Cornell engineers have developed machine learning models to simplify and reinforce models to calculate the fine particulate matter (PM2.5) contained in urban air pollution. Described in a paper in the journal Transportation Research Part D: Transport and Environment, the modeling approach has low data requirements and is computationally efficient. Previous.