Differences in the Estimation of Wildfire-Associated Air Pollution by Satellite Mapping of Smoke Plumes and Ground-Level Monitoring

Mortality attributable to PM2.5 from wildland fires in California from 2008 to 2018

In California, wildfire risk and severity have grown substantially in the last several decades. Research has characterized extensive adverse health impacts from exposure to wildfire-attributable fine particulate matter (PM2.5), but few studies have quantified long-term outcomes, and none have used a wildfire-specific chronic dose-response mortality coefficient. Here, we quantified the mortality burden for PM2.5 exposure from California fires from 2008 to 2018 using Community Multiscale Air Quality modeling system wildland fire PM2.5 estimates. We used a concentration-response function for PM2.5, applying ZIP code-level mortality data and an estimated wildfire-specific dose-response coefficient accounting for the likely toxicity of wildfire smoke. We estimate a total of 52,480 to 55,710 premature deaths are attributable to wildland fire PM2.5 over the 11-year period with respect to two exposure scenarios, equating to an economic impact of $432 to $456 billion. These findings extend evidence on climate-related health impacts, suggesting that wildfires account for a greater mortality and economic burden than indicated by earlier studies.

Air Pollution and Atopic Dermatitis, from Molecular Mechanisms to Population-Level Evidence: A Review

Atopic dermatitis (AD) has increased in prevalence to become the most common inflammatory skin condition globally, and geographic variation and migration studies suggest an important role for environmental triggers. Air pollution, especially due to industrialization and wildfires, may contribute to the development and exacerbation of AD. We provide a comprehensive, multidisciplinary review of existing molecular and epidemiologic studies on the associations of air pollutants and AD symptoms, prevalence, incidence, severity, and clinic visits. Cell and animal studies demonstrated that air pollutants contribute to AD symptoms and disease by activating the aryl hydrocarbon receptor pathway, promoting oxidative stress, initiating a proinflammatory response, and disrupting the skin barrier function. Epidemiologic studies overall report that air pollution is associated with AD among both children and adults, though the results are not consistent among cross-sectional studies. Studies on healthcare use for AD found positive correlations between medical visits for AD and air pollutants. As the air quality worsens in many areas globally, it is important to recognize how this can increase the risk for AD, to be aware of the increased demand for AD-related medical care, and to understand how to counsel patients regarding their skin health. Further research is needed to develop treatments that prevent or mitigate air pollution-related AD symptoms.

A novel ensemble-based statistical approach to estimate daily wildfire-specific PM2.5 in California (2006-2020)

Though fine particulate matter (PM2.5) has decreased in the United States (U.S.) in the past two decades, the increasing frequency, duration, and severity of wildfires significantly (though episodically) impairs air quality in wildfire-prone regions and beyond. Increasing PM2.5 concentrations derived from wildfire smoke and associated impacts on public health require dedicated epidemiological studies. Main sources of PM2.5 data are provided by government-operated monitors sparsely located across U.S., leaving several regions and potentially vulnerable populations unmonitored. Current approaches to estimate PM2.5 concentrations in unmonitored areas often rely on big data, such as satellite-derived aerosol properties and meteorological variables, apply computationally-intensive deterministic modeling, and do not distinguish wildfire-specific PM2.5 from other sources of emissions such as traffic and industrial sources. Furthermore, modelling wildfire-specific PM2.5 presents a challenge since measurements of the smoke contribution to PM2.5 pollution are not available. Here, we aim to use statistical methods to isolate wildfire-specific PM2.5 from other sources of emissions. Our study presents an ensemble model that optimally combines multiple machine learning algorithms (including gradient boosting machine, random forest and deep learning), and a large set of explanatory variables to, first, estimate daily PM2.5 concentrations at the ZIP code level, a relevant spatiotemporal resolution for epidemiological studies. Subsequently, we propose a novel implementation of an imputation approach to estimate the wildfire-specific PM2.5 concentrations that could be applied geographical regions in the US or worldwide. Our ensemble model achieved comparable results to previous machine learning studies for PM2.5 prediction while avoiding processing larger, computationally intensive datasets. Our study is the first to apply a suite of statistical models using readily available datasets to provide daily wildfire-specific PM2.5 at a fine spatial scale for a 15-year period, thus providing a relevant spatiotemporal resolution and timely contribution for epidemiological studies.

Using wildland fire smoke modeling data in gerontological health research (California, 2007-2018)

Widespread population exposure to wildland fire smoke underscores the urgent need for new techniques to characterize fire-derived pollution for epidemiologic studies and to build climate-resilient communities especially for aging populations. Using atmospheric chemical transport modeling, we examined air quality with and without wildland fire smoke PM_2.5. In 12-km gridded output, the 24-hour average concentration of all-source PM_2.5 in California (2007-2018) was 5.16 μg/m³ (S.D. 4.66 μg/m³). The average concentration of fire-PM_2.5 in California by year was 1.61 μg/m³ (~30% of total PM_2.5). The contribution of fire-source PM_2.5 ranged from 6.8% to 49%. We define a "smokewave" as two or more consecutive days with modeled levels above 35 μg/m³. Based on model-derived fire-PM_2.5, 99.5% of California's population lived in a county that experienced at least one smokewave from 2007 to 2018, yet understanding of the impact of smoke on the health of aging populations is limited. Approximately 2.7 million (56%) of California residents aged 65+ years lived in counties representing the top 3 quartiles of fire-PM_2.5 concentrations (2007-2018). For each year (2007-2018), grid cells containing skilled nursing facilities had significantly higher mean concentrations of all-source PM_2.5 than cells without those facilities, but they also had generally lower mean concentrations of wildland fire-specific PM_2.5. Compared to rural monitors in California, model predictions of wildland fire impacts on daily average PM_2.5 carbon (organic and elemental) performed well most years but tended to overestimate wildland fire impacts for high-fire years. The modeling system isolated wildland fire PM_2.5 from other sources at monitored and unmonitored locations, which is important for understanding exposures for aging population in health studies.

Assessment of Smoke Pollution Caused by Wildfires in the Baikal Region (Russia)

Climate change has increased the prevalence of wildfires, resulting in longer fire seasons and larger geographic area burned. The aim of this work was to assess the air pollution and health risk to the population caused during exposure to smoke in fire season. The study design included: an analysis of long-term air pollution to determine background levels; an analysis of short-term (<24 h) and subchronic (10–14 days) concentrations during wildfires; and an assessment of the health risk in the industrial center of the Baikal region (Russia). In Irkutsk, at a distance of 2000 km from the fire focal points, the maximum short-term concentrations of pollution were noted during the smoke period, when the average CO level increased 2.4 times, and PM1 increased 1.4 times relative to the background levels in August 2021. In Bratsk, located near the fires, the increases in short-term concentrations were: CO—21.0; SO2—13.0; formaldehyde—12.0; TPM—4.4 times. The hazard indices of respiratory and coronary diseases in the burning period exceeded the acceptable level. Acute reactions to smoke can be expected in 30% of the exposed population near fires and 11% in remote areas (Bratsk). The results obtained from the remote sensing of atmospheric smoke can be used to urgently resolve the issue of organizing medical assistance or evacuating the population groups most sensitive to the effects of smoke in fire season.