CAREER: Mechanistically Informed Modeling of 3D Urban Morphology and Real-Time Exposure to PM2.5

Abstract This CAREER project, led by Dr. Tianjun Lu, aims to advance high-resolution, interpretable air pollution exposure modeling by uncovering how built environment features and real-time dynamics influence PM2.5 concentrations in urban settings. Existing models often overlook the role of 3D built environment, traffic variability, and localized meteorological patterns in shaping exposure at fine scales. To address this gap, Dr. Lu will develop annual and hourly land use regression (LUR) models that integrate 3D morphological data, real-time traffic, and weather metrics, with empirical validation supported by a WiFi-enabled sensor network deployed across multiple cities in the United States. The project also features a robust educational component: undergraduate students will engage in hands-on air quality monitoring and data analysis through CPH 320 coursework and an additional built environment-related course, while public health and urban planning professionals from Lexington KY will participate in interactive workshops using a geospatial dashboard co-developed with graduate students. These activities involve students and professionals solely as participants in educational training, not as human research subjects for health-related data collection. Together, the research and educational activities will generate a scalable, mechanism-informed exposure modeling framework and a replicable model for translating air quality science into actionable knowledge for students, professionals, and communities.