, scientific and technological advancement has propelled rapid development and metropolitan expansion in urban centers across the world. With populations shifting to urban areas for better opportunities and desired services, urban morphology is changing to accommodate this growth. However, population dynamics and increased urbanization has resulted in densely packed housing, crowded streets, and evolution of physical and chemical transportation mechanisms, all which contributes to the heat accumulation phenomenon known as the Urban Heat Island (UHI) effect. Characterized as an important aspect in urban environments, UHI can influence local climate, increase the demand for energy resources needed for cooling, and affect the livelihoods and health of individuals that reside within the urban boundaries. In addition to the UHI effect, urban areas experience significantly higher levels of pollution due to various industrial and human activities relative to outlying areas. The interaction between UHI and its influence on pollutant concentrations, specifically ozone, is of importance, given the projection of temperature increases with climate change. Past studies have shown a connection between UHI and O3 production, however, none have explored the UHI-O3 effect within the context of specific urban infrastructure configurations, layouts or proximities. This is partly due to a lack of high-resolution spatial data for air quality and specific urban infrastructure. However, knowing the connection of urban morphology to UHI and enhanced O3 formation is essential to our understanding how to design resilient and sustainable urban environments and enact effective pollution mitigation measures. To assess the impact of the UHI on O3 formation, concentrations of Nitrogen Oxides (NOx), and O3, are extracted from the Chemical Transport Model, known as the Community Multiscale Air Quality Model (CMAQ), along with several meteorological variables from the Weather Research Forecasting (WRF) model. Focusing on the urban area of Atlanta, Georgia, the aggregated data underwent systematic variable selection using stepwise regression and the ideal predictors were then analyzed through multiple regression models and principal component analysis (PCA) to evaluate the relationship between modeled spatial ozone concentrations and meteorological variables in effort to understand the UHI effect. Initial results show spatial associations with elevated surface temperatures to O3. Further delineation with urban spatial density was conducted to assess the interaction with urban morphology.
