Application of GIS technology on atmospheric environment simulation research in Hefei, Anhui province, China

The GIS technology is a good tool to improve the spatial distribution of emissions, which can improve the model performance indirectly. This article chose An Hui Province with Hefei City as the research area. In this study, the GIS data, including information of land use, road, population in Anhui province, was used to optimize the spatial distribution of emission sources. The 1km*1km population data (cited) is used to optimize the anthropogenic emissions, while the agricultural NH3 is distributed followed the coverage of agriculture from MODIS land use data. After optimization, the spatial distribution of the regional emission sources turned into a complicated pattern that has the characteristics of GIS information from a simple pattern. The results of the Air Quality Model CMAQ that is driven with the emission sources before optimization and that after optimization was preliminarily validated with the station observation data. The validation results show: 1.The simulation results of CO and PM2.5 by optimized emission sources had some improvement comparing with the simulation results by normal emission sources when they were validated with observation data. The simulation results of PM2.5 in Yaohai were chosen as an example. The mean bias of simulation after optimization is -6.55 and that of simulation before optimization is -16.84.And mean error of simulation after optimization comparing with simulation before optimization is reduced from 22.07 to 13.69, and correlation coefficient R is increased from -0.22 to 0.23. 2. The simulation results are abnormal in a given period, mainly in the rush hour, and need to be corrected in the later period. The results show that the accuracy of simulation results after spatial optimization with GIS tools is improved, and GIS data has some effects on improving the results of atmospheric environment simulation in Hefei City and Anhui province. Subsequent work will be to refine the GIS data, to improve the model grid resolution and to add more information about emissions, in order to improve the precision of the simulation, and to add satellite observation data to further verify the conclusion.