Learning Global Variations in Outdoor PM_2.5 Concentrations with Satellite Images

The World Health Organization identifies outdoor fine particulate air pollution (PM2.5) as a leading risk factor for premature mortality globally. As such, understanding the global distribution of PM2.5 is an essential precursor towards implementing pollution mitigation strategies and modelling global public health. Here, we present a convolutional neural network based approach for estimating annual average outdoor PM2.5 concentrations using only satellite images. The resulting model achieves comparable performance to current state-of-the-art statistical models.

Speaker bio: - Kris Y Hong is a research assistant and prospective PhD student in the Weichenthal Lab at McGill University, in Montreal, Canada. His interests lie in applying current statistical and machine learning techniques towards solving humanitarian and environmental challenges. Prior to joining McGill, he was a data analyst at the British Columbia Centre for Disease Control while receiving his B.Sc. in Statistics from the University of British Columbia.