California Wildfires Research Articles

Tilapia Fish Skin Treatment of Third-Degree Skin Burns in Murine Model.

This study explored the feasibility of using fish skin bandages as a therapeutic option for third-degree skin burns. Following the California wildfires, clinical observations of animals with third-degree skin burns demonstrated increased comfort levels and reduced pain when treated with tilapia fish skin. Despite the promises of this therapy, there are few studies explaining the healing mechanisms behind the application of tilapia fish skin. In this study, mice with third-degree burns were treated with either a hydrocolloid adhesive bandage (control) (n = 16) or fish skin (n = 16) 7 days post-burn. Mice were subjected to histologic, hematologic, molecular, and gross evaluation at days 7, 16, and 28 post-burn. The fish skin offered no benefit to overall wound closure compared to hydrocolloids. Additionally, we detected no difference between fish skin and control treatments in regard to hypermetabolism or hematologic values. However, the fish skin groups exhibited 2 times more vascularization and 2 times higher expression of antimicrobial defensin peptide in comparison to controls. Proteomic analysis of the fish skin revealed the presence of antimicrobial peptides. Collectively, these data suggest that fish skin can serve as an innovative and cost-effective therapeutic alternative for burn victims to facilitate vascularization and reduce bacterial infection.

Gas–particle partitioning of semivolatile organic compounds when wildfire smoke comes to town

Abstract. Wildfires have become an increasingly important source of organic gases and particulate matter in the western USA. A large fraction of organic particulate matter emitted in wildfires is semivolatile, and the oxidation of organic gases in smoke can form lower-volatility products that then condense on smoke particulates. In this research, we measured the gas- and particle-phase concentrations of semivolatile organic compounds (SVOCs) during the 2017 northern California wildfires in a downwind urban area, using semivolatile thermal desorption aerosol gas chromatography (SV-TAG), and measured SVOCs in a rural site affected by biomass burning using cTAG (comprehensive thermal desorption aerosol gas chromatography mass spectrometry) in Idaho in 2018. Commonly used biomass burning markers such as levoglucosan, mannosan, and nitrocatechols were found to stay predominantly in the particle phase, even when the ambient organic aerosol (OA) was relatively low. The phase partitioning of SVOCs is observed to be dependent on their saturation vapor pressure, while the equilibrium absorption model underpredicts the particle-phase fraction of most of the compounds measured. Wildfire organic aerosol enhanced the condensation of polar compounds into the particle phase but not some nonpolar compounds, such as polycyclic aromatic hydrocarbons.

Space-Based Observations of Ozone Precursors within California Wildfire Plumes and the Impacts on Ozone-NOx-VOC Chemistry.

The frequency of wildfires in the western United States has escalated in recent decades. Here we examine the impacts of wildfires on ground-level ozone (O3) precursors and the O3-NOx-VOC chemistry from the source to downwind urban areas. We use satellite retrievals of nitrogen dioxide (NO2) and formaldehyde (HCHO, an indicator of VOC) from the Tropospheric Monitoring Instrument (TROPOMI) to track the evolution of O3 precursors from wildfires over California from 2018 to 2020. We improved these satellite retrievals by updating the a priori profiles and explicitly accounting for the effects of smoke aerosols. TROPOMI observations reveal that the extensive and intense fire smoke in 2020 led to an overall increase in statewide annual average HCHO and NO2 columns by 16% and 9%. The increase in the level of NO2 offsets the anthropogenic NOx emission reduction from the COVID-19 lockdown. The enhancement of NO2 within fire plumes is concentrated near the regions actively burning, whereas the enhancement of HCHO is far-reaching, extending from the source regions to urban areas downwind due to the secondary production of HCHO from longer-lived VOCs such as ethene. Consequently, a larger increase in NOx occurs in NOx-limited source regions, while a greater increase in HCHO occurs in VOC-limited urban areas, both contributing to more efficient O3 production.

Cattle Grazing Moderates Greenhouse Gas and Particulate Matter Emissions from California Grassland Wildfires

Between 2010 and 2020, an average of 36,037 hectares of grassland burned in wildfires in California each year, emitting greenhouse gasses (GHGs) and particulate matter (PM). These emissions impact climate and human health. Cattle grazing removes herbaceous fuel through the consumption of forage; however, ruminant digestion also emits GHGs. The purpose of this study was to examine the GHG and PM impact of livestock grazing in grasslands that go on to burn. We used Monte Carlo simulation to determine whether forage consumption by livestock led to reductions in grassland wildfire emissions and whether these reductions outweighed the emissions from the digestion of that forage. We estimate that between 2010 and 2020, an average of 11,590 metric tons (MT) of herbaceous fuel were removed by cattle annually from grasslands in California that went on to burn. This resulted in annual wildfire emission reductions ranging between 0.001 and 0.025 million metric tons (MMT) of CO2 equivalents (CO2e) and between 11 and 314 MT of PM2.5; a small fraction of total GHG and PM emissions from wildfires in California. We also evaluated the change in emissions if burned grasslands in California’s Central and North Coast regions—where removing grazing can lead to the encroachment of shrubs into grasslands—were instead shrublands. If the grasslands that burned in these regions in 2020 had instead been shrublands, we estimate that as much as 0.90 MMT more CO2e and 8448 MT more PM2.5 would have been emitted by wildfires, highlighting the long-term implications of livestock grazing.

Prediction and model evaluation for space–time data

ABSTRACT Evaluation metrics for prediction error, model selection and model averaging on space–time data are understudied and poorly understood. The absence of independent replication makes prediction ambiguous as a concept and renders evaluation procedures developed for independent data inappropriate for most space–time prediction problems. Motivated by air pollution data collected during California wildfires in 2008, this manuscript attempts a formalization of the true prediction error associated with spatial interpolation. We investigate a variety of cross-validation (CV) procedures employing both simulations and case studies to provide insight into the nature of the estimand targeted by alternative data partition strategies. Consistent with recent best practice, we find that location-based cross-validation is appropriate for estimating spatial interpolation error as in our analysis of the California wildfire data. Interestingly, commonly held notions of bias-variance trade-off of CV fold size do not trivially apply to dependent data, and we recommend leave-one-location-out (LOLO) CV as the preferred prediction error metric for spatial interpolation.

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.

A methodology to estimate postdisaster unmet housing needs using limited data: Application to the 2017 California wildfires.

In the United States, assistance from the Department of Housing and Urban Development (HUD) plays an essential role in supporting the postdisaster recovery of states with unmet housing needs. HUD requires data on unmet needs to appropriate recovery funds. Ground truth data are not available for months after a disaster, however, so HUD uses a simplified approach to estimate unmet housing needs. State authorities argue that HUD's simplified approach underestimates the state's needs. This article presents a methodology to estimate postdisaster unmet housing needs that is accurate and relies only on data obtained shortly after a disaster. Data on the number of damaged buildings are combined with models for expected repair costs. Statistical models for aid distributed by the Federal Emergency Management Agency (FEMA) and the Small Business Administration (SBA) are then developed and used to forecast funding provided by those agencies. With these forecasts, the unmet need to be funded by HUD is estimated. The approach can be used for multiple states and hazard types. As validation, the proposed methodology is used to estimate the unmet housing needs following disasters that struck California in 2017. California authorities suggest that HUD's methodology underestimated the state's needs by a factor of 20. Conversely, the proposed methodology can replicate the estimates by the state authorities and provide accounts of losses, the amount of funding from FEMA and SBA, and the total unmet housing needs without requiring data unavailable shortly after a disaster. Thus, the proposed methodology can help improve HUD's funding appropriation withoutdelays.

Attention-Based Wildland Fire Spread Modeling Using Fire-Tracking Satellite Observations

Modeling the spread of wildland fires is essential for assessing and managing fire risks. However, this task remains challenging due to the partially stochastic nature of fire behavior and the limited availability of observational data with high spatial and temporal resolutions. Herein, we propose an attention-based deep learning modeling approach that can be used to learn the complex behaviors of wildfires across different fire-prone regions. We integrate optimized spatial and channel attention modules with a convolutional neural network (CNN) modeling architecture and train the attention-based fire spread models using a recently derived fire-tracking satellite observational dataset in conjunction with corresponding fuel, terrain, and weather conditions. The evaluation results and their comparison with benchmark models, such as a deeper and more complex autoencoder model and the semi-empirical FARSITE fire behavior model, demonstrate the effectiveness of the attention-based models. These new data-driven fire spread models exhibit promising modeling performances in both the next-step prediction (i.e., predicting fire progression from one timestep earlier) and recursive prediction (i.e., recursively predicting final fire perimeters from initial ignition points) of observed large wildfires in California, and they provide a foundation for further practical applications including short-term active fire spread prediction and long-term fire risk assessment.

β‐Pinene + ethanol attracts more red turpentine beetles than carene+ ethanol, with or without traces of frontalin, at prescribed burn or thinned sites

Abstract Red turpentine beetle, Dendroctonus valens LeConte (Coleoptera: Curculionidae: Scolytinae), previously responded more strongly to (−)‐β‐pinene + ethanol than (+)‐3‐carene + ethanol lures at sites burned the prior year by wildfire in Oregon and northeastern California, whereas at a thinned‐unburned Arizona site (+)‐3‐carene + ethanol was the stronger attractant. This discrepancy was further examined to tease apart whether D. valens attraction varies by region or previous forest disturbance types. Here, (−)‐β‐pinene + ethanol and (+)‐3‐carene + ethanol lures were tested in pine stands at two Oregon sites disturbed the previous year by a prescribed burn or thinning only. Both lures were tested also with or without trace amounts of the pheromone frontalin, as its presence enhanced attractions in China but had not been tested in North America. At both sites, regardless of prior forest disturbance, (−)‐β‐pinene + ethanol lures attracted the most beetles. Lures releasing trace frontalin attracted more beetles than their corresponding lures without it at both sites, except in one case. Overall, previous year disturbances from disparate management treatments had minimal influence on lure attraction to D. valens. For detection, monitoring or management (−)‐β‐pinene + ethanol + frontalin in trace amounts attracts the most beetles of lures tested to date in Pacific Northwest pine forests.

It’s about Time: The Wildfire Ecologies of Contingent Research and Planning

Abstract This article is a reflection on doing wildfire research aimed at shaping public policy in Orange County, California, during the COVID-19 pandemic. Its focus is on the little-known efforts of fire mitigation by Latinx migrant workers. In this article, I discuss how the COVID-19 pandemic made me shift research focus from seeking to understand how workers’ ecological knowledge might shape fire mitigation policy to a prioritization of workers’ precarious “essential labor” on the “front” front lines of fire prevention. I discuss how the temporalities of the pandemic, wildfire, and research played out across the labor terrain of the Southern California wildfire mitigation efforts and within my own applied research. Specifically, I discuss how COVID-19 university research “ramped down,” and stay-at-home orders prevented me from being embedded with workers in the county’s canyons, as I had planned, and how I had to learn to adjust my funded research. The outcome required doing applied research by letting go of continuity, by dwelling in disjointed COVID-19 temporalities that settled over the county’s flammable chaparral where essential labor serves as an extension of a failing settler colonial fire management practice that requires worker vulnerability to inoculate the lives of those living in the county’s wildfire risk regions.

Extreme weather events and HIV: development of a conceptual framework through qualitative interviews with people with HIV impacted by the California wildfires and their clinicians

BackgroundPeople with HIV (PWH) are disproportionately vulnerable to the impacts of wildfires, given the need for frequent access to healthcare systems, higher burden of comorbidities, higher food insecurity, mental and behavioral health challenges, and challenges of living with HIV in a rural area. In this study, we aim to better understand the pathways through which wildfires impact health outcomes among PWH.MethodsFrom October 2021 through February 2022, we conducted individual semi-structured qualitative interviews with PWH impacted by the Northern California wildfires and clinicians of PWH who were impacted by wildfires. The study aims were to explore the influence of wildfires on the health of PWH and to discuss measures at the individual, clinic, and system levels that helped to mitigate these impacts.ResultsWe interviewed 15 PWH and 7 clinicians. While some PWH felt that surviving the HIV epidemic added to their resilience against wildfires, many felt that the wildfires compounded the HIV-related traumas that they have experienced. Participants outlined five main routes by which wildfires negatively impacted their health: (1) access to healthcare (medications, clinics, clinic staff), (2) mental health (trauma; anxiety, depression, or stress; sleep disturbances; coping strategies), (3) physical health (cardiopulmonary, other co-morbidities), (4) social/economic impacts (housing, finances, community), and (5) nutrition and exercise. The recommendations for future wildfire preparedness were at the (1) individual-level (what to have during evacuation), (2) pharmacy-level (procedural, staffing), and (3) clinic- or county-level (funds and vouchers; case management; mental health services; emergency response planning; other services such as telehealth, home visits, home laboratory testing).ConclusionsBased on our data and prior research, we devised a conceptual framework that acknowledges the impact of wildfires at the community-, household-, and individual-level with implications for physical and mental health outcomes among PWH. These findings and framework can help in developing future interventions, programs, and policies to mitigate the cumulative impacts of extreme weather events on the health of PWH, particularly among individuals living in rural areas. Further studies are needed to examine health system strengthening strategies, innovative methods to improve access to healthcare, and community resilience through disaster preparedness.Trial registrationN/A.

Searching the flames: Trends in global and regional public interest in wildfires

Interactions between humans and wildfires have increased in many regions over the last decades driven by climate and land-use changes. A shift towards more adaptive fire management and policies is urgently needed but remains difficult to achieve. Better understanding of public interest in wildfire can facilitate this transition, as the public is a key driver for policy decisions. We used Google Trends to assess temporal patterns (2004–2020) in public interest on wildfires worldwide and in five case study countries (Australia, Canada, Indonesia, Portugal, USA). Public interest consistently shows a cyclic pattern with low background and short-lasting spikes during fire seasons and catastrophic events. Wildfires that receive the most interest worldwide are located in Western countries, especially the USA. There is usually high demand for news on wildfires when spikes in interest happen. Overall global interest in wildfire has risen twice: first for a short period in 2007–2008, concomitant to catastrophic wildfires in California, and again since 2017, probably triggered by a series of catastrophic fire events around the globe. Nevertheless, public interest in wildfire is low when compared with socioeconomically more costly earthquakes or hurricanes. The short and seasonal interest in wildfire may present an important obstacle to the implementation of wildfire mitigation policies that require year-round approaches. However, the fact that the public uses the internet to obtain basic knowledge about wildfire functioning and impacts, especially during the interest spikes, can facilitate targeting awareness campaigns. These could be not only about wildfires but also about broader related environmental issues.

Vegetation type change in California’s Northern Bay Area: A comparison of contemporary and historical aerial imagery

The North Bay area of California is a populous and ecologically diverse area that has experienced significant changes in the past century, as well as a series of recent wildfires, after over a century of fire suppression practices. While much research has been conducted quantifying drivers and patterns of vegetation change in conifer-dominated ecosystems, and how such changes have influenced current trends in fire behavior, studies of similar focus and scale are rarer in non-conifer ecosystems, including mixed-hardwood forests or shrub-dominated ecosystems in central and coastal Northern California. This is despite the fact that ecosystems other than conifer forests make up the majority of area burned in California wildfires. As such, expanding research focused on patterns of large-scale vegetation change as a possible driver of this trend in this area is a priority. In this study, we sought to map the overall extent and patch sizes of broad vegetation classes across a 52,000 ha study area based on historical (1948) and contemporary (2014) aerial imagery and to investigate shifts in vegetation patterns, as well as potential pathways and drivers of detected changes. We classified vegetation types through segmenting our imagery into homogenous polygons, and assigning broad vegetation categories using a random forest algorithm. We then analyzed patterns of change using spatial statistics and conditional inference tree analyses. We detected a large increase (12%) in the relative landscape proportion and average patch size of the forest class, characterized by dense tree canopy cover. Woodlands and shrub patches were most susceptible to type change, with the majority (57% and 65%, respectively) of converted areas subsequently identified as denser forest stands. By contrast, herbaceous and forest patches were most persistent. We additionally found that disturbance history, specifically whether an area burned or not, and topographic variables, including elevation and aspect, were important influences on the likelihood of vegetation persistence, while slope and water availability were not. Historical aerial imagery, which provides fine resolution and accurate data over a large spatial scale, is a useful tool for detecting landscape-scale vegetation shifts in ecosystems where widespread vegetation monitoring was not common historically. The marked increase in dense forest we detected, specifically due to the conversion of large areas of shrub and woodland vegetation may correspond to higher surface and ladder fuel load and continuity, and potentially higher wildfire risk. Fuel reduction treatments typically implemented in conifer-dominated forests may also be warranted in these mixed hardwood forests. However, more research is needed to understand drivers of change in non-conifer-dominated ecosystems in California and how such change influences wildfire behavior.

A multi-modal wildfire prediction and early-warning system based on a novel machine learning framework

Wildfires are increasingly impacting the environment and human health. Among the top 20 California wildfires, those in 2020–2021 burned more acres than the last century combined. Lack of an adequate early warning system impacts the health and safety of vulnerable populations disproportionately and widens the inequality gap. In this project, a multi-modal wildfire prediction and early warning system has been developed based on a novel spatio-temporal machine learning architecture. A comprehensive wildfire database with over 37 million data points was created, including the historical wildfires, environmental and meteorological sensor data from the Environmental Protection Agency, and geological data. The data was augmented into 2.53 km × 2.53 km square grids to overcome the sensor network coverage limitations. Leading and trailing indicators for the wildfires are proposed, classified, and tested. The leading indicators are correlated to the risks of wildfire conception, whereas the trailing indicators are correlated to the byproducts of the wildfires. Additionally, geological data was incorporated to provide additional information for better assessment on wildfire risks and propagation. Next, a novel U-Convolutional Long Short-Term Memory (ULSTM) neural network was developed to extract key spatial and temporal features of the dataset, specifically to address the spatial nature of the location of the wildfire and time-progression temporal nature of the wildfire evolution. Through iterative improvements and optimization, the final ULSTM network architecture, trained with data from 2012 to 2017, achieved >97% accuracy for predicting wildfires in 2018, as compared to ∼76% using traditional Convolutional Neural Network (CNN) techniques. The final model was applied to conduct a retrospective study for the 2018–2022 wildfire seasons, and successfully predicted 85.7% of wildfires >300 K acres in size. This technique could enable fire departments to anticipate and prevent wildfires before they strike and provide early warnings for at-risk individuals for better preparation, thereby saving lives, protecting the environment, and avoiding economic damages.

Risk of systemic fungal infections after exposure to wildfires: a population-based, retrospective study in California

Large-scale wildfires in California, USA, are increasing in both size and frequency, with substantial health consequences. The capacity for wildfire smoke to displace microbes and cause clinically significant fungal infections is poorly understood. We aimed to determine whether exposure to wildfire smoke was associated with an increased risk of hospital admissions for systemic fungal infections.In this population-based, retrospective study, we used hospital administrative data from 22 hospitals in California, USA, to analyse the association between wildfire smoke exposure and monthly hospital admissions for aspergillosis and coccidioidomycosis. We included hospitals that were members of the Vizient Clinical Data Base or Resource Manager during the study and excluded those that did not have complete reporting into Vizient during the study period. Smoke exposure was estimated using satellite-imaged smoke plumes in the hospital county. Incident rate ratios were calculated for all infection types 1 month and 3 months after smoke exposure.Between Oct 1, 2014, and May 31, 2018, there were a median of 1638 annual admissions per hospital in the study sample. Individual patient demographics were not collected. We did not observe an association between smoke exposure and rate of hospital admission for aspergillosis. However, hospital admission for coccidioidomycosis increased by 20% (95% CI 5-38) in the month following any smoke exposure. Hospital admission increased by 2% (0-4) for every day that there had been smoke exposure in the previous month, after adjustment for temperature and temporal trend. Similar results were obtained with smoke exposure data from the 3 months before admission.In the months following wildfire smoke exposure, California hospitals saw increased coccidioidomycosis infections. Given the projected increase in California wildfires and their expansion in endemic territories of soil-dwelling fungi, the ability for wildfire smoke to carry microbes and cause human disease warrants further research.None.

Ground solar absorption observations of total column CO, CO2, CH4, and aerosol optical depth from California's Sequoia Lightning Complex Fire: emission factors and modified combustion efficiency at regional scales

Abstract. With global wildfires becoming more widespread and severe, tracking their emissions of greenhouse gases and air pollutants is becoming increasingly important. Wildfire emissions have primarily been characterized by in situ laboratory and field observations at fine scales. While this approach captures the mechanisms relating emissions to combustion phase and fuel properties, their evaluation on regional-scale plumes has been limited. In this study, we report remote observations of total column trace gases and aerosols during the 2020 wildfire season from smoke plumes in the Sierra Nevada of California with an EM27/SUN solar Fourier transform infrared (FTIR) spectrometer. We derive total column aerosol optical depth (AOD), emission factors (EFs) and modified combustion efficiency (MCE) for these fires and evaluate relationships between them, based on combustion phase at regional scales. We demonstrate that the EM27/SUN effectively detects changes in CO, CO2, and CH4 in the atmospheric column at ∼10 km horizontal scales that are attributed to wildfire emissions. These observations are used to derive total column EFCO of 120.5±12.2 and EFCH4 of 4.3±0.8 for a regional smoke plume event in mixed combustion phases. These values are consistent with in situ relationships measured in similar temperate coniferous forest wildfires. FTIR-derived AOD was compared to a nearby AERONET (AErosol RObotic NETwork) station and observed ratios of XCO to AOD were consistent with those previously observed from satellites. We also show that co-located XCO observations from the TROPOspheric Monitoring Instrument (TROPOMI) satellite-based instrument are 9.7±1.3 % higher than our EM27/SUN observations during the wildfire period. Finally, we put wildfire CH4 emissions in context of the California state CH4 budget and estimate that 213.7±49.8 Gg CH4 were emitted by large wildfires in California during 2020, about 13.7 % of the total state CH4 emissions in 2020. Our work demonstrates a novel application of the ground-based EM27/SUN solar spectrometers in wildfire monitoring by integrating regional-scale measurements of trace gases and aerosols from smoke plumes.

Post-disaster (im)mobility aspiration and capability formation: case study of Southern California wildfire

Scholarship on the environmental dimensions of migration demonstrates the complex interplay of climatic and non-climatic factors which combine to create a potential for migration. Yet in times of environmental crisis or change, not everyone aspires to or is capable of moving to reduce their vulnerability. When, why, and how populations vulnerable to hazard risks decide not to migrate remains a significant gap in our understanding of the migration—environment relationship. Analysis of data from 38 qualitative interviews shows how Los Angeles County residents—after surviving the 2018 Woolsey Fire—developed aspirations to stay and/or rebuild, depending on the attachments and meanings associated with their communities. This paper also seeks to clarify the concept of capabilities to stay by considering separately the capabilities to return and rebuild from the capabilities to cultivate preparedness. Many who stayed also worked to strengthen community resilience to alleviate concerns of future wildfire risk. Some residents expressed individual commitments to stay and defend homes during future fires, while well-equipped volunteer fire brigades have proliferated in more affluent areas. Community mobilizations pressured local government and fire services to address the perceived institutional failure during previous fire responses and fostered feelings of collective efficacy among residents which increased their confidence to remain in high wildfire risk communities.

Effect modification of the association between fine particulate air pollution during a wildfire event and respiratory health by area-level measures of socio-economic status, race/ethnicity, and smoking prevalence

Fine particulate air pollution (PM2.5) is decreasing in most areas of the United States, except for areas most affected by wildfires, where increasing trends in PM2.5 can be attributed to wildfire smoke. The frequency and duration of large wildfires and the length of the wildfire season have all increased in recent decades, partially due to climate change, and wildfire risk is projected to increase further in many regions including the western United States. Increasingly, empirical evidence suggests differential health effects from air pollution by class and race; however, few studies have investigated such differential health impacts from air pollution during a wildfire event. We investigated differential risk of respiratory health impacts during the 2008 northern California wildfires by a comprehensive list of socio-economic status (SES), race/ethnicity, and smoking prevalence variables. Regardless of SES level across nine measures of SES, we found significant associations between PM2.5 and asthma hospitalizations and emergency department (ED) visits during these wildfires. Differential respiratory health risk was found by SES for ED visits for chronic obstructive pulmonary disease where the highest risks were in ZIP codes with the lowest SES levels. Findings for differential effects by race/ethnicity were less consistent across health outcomes. We found that ZIP codes with higher prevalence of smokers had greater risk of ED visits for asthma and pneumonia. Our study suggests that public health efforts to decrease exposures to high levels of air pollution during wildfires should focus on lower SES communities.

Disaster exposure and patterns of disaster preparedness: A multilevel social vulnerability and engagement perspective

This study examined how community vulnerabilities contextualized the impact of exposure to five major disasters in 2017 on individuals' disaster preparedness. We pooled two panels of the Federal Emergency Management Agency (FEMA)'s National Household Survey in 2017 (pre-disaster) and 2018 (post-disaster) and further merged the data with 15 Centers for Disease Control and Prevention Social Vulnerability Index factors to examine the moderating impact of county-level vulnerabilities. Latent class analysis was used to identify patterns of disaster preparedness based on six preparedness items defined by FEMA with a total of 10,045 individuals. Three groups were identified and named basic preparedness (BP) group, high preparedness: socially engaged (HP-SE) group, and high preparedness: advanced socially engaged (HP-ASE) group. A working sample with a smaller sample size was constructed to include 2,179 individuals from 92 counties with disaster declarations of Hurricane Harvey, Hurricane Irma, Hurricane Maria, Hurricane Nate, and California Wildfires in 2017. Multinomial logistic regression indicated that post-disaster respondents were more likely to be in the HP-SE relative to the BP group. Two-level multinomial logistic regression showed that post-disaster respondents in communities with higher percentages of single-parent households and those of no vehicles were less likely to be in the HP-SE vs. the BP group. Post-disaster respondents in communities with higher percentages of unemployment, no high school diploma, and occupied housing units with more people than rooms were less likely to be in the HP-ASE vs. the BP group. Post-disaster respondents in communities with higher percentages of older adults and mobile homes were more likely to be in the HP-SE vs. the BP group. The findings also highlighted the importance of social engagement in disaster preparedness disparities and the need for community-level intervention to promote individuals' disaster preparedness.

Using Satellite‐Derived Fire Arrival Times for Coupled Wildfire‐Air Quality Simulations at Regional Scales of the 2020 California Wildfire Season

AbstractWildfire frequency has increased in the Western US over recent decades, driven by climate change and a legacy of forest management practices. Consequently, human structures, health, and life are increasingly at risk due to wildfires. Furthermore, wildfire smoke presents a growing hazard for regional and national air quality. In response, many scientific tools have been developed to study and forecast wildfire behavior, or test interventions that may mitigate risk. In this study, we present a retrospective analysis of 1 month of the 2020 Northern California wildfire season, when many wildfires with varying environments and behavior impacted regional air quality. We simulated this period using a coupled numerical weather prediction model with online atmospheric chemistry, and compare two approaches to representing smoke emissions: an online fire spread model driven by remotely sensed fire arrival times and a biomass burning emissions inventory. First, we quantify the differences in smoke emissions and timing of fire activity, and characterize the subsequent impact on estimates of smoke emissions. Next, we compare the simulated smoke to surface observations and remotely sensed smoke; we find that despite differences in the simulated smoke surface concentrations, the two models achieve similar levels of accuracy. We present a detailed comparison between the performance and relative strengths of both approaches, and discuss potential refinements that could further improve future simulations of wildfire smoke. Finally, we characterize the interactions between smoke and meteorology during this event, and discuss the implications that increases in regional smoke may have on future meteorological conditions.