Assessment of Wildfire-Related Air Quality Exposures in the Built Environment and the Related Impacts on Older Adult Health in California

To better understand personal risks and exposures to smoke from biomass burning, in particular wildland fire activity, information from both the ambient and built environment must be combined with behavioral data. The work presented here represents findings from a pilot NIH-funded study that is focused on quantification of wildfire smoke exposure for the elderly in California through a combination of these methods. Using the Weather Research and Forecasting (WRF) model coupled to the Community Multiscale Air Quality (CMAQ) model with data assimilation of CO and aerosol optical depth, indoor and outdoor PurpleAir observations of PM_2.5, and in-home modeling of air quality using CONTAM, a multizone indoor air quality and ventilation program, we can estimate wildfire smoke concentrations in both the ambient and built environment. To capture social and behavioral data a random selection of approximately 100 households were enrolled among census tracts in the highest and lowest quintiles of historic wildfire-specific CO concentrations based on modeled 14-year WRF-CMAQ GSI estimates. In addition, we installed 40 PurpleAir sensors both inside and outside of 20 homes and evaluated the sensors, nearby EPA AQS observations and the WRF-Chem chemical forecast produced by NSF NCAR for the 2024 wildfire season. The combination of cohort interviews and in-home air quality modeling for this dataset shows how behavior, characteristics of the built home environment (e.g. HVAC characteristics, home age, home type), and proximity to wildland fire events impacts a person’s exposure and risk to both local and remote wildfire smoke.