Temporal MLP Network for PM 2.5 Estimation

Particulate Matter (PM) 2.5 is a critical factor to measure in the environment. However, accurate PM 2.5 measurement relies on special devices, which leads to limited spatial coverage. This paper focuses on the development of PM 2.5 estimation based on satellite images. We have developed a deep temporal multiple layer perception (MLP) neural network based on various satellite imaging inputs captured from different time periods together with meteorological inputs. In addition, the temporal MLP simultaneously learns both PM 2.5 and PM 10 which can improve the performance of each task. Experiments demonstrate that our method outperforms other machine learning algorithms used in PM 2.5 estimation.