The advancement of new exposure assessment techniques has facilitated the development of several wildland fire smoke datasets employing varied smoke estimation methods. Exposure to wildland fire smoke can increase respiratory health risks, particularly among vulnerable groups such as the elderly. This study compares estimates of wildland fire smoke fine particulate matter (“smoke PM2.5”) in California from 2008 to 2018 across three datasets and quantifies differences in the attributable respiratory health burden among elderly populations from utilizing different smoke datasets. Smoke estimates from Childs and Casey, which rely on similar input datasets, were more similar than those from CMAQ in terms of correlation, spatial distributions, and temporal trends. Approximately 1,300-5,400 respiratory morbidity impacts among the elderly are attributable to smoke PM2.5 in California during the study period. Using different smoke datasets and dose-response values yielded discrepancies in health impact estimates, with larger discrepancies between CMAQ with Childs and Casey. Differences in smoke PM2.5 and health impact estimates can affect future wildland fire policies that rely on these estimates to address health burdens.

The dissertation presents three papers examining exposure to extreme heat and wildfire in the Western United States. In the first chapter, I develop a framework for analyzing transit passenger exposure to extreme heat in Maricopa County and then implement an optimization algorithm for minimizing wait times through the reallocation of buses across the transit network. In simulating the reconfiguration of buses, I find the potential for small adjustments to produce large reductions in wait time for vulnerable populations. This work also formulates a way to measure passenger vulnerability with an activity-based model that accounts for the distinct demographics of transit riders.

In the second chapter, I study the prevalence of ground level wildfire smoke, specifically particulate matter 2.5�m in diameter, in California during the 2020 wildfire season - the most severe wildfire season ever recorded by the state. For the first time, I study how frequently extreme smoke levels at surface level coincide with extreme heat in space and time. These interactions can influence adaptive behaviors and studies show evidence of increased hospitalizations when these hazards co-occur. I find that a majority of Californians experienced at least one day of concurrent heat and smoke in 2020 and that these events were concentrated in more rural areas of the State. This case study motivates the integration of multi-hazard frameworks in both public and private sector risk planning.

In the final chapter, I examine wildfire risk factors for residential property in California. I leverage a dataset collected by CAL FIRE enumerators who record the features of a home and categorize the level of damage after every named incident. I enhance this dataset using remotely sensed detections of wildfire to impute the date when a home burned from which I then estimate time-varying weather risk factors like humidity, temperature and wind as well as fire intensity. I then use these features to train a predictive model to be used by homeowners or insurance carriers to better estimate the vulnerability of their property.

This dissertation employs a multi-satellite synergy algorithm and a chemical transport model to investigate atmospheric composition changes and public health impacts resulting from the 2020 wildfires in the Western United States. The study synergizes data from the CrIS and TROPOMI satellite instruments to analyze carbon monoxide (CO) and evaluates how these two instruments sensed CO separately and in synergy. Results indicate significantly higher daily average CO columns in the Western U.S. compared to the Central and Eastern U.S., with TROPOMI revealing higher values near fire sources, suggesting stronger contributions from close-to-surface concentrations. Validation against ground-based TCCON and NDACC's FTIR CO column estimates demonstrated Normalized Mean Error of less than 24% for CrIS and 32% for TROPOMI. The synergy between TROPOMI and CrIS CO columns was evaluated by assessing the elevated smoke plume on September 15, 2020, against a balloon-borne retrieval from AirCore. It was found that even when deviations were present in CrIS's predicted profile, consistency between TROPOMI and CrIS CO columns was maintained for lofted plumes. Overall, this analysis shows that CrIS and TROPOMI provide complementary information on CO, enhancing the understanding of CO distribution during the wildfires.

The dissertation then focuses on a detailed study of fire-specific PM2.5 emissions, employing the GEOS-Chem chemical transport model. This section reveals that the 2020 wildfires resulted in an unprecedented emission of 328 million tons PM2.5 across the Western U.S., far exceeding the total emissions of the previous three years. It highlights that California alone was responsible for 18% of the six-year total PM2.5 emissions in 2020. The study revealed that certain locations in the Western United States experienced prolonged periods of hazardous air quality conditions that exceeded the EPA's 24-hour limits for more than 40 days. In total, there were 492 million person-days of exposure to poor air quality in the Western U.S. in 2020. This study emphasizes the importance of distinguishing between emissions from fire-specific smoke and smoke from multiple sources due to the higher toxicity of wildfire smoke.

The aim of this research is to highlight the importance of understanding the impact of wildfires on the environment and populations, especially considering their increasing frequency and severity.