Wildfire Smoke Research Articles

The effect of enriched versus depleted housing on eucalyptus smoke-induced cardiovascular dysfunction in mice

Objectives: Living conditions play a major role in health and well-being, particularly for the cardiovascular and pulmonary systems. Depleted housing contributes to impairment and development of disease, but how it impacts body resiliency during exposure to environmental stressors is unknown. This study examined the effect of depleted (DH) versus enriched housing (EH) on cardiopulmonary function and subsequent responses to wildfire smoke. Materials and Methods: Two cohorts of healthy female mice, one of them surgically implanted with radiotelemeters for the measurement of electrocardiogram, body temperature (Tco) and activity, were housed in either DH or EH for 7 weeks. Telemetered mice were exposed for 1 h to filtered air (FA) and then flaming eucalyptus wildfire smoke (WS) while untelemetered mice, which were used for ventilatory assessment and tissue collection, were exposed to either FA or WS. Animals were continuously monitored for 5–7 days after exposure. Results: EH prevented a decrease in Tco after radiotelemetry surgery. EH mice also had significantly higher activity levels and lower heart rate during and after FA and WS. Moreover, EH caused a decreased number of cardiac arrhythmias during WS. WS caused ventilatory depression in DH mice but not EH mice. Housing enrichment also upregulated the expression of cardioprotective genes in the heart. Conclusions: The results of this study indicate that housing conditions impact overall health and cardiopulmonary function. More importantly, depleted housing appears to worsen the response to air pollution. Thus, non-chemical factors should be considered when assessing the susceptibility of populations, especially when it comes to extreme environmental events.

A Preliminary Case Study on the Compounding Effects of Local Emissions and Upstream Wildfires on Urban Air Pollution

Interactions between urban and wildfire pollution emissions are active areas of research, with numerous aircraft field campaigns and satellite analyses of wildfire pollution being conducted in recent years. Several studies have found that elevated ozone and particulate pollution levels are both generally associated with wildfire smoke in urban areas. We measured pollutant concentrations at two Utah Division of Air Quality regulatory air quality observation sites and a local hot spot (a COVID-19 testing site) within a 48 h period of increasing wildfire smoke impacts that occurred in Salt Lake City, UT (USA) between 20 and 22 August 2020. The wildfire plume, which passed through the study area during an elevated ozone period during the summer, resulted in increased criteria pollutant and greenhouse gas concentrations. Methane (CH4) and fine particulate matter (PM2.5) increased at comparable rates, and increased NOx led to more ozone. The nitrogen oxide/ozone (NOx/O3) cycle was clearly demonstrated throughout the study period, with NOx titration reducing nighttime ozone. These findings help to illustrate how the compounding effects of urban emissions and exceptional pollution events, such as wildfires, may pose substantial health risks. This preliminary case study supports conducting an expanded, longer-term study on the interactions of variable intensity wildfire smoke plumes on urban air pollution exposure, in addition to the subsequent need to inform health and risk policy in these complex systems.

Wildfire smoke reduces lake ecosystem metabolic rates unequally across a trophic gradient

Wildfire smoke covers entire continents, depositing aerosols and reducing solar radiation fluxes to millions of freshwater ecosystems, yet little is known about impacts on lakes. Here, we quantified trends in the spatial extent of smoke cover in California, USA, and assessed responses of gross primary production and ecosystem respiration to smoke in 10 lakes spanning a gradient in water clarity and nutrient concentrations. From 2006 − 2022, the maximum extent of medium or high-density smoke occurring between June-October increased by 300,000 km2. In the three smokiest years (2018, 2020, 2021), lakes experienced 23 − 45 medium or high-density smoke days, characterized by 20% lower shortwave radiation fluxes and five-fold higher atmospheric fine particulate matter concentrations. Ecosystem respiration generally declined during smoke cover, especially in low-nutrient, cold lakes, whereas responses of primary production were more variable. Lake attributes and seasonal timing of wildfires will mediate the effects of smoke on lakes.

Practical considerations for using low-cost sensors to assess wildfire smoke exposure in school and childcare settings.

More frequent and intense wildfires will increase concentrations of smoke in schools and childcare settings. Low-cost sensors can assess fine particulate matter (PM2.5) concentrations with high spatial and temporal resolution. We sought to optimize the use of sensors for decision-making in schools and childcare settings during wildfire smoke to reduce children's exposure to PM2.5. We measured PM2.5 concentrations indoors and outdoors at four schools in Washington State during wildfire smoke in 2020-2021 using low-cost sensors and gravimetric samplers. We randomly sampled 5-min segments of low-cost sensor data to create simulations of brief portable handheld measurements. During wildfire smoke episodes (lasting 4-19 days), median hourly PM2.5 concentrations at different locations inside a single facility varied by up to 49.6 µg/m3 (maximum difference) during school hours. Median hourly indoor/outdoor ratios across schools ranged from 0.22 to 0.91. Within-school differences in concentrations indicated that it is important to collect measurements throughout a facility. Simulation results suggested that making handheld measurements more often and over multiple days better approximates indoor/outdoor ratios for wildfire smoke. During a period of unstable air quality, PM2.5 over the next hour indoors was more highly correlated with the last 10-min of data (mean R2 = 0.94) compared with the last 3-h (mean R2 = 0.60), indicating that higher temporal resolution data is most informative for decisions about near-term activities indoors. As wildfires continue to increase in frequency and severity, staff at schools and childcare facilities are increasingly faced with decisions around youth activities, building use, and air filtration needs during wildfire smoke episodes. Staff are increasingly using low-cost sensors for localized outdoor and indoor PM2.5 measurements, but guidance in using and interpreting low-cost sensor data is lacking. This paper provides relevant information applicable for guidance in using low-cost sensors for wildfire smoke response.

Forest Smoke-Fire Net (FSF Net): A Wildfire Smoke Detection Model That Combines MODIS Remote Sensing Images with Regional Dynamic Brightness Temperature Thresholds

Satellite remote sensing plays a significant role in the detection of smoke from forest fires. However, existing methods for detecting smoke from forest fires based on remote sensing images rely solely on the information provided by the images, overlooking the positional information and brightness temperature of the fire spots in forest fires. This oversight significantly increases the probability of misjudging smoke plumes. This paper proposes a smoke detection model, Forest Smoke-Fire Net (FSF Net), which integrates wildfire smoke images with the dynamic brightness temperature information of the region. The MODIS_Smoke_FPT dataset was constructed using a Moderate Resolution Imaging Spectroradiometer (MODIS), the meteorological information at the site of the fire, and elevation data to determine the location of smoke and the brightness temperature threshold for wildfires. Deep learning and machine learning models were trained separately using the image data and fire spot area data provided by the dataset. The performance of the deep learning model was evaluated using metric MAP, while the regression performance of machine learning was assessed with Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The selected machine learning and deep learning models were organically integrated. The results show that the Mask_RCNN_ResNet50_FPN and XGR models performed best among the deep learning and machine learning models, respectively. Combining the two models achieved good smoke detection results (Precisionsmoke=89.12%). Compared with wildfire smoke detection models that solely use image recognition, the model proposed in this paper demonstrates stronger applicability in improving the precision of smoke detection, thereby providing beneficial support for the timely detection of forest fires and applications of remote sensing.

Enriched housing differentially alters allostatic load and cardiopulmonary responses to wildfire-related smoke in male and female mice

ABSTRACT Living conditions are an important modifier of individual health outcomes and may lead to higher allostatic load (AL). However, housing-induced cardiovascular and immune effects contributing to altered environmental responsiveness remain understudied. This investigation was conducted to examine the influence of enriched (EH) versus depleted housing (DH) conditions on cardiopulmonary functions, systemic immune responses, and allostatic load in response to a single wildfire smoke (WS) exposure in mice. Male and female C57BL/6J mice were divided into EH or DH for 22 weeks, and cardiopulmonary assessments measured before and after exposures to either one-hr filtered air (FA) or flaming eucalyptus WS exposure. Male and female DH mice exhibited increased heart rate (HR) and left ventricular mass (LVM), as well as reduced stroke volume and end diastolic volume (EDV) one week following exposure to WS. Female DH mice displayed significantly elevated levels of IL-2, IL-17, corticosterone and hemoglobin A1c (HbA1c) following WS, while female in EH mice higher epinephrine levels were detected. Female mice exhibited higher AL than males with DH, which was potentiated post-WS exposure. Thus, DH increased susceptibility to extreme air pollution in a gender-dependent manner suggesting that living conditions need to be evaluated as a modifier of toxicological responses.

Impacts of heat and wildfire on preterm birth

BackgroundClimate change continues to increase the frequency, intensity, and duration of heat events and wildfires, both of which are associated with adverse pregnancy outcomes. Few studies simultaneously evaluated exposures to these increasingly common exposures. ObjectivesWe investigated the relationship between exposure to heat and wildfire smoke and preterm birth (PTB). MethodsIn this time-stratified case-crossover study, participants consisted of 85,806 California singleton PTBs (20–36 gestational weeks) from May through October of 2015–2019. Birthing parent ZIP codes were linked to high-resolution daily weather, PM2.5 from wildfire smoke, and ambient air pollution data. Heat day was defined as a day with apparent temperature >98th percentile within each ZIP code and heat wave was defined as ≥2 consecutive heat days. Wildfire-smoke day was defined as a day with any exposure to wildfire-smoke PM2.5. Conditional logistic regression was used to calculate the odds ratio (OR) and 95% confidence intervals (CI) comparing exposures during a hazard period (lags 0–6) compared to control periods. Analyses were adjusted for relative humidity, fine particles, and ozone. ResultsWildfire-smoke days were associated with 3.0% increased odds of PTB (ORlag0: 1.03, CI: 1.00–1.05). Compared with white participants, associations appeared stronger among Black, Hispanic, Asian, and American Indians/Alaskan Native participants. Heatwave days (ORlag2: 1.07, CI: 1.02–1.13) were positively associated with PTB, with stronger associations among those simultaneously exposed to wildfire smoke days (ORlag2: 1.19, CI: 1.11–1.27). Similar findings were observed for heat days and when other temperature metrics (e.g., maximum, minimum) were used. DiscussionHeat and wildfire increased PTB risk with evidence of synergism. As the occurrence and co-occurrence of these events increase, exposure reduction among pregnant people is critical, especially among racial/ethnic minorities.

Evaluating Phoenix Metropolitan Area Ozone Behavior Using Ground-Based Sampling, Modeling, and Satellite Retrievals

An oxidizing and harmful pollutant gas, tropospheric ozone is a product of a complex set of photochemical reactions that can make it difficult to enact effective control measures. A better understanding of its precursors including volatile organic compounds (VOCs) and nitrogen oxides (NOx) and their spatial distribution can enable policymakers to focus their control efforts. In this study we used low-cost sensors (LCSs) to increase the spatial resolution of an existing NO2 monitoring network in addition to VOC sampling to better understand summer ozone formation in Maricopa County, Arizona, and observed that afternoon O3 values at the downwind sites were significantly correlated, ~0.27, to the morning NO2 × rate values at the urban sites. Additionally, we looked at the impact of wildfire smoke on ozone exceedances and compared non-smoke days to smoke days. The average O3 on smoke days was approximately 20% higher than on non-smoke days, however, the average NO2 concentration multiplied by estimated photolysis rate (NO2 × rate) values were only 2% higher on smoke days. Finally, we evaluated the ozone sensitivity of the region by calculating HCHO/NO2 ratios using three different datasets: ground, satellite, and model. Although the satellite dataset produced higher HCHO/NO2 ratios than the other datasets, when the proper regime thresholds are applied the three datasets consistently show transition and VOC-limited O3 production regimes over the Phoenix metro area. This suggests a need to implement more VOC emission controls in order to reach O3 attainment in the county.

Evaluation of Wildfire Plume Injection Heights Estimated from Operational Weather Radar Observations Using Airborne Lidar Retrievals

AbstractThe vertical distribution of wildfire smoke aerosols is important in determining its environmental impacts but existing observations of smoke heights generally do not possess the temporal resolution required to fully resolve the diurnal behavior of wildfire smoke injection. We use Weather Surveillance Radar‐1988 Doppler (WSR‐88D) dual polarization data to estimate injection heights of Biomass Burning Debris (BBD) generated by fires. We detect BBD as a surrogate for smoke aerosols, which are often collocated with BBD near the fire but are not within the size range detectable by these radars. Injection heights of BBD are derived for 2–10 August 2019, using WSR‐88D reflectivity (Z ≥ 10 dBZ) and dual polarization correlation coefficients (0.2 < C.C < 0.9) to study the Williams Flats fire. Results show the expected diurnal cycles with maximum injection heights present during the late afternoon period when the fire's intensity and convective mixing are maximized. WSR‐88D and airborne lidar injection height comparisons reveal that this method is sensitive to outliers and generally overpredicts maximum heights by 40%, though mean and median heights are better captured (<20% mean error). WSR‐88D heights between the 75th and 90th percentile seem to accurately represent the maximum heights, with the exception of heights estimated during the occurrence of a pyro‐cumulonimbus. Location specific mapping of WSR‐88D and lidar injection heights reveal that they diverge further away from the fire as expected due to BBD settling. Most importantly, WSR‐88D‐derived injection height estimates provide near continuous smoke height information, allowing for the study of diurnal variability of smoke injections.

Wildfires and climate justice: future wildfire events predicted to disproportionally impact socioeconomically vulnerable communities in North Carolina.

Wildfire events are becoming increasingly common across many areas of the United States, including North Carolina (NC). Wildfires can cause immediate damage to properties, and wildfire smoke conditions can harm the overall health of exposed communities. It is critical to identify communities at increased risk of wildfire events, particularly in areas with that have sociodemographic disparities and low socioeconomic status (SES) that may exacerbate incurred impacts of wildfire events. This study set out to: (1) characterize the distribution of wildfire risk across NC; (2) implement integrative cluster analyses to identify regions that contain communities with increased vulnerability to the impacts of wildfire events due to sociodemographic characteristics; (3) provide summary-level statistics of populations with highest wildfire risk, highlighting SES and housing cost factors; and (4) disseminate wildfire risk information via our online web application, ENVIROSCAN. Wildfire hazard potential (WHP) indices were organized at the census tract-level, and distributions were analyzed for spatial autocorrelation via global and local Moran's tests. Sociodemographic characteristics were analyzed via k-means analysis to identify clusters with distinct SES patterns to characterize regions of similar sociodemographic/socioeconomic disparities. These SES groupings were overlayed with housing and wildfire risk profiles to establish patterns of risk across NC. Resulting geospatial analyses identified areas largely in Southeastern NC with high risk of wildfires that were significantly correlated with neighboring regions with high WHP, highlighting adjacent regions of high risk for future wildfire events. Cluster-based analysis of SES factors resulted in three groups of regions categorized through distinct SES profiling; two of these clusters (Clusters 2 and 3) contained indicators of high SES vulnerability. Cluster 2 contained a higher percentage of younger (<5 years), non-white, Hispanic and/or Latino residents; while Cluster 3 had the highest mean WHP and was characterized by a higher percentage of non-white residents, poverty, and less than a high school education. Counties of particular SES and WHP-combined vulnerability include those with majority non-white residents, tribal communities, and below poverty level households largely located in Southeastern NC. WHP values per census tract were dispersed to the public via the ENVIROSCAN application, alongside other environmentally-relevant data.

Impact of Wildfires on Cardiovascular Health.

Wildfire smoke (WFS) is a mixture of respirable particulate matter, environmental gases, and other hazardous pollutants that originate from the unplanned burning of arid vegetation during wildfires. The increasing size and frequency of recent wildfires has escalated public and occupational health concerns regarding WFS inhalation, by either individuals living nearby and downstream an active fire or wildland firefighters and other workers that face unavoidable exposure because of their profession. In this review, we first synthesize current evidence from environmental, controlled, and interventional human exposure studies, to highlight positive associations between WFS inhalation and cardiovascular morbidity and mortality. Motivated by these findings, we discuss preventative measures and suggest interventions to mitigate the cardiovascular impact of wildfires. We then review animal and cell exposure studies to call attention on the pathophysiological processes that support the deterioration of cardiovascular tissues and organs in response to WFS inhalation. Acknowledging the challenges of integrating evidence across independent sources, we contextualize laboratory-scale exposure approaches according to the biological processes that they model and offer suggestions for ensuring relevance to the human condition. Noting that wildfires are significant contributors to ambient air pollution, we compare the biological responses triggered by WFS to those of other harmful pollutants. We also review evidence for how WFS inhalation may trigger mechanisms that have been proposed as mediators of adverse cardiovascular effects upon exposure to air pollution. We finally conclude by highlighting research areas that demand further consideration. Overall, we aspire for this work to serve as a catalyst for regulatory initiatives to mitigate the adverse cardiovascular effects of WFS inhalation in the community and alleviate the occupational risk in wildland firefighters.

Application of quasi-empirical orthogonal functions to estimate wildfire impacts in northwestern Spain

Galicia (NW Spain) is one of the most fire-prone regions in Southern Europe. In the summer of 2022, a total of thirteen wildfires each exceeding 500 ha were reported in this area, with ten of these large fires occurring in the Ourense region. To study the impacts of wildfire smoke plumes on ambient air PM2.5 concentrations, a network of 18 PurpleAir monitors was deployed across the Galicia region during July and August 2022. The PM2.5 concentration data were then used as input to test the applicability of quasi-empirical orthogonal functions (QEOFs obtained with Positive Matrix Factorization (PMF)) to characterize the spatial variability of wildfire smoke impacts on air quality. HYSPLIT back-trajectory analysis and Concentration-Weighted Trajectory (CWT) models were implemented, and the results from these tools were combined with source contributions. As a result, 19 wildfires were identified and linked with peak ambient PM2.5 concentrations (>300 μg/m3 of PM2.5; 1-h mean). Specifically, the Folgoso do Courel fire emerged as a significant contributor to these high concentrations and played an important role in influencing a significant number of the identified factors. Moreover, the results also suggested that emissions from fires in Portugal reached the study area, contributing additional impacts on air quality. These results demonstrated that this approach was useful in identifying the emission source areas contributing to observed PM2.5 concentrations during wildfire events. The PM2.5 concentration maps resulting from the CWT analysis were also valuable in understanding the short- and long-term exposures to PM2.5 from wildfire smoke.

A Control-Theoretic Spatio-Temporal Model for Wildfire Smoke Propagation Using UAV-Based Air Pollutant Measurements

Wildfires have the potential to cause severe damage to vegetation, property and most importantly, human life. In order to minimize these negative impacts, it is crucial that wildfires are detected at the earliest possible stages. A potential solution for early wildfire detection is to utilize unmanned aerial vehicles (UAVs) that are capable of tracking the chemical concentration gradient of smoke emitted by wildfires. A spatiotemporal model of wildfire smoke plume dynamics can allow for efficient tracking of the chemicals by utilizing both real-time information from sensors as well as future information from the model predictions. This study investigates a spatiotemporal modeling approach based on subspace identification (SID) to develop a data-driven smoke plume dynamics model for the purposes of early wildfire detection. The model was learned using CO2 concentration data which were collected using an air quality sensor package onboard a UAV during two prescribed burn experiments. Our model was evaluated by comparing the predicted values to the measured values at random locations and showed mean errors of 6.782 ppm and 30.01 ppm from the two experiments. Additionally, our model was shown to outperform the commonly used Gaussian puff model (GPM) which showed mean errors of 25.799 ppm and 104.492 ppm, respectively.

How heating tracers drive self-lofting long-lived stratospheric anticyclones: simple dynamical models

Abstract. Long-lived “bubbles” of wildfire smoke or volcanic aerosol have recently been observed in the stratosphere, co-located with ozone, carbon monoxide, and water vapour anomalies. These bubbles often survive for several weeks, during which time they ascend through vertical distances of 15 km or more. Meteorological analysis data suggest that this aerosol is contained within strong, persistent anticyclonic vortices. Absorption of solar radiation by the aerosol is hypothesised to drive the ascent of the bubbles, but the dynamics of how this heating gives rise to a single-sign anticyclonic vorticity anomaly have thus far been unclear. We present a description of heating-driven stratospheric vortices, based on an axisymmetric balanced model. The simplest version of this model includes a specified localised heating moving upwards at fixed velocity and produces a steadily translating solution with a single-signed anticyclonic vortex co-located with the heating, with corresponding temperature anomalies forming a vertical dipole, matching observations. A more complex version includes the two-way interaction between a heating tracer, representing the aerosol, and the dynamics. An evolving tracer provides heating which drives a secondary circulation, and this in turn transports the tracer. Through this two-way interaction an initial distribution of tracer drives a circulation and forms a self-lofting tracer-filled anticyclonic vortex. Scaling arguments show that upward velocity is proportional to heating magnitude, but the magnitude of peak quasigeostrophic vorticity is O(f) (f is the Coriolis parameter) and independent of the heating magnitude. Estimates of vorticity from observations match our theoretical predictions. We discuss 3-D effects such as vortex stripping and dispersion of tracer outside the vortex by the large-scale flow, which cannot be captured explicitly by the axisymmetric model and are likely to be important in the real atmosphere. The large O(f) vorticity of the fully developed anticyclones explains their observed persistence and their effective confinement of tracers. To further investigate the early stages of formation of tracer-filled vortices, we consider an idealised configuration of a homogeneous tracer layer. A linearised calculation reveals that the upper part of the layer is destabilised due to the decrease in tracer concentrations with height there, which sets up a self-reinforcing effect where upward lofting of tracer results in stronger heating and hence stronger lofting. Small amplitude disturbances form isolated tracer plumes that ascend out of the initial layer, indicative of a self-organisation of the flow. The relevance of these idealised models to formation and persistence of tracer-filled vortices in the real atmosphere is discussed, and it is suggested that a key factor in their formation is the time taken to reach the fully developed stage, which is shorter for strong heating rates.

A Mixed Method Study to Explore How Maintenance Personnel Can Enhance Wildfire Smoke Resilience at Long-Term Care Facilities in the US Mountain West

Wildfire activity is increasing around the world, concurrent with climate change, and mitigation strategies for protecting vulnerable populations are desperately needed. Because inhaled particles are deleterious to respiratory health, particularly among older adults with co-morbidities, we engaged maintenance personnel working in long term care facilities located in the Western US. Our objective was to identify opportunities to build resilience during wildfire smoke events. We implemented a virtual workshop that addressed wildfire smoke health impacts as well as strategies to assess and maintain indoor air quality. A total of 24 maintenance personnel attended the virtual workshop and 14 participated in a quantitative survey. Workshop attendees found value in the material and there was enthusiasm for educational resources and enhancing indoor air quality. Four months later, four maintenance staff participated in a follow-up interview. Our qualitative assessment revealed the following themes: awareness and prioritization, application of knowledge, barriers, and educational resources. Access to real-time actionable air quality data was a consistent feature across these themes. Maintenance personnel could play a key role in a facility’s ability to prepare for and respond to wildfire smoke events, and this study highlights potential challenges and opportunities to involving them in resilience building strategies.

The Environment and Headache: a Narrative Review.

In this narrative review, we summarize the peer-reviewed literature published between 2017 and 2022 that evaluated ambient environmental risk factors for primary headache disorders, which affect more than half of the population globally. Primary headache disorders include migraine, tension-type headache (TTH), and trigeminal and autonomic cephalalgias (TAC). We identified 17 articles that met the inclusion criteria via PubMed or Google Scholar. Seven studies (41%) relied on data from US populations. The remaining studies were conducted in China, Taiwan, Germany, Ghana, Japan, the Netherlands, South Korea, and Turkey. Air pollution was the most frequently assessed environmental risk factor. Most studies were cross-sectional and focused on all-cause or migraine headaches; one study included TTH, and none included TAC. Short-term exposure to fine particulate matter (PM2.5) was not consistently associated with headache endpoints, but long-term exposure to PM2.5 was associated with migraine headache prevalence and severity across multiple studies. Elevated ambient temperature, changes in weather, oil and gas well exposure, and less natural greenspace, but not noise pollution, were also associated with headache. No studies considered water pollution, metal exposure, ultrafine particulate matter, or wildfire smoke exposure. There is a need for ongoing research focused on headache and the environment. Study designs with the greatest explanatory power may include longitudinal studies that capture the episodic nature of headache and case-crossover analysis, which control for time-invariant individual-level confounders by design. There is also a clear need for research that considers comorbid psychiatric illness and socioeconomic position as powerful modifiers of the effect of the environment on headache.

Seasonal extreme temperatures and short-term fine particulate matter increases pediatric respiratory healthcare encounters in a sparsely populated region of the intermountain western United States

BackgroundWestern Montana, USA, experiences complex air pollution patterns with predominant exposure sources from summer wildfire smoke and winter wood smoke. In addition, climate change related temperatures events are becoming more extreme and expected to contribute to increases in hospital admissions for a range of health outcomes. Evaluating while accounting for these exposures (air pollution and temperature) that often occur simultaneously and may act synergistically on health is becoming more important.MethodsWe explored short-term exposure to air pollution on children’s respiratory health outcomes and how extreme temperature or seasonal period modify the risk of air pollution-associated healthcare events. The main outcome measure included individual-based address located respiratory-related healthcare visits for three categories: asthma, lower respiratory tract infections (LRTI), and upper respiratory tract infections (URTI) across western Montana for ages 0–17 from 2017–2020. We used a time-stratified, case-crossover analysis with distributed lag models to identify sensitive exposure windows of fine particulate matter (PM2.5) lagged from 0 (same-day) to 14 prior-days modified by temperature or season.ResultsFor asthma, increases of 1 µg/m3 in PM2.5 exposure 7–13 days prior a healthcare visit date was associated with increased odds that were magnified during median to colder temperatures and winter periods. For LRTIs, 1 µg/m3 increases during 12 days of cumulative PM2.5 with peak exposure periods between 6–12 days before healthcare visit date was associated with elevated LRTI events, also heightened in median to colder temperatures but no seasonal effect was observed. For URTIs, 1 unit increases during 13 days of cumulative PM2.5 with peak exposure periods between 4–10 days prior event date was associated with greater risk for URTIs visits that were intensified during median to hotter temperatures and spring to summer periods.ConclusionsDelayed, short-term exposure increases of PM2.5 were associated with elevated odds of all three pediatric respiratory healthcare visit categories in a sparsely population area of the inter-Rocky Mountains, USA. PM2.5 in colder temperatures tended to increase instances of asthma and LRTIs, while PM2.5 during hotter periods increased URTIs.

YOLO-Based Models for Smoke and Wildfire Detection in Ground and Aerial Images

Wildland fires negatively impact forest biodiversity and human lives. They also spread very rapidly. Early detection of smoke and fires plays a crucial role in improving the efficiency of firefighting operations. Deep learning techniques are used to detect fires and smoke. However, the different shapes, sizes, and colors of smoke and fires make their detection a challenging task. In this paper, recent YOLO-based algorithms are adopted and implemented for detecting and localizing smoke and wildfires within ground and aerial images. Notably, the YOLOv7x model achieved the best performance with an mAP (mean Average Precision) score of 80.40% and fast detection speed, outperforming the baseline models in detecting both smoke and wildfires. YOLOv8s obtained a high mAP of 98.10% in identifying and localizing only wildfire smoke. These models demonstrated their significant potential in handling challenging scenarios, including detecting small fire and smoke areas; varying fire and smoke features such as shape, size, and colors; the complexity of background, which can include diverse terrain, weather conditions, and vegetation; and addressing visual similarities among smoke, fog, and clouds and the the visual resemblances among fire, lighting, and sun glare.

Large transboundary health impact of Arctic wildfire smoke

Rapid warming at high latitudes, particularly in Siberia, has led to large wildfires in recent years that cause widespread smoke plumes. These fires lead to substantial deterioration in summer air quality in the region, with a factor 4 increase in summer fine particulate matter (PM2.5) concentrations in parts of Siberia during 1998–2020. Exposure to PM2.5 is associated with increased risk of mortality due to cardiovascular and respiratory disease, and the atmospheric lifetime of PM2.5 means that it can be efficiently transported between regions and nations. We used the Community Earth System Model to quantify the fraction of PM2.5 attributed to high latitude wildfires that occur in the Arctic Council member states and estimated the attributable health impact locally and in neighbouring countries. During 2001–2020 we attribute ~21,000 excess deaths to Arctic Council wildfires on average each year, of which ~8000 occur in countries outside the Arctic Council. Our analysis shows that the health impact of Arctic wildfires decreased during 2001–2020, despite the increase of wildfire-sourced PM2.5, which we suggest is due to a northwards shift in the average latitude of Siberian wildfires, reducing their impact on more densely populated regions.

Quantifying the impacts of Canadian wildfires on regional air pollution networks

Wildfire smoke greatly impacts regional atmospheric systems, causing changes in the behavior of pollution. However, the impacts of wildfire smoke on pollution behavior are not easily quantifiable due to the complex nature of atmospheric systems. Air pollution correlation networks have been used to quantify air pollution behavior during ambient conditions. However, it is unknown how extreme pollution events impact these networks. Therefore, we propose a multidimensional air pollution correlation network framework to quantify the impacts of wildfires on air pollution behavior. The impacts are quantified by comparing two time periods, one during the 2023 Canadian wildfires and one during normal conditions with two complex network types for each period. In this study, the value network represents PM2.5 concentrations and the rate network represents the rate of change of PM2.5 concentrations. Wildfires' impacts on air pollution behavior are captured by structural changes in the networks. The wildfires caused a discontinuous phase transition during percolation in both network types which represents non-random organization of the most significant spatiotemporal correlations. Additionally, wildfires caused changes to the connectivity of stations leading to more interconnected networks with different influential stations. During the wildfire period, highly polluted areas are more likely to form connections in the network, quantified by an 86 % and 19 % increase in the connectivity of the value and rate networks respectively compared to the normal period. In this study, we create novel understandings of the impacts of wildfires on air pollution correlation networks, show how our method can create important insights into air pollution patterns, and discuss potential applications of our methodologies. This study aims to enhance capabilities for wildfire smoke exposure mitigation and response strategies.