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. In 12-km gridded output, the 24-hour average concentration of all-source PM in California (2007-2018) was 5.16 μg/m (S.D. 4.66 μg/m). The average concentration of fire-PM in California by year was 1.61 μg/m (~30% of total PM). The contribution of fire-source PM ranged from 6.8% to 49%. We define a "smokewave" as two or more consecutive days with modeled levels above 35 μg/m. Based on model-derived fire-PM, 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 concentrations (2007-2018). For each year (2007-2018), grid cells containing skilled nursing facilities had significantly higher mean concentrations of all-source PM than cells without those facilities, but they also had generally lower mean concentrations of wildland fire-specific PM. Compared to rural monitors in California, model predictions of wildland fire impacts on daily average PM 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 from other sources at monitored and unmonitored locations, which is important for understanding exposures for aging population in health studies.