Outdoor air pollution, especially particulate matter with a diameter of 2.5 microns or less, is a major threat to public health. Pollution levels in cities often vary among neighborhoods. Building design, street layout, traffic, and weather affect how pollution spreads and accumulates. However, current air pollution models often cannot fully explain why some places become pollution hot spots or how city design could reduce exposure. This project will study how the shape of cities, such as building heights and street layouts, affects air pollution levels. The research will combine air quality measurements, geospatial and traffic data, and artificial intelligence (AI) models to understand where and when pollution accumulates. The project will produce accessible tools to help city planners and engineers to identify risks and explore mitigation strategies. Students will help deploy air sensors and analyze the data while workshops will train planners and public health officials to use these tools to support healthier cities. This project will develop a mechanistically-informed modeling framework that integrates 3D urban morphology data, real-time environmental observations, and interpretable AI to quantify how the built environment influences PM2.5 exposure. It will include two interconnected objectives. First, annual air quality models will be developed for six major U.S. cities by combining standardized morphological indicators from the Local Climate Zone (LCZ) framework (e.g., sky view factor, building surface fraction) with regulatory monitoring and low-cost sensor data to train interpretable machine learning models. These models will link urban structural features to pollution concentrations and use Shapley Additive explanations (SHAP) to interpret the nonlinear relationships. Second, high-resolution hourly exposure models will be developed in Lexington, Kentucky using a dense sensor network, mobile monitoring, and time-series traffic and meteorological data to