Measuring long-term exposure to wildfire PM_2.5 in California: Time-varying inequities in environmental burden

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/m³; 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/m³ 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.