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Aerosol Optical Depth (AOD) is one of the most important parameters of aerosol and a key physical quantity to characterize atmospheric turbidity and air pollution. Accurate retrieval of AOD is of great significance for air quality assessment. However, the spatial resolution of the currently widely used Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products is too low to meet the application research of atmospheric environment at the regional scale. In 2013, China launched the Gaofen-1 (GF-1) satellite, which provides a new idea for AOD retrieval. In this paper, we apply the synergetic use of TERRA and AQUA satellite MODIS data to calculate the high-resolution AOD over Beijing based on the Synergetic Retrieval of Aerosol Properties algorithm (SRAP) and discussed scale conversion problems between AODs with different resolutions. To obtain the 100 m MODIS data, we use GF-1 wide-field-of-view data to downscale 1 km MODIS data based on mutual information method. The retrieved AOD has a spatial resolution of 100 m and can cover many land surface types. Preliminary validation was carried out with the Aerosol Robotic Network (AERONET) ground observation data. The correlation coefficient is about 0.88, and the root-mean-square error is about 0.15. Due to the high resolution of retrieved results, more detailed features can be provided in the spatial distribution. The experimental results show that the method has high precision, and further verification work is continuing. [ABSTRACT FROM AUTHOR]

With the economic growth and increasing urbanization in the last three decades, the air quality over China has continuously degraded, which poses a great threat to human health. The concentration of fine particulate matter (PM2.5) directly affects the mortality of people living in the polluted areas where air quality is poor. The Beijing-Tianjin-Hebei (BTH) region, one of the well organized urban regions in northern China, has suffered with poor air quality and atmospheric pollution due to recent growth of the industrial sector and vehicle emissions. In the present study, we used the back propagation neural network model approach to estimate the spatial distribution of PM2.5 concentration in the BTH region for the period January 2014-December 2016, combining the satellite-derived aerosol optical depth (S-DAOD) and meteorological data. The results were validated using the ground PM2.5 data. The general method including all PM2.5 training data and 10-fold cross-method have been used for validation for PM2.5 estimation (R2 = 0.68, RMSE = 20.99 for general validation; R2 = 0.54, RMSE = 24.13 for cross-method validation). The study provides a new approach to monitoring the distribution of PM2.5 concentration. The results discussed in the present paper will be of great help to government agencies in developing and implementing environmental conservation policy. [ABSTRACT FROM AUTHOR]