Search Results (169)

Forest fires release various chemical compounds that directly degrade air quality and endanger human health. This study examines the occurrence of forest fires in six Brazilian biomes over a 22-year period (1999–2021). The primary purpose is to identify significant meteorological predictors for the monthly number of hot spots using a standardized statistical framework. Fire hotspots were identified using satellite thermal sensors (AVHRR and MODIS), and we employed a standardized negative binomial regression modeling approach to analyze the relationship between meteorological variables and fire hotspots in all six Brazilian biomes simultaneously, providing a comprehensive comparative perspective often lacking in studies focused on isolated regions. The results show that the Amazon and Cerrado biomes have the highest absolute number of fires, which is consistent with their size and vegetation structure. To avoid bias associated with biome size, fire occurrence was additionally estimated using hotspot density normalized by biome area (hotspots per km²). Using these models, significant factors for fire occurrence were identified, namely the main meteorological variables—temperature, precipitation and wind speed. By comparing the performance of the models in different biomes, we aimed to better understand regional fire dynamics. The model’s ability to predict the expected number of fires based on these variables provides a key tool for preventive air quality monitoring. Such a predictive model serves as a basis for developing early warning systems, assessing potential health risks for the population, and adopting targeted fire management policies. Full article

Injection-molding purges are heterogeneous, bulky residues whose uncertain composition and irregular geometry hinder direct reinsertion, making cold shredding costly and maintenance-intensive. This work develops a low-infrastructure solar-assisted pre-processing route using a PMMA Fresnel lens to induce controlled sub-onset softening and enable clean shear cutting without destructive thermal histories. The sub-onset softening is here defined into a viscoelastically active range (at or above T_g for the amorphous phase) while remaining below the melting onset (T_m, onset) and below the onset of thermal degradation (T_d, onset). The station was engineered via QFD and risk-oriented design tools, while a weighted Pugh matrix selected shear cutting over saw-based alternatives. A screening factorial DOE showed that lens height, angle, and their interaction significantly govern focal-spot diameter and receiver temperature, yielding linear relations for conservative set-point selection. Receiver benchmarking further indicated that copper reaches substantially higher temperatures than graphite under identical exposure conditions, supporting copper as the simplest, rapid-heating receiver. Under DOE-calibrated operation, tear-free shear cutting was achieved across representative purge families (PP–ABS, PC–ABS–PP, PA66, PA66-filler, and POM) without forced convection. From a recycling and waste-management perspective, the approach converts bulky purge scrap into mill-compatible feedstock with reduced mechanical resistance, lowering tool wear and fines generation, accelerating downsizing, and limiting stockpiling that elevates combustible-inventory fire risk. Overall, the proposed DOE-calibrated, operator-friendly framework improves recycling feasibility by enabling safer handling, more stable preprocessing throughput, and reduced reliance on disposal or long-term storage for heterogeneous industrial purges. Full article

Two-dimensional cellular automata (CA) models are widely used for wildfire simulation due to their clean representation of environment and fire mechanics and their computational efficiency. In this review we describe the mechanisms through which forestry fuel characteristics, topographic features, firefighting suppression strategies, fire spotting behavior and meteorological conditions are represented and integrated within these models. While these models are effective for large scale simulations, in which high precision is not critical, their reliance on discrete representations of space and time, along with simplified local state transition rules, introduces additional challenges and limitations. This review presents key methodologies, hybrid implementations, and model extensions of CA-based wildfire simulation models, highlighting their inherent strengths, limitations, and practical challenges. In addition, it provides a classification of the computational and simulation techniques applied to wildfire spread and behavior. Full article

With the rapid expansion of renewable energy and the acceleration of electricity market reforms, coal-fired units are facing increasing difficulty in recovering fixed costs due to marginal cost-based bidding competition and depressed clearing prices caused by low-cost renewable integration, circumstances in which reasonable returns and investment incentives for coal-fired power plants are not guaranteed. To address this issue, this paper proposes a capacity cost compensation mechanism for coal-fired power in the electricity market environment. First, a joint clearing model for the electricity spot market considering both energy and reserve services is established, and annual market operation simulations are conducted to obtain unit output schedules, clearing prices, and annual revenues. Second, based on the long-term simulation results, the marginal clearing probability and fixed cost recovery deficit of each coal-fired unit are calculated, and a capacity compensation pricing method based on marginal clearing probability weighting is proposed to determine the system unit capacity compensation price. Subsequently, the compensated capacity is determined using the availability factor method, comprehensively reflecting each unit’s actual contribution to system capacity adequacy. Finally, case studies conducted on a modified IEEE 30-bus system validate the effectiveness of the proposed mechanism. The results demonstrate that following the implementation of the proposed mechanism, the investment payback periods of all coal-fired units are reduced to within the planned 20-year horizon, thereby ensuring the sustainable operation of coal-fired units and maintaining adequate reliability margins in the power system. Full article

This paper proposes an approach to digitizing the environmental passport for areas where detachable parts of launch vehicles fall in Kazakhstan based on an interactive geographic information system platform and smart maps. An example is considered for zone U-4 (“Ulytau” district of the “Karaganda” region), which includes the fall zones of “Soyuz” launch vehicle blocks (IZ 26, 32, 34, 42, 56). The natural and climatic factors and hazards of the territory are analyzed: the total area of the zones under consideration exceeds 4.1 million hectares, annual precipitation varies between 218 and 289 mm, strong winds of 5.0–6.8 m/s are characteristic, and a high level of fire hazard can develop within 6–7 days. Data on fires for 2021 are provided. For an integrated assessment, a normalized system criterion, environmental sustainability indicator (0–1), has been introduced, aggregating four groups of criteria (chemical, mechanical, pyrogenic, biota) with a breakdown of contributions and calculation of uncertainty (σ and 95% CI). The system criterion of environmental sustainability map identifies local ‘hot spots’ with levels of around 0.8–1.0, while the uncertainty map shows maximums of up to 0.12–0.14 (with background values of ~0.02–0.08), which increases the validity of management decisions on monitoring and reclamation. Full article

With the advancement of unmanned aerial vehicle (UAV) technology, its use in ecological monitoring and safety management of national parks has expanded significantly. However, object detection in complex scenes remains challenging due to environmental complexity, background interference, and occlusion. To address these issues, this paper proposes two improved YOLOv8-based models, YOLOv8-StarNet-CGA and SCS-YOLOv8, for detecting pine wilt disease-infected trees, under-construction farmhouses, and forest fires. In YOLOv8-StarNet-CGA, the StarNet module and Content-Guided Attention (CGA) are integrated into the backbone to enhance global feature extraction and focus on critical regions through dynamic weight adjustment. In SCS-YOLOv8, the original CIoU loss is also replaced with SIoU loss to optimize shape and orientation consistency, improving robustness. Experiments on UAV datasets covering diverse national park scenes demonstrate the effectiveness of the models. Results show that the improved models substantially outperform the original YOLOv8 in Precision, Recall, and mAP50. For pine wilt disease caused by the pine wood nematode Bursaphelenchus xylophilus, YOLOv8-StarNet-CGA achieves 8.6% higher Precision and 11.7% higher mAP50, facilitating early diagnosis and intervention of the disease. In under-construction farmhouse scenarios, Precision rises by 11% and mAP50 by 10.1%, lowering annual inspection labor by nearly 30% and improving oversight. For forest fires, SCS-YOLOv8 is more effective, with Precision improved by 7.2% and mAP50 by 6.3%. The improved detection model enables earlier identification of fire spots, thereby providing additional response time for emergency intervention, helping to mitigate fire spread and reduce the loss of forest resources. Both models also reduce GFLOPs and computational complexity, striking a balance between efficiency and accuracy, and showing strong potential for UAV deployment. Full article

Video footage of a recent California wildfire confirmed that dangerous fire spread can lead to unsurvivable foreground conditions. This process thus needs enhanced awareness across the wildfire sector. The fire moved sideways, downwind of a ridgeline, and formed dense, rapidly spreading spot-fires. Effective lateral rates-of-spread up to 20 km h⁻¹ were measured. This is discussed in detail. A HPWREN camera system was installed on Santiago Peak in California. The Airport Fire, on two consecutive days, burned past the cameras by means of vorticity-driven lateral spread (VLS). This provided the most complete sets of time-series observations of VLS on a landscape-scale. Some remarkable measurements are derived from the observations. The overall lateral rate-of-spread averaged at 1.9 km h⁻¹. Around plume touch-down events, that speed rose to 4 km h⁻¹, but also peaked at 20 km h⁻¹. The effective downwind spread of the overall fire envelope was 45 km h⁻¹. A major spot-fire had a slope-affected headfire rate-of-spread of 15 km h⁻¹ (equivalent to c. 2 km h⁻¹ on flat ground) and a burn rate of 60 ha h⁻¹. The implications for fireground safety are extreme. An emphasis must be placed on predicting these events, as any burnover entrapments may well be unsurvivable. Avoiding a burnover requires good predictive capability, and observations such as these are critical for calibration. Full article

Respiratory infections represent one of the leading causes of pediatric consultations and hospitalizations in Chile, where rapid etiological identification is essential for clinical decision-making. We evaluated the impact of implementing the BIOFIRE^® SPOTFIRE^® Respiratory (R) Panel in the pediatric Emergency Department of a public referral hospital in Santiago, using a pre–post cross-sectional design comparing two winter periods (July 2023 vs. July 2024). Clinical records, laboratory data, and operational indicators were analyzed to assess changes in diagnostic yield, turnaround time, hospitalizations, discharges, supplementary test requests, and antimicrobial use. A total of 470 patients were included (224 in 2023; 246 in 2024). The etiological detection rate increased from 58.0% to 87.8% after the implementation of Spotfire^® (p < 0.0001), with marked increases in the identification of adenovirus, RSV, rhinovirus/enterovirus, and seasonal coronaviruses. Rapid molecular testing was associated with a significant rise in emergency department discharges (23.7% vs. 57.3%; p < 0.0001) and a reduction in hospitalizations (76.3% vs. 42.7%; p < 0.0001) and readmissions (9.2% vs. 0.5%; p < 0.0001). Requests for complete blood counts, chest X-rays, and antimicrobial prescriptions at discharge also decreased significantly. These effects persisted in key subgroups, including infants and children with comorbidities. In this high-demand winter setting, the BIOFIRE^® SPOTFIRE^® R Panel improved diagnostic performance and supported more efficient and targeted clinical management. Full article

Background: Bordetella pertussis causes pertussis, a respiratory infection with whooping cough. Despite a high vaccine coverage, pertussis resurged post-COVID-19 pandemic. Meanwhile, isolates resistant to macrolides—the first-line therapy—have increased in several countries, including Japan. Culturing B. pertussis and detecting resistance are difficult; reports remain limited in Japan. Methods: From March to August 2025, we collected nasopharyngeal samples from children aged 0–15 years with suspected pertussis at six Japanese clinics. Pediatricians obtained swabs and tested them using gene-amplification kits (e.g., BioFire^® SpotFire^® in four clinics, LAMP Pertussis Detection^® in two clinics). B. pertussis was confirmed by PCR; isolates were sequenced to identify macrolide-resistant mutations. Results: Samples were taken from 54 children, the number of boys and girls was 34 and 20, and their median age was 12 years old. Among 54 B. pertussis isolates, 43/52 (82.7%) sequenced strains harbored the A2047G mutation associated with macrolide resistance. Resistance rates at each clinic varied from 40% to 96%. Conclusions: These findings indicate a post-pandemic rise in macrolide-resistant B. pertussis in Japan. Ongoing resistance surveillance is essential, and repurposing residual clinical samples after routine testing is useful given culture and detection challenges. Full article

Exogenous fires in underground coal mines are characterized by low illumination, smoke occlusion, heavy dust loading and pseudo fire sources, which jointly degrade image quality and cause missed and false alarms in visual detection. To achieve accurate and real-time early warning under such conditions, this paper proposes a mine exogenous fire detection algorithm based on an improved YOLOv9m, termed PPL-YOLO-F-C. First, a lightweight PP-LCNet backbone is embedded into YOLOv9m to reduce the number of parameters and GFLOPs while maintaining multi-scale feature representation suitable for deployment on resource-constrained edge devices. Second, a Fully Connected Attention (FCAttention) module is introduced to perform fine-grained frequency–channel calibration, enhancing discriminative flame and smoke features and suppressing low-frequency background clutter and non-flame textures. Third, the original upsampling operators in the neck are replaced by the CARAFE content-aware dynamic upsampler to recover blurred flame contours and tenuous smoke edges and to strengthen small-object perception. In addition, an MPDIoU-based bounding-box regression loss is adopted to improve geometric sensitivity and localization accuracy for small fire spots. Experiments on a self-constructed mine fire image dataset comprising 3000 samples show that the proposed PPL-YOLO-F-C model achieves a precision of 97.36%, a recall of 84.91%, mAP@50 of 96.49% and mAP@50:95 of 76.6%, outperforming Faster R-CNN, YOLOv5m, YOLOv7 and YOLOv8m while using fewer parameters and lower computational cost. The results demonstrate that the proposed algorithm provides a robust and efficient solution for real-time exogenous fire detection and edge deployment in complex underground mine environments. Full article

Fire spotting (FS), the process by which firebrands are lofted, transported, and ignite new fires ahead of the main flame front, plays a critical role in escalating extreme wildfire events. This systematic literature review (SLR) analyzes peer-reviewed articles and book chapters published in English from 2000 to 2023 to assess the evolution of FS models, identify prevailing methodologies, and highlight existing gaps. Following a PRISMA-guided approach, 102 studies were selected from Scopus, Web of Science, and Google Scholar, with searches conducted up to December 2023. The results indicate a marked increase in scientific interest after 2010. Thematic and bibliometric analyses reveal a dominant research focus on integrating the FS model within existing and new fire spread models, as well as empirical research and individual FS phases, particularly firebrand transport and ignition. However, generation and ignition FS phases, physics-based FS models (encompassing all FS phases), and integrated operational models remain underexplored. Modeling strategies have advanced from empirical and semi-empirical approaches to machine learning and physical-mechanistic simulations. Despite advancements, most models still struggle to replicate the stochastic and nonlinear nature of spotting. Geographically, research is concentrated in the United States, Australia, and parts of Europe, with notable gaps in representation across the Global South. This review underscores the need for interdisciplinary, data-driven, and regionally inclusive approaches to improve the predictive accuracy and operational applicability of FS models under future climate scenarios. Full article

This study develops, implements, and evaluates the Firebrand Spotting parameterization within the WRF-Fire coupled fire–atmosphere modeling system. Fire spotting is an important mechanism characterizing fire spread in wind-driven events. It can accelerate the rate of spread and enable the fire to spread over streams and barriers such as highways. Without the capability to simulate fire spotting, wind-driven fire simulations cannot accurately represent fire behavior. In the Firebrand Spotting parameterization, firebrands are generated with a set of fixed properties, from locations vertically aligned with the leading fire line. Firebrands are transported using a Lagrangian framework accounting for particle burnout (combustion) through an MPI-compatible implementation within WRF-Fire. Fire spots may occur when firebrands land on unburned grid points. The parameterization is verified through idealized simulations and its application is demonstrated for the 2021 Marshall Fire, Colorado. The simulations are assessed using the observed fire perimeter and time of arrival at multiple locations identified from social media footage and official documents. All simulations using a range of ignition thresholds outperform the control without spotting. Simulations accounting for fire spots show more accurate fire arrival times (i.e., reflecting a better fire rate of spread), despite producing a generally larger fire area. The Heidke Skill Score (Cohen’s Kappa) for the burn area ranges between 0.62 and 0.78 for simulations with fire spots compared to 0.47 for the control. These results show that the parameterization consistently improves the fire forecast verification metrics, while also underscoring future work priorities, including advancing the generation and ignition components. Full article

Firefighters are inherently exposed to soot and polycyclic aromatic hydrocarbons (PAHs) at work. In this repeated measures study, we assessed if three different interventions reduced PAH exposure. For each sub-study, the firefighters participated in two sampling periods and thereby served as their own controls. The first period served as baseline, while the second period was the intervention period where the participants received education on health effects of soot, information on own PAH exposure, and participated in one of three interventions: (1) sauna after fire calls, (2) use of fire suits with improved barrier, and (3) showering after every fire call. We recruited 26 firefighters from three different fire stations. Dermal wipes were assessed for 16 PAHs and spot urine for eight hydroxylated metabolites. Pre-shift PAH burden was significantly reduced compared to our previous biomonitoring study. Post-shift levels of two PAH metabolites (1-hydroxypyrene and 1-hydroxyfluorene) were increased for firefighters after a work shift without fire calls compared to pre-shift. The sauna intervention significantly reduced the levels of all the measured urinary PAH metabolites while the dermal PAH exposure remained unaffected. The fire suit intervention yielded more inconsistent results. While standard shower reduced dermal PAH levels, no additional effects were observed for the shower intervention. Full article

With the rapid expansion of variable renewable energy, coal-fired units are increasingly operated at low load, where non-convex cost characteristics pose challenges for spot market clearing. This study reviews and improves existing low-load generation cost models, introducing three key enhancements: (1) integrating piecewise linearization with the marginal cost approach to reduce computational burden; (2) removing redundant binary variables and incorporating previously omitted cost components to improve clearing efficiency; and (3) developing a fuel cost model that combines quasi-fixed and marginal costs for low-load generation with firing and combustion support (FCS), enabling the joint optimization of low-load and normal operations. Applied to 6-bus and provincial systems, the proposed approach achieves speed-ups of 11.3× and 6.3× over the benchmark model (Model I) while maintaining accuracy, demonstrating both its efficiency and practical applicability. Full article

In this study, typical haze pollution influenced by biomass burning (BB) activities in Changsha in the autumn of 2024 was investigated through the mixing state and evolution process of BB particles via the real-time measurement of single-particle aerosol mass spectrometry (SPAMS). From the clean period to the haze period, the PM_2.5 concentration increased from 25 μg·m⁻³ at 12:00 to 273 μg·m⁻³ at 21:00 on 12 October, and the proportion of total BB single particles in the total detected particles increased from 17.2% to 54%. This indicates that the rapid increase in PM_2.5 concentration was accompanied by a concurrent increase in the contribution of particles originating from BB sources. The detected BB particles were classified into two types based on their mixing states and temporal variations: BB1 and BB2, which accounted for 71.7% and 28.3% of the total BB particles, respectively. The analysis of backward trajectories and fire spots suggested that BB1 particles originated from straw burning emissions at northern Changsha, while BB2 particles were primarily related to local nighttime cooking emissions in Changsha. In addition, a special type of K-containing single particles without K cluster ions was found closely associated with BB1 type particles, which were designated as secondarily processed BB particles (BB-sec). The BB-sec particles contained abundant sulfate and ammonium signals and showed lagged appearance after the peak of BB1-type particles, which was possibly due to the aging and formation of ammonium sulfate on the freshly emitted particles. In all, this study provides insights into understanding the substantial impact of BB sources on regional air quality during the crop harvest season and the appropriate disposal of crop straw, including conversion into high-efficiency fuel through secondary processing or clean energy via biological fermentation, which is of great significance for the mitigation of local haze pollution. Full article