文献类型：期刊文献

英文题名：Performance evaluation of an integrated constructed wetland used to treat a contaminated aquatic environment

作者：Zhang, Yan[1] Cui, Lijuan[1] Li, Wei[1] Zhang, Manyin[1] Zhao, Xinsheng[1] Wang, Yifei[1]

通信作者：Cui, LJ[1]

机构：[1]Chinese Acad Forestry, Inst Wetland Res, Haidian 100091, Peoples R China

年份：2014

卷号：22

期号：5

起止页码：493-507

外文期刊名：WETLANDS ECOLOGY AND MANAGEMENT

收录：;Scopus(收录号：2-s2.0-84943010217);WOS:【SCI-EXPANDED(收录号:WOS:000342229200003)】；

基金：This study was funded by the Cultivation and Development of the Science and Technology Innovation Bases Project: Study on the Key Problems of Phosphorus Dynamics in Constructed Wetlands (Z131105002813016), and the National Nonprofit Institute Research Grant of the Chinese Academy of Forestry: Dynamic mechanism of phosphorus removal in a subsurface constructed wetland (CAFINT2013C13). We are grateful to all of the research team members for their helpful comments and advice.

语种：英文

外文关键词：Integrated constructed wetland; Eutrophication; Principal component analysis; Artificial neural network

摘要：The treatment performance of an integrated constructed wetland (ICW) that was in operation for 3 years was evaluated. Artificial neural network modeling was used to predict contaminant treatment efficiencies based on easily measured field parameters. The estimates for average yearly removals of total phosphorus (TP), total nitrogen (TN), chemical oxygen demand (COD), and total suspended solids (TSS) were 0.81 +/- A 0.18, 7.17 +/- A 1.62, 63.80 +/- A 17.41, and 126.12 +/- A 48.61 g m(-2) d(-1), respectively. Removal velocities of contaminants were determined from analyses of inlet-outlet datasets. The areal removal rate constants were 0.46, 0.73, 0.44, and 0.82 m d(-1) for TP, TN, COD, and TSS, respectively. The presence of high background concentrations of contaminants (TP: 0.01 mg L-1, TN: 0.13 mg L-1, COD: 6.43 mg L-1, TSS: 14.83 mg L-1) indicated that the water in the ICW was mesotrophic. Statistical methods (i.e., principal component analysis (PCA), forward selection, and correlation analysis) were used to select optimal input subsets for different contaminants. These data subsets were subsequently used for model development. To find the optimal network architectures, a genetic algorithm was introduced to the learning processes. The models were competent at providing reasonable matches between the measured and the predicted effluent concentrations of TP (R-2 = 0.9711), TN (R-2 = 0.8875), COD (R-2 = 0.9359), and TSS (R-2 = 0.9164). The results of the models provided information that will be useful for the design and modification of constructed wetlands.