Impact Of Wildfires Research Articles

Influence of Fuel Properties on the Light Absorption of Fresh and Laboratory-Aged Atmospheric Brown Carbon Produced from Realistic Combustion of Boreal Peat and Spruce Foliage.

Climate change has exacerbated fire activity in the boreal region. Consequently, smoldering boreal peatland fires are an increasingly important source of light-absorbing atmospheric organic carbon ("brown carbon"; BrC). To date, however, BrC from this source remains largely unstudied, which limits our ability to predict its climate impact. Here, we use size-exclusion chromatography coupled with diode array UV-vis detection to examine the molecular-size-dependent light absorption properties of fresh and photoaged aqueous BrC extracts collected during laboratory combustion of boreal peat and live spruce foliage. The atmospheric stability of BrC extracts varies with chromophore molecular size and fuel type: in particular, the high-molecular-weight fractions of both peat- and spruce-BrC are more resistant to photobleaching than their corresponding low-molecular-weight fractions, and total light absorption by peat-BrC persists over longer illumination timescales than that of spruce-BrC. Importantly, the BrC molecular size distribution itself varies with fuel properties (e.g., moisture content) and to an even greater extent with fuel type. Overall, our findings suggest that the accurate estimation of BrC radiative forcing, and the overall climate impact of wildfires, will require atmospheric models to consider the impact of regional diversity in vegetation/fuel types.

The wildfire suppression problem with multiple types of resources

The frequency and impact of wildfires have considerably increased in the past decade, due to the extreme weather conditions as well as the increased population density. The aim of this study is to introduce, model, and solve a wildfire suppression problem that involves multiple types of fire suppression resources and their operational characteristics. Two integer programming (IP) formulations, a basic IP and its reformulation with combinatorial Benders’ cuts, are presented. The performances of the proposed formulations are evaluated on a set of randomly generated instances. The results indicate that the proposed formulations are able to obtain high quality upper and lower bounds. Extensive numerical experiments are performed to analyse the effects of several operational constraints on the computational performance of the models. A case study arising in Yatağan district of Muğla province of Türkiye is presented.

Heterogeneity in post-fire thermal responses across Pacific Northwest streams: A multi-site study

Over the past century, water temperatures in many streams across the Pacific Northwest (PNW) have steadily risen, shrinking endangered salmonid habitats. The warming of PNW stream reaches can be further accelerated by wildfires burning forest stands that provide shade to streams. However, previous research on the effect of wildfires on stream water temperatures has focused on individual streams or burn events, limiting our understanding of the diversity in post-fire thermal responses across PNW streams. To bridge this knowledge gap, we assessed the impact of wildfires on daily summer water temperatures across 31 PNW stream sites, where 10–100% of their riparian area burned. To ensure robustness of our results, we employed multiple approaches to characterize and quantify fire effects on post-fire stream water temperature changes.Averaged across the 31 burned sites, wildfires corresponded to a 0.3 – 1°C increase in daily summer water temperatures over the subsequent three years. Nonetheless, post-fire summer thermal responses displayed extensive heterogeneity across burned sites where the likelihood and rate of a post-fire summer water temperature warming was higher for stream sites with greater proportion of their riparian area burned under high severity. Also, watershed features such as basin area, post-fire weather, bedrock permeability, pre-fire riparian forest cover, and winter snowpack depth were identified as strong predictors of the post-fire summer water temperature responses across burned sites. Our study offers a multi-site perspective on the effect of wildfires on summer stream temperatures in the PNW, providing insights that can inform freshwater management efforts beyond individual streams and basins.

The Measured Impact of Wildfires on Ozone in Western Canada From 2001 to 2019

AbstractThe impacts on atmospheric ozone (O3) due to wildfires are difficult to characterize due to the many factors that affect O3's formation rate and the episodic nature of fire events. This study uses a very large set of air quality data (518,987 6‐hr data points) collected in Western Canada from 2001 to 2019 to determine the prevalence and severity of fire‐driven increases to measured O3 values. Wildfire events are identified using the automated Trajectory‐Fire Interception Method (TFIM), looking for interceptions between HYSPLIT back‐trajectories and wildfire hotspots. As with other studies, which have used more restricted sets of measurements, the results from this large‐scale, data‐driven approach indicate increases in the O3 mixing ratio with wildfire impact, on average ∼2 ppbv across all wildfire time periods. To understand the factors which lead to the largest increases, and to better compare to other studies looking at individual fire events, wildfire events are classified using their distance from the air quality measurement location, time of measurement, and corresponding PM2.5 value. Increases to O3 are largest during the daytime, when fires occur close to the air quality measurement, and with corresponding measurements of PM2.5 > 25 μg/m3. When an upper‐limit correction for the bias in UV photometric detection of ozone with MnCl2 scrubbers is applied, the analysis still yields a persistent increase in O3 during wildfires except for the highest PM2.5 levels. However, a more accurate correction to the potential bias is needed to fully understand the magnitude of the impact of wildfires on O3.

Assessing Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2 Data for Large-Scale Wildfire-Burned Area Mapping: Insights from the 2017–2019 Canada Wildfires

Wildfires play a crucial role in the transformation of forest ecosystems and exert a significant influence on the global climate over geological timescales. Recent shifts in climate patterns and intensified human–forest interactions have led to an increase in the incidence of wildfires. These fires are characterized by their extensive coverage, higher frequency, and prolonged duration, rendering them increasingly destructive. To mitigate the impact of wildfires on climate change, ecosystems, and biodiversity, it is imperative to conduct systematic monitoring of wildfire progression and evaluate their environmental repercussions on a global scale. Satellite remote sensing is a powerful tool, offering precise and timely data on terrestrial changes, and has been extensively utilized for wildfire identification, tracking, and impact assessment at both local and regional levels. The Canada Centre for Mapping and Earth Observation, in collaboration with the Canadian Forest Service, has developed a comprehensive National Burned Area Composite (NBAC). This composite serves as a benchmark for curating a bi-temporal multi-source satellite image dataset for change detection, compiled from the archives of Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2. To our knowledge, this dataset is the inaugural large-scale, multi-source, and multi-frequency satellite image dataset with 20 m spatial resolution for wildfire mapping, monitoring, and evaluation. It harbors significant potential for enhancing wildfire management strategies, building upon the profound advancements in deep learning that have contributed to the field of remote sensing. Based on our curated dataset, which encompasses major wildfire events in Canada, we conducted a systematic evaluation of the capability of multi-source satellite earth observation data in identifying wildfire-burned areas using statistical analysis and deep learning. Our analysis compares the difference between burned and unburned areas using post-event observation solely or bi-temporal (pre- and post-event) observations across diverse land cover types. We demonstrate that optical satellite data yield higher separability than C-Band and L-Band Synthetic Aperture Radar (SAR), which exhibit considerable overlap in burned and unburned sample distribution, as evidenced by SAR-based boxplots. With U-Net, we further explore how different input channels influence the detection accuracy. Our findings reveal that deep neural networks enhance SAR’s performance in mapping burned areas. Notably, C-Band SAR shows a higher dependency on pre-event data than L-Band SAR for effective detection. A comparative analysis of U-Net and its variants indicates that U-Net works best with single-sensor data, while the late fusion architecture marginally surpasses others in the fusion of optical and SAR data. Accuracy across sensors is highest in closed forests, with sequentially lower performance in open forests, shrubs, and grasslands. Future work will extend the data from both spatial and temporal dimensions to encompass varied vegetation types and climate zones, furthering our understanding of multi-source and multi-frequency satellite remote sensing capabilities in wildfire detection and monitoring.

RETRACTED ARTICLE: Wildfire risk exploration: leveraging SHAP and TabNet for precise factor analysis

BackgroundUnderstanding the intricacies of wildfire impact across diverse geographical landscapes necessitates a nuanced comprehension of fire dynamics and areas of vulnerability, particularly in regions prone to high wildfire risks. Machine learning (ML) stands as a formidable ally in addressing the complexities associated with predicting and mapping these risks, offering advanced analytical capabilities. Nevertheless, the reliability of such ML approaches is heavily contingent on the integrity of data and the robustness of training protocols. The scientific community has raised concerns about the transparency and interpretability of ML models in the context of wildfire management, recognizing the need for these models to be both accurate and understandable. The often-opaque nature of complex ML algorithms can obscure the rationale behind their outputs, making it imperative to prioritize clarity and interpretability to ensure that model predictions are not only precise but also actionable. Furthermore, a thorough evaluation of model performance must account for multiple critical factors to ensure the utility and dependability of the results in practical wildfire suppression and management strategies.ResultsThis study unveils a sophisticated spatial deep learning framework grounded in TabNet technology, tailored specifically for delineating areas susceptible to wildfires. To elucidate the predictive interplay between the model’s outputs and the contributing variables across a spectrum of inputs, we embark on an exhaustive analysis using SHapley Additive exPlanations (SHAP). This approach affords a granular understanding of how individual features sway the model’s predictions. Furthermore, the robustness of the predictive model is rigorously validated through 5-fold cross-validation techniques, ensuring the dependability of the findings. The research meticulously investigates the spatial heterogeneity of wildfire susceptibility within the designated study locale, unearthing pivotal insights into the nuanced fabric of fire risk that is distinctly local in nature.ConclusionUtilizing SHapley Additive exPlanations (SHAP) visualizations, this research meticulously identifies key variables, quantifies their importance, and demystifies the decision-making mechanics of the model. Critical factors, including temperature, elevation, the Normalized Difference Vegetation Index (NDVI), aspect, and wind speed, are discerned to have significant sway over the predictions of wildfire susceptibility. The findings of this study accentuate the criticality of transparency in modeling, which facilitates a deeper understanding of wildfire risk factors. By shedding light on the significant predictors within the models, this work enhances our ability to interpret complex predictive models and drives forward the field of wildfire risk management, ultimately contributing to the development of more effective prevention and mitigation strategies.

Are fire intensity and burn severity associated? Advancing our understanding of FRP and NBR metrics from Himawari-8/9 and Sentinel-2

Burn severity has been widely studied. Typical approaches use spectral differencing indices from remotely sensed data to extrapolate in-situ severity assessments. Next generation geostationary data offer near-continuous fire behaviour information, which has been used for fire detection and monitoring but remains underutilized for fire impact estimation. Here, we explore the association between remotely sensed fire intensity metrics and spectral differencing severity indices to understand whether and where they describe similar wildfire effects. The commonly used Differenced Normalised Burn Ratio (dNBR) severity index was calculated for Advanced Himawari Imager (AHI − 2 km) and Sentinel-2 (20 m) data and compared to different Fire Radiative Power (FRP) metrics derived from fire hotspot detections from AHI data across Australia. The comparison was implemented through different stratifications based on biogeographical region, land cover, fire type, and percentage of AHI pixel burned (fire fractional cover). The results indicate that FRP and dNBR metrics do not correlate in most scenarios, noting correlations being marginally stronger for hotter fires. However, correlations become significantly stronger when data are grouped using fire type information and fire fractional cover, with correlations peaking (R = 0.75) for large fires that burned 41–60 % of an AHI pixel. In conclusion, remotely sensed fire intensity and severity proxies capture different aspects of wildfire impact, that only correlate with each other after using auxiliary data. Spectral differencing severity metrics have been used extensively during the past decades, however high-frequency fire intensity estimations have the potential to augment the existing information and reveal new ways of characterizing wildfire impact over large areas.

Pyrogeography of the Western Great Plains: A 40-Year History of Fire in Semi-Arid Rangelands

This study describes spatial and temporal patterns in fire across the US Western Great Plains over the last 40 years. Although pyrogeographic studies have explored the nexus of fire patterns in relation to the bio-physical environment and socio-ecological trends, most of this research has focused on forested ecosystems and regions long known for conflict between wildfires and human development, especially at the wildland–urban interface. But evidence suggests large wildfire activity is increasing in the US Great Plains, and the Western Great Plains—a Land Resource Region comprised of four ecoregions, Northwestern Plains, High Plains, Nebraska Sandhills, and Southwestern Tablelands—not only contains some of the largest areas of rangeland in the US but also the highest concentration of public land in the Great Plains. As such, the Western Great Plains provides an opportunity to explore fire activity in primarily rural landscapes with a combination of public and private ownership, all dominated by rangeland vegetation. We combined several publicly-available datasets containing fire records between 1992 and 2020 to create two databases, one with georeferenced point data on 60,575 wildfire events in the region, and another with georeferenced perimeter data for 2665 fires. Ignition by humans was the dominant cause of fires. No ecoregion showed a statistically significant trend towards either increasing or decreasing the annual burned area. The Northwestern Plains had the most burned area and the greatest number of incidents—consistently around or above 1000 incidents per year since 1992—with the majority in July. The High Plains showed the greatest increase in annual fire incidence, never reaching more than 200–300 per year 1992–2009, and averaging above 1000 incidents per year since 2010. Few long-term trends in human population, weather, or fuel metrics appear strongly associated with fire patterns in any ecoregion, although the years 2006, 2012, and 2017 stood out for their levels of fire activity, and these years often frequently logged extreme values in wildland fuel metrics. These relationships merit much closer examination in the Western Great Plains, because like other rangeland-dominated landscapes, the fine fuels that comprise these wildland fuelbeds are much more responsive to fine-scale changes in moisture conditions. Rural Western Great Plains landscapes are a mosaic of public and private land ownership, and an increasing impact of wildfires on public grazing lands—which are often situated within other jurisdictions or ownership—will likely have an impact on rural livelihoods.

Wildfire impact on soil microbiome life history traits and roles in ecosystem carbon cycling

Abstract Wildfires, which are increasing in frequency and severity with climate change, reduce soil microbial biomass and alter microbial community composition and function. The soil microbiome plays a vital role in carbon (C) and nitrogen (N) cycling, but its complexity makes it challenging to predict post-wildfire soil microbial dynamics and resulting impacts on ecosystem biogeochemistry. The application of biogeochemically relevant conceptual trait-based frameworks to the soil microbiome can distill this complexity, enabling enhanced predictability of soil microbiome recovery following wildfire and subsequent impacts to biogeochemical cycles. Conceptual frameworks that have direct links to soil C and N cycling have been developed for the soil microbiome; the Y-A-S framework overviews soil microbiome life history strategies that have tradeoffs with one another and others have proposed frameworks specific to wildfire. Here, we aimed to delineate post-wildfire changes of bacterial traits in western US coniferous forests to inform how severe wildfire influences soil microbiome recovery and resultant biogeochemical cycling. We utilized a comprehensive metagenome-assembled genome catalog from post-wildfire soils representing 1 to 11 years following low- and high-severity burning to identify traits that enable the persistence of microbial taxa in burned soils and influence ecosystem C and N cycling. We found that high-severity wildfire initially selects for fast growers and, up to a decade post-fire, taxa that invest in genes for acquiring diverse resources from the external environment, which in combination could increase soil C losses. This work begins to disentangle how climate change–induced shifts in wildfire behavior might alter microbially mediated soil biogeochemical cycling.

Accelerating decline of wildfires in China in the 21st century

Global wildfires have increased in frequency and intensity, especially in temperate regions, in the context of global warming. However, the spatiotemporal characteristics of wildfire frequency and intensity are still not well understood. Using Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6) wildfire products during 2003–2022, here, we analyzed the spatiotemporal patterns and evolutionary characteristics of the frequency and intensity of wildfires in China. The main conclusions are: (1) Over the past 20 years, the wildfire frequency and fire radiative power (FRP) have declined at rates of –1920/year (p < 0.001) and –76492 MW/year (p < 0.001), respectively, showing a highly significant downward trend, with declines of up to 63 and 81%. (2) Wildfires in China show a single peak pattern of high incidence in winter and spring, with the most frequent in March, followed by February and April. (3) The overall spatial distribution of wildfires in China is characterized by a bimodal distribution, with more in the south and less in the north. Wildfires are most abundant but less intense in the southern region(SR), fewer but most intense in the northeastern region(NER), and significantly influenced by El Niño in the southwestern region(SWR), with significant regional differences in wildfires in China. (4) The average FRP of wildfire spots presented a decreasing trend from 47 MW/spot in 2003 to only 25 MW/spot in 2022. This may be due to more dispersed wildfires, rather than concentrated wildfires. (5) The frequency and FRP of wildfires showed an overall negative correlation with the Standardized Precipitation Evapotranspiration Index (SPEI). Drought events (negative SPEI) have a significant effect on wildfires, especially in SR. This study demonstrates the effectiveness of current fire suppression policies in China in terms of disaster prevention and mitigation, and further provides data to further explore and analyze the impact of wildfires on the regional environment.

Employing Copernicus Land Service and Sentinel-2 Satellite Mission Data to Assess the Spatial Dynamics and Distribution of the Extreme Forest Fires of 2023 in Greece

In 2023, Greece faced its worst wildfire season, with nine major fires causing unprecedented environmental damage of 1470.31 km2. This article uses Copernicus Land Monitoring Service and Sentinel-2 data, employing advanced remote sensing and GIS techniques to analyze spatial dynamics, map burn severity, assess fire extent, and highlight pre-fire tree density and land cover. The study focuses on the catastrophic fire in the Evros region and the damage to the National Forest Park of Dadia–Lefkimmi–Soufli. It also analyzes significant fires in Rhodes, Attica, Thessaly, Evia, Corfu, and Magnesia, emphasizing the compounded challenges posed by terrain, climate, and human factors in those areas. Additionally, the climate data for each affected area were compared with the weather conditions prevailing at the time of the fires. Copernicus Land Cover and Tree Density data are integrated to aid future management, assessment, and restoration. The analysis of maps and fire statistics underscores a notable pattern: areas with higher pre-fire tree density experienced correspondingly higher burn severity. This research underscores the crucial role of such data in assessing wildfire impact. In addition, compared with Copernicus Emergency Management Service, the burned area maps validate the accuracy and reliability of the utilized satellite data. The total burned area was assessed with a high accuracy rate of 96.28%.

Impacts of wildfire on soil characteristics in subtropical Chir Pine forests of Murree, Pakistan

Wildfires are a natural component of forest ecosystem. These fires are a major cause of significant perturbations in the forest habitat due to temperature extremes and drought conditions particularly in summer. Wildfires occur frequently during summer in the subtropical Chir pine forests of Murree, Pakistan. These fires influence soil characteristics as well as tree-species growth and survival. The present study was thus conducted to investigate soil characteristics of wildfire-affected and non-affected areas. Four sites were selected based on wildfire incidents in the study area. Soil samples were collected from the fire-affected and adjacent non-affected areas. The collected soil samples were analyzed for physical and chemical characteristics using appropriate methods. The study showed that bulk density, moisture content, electrical conductivity, and soil nitrogen and soil carbon content declined in the burnt soil. However, soil pH, and phosphorus and potassium contents were higher in the burnt soils than those in the non-burnt soils. Generally, low intensity forest fire (surface fire) is an important component of forest ecosystem and it enhances natural regeneration as well as soil fertility. It is suggested that government forest departments should take into account burn severity while developing hazard reduction and restoration strategies of wildfire affected areas.

In the line of fire: Analyzing burning impacts on air pollution and air quality in an Amazonian city, Brazil

Brazil is grappling with escalating wildfire incidents, particularly in the ecotone region of Cerrado-Amazon, attributed to climate change and anthropogenic factors. These wildfires significantly impact air quality, posing health and environmental risks to the population. This study aimed to assess the influence of wildfires on air quality in Sinop, a city located in Northern Mato Grosso, Brazil. The primary focus was on understanding the behavior of air pollutants during and after wildfire events. Air quality data were obtained from modeling dataset, while meteorological data from ground-based monitoring, and wildfire data from the National Institute of Spatial Research of Brazil. Statistical analyses were employed to investigate pollutant variations and their relationships with meteorological parameters and wildfire intensity indicators. The study revealed a significant impact of wildfires on pollutant concentrations, with ozone levels rising immediately after fires and remaining elevated for several days. The particulate matter (PM2.5 and PM10) concentrations increased a few days after fire events. Fire radiative power (FRP) correlated with O3 levels, suggesting it is an intensity indicator. Temperature exhibited a consistent positive correlation with all pollutants, highlighting its role in pollutant dynamics. The findings underscore the multifaceted relationship between wildfires, meteorological conditions, and pollutant concentrations pointing to the importance of holistic air quality management strategies in fire-prone regions. Such studies are crucial for guiding environmental policies, safeguarding public health, and advancing scientific understanding in the face of escalating wildfire challenges.

Toward Rapid Balloon Experiments for Sudden Aerosol Injection in the Stratosphere (REAS) by Volcanic Eruptions and Wildfires

Abstract Stratospheric aerosols are greatly influenced by medium-to-large volcanic eruptions. Over the last few years, extreme wildfires have been identified as new sources of stratospheric particles, in the form of carbonaceous aerosols injected by pyrocumulonimbus (pyroCb) events in the upper troposphere and lower stratosphere, associated with significant impacts on climate and ozone chemistry. To assess the impact of wildfires and volcanic eruptions on stratospheric aerosol loadings in the Northern Hemisphere, the Rapid Balloon Experiments for Sudden Aerosol Injection in the Stratosphere (REAS) project has been initiated. REAS is an international initiative that aims to respond to sudden events impacting stratospheric aerosol composition. Seventeen balloons were launched from Reims, eastern France, between November 2021 and January 2022 to quantify the atmospheric content for both aerosols and trace/greenhouse gases from the ground up to stratospheric levels. The main measurements concerned trace gases (CO/CO2 as tracers of smoke) and aerosol together with ozone using instruments such as a gas collector, optical particle counters, backscatter sondes, an aerosol sampler, an aerosol impactor, and ozonesondes. The Groupe de Spectrométrie Moléculaire et Atmosphérique (GSMA) launch facility provided unique possibilities of combining multiple measurements in one flight thanks to medium flights (corresponding to a 6 kg payload). While no major event impacted the stratosphere during the campaign, we particularly discuss the influence of the aged volcanic plume from La Soufrière volcano (Saint Vincent island) and smoke particles from series of pyroCb events that took place in North America. The burden as well as the optical and microphysical properties of the observed aerosols are quantified from these in situ observations in association with various satellite data.

Impact of wildfire smoke on ozone concentrations using a Generalized Additive model in Salt Lake City, Utah, USA, 2006–2022

ABSTRACT We investigated the impact of wildfires on maximum daily 8-hr average ozone concentrations (MDA8 O3) at four sites in Salt Lake City (SLC), Utah for May to September for 2006–2022. Smoke days, which were identified by a combination of overhead satellite smoke detection and surface PM2.5 data and accounted for approximately 9% of the total number of days, exhibited O3 levels 6.8 to 8.9 ppb higher than no-smoke days and were predominantly characterized by high daily maximum temperatures and low relative humidity. A Generalized Additive Model (GAM) was developed to quantify the impact of wildfire contributions to O3. The GAM, which provides smooth functions that make the interpretation of relationships more intuitive, employed 17 predictors and demonstrated reliable performance in various evaluation metrics. The mean of the residuals for all sites was approximately zero for the training and cross-validation data and 5.1 ppb for smoke days. We developed three approaches to estimate the contribution of smoke to O3 from the model residuals. These generate a minimum and maximum contribution for each smoke day. The average of the minimum and maximum wildfire contributions to O3 for the SLC sites was 5.1 and 8.5 ppb, respectively. Between 2006 and 2022, an increasing trend in the wildfire contributions to O3 was observed in SLC. Moreover, trends of the fourth-highest MDA8 O3 before and after removing the wildfire contributions to O3 at the SLC Hawthorne site in 2006–2022 were quite different. Whereas the unadjusted data do not meet the current O3 standard, after removing the contributions from wildfires the SLC region is close to achieving levels that are consistent with meeting the O3 standard. We also found that the wildfire contribution during smoke days was particularly high under conditions of high temperature, high PM2.5 concentration, and low cloud fraction. Implications: In this study, we quantified the impact of wildfires on maximum daily 8-hr average ozone concentrations (MDA8 O3) in Salt Lake City, Utah, using a Generalized Additive Model (GAM). The GAM results demonstrate the importance of wildfires as contributors to O3 air pollution. Our results suggest that states could use the GAM approach to assist in quantifying the wildfire contribution to MDA8 O3 under the U.S. EPA exceptional events rule. These findings also highlight the need for strategies to manage wildfires and their subsequent impacts on air quality in an era of climate warming.

Developing customized fuel models for shrub and bracken communities in Galicia (NW Spain)

Geospatial fire behaviour and fire hazard simulators, fire effects models and smoke emission software commonly use standard fuel models in order to simplify data collection and the inclusion of complex fuel scenarios. These fuel models are often mapped using remotely sensed data. However, given the great complexity of fuelbeds, with properties that vary widely in both time and space, the use of these standard fuel models can greatly limit accurate fuel mapping. This affects fuel hazard assessment, fuel reduction treatment plans, fire management decision-making and evaluation of the environmental impact of wildfire. In this study, we developed unique customized fire behaviour fuel models for shrub and bracken communities, by using k-medoids clustering analysis based on both fuel structural characteristics and potential fire behaviour. We used an original database of 722 destructive sample plots in nine different shrub and bracken communities covering the entire distribution area in Galicia (NW Spain), one of the regions in Europe most affected by forest fires. Measurements of cover, height and fuel fractions loads differentiated by size and vegetative state (live or dead) were used to estimate the potential rate of fire spread with five different models including fireline intensity, heat per unit area and the flame length for each sampling site and considering extreme environmental conditions. The optimal number of clusters was established by combining practical knowledge about the shrubland communities under study and their associated fire behaviour, with maximization of the mean value of the silhouette variable and minimization of the within-cluster sum of squares. The structural characteristics of the medoids derived from the analysis were associated with each of the proposed customized fuel models. Finally, a simple dichotomous classification based only on shrub height was developed to enable construction of spatially explicit fuel model maps based on remotely sensed data. Thus, the methodology applied allows generation of a more realistic representation of fuel distribution in the landscape, based on fuel structure measurements of natural regional ecosystems rather than on the use of standard models. We believe that the proposed methodology is generally applicable to communities composed of other shrub and fern species in different biogeographical regions.

Experimental and Numerical Evaluation of a Wildland–Urban Interface Fire Scenario

This paper presents the results obtained from a field fire test, aiming to reproduce a wildland–urban interface scenario to collect relevant information concerning the impact of wildfires on the built environment. The objective was to understand heat transfer mechanisms from forest fires to structures. During the fire test, the temperatures at the exposed face of one building component were monitored, as well as those in the vicinity of that component, using thermal imaging. The detailed characterization of the field test and building component and obtained experimental results of the fire test were then used to develop and validate a complex computational fluid dynamics model (full physics models) using the Fire Dynamics Simulator (FDS). Several numerical models were previously developed to reproduce the behaviour of individual shrubs and trees in fires considering available results in the literature. The developed Computational Fluid Dynamics (CFD) models can accurately reproduce the field test, including the fire spread and the temperature evolution on the surface of the exposed construction component. The obtained maximum temperature in the construction element was 1038 °C, whereas the maximum average temperature was approximately 638 °C. According to the results from the numerical model, the construction element was exposed to a very high heat flux (above 40 kW/m2), indicating direct contact of the flames with the construction element. The use of CFD enables the quantification of the characteristics of the fire and the exposure of structures to fire in the wildland–urban interface (WUI), allowing for the definition of a performance-based design approach for buildings in the WUI. This contributes to developing safe and resilient structures, as well as mitigating and reducing the impacts of wildfires in the built environment.

Long-term mortality burden trends attributed to black carbon and PM2·5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study

Long-term improvements in air quality and public health in the continental USA were disrupted over the past decade by increased fire emissions that potentially offset the decrease in anthropogenic emissions. This study aims to estimate trends in black carbon and PM2·5 concentrations and their attributable mortality burden across the USA. In this study, we derived daily concentrations of PM2·5 and its highly toxic black carbon component at a 1-km resolution in the USA from 2000 to 2020 via deep learning that integrated big data from satellites, models, and surface observations. We estimated the annual PM2·5-attributable and black carbon-attributable mortality burden at each 1-km2 grid using concentration-response functions collected from a national cohort study and a meta-analysis study, respectively. We investigated the spatiotemporal linear-regressed trends in PM2·5 and black carbon pollution and their associated premature deaths from 2000 to 2020, and the impact of wildfires on air quality and public health. Our results showed that PM2·5 and black carbon estimates are reliable, with sample-based cross-validated coefficients of determination of 0·82 and 0·80, respectively, for daily estimates (0·97 and 0·95 for monthly estimates). Both PM2·5 and black carbon in the USA showed significantly decreasing trends overall during 2000 to 2020 (22% decrease for PM2·5 and 11% decrease for black carbon), leading to a reduction of around 4200 premature deaths per year (95% CI 2960-5050). However, since 2010, the decreasing trends of fine particles and premature deaths have reversed to increase in the western USA (55% increase in PM2·5, 86% increase in black carbon, and increase of 670 premature deaths [460-810]), while remaining mostly unchanged in the eastern USA. The western USA showed large interannual fluctuations that were attributable to the increasing incidence of wildfires. Furthermore, the black carbon-to-PM2·5 mass ratio increased annually by 2·4% across the USA, mainly due to increasing wildfire emissions in the western USA and more rapid reductions of other components in the eastern USA, suggesting a potential increase in the relative toxicity of PM2·5. 100% of populated areas in the USA have experienced at least one day of PM2·5 pollution exceeding the daily air quality guideline level of 15 μg/m3 during 2000-2020, with 99% experiencing at least 7 days and 85% experiencing at least 30 days. The recent widespread wildfires have greatly increased the daily exposure risks in the western USA, and have also impacted the midwestern USA due to the long-range transport of smoke. Wildfires have become increasingly intensive and frequent in the western USA, resulting in a significant increase in smoke-related emissions in populated areas. This increase is likely to have contributed to a decline in air quality and an increase in attributable mortality. Reducing fire risk via effective policies besides mitigation of climate warming, such as wildfire prevention and management, forest restoration, and new revenue generation, could substantially improve air quality and public health in the coming decades. National Aeronautics and Space Administration (NASA) Applied Science programme, NASA MODIS maintenance programme, NASA MAIA satellite mission programme, NASA GMAO core fund, National Oceanic and Atmospheric Administration (NOAA) GEO-XO project, NOAA Atmospheric Chemistry, Carbon Cycle, and Climate (AC4) programme, and NOAA Educational Partnership Program with Minority Serving Institutions.

Impacts of wildfires and strategies to accelerate secondary succession: A comprehensive analysis

Wildfires pose a significant and growing threat to ecosystems, leading to loss of biodiversity, water contamination, and alteration of habitats. This paper examines the devastating impacts of wildfires, with a focus on California, and explores the natural process of secondary succession in coastal forests. It further presents a comprehensive set of solutions to accelerate this process and mitigate the effects of wildfires. Strategies include immediate interventions like mulching, controlling invasive species through geo-spatial mapping, utilizing artificial structures such as seed banks and erosion control matting, and strategic planting and seeding. The paper emphasizes the need for collaboration among governments, researchers, and communities to implement these solutions effectively. It offers a roadmap for creating resilient ecosystems that can recover more quickly and sustainably from wildfires, highlighting the importance of innovation, education, and commitment to safeguarding our natural environment.

Impact of wildfires on regional ozone and PM2.5: Considering the light absorption of Brown carbon

The influence of wildfire brown carbon (BrC) in moderating the formation of O3 and fine particulate matter (PM2.5) due to its absorption of ultraviolet (UV) radiation is, for the first time, investigated using a modified community multiscale air quality (CMAQ) model. The wavelength-dependent imaginary refractive index of the organic carbon from wildfires, which are needed by the CMAQ model for its inline photolysis rate calculation, are generated experimentally from wood burning aerosols from a combustion chamber. On high emission days of the Bastrop County Complex fire in Texas in early September 2011, BrC UV absorption reduces the daytime average NO2 photolysis rate by up to 38% in the core region of the wildfire plume compared to the base scenario that does not consider BrC absorption. Consequently, O3 production in the smoke plume is suppressed compared to the base scenario due to reduced HOx radical concentrations. In the core region, the predicted O3 increase due to wildfire reaches as high as 47–123 ppb without considering BrC absorption, the predicted increase of O3 is 5–15% lower when BrC absorption is considered. Similarly, considering the BrC UV absorption leads to approximately 1% (or ∼2–3 μg m−3) lower estimation of the wildfire emissions’ impact on total PM2.5. This change is small because secondary aerosols, which are the components affected by BrC absorption, only account for a small fraction of the total PM2.5 in wildfire impacted regions in this study. In addition, our study shows that assumptions about aerosol mixing state (core-shell vs. homogeneous) in the inline photolysis rate calculation would not significantly affect out assessment of the impact of wildfire BrC light absorption on O3 and PM2.5.