Morphological and elemental characterization of fine and ultrafine particulate matter generated from fires.

Characteristic Compartment Fire Behaviour — A theoretical study encompassing a broader range of regimes

This research investigated the impact of using lecithin and casein on lignocellulosic fiberboards on their characteristics and properties, including fire resistance. The six experimental variants created included: (1) unmodified reference fiberboards, (2) fiberboards coated with casein only, (3) fiberboards that were vacuum-impregnated with rapeseed or (4) soy lecithin, and (5, 6) fiberboards that were both vacuum-impregnated with lecithin and coated with casein. Evaluation of the board’s mass uptake, density profile, modulus of elasticity, compressive strength and fire behavior (single face exposure to mass loss, maximum posterior temperature, and area burned) demonstrated that vacuum-impregnation with lecithin was the primary driving force behind mass uptake (producing minor densification of the surface), while the casein coating produced only very minor changes to mechanical properties and modestly modified the fire performance. Lecithin alone produced an increase in both mass loss and area burned while producing a decrease in maximum posterior temperature (about 20%–25%). Lecithin-impregnated boards that were also casein-coated displayed a synergistic effect; these boards provided intermediate mechanical properties with the highest levels of fire performance (approximately 20%–30% lower than the reference fiberboards) in terms of both mass loss and area burned while also having approximately 20%–30% lower maximum posterior temperature compared to the reference.

This study investigates the fire behaviour of Battery Electric Vehicles (BEVs) and Internal Combustion Engine Vehicles (ICEVs) in confined environments such as underground parking facilities and tunnels. Using the Fire Dynamics Simulator (FDS), several scenarios were modelled to analyse the effects of ventilation and automatic suppression systems on fire growth, heat release, and smoke propagation. Three ventilation configurations—reduced, standard, and increased airflow—were evaluated to determine their influence on combustion dynamics and thermal development. Results show that BEV fires produce higher peak Heat Release Rates (up to 7 MW) and longer combustion durations than ICEVs, mainly due to self-sustained battery reactions. Increased ventilation enhances smoke removal but intensifies flames and radiant heat transfer, while limited airflow restricts combustion yet leads to hazardous smoke accumulation. The inclusion of a sprinkler system effectively reduced temperatures by over 60% within 100 s of activation, though residual heat in BEVs poses a risk of re-ignition. This underlines the need for tailored ventilation and suppression strategies in modern underground facilities to ensure safety in the transition toward electric mobility.

The annual volume of battery waste steadily increases, yet its recycling rate stays sluggish. Notably, battery waste comprises various transition metals, which hold a high potential in the flame-retardant field. This study explores a recycling-based strategy to repurpose spent nickel-cobalt-manganese (NCM) battery cathodes as flame-retardant additives in thermoplastic polyurethane (TPU). Unlike conventional metal oxides, the multicomponent transition metal nature of recycled NCM provides a unique catalytic interface with ammonium polyphosphate (APP), influencing char formation and fire behavior. In response, this study puts forward an innovative approach to utilize solid battery waste. Specifically, we have recycled NCM material from the battery cathode and incorporated it into a TPU elastomer, combining with APP. The interactive effect originates from interface-mediated catalytic carbonization between NCM and APP within the TPU matrix, leading to enhanced flame-retardant performance. As revealed, the limiting oxygen index (LOI) of the APP8.75/NCM0.25/TPU composite reaches 26.8 vol %, higher than that of neat TPU (22.5 vol %) and comparable to APP9/TPU (27.3 vol %). The fire property results further illustrate that, as for the APP8/NCM1/TPU composite, the peak heat release rate (PHRR), total heat release (THR), total smoke release (TSR), peak CO release, and peak CO2 release are reduced by 80.0, 56.7, 54.5, 39.8, and 56.9% respectively. Overall, these findings clearly indicate that the APP/NCM flame-retardant system effectively enhances the fire safety of the composite. This study not only paves the way for the high-value utilization of NCM but also offers novel insights into the development of flame-retardant polymer composites.

Abstract Background Wildland fuels are fundamental variables in modeled predictions of fire behavior and effects. In forest ecosystems, accumulated forest floor layers, including recently fallen litter and highly decomposed organic material (i.e., duff), often constitute most surface fuel—biomass and stored carbon. Associated error in estimated litter and duff biomass can thus propagate to major sources of uncertainty in predicted fire effects, including tree mortality and smoke production. Distributions of forest floor biomass are difficult to measure, and estimates of litter and duff biomass typically involve high uncertainty. In this study, we evaluated relationships between forest floor characteristics (i.e., depth, biomass, and bulk density) and how forest floor layers vary between locations against the boles of trees, inside tree crown drip lines, and outside tree crown drip lines. Our study focused on pine-dominated systems with open forest canopies including southeastern (SE) pine mesic flatwoods, SE loblolly pine plantations with sweetgum understories, and western (W) ponderosa pine and mixed conifer forests. Results Across the three forest types, litter depths were highest in SE flatwood sites and lowest in W pine sites, but W pine litter generally had higher bulk density and thus greater biomass than SE pine sites. Duff depth and biomass were greater in W sites, likely related to less frequent burning and slower rates of decomposition than SE forests. Linear regression models were constructed to predict litter and duff biomass from sampled depth and transect positions for each forest type. Even with high variance among samples, there were significant differences in litter and especially duff characteristics across transect positions. Across all forest types, litter and duff accumulations generally were significantly greater inside than outside tree crowns, particularly when bole and outside positions were compared. Conclusions Modeled relationships revealed significant trends that could inform process models of forest floor development and decomposition over time. With increased availability of stem and crown mapping based on lidar metrics, estimates of forest floor accumulations can be made through mapping of tree crown positions and predictive modeling of litter and duff biomass in relation to tree crowns.

Abstract. Firefighter entrapments occur when wildfires suddenly transition into extreme wildfire events (EWEs). These transitions are often caused by pyroconvective fire-atmosphere coupling, triggered by a combination of high fire intensity and atmospheric vertical thermodynamic structure. Pyroconvection indices calculated using coarse atmospheric modeling data crudely detect these dynamic transitions due to highly localized atmospheric processes and changes in atmospheric conditions caused by the fire. Consequently, fire managers may remain unaware that fire behavior intensification due to fire-atmosphere coupling is outdating the safety protocols in place. This study presents a new in-plume profiling methodology to improve the assessment of fire-atmosphere interaction dynamics in real-time. As proof of concept, we analyzed 173 successful sondes (148 in-plume) launched during the 2021–2025 fire seasons in Spain, Chile, Greece, and the Netherlands. As a strategy to measure the fire-atmosphere coupling, we propose simultaneously launching two radiosondes: one to measure ambient conditions and another to capture data within the plume updraft. Comparing these profiles, we measure in-situ and in-real time the modification of state variables by the fire-atmosphere interaction. These new observations and methodology improve our assessment of pyroconvection dynamics, demonstrating practical implications that support their use by incident management teams. It has the potential to enhance awareness of possible near-accidents and tactical failures during extreme pyroconvective wildfire events. Additionally, it offers a comprehensive observational dataset to improve pyroconvection nowcasting and advance research on fire-atmosphere interaction.

This study investigates the fire behaviour of building-integrated photovoltaic (PV) claddings, focusing on the progression from glass failure to ignition under different cavity conditions. Experimental tests were conducted on two common PV cladding types: bifacial dual-glass (GG) and monofacial glass–plastic (GP) modules. Results revealed that GP modules exhibited faster burning and higher peak heat release rates (HRR), reaching up to 600 kW, while GG modules burned more slowly with peak HRR between 50 and 100 kW. Cavity conditions, including depth, ventilation, and operational energization, were found to be vital in determining glass breakage, occurring between 400 and 550 °C, and cavity ignition and subsequent flame spread. The relationship between cavity fire dynamics and glass breakage suggests the importance of system design, particularly regarding cavity ventilation and flame barriers, for mitigating upward fire propagation. These results establish a basis for advancing numerical fire models through integration of critical parameters such as material properties, glass breakage, cavity ignition, and cavity configuration. This approach supports comprehensive real-scale analysis to guide the development of effective design recommendations, ultimately improving fire safety in PV-integrated construction.

Spontaneous activity of peripheral sensory nerve fibers is one of the main drivers of neuropathic pain. It can be assessed in microneurography recordings of patients' C fibers and in patch-clamp recordings of dissociated dorsal root ganglia from humans and rodents. In microneurography of human C fibers, a distinct subgroup of neurons, the so-called mechano-insensitive (CMi) or sleeping nociceptors, shows spontaneous activity during neuropathic pain. It was shown before that sensory neurons from patient-derived induced pluripotent stem cells (iSNs) can be used to model this increased spontaneous activity in vitro, suggesting that a disease relevant cell type is generated with this approach. The origin of the spontaneous activity in human C fibers is not fully understood. Derived sensory neurons offer the unique possibility to study patient-derived, single-cell function, allowing for identification of potential mechanisms underlying spontaneous C-fiber activity. Here, we identify 4 distinct functional subtypes of iSNs from healthy donors and a patient suffering from the neuropathic pain syndrome inherited erythromelalgia using patch-clamp recordings. Similar to microneurography recordings from the same patient, spontaneous activity is restricted to 1 functional subgroup that shows tonic firing behavior and seems to be especially prone to develop neuronal hyperexcitability. We demonstrate that spontaneous activity correlates with a reduced voltage threshold of action potential generation and increased spontaneous depolarizing fluctuations of the membrane potential. Our findings reveal that only the tonically firing functional subclass of iSNs shows spontaneous activity and suggest that these neurons may be related to the pathologically active CMi fibers identified during microneurography recordings in patients with pain.

Abstract. Wildfires are fundamental ecological processes that shape terrestrial ecosystems and influence atmospheric dynamics. This study presents a global assessment of fire frequency, intensity, and burned extent over a 24-year period (2001-2024) using multi-source satellite observations. Spatio-temporal variability of forest fire activity and the critical role of seasonality in modulating fire behaviour has been captured in a grid of 0.25°. Our results reveal clear regional and hemispheric patterns wherein southern and central Africa, northern Australia, and parts of South America consistently exhibit the highest fire activity, with strong spatial and seasonal variability. In contrast, the Northern Hemisphere remains relatively stable, with lower fire occurrence and limited changes over time, aside from modest seasonal fluctuations in certain regions. Seasonal dynamics are especially pronounced in the tropics, reflecting variations in climatic drivers and fuel availability. To explore interactions among fire parameters, we conduct grid-level correlation analyses, revealing strong positive associations among frequency, intensity, and extent in tropical and subtropical regions. These relationships weaken or decouple in temperate and boreal zones, highlighting the influence of seasonal climate and vegetation dynamics. Building on these insights, clustering-based classification was used to delineate global fire regimes based on the combined behavior of the three parameters. The resulting maps reveal distinct spatial configurations and temporal evolution, with dynamic regime shifts across much of the Southern Hemisphere and comparatively stable regimes in the north.

Timber-encased steel composite (TESC) systems have emerged as a promising structural solution combining strength, sustainability, and enhanced fire performance. This meta-analytic review synthesizes experimental and numerical findings reported between 2020 and 2025 to assess the influence of timber encasement on the fire resistance of steel members. Data from full-scale and small-scale fire tests were statistically aggregated using random-effects models to determine pooled fire resistance and to quantify the effects of parameters such as timber thickness and moisture content. Results show that full timber encasement markedly delays steel heating and improves fire endurance. On average, each additional millimeter of timber cover contributes approximately 1.9 minutes of fire resistance (p < 0.01), with 50 mm encasement achieving roughly one hour of protection under ISO 834 conditions. Moisture within the timber further reduces the rate of temperature rise by absorbing latent heat during evaporation. The study confirms that the insulating and charring behavior of timber functions as an effective passive fire-protection layer, offering an alternative to conventional coatings or boards. Design implications are significant: empirical correlations between cover thickness and fire resistance can inform future fire-design models and code calibrations. Remaining research needs include long-term performance of composite joints and validation under realistic fire scenarios. Overall, the review provides quantitative evidence supporting timber encasement as a viable, sustainable, and code-integral approach for improving the fire safety of composite steel structures.

Abstract. Large-scale forest fire modeling necessitates high-resolution spatial data and complex fuel parameterization, resulting in substantial computational demands. While significant progress has been made in countries such as the USA, Australia, and parts of Europe, particularly in region-specific fuel parameter development. Comparable studies for Indian forests remain limited. This research addresses that gap through extensive field investigations in the Sikkim Himalayas, leading to the generation of localized fuel parameters and their integration into high-resolution (1 m) fire simulations. We employed the Fire Dynamics Simulator (FDS), a Computational Fluid Dynamics (CFD)-based tool, to model forest fire behavior, executing simulations on a High-Performance Computing (HPC) platform. Pre-processing and formatting of input data were facilitated using open-source GIS software, ensuring compatibility with FDS requirements. Validation was performed by comparing simulated outputs with burnt area extents derived from post-fire satellite imagery. By integrating FDS, HPC and field investigations specific to Sikkim forests, this approach offers new insights into forest fire simulation tailored to the Sikkim context. Leveraging parallel computing enables faster simulation outputs, providing valuable support to decision-makers for more effective fire management and mitigation strategies.

Forest fires cause major environmental and socio-economic impacts, with the greatest risk for people occurring in the wildland-urban interface (WUI). In Ibero-Atlantic landscapes, where dispersed settlements are expanding into wildlands and vegetation is encroaching on populated areas, both the frequency and severity of WUI fires are rising sharply. The objective of this study was to develop a new methodological approach to assess fire risk in the WUI of Ibero-Atlantic heterogeneous landscapes under the assumption that fire impacts on population entities may occur by direct or indirect exposure to different types of vegetation. Based on landscape configuration analysis, expert knowledge of fire behaviour across vegetation types and on-site observations of fire impact on buildings and other infrastructures, we developed a multi-ring system around different types of population entities to characterize pre-fire vegetation patterns and fire severity across zones of influence (rings) within the WUI. The relationships between vegetation and severity (estimated with the Relativized Burn Ratio –RBR– spectral index derived from Sentinel-2 satellite imagery) were evaluated using multivariate linear regression models, with a backward stepwise procedure, at two levels: WUI and ring. This framework was tested in the large forest fire of Foyedo (Asturias, NW Spain) that affected more than 10,000 ha in the spring of 2023. Vegetation changed across the WUI, reflecting a land-use gradient from more managed vegetation near settlements to less managed and more natural types in outer zones, with fire severity increasing outward. The main drivers of fire severity at WUI level were vegetation type and vertical complexity. At ring level, the pattern was similar, with the percentage of heathlands and shrublands being the best predictor of fire severity in all rings. In the outermost ring, pine and eucalyptus plantations were also directly related to fire severity. Our findings underscore the need to develop spatially complex analytical frameworks accounting for different exposures across the WUI in order to guide effective vegetation management for forest fire prevention in the Atlantic landscapes of the Iberian Peninsula.

ABSTRACT In this work, the effects of using spent dry chemical powder (DCP) from fire extinguishers on the physicomechanical, hygroscopic and fire performance of urea-formaldehyde (UF) bonded particleboards (PB) were investigated. Obtained results proved that the incorporation of spent dry chemical powder (DCP) into UF-bonded particleboards significantly affected their performance. Mechanical and hygroscopic properties deteriorated with increasing DCP content: internal bond strength decreased by 93%, modulus of rupture by 65%, and modulus of elasticity by 31%, while thickness swelling after 24 h increased by 132%. These effects are attributed to the acidic and hygroscopic nature of MAP and AS, which disrupt UF curing and promote moisture uptake. In contrast, DCP markedly improved fire behaviour. At 10% loading, the maximum average rate of heat emission decreased by 25%, peak HRR by 20%, total heat release by 14%, total CO by 45%, and total smoke production by 42%, while residue mass increased by 149%. Although the improvement in fire performance is moderate compared to advanced flame-retardant systems, DCP enhances nearly all major fire-related parameters simultaneously. The results highlight a clear trade-off between fire safety and mechanical integrity, indicating the need for optimised resin formulations or combined fire-retardant strategies in future work.

Live fuel moisture content (LFMC) is important in vegetation flammability and often considered in fire behaviour modelling and fire danger forecasting. Yet little is known about LFMC in evergreen fynbos shrublands of the Cape Floristic Region which sustain regular high-severity canopy fires in the warm dry season. Temporal variation in LFMC was assessed over 21 months in three prominent plant structural guilds in mesic mountain fynbos in the southeastern Cape Floristic Region in relation to season and weather conditions. Average LFMC ranged from 104%–161% and was significantly higher in proteoids (deep-rooted macrophyllous overstorey shrubs) than in ericoids (shallow-rooted microphyllous understorey shrubs) and restioids (rhizomatous graminoids). Proteoid LFMC fluctuated least among the seasons, while ericoid LFMC peaked in autumn and restioid LFMC in winter. LFMC of proteoids and restioids, respectively, was not correlated with the Canadian fire weather index or its constituent drought indices, the Keetch-Byram drought index, two- or six-month cumulative rainfall, or average temperature of the preceding two months. Ericoid LFMC was significantly correlated with the latter two measures, suggesting a higher level of sensitivity to moisture availability than proteoids and restioids. The differences in LFMC among the fynbos guilds in absolute terms and patterns of seasonal variation align with other evidence of plant-water relations and growth periodicity in these guilds. The lack of responsiveness of LFMC of mixed-species samples representing fynbos guilds to short- and medium-term weather influences implies that incorporating meteorological proxies for LFMC into fire danger forecasting is likely to be arduous in diverse fynbos shrublands.

Background Wildfire suppression is shaped by a complex interplay of environmental conditions, resource allocation and management strategies. Aims Examining the containment of the 2021 Schneider Springs Fire in the Eastern Cascades of Washington State, USA, we emphasise critical roles of variable selection, representative sampling and suppression-specific factors. Methods Using descriptive, predictive and causal models, we assessed the influence of weather conditions, terrain features, personnel availability, tree canopy cover, fire containment lines, and previously identified ‘best available’ containment features. Key results High vapour pressure deficit and strong winds were consistently associated with declining containment success. Terrain features such as valleys and ridges facilitated suppression operations, while steep slopes posed challenges. Additional personnel improved containment outcomes, though with diminishing returns in descriptive and predictive models. Tree canopy cover breaks enhanced suppression effectiveness, but with declining utility during windy conditions. Containment lines played a pivotal role, whereas the role of pre-identified containment features was context-dependent, likely influenced by broader strategic decisions. Conclusions Wildfire containment wasinfluenced by multiple variables, and suppression strategies were situationally determined. Causal models provided valuable insights by isolating total effects of primary variables. Implications Findings underscore adaptive fire management strategies that incorporate context-specific information. Future research should integrate fine-scale weather metrics and additional fire behaviour drivers that guide effective decision-making during dynamic operations.

Concrete-filled steel tube (CFST) columns are composite structural elements preferred in various engineering structures due to their superior properties compared to those of traditional structural elements. However, fire resistance analyses are complex due to CFST columns consisting of two components with different thermal and mechanical properties. Significant challenges arise because current design codes and guidelines do not provide clear guidance for determining the time-dependent fire performance of these composite elements. This study aimed to address the existing design gap by investigating the fire behavior of circular long CFST columns under axial compressive load and developing robust, accurate, and reliable design models to predict their fire performance. To this end, an up-to-date database consisting of 62 data-points obtained from experimental studies involving variable material properties, dimensions, and load ratios was created. Analytical design models were meticulously developed using two advanced soft computing techniques: artificial neural networks (ANNs) and genetic expression programming (GEP). The model inputs were determined as six main independent parameters: steel tube diameter (D), wall thickness (ts), concrete compressive strength (fc), steel yield strength (fsy), the slenderness ratio (L/D), and the load ratio (μ). The performance of the developed models was comprehensively compared with experimental data and existing design models. While existing design formulas could not predict time-based fire performance, the developed models demonstrated superior prediction accuracy. The GEP-based model performed well with an R-squared value of 0.937, while the ANN-based model achieved the highest prediction performance with an R-squared value of 0.972. Furthermore, the ANN model demonstrated its excellent prediction capability with a minimal mean absolute percentage error (MAPE = 4.41). Based on the nRMSE classification, the GEP-based model proved to be in the good performance category with an nRMSE value of 0.15, whereas the ANN model was in the excellent performance category with a value of 0.10. Fitness function (f) and performance index (PI) values were used to assess the models’ accuracy; the ANN (f = 1.13; PI = 0.05) and GEP (f = 1.19; PI = 0.08) models demonstrated statistical reliability by offering values appropriate for the expected targets (f ≈ 1; PI ≈ 0). Consequently, it was concluded that these statistically convincing and reliable design models can be used to consistently and accurately predict the time-dependent fire resistance of axially loaded, circular, long CFST columns when adequate design formulas are not available in existing codes.

Abstract The study of relationship emotions, a set of emotional and psychological responses that arise in a relationship, can help develop more humanized artificial intelligence, improve human-computer interaction, and even create more immersive experiences in virtual and augmented reality. Due to the nonlinear and feedback-driven nature of relational affect, which aligns closely with chaos theory, and the ability of LIF neuron models to simulate dopamine-related electrical activity in brain nuclei, this study innovatively integrates both approaches. By linking the membrane potential signals of LIF neurons to relational affect equations, it achieves a refined modeling of the mechanisms underlying relational affect generation. This paper adds the LIF neuron model to the relationship emotion model to construct a new LIF relationship emotion model (LRM). The effect of the parameters in the LRM on the relationship emotions generated by the model is also investigated using numerical analysis. This includes the firing behavior produced by LIF neurons and a study of relationship emotions produced by different initial relationship emotion states under the same conditions. Finally, the feasibility of LRM is verified by a DSP platform. This process verifies the feasibility of LRM but also provides new ideas and methods for future research in affective computing and human-computer interaction.

Abstract This study investigates the Serra da Estrela megafire, located in Portugal, which occurred in August 2022, aiming to explore how changes in weather conditions can modify fire behavior in a fire burning during several days. The study uses a set of fire–atmosphere coupled simulations, referred as EXPn. The MesoNH atmospheric model was coupled to the ForeFire fire propagation model and configured with three nested domains, each comprising a 150 × 150 horizontal grid, with resolutions of 1500 m, 500 m, and 250 m. Regarding the experiments, EXP1 [IGN] corresponds to the ignition phase on 6 August, EXP2 [PYRO] to the development of pyro‐convective clouds on 10 August, whereas EXP3 [REAC] represents the fire reactivation period on 15 August. The first and third periods showed that smoke plumes were transported mainly within the boundary layer, whereas in the second period pyro‐convection reached the middle tropospheric levels, allowing the formation of pyro‐convective clouds. Such clouds were confirmed from radar and satellite observations. Beside the fire‐generated clouds, the study highlighted that propagation of the fire front can suddenly shift due to the interaction between weather, fire and complex terrain. This study confirmed the importance of the use of coupled atmosphere–fire models to represent fire dynamics and their impact on local meteorology. In a global perspective, such an approach provides a framework for assessing wildfire–atmosphere interactions in diverse environments, supporting improved prediction of large wildfires. Overall, it underscores the influence of evolving atmospheric conditions on fire behavior, which is critical for understanding the development of megafires capable of burning over several weeks. This study highlights the crucial need to expand our scientific understanding of fire behavior, enabling its application to other regions worldwide.

The hidden variable: Impacts of human decision-making on prescribed fire outcomes.