搜索结果：wildfires

In the western United States, there has been a significant increase in both the size and number of wildfires associated with the increasing drought conditions. The six largest wildfires in U.S. history have occurred in the last seven years. Four of the six fires were in California (2017 Tubbs Fire, 2018 Camp Fire, 2020 Bay Area Fire, and 2021 Dixie Fire), where the mountains create complex atmospheric flows that lead to terrain-induced wildfires from dry, downslope winds. To investigate the health outcomes associated with smoke exposure the spatiotemporal distribution of smoke plume concentrations is required. Air quality monitors are sparse and do not quantify pollutant concentrations solely from smoke plumes. To estimate smoke impacts, the monitoring data can be supplemented with information from satellites, wildfire emissions inventories, dispersion models, and chemical transport models. Results of air quality modeling efforts to simulate wildfire smoke plume transport in the western U.S. and estimate smoke exposure are presented here. The focus of this work is on two recent modeling efforts (1) a novel smoke exposure modeling framework that provides estimates by fuel type, fire size, and plume age and (2) gap-filling approaches that leverage machine learning algorithms to increase the usability of satellite aerosol remote sensing products for estimating wildfire smoke exposure.

Exposure to wildfire-associated smoke has increased in recent years in the Northeast of the United States. Of particular note was the smoke event in June 2023 when plumes from Canadian wildfires caused a high pollution event in New York State. We used data from Stony Brook Hospital to assess the effect of the June 2023 smoke event on healthcare encounters, including emergency department (ED) visits and inpatient admissions. We examined all-cause encounters, cardiovascular diseases (CVDs), hypertension, respiratory diseases, and asthma. We then extended our study to look at longer term trends using a time-series analysis looking at the effect of exposure to wildfire smoke-associated PM2.5 on healthcare encounters. We studied the association between exposure to wildfire-associated PM2.5 at the county level to daily hospital visits between January 2014 through July 2023. Other exposures of interest included non-smoke PM2.5, ozone, and temperature. We found increased rates of total hospital visits for CVD (rate ratio: 1.34 (95% CI: 1.07-1.68)) and hypertension (rate ratio: 1.47, (95% CI: 1.08-2.00)) during the smoke event in 2023 as compared to the reference period, driven primarily by increased ED visits. We also found an increase in the rate of inpatient respiratory admissions (rate ratio: 1.60 (195% CI: 1.03-2.48) as compared to the reference period. Our time-series analysis showed an increased rate of total encounters with exposure to wildfire-smoke PM2.5. Higher temperatures were also associated with increased rates of all-cause health care encounters as well as cardiovascular and respiratory encounters. Our study found adverse outcomes related to exposure to wildfire-associated air pollution in a suburban community during a high exposure wildfire smoke event. It highlights the need for further local mitigation and adaptation measures in response to increasing wildfires.

Prescribed burning has been adopted as a strategic tool within Integrated Fire Management (IFM) of pyrophytic ecosystems, and there is a growing need to provide evidence supporting its effectiveness in reducing wildfire occurrence. As the first study to systematically quantify the contributions of prescribed burning in Amazonian savanna ecosystems, it evaluates its effects on fire dynamics in the largest savanna enclave in the southern Brazilian Amazon, with emphasis on fire seasonality, fire size, and the extent of fire-affected sensitive vegetation. A fire-scar database was compiled using semi-automated classification procedures applied to Landsat imagery, and records were classified by fire type, seasonality, and size classes. The analysis was segmented into three periods that coincide with abrupt changes in the territorial fire management of the area: T1 (1998-2006), before establishment of Campos Amazônicos National Park; T2 (2007-2015), fire-exclusion policy; T3 (2016-2024), IFM with prescribed burns. Analysis of variance was used to determine whether statistically significant differences occurred among dependent variables derived from fire dynamics patterns across the study periods. Over the past 27 years, a total of 16,589 km2 of burned area was detected, distributed across 2962 fires. The average percentage of sensitive vegetation burned in wildfires was 3.3 times higher than in prescribed burns (5.2 ± 13.4% for wildfires, compared with 1.6 ± 2.9% for prescribed burns). The use of prescribed burning in T3 was associated with a 31.4% reduction in the percentage prevalence of burned areas in the late dry season compared to T2 (F = 7.297; p = 0.00335), along with a 29.2% reduction in the annual proportion of fire-affected sensitive vegetation. The findings underscore the effectiveness of prescribed burning as a tool for reducing late dry season burned-area prevalence and protecting sensitive vegetation. Prescribed burning should be incorporated into IFM strategies in Amazonian open ecosystems to manage accumulated and continuous fuel in savanna and grassland environments and enhance ecosystem resilience under current and future climate conditions.

The increasing frequency and severity of wildfires has necessitated assessing fire effects on soil systems. Variation in fuel loads and fire effects create landscape mosaics with distinct abiotic properties, a heterogeneity that has been dubbed pyrodiversity. Expanding on the pyrodiversity framework, the pyrodiversity-biodiversity hypothesis posits that pyrodiversity increases niche diversity, thereby promoting biodiversity. This hypothesis, however, has remained untested for soil microbes and microeukaryotes. We explored this hypothesis for soil fungal communities using pre- and post-fire data from three empirical fuel load manipulations and across a total of five different vegetation contexts. We first compared pre- and post-fire abiotic heterogeneity to test whether fuel load manipulations would lead to greater environmental heterogeneity, particularly in soil properties. We then tested whether such manipulations led to greater fungal biodiversity as measured by fungal richness, β-diversity, and community dispersion. Labile abiotic soil resource (e.g. plant available phosphorus and inorganic nitrogen) heterogeneity increased post-fire, but this effect depended on the experimental context. In contrast, we observed little evidence for pyrodiversity-associated increases in post-fire fungal richness or diversity; community dispersion increased only in the study with the most extreme fuel load manipulations. Although our analyses did not clearly answer whether pyrodiversity begets biodiversity, our results highlight the nuances of soil responses to fire. Pyrodiversity-biodiversity linkages appear to depend on the system and on the diversity metric: the hypothesis had no support based on fungal richness, but community dispersion provided some support, even if only in one experiment. Understanding system-specific responses may be particularly important as fires increase in systems where they have been suppressed or have been historically rare.

Pollutants in smoke from wildland and wildland urban interface (WUI) fires present an increasing risk to outdoor workers, field technicians, and others as the frequency and severity of these fires increase due to expansion of the WUI, changing land management practices, and climate change. Recent advancements have improved our understanding and ability to track and model wildfires, smoke plume chemistry, and associated health impacts. There are indications that smoke from burning certain fuels, including artificial materials present in many WUI fires and other biomass burned under certain conditions, may present enhanced health risks relative to smoke from typical biomass-only fires. However, large uncertainties, data gaps, and a lack of integration of available tools and datasets present substantial challenges in making recommendations and developing guidance to protect outdoor workers. Notwithstanding these uncertainties and data gaps, outdoor workers are exposed to potentially harmful wildfire pollutants with limited health protection guidance. There is a pressing need to advance the understanding of the relative risk of wildfire smoke based on the burned fuel and to provide tools to support real-time decision-making to protect the health of outdoor workers. Therefore, researchers and health professionals need to fill data gaps and develop integrated tools to provide decision-makers with clear guidance. This new guidance should be action-oriented, reflect current knowledge, and highlight remaining uncertainties. With these resources, decision makers can better strategize when and where outdoor work needs to be done to best protect those doing the work.

Providing access to clean drinking water is a critical challenge in the aftermath of natural disasters. The aim of this study is to conduct a bibliometric analysis of research trends on drinking water provision in disaster-affected areas, identifying strengths and weaknesses in the global research community. This study conducted a bibliometric analysis of research on drinking water supply in disaster-affected areas from 2004 to 2024 using Scopus. A comprehensive search query was employed to include relevant studies, excluding unrelated topics. Data were exported to Excel for analysis, and VOSviewer software was used to create network maps based on co-occurrence relationships, author collaborations, and citation patterns. This study analyzes 709 publications on drinking water supply in disaster-affected areas from 2004 to 2024, sourced from 109 countries and authored by 2804 researchers. The findings indicate a consistent rise in publications, reaching a peak in 2021, with a predominant focus on environmental science, engineering, and social sciences. Peer-reviewed journal articles dominate, followed by conference papers. Prominent authors such as Rosario-Ortiz and Pieper have significantly influenced the field. Research clusters highlight wildfire impacts, water quality management, and treatment systems. Major journals include Water and Journal of Hydrology, with the United States, China, and Australia leading global contributions. This study analyzes research trends in providing drinking water during natural disasters, highlighting a focus on floods and wildfires. It emphasizes the need for more attention to water crises caused by earthquakes and tsunamis, the use of predictive models, smart systems, and international collaborations in future research.

Decades of improvement in surface ozone exposure in the United States risk being reversed by wildfires.

Extreme weather events are increasing in frequency and severity, disrupting health care services across the cancer care continuum. Oncology professionals and patients are directly affected by these events, yet preparedness and resilience planning specific to cancer care remain limited. This commentary synthesizes current knowledge on the impacts of extreme weather events on oncology care and draws on lessons from prior disasters to highlight opportunities for action. Three illustrative case studies regarding Hurricane Katrina, California wildfires, and Texas Winter Storm Uri highlight commonalities between different types of disasters and geographic locations, including patterns of disruption related to infrastructure damage, power outages, workforce strain, and challenges to continuity of care. Existing evidence-informed strategies to enhance resilience across clinical, institutional, and policy domains include integrating climate resilience into clinical training and practice, adapting infrastructure and operations, reviewing local climate vulnerabilities, and strengthening partnerships with community organizations. As extreme weather events increasingly threaten cancer care delivery and patient outcomes, proactive, coordinated efforts by oncology professionals and health systems are essential to maintain high-quality care in a changing environment.

Climate change is exacerbating wildfires, resulting in an increased influx of pyrogenic dissolved organic matter (pyDOM) into aquatic systems. The distinctive molecular signature of pyDOM, which is markedly different from that of conventional humic substances, is hypothesized to have an impact on its environmental fate, especially during coagulation that is a crucial process for carbon sedimentation and water purification. Nevertheless, the specific removal mechanisms and the resulting floc architectures remain unclear. Through a comparative investigation of pyDOM from subtropical burnt soils and standard humic acid (HA) during aluminum sulfate coagulation, we have discovered a selective coagulation mechanism governed by pyDOM's molecular properties. pyDOM with condensed aromatics and oxygenated aliphatics is removed via synergistic π-π stacking and Al coordination. This stands in stark contrast to the charge neutralization dominated pathway observed for HA. The pyDOM-specific pathway gives rise to larger, denser flocs with a wider size distribution. At the molecular level, aggregation initiates with the condensation of aromatic cores, and oxygen and heteroatom functional groups form stable Al-O-C complexes, acting as chemical cross-links that strengthen floc integrity. This research identifies a previously neglected coagulation paradigm unique to pyDOM, which differs fundamentally from the traditional HA-centric framework, thus enhancing our mechanistic understanding of pyDOM-driven particle assembly and carbon export. These findings are essential for precisely predicting carbon stability in fire-affected watersheds and for devising effective water treatment strategies in wildfire-prone regions.

Wildfire smoke is an increasing disturbance across much of the globe that has received little attention as a selective force in animal behavior. Animals may have evolved behavioral strategies to cope with smoke exposure, but what those strategies are and how they affect health outcomes are poorly described. Here we introduce a tiered "Stay, Shift, Go" framework to characterize animal responses to wildfire smoke disturbance. During periods of toxic smoke, animals may (1) stay, resisting changes in behavior (2) shift, altering their behavior to reduce negative impacts, or (3) go, relocating to places with cleaner air. Responses will be constrained by animal traits, such as locomotion, and may scale depending on the extent, duration, and intensity of smoke exposure. We hypothesized that American robins (Turdus migratorius), a highly mobile, partial migrant songbird living in a fire-prone habitat, would utilize a Go strategy to avoid smoke from nearby wildfires during the post-breeding season. However, we found that they made shorter-distance movements as wildfire smoke intensified. Robins did not alter orientation behavior relative to the direction of the wind under light smoke, but they were more likely to orient into the wind when heavy smoke was present, which tended to reduce smoke exposure. Our results suggest that American robins shift their behavior during wildfire smoke events, potentially to reduce exposure to toxic air. While they do not immediately relocate, they may use different strategies as smoke becomes more intense-supporting our hypothesis that animal responses to wildfire smoke are complex and dose-dependent.

Genetic data have long been applied in conservation and management for threatened and endangered species to track effects of inbreeding, hybridization, and population structure. However, when a reference genome is lacking, there is inadequate information to confidently predict extinction risk or potential for local adaptation to varying environmental factors. A reference genome provides information about the identity and function of key gene families that likely affect organismal performance in nature. A well-resolved genome is therefore vital to future conservation planning for protected species. Gila Trout have undergone severe genetic and demographic bottlenecks due to habitat fragmentation and climate-based drought and wildfires in the Gila national forest, NM. The species is currently managed under a recovery program that works to ensure the preservation of five genetically and morphologically distinct Gila Trout lineages. Here we present a chromosome-level assembly and annotation of the genome of Gila Trout Oncorhynchus gilae, a threatened trout species closely related to the Rainbow Trout. We verify that the Gila Trout genome is composed of 28 chromosomes, of which, 24 are metacentric and four are telocentric. Gila Trout maintains karyotypes of key chromosomal inversions observed in ancestral salmonids. Annotation and comparison of genes involved in adaptive immunity in the Gila Trout genome against other salmonids reveals conservation of coding regions but high variability within the introns and non-coding regions around the genes that could bolster immunocompetence in the face of novel pathogens. This genome provides a key resource for the further monitoring and conservation of the Gila Trout.

As climate change increases the frequency and severity of disasters, proactive planning for post-disaster housing recovery is essential to mitigate long-term social and economic disruption. Computational models can support this planning by simulating potential recovery trajectories, yet many existing approaches are limited by overwhelming data requirements or narrow applicability to past events. Here, we introduce RAAbIT (Recovery Assessment using Agent-based Tools), a novel agent-based model designed to simulate housing recovery using data available within weeks of a disaster. RAAbIT models individual households, insurers, and contractors as agents governed by empirical behavior rules, and incorporates modifiable system-level constraints, such as contractor availability, to reflect context-specific recovery dynamics. We demonstrate the model's utility by hindcasting two California wildfires-the 2017 Tubbs Fire in Santa Rosa and the 2018 Camp Fire in Paradise-and capturing their divergent recovery trajectories. Despite similar hazards, the two communities experienced significantly different reconstruction outcomes, with Santa Rosa rebuilding 57% of destroyed homes and Paradise only 9% within five years. RAAbIT can reproduce temporal and spatial patterns of recovery observed in building permit and construction data. By balancing generalizability with data realism, RAAbIT provides a flexible and transferable tool for post-disaster recovery planning, supporting more effective decision-making under uncertainty and enhancing community resilience in the face of escalating climate risks.

Avoiding human fatalities during wildfires is a key public policy objective. Outward road access, or the number of egress routes, is widely assumed to influence wildfire fatalities, yet few studies have quantified if or when this factor becomes critical. To address this gap, we assembled a dataset on community-level wildfire fatality counts and combined it with nationally consistent community egress for the United States, finding that cumulative fatalities are sharply concentrated in communities with very few exits, declining steeply to roughly six nonresidential roads, beyond which additional routes confer minimal further risk reduction. Extending this analysis nationally, we mapped all small communities (<50,000 residents) to identify geographic confluence of limited egress and high wildfire hazard, highlighting regions where road constraints could directly amplify fatalities. Across the United States, 17.7 million people live in communities below this critical egress threshold, including 2.5 million in high wildfire hazard areas. Although most high-risk communities are in the western United States, unexpected hotspots appear in Oklahoma, Florida, and Hawai'i. As wildfire hazard continues to expand with climate change, fuel accumulation, and development in the wildland-urban interface, even more communities may be at risk. Targeted investment in road infrastructure, improved evacuation communication and preparedness, and development of preplanned refuge options together offer complementary and actionable pathways to reduce wildfire fatalities and build nationwide resilience.

Air pollution, caused by both natural events and human activities, poses serious threats to human health and ecosystems. Remote sensing involves measuring electromagnetic radiation reflected, emitted, or scattered by a target object or area. This comprehensive review explores the latest advances in remote sensing technologies, specifically ground-based, aerial, and satellite systems, and their applications for monitoring various natural and human-made pollution sources. These sources include volcanic eruptions, wildfires, transportation, industrial emissions, agricultural activities, and landfills. The review systematically categorizes these pollution sources and compares how different system setups, operational methods, and effectiveness vary across air quality monitoring techniques. A key focus is on the rapid progress in sensor technology, driven by the growing need for accurate, real-time, and scalable air quality monitoring solutions. Modern sensing platforms, such as high-resolution satellite imagers, multispectral and hyperspectral sensors, and advanced data fusion techniques, are recognized for their transformative impact. However, the field faces significant challenges, including sensor calibration, standardizing data across platforms, and integrating diverse datasets. Limitations in spatial and temporal resolution, especially in satellite-based monitoring, highlight the importance of hybrid systems that combine multiple sensing methods. The review concludes by emphasizing the need for ongoing innovation, interdisciplinary collaboration, and the integration of advanced sensing technologies into national and global regulatory frameworks. These efforts are crucial for improving environmental management, protecting public health, and strengthening climate resilience.

This study explores the integration of Earth Observation (EO) data within Italy's civil protection and disaster risk management (DRM) framework, with a focus on enhancing response, preparedness, and recovery efforts. Recognizing the critical role of satellite and in-situ EO products in monitoring hazards such as earthquakes, volcanic activity, meteo-hydrological (i.e., landslides, floods, land subsidence, droughts), wildfires, and technological hazards, the research evaluates current European Copernicus services and products, and their applicability to Italian civil protection needs. Through the creation of a comprehensive database of the Italian Civil Protection Department's (DPC) needs and technical requirements, the study identifies existing gaps and unexploited opportunities for EO data utilisation. It emphasizes the importance of a user need-driven approach, with potential applicability across Europe, to inform future mission planning, service improvements, and the ongoing development of the Italian IRIDE EO constellation.

Accurate PM2.5 monitoring in complex terrain is challenged by sparse regulatory networks and distinct seasonal pollution sources, like winter temperature inversions and summer wildfires. This study develops and evaluates a hybrid modeling framework that integrates bias-corrected low-cost sensor data to improve spatial and temporal PM2.5 mapping in Montana, USA. We first applied a local, observation-based bias correction to dense PurpleAir sensor network, substantially improving agreement with reference monitors (R2 increased from 0.750 for a generic correction to 0.838). This corrected data was used to create high-resolution smog potential (smogP) surface, a static layer representing terrain-driven pollution accumulation, which explained 59.5% of the spatial variance of mean PM2.5 during wintertime inversion events. Daily 600&#xa0;m resolution PM2.5 grids were then generated using geographically weighted regression framework. Cross-validation revealed a strong seasonal dichotomy in model performance: during the winter inversion season (November-March), models incorporating the smogP layer were critical and consistently outperformed other approaches. Conversely, during the summer wildfire season (May-September), the high density of sensor data alone was often sufficient, with simpler spatial interpolation models proving as effective at capturing the diffuse nature of large smoke plumes than models constrained by static covariates like smogP or satellite aerosol optical depth (AOD). We conclude that the optimal strategy for PM2.5 mapping in complex terrain is season-dependent, and this work provides a framework for leveraging citizen-science data to enhance air quality forecasting in these challenging environments. SYNOPSIS: Effective PM2.5 monitoring/modeling in complex terrain requires a seasonally adaptive strategy, using a terrain-based smog potential model for winter inversions and flexible spatial interpolation of dense sensor data for summer wildfire smoke.

Wildfires are increasingly affecting forest ecosystems worldwide, with potentially long-lasting impacts on soil organic matter and carbon fluxes. Transformations of dissolved organic matter (DOM) by fire are particularly important, as they regulate many biogeochemical and physical processes in soils. We examined changes in DOM from two eucalypt forest ecosystems in southwest Australia that were variably impacted by a large wildfire five years earlier. DOM was extracted from loamy soils under karri forest (Eucalyptus diversicolor F. Muell) and from sandy soils under jarrah forest (Eucalyptus marginata Donn ex. Smith). Soils were collected from low and high severity burned areas and adjacent, unburned sites. To assess molecular characteristics, we combined 1HNMR with excitation-emission matrices (EEMs). Our results show that fire legacy was more evident in DOM from loamy soils, where the concentration of dissolved organic carbon (DOC) and the molecular weight of DOM was lower in sites burnt at high severity than unburnt sites. The 1H NMR spectra of unburnt sites in the loamy soils were characterised by oxygenated structures, whereas sites exposed to high severity fires exhibited a dominance of aliphatic structures. By contrast, sandy soils showed little change in 1H NMR spectra or fluorescence signatures across burned and unburned areas. Importantly, there was no evidence of aromatic structures in burned soils from either forest type, as indicated by the absence of increased aromaticity (SUVA254) or aromatic 1H NMR signals. Our findings highlight that wildfire can impart long-term changes to DOM, particularly in loamy soils, with implications for carbon cycling and ecosystem recovery.