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.
Fine particles less than 2.5 microns in aerodynamic diameter (PM2.5) has found to threat human health and environment. The formation and diffusion of PM2.5 are closely related to the meteorological elements. Many scholars have studied the influence mechanism of meteorological elements to PM2.5. However, most of these researches mainly focus on some serious short-term atmospheric pollution, long-term research is rare. In addition, the impact of meteorological elements on PM2.5 has regional characteristics. This paper takes Shanghai as study area, applying PM2.5 concentrations from China environmental monitoring stations and reanalysis meteorological data from 2014 to 2016.. Through qualitative and quantitative analysis, this paper got the change characteristics of PM2.5 in Shanghai in recent three years, and the correlation between PM2.5 and relative humidity, temperature, wind and boundary layer height. Relative humidity is positively correlated with PM2.5, while U wind is negatively correlated with PM2.5. And there are seasonal differences in the correlation between PM2.5 and temperature, V wind and boundary layer height.
People in Huaihe River Basin and Shanghai have been suffering from severe air pollution of nitrogen dioxide and sulfur dioxide due to the development of heavy industry. Traditional ambient monitoring station measurements can provide real-time accurate data, but it is limited due to the less number of monitoring sites. Satellite observation data from remote sensing can provide a wide range opollutants concentrations in long-time sequence. Top-down approaches based on satellite data can be effectively applied to estimate the ground concentrations of pollutants. In this paper, the tropospheric pollutants columns from the Ozone Monitoring Instrument(OMI) were used to analyse the seasonal variation of NO<sub>2</sub> and SO<sub>2</sub> in 2015. Moreover, the ground-level NO<sub>2</sub> and SO<sub>2</sub> concentrations of the Huaihe River Basin and Shanghai at this time were estimated by the data and meteorological data. The results show that: the concentrations of NO<sub>2</sub> and SO<sub>2</sub> are highest in winter, and high-value areas are mainly located in Shandong and Northern Henan. Estimating the ground-level NO<sub>2</sub> and SO<sub>2</sub> concentrations based on satellite observations is reliable with the validation R<sup>2</sup> 0.48 and 0.47 respectively. Finally, The spatial distribution of satellite-derived annual mean NO<sub>2</sub> and SO<sub>2</sub> has a similar characteristics to the satellite columns.