Abstract:The air quality in China, particularly the PM 2.5 (particles less than 2.5 μm in aerodynamic diameter) level, has become an increasing public concern because of its relation to health risks. The distribution of PM 2.5 concentrations has a close relationship with multiple geographic and socioeconomic factors, but the lack of reliable data has been the main obstacle to studying this topic. Based on the newly published Annual Average PM 2.5 gridded data, together with land use data, gridded population data and Gross Domestic Product (GDP) data, this paper explored the spatial-temporal characteristics of PM 2.5 concentrations and the factors impacting those concentrations in China for the years of 2001-2010. The contributions of urban areas, high population and economic development to PM 2.5 concentrations were analyzed using the Geographically Weighted OPEN ACCESS Int. J. Environ. Res. Public Health 2014, 11 174Regression (GWR) model. The results indicated that the spatial pattern of PM 2.5 concentrations in China remained stable during the period 2001-2010; high concentrations of PM 2.5 are mostly found in regions with high populations and rapid urban expansion, including the Beijing-Tianjin-Hebei region in North China, East China (including the Shandong, Anhui and Jiangsu provinces) and Henan province. Increasing populations, local economic growth and urban expansion are the three main driving forces impacting PM 2.5 concentrations.

Epidemiological studies around the world have reported that fine particulate matter (PM2.5) is closely associated with human health. The distribution of PM2.5 concentrations is influenced by multiple geographic and socioeconomic factors. Using a remote-sensing-derived PM2.5 dataset, this paper explores the relationship between PM2.5 concentrations and meteorological parameters and their spatial variance in China for the period 2001–2010. The spatial variations of the relationships between the annual average PM2.5, the annual average precipitation (AAP), and the annual average temperature (AAT) were evaluated using the Geographically Weighted Regression (GWR) model. The results indicated that PM2.5 had a strong and stable correlation with meteorological parameters. In particular, PM2.5 had a negative correlation with precipitation and a positive correlation with temperature. In addition, the relationship between the variables changed over space, and the strong negative correlation between PM2.5 and the AAP mainly appeared in the warm temperate semihumid region and northern subtropical humid region in 2001 and 2010, with some localized differences. The strong positive correlation between the PM2.5 and the AAT mainly occurred in the mid-temperate semiarid region, the humid, semihumid, and semiarid warm temperate regions, and the northern subtropical humid region in 2001 and 2010.

No abstract

The Donghuantuo coal mine is geologically unusual, with 60 normal faults, 18 reverse faults, and 1 syncline. The coal seam floor is highly fractured and the fractures act as conduits for groundwater, which flows from the Ordina limestone aquifer into the no. 12 coal seam. From 2005 to 2010, there were 7 water-inrush disasters through the floor of this coal seam. The largest waterinrush event exceeded 63 m 3 /min; there are five points where the water-inrush continues to exceed 1.0 m 3 /min. Comprehensive modeling of the probability of water-inrush through the floor is required to reduce the likelihood and severity of such events. The water-inrush situation was assessed using a GIS-based Bayesian network (BN). In the developed BN-GIS model, the geometry of the coal mine working face was incorporated in suitable detail and resolution. The results of the modeling compared well with field water-inrush observations. Based on documented water-inrush events, the accuracy of the fit of the model data is 83.4 %, and the probability of making an incorrect prediction is less than 0.5, which means that using this method could significantly enhance coal production at the mine.

No abstract