[Research on hyperspectral information parameters of chlorophyll content of rice leaf in Cd-polluted soil environment].

The remote sensing pollution mechanism in Cd-polluted soil is discussed depending on the research into the chlorophyll content of Cd-polluted rice leaf in the present paper. The response models of remote sensing information parameters, which reflected chlorophyll content variety of rice canopy with soil Cd pollution degree, were established based on Hyperion satellite data and a great number of ground experiment data. To extract sensitive remote sensing parameters for Cd pollution, multiple discriminant analysis (MDA) was applied to the reflectivity of 447-925 nm in Hyperion data and five remote sensing information parameters, including MCARI, NPCI, RVSI, NDVI and Depth671. Experiments indicated that MCARI is the most sensitive parameter to the chlorophyll content of Cd-polluted rice, whose response coefficient is 0.59. In the extent of 1.0-2.0 mg x kg(-1) of Cd pollution concentration in soil, MCARI curve shows a small decline. In the extent of 2.0-3.0 mg x kg(-1) of Cd pollution concentration in soil, MCARI curve is horizontal. Above 3.0 mg x kg(-1), MCARI shows a significant drop trend and so on. The research results showed that the chlorophyll content is a good indicator for nutrition situation of plant, capacity of photosynthesis and each developmental stage. And the chlorophyll remote sensing parameters in crop have a great significance for monitoring heavy metal pollution This study will help improve the precision and limitation of statistical methods and provide theoretical basis for and technical approach to monitoring soil Cd pollution in large area using hyperspectral remote sensing technology. However, the precision of pollution model needs to be improved.

[Socio-economic Factors Influencing the Spatial Distribution of PM2.5 Concentrations in China: An Exploratory Analysis].

In recent years, the PM2.5 pollution in China has become a top environmental and health concern, involving the characterization of healthy effects over a broad spatial area with uneven geographical distribution. This research aims to explore the influential factors for the PM2.5 distribution from a socio-economic perspective, based on the observations from China's 1497 monitoring sites in 2015. First, the Moran's I index and the local indicators of spatial association (LISA) were computed to outline the distribution of PM2.5 on a national scale using provincial-level divisions. Second, the correlation between the spatial distribution of PM2.5 and socio-economic factors were analyzed by ordinary least squares (OLS) and geo-weighted regression (GWR) models. The results indicated that the GWR model explained the causal relationships better. Generally, Beijing, Tianjin, and Hebei had peak levels of PM2.5, while Guangxi, Sichuan, and several other southern provinces had the lowest levels. Particularly, forest coverage rate and electricity consumption per capita were negatively correlated with the concentration of PM2.5. In this study, the vehicle ownership per capita proved to be the most significant factor that positively contributed to the concentration.