Search Results (2,509)

The exacerbation of wildfires caused by global warming poses a significant threat to human health and environmental integrity. This review examines the particulate matter (PM) and gaseous pollutants resulting from fire incidents and their impacts on individual health, with a specific focus on the occupational hazards faced by firefighters. Of particular concern is the release of carbon-containing gases and fine particulate matter (PM_2.5) from forest fires and urban conflagrations, which exceed the recommended limits and pose severe health risks. Firefighters exposed to these pollutants demonstrate an elevated risk of developing pulmonary and cardiovascular diseases and cancer compared to the general population, indicating an urgent need for enhanced protective measures and health management strategies for firefighters. Through a meticulous analysis of the current research findings, this review delineates future research directions, focusing on the composition and properties of these pollutants, the impacts of fire-emitted pollutants on human health, and the development of novel protective technologies. Full article

High temperatures and droughts are two natural disasters that cause forest fires. During climate change, the frequent occurrence of high temperatures, droughts, and their coupled effects significantly increase the forest fire risk. To reveal the seasonal and spatial differences in the coupled effects of high temperatures and droughts on forest fires, this study used the Copula method and proposed the compound extremely high-temperature and drought event index (CTDI). The results indicated that the study area was subject to frequent forest fires in spring (71.56%), and the burned areas were mainly located in forests (40.83%) and the transition zone between farmland and forests (36.91%). The probability of forest fires in summer increased with high temperatures and drought intensity, with high temperatures playing a dominant role. The highest forest fire hazard occurred in summer (>0.98). The probability of a forest fire occurring under extreme meteorological conditions in summer and fall was more than twice as high as that in the same zone under non-extreme conditions. Droughts play a significant role in the occurrence and spread of forest fires during fall. These results can provide decision-making support for forest fire warnings and fire fighting in the Northeast China forest zone. Full article

Forest fires pose a significant threat to forest resources and wildlife. To balance accuracy and parameter efficiency in forest fire detection, this study proposes an improved model, Mcan-YOLO, based on YOLOv7. In the Neck section, the asymptotic feature pyramid network (AFPN) was employed to effectively capture multi-scale information, replacing the traditional module. Additionally, the content-aware reassembly of features (CARAFE) replaced the conventional upsampling method, further reducing the number of parameters. The normalization-based attention module (NAM) was integrated after the ELAN-T module to enhance the recognition of various fire smoke features, and the Mish activation function was used to optimize model convergence. A real fire smoke dataset was constructed using the mean structural similarity (MSSIM) algorithm for model training and validation. The experimental results showed that, compared to YOLOv7-tiny, Mcan-YOLO improved precision by 4.6%, recall by 6.5%, and mAP50 by 4.7%, while reducing the number of parameters by 5%. Compared with other mainstream algorithms, Mcan-YOLO achieved better precision with fewer parameters. Full article

This article describes a quadcopter drone with foldable arms, which we have named the Peregrine Falcon. It can take off from a moving vehicle by barrel launching and perform its mission in the air after autonomously spreading its arms. The design helps the drone to be put into operation quickly, which is a great advantage for applications in industries such as forest fire reconnaissance. This paper introduces the design process of the Peregrine Falcon from the aspects of structure, avionics and control algorithm, and proposes control compensation methods for the large attitude change generated during the barrel launching process and the vertical drift problem after impact, respectively. The stability and feasibility of the Peregrine Falcon in the static and dynamic launching process are verified by flying experiments. Full article

The world’s forests are being increasingly disturbed from exposure to the compounding impacts of land use and climate change, in addition to natural disturbance regimes. Boreal forests have a lower level of deforestation compared to tropical forests, and while they have higher levels of natural disturbances, the accumulated impact of forest management for commodity production coupled with worsening fire weather conditions and other climate-related stressors is resulting in ecosystem degradation and loss of biodiversity. We used satellite-based time-series analysis of two canopy indices—canopy photosynthesis and canopy water stress—to calculate an index that maps the relative stability of forest canopies in the Canadian provinces of Ontario and Quebec. By drawing upon available spatial time-series data on logging, wildfire, and insect infestation impacts, we were able to attribute the causal determinants of areas identified as having unstable forest canopy. The slope of the two indices that comprise the stability index also provided information as to where the forest is recovering from prior disturbances. The stability analyses and associated spatial datasets are available in an interactive web-based mapping app. that can be used to map disturbed forest canopies and the attribution of disturbances to human or natural causes. This information can assist decision-makers in identifying areas that are potentially ecologically degraded and in need of restoration and those stable areas that are a priority for protection. Full article

The number and area of forest fires in northern Europe have been dramatically reduced during the past century, and several fire-favoured species are now threatened. To promote the recovery of these species, prescribed burning is often used as a conservation measure, and to optimise the use of these conservation burns, knowledge is needed on suitable fire frequency, size and placement in the landscape. The aim of this study was to analyse the effect of recent fire history (12 yrs) on beetles sampled using smoke attraction traps at 21 forest sites in a 10,000 km² region. We analysed the odds of finding a fire-favoured beetle species or individual among the beetles in each trap using a new spatiotemporal connectivity measure and compared the results to non-fire-favoured and saproxylic species. For fire-favoured beetles, both the number of species and individuals significantly increased with connectivity to previous fires, while the other two groups did not. The spatiotemporal connectivity that best explained the patterns suggests that fire-favoured beetles mainly respond to fires within a 2 km range up to 2–3 years after the fire. Hence, to preserve fire-favoured insects, prescribed fires must be close in space and time to other fires—whether prescribed or natural. Full article

Forest fires are increasingly destructive, contributing to significant ecological damage, carbon emissions, and economic losses. Monitoring these fires promptly and accurately, particularly by delineating fire perimeters, is critical for mitigating their impact. Satellite-based remote sensing, especially using active fire products from VIIRS and MODIS, has proven indispensable for real-time forest fire monitoring. Despite advancements, challenges remain in accurately clustering and delineating fire perimeters in a timely manner, as many existing methods rely on manual processing, resulting in delays. Active fire perimeter (AFP) and Timely Active Fire Progression (TAFP) models were developed which aim to be an automated approach for clustering active fire data points and delineating perimeters. The results demonstrated that the combined dataset achieved the highest matching rate of 85.13% for fire perimeters across all size classes, with a 95.95% clustering accuracy for fires ≥100 ha. However, the accuracy decreased for smaller fires. Overall, 1500 m radii with alpha values of 0.1 were found to be the most effective for fire perimeter delineation, particularly when applied at larger radii. The proposed models can play a critical role in improving operational responses by fire management agencies, helping to mitigate the destructive impact of forest fires more effectively. Full article

Wildfires pose a growing threat to Mediterranean ecosystems. This study employs advanced classification techniques for shrub fractional cover mapping from satellite imagery in a fire-prone landscape in Quinta da França (QF), Portugal. The study area is characterized by fine-grained heterogeneous land cover and a Mediterranean climate. In this type of landscape, shrub encroachment after land abandonment and wildfires constitutes a threat to ecosystem resilience—in particular, by increasing the susceptibility to more frequent and large fires. High-resolution mapping of shrub cover is, therefore, an important contribution to landscape management for fire prevention. Here, a 20 cm resolution land cover map was used to label 10 m Sentinel-2 pixels according to their shrub cover percentage (three categories: 0%, >0%–50%, and >50%) for training and testing. Three distinct algorithms, namely Support Vector Machine (SVM), Artificial Neural Networks (ANNs), and Random Forest (RF), were tested for this purpose. RF excelled, achieving the highest precision (82%–88%), recall (77%–92%), and F1 score (83%–88%) across all categories (test and validation sets) compared to SVM and ANN, demonstrating its superior ability to accurately predict shrub fractional cover. Analysis of confusion matrices revealed RF’s superior ability to accurately predict shrub fractional cover (higher true positives) with fewer misclassifications (lower false positives and false negatives). McNemar’s test indicated statistically significant differences (p value < 0.05) between all models, consolidating RF’s dominance. The development of shrub fractional cover maps and derived map products is anticipated to leverage key information to support landscape management, such as for the assessment of fire hazard and the more effective planning of preventive actions. Full article

Coal-fired power plants account for a large share of the power generation market in China. The mainstream method of desulfurization employed in the coal-fired power generation sector now is wet flue gas desulfurization. This process is known to have a high cost and be energy-/materially intensive. Due to the complicated desulfurization mechanism, it is challenging to improve the overall sustainability profile involving energy-, cost-, and resource-relevant objectives via traditional mechanistic models. As such, the present study formulated a data-driven many-objective model for the sustainability of the desulfurization process. We preprocessed the actual operation data collected from the desulfurization tower in a domestic ultra-supercritical coal-fired power plant with a 600 MW unit. The extreme random forest algorithm was adopted to approximate the objective functions as prediction models for four objectives, namely, desulfurization efficiency, unit power consumption, limestone supply, and unit operation cost. Three metrics were utilized to evaluate the performance of prediction. Then, we incorporated differential evolution and non-dominated sorting genetic algorithm-III to optimize the multiple parameters and obtain the Pareto front. The results indicated that the correlation coefficient (R2) values of the prediction models were greater than 0.97. Compared with the original operation condition, the operation under optimized parameters could improve the desulfurization efficiency by 0.25% on average and reduce energy, cost, and slurry consumption significantly. This study would help develop operation strategies to improve the sustainability of coal-fired power plants. Full article

This study is focused on the modelling of the production of bio-oil by thermochemical liquefaction. Species Acacia melanoxylon was used as the source of biomass, the standard chemical 2-Ethylhexanol (2-EHEX) was used as solvent, p-Toluenesulfonic acid (pTSA) was used as the catalyst, and acetone was used for the washing process. This procedure consisted of a moderate acid-catalysed liquefaction process and was applied at 3 different temperatures to determine the proper model: 100, 135, and 170 °C, and at 30-, 115-, and 200-min periods with 0.5%, 5.25%, and 10% (m/m) catalyst concentrations of overall mass. Optimized results showed a bio-oil yield of 83.29% and an HHV of 34.31 MJ/kg. A central composite face-centred (CCF) design was applied to the liquefaction reaction optimization. Reaction time, reaction temperature, as well as catalyst concentration, were chosen as independent variables. The resulting model exhibited very good results, with a highly adjusted R-squared (1.000). The liquefied products and biochar samples were characterized by Fourier-transformed infrared (FTIR) and thermogravimetric analysis (TGA); scanning electron microscopy (SEM) was also performed. The results show that invasive species such as acacia may have very good potential to generate biofuels and utilize lignocellulosic biomass in different ways. Additionally, using acacia as feedstock for bio-oil liquefaction will allow the valorisation of woody biomass and prevent forest fires as well. Besides, this process may provide a chance to control the invasive species in the forests, reduce the effect of forest fires, and produce bio-oil as a renewable energy. Full article

This study presents a novel approach to emergency vehicle classification that leverages a comprehensive set of informative audio features to distinguish between ambulance sirens, fire truck sirens, and traffic noise. A unique contribution lies in combining time domain features, including root mean square (RMS) and zero-crossing rate, to capture the temporal characteristics, like signal energy changes, with frequency domain features derived from short-time Fourier transform (STFT). These include spectral centroid, spectral bandwidth, and spectral roll-off, providing insights into the sound’s frequency content for differentiating siren patterns from traffic noise. Additionally, Mel-frequency cepstral coefficients (MFCCs) are incorporated to capture the human-like auditory perception of the spectral information. This combination captures both temporal and spectral characteristics of the audio signals, enhancing the model’s ability to discriminate between emergency vehicles and traffic noise compared to using features from a single domain. A significant contribution of this study is the integration of data augmentation techniques that replicate real-world conditions, including the Doppler effect and noise environment considerations. This study further investigates the effectiveness of different machine learning algorithms applied to the extracted features, performing a comparative analysis to determine the most effective classifier for this task. This analysis reveals that the support vector machine (SVM) achieves the highest accuracy of 99.5%, followed by random forest (RF) and k-nearest neighbors (KNNs) at 98.5%, while AdaBoost lags at 96.0% and long short-term memory (LSTM) has an accuracy of 93%. We also demonstrate the effectiveness of a stacked ensemble classifier, and utilizing these base learners achieves an accuracy of 99.5%. Furthermore, this study conducted leave-one-out cross-validation (LOOCV) to validate the results, with SVM and RF achieving accuracies of 98.5%, followed by KNN and AdaBoost, which are 97.0% and 90.5%. These findings indicate the superior performance of advanced ML techniques in emergency vehicle classification. Full article

Recurrent wildfires can negatively affect soil quality, and post-fire soil quality recovery is critical for maintaining sustainable ecosystem development. The objective of this study was to evaluate the changes and recovery of soil properties and soil quality in the forests of dry/warm river valleys in southwest China after disturbance by high-severity fires. In this study, the impact of fire on soil properties and soil quality was investigated for three years post-fire. Unburned forest land with a similar natural environment compared to the fire area was used as a control. Soil samples were collected from three different depths of 0–10 cm, 10–20 cm, and 20–30 cm, respectively. Principal component analysis (PCA) combined with the Norm value was used to select the minimum data set (MDS), thus calculating the soil quality index (SQI). The results showed that the soil properties changed significantly after high-severity fires. On average, soil bulk density (0.91 g/cm³, p = 0.001), total nitrogen (0.12 g/kg, p = 0.000), total phosphorus (0.10 g/kg, p = 0.000), and total potassium (5.55 g/kg, p = 0.000) were significantly lower in the burned areas than in the unburned areas at the first sampling. These indicators increased in the following three years but still did not recover to unburned levels. Compared with the above indicators, soil porosity and organic matter increased post-fire, but gradually decreased over time. Soil clay, geometric mean diameter, and total potassium were included in the MDS. The SQI was ranked as unburned > 3 years > 2 years > 1 year > 6 months. The SQI was significantly (p = 0.001) reduced six months post-fire by an average of 36%, and, after three years of recovery, the soil quality of the post-fire areas could be restored to 81% of soil in unburned areas. Apparently, high-severity fires caused changes in soil properties, thereby significantly decreasing soil quality. Soil quality gradually improved with increasing restoration time. However, the complete recovery of soil quality post-fire in forest land in the dry/warm river valley will take a longer time. Full article

Forest fires are disturbance events that can impact biological assemblages at multiple scales. In this study, the structures of breeding bird communities in cork oak patches located in an agro-mosaic suburban landscape of central Italy (Rome) were compared at the local scale with a fine-grained mapping method before (2018) and after (2023) a fire event occurred in July 2022. The analyses did not reveal any significant changes in the density of territorial pairs or in the diversity metrics, both univariate (Shannon–Wiener index, evenness, Margalef normalized richness) and bivariate (Whittaker and k-dominance plots, abundance/biomass curves) of diversity. Even when the guilds of strictly forest-related species were compared, no differences emerged before and after the fire. This counterintuitive phenomenon may be due to the characteristics of the dominant tree, the cork oak (Quercus suber), a sclerophilous tree that is very resilient to fires and able to recover foliage in the following spring season, thus allowing rapid bird recolonization. However, other small-scale phenomena (e.g., the ‘crowding effect’ and local dispersal of territorial pairs from remnant wood patches not affected by fire) may explain this lack of change in breeding bird density and diversity. Further studies should be carried out at larger spatial and temporal scales and at different levels of fire frequency and intensity to confirm these responses at the guild/community level in suburban cork oak wood patches. Full article

Forest fire is one of the dominant disturbances in the forests of Heilongjiang Province, China, and is one of the most rapid response predictors that indicate the impact of climate change on forests. This study calculated the Canadian FWI (Fire Weather Index) and its components from meteorological record over past years, and a linear model was built from the monthly mean FWI and monthly fire numbers. The significance test showed that fire numbers and FWI had a very pronounced correlation, and monthly mean FWI was suitable for predicting the monthly fire numbers in this region. Then FWI and its components were calculated from the SRES (IPCC Special Report on Emission Scenarios) A2 and B2 climatic scenarios, and the linear model was rebuilt to be suitable for the climatic scenarios. The results indicated that fire numbers would increase by 2.98–129.97% and −2.86–103.30% in the A2 and B2 climatic scenarios during 2020–2090, respectively. The monthly variation tendency of the FWI components is similar in the A2 and B2 climatic scenarios. The increasing fire risk is uneven across months in these two climatic scenarios. The monthly analysis showed that the FFMC (Fine Fuel Moisture Code) would increase dramatically in summer, and the decreasing precipitation in summer would contribute greatly to this tendency. The FWI would increase rapidly from the spring fire season to the autumn fire season, and the FWI would have the most rapid increase in speed in the spring fire season. DMC (Duff Moisture Code) and DC (Drought Code) have relatively balanced rates of increasing from spring to autumn. The change in the FWI in this region is uneven in space as well. In early 21st century, the FWI of the north of Heilongjiang Province would increase more rapidly than the south, whereas the FWI of the middle and south of Heilongjiang Province would gradually catch up with the increasing speed of the north from the middle of 21st century. The changes in the FWI across seasons and space would influence the fire management policy in this region, and the increasing fire numbers and variations in the FWI scross season and space suggest that suitable development of the management of fire sources and forest fuel should be conducted. Full article

Destructive wildfires pose a serious threat to ecosystems, economic development, and human life and property safety. If wildfires can be extinguished in a relatively short period of time after they occur, the losses caused by wildfires will be greatly reduced. Although deep learning methods have been shown to have powerful feature extraction capabilities, many current models still have poor generalization performance when faced with complex tasks. To this end, in this study, we considered introducing attention modules both inside and outside the nested U-shaped structure and trained a neural network model based on the U2-Net architecture to enable the model to suppress the activation of irrelevant areas. Compared with baseline models such as U-Net, our model has made great progress on the test set, with an F1 score improvement of at least 2.8%. The experimental results indicate that the model we proposed has certain practicality and can provide a significant scientific basis for forest fire management and emergency decision-making. Full article