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Emerging pollutants are frequently detected in surface water, threatening the regional aquatic ecosystem and human health. Due to their complex types and large differences in risk and toxicity, research based on the comprehensive assessment of the pollution characteristics to determine the new priority pollutants remains incomplete. This study established a multi-criterion scoring method targeting 41 emerging pollutants with the goal of protecting aquatic organisms and human health, using five key indicators including environmental exposure level, persistence, bioaccumulation, ecological risk, and health risk of pollutants. The emerging pollutants were screened and identified in the surface water of Tianjin. The priority levels of different congeners were divided, and the ecological and health risks of pollutants in the priority control list were evaluated. The results showed that 41 emerging pollutants were generally detected in the study area. The average concentration of sulfonamide antibiotics (SAs) and other drugs (Others) were the largest, with 200.04 ng·L

Based on environmental statistics data from 1993 to 2005, spatial distribution and temporal tendency of the environmental pollution and destruction accidents and their external causes were analyzed by using GIS and non-parametric correlation methods. It was concluded that (1) during the study period, annual environmental pollution accidents was maximally 3001 times in 1994 and minimally 1406 in 2005, while the frequency decreased in general. In addition, water and air accidents occupied the most; (2) environmental pollution and destruction accidents centralized in southeast and middle parts of China, mainly in Hunan, Sichuan, and Guangxi; (3) factors including population, GDP, company number and industrial waste water discharge had positive impacts on frequency of environmental pollution and destruction accidents, while in developed provinces the frequency was only correlated with company number.

Analytic hierarchy process and a specialist investigation were applied to value the three PCBs treatment technologies qualitatively and quantitatively, in which the environmental, technological, social and economic factors were considered. The most important factor is thought to be environmental impact. Incineration is proved to be the most suitable technology in this period according to the research. For the high concentration of PCBs pollutants, or the low concentration of PCBs pollutants with good economic performance and large quantity of waste, incineration is considered the best. For the low concentration of PCBs pollutants in the area with bad economic performance and with little quantity of waste, cement kiln and landfill are thought to be suitable. It is also suggested that pollutants be treated at the nearest place. What's more, the measures to improve the three technologies are discussed and the policy comments on PCBs treatment are presented.

Topsoil samples around the Guanting Reservoir ranging from 2 - 10km were measured for heavy metals concentrations. GIS and geostatistical techniques were applied to analyze the special variation and eco-environmental risk of heavy metals. The results show that Cd is the major contamination in this area, 3.4 fold higher than the national background level, with a mean of 0.68 mg/kg and a standard deviation of 0.17. The area in which Cd concentration is higher than the second level of national standard accounts for about 40% of the whole study area. Spacial variation of heavy metals (except Ni) mainly is induced by the stochastic factors-mostly anthropogenic activities, e.g. fertilizer application, cultivation and planting modes, etc. The spatial pattern of heavy metals approximately declines in the W-N and N-S direction, with the highest value in the region between Yanghe River and Huailai County. The complex contamination index of heavy metals has the same distribution as their concentrations. However, the complex eco-risk index with higher value mainly occurs in vicinities of Huailai County, Yanqing County and Beixinpu town. It suggests that the town with higher population density and industrialization will induce both environmental pollution and ecological risk.