Nitrate pollution in groundwater is a serious water quality problem that increases the risk of developing various cancers.Groundwater is the most important water resource and supports a population of 5 million in Anya...Nitrate pollution in groundwater is a serious water quality problem that increases the risk of developing various cancers.Groundwater is the most important water resource and supports a population of 5 million in Anyang area of the southern part of the North China Plain. Determining the source of nitrate pollution is the challenge in hydrology area due to the complex processes of migration and transformation. A new method is presented to determine the source of nitrogen pollution by combining the composition characteristics of stable carbon isotope in dissolved organic carbon in groundwater. The source of groundwater nitrate is dominated by agricultural fertilizers, as well as manure and wastewater. Mineralization, nitrification and mixing processes occur in the groundwater recharge area, whereas the confined groundwater area is dominated by denitrification processes.展开更多
Considering the non-linear, complex and muhivariable process of biological denitrification, an activated sludge process was introduced to remove nitrate in groundwater with the aid of artificial neural networks （ANN...Considering the non-linear, complex and muhivariable process of biological denitrification, an activated sludge process was introduced to remove nitrate in groundwater with the aid of artificial neural networks （ANN） to evaluate the nitrate removal effect： The parameters such as COD, NH3-N, NO3^- -N, NO2^- -N, MISS, DO, etc. , were used for input nodes, and COD, NH3-N, NO3^- -N, NO2^- -N were selected for output nodes. Experimental ANN training results show that ANN was able to predict the output water quality parameters very well. Most of relative errors of NO3^- -N and COD were in the range of ± 10% and ±5% respectively. The results predicted by ANN model of nitrate removal in groundwater produced good agreement with the experimental data. Though ANN model can optimize effect of the whole system, it cannot replace the water treatment process.展开更多
基金Projects(41072179,41002083)supported by the National Natural Science Foundation of China
文摘Nitrate pollution in groundwater is a serious water quality problem that increases the risk of developing various cancers.Groundwater is the most important water resource and supports a population of 5 million in Anyang area of the southern part of the North China Plain. Determining the source of nitrate pollution is the challenge in hydrology area due to the complex processes of migration and transformation. A new method is presented to determine the source of nitrogen pollution by combining the composition characteristics of stable carbon isotope in dissolved organic carbon in groundwater. The source of groundwater nitrate is dominated by agricultural fertilizers, as well as manure and wastewater. Mineralization, nitrification and mixing processes occur in the groundwater recharge area, whereas the confined groundwater area is dominated by denitrification processes.
基金National Hi-Tech Research and Development Program of China (Grant No.863-2003AA601120).
文摘Considering the non-linear, complex and muhivariable process of biological denitrification, an activated sludge process was introduced to remove nitrate in groundwater with the aid of artificial neural networks （ANN） to evaluate the nitrate removal effect： The parameters such as COD, NH3-N, NO3^- -N, NO2^- -N, MISS, DO, etc. , were used for input nodes, and COD, NH3-N, NO3^- -N, NO2^- -N were selected for output nodes. Experimental ANN training results show that ANN was able to predict the output water quality parameters very well. Most of relative errors of NO3^- -N and COD were in the range of ± 10% and ±5% respectively. The results predicted by ANN model of nitrate removal in groundwater produced good agreement with the experimental data. Though ANN model can optimize effect of the whole system, it cannot replace the water treatment process.
