Forest Fire Smoke Research Articles

PDAM–STPNNet: A Small Target Detection Approach for Wildland Fire Smoke through Remote Sensing Images

The target detection of smoke through remote sensing images obtained by means of unmanned aerial vehicles (UAVs) can be effective for monitoring early forest fires. However, smoke targets in UAV images are often small and difficult to detect accurately. In this paper, we use YOLOX-L as a baseline and propose a forest smoke detection network based on the parallel spatial domain attention mechanism and a small-scale transformer feature pyramid network (PDAM–STPNNet). First, to enhance the proportion of small forest fire smoke targets in the dataset, we use component stitching data enhancement to generate small forest fire smoke target images in a scaled collage. Then, to fully extract the texture features of smoke, we propose a parallel spatial domain attention mechanism (PDAM) to consider the local and global textures of smoke with symmetry. Finally, we propose a small-scale transformer feature pyramid network (STPN), which uses the transformer encoder to replace all CSP_2 blocks in turn on top of YOLOX-L’s FPN, effectively improving the model’s ability to extract small-target smoke. We validated the effectiveness of our model with recourse to a home-made dataset, the Wildfire Observers and Smoke Recognition Homepage, and the Bowfire dataset. The experiments show that our method has a better detection capability than previous methods.

A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting

Forest fire smoke detection based on deep learning has been widely studied. Labeling the smoke image is a necessity when building datasets of target detection and semantic segmentation. The uncertainty in labeling the forest fire smoke pixels caused by the non-uniform diffusion of smoke particles will affect the recognition accuracy of the deep learning model. To overcome the labeling ambiguity, the weighted idea was proposed in this paper for the first time. First, the pixel-concentration relationship between the gray value and the concentration of forest fire smoke pixels in the image was established. Second, the loss function of the semantic segmentation method based on concentration weighting was built and improved; thus, the network could pay attention to the smoke pixels differently, an effort to better segment smoke by weighting the loss calculation of smoke pixels. Finally, based on the established forest fire smoke dataset, selection of the optimum weighted factors was made through experiments. mIoU based on the weighted method increased by 1.52% than the unweighted method. The weighted method cannot only be applied to the semantic segmentation and target detection of forest fire smoke, but also has a certain significance to other dispersive target recognition.

The manifold embedded selective pseudo-labeling algorithm and transfer learning of small sample dataset

Special scene classification and identification tasks are not easily fulfilled to obtain samples, which results in a shortage of samples. The focus of current researches lies in how to use source domain data (or auxiliary domain data) to build domain adaption transfer learning models and to improve the classification accuracy and performance of small sample machine learning in these special and difficult scenes. In this paper, a model of deep convolution and Grassmann manifold embedded selective pseudo-labeling algorithm (DC-GMESPL) is proposed to enable transfer learning classifications among multiple small sample datasets. Firstly, DC-GMESPL algorithm uses satellite remote sensing image sample data as the source domain to extract the smoke features simultaneously from both the source domain and the target domain based on the Resnet50 deep transfer network. This is done for such special scene of the target domain as the lack of local sample data for forest fire smoke video images. Secondly, DC-GMESPL algorithm makes the source domain feature distribution aligned with the target domain feature distribution. The distance between the source domain and the target domain feature distribution is minimized by removing the correlation between the source domain features and re-correlation with the target domain. And then the target domain data is pseudo-labeled by selective pseudo-labeling algorithm in Grassmann manifold space. Finally, a trainable model is constructed to complete the transfer classification between small sample datasets. The model of this paper is evaluated by transfer learning between satellite remote sensing image and video image datasets. Experiments show that DC-GMESPL transfer accuracy is higher than DC-CMEDA, Easy TL, CMMS and SPL respectively. Compared with our former DC-CMEDA, the transfer accuracy of our new DC-GMESPL algorithm has been further improved. The transfer accuracy of DC-GMESPL from satellite remote sensing image to video image has been improved by 0.50%, the transfer accuracy from video image to satellite remote sensing image has been improved by 8.50% and then, the performance has been greatly improved.

A New Predictive Framework for Amazon Forest Fire Smoke Dispersion over South America

AbstractAerosol particles from forest fire events in the Amazon can be effectively transported to urban areas in southeastern South America, thus affecting the air quality over those regions. A combination of observational data and a comprehensive air quality modeling system capable of anticipating acute air pollution episodes is therefore required. A new predictive framework for Amazon forest fire smoke dispersion over South America has been developed based on the Weather Research and Forecasting with Chemistry community (WRF-Chem) model. Two experiments of 48-h simulations over South America were performed by using this system at 20-km horizontal resolution, on a daily basis, during August and September of 2018 and 2019. The experiment in 2019 included the very strong 3-week forest fire event, when the São Paulo metropolitan area, located in southeastern South America, was plunged into darkness on August 19. The model results were satisfactorily compared against satellite-based data products and in situ measurements collected from air quality monitoring sites. The system is executed daily immediately after the CPTEC Satellite Division releases the latest active fire locations data and provides 48-h forecasts of regional distributions of chemical species such as CO, PM2.5, and O3. The new modeling system will be used as a benchmark within the framework of the Chemistry of the Atmosphere–Field Experiment in Brazil (CAFE-Brazil) project, which will take place in 2022 over the Amazon.

Tempo-spatial patterns of PM2.5 measured using a portable particulate monitor around a mine complex in Canada's Arctic.

Mining activities in Canada's pristine Arctic (e.g., driving on unpacked roads, blasts, rock grinding, diesel combustion, and garbage incineration) could add local sources of airborne fine particulate matter with a diameter of < 2.5 μm (PM2.5) to their surrounding area. The increase in PM2.5 above the background level around a mine represents a potential disturbance to caribou. To quantify the spatial distribution of the elevated PM2.5, we investigated three different sampling schemes to measure PM2.5 concentration using a portable monitor. We found that the best sampling scheme was to use the regional background PM2.5 as the reference and analyze the anomaly of PM2.5 measured at sites around the mine complex from the background level. The regional background PM2.5 values were measured at the Daring Lake Tundra Research Station during 2018 and 2019. Our results indicated that the background PM2.5 was not a low and constant value but varied with rain events, wind direction, and the impacts of forest fire smoke. After excluding periods affected by forest fires smokes, we found the background PM2.5 was close to 0 μg m-3 for the first few hours after rain, and then increased logistically with the time after rain (tar) to the maximum of 5 (or 10) μg m-3 when the wind came from the north (or south) of the NW-SE axis. The NW-SE axis in western Canada divides the tundra north with few anthropogenic PM2.5 sources from the forested south with many PM2.5 sources from forest fire smokes and human activities. Analyses of PM2.5 anomaly from the background (i.e., PM2.5 measured at a site around the mining complex-the background level at the corresponding tar and wind direction) revealed that the zone of elevated PM2.5 around the mine (Zepm) expanded with tar. In the first few hours after rain, PM2.5 was close to 0 everywhere except within meters of a source (e.g., a truck exhaust) in the downwind direction. During tar = 6 to 96 h, Zepm expanded to 6.3 km in the downwind direction when the wind came from south of the NW-SE axis. A similar result was found in the downwind direction when the wind came from north of the NW-SE axis, with Zepm = 4.4 km. In the upwind direction, the value of Zepm was much smaller, being 0.7 km (or 1.0 km) when the wind came from the north (or south) of the NW-SE axis. For the period of tar between 96 and 192 hours, Zepm further expanded to 21.2 km when the wind from the south of the NW-SE axis. The results from this study indicated that this reference paradigm that uses the regional background PM2.5 as the reference in combination with a portable PM2.5 monitor worked well for quantifying the tempo-spatial patterns of PM2.5 at locations in remote and mostly pristine Arctic. However, their effectiveness for other regions needs further investigation.

ASSESSING THE SIMILARITIES OF 3D SIMULATION MODEL OUTCOMES

Abstract. The recent advancement of simulation modeling to represent phenomena in three spatial dimensions (3D) requires the development of techniques that will allow comparison of the modeling outputs in multiple dimensions. However, many existing techniques for map comparison in two spatial dimensions (2D) have been developed from non-spatial method such Cohen’s Kappa. These techniques are not yet fully extended to deal with 3D map data or simulation outcomes. Therefore, the main objective of this study is to investigate the use of the 3D Accuracy and 3D Cohen’s Kappa coefficients to compare simulation model outputs in 3D. An existing agent-based model (ABM) of forest-fire smoke propagation was used to generate multiple scenarios for the purpose of comparing 3D simulation outputs. The results for 3D Accuracy and 3D Cohen’s Kappa produces meaningful values when comparing several scenarios with different 3D ABM outputs. This study emphasizes the need for the development of more advanced simulation output comparison techniques that operate in 3D and potentially over time (4D).

A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.

Spatial distribution of particulate matter 2.5 released from surface fuel combustion of Pinus koraiensis – A laboratory simulation study

High concentration particulate matter 2.5 released from forest fires, in addition to direct burns and asphyxia, PM2.5 is one of the main pollutants which threaten the safety of forest fire fighter. Therefore, to assess spatial distribution of PM2.5, a simulation study was conducted. Fuel beds with different moisture contents and loads were constructed. 144 times burning experiments were carried out under different wind speeds by using wind tunnel device. PM2.5 particles at different spatial points were collected and calculated. The results show that, in the two of three variables interaction between wind speed, fuel load, and, except fuel moisture content, wind speed and fuel load are positively correlated with the PM2.5 concentrations. From PM2.5 concentration which collected at each point in the horizontal and vertical directions, the overall trend is that PM2.5 concentration increases along the horizontal downwind direction (C and D higer than A and B) and the vertical upward direction (A and C higer than B and D) Based on BP neural network, the spatial distribution model of PM2.5 concentration with single hidden layer was established. The prediction accuracy of modeling samples and validation samples is balanced when hidden layer node is 5. This study will help to make reference for PM2.5 occupational exposure standards, forest fire smoke management and forest fire management in China.

Design of Work System for Reducing Pollution and Forest Fire Smoke

Air quality has an impact on human life. The incidence of forest and land fires has caused many casualties. On the other hand, poor air quality as a result of forest and land fires also threatens human life directly. Therefore a Pollution and Smoke Reduction Tool was designed as a solution to the problems of pollution and smoke due to forest fires. The purpose of writing this article is to describe the design and manufacture of Pollution and Smoke Reducers as well as its working principles. The writing method used is descriptive qualitative, with data collection techniques in the form of literature studies to strengthen ideas. Pollution and Smoke Reducers are tools that can convert particulate CO (PM) PM10 and PM2.5 into CO_2 and burn particulates PM10 and PM2.5 until they disappear. Pollution and Smoke Reducers are the development of research on catalytic converters and diesel particulate filters by utilizing a fan/blower as a smoke suction agent and a heater to heat the smoke until it burns completely. Pollution and Smoke Reducers are also equipped with wire mesh and fiber to trap particulates and hold them until they burn entirely. Pollution and Smoke Reducers are designed with several materials and tools specifically designed to reduce the direct impact of forest and land fires by burning PM 10 and PM2.5 and lowering CO emissions. The smoke reduction capability based on the designed design has smoke and particulate reduction capacity of ±43,4769880184/ft3. The capacity of the smoke and particulate suction rate is 21500ft3/minutes with the ability of the significant smoke suction rate so that smoke and particulates due to forest fires can enter the equipment and do not fly freely under the wind. Hence, the air that is inhaled by the community has better potential and safe for health.

Computer Vision-Based Wildfire Smoke Detection Using UAVs

This paper presents a new methodology based on texture and color for the detection and monitoring of different sources of forest fire smoke using unmanned aerial vehicles (UAVs). A novel dataset has been gathered comprised of thin smoke and dense smoke generated from the dry leaves on the floor of the forest, which is a source of igniting forest fires. A classification task has been done by training a feature extractor to check the feasibility of the proposed dataset. A meta-architecture is trained above the feature extractor to check the dataset viability for smoke detection and tracking. Results have been obtained by implementing the proposed methodology on forest fire smoke images, smoke videos taken on a stand by the camera, and real-time UAV footages. A microaverage F1-score of 0.865 has been achieved with different test videos. An F1-score of 0.870 has been achieved on real UAV footage of wildfire smoke. The structural similarity index has been used to show some of the difficulties encountered in smoke detection, along with examples.

ANALISIS PENGARUH SUHU UDARA, CURAH HUJAN, KELEMBABAN UDARA DAN KECEPATAN ANGIN TERHADAP ARAH PENYEBARAN DAN AKUMULASI PARTICULATE MATTER (PM10): STUDI KASUS KOTA PEKANBARU

Forest fires in Riau Province are the biggest contributor to air pollution that spreads to the urben area of Pekanbaru. PM 10 is one of the most dangerous elements contained in forest fire smoke. This study aims to map areas that have accumulated the spread of PM 10 in the city of Pekanbaru. This research has been carried out using a data interpretation methodology using data on the value of PM 10 , rainfall, air temperature, humidity, wind direction and wind speed in the form of monthly data from 2015 – 2019. Data processing is carried out using Microsoft office excel 2017, SPSS, surfer 17, google earth pro. The result of the analysis there is a significant effect of wearther parameters on PM 10 . Wind speed has a strong influence on the spread of PM 10 . Mapping of wind distribution patterns and distribution patters of PM 10 in sub-district Limapuluh every year and the wind direction accoriding to the wrplot flower chart always goes to sub-direction Limapuluh.

Characterization of forest fire and Saharan desert dust aerosols over south-western Europe using a multi-wavelength Raman lidar and Sun-photometer

In the morning of 21 July 2019, a dense forest fire smoke plume was detected over Évora (Portugal) in the lower troposphere. Around 13:00 UTC, a Sahara dust layer was detected by lidar between 3000 and 4000 m height above sea level. In this work results of these events using an EARLINET Raman lidar and AERONET Sun-photometer measurements are reported and discussed: the dense smoke layer, the mixing of smoke with dust and the lofted dust layer that crossed Évora. During the morning, in-situ hourly mean black carbon concentration reached a maximum of 6.28 ± 0.73 μgm−3. The AERONET fine mode aerosol optical depth at 500 nm reached 0.77 and the Ångström exponent at 440–870 nm varied between 1.45 and 1.82, indicating the presence of fine smoke particles in the atmospheric column. In the beginning of the afternoon, the lidar profiles indicated the presence of a smoke layer followed by two thin layers of pollution mixed with dust and a lofted dust layer. The smoke particles caused low particle linear depolarization ratios at 532 nm with mean value of 0.059 ± 0.002 in the smoke layer. In contrast, the lofted dust layer caused mean particle linear depolarization ratio at 532 nm of 0.26 ± 0.01. Mass concentrations were in the order of 22–29 μg m−3 in the smoke layer and 60–178 μg m−3 in the lofted dust layer. During the afternoon and at night, mixing dust layers were identified by using lidar data. In the afternoon, two aerosol layers exhibited mean particle linear depolarization ratios at 532 nm of 0.23 ± 0.01 and 0.25 ± 0.01 with the dust dominating the extinction and mass concentration in both aerosol layers. Mass concentrations of dust were in the order of 101–266 μg m−3 and 159–195 μg m−3. At night, the particle linear depolarization ratio presented low variability across the identified layer with mean value of 0.20 ± 0.01. The Ångström exponents were low, the mean lidar ratios were 50 ± 6 sr (355 nm) and 42 ± 4 sr (532 nm) and the aerosol optical depth was 0.32 (355 nm) and 0.30 (532 nm). Mass concentrations ranged in 10–28 μg m−3 for non-dust and in 77–186 μg m−3 for dust.

Lidar detection and characterization of multi-type aerosol layers in the troposphere above Sofia, Bulgaria

Aerosols can directly and strongly affect the atmospheric energy budget, the climatic processes, the ecological state of the environment, and the biosphere and human health. This is why the detection and identification of aerosols in terms of type and origin and their optical and microphysical characterization are of great scientific and practical importance. Lidars are a widely recognized and proven tool for detecting and studying atmospheric aerosols. In this paper, we present results of lidar detection and characterization of aerosol layers present in the troposphere over the city of Sofia. The measurements were performed at the wavelengths of 1064 nm and 532 nm by using the Nd:YAG-laser based lidar of Sofia lidar station, part of the European Aerosol Research Lidar Network (EARLINET) and the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS). Time-averaged vertical profiles of the aerosol backscatter coefficient and the backscatter-related Ångström exponent were retrieved and analyzed. Color-coded height-time diagrams of the vertical mass distribution in the observed layers and its temporal dynamics were also obtained. Based on information from forecast models, it is concluded that the aerosol layers registered contained marine aerosols, forest fire smoke, indirectly and directly transported dust from Sahara, as well as continental, regional and local aerosols from different sources.

Forest Fire Smoke Recognition Based on Anchor Box Adaptive Generation Method

There are major problems in the field of image-based forest fire smoke detection, including the low recognition rate caused by the changeable and complex state of smoke in the forest environment and the high false alarm rate caused by various interferential objects in the recognition process. Here, a forest fire smoke identification method based on the integration of environmental information is proposed. The model uses (1) the Faster R-CNN as the basic framework, (2) a component perception module to generate a receptive field of integrated environmental information through separable convolution to improve recognition accuracy, and (3) a multi-level Region of Interest (ROI)pooling structure to reduce the deviation caused by rounding in the ROI pooling process. The results showed that the model achieved a recognition accuracy rate of 96.72%, an Intersection Over Union (IOU) of 78.96%, and an average recognition speed for each picture of 1.5 ms; the false alarm rate was 2.35% and the false-negative rate was 3.28%. Compared with other models, the proposed model can effectively enhance the recognition accuracy and recognition speed of forest fire smoke, which provides a technical basis for the real-time and accurate detection of forest fires.

Atmospheric evolution of emissions from a boreal forest fire: the formation of highly functionalized oxygen-, nitrogen-, and sulfur-containing organic compounds

Abstract. Forest fires are major contributors of reactive gas- and particle-phase organic compounds to the atmosphere. We used offline high-resolution tandem mass spectrometry to perform a molecular-level speciation of gas- and particle-phase compounds sampled via aircraft from an evolving boreal forest fire smoke plume in Saskatchewan, Canada. We observed diverse multifunctional compounds containing oxygen, nitrogen, and sulfur (CHONS), whose structures, formation, and impacts are understudied. The dilution-corrected absolute ion abundance of particle-phase CHONS compounds increased with plume age by a factor of 6.4 over the first 4 h of downwind transport, and their relative contribution to the observed functionalized organic aerosol (OA) mixture increased from 19 % to 40 %. The dilution-corrected absolute ion abundance of particle-phase compounds with sulfide functional groups increased by a factor of 13 with plume age, and their relative contribution to observed OA increased from 4 % to 40 %. Sulfides were present in up to 75 % of CHONS compounds and the increases in sulfides were accompanied by increases in ring-bound nitrogen; both increased together with CHONS prevalence. A complex mixture of intermediate- and semi-volatile gas-phase organic sulfur species was observed in emissions from the fire and depleted downwind, representing potential precursors to particle-phase CHONS compounds. These results demonstrate CHONS formation from nitrogen- and oxygen-containing biomass burning emissions in the presence of reduced sulfur species. In addition, they highlight chemical pathways that may also be relevant in situations with elevated emissions of nitrogen- and sulfur-containing organic compounds from residential biomass burning and fossil fuel use (e.g., coal), respectively.

Patchwise dictionary learning for video forest fire smoke detection in wavelet domain

Fire smoke detection in forest faces much more challenges than in local areas or indoors. Conventional fire smoke methods using static features extracted form single image directly cannot handle some disturbed factors exist in forest, such as shaking trees and fog/haze. On the other hand, it is difficult to capture the dynamic features of fire smoke while they are vital for fire smoke detection. This paper proposes an effective method of extracting features based on sequential images for smoke detection, quasi-dynamic features are extracted using on pixel-block arranged image sets. First, consecutive frames are rearranged into a new big image based on a pixel-block rule, so that the new image still keep the whole structure of original image on block level, and the change of different pixels in the same block, which comes from the same position of each frame, can reflect the dynamic features by special process. Second, to process the new arranged big image, driven by single image wavelet transform for denoting local signal characteristics, Dual Tree-Complex Wavelet Transform is utilized to decompose the new big image into eight images with the same size of the source image after several level-decomposition, including four directions (HH, HL, LH, LL) with two decomposed images in each direction, so that the changes from smoke in different orientation can be extracted and depicted in these subbands. Third, a multi-scale dictionary learning method is proposed to learn dictionaries of each group images. Furthermore, elastic net is introduced for sparse coding and feature coefficients extraction. All operations above contribute to a significant improvement for smoke detection in this paper. Extensive experiments are performed to validate the effectiveness of the proposed approach.

Forest fire smoke detection under complex backgrounds using TRPCA and TSVB

It is difficult to detect forest fires in complex backgrounds owing to the many interfering factors in forest fire smoke. In this paper, a novel method that combines Time Domain Robust Principal Component Analysis (TRPCA) and a Two-Stream Composed of Visual Geometry Group Network (VGG) and Bi-Long Short-Term Memory (BLSTM) (TSVB) model is proposed for forest fire smoke detection. First, features are extracted from the smoke video from the spatial stream (static) and time stream (dynamic). For the spatial stream, static features are extracted from a single-frame image of the smoke video using the VGG network. For the time stream, continuous-frame binary images of the smoke are obtained using the TRPCA algorithm. Then, the dynamic features of the smoke are extracted by VGG and BLSTM. Finally, the static and dynamic features are fused using a concatenate function to achieve forest fire smoke detection. The experimental results show that compared with the single-feature model, the proposed method effectively improves learning ability and prediction ability, and shows strong robustness against interference factors in a complex background, with accuracy of forest fire smoke detection reaching 90.6%.

Experimental study of the gonadotoxic effect of forest fire smoke

Introduction. The study of the effects of air smoke during natural fires on the reproductive system is the actual scientific problem at present. Material and methods. The experimental study took place in 2 stages. At the first stage of the study, white male rats were exposed to the smoke-bearing effects of wildfire for 7 days (4 hours a day), and at the second stage, 5 days a week (4 hours a day) for one month. Immediately after the end of the exposure, the animals were decapitated under light ether anesthesia to study the morphofunctional state of the reproductive system, which included counting the total number of spermatogonia, the number of tubules with desquamated epithelium, as well as determining the spermatogenesis index and the number of Leydig cells. Results. The functional state of the reproductive system of male white rats after exposure to smoke for 1 week did not have statistically significant differences from the animals of the control group. At the same time, an increment in the duration of exposure (4 weeks) led to significant morphological and functional disorders of the reproductive system of male white rats, characterized by a decrease in the spermatogenesis index with a simultaneous decrease in the percentage of spermatogonia and the number of interstitial Leydig cells. Discussion. Male reproductive health can be a sensitive marker of environmental factors, and there is no evidence of smoke from natural fires in modern literature. This study justifies the need to develop new approaches to the diagnosis and prevention of adverse effects on the body for a long stay in a smoke area. Conclusion. The results of the study indicate the prolonged exposure to combustion products released during a forest fire to lead to inhibition of reproductive function in male white rats.

Early forest fire smoke detection based on aerial video

The early detection of forest fire smoke has more research value than the detection of forest fire flames. This article mainly introduces the content of early forest fire smoke detection based on background blur model and aerial video. First, the background image of the video is extracted by establishing a background model. Then, through wavelet transform, the energy value of the background composite image is compared with the energy value of the target composite image when smoke appears, and the presence of smoke is detected by threshold processing. Experimental results show that the average recognition rate of smoke is as high as 96.94%.

Smoke-related health impacts of an unprecedented 2019/2020 forest fire season in Victoria, Australia

Background Victoria is a state in south-eastern Australia with a population of 6.6 million people. The 21st of November 2019 was the start of an unprecedented forest fire season during which 1.5 million hectares were burnt in Victoria. The fires were preceded by record fire danger weather in Australia and smoke affected large populations for a longer duration and with higher concentrations than previous fire seasons. As this was a recent event, detailed analysis of the wide-ranging health impacts are ongoing. Methods EPA Victoria commenced monitoring fine particles on 25th November 2019. A total of 32 air monitors measuring PM2.5 were deployed across the state. Preliminary data on emergency department presentations from the Victorian Emergency Minimum Dataset were provided by the Victorian Department of Health and Human Services. Results Air pollution from the forest fires was worst in the eastern half of the State. The 24-hour air quality standard for PM2.5 (25 µg/m3) was exceeded for 50 days in the East Gippsland region. The maximum 24 hour average PM2.5 was 3,253 µg/m3 and was recorded in the town of Bright in the north east. The 1hr peak at Bright was 6,956 µg/m3. Initial descriptive analyses of Victorian emergency department data clearly demonstrate an increase in respiratory-related emergency department presentations during this period. For example, in January 2020 Victorian emergency departments reported over 1,500 additional respiratory-related presentations compared to the same period the previous year. More detailed analyses investigating the association between forest fire smoke exposure and health effects using hospital admissions, Ambulance Victoria call-outs and calls to Nurse-on-Call are in progress. Conclusions Preliminary analyses have revealed major increases in respiratory-related emergency department presentations during an unprecedented 2019/2020 forest fire season in Victoria, Australia. Results of further analysis will be presented at the conference.