Retrieval Of AOD And PM2.5 Concentrations Over Urban Areas Using MODIS Data

Atmospheric aerosols play an important part in the Earth’s radiation balance as well as global climate change, aerosols also have very important impact on environment as well as human and other organisms’ health. Now, people constantly improve their awareness, especially inhalable aerosol grows in importance, detecting the aerosol optical depth (AOD) and PM2.5 concentration precisely is very important for ambient air quality as well as climate conditions. Detection of AOD by satellite and remote sensing is currently one of the hotest issues of domestic and foreign research, diffierent from the traditional monitoring method, this method has much more advantanges, for emample wide area coverage, fast and convenient etc. So it is possible for people to know the regional changes of AOD real time over large area. Now, detection aerosol by RS technology has reached a high level in marine and dense vegetation land areas, but result is not ideal for urban areas, the higher surface reflectance in urban areas is a bottleneck of AOD retrieval.Focus on the high surface reflectance and low accuracy of the AOD products of urban areas, this paper propose an algorithm coupled with surface reflectance to get red band surface reflectance, based on Dens Dark Vegetation algorithm and geometrical optics model theory, to distinguish urban reflectivity from other targets. Considering the appropriate aerosol model which adapt to season and other proper parameters, this paper uses 6S model to establish look-up table, thus retrieve AOD for urban as well as other high reflectance areas. This paper take Shenyang region as pilot area, then retrieve the AOD and PM2.5 concentration of Shenyang in 2013 based on MODIS data, thus get lkm resolution distribution map, and then analyzed the results in spatial, intensity and temporal. At last, real-time monitoring data from the ground monitor station is used to verify the outcome, the consistency is 0.9 when the weather is sunny. The research shows that this algorithm has relatively higher precision and certain universality. This method has better applicability to retrieve AOD and PM2.5 concentration by remote sensing in Shenyang and Liaoning Provience, and owes practice and reference significance, and it has a high value in terms of atmospheric environment monitoring.