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We propose a new method to estimate surface-level particulate matter (PM) concentrations by using satellite-retrieved Aerosol Optical Thickness (AOT). This method considers the distribution and variation of Planetary Boundary Layer (PBL) height and relative humidity (RH) at the regional scale. The method estimates surface-level particulate matter concentrations using the data simulated by an atmospheric boundary layer model RAMS and satellite-retrieved AOT. By incorporation MODIS AOT, PBL height and RH simulated by RAMS, this method is applied to estimate the surface-level PM2.5 concentrations in North China region. The result is evaluated by using 16 ground-based observations deployed in the research region, and the result shows a good agreement between estimated PM2.5 concentrations and observations, and the coefficient of determination R 2 is 0.61 between the estimated PM2.5 concentrations and the observations. In addition, surface-level PM2.5 concentrations are also estimated by using MODIS AOT, ground-based LIDAR observations and RH measurements. A comparison between the two estimated PM2.5 concentrations shows that the new method proposed in this paper is better than the traditional method. The coefficient of determination R 2 is improved from 0.32 to 0.62.

PM2.5 has become one of the primary pollutants in China, and heavy smogs usually occurred over the eastern China in recent years. Some scientific issues such as the sources of the fine particles, the formation mechanism of the severe pollutions and the interactions between air quality and regional climate change, are hot points in the field of air quality monitoring and modeling studies. Satellite remote sensing has been recognized as an important and effective approach to analyze these scientific issues. We have developed a series of retrieval algorithms to assess the spatial and temporal variations of haze pollutions, aerosols, gaseous pollutants, and greenhouse gases (GHGs). This paper puts forward three directions of future research in atmospheric environment remote sensing in China. The first one is to improve the satellite retrieving methods of atmospheric components in order to better quantify their amounts over China. The second one is to enhance the performance of atmospheric models by refining emission inventory and by assisting the source apportionment of pollutants based on satellite data. So far, there are still many unclear problems concerning atmospheric emissions and formation processes of the haze clouds, which may introduce large uncertainties in model simulations. The last one is to advance our understanding of the complex interactions between haze clouds and regional climate change in the eastern China.