Spatial modelling of particulate matter air pollution sensor measurements collected by community scientists while cycling, land use regression with spatial cross-validation, and applications of machine learning for data correction

Fine particulate matter air pollution is a global issue; cycling is a global activity. In our paper, particulate matter less than 2.5 μm (PM2.5) air pollution data obtained by community scientists while cycling is used to develop high-resolution spatial air pollution maps. Mapping is completed using a land use regression model for Charlotte, North Carolina. The air pollution observations were obtained with a low-cost sensor. We evaluated the …