MODELING THE IMPACT OF PARTICULATE MATTER ON RESPIRATORY HOSPITAL ADMISSIONS IN ALASKA DURING THE WILDFIRE SEASON

Abstract

Wildfire-derived smoke poses a global hazard to residents of urban and rural areas alike. Studies have documented the adverse health effects associated with exposure to wildfire-derived smoke, which include respiratory and cardiovascular morbidity and mortality. The State of Alaska is a natural case study to understand smoke exposure assessment in rural, sparsely populated areas, where most pollution in late spring and early fall can be attributed to wildfires. This dissertation addresses critical methodological gaps in wildfire-derived PM2.5 exposure assessment. There are three distinct studies in this dissertation. Following an introductory chapter, chapter two is a stock take of existing surface monitoring on the country and global scales as it can be applied to monitoring wildfire-derived smoke. Ground-based air quality monitors are the operating standard for assessing air quality for regulatory purposes, but there are significant gaps in the coverage of monitors, especially in sparsely populated areas of the world that warrant approaches that use satellite data and other modeling approaches. Chapter 3 presents a computationally light modeling approach that derives PM2.5 at 1 km resolution using satellite-derived fire metrics with cross-validated R2 values over 0.80. Chapter 4 demonstrates how selecting exposure datasets in common epidemiological studies is a fundamentally important decision. Sensitivity analyses to test the robustness of results are standard practice, but changes are made within the same single exposure dataset. This research underscores the importance of interdisciplinary approaches bridging geospatial science, public health, and epidemiology. Future work should incorporate social science perspectives and develop open-source tools to translate air quality models into actionable resources for wildfire preparedness and response. As Alaska faces growing wildfire risk, improved exposure estimation methods are critical for protecting vulnerable populations.