Agricultural nonpoint source (NPS) pollution has been the most important threat to water environment quality. Understanding the spatial distribution of NPS pollution potential risk is important for taking effective measures to control and reduce NPS pollution. A Transformed-Agricultural Nonpoint Pollution Potential Index (T-APPI) model was constructed for evaluating the national NPS pollution potential risk in this study; it was also combined with remote sensing and geographic information system techniques for evaluation on the large scale and at 1 km2 spatial resolution. This model considers many factors contributing to the NPS pollution as the original APPI model, summarized as four indicators of the runoff, sediment production, chemical use and the people and animal load. These four indicators were analysed in detail at 1 km2 spatial resolution throughout China. The T-APPI model distinguished the four indicators into pollution source factors and transport process factors; it also took their relationship into consideration. The studied results showed that T-APPI is a credible and convenient method for NPS pollution potential risk evaluation. The results also indicated that the highest NPS pollution potential risk is distributed in the middle-southern Jiangsu province. Several other regions, including the North China Plain, Chengdu Basin Plain, Jianghan Plain, cultivated lands in Guangdong and Guangxi provinces, also showed serious NPS pollution potential. This study can provide a scientific reference for predicting the future NPS pollution risk throughout China and may be helpful for taking reasonable and effective measures for preventing and controlling NPS pollution.

The Gannan region is the largest navel orange planting area in the world and has the largest production in China. However, about 5 million tons of navel orange waste (NOW) produced annually. NOW has a great environmental risk because of its high content of organic matter and moisture. Anaerobic digestion of NOW with high nitrogen content waste is a promising alternative to treat these wastes. The effect of swine manure (SM), waste active sludge (WAS) as co-substrates and different mixing ratio were examined in three batch-scale studies. In the first investigation, co-digestion of NOW with SM resulted low methane yield and high concentration of VFAs. In the second investigation, NOW was co-digested with WAS, the methane yield was improved by 260% when the mixing ratio of NOW to WAS (VS/VS) was shifted from 1:2 to 2:1. In the third investigation, the co-digestion of NOW with SM and WAS was conducted. Co-digestion of three substrates has higher methane yield than that of previous two studies, with the exception of equal amounts of NOW with co-substrates (mixing ratio of NOW to SM to WAS was 2:1:1). The highest methane yield of all experiments was 0.20 m3 kg-1VS added while the mixing ratio of NOW to SM to WAS was 1:2:1. It seemed to obtain stable digestion performance, the mixing ratio of co-substates to NOW should not be lower than 1:1. WAS was a better co-substrate than SM, as WAS was capable to supply more organic nitrogen to create positive synergistic effects.

For nuclear power plant, station black-out (SBO) is the events that contribute significantly to the level-I core damage risk. For an SBO, it is assumed that both the off-site power and on-site diesel generators fail to supply alternating-current power for the plant systems. SBO induced steam generator tube rupture (SGTR) is a concern because the steam generator (SG) tubes are parts of the reactor coolant pressure boundary and failure of the SG tubes may lead to fission products bypassing the containment. The SG tube integrity may be challenged by high temperature and high pressure conditions and may have a potential to fail due to creep rupture. This study focuses on the probability of SBO induced SGTR accidents under the station blackout (SBO) with RCS integrity, seal LOCA and steam relief valves remaining stuck open for the reference plant. At last, the sensitivity of the tube thick is studied.