Cornell team develops computationally efficient machine learning models for hyperlocal models of PM2.5 concentrations - Green Car Congress

Cornell engineers have developed machine learning models to simplify and reinforce models to calculate the fine particulate matter (PM2.5) 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 methods to gauge air pollution were cumbersome and reliant on extraordinary amounts of data points. Older models to calculate particulate matter were computationally and mechanically consuming and complex. But if you develop an easily accessible data model, with the help of artificial intelligence filling in some of the blanks, you can have an accurate model at a local scale.