Abstract
Wildfires have become more frequent and intense due to climate change and outdoor wildfire fine particulate matter (PM(2.5)) concentrations differ from relatively smoothly varying total PM(2.5). Thus, we introduced a conceptual model for computing long-term wildfire PM(2.5) and assessed disproportionate exposures among marginalized communities. We used monitoring data and statistical techniques to characterize annual wildfire PM(2.5) exposure based on intermittent and extreme daily wildfire PM(2.5) concentrations in California census tracts (2006 to 2020). Metrics included: 1) weeks with wildfire PM(2.5) < 5 μg/m(3); 2) days with non-zero wildfire PM(2.5); 3) mean wildfire PM(2.5) during peak exposure week; 4) smoke waves (≥2 consecutive days with <15 μg/m(3) wildfire PM(2.5)); and 5) mean annual wildfire PM(2.5) concentration. We classified tracts by their racial/ethnic composition and CalEnviroScreen (CES) score, an environmental and social vulnerability composite measure. We examined associations of CES and racial/ethnic composition with the wildfire PM(2.5) metrics using mixed-effects models. Averaged 2006 to 2020, we detected little difference in exposure by CES score or racial/ethnic composition, except for non-Hispanic American Indian and Alaska Native populations, where a 1-SD increase was associated with higher exposure for 4/5 metrics. CES or racial/ethnic × year interaction term models revealed exposure disparities in some years. Compared to their California-wide representation, the exposed populations of non-Hispanic American Indian and Alaska Native (1.68×, 95% CI: 1.01 to 2.81), white (1.13×, 95% CI: 0.99 to 1.32), and multiracial (1.06×, 95% CI: 0.97 to 1.23) people were over-represented from 2006 to 2020. In conclusion, during our study period in California, we detected disproportionate long-term wildfire PM(2.5) exposure for several racial/ethnic groups.