Modeling Daily Plume Specific Smoke Concentrations for Health Effects Studies with Estimates of Fire Size, Plume Age, and Fuel Type

Inhaling smoke PM_2.5 can lead to acute health effects like asthma and lung irritation, making it essential to estimate smoke exposure on short time scales. Epidemiological studies that assess these effects need daily, fire-specific ground-level PM_2.5 data, and missing emission information can lead to underestimates. This paper presents a method to estimate daily fire-specific PM_2.5 smoke concentrations in the western United States from 2007 to 2019. Our model uses fire characteristics (e.g., fuel type, fire size, and distance) and updated fire emission inputs in an atmospheric dispersion model to simulate where smoke travels and at what concentration. We then apply a Bayesian time-series model to ground-based EPA monitors to isolate the smoke-specific portion of total PM_2.5, accounting for meteorology and season. This approach allows us to assess spatial variation in smoke exposure and investigate the role of fire attributes. For example, Lindon, UT experienced 398 fires with modest average concentrations (∼2 μg m³), while Carson City, NV saw fewer fires (177) but more intense exposures (∼6 μg m³, max 159 μg m³). These contrasts highlight the value of linking fire characteristics to daily exposure in health studies and underscore the need to consider transported smoke in fire management strategies.