Numerical Simulation and Evaluation of Constructed Wetland Designs for Effluent Treatment Based on MIKE21

Numerical Simulation and Evaluation of Constructed Wetland Designs for Effluent Treatment Based on MIKE21 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Numerical Simulation and Evaluation of Constructed Wetland Designs for Effluent Treatment Based on MIKE21 Xing Xiong, Shanrui Yang, Junxiang Zhang, Jiafan Chen, Xinyu Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4552346/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Constructed Wetlands for Effluent treatment (E-CW) play a vital role in the degradation of pollutants, purification of water, and the improvement of freshwater ecosystems. However, conventional designs often lack a methodical approach for quantifying the efficacy of these wetlands. The present study utilized the MIKE21 Hydrodynamic (HD) module in conjunction with the ECO-Lab Water Quality (AD) module to perform a numerical simulation of the Constructed Wetland for Effluent. The key parameters involved in effective water purification were calibrated and the system's ability to treat effluents from wastewater treatment facilities was assessed. The findings demonstrated significant removal efficiencies for Chemical Oxygen Demand (COD), Total Nitrogen (TN), Total Phosphorus (TP), and ammonia (NH 3 -N), with average rates of 51.14%, 43.14%, 63.82%, and 54.38%, respectively. In addition, the simulations exhibited a high degree of accuracy, with hydrodynamic predictions deviating by less than 5% and water quality approximations by less than 15%. Additionally, the use of numerical simulations can provide valuable guidelines for the future design and functional assessment of wetlands by offering crucial insights that aid in the optimization of purification processes and vegetation selection. Earth and environmental sciences/Environmental sciences/Environmental chemistry/Pollution remediation Earth and environmental sciences/Environmental sciences/Environmental impact Biological sciences/Ecology/Wetlands ecology Constructed wetland pollutant removal Numerical simulation MIKE21 Hydrodynamic Water Quality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Rapid urbanization results in the production of substantial amounts of industrial and domestic wastewater, exacerbating the problem of urban water pollution 1 . The improper management of this wastewater frequently leads to higher levels of Chemical Oxygen Demand (COD), Biological Oxygen Demand (BOD), nitrogen, and phosphorus 2 – 3 . This, in turn, depletes dissolved oxygen levels, encourages eutrophication, and facilitates the spread of environmental hazards. In this context, the challenges associated with intercepting the release of untreated wastewater and leveraging natural waterways to mitigate surface water pollution underscore the pivotal role of wastewater treatment facilities in controlling pollution 4 – 5 . Effluents, which comprise the secondary wastewater generated during the wastewater treatment process, still contain residual amounts of organic matter, nitrogen, phosphorus, and other pollutants even after undergoing preliminary treatment. Thus, additional treatment of the discharged water is necessary to comply with environmental protection standards. For decades, researchers have been investigating numerous nature-based solutions as cost-effective alternatives to conventional wastewater management systems, which are expensive and technically complex 6 . In this context, the use of constructed wetlands, which are nature-based solutions, not only ensure the sustainable management of wastewater but also offer additional ecosystem services and social benefits, thereby supporting a wider concept of a circular economy. Constructed Wetlands (CWs) represent cost-effective, high-performance, and environmentally friendly infrastructures that effectively process pollutants by utilizing the collaborative actions of soil, macrophytes, and microbial communities 7 . These systems utilize natural processes to transform and eliminate contaminants in wastewater, making them an excellent solution for managing wastewater and a promising alternative in decentralized wastewater treatment strategies 8 – 9 . Furthermore, when compared to the traditional methods of treating wastewater, CWs require less energy and have lower maintenance expenses 10 . Correspondingly, water bodies that were previously categorized as Class V or lower can attain quality standards ranging from Class III to IV after treatment. In addition, the nutrient removal efficiency in CWs is governed by the dynamics of the root zone, which are influenced by the plants' ability to adsorb nutrients and the design of the system. Thus, implementing subsurface flow wetlands using tailored media, followed by their integration into surface or subsurface flow systems, can significantly enhance the purification of effluents. As a densely populated and economically robust province in China, Jiangsu Province has experienced a significant degree of urbanization and faces numerous challenges in the realm of urban sewage treatment. To date, the province has successfully completed 39 constructed wetland projects, spanning over an area of more than 1400 hectares (hm 2 ). Post-construction, the wastewater from these facilities are likely to meet the Grade IV standards for surface water quality. Accordingly, the establishment and operation of these CWs serve a dual purpose: 1. enhancing the quality of discharged water and reducing operational expenses for sewage treatment facilities; 2. elevating the ecological safety and sustainability of important water systems such as the Yangtze River, Tai Lake, and the Grand Canal. To optimally utilize limited spaces and control the associated costs, the rational design of CWs is essential. Contemporary research methodologies for CW design include both experimental simulations and numerical simulations 11 . The former approach, referred to as a "black box", primarily depends on empirical data from previous experiments to investigate the mechanisms of pollutant removal, the selection of plant species and substrates, and the resilience of CWs to sudden increases in pollutant loads in real-world scenarios 12 – 13 . However, the use of numerical modeling has become increasingly prevalent for predicting flow patterns and other internal processes in recent years due to the challenges associated with identifying wetland types, process combinations, and load capacities in CW design. Furthermore, it also allows for the evaluation and refinement of CW functionalities 14 . In this context, two-dimensional models have been extensively used in CW research to analyze the impact of geometric configurations on retention times, flow dynamics, and pollutant kinetics despite their drawbacks 15 – 16 . Numerous studies have recently incorporated three-dimensional numerical models into CW simulations, thus, revealing differences in total phosphorus (TP) removal efficiencies between horizontal subsurface flow (HSSF) and wave subsurface flow (WSSF) CWs 16 . In this context, the use of tools such as MODFLOW and MODPATH enable the numerical modeling of CW flow patterns, thereby enhancing the design process by evaluating the impact of different hydraulic conductivities and inflow patterns on the distribution of flow across CW sections 17 . In wetland design, accounting for the hydrodynamics and plant configurations within CWs is imperative. Selecting species capable of tolerating and removing pollutants is essential because aquatic plants have significant tolerance to pollutants, facilitate hyperaccumulation of contaminants, exhibit rapid growth, and demonstrate the ability to accumulate biomass, rendering them vital components of CWs 18 . Several studies have emphasized the potential of using different aquatic plants in CWs to replicate the natural dynamics of pollutant concentrations in wetlands by adjusting purification parameters 19 . Thus, the selection of appropriate plant species is a key design factor that must be considered when designing Engineered Constructed Wetlands (E-CWs) in order to greatly enhance their efficiency 20 . However, field measurement approaches require a significant amount of time for long-term water quality assessments, despite their predominance in recent research 21 . Furthermore, in addressing the complexities of designing CWs across varied types, sizes, and technological processes, the use of physical simulation experiments and onsite measurements often presents prohibitive costs and practical challenges. Consequently, these issues have pivoted the focus towards leveraging numerical models to simulate the hydrodynamics and water quality in CWs, providing a cost-efficient and practical approach for design exploration. Presently, a wide range of numerical models is used to support the optimization of plant arrangements and refinement of design methodologies in wetlands. Among these, widely utilized water environment simulation tools include QUAL2K, WASP7, EPDRiv1, and MIKE. MIKE21, a mathematical model developed by the Danish Hydraulic Institute (DHI), the "HD" hydrodynamic module in MIKE21 is capable of simulating flow field conditions that are unique to E-CW, the "Eco-Lab" module's "AD" water quality component enables the modification of pollutant diffusion and biochemical reaction coefficients, thereby facilitating simulations of pollutant concentration and diffusion processes 22 – 23 . These simulations assist in optimizing design by comparing them to discharge standards. The present study seeks to utilize these numerical models to simulate the water environment of E-CW in order to evaluate the effectiveness of pollutant removal and the purification performance of individual wetland units, while considering cost and spatial limitations. The E-CW, equipped with separate purification modules and a flat base, received incoming flow from nearby sewage treatment plant-processed effluent. Accordingly, the following three objectives were proposed: 1. to develop a MIKE21 numerical model for E-CW by combining wetland unit processes, in order to simulate hydrodynamic and purification processes; 2. to collect pollutant concentration data for each E-CW unit for the calibration of purification parameters; and 3. to compare these findings with long-term water quality monitoring data, in order to assess the accuracy of the simulation, thus aiming to provide valuable insights for the optimized process design of constructed wetlands for effluent treatment. Materials and Methods Study area The Yingtai Constructed Wetland for Effluent (E-CW) is situated in the High-tech Zone of Hai'an City, Nantong, Jiangsu Province, China. The wetland is situated in close proximity to a densely populated urban area and is surrounded by rice paddies and rivers. Correspondingly, the Yingtai Wastewater Treatment Plant is tasked with treating both industrial and domestic wastewater from the entire High-tech Zone. The treated effluent then flows into the adjacent E-CW, as shown in Fig. 1 . Upon its completion, the E-CW is anticipated to generate effluent that conforms to Class IV surface water standards, enabling diverse possibilities for reuse. More precisely, 35% of the processed wastewater is allocated for the purpose of watering urban green spaces, maintaining roads, and serving as cooling water in industrial operations. On the other hand, the remaining 65% is intended for discharge intended to be released into nearby rivers. This dual strategy not only enhances the quality of the wastewater treatment plant's output but also contributes to ecological improvement by decreasing pollution in aquatic ecosystems. The initiative also reduces overall pollutant discharge, thereby improving the ecological health of the aquatic environment in Hai'an City. Technology and vegetation in E-CW Previous studies indicate that subsurface flow wetlands exhibit elevated expenses in terms of construction, operation, and maintenance, and are susceptible to substrate clogging 6 . In contrast, surface flow wetlands, despite having smaller hydraulic loads, are more cost-effective and require less maintenance. In the current study, the E-CW primarily focused on the treatment of low-pollution tailwater (meeting Grade A standards) from wastewater treatment plants. Accordingly, there was no need for a higher hydraulic load. The process flow consisted of an integrated system of "ecological stabilization pond + surface flow wetland + porous media ecological filter bed + submerged plant wetland." The primary objective of the pretreatment area in the ecological pond was to facilitate denitrification, remove nitrogen, and enhance the biodegradability of organic matter in the water. It is worth noting that the pond does not have aeration or push-flow systems. Instead, it relies on the remaining dissolved oxygen in the effluent and the hydraulic gradient for the flow from upstream to downstream. The conversion of recalcitrant organic matter into biodegradable forms enhances the biodegradability of the effluent, leading to a reduction in COD. Furthermore, the ecological pond utilizes ecological islands to redirect the tailwater: On one hand, it prolongs the path of the wastewater to create a continuous flow state; on the other hand, the ecological islands fulfill the criteria for landscaping and naturalization. Moreover, the shallow water type surface flow wetland system with controllable water level operates similarly to natural marsh wetlands 24 . Plants independently carry out processes like growth, wilting, and decay, relying on the humus in the substrate and organic matter from plant residues as a source of carbon to complete denitrification and de-nitration. During periods of high water levels, the wetland system functions as a conventional surface flow wetland, facilitating the organized cultivation of plants and timely rotation of crops. In addition, the porous media filter bed unit is designed to capture and remove suspended solids from water. It achieves this by using porous and lightweight concrete fillers that are effective in adsorbing phosphorus and facilitating biodegradation, aiming primarily to improve water clarity. The submerged plant unit is positioned at the terminal point of the wetland water quality purification system, boasting the most extended period of hydraulic retention. Furthermore, the final step in ensuring water quality is achieved through the comprehensive action of pollutants acting on the roots of submerged plants, microbial degradation, and redox reactions. The ecological pond had a design area of 10,240 m². The water surface area of the pond was 4,853 m² and it had an effective volume of 9,708 m³. The slope ratio was established as 1:3, the water depth is set at 2 m, with a surcharge of 0.5 m, and the hydraulic retention time of 7.06 h. In addition, the ecological floating islands occupied 25% of the pond's water surface. They were designed in a hexagonal shape, with each side of the unit measuring 1.0 m. Each floating island covered an area of 2.6 m², and 40 such islands were assembled into 12 composite islands, each spreading over 104 m², uniformly across the pond. The planting on these composite islands followed a radial pattern, commencing from the center and extending outwards. It began with Nymphaea , followed by Iris pseudacorus , Ipomoea aquatica , and Hydrocotyle vulgaris . The vegetation on the slope was arranged in a descending order from the crest to the base, with plants such as Iris sibirica , Vallisneria , and Phragmites . The plants were planted from 0.3 m above to 0.5 m below the water surface. The surface flow wetland section had a design area of 10,240 m², a water surface area of 9,613 m², and an effective volume of 6,738 m³. The slope ratio in this case was also 1:3, with a typical water depth of 0.7 m, a surcharge of 0.3 m, and a hydraulic retention time of 4.9 h. Water level adjustment is crucial for the regrowth of aquatic plants during the plant rotation seasons. Therefore, from mid-April to late November, the wetland's operation shifted to a low water level. This was achieved by using rectifying weirs to control the water level, ensuring it does not exceed 0.3 m in depth. The porous media filter bed had a surface area of 2,100 m², with an effective depth of 0.8 m. The primary component of this system comprised a porous autoclaved aerated concrete, which significantly improves the efficiency of phosphorus removal. The filler beds used in this construction consisted of units measuring 2.0 m × 0.5 m × 0.4 m, which were filled with lightweight concrete granules and enclosed within gabion meshes. These beds were placed between units of porous concrete dams. Foaming agents were mechanically aerated to achieve complete expansion and then thoroughly mixed with cement slurry to create the porous autoclaved aerated concrete filler. This filler was distinguished by its large specific surface area, which was conducive to pollutant adsorption and facilitated the formation of microbial films. The submerged plant wetland covered an area of 6,898 m², with a water surface area of 6,488 m² and an effective volume of 16,225 m³. The slope ratio was maintained at a ratio of 1:3, the water depth was maintained at 2.5 m, with a surcharge of 0.3 m, and a hydraulic retention time of 11.8 h. The bank slope, which had a gradual incline of 1:3 into the water, created an ecological slope protection. The arrangement of vegetation in the pond varied from terrestrial herbs near the shore to hydrophilic plants, emergent plants, and finally submerged plants, with the bottom of the pond populated by Scirpus , Typha , and Lemna . The process flow of E-CW is shown in Fig. 2 . 3D model construction To begin constructing the model, the initial step involved to generate a wetland boundary data file. This was carried out using the Mesh Generator tool in MIKE to import the boundary terrain file of the E-CW. Meanwhile, the inlets and outlets of the E-CW, as well as the boundary lines, were defined in the model. Once the wetland model and the actual terrain data are confirmed to be consistent, the E-CW was partitioned using unstructured triangular mesh elements, resulting in a total of 4,438 triangular adaptive meshes. Subsequently, the 3D terrain model was generated by drawing the y-axis terrain of the model based on the precise elevation of the engineering construction 25 , and is shown in Fig. 3 . Hydrodynamic Simulation Approach Following the construction of the 3D E-CW model, the HD module of the MIKE-21 software was utilized to carry out hydrodynamic simulations on the E-CW. The hydrodynamic simulation utilized the three-dimensional incompressible Navier-Stokes (N-S) equations with an R-E averaged distribution, while adhering to the Boussinesq assumption of eddy viscosity 26 . This module enabled to comprehensively capture the intricacy and intricacy of fluid motion within the intricate wetland system. In addition, the simulation process incorporated hydrodynamic behaviors such as buoyancy effects and bottom friction within the wetland system, ensuring the model's accuracy and reliability in characterizing turbulent structures and water movement. The continuity equation for two-dimensional water flow is shown in Eq. (1), and the momentum equations for two-dimensional water flow are presented in Equations (2) to (4). \(\begin{array}{c}\frac{\partial h}{\partial t}+\frac{\partial h\stackrel{-}{u}}{\partial x}+\frac{\partial h\stackrel{-}{v}}{\partial y}=hS\#（1）\end{array}.\) $$\frac{\partial h\stackrel{-}{u}}{\partial t}+\frac{\partial h\stackrel{-}{u}}{\partial x}+\frac{\partial h\stackrel{-}{uv}}{\partial y}=-gh\frac{\partial \eta }{\partial x}-\frac{h}{{\rho }_{0}}\frac{\partial {P}_{\text{a}}}{\partial x}-\frac{g{h}^{2}}{2{\rho }_{0}}\frac{\partial \rho }{\partial x}+\frac{{\tau }_{\text{x}\text{x}}}{{\rho }_{0}}-\frac{{\tau }_{\text{b}\text{x}}}{{\rho }_{0}}-\frac{1}{{\rho }_{0}}\left(\frac{\partial {s}_{\text{x}\text{x}}}{\partial x}+\frac{\partial {s}_{\text{x}\text{y}}}{\partial y}\right)$$ $$\begin{array}{c}+\frac{\partial }{\partial x}\left(h{T}_{\text{x}\text{x}}\right)+\frac{\partial }{\partial y}\left(h{T}_{\text{x}\text{y}}\right)+h{u}_{\text{s}}S\#\left(2\right),\end{array}$$ $$\frac{\partial h\stackrel{-}{v}}{\partial t}+\frac{\partial h\stackrel{-}{uv}}{\partial x}+\frac{\partial h{\stackrel{-}{v}}^{2}}{\partial y}=-gh\frac{\partial \eta }{\partial y}-\frac{h}{{\rho }_{0}}\frac{\partial {p}_{\text{a}}}{\partial y}-\frac{g{h}^{2}}{2{\rho }_{0}}\frac{\partial \rho }{\partial y}+\frac{{\tau }_{\text{s}\text{y}}}{{\rho }_{0}}-\frac{{\tau }_{\text{b}\text{y}}}{{\rho }_{0}}-\frac{1}{{\rho }_{0}}\left(\frac{\partial {s}_{\text{y}\text{x}}}{\partial x}+\frac{\partial {s}_{\text{y}\text{y}}}{\partial y}\right)$$ $$\begin{array}{c}+\frac{\partial }{\partial x}\left(h{T}_{\text{x}\text{y}}\right)+\frac{\partial }{\partial y}\left(h{T}_{\text{y}\text{y}}\right)+h{v}_{\text{s}}S\#\left(3\right),\end{array}$$ $$h\stackrel{-}{u}={\int }_{-d}^{\eta }udz$$ $$\begin{array}{c}h\stackrel{-}{v}={\int }_{-d}^{\eta }vdz\#\left(4\right).\end{array}$$ In the aforementioned equations, \(t\) represents time, and x and y denote the coordinates within the Cartesian coordinate system. The variable d denotes the static water depth, while \(\eta\) signifies the water level. The total water depth is given by \(h=d+\eta\) . The terms \(u\) and \(v\) represent the velocity components in the \(x\) and \(y\) directions, respectively. The letter \(g\) is used to denote the acceleration due to gravity, and \(\rho\) symbolizes the water's density. The term T encompasses lateral stress components, which include aspects like viscous friction, turbulent friction, and differential advection. In addition, the components of radiation stress in various directions are represented by \({S}_{\text{x}\text{x}}\) , \({S}_{\text{x}\text{y}}\) , and \({S}_{\text{y}\text{y}}\) , while \(S\) denotes the source and sink terms. The velocities of water flow associated with these source and sink terms are indicated by \({u}_{\text{s}}\) and \({v}_{\text{s}}\) . Water Quality Simulation The E-CW inlet was serviced by a wastewater treatment plant that spanned 3.3 hectares and had a sewage treatment capacity of 20,000 m³/day. The effluent quality of the sewage treatment plant is shown in Table 1 . Table 1 Water quality parameters of the Yingtai Sewage Treatment Plant effluent. Water quality types COD/(mg.L − 1 ) TN/ (mg.L − 1 ) NH 3 -N/ (mg.L − 1 ) TP/ (mg.L − 1 ) The water quality of the effluent from Phase I 36–51 10–12 2–5 0.2–0.5 The designed effluent water quality ≤ 30 ≤ 1.5 ≤ 1.5 ≤ 0.3 The measured average effluent water quality 21 1.56 0.59 0.13 The current study aimed to use MIKE21 software to simulate water quality and meet the design requirement of improving the effluent at the E-CW outlet to meet the standards of Category IV surface water. Initially, the software utilized the Time Series tool to preprocess water quality data at the inlet and outlet of each unit. This process involved obtaining time series files through linear interpolation. Subsequently, the initial calculation parameters for the purification of water quality were established, which encompassed the first-order reaction kinetic coefficients and the absorption coefficients of plants and microbes. Then, the "AD" module from ECO Lab was coupled with the hydrodynamic simulation outcomes to carry out water quality simulation. Following six simulation iterations, the water quality purification parameters were adjusted to achieve calibration. The water quality model relied on vertically integrated conservation equations for mass and momentum, as depicted in Eq. (5). \(\begin{array}{c}\frac{\partial \text{A}c}{\partial t}+\frac{\partial \text{Q}c}{\partial x}-\frac{\partial }{\partial x}\left(\text{A}\text{D}\frac{\partial c}{\partial x}\right)=-AKc+{C}_{2}c\#\left(5\right).\end{array}\) In this above-mentioned equation, \(c\) denotes the concentration of the targeted water quality indicator while \(\text{D}\) represents the diffusion coefficient, which is a measure of how quickly pollutants in the water spread in the direction of water flow. \(\text{K}\) represents the total attenuation coefficient, while \({C}_{2}\) denotes the concentration of the source and sink terms. When considering the impact of convective diffusion in the specific water body, this approach involved utilizing central and spatial implicit difference formats to computationally solve the equation set. These equations can be derived through estimation. $$\begin{array}{c}D=a{v}^{\text{b}}\#\left(6\right).\end{array}$$ Within these equations, \(v\) stands for velocity, derived from the outcomes of the hydrodynamic model. The parameters a and \(b\) are predefined settings within the model's framework. Results and discussion Hydrodynamic Simulation results The present study incorporated hydrodynamic model parameters, such as bed roughness, eddy viscosity coefficient, and configurations for internal structures, from established wetland designs in similar research, in order to develop the constructed wetland design 27 . The objective was to refine the model to ensure that the differences between the simulated and measured water levels and flow velocities did not exceed 5%, as specified in Table 2 . By setting the model parameters to a specific eddy viscosity coefficient of 0.28 and a bed roughness of 32 m 1/3 .s, the hydrodynamic simulations were able to effectively keep the relative errors below 5%. The ecological pond component was simulated using a water level of 2.0 m and flow velocities ranging from 0.12 to 0.18 m/s, in order to optimize the initial purification of effluent. The reduced flow rate was crucial in enabling the ecological floating beds to effectively reduce pollutant levels (Fig. 4 ). In contrast, the surface flow wetland section, characterized by a water level of 0.72 m and flow velocities ranging from 0.36–0.60 m/s, exhibited increased flow rates as a result of the design of the overflow weir plate. This increased flow rate promoted the growth and function of aerobic microorganisms, which improved the breakdown of organic matter. Furthermore, it prevented suspended solids and particles from progressing to later stages of treatment. In addition, the design of the porous media filter bed component included a water level of 0.85m and flow velocities ranging from 0.18–0.36m/s. This design enabled suspended solids to settle or be captured more effectively. The reduced flow speeds also minimized shear forces and prevented particle resuspension. This improved the efficiency of removing pollutants from the bed. Moreover, the prolonged duration of water retention in the bed maximized pollutant adsorption by leveraging the large specific surface areas of the porous and foam concrete materials, allowing for more efficient interactions between microorganisms and pollutants. In addition, the submerged plant component, simulated at a water level of 2.50 m with flow velocities ranging from 0.12–0.18 m/s, demonstrated the positive impact of reduced speeds and longer hydraulic retention times on sedimentation, adsorption, and biodegradation mechanisms. Table 2 Parameters of the E-CW hydrodynamic model. Wetland unit Ecological stabilization pond Surface flow wetland Submerged plant wetland Calculation period 3600s, total of 2208 steps 3600s, total of 2208 steps 3600s, total of 2208 steps Eddy viscosity coefficient 0.28 0.28 0.28 Bed roughness (m 1/3 .s) 32 32 32 Inflow boundary Flow boundary 0.014 m 3 .s − 1 Flow boundary 0.014 m 3 .s − 1 Flow boundary 0.014 m 3 .s − 1 Outflow boundary Flow boundary − 0.014 m 3 .s − 1 Water level boundary 0.72 m Water level boundary 2.90 m Internal structure setting weir discharge coefficient of 1.838, weir width of 1.8 m, and weir height of 0.5 m Water Quality Simulation and Parameter Calibration Leveraging the hydrodynamic model parameters of the E-CW, the water quality at the wetland's outflow was simulated by integrating the "AD" module of ECO Lab. The simulation spanned from November 1, 2020, to January 31, 2021, with a time step of 3600 s over 2208 steps. The model incorporated fundamental hydrodynamic principles and introduced diffusion coefficients and processes for pollutants, with coefficients ranging from 0.01 to 20 \(\) m 2 /s. The initial conditions for the inflow pollutants were determined using the monitored water quality: COD = 40 mg/L, TN = 10.61 mg/L, TP = 0.29 mg/L, NH 3 -N = 2.04 mg/L. The concentration changes of various pollutants exhibited significant variations due to the disparities in water levels, flow rates, and processes across each wetland unit (Fig. 5 ). As observed, the ecological pond unit demonstrated a significant impact on the removal of COD, likely due to the prolonged contact time between COD and the microbes and plants on the ecological floating islands. In addition, the porous media filter bed unit played a crucial role in removing phosphorus. The porous structure of the media was able to capture suspended solids and particles that were attached to phosphorus, leading to their deposition on the filter bed. Accordingly, the simulations revealed that the ecological pond achieved average removal rates of 12.02% for COD, 22.94% for TN, 8.36% for TP, and 21.36% for NH 3 -N. Furthermore, in the surface flow wetland, the corresponding rates were 12.41% for COD, 7.82% for TN, 22.65% for TP, and 8.95% for NH 3 -N. On the other hand, the porous media filter bed unit achieved removal rates of 34.61% for COD, 9.77% for TN, 7.33% for TP, and 33.83% for NH 3 -N. Lastly, the submerged plant wetland had removal rates of 2.98% for COD, 24.91% for TN, 23.40% for TP, and 18.10% for NH 3 -N. Before the simulation began, 5 cross-sections were set up at the endpoints and outlets of the four wetland units to record the concentration changes of the four pollutants along the way (Fig. 6 ). Accordingly, the E-CW showed significant removal effects on the four pollutants, with the pollutants gradually decreasing along the wetland unit and the outflow water quality being stable. Based on the water quality data from the inlet and outlet of the wetland, the average removal rates of the four pollutants in the entire tailwater wetland were calculated to be 51.14%, 43.14%, 63.82%, and 54.38%, respectively. Accordingly, their pollutant reduction loads were estimated to be 1.87 g.m − 2 .d − 1 , 0.46 g.m − 2 .d − 1 , 0.098 g.m − 2 .d − 1 , and 0.031 g.m − 2 .d − 1 , respectively. In summary, the effluent treated by the E-CW can meet the Class IV surface water standard. Error analysis of simulated and measured values of E-CW water quality The accuracy of the established water quality model was assessed by analyzing and comparing simulated and actual measurements of four pollution indicators. illustrates the comparison between the simulated and measured values of different pollutants at the outflow of the E-CW, at regular intervals. Although fluctuations in the concentration of the tailwater output was observed at each step, the measured and simulated values of the pollutants exhibited a consistent pattern of change. Accordingly, the predictive accuracy of the model was assessed in relation to actual measurements using the Kruskal-Wallis non-parametric test. The results showed that the P-values for all state variables were greater than 0.05. This result corroborates the hypothesis that the simulated and measured values exhibit a similar trend, suggesting that there was no notable disparity in their distributions. While the Kruskal-Wallis test can detect statistical differences between simulated and measured values, it is not adequate for fully assessing the accuracy of model calculations. The primary purpose of the Kruskal-Wallis test is to ascertain whether multiple independent samples originate from the identical distribution. However, it does not yield direct quantitative data regarding the specific discrepancies between simulated and measured values. Thus, the relative errors between the simulated and measured values were calculated after confirming the consistency of the distribution trends in order to more accurately evaluate the model's accuracy. The experiment demonstrated a good fit between simulation results and measured values, as indicated by the relative errors of 4.88% for COD, 10.02% for TN, 3.25% for TP, and 12.69% for NH 3 -N. Furthermore, all of these relative errors were within the acceptable range of 15%. In general, a warm climate facilitates the proliferation of plants and microorganisms, thereby enhancing the uptake and breakdown of pollutants 28 . Thus, empirical measurements were performed between November 2020 and January 2021, coinciding with the winter season in Hai'an City. The measurements indicate that the purification of different pollutants all complied with the standards, thus demonstrating the viability of E-CW for year-round sewage treatment. However, the present study also encompasses other errors. In terms of water quality monitoring, long-term monitoring of wetland water quality was not conducted, as carried out in similar studies reported previously. This was attributed to the fact that the primary objective of the current study was to establish a reference for the use of numerical simulation methods in wetland design. Furthermore, in terms of environmental indicators, the wetland was found to be rarely affected by severe weather conditions and excessive hydraulic loads. As a result, excluded wetland climate was excluded from the simulation scope of the current study. However, this is likely to have resulted in some inaccuracies in the numerical simulation results of the wetland. Thus, future investigations focusing on the effects of various seasons and plant species on the efficiency of purification in constructed wetlands, in conjunction with the study of hydrodynamics and water quality simulation are deemed essential, develop a comprehensive workflow for the design of numerical simulation-assisted constructed wetlands. Conclusions In the current study, a numerical model of the E-CW was constructed based on engineering design principles. By conducting six simulation runs and carefully calibrating the parameters involved, along with three months of thorough water quality monitoring, the post-construction hydrodynamic behavior and water quality dynamics of the wetland were successfully simulated. The hydrodynamic simulation exhibited a relative error of less than 5%, while the water quality simulation demonstrated a relative error of less than 15%, thereby satisfying our simulation expectations. In addition, the numerical model of E-CW was able to generate the local flow velocity vectors for each purification unit, enabling us to optimize the flow velocity and hydraulic retention time of the wetland units during the design phase. This optimization enhanced the conditions for the growth and activity of wetland plants and microorganisms resulting in a more favorable environment. For instance, the combined action of anaerobic and aerobic zones in surface flow wetlands facilitated the elimination of nitrogen. Furthermore, when the relative error standard was met, the average removal rates of COD, TP, TN, and NH 3 -N in E-CW were also significantly higher. This was observed by collecting their concentrations at 5 cross-sections of the wetland unit, which all showed varying degrees of decrease (Fig. 5 ). The use of MIKE21 facilitated the visual demonstration of the movement of pollutants through wetlands, aiding in the design of complex wetland process combinations. This, subsequently, improved the efficiency of wetland units in purifying pollutants, thereby allowing for the optimization of plant configuration based on the specific purification requirements of the wetland. As observed, the findings derived from this study are likely to aid in implementing numerical simulation techniques in the design of constructed wetlands. Declarations Author Contributions All authors contributed to the study conception and design. Material preparation was performed by X.Z., data collection was performed by J.Z. and J.C., data analysis and simulation experiment were performed by S.Y. In addition, S.Y. and X.X. wrote the main manuscript text and prepared figures 1-6. All authors reviewed the manuscript. Funding This research is supported by the Ministry of Education of China's Fund for Humanities and Social Sciences Research (Grant No. 23YJAZH200). Data availability The data used or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval Not applicable. Consent for publication All authors approved the final manuscript for submission and publications. Competing interests The authors declare no competing interests. References Saeed, Tanveer, Nehreen Majed, Tanbir Khan, & Hena Mallika. Two-Stage Constructed Wetland Systems for Polluted Surface Water Treatment.” Journal of Environmental Management 249 (November): 109379 (2019). Cao, Wenping, Yinmei Wang, Ling Sun, Jinlong Jiang, & Yanqiu Zhang. Removal of Nitrogenous Compounds from Polluted River Water by Floating Constructed Wetlands Using Rice Straw and Ceramsite as Substrates under Low Temperature Conditions. Ecological Engineering 88 (March): 77–81 (2016). Fengle, Yang, Zhang Xianzhi, Li Jinhua, Zhao Hongfeng, Jin Fangming, & Baoxue Zhou. Analysis and Evaluation of the Treatment and Reuse of Tailwater: A Case Study in Erhai Lake. Journal of Cleaner Production 327 (December): 129435 (2021). Shao, YY, Pei, HY, WR, Chanway, CP, Meng, & PP. Bioaugmentation in Lab Scale Constructed Wetland Microcosms for Treating Polluted River Water and Domestic Wastewater in Northern China. INT BIODETER BIODEGR 95 : 151–59 (2014). Yuan, Xingcheng, Xin Qian, Ruibin Zhang, Rui Ye, & Wei Hu. Performance and Microbial Community Analysis of a Novel Bio-Cord Carrier during Treatment of a Polluted River. Bioresource Technology 117 : 33–39 (2012). Wei, Dingbing, Rajendra Prasad Singh, Yangke Li, & Dafang Fu. 2020. Nitrogen Removal Efficiency of Surface Flow Constructed Wetland for Treating Slightly Polluted River Water. Environmental Science and Pollution Research 27 (20): 24902–24913 (2020). Saeed, Tanveer, Md Jihad Miah, Nehreen Majed, Mahmudul Hasan, & Tanbir Khan. Pollutant Removal from Landfill Leachate Employing Two-Stage Constructed Wetland Mesocosms: Co-Treatment with Municipal Sewage. Environmental Science and Pollution Research 27 (22): 28316–28332 (2020). Stefanakis, Alexandros I. The Role of Constructed Wetlands as Green Infrastructure for Sustainable Urban Water Management. Sustainability 11 (24): 6981 (2019). A, Dan, Yang-yang Deng, Qin-mei Guo, Yu Jiang, & Chun-xing Chen. A Three-Year Study on the Treatment of Domestic-Industrial Mixed Wastewater Using a Full-Scale Hybrid Constructed Wetland. Environmental Science and Pollution Research 30 (11): 31256–31267 (2022). Nan, Xi, Stevo Lavrnić, Giuseppe Mancuso, & Attilio Toscano. Effects of Design and Operational Conditions on the Performance of Constructed Wetlands for Agricultural Pollution Control – Critical Review. Water, Air, & Soil Pollution 234 (7): 434 (2023). Guo, Changqiang, Di Wan, Yalong Li, Qing Zhu, Yufeng Luo, Wenbing Luo, & Yuanlai Cui. Quantitative Prediction of the Hydraulic Performance of Free Water Surface Constructed Wetlands by Integrating Numerical Simulation and Machine Learning. Journal of Environmental Management 337 (July): 117745 (2023). Singh, Priyanka, Gurudatta Singh, Anubhuti Singh, Virendra Kumar Mishra, & Reetika Shukla. Macrophytes for Utilization in Constructed Wetland as Efficient Species for Phytoremediation of Emerging Contaminants from Wastewater. Wetlands 44 (2): 22 (2024). Wei, Ting, Yaqian Zhao, Jia Guo, Bin Ji, Álvaro Pun García, & Abraham Esteve Núñez. Developing a Novel Lightweight Substrate for Constructed Treatment Wetland: The Idea and the Reality. Journal of Water Process Engineering 57 (January): 104587 (2024). Kumar, Satish, Ankit Agarwal, Vasant Govind Kumar Villuri, Srinivas Pasupuleti, Dheeraj Kumar, Deo Raj Kaushal, Ashwin Kumar Gosain, Axel Bronstert, & Bellie Sivakumar. Constructed Wetland Management in Urban Catchments for Mitigating Floods. Stochastic Environmental Research and Risk Assessment 35 (10): 2105–2124 (2021). Liu, Yin, Yunzhong Jiang, Shuanghu Zhang, Dan Wang, & Huan Chen. Application of a Linked Hydrodynamic–Groundwater Model for Accurate Groundwater Simulation in Floodplain Areas: A Case Study of Irtysh River, China. Water 15 (17): 3059 (2023). Wang, Jun, Sui-liang Huang, Cheng-da He, & Chiu-On Ng. Numerical Analysis of the Performance of Horizontal and Wavy Subsurface Flow Constructed Wetlands. Journal of Hydrodynamics 23 (3): 339–47 (2011). Fioreze, Mariele, & Malva Andrea Mancuso. MODFLOW and MODPATH for Hydrodynamic Simulation of Porous Media in Horizontal Subsurface Flow Constructed Wetlands: A Tool for Design Criteria. Ecological Engineering 130 (May): 45–52 (2019). Vymazal, Jan. “Plants in Constructed, Restored and Created Wetlands. Ecological Engineering , Plants in constructed, restored and created wetlands, 61 (December): 501–504 (2013). Yin, Hang, Wenyan Liang, & Xin Cao. Self-Purification Mode of Still-Water Ponds in Urban Parks Based on In Situ Ecological Remediation Design. Land 11 (10): 1676 (2022). Chen, Xinyi, Juan Wu, Fei Zhong, Shaole Yu, Kejian Chen, Xiangqian Zeng, Dongling Duan, & Shuiping Cheng. Mechanism of Iris Sibirica and Aeration Combination on Promoting the Water Purification Performance of Constructed Wetland under Low Temperature. Environmental Science and Pollution Research 31 (13): 19715–19724 (2024). Samadi, Mohammad Taghi, Ghorban Asgari, Mostafa Leili, & Sonia Chavoshi. Integrated Modified Septic Tank and Constructed Wetland: An Alternative Green Technology for Phytoremediation of Highly Polluted Leachate. Biomass Conversion and Biorefinery (2023). Zhang, Xianqi, Bingsen Duan, Shaoyu He, & Yaohui Lu. Simulation Study on the Impact of Ecological Water Replenishment on Reservoir Water Environment Based on Mike21——Taking Baiguishan Reservoir as an Example. Ecological Indicators 138 (May): 108802 (2022). Zhao, Lidong, Ting Zhang, Jianzhu Li, Libin Zhang, and Ping Feng. Numerical Simulation Study of Urban Hydrological Effects under Low Impact Development with a Physical Experimental Basis. Journal of Hydrology 618 (March): 129191 (2023). Ji, Zhen-Gang, & Kang-Ren Jin. An Integrated Environmental Model for a Surface Flow Constructed Wetland: Water Quality Processes. Ecological Engineering 86 (January): 247–61 (2016). Song, Chao, Quanxi Shao, Xiaohong Chen, and Yingshan Liang. Evaluating and Understanding Tide-River Interactions Based on Both Physical Models and Data Analysis. Journal of Hydrology 632 (March): 130765 (2024). Chondros, Michalis K., Iason G. Koutsourelakis, & Constantine D. Memos. A Boussinesq-Type Model Incorporating Random Wave-Breaking. Journal of Hydraulic Research 49(4): 529–538 (2011). Zhang, Xianqi, Yu Qi, Fang Liu, Haiyang Li, & Shifeng Sun. Predicting Effects of Non-Point Source Pollution Emission Control Schemes Based on VMD-BiLSTM and MIKE21. Environmental Modeling & Assessment (2024). Vega De Lille, M. I., M. A. Hernández Cardona, Y. A. Tzakum Xicum, G. Giácoman-Vallejos, & C. A. Quintal-Franco. Hybrid Constructed Wetlands System for Domestic Wastewater Treatment under Tropical Climate: Effect of Recirculation Strategies on Nitrogen Removal. Ecological Engineering 166 (August): 106243 (2021). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4552346","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":318342040,"identity":"f3b2e029-a3f2-4560-963e-f9246e29fa6d","order_by":0,"name":"Xing Xiong","email":"","orcid":"","institution":"Department of Landscape Architecture, Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xing","middleName":"","lastName":"Xiong","suffix":""},{"id":318342042,"identity":"c2f386e5-f479-45b4-ba76-f7bdbe701620","order_by":1,"name":"Shanrui Yang","email":"","orcid":"","institution":"Department of Landscape Architecture, Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Shanrui","middleName":"","lastName":"Yang","suffix":""},{"id":318342044,"identity":"e458f1f0-4160-403d-bf36-39b5e527245e","order_by":2,"name":"Junxiang Zhang","email":"","orcid":"","institution":"Department of Landscape Architecture, Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Junxiang","middleName":"","lastName":"Zhang","suffix":""},{"id":318342045,"identity":"02c7d446-b306-4998-af4c-93cdf249098b","order_by":3,"name":"Jiafan Chen","email":"","orcid":"","institution":"Department of Landscape Architecture, Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jiafan","middleName":"","lastName":"Chen","suffix":""},{"id":318342046,"identity":"21e49416-af9b-4c01-ab44-45f667b50ce6","order_by":4,"name":"Xinyu Zhang","email":"","orcid":"","institution":"Department of Landscape Architecture, Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Zhang","suffix":""},{"id":318342047,"identity":"e9085cd2-06fb-4a3b-81f2-3b0a7587fddc","order_by":5,"name":"Qinghai Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYDACZjCSkGNgOADlEqvFmAQtUGWJDQg2AcB3nPfw64Iai/TtjIefSTBUWCc2sJ89gFeL5GG+NOsZxyRydzYcM5NgOJOe2MCTl4BXi8FhHjNjHjaJ3A0HDphJMLYdTmyQ4DEgQss/iXSDA8e/STD+I06L8WPeNokEgwNngLY0EKFFEmgLM2+fhOGGA2eKLRKOpRu38eTg18J3/ozxZ55vdfIGN45vvPGhxlq2n/0Mfi3ACGSTADMkDjAwJABpNvzqwVqYP4AZ/A0E1Y6CUTAKRsEIBQBEnEQAN6WLFgAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Landscape Architecture, Nanjing Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Qinghai","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-06-09 03:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4552346/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4552346/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59189081,"identity":"67f2d1d0-0a17-4822-8ec5-bd36605b0832","added_by":"auto","created_at":"2024-06-27 12:45:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27505425,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Aerial view of the E-CW (b) Location of the Yingtai E-CW.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4552346/v1/bb4c38758bce92be35df7987.jpg"},{"id":59188598,"identity":"2d43edcc-25fe-4b57-a9a0-a45a59ec9263","added_by":"auto","created_at":"2024-06-27 12:37:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31482468,"visible":true,"origin":"","legend":"\u003cp\u003eCombination of Wetland Units in the E-CW.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4552346/v1/b82b0f0172330caf5367eca7.jpg"},{"id":59188596,"identity":"fe012d8a-eb6a-4a32-ba85-70f08985bfcd","added_by":"auto","created_at":"2024-06-27 12:37:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9637450,"visible":true,"origin":"","legend":"\u003cp\u003eE-CW grid delineation and terrain construction; (a) Construction of Triangular Mesh; (b) 3D Terrain model; (c) Current direction; (d) Still water depth.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4552346/v1/b88df689adc3905b19644edf.jpg"},{"id":59188593,"identity":"d5c4698c-fc6c-446f-963b-c5fdbc9b3da7","added_by":"auto","created_at":"2024-06-27 12:37:28","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":9603162,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Velocity vector of E-CW; (b) Velocity vector of overflow weir; (c) Velocity vector of landscape pile.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4552346/v1/7bc29fb264e825c5efa74e4c.jpg"},{"id":59188595,"identity":"21ce0c79-4789-413e-8661-1e7e02bb816d","added_by":"auto","created_at":"2024-06-27 12:37:28","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":9809153,"visible":true,"origin":"","legend":"\u003cp\u003eConcentrations of the pollutant in the effluent from each unit.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4552346/v1/bb26f189ea86be381aa666fc.jpg"},{"id":59188594,"identity":"1e9ccf00-93af-499b-96ed-0443beb694e8","added_by":"auto","created_at":"2024-06-27 12:37:28","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":8101146,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Pollutant concentrations at 5 sampling points; (b) Scattered comparison of simulated and measured Values of pollution indicators.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4552346/v1/797aa160037ad55bd27adad5.jpg"},{"id":60870841,"identity":"71b33203-645b-4752-8391-26ee145c45e3","added_by":"auto","created_at":"2024-07-23 04:47:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":96850955,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4552346/v1/27cb0aad-92b5-41bf-a11a-2af64b3db6b4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Numerical Simulation and Evaluation of Constructed Wetland Designs for Effluent Treatment Based on MIKE21","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRapid urbanization results in the production of substantial amounts of industrial and domestic wastewater, exacerbating the problem of urban water pollution\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The improper management of this wastewater frequently leads to higher levels of Chemical Oxygen Demand (COD), Biological Oxygen Demand (BOD), nitrogen, and phosphorus\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This, in turn, depletes dissolved oxygen levels, encourages eutrophication, and facilitates the spread of environmental hazards. In this context, the challenges associated with intercepting the release of untreated wastewater and leveraging natural waterways to mitigate surface water pollution underscore the pivotal role of wastewater treatment facilities in controlling pollution\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Effluents, which comprise the secondary wastewater generated during the wastewater treatment process, still contain residual amounts of organic matter, nitrogen, phosphorus, and other pollutants even after undergoing preliminary treatment. Thus, additional treatment of the discharged water is necessary to comply with environmental protection standards. For decades, researchers have been investigating numerous nature-based solutions as cost-effective alternatives to conventional wastewater management systems, which are expensive and technically complex\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In this context, the use of constructed wetlands, which are nature-based solutions, not only ensure the sustainable management of wastewater but also offer additional ecosystem services and social benefits, thereby supporting a wider concept of a circular economy.\u003c/p\u003e \u003cp\u003eConstructed Wetlands (CWs) represent cost-effective, high-performance, and environmentally friendly infrastructures that effectively process pollutants by utilizing the collaborative actions of soil, macrophytes, and microbial communities\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. These systems utilize natural processes to transform and eliminate contaminants in wastewater, making them an excellent solution for managing wastewater and a promising alternative in decentralized wastewater treatment strategies\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Furthermore, when compared to the traditional methods of treating wastewater, CWs require less energy and have lower maintenance expenses\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Correspondingly, water bodies that were previously categorized as Class V or lower can attain quality standards ranging from Class III to IV after treatment. In addition, the nutrient removal efficiency in CWs is governed by the dynamics of the root zone, which are influenced by the plants' ability to adsorb nutrients and the design of the system. Thus, implementing subsurface flow wetlands using tailored media, followed by their integration into surface or subsurface flow systems, can significantly enhance the purification of effluents.\u003c/p\u003e \u003cp\u003eAs a densely populated and economically robust province in China, Jiangsu Province has experienced a significant degree of urbanization and faces numerous challenges in the realm of urban sewage treatment. To date, the province has successfully completed 39 constructed wetland projects, spanning over an area of more than 1400 hectares (hm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e). Post-construction, the wastewater from these facilities are likely to meet the Grade IV standards for surface water quality. Accordingly, the establishment and operation of these CWs serve a dual purpose: 1. enhancing the quality of discharged water and reducing operational expenses for sewage treatment facilities; 2. elevating the ecological safety and sustainability of important water systems such as the Yangtze River, Tai Lake, and the Grand Canal.\u003c/p\u003e \u003cp\u003eTo optimally utilize limited spaces and control the associated costs, the rational design of CWs is essential. Contemporary research methodologies for CW design include both experimental simulations and numerical simulations\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The former approach, referred to as a \"black box\", primarily depends on empirical data from previous experiments to investigate the mechanisms of pollutant removal, the selection of plant species and substrates, and the resilience of CWs to sudden increases in pollutant loads in real-world scenarios\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, the use of numerical modeling has become increasingly prevalent for predicting flow patterns and other internal processes in recent years due to the challenges associated with identifying wetland types, process combinations, and load capacities in CW design. Furthermore, it also allows for the evaluation and refinement of CW functionalities\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In this context, two-dimensional models have been extensively used in CW research to analyze the impact of geometric configurations on retention times, flow dynamics, and pollutant kinetics despite their drawbacks\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Numerous studies have recently incorporated three-dimensional numerical models into CW simulations, thus, revealing differences in total phosphorus (TP) removal efficiencies between horizontal subsurface flow (HSSF) and wave subsurface flow (WSSF) CWs\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In this context, the use of tools such as MODFLOW and MODPATH enable the numerical modeling of CW flow patterns, thereby enhancing the design process by evaluating the impact of different hydraulic conductivities and inflow patterns on the distribution of flow across CW sections\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In wetland design, accounting for the hydrodynamics and plant configurations within CWs is imperative. Selecting species capable of tolerating and removing pollutants is essential because aquatic plants have significant tolerance to pollutants, facilitate hyperaccumulation of contaminants, exhibit rapid growth, and demonstrate the ability to accumulate biomass, rendering them vital components of CWs\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Several studies have emphasized the potential of using different aquatic plants in CWs to replicate the natural dynamics of pollutant concentrations in wetlands by adjusting purification parameters\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Thus, the selection of appropriate plant species is a key design factor that must be considered when designing Engineered Constructed Wetlands (E-CWs) in order to greatly enhance their efficiency\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. However, field measurement approaches require a significant amount of time for long-term water quality assessments, despite their predominance in recent research\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Furthermore, in addressing the complexities of designing CWs across varied types, sizes, and technological processes, the use of physical simulation experiments and onsite measurements often presents prohibitive costs and practical challenges. Consequently, these issues have pivoted the focus towards leveraging numerical models to simulate the hydrodynamics and water quality in CWs, providing a cost-efficient and practical approach for design exploration. Presently, a wide range of numerical models is used to support the optimization of plant arrangements and refinement of design methodologies in wetlands. Among these, widely utilized water environment simulation tools include QUAL2K, WASP7, EPDRiv1, and MIKE.\u003c/p\u003e \u003cp\u003eMIKE21, a mathematical model developed by the Danish Hydraulic Institute (DHI), the \"HD\" hydrodynamic module in MIKE21 is capable of simulating flow field conditions that are unique to E-CW, the \"Eco-Lab\" module's \"AD\" water quality component enables the modification of pollutant diffusion and biochemical reaction coefficients, thereby facilitating simulations of pollutant concentration and diffusion processes \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. These simulations assist in optimizing design by comparing them to discharge standards. The present study seeks to utilize these numerical models to simulate the water environment of E-CW in order to evaluate the effectiveness of pollutant removal and the purification performance of individual wetland units, while considering cost and spatial limitations. The E-CW, equipped with separate purification modules and a flat base, received incoming flow from nearby sewage treatment plant-processed effluent. Accordingly, the following three objectives were proposed: 1. to develop a MIKE21 numerical model for E-CW by combining wetland unit processes, in order to simulate hydrodynamic and purification processes; 2. to collect pollutant concentration data for each E-CW unit for the calibration of purification parameters; and 3. to compare these findings with long-term water quality monitoring data, in order to assess the accuracy of the simulation, thus aiming to provide valuable insights for the optimized process design of constructed wetlands for effluent treatment.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe Yingtai Constructed Wetland for Effluent (E-CW) is situated in the High-tech Zone of Hai'an City, Nantong, Jiangsu Province, China. The wetland is situated in close proximity to a densely populated urban area and is surrounded by rice paddies and rivers. Correspondingly, the Yingtai Wastewater Treatment Plant is tasked with treating both industrial and domestic wastewater from the entire High-tech Zone. The treated effluent then flows into the adjacent E-CW, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Upon its completion, the E-CW is anticipated to generate effluent that conforms to Class IV surface water standards, enabling diverse possibilities for reuse. More precisely, 35% of the processed wastewater is allocated for the purpose of watering urban green spaces, maintaining roads, and serving as cooling water in industrial operations. On the other hand, the remaining 65% is intended for discharge intended to be released into nearby rivers. This dual strategy not only enhances the quality of the wastewater treatment plant's output but also contributes to ecological improvement by decreasing pollution in aquatic ecosystems. The initiative also reduces overall pollutant discharge, thereby improving the ecological health of the aquatic environment in Hai'an City.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTechnology and vegetation in E-CW\u003c/h2\u003e \u003cp\u003ePrevious studies indicate that subsurface flow wetlands exhibit elevated expenses in terms of construction, operation, and maintenance, and are susceptible to substrate clogging \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In contrast, surface flow wetlands, despite having smaller hydraulic loads, are more cost-effective and require less maintenance. In the current study, the E-CW primarily focused on the treatment of low-pollution tailwater (meeting Grade A standards) from wastewater treatment plants. Accordingly, there was no need for a higher hydraulic load. The process flow consisted of an integrated system of \"ecological stabilization pond\u0026thinsp;+\u0026thinsp;surface flow wetland\u0026thinsp;+\u0026thinsp;porous media ecological filter bed\u0026thinsp;+\u0026thinsp;submerged plant wetland.\" The primary objective of the pretreatment area in the ecological pond was to facilitate denitrification, remove nitrogen, and enhance the biodegradability of organic matter in the water. It is worth noting that the pond does not have aeration or push-flow systems. Instead, it relies on the remaining dissolved oxygen in the effluent and the hydraulic gradient for the flow from upstream to downstream. The conversion of recalcitrant organic matter into biodegradable forms enhances the biodegradability of the effluent, leading to a reduction in COD. Furthermore, the ecological pond utilizes ecological islands to redirect the tailwater: On one hand, it prolongs the path of the wastewater to create a continuous flow state; on the other hand, the ecological islands fulfill the criteria for landscaping and naturalization. Moreover, the shallow water type surface flow wetland system with controllable water level operates similarly to natural marsh wetlands\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Plants independently carry out processes like growth, wilting, and decay, relying on the humus in the substrate and organic matter from plant residues as a source of carbon to complete denitrification and de-nitration. During periods of high water levels, the wetland system functions as a conventional surface flow wetland, facilitating the organized cultivation of plants and timely rotation of crops. In addition, the porous media filter bed unit is designed to capture and remove suspended solids from water. It achieves this by using porous and lightweight concrete fillers that are effective in adsorbing phosphorus and facilitating biodegradation, aiming primarily to improve water clarity. The submerged plant unit is positioned at the terminal point of the wetland water quality purification system, boasting the most extended period of hydraulic retention. Furthermore, the final step in ensuring water quality is achieved through the comprehensive action of pollutants acting on the roots of submerged plants, microbial degradation, and redox reactions.\u003c/p\u003e \u003cp\u003eThe ecological pond had a design area of 10,240 m\u0026sup2;. The water surface area of the pond was 4,853 m\u0026sup2; and it had an effective volume of 9,708 m\u0026sup3;. The slope ratio was established as 1:3, the water depth is set at 2 m, with a surcharge of 0.5 m, and the hydraulic retention time of 7.06 h. In addition, the ecological floating islands occupied 25% of the pond's water surface. They were designed in a hexagonal shape, with each side of the unit measuring 1.0 m. Each floating island covered an area of 2.6 m\u0026sup2;, and 40 such islands were assembled into 12 composite islands, each spreading over 104 m\u0026sup2;, uniformly across the pond. The planting on these composite islands followed a radial pattern, commencing from the center and extending outwards. It began with \u003cem\u003eNymphaea\u003c/em\u003e, followed by \u003cem\u003eIris pseudacorus\u003c/em\u003e, \u003cem\u003eIpomoea aquatica\u003c/em\u003e, and \u003cem\u003eHydrocotyle vulgaris\u003c/em\u003e. The vegetation on the slope was arranged in a descending order from the crest to the base, with plants such as \u003cem\u003eIris sibirica\u003c/em\u003e, \u003cem\u003eVallisneria\u003c/em\u003e, and \u003cem\u003ePhragmites\u003c/em\u003e. The plants were planted from 0.3 m above to 0.5 m below the water surface.\u003c/p\u003e \u003cp\u003eThe surface flow wetland section had a design area of 10,240 m\u0026sup2;, a water surface area of 9,613 m\u0026sup2;, and an effective volume of 6,738 m\u0026sup3;. The slope ratio in this case was also 1:3, with a typical water depth of 0.7 m, a surcharge of 0.3 m, and a hydraulic retention time of 4.9 h. Water level adjustment is crucial for the regrowth of aquatic plants during the plant rotation seasons. Therefore, from mid-April to late November, the wetland's operation shifted to a low water level. This was achieved by using rectifying weirs to control the water level, ensuring it does not exceed 0.3 m in depth.\u003c/p\u003e \u003cp\u003eThe porous media filter bed had a surface area of 2,100 m\u0026sup2;, with an effective depth of 0.8 m. The primary component of this system comprised a porous autoclaved aerated concrete, which significantly improves the efficiency of phosphorus removal. The filler beds used in this construction consisted of units measuring 2.0 m \u0026times; 0.5 m \u0026times; 0.4 m, which were filled with lightweight concrete granules and enclosed within gabion meshes. These beds were placed between units of porous concrete dams. Foaming agents were mechanically aerated to achieve complete expansion and then thoroughly mixed with cement slurry to create the porous autoclaved aerated concrete filler. This filler was distinguished by its large specific surface area, which was conducive to pollutant adsorption and facilitated the formation of microbial films.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe submerged plant wetland covered an area of 6,898 m\u0026sup2;, with a water surface area of 6,488 m\u0026sup2; and an effective volume of 16,225 m\u0026sup3;. The slope ratio was maintained at a ratio of 1:3, the water depth was maintained at 2.5 m, with a surcharge of 0.3 m, and a hydraulic retention time of 11.8 h. The bank slope, which had a gradual incline of 1:3 into the water, created an ecological slope protection. The arrangement of vegetation in the pond varied from terrestrial herbs near the shore to hydrophilic plants, emergent plants, and finally submerged plants, with the bottom of the pond populated by \u003cem\u003eScirpus\u003c/em\u003e, \u003cem\u003eTypha\u003c/em\u003e, and \u003cem\u003eLemna\u003c/em\u003e. The process flow of E-CW is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3D model construction\u003c/h2\u003e \u003cp\u003eTo begin constructing the model, the initial step involved to generate a wetland boundary data file. This was carried out using the Mesh Generator tool in MIKE to import the boundary terrain file of the E-CW. Meanwhile, the inlets and outlets of the E-CW, as well as the boundary lines, were defined in the model. Once the wetland model and the actual terrain data are confirmed to be consistent, the E-CW was partitioned using unstructured triangular mesh elements, resulting in a total of 4,438 triangular adaptive meshes. Subsequently, the 3D terrain model was generated by drawing the y-axis terrain of the model based on the precise elevation of the engineering construction\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, and is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eHydrodynamic Simulation Approach\u003c/h2\u003e \u003cp\u003eFollowing the construction of the 3D E-CW model, the HD module of the MIKE-21 software was utilized to carry out hydrodynamic simulations on the E-CW. The hydrodynamic simulation utilized the three-dimensional incompressible Navier-Stokes (N-S) equations with an R-E averaged distribution, while adhering to the Boussinesq assumption of eddy viscosity\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This module enabled to comprehensively capture the intricacy and intricacy of fluid motion within the intricate wetland system. In addition, the simulation process incorporated hydrodynamic behaviors such as buoyancy effects and bottom friction within the wetland system, ensuring the model's accuracy and reliability in characterizing turbulent structures and water movement. The continuity equation for two-dimensional water flow is shown in Eq.\u0026nbsp;(1), and the momentum equations for two-dimensional water flow are presented in Equations (2) to (4).\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\begin{array}{c}\\frac{\\partial h}{\\partial t}+\\frac{\\partial h\\stackrel{-}{u}}{\\partial x}+\\frac{\\partial h\\stackrel{-}{v}}{\\partial y}=hS\\#（1）\\end{array}.\\)\u003c/span\u003e\u003c/span\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\frac{\\partial h\\stackrel{-}{u}}{\\partial t}+\\frac{\\partial h\\stackrel{-}{u}}{\\partial x}+\\frac{\\partial h\\stackrel{-}{uv}}{\\partial y}=-gh\\frac{\\partial \\eta }{\\partial x}-\\frac{h}{{\\rho }_{0}}\\frac{\\partial {P}_{\\text{a}}}{\\partial x}-\\frac{g{h}^{2}}{2{\\rho }_{0}}\\frac{\\partial \\rho }{\\partial x}+\\frac{{\\tau }_{\\text{x}\\text{x}}}{{\\rho }_{0}}-\\frac{{\\tau }_{\\text{b}\\text{x}}}{{\\rho }_{0}}-\\frac{1}{{\\rho }_{0}}\\left(\\frac{\\partial {s}_{\\text{x}\\text{x}}}{\\partial x}+\\frac{\\partial {s}_{\\text{x}\\text{y}}}{\\partial y}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}+\\frac{\\partial }{\\partial x}\\left(h{T}_{\\text{x}\\text{x}}\\right)+\\frac{\\partial }{\\partial y}\\left(h{T}_{\\text{x}\\text{y}}\\right)+h{u}_{\\text{s}}S\\#\\left(2\\right),\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\frac{\\partial h\\stackrel{-}{v}}{\\partial t}+\\frac{\\partial h\\stackrel{-}{uv}}{\\partial x}+\\frac{\\partial h{\\stackrel{-}{v}}^{2}}{\\partial y}=-gh\\frac{\\partial \\eta }{\\partial y}-\\frac{h}{{\\rho }_{0}}\\frac{\\partial {p}_{\\text{a}}}{\\partial y}-\\frac{g{h}^{2}}{2{\\rho }_{0}}\\frac{\\partial \\rho }{\\partial y}+\\frac{{\\tau }_{\\text{s}\\text{y}}}{{\\rho }_{0}}-\\frac{{\\tau }_{\\text{b}\\text{y}}}{{\\rho }_{0}}-\\frac{1}{{\\rho }_{0}}\\left(\\frac{\\partial {s}_{\\text{y}\\text{x}}}{\\partial x}+\\frac{\\partial {s}_{\\text{y}\\text{y}}}{\\partial y}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}+\\frac{\\partial }{\\partial x}\\left(h{T}_{\\text{x}\\text{y}}\\right)+\\frac{\\partial }{\\partial y}\\left(h{T}_{\\text{y}\\text{y}}\\right)+h{v}_{\\text{s}}S\\#\\left(3\\right),\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$h\\stackrel{-}{u}={\\int }_{-d}^{\\eta }udz$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}h\\stackrel{-}{v}={\\int }_{-d}^{\\eta }vdz\\#\\left(4\\right).\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the aforementioned equations, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(t\\)\u003c/span\u003e\u003c/span\u003e represents time, and \u003cem\u003ex\u003c/em\u003e and \u003cem\u003ey\u003c/em\u003e denote the coordinates within the Cartesian coordinate system. The variable \u003cem\u003ed\u003c/em\u003e denotes the static water depth, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\eta\\)\u003c/span\u003e\u003c/span\u003e signifies the water level. The total water depth is given by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(h=d+\\eta\\)\u003c/span\u003e\u003c/span\u003e. The terms \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(u\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(v\\)\u003c/span\u003e\u003c/span\u003e represent the velocity components in the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(x\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(y\\)\u003c/span\u003e\u003c/span\u003e directions, respectively. The letter \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(g\\)\u003c/span\u003e\u003c/span\u003e is used to denote the acceleration due to gravity, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho\\)\u003c/span\u003e\u003c/span\u003e symbolizes the water's density. The term \u003cem\u003eT\u003c/em\u003e encompasses lateral stress components, which include aspects like viscous friction, turbulent friction, and differential advection. In addition, the components of radiation stress in various directions are represented by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{\\text{x}\\text{x}}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{\\text{x}\\text{y}}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{\\text{y}\\text{y}}\\)\u003c/span\u003e\u003c/span\u003e, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(S\\)\u003c/span\u003e\u003c/span\u003e denotes the source and sink terms. The velocities of water flow associated with these source and sink terms are indicated by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({u}_{\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({v}_{\\text{s}}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eWater Quality Simulation\u003c/h2\u003e \u003cp\u003eThe E-CW inlet was serviced by a wastewater treatment plant that spanned 3.3 hectares and had a sewage treatment capacity of 20,000 m\u0026sup3;/day. The effluent quality of the sewage treatment plant is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWater quality parameters of the Yingtai Sewage Treatment Plant effluent.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWater quality types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOD/(mg.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTN/\u003c/p\u003e \u003cp\u003e(mg.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e-N/\u003c/p\u003e \u003cp\u003e(mg.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTP/\u003c/p\u003e \u003cp\u003e(mg.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe water quality of the effluent from Phase I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e36\u0026ndash;51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u0026ndash;0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe designed effluent water quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe measured average effluent water quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe current study aimed to use MIKE21 software to simulate water quality and meet the design requirement of improving the effluent at the E-CW outlet to meet the standards of Category IV surface water. Initially, the software utilized the Time Series tool to preprocess water quality data at the inlet and outlet of each unit. This process involved obtaining time series files through linear interpolation. Subsequently, the initial calculation parameters for the purification of water quality were established, which encompassed the first-order reaction kinetic coefficients and the absorption coefficients of plants and microbes. Then, the \"AD\" module from ECO Lab was coupled with the hydrodynamic simulation outcomes to carry out water quality simulation. Following six simulation iterations, the water quality purification parameters were adjusted to achieve calibration. The water quality model relied on vertically integrated conservation equations for mass and momentum, as depicted in Eq.\u0026nbsp;(5).\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\begin{array}{c}\\frac{\\partial \\text{A}c}{\\partial t}+\\frac{\\partial \\text{Q}c}{\\partial x}-\\frac{\\partial }{\\partial x}\\left(\\text{A}\\text{D}\\frac{\\partial c}{\\partial x}\\right)=-AKc+{C}_{2}c\\#\\left(5\\right).\\end{array}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eIn this above-mentioned equation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(c\\)\u003c/span\u003e\u003c/span\u003e denotes the concentration of the targeted water quality indicator while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{D}\\)\u003c/span\u003e\u003c/span\u003e represents the diffusion coefficient, which is a measure of how quickly pollutants in the water spread in the direction of water flow. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{K}\\)\u003c/span\u003e\u003c/span\u003e represents the total attenuation coefficient, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{2}\\)\u003c/span\u003e\u003c/span\u003e denotes the concentration of the source and sink terms. When considering the impact of convective diffusion in the specific water body, this approach involved utilizing central and spatial implicit difference formats to computationally solve the equation set. These equations can be derived through estimation.\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}D=a{v}^{\\text{b}}\\#\\left(6\\right).\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWithin these equations, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(v\\)\u003c/span\u003e\u003c/span\u003e stands for velocity, derived from the outcomes of the hydrodynamic model. The parameters \u003cem\u003ea\u003c/em\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(b\\)\u003c/span\u003e\u003c/span\u003e are predefined settings within the model's framework.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eHydrodynamic Simulation results\u003c/h2\u003e \u003cp\u003eThe present study incorporated hydrodynamic model parameters, such as bed roughness, eddy viscosity coefficient, and configurations for internal structures, from established wetland designs in similar research, in order to develop the constructed wetland design \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The objective was to refine the model to ensure that the differences between the simulated and measured water levels and flow velocities did not exceed 5%, as specified in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. By setting the model parameters to a specific eddy viscosity coefficient of 0.28 and a bed roughness of 32 m\u003csup\u003e1/3\u003c/sup\u003e.s, the hydrodynamic simulations were able to effectively keep the relative errors below 5%. The ecological pond component was simulated using a water level of 2.0 m and flow velocities ranging from 0.12 to 0.18 m/s, in order to optimize the initial purification of effluent. The reduced flow rate was crucial in enabling the ecological floating beds to effectively reduce pollutant levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In contrast, the surface flow wetland section, characterized by a water level of 0.72 m and flow velocities ranging from 0.36\u0026ndash;0.60 m/s, exhibited increased flow rates as a result of the design of the overflow weir plate. This increased flow rate promoted the growth and function of aerobic microorganisms, which improved the breakdown of organic matter. Furthermore, it prevented suspended solids and particles from progressing to later stages of treatment. In addition, the design of the porous media filter bed component included a water level of 0.85m and flow velocities ranging from 0.18\u0026ndash;0.36m/s. This design enabled suspended solids to settle or be captured more effectively. The reduced flow speeds also minimized shear forces and prevented particle resuspension. This improved the efficiency of removing pollutants from the bed. Moreover, the prolonged duration of water retention in the bed maximized pollutant adsorption by leveraging the large specific surface areas of the porous and foam concrete materials, allowing for more efficient interactions between microorganisms and pollutants. In addition, the submerged plant component, simulated at a water level of 2.50 m with flow velocities ranging from 0.12\u0026ndash;0.18 m/s, demonstrated the positive impact of reduced speeds and longer hydraulic retention times on sedimentation, adsorption, and biodegradation mechanisms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters of the E-CW hydrodynamic model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWetland unit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEcological stabilization pond\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurface flow wetland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubmerged plant wetland\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalculation period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3600s, total of 2208 steps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3600s, total of 2208 steps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3600s, total of 2208 steps\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEddy viscosity coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBed roughness (m\u003csup\u003e1/3\u003c/sup\u003e.s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflow boundary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlow boundary 0.014 m\u003csup\u003e3\u003c/sup\u003e.s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFlow boundary 0.014 m\u003csup\u003e3\u003c/sup\u003e.s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlow boundary 0.014 m\u003csup\u003e3\u003c/sup\u003e.s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutflow boundary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlow boundary \u0026minus;\u0026thinsp;0.014 m\u003csup\u003e3\u003c/sup\u003e.s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater level boundary 0.72 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWater level boundary\u003c/p\u003e \u003cp\u003e2.90 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternal structure setting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eweir discharge coefficient of 1.838, weir width of 1.8 m, and weir height of 0.5 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eWater Quality Simulation and Parameter Calibration\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLeveraging the hydrodynamic model parameters of the E-CW, the water quality at the wetland's outflow was simulated by integrating the \"AD\" module of ECO Lab. The simulation spanned from November 1, 2020, to January 31, 2021, with a time step of 3600 s over 2208 steps. The model incorporated fundamental hydrodynamic principles and introduced diffusion coefficients and processes for pollutants, with coefficients ranging from 0.01 to 20\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\)\u003c/span\u003e\u003c/span\u003em\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e/s. The initial conditions for the inflow pollutants were determined using the monitored water quality: COD\u0026thinsp;=\u0026thinsp;40 mg/L, TN\u0026thinsp;=\u0026thinsp;10.61 mg/L, TP\u0026thinsp;=\u0026thinsp;0.29 mg/L, NH\u003csub\u003e3\u003c/sub\u003e-N\u0026thinsp;=\u0026thinsp;2.04 mg/L. The concentration changes of various pollutants exhibited significant variations due to the disparities in water levels, flow rates, and processes across each wetland unit (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). As observed, the ecological pond unit demonstrated a significant impact on the removal of COD, likely due to the prolonged contact time between COD and the microbes and plants on the ecological floating islands. In addition, the porous media filter bed unit played a crucial role in removing phosphorus. The porous structure of the media was able to capture suspended solids and particles that were attached to phosphorus, leading to their deposition on the filter bed. Accordingly, the simulations revealed that the ecological pond achieved average removal rates of 12.02% for COD, 22.94% for TN, 8.36% for TP, and 21.36% for NH\u003csub\u003e3\u003c/sub\u003e-N. Furthermore, in the surface flow wetland, the corresponding rates were 12.41% for COD, 7.82% for TN, 22.65% for TP, and 8.95% for NH\u003csub\u003e3\u003c/sub\u003e-N. On the other hand, the porous media filter bed unit achieved removal rates of 34.61% for COD, 9.77% for TN, 7.33% for TP, and 33.83% for NH\u003csub\u003e3\u003c/sub\u003e-N. Lastly, the submerged plant wetland had removal rates of 2.98% for COD, 24.91% for TN, 23.40% for TP, and 18.10% for NH\u003csub\u003e3\u003c/sub\u003e-N.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBefore the simulation began, 5 cross-sections were set up at the endpoints and outlets of the four wetland units to record the concentration changes of the four pollutants along the way (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Accordingly, the E-CW showed significant removal effects on the four pollutants, with the pollutants gradually decreasing along the wetland unit and the outflow water quality being stable. Based on the water quality data from the inlet and outlet of the wetland, the average removal rates of the four pollutants in the entire tailwater wetland were calculated to be 51.14%, 43.14%, 63.82%, and 54.38%, respectively. Accordingly, their pollutant reduction loads were estimated to be 1.87 g.m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e.d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 0.46 g.m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e.d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 0.098 g.m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e.d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 0.031 g.m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e.d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. In summary, the effluent treated by the E-CW can meet the Class IV surface water standard.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eError analysis of simulated and measured values of E-CW water quality\u003c/h2\u003e \u003cp\u003eThe accuracy of the established water quality model was assessed by analyzing and comparing simulated and actual measurements of four pollution indicators. illustrates the comparison between the simulated and measured values of different pollutants at the outflow of the E-CW, at regular intervals. Although fluctuations in the concentration of the tailwater output was observed at each step, the measured and simulated values of the pollutants exhibited a consistent pattern of change. Accordingly, the predictive accuracy of the model was assessed in relation to actual measurements using the Kruskal-Wallis non-parametric test. The results showed that the P-values for all state variables were greater than 0.05. This result corroborates the hypothesis that the simulated and measured values exhibit a similar trend, suggesting that there was no notable disparity in their distributions. While the Kruskal-Wallis test can detect statistical differences between simulated and measured values, it is not adequate for fully assessing the accuracy of model calculations. The primary purpose of the Kruskal-Wallis test is to ascertain whether multiple independent samples originate from the identical distribution. However, it does not yield direct quantitative data regarding the specific discrepancies between simulated and measured values. Thus, the relative errors between the simulated and measured values were calculated after confirming the consistency of the distribution trends in order to more accurately evaluate the model's accuracy. The experiment demonstrated a good fit between simulation results and measured values, as indicated by the relative errors of 4.88% for COD, 10.02% for TN, 3.25% for TP, and 12.69% for NH\u003csub\u003e3\u003c/sub\u003e-N. Furthermore, all of these relative errors were within the acceptable range of 15%. In general, a warm climate facilitates the proliferation of plants and microorganisms, thereby enhancing the uptake and breakdown of pollutants\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Thus, empirical measurements were performed between November 2020 and January 2021, coinciding with the winter season in Hai'an City. The measurements indicate that the purification of different pollutants all complied with the standards, thus demonstrating the viability of E-CW for year-round sewage treatment.\u003c/p\u003e \u003cp\u003eHowever, the present study also encompasses other errors. In terms of water quality monitoring, long-term monitoring of wetland water quality was not conducted, as carried out in similar studies reported previously. This was attributed to the fact that the primary objective of the current study was to establish a reference for the use of numerical simulation methods in wetland design. Furthermore, in terms of environmental indicators, the wetland was found to be rarely affected by severe weather conditions and excessive hydraulic loads. As a result, excluded wetland climate was excluded from the simulation scope of the current study. However, this is likely to have resulted in some inaccuracies in the numerical simulation results of the wetland. Thus, future investigations focusing on the effects of various seasons and plant species on the efficiency of purification in constructed wetlands, in conjunction with the study of hydrodynamics and water quality simulation are deemed essential, develop a comprehensive workflow for the design of numerical simulation-assisted constructed wetlands.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn the current study, a numerical model of the E-CW was constructed based on engineering design principles. By conducting six simulation runs and carefully calibrating the parameters involved, along with three months of thorough water quality monitoring, the post-construction hydrodynamic behavior and water quality dynamics of the wetland were successfully simulated. The hydrodynamic simulation exhibited a relative error of less than 5%, while the water quality simulation demonstrated a relative error of less than 15%, thereby satisfying our simulation expectations. In addition, the numerical model of E-CW was able to generate the local flow velocity vectors for each purification unit, enabling us to optimize the flow velocity and hydraulic retention time of the wetland units during the design phase. This optimization enhanced the conditions for the growth and activity of wetland plants and microorganisms resulting in a more favorable environment. For instance, the combined action of anaerobic and aerobic zones in surface flow wetlands facilitated the elimination of nitrogen. Furthermore, when the relative error standard was met, the average removal rates of COD, TP, TN, and NH\u003csub\u003e3\u003c/sub\u003e-N in E-CW were also significantly higher. This was observed by collecting their concentrations at 5 cross-sections of the wetland unit, which all showed varying degrees of decrease (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The use of MIKE21 facilitated the visual demonstration of the movement of pollutants through wetlands, aiding in the design of complex wetland process combinations. This, subsequently, improved the efficiency of wetland units in purifying pollutants, thereby allowing for the optimization of plant configuration based on the specific purification requirements of the wetland. As observed, the findings derived from this study are likely to aid in implementing numerical simulation techniques in the design of constructed wetlands.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eAuthor Contributions\u003c/h3\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation was performed by X.Z., data collection was performed by J.Z. and J.C., data analysis and simulation experiment were performed by S.Y. In addition, S.Y. and X.X. wrote the main manuscript text and prepared figures 1-6. All authors reviewed the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis research is supported by the Ministry of Education of China\u0026apos;s Fund for Humanities and Social Sciences Research (Grant No. 23YJAZH200).\u003c/p\u003e\n\u003ch3\u003eData availability\u003c/h3\u003e\n\u003cp\u003eThe data used or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch3\u003eEthics approval\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003e\u0026nbsp;All authors approved the final manuscript for submission and publications.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSaeed, Tanveer, Nehreen Majed, Tanbir Khan, \u0026amp; Hena Mallika. 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Hybrid Constructed Wetlands System for Domestic Wastewater Treatment under Tropical Climate: Effect of Recirculation Strategies on Nitrogen Removal. \u003cem\u003eEcological Engineering\u003c/em\u003e \u003cstrong\u003e166\u003c/strong\u003e(August): 106243 (2021). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Constructed wetland, pollutant removal, Numerical simulation, MIKE21, Hydrodynamic, Water Quality","lastPublishedDoi":"10.21203/rs.3.rs-4552346/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4552346/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eConstructed Wetlands for Effluent treatment (E-CW) play a vital role in the degradation of pollutants, purification of water, and the improvement of freshwater ecosystems. However, conventional designs often lack a methodical approach for quantifying the efficacy of these wetlands. The present study utilized the MIKE21 Hydrodynamic (HD) module in conjunction with the ECO-Lab Water Quality (AD) module to perform a numerical simulation of the Constructed Wetland for Effluent. The key parameters involved in effective water purification were calibrated and the system's ability to treat effluents from wastewater treatment facilities was assessed. The findings demonstrated significant removal efficiencies for Chemical Oxygen Demand (COD), Total Nitrogen (TN), Total Phosphorus (TP), and ammonia (NH\u003csub\u003e3\u003c/sub\u003e-N), with average rates of 51.14%, 43.14%, 63.82%, and 54.38%, respectively. In addition, the simulations exhibited a high degree of accuracy, with hydrodynamic predictions deviating by less than 5% and water quality approximations by less than 15%. Additionally, the use of numerical simulations can provide valuable guidelines for the future design and functional assessment of wetlands by offering crucial insights that aid in the optimization of purification processes and vegetation selection.\u003c/p\u003e","manuscriptTitle":"Numerical Simulation and Evaluation of Constructed Wetland Designs for Effluent Treatment Based on MIKE21","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-27 12:37:23","doi":"10.21203/rs.3.rs-4552346/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ca13ae9e-bb51-4baf-8d02-a06ba34e049c","owner":[],"postedDate":"June 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33659186,"name":"Earth and environmental sciences/Environmental sciences/Environmental chemistry/Pollution remediation"},{"id":33659187,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":33659188,"name":"Biological sciences/Ecology/Wetlands ecology"}],"tags":[],"updatedAt":"2024-07-23T04:38:31+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-27 12:37:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4552346","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4552346","identity":"rs-4552346","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}