Human exposure to atmospheric aerosols is increasingly recognized as the leading global environmental determinant of health and longevity. However, ground-based monitoring remains sparse in many regions of the world. Deep learning offers immense potential to advance understanding of air quality by leveraging large satellite remote sensing datasets. However, the spatial heterogeneity and autocorrelation of ground-based measurements pose challenges to the training and testing of algorithms for true out of sample prediction. Thus, algorithms benefit from additional process-based constraints from a chemical transport model and targeted ground-based measurements of aerosol chemical composition. This talk will highlight recent advances in applying deep learning by building on satellite remote sensing, global modeling, and ground-based measurements to improve understanding of air quality and planetary health from global toward urban scales