Establishment of PM2.5 Prediction Model Based on MAIAC AOD Data of High Resolution Remote Sensing Images

Abstract

Long-term exposure to seriously polluted atmospheric environment is closely related to a variety of human diseases. The long-term and continuous monitoring of PM[Formula: see text] (the aerodynamic particle diameter is less than or equal to 2.5[Formula: see text][Formula: see text]m) is of great significance as it is one of the major air pollutants. Because of the small number of PM[Formula: see text] monitoring stations in China, it is very difficult to accurately estimate the continuous scale of air pollution, but PM[Formula: see text] concentration can be acquired by using the aerosol products inversed by remote sensing data. However, due to the limitation of the inversion algorithm, the traditionally used moderate resolution imaging spectroradiometer (MODIS) aerosol products cannot meet the precision and practical requirements in both spatial resolution and effective numerical coverage. This paper adopts MAIAC AOD, i.e. the aerosol optical depth (AOD) products produced by using the new multi-angle implementation of atmospheric correction (MAIAC) algorithm, and we take the Beijing–Tianjin–Hebei region in China as the research area. Based on the neural network algorithm, the MAIAC AOD data is mainly used and the meteorological parameter data is supplemented to establish the PM[Formula: see text] concentration prediction model to obtain the PM[Formula: see text] concentration distribution map. The prediction accuracy, the effective numerical coverage and spatial resolution have been greatly improved.