Improved solute transport and pollutant degradation model of free water surface constructed wetlands considering significant linear correlation between model parameters

Clarifying the correlations between hydraulic indicators evaluating the hydraulic performance of free water surface constructed wetlands

Previous studies have shown a strong linear correlation between the common hydraulic indicators of free water surface constructed wetlands (FWS CWs), which results in the waste of computing resources and confusion in evaluating the hydraulic performance of FWS CWs. There is an urgent need to define a relatively independent and authoritative hydraulic indicator. In this study, based on three years' data, the correlations among five representative hydraulic indicators, that is, short-circuit index (φ10), Morrill dispersion index (MDI), effective volume ratio (e), moment index (MI), and hydraulic efficiency (λe), were analyzed by combining a variety of methods. The results of the correlation analysis and principal component analysis clearly showed the positive correlations among φ10, e, MI, and λe (p < 0.01), and the strong negative correlations between MDI and the remaining four indexes, which were further confirmed by redundancy analysis (RDA). Most importantly, the significant correlations between MI vs e and φ10 vs e were proved through rigorous mathematical reasoning for the first time. Besides, the results of RDA indicated that the studied design parameters, namely, water depth, hydraulic loading rate, plant spacing, aspect ratio, layout of inlet and outlet, and plant species, could generally explain the variation in hydraulic indicators. The layout of inlet and outlet displayed positive effects on hydraulic performance, and the water depth exhibited negative effects. Combining the clarity of physical meaning, convenience of calculation, and universality of reflecting both hydraulic and treatment performances, e was recommended as the most authoritative hydraulic indicator to evaluate hydraulic performance. The clustering results based on e were highly consistent with those based on the comprehensive principal component score. Wetlands with a combination of lower water depth and better layout of inlet and outlet usually have better hydraulic performance. This study successfully revealed the significant correlations among hydraulic indicators and their sources, and recommended a unique hydraulic indicator to authoritatively evaluate the hydraulic performance of FWS CWs.

Development of a novel sizing approach for passive mine water treatment systems based on ferric iron sedimentation kinetics

The removal of dissolved and particulate iron (Fe) from contaminated mine drainage is an omnipresent challenge in, and legacy of, the mining industry worldwide. The sizing of settling ponds and surface-flow wetlands for passive Fe removal from circumneutral, ferruginous mine water is based either on a linear (concentration-independent) area-adjusted removal rate or flat assignment of an experience-based retention time, neither of which reflects the underlying Fe removal kinetics. In this study, we evaluated the Fe removal performance of a pilot-scale passive system operating in three identical, parallel lines for treatment of mining-influenced, ferruginous seepage water to determine and parameterise a robust, application-orientated model approach for sizing of settling ponds and surface-flow wetlands, each. By systematically varying flow rates (and thus residence time), we were able to demonstrate that the sedimentation-driven removal of particulate hydrous ferric oxides in settling ponds may be approximated by a simplified first-order approach at low to moderate Fe levels. The first-order coefficient was found in the order of 2.1(±0.7) × 10^-2 h^-1, which corresponds well with previous laboratory studies. The sedimentation kinetics may be combined with the preceding Fe(II) oxidation kinetics to estimate the required residence time for pre-treatment of ferruginous mine water in settling ponds. In contrast, Fe removal in surface-flow wetlands is more complex due to the phytologic component, which is why we advanced the established area-adjusted Fe removal approach by parameterising the underlying concentration-dependency for polishing of pre-treated mine water. The quantitative results of this study provide a novel, conservative approach for customised sizing of settling ponds and wetlands in integrated passive mine water treatment systems.

Experimental checking and modeling of the influence of operation conditions on the first order kinetic constants in free water surface wetlands

The most commonly used model in constructed wetlands is the first-order removal model, and first order kinetic constants (k) are the key parameters. The presumption is often made that k are constants. However, it is possible that k are functions of operating conditions, but the influence of operation conditions on k is unclear. In this study, response surface methodology was used to explore the variation patterns of k_a (area rate constants) and k_V (volume rate constants) for the removal of total nitrogen (TN) and total phosphorus (TP) in free water surface (FWS) wetlands. The experimental variables included hydraulic loading rate (HLR), water depth, and inlet concentration (C_in). The results showed that k_V was more variable than k_a, and the area-based first-order model is more suitable for simulating TN and TP in FWS wetlands. Inlet concentration (C_in) was significant for k_a; C_in and water depth were significant for k_V; HLR and the interaction between factors were insignificant. The effects of C_in on k_a and k_V can be described by an upward convex quadratic curve, while the effect of water depth on k_V demonstrates a downward convex quadratic curve. The first-order area rate constant for TN removal was given by k = -47.66 + 22.01 C_in - 1.154 C_in²; the first-order area rate constant for TP removal was given by k = -27.75 + 95.88 C_in - 30.73 C_in². Based on the variation patterns, the traditional k-C model was modified to the k^ψ-C model. The k^ψ-C model produced the best results at simulating the outlet concentration and removal efficiency (RE).

Machine learning exhibited excellent advantages in the performance simulation and prediction of free water surface constructed wetlands

Optimizing the design and operation parameters of free water surface constructed wetlands (FWS CWs) in runoff regulation and wastewater treatment is necessary to improve the comprehensive performance. In this study, nine machine learning (ML) algorithms were successfully developed to optimize the parameter combinations for FWS CWs. The scale effect of surface area on wetland performance was determined based on consistently smaller predictions (-6.2% to -28.9%) of the nine well-established ML algorithms. The models most suitable for FWS CW performance simulation and prediction were random forest and extra trees algorithms because of their high R² values (0.818 in both) with the training set and low mean absolute relative errors (4.7% and 3.8%, respectively) with the test set. Results from feature analysis of the six tree-based algorithms emphasized the importance of water depth and layout of inlet and outlet, and revealed the negligible effect of the aspect ratio. Feature importance and partial dependence analysis enhanced the interpretability of the tree-based algorithms. The proposed ML algorithms enabled the implementation of an extended scenario at a low cost in real time. Therefore, ML algorithms are suitable for expressing the complex and uncertain effects of the design and operation parameters on the performance of FWS CWs. Acquiring datasets consisting of more extensive, uniform, and unbiased parameter combinations is crucial for developing more robust and practical ML algorithms for the optimal design of FWS CWs.