Climate change has intensified global wildfire risks, yet national-scale prediction remains challenging due to the difficulty of consistently monitoring fuel conditions and human ignition factors. This study introduces calendar-based time proxy variables as structural surrogates for these unobservable drivers and integrates them with the Canadian Fire Weather Index (FWI) within a parsimonious framework for seasonally fire-prone regions such as South Korea. Using 15 years of nationwide wildfire records and daily observations from 100 ASOS stations (2011–2025), predictive performance was evaluated across eight models and five feature sets (Time-only, Weather-only, Weather + Time, FWI-only, and FWI + Time). Based on test-set mean AUC, the Time-only feature set reached 0.7374, clearly exceeding the random-classifier baseline (AUC = 0.5) and indicating the independent predictive value of time proxy variables. Furthermore, integrating time proxies with FWI improved performance, with the best model (CatBoost) achieving test AUC = 0.8394 and Recall = 0.6019. Multi-model SHAP analysis revealed complementary contributions of FWI components (53.7% ± 4.7%) and time proxy variables (46.3% ± 4.7%). Overall, the results demonstrate that a simple yet structured input design based on time proxy variables provides meaningful predictive performance for nationwide wildfire early warning systems.

This study establishes an experimental platform consisting of an adjustable inclined surface and a cross-slope wind system. Turbulent diffusion flames are investigated by examining the variation characteristics of flame morphology under slope angles of 10–40°, cross-slope wind velocities of 0.8–2.0 m/s, and heat release rates of 15.38–61.50 kW. The results show that variations in slope angle change the components of buoyancy in the normal and tangential directions. The normal component influences the lifting of the flame perpendicularly to the slope, while the tangential component, together with differences in air entrainment on both sides of the flame, promotes flame inclination and spreading along the slope surface. The cross-slope wind enhances the horizontal stretching and attachment tendency of the flame through inertial shear, while simultaneously suppressing flame height and its development along the slope. The coupled effects of these factors cause the flame morphology to gradually transition from a nearly vertical state to an attached state. Based on dimensionless analysis, empirical correlations of flame morphology parameters are established by introducing the cross-slope wind Froude number, dimensionless heat release rate, the density ratio of propane to air, and a slope function. Within the experimental range of this study, the data under various conditions show good collapse and correlation under the selected dimensionless parameters.

Recruitment of firefighters is, in part, hindered due to attrition from fire academies. This study explored initial fitness differences between those who graduated (GRAD) or were released (REL) from the academy. During the first week of the academy, recruits (N = 407; GRAD = 354, REL = 53; 26.6 ± 7.2 yrs; 177.6 ± 8.6 cm; 87.9 ± 17.2 kg) completed an assessment battery including: body composition using skinfold calipers to estimate percent body fat (BF) and fat-free mass (FFM); shoulder mobility via Apley’s scratch test (APLEY); aerobic fitness (VO2peak) and heart rate recovery (HRR1min) estimated from the five-minute Forestry step test; muscular strength via the sum of right and left handgrip (SHG); and muscular endurance via a paced two-minute push-up test (PU). A t-test identified age differences between GRAD and REL, followed by separate ANCOVAs for each fitness measure, and logistic regression to identify the ability of fitness measures to predict academy outcome. GRAD had a lower age and BF and a higher FFM, VO2peak, SHG, and PU, but did not differ in APLEY or HRR1min. The full model predicting release was significant; age, BF, and FFM were significant predictors. These results provide pre-fire academy preparation guidance for optimizing the potential for successful academy completion.

The lack of scientific methods for selecting monitoring indicators and equipment undermines the efficiency of forest fire emergency response. To address this gap, we developed a novel evaluation model for emergency monitoring indicators based on the spatiotemporal evolution of forest fires. The model, comprising four primary and eight secondary factors, leverages a hybrid TriFAHP and DBN approach to objectively determine factor weights based on survey data from 20 domain experts. The results indicate that the primary factor weights rank as follows: Monitorability (0.3807) > Timeliness (0.3353) > Sensitivity (0.1874) > Feasibility (0.0966). Four indicators (wind speed, temperature, flame, and gas) were identified as the most suitable for core monitoring. Furthermore, stage-specific monitoring strategies were proposed, prioritizing different core indicators across the ignition, spread, and fully developed fire stages. An indicator and equipment association was established, recommending optimal configurations such as UAV-mounted thermal imagers and lidar anemometers. The practical applicability of the proposed framework was successfully validated through real-world case studies, including the 2019 to 2020 Australia bushfires. This study provides a standardized framework aligning indicators, equipment, and scenarios, offering theoretical and practical guidance for optimizing emergency monitoring systems.

We analyzed wildfire hazard profiles across the Hyrcanian temperate forests of northern Iran (Guilan Province) by simulating a large set of wildfires with FlamMap MTT. We first derived geospatial data on terrain, fuel models, weather conditions, and historical wildfire occurrence (1992–2022) for the study area. We stratified fire weather conditions and fuel moisture based on the bioclimatic classification of the study area, considering observed extreme fire weather, as well as observed and random fire ignition locations for the simulations. The wildfire simulations were used to estimate burn probability (BP), conditional flame length (CFL), fire size (FS), and crown fire probability (CFP). BP ranged from 0 to 5.0 × 10−2, with mean values of 1.3 × 10−3 and 1.1 × 10−3 for observed and random scenarios, respectively. The mean value of CFL from random ignition simulations (0.78 m) was substantially higher than that obtained in the observed ignition simulations (0.54 m), ranging from 0 to 6.75 m. We evidenced significant differences between observed and random ignition simulations for all wildfire hazard metrics. The highest wildfire hazard profiles were observed in the Cold-Mountainous bioclimatic zone under the random ignition simulations. On average, the annual number of anthropic structures threatened by wildfires ranged from 97 (observed scenario) to 123 (random scenario). This research provides detailed and spatially explicit fire hazard and exposure maps to inform fire modeling, land management, and policy actions.

Early detection is a key tool for mitigating the devastating effects of wildfires. Single-frame detection methods that do not consider inter-frame dependencies often fail to detect smoke plumes at the earliest stage and at greater distances, or produce excessive false alarms. Biological vision is particularly sensitive to motion cues, and this translates well to automated systems. Recent temporal-memory approaches have demonstrated improved performance over purely spatial methods, but typically rely on complex, computationally heavy multi-stage architectures. This study investigates the possibility of encoding temporal and contextual information into additional image channels as a basis for compiling data models with increased information content. Seven distinct data models were proposed, and corresponding datasets were generated to train standard YOLO architectures without modifications to the network structure. The datasets were compiled from real wildfire footage collected from an operational wildfire surveillance system in Croatia, comprising 333 annotated sequences of real fires recorded between 2018 and 2024. Experimental evaluation compared the performance of YOLO models trained on the information-enriched datasets with those trained on standard RGB images. Based on the results, the best data model for early wildfire smoke detection, combining original RGB channels with short-term and long-term temporal memory, was selected. Comparative evaluation demonstrated improved detection accuracy, achieving up to 5 percent higher true-positive detection rate for models trained on spatio-temporal data compared to standard RGB images, while maintaining low inference latency. The proposed approach shifts the focus to the structure and information content of the data while preserving the efficiency of standard convolutional neural network architectures. This approach could be applied to other problems requiring high efficiency and real-time operation, where temporal and contextual information can improve detection performance.

This study experimentally investigates the combustion behavior of RP-3 aviation kerosene pool fires (300~800 mm) within a confined space, specifically focusing on the complex interaction between buoyancy-driven plumes and mechanical negative pressure ventilation. By integrating high-precision mass loss measurements with multiple characteristic parameters, this research uniquely characterizes the transition of energy feedback mechanisms under confined suction flow. Results show that ventilation enhances combustion intensity and compresses the fire cycle. For an 800 mm pool, the peak mass loss rate rose by 57.1%, from 16.71 g/s to 26.25 g/s. This enhancement stems from boundary layer thinning, which transitions the combustion from diffusion-controlled to kinetics-controlled. Ventilation also induces severe flame tilt with a non-monotonic trend. The tilt angle peaks at 84° for 600 mm pools but drops to 64° at 800 mm as buoyancy momentum increases. Additionally, an energy contrast of vertical cooling and horizontal heating was observed. Axial peak temperatures decreased by 20%, while downwind thermal radiation flux increased by up to 125%. The ventilation system essentially acts as a directional energy projector, shifting heat loads toward the downwind region. These findings support the optimization of fire safety and detection designs for industrial ventilation systems. This study experimentally investigates the combustion behavior of RP-3 aviation kerosene pool fires (300–800 mm) within a confined space, specifically focusing on the complex interaction between buoyancy-driven plumes and mechanical negative pressure ventilation. By integrating high-precision mass loss measurements with multi-point thermal and imaging diagnostics, this research uniquely characterizes the transition of energy feedback mechanisms under confined suction flow.

Estimating wildfire greenhouse gas (GHG) emissions in Mediterranean landscapes is challenging due to heterogeneous fuel mosaics and limited scalability of field-based approaches. This study presents a Geographic Information System (GIS) based framework that integrates land-cover data, pre-fire biomass estimates, fire severity mapping, and established emission factors to produce spatially explicit estimates of biomass consumption and GHG emissions. Fire severity was derived from multitemporal Sentinel-2 imagery using the differenced Normalized Burn Ratio (ΔNBR) and combined with land-cover information to define vegetation–severity classes for emission estimation. A key innovation is the identification of co-occurring vegetation types within the same spatial units, allowing emissions to be quantified across vegetation mixtures rather than single classes, providing a more realistic representation of Mediterranean forests. Applied to the 2022 Bejis wildfire, pre-fire biomass within the burned area was 673,601 tons. Coniferous forests dominated, but co-occurrence with shrubland and herbaceous layers produced the highest emission contributions, highlighting the role of vegetation interactions. Total emissions were estimated at 625,938 tons of equivalent CO2, and comparison with large-scale datasets (CAMS Global Fire Assimilation System, Global Fire Emissions Database) shows general coherence. This severity-driven, vegetation-explicit framework demonstrates robust potential for quantifying wildfire emissions across heterogeneous Mediterranean landscapes, though uncertainties remain due to pre-defined biomass, burning efficiency, emission factors, assumptions in fire severity mapping, and limited field validation. The approach can support improved regional GHG inventories and wildfire management strategies.

Quercus suber bark, known as cork, is an important fire-adaptive trait of this Mediterranean species. However, the increased frequency of wildfires and poor forest management practices can be significant challenges in managing the sustainable exploitation of cork oak stands. This study evaluates cork’s thermal behavior and organoleptic quality for commercial applications under three experimental fire scenarios: prescribed burn, low-intensity wildfire, and high-intensity wildfire. Bench-scale tests were conducted using a vertical mass loss calorimeter to simulate heat exposure levels, measuring temperature changes at four cork depths and quantifying heat-induced damage. Morphological traits—cork thickness, corkback thickness, and relative humidity—were recorded as predictor variables. Additionally, organoleptic and aromatic analyses were performed to assess the suitability of fire-exposed cork for wine stopper production. Results were consistent with the available literature, confirming that cork thickness significantly reduces the maximum temperature at the phellogen level. Specifically, mean cork thickness showed a significant negative effect on Tmax4 (β = −0.02, p < 0.001), indicating a consistent decrease in internal temperatures with increasing thickness across all heat flux levels. By contrast, cork consumption (mass loss) was primarily driven by heat flux intensity rather than cork structural traits. Aromatic profiling and organoleptic analysis revealed the presence of smoke-related compounds in cork samples exhibiting external carbonization. This effect was observed under higher heat flux exposure (particularly at 25 and 50 kW m−2), where visible charring occurred. Under these conditions, commercial quality may be partially compromised, whereas samples without external carbonization did not show comparable aromatic alteration. Further field validation is recommended.

As part of efforts to support the transition toward a zero-carbon future, this research evaluates how the use of natural gas and liquefied petroleum gas under lean burn conditions affects the energy efficiency and environmental outcomes of a diesel engine that has been retrofitted to operate with spark ignition. The assessment of the ecological potential of these low-carbon gaseous fuels was performed at the engine test bed at optimum spark advance set from the condition of achieving maximum brake thermal efficiency (i.e., lowest carbon dioxide emission, CO2). The results found with lean mixtures are compared to those obtained under stoichiometric conditions, as well as to those from a commercial gasoline engine of comparable size, equally operated at stoichiometry. With lean burning, a clear improvement is observed for all operating points in terms of brake thermal efficiency with respect to the stoichiometric operation. The results highlight a slightly greater improvement when operating with natural gas lean mixtures: between (1.35 and 2.35) percentage points gained in this case, compared to (1.15–2.10) percentage points gained in the case of liquefied petroleum gas. As for CO2, a maximum 28% reduction when using natural gas is achieved with lean operation with respect to the commercial gasoline engine. Using lean mixtures also brings an important reduction in the engine-out pollutants (carbon monoxide, nitric oxides and particulate number). However, with respect to stoichiometric operation, cyclic variability of the prototype degrades with lean burning but remains lower than one of the baseline commercial gasoline engines.