Forest fire susceptibility assessment under small sample scenario: A semi-supervised learning approach using transductive support vector machine

Forest fires threaten global ecosystems, socio-economic structures, and public safety. Accurately assessing forest fire susceptibility is critical for effective environmental management. Supervised learning methods dominate this assessment, relying on a substantial dataset of forest fire occurrences for model training. However, obtaining precise forest fire location data remains challenging. To address this issue, semi-supervised learning emerges as a viable solution, leveraging both a limited set of collected samples and unlabeled data containing environmental factors for training. Our study employed the transductive support vector machine (TSVM), a key semi-supervised learning method, to assess forest fire susceptibility in scenarios with limited samples. We conducted a comparative analysis, evaluating its performance against widely used supervised learning methods. The assessment area for forest fire susceptibility lies in Dayu County, Jiangxi Province, China, renowned for its vast forest cover and frequent fire incidents. We analyzed and generated maps depicting forest fire susceptibility, evaluating prediction accuracies for both supervised and semi-supervised learning methods across various small sample scenarios (e.g., 4, 8, 12, 16, 20, 24, 28, and 32 samples). Our findings indicate that TSVM exhibits superior prediction accuracy compared to supervised learning with limited samples, yielding more plausible forest fire susceptibility maps. For instance, at sample sizes of 4, 16, and 28, TSVM achieves prediction accuracies of approximately 0.8037, 0.9257, and 0.9583, respectively. In contrast, random forests, the top performers in supervised learning, demonstrate accuracies of approximately 0.7424, 0.8916, and 0.9431, respectively, for the same small sample sizes. Additionally, we discussed three key aspects: TSVM parameter configuration, the impact of unlabeled sample size, and performance within typical sample sizes. Our findings support semi-supervised learning as a promising approach compared to supervised learning for forest fire susceptibility assessment and mapping, particularly in scenarios with small sample sizes.

Application of quasi-empirical orthogonal functions to estimate wildfire impacts in northwestern Spain

Galicia (NW Spain) is one of the most fire-prone regions in Southern Europe. In the summer of 2022, a total of thirteen wildfires exceeding 500 ha were reported in this area, with ten of these large fires occurring in the Ourense region. To study the impacts of wildfire smoke plumes on ambient air PM2.5 concentrations, a network of 18 PurpleAir monitors was deployed across the Galicia region during July and August 2022. The PM2.5 concentration data were then used as input to test the applicability of quasi-empirical orthogonal functions (QEOFs obtained with Positive Matrix Factorization (PMF)) to characterize the spatial variability of wildfire smoke impacts on air quality. HYSPLIT back-trajectory analysis and Concentration-Weighted Trajectory (CWT) models were implemented, and the results from these tools were combined with source contributions. As a result, 19 wildfires were identified and linked with peak ambient PM2.5 concentrations (>300 μg/m3 of PM2.5; 1-h mean). Specifically, the Folgoso do Courel fire emerged as a significant contributor to these high concentrations and played an important role in influencing a significant number of the identified factors. Moreover, the results also suggested that emissions from fires in Portugal reached the study area, contributing additional impacts on air quality. These results demonstrated that this approach was useful in identifying the emission source areas contributing to observed PM2.5 concentrations during wildfire events. The PM2.5 concentration maps resulting from the CWT analysis were also valuable in understanding the short- and long-term exposures to PM2.5 from wildfire smoke.

Spatiotemporal dynamics of forest fires in the context of climate change: a review

Forest fires are sudden, destructive, hazardous, and challenging to manage and rescue, earning them a place on UNESCO's list of the world's eight major natural disasters. Currently, amid global warming, all countries worldwide have entered a period of high forest fire incidence. Due to global warming, the frequency of forest fires has accelerated, the likelihood of large fires has increased, and the spatial and temporal dynamics of forest fires have shown different trends. Therefore, the impact of climate change on the spatiotemporal dynamics of forest fires has become a hot issue in the field of forest fire research in recent years. Therefore, it is of great significance and necessity to conduct a review of the research in this area. This review delves into the interactions and impacts between climate change and the spatiotemporal dynamics of forest fires. To address this issue, scholars have mainly adopted the following research methods: first, statistical analysis methods, second, the establishment of spatiotemporal prediction models for meteorology and forest fires, and third, the coupling of climate models with forest fire risk forecasting models. The statistical analysis method relies on the analysis of historical meteorological and fire-related data to study the effects of climate change and meteorological factors on fire occurrence. Meanwhile, forest fire prediction models utilize technical tools such as remote sensing. These models synthesize historical meteorological and fire-related data, incorporating key meteorological factors such as temperature, rainfall, relative humidity, and wind. The models revealed the spatial and temporal distribution patterns of fires, identified key drivers, and explored the interactions between climate change and forest fire dynamics, culminating in the construction of predictive models. With the deepening of the study, the coupling of climate models and fire risk ranking systems became a trend in the prediction of forest fire risk trends. Moreover, as the climate warms, the increased frequency of extreme weather events like heatwaves, droughts, snow and ice storms, and El Niño-Southern Oscillation (ENSO) has accelerated forest fire occurrences and raised the risk of major fires. This review offers valuable technical insights by comprehensively analyzing the spatial and temporal characteristics of forest fires, elucidating key meteorological drivers, and exploring potential mechanisms. These insights serve as a scientific foundation for preventive measures and effective forest fire management. In the face of a changing climate, this synthesis contributes to the development of informed strategies to mitigate the escalating threat of forest fires.

Forest fire vulnerability in Nepal's chure region: Investigating the influencing factors using generalized linear model

The Chure region, among the world's youngest mountains, stands out as highly susceptible to natural calamities, particularly forest fires. The region has consistently experienced forest fire incidents, resulting in the degradation of valuable natural and anthropogenic resources. Despite its vulnerability, there have been limited studies to understand the relationship of various causative factors for the recurring fire problem. Hence, to comprehend the influencing factors for the recurring forest fire problem and its extent, we utilized generalized linear modeling under binary logistic regression to combine the dependent variable of satellite detected fire points and various independent variables. We conducted a variance inflation factor (VIF) test and correlation matrix to identify the 14 suitable variables for the study. The analysis revealed that forest fires occurred mostly during the three pre-monsoon periods and had a significant positive relation with the area under forest, rangeland, bare-grounds, and Normalized Difference Vegetation Index (NDVI) (P < 0.05). Consequently, our model showed that the probability of fire incidents decreases with elevation, precipitation, and population density (P < 0.05). Among the significant variables, the forest areas emerges as the most influencing factor, followed by precipitation, elevation, area of rangeland, population density, NDVI, and the area of bare ground. The validation of the model was done through the area under the curve (AUC = 0.92) and accuracy (ACC = 0.89) assessments, which showed the model performed excellently in terms of predictive capabilities. The modeling result and the forest fire susceptible map provide valuable insights into the forest fire vulnerability in the region, offering baseline information about forest fires that will be helpful for line agencies to prepare management strategies to further prevent the deterioration of the region.

Release of biogenic volatile organic compounds and physiological responses of two sub-tropical tree species to smoke derived from forest fire

Forests emit a large amount of biogenic volatile organic compounds (BVOCs) in response to biotic and abiotic stress. Despite frequent occurrence of large forest fires in recent years, the impact of smoke stress derived from these forest fires on the emission of BVOCs is largely unexplored. Thus, the aims of the study were to quantify the amount and composition of BVOCs released by two sub-tropical tree species, Cunninghamia lanceolata and Schima superba, in response to exposure to smoke. Physiological responses and their relationship with BVOCs were also investigated. The results showed that smoke treatments significantly (p < 0.001) promoted short-term release of BVOCs by C. lanceolata leaves than S. superba; and alkanes, olefins and benzene homologs were identified as major classes of BVOCs. Both C. lanceolata and S. superba seedlings showed significant (p < 0.005) physiological responses after being smoke-stressed where photosynthetic rate remained unaffected, chlorophyll content greatly reduced and Activities of anti-oxidant enzymes and the malondialdehyde content generally increased with the increase in smoke concentration. Activities of anti-oxidant enzymes showed mainly positive correlations with the major BVOCs. In conclusion, the release of BVOCs following smoke stress is species-specific and there exists a link between activities of antioxidant enzymes and BVOCs released. The findings provide insight about management of forest fires in order to control excessive emission of smoke that would trigger increased release of BVOCs.

Optimal Rotation Age in Fast Growing Plantations: A Dynamical Optimization Problem

Forest plantations are economically and environmentally relevant, as they play a key role in timber production and carbon capture. It is expected that the future climate change scenario affects forest growth and modify the rotation age for timber production. However, mathematical models on the effect of climate change on the rotation age for timber production remain still limited. We aim to determine the optimal rotation age that maximizes the net economic benefit of timber volume in a negative scenario from the climatic point of view. For this purpose, a bioeconomic optimal control problem was formulated from a system of Ordinary Differential Equations (ODEs) governed by the state variables live biomass volume, intrinsic growth rate, and area affected by fire. Then, four control variables were associated to the system, representing forest management activities, which are felling, thinning, reforestation, and fire prevention. The existence of optimal control solutions was demonstrated, and the solutions of the optimal control problem were also characterized using Pontryagin's Maximum Principle. The solutions of the model were approximated numerically by the Forward-Backward Sweep method. To validate the model, two scenarios were considered: a realistic scenario that represents current forestry activities for the exotic species Pinus radiata D. Don, and a pessimistic scenario, which considers environmental conditions conducive to a higher occurrence of forest fires. The optimal solution that maximizes the net benefit of timber volume consists of a strategy that considers all four control variables simultaneously. For felling and thinning, regardless of the scenario considered, the optimal strategy is to spend on both activities depending on the amount of biomass in the field. Similarly, for reforestation, the optimal strategy is to spend as the forest is harvested. In the case of fire prevention, in the realistic scenario, the optimal strategy consists of reducing the expenses in fire prevention because the incidence of fires is lower, whereas in the pessimistic scenario, the opposite is true. It is concluded that the optimal rotation age that maximizes the net economic benefit of timber volume in P. radiata plantations is 24 and 19 years for the realistic and pessimistic scenarios, respectively. This corroborates that the presence of fires influences the determination of the optimal rotation age, and as a consequence, the net economic benefit.

Prediction of air pollutant concentrations based on the long short-term memory neural network

In recent years, environmental problems caused by air pollutants have received increasing attention. Effective prediction of air pollutant concentrations is an important way to protect the public from harm. Recently, due to extreme climate and social development, the forest fire frequency has increased. During the biomass combustion process caused by forest fires, the content of particulate matter (PM) in the atmosphere increases significantly. However, most existing air pollutant concentration prediction methods do not consider the considerable impact of forest fires, and effective long-term prediction models have not been established to provide early warnings for harmful gases. Therefore, in this paper, we collected a daily air quality data set (aerodynamic diameter smaller than 2.5 µm, PM2.5) for Heilongjiang Province, China, from 2017 to 2023 and A novel Long Short-Term Memory (LSTM) model was proposed to effectively predict the situation of air pollutants. The model could automatically extract information of the effective time step from the historical data set and combine forest fire disturbance and climate data as auxiliary data to improve the model prediction ability. Moreover, we created artificial neural network (ANN) and permissive regression (support vector machine, SVR) models for comparative experiments. The results showed that the precision accuracy of the developed LSTM model is higher. Unlike the other models, the LSTM neural network model could effectively predict the concentration of air pollutants in long-term series. Regarding long-term observation missions (7 days), the proposed model performed well and stably, with R2 reaching over 88%.

Satellite-based ensemble intelligent approach for predicting forest fire: a case of the Hyrcanian forest in Iran

A machine learning-based approach is applied to simulate and forecast forest fires in the Golestan province in Iran. A dataset for no-fire, medium confidence (MC) fire events, and high confidence (HC) fire events is constructed from MODIS-MOD14A2. Nine climate variables from NASA's FLDAS are used as input variables, and 12 dates and 915 study points are considered. Three machine learning ensemble multi-label classifiers, gradient boosting (GBC), random forest (RFC), and extremely randomized tree (ETC), are used for forest fire simulation for the period 2000 to 2021, and ETC is found to be the most accurate classifier. Future fire projection for the near-future period of 2030 to 2050 is carried out with the ETC model, using CMIP6 EC-Earth3-SSP245 General Circulation Model (GCM) data. It is projected that MC forest fire occurrences will decrease, while HC forest fire occurrences will increase, and that the summer months, especially September, will be the most affected by fire.

Applicability analysis of flame height estimation based on Byram's fireline intensity model under flat and windless conditions

Forest fire have a serious impact on forest ecosystems, the safety of people's lives and property, and social stability. The height of surface flames, as the main indicator of forest fire behavior, which is an important parameter for forest fire management. The relationship between fireline intensity and flame height proposed by Byram has been widely used in estimating flame height; however, its applicability to the surface fuel of typical forest stands in the Yunnan-Guizhou Plateau of China has not yet been analyzed. In this study, the surface fuel in the area was taken as the research object, and the flame height of different fuel bed characteristics was measured through an indoor burning experiment. The applicability of three methods-the directly used Byram's model, corrected model, and re-established prediction model-was analyzed to estimate the flame height in the Yunnan-Guizhou Plateau. We found that the flame height of the typical forest stands in the Yunnan-Guizhou Plateau ranged from 0.05 to 1.2 m and was significantly affected by the moisture content, load, and height of the fuel bed. Although the fireline intensity exhibited a significant linear relationship with the flame height, directly using Byram's method to predict the flame height of surface fires was impractical, as its mean prediction error exceeded 150%. The mean relative errors of the prediction model obtained by modifying Byram's method and that based on the characteristics of the fuel bed were both within 15%, which is significantly lower than that of the original Byram's method. Based on the results of this study, we propose two methods that are suitable for predicting the flame height of surface fires in the typical forests of the Yunnan-Guizhou Plateau in China, which is of great significance for further understanding the relationship between flame height, fireline intensity, and scientific forest fire management.

Forest fire surveillance systems: A review of deep learning methods

This review aims to critically examine the existing state-of-the-art forest fire detection systems that are based on deep learning methods. In general, forest fire incidences bring significant negative impact to the economy, environment, and society. One of the crucial mitigation actions that needs to be readied is an effective forest fire detection system that are able to automatically notify the relevant parties on the incidence of forest fire as early as possible. This review paper has examined in details 37 research articles that have implemented deep learning (DL) model for forest fire detection, which were published between January 2018 and 2023. In this paper, in depth analysis has been performed to identify the quantity and type of data that includes images and video datasets, as well as data augmentation methods and the deep model architecture. This paper is structured into five subsections, each of which focuses on a specific application of deep learning (DL) in the context of forest fire detection. These subsections include 1) classification, 2) detection, 3) detection and classification, 4) segmentation, and 5) segmentation and classification. To compare the model's performance, the methods were evaluated using comprehensive metrics like accuracy, mean average precision (mAP), F1-Score, mean pixel accuracy (MPA), etc. From the findings, of the usage of DL models for forest fire surveillance systems have yielded favourable outcomes, whereby the majority of studies managed to achieve accuracy rates that exceeds 90%. To further enhance the efficacy of these models, future research can explore the optimal fine-tuning of the hyper-parameters, integrate various satellite data, implement generative data augmentation techniques, and refine the DL model architecture. In conclusion, this paper highlights the potential of deep learning methods in enhancing forest fire detection that is crucial for forest fire management and mitigation.

Quantifying forest resilience post forest fire disturbances using time-series satellite data

Quantification of forest resilience will help us to manage the sustainability of the forest environment and the safety of biodiversity. Measuring forest resilience is crucial for ensuring long-term health of the forest ecosystem in the face of ongoing environmental changes and disturbances. This study focuses on providing a framework to estimate forest resilience scores to assess the vegetation condition after a disturbance. The resilience calculation framework provided uses number of recovery days, the phenological performance level of vegetation in the year when the disturbance took place, long-term mean phenological performance, and greenness levels in subsequent year to calculate the final resilience score at each pixel. Recovery of forests using Landsat data with the help of Normalized Difference Vegetation Index or Normalized Burn Ratio poses a challenge for continuous monitoring of forested landscapes due to cloud cover and availability of scenes at continuous intervals in Landsat datasets. In this regard, MODIS 16-day EVI products were used in this study (2001 to 2020) for monitoring vegetation health before, during, and after the disaster. Bandhavgarh National Park (BNP) located in Madhya Pradesh, India is considered for this study as it witnessed major forest fire breakouts in the second half of March 2018. The objectives of the study are the following: (1) to estimate post-fire recovery days and (2) to formulate new resilience index. The study revealed that the northern part of BNP is more vulnerable and shows slow recovery. The relationship between occupation of people living inside and in the neighboring area with forest resilience is also investigated in this study.

Spatiotemporal distribution of air pollutants during a heat wave-induced forest fire event in Uttarakhand

Prevailing dry conditions and rainfall deficit during the spring season in North India led to heat wave conditions which resulted in widespread and intense forest fire events in the Himalayan state of Uttarakhand during April 16-30, 2022. A total of 7589 active fires were detected by VIIRS during the second half of April 2022 compared to 1558 during the first half. The TROPOMI observed total column values of CO and NO2 increased by 4.4% and 11.7%, respectively during April 16-30, 2022 with respect to April 1-15, 2022. A noticeable increase in surface level concentration of trace gases was also observed at Dehradun. In situ measurements of CO, NOx, and O3 during April 16-30, 2022 show an increase of 133, 35, and 6% compared to previous year observations during the same period. Weather Research and Forecasting model with chemistry (WRF-Chem) is utilized to quantitatively estimate the contribution of this event on the distribution of air pollutants over this state. The model results were evaluated against ERA5 reanalysis, upper air soundings, and TROPOMI-retrieved total column density (TCD) of CO, NO2, and O3. Two simulations with (Fire) and without (NoFire) biomass burning emissions input were performed to quantify the contribution of forest fires to the concentration of trace gases and particulates. The CO, NO2, and O3 emitted/produced from forest fire over Uttarakhand during April 2022 contributed approximately 39.95, 35.73, and 9.97% to the surface concentration of respective gas. In the case of aerosols, it was around 71.20, 71.44, and 33.62% for PM2.5, PM10, and BC respectively. The vertical profile analysis of pollutants revealed that extreme forest fire events can perturb the distribution of air pollutants from the surface up to 450 hPa.

Environmental effects of a management method used after fire on development of temperate Scots pine ecosystem: a 15-year study from Poland

Due to the ongoing climate changes, temperate forests are increasingly exposed to fires. However, until now the functioning of post-fire temperate forest ecosystems with regard to used forest management method has been weakly recognized. Here, we examined three variants of forest restoration after fire (two variants of natural regeneration with no soil preparation-NR, and artificial restoration by planting following soil preparation-AR) regarding their environmental consequences in development of post-fire Scots pine (Pinus sylvestris) ecosystem. The study was conducted using a 15-year timespan in a long-term research site located in the Cierpiszewo area (N Poland) being one of the biggest post-fire grounds in European temperate forests in last decades. We focused on soil and microclimatic variables as well as on growth dynamics of post-fire pines generation. We found that the restoration rates of soil organic matter, carbon and most studied nutritional elements stocks were higher in NR plots than in AR. This could be primarily linked to the higher (p < 0.05) density of pines in naturally regenerated plots, and the subsequent faster organic horizon reconstruction after fire. The difference in tree density also involved regular differences in air and soil temperature among plots: consistently higher in AR than in both NR plots. In turn, lower water uptake by trees in AR implied that soil moisture was constantly the highest in this plot. Our study delivers strong arguments to pay more attention to restore post-fire forest areas with the use of natural regeneration with no soil preparation.

Assessment of forest fire emissions in Uttarakhand State, India, using Open Geospatial data and Google Earth Engine

In the recent past, forest fires have increased due to the changing climate pattern. It is necessary to analyse and quantify various gaseous emissions so as to mitigate their harmful effects on air pollution. Satellite remote sensing data provides an opportunity to study the greenhouse gases in the atmosphere. The multispectral sensor of the Tropospheric Monitoring Instrument (Sentinel-5) is capable of recording the reflectance of wavelengths vital for measuring the atmospheric concentrations of methane, formaldehyde, aerosol, carbon monoxide, etc., at a spatial resolution of 0.01°. The present study utilized the Google Earth Engine (GEE) platform to study the emissions caused by forest fires in four districts of Uttarakhand State of India, which witnessed unprecedented fires in April-May 2021. All the datasets were ingested in GEE, which has the capability to analyse large datasets without the need to download them. The pre-fire period chosen was September 2020; the fire period was February-May 2021, and the post-fire period was June 2021. The variables chosen were aerosol absorbing index (AAI), carbon monoxide (CO) and nitrogen dioxide (NO2). The climate parameter temperature (Moderate Resolution Imaging Spectroradiometer Land Surface Temperature) and precipitation (from Climate Hazards Group InfraRed Precipitation (CHIRPS) Pentad) were also studied for the period mentioned. The results indicate a different trend for emissions in each district. For AAI, maximum emissions were noted in district Nainital followed by Almora, Tehri Garhwal and Garhwal. For CO emissions, the most affected district was Almora followed by Nainital, Garhwal and Tehri Garhwal. For NO2 emissions, the most affected district was Garhwal, followed by Nainital, Tehri Garhwal and Almora. Delta Normalized Burn Ratio was computed from Sentinel data (difference of pre-fire and post-fire images) to assess the burnt area severity. The Delta Normalized Burn Ratio values observed that the district with the most burnt area is Garhwal, followed by Nainital, Almora and Tehri Garhwal. The elevated temperatures and scanty rainfall patterns regulated the intensity and duration of forest fire. Monitoring the gaseous emissions as a consequence of forest fire in the GEE platform is much easier and more convenient at a regional level. Such data is much needed for mitigation measures to be implemented in time.

Local stakeholder perceptions of forest degradation: Keys to sustainable tropical forest management

Land use policies and planning in Latin America have been partially successful in halting deforestation yet have not stopped forest degradation. Here, we study the different stakeholders' perspectives of the drivers of forest degradation. We use Colombia as a case study for understanding synergies and trade-offs of the sustainable development goals (SDGs) and analyzed what the most important causes are, to whom it matters, and their regional contribution. We identified a common perception, but miscommunication and misunderstandings occur between local- and national-level actors in terms of their views on responsibilities and rates of change. The results are a call for action. Cross-scale governance is necessary to improve the design and implementation of policies for forest management at the subnational and local levels and to ensure that we move toward sustainable development without worsening existing inequalities. It is essential that countries provide the enabling conditions to develop a coherent governing framework.

Determining the suitable settlement areas in Alanya with GIS-based site selection analyses

Urbanization, which is defined as an irreversible global-scale problem nowadays, necessitates the foundation of new settlement areas. In general, no sufficient scientific assessment and analysis were performed during these processes, and thus, various natural disasters cost the loss of many lives and properties every year. Nevertheless, considering the areas that are risky in terms of natural disasters during the selection of settlement areas might prevent a large-scale loss of lives and properties because of natural disasters. Within the scope of this study, it was aimed to determine suitable settlement areas in the Alanya district, which is one of the significant points of interest for tourists in our country and has a large population and new settlement areas because of this increasing population. Within this scope, besides the risks of flood and landslide that are the most important natural disasters in the region, and a forest fire that is among the most significant risks for the region, also the biocomfort zones were included in the assessments. As a result of the study, it was determined that the most important natural disaster risk was flooding in a large portion of the region and that only 6.72% of the study area was suitable for settlement in terms of all the criteria examined in the present study.

Potentially toxic elements in smoke particles and residual ashes by biomass combustion from Huangshi National Mine Park, China

This paper investigates the fractional and spatial distribution characteristics of potentially toxic elements (PTEs) in smoke particles and residual ashes from mine-park-biomass combustion. It then evaluates the consequential potential environment risk by using a Geo-accumulation index and Nemerow pollution index methods. Biomass combustibles are comprised of Camphor leaves (CL), Camphor dead-branch (CB), Ramie (RA), Miscanthus sinensis (MS), and Dryopteris (DR). The results show that the products of combustion contain PTEs, As, Cr, Cu, and Zn, etc. Among them, the content of As, Cr, Cu, Pb elements in smoke particles of CB was higher than other combustibles. Moreover, Cr, Mn, Ni, and Pb in residual ashes of CL were higher than others. The proportion of acid-soluble and reducible fraction of As in residual ash was higher, while Cr existed mainly in the oxidizable and residual fraction. Besides, the available state of As gradually decreased from 74% (400 °C) to 41% (800 °C), indicating that the increase of temperature significantly reduced the bioavailability of As. Meanwhile, with the increase of temperature, the concentration of PTEs in smoke particles decreased and PTEs in residual ashes increased in different degrees. The risk evaluation results indicate that PTEs may cause moderate or higher levels of contamination. The overall contamination level of PTEs in the residual ashes of CB was higher than that of other plant. The results show in this study would contribute to understanding the environmental risks of wildfire and prescribed burning in PTEs-contaminated areas.

Effects of forest degradation classification on the uncertainty of aboveground carbon estimates in the Amazon

BACKGROUND

Tropical forests are critical for the global carbon budget, yet they have been threatened by deforestation and forest degradation by fire, selective logging, and fragmentation. Existing uncertainties on land cover classification and in biomass estimates hinder accurate attribution of carbon emissions to specific forest classes. In this study, we used textural metrics derived from PlanetScope images to implement a probabilistic classification framework to identify intact, logged and burned forests in three Amazonian sites. We also estimated biomass for these forest classes using airborne lidar and compared biomass uncertainties using the lidar-derived estimates only to biomass uncertainties considering the forest degradation classification as well.

RESULTS

Our classification approach reached overall accuracy of 0.86, with accuracy at individual sites varying from 0.69 to 0.93. Logged forests showed variable biomass changes, while burned forests showed an average carbon loss of 35%. We found that including uncertainty in forest degradation classification significantly increased uncertainty and decreased estimates of mean carbon density in two of the three test sites.

CONCLUSIONS

Our findings indicate that the attribution of biomass changes to forest degradation classes needs to account for the uncertainty in forest degradation classification. By combining very high-resolution images with lidar data, we could attribute carbon stock changes to specific pathways of forest degradation. This approach also allows quantifying uncertainties of carbon emissions associated with forest degradation through logging and fire. Both the attribution and uncertainty quantification provide critical information for national greenhouse gas inventories.

Remote sensing technology for rapid extraction of burned areas and ecosystem environmental assessment

Forest fires are one of the significant disturbances in forest ecosystems. It is essential to extract burned areas rapidly and accurately to formulate forest restoration strategies and plan restoration plans. In this work, we constructed decision trees and used a combination of differential normalized burn ratio (dNBR) index and OTSU threshold method to extract the heavily and mildly burned areas. The applicability of this method was evaluated with three fires in Muli County, Sichuan, China, and we concluded that the extraction accuracy of this method could reach 97.69% and 96.37% for small area forest fires, while the extraction accuracy was lower for large area fires, only 89.32%. In addition, the remote sensing environment index (RSEI) was used to evaluate the ecological environment changes. It analyzed the change of the RSEI level through the transition matrix, and all three fires showed that the changes in RSEI were stronger for heavily burned areas than for mildly burned areas, after the forest fire the ecological environment (RSEI) was reduced from good to moderate. These results realized the quantitative evaluation and dynamic evaluation of the ecological environment condition, providing an essential basis for the restoration, decision making and management of the affected forests.

Contrasting effects of low-severity fire on stemflow production between coexisting pine and oak trees

As climate change intensifies, fires events are predicted to increase in forest ecosystems. Fire alters the ecosystem structure and consequently, the hydrological cycle. However, little is known about the impacts of forest fire on stemflow. A field experiment was conducted to evaluate the short-term response of stemflow production to low-severity fire in a coniferous and broadleaved mixed forest. Results demonstrated low-severity fire changed stemflow yield and had insignificant effect on the correlation between stemflow efficiency and rainfall or plant morphological variables. In unburned site Quercus acutissima and Pinus massoniana and in burned site Q. acutissima and P. massoniana, stemflow percentage averaged 3.86, 0.37, 1.20, and 0.47 %, whereas funneling ratio averaged 38.8, 4.2, 11.4, and 5.1, respectively. Fire substantially decreased the stemflow percentage and funneling ratio of Q. acutissima (P < 0.05) and slightly enhanced P. massoniana (P > 0.05). The responses of stemflow production to fire differed significantly between oak and pine trees. Fire made Q. acutissima become less effective in funneling rain to the forest ground, which is attributed to that the scaly bark was burned to highly furrowed bark that delivers less water to tree base. Burned P. massoniana was more productive in draining stemflow relative to unburned trees and is attributed to the bark which was still flaky regardless of. Additionally, the higher canopy openness allows more rain to funnel to the trunk. Stemflow efficiency was reduced in response to fire and limited the transfer of water and nutrients from canopy to soil and can reduce the competitiveness of Q. acutissima after fire disturbance.

Mapping burn severity and monitoring CO content in Türkiye's 2021 Wildfires, using Sentinel-2 and Sentinel-5P satellite data on the GEE platform

This study investigated forest fires in the Mediterranean of Türkiye between July 28, 2021, and August 11, 2021. Burn severity maps were produced with the difference normalised burned ratio index (dNBR) and difference normalised difference vegetation index (dNDVI) using Sentinel-2 images on the Google Earth Engine (GEE) cloud platform. The burned areas were estimated based on the determined burning severity degrees. Vegetation density losses in burned areas were analysed using the normalised difference vegetation index (NDVI) time series. At the same time, the post-fire Carbon Monoxide (CO) column number densities were determined using the Sentinel-5P satellite data. According to the burn severity maps obtained with dNBR, the sum of high and moderate severity areas constitutes 34.64%, 20.57%, 46.43%, 51.50% and 18.88% of the entire area in Manavgat, Gündoğmuş, Marmaris, Bodrum and Köyceğiz districts, respectively. Likewise, according to the burn severity maps obtained with dNDVI, the sum of the areas of very high severity and high severity constitutes 41.17%, 30.16%, 30.50%, 42.35%, and 10.40% of the entire region, respectively. In post-fire NDVI time series analyses, sharp decreases were observed in NDVI values from 0.8 to 0.1 in all burned areas. While the Tropospheric CO column number density was 0.03 mol/m² in all regions burned before the fire, it was observed that this value increased to 0.14 mol/m² after the fire. Moreover, when the area was examined more broadly with Sentinel 5P data, it was observed that the amount of CO increased up to a maximum value of 0.333 mol/m². The results of this study present significant information in terms of determining the severity of forest fires in the Mediterranean region in 2021 and the determination of the CO column number density after the fire. In addition, monitoring polluting gases with RS techniques after forest fires is essential in understanding the extent of the damage they can cause to the environment.

Composition of inorganic elements in fine particulate matter emitted during surface fire in relation to moisture content of forest floor combustibles

The moisture content of combustible material on the forest floor is constantly changing due to environmental factors, which have a direct impact on the composition and emission intensity of particulate matter released during fire. In this study, an indoor biomass combustion analysis device was used to analyze the emission characteristics of fine particulate matter (PM_2.5) from combustion of herbaceous combustible materials with different moisture contents (0%, 15%, and 30%). The composition of inorganic elements in PM_2.5 (Zn, K, Mg, Ca, and other 13 measurable elements) were determined by inductively coupled plasma-mass spectrometer (ICP-MS). The results showed that the PM_2.5 emission factor increased significantly with the increase of moisture content of combustible materials in the range of 11.63 ± 0.55 for dry samples to 36.71 ± 1.21 g/kg for samples with 30% moisture content. The main elemental components of PM_2.5 were K, Zn, Ca, Mg, and Na and K, Ca, Mg, and Na emission factors increased with the increase of moisture content of combustibles. The proportion of macronutrients in PM_2.5 released by combustion of each herb increased as the moisture content increased, but the proportion of trace elements gradually decreased. There was a good correlation between elemental composition of PM_2.5 and that of herbaceous combustibles. The results provide evidence that the moisture content of combustible materials has a significant effect on the emission of inorganic elements in particulate matter, and hence cautions should be exercised during fuel reduction treatments, such as early prescribed fire.

Trace gas emissions from laboratory combustion of leaves typically consumed in forest fires in Southwest China

Forest fires are becoming increasingly severe and frequent due to global climate change. Trace gases emitted from forest fires significantly affect atmospheric chemistry and climate change on a regional and global scale. Forest fires occur frequently in Southwest China, but systematic studies on trace gas emissions from forest fires in Southwest China are rare. Leaves of seven typical vegetation fuels based on their prominence in forest fires consumption in Southwest China were burned in a self-designed combustion device and the emission factors of eighteen trace gases (greenhouse gases, non-methane organic gases, nitrogenous gases, hydrogen chloride, and sulfur dioxide) at specific combustion stages (flaming and smoldering) were determined by using Fourier transform infrared spectroscopy, respectively. The emission factors data presented were compared with previous studies and can aid in the construction of an emission inventory. Pine needle combustion released a greater amount of methane in the smoldering stage than other broadleaf combustion. Peak values of emission factors for methane and non-methane organic gas are emitted by the smoldering of vegetation (Pinus kesiya and Pinus yunnanensis), which is endemic to forest fires in Southwest China. The emission factor for oxygenated volatile organic compounds (OVOCs) in the smoldering stage is greater than the flaming stage. This work established the relationship between modified combustion efficiency (MCE) with emission factors of hydrocarbons (except acetylene) and OVOCs. The results show that exponential fitting is more suitable than linear fitting for the seven leaf fuels (four broadleaf and three coniferous). However, the emission factors from the combustion of three coniferous fuels relative to all fuels are linear with MCE. Findings demonstrated that different combustion stages and fuel types have significant impacts on the emission factors, which also highlighted the importance of studying regional emissions.

Forest Fire Characterization Using Landsat-8 Satellite Data in Dalma Wildlife Sanctuary

A forest fire has caused a loss of biodiversity and forest heterogeneity and resulted in forest degradation and fragmentation. Remote sensing techniques have been widely used for locating and delineating forest fires. The present study has employed Landsat-8 satellite data during 2014-2020 for spatiotemporal analysis of forest fire in Dalma Wildlife Sanctuary (DWS). Normalized burn ratio (NBR) has been used to delineate forest fire-affected locations along with visual interpretation techniques. The results showed that an extensive area was burnt and deforested due to forest fire in DWS during 2014-2020. The burned areas due to forest fires within the notified forest boundary in DWS were 12.11 km², 25.5 km², 22.45 km², 9.11 km², 24.44 km², 10.09 km², and 1 km² during 2014, 2015, 2016, 2017, 2018, 2019, and 2020, respectively, whereas burned areas outside notified boundary were 2.24 km², 4.15 km², 1.48 km², 3.29 km², 3.31 km², 1.9 km², and 0.1 km². According to visual image interpretation, the highest burned area was found in 2015 (25.5 km²), whilst the least affected was found in 2020 (1 km²), and fires were mainly seen in the degraded forests and open forest regions. The present study revealed that forest fire is more dominant in Asanbani, Pardih, Bhelaipahari, Gobargushi, Bamri, Andharjhor, Somadih, Koira, Tetla, Bochkamkocha, Sah, Rbera, and Jamdih locations/beats in DWS. Thereby, these forest beats need attention from forest managers to control fire-mediated forest degradation for the conservation and restoration of forests in DWS.

Capture of fire smoke particles by leaves of Cunninghamia lanceolata and Schima superba, and importance of leaf characteristics

Emission of particulate matter (PM) during forest fires is a major source of air pollution and hence purification of atmospheric pollution has gained increasing importance. Trees can absorb polluting gases and fine particles by their leaves from the atmosphere and act as a sustainable air purification filter. However, the capture efficiency varies among tree species; thus exploring the ability of forest trees to capture smoke PM released during forest fires provides a basis for assessing net emissions from forest fires and the impact of smoke on forest ecosystems. In this study, the main afforestation tree species, Cunninghamia lanceolata (Lamb.) Hook, and a fire-resistant tree species, Schima superba Gardn.et Champ, in southern China were exposed to different smoke concentrations by simulating forest fire. The amount of PM per unit leaf area, absorption of nutrient element, leaf surface characteristics and antioxidant enzyme activities were determined. The main findings were: (1) The total quantity of PM captured by unit leaf area (μg·cm^-2) of C. lanceolata was 28.25 ± 1.12, 30.52 ± 3.43 and 33.14 ± 3.00 in low, intermediate and high smoke concentrations, respectively. The corresponding values for S. superba was 5.96 ± 0.56, 10.09 ± 1.13 and 12.27 ± 0.39, respectively. (2) Both species had weak absorption capacity for inorganic ions in the PM. (3) The purification of smoke PM by leaves was mainly related to leaf surface roughness, where it was higher for C. lanceolata than S. superba leaves. (4) Smoke treatment positively affected the contents of chlorophyll and soluble protein as well as increased antioxidant enzyme activities. In conclusion, the findings highlight the importance of leaf structural characteristics in capturing smoke particles and C. lanceolata is better suited for purification of atmospheric smoke particles following forest fire than S. superba.

Post-fire management effects on hillslope-stream sediment connectivity in a Mediterranean forest ecosystem

Forest fires intensify sediment transport and aggravate local and off-site consequences of soil erosion. This study evaluates the influence of post-fire measures on structural and functional sediment connectivity (SC) in five fire-affected Mediterranean catchments, which include 929 sub-catchments, by using the "aggregated index of connectivity" (AIC) at two temporal scenarios: I) immediately after the fire and before implementing post-fire practices ('Pre-man'), and II) two years after the fire ('Post-man'). The latter includes all the emergency stabilization practices, that are hillslope barriers, check-dams and afforestation. The stream system was set as the target of the computation (STR), to be representative of intense rainfall-runoff events with effective sediment delivery outside the catchments. Output normalization (AIC_N) allows comparing the results of the five basins between them. The sedimentological analysis is based on specific sediment yield (SSY) -measured at the check-dams installed after the fire -, and this data is used for output evaluation. Stream density and slope variables were the most influential factors on AIC_N-STR results at the sub-catchment scale. Post-fire hillslope treatments (barriers when built in high densities and afforestation) significantly reduced AIC_N-STR in comparison with untreated areas in both structural and functional approaches. Despite the presence of hillslope treatments, the higher erosive rainfall conditions resulted in higher AIC_N-STR values in the Post-man scenario (functional approach). A positive and good correlation was found between the measured SSY and the AIC_N-STR changes due to the post-fire practices and vegetation recovery, showing the good correspondence of the computation results and the real sediment dynamics of the studied catchments. Overall, AIC_N demonstrated to be a useful and versatile tool for post-fire management, which needs further research to optimize its applicability.

Hotspot and trend analysis of forest fires and its relation to climatic factors in the western Himalayas

Forest fire is one of the main issues of forest ecosystems around the world which has resulted in loss of biodiversity, forest degradation, soil erosion, and greenhouse gas emission. Ironically, the information on the forest fire regime and its pattern are still lacking in the Himalayan region. In this study, Moderate-Resolution Imaging Spectroradiometer active fire data products from 2001 to 2020 have been analysed for understanding the forest fire trends and its hotspots patterns during the active fire season (February to June). About 1347 average fire counts/year were recorded in six natural vegetations with the highest number of fires observed during the year 2012 (n = 3096) and minimum in 2011 (n = 210). Mann-Kendall trends analysis for the spatial and temporal pattern of fires indicated that there is a significant increase of forest fires towards higher elevation. Forest fire hotspot analysis using fire radiative power, fire frequency, and fire density showed that Uttarakhand is the most forest fire-prone state as compared to other north-western Himalayan states. It is also revealed that the May month has a higher number of fire counts and the evergreen needle forests have higher fire frequencies amongst the forest types. The forest fires were found to be more influenced by land surface temperature as compared to rainfall. The outcomes in this study on the temporal and spatial patterns for forest fire can be used for forest fire modelling.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11069-022-05530-5.

Examining the status of forest fire emission in 2020 and its connection to COVID-19 incidents in West Coast regions of the United States

Forest fires impact on soil, water, and biota resources. The current forest fires in the West Coast of the United States (US) profoundly impacted the atmosphere and air quality across the ecosystems and have caused severe environmental and public health burdens. Forest fire led emissions could significantly exacerbate the air pollution level and, therefore, would play a critical role if the same occurs together with any epidemic and pandemic health crisis. Limited research is done so far to examine its impact in connection to the current pandemic. As of October 21, nearly 8.2 million acres of forest area were burned, with more than 25 casualties reported so far. In-situ air pollution data were utilized to examine the effects of the 2020 forest fire on atmosphere and coronavirus (COVID-19) casualties. The spatial-temporal concentrations of particulate matter (PM_2.5 and PM₁₀) and Nitrogen Dioxide (NO₂) were collected from August 1 to October 30 for 2020 (the fire year) and 2019 (the reference year). Both spatial (Multiscale Geographically Weighted Regression) and non-spatial (Negative Binomial Regression) analyses were performed to assess the adverse effects of fire emission on human health. The in-situ data-led measurements showed that the maximum increases in PM_2.5, PM₁₀, and NO₂ concentrations (μg/m³) were clustered in the West Coastal fire-prone states during August 1 - October 30, 2020. The average concentration (μg/m³) of particulate matter (PM_2.5 and PM₁₀) and NO₂ was increased in all the fire states severely affected by forest fires. The average PM_2.5 concentrations (μg/m³) over the period were recorded as 7.9, 6.3, 5.5, and 5.2 for California, Colorado, Oregon, and Washington in 2019, increasing up to 24.9, 13.4, 25.0, and 17.0 in 2020. Both spatial and non-spatial regression models exhibited a statistically significant association between fire emission and COVID-19 incidents. Such association has been demonstrated robust and stable by a total of 30 models developed for analyzing the spatial non-stationary and local association. More in-depth research is needed to better understand the complex relationship between forest fire emission and human health.

Hydrophobicity of soils affected by fires: An assessment using molecular markers from ultra-high resolution mass spectrometry

Soil water repellency (SWR) is a physical property due to a complex interaction of factors (e.g., fire, soil organic matter, soil texture) that reduces the soil water infiltration capacity. Traditionally, SWR is attributed to the accumulation and redistribution of hydrophobic compounds within soil profile. To obtain further insight into chemical compounds, which could be associated with SWR, a study was done on coarse (1-2 mm) and fine (< 0.05 mm) granulometric fractions of burned and unburned sandy soils under two Mediterranean vegetation biomes from Doñana National Park (Spain). The water drop penetration time (WDPT) test was used to assess the SWR. The molecular composition of extracted humic substances from the soil organic matter (SOM) was determined by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS). Partial least squares (PLS) regressions showed that the SWR can be predicted (P = 0.006) solely based on the abundances of approximately 1200 common compounds determined by FT-ICR/MS. This model confirmed the significant correlation between a specific SOM molecular composition and the SWR. The comparative analysis revealed that the SWR in the burned samples was significantly (P < 0.05) related to the abundance of aromatic and condensed compounds, while in the unburned samples there was a significant influence of aromatic hydrocarbons and lignin compounds. In the fine fraction, lipid compounds were significantly associated with the SWR. Contrastingly, the coarse fraction did not show any correlation. Alternatively, soils with a high SWR were significantly related to the presence of lipids and lignin. This analysis showed that combining FT-ICR/MS molecular characterizations with statistical treatments is a powerful approach for exploratory analysis suggesting that the structural features associated with SWR in the studied soils are different depending on the types of vegetation or the soil physical fractions with different particle size.

A wildfire growth prediction and evaluation approach using Landsat and MODIS data

The increasing wildfire occurrence due to global climate changes urged the improvement of present wildfire growth prediction and evaluation methods. This study aimed to propose novel solutions to their two primary limitations, including the lack of robust fuel classification method and the low spatial resolution of wildfire growth accuracy assessment while ensuring wide applicability using open data satellite missions and software. The first objective was to create a robust two-step fuel model classification method consisted of the supervised machine learning classification of generalized land cover classes in the 1st level and their individual unsupervised classification to vegetation subtypes in the 2nd level. The second objective was creating a wildfire prediction accuracy assessment method using MODIS 250 m images, which overcome the limitations of low spatial resolution while preserving sub-daily temporal resolution. The wildfire on the Korčula island in Croatia was analyzed in the study, being specific for its long duration from 18 to 24 July 2015. The wildfire ignition occurred in the isolated area, which prolonged the response time from emergency agencies. Random Forest (RF) with input Landsat 8 spectral bands and indices resulted in the highest classification accuracy in the 1st classification level with an overall agreement of 83.6%. The vegetation subclasses from the 2nd classification level were matched to the 13 standard fuel models for the input in FARSITE software. The predicted wildfire evaluation showed the highest mean accuracy of 0.906 for the first two days, which decreased to 0.722 in the latter stages of the active wildfire caused by overprediction. The proposed two-step fuel model classification presented a cost-efficient solution to the fuel map creation in any part of the world, with a disadvantage of no in-situ ground truth identification and accuracy assessment for 2nd classification level. The evaluation of wildfire growth prediction with 250 m images enabled high spatial and temporal resolution of the assessment, while its limitations of wildfire overprediction and the negative effects of wildfire smoke in MODIS images should be addressed in future research.

Australian fire nourishes ocean phytoplankton bloom

An unprecedented devastating forest fire occurred in Australia from September 2019 to March 2020. Satellite observations revealed that this rare fire event in Australia destroyed a record amount of more than 202,387 km² of forest, including 56,471 km² in eastern Australia, which is mostly composed of evergreen forest. The released aerosols contained essential nutrients for the growth of marine phytoplankton and were transported by westerly winds over the Southern Ocean, with rainfall-induced deposition to the ocean beneath. Here, we show that a prominent oceanic bloom, indicated by the rapid growth of phytoplankton, took place in the Southern Ocean along the trajectory of fire-born aerosols in response to atmospheric deposition. Calculations of carbon released during the fire versus carbon absorbed by the oceanic phytoplankton bloom suggest that they were nearly equal. This finding illustrates the critical role of the oceans in mitigating natural and anthropogenic carbon dioxide releases to the atmosphere, which are a primary driver of climate change.

An estimate of the area of occupancy and population size of Brachycephalus tridactylus (Anura: Brachycephalidae) to reassess its conservation status, with a proposal for conservation measures

Background

We are experiencing a global crisis in conservation, which has led to the prioritization of targets, such as nations, regions, and animal groups, which are necessary while resources are disputed. Brazil is a priority not only because of its megadiversity, high rates of endemism, and frequent descriptions of new species but also because of its high levels of deforestation. Among the species groups prioritized for conservation is the anurans (Amphibia: Anura), the population of which is severely declining. One group of anurans is the genus Brachycephalus, which includes 37 endemic species in the Brazilian Atlantic Rainforest. Some of these species have highly restricted distributions (<100 ha). Thirty new species have been described since 2000, and 55.3% of all species are threatened with extinction. Brachycephalus tridactylus was only recently described and remains restricted to its type locality. Because of its reduced geographical distribution (0.41 km²), it has been proposed to be considered as Vulnerable. The objective of this study is to reevaluate the conservation status of Brachycephalus tridactylus and propose conservation measures.

Methods

We searched for new populations during 2016–2020, evaluated in loco impacts and potential impacts on the species’ population, and performed an analysis of the density of this population and estimated its size. International Union for Conservation of Nature (IUCN) criteria were used to assess the conservation status of the species.

Results

We recorded the species in seven new localities (from 715–1,140 m above sea level) in the state of São Paulo up to 33 km from the type locality of the species (in state of Paraná). We estimated the area of occupancy as 148.44 km², densities as one calling male per 4.05 m² and 130.00 m², and a total population size of 4,429,722 adult individuals. Based on our finding, we proposed three lines of management: (1) formation of fire brigades, (2) management of residents’ mules in the conservation unit and surrounding areas, and (3) management of degraded areas. We recommend changing the species’ conservation status from Vulnerable to Endangered because of its fragmented distribution and decline in the area of occupancy and in the quality of its habitat. Our results have expanded the species previous geographic distribution and delimited areas without previous records. Our estimates of population density and size are in accordance with those verified for congeners. The conservation of this species benefits the environments and other species that inhabit them, being, therefore, strategic for receiving conservation actions that will spread throughout the ecosystem.

Climate change induced declines in fuel moisture may turn currently fire-free Pyrenean mountain forests into fire-prone ecosystems

Fuel moisture limits the availability of fuel to wildfires in many forest areas worldwide, but the effects of climate change on moisture constraints remain largely unknown. Here we addressed how climate affects fuel moisture in pine stands from Catalonia, NE Spain, and the potential effects of increasing climate aridity on burned area in the Pyrenees, a mesic mountainous area where fire is currently rare. We first quantified variation in fuel moisture in six sites distributed across an altitudinal gradient where the long-term mean annual temperature and precipitation vary by 6-15 °C and 395-933 mm, respectively. We observed significant spatial variation in live (78-162%) and dead (10-15%) fuel moisture across sites. The pattern of variation was negatively linked (r = |0.6|-|0.9|) to increases in vapor pressure deficit (VPD) and in the Aridity Index. Using seasonal fire records over 2006-2020, we observed that summer burned area in the Mediterranean forests of Northeast Spain and Southern France was strongly dependent on VPD (r = 0.93), the major driver (and predictor) of dead fuel moisture content (DFMC) at our sites. Based on the difference between VPD thresholds associated with large wildfire seasons in the Mediterranean (3.6 kPa) and the maximum VPD observed in surrounding Pyrenean mountains (3.1 kPa), we quantified the "safety margin" for Pyrenean forests (difference between actual VPD and that associated with large wildfires) at 0.5 kPa. The effects of live fuel moisture content (LFMC) on burned area were not significant under current conditions, a situation that may change with projected increases in climate aridity. Overall, our results indicate that DFMC in currently fire-free areas in Europe, like the Pyrenees, with vast amounts of fuel in many forest stands, may reach critical dryness thresholds beyond the safety margin and experience large wildfires after only mild increases in VPD, although LFMC can modulate the response.

Insights into sources and occurrence of oxy- and nitro-PAHs in the alberta oil sands region using a network of passive air samplers

Mining-related activities in the Alberta Oil Sands Region (AOSR) are known to emit polycyclic aromatic hydrocarbons (PAHs) and related compounds to ambient air. This is a concern due to the toxicity of PAHs, including their transformation products such as nitrated (NPAHs) and oxygenated (OPAHs) PAHs. This is the first study that provided a more extensive outlook into the sources, occurrence in air, and spatial and seasonal patterns of NPAHs and OPAHs in the AOSR by using passive air sampling. A sampling campaign from 2013 to 2016 revealed concentrations of NPAHs that were much lower than those of OPAHs. The highest concentrations of NPAHs were concentrated in the region associated with extensive mining activities, with ∑NPAH concentrations ranging from 20 to 250 pg/m³. Within the oil sands (OS) mineable area, NPAHs associated with primary release appear more commonly, while NPAHs produced via oxidative transformation are predominant outside of this area. The concentrations of ∑OPAH ranged from 400 to 2400 pg/m³, with the highest air concentrations in the region located south of the main OS activity zone, with peak concentrations attributed to a 2016 forest fire event. Uptake of PAHs from ambient air and their subsequent conversion to generate OPAHs is believed to play an important role in wildfire emissions of OPAHs. The seasonal trend investigation was inconclusive, with NPAHs slightly higher during the winter, while OPAHs were slightly elevated during summer. A preliminary comparison of ambient concentrations of OPAHs and NPAHs in the AOSR to measurements in the Greater Toronto Area revealed a similar range of concentrations, but also a unique presence of certain NPAHs such as 4-nitrobiphenyl, 2-nitrodibenzothiophene, 2,8-dinitrodibenzothiophene and 6-nitrobenzo-(a)-pyrene. This indicates that AOSR might have its own NPAH profile - creating the need to better understand associated NPAH toxicity and propensity for long range transport.

Rapid estimation of CO2 emissions from forest fire events using cloud-based computation of google earth engine

One of the main sources of greenhouse gases is forest fire, with carbon dioxide as its main constituent. With increasing global surface temperatures, the probability of forest fire events also increases. A method that enables rapid quantification of emissions is even more necessary to estimate the environmental impact. This study introduces the application of the Google Earth Engine platform to monitor burned areas in forest fire events in Mount Arjuno, Indonesia, during the 2016-2019 period, using Landsat-8 and Sentinel-2 satellite imageries. The events particularly affected grassland and tropical forest areas, as well as a fraction of agricultural areas, with a total estimated emission of 2.5 × 10³ tCO₂/km² burned area. Higher carbon dioxide emissions were also observed, consistent with the higher local surface temperature as well as the CO total column mixing ratio average retrieved from Sentinel-5 p Tropospheric Monitoring Instrument during the period of analysis.

Effect of forest floor fuel moisture content on chemical components of PM2.5 emitted during combustion

The moisture content of forest floor fuels changes continuously with the influence of environmental factors; thus it has an important impact on the concentration and chemical composition of particulate matter emitted during forest fire. However, most previous studies quantify emissions of particulate matter and constituents using dry samples. In this study, we use a self-designed semi closed combustion simulator to quantify emission of total carbon (TC), organic carbon (OC), elemental carbon (EC) and water-soluble ions in fine particulate matter (PM_2.5) using fuels of four tree species that differ in moisture content (0, 10, 20 and 30%). The results showed that the emissions of TC, OC and EC and total water-soluble inorganic ions increased significantly (<0.05) with increasing moisture content of fuels, and fuels of coniferous species emitted significantly more pollutants than fuels of broadleaved species. Similarly combustion of leaf samples emitted more carbonaceous components and water-soluble ions than combustion of branches. K⁺, NH₄⁺ and Cl^- were the main components of water-soluble ionic species, and emissions of K⁺, Ca²⁺, Na⁺, Mg²⁺, NH₄⁺, Cl^-, Br^-, NO₃^-, NO₂^-, SO₄^2- increased with increasing moisture content of fuels. Fuel moisture content had a great impact on the inorganic salt composition in the particulate matter emitted during combustion. The findings have an important implication on the use of prescribed early fire as management tools as the moisture content of the fuels early during the dry season is still high.

Optimization studies for hydrothermal gasification of partially burnt wood from forest fires for hydrogen-rich syngas production using Taguchi experimental design

Forest fires significantly affect the wildlife, vegetation, composition and structure of the forests. This study explores the potential of partially burnt wood recovered in the aftermath of a recent Canadian forest fire incident as a feedstock for generating hydrogen-rich syngas through hydrothermal gasification. Partially burnt wood was gasified in hydrothermal conditions to study the influence of process temperature (300-500 °C), residence time (15-45 min), feed concentration (10-20 wt%) and biomass particle size (0.13 mm and 0.8 mm) using the statistical Taguchi method. Maximum hydrogen yield and total gas yield of 5.26 mmol/g and 11.88 mmol/g, respectively were obtained under optimized process conditions at 500 °C in 45 min with 10 wt% feed concentration using biomass particle size of 0.13 mm. The results from the mean of hydrogen yield show that the contribution of each experimental factors was in the order of temperature > feed concentration > residence time > biomass particle size. Other gaseous products obtained at optimum conditions include CO₂ (3.43 mmol/g), CH₄ (3.13 mmol/g) and C₂-C₄ hydrocarbons (0.06 mmol/g).

Forest fires and deforestation in the central Amazon: Effects of landscape and climate on spatial and temporal dynamics

Forest fires and deforestation are the main threats to the Amazon forest. Extreme drought events exacerbate the impact of forest fire in the Amazon, and these drought events are predicted to become more frequent due to climate change. Fire escapes into the forest from agriculture and pasture areas. We assessed the potential drivers of deforestation and forest fires in the central Brazilian Amazon and show that over a period of 31 years (1985-2015) forest fires occurred only in years of extreme drought induced by El Niño (1997, 2009 and 2015). The association of forest fires with strong El Niños shows the vulnerability of forest to climate change. The areas deforested were closely associated with navigable rivers: 62% of the total deforestation from 2000 to 2018 was located within the 2 km of rivers. There was a notable increase in deforestation and forest fire during the 2015 El Niño in comparison to previous years. Only a small part of the forest that burned was deforested in the years following the wildfires: 7% (1997), 3% (2009) and 1.5% (2015). Forest close to roads, rivers and established deforestation is susceptible to deforestation and fire since these areas are attractive for agriculture and pasture. Indigenous land was shown to be important in protecting the forest, while rural settlement projects attracted both forest fire and deforestation. Of the total area in settlement projects, 40% was affected by forest fires and 17% was deforested. Rivers are particularly important for deforestation in this part of Amazonia, and efforts to protect forest along the rivers are therefore necessary. The ability to predict where deforestation and fires are most likely to occur is important for designing policies for preventative actions.

Post-fire forest restoration in the humid tropics: A synthesis of available strategies and knowledge gaps for effective restoration

Humid tropical forests are increasingly exposed to devastating wildfires. Major efforts are needed to prevent fire-related tipping points and to enable the effective recovery of fire-affected areas. Here, we provide a synthesis of the most common forest restoration strategies, thereby focusing on post-fire forest dynamics in the humid tropics. A variety of restoration strategies can be adopted in restoring humid tropical forests, including natural regeneration, assisted natural regeneration (i.e. fire breaks, weed control, erosion control, topsoil replacement, peatland rewetting), enrichment planting (i.e. planting nursery-raised seedlings, direct seeding) and commercial restoration (i.e. plantation forests, agroforestry). Our analysis shows that while natural regeneration can be effective under favourable ecological conditions, humid tropical forests are often ill-adapted to fire, and therefore less likely to recover unassisted after a wildfire event. Active restoration practices may be more effective, but can be costly and challenging to implement. We also identify gaps in knowledge needed for effective restoration of humid tropical forests after fire, hereby taking into account the ecosystems and socio-economic conditions in which these fires occur. We suggest to incorporate fire severity in future studies, to better understand and predict post-fire ecosystem responses. In addition, as fire poses a recurring and intensifying threat throughout the recovery process, more emphasis should be placed on post-restoration management and the prevention of fire throughout the different phases of the restoration process. Furthermore, as tropical wildfires are increasing in scale, establishing collaborative capacity and setting priorities for efficient resource allocation should become a major priority for restoration practitioners in the humid tropics. Finally, as global fire regimes are changing and expected to intensify in the context of climate change, land use and land cover change, we suggest to put continuous effort into fire monitoring and modelling to inform the development of effective restoration strategies in the long-run.

Peat-forest burning smoke in Maritime Continent: Impacts on receptor PM2.5 and implications at emission sources

This study characterizes the impacts of transported peat-forest (PF) burning smoke on an urban environment and evaluates associated source burning conditions based on carbon properties of PM_2.5 at the receptor site. We developed and validated a three-step classification that enables systematic and more rapid identification of PF smoke impacts on a tropical urban environment with diverse emissions and complex atmospheric processes. This approach was used to characterize over 300 daily PM_2.5 data collected during 2011-2013, 2015 and 2019 in Singapore. A levoglucosan concentration of ≥0.1 μg/m³ criterion indicates dominant impacts of transported PF smoke on urban fine aerosols. This approach can be used in other ambient environments for practical and location-dependent applications. Organic carbon (OC) concentrations (as OC indicator) can be an alternate to levoglucosan for assessing smoke impacts on urban environments. Applying the OC concentration indicator identifies smoke impacts on ∼80% of daily samples in 2019 and shows an accuracy of 51-86% for hourly evaluation. Following the systematic identification of urban PM_2.5 predominantly affected by PF smoke in 2011-2013, 2015 and 2019, we assessed the concentration ratio of char-EC/soot-EC as an indicator of smoldering- or flaming-dominated burning emissions. When under the influence of transported PF smoke, the mean concentration ratio of char-EC to soot-EC in urban PM_2.5 decreased by >70% from 8.2 in 2011 to 2.3 in 2015 but increased to 3.8 in 2019 (p < 0.05). The reversed trend with a 65% increase from 2015 to 2019 shows stronger smoldering relative to flaming, indicating a higher level of soil moisture at smoke origins, possibly associated with rewetting and revegetating peatlands since 2016.

Plant material and its biochar differ in their effects on nitrogen mineralization and nitrification in a subtropical forest soil

Natural wildfires have a great effect on soil N transformation in subtropical forest. The pyrogenic organic matter (PyOM) in forest soils is mainly derived from the plant material burnt during forest fires, which affects soil N composition, N mineralization and nitrification. This study examined the effects of typical fresh plant material (leaves and twigs of Castanopsis sclerophylla, representing litter) and its biochar (representing PyOM) on N mineralization and nitrification in a subtropical forest soil. The soils were incubated with the plant material (PM), its biochar (BC) and their combinations for 84 days. Both PM and BC considerably increased soil pH and dissolved organic C, whereas PM decreased NO₃^--N and dissolved organic N. The additions of PM alone, and its combinations with BC resulted in net N immobilization. The rates of net N mineralization rapidly increased in first 14 days and then became stable following the addition of PM to soil. Moreover, the additions of PM and BC increased the abundances of archaeal amoA and bacterial amoA, especially with PM. The abundance of bacterial amoA correlated positively with soil pH and dissolved organic C, while archaeal amoA showed the opposite. Biochar affected soil properties and N transformation more significantly in the presence of PM, highlighting the need for further research on the interactions of plant litter and its biochar.

Hybrid inversion of radiative transfer models based on high spatial resolution satellite reflectance data improves fractional vegetation cover retrieval in heterogeneous ecological systems after fire

In forest landscapes affected by fire, the estimation of fractional vegetation cover (FVC) from remote sensing data using radiative transfer models (RTMs) enables to evaluate the ecological impact of such disturbance across plant communities at different spatio-temporal scales. Even though, when landscapes are highly heterogeneous, the fine-scale ground spatial variation might not be properly captured if FVC products are provided at moderate or coarse spatial scales, as typical of most of operational Earth observing satellite missions. The objective of this study was to evaluate the potential of a RTM inversion approach for estimating FVC from satellite reflectance data at high spatial resolution as compared to the standard use of coarser imagery. The study was conducted both at landscape and plant community levels within the perimeter of a megafire that occurred in western Mediterranean Basin. We developed a hybrid retrieval scheme based on PROSAIL-D RTM simulations to create a training dataset of top-of-canopy spectral reflectance and the corresponding FVC for the dominant plant communities. The machine learning algorithm Gaussian Processes Regression (GPR) was learned on the training dataset to model the relationship between canopy reflectance and FVC. The GPR model was then applied to retrieve FVC from WorldView-3 (spatial resolution of 2 m) and Sentinel-2 (spatial resolution of 20 m) surface reflectance bands. A set of 75 plots of 2x2m and 45 plots of 20x20m was distributed under a stratified schema across the focal plant communities within the fire perimeter to validate FVC satellite derived retrieval. At landscape scale, the accuracy of the FVC retrieval was substantially higher from WorldView-3 (R² = 0.83; RMSE = 7.92%) than from Sentinel-2 (R² = 0.73; RMSE = 11.89%). At community level, FVC retrieval was more accurate for oak forests than for heathlands and broomlands. The retrieval from WorldView-3 minimized the over- and under-estimation effects at low and high field sampled vegetation cover, respectively. These findings emphasize the effectiveness of high spatial resolution satellite reflectance data to capture FVC ground spatial variability in heterogeneous burned areas using a hybrid RTM retrieval method.

Impact of wildfires on SO2 detoxification mechanisms in leaves of oak and beech trees

Frequency and intensity of wildfire occurrences are dramatically increasing worldwide due to global climate change, having a devastating effect on the entire ecosystem including plants. Moreover, distribution of fire-smoke can influence the natural environment over very long distances, i.e. hundreds of kilometres. Dry plant matter contains 0.1-0.9% (w/w) sulphur, which is mainly released during combustion into the atmosphere as sulphur dioxide (SO₂) resulting in local concentrations of up to 3000 nL L^-1. SO₂ is a highly hazardous gas, which enters plants mostly via the stomata. Toxic sulphite is formed inside the leaves due to conversion of SO₂. Plants as sessile organisms cannot escape from threats, why they evolved an impressive diversity of molecular defence mechanisms. In the present study, two recent wildfires in Germany were evaluated to analyse the effect of SO₂ released into the atmosphere on deciduous trees: the Meppen peat fire in 2018 and the forest fire close to Luebtheen in 2019. Collected leaf material from beech (Fagus sylvatica) and oak (Quercus robur) was examined with respect to detoxification of sulphur surplus due to the exposure to elevated SO₂. An induced stress reaction in both species was indicated by a 1.5-fold increase in oxidized glutathione. In beech leaves, the enzymatic activities of the sulphite detoxification enzymes sulphite oxidase and apoplastic peroxidases were increased 5-fold and a trend of sulphate accumulation was observed. In contrast, oaks did not regulate these enzymes during smoke exposure, however, the constitutive activity is 10-fold and 3-fold higher than in beech. These results show for the first time sulphite detoxification strategies of trees in situ after natural smoke exposure. Beech and oak trees survived short-term SO₂ fumigation due to exclusion of toxic gases and different oxidative detoxification strategies. Beeches use efficient upregulation of oxidative sulphite detoxification enzymes, while oaks hold a constitutively high enzyme-pool available.

The fate of anthropogenic Pb in soils; years after Pb terminated as a fuel additive; Northern Israel

The source for Lead (Pb) pollution in soils from the heavily industrialized area located along the coast of the Eastern Mediterranean, Haifa Bay, Northern Israel, is studied using the lead isotopic composition. The uniqueness of the studied data set is that it includes samples of soils, road-wash, and storm-dust sampled for nearly three decades (1988-2017). Road-wash sediments are similar in both elemental and Pb isotopic composition to soils sampled in the same year (2010), indicating re-suspension of local soil, as its origin. Soils sampled during and before 1993 show no evidence for Pb contamination (bulk soil values), although Pb as an additive was already in use. Furthermore, soil overturns hinder the possibility to trace changes in the Pb isotopic composition with time in soils of the same location. Soils sampled from 1995-8 to 2013 were significantly dominated by Post-1992 Pb additive, pointing to Pb's peak as an additive. Soils Pb and Zn Enrichment factors for most samples are below 5, and their anthropogenic source is likely common. Forest fire enriched Pb and Zn in the soil, and their Pb isotope compositions reflect this enrichment. Lead from the Hod Assaf recycling plant detected up to some 2.5 km away, and although not analyzed in the current study, dioxin-like compounds possibly accompanied Pb.

Effectiveness of soil erosion barriers to reduce sediment connectivity at small basin scale in a fire-affected forest

Forest fires and post-fire management practices (PFMP) cause changes in the hydrological response of a hillslope. This study evaluates the effect of log erosion barriers (LB) and Easy-Barriers® (EB) on the spatial patterns and values of structural sediment connectivity (SC) in a Mediterranean mountainous pine forest affected by an arson fire in August 2017. A drone flight was done in July 2019 (23 months after the fire and 11 months after the PFMP) to obtain a high-resolution orthomosaic and DEM (at 0.05 m). Two contrasted areas, with and without PFMP, were selected along the same hillslope and 26 small basins were identified: 16 in the treated area (mean area, slope and vegetation recovery of 916 m², 60% and 25%; with 94 LB and 39 EB) and 10 in the untreated area (1952 m², 75% and 20%). The aggregated index of sediment connectivity (AIC) was chosen to compute SC in three temporal scenarios: Before and just after the fire and when all PFMP were implemented including the incipient vegetation recovery. Output normalization allowed the comparison of the non-nested basins among them. After accounting the intrinsic differences among the basins and areas, and the temporal changes of SC between the three scenarios, the contribution of the barriers was estimated in 27% from the total decrease of SC in the treated area (-8.5%). The remaining 73% was explained by the vegetation recovery. The effectiveness of the LB (11.3% on average) and EB (13.4%) did not diminish with increasing slope gradients. These percentages become relevant considering the small area affected by the LB (2.8%) and EB (1.3%). Independent metrics (convergence index, flow width, flat areas and LS factor) also reported clear differences between the two areas -higher soil erosive intensity in the untreated area- and in accordance with the AIC results.

Influence of climatic factor of changes in forest fire danger and fire season length in Turkey

In contrast to the expectations of an increase in annual fire activity and the severity of fire season due to climate change and large fires, which have been occurring in recent years, a downtrend has been identified in fire activity in many studies conducted for the whole of Europe in recent years. Similarly, in Turkey, according to the General Directorate of Forestry statistics, while there is an increase in the number of annual fires, the burnt area has a downtrend pattern. In this study, fire activity and climate data statistics for Turkey were examined along with the fire season length and severity. The results obtained conform with the studies conducted in places from Spain at the westernmost part of Mediterranean Europe to Israel at the easternmost part of the Mediterranean. Considering the changes in temperatures, temperature rise of 2 to 3 °C was detected at all stations in the study area. No decrease was observed in the average temperatures at any of the stations within the study period between 1940 and 2018. On the other hand, the precipitation trend varied according to the stations. Although there have been increases in precipitation in Fethiye, Isparta, and Marmaris since 1960, the decrease in precipitation by 132 mm in Afyon since 1970 and the decrease in precipitation by 137 mm in Bodrum since 1940 are attention-grabbing. These stations are followed by Izmir station with 66 mm and Cesme station with 37 mm of decrease, despite being smaller decreases. In the study, the long-term (1940-2018) data of the meteorological stations discussed within the study, the Canadian Fire Weather Index (FWI) and the Fine Fuel Moisture Code (FFMC) values were calculated. According to the FWI results used in determining the severity and length of fire season on the coastline of Turkey from the northern Aegean to Antalya, the likelihood of large fires decreased by about 52% in 2018 compared to 1970. This decrease in FWI value indicates that the fire severity is reduced. The specified decrease in fire severity also explains the reason of the decrease in the burnt area that occurred over the years in Turkey. No significant change was observed in the FFMC values indicating the possibility of human-induced fires between 1970 and 2018.

Mercury: What can we learn from the Amazon?

Mercury is among the ten most dangerous chemicals for public health, and is a priority concern for the 128 signatory countries of the Minamata Convention. Mercury emissions to the atmosphere increased 20% between 2010 and 2015, with South America, Sub-Saharan Africa and Southeast Asia as the main contributors. Approximately 80% of the total mercury emissions in South America is from the Amazon, where the presence of the metal is ubiquitous and highly dynamic. The presence of this metal is likely increasing, with global consequences, due to events of the last two years including extensive biomass burning and deforestation, as well as mining activities and the construction of large-scale projects, such as dams. Here we present a concise profile of this mobilization, highlighting the human exposure to this metal in areas without mining history. Mercury reaches the food chain in its most toxic form, methylmercury, intoxicating human populations through the intake of contaminated fish. Amazonian populations present levels over 6 ppm of hair mercury and, according to the 175:250:5:1 ratio for methylmercury intake : mercury hair : mercury brain : mercury blood, consume 2-6 times the internationally recognized reference doses. This exposure is alarmingly higher than that of other populations worldwide. A possible biphasic behavior of the mercury-related phenomena, with consequences that may not be observed in populations with lower levels, is hypothesized, supporting the need of improving our knowledge of this type of chronic exposure. It is urgent that we address this serious public health problem in the Amazon, especially considering that human exposure may be increasing in the near future. All actions in this region carry the potential to have global repercussions.

Biomass burning spatiotemporal variations over South and Southeast Asia

In this study, Moderate Resolution Imaging Spectroradiometer active fire and land use products were integrated to extract and classify biomass burning (BB) data for South Asia (SA) and Southeast Asia (SEA). Several trend and geographic distribution analyses were conducted at the grid (0.25° × 0.25°) and regional scales. As the principal local form of BB, crop residue burning (CRB) in SA increased by 844 spots/yr, and the Mann-Kendall (MK) τ reached 0.61. Additionally, the CRB in Punjab-Haryana, a region a well-known for severest CRB, presented a significant declining trend. BB in mainland SEA was much more intense and was dominated by forest and shrubland fires. Forest fires in mainland SEA declined at a rate of -209 spots/yr, and shrubland fire conversely grew at a rate of 803 spots/yr, which was likely related to the dramatic land cover change induced by the local swidden agriculture. Unlike other regions, BB in equatorial SEA primarily occurred in the second half of the year (August to October), and it was extremely vulnerable to El Niño events. When the annual sea surface temperature anomalies within the Niño 3 region improved by 1 °C, the annual BB spots and fire radiative power in equatorial SEA increased by 5.18 × 10⁴ and 2.40 × 10⁶ MW, respectively. Although the interannual variations in equatorial SEA were dramatic, the robust Siegel's repeated median estimator still revealed that equatorial SEA BB significantly declined by -1825 spots/yr. This regional decline reflects government endeavors to curb indigenous BB. However, regions with enhanced BB still need to draw more attention, and it is imperative for the Indonesian government to take substantial measures to reduce anthropogenic fire sources during El Niño events.

Polycyclic aromatic compounds (PACs) in the Canadian environment: Exposure and effects on wildlife

Polycyclic aromatic compounds (PACs) are ubiquitous in the environment. Wildlife (including fish) are chronically exposed to PACs through air, water, sediment, soil, and/or dietary routes. Exposures are highest near industrial or urban sites, such as aluminum smelters and oil sands mines, or near natural sources such as forest fires. This review assesses the exposure and toxicity of PACs to wildlife, with a focus on the Canadian environment. Most published field studies measured PAC concentrations in tissues of invertebrates, fish, and birds, with fewer studies of amphibians and mammals. In general, PAC concentrations measured in Canadian wildlife tissues were under the benzo[a]pyrene (BaP) guideline for human consumption. Health effects of PAC exposure include embryotoxicity, deformities, cardiotoxicity, DNA damage, changes to DNA methylation, oxidative stress, endocrine disruption, and impaired reproduction. Much of the toxicity of PACs can be attributed to their bioavailability, and the extent to which certain PACs are transformed into more toxic metabolites by cytochrome P450 enzymes. As most mechanistic studies are limited to individual polycyclic aromatic hydrocarbons (PAHs), particularly BaP, research on other PACs and PAC-containing complex mixtures is required to understand the environmental significance of PAC exposure and toxicity. Additional work on responses to PACs in amphibians, reptiles, and semi-aquatic mammals, and development of molecular markers for early detection of biological responses to PACs would provide a stronger biological and ecological justification for regulating PAC emissions to protect Canadian wildlife.

Emission of atmospheric pollutants during forest fire in boreal region of China

Data on emission of atmospheric pollutants at local scale is essential for accurately modelling forest fire emission at regional scale. In this study, we quantified emission factor (EF) of gaseous pollutants (CO, CO2, NOx, hydrocarbons, organic carbon, and inorganic elements), fine particulate matter (PM_2.5), water soluble inorganic ions, and non-methane hydrocarbons (NMHCs) from leaves, branches and barks of five dominant tree species in Chinese boreal region. Results demonstrate that the emission factors of different pollutants varied among tree species and fuel typology. The average total EF (leaves + branches + barks) of different species ranged from 922 ± 116 mg/g to 1383 ± 134 mg/g for CO₂; 225 ± 109 mg/g to 277 ± 21 mg/g for CO; 0.6 ± 0.2 mg/g to 3 ± 0.7 mg/g for NO_x; 32 ± 6 mg/g to 55 ± 7 mg/g for hydrocarbons; 3 ± 0.3 mg/g to 6 ± 0.7 mg/g for organic carbon; 0.6 ± 0.1 mg/g to 2 ± 0.1 mg/g for elemental carbon; and 4 ± 0.7 mg/g to 12 ± 1 mg/g for PM_2.5. The total water soluble ions ranged from 5 ± 0.6 mg/kg to 12 ± 1.3 mg/g. For most of the pollutants, combustion of barks emitted more than that of leaves and branches. A total of 48 types of NMHCs (19 alkanes, 15 alkenes, and 14 aromatic compounds) were released during combustion of leaves, barks, and branches of tree species, with EF ranged from 982 mg/g to 1375 mg/g. Alkenes and i-butane, 1-butene, 1,3-butadiene, Isoprene, 4-Methyl-1-pentene, p-Xylene and benzene were the major ozone-forming compounds. Our results provide a comprehensive emission data by species and fuel typology that can be useful for modelling climate change, source apportionment and atmospheric photochemistry.