Levy, Jonathan I.Buckley, Laura2025-01-172025-01-172025https://hdl.handle.net/2144/496012025Ambient air pollution poses significant health risks, with extensive research linking pollutants like PM2.5, NO2, and O3 to increased mortality and morbidity. The complex interplay between these pollutants, their sources, and atmospheric dynamics creates challenges for effective air quality management. Moreover, sociodemographic inequities in exposure to air pollution persist across multiple geographic scales, with marginalized communities facing disproportionate burdens due to historical and present-day inequities. Recent technological advancements in remote sensing, chemical transport modeling, and data integration have dramatically improved our ability to characterize air pollution exposure at fine spatial scales, even in areas lacking traditional monitoring networks. This enhanced understanding is crucial as the world grapples with climate change, presenting a unique opportunity to build solutions that simultaneously improve air quality, reduce existing inequities, and mitigate the worst impacts of our shifting climate.This dissertation explores the complex interplay between air pollution, climate change mitigation strategies, and the magnitude and distribution of health equity outcomes through three interconnected studies, each addressing fundamental aspects of air pollution exposure and health risk modeling at different geographic scales. The research examines the health benefits and equity implications of transportation emissions reduction scenarios and vehicle electrification strategies in the United States, while also providing insight regarding the health impacts of NO2 exposure in Mexico. This work collectively provides insight on alternative approaches for air pollution exposure modeling and for characterization of equity, helping to illuminate pathways for designing more impactful, equitable, and health-enhancing policies. In Chapter Two, we explore the equity implications of various transportation emissions reduction scenarios in the northeastern United States, focusing on four distinct equity constructs: racial/ethnic exposure inequities, benefits to environmental justice communities, distribution of benefits among participating states, and rural-urban share of benefits. Using advanced chemical transport modeling, we analyze scenarios for reducing directly emitted fine particulate matter across 12 Northeast states and the District of Columbia, revealing tradeoffs among different equity constructs. Our findings highlight that scenarios resulting in greater reductions in population-weighted primary PM2.5 exposure were generally those centered in states with large urban areas, leading to greater reductions in racial/ethnic exposure inequities but higher between state or rural/urban inequality. Conversely, scenarios targeting uniform percentage emission reductions from trucks better address rural/urban inequalities but lead to smaller reductions in racial/ethnic inequity. In Chapter Three, we evaluate the impacts of vehicle electrification strategies in the Boston metropolitan area of the Northeast United States, focusing on their potential to reduce emissions, improve health outcomes, and address existing exposure and health inequities among racial and ethnic groups. Using high-resolution chemical transport modeling, we examine a set of scenarios targeting different vehicle types within unique regions of the metropolitan area. Our findings highlight that while targeting larger vehicle fleets in suburban areas yielded greater overall health improvements, concentrating efforts on heavy-duty trucks and high-emitting vehicles in urban core areas proved most effective in reducing inequities on a per-vehicle basis. Our findings underline the importance of considering multiple pollutants and utilizing detailed health data in policy decision-making. The final study in Chapter Four assesses the public health burden of NO2 exposure in Mexico, highlighting uncertainties in health impact assessment modeling. This work utilizes two globally modeled ground-level NO2 datasets alongside TROPOMI satellite-derived tropospheric NO2 data to analyze spatial patterns in the pollutant across Mexico and their effects on population exposure estimates and health impact calculations, with different concentration-response functions also evaluated. The analysis reveals tens of thousands of premature deaths annually attributable to ambient NO2 exposure across Mexico annually. The study finds that health estimates vary more with the choice of concentration-response function at the national scale than the exposure dataset, though it is important to note only two exposure datasets were compared. Notable differences emerge between these exposure datasets, however, at the state level, particularly near Mexico City. While demographic patterns are consistent, differences are observed for smaller subpopulations like Indigenous language speakers. This work describes the notable health impacts of NO2 across Mexico, which were previously challenging to define due to limited air monitoring networks. It also highlights the complexities involved in selecting the most appropriate inputs for air pollution health impact assessments at different geographic scales. In conclusion, this dissertation underscores the importance of applying air pollution modeling techniques that fit both the pollutants of interest and the policy context, with heightened importance when considering local or regional contexts. Although the three chapters encompass diverse geographic scales and methodological frameworks, a recurring theme relates to the potential tradeoffs between overall public health improvements and targeted equity gains. Analyses that elucidate these tradeoffs and describe the attributes of policies that perform best across multiple endpoints will be maximally informative. These insights lay a groundwork for future research and policy development that simultaneously address air quality, climate change, and health equity.en-USAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Environmental healthHealth impact assessmentThesis/Dissertation2025-01-160000-0001-8054-2392