Changes in lung function and fractional exhaled nitric oxide across wildfire seasons in the wildland firefighter exposure and health effect (WFFEHE) study.

Abstract. Landscape fires emit climate-influencing greenhouse gases and aerosols. The vast majority of landscape fire emissions originate from tropical savannas, especially in Africa. During the fire season climatic conditions change, and fires burning later consume drier vegetation and occur in drier weather conditions than earlier fires. Previous studies have shown that it is possible to reduce emissions of some greenhouse gases (CH4 and N2O) by using “prescribed” fires, i.e. deliberate burning in the early dry season. In this study we examine the climate effect of (deliberately) changing fire regimes beyond CH4 and N2O, including aerosols and other short-lived species, CO2, and changes to surface albedo. We find that in general shifting burning earlier in a single fire season results in global negative climate forcing (cooling) of around −0.001 to −0.002 W m−2 (long-term) or −0.006 (short-term) W m−2, compared to less than −0.0005 W m−2 if only considering CH4 and N2O. Forcing from shifting burning later in contrast is negligible in the long term. CO2 emissions reduction through emission factor changes and burned area reduction is the largest contributing factor, though especially in the short term albedo effects are also substantial. Shifting fire activity towards the late fire season generally produces a positive climate forcing (warming) of a smaller magnitude. We find too that some localities within our study area have a potentially disproportionately large impact on our results, such that the efficacy of any fire regime change with respect to climate forcing must be carefully considered on a local scale.

Effects of Seasonal Fires and Occurrence on Tree Species Density across Fire Treatment in Makambu, Kavango West Region, Namibia

Abstract Fires have a significant impact on the regional carbon budget, the ecosystem, and public health. We analyzed the fire dynamics and its impact on carbon flux across three fire prone regions in South Asia, Region-1 (southwestern Nepal, Uttarakhand), Region-2 (central India), and Region-3 (northeast India) from 2010 to 2021, with a focus on the significant fire season of February, March, and April (FMA) of 2021. We find high burned areas (5,000-10,000 km 2 ), and fire carbon emissions (0.3-4 TgC season –1 ) across these regions in FMA, 2021, as compared to a climatological mean from 2010–2020. Each of the three regions shows distinct drivers that preceded the fires. In Region-1, snow-induced soil moisture deficits drive fire activity, leading to a subsequent decline in gross primary production (GPP). In Region-2, human activities, likely cropland burning, contributed to the forest fire. In Region-3, the scattered distribution of burned areas hints that human activity is the likely cause of the forest fire. During FMA, 2021, fire carbon emission in Region-1 (~4 TgC) were almost twice of the fossil fuel emissions (~2.2 TgC), while in Region-2 (~3.8 TgC), it remained below fossil fuel emissions (~16 TgC). In both regions, emissions from forests and croplands contributed equally to the total fire carbon emissions. In Region-3, fire carbon emissions exceeded fossil fuel emissions in 2012 (~4.7 TgC), 2013 (~6.18 TgC), and 2014 (~9.75 TgC) but remained lower in 2021 (~3.37 TgC), with most emissions originating from forests. This analysis highlights the critical role of forest fires in the carbon budget, the ecosystem and the need for better forest carbon management.

Background Dead fine fuel moisture content (FMC) is critical for predicting fire behavior and effects. Spatiotemporal variation in FMC occurs due to to variability in atmospheric conditions at the fuel interface, which is influenced by interacting factors including local forest structure and topography. Previous research has primarily examined these patterns over coarse spatial scales and relied on few factors to explain variability. Aims In this study, we monitored the spatiotemporal variability in FMC and characterized how controls of FMC vary over a fire season. FMC was sampled at 80 locations 21 times (approximately weekly) through the summer season in a 17.6 ha southern Rocky Mountain mixed-conifer forest. Key results Results indicate that FMC variability declines during drier periods and that the influence of forest structure and topography on FMC is constant through time under fluctuating precipitation patterns. FMC values are autocorrelated over spatial and temporal scales and are highly variable over fine spatial scales. Conclusions Understanding the full magnitude of FMC variability is important for achieving management objectives under both prescribed and wildfire conditions. Implications Further research into FMC variability and its controls could lead to more reliable models and tools allowing managers to better predict fire behavior and effects.

Abstract Background Little is known about the fire history of the Atlantic cypress swamps, which are found along the coast of the United States from Maine to Florida. By utilizing historical records from journals and newspapers, we have documented the fire history of the Delmarva Peninsula’s Great Cypress Swamp. In the late eighteenth century, this swamp was estimated to cover nearly 50,000 acres (20,234 ha). Results Between 1782 and 1941, the Great Cypress Swamp experienced 18 documented fires. The fire season in the swamp lasted from May to November, with the highest number of fires—one-third—occurring in July. The mean fire return interval (MFRI) was 5.33 years. Generally, both the Palmer Drought Severity Index (PDSI) and newspaper reports indicated that fires were more likely to occur during times of drought. Notably, the 1930 fire, which lasted from August to October, occurred under severe drought conditions. Conclusions Over time, the Great Cypress Swamp has undergone significant human alteration due to timber harvesting for shingle production and ditching and drainage efforts associated with agricultural expansion. The removal of the canopy, as well as drainage, likely intensified the effects of droughts by drying out the peat and increasing the risk of fire. In most cases, the exact cause of the fires remains unknown, but they were most likely of anthropogenic origin, such as careless smoking and the burning of dry peat to access buried logs.

Abstract Background: As climate conditions intensify fire seasons, human exposure to wildfire smoke becomes more significant, posing health risks and disrupting emotional and social well-being. Academic literature exploring how people perceive and respond to wildfire smoke has grown rapidly in the past decade. We conducted this systematic review to understand the theoretical models and disciplinary focus of existing research, examine the application of behavioral theories to smoke protection behaviors, and identify emergent themes and research gaps. Methods/Design: Following PRISMA standards, we systematically reviewed papers focusing on individual-level human perception or behavioral responses to wildfire smoke events. We extracted information on eight psychosocial factors across three theoretical levels: intrapersonal (cognitive processes), interpersonal (social influences), and contextual factors. Results: Our review of 39 studies reveals an emerging field concentrated in the Western US and dominated by public health perspectives. Only 10 studies employed explicit theoretical models. Smoke exposure evokes strong emotional responses and significantly disrupts daily routines. We found complex relationships between psychosocial factors: while people generally recognize smoke as threatening, the relationship between threat perception and protective action remains complex, with mixed findings regarding past experience and coping appraisal. Social networks play a paradoxical role, providing crucial support during smoke events while being disrupted by smoke-induced isolation that prevents normal social gathering. Social norms around smoke protection remain underexplored. Socially vulnerable populations are underrepresented despite facing heightened exposure risks. Discussion: We identify significant research gaps regarding emotional responses, social norms, and community-level interventions. Enhanced understanding of wildfire smoke responses can improve interventions and policies to promote public health and community adaptation to wildfire smoke.

Abstract In 2020, a record‐breaking 10.1 million acres burned in US wildfires. To investigate this prolific fire season's impacts on US air quality, the National Oceanic and Atmospheric Administration (NOAA)'s 72‐hr Unified Forecast System Air Quality Model (UFS‐AQM) is run once‐daily from 15 August–30 September 2020. A meteorological and air quality‐based forecast verification is carried out for this period. The 72‐hr forecasts of near‐surface temperature, moisture, and winds perform quite well against observations in terms of correlation and bias. The UFS‐AQM surface ozone and fine particulate matter (PM 2.5 ) often concur with US Environmental Protection Agency (EPA) AirNow station data, though the model ozone displays a consistent positive bias ranging from 8.5 to 15.4 ppb. Anomalously poor PM 2.5 predictions are identified during extreme wildfire activity in the Pacific Northwest between 13 and 20 September. Here, in addition to periods of widespread cloudiness, smoke is quite thick. As a result, fire activity is obscured from satellite detection. The UFS‐AQM thus underestimates wildfire emissions in the Pacific Northwest. The lack of dynamic aerosol–radiation interactions in UFS‐AQM coupled with uncertainties in fire emissions leads to a large underestimation of surface PM 2.5 there, along with high temperature and low humidity biases. Finally, satellite‐based measurements are also employed to evaluate the UFS‐AQM's performance in the deeper troposphere. The Visible Infrared Imaging Radiometer Suite 550‐nm aerosol optical depth provides additional insights into the UFS‐AQM's handling of smoke transport, while the TROPOspheric Monitoring Instrument suggests UFS‐AQM suffers from overpredictions of NO 2 and CO in active fire regions.

Although the atmospheric boundary layer height (ABLH) is a highly relevant parameter for various meteorological studies, the analysis of its behavior remains undersampled in South America, especially in Brazil. In this context, this work presents a monthly characterization of the ABLH during the convective period (Convective Boundary Layer Height-CBLH) using radiosonde data and a comparison between the monthly patterns obtained from ERA5 and COSMIC-2 data. The results demonstrate that, based on radiosonde data, the CBLH can be grouped into six regions (Northern Amazon, North, Northeast, Midwest, Southeast, and South), with seasonality varying according to the continentality and the climate to which they are exposed. The ERA5 and COSMIC-2 data show considerable agreement for most of the year [average absolute difference of [362 ± 182] m] and demonstrate the same seasonality observed in radiosondes for the North Amazon, North, Northeast, Southeast, and South regions. The highest discrepancies between ERA5 and COSMIC-2 occur during the fire season, mainly at Midwest region, reaching 802 m in July, likely linked to the sensitivity of the COSMIC-2 to fire plumes.

Forest degradation in the Amazon increased by 400% in 2024. It was largely driven by wildfires during the forest’s worst fire season in more than 20 years.

Where there is smoke, there is fire: long- and mid-range biomass burning role on São Paulo's state air quality.

ABSTRACT Aim As fire activity changes globally, we need to better understand the spatial and temporal characteristics of the individual events that, when aggregated, constitute fire regimes. Most global studies analyze point detections of burned area, without delineating or considering the properties of individual events. Furthermore, there is a critical need to understand fire patterns within the context of the geopolitical boundaries within which fires are managed. Location Global. Time Period 2003–2020. Major Taxa Studied Fire. Methods We divided 241 countries by Köppen‐Geiger climate classifications and quantified four event‐based fire regime metrics: size, duration, and mean and maximum growth rate; and four area‐based metrics: burned area, number of fires, season length, and season peak. We examined the correlations among fire regime components, and between each fire regime component and climate normals. We quantified temporal trends, and used mixed models to analyze how climate and landcover change were associated with event‐based components of fire regimes. Results Event‐based metrics were weakly correlated with area‐based metrics. Countries with warmer and less variable climates had high burned area, more fire events, longer season lengths and shorter event durations. Countries with high annual temperature range and low precipitation tended to have fewer events but larger fires that were faster‐spreading and occurred later in the year. The growth rate and size of individual fire events are increasing in 18% and 21% of regions we analysed, respectively. Interannual variability in size and growth rate was associated with aridity increases in boreal areas, and landcover changes in arid areas. Main Conclusions Drivers of burned area and fire seasonality are well understood but largely unrelated to the properties of individual events. A more detailed understanding of the spatial and temporal aspects of fire events at broad scales will assist fire management efforts in preparing for a warmer future.

Background Indigenous people used fire in Australia’s deserts over millennia. Colonisation interrupted these practices, but Indigenous and conservation sectors are now restoring desert fire management for cultural, social and biodiversity outcomes. However, evaluating progress is difficult – inter-fire intervals are long and variable – and fire regimes are dominated by extensive fires after above-average rainfall. Aim To determine whether, despite these challenges, a decade of fire management has influenced fire regimes at four large, separated locations (each 1250–7850 km2) in Australia’s spinifex deserts. Methods We used Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery to create a >20 year fire history (1997–2019) at four locations, then investigated temporal patterns in seven fire regime metrics, whilst accounting for rainfall variation. Results Management caused (1) a change in fire season (more burns in cooler months); (2) a larger number of smaller fires; (3) an increased seral heterogeneity with burnt/unburnt patch sizes decreased, time-since-fire recovery stages more evenly distributed and mature vegetation extent stabilised. These changes occurred despite above-average rainfall in 2010–2011. Conclusions Management can change desert fire regimes, bringing expected cultural and biodiversity benefits. We provide recommendations for further improvement, noting that prescribed burning in remote deserts is operationally challenging, and investment is needed to meet capacity gaps, and knowledge sharing, monitoring, and research priorities.

The Neotropical Savanna (Cerrado) is a fire-prone biome characterized by seasonal climate, nutrient-poor soils, and variable fire regimes. While fire-induced germination responses are well documented in Cerrado plants, the role of seed size in mediating thermal tolerance remains poorly understood. Here, we investigate how seed size and fire-related heat treatments influence germination in Hymenaea stigonocarpa, a keystone Cerrado tree species. Specifically, we test the predictions that (i) low to moderate fire temperatures (<270 °C) do not impair seed germination and (ii) larger seeds exhibit greater heat tolerance than smaller seeds. We exposed 360 seeds from 30 individual trees to five heat-shock treatments (27, 100, 150, 200, and 270 °C) simulating fire intensities typically experienced in the Cerrado. Our results show that H. stigonocarpa produces relatively large seeds with an average germination rate of approximately 42%. The average time required for germination was 12.18 ± 0.43 (average ± standard error) days. The time required for seed germination varied significantly as a function of heat-shock treatment and seed mass, with seeds exposed to the highest temperature (270 °C) taking longer to germinate. Moreover, seed mass had a positive effect on the time required for seed germination. The germination percentage remains stable across heat treatments and seed sizes, indicating that H. stigonocarpa seeds exhibit characteristics typical of heat-tolerant species rather than those of heat-stimulated species. Our study showed that H. stigonocarpa trees produce large seeds that germinate quickly and are tolerant to moderate temperatures. These seed traits play a crucial role in the reproductive success of individual plants in fire-prone, nutrient-poor, and water-limited ecosystems. Furthermore, our results offer important guidance by emphasizing the role of seed size in effective restoration initiatives.

The risk of forest fires is affected by various factors such as vegetation density, topography, human activities, and climate patterns. These factors remain relatively constant over time, at least during the fire season. To manage forests and ensure protection against fires, fire-cycle analysis is performed which includes creating a map of potential fire ignition and preparing a vulnerability map that can assist in controlling the spread of fire. Accurate data is crucial for forest management, and geospatial technology provides reliable information. By providing accurate information, geospatial technology can help prevent and mitigate damage caused by forest fires, while also promoting sustainable land use practices. The study focused on assessing forest fire risk in the Malkangiri district of Odisha, India, using geospatial technology and the AHP method. The final risk map was categorized into five zones, namely very high, high, moderate, low, and very low, which can help guide forest management and firefighting efforts in the area. To validate these forest fire risk zones, the study used fire points data from the office of PCCF, Odisha from FIRMS. The results showed that the forest fire risk was high in the low to moderate elevation ranges, with most fire points overlapping in the very high-risk zones of the map. Anthropogenic activities have been a major cause of forest fires in tropical regions. Overall, the study demonstrated the effectiveness of using geospatial technologies and the AHP method for assessing forest fire risk. The results can help in developing strategies to prevent and mitigate the impact of forest fires, particularly in areas with high-risk zones, such as the Malkangiri district of Odisha, India.

Seasonal fires in northern Thailand are a persistent environmental and public health concern, yet existing fire probability mapping approaches in Thailand rely heavily on subjective multi-criteria analysis (MCA) methods and temporally static data aggregation methods. To address these limitations, we present a flexible, replicable, and operationally viable seasonal fire probability mapping methodology using a Random Forest (RF) machine learning model in the Google Earth Engine (GEE) platform. We trained the model on historical fire occurrence and fire predictor layers from 2016–2023 and applied it to 2024 conditions to generate a probabilistic fire prediction. Our novel approach improves upon existing operational methods and scientific literature in several ways. It uses a more representative sample design which is agnostic to the burn history of fire presences and absences, pairs fire and fire predictor data from each year to account for interannual variation in conditions, empirically refines the most influential fire predictors from a comprehensive set of predictors, and provides a reproducible and accessible framework using GEE. Predictor variables include both socioeconomic and environmental drivers of fire, such as topography, fuels, potential fire behavior, forest type, vegetation characteristics, climate, water availability, crop type, recent burn history, and human influence and accessibility. The model achieves an Area Under the Curve (AUC) of 0.841 when applied to 2016–2023 data and 0.848 when applied to 2024 data, indicating strong discriminatory power despite the additional spatial and temporal variability introduced by our sample design. The highest fire probabilities emerge in forested and agricultural areas at mid elevations and near human settlements and roads, which aligns well with the known anthropogenic drivers of fire in Thailand. Distinct areas of model uncertainty are also apparent in cropland and forests which are only burned intermittently, highlighting the importance of accounting for localized burning cycles. Variable importance analysis using the Gini Impurity Index identifies both natural and anthropogenic predictors as key and nearly equally important predictors of fire, including certain forest and crop types, vegetation characteristics, topography, climate, human influence and accessibility, water availability, and recent burn history. Our findings demonstrate the heavy influence of data preprocessing and model design choices on model results. The model outputs are provided as interpretable probability maps and the methods can be adapted to future years or augmented with local datasets. Our methodology presents a scalable advancement in wildfire probability mapping with machine learning and open-source tools, particularly for data-constrained landscapes. It will support Thailand’s fire managers in proactive fire response and planning and also inform broader regional fire risk assessment efforts.

Fire is a fundamental force that shapes ecosystems by influencing vegetation composition, succession, and structural diversity. Fire regimes, defined by fire frequency, intensity, and seasonality, vary across ecosystems and are critical in fire-dependent landscapes. In the Florida Everglades, fire is a key driver of ecological dynamics, interacting with hydrology and the structure of vegetation. This study defines contemporary fire regimes by describing fire patterns from 1978 to 2023, utilizing fire perimeter data from Everglades National Park and Big Cypress National Preserve. Our findings reveal a highly variable annual burned area with a strong increasing trend. Prescribed fires were the foundation of trends in fire activity, as wildfires remained stable over the study period. Across the Everglades, fire return intervals differed between ecosystems, with upland ecosystems experiencing more frequent fires than wetland ecosystems. Our findings highlight the role of fire management in shaping modern fire regimes and underscore the importance of prescribed burns in maintaining ecosystem function and resilience in the Everglades.

Abstract With increased urbanization, fires in the wildland urban interface (WUI) have become a severe problem worldwide. The unique features of WUI may influence the atmospheric flows in the vicinity of fire. This study utilizes the parallelized large eddy simulation model (PALM) system for fire‐atmosphere simulations of Bottle Lake Forest, Christchurch, New Zealand. Over 3,000 residential buildings are situated around the 7 forest, with many homes only 50 m away from the forest edge. We conducted high‐fidelity fire‐atmosphere simulations with the finest grid spacing of 4 m. Wildland forest (WF) and flat terrain simulations were conducted to provide a reference for comparison with WUI simulations. Fire‐weather conditions for the 2022/2023 New Zealand fire season were selected based on the Fire Weather Index (FWI). Data from previous fire field campaigns were obtained to represent a low‐intensity fire heat forcing. The results reveal a pulsing behavior in downwind heat transport when the forest canopy is included. Furthermore, the presence of the WUI is associated with extended downwind fire heat transport compared to WF and flat terrain scenarios. This study is the first to simulate atmospheric flows near fires in a WUI setting with such high fidelity. The findings highlight the critical role of WUI features in shaping fire‐atmosphere dynamics, though further research is required to disentangle the contributions of individual WUI components to these effects.

Climate change and land mismanagement are creating increasingly fire-prone built and natural environments. However, despite worsening fire seasons, evidence is lacking globally for trends in socially and economically disastrous wildfires, partly due to sparse systematic records. Using a 44-year dataset (1980 to 2023) we analyze the distribution, trends, and climatic conditions connected with the most lethal and costly wildfires. Disastrous wildfires occurred globally over this period but were concentrated in the Mediterranean and temperate conifer biomes. Disaster risk was highest where highly energetic daily fire events intersected affluent, populated areas. Economic disasters increased sharply from 2015 onward, with 43% of the 200 most damaging events occurring in the last decade. Disasters coincided with increasingly extreme climatic conditions, highlighting the urgent need to adapt to a more fire-prone world.

A generalised model of rainforest vulnerability to fire in eastern Australia.