Cornell team develops computationally efficient machine learning models for hyperlocal models of PM2.5 concentrations
17 January 2023
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... Read more →