Biomonitoring of firefighters' exposure to priority pollutant metal(loid)s during wildland fire combat missions: Impact on urinary levels and health risks.

Wildland firefighters are exposed to metal(loid)s released during wildfires through vegetation combustion, which also promotes remobilization of accumulated anthropogenic metal(loid)s. Studies biomonitoring metal(loid)s exposure promoted exclusively by wildfire suppression activities are lacking. This work aimed to characterize, for the first time, the impact of real-life wildland firefighting operations on urinary levels of priority pollutant metal(loid)s [14 included in ATSDR, 11 in USEPA, and 4 in Human Biomonitoring for Europe Initiative priority lists] in firefighters. Spot urines were sampled pre-exposure (105 non-smokers, 76 smokers) and post-exposure to firefighting activities (20 non-smokers, 25 smokers); among those, paired samples were collected from 14 non-smoking and 24 smoking firefighters. Smokers displayed significantly higher baseline levels of zinc (28 %), lithium (29 %), cadmium (55 %), rubidium (13 %), and copper (20 %) than non-smokers. Following wildfire suppression, the concentration of the WHO potentially toxic metal(loid)s rose from 2 % to 3 % in smokers and 2 % to 5 % in non-smokers (up to 4 % for all firefighters and up to 5 % in paired samples). Levels of nickel (33-53 %), antimony (45-56 %), and cesium (40-47 %) increased significantly post-exposure in non-smokers (in all firefighters and in paired samples), whose urinary concentrations were generally more impacted by wildfire emissions than those of smokers. Arsenic (80 %) displayed the only significant increase post-exposure in smokers, being the best discriminant of exposure to wildfire emissions in these subjects. Significant positive correlations were found for age and/or career length with cadmium, lead, barium, strontium, and mercury, and for body mass index with arsenic. The reference/guidance values were exceeded for arsenic, zinc, cesium, nickel, antimony, cadmium, lead, thallium, mercury, copper, and cobalt in 1-90 % of firefighters suggesting augmented health risks due to wildfire combating and emphasizing the need of mitigation strategies. This study also provides biomonitoring data to help setting reference values for the occupationally exposed part of population.

Integrating machine learning for enhanced wildfire severity prediction: A study in the Upper Colorado River basin.

Wildfires pose significant threats worldwide, requiring accurate prediction for mitigation. This study uses machine learning techniques to forecast wildfire severity in the Upper Colorado River basin. Datasets from 1984 to 2019 and key indicators like weather conditions and land use were employed. Random Forest outperformed Artificial Neural Network, achieving 72 % accuracy. Influential predictors include air temperature, vapor pressure deficit, NDVI, and fuel moisture. Solar radiation, SPEI, precipitation, and evapotranspiration also contribute significantly. Validation against actual severities from 2016 to 2019 showed mean prediction errors of 11.2 %, affirming the model's reliability. These results highlight the efficacy of machine learning in understanding wildfire severity, especially in vulnerable regions.

Craniometric determinants of the fitted filtration efficiency of disposable masks.

Introduction: Exposure to harmful aerosols is of increasing public health concern due to the SARS-CoV-2 pandemic and wildland fires. These events have prompted risk reduction behaviors, notably the use of disposable respiratory protection. This project investigated whether craniofacial morphology impacts the efficiency of disposable masks (N95, KN95, surgical masks, KF94) most often worn by the public to protect against toxic and infectious aerosols. This project was registered with ClinicaltTrials.gov (NCT05388201; registration May 18, 2022). Methods: One-hundred participants (50 men, 50 women) visited the Environmental Protection Agency's Human Studies Facility in Chapel Hill, NC between 2022-2023. Craniometrics and 3D scans were used to separate participants into four clusters. Boosting and elastic net regression yielded five measurements (bizygomatic breadth, nose length, bizygomatic nasal arc, neck circumference, ear breadth) that were the best predictors of filtration efficiency based on overall model fit. Fitted filtration efficiency was quantified for each mask at baseline and when tightened using an ear-loop clip. Results: The mean unmodified mask performance ranged from 55.3% (15.7%) in the large KF94 to 69.5% (12.3%) in the KN95. Modified performance ranged from 66.3% (9.4%) in the surgical to 80.7% (12.0%) in the KN95. Clusters with larger face width and neck circumference had higher unmodified mask efficiency. Larger nose gap area and nose length decreased modified mask performance. Discussion: We identify face width, nose size, nose shape, neck circumference, and ear breadth as specific features that modulate disposable mask fit in both unmodified and modified conditions. This information can optimize guidance on respiratory protection afforded by disposable ear-loop masks.

Blinded by smoke: Wildfire smoke exposure and eye irritation in Australian wildland firefighters.

PURPOSE: Wildfire occurrence is increasing worldwide, putting firefighters and general public at increased risk of eye injuries from smoke exposure. This study explored ocular symptoms and use of protective eyewear amongst wildland firefighters in Australia. METHODS: Australian wildland firefighters were invited to complete an online survey about the occurrence of eye irritation, use of protective eyewear and behaviours associated with occupational smoke exposure. Responses were analysed using logistic regression and qualitative inductive content analysis. RESULTS: 338 wildland firefighters completed the survey. Eye irritation was reported by 90% of firefighters at least sometimes during work and by 70% after work. Frequency of eye irritation was greater amongst females than males (OR 2.01, CI 1.22 - 3.31, p<0.05). Protective eyewear was used often or always by 67% of firefighters on the fireground, however 55% had to remove their protective eyewear due to sweat, fogging or another reason. Goggles were more likely to be removed compared to sunglasses and safety glasses (OR 4.28, CI 2.75 - 6.68, p<0.001). Firefighters reported that, at times smoke exposure necessitated eye closure and impaired vision on the fireground. Firefighters also reported that protective eyewear helped to reduce eye symptoms, but its consistent use on the fireground was difficult. The severity and recovery from eye symptoms varied between participants. CONCLUSION: Australian wildland firefighters frequently experience eye irritation from smoke exposure, and this can affect operational capabilities. These findings can support the development of evidence-based strategies to help protect and aid recovery of the eye surface following smoke exposure.

Impacts of ash-induced environmental alkalinization on fish physiology, and their implications to wildfire-scarred watersheds.

Changes in land use, a warming climate and increased drought have amplified wildfire frequency and magnitude globally. Subsequent rainfall in wildfire-scarred watersheds washes ash into aquatic systems, increasing water pH and exposing organisms to environmental alkalinization. In this study, 15 or 20 °C-acclimated Chinook salmon (Oncorhynchus tshawytscha) yearlings were exposed to an environmentally-relevant ash concentration (0.25 % w/v), increasing water pH from ~8.1 to ~9.2. Salmon experienced significant disturbance to blood plasma pH (pHe) and red blood cell intracellular pH (RBC pHi) within 1 h, but recovered within 24 h. Impacts on plasma ion concentrations were relatively mild, and plasma glucose increased by 2- to 4-fold at both temperatures. Temperature-specific differences were observed: 20 °C salmon recovered their pHe more rapidly, perhaps facilitated by higher basal metabolism and anaerobic metabolic H+ production. Additionally, 20 °C salmon experienced dramatically greater spikes in plasma total ammonia, [NH3] and [NH4+] after 1 h of exposure that decreased over time, whereas 15 °C salmon experienced a gradual nitrogenous waste accumulation. Despite pHe and RBC pHi recovery and non-lethal nitrogenous waste levels, we observed 20 % and 33 % mortality in 15 and 20 °C treatments within 12 h of exposure, respectively. The mortalities cannot be explained by high water pH alone, nor was it likely to be singularly attributable to a heavy metal or organic compound released from ash input. This demonstrates post-wildfire ash input can induce lethal yet previously unexplored physiological disturbances in fish, and further highlights the complex interaction with warmer temperatures typical of wildfire-scarred landscapes.

Dwelling proximity to wildfire and spina bifida risk in offspring.

OBJECTIVE: To evaluate the association between wildfire exposure in pregnancy and spina bifida risk. METHODS: This retrospective cohort study used the California Office of Statewide Health Planning and Development Linked Birth File with hospital discharge data between 2007 and 2010. The Birth File data were merged with the California Department of Forestry and Fire Protection data of the same year. Spina bifida was identified by its corresponding ICD-9 code listed on the hospital discharge of the newborn. Wildfire exposure was determined based on the zip code of the woman's home address. Pregnancy was considered exposed to wildfire if the mother lived within 15 miles of a wildfire during the pregnancy or within 30 days prior to pregnancy. RESULTS: There were 2,093,185 births and 659 cases of spina bifida between 2007 and 2010. The births were analyzed using multivariable logistic regression models and adjusted for potential confounders. Exposure to wildfire in the first trimester was associated with higher odds of spina bifida (aOR= 1.43 [1.11-1.84], p-value = 0.01). Wildfire exposure 30 days before the last menstrual period and during the second and third trimesters were not associated with higher spina bifida risk. CONCLUSION: Wildfire exposure has shown an increased risk of spina bifida during the early stages of pregnancy.

Robust differential gene expression patterns in the pre-frontal cortex of male mice exposed to an occupationally relevant dose of laboratory generated wildfire smoke.

Wildfires have become common global phenomena concurrent with warmer and drier climates and are now major contributors to ambient air pollution world-wide. Exposure to wildfire smoke has been classically associated with adverse cardiopulmonary health outcomes, especially in vulnerable populations. Recent work has expanded our understanding of wildfire smoke toxicology to include effects on the central nervous system and reproductive function; however, the neurotoxic profile of this toxicant remains ill-explored in an occupational context. Here, we sought to address this by using RNA sequencing to examine transcriptomic signatures in the pre-frontal cortex of male mice modeling career wildland firefighter smoke exposure. We report robust changes in gene expression profiles between smoke exposed samples and filtered air controls, evidenced by 2,862 differentially expressed genes (51.2% increased). We further characterized the functional relevance of these genes highlighting enriched pathways related to synaptic transmission, neuroplasticity, blood-brain barrier integrity, and neurotransmitter metabolism. Additionally, we identified possible contributors to these alterations through protein-protein interaction network mapping, which revealed a central node at ß-catenin and secondary hubs centered around mitochondrial oxidases, the Wnt signaling pathway, and gene expression machinery. The data reported here will serve as the foundation for future experiments aiming to characterize the phenotypic effects and mechanistic underpinnings of occupational wildfire smoke neurotoxicology.

Understanding fire regimes: A biogeographical perspective.

Fire regimes are often considered to be either driven by climate, fuel load or human activities. A significant proportion of fires across various ecosystems occur via large fire events. Recently, suggestions have been made that fires are becoming more severe and frequent as a consequence of current climate change. Although there are many factors influencing fire events, scientists have not found a suitable framework that can provide for understanding fires at the macroscale level. This review article proposes a new conceptual framework to better understand fire regimes. The proposed framework relies on a biogeographical perspective of fire regimes that include characteristics that have been underestimated in previous frameworks and to mitigate time as well as spatial scale issues at the macrolevel.

Particulate matter from wildfire smoke in northern Colorado appears to be associated with conjunctivitis in dogs.

OBJECTIVE: To evaluate ocular surface parameters in dogs with normal eyes when exposed to 3 different air quality index (AQI) categories corresponding to levels of normal air pollutants ("good," 0 to 50; "moderate," 51 to 100) and wildfire smoke ("smoke," 101 to 150). ANIMALS: 15 privately owned dogs. METHODS: A prospective cohort study with dogs living in northern Colorado. Ocular surface parameters (conjunctival chemosis and hyperemia, Schirmer tear test-1, tear film break-up time, fluorescein stain, conjunctival microbiology, etc) were evaluated when the AQI was reported in 1 of the 3 categories (good, moderate, and smoke) for 3 consecutive days. The AQI and air pollutant levels (particulate matter < 2.5 µm in diameter [PM2.5], ozone, etc) were retrieved from the AirNow database. RESULTS: Due to scheduling conflicts, only 7 dogs were examined during the smoke category. Average AQI in the 3 categories were good, 44.1; moderate, 73.7; and smoke, 103.7. The odds for more severe hyperemia and more severe chemosis for smoke were 5.39 and 7,853.02 times the odds, respectively, when compared to good AQI. Additionally, the odds for more severe chemosis were 34,656.62 times the odds for smoke when compared to moderate AQI. A significant relationship was found between chemosis and PM2.5. CONCLUSION: Exposure to increased AQI related to wildfire smoke caused a significant increase in conjunctivitis. The significant relationship between chemosis and PM2.5 could indicate that PM2.5 in wildfire smoke is associated with an inflammatory factor. CLINICAL RELEVANCE: Preventive measures (eg, use of eyewash, artificial tears, or eye protection) for dogs that are exposed to wildfire smoke should be instituted to decrease the risk of ocular irritation.

A comprehensive taxonomy for forest fire risk assessment: bridging methodological gaps and proposing future directions.

Forest fire risk assessment plays a crucial role in the environmental management of natural hazards, serving as a key tool in the prevention of forest fires and the protection of various species. As these risks continue to evolve with environmental changes, the pertinence of contemporary research in this field remains undiminished. This review constructs a comprehensive taxonomic framework for classifying the existing body of literature on forest fire risk assessment within forestry studies. The developed taxonomy categorizes existing studies into 8 primary categories and 23 subcategories, offering a structured perspective on the methodologies and focus areas prevalent in the domain. We categorize a sample of 170 articles to present recent trends and identify research gaps in forest fire risk assessment literature. The classification facilitates a critical evaluation of the current research landscape, identifying areas in need of further exploration. Particularly, our review identifies underrepresented methodologies such as optimization modeling and some advanced machine learning techniques, which present routes for future inquiry. Moreover, the review underscores the necessity for model development that is tailored to specific regional data sets but also adaptable to global data resources, striking a balance between local specificity and broad applicability. Emphasizing the dynamic nature of forest fire behavior, we advocate for models that integrate the burgeoning field of machine learning and multi-criteria decision analysis to refine predictive accuracy and operational effectiveness in fire risk assessment. This study highlights the great potential for new ideas in modeling techniques and emphasizes the need for increased collaboration among research communities to improve the effectiveness of assessing forest fire risks.

Moderating effects of past wildfire on reburn severity depend on climate and initial severity in Western US forests.

Rising global fire activity is increasing the prevalence of repeated short-interval burning (reburning) in forests worldwide. In forests that historically experienced frequent-fire regimes, high-severity fire exacerbates the severity of subsequent fires by increasing prevalence of shrubs and/or by creating drier understory conditions. Low- to moderate-severity fire, in contrast, can moderate future fire behavior by reducing fuel loads. The extent to which previous fires moderate future fire severity will powerfully affect fire-prone forest ecosystem trajectories over the next century. Further, knowing where and when a wildfire may act as a landscape-scale fuel treatment can help direct pre- and post-fire management efforts. We leverage satellite imagery and fire progression mapping to model reburn dynamics within forests that initially burned at low/moderate severity in 726 unique fire pair events over a 36-year period across four large fire-prone Western US ecoregions. We ask (1) how strong are the moderating effects of low- to moderate-severity fire on future fire severity, (2) how long do moderating effects last, and (3) how does the time between fires (a proxy for fuel accumulation) interact with initial fire severity, day-of-burning weather conditions, and climate to influence reburn severity. Short-interval reburns primarily occurred in dry- and moist-mixed conifer forests with historically frequent-fire regimes. Previous fire moderated reburn severity in all ecoregions with the strongest effects occurring in the California Coast and Western Mountains and the average duration of moderating effects ranging from 13 years in the Western Mountains to >36 years in the California Coast. The strength and duration of moderating effects depended on climate and initial fire severity in some regions, reflecting differences in post-fire fuel accumulation. In the California Coast, moderating effects lasted longer in cooler and wetter forests. In the Western Mountains, moderating effects were stronger and longer lasting in forests that initially burned at higher severity. Moderating effects were largely robust to fire weather, suggesting that previous fire can mediate future fire severity even under extreme conditions. Our findings demonstrate that low- to moderate-severity fire buffers future fire severity in historically frequent-fire forests, underlining the importance of wildfire as a restoration tool for adapting to global change.

Efficient First-Order Algorithms for Large-Scale, Non-Smooth Maximum Entropy Models with Application to Wildfire Science.

Maximum entropy (MaxEnt) models are a class of statistical models that use the maximum entropy principle to estimate probability distributions from data. Due to the size of modern data sets, MaxEnt models need efficient optimization algorithms to scale well for big data applications. State-of-the-art algorithms for MaxEnt models, however, were not originally designed to handle big data sets; these algorithms either rely on technical devices that may yield unreliable numerical results, scale poorly, or require smoothness assumptions that many practical MaxEnt models lack. In this paper, we present novel optimization algorithms that overcome the shortcomings of state-of-the-art algorithms for training large-scale, non-smooth MaxEnt models. Our proposed first-order algorithms leverage the Kullback-Leibler divergence to train large-scale and non-smooth MaxEnt models efficiently. For MaxEnt models with discrete probability distribution of n elements built from samples, each containing m features, the stepsize parameter estimation and iterations in our algorithms scale on the order of O(mn) operations and can be trivially parallelized. Moreover, the strong ℓ1 convexity of the Kullback-Leibler divergence allows for larger stepsize parameters, thereby speeding up the convergence rate of our algorithms. To illustrate the efficiency of our novel algorithms, we consider the problem of estimating probabilities of fire occurrences as a function of ecological features in the Western US MTBS-Interagency wildfire data set. Our numerical results show that our algorithms outperform the state of the art by one order of magnitude and yield results that agree with physical models of wildfire occurrence and previous statistical analyses of wildfire drivers.

Effects of Wildfire on Soil CO2 Emission and Bacterial Community in Plantations.

In order to study the effects of wildfires on soil carbon dioxide (CO2) emissions and microbial communities in planted forests, Pinus massoniana Lamb. and Cunninghamia lanceolata (Lamb.) Hook. forests were selected as the research subjects. Through a culture test with 60 days of indoor constant temperature, the soil physical and chemical properties, organic carbon mineralization, organic carbon components, enzyme activity, and microbial community structure changes of the two plantations after fire were analyzed. The results showed that wildfires significantly reduced soil CO2 emissions from the Pinus massoniana forests and Cunninghamia lanceolata forests by 270.67 mg·kg-1 and 470.40 mg·kg-1, respectively, with Cunninghamia lanceolata forests exhibiting the greatest reduction in soil CO2 emissions compared to unburned soils. Bioinformatics analysis revealed that the abundance of soil Proteobacteria in the Pinus massoniana and Cunninghamia lanceolata forests decreased by 6.00% and 4.55%, respectively, after wildfires. Additionally, redundancy analysis indicated a significant positive correlation between Proteobacteria and soil CO2 emissions, suggesting that the decrease in Proteobacteria may inhibit soil CO2 emissions. The Cunninghamia lanceolata forests exhibited a significant increase in soil available nutrients and inhibition of enzyme activities after the wildfire. Additionally, soil CO2 emissions decreased more, indicating a stronger adaptive capacity to environmental changes following the wildfire. In summary, wildfire in the Cunninghamia lanceolata forests led to the most pronounced reduction in soil CO2 emissions, thereby mitigating soil carbon emissions in the region.

Automatic Methodology for Forest Fire Mapping with SuperDove Imagery.

The global increase in wildfires due to climate change highlights the need for accurate wildfire mapping. This study performs a proof of concept on the usefulness of SuperDove imagery for wildfire mapping. To address this topic, we present an automatic methodology that combines the use of various vegetation indices with clustering algorithms (bisecting k-means and k-means) to analyze images before and after fires, with the aim of improving the precision of the burned area and severity assessments. The results demonstrate the potential of using this PlanetScope sensor, showing that the methodology effectively delineates burned areas and classifies them by severity level, in comparison with data from the Copernicus Emergency Management Service (CEMS). Thus, the potential of the SuperDove satellite sensor constellation for fire monitoring is highlighted, despite its limitations regarding radiometric distortion and the absence of Short-Wave Infrared (SWIR) bands, suggesting that the methodology could contribute to better fire management strategies.

Substantial Underestimation of Fine-Mode Aerosol Loading from Wildfires and Its Radiative Effects in Current Satellite-Based Retrievals over the United States.

Wildfires generate abundant smoke primarily composed of fine-mode aerosols. However, accurately measuring the fine-mode aerosol optical depth (fAOD) is highly uncertain in most existing satellite-based aerosol products. Deep learning offers promise for inferring fAOD, but little has been done using multiangle satellite data. We developed an innovative angle-dependent deep-learning model (ADLM) that accounts for angular diversity in dual-angle observations. The model captures aerosol properties observed from dual angles in the contiguous United States and explores the potential of Greenhouse gases Observing Satellite-2's (GOSAT-2) measurements to retrieve fAOD at a 460 m spatial resolution. The ADLM demonstrates a strong performance through rigorous validation against ground-based data, revealing small biases. By comparison, the official fAOD product from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Multiangle Imaging Spectroradiometer (MISR) during wildfire events is underestimated by more than 40% over western USA. This leads to significant differences in estimates of aerosol radiative forcing (ARF) from wildfires. The ADLM shows more than 20% stronger ARF than the MODIS, VIIRS, and MISR estimates, highlighting a greater impact of wildfire fAOD on Earth's energy balance.

Gestational exposure to wildfire PM2.5 and its specific components and the risk of gestational hypertension and eclampsia in the southwestern United States.

In the southwestern United States, the frequency of summer wildfires has elevated ambient PM2.5 concentrations and rates of adverse birth outcomes. Notably, hypertensive disorders in pregnancy (HDP) constitute a significant determinant associated with maternal mortality and adverse birth outcomes. Despite the accumulating body of evidence, scant research has delved into the correlation between chemical components of wildfire PM2.5 and the risk of HDP. Derived from data provided by the National Center for Health Statistics, singleton births from >2.68 million pregnant women were selected across 8 states (Arizona, AZ; California, CA, Idaho, ID, Montana, MT; Nevada, NV; Oregon, OR; Utah, UT, and Wyoming, WY) in the southwestern US from 2001 to 2004. A spatiotemporal model and a Goddard Earth Observing System chemical transport model were employed to forecast daily concentrations of total and wildfire PM2.5-derived exposure. Various modeling techniques including unadjusted analyses, covariate-adjusted models, propensity-score matching, and double robust typical logit models were applied to assess the relationship between wildfire PM2.5 exposure and gestational hypertension and eclampsia. Exposure to fire PM2.5, fire-sourced black carbon (BC) and organic carbon (OC) were associated with an augmented risk of gestational hypertension (ORPM2.5 = 1.125, 95 % CI: 1.109,1.141; ORBC = 1.247, 95 % CI: 1.214,1.281; OROC = 1.153, 95 % CI: 1.132, 1.174) and eclampsia (ORPM2.5 = 1.217, 95 % CI: 1.145,1.293; ORBC = 1.458, 95 % CI: 1.291,1.646; OROC = 1.309, 95 % CI: 1.208,1.418) during the pregnancy exposure window with the strongest effect. The associations were stronger that the observed effects of ambient PM2.5 in which the sources primarily came from urban emissions. Social vulnerability index (SVI), education years, pre-pregnancy diabetes, and hypertension acted as effect modifiers. Gestational exposure to wildfire PM2.5 and specific chemical components (BC and OC) increased gestational hypertension and eclampsia risk in the southwestern United States.

Black carbon and particulate matter concentrations amid central Chile's extreme wildfires.

The increase in the frequency and severity of global wildfires has been largely influenced by climate change and land use changes. From February 2 to 6, 2024, central Chile experienced its most devastating wildland-urban interface wildfire in history, severely impacting the Valparaíso region. This catastrophic event, which led to extensive forest destruction, the loss of thousands of homes, and over a hundred human fatalities, directly impacted the area surrounding the campus of Federico Santa María Technical University. In that period, an air quality monitoring campaign was set up on the campus to measure black carbon (BC) and particulate matter (PM) during the wildfire season. The monitoring station was located directly within the smoke plume, allowing for the collection of unprecedented air quality data. Extremely high concentrations of BC at 880 nm were reported during the wildfires, with a daily mean (±σ) of 14.83 ± 19.52 µg m-3. Peak concentrations measured at 880 nm and 375 nm reached 812.89 µg m-3 and 1561.24 µg m-3, respectively. The maximum daily mean BC concentrations at these wavelengths were 55 and 99 times higher, respectively, compared to the pre-event period. The mean Ångström absorbing coefficient during the event was 1.66, indicating biomass burning as the primary BC source, while the maximum BC/PM2.5 ratio (at 375 nm) reached 57 %. From February 2 to 5, 2024, PM concentrations exceeded the Chilean air quality standard by 82 % and 198 % for coarse and fine particles, respectively. These levels are 4.7 and 6.0 times higher than the World Health Organization's recommendations. These elevated concentrations persisted for up to three days after the fire was extinguished. This study provides unique evidence of the rapid deterioration of regional air quality during a wildfire event using in situ measurements, serving as a stark reminder of the far-reaching consequences of a warming climate.

Fire up Biosensor Technology to Assess the Vitality of Trees after Wildfires.

The development of tools to quickly identify the fate of damaged trees after a stress event such as a wildfire is of great importance. In this context, an innovative approach to assess irreversible physiological damage in trees could help to support the planning of management decisions for disturbed sites to restore biodiversity, protect the environment and understand the adaptations of ecosystem functionality. The vitality of trees can be estimated by several physiological indicators, such as cambium activity and the amount of starch and soluble sugars, while the accumulation of ethanol in the cambial cells and phloem is considered an alarm sign of cell death. However, their determination requires time-consuming laboratory protocols, making the approach impractical in the field. Biosensors hold considerable promise for substantially advancing this field. The general objective of this review is to define a system for quantifying the plant vitality in forest areas exposed to fire. This review describes recent electrochemical biosensors that can detect plant molecules, focusing on biosensors for glucose, fructose, and ethanol as indicators of tree vitality.

Wildfire Impacts on O3 in the Continental United States Using PM2.5 and a Generalized Additive Model (2018-2023).

We examined PM2.5 and Hazard Mapping System smoke plume satellite data at ∼600 United States (US) air monitoring stations to identify surface smoke on 14.0% of all May-September days for 2018-2023, with large influences in 2020 and 2021, due to California fires, and 2023, due to Canadian fires. Days with smoke have an average of 11 µg m-3 more PM2.5 and 8 ppb higher maximum daily 8 h average (MDA8) O3 concentrations than nonsmoke days, and they also account for 94% of all days that exceed the daily PM2.5 health standard (35 µg m-3) and 36% of all days that exceed the O3 health standard (70 ppb). To estimate the smoke contributions to the O3 MDA8, Generalized Additive Models (GAMs) were built for each site using the nonsmoke day data and up to 8 predictors. The mean and standard deviation of the residuals from the GAMs were 0 ± 6.1 ppb for the nonsmoke day data and 4.3 ± 7.9 ppb for the smoke day data, indicating a significant enhancement in the MDA8 O3 on smoke days. We found positive residuals on 72% of the smoke days and for these days, we calculate an average smoke contribution to the O3 MDA8 of 7.8 ± 6.0 ppb. Over the 6 year period, the percentage of exceedance days due to smoke in the continental US was 25% of all exceedance days, and the highest was in 2023 (38%). In 2023, the Central US experienced an unusually high number of exceedance days, 1522, with 52% of these impacted by smoke, while the Eastern US had fewer exceedance days, 288, with 78% of these impacted by smoke. Our results demonstrate the importance of wildland fires as contributors to exceedances of the health-based national air quality standards for PM2.5 and O3.

Elemental composition of post-wildfire biomass ashes and partly burned woody species in Bohemian Switzerland National Park, Czech Republic.

The study explored the post-wildfire elemental composition of parts (wood, bark, branch, cone, trunk, litter, twig, needle, sward, fallow, sapling, etc.) and by-products (biomass ashes, partly burnt parts, and char) of different woody species in the Bohemian Switzerland National Park, Czech Republic, and considered their effects on soils. Multi-elemental analysis of the fire by-products of the woody species was determined with inductively coupled plasma-optical emission spectrometry and mass spectrometry and compared with control biomass samples unaffected by wildfire. Most fire by-products were slightly alkaline, with acidic ashes obtained from blueberry wood. The by-products of the wildfire were characterized by varied total contents of macro (P, Ca, K, Mg, and S), micro (Na, Mn, Fe, Cu, and Zn), and other elements (B, Co, Mo, and V) vital to soil fertility and plant growth. The mean content of macro elements in the biomass ashes was up to 4.16 P, 23.5 Ca, 2.48 Mg, 63 K, and 5.57 S g kg-1. These values were comparatively lower than published data for ashes obtained under optimized conditions, e.g., those combusted in power generation facilities. Conversely, partly burnt parts-an indication of incomplete combustion-had higher 9.22 P, 79 Ca, and 5.99 Mg g kg-1 contents in spruce needles than in biomass ashes and the control. Variations in woody species and anthropogenic activities in areas of wildfires produced varied As, Cd, Cr, Ni, and Pb contents above EU fertilizer regulation. Caution in applying biomass ashes from wildfires on fields is required due to risk/toxic elements input from anthropogenic activities. Wildfire effects on the elemental composition of woody species can provide information on plant parts most suitable for biomass ashes for soil and ecosystem safety.