Wildfire Season Research Articles

Antecedent Hydrometeorological Conditions of Wildfire Occurrence in the Western U.S. in a Changing Climate

AbstractWildfires have significant hydrological and ecological impacts in the western U.S. Using a high‐resolution regional climate simulation and wildfire observations for 1984–2018, this study investigates the antecedent hydrometeorological conditions (AHCs) of wildfires in the western U.S. During the warm season (April‐September), the wildfire AHCs feature diverse surface pressure (PS), soil moisture, and longwave/shortwave radiation (LW/SW) conditions. K‐means clustering classifies wildfires into four types with distinct AHCs: low‐PS‐type and high‐PS‐type with lower and higher PS anomalies, respectively, LW‐type featuring intense LW but weak SW anomalies, and wet‐soil‐type with wet soil anomalies. Each fire cluster represents 22%–27% of all the wildfires, featuring different combinations of climate and vegetation conditions and their diverse relations to regional hydrometeorological conditions, with wet‐soil‐type fires often exhibiting opposite correlations with AHCs compared to those of the other three types. In five major Köppen climate zones over the western U.S., clustering‐based predictions improve the seasonal wildfire prediction accuracy (R2) by 10% compared to prediction without classification. Such improvement comes from separating the opposite relationships between wet‐soil‐type fires and their seasonal AHCs from the other three types, along with separating LW‐type fires, which include most of the lightning‐ignited fires that occur more randomly. Increases in wildfire occurrence during 1984–2018 are dominated by the increases in the LW‐type fires, while the wet‐soil‐type fires have decreased, consistent with the long‐term drying in the western U.S.

Abstract 13295: The Effects of Short-Term Exposure to Fine Particulate Matter and Ozone on Attending Outpatient Clinic Visit Appointments Among Patients With Depressive Symptoms

Background: Air pollution is increasingly recognized as an environmental risk factor for mental health. However, data regarding the short-term influence on depressive symptoms are limited. It is known that depressed patients (pts) tend to be less adherent to prescribed medical regimens, including appointments. Therefore, we sought to determine how depression during times of increased particulate matter (PM 2.5 ) and ozone during winter inversion (Nov-Mar) and wildfire seasons (June-Oct) affected pt behavior of attending clinic visit appointments. Methods: Pts (n=142,930) who completed a PHQ-9 as an outpatient between Mar 2003-Apr 2022 and resided on Utah’s Wasatch Front were studied. PHQ-9 results were used to classify depression by no depressive symptoms (<10) or any depressive symptoms ( > 10), with further stratification into severe ( > 20). A case-crossover design was used with odd ratios computed by Poisson regression. Results: A total of 43% (n=60,724) of pts had depressive symptoms, with 25% (n=15,278) being severe. Depressed pts were younger (37 vs. 45 yrs), female (62% vs. 56%), more often smoked (24% vs. 17%), and had fewer comorbidities. The likelihood of attending clinic visits was decreased when the moving average (mAvg) of PM 2.5 was increased in the 2 days and week prior among those with depression, particularly when severe (Figure a). Levels of PM 2.5 on lag day 0 did not affect visit attendance. Increases in ozone were associated with a decrease in clinic attendance (Figure b). Air pollution levels >7 to 28 days prior were not associated with clinic attendance. Conclusion: Short-term increases in air pollution, particularly in average PM 2.5 during inversion season, in the days and week prior were associated with a decrease in attending clinic visit appointments among depressed pts. These results could have healthcare delivery implications since people with depression were less likely to show up to non-emergent medical visits when the air has been polluted.

Abstract 12441: Ambient Fine Particulate Matter Air Pollution but Not Ozone is Associated With Elevated Risk of All-Cause Hospitalization in Patients With a Prior Diagnosis of Atrial Fibrillation

Introduction: Short-term increases (days to weeks) in fine particulate matter (PM 2.5 ) air pollution are associated with atrial fibrillation (AF) episodes. We previously reported that PM 2.5 was not associated with severe AF measured as emergency or inpatient hospitalization for the primary diagnosis of AF. Mild AF, though, may influence all-cause hospitalizations. This study evaluated if short-term increases in PM 2.5 and ozone were associated with all-cause hospitalization at 11 Intermountain Health hospitals in patients with AF, and if this differed between wildfire (June-Oct.) and winter inversion (Nov.-March) seasons. Methods: Patients carrying an AF diagnosis (N=347,157) were evaluated for all-cause hospitalization and, secondarily, AF hospitalization (N=25,601) and first AF diagnosis (N=77,893). PM 2.5 levels were measured from January 1, 1999 to March 31, 2022 (ozone: 2006-2022) in subjects residing on Utah’s Wasatch Front. Using the case-crossover design that matched individual-level characteristics, Poisson regression also adjusted for weather variables. Results: Mean age was 72±15 years and 51.0% were female. Overall, PM 2.5 increases at a lag of 0 days, 1 day, 3-day moving average (mAvg) (days 0-2), and 7-day mAvg (days 0-6) were associated with all-cause hospitalization [respectively: odds ratio (OR)=1.010, 1.008, 1.011, 1.009 per +10 μg/m 3 ; p=0.0004, 0.006, 0.0004, 0.028]. Associations differed by seasons (Figure). Regressions entering both ozone and PM 2.5 mildly increased PM 2.5 ORs for all-cause hospitalization. PM 2.5 was not associated with first AF diagnosis. Increases in ozone were not associated with higher risk of any outcome. Conclusions: Short-term increases in PM 2.5 air pollution were associated with all-cause hospitalization in patients with a prior AF diagnosis. Lag structure varied for wildfire and inversion seasons, with greater effect sizes in wildfire season. Increases in ozone did not associate with elevated risk.

Abstract 12409: Ambient Fine Particulate Matter and Ozone Air Pollution Are Differentially Associated With Hospitalization for Unstable Angina and Acute Myocardial Infarction During Wildfire and Inversion Seasons

Background: Short-term increases (days to weeks) in fine particulate matter (PM 2.5 ) air pollution are associated with hospitalization for acute coronary syndromes (ACS). A changing climate is shifting sources of PM 2.5 toward summer wildfires in the western US. This study evaluated if short-term increases in PM 2.5 and ozone were associated with ACS risks in wildfire (June-October) and winter inversion (Nov.-March) seasons. Methods: Case-crossover analyses examined N=21,414 subjects with emergency or inpatient hospitalization at 11 Intermountain hospitals for ACS, including the primary diagnosis of myocardial infarction (AMI, n=20,287) or unstable angina (USA, n=1,127). PM 2.5 data were collected in January 1, 1999 to March 31, 2022 (ozone: 2006-2022) from community monitors on Utah’s Wasatch Front. Odds ratios (OR) and 95% confidence intervals (CI) were computed by Poisson regression with adjustment for weather data. Results: Subjects averaged 66±14 years of age and 31.0% were female. Overall PM 2.5 increases were associated with concurrent day (lag 0 days) ACS hospitalizations (OR=1.024 per +10 μg/m 3 , CI: 1.004, 1.043, p=0.019), driven by AMI (OR=1.025 per +10 μg/m 3 , CI: 1.004, 1.045, p=0.017). USA admissions were delayed to the second week [lag moving average (mAvg) days 7-13] after PM 2.5 increases (OR=1.090 per +10 μg/m 3 , CI: 1.024, 1.159, p=0.007) and PM 2.5 associations with USA were strengthened by adjustment for ozone. Analyzed by wildfire and inversion seasons, PM 2.5 associations differed for AMI and USA (Figure). Short-term increases in ozone in wildfire season were associated with lower USA risk at lag 1, lag 2, 3-day mAvg 0-2, and 1-week mAvg 0-6 and lower AMI risk at mAvg 7-13, but not with inversion season risks. Conclusions: Short-term increases in PM 2.5 air pollution were associated with ACS hospitalizations for AMI and USA, with patterns of risk differing between wildfire and inversion seasons. Ozone increases were not associated with greater risk.

Attribution of Seasonal Wildfire Risk to Changes in Climate: A Statistical Extremes Approach

Abstract Wildfire risk is greatest during high winds after sustained periods of dry and hot conditions. This paper is a statistical extreme-event risk attribution study that aims to answer whether extreme wildfire seasons are more likely now than under past climate. This requires modeling temporal dependence at extreme levels. We propose the use of transformed-linear time series models, which are constructed similarly to traditional autoregressive–moving-average (ARMA) models while having a dependence structure that is tied to a widely used framework for extremes (regular variation). We fit the models to the extreme values of the seasonally adjusted fire weather index (FWI) time series to capture the dependence in the upper tail for past and present climate. We simulate 10 000 fire seasons from each fitted model and compare the proportion of simulated high-risk fire seasons to quantify the increase in risk. Our method suggests that the risk of experiencing an extreme wildfire season in Grand Lake, Colorado, under current climate has increased dramatically relative to the risk under the climate of the mid-twentieth century. Our method also finds some evidence of increased risk of extreme wildfire seasons in Quincy, California, but large uncertainties do not allow us to reject a null hypothesis of no change.

Performance of Fire Danger Indices and Their Utility in Predicting Future Wildfire Danger Over the Conterminous United States

AbstractPredicting current and future wildfire frequency and size is central to wildfire control and management. Multiple fire danger indices (FDIs) that incorporate weather and fuel conditions have been developed and utilized to support wildfire predictions and risk assessment. However, the scale‐dependent performance of individual FDIs remains poorly understood, which leads to large uncertainty in the estimated fire sizes under climate change. Here, we calculate four commonly used FDIs over the conterminous United States using high‐resolution (4 km) climate and fuel data sets for the 1984–2019 period. The relationships of these four FDIs to the observed wildfire sizes show that higher values of FDIs correlate to larger total fire sizes; this correlation is more robust at larger spatial scales. Sensitivity analysis indicates that the daily minimum relative humidity and precipitation are the most important drivers of the annual mean fire danger. In the instances of extreme fire danger, wind speed becomes a critical factor and should be considered in the calculation of the FDI. To assess the impact of climate change on future fire size, we calculate the present‐day and end‐of‐century FDIs using the 12 km regional climate model simulations. The four FDIs generally predict consistent changes in future fire potential, suggesting an overall higher fire potential in conjunction with a prolonged wildfire season in future climate. Regionally, the four FDIs also reveal similar seasonal patterns as the enhancement arises mostly in spring and summer over the southwest US while in summer and fall over the northern and eastern US.

Enhancing Air Quality Forecasts Across the Contiguous United States (CONUS) During Wildfires Using Analog-Based Post-Processing Methods

With our growing understanding of the risks of air pollution to human health, air quality forecasting has become a very important tool to enable decision-makers to take preventive and corrective measures for current and future policies. In addition, accurate predictions of air quality can help predict and mitigate the impacts of wildfires on human health, which have an increased risk due to anthropogenic climate change. However, errors in air quality forecasts limit their value in decision-making processes. Thus, increasing the accuracy of air quality forecasts is of significant importance. In this study, we have utilized the Community Multiscale Air Quality (CMAQ) modeling system with a 12 km horizontal grid resolution to generate daily 48-h fine particulate matter (PM2.5) forecasts for the Contiguous United States (CONUS) domain for June 1st through September 29th (major wildfire season) during 2015–2021. We conduct CMAQ offline simulations using meteorological inputs generated by the NOAA’s Unified Forecast System (UFS) numerical weather prediction model. We analyze the performance of the CMAQ PM2.5 and UFS meteorological forecasts over seven years of simulations for Environmental Protection Agency (EPA)-defined ten regions using the Air Quality System (AQS) ambient air pollution data from over a thousand monitoring sites across the CONUS. We have found that on average, the CMAQ model performs better in the eastern CONUS with the lowest Root Mean Square Error (RMSE) (10–30 μg/m3) while in the west, where wildfires are prevalent, the model has the highest RMSE of up to 120 μg/m3. To address these substantial model errors, we advance the field by applying Analog Ensemble (AnEn), a state-of-the-art statistical-dynamical method, to enhance PM2.5 forecast accuracy, with a particular focus on wildfire events, when producing an accurate forecast is highly challenging. We further refine this approach by incorporating a Carbon Monoxide-FIRE (CO-FIRE) tracer to precisely monitor fire smoke. In parallel to our objective and for the first time, we introduce two novel predictors, i.e., a CO−FIRE tracer in the model, and the maximum observed PM2.5 concentrations from the previous day (PM2.5−preday−max), and evaluate their potential significance in improving forecast accuracy, which aligns with the primary goal of our study. Despite the challenges of using AnEn for wildfires, we demonstrate that it has the potential to improve the CMAQ model forecast over the CONUS. We find that AnEn decreases the model RMSE by up to 25%, including additional 7% and 15% reduction by CO−FIRE and PM2.5−preday−max predictors, respectively, at different forecast lead times. In addition, the correlation between AnEn forecasts and observations is 20%–40% higher than that between CMAQ and observations. The Mean Bias Error (MBE) for AnEn forecasts is consistent and approximately −0.5 μg/m3 whereas CMAQ MBE varies between −1 and +1 μg/m3 between 0–48 forecast hours. AnEn significantly improves the PM2.5 forecast results during its highest episodes. During the initial days of wildfires, AnEn performs similarly to CMAQ. However, it soon catches up and decreases the error significantly.

Spatial-temporal characteristics of more than 50,000 wildfires in Japan from 1995 to 2020

The spatial-temporal characteristics of wildfires were summarized based on fire reports covering 55,863 wildfires with a total burned area of 289.91 km2 from 1995 to 2020 in Japan. The long-term decreasing trend was largely influenced by changes in social aspects, while the annual fluctuation was determined by large-scale events. The wildfire season started around early January in the southern part and gradually shifted northward over approximately three months. A total of 98.77% of cases were human-induced, and 1.23% were caused by lightning. Regarding response time after receiving the call, firefighting was started within 30 min in 89% of cases and controlled within 1 h in 85% of cases, where spatial characteristics were insignificant. Full-time fire departments utilized artificial water sources primarily in 72% of cases and natural water sources in 28%, while the rate using natural water sources increased as the burned area increased. Natural water sources from ponds and rivers exhibited regionalities. Cluster analysis revealed five wildfire regions reflecting natural and anthropogenic factors. Furthermore, the Mann–Kendall trend test concluded that the initiation process decreased almost uniformly across the country, while regional differences existed regarding the expansion process. Additionally, only human-induced wildfires had a significant decreasing trend.

Fire-Pollutant-Atmosphere Components and Its Impact on Mortality in Portugal During Wildfire Seasons.

This study analyzed fire-pollutant-meteorological variables and their impact on cardio-respiratory mortality in Portugal during wildfire season. Data of burned area, particulate matter with a diameter of 10 or 2.5μm (μm) or less (PM10, PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), temperature, relative humidity, wind speed, aerosol optical depth and mortality rates of Circulatory System Disease (CSD), Respiratory System Disease (RSD), Pneumonia (PNEU), Chronic Obstructive Pulmonary Disease, and Asthma (ASMA), were used. Only the months of 2011-2020 wildfire season (June-July-August-September-October) with a burned area greater than 1,000ha were considered. Principal component analysis was used on fire-pollutant-meteorological variables to create two indices called Pollutant-Burning Interaction (PBI) and Atmospheric-Pollutant Interaction (API). PBI was strongly correlated with the air pollutants and burned area while API was strongly correlated with temperature and relative humidity, and O3. Cluster analysis applied to PBI-API divided the data into two Clusters. Cluster 1 included colder and wetter months and higher NO2 concentration. Cluster 2 included warmer and dried months, and higher PM10, PM2.5, CO, and O3 concentrations. The clusters were subjected to Principal Component Linear Regression to better understand the relationship between mortality and PBI-API indices. Cluster 1 showed statistically significant (p-value<0.05) correlation (r) between RSDxPBI (r RSD=0.58) and PNEUxPBI (r PNEU=0.67). Cluster 2 showed statistically significant correlations between RSDxPBI (r RSD=0.48), PNEUxPBI (r PNEU=0.47), COPDxPBI (r COPD=0.45), CSDxAPI (r CSD=0.70), RSDxAPI (r CSD=0.71), PNEUxAPI (r PNEU=0.49), and COPDxAPI (r PNEU=0.62). Cluster 2 analysis indicates that the warmest, driest, and most polluted months of the wildfire season were associated with cardio-respiratory mortality.

Remote sensing and model analysis of biomass burning smoke transported across the Atlantic during the 2020 Western US wildfire season

Biomass burning is the main source of air pollution in several regions worldwide nowadays. This predominance is expected to increase in the upcoming years as a result of the rising number of devastating wildfires due to climate change. Harmful pollutants contained in the smoke emitted by fires can alter downwind air quality both locally and remotely as a consequence of the recurrent transport of biomass burning plumes across thousands of kilometers. Here, we demonstrate how observations of carbon monoxide and aerosol optical depth retrieved from polar orbiting and geostationary meteorological satellites can be used to study the long-range transport and evolution of smoke plumes. This is illustrated through the megafire events that occurred during summer 2020 in the Western United States and the transport of the emitted smoke across the Atlantic Ocean to Europe. Analyses from the Copernicus Atmosphere Monitoring Service, which combine satellite observations with an atmospheric model, are used for comparison across the region of study and along simulated air parcel trajectories. Lidar observation from spaceborne and ground-based instruments are used to verify consistency of passive observations. Results show the potential of joint satellite-model analysis to understand the emission, transport, and processing of smoke across the world.

Increase in Arctic Oscillations explains most interannual variability in Russia’s wildfires

Over the past two decades, the escalating emissions of greenhouse gases from boreal wildfires in the Northern Hemisphere have drawn significant attention, underscoring an unprecedented wildfire season in 2021. Our calculations indicate that between 2002 and 2020, wildfires in Russia released approximately 726 ± 280 Tg CO2eqv yr−1. This aligns closely with similar estimates derived from remote sensing data, far surpassing the earlier approximations found in the Russian National Inventory Report (NIR) by a factor of 2 to 3. Notably, in 2021 alone, Russia’s wildfires emitted an exceptionally high amount of 1,700 Tg CO2eqv, exceeding the carbon emissions from the country’s fossil fuel consumption. Consequently, this situation led to an almost complete counterbalance of carbon assimilation by Russian forests. Our analysis attributes over 50% of the variation in wildfire frequency between 2002 and 2021 to shifts in the Arctic Oscillation (AO). This suggests a potential for utilizing AO as a predictive variable for wildfires. It’s noteworthy that the AO itself is influenced by the sustained regression of Arctic sea-ice. From this, it can be inferred that in the foreseeable future, Russian forests might undergo a transition from their role as carbon sinks to the potential net contributors of carbon to the atmosphere.

Temporal Characteristics and Sources of PM2.5 in Porto Velho of Amazon Region in Brazil from 2020 to 2022

Our study analyzed PM2.5 concentrations in Porto Velho, Rondônia, during the April 2020 and October 2022 wildfire seasons. This study aimed to evaluate the temporal characteristics of PM2.5 and the influence of long-distance pollution sources. Using PurpleAir data, we found that the average PM2.5 concentration was 17.7 ± 24.0 µg m−3, with significant spikes in August. PM2.5 concentrations decreased during the day but rose from nighttime to morning. The PM2.5 concentration was observed to be distributed at a high level of 36.3 ± 31.1 µg m−3 in slow westerly winds. Moreover, even in the dominant northerly wind conditions, a similarly high concentration of PM2.5 was detected, measuring at 33.2 ± 28.3 µg m−3. Air masses mainly originated from northeastern, southeastern, and southern regions, passing through Paraguay and Bolivia. Furthermore, PM2.5 in Porto Velho was influenced by Brazil’s northern and Central-West areas. To meet the Sustainable Development Goal (SDG) Indicator 11.6.2 for clean air, it is recommended that wildfires in Porto Velho’s northern and western regions be reduced, and more robust deforestation policies are needed in areas with long-distance pollution sources.

Emergency department visits associated with wildfire smoke events in California, 2016–2019

Wildfire smoke has been associated with adverse respiratory outcomes, but the impacts of wildfire on other health outcomes and sensitive subpopulations are not fully understood. We examined associations between smoke events and emergency department visits (EDVs) for respiratory, cardiovascular, diabetes, and mental health outcomes in California during the wildfire season June–December 2016–2019. Daily, zip code tabulation area-level wildfire-specific fine particulate matter (PM2.5) concentrations were aggregated to air basins. A “smoke event” was defined as an air basin-day with a wildfire-specific PM2.5 concentration at or above the 98th percentile across all air basin-days (threshold = 13.5 μg/m3). We conducted a two-stage time-series analysis using quasi-Poisson regression considering lag effects and random effects meta-analysis. We also conducted analyses stratified by race/ethnicity, age, and sex to assess potential effect modification. Smoke events were associated with an increased risk of EDVs for all respiratory diseases at lag 1 [14.4%, 95% confidence interval (CI): (6.8, 22.5)], asthma at lag 0 [57.1% (44.5, 70.8)], and chronic lower respiratory disease at lag 0 [12.7% (6.2, 19.6)]. We also found positive associations with EDVs for all cardiovascular diseases at lag 10. Mixed results were observed for mental health outcomes. Stratified results revealed potential disparities by race/ethnicity. Short-term exposure to smoke events was associated with increased respiratory and schizophrenia EDVs. Cardiovascular impacts may be delayed compared to respiratory outcomes.

Effects of solvent extracted organic matter from outdoor air pollution on human type II pneumocytes: Molecular and proteomic analysis

Outdoor air pollution is responsible for the exacerbation of respiratory diseases in humans. Particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) is one of the main components of outdoor air pollution, and solvent extracted organic matter (SEOM) is adsorbed to the main PM2.5 core. Some of the biological effects of black carbon and polycyclic aromatic hydrocarbons, which are components of PM2.5, are known, but the response of respiratory cell lineages to SEOM exposure has not been described until now. The aim of this study was to obtain SEOM from PM2.5 and analyze the molecular and proteomic effects on human type II pneumocytes. PM2.5 was collected from Mexico City in the wildfire season and the SEOM was characterized to be exposed on human type II pneumocytes. The effects were compared with benzo [a] pyrene (B[a]P) and hydrogen peroxide (H2O2). The results showed that SEOM induced a decrease in surfactant and deregulation in the molecular protein and lipid pattern analyzed by reflection-Fourier transform infrared (ATR-FTIR) spectroscopy on human type II pneumocytes after 24 h. The molecular alterations induced by SEOM were not shared by those induced by B[a]P nor H2O2, which highlights specific SEOM effects. In addition, proteomic patterns by quantitative MS analysis revealed a downregulation of 171 proteins and upregulation of 134 proteins analyzed in the STRING database. The deregulation was associated with positive regulation of apoptotic clearance, removal of superoxide radicals, and positive regulation of heterotypic cell-cell adhesion processes, while ATP metabolism, nucleotide process, and cellular metabolism were also affected. Through this study, we conclude that SEOM extracted from PM2.5 exerts alterations in molecular patterns of protein and lipids, surfactant expression, and deregulation of metabolic pathways of type II pneumocytes after 24 h of exposure in absence of cytotoxicity, which warns about apparent SEOM silent effects.

Satellite Evidence of HONO/NO2 Increase With Fire Radiative Power

AbstractWildfires are important sources of atmospheric reactive nitrogen. The reactive nitrogen species partitioning generally depends on fire characteristics. One reactive nitrogen compound, nitrous acid (HONO), is a source of hydroxyl radicals and nitric oxide, which can impact the oxidizing capacity of the atmosphere and fire plume chemistry and composition. We study the Australian wildfire season of 2019–2020, known as Black Summer, where numerous large and intense wildfires burned throughout the continent. We use HONO and nitrogen dioxide (NO2) from the TROPOspheric Monitoring Instrument (TROPOMI) and fire radiative power (FRP) from the Visible Infrared Imaging Radiometer Suite to investigate HONO and NO2 relationships with fire characteristics. The ratio of HONO to NO2 increases linearly with FRP both in Australia and globally. Both Australian and global fire relationships depend strongly on land cover type. These relationships can be applied to emission inventories to improve wildfire emission representation in models.

MAPPING FIRE SEVERITY FROM RECENT CALIFORNIA WILDFIRES USING SATELLITE IMAGERY

Abstract. Urban sprawl has become a huge concern for cities like Los Angeles, New York, and Chicago in recent years. As urban sprawl pushes urbanization into city suburbs and outskirts, forest fragmentation becomes evidently prevalent and exposes forests to high temperatures, pollution, pests, and fires that threaten forest health. A 2021 report titled Rebuilding for a Resilient Recovery affirmed that the frequency and damage potential of wildfires have been exacerbated by climate change and urban sprawl especially in California. Globally, these fires can be attributed to both natural and anthropogenic drivers such as deforestation, agriculture, mining, and industrialization. Future projections predict that these incidences of fires will only worsen as the planet continues to warm further, with emphasis on the spread and intensities of the annual California wildfires over the decade. Quantifying the consequences of these fires on global climate change has become crucial and with the emergence of advanced GIS mapping tools, focus, visualization, and interpretation of fire and burn severity has become easier. However, knowledge and understanding of wildfire dynamics is limited especially in terms of fuel load, impacts on vegetation health, aerosol release and associated movement in the atmosphere. It is therefore important to address these gaps to make better and informed actions towards forest use, protection, management, and policies and broadly towards ambitious climate goals such as the UN’s Carbon Neutral goal by 2050. This study uses Sentinel 2A data from the Copernicus fleet between 2018 and 2022 to identify and assess the burn severity of affected areas in Sonoma County, California. The aim of the study is to understand the impacts of fires of fire on vegetation health and the post-fire recovery process. The Normalized Burn Ration Index (NBRI) was used to identify and measure the extent of the burnt areas within the county and their severity and Normalized Difference Vegetation Index (NDVI) was used as a measure of forest heath. The results show that Sonoma County has become a high burn severity area with a major decrease in unburned areas between 2018 and 2022. NDVI values recorded all decrease from January to December for all the years because of pre-fire season drought. The wildfire season begins in May and before then there are seasonal droughts that occur hence accounting for the initial decline in NDVI. The least values recorded were between 0.5 and 0.57 for September, indicating sparse and unhealthy vegetation because of sharp declines during the fire season.

Abrupt, climate-induced increase in wildfires in British Columbia since the mid-2000s

In the province of British Columbia, Canada, four of the most severe wildfire seasons of the last century occurred in the past 7 years: 2017, 2018, 2021, and 2023. To investigate trends in wildfire activity and fire-conducive climate, we conducted an analysis of mapped wildfire perimeters and annual climate data for the period of 1919–2021. Results show that after a century-long decline, fire activity increased from 2005 onwards, coinciding with a sharp reversal in the wetting trend of the 20th century. Even as precipitation levels remain high, moisture deficits have increased due to rapid warming and increased evaporative demand. Bottom-up factors further influence fire activity, as the legacy of past wildfires, insect outbreaks, and land-use practices continually influence fire regimes. The compound effects of climate-induced moisture changes and altered fuels now force British Columbians to confront the harsh reality of more frequent years of intense and prolonged wildfire activity.

Responding to multiple events in 2020: A qualitative study exploring areas of growth and development among California college students

In 2020, the US was consumed by the COVID-19 pandemic and racial justice protests. California was further compounded by a devastating fall wildfire season. Negative effects related to these events have been documented in research. Objective: This study sought to expand the literature by exploring areas of personal growth and development among college students with a lens on the impact of these three events. Methods: A diverse group of undergraduate and graduate students enrolled in a large public university system in California were recruited to participate in one of six focus groups held during the winter of 2020/2021 with 35 students. Findings: Thematic analysis was used to develop three themes: 1) experiencing fluctuation and repetition, 2) understanding myself and the world, and 3) finding hope and wanting action. Conclusions: Findings suggest opportunities for academic institutions to enhance critical thinking about contemporary issues and support students in developing skills to navigate change and transition successfully.

Evidence for multi-decadal fuel buildup in a large California wildfire from smoke radiocarbon measurements

In recent decades, there has been a significant increase in annual area burned in California’s Sierra Nevada mountains. This rise in fire activity has prompted the need to understand how historical forest management practices affect fuel composition and emissions. Here we examined the total carbon (TC) concentration and radiocarbon abundance (Δ14C) of particulate matter (PM) emitted by the KNP Complex Fire, which occurred during California’s 2021 wildfire season and affected several groves of giant sequoia trees in the southern Sierra Nevada. During a 26 h sampling period, we measured concentrations of fine airborne PM (PM2.5), as well as dry air mole fractions of carbon monoxide (CO) and methane (CH4), using a ground-based mobile laboratory. We also collected filter samples of PM2.5 for analysis of TC concentration and Δ14C. High correlation among PM2.5, CO, and CH4 time series confirmed that our PM2.5 measurements captured variability in wildfire emissions. Using a Keeling plot approach, we determined that the mean Δ14C of PM2.5 was 111.6 ± 7.7‰ (n = 12), which was considerably enriched relative to atmospheric carbon dioxide in the northern hemisphere in 2021 (−3.2 ± 1.4‰). Combining these Δ14C data with a steady-state one-box ecosystem model, we estimated that the mean age of fuels combusted in the KNP Complex Fire was 40 years, with a range of 29–57 years. These results provide evidence for emissions originating from woody biomass, larger-diameter fine fuels, and coarse woody debris that have accumulated over multiple decades. This is consistent with independent field observations that indicate high fire intensity contributed to widespread giant sequoia mortality. With the expanded use of prescribed fires planned over the next decade in California to mitigate wildfire impacts, our measurement approach has the potential to provide regionally-integrated estimates of the effectiveness of fuel treatment programs.

Regional participation trends for community wildfire preparedness program Firewise USA

Community-wide wildfire mitigation can effectively protect homes from structure ignition. The Firewise USA program provides a framework for grassroots wildfire preparedness. Here, we examine the 500 Firewise USA sites in California to understand participation and demographic trends. We find important regional differences regarding the influence of underlying fire hazard, fire history, and other Firewise sites on new site formation. Sites in the Bay Area and Sierras respond strongly to fire history and proximity to other Firewise sites, while Northern and Southern California have few Firewise sites despite underlying hazardous conditions and large fire history. Firewise sites are often whiter, older, and more well-educated than California’s median population, potentially leaving out many communities that do not meet this demographic profile but face severe risks from wildfires. These findings offer important insights into the factors motivating communities to pursue wildfire protection, particularly important given recent severe and destructive wildfire seasons.