Wildland fire behaviour is strongly governed by the coupled effects of wind and terrain slope, yet the literature remains fragmented across experimental, empirical, mathematical, and physics-based modelling traditions. A systematic scoping review with narrative synthesis was performed (Web of Science, Scopus, and Google Scholar plus citation chaining), screening studies for explicit wind–slope treatment with reported forcings and outcomes. Across more than 150 studies, slope benches, wind tunnels, trenches/canyons, and field burns show that upslope–wind alignment promotes flame attachment and a shift from radiation-led to convection-led preheating (often near 20–30° slopes and moderate winds), whereas opposing or downslope forcing lifts flames and suppresses spread; confined geometries can trigger eruptive acceleration. Mathematical analogues and empirical models provide fast predictions using compact wind/slope modifiers and enable scenario and burn-probability mapping but typically prescribe coupling and miss regime transitions. Physics-based LES/CFD and coupled atmosphere–fire systems resolve terrain–flow feedback sand can yield reduced-order laws suitable for embedding into operational tools, albeit at higher computational cost and with validation gaps. Benchmarks are consolidated, approaches are compared using a common rubric (fidelity, validation, applicability, cost, and operational utility), and priorities are identified for cross-scale datasets, firebrand transport in complex terrain, and real-time coupled prediction.

This study investigates the pyrolysis behavior of loblolly pine through thermogravimetric (TGA) and derivative thermogravimetric (DTG) analysis under varying nitrogen flow rates of 5–40 mL min−1 and heating rates of 5–20 °C min−1. The pyrolysis proceeded through three distinct phases: Phase I: initial moisture release, Phase II: active devolatilization, and Phase III: char formation. Kinetic modeling using both integral and differential forms of the Coats–Redfern method revealed distinct mechanistic interpretations. The integral approach primarily identified diffusion-controlled models (D1, D3) during moisture and char stages and reaction-order or contraction models (F2, R2) during devolatilization, with activation energies ranging from 8.89 to 70.48 kJ mol−1. In contrast, the differential method captured sharper transitions and favored complex nucleation and growth mechanisms (A3, A4) and power laws (P3, P4), yielding higher activation energies up to 111.29 kJ mol−1 in Phase II. These results underscore the influence of both inert gas flow and thermal ramp on pyrolysis reactivity and demonstrate that kinetic model selection significantly affects activation energy interpretation. The findings contribute to a more nuanced understanding of biomass pyrolysis and offer insights into reactor design and process optimization in thermochemical conversion systems.

Coniferous forests account for 78% of the western US forests and store a substantial amount of carbon. Wildfires significantly alter vegetation structure and associated forest carbon stocks. This study evaluates postfire biomass recovery trajectories (1984–2017) and total biomass accumulation in conifer forests that historically experienced low-severity, high-frequency fire regimes in the western US using recently launched Global Ecosystem Dynamic Investigations (GEDI) mission lidar data. All three ecoregions studied, including the Pacific Northwest (PNW), Southern Rockies (SR), and Northern Rockies (NR), show site-specific biomass recovery trajectories shaped by fire severity. The recovery trajectories were characterized by an initial decline and a variable gain with time since fire across the three ecoregions. Regions with low burn severity recovered to the unburned background state within the first three decades, while regions with higher burn severity only recovered in the Northern Rockies after five decades without fire. Moderate- and high-severity burned areas in both SR and PNW exhibited slow declines or sustained low biomass periods following fires, implying potential ecosystem transformation or an arrested state of lower biomass. Time since fire and fire severity were identified as the most significant drivers of postfire biomass recovery, likely because they reflect both reduced seed availability and the process of seedling establishment and regeneration. In addition, distance to unburned area, drought (measured using the Standardized Precipitation Evapotranspiration Index (SPEI)), elevation, and fire size were important drivers of biomass recovery. Our results demonstrate that all three ecoregions experienced a loss of overall biomass (15–23% (+/−40%)), with the largest losses occurring in the areas with high-severity burns (59% (+/−23%)) in the Southern Rockies compared to unburned forests within the first three decades. This study thus confirms GEDI’s ability to assess disturbance-driven vegetation biomass dynamics and provides an open-science methodology that could be utilized for other regions. In conclusion, our study indicates that an increase in fire severity within low-severity, high-frequency fire regimes, beyond historically observed levels, results in greater carbon losses. It is therefore important to consider the effects of increases in fire severity on vegetation recovery trajectories to infer the future carbon potential in these ecosystems.

Aiming at the insufficient integration of real-gas effects and the unclear parameter influence mechanisms in predicting high-pressure hydrogen leakage flame length, this paper proposes a refined predictive model that systematically incorporates the real-gas critical flow factor (Cr*). By dynamically correcting the mass flow rate calculation under high-pressure conditions, the model significantly improves prediction accuracy (relative error in mass flow rate < 3%). A parametric analysis reveals that the flame length is approximately three times more sensitive to the leakage orifice diameter than to the storage pressure (L∝D1.041P00.347), providing a quantitative basis for inherent safety design. Validated by experimental datasets, the model demonstrates good accuracy. It can be employed for safety distance demarcation and risk assessment at hydrogen refueling stations and storage facilities.

Combustion duration is a fire behaviour feature relevant for both the effects and management of fire. We burned small-scale laboratory fuel beds (n = 135) of eight fuel types and developed empirical models to describe variation in flame residence and burn-out times, and fuel mass fraction loss rates during flaming and non-flaming combustion; each fuel sample was ignited at once and burned as a pile. Surface area-to-mass ratio of the fuel particles, by itself, allowed accurate prediction of all combustion properties with better performance than surface area-to-volume ratio. Fuel bed structure was also shown to have an influence, fuel load being the variable that further improved all predictions. This work provides evidence that surface area-to-mass ratio is an adequate descriptor of the combustion characteristics of forest fuel beds. Our expectation is that this approach will assist future modelling efforts to obtain simple empirical models to predict the combustion features of free-spreading fires in a wide range of vegetation types.

Páramos, high-mountain tropical ecosystems, are crucial for carbon storage and water regulation for many Andean cities. However, they are subjected to wildland fires that threaten the ecosystem services they provide. Fire activity varies substantially among páramos, making it essential to understand the drivers of this spatial variability. This study evaluates the relative influence of anthropogenic and biophysical factors on fire occurrence in Colombian páramos, analyzing burned area data from 2000 to 2022 using a Random Forest model. Results indicate that fire occurrence is shaped by the interaction between human pressures and biophysical characteristics. Annual precipitation was the most influential predictor: areas with lower mean annual precipitation (<1000–1500 mm/year) were linked to greater burned area. Vegetation cover, assessed using the Normalized Difference Vegetation Index (NDVI), showed a hump-shaped relationship, with intermediate greenness levels (0.13–0.25) being most prone to burning. Anthropogenic factors, especially proximity to buildings and agricultural zones, also had a significant impact. Our results show that fire occurrence in páramos cannot be attributed solely to human pressures but results from the combined effect of anthropogenic and biophysical drivers. Understanding of these interactions underscores the need for socio-ecological perspectives to guide integrated and adaptive management of strategic high-mountain ecosystems.

Wildfire escalation is increasingly threatening ecosystems and communities in Khyber Pakhtunkhwa (KP), Pakistan, particularly in forest and rangeland landscapes where ecological flammability interacts with human activity. While environmental and climatic drivers are well studied, governance factors remain underexplored despite their decisive role in shaping how ecological risk translates into disasters. Regional forests show considerable ecological diversity, including chir pine-dominated stands, mixed temperate conifer forests, broadleaved oak-associated systems, and shrub rangeland mosaics, each differing in fuel structure and fire behavior. Dependence on fuelwood collection, grazing, and forest access further influences ignition probability and fire spread. This study examines how governance failures influence wildfire risk and severity through a Governance-Fire Risk Framework. Governance is treated as a determining institutional condition affecting prevention capacity, regulation of hazardous land use, fuel management, and emergency response effectiveness. A cross-sectional survey of 540 stakeholders from rural (Dir Lower, Dir Upper) and peri-urban districts (Swat, Mansehra, Abbottabad) was analyzed using SPSS (version 26) and AMOS (version 24) (CFA and SEM). Governance failure significantly escalates wildfire risk through delayed emergency response, regulatory non-compliance, political interference, and weak institutional coordination. Institutional preparedness and response capacity reduce risks, whereas corruption intensifies them. Corruption functions through illegal land conversion, diversion of fire management resources, procurement irregularities, nepotistic staffing, and selective enforcement, increasing ignition sources, fuel accumulation, and response delays. Rural districts show stronger governance-fire linkages. Wildfire escalation in KP is governance-driven in interaction with ecological conditions and community dependence on forest resources. Effective mitigation requires anti-corruption measures, rapid response systems, stronger enforcement, and improved preparedness. The study offers a transferable governance-focused framework for wildfire management in fire-prone developing regions.

To effectively prevent and control coal spontaneous combustion, a novel heat-sensitive hydrogel for mine fire prevention and extinguishment was developed using hydroxypropyl methylcellulose (HPMC) and the organic flame-retardant, sodium alginate (SA). The hydrogel was prepared through single-factor variable control and material compounding. First, the optimal formulation of the hydrogel was determined using analytical instruments and techniques, including a viscometer, vacuum drying oven, and the inverted test tube method. Subsequently, its microstructural characteristics were examined using scanning electron microscopy (SEM) and infrared spectroscopy (FTIR). Finally, a fire suppression test platform was established to perform comparative experiments, verifying the hydrogel’s fire prevention, extinguishing, and cooling performance. Experimental results demonstrated that the optimal hydrogel formulation consists of 2.5 wt% HPMC and 0.3 wt% SA. At this ratio, the hydrogel exhibits excellent fluidity and water retention, ensuring prolonged coverage and wetting of coal surfaces. The gel undergoes a sol–gel phase transition at 58 °C, enabling it to fill voids, bind and reinforce coal particles, and reduce exposed surface area. After drying, the hydrogel forms a uniformly smooth surface capable of both coating the coal body and encapsulating individual coal particles. Following the hydrogel treatment, the coal sample retains its original functional groups, indicating that no chemical reactions occur during mixing. Compared with traditional inhibitors, the hydrogel demonstrates superior fire suppression performance, more effectively covering and encapsulating burning coal. It rapidly reduces the temperature to 28 °C by the cooling effect of water evaporation from the hydrogel, and it maintains thermal stability, achieving outstanding fire-extinguishing efficiency.

Stairwells constitute critical escape routes for emergency evacuation during building disasters. The spread of fire smoke and the failure of lighting systems can significantly reduce visibility within stairwells, thereby adversely affecting evacuation speed. This issue is particularly pronounced in super high-rise buildings. In this study, a typical super high-rise building was selected as the experimental site. The variation laws of key parameters such as evacuation time, speed, and heart rate were investigated for groups with different gender proportions in stairwells under different visibility conditions. The experimental results show that: First, collaboration within multi-person groups can effectively mitigate the adverse impact of reduced visibility on evacuation speed. Second, different gender proportions within groups affect evacuation speed, with groups having a higher proportion of males demonstrating relatively faster evacuation speed. Third, under identical visibility conditions, the heart rates of multi-person groups during evacuation are generally lower than those of individual groups; in low-visibility environments, the heart rates of members within the same group are significantly higher than those under normal visibility conditions. Accordingly, this study proposes a mixed-gender group evacuation strategy under low-visibility conditions. The findings provide empirical data support for the formulation of emergency evacuation response strategies in super high-rise buildings.

To improve the accuracy of wildfire risk identification in areas adjacent to power transmission corridors, this study proposes a wildfire early warning method that integrates refined land cover segmentation and multimodal feature deep learning. First, an improved bi-branch semantic segmentation network (BuildFormer++) is used to perform refined classification of high-resolution remote sensing images, extracting six types of land cover information, including forest and cultivated land. Second, a multi-dimensional feature set integrating land cover, topography, climate, and human activities is constructed and input into a multimodal wildfire point prediction network for deep feature fusion and probabilistic modeling. Experimental results show that the proposed segmentation network achieves a mean intersection–union ratio (mIoU) of 40.68% in the semantic segmentation task; the early warning model achieves an accuracy of 85.37%, an F1 score of 93.15%, and an ROC-AUC of 85.42% in risk prediction, significantly outperforming comparative methods. The “refined segmentation–feature fusion–risk prediction” framework constructed by this method can provide reliable technical support for the operation and maintenance safety and fire prevention of power transmission corridors.