Abstract:

Results demonstrate that the proposed methodologies substantially improve the computational feasibility of Bayesian source inversion while preserving predictive accuracy. Furthermore, the uncertainty propagation analyses reveal that building-height uncertainty can significantly affect urban flow structures, pollutant concentration distributions, and associated environmental assessments. Collectively, this work establishes a unified uncertainty-aware framework that addresses both inverse and forward urban air pollution modeling problems, advancing the state--of--the--art in source identification, urban morphology characterization, and uncertainty propagation. The proposed framework supports more reliable environmental decision-making, urban planning, and air quality management in complex urban environments.