward side, specifically between 0.5 m and 3 m, to ignite the combustible materials on the carriage surface, resulting in fire spread and multiple fire incidents. The relationship between wind speed and the angle of deflection from the fire source was determined using relevant physics principles. Additionally, the relationship between wind speed and the trajectory of water mist spraying was established. It was proposed to optimize the position of the water mist based on its deviation, and the results indicated that under critical wind speed conditions, when the water mist spraying is offset approximately 5 m towards the upwind side of the fire source, it can act more directly on the surface of the fire source. Numerical simulation results show a significant reduction in the maximum temperature and effective control of fire spread. Under critical wind speed conditions, the localized average temperature of the fire decreased by approximately 140 °C when spraying was applied, compared to the conditions without spraying, and the peak temperature decreased by about 190 °C. This modification scheme can effectively suppress the threat of fire to personnel evacuation under simulated working conditions, reflecting effective control over fires. Additionally, it provides theoretical support for the study of fire patterns in tunnels and emergency response measures. Full article

The wildland–urban interface (WUI) has been a global phenomenon, yet parameter threshold determination remains a persistent challenge in this field. In China, a significant research gap exists in the development of WUI mapping methodology. This study proposes a novel mapping approach that delineates the WUI by integrating both vegetation and building environment perspectives. GaoFen 1 Panchromatic Multi-spectral Sensor (GF1-PMS) imagery was leveraged as the data source. Building location was extracted using object-oriented and hierarchical classification techniques, and the pixel dichotomy method was employed to estimate fractional vegetation coverage (FVC). Building location and FVC were used as input for the WUI mapping. In this methodology, the threshold of FVC was determined by incorporating the remote sensing characteristics of the WUI types, whereas the buffer range of vegetation was refined through sensitivity analysis. The proposed method demonstrated high applicability in Anning City, achieving an overall accuracy of 88.56%. The total WUI area amounted to 49,578.05 ha, accounting for 38.08% of Anning City’s entire area. Spatially, the intermix WUI was predominantly distributed in the Taiping sub-district of Anning City, while the interface WUI was mainly concentrated in the Bajie sub-district of Anning City. MODIS fire spots from 2003 to 2022 were primarily clustered in the Qinglong sub-district, Wenquan sub-district, and Caopu sub-district of Anning City. Our findings indicated a spatial overlap between the WUI and fire-prone areas in Anning City. This study presents an effective methodology for threshold determination and WUI mapping, making up for the scarcity of mapping methodologies in China. Moreover, our approach offers valuable insights for a wise decision in fire risk. Full article

The Roman site at Makariopolsko village in Northeastern Bulgaria has been identified as a ceramic production center, featuring single- and double-chamber kilns, abundant ceramic material, and a nearby water source. Geological assessments also reveal local clay deposits. Previous archaeological studies have noted similar Roman production sites in the region, primarily focusing on the study of the kilns and the macroscopic description and classification of the ceramics. However, there has been a lack of research into the pottery’s composition and the sourcing of raw materials, which is essential for understanding the area’s cultural and economic context. This study aims to determine the raw material and firing temperature of the ceramic from the site at Makariopolsko village. Clay samples (both raw and fired at 1100 °C) and ceramic were subjected to chemical, statistical, phase X-ray structural, and thermal analyses. The findings indicate the use of calcareous illite–kaolinitic clay, sourced locally, with an added sandy component. The ceramics were fired at temperatures of 570–760 °C and 920–945 °C. These results, which support the site’s identification as a pottery production center, highlight advanced pottery skills and the dual functional capabilities of the kilns. Additionally, they pave the way for further research into regional production center relationships. Full article

Costa Rica is located along the narrow isthmus that connected South America to North America beginning in the mid-Cenozoic. The exchange of vertebrates between the two continents has received considerable study, but paleobotanical aspects are less known. The Pacific coast “ring of fire” volcanoes produced abundant hyaloclastic material that provided a source of silica for wood petrifaction, and the tropical forests contained diverse taxa. This combination resulted in the preservation of petrified wood at many sites in Costa Rica. Fossil wood ranges in age from Lower Miocene to Middle Pleistocene, but Miocene specimens are the most common. Our research involved the study of 54 specimens, with the goal of determining their mineral compositions and interpreting the fossilization processes. Data came from thin-section optical microscopy, SEM images, and X-ray diffraction. Two specimens were found to be mineralized with calcite, but most of the woods contained crystalline quartz and/or opal-CT. The preservation of anatomical detail is highly variable. Some specimens show evidence of decay or structural deformation that preceded mineralization, but other woods have well-preserved cell structures. This preliminary study demonstrates the abundance and botanical diversity of fossil wood in Costa Rica, hopefully opening a door into future studies that will consider the taxonomy and evolutionary aspects of the country’s fossil forests. Full article

This article demonstrates that machine fires caused by a belt transmission are a fundamental and current research problem. The aim of this work is to identify the hazards during thermal decomposition and combustion of a transmission with a toothed belt, used as a drive or conveyor belt to synchronise mechanisms. The analysis distinguished belts in a polyurethane or rubber cushion with a Kevlar, steel, or polyurethane cord. The belts’ composite structure can be a source of unpredictable emissions and toxic substances of varying concentrations and compositions during thermal decomposition and combustion. To evaluate the compared belts, a testing methodology was used to determine the toxicometric indicators (W_LC50SM), according to which it was possible to assess the toxicity of the thermal decomposition and combustion products following EU standards. The analysis was carried out based on the recorded emissions of chemical compounds during the thermal decomposition and combustion of polymer materials at three different temperatures (450, 550, and 750 °C). The least favourable toxicometric indicators (W_LC50SM) are found in rubber cushion belts, which are very toxic (about 13 g/m³) and toxic (about 40 g/m³) materials. The results show that thermoplastic polyurethane cushion belts are moderately toxic materials, with a W_LC50SM index ranging from 411 g/m³ to 598 g/m³. Full article

Polyurethane foam (PF) is a versatile polymer widely used in various applications. By changing the composition of polyol and isocyanate, these foams can be classified into rigid polyurethane foams (PUFRs) and flexible polyurethane foams (PUFFs). The flexible polyurethane foam (PUFFs) is well known for its sound absorption capacities; nevertheless, its flammability poses significant safety hazards. The purpose of this study is to look into how flexible polyurethane foam reacts to fire, specifically its combustion properties, and the risks that come with them. The study aims to find out the rates of horizontal and vertical burning, the make-up of the reaction products, and the temperatures that build up inside the polyurethane foam mass when a support pole is placed in front of the stage and sound-absorbing material is added to stop stage sounds from reverberating. There were performed experiments to determine the fire behavior of the samples in contact with an ignition source in the form of a small flame and experiments to determine the ignition temperature of the sound-absorbing sponge, where it was found that vertical position accelerates combustion, and in practical applications, this aspect must be considered for fire prevention. To determine the combustion gases, several methods were used, namely spectrophotometric, ion chromatography, and gas-chromatographic methods. Analysis of the gases resulting from the combustion of the sound-absorbing sponge indicates the presence of dangerous toxic compounds (hydrogen cyanide, carbon monoxide, and hydrochloric acid), which can endanger human health in the event of a fire. Full article

With the safety and reliability of the electrical equipment used in information systems becoming more important, prefabricated fire-extinguishing devices using heptafluoropropane as the extinguishing agent have broad application prospects. However, few studies have focused on the vaporization performance of heptafluoropropane in the context of fire suppression and the safe distance for electrical equipment. This study proposes a numerical simulation model to analyze the vaporization and cooling performance of sprayed heptafluoropropane. First, experimental measurements with no fire source are performed to verify the numerical model. Through numerical and experimental methods, the temperature, concentration, and velocity distribution of the sprayed heptafluoropropane are analyzed to improve its vaporization performance and determine the safe distance. Finally, heptafluoropropane spraying with a fire source is simulated, allowing for the discussion of its cooling effect and fire-extinguishing performance. The results illustrate that the mass ratio of liquid and gas phases in the sprayed heptafluoropropane are 20.2% and 79.8%, respectively. Heptafluoropropane spraying reduced the average temperature in the protective room, with the final value reaching 270 K. The mass fraction of the heptafluoropropane maintained a value of 0.1 at a distance of 0.8 m in front of the nozzle axis. The main findings of this research indicate the temperature variation and fluid flow performance associated with heptafluoropropane spraying, as well as providing a reference value for a safe distance from the nozzle. Full article

Wildfires are increasingly frequent and severe, posing substantial risks to ecosystems, communities, and infrastructure. Accurately mapping wildfire severity (WSM) using remote sensing and machine learning (ML) is critical for evaluating damages, informing recovery efforts, and guiding long-term mitigation strategies. Stacking ensemble ML (SEML) enhances predictive accuracy and robustness by combining multiple diverse models into a single meta-learned predictor. This approach leverages the complementary strengths of individual base learners while reducing variance, ultimately improving model reliability. This study aims to optimize a SEML framework to (1) identify the most effective ML models for use as base learners and meta-learners, and (2) determine the optimal number of base models needed for robust and accurate wildfire severity predictions. The study utilizes six ML models—Random Forests (RF), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Linear Regression (LR), Adaptive Boosting (AB), and Multilayer Perceptron (MLP)—to construct an SEML. To quantify wildfire impacts, we extracted 118 spectral indices from post-fire Landsat-8 data and incorporated four additional predictors (land cover, elevation, slope, and aspect). A dataset of 911 CBI observations from 18 wildfire events was used for training, and models were validated through cross-validation and bootstrapping to ensure robustness. To address multicollinearity and reduce computational complexity, we applied Linear Discriminant Analysis (LDA) and condensed the dataset into three primary components. Our results indicated that simpler models, notably LR and KNN, performed well as meta-learners, with LR achieving the highest predictive accuracy. Moreover, using only two base learners (RF and SVM) was sufficient to realize optimal SEML performance, with an overall accuracy and precision of 0.661, recall of 0.662, and F1-score of 0.656. These findings demonstrate that SEML can enhance wildfire severity mapping by improving prediction accuracy and supporting more informed resource allocation and management decisions. Future research should explore additional meta-learning approaches and incorporate emerging remote sensing data sources such as hyperspectral and LiDAR. Full article

DNA analysis of environmental samples (eDNA) provides a non-intrusive approach to identify organisms, characterize biological communities, and assess biodiversity, including the detection and monitoring of invasive plant effects. However, the use of eDNA for specific applications, such as targeted-species detection, geographic and floral source tracing, and assessment of invasive plant ecological and environmental effects, requires the development of species-specific genetic primers. Chinese tallow (Triadica sebifera (L.) Small) is a non-native high-impact invader, capable of changing fire regimes, native biodiversity, nutrient cycling, and wildlife habitat and populations, that is expanding in range and abundance throughout the southern United States. In this study, we investigated and identified specific genetic sites, markers, in the tallow chloroplast genome and developed sets of primers for tallow eDNA detection. Two sets of tallow primers were developed, tallow-specific primers and tallow-related primers. Both sets of primers can be used for tallow eDNA detection, with higher target specificity for tallow-specific primers. Primers were subsequently validated for target specificity against closely related species, samples of tallow tissue, and honey and honey bee-collected pollen from areas with tallow. We found that tallow-specific primers differentiated tallow eDNA from closely related species, demonstrating target specificity. Furthermore, a sequence analysis of the tallow-related primers in the polymerase chain reaction accurately distinguished members of the Hippomaninae subtribe, including tallow, from other subtribe or subfamily members within the Euphorbiaceae. Ultimately, the genetic markers and the corresponding sets of primers will facilitate eDNA analysis of tallow for several applications, including detection and monitoring in water and soil, assurance of honey quality and floral source tracing, and perhaps serving as a model for determining plant use by pollinators. Full article