Spatio-temporal modeling of PM risk mapping using three machine learning algorithms.

Urban air pollution is one of the most critical issues that affect the environment, community health, economy, and management of urban areas. From a public health perspective, PM is one of the primary air pollutants, especially in Tehran's metropolis. Owing to the different patterns of PM in different seasons, Spatio-temporal modeling and identification of high-risk areas to reduce its effects seems necessary.