Mary Angelique’s dissertation, “Climate change, inequality, and urbanization as drivers of air pollution,” explores the effects of these social issues on tropospheric ozone and nitrogen dioxide pollution. Demetillo is passionate about the application of data science methods and insight to address critical issues especially environmental injustice. With training and foundation in physical sciences, she currently analyzes high-resolution aircraft and satellite datasets to understand air pollution inequality within cities. Prior to joining the Jefferson Scholars Foundation community, Demetillo was Data Science Institute Presidential Fellow, a Virginia Space Grant Consortium Research Fellow, and most recently, a recipient of the NASA FINESST research grant.
Climate change, inequality, and urbanization as drivers of air pollution
Reactive trace gases comprise just a fraction of the Earth’s atmosphere, yet their chemistry strongly influences many atmospheric processes, air pollution levels, Earth’s climate, and human and ecosystem health. Tropospheric ozone (O3) is a greenhouse gas, an oxidant, and a harmful air pollutant. Nitrogen oxides (NOx) drive O3 production, broadly control the tropospheric oxidative capacity by regulating hydroxyl radical lifetimes, and, in cities, may be harmful pollutants themselves. While air pollution levels have decreased across the U.S., and in many cities around the world, there are still major uncertainties in the sources, the processes influencing spatiotemporal variability, and the impacts of O3 and NO2 pollution. In this dissertation, I explore the effects of three social issues that influence O3 and NO2 chemistry and composition: climate change, inequity, and urbanization. Specifically, I investigate: (1) impacts of severe drought on the chemical production and loss of high O3; (2) spatial and temporal variability in neighborhood-level NO2 burdens and their drivers in U.S. cities with community sociodemographics, including the advance of new analytical tools; and (3) sources and consequences of NO2 spatial variability in the rapidly growing city of Dakar, Senegal, in part through application of Deep Neural Networks (DNNs) to improve land-use regression modeling.