For the past few years, the aerosol pollution in Shanghai is getting worse, leading to the haze weather and air quality deterioration as well. This paper is a comparative study on reliability and applicability of the spatial interpolation methods for the regional air quality evaluation, the daily data of the air quality indices (AQI, PM<sub>2.5</sub> and PM<sub>10</sub>) comes from the Shanghai automatic monitoring stations, which helps us to compare the different interpolation methods in testing and measuring various air pollutants in Shanghai. Inverse Distance Weighted (IDW), Spline and Kriging were respectively used for the calculation of spatial interpolation. With the aforementioned methods we can compare the interpolation methods and gain the four indices, such as the mean error (ME), the mean relative error (MRE), the root mean squared error (RMSIE), and the correlation coefficient (R<sup>2</sup>) , which help us make a comprehensive comparative analysis of the spatial interpolation methods for the Shanghai regional air quality. The result shows that the IDW method is optimal for PM<sub>2.5</sub> concentration and AQI, while Kriging Method is the Best for the concentration of PM<sub>10</sub>. We can also find that Seasonal characteristics and different spatial aggregation characteristics have a significant impact on the interpolated results of air pollutants.
With the rapid development of urbanization, the dynamic evolution of urban expansion has become one of the hot topics throughout the world. Thus, modeling and predicting the urban expansion in the future is one of the effective methods for the study of urban growth. Based on the rapid urbanization in Shanghai, our study uses four years of land use data (1995, 2000, 2005 and 2010), DEM and two years of traffic roads data (2005 and 2010) to obtain the optimal parameters of urban growth through model calibration. And the results of calibration were used to simulate and predict the land use change in 2040 under different scenarios of excluded layers. The results show that the urban growth in Shanghai is more often grow along the edge of existing urban centers and the transportation network with the relatively high spread coefficient (43) and road coefficient (66), while the dispersion, breed and slope coefficient are relatively low. The SLEUTH simulation with these five parameters possessed satisfactory capability of predicting land use changes with the kappa coefficient of 0.8628 and an appropriate Lee-Sallee index of 0.8139. The result shows that the urban areas in Shanghai increase significantly in 2040, while the rural area, grass and other construction area are decreased. Therefore, SLEUTH can better predict the spatial changes of land use and provide some theoretical support and decision-making basis for the urban-rural planning in Shanghai.