Search Results (2,159)

Forest fires are key disturbance factors in forest ecosystems, and soil fungi play an irreplaceable role in post-fire recovery. This study focused on forest areas burned in 2000 in the Daxing’anling region of China, targeting long-term recovery sites with different fire intensities. Illumina MiSeq sequencing was used to analyze the structural characteristics of fungal communities and their environmental drivers. Results showed that compared with the control check (CK), the Shannon index of the low fire group (L) increased significantly (p < 0.05), while moderate (M) and high (H) fire groups reduced fungal diversity significantly. PCoA indicated significant differences in community structure (R² = 0.97, p = 0.001). In highly burned areas, the relative abundance of Ascomycota reached 94.17%, and Basidiomycota lost its dominance. Spearman analysis showed that pH, available phosphorus, available potassium, soil fluorescein diacetate hydrolase, soil dehydrogenase, and soil urease were significantly positively correlated with fungal alpha diversity. RDA revealed that total nitrogen, available phosphorus, soil water content, alkaline nitrogen, active potassium, and dissolved organic carbon had extremely significant effects on soil fungal community composition (p < 0.01). Co-occurrence network analysis indicated that symbiotic relationships dominated all groups. Networks in L and M groups were more complex, while that in H group was simplified and severely damaged. This study indicated that after long-term recovery, soil fungal communities in low fire areas returned to pre-fire levels; those in moderate and high fire areas did not recover, with high fire burns causing severe damage and community structure reorganization. Full article

Burned area, fire severity, and suppression expenditures have increased in British Columbia in recent decades with climate change. Approximately 80% of suppression expenditures are attributable to wildfires near the Wildland–Urban Interface (WUI). Evaluating the potential for fuel management to reduce suppression expenditures is essential to mitigating demands on fire response resources and reducing impacts on communities. One management approach is to increase the proportion of deciduous tree species, which have a lower propensity for crown fire. Using fire suppression expenditure data from 1981 to 2014, we applied the machine learning method causal forests (CFs) to estimate the effect of the proportion of conifer forest cover on suppression expenditures for WUI fires and how these effects varied with other influential factors (i.e., heterogenous treatment effects). Across all fires, the effect of conifer cover on suppression expenditures was stronger on private land compared to public land, under high fire danger measured by daily severity ratings (DSRs), which reflect wind speed and fuel moisture, and for fires igniting earlier in the calendar year, based on Julian day. These findings provide insights into prioritizing wildland fuel treatment when budgets are limited. The CFs approach demonstrates potential for broader applications in fire risk mitigation and analysis beyond the scope of the current data. CFs may also be valuable in other areas of forest research where heterogenous treatment effects are common. Full article

Remote sensing of wildland fires has become an integral part of fire science. Airborne sensors provide high spatial resolution and can provide high temporal resolution, enabling fire behavior monitoring at fine scales. Fire agencies frequently use airborne long-wave infrared (LWIR) imagery for fire monitoring and to aid in operational decision-making. While tactical remote sensing systems may differ from scientific instruments, our objective is to illustrate that operational support data has the capacity to aid scientific fire behavior studies and to facilitate the data analysis. We present an image processing algorithm that automatically delineates active fire edges in tactical LWIR orthomosaics. Several thresholding and edge detection methodologies were investigated and combined into a new algorithm. Our proposed method was tested on tactical LWIR imagery acquired during several fires in California in 2020 and compared to manually annotated mosaics. Jaccard index values ranged from 0.725 to 0.928. The semi-automated algorithm successfully extracted active fire edges over a wide range of image complexity. These results contribute to the integration of infrared fire observations captured during firefighting operations into scientific studies of fire spread and support landscape-scale fire behavior modeling efforts. Full article

Forest ecosystems are vital to sustainable development, contributing to economic, environmental and social well-being. However, the increasing frequency and severity of wildfires threaten these ecosystems, demanding more effective and integrated fire management (IFM) strategies. Current suppression efforts face limitations due to high resource demands and the need for timely, informed decision-making under uncertain conditions. This paper presents the SILVANUS project’s approach to developing an advanced Decision Support System (DSS) designed to assist incident commanders in optimizing resource allocation during wildfire events. Leveraging Geographic Information Systems (GIS), real-time data collection, AI-enhanced analytics and multicriteria optimization algorithms, the SILVANUS DSS component integrates diverse data sources to support dynamic, risk-informed decisions. The system operates within a cloud-edge infrastructure to ensure scalability, interoperability and secure data management. We detail the formalization of the resource allocation problem, describe the implementation of the DSS within the SILVANUS platform, and evaluate its performance in both controlled simulations and real-world pilot scenarios. The results demonstrate the system’s potential to enhance situational awareness and improve the effectiveness of wildfire response operations. Full article

In the context of climate change, increasingly severe forest fires present a significant threat to lives, property, ecosystem functionality, and the sustainable development of forest resources. As a result, there is an urgent need for rapid, efficient, and environmentally friendly technologies for fire suppression and containment. This paper begins by reviewing the current research on forest fire extinguishing agents and materials that hold promise for effective fire suppression. Among these agents, gaseous and foam extinguishing materials exhibit drawbacks such as low efficiency or significant environmental hazards. In contrast, natural polymer hydrogels, which are high in water content, environmentally friendly, and biodegradable, show significant potential for developing clean and efficient extinguishing materials. Furthermore, this paper discusses existing environmental assessment standards for fire extinguishing agents, as well as the assessment systems proposed in various studies. It finds that, while universal assessment standards are fairly well-established, current research primarily focuses on enhancing fire suppression performance. However, the environmental performance assessment of forest fire extinguishing agents—often used in large quantities—remains inadequate. Therefore, there is an urgent need to establish a comprehensive and systematic environmental assessment system to address this theoretical and practical gap. Full article

In recent years, ecosystems in Saudi Arabia have experienced severe degradation due to factors such as hyperaridity, overgrazing, climate change, urban expansion, and an increase in uncontrolled wildfires. Among these, wildfires have emerged as the second most significant threat to forests after urban expansion. This study aims to map wildfire susceptibility in southwestern Saudi Arabia by identifying key driving factors and evaluating the performance of several machine learning models under conditions of limited and imbalanced data. The models tested include Maxent, logistic regression, random forest, XGBoost, and support vector machine. In addition, an NDVI-based phenological approach was applied to assess seasonal vegetation dynamics and to compare its effectiveness with conventional machine learning-based susceptibility mapping. All methods generated effective wildfire risk maps, with Maxent achieving the highest predictive accuracy (AUC = 0.974). The results indicate that human activities and dense vegetation cover are the primary contributors to wildfire occurrence. This research provides valuable insights for wildfire risk assessment in data-scarce regions and supports proactive fire management strategies in non-traditional fire-prone environments. Full article

Introduction: Charred bodies represents a significant challenge for forensic pathologists due to the destructive effects of fire on human remains. Although most fire-related deaths are accidental, cases of suicide and homicide are not uncommon. Case Report: We report a peculiar case of a severely burned body discovered inside a torched vehicle. Under judicial investigation, a full autopsy was performed, including macroscopic and microscopic examination of key anatomical structures: the inspection of oral cavity revealed no soot deposits; a foreign object (a chestnut) was found anterior to the epiglottis, though not lodged within the glottis; no thermal injuries or soot were observed in the upper or lower airways. Histological analysis excluded thermal damage at the alveolar–capillary interface. Alveolar spaces appeared both hyperinflated and ectatic, likely due to septal rupture, suggestive of acute pulmonary emphysema and multiorgan congestion. Carboxyhemoglobin levels were below 5%, indicating a low level which did not support intravital inhalation of combustion gases. Based on the comprehensive medico-legal findings, the cause of death was attributed to an asphyxial mechanism. It was further demonstrated that the burning of the body occurred post-mortem. DNA extraction from two dental specimens enabled positive identification of the victim. Subsequent investigations confirmed the case to be a femicide. The perpetrator, following a domestic altercation over jealousy, suffocated his young wife and attempted to simulate accidental choking by placing a chestnut in her mouth. He then staged a vehicular fire to mimic a fatal accident. Conclusions: The case underlines that a multidisciplinary forensic approach is essential, and must integrate different methodologies and the analysis of both circumstantial evidence and scene investigation. Full article

This single high-impact case study investigates the impact of planetary boundary layer (PBL) representation on long-range transport of Amazon fire smoke that reached the Metropolitan Area of São Paulo (MASP) from 15 to 20 August 2019, using the WRF model to compare three PBL schemes (MYNN 2.5, YSU, and BouLac) and three source-tagged tracers. The simulations are evaluated against MODIS-derived aerosol optical depth (AOD), the Light Detection and Ranging (LiDAR) time–height curtain over MASP, and HYSPLIT forward trajectories. Transport is diagnosed along the source-to-MASP pathway using six-hourly cross-sections and two integrative metrics: the projected mean wind in the 700–600 hPa layer and the vertical moment of tracer mass above the boundary layer. Outflow and downwind impact are strongest when a persistent reservoir between 2 and 4 km coexists with projected winds for several hours. In this episode, MYNN maintains an elevated 2–5 km transport layer and matches the observed arrival time and altitude, YSU yields a denser but delayed column, and BouLac produces discontinuous pulses with reduced coherence over the city. A negatively tilted trough, jet coupling, and a nearly stationary front establish a northwest-to-southeast corridor consistent across model fields, trajectories, and satellite signal. Seasonal robustness should be assessed with multi-event, multi-model analyses. Full article

Coal spontaneous combustion in arid regions poses severe threats to both ecological security and resource sustainability. Focusing on the detection challenges in Fire Zone No. 1 of the Miaoergou Coalfield, Xinjiang, this study proposes an Integrated Geophysical Collaborative Detection Framework that combines high-precision magnetic surveys, spontaneous potential (SP) measurements, and transient electromagnetic (TEM) methods. This innovative framework effectively overcomes the limitations of traditional single-method detection approaches, enabling the precise delineation of fire zone boundaries and the accurate characterization of spatial dynamics of coal fires. The key findings of the study are as follows: (1) High-magnetic anomalies (with a maximum ΔT of 1886.3 nT) exhibit a strong correlation with magnetite-enriched burnt rocks and dense fracture networks (density > 15 fractures/m), with a correlation coefficient (R²) of 0.89; (2) Negative SP anomalies (with a minimum SP of −38.17 mV) can effectively reflect redox interfaces and water-saturated zones (moisture content > 18%), forming a “positive–negative–positive” annular spatial structure where the boundary gradient exceeds 3 mV/m; (3) TEM measurements identify high-resistivity anomalies (resistivity ρ = 260–320 Ω·m), which correspond to non-waterlogged goaf collapse areas. Spatial integration analysis of the three sets of geophysical data shows an anomaly overlap rate of over 85%, and this result is further validated by borehole data with an error margin of less than 10%. This study demonstrates that multi-parameter geophysical coupling can effectively characterize the thermo-hydro-chemical processes associated with coal fires, thereby providing critical technical support for the accurate identification of fire boundaries and the implementation of disaster mitigation measures in arid regions. Full article

Fire blight, caused by Erwinia amylovora, is a severe disease impacting pome fruit production worldwide, including in Saudi Arabia. This study evaluated antibiotic sensitivity and the potential of chemical and elicitor treatments to suppress E. amylovora isolates collected from various regions in Saudi Arabia. In the in vitro assays, at low antibiotic levels (10 µg/mL streptomycin and 25 µg/mL oxytetracycline), all Saudi Arabian strains exhibited minimal inhibition (zones ≤ 14 mm). Two isolates displayed partial tolerance at an intermediate oxytetracycline concentration (50 µg/mL). True sensitivity (zones > 18 mm) was mainly observed at the highest tested oxytetracycline dose (100 µg/mL). Regarding copper sulfate, all isolates showed no inhibition between 0.02 and 0.08 mM, while all isolates exhibited intermediate susceptibility at 0.16 mM. The second experimental phase examined in planta effects of streptomycin, salicylic acid (SA), and their combination on disease development in artificially inoculated apple (Malus domestica) shoots under greenhouse conditions. Both streptomycin and SA significantly reduced fire blight incidence (by 75%) and symptom severity, while the combined treatment yielded the greatest reduction in shoot necrosis and bacterial load. This is the first report demonstrating that SA, particularly when used in combination with streptomycin, can effectively suppress fire blight in Saudi Arabia. These results stress the importance of integrating resistance inducers into fire blight management strategies to counter the rise in antimicrobial resistance. Full article

This review article critically examines the fire performance of cross-laminated timber (CLT), a key structural material for sustainable construction, by synthesising recent advancements in both experimental and numerical research. It identifies a critical gap between experimental findings and numerical models, offering insights to refine future fire-safe design and research. The article assesses fire design strategies across major international standards and reviews experimental fire testing of CLT elements, highlighting how adhesives, protective cladding, layer thickness, load levels, and support conditions affect fire resistance. This article also summarises CLT compartment tests, focusing on how openings, ventilation size, and protective cladding affect fire dynamics and CLT degradation. A literature review of numerically modelled CLT specimens under fire load is compiled and evaluated based on several criteria, including material characterisation, mesh characteristics, and modelling procedures. Subsequently, the outcomes of two distinct approaches are evaluated, emphasising the disparities in the techniques employed and the difficulties inherent in performing more precise numerical simulations. The article will bridge and inform the gap between experimental tests and numerical analysis, focusing on identifying suitable approaches for such simulations. The study aims to provide a broader understanding of the topic and promote the development of fire-safe design and modelling of engineered timber construction using CLT. Full article

Protected area coverage is set to expand in response to climate change and the biodiversity crisis, but we lack assessments of wildfire incidence in protected areas. Here, we quantify biogeographical variation in global patterns of burned area in protected areas. During the twenty-first century, wildfires have burned 2 billion hectares of protected areas—an area the size of Russia and India combined—and, while protected areas only cover 19.2% of semi-natural ecosystems, they concentrate 28.5% of the area burned annually. Wildfire in protected areas increased significantly between 2001 and 2024 (+0.46% yr⁻¹), even after taking into account increases in protected area (+0.27% yr⁻¹), pointing to a disproportional impact of fire on protected areas under increasingly severe fire weather. This pattern showed marked variation across biomes, with the largest disproportionate increases occurring in fire-prone biomes (e.g., Mediterranean and dry tropical forests, tropical grasslands, and xeric shrublands). There were important exceptions to this general trend, and protected area fire was lower than expected in biomes where fire activity is naturally limited by moisture (e.g., tropical rainforests or montane grasslands). Wildfires are important for the health of many ecosystems, and such values of burned area will not always mean a negative outcome. Amidst concerted efforts to expand protected area coverage, such as the Global Biodiversity Framework, our results highlight the need for new management strategies that address the globally increasing impacts of burned area across protected areas under unabated climate change. Full article

The increasing frequency and intensity of wildfires pose severe risks to ecosystems, infrastructure, and human safety. In wildland–urban interface (WUI) areas, nearby vegetation strongly influences building ignition risk through flame contact and radiant heat exposure. However, limited research has leveraged Unmanned Aerial Systems (UAS) remote sensing (RS) to capture species-specific vegetation geometry and predict thermal responses during ignition events This study proposes a two-stage framework integrating UAS-based multispectral (MS) imagery, LiDAR data, and Fire Dynamics Simulator (FDS) modeling to estimate the maximum temperature (T) and heat flux (HF) of outdoor vegetation, focusing on Syzygium smithii (Lilly Pilly). The study data was collected at a plant nursery at Queensland, Australia. A total of 72 commercially available outdoor vegetation samples were classified into 11 classes based on pixel counts. In the first stage, ensemble learning and watershed segmentation were employed to segment target vegetation patches. Vegetation UAS-LiDAR point cloud delineation was performed using Raycloudtools, then projected onto a 2D raster to generate instance ID maps. The delineated point clouds associated with the target vegetation were filtered using georeferenced vegetation patches. In the second stage, cone-shaped synthetic models of Lilly Pilly were simulated in FDS, and the resulting data from the sensor grid placed near the vegetation in the simulation environment were used to train an XGBoost model to predict T and HF based on vegetation height (H) and crown diameter (D). The point cloud delineation successfully extracted all Lilly Pilly vegetation within the test region. The thermal response prediction model demonstrated high accuracy, achieving an RMSE of 0.0547 °C and R² of 0.9971 for T, and an RMSE of 0.1372 kW/m² with an R² of 0.9933 for HF. This study demonstrates the framework’s feasibility using a single vegetation species under controlled ignition simulation conditions and establishes a scalable foundation for extending its applicability to diverse vegetation types and environmental conditions. Full article

The structure, chemical composition, thermal stability, and abuse responses of cathode materials are critical to the safety and economy of lithium-ion batteries (LIBs). This review systematically summarizes advances in research on how cathode materials influence LIB thermal runaway (TR) behavior. It analyzes the oxygen release from cathodes in TR mechanisms and the hazards of such oxygen generation during TR, expounds on how differences in cathode structure, chemical composition, and thermal stability affect TR behavior, and summarizes the thermal characteristics of LIBs with different cathodes under mechanical, electrical, and thermal abuse. Results indicate that oxygen released from cathode decomposition during TR oxidizes electrolytes, releasing substantial heat and gas and causing more severe TR hazards. Structural instability of cathodes leads to accelerated release of lattice oxygen, speeding up TR initiation. Chemical composition regulates thermal stability, phase transition pathways, and gas generation rates during TR, while elemental ratios affect the ease of TR triggering. Cathodes with poor thermal stability have lower thermal decomposition onset temperatures, making TR more likely to occur and intensifying reaction severity. All three abuse types trigger inherent risks of cathodes, inducing TR and significantly increasing its occurrence probability. Differences in intrinsic properties further extend to the system level, also influencing thermal runaway propagation and fire dynamics at the module level. Future research focusing on the intrinsic properties of cathodes and external abuse is of great significance for addressing LIB TR behavior. Full article

This study investigates the use of wheat straw ash (WSA) as a sustainable supplementary cementitious material, focusing on its mechanical performance optimization and environmental implications. WSA (ASTM C618, Class F), produced via controlled calcination at 700 °C, was used to replace cement at 2.5, 5, 7.5, 10% by mass. The optimal performance was observed at 5% substitution, achieving a 90-day compressive strength of 48.42 MPa (+4.7%) and a 28-day flexural strength of 7.93 MPa (+6.6%). To contextualize these findings, a multi-technique analytical approach was employed, including scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FT-IR), and ultrasonic pulse velocity (UPV). These methods confirmed that WSA enhances portlandite consumption through pozzolanic reactivity and improves matrix densification via secondary C-S-H gel formation. Additionally, satellite (Sentinel-5P) and ground-based measurements during a severe stubble fire event in Diyarbakir (20–24 June 2024) documented a fourfold increase in PM₁₀ concentrations (157 μg/m³ compared to the June average of ≈35 μg/m³), alongside 23% and 41% rises in NO₂ and SO₂ levels, respectively. These findings demonstrate that wheat straw ash utilization can mitigate agricultural waste burning, improve air quality, and reduce the carbon footprint of cement production. The study highlights WSA’s potential as a high-performance, eco-efficient construction material aligned with circular economy principles. Full article