Performance evaluation of an integrated constructed wetland used to treat a contaminated aquatic environment

Abstract

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 ± 0.18, 7.17 ± 1.62, 63.80 ± 17.41, and 126.12 ± 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 (R2 = 0.9711), TN (R2 = 0.8875), COD (R2 = 0.9359), and TSS (R2 = 0.9164). The results of the models provided information that will be useful for the design and modification of constructed wetlands.