Wildfire Episode Research Articles

Neuroinflammation is dependent on sex and ovarian hormone presence following acute woodsmoke exposure

Woodsmoke (WS) exposure is associated with significant health-related sequelae. Different populations can potentially exhibit varying susceptibility, based on endocrine phenotypes, to WS and investigating neurological impacts following inhaled WS is a growing area of research. In this study, a whole-body inhalation chamber was used to expose both male and female C57BL/6 mice (n = 8 per group) to either control filtered air (FA) or acute WS (0.861 ± 0.210 mg/m3) for 4 h/d for 2 days. Neuroinflammatory and lipid-based biological markers were then assessed. In a second set of studies, female mice were divided into two groups: one group was ovariectomized (OVX) to simulate an ovarian hormone-deficient state (surgical menopause), and the other underwent Sham surgery as controls, to mechanistically assess the impact of ovarian hormone presence on neuroinflammation following FA and acute WS exposure to simulate an acute wildfire episode. There was a statistically significant impact of sex (P ≤ 0.05) and statistically significant interactions between sex and treatment in IL-1β, CXCL-1, TGF-β, and IL-6 brain relative gene expression. Hippocampal and cortex genes also exhibited significant changes in acute WS-exposed Sham and OVX mice, particularly in TGF-β (hippocampus) and CCL-2 and CXCL-1 (cortex). Cortex GFAP optical density (OD) showed a notable elevation in male mice exposed to acute WS, compared to the control FA. Sham and OVX females demonstrated differential GFAP expression, depending on brain region. Overall, targeted lipidomics in phosphatidylcholine (PC) and phosphatidylethanolamine (PE) serum and brain lipids demonstrated more significant changes between control FA and acute WS exposure in female mice, compared to males. In summary, male and female mice show distinct neuroinflammatory markers in response to acute WS exposure. Furthermore, ovarian hormone deficiency may impact the neuroinflammatory response following an acute WS event.

Seamless Modeling of Direct and Indirect Aerosol Effects during April 2020 Wildfire Episode in Ukraine

Wildfires frequently occur in Ukraine during agricultural open-burning seasons in spring and autumn. High aerosol concentrations from fire emissions can significantly affect meteorological processes via direct and indirect aerosol effects. To study these impacts, we selected a severe wildfire episode from April 2020 in the Chornobyl Exclusion Zone (CEZ) and its surrounding area as a case study. We employed the Enviro-HIRLAM modeling system to simulate reference (REF) meteorological conditions, along with direct (DAE), indirect (IDAE), and combined (COMB) aerosol effects. In our simulations, black carbon (BC) and organic carbon (OC) comprised 70–80% of all aerosol mass in the region, represented in two layers of higher concentrations: one near the surface and the other 3–4 km above the surface. Our simulations showed that the inclusion of aerosol effects into the modeling framework led to colder (up to −3 °C) and drier (relative humidity drop up to −20%) conditions near the surface. We also observed localized changes in cloudiness, precipitation (mainly redistribution), and wind speed (up to ±4 m/s), particularly during the movement of atmospheric cold fronts. Larger uncertainties were observed in coarser model simulations when direct aerosol effects were considered. Quantifying the aerosol effects is crucial for predicting and promptly detecting changes that could exacerbate unfavorable weather conditions and wildfires. Such knowledge is essential for improving the effectiveness of emergency response measures.

Australian Black summer smoke signal on Antarctic aerosol collected between New Zealand and the Ross sea

Open biomass burning (BB) events are a well-known primary aerosol source, resulting in the emission of significant amount of gaseous and particulate matter and affecting Earth's radiation budget. The 2019–2020 summer, known as “Australian Black Summer”, showed exceptional duration and intensity of seasonal wildfires, triggered by high temperatures and severe droughts. Since increasing megafires are predicted due to expected climate changes, it is critical to study the impact of BB aerosol on a large scale and evaluate related transport processes. In this study, five aerosol samples (total suspended particles with a diameter >1 μm) were collected during the XXXV Italian Expedition in Antarctica on board of the R/V Laura Bassi from 6th of January to February 16, 2020, along the sailing route from Lyttelton harbor (New Zealand) to Terra Nova Bay (Antarctica). Levoglucosan and its isomers have been analyzed as markers of BB, together with polycyclic aromatic hydrocarbons (PAHs), sucrose and alcohol sugars. Ionic species and carboxylic acids have been analyzed to support the identification of aerosol sources and its aging. Results showed high levoglucosan concentrations (325–1266 pg m−3) during the campaign, suggesting the widespread presence of smoke in the region, because of huge wildfire releases. Backward trajectories indicated the presence of long-range atmospheric transport from South America, probably carrying wildfires plume, in agreement with literature. Regional sources have been suggested for PAHs, particularly for 3–4 rings' compounds; monosaccharides, sucrose, arabitol, and mannitol were related to marine and biogenic contributions. In a warming climate scenario, more frequent and extensive wildfire episodes are expected in Australia, potentially altering albedo, aerosol radiative properties, and cloud interactions. Therefore, it is crucial to strengthens the investigations on the regional climatic effects of these events in Antarctica.

Impacts on Urban VOCs and PM2.5 during a Wildfire Episode

This study focuses on the impact of wildfire smoke emissions on regional, urban air quality during a wildfire event. We measured volatile organic compounds (VOCs) and fine particulate matter (PM2.5) in the San Francisco Bay Area to assess air quality during a wildfire event and compared them to those in a later non-wildfire period. VOCs were collected using thermal desorption tubes and quantified using thermal desorption-gas chromatography/mass spectrometry (TD-GC/MS). Elevated concentrations of VOCs such as 1,3,5-trimethylbenzene (0.33 ± 0.01 µg/m3), benzene (1.03 ± 0.02 µg/m3), toluene (2.15 ± 0.04 µg/m3), ethylbenzene (0.60 ± 0.02 µg/m3), and m, p-xylene (0.77 ± 0.07 µg/m3) were observed in the wildfire event. Compared with that in the non-wildfire season, the toluene concentration during the wildfire period was more than three times the non-wildfire concentration. Similarly, the benzene concentration during the wildfire was almost four times higher, and that of p, m-xylene was three times higher. The higher wildfire levels were statistically significant for sec-butylbenzene, 1,2,4-trimethylbenzene, n-propylbenzene, o-xylene, styrene, 1,3,5-trimethylbenzene, benzene, toluene, ethylbenzene, and p,m-xylene (p < 0.00001). These higher VOC levels compared with those for the non-fire period may potentially pose a public health concern. Open face passive sampler (OFPS)-collected PM was analyzed using scanning electron microscopy/energy dispersive X-ray spectroscopy (SEM/EDS) and revealed organic carbon tar balls. The highest daily average beta-attenuation-monitored PM2.5 during the fire period was 200 µg/m3 and the highest hourly average was 270 µg/m3. Monitoring gas phase species in addition to PM concentrations is useful during wildfire season to inform public health guidance.

Climatic drivers of the Canadian wildfire episode in 2023

Extreme wildfires broke out in Canada from May 2023 and persisted four months. Tremendous fire emissions posed significant impacts on the air quality in both local and downwind regions. Here, the authors explored the meteorological factors driving this wildfire episode and the associated large-scale circulation. Relative to the mean value of 2001–2022, the 2023 Canadian fires released 0.37 Gt more CO2 (527.1%) during May–August. Such enhancement was strongly associated with the anomalous surface warming, especially in May–June, when the daily maximum temperature on average increased by 8.11°C, with regional hotspots up to 10°C in central and eastern Canada. Consistent with the surface warming, positive anomalies of geopotential height were observed at high levels in central and western Canada. The dispersion of Rossby waves led to the long-lasting dominance of high-pressure systems and the consequent warming through anomalous subsidence. The phase-locked wave pattern induced by favorable topography and the strong warming tendency in the pan-Arctic regions are expected to jointly escalate the probability of extreme wildfires over central and western Canada in the future.摘要2023年5月加拿大发生极端野火事件并持续4个月时间. 野火污染物排放对当地和下风向的空气质量造成了严重影响. 本文对驱动此次野火事件的气象因子和相关大尺度环流进行了探究. 研究表明, 此次极端野火排放的二氧化碳相较往年同期增长了0.37Gt (527.1%) . 受罗斯贝波频散影响, 同期加拿大中西部区域出现持续的位势高度正异常, 促进气流下沉并引发局地高温, 中西部区域5–6月最高温平均上升8.11°C, 部分区域甚至超过10°C, 导致野火排放的显著增长. 受有利地形和泛北极地区快速增暖的影响, 预期未来加拿大中西部发生极端野火的概率可能会显著上升.

Exploring the use of masks for protection against the effects of wildfire smoke among people with preexisting respiratory conditions

BackgroundThe impact of wildfire smoke is a growing public health issue, especially for those living with preexisting respiratory conditions. Understanding perceptions and behaviors relevant to the use of individual protective strategies, and how these affect the adoption of these strategies, is critical for the development of future communication and support interventions. This study focused on the use of masks by people living in the Australian community with asthma or chronic obstructive pulmonary disease (COPD).MethodsSemi-structured phone interviews were undertaken with people living in the community aged 18 years and over. Participants lived in a bushfire-prone area and reported having been diagnosed with asthma or COPD.ResultsTwenty interviews were undertaken between July and September 2021. We found that, during wildfire episodes, there was an overwhelming reliance on closing windows and staying inside as a means of mitigating exposure to smoke. There was limited use of masks for this purpose. Even among those who had worn a mask, there was little consideration given to the type of mask or respirator used. Reliance on sensory experiences with smoke was a common prompt to adopting an avoidance behavior. Participants lacked confidence in the information available from air-quality apps and websites, however they were receptive to the idea of using masks in the future.ConclusionsWhilst COVID-19 has changed the nature of community mask use over the last couple of years, there is no guarantee that this event will influence an individual’s mask behavior during other events like bushfires. Instead, we must create social support processes for early and appropriate mask use, including the use of air quality monitoring.

Potential Health Impacts from a Wildfire Smoke Plume over Region Jämtland Härjedalen, Sweden

In the summer of 2018, Sweden experienced widespread wildfires, particularly in the region of Jämtland Härjedalen during the final weeks of July. We previously conducted an epidemiological study and investigated acute respiratory health effects in eight municipalities relation to the wildfire air pollution. In this study, we aimed to estimate the potential health impacts under less favorable conditions with different locations of the major fires. Our scenarios focused on the most intense plume from the 2018 wildfire episode affecting the largest municipality, which is the region’s only city. Combining modeled PM2.5 concentrations, gridded population data, and exposure–response functions, we assessed the relative increase in acute health effects. The cumulative population-weighted 24 h PM2.5 exposure during the nine highest-level days reached 207 μg/m3 days for 63,227 inhabitants. We observed a small number of excess cases, particularly in emergency unit visits for asthma, with 13 additional cases compared to the normal 12. Overall, our scenario-based health impact assessment indicates minor effects on the studied endpoints due to factors such as the relatively small population, limited exposure period, and moderate increase in exposure compared to similar assessments. Nonetheless, considering the expected rise in fire potential due to global warming and the long-range transport of wildfire smoke, raising awareness of the potential health risks in this region is important.

Preliminary Use of Convection-allowing Models in Fire Weather

Multiple high-impact wildfire episodes on the southern Great Plains in 2021/22 provided unique opportunities to demonstrate the emerging utility of Convection-allowing Models (CAMs) in fire-weather forecasting. This short contribution article will present preliminary analyses of the deterministic Texas Tech Real Time Weather Prediction System’s Red Flag Threat Index (RFTI) compared to wildfire activity observed via the Geostationary Operational Environmental Satellite-16 during four southern Great Plains wildfire outbreaks. Visual side-by-side comparisons of model-predicted RFTI and satellite-detected wildfires will be shown in static and animated displays that demonstrate the model’s prognostic signal in depicting fire-outbreak evolution. The data analyses are supplemented with preliminary information from state forestry agencies that provide context to predicted RFTI relative to size-based categorization of observed wildfires and human casualties. In addition, use of the National Severe Storm Laboratory’s Warn-on-Forecast System to provide short-term updates on the evolution of fire-effective atmospheric features that promote new fire ignition, problematic spread, and extreme fire behavior is also demonstrated. The examples presented here suggest that CAMs serve an important role in the mesoscale prediction of dangerous wildfire conditions. With this novel use of CAMs in fire meteorology, the authors advocate for expanded availability of fire weather-specific fields and parameters in high-resolution numerical weather prediction systems that would improve wildfire forecasts and associated impact-based decision support.

Wildfire-Derived Nitrogen Aerosols Threaten the Fragile Ecosystem in Himalayas and Tibetan Plateau.

Himalayas and Tibetan Plateau (HTP) is important for global biodiversity and regional sustainable development. While numerous studies have revealed that the ecosystem in this unique and pristine region is changing, their exact causes are still poorly understood. Here, we present a year-round (23 March 2017 to 19 March 2018) ground- and satellite-based atmospheric observation at the Qomolangma monitoring station (QOMS, 4276 m a.s.l.). Based on a comprehensive chemical and stable isotope (15N) analysis of nitrogen compounds and satellite observations, we provide unequivocal evidence that wildfire emissions in South Asia can come across the Himalayas and threaten the HTP's ecosystem. Such wildfire episodes, mostly occurring in spring (March-April), not only substantially enhanced the aerosol nitrogen concentration but also altered its composition (i.e., rendering it more bioavailable). We estimated a nitrogen deposition flux at QOMS of ∼10 kg N ha-1 yr-1, which is approximately twice the lower value of the critical load range reported for the Alpine ecosystem. Such adverse impact is particularly concerning, given the anticipated increase of wildfire activities in the future under climate change.

Public health applications of historical smoke forecasts: An evaluation of archived BlueSky data for the coterminous United States, 2015–2018

BackgroundWildfires are increasing in magnitude, frequency, and severity. Populations in the wildland-urban interface and in downwind communities are at increased risk of exposure to elevated concentrations of fine particulate matter (PM2.5) and other harmful components of wildfire smoke. We conducted this analysis to evaluate the use of modeled predictions of wildfire smoke to create county-level measures of smoke exposure for public health research and surveillance. MethodsWe evaluated four years (2015–2018) of grid-based North American Mesoscale (NAM)-derived PM2.5 forecasts from the U.S. Forest Service BlueSky modeling framework with monitoring data from the Environmental Protection Agency Air Quality System (AQS), the Interagency Monitoring of Protected Visual Environments (IMPROVE), the Western Regional Climate Center (WRCC), and the Interagency Real Time Smoke Monitoring (AIRSIS) programs. To assess relationships between model-derived estimates and monitor-based observations, we assessed Spearman's correlations by spatial (i.e., county, level of urbanization, states in the western United States impacted by major wildfires, and climate regions) and temporal (i.e., month and wildfire activity periods) characteristics. We then generated county-level smoke estimates and examined spatial and temporal patterns in total and person-days of smoke exposure. ResultsAcross all counties in the coterminous United States and for all days, the correlation between county-level model- and monitor-derived PM2.5 estimates was 0.14 (p < 0.001). Correlations were stronger using data from temporary monitors and for areas and days impacted by high wildfire smoke, especially in the western United States. Correlations between county-level model- and monitor-derived estimates in non-metropolitan counties, and at higher concentrations ranged from 0.25 to 0.54 (p < 0.001). ConclusionsIn general, public health practitioners and health researchers need to consider the pros and cons associated with modeled data products for conducting health analyses. Our results support the use of model-derived smoke estimates to identify communities impacted by heavy smoke events, especially during emergency response and for communities located near wildfire episodes.

Suppression of the phenolic SOA formation in the presence of electrolytic inorganic seed

Phenolic compounds are largely attributed to wildfire gases and rapidly react with atmospheric oxidants to form persistent phenoxy free radicals, which influence atmospheric chemistry and secondary organic aerosol (SOA) formation. In this study, phenol or o-cresol was photochemically oxidized under various conditions (NOx levels, humidity, and seed conditions) in an outdoor photochemical reactor. Unexpectedly, SOA growth of both phenols was suppressed in the presence of salted aqueous aerosol compared to non-seed SOA. This discovery is different from the typical SOA formation of aromatic or biogenic hydrocarbons, which show noticeably higher SOA yields via organic aqueous reactions. Phenol, o-cresol, and their phenolic products (e.g., catechols) are absorbed in aqueous aerosol and form phenoxy radicals via heterogeneous reactions under sunlight. The resulting phenoxy radicals are redistributed between the gas and particle phases. Gaseous phenoxy radicals quickly react with ozone to form phenyl peroxide radicals and regenerated through a NOx cycle to retard phenol oxidation and SOA formation. The explicit oxidation mechanisms of phenol or o-cresol in the absence of aqueous phase were derived including the Master Chemical Mechanism (MCM v3.3.1) and the path for peroxy radical adducts originating from the addition of an OH radical to phenols to form low volatility products (e.g., multi-hydroxy aromatics). The resulting gas mechanisms of phenol or o-cresol were, then, applied to the Unified Partitioning Aerosol Phase Reaction (UNIPAR) model to predict SOA formation via multiphase partitioning of organics and aerosol-phase oligomerization. The model well simulated chamber-generated phenolic SOA in absence of wet-inorganic seed, but significantly overestimated SOA mass in presence of wet seed. This study suggests that heterogeneous chemistry to form phenoxy radicals needs to be included to improve SOA prediction from phenols. The suppression of atmospheric oxidation due to phenoxy radicals in wet inorganic aerosol can explain the low SOA formation during wildfire episodes.

Assessing Sumatran Peat Vulnerability to Fire under Various Condition of ENSO Phases Using Machine Learning Approaches

In recent decades, catastrophic wildfire episodes within the Sumatran peatland have contributed to a large amount of greenhouse gas emissions. The El-Nino Southern Oscillation (ENSO) modulates the occurrence of fires in Indonesia through prolonged hydrological drought. Thus, assessing peatland vulnerability to fires and understanding the underlying drivers are essential to developing adaptation and mitigation strategies for peatland. Here, we quantify the vulnerability of Sumatran peat to fires under various ENSO conditions (i.e., El-Nino, La-Nina, and Normal phases) using correlative modelling approaches. This study used climatic (i.e., annual precipitation, SPI, and KBDI), biophysical (i.e., below-ground biomass, elevation, slope, and NBR), and proxies to anthropogenic disturbance variables (i.e., access to road, access to forests, access to cities, human modification, and human population) to assess fire vulnerability within Sumatran peatlands. We created an ensemble model based on various machine learning approaches (i.e., random forest, support vector machine, maximum entropy, and boosted regression tree). We found that the ensemble model performed better compared to a single algorithm for depicting fire vulnerability within Sumatran peatlands. The NBR highly contributed to the vulnerability of peatland to fire in Sumatra in all ENSO phases, followed by the anthropogenic variables. We found that the high to very-high peat vulnerability to fire increases during El-Nino conditions with variations in its spatial patterns occurring under different ENSO phases. This study provides spatially explicit information to support the management of peat fires, which will be particularly useful for identifying peatland restoration priorities based on peatland vulnerability to fire maps. Our findings highlight Riau’s peatland as being the area most prone to fires area on Sumatra Island. Therefore, the groundwater level within this area should be intensively monitored to prevent peatland fires. In addition, conserving intact forests within peatland through the moratorium strategy and restoring the degraded peatland ecosystem through canal blocking is also crucial to coping with global climate change.

Flame out! End-Triassic mass extinction polycyclic aromatic hydrocarbons reflect more than just fire

Global warming induced-wildfires of the 21st century reveal the catastrophic effects that widespread biomass burning has on flora and fauna. During mass extinction events, similar wildfire episodes are considered to play an important role in driving perturbations in terrestrial ecosystems. To better evaluate the record of biomass burning and potential carbon cycle feedbacks at the end-Triassic mass extinction (∼202 Ma; ETE), we investigated the relative abundances of a range of polycyclic aromatic hydrocarbons (PAHs) and the δ13C values of regular isoprenoids and n-alkanes at key sections in the SW UK. These data reveal little evidence for intensive wildfire activity during the extinction event, in contrast to what has been reported elsewhere in European, Chinese, and Greenland ETE sections. Herein, PAHs instead reflect greater contributions from an episode of soil erosion that we attribute to Large Igneous Province (LIP)-driven acid rain, and possible distal sources of smoke, suggestive of fire elsewhere in the UK/European basins. This terrestrial ecosystem perturbation is coincident with those in the marine realm, indicating ecosystem perturbations occurred across multiple habitats throughout the latest Rhaetian in the SW UK. Additionally, this geochemical approach reveals that the precursor carbon isotope excursion (CIE) routinely used in chemostratigraphic correlations is unrelated to LIP activity, but instead results from the increased input of terrestrially derived 13C-depleted plant material. Furthermore, we find the initial CIE (commonly used to mark the extinction level, but which is now known to precede the ETE) is also unrelated to biomass burning. Collectively, these data reveal that processes other than combustion of terrestrial material are important for the terrestrial phase of the ETE in the SW UK. Similar investigations are required on other ETE sections, both those in close proximity to the LIP driving the extinction and those further afield, to more clearly determine the negative effect(s) of LIPs and their geographic extent in the terrestrial realm.

Impacts on Urban Vocs and Pm2.5 During a Wildfire Episode

Impacts on Urban Vocs and Pm2.5 During a Wildfire Episode

From air quality sensors to sensor networks: Things we need to learn

As a potential complement to traditional regulatory instruments, low-cost air quality sensors (LCAQS) can be deployed in dense monitoring networks to provide timely and comprehensive snapshots of pollutant concentrations and their spatial and temporal variability at various scales with relatively less cost and labor. However, a lack of practical guidance and a limited understanding of sensor data quality hinder the widespread application of this emerging technology. We leveraged air quality data collected from state and local monitoring agencies in metropolitan areas of the United States to evaluate how low-cost sensors could be deployed across the U.S. We found that ozone, as a secondary pollutant, is more homogeneous than other pollutants at various scales. PM2.5, CO, and NO2 displayed homogeneities that varied by city, making it challenging to design a uniform network that was suitable across geographies. Our low-cost sensor data in New York City indicated that PM2.5 sensors track well with light-scattering reference methods, particularly at low concentrations. The same phenomenon was also found after thoroughly evaluating sensor evaluation reports from the Air Quality Sensor Performance Evaluation Center (AQ-SPEC). Furthermore, LCAQS data collected during wildfire episodes in Portland, OR show that a real-time (i.e. in situ) machine learning calibration process is a promising approach to address the data quality challenges persisting in LCAQS applications. Our research highlights the urgency and importance of practical guidance for deploying LCAQS.

Outdoor and indoor concentrations of size-resolved particulate matter during a wildfire episode in interior Alaska and the impact of ventilation

Outdoor and indoor concentrations of size-resolved particulate matter during a wildfire episode in interior Alaska and the impact of ventilation

Health Impact Assessment of the 2020 Washington State Wildfire Smoke Episode: Excess Health Burden Attributable to Increased PM2.5 Exposures and Potential Exposure Reductions.

Major wildfires starting in the summer of 2020 along the west coast of the United States made PM2.5 concentrations in this region rank among the highest in the world. Washington was impacted both by active wildfires in the state and aged wood smoke transported from fires in Oregon and California. This study aims to estimate the magnitude and disproportionate spatial impacts of increased PM2.5 concentrations attributable to these wildfires on population health. Daily PM2.5 concentrations for each county before and during the 2020 Washington wildfire episode (September 7–19) were obtained from regulatory air monitors. Utilizing previously established concentration‐response function (CRF) of PM2.5 (CRF of total PM2.5) and odds ratio (OR) of wildfire smoke days (OR of wildfire smoke days) for mortality, we estimated excess mortality attributable to the increased PM2.5 concentrations in Washington. On average, daily PM2.5 concentrations increased 97.1 μg/m3 during the wildfire smoke episode. With CRF of total PM2.5, the 13‐day exposure to wildfire smoke was estimated to lead to 92.2 (95% CI: 0.0, 178.7) more all‐cause mortality cases; with OR of wildfire smoke days, 38.4 (95% CI: 0.0, 93.3) increased all‐cause mortality cases and 15.1 (95% CI: 0.0, 27.9) increased respiratory mortality cases were attributable to the wildfire smoke episode. The potential impact of avoiding elevated PM2.5 exposures during wildfire events significantly reduced the mortality burden. Because wildfire smoke episodes are likely to impact the Pacific Northwest in future years, continued preparedness and mitigations to reduce exposures to wildfire smoke are necessary to avoid excess health burden.

Composition of particulate matter during a wildfire smoke episode in an urban area

The composition of wildfire smoke particulate matter (PM) was investigated during a 2018 smoke episode in the San Francisco Bay Area and compared to non-wildfire periods. Scanning electron microscopy (SEM) of passive PM samples deployed at four sites exhibited a higher concentration of submicron, spherical, carbonaceous PM (“tar balls”) during the wildfire period compared to the non-wildfire period. Coarse ash aluminosilicates and potassium bearing particles were enhanced as well. Disproportionately more UV light was absorbed by PM during the wildfire period than during the non-wildfire period, indicating a higher organic content consistent with the observed tar balls. An indoor PM sample from a residence operating a medium-efficiency furnace filter during the smoke episode exhibited fine, carbonaceous PM comparable to outdoors, but reduced coarse ash PM. Indoor/outdoor ratios for PM2.5, PM10-2.5, and PM10 were 0.6, 0.1, and 0.5 respectively. SEM of the wildfire furnace filter revealed a greater proportion of tar balls compared to a non-wildfire filter. Relatively rare particles enriched in Cu, Zn, Sn and Pb were observed in wildfire samples, but not in non-wildfire samples. Higher concentrations of Cu, Zn, and Pb during the wildfire were confirmed by inductively coupled plasma mass spectrometry (ICP-MS) of the furnace filter. The use of low-cost passive samplers enabled rapid deployment at multiple locations and indoors and outdoors during the wildfire episode. These measurements suggest smoke impacts were regional in scale and similar across sites, though local variations in PM2.5 magnitudes, particle types, and confounding by local sources were observed.Copyright © 2021 American Association for Aerosol Research

Field measurements of PM2.5 infiltration factor and portable air cleaner effectiveness during wildfire episodes in US residences

Wildfires have frequently occurred in the western United States (US) during the summer and fall seasons in recent years. This study measures the PM2.5 infiltration factor in seven residences recruited from five dense communities in Seattle, Washington, during a 2020 wildfire episode and evaluates the impacts of HEPA-based portable air cleaner (PAC) use on reducing indoor PM2.5 levels. All residences with windows closed went through an 18-to-24-h no filtration session, with five of seven following that period with an 18-to-24-h filtration session. Auto-mode PACs, which automatically adjust the fan speed based on the surrounding PM2.5 levels, were used for the filtration session. 10-s resolved indoor PM2.5 levels were measured in each residence's living room, while hourly outdoor levels were collected from the nearest governmental air quality monitoring station to each residence. Additionally, a time-activity diary in minute resolution was collected from each household. With the impacts of indoor sources excluded, indoor PM2.5 mass balance models were developed to estimate the PM2.5 indoor/outdoor (I/O) ratios, PAC effectiveness, and decay-related parameters. Among the seven residences, the mean infiltration factor ranged from 0.33 (standard deviation [SD]: 0.06) to 0.76 (SD: 0.05). The use of auto-mode PAC led to a 48%–78% decrease of indoor PM2.5 levels after adjusting for outdoor PM2.5 levels and indoor sources. The mean (SD) air exchange rates ranged from 0.30 (0.13) h−1 to 1.41 (3.18) h−1 while the PM2.5 deposition rate ranged from 0.10 (0.54) h−1 to 0.49 (0.47) h−1. These findings suggest that staying indoors, a common protective measure during wildfire episodes, is insufficient to prevent people's excess exposure to wildfire smoke, and provides quantitative evidence to support the utilization of auto-mode PACs during wildfire events in the US.

The U.S. EPA wildland fire sensor challenge: Performance and evaluation of solver submitted multi-pollutant sensor systems

Wildland fires can emit substantial amounts of air pollution that may pose a risk to those in proximity (e.g., first responders, nearby residents) as well as downwind populations. Quickly deploying air pollution measurement capabilities in response to incidents has been limited to date by the cost, complexity of implementation, and measurement accuracy. Emerging technologies including miniaturized direct-reading sensors, compact microprocessors, and wireless data communications provide new opportunities to detect air pollution in real time. The U.S. Environmental Protection Agency (EPA) partnered with other U.S. federal agencies (CDC, NASA, NPS, NOAA, USFS) to sponsor the Wildland Fire Sensor Challenge. EPA and partnering organizations share the desire to advance wildland fire air measurement technology to be easier to deploy, suitable to use for high concentration events, and durable to withstand difficult field conditions, with the ability to report high time resolution data continuously and wirelessly. The Wildland Fire Sensor Challenge encouraged innovation worldwide to develop sensor prototypes capable of measuring fine particulate matter (PM2.5), carbon monoxide (CO), carbon dioxide (CO2), and ozone (O3) during wildfire episodes. The importance of using federal reference method (FRM) versus federal equivalent method (FEM) instruments to evaluate performance in biomass smoke is discussed. Ten solvers from three countries submitted sensor systems for evaluation as part of the challenge. The sensor evaluation results including sensor accuracy, precision, linearity, and operability are presented and discussed, and three challenge winners are announced. Raw solver submitted PM2.5 sensor accuracies of the winners ranged from ~22 to 32%, while smoke specific EPA regression calibrations improved the accuracies to ~75–83% demonstrating the potential of these systems in providing reasonable accuracies over conditions that are typical during wildland fire events.