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The 10 typical wetlands in Xianlin New Townof Nanjing were classified into three categories, including rural wetland, suburban wetland, and urban wetland according to the influence of urbanization as well as the characteristics of wetland and LUCC of catchment regions. RDA was used to analyse the relationships between nitrogen and phosphorus in urban wetland and various types and patterns of land use. It was found that the water quality of the urban wetlands presented to be worse than that from rural wetlands, followed by sub urban wetlands. Secondly, according to all investigated wetlands, TP and TN turned out to be higher during the wet seasons than dry seasons. In addition, significant differences of TP were observed between wet and dry seasons for rural and suburban wetlands, and it was not so obvious for urban wetlands. However, the differences of TN was opposite to that of TP. Thirdly, factors affecting the water quality of wetlands were comprised of types and patterns of land use, and thus significant positive relationships were found between the concentrations of TN and TP and the impervious land, while negative correlations for meadows, woodlands and wetlands. What's more, higher remarkable differences were found in wetlands than those from meadows and woodlands. Regarding to patterns of land use, TP, TN concentrations were negatively correlated with the average patch shape in the dry and wet seasons, whereas positively relationships were observed for patch density and diversity index; furthermore, with refer to the impact of adjacent landscape, significant relationships were found between the content of TN and the patterns of land use and thus, a negative correlation in the wet season and a positive correlation in the dry season were observed, respectively.

Taking the core part of Yancheng national nature reserve as the study area, according to soil sampling analysis of coastal wetlands in April and May 2011 land the 2011 ETM + remote sensing image, the spatial difference characteristic of coastal wetlands soil moisture and salinity, and the relationship with vegetation under natural conditions, were investigated with the model of correspondence analysis (CCA), linear regression simulation and geo-statistical method. The results showed: Firstly, the average level of the soil moisture was fluctuating between 36.820% and 46.333% , and the soil salinity was between 0.347% and 1.328% , in a more detailed sense, the Spartina swamp was the highest, followed by the mudflats swamp, the Suaeda salsa swamp, and the Reed marsh. Secondly, the spatial variation of soil moisture was consistent with that of the salinity, and the degree of variation in the east-west direction was greater than that in the north-south. The maximum soil moisture and salinity were found in the southwest Spartina swamp. The minimum was in the Reed swamp. The soil moisture and salinity were divided into 5 levels, from I to V. Level IV occupied the highest proportion, which were 36.156% and 28.531% , respectively. Finally, different landscape types with the combination of soil moisture and salinity showed a common feature that the moisture and salinity were from both high to low. The soil moisture value of Reed marshes was lower than 40.116% and the salinity value was lower than 0. 676% . The soil moisture value of Suaeda salsa marshes was between 38. 162% and 46. 403% and the salinity value was between 0.417% and 1.295%. The soil moisture value of Spartina swamp was higher than 43.214% and the salinity was higher than 1.090%. The soil moisture value of beach was higher than 43.214% and the salinity was higher than 0.677%.

Fly ash was sampled at the inlet and outlet of ash collectors in four different coal-fired utility boilers using 13-stage low pressure impactor (LPI). The mass distribution, emission characteristics and the composition at different size particle of PM10 were studied. The results show that PM10 of the four boilers have a similar himodal distribution, with two peaks formed around 0.1 microm and 2.36 - 3.95 microm, respectively. The lowest efficiency of ash collectors was between 50% - 65% when the particle sizes were around 0.1 - 1 microm, no matter Venturi water membrane dust collector or ESP was used. Ash collectors show different removal efficiencies to various particle sizes PM. The removal efficiency of ash collectors was about 96% around 10 microm, while under 1 microm it was between 62% - 83%. The chemical composition of the size-segregated ash showed that the element S and Na were obvious enrichment in finer ash, which is possibly formed via vaporization and subsequent condensation of inorganic matter. While the refractory oxides were the major composition in bigger size ash, which may be formed via char fragmentation, excluded mineral fragmentation and included mineral coalescence.