Development and evaluation of a process-based model to assess nutrient removal in floating treatment wetlands

Science of the Total Environment 694 (2019) 133633

Contents lists available at ScienceDirect

Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Development and evaluation of a process-based model to assess nutrient removal in floating treatment wetlands Yan Wang, Bowen Sun ⁎, Xueping Gao, Na Li State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, No. 135, Yaguan Road, Haihe Education Park, Jinnan District, Tianjin 300072, China

H I G H L I G H T S • A FTW model considered temperature effective and phosphorus dynamic was developed. • Nitrogen removal was sensitive to parameters about plant and microbial. • The response of nitrogen removal and plant uptake contribution was explored. • Nitrogen to phosphorus ratio in plant tissue was an indicator of nutrient limitation.

G R A P H I C A L

A B S T R A C T

Plant growth submodel Photosynthesis

Nitrogen submodel N2+N2O

Phosphorus submodel Nutrients transfer

Biomass allocation

Denitrification

NO3Uptake

Uptake

NH4+

Mineralization

Decay

Decay

Decay

Nitrification

OrgN

Sedimentation

OrgP

Mineralization

Sedimentation

PO43Coprecipitation

Sediment

a r t i c l e

i n f o

Article history: Received 24 May 2019 Received in revised form 23 July 2019 Accepted 26 July 2019 Available online 30 July 2019 Editor: Ashantha Goonetilleke Keywords: Nitrogen removal Phosphorus removal Global sensitivity analysis Plant uptake contribution FTW operating conditions Plant characteristic parameters

a b s t r a c t Modelling is a useful tool for comprehensively describing the processes occurring in floating treatment wetlands (FTWs). However, temperature effects and phosphorus dynamics are not considered in the current FTW models. Therefore, a process-based model comprised of a plant growth submodel, a nitrogen dynamic submodel and a phosphorus dynamic submodel was developed to understand the complicated processes occurring in FTWs. The model was fully calibrated using a mesocosm FTW system operated for 168 days. Global sensitivity analysis revealed that nitrogen removal performance was predominantly sensitive to parameters representing plant characteristics and microbial activity. Because of the high concentration of organic matter, mineralization and sedimentation played important roles in nitrogen and phosphorus removal. In addition, the coprecipitation rate of phosphate also had a significant influence on phosphorus removal performance. When further investigation was applied to understand the behavior of the model, the ratio of nitrogen to phosphorus in plant tissue was found to be an indicator of the nutrient limitation in the water column. Furthermore, the model illustrated that both FTW operating conditions and plant characteristic parameters exerted an important influence on nitrogen removal and plant uptake contribution. Therefore, the selection of appropriate operating conditions and plant species can achieve high nutrients removal and make effective use of plants in FTWs. The model provides a useful

⁎ Corresponding author. E-mail addresses: [email protected] (Y. Wang), [email protected] (B. Sun), [email protected] (X. Gao), [email protected] (N. Li).

https://doi.org/10.1016/j.scitotenv.2019.133633 0048-9697/© 2019 Elsevier B.V. All rights reserved.

2

Y. Wang et al. / Science of the Total Environment 694 (2019) 133633

tool for assessing the nutrients removal performance of FTWs and for evaluating strategies for them in design and operation. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Floating treatment wetlands (FTWs) consist of aquatic plants established upon a floating raft, floating on water surface without occupying any land area (Headley and Tanner, 2012; Lucke et al., 2019). FTWs, as an ecologically engineered plant-periphyton complex remediation technology, provide a promising alternative to traditional approaches to polluted water treatment (Gao et al., 2019). The technique has been widely used for the treatment of stormwater (Lynch et al., 2015; McAndrew et al., 2016), domestic sewage (Benvenuti et al., 2018), lake and river water (Dunne et al., 2015; Ning et al., 2014; Schwammberger et al., 2019), aquaculture wastewater (Li and Li, 2009) and industry wastewater (Tara et al., 2019). The nutrients removal performance of FTWs depends on a large amount of factors, such as the compositions and concentrations of inflow nutrients (Dai et al., 2018; Tan et al., 2019), hydraulic retention time (Yang et al., 2008), plant species and coverage (Garcia Chance and White, 2018; Saad et al., 2016), microbial activity (Gao et al., 2018) and temperature (Van de Moortel et al., 2010). Modelling has been shown to be a powerful approach to improving the understanding of the complicated processes of nutrient removal in aquatic ecosystems. Therefore, some effort has been made to develop the mathematical models for FTWs, which can primarily be grouped into two types viz., black-box models and process-based models. For the black-box models, Headley and Tanner (2012) used a P-k-C* model to calculate the first-order areal rate coefficient (k). Wang and Sample (2013) proposed a first order kinetics model, where the apparent uptake rates (vf) of FTWs were used to describe the nitrogen and phosphorus removal performance. Compared to the black-box models, that fail to consider the inherent mechanisms involved in nutrient removal, process-based models specifically consider the various processes occurring in FTWs. Therefore, process-based models are considered to be a powerful tool for understanding the system performance (Defo et al., 2017). So far, two system dynamics models have been developed for FTWs (Marimon et al., 2013; McAndrew and Ahn, 2017). The model proposed by Marimon et al. (2013) represented the physical, chemical and biological processes of nitrogen removal. In their model, ammonium, nitrate and organic nitrogen existing in water, sediment and plant tissue were incorporated into the nitrogen cycle. McAndrew and Ahn (2017) also developed a system dynamics model, which was comprised of a hydrological submodel, a plant growth submodel and a nitrogen submodel. Among these existing processes-based models, temperature adjustments were unnecessary because the climate effects on plant growth and microbial activity were deemed to be minimal for the study area (Marimon et al., 2013). However, it has been reported that when temperature ranges from 10 °C to 35 °C, plant growth rates increase accordingly (Hu et al., 2010; Xu et al., 2017). Furthermore, low temperatures could inhibit the microbial activity and the plant uptake of nutrients in FTWs (Wang et al., 2012). Therefore, consideration of the effects of temperature would make these models more applicable to regions with wide temperature fluctuation. Additionally, the biomass production by plants in FTWs can be limited by both nitrogen and phosphorus availability (Bi et al., 2019). For example, it has been reported that plant growth and nutrients use showed a strong dependence on the relative supply of nitrogen and phosphorus (Güsewell, 2005). Therefore, in cases where phosphorus is limited, this should be taken into account in FTW model. In this study, a process-based model for nitrogen and phosphorus removal in FTWs was developed. Then a global sensitivity analysis was

used to identify the influential parameters for nutrients removal performance. It is still under debate whether plant uptake contributes most to nutrient removal in FTWs. Aiming at this problem, we hypothesized that nitrogen removal performance and plant uptake contribution are influenced by FTW operating conditions and plant characteristic parameters. The developed model was applied to a series of modelling scenarios to test the hypothesis. 2. Materials and methods 2.1. Model description The floating treatment wetlands model (FTWMOD) is a biogeochemical process-based model that was developed to describe the behavior of nitrogen and phosphorus in FTWs. The FTWMOD consists of three interconnected submodels: (1) a plant growth submodel; (2) a nitrogen dynamics submodel; and (3) a phosphorus dynamics submodel. The model is integrated by the fourth-order Runge-Kutta method with a 0.1 day time step. Several assumptions were made in the FTWMOD to simplify the complex ecological processes and to overcome a lack of available experimental data, as follows: (1) the nitrogen/phosphorus content of shoots/ roots is calculated as a product of shoot/root biomass and the ratio of nitrogen/phosphorus to shoot/root dry biomass, which is assumed to be a constant in this model; (2) different forms of organic nitrogen/phosphorus are simplified to one variable due to a lack of supporting data; (3) algae are not included in the model because low algal biomass is observed in the water column constructed with FTWs (West et al., 2017); (4) the limiting effect of light on plant growth is not considered. 2.1.1. Plant growth submodel The plant growth submodel incorporates two state variables, namely shoot biomass (biomass above the floating raft) and root biomass (biomass below the floating raft). The structure of the submodel is modified based on a submerged aquatic vegetation model (Cerco and Moore, 2001). In the FTWMOD, shoots convert solar energy to plant biomass during photosynthesis, while roots assimilate nutrients directly from the water to promote plant growth. Root-litter fallen into the water will decompose to organic and inorganic matter, while all shoot-litter remains at the surface of the floating raft. Shoot and root growth are expressed as Eqs. (1) and (2): dBSH ¼ ðð1−FSTRÞP SH −RSH ÞBSH dt

ð1Þ

dBRO ¼ FSTR  P SH  BSH −RRO  BRO dt

ð2Þ

where BSH and BRO are the shoot and root biomass of the plants (g DW m−2), respectively; PSH is the growth rate of plant shoots (day−1); RSH and RRO are decay rate of plant shoots and roots, respectively (day−1); and FSTR is the fraction of shoot photosynthetic products directly transferred to the roots. The growth rate of plant shoots in FTWs is limited by nutrient concentrations in the water column and temperature. As plants growing in the substrates of FTWs are often exposed to direct sunlight, water depth and transparency have little impact on the plant growth. The effect of light limitation is not, therefore, considered in the FTWMOD (f3

Y. Wang et al. / Science of the Total Environment 694 (2019) 133633

roots at the reference temperature (day−1), respectively; and KTRSH and KTRRO are the effects of temperature on the shoots and roots decay rates (°C−1), respectively.

(I) = 1.0). PSH can be calculated by Eq. (3). P SH ¼ PMSH  f 1 ðNÞ  f 2 ðT Þ  f 3 ðI Þ

ð3Þ


f 1 ðNÞ ¼ min

NH4 þ NO3 PO4 ; KHNPL þ ðNH4 þ NO3 Þ KHPPL þ PO4

8 2 > < e−KT1P ðT−TP1P Þ f 2 ðT Þ ¼ 1:0 > : −KT2P ðT−TP2P Þ2 e



2.1.2. Nitrogen dynamic submodel Organic nitrogen (OrgN), ammonia nitrogen (NH+ 4 -N) and nitrate nitrogen (NO− 3 -N) in the water column are modeled in the nitrogen dynamic submodel. Fig. 1 depicts the conceptual model for the nitrogen dynamics in FTWs. Inflow and decay of root tissue are sources of OrgN. The decay of shoot tissue is not considered because the shootlitter will fall onto the surface of the floating raft rather than directly into the water column. Root decomposition products consist of OrgN and NH+ 4 -N as described by Chapra (2008). Mineralization and sedimentation are considered to be the main removal mechanisms of OrgN in the model. NH+ 4 -N in the model can be reduced by nitrification and plant uptake. Ammonia volatilization is not included in the model because pH in FTWs is generally b8 (Di Luca et al., 2019; Wang et al., 2014), while appreciable ammonia volatilization may occur at pH N 8 (Hammer and Knight, 1994). The major processes considered in modelling the transformation of NO− 3 -N include denitrification and plant up− take. The kinetic equations for OrgN, NH+ 4 -N and NO3 -N in the water column (mg L−1) can be expressed as Eqs. (8), (9) and (10).

ð4Þ

if T ≤TP1P if TP1P bTbTP2P if T ≥TP2P

ð5Þ

where PMSH is the maximum shoots growth rate under optimal conditions (day−1); f1(N) and f2(T) are the nutrients and temperature limitation coefficients, respectively; KHNPL and KHPPL are the plant halfsaturation constant for nitrogen and phosphorus uptake from water, respectively (mg L−1); T is the water temperature (°C); TP1p b T b TP2p is the optimal temperature range for plant growth (°C); KT1p is the effect of temperatures below TP1p on plant growth (°C−2); and KT2p is the effect of temperature above TP2p on plant growth (°C−2). The decay rate of shoots and roots can be calculated by Eqs. (6) and (7). RSH ¼ RM SH  eKTRSH ðT−TRSH Þ

ð6Þ

RRO ¼ RM RO  eKTRRO ðT−TRRO Þ

ð7Þ

3

dOrgN SFTW ¼ FNOPL  RRO  LPLNM  BRO −NMI  OrgN− dt VW 1 KNSED  OrgN þ ðQ  OrgNIN −Q OUT  OrgNÞ V W IN

where TRSH and TRRO are reference temperatures for shoots and roots (°C), respectively; RMSH and RMRO are the reference decay rates for shoots and

Plant growth submodel Photosynthesis

Nitrogen submodel

Phosphorus submodel

N2+N2O

Nutrients transfer

Biomass allocation

Denitrification

NO3Uptake

Uptake

Decay

Nitrification

NH4+

Mineralization

Decay

Decay

OrgN

Sedimentation

OrgP

Mineralization

PO43Coprecipitation

Sedimentation

Sediment Fig. 1. Nitrogen and phosphorus processes in floating treatment wetlands.

ð8Þ

4

Y. Wang et al. / Science of the Total Environment 694 (2019) 133633

dNH4 SFTW ¼ NMI  OrgN þ FNIPL  RRO  LPLNM  BRO −Nit  NH4 − dt Vw ð9Þ SFTW 1 LPLNM  PNPL  P SH  BSH þ ðQ IN  NHIN −Q OUT  NH4 Þ V Vw

mineralization and sedimentation are the main sinks. Plant uptake and coprecipitation are considered to be the main removal mechanisms of PO3− 4 -P in the model, as reported by Tanner and Headley (2011). The −1 kinetic equations for OrgP and PO3− ) 4 -P in the water column (mg L are expressed as Eqs. (17) and (18).

dNO3 SFTW ¼ Nit  NH4 − LPLNMð1−PNPL Þ  P SH  BSH − dt Vw 1 Denit  NO3 þ ðQ IN  NOIN −Q OUT  NO3 Þ V

dOrgP SFTW ¼ FPOPL  RRO  LPLPM  BRO −PMI  OrgP− dt Vw 1 KPSED  OrgP þ ðQ  OrgP IN −Q OUT  OrgPÞ V W IN

ð10Þ

where SFTW is the water surface area (m−2). VW is the water volume (m3). FNOPL and FNIPL are the fractions of nitrogen metabolized by plants as OrgN and NH+ 4 -N, respectively; LPLNM is the ratio of nitrogen to plant biomass. NMI and KNSED are the mineralization rate and the sedimentation rate of OrgN (day−1), respectively; PNPL is the NH+ 4 -N uptake preference factor for the plants; Nit and Denit are the nitrification rate and the denitrification rate (day−1), respectively; QIN and QOUT are the influent and the effluent flow rate (m3 day−1), respectively; and OrgNIN, NHIN and NOIN are the influent concentration of OrgN, NH+ 4 -N and −1 NO− ), respectively. 3 -N (mg L NMI, Nit and Denit in Eqs. (8), (9) and (10) are expressed as a function of temperature, as described by the flowing equations: NMI ¼ NMIM  eKNMIðT−TNMIÞ

ð11Þ

NH4 f ðT Þ Nit ¼ Nitm KHNit þ NH4 Nit ( f Nit ðT Þ ¼

2

e−KNit1ðT−TNitÞ 2 e−KNit2ðT−TNitÞ

ð12Þ if T ≤TNit if T NTNit

ð13Þ

NO3 f ðT Þ Denit ¼ Denitm KHDenit þ NO3 Denit ( f Denit ðT Þ ¼

2

e−KDenit1ðT−TDenit Þ 2 e−KDenit2ðT−TDenit Þ

ð14Þ

if T ≤TDenit if T NTDenit

ð15Þ −1

where NMIM is the mineralization rate of OrgN (day ) at the reference temperature TNMI (°C); KNMI is the effect of temperature on OrgN mineralization (°C−1); TNit and TDenit are the optimum temperature for nitrification and denitrification (°C), respectively; Nitm and Denitm are the maximum nitrification and denitrification rates at TNit and Tdenit (day−1), respectively; KHNit and KHDenit are the nitrification halfsaturation constant for NH+ 4 -N and the denitrification half-saturation −1 constant for NO− ), respectively; KNit1 is effect of tempera3 -N (mg L tures below TNit on the nitrification rate (°C−2); KNit2 is effect of temperatures above TNit on the nitrification rate (°C−2); KDenit1 is effect of temperatures below TDenit on the denitrification rate (°C−2); and KDenit2 is effect of temperatures above TDenit on the denitrification rate (°C−2). − The preferential uptake of NH+ 4 -N by plants over NO3 -N is modeled using the preference factor PNPL (Gargallo et al., 2017) as Eq. (16), PNPL

NH4  NO3 NH4  KHNPL þ ¼ ðKHNPL þ NH4 ÞðKHNPL þ NO3 Þ ðNH4 þ NO3 ÞðKHNPL þ NO3 Þ ð16Þ

ð17Þ

dPO4 SFTW SFTW ¼ PMI  OrgP þ FPI PL  RRO  LPLPM  BRO − LPLPM  P SH  BSH − dt Vw Vw 1 ðQ  POIN −Q OUT  PO4 Þ KPCO  PO4 þ ð18Þ V W IN

where FPOPL and FPIPL are the fractions of metabolized phosphorus by plants as OrgP and PO3− 4 -P, respectively; LPLPM is the ratio of phosphorus to plant biomass. PMI and KPSED are the mineralization rate and the sedimentation rate of OrgP (day−1), respectively; KPCO is the ratio of −1 coprecipitation of PO3− ); and OrgPIN and POIN are the influent 4 -P (day 3− concentrations of OrgP and PO4 -P (mg L−1), respectively. PMI in Eq. (17) is expressed as a function of temperature, as follows: PMI ¼ PMIM  eKPMIðT−TPMIÞ

ð19Þ

where PMIM is the mineralization rate of OrgP (day−1) at reference temperature TPMI (°C); and KPMI is the effect of temperature on OrgP mineralization (°C−1). 2.1.4. Other processes of the model Dissolved oxygen (DO), being a key parameter for water quality assessment in aquatic systems, is also included in the model. The main DO sources include reaeration and external loads. The main DO sinks include plant roots decomposition, NH+ 4 -N nitrification and chemical oxygen demand (COD) oxidation. The kinetic equations for DO and COD in the water column (mg L−1) can be expressed as Eqs. (20) and (21). dDO SCFW RRO  LPLOM  BRO −AONT  Nit  NH4− ¼ K r ðDOs −DOÞ− Vw dt DO 1 ðQ  DOIN −Q OUT  DOÞ KCOD  COD þ KHCOD þ DO V w IN

ð20Þ

dCOD DO 1 ¼− KCOD  COD þ ðQ  CODIN −Q OUT  CODÞ dt KHCOD þ DO V w IN ð21Þ where Kr is the reaeration coefficient (day−1); DOs is the saturation DO concentration (mg L−1); LPLOM is the rate of oxygen to plant biomass; AONT is the mass of DO consumed per unit mass of NH+ 4 -N; KHCOD is the half-saturation constant of DO required for COD oxidation (mg L−1); KCOD is the oxidation rate of COD (day−1); DOIN and CODIN are the influent concentration of DO and COD (mg L−1), respectively. KCOD is expressed as a function of temperature, as follows: KCOD ¼ KRCOD  eKTCODðT−TRCODÞ

ð22Þ

where KRCOD is the oxidation rate of COD (day−1) at reference temperature TRCOD (°C); and KTCOD is the effect of temperature on COD oxidation (°C−1).

where KHNPL is the half-saturation coefficient for nitrogen uptake for plants.

2.2. Calibration and validation

2.1.3. Phosphorus dynamics submodel Organic phosphorus (OrgP) and phosphate (PO3− 4 -P) in the water column are modeled in the phosphorus dynamics submodel. Fig. 1 depicts the conceptual model for phosphorus dynamic in FTWs. As for OrgP, inflow and decay of root-litter are the main sources,

The geometry, boundary condition and initial condition of the model were chosen to match the mesocosm described in Wang and Sample (2014) and Wang et al. (2014). These studies were chosen for the calibration and validation because they reported both time variation of plant biomass and nutrient removal efficiency. The experiment was

Y. Wang et al. / Science of the Total Environment 694 (2019) 133633

conducted in tanks contained 133 L pond water at 0.32 m operational water depth. Floating treatment wetlands, with an area of 0.29 m2 and plant density of 10.4 plants/m2, were constructed in the tanks. The experiment lasted for 168 days and was equally divided into six stages, during which the water temperature ranged from 12.4 to 25.3 °C. Fresh pond water was pumped to the tanks every seven days after the replaced water was drained. Plants in FTWs were replaced every stage to measure the plant biomass. The initial water quality of the pond water and plant (Pontederia cordata L.) biomass at the beginning of each stage were taken from Wang and Sample (2014) and Wang et al. (2015). The initial conditions and all the model parameters are listed in Tables S1 and S2. Six parameters (mean removal efficiency of TN, OrgN, TP, OrgP, shoot biomass and root biomass) in each stage obtained from Wang and Sample (2014) and Wang et al. (2014) were used to calibrate and validate the model. The root mean square error (RMSE) and the coefficient of determination (R2) were applied to evaluate the deviation between the experimental data and simulation data (Gao et al., 2017). sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi N ∑i¼1 ðMi −Si Þ2 RMSE ¼ N 32    N N ∑i¼1 M i ∑i¼1 Si 6 7 7 R2 ¼ 6 4rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  2ffirffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  2ffi5 N N N N 2 2 N∑i¼1 Si − ∑i¼1 Si N∑i¼1 M i − ∑i¼1 Mi

ð23Þ

2

N N∑i¼1 Mi Si −

ð24Þ

where Mi and Si are the experimental data and simulation data, respectively; and N is the number of data sets. 2.3. Sensitivity analysis A global sensitivity analysis was conducted to better understand the response of the FTWMOD outputs to the input parameters. PAWN is a moment-independent sensitivity analysis method, which obtains sensitivity indices of parameter Xi based on the distance between the unconditional cumulative distribution function of output Y (FY(Y)) and the conditional cumulative distribution function of output Y (FY∣X i(Y)) using Eq. (25) (Pianosi and Wagener, 2015, 2018):   KSðX i Þ ¼ max  F Y ðY Þ−F YjX i ðY Þ Y

ð25Þ

where KS(Xi) is the Kolmogorov-Smirnov statistic of Xi. As KS depends on the fixed value of Xi, the median of KS over all possible values of Xi was used to drive the PAWN sensitivity index Ti (0 b Ti b 1): T i ¼ med ½KSðX i Þ Xi

ð26Þ

Eqs. (25) and (26) were approximated using the generic sampling method as described by Pianosi and Wagener (2018). In the evaluation, 8000 Latin hypercube samples and 10 equally spaced intervals for the input parameters were determined through trials and analysis. However, because of the limited size samples, the Ti for non- influential parameters may be slightly larger than zero. Therefore, a dummy parameter was introduced to identify the influential parameters. Dummy parameter, which has no influence of model structure and output, was calculated according to the input-output dataset (Khorashadi Zadeh et al., 2017; Pianosi and Wagener, 2018). Parameters for which Ti are significantly larger than the dummy Ti can be classified as influential; while those with Ti equal to or less than the dummy Ti are potentially non-influential. Bootstrapping was used to estimate the 95% confidence interval. PAWN was performed by varying all of the parameters listed in Table 2, with a ±30% factor value range, while holding all other

5

− parameters constant. The average removal efficiency of NH+ 4 -N, NO3 N, TN, PO3− 4 -P and TP were chosen as the performance metrics.

2.4. Model application Application of the FTWMOD was used to better understand the influence of FTW operating conditions (i.e., hydraulic retention time (HRT), plant density (BPL); the initial concentration of TN in the water column (TN), the ratio of inorganic nitrogen to organic nitrogen (IN: OrgN) and the ratio of nitrogen to phosphorus in the water column (N:P)) and plant characteristic parameters (i.e., the maximum growth rate of plant (PMSH); the ratio of nitrogen to dry biomass of plant (LPLNM), the ratio of nitrogen to phosphorus of plant (LPLNM:LPLPM) and the fraction of shoot photosynthetic products directly transferred to root (FSTR)) on TN removal efficiency (RETN) and plant uptake contribution to TN removal (PLTN) in FTWs. The baseline of FTW operating conditions were determined according to the literature and experience, while the plant characteristic parameters were based on the calibrated model. The FTWMOD was run by changing the value of one parameter (Table 1) at a time while keeping the other parameters constant. For example, the response of RETN and PLTN to the initial concentration of TN in the water column was iteratively simulated within the TN initial concentration range of 0.5–15.0 mg L−1 while other parameters were set to the baseline. To further illustrate the behavior of the model, the RETN and PLTN were modeled when two parameters were changed simultaneously in some cases. 2.5. Development and analysis tools The FTWMOD is developed with a FORTRAN code using the Intel FORTRAN Compiler XE 13.1 with Microsoft Visual Studio 11.0. Oneway ANOVA was used to evaluate the significance difference of PAWN sensitivity index between model parameters and dummy parameter at 0.01 level using SPSS 18.0 (SPSS Inc., Chicago, IL, USA). 3. Results and discussion 3.1. Calibration and validation results The simulation data about the mean removal efficiency of TP, OrgP, TN and OrgN in each stage (calculated in the same way as Wang and Sample (2014)) and the plant biomass at the end of each stage (exported from the FTWMOD) were compared with the experimental data (Fig. 2). The coefficient of determination (R2) and the RMSE were 0.83 and 0.04 for OrgN, 0.81 and 0.06 for TN, 0.71 and 0.04 for OrgP, 0.83 and 0.03 for TP, 0.86 and 2.82 for shoot biomass and 0.98 and 3.37 for root biomass, respectively. These results demonstrated the good performance of the FTWMOD. Before the water in the tanks was replaced, the concentration of TN and TP were exported from the model (Fig. 3). During the first three stages, the outflow concentration of TN and TP decreased when the average temperature in each stage increase from 21.9 °C to 25.3 °C. Then, as the average temperature decreased to 12.4 °C in the last three stages, the removal performance of TN and TP also decreased. Furthermore, the shoot and root biomass at the end of each stage were exported from the

Table 1 Baseline and variation range values of parameters for model application. TN Parameters HRT BPL (mg (day) (g m−2) L−1) Baseline Minimum Maximum

7 1 28

29 1 100

3 0.5 15

IN: N: OrgN P 2 0.5 10

10 1 50

PMSH LPLNM (day−1) (mg g−1) 0.16 0.05 0.5

20.24 5 50

LPLNM: LPLPM

FSTR

21.31 1 50

0.37 0.1 0.7

6

Y. Wang et al. / Science of the Total Environment 694 (2019) 133633

Fig. 2. R2 and RMSE for removal efficiency of (a) OrgN, (b) TN, (c) OrgP and (d) TP, and (e) shoot biomass and (f) root biomass.

model, then the relatively growth rate of shoots and roots was calculated (Fig. 4). The modelling results showed that the relatively growth rate of shoots and roots was high in the first three stages, while dropped below zero in the last two stages. The temporal evolution of TN and TP concentration and plant relatively growth rate indicated that the FTWMOD performed well in a temperature range of 12.4 to 25.3 °C.

3.2. Model sensitivity analysis Sensitivity analysis can be used to identify the most influential parameters for the performance metrics. The sensitivities of the 25 model parameters (Table 2) with respect to the five performance metrics are shown in Fig. 5.

There were 13–16 parameters that showed a significant influence on nitrogen removal efficiency (Fig. 5a–c). It appears that plant uptake and microbial nitrification and denitrification are the main processes of inorganic nitrogen removal in FTWs. Among all of the plant characteristic parameters, PMSH and LPLNM were the two most influential parameters − + − for NH+ 4 -N and NO3 -N removal. Specifically, the NH4 -N and NO3 -N removal was influenced by KT1P, which means that temperature appears to be a crucial factor for plant nitrogen removal performance. As such, it is necessary to consider the effect of temperature in FTW models, especially when applied to regions experiencing a wide range of tempera− tures. Interestingly, the sensitivity index of FSTR for NH+ 4 -N and NO3 N removal were significant higher than the dummy parameter, being ranked 6th and 8th, respectively. A Phragmites australis growth dynamic model also showed that the fraction of photosynthesis translocation to

Fig. 3. Temporal evolution of the simulated TN and TP concentration.

Y. Wang et al. / Science of the Total Environment 694 (2019) 133633

7

respectively (Fig. 5d). Organic phosphorus mineralization (PMIM) and sedimentation (KPSED) also influenced the PO3− 4 -P removal. As shown in Fig. 5e, the flowing parameters were found to have significant impacts on the TP removal (listed in the decreasing order of sensitivity index): KPSED, KPCO, PMIM, PMSH and RMRO. And some experiment studies also reported that sedimentation is the main process for TP removal in FTWs (Tanner and Headley, 2011). Additionally, the decay of plant roots releases nutrient back the water column (Chapra, 2008; Zhou and Wang, 2010). As such, the sensitivity index of RMRO for TP removal was slightly greater than the dummy parameter. 3.3. Nitrogen removal performance in FTWs Nitrogen removal in FTWs is mediated by anammox, nitrification, denitrification, plant uptake, filtration by root systems and sedimentation, which greatly depends on FTW operating conditions and plant characteristic parameters. Fig. 6 illustrates how RETN and PLTN are affected by the FTW operating conditions and plant characteristic parameters. Fig. 4. Simulated shoot and root biomass at the end of each stage and shoot and root relatively growth rate in each stage.

below-ground structures was one of the most sensitive parameters for plant biomass (Asaeda and Karunaratne, 2000), which would further influence the nutrient uptake. Additionally, the other plant characteristic parameters such as RMSH, RMRO and KHPPL also had a significant influ− ence on NH+ 4 -N and NO3 -N removal. Particularly, KHPPL, PMIM and KPCO influenced the nitrogen removal, meaning that phosphorus could be a limiting factor for plant growth in some cases. Regarding the microbial processes, it appeared that NH+ 4 -N removal was influenced by nitrification (as represented by Nitm and KHNit), while NO− 3 N removal was mediated by nitrification and denitrification (as represented by Denitm and KHDenit). More importantly, because of the high ratio of organic nitrogen to inorganic nitrogen in the inflow, the sensi− tivity indices of NMIM for NH+ 4 -N and NO3 -N removal were both ranked 2nd. Coprecipitation and plant uptake were the two main removal processes for PO3− 4 -P in FTWs. As such, KPCO, LPLLM and PMSH had a significant influence on PO3− 4 -P removal, ranking 1st, 4th and 5th,

3.3.1. The influence of FTW operating conditions Model simulation indicated that RETN increased with HRT (Fig. 6a). RETN was only 21.4% for HRT = 1 day. When HRT increased to 7 days, RETN increased almost threefold to 62.9%. However, when HRT was further increased to 28 days, RETN increased slowly to 78.7%. A FTW process-based model developed by McAndrew and Ahn (2017) also reported a positive relationship between HRT and nitrogen removal. Model simulations showed that RETN increased slightly at initial plant density below 25.0 g m−2 but stabilized at 66% when initial plant density exceeded 25.0 g m−2 (Fig. 6b). This finding was consistent with the modelling results of Marimon et al. (2013), who reported that inorganic nitrogen load in water column decreased as the plant density increase from 160 to 320 plant m−2 with 25% and 30% FTW surface area coverage; while further increasing the plant density to 480, the decreased tendency is slowing. Some experimental studies have also reported that initial plant density had a limiting effect on TN removal (Garcia Chance and White, 2018; Xiang and Peng, 2015). When TN concentration in the water column is low, nutrient availability can be a limiting factor for plant growth and microbial metabolism (Chapra, 2008). As such, RETN exhibited an upward trend when TN inflow concentration

Table 2 FTWs parameters used for global sensitivity analysis. Parameters groups Plant

Nitrogen

Phosphorus

Parameter PMSH KT1P KT2P KTRSH KTRRO FSTR RMSH RMRO KHNPL KHPPL LPLNM KNSED NMIM KNMI Nitm Denitm KNit KDenit KHNit KHdenit LPLPM KPSED KPCO PMIM KPMI

Description

Units

Value

Maximum growth rate of plant shoot Effect of temperature below TP1P on shoot growth rate Effect of temperature above TP2P on shoot growth rate Effect of temperature on shoot respiration rate Effect of temperature on root respiration rate Fraction of shoot photosynthetic products directly transferred to root Shoot death rate at reference temperature Root death rate at reference temperature Half-saturation constant for nitrogen uptake Half-saturation constant for phosphorus uptake Ratio of nitrogen to plant biomass Sedimentation rate of OrgN Mineralization rate of OrgN in reference temperature Effect of temperature on mineralization rate of OrgN Maximum nitrification rate Maximum denitrification rate Effect of temperature on nitrification rate Effect of temperature on denitrification rate Nitrification half-saturation constant for ammonium Denitrification half-saturation constant for nitrate Ratio of phosphorus to plant biomass Sedimentation rate of OrgP Coprecipitation rate of PO3− 4 -P Mineralization rate of OrgP in reference temperature Effect of temperature on mineralization rate of OrgP

day−1 °C−2 °C−2 °C−1 °C−1 – day−1 day−1 mg L−1 mg L−1 mg N g−1 day−1 day−1 °C−1 day−1 day−1 °C−2 °C−2 mg L−1 mg L−1 mg P g−1 day−1 day−1 day−1 °C -1

0.16 0.06 0.05 0.10 0.12 0.37 0.03 0.02 0.01 0.005 20.24 (Wang et al., 2014) 0.03 0.04 0.05 0.29 0.62 0.0001 0.0001 0.1 0.1 0.95 (Wang et al., 2014) 0.09 0.28 0.07 0.06

8

Y. Wang et al. / Science of the Total Environment 694 (2019) 133633

− 3− Fig. 5. PAWN sensitivity indices for the FTWMOD with respect to the removal efficiency of (a) NH+ 4 -N, (b) NO3 -N, (c) TN, (d) PO4 -P and (e) TP. The red lines present the dummy parameter results. Parameters below the dashed line had significant influence on the performance metrics compared to the dummy parameter (p b 0.01). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

was increased from 0.5 mg L−1 to 1.4 mg L−1. As the inflow TN concentration was increased, nutrients were no longer a limitation for plant growth and microbial metabolism but a nuisance, which resulted in a decrease of RETN. Anammox, nitrification, denitrification and plant uptake are the main process of nitrogen removal in FTWs (Pavlineri et al., 2017). As such, the higher proportion of inorganic nitrogen to organic nitrogen in the inflow, the more efficiency of TN removal (Fig. 6d). When N:P b 20 (TN = 3 mg L−1), RETN remained almost constant at 63% and this was irrespective of changes in N:P. However, a negative relationship between N:P and RETN was apparent as a result of the lack of phosphorus when N:P N 20. This critical value of N:P is thought to be related to the ratio of nitrogen to phosphorus in plant tissue (LPLNM: LPLPM). 3.3.2. The influence of plant characteristic parameters A high number of studies have demonstrated that there are great differences in nutrient removal performance among different plant species (Geng et al., 2017; West et al., 2017). The model simulations showed that RETN increased with PMSH (Fig. 6f) and LPLNM (Fig. 6g). However, when PMSH N 0.2 day−1 or LPLNM N 20, the increasing trend of RETN slowed down and stabilized at approximately 70% and 66%, respectively. When LPLNM:LPLPM b 10, RETN increased with LPLNM:LPLPM apparently; when LPLNM:LPLPM N 10, RETN remained relatively constant at 63% irrespective of the changes in LPLNM:LPLPM. The response of RETN to LPLNM:LPLPM (Fig. 6h) and N:P (Fig. 6e) showed a striking contrast. At the critical point in Fig. 6e and Fig. 6h, phosphorus became a limitation for plant growth when N:P increased or when LPLNM:LPLPM decreased. It has been previously reported that the LPLNM:LPMPM can be an effective predictor of nutrient limitation (Koerselman and Meuleman, 1996; Tessier and Raynal, 2003). To further illustrate the effect of N:P and LPLNM:LPMPM on RETN, an additional scenario was simulated where N:P and LPLNM:LPLPM were changed simultaneously. As shown in Fig. 7a, on the critical line, the value of N:P was roughly equal to LPLNM:LPLPM—which ranged from 2 to 20 among various plant species used in FTWs (Keizer-Vlek et al., 2014)—when the latter was b20. The result of this simulation scenario suggested the necessity of integrating the phosphorus submodel, especially when phosphorus is a limitation factor for plant growth in FTWs. The sensitivity analysis results suggested that FSTR had a significant influence on TN removal. Fig. 6i showed that RETN decrease with an increase in FSTR. In fact, a larger FSTR means that more shoot

photosynthetic products are allocated to the roots. However, the primary productivity is positively related to shoot biomass (Hantush et al., 2013). The value of FSTR depends on the environment experienced by the plants, such as light, nutrients and water (Hermans et al., 2006; Poorter and Nagel, 2000). 3.4. Plant uptake contribution to nitrogen removal in FTWs Whether plant uptake is the major contributor to nutrient removal in FTWs has always been highly debated. The contribution of plant uptake to nutrient removal seems to depend on plant features (i.e. plant growth rate and nutrient storage capacity and storage location) (Pavlineri et al., 2017) and operating conditions (i.e. nutrient concentrations and the characteristics of nutrients in the influent, plant density and HRT) (Li et al., 2010; Spangler et al., 2019; Zhang et al., 2018). The black dashed line in Fig. 6 illustrated the influence of FTW operating conditions and plant characteristic parameters to PLTN. The simulation results showed that plant uptake contributed the most to nitrogen removal (58.5%) when HRT = 11.2 day. HRT, which was considered to be a key factor for nitrogen removal performance as described in the previous section, can directly affect the contact time between treatment water and plant roots in FTWs (Headley et al., 2006). This means that a low HRT would decrease the processing time of plants with respect to nutrients dissolved in the water, thus lowering the contribution of plant uptake to nitrogen removal. With a high HRT, nutrients in the water column might be depleted and become a limitation for plant growth, which will also reduce the function of plant in FTWs. According to the modelling results, when HRT N 11.2 day, the concentration of inorganic nitrogen is below 0.01 mg L−1. Therefore, an appropriate HRT is important for maximizing the efficiency of nitrogen removal by plants. With an increase in plant density, an increasing amount of nitrogen is transferred from the water column to plant tissues, resulting in a corresponding increase in PLTN (Fig. 6b). Although RETN showed an increasing trend when the concentration of TN in the inflow was b1.4 mg L−1, PLTN decreased steadily while keeping inflow concentration of TN increasing (Fig. 6c). Spangler et al. (2019) also reported that the contribution of plant uptake to nitrogen removal was higher for Pontederia cordata and Juncus effusus at low nutrient concentration than that at high nutrient concentration. In contrast, some studies have drawn the opposite conclusion (Li and Guo, 2017). These conflicting findings may be related to plant species, environmental conditions

Y. Wang et al. / Science of the Total Environment 694 (2019) 133633

9

Fig. 6. The response of TN removal efficiency (RETN) and plant uptake contribution to TN removal (PLTN) to FTW operating conditions: (a) hydraulic retention time, HRT, (b) plant density, BPL, (c) the concentration of TN in the inflow, (d) the ratio of inorganic nitrogen to organic nitrogen in the inflow, IN:OrgN, (e) the ratio of nitrogen to phosphorus in the inflow, N:P and plant characteristic parameters: (f) the maximum growth rate of plants, PMSH, (g) the ratio of nitrogen to dry biomass of plants, LPLNM, (h) the ratio of nitrogen to phosphorus on plant tissues, LPLNM:LPLPM and (i) the fraction of shoot photosynthetic products directly transferred to roots, FSTR.

Fig. 7. Model calculation of (a) TN removal efficiency (RETN), (b) plant uptake contribution to TN removal (PLTN) while N:P and LPLNM:LPLPM simultaneously varied.

10

Y. Wang et al. / Science of the Total Environment 694 (2019) 133633

and microbial activity in FTWs. Similarly to the response of PLTN to the concentrations of TN in the inflow, PLTN also decreased with the IN: OrgN of the inflow (Fig. 6d). According to the results described in Section 3.3.1, it was predictable that PLTN started to decrease when the N:P of inflow was equal to 20, while remained constant at 56.5% when N:P was b20 (Fig. 6e). The response of PLTN to plant characteristic parameters exhibited an extremely similar trend with RETN (Fig. 6f–i). Therefore, plants with high growth rates (represented by PMSH), strong nitrogen storage capacity (represented by LPLNM) and efficiency uptake capacity (represented by KHNPL) might be more contributive on nitrogen removal in FTWs. Additionally, plants with high FSTR might contribute less to nitrogen removal. 3.5. Limitations of the model and further recommendations Microorganism in water column and periphyton attached on root surface play an important role in nutrient cycling. Although the FTWMOD considers some microbial action, such as mineralization, nitrification and denitrification, microbial communities are not modeled. Dissolved oxygen that has garnered particular attention is one of the most important element for the life of fish, invertebrates, bacteria and plants. Therefore, dissolved oxygen is included in the FTWMOD. Unfortunately, this part is not calibrated due to a lack of the concentration of chemical oxygen demand which is a major sink of dissolved oxygen. The FTWMOD is only examined using a mesocosm study which may magnify the FTW nutrients removal performance (Wang and Sample, 2014). So it should be considered to extrapolate the results to larger systems. Further examination of the model should be performed with a large-scale FTWs to test its flexibility when applied to environmental engineering. Furthermore, plant roots and floating raft can influence the hydraulics of the system, which has been reported to be a key factor to determine the nutrient removal performance in FTWs (Liu et al., 2019; Lucke et al., 2019). Therefore, coupling a hydrodynamic model with the FTWMOD to develop a three-dimensional hydroenvironmental model would provide a better illustration on nutrients removal in FTWs. 4. Conclusion A new process-based model FTWMOD was developed to simulate the processes of nitrogen and phosphorus removal in FTWs. The FTWMOD was calibrated using a mesocosm experiment. The calibration results of the model showed good agreement between the experimental and simulation data. The results of global sensitivity analysis revealed the significant influential parameters on nitrogen and phosphorus removal. Nitrogen removal was found to be sensitive to the parameters related to plant characteristics and microbial activity. In addition, the mineralization and sedimentation of organic matter had a significant influence on both nitrogen and phosphorus removal. Particularly, the temperature effect coefficient with respect to plant growth was a sensitive parameter for nitrate, ammonium and phosphate removal. The model application provides a deeper understanding of the influence of FTW operating conditions and plant characteristic parameters on nitrogen removal performance and plant uptake contribution. Nitrogen removal efficiency increases with HRT, while plant uptake contributes most when HRT = 11.2 day. Specifically, N:P in plant tissues was found to be an indicator of nutrients limitation in the water column. When N:P in the water column was greater than that in the plant tissues, phosphorus was a limitation for plant growth and had an obvious effect on nitrogen removal. In practice, the selection of appropriate operating conditions and an informed choice of plant species can help achieve high nutrient removal and maximize the efficiency of nutrient assimilated by plants in FTWs.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper and declaration of competing interest statement has been supplemented. Acknowledgements This study was supported by the Major Science and Technology Program for Water Pollution Control and Treatment (2018ZX07105-002), the Science Fund for Creative Research Group of the National Natural Science Foundation of China (51621092) and Natural Science Foundation of Tianjin (15JCYBJC22600). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.133633.

References Asaeda, T., Karunaratne, S., 2000. Dynamic modeling of the growth of Phragmites australis: model description. Aquat. Bot. 67 (4), 301–318. https://doi.org/10.1016/S0304-3770 (00)00095-4. Benvenuti, T., Hamerski, F., Giacobbo, A., Bernardes, A.M., Zoppas-Ferreira, J., Rodrigues, M.A., 2018. Constructed floating wetland for the treatment of domestic sewage: a real-scale study. J. Environ. Chem. Eng. 6 (5), 5706–5711. https://doi.org/10.1016/j. jece.2018.08.067. Bi, R., Zhou, C.Y., Jia, Y.F., Wang, S.F., Li, P., Reichwaldt, E.S., Liu, W.H., 2019. Giving waterbodies the treatment they need: a critical review of the application of constructed floating wetlands. J. Environ. Manag. 238, 484–498. https://doi.org/ 10.1016/j.jenvman.2019.02.064. Cerco, C.F., Moore, K., 2001. System-wide submerged aquatic vegetation model for Chesapeake Bay. Estuaries 24 (4), 522–534. https://doi.org/10.2307/1353254. Chapra, S.C., 2008. Surface Water-quality Modeling. Waveland Press. Dai, J., He, S., Zhou, W., Huang, J., Chen, S., Zeng, X., 2018. Integrated ecological floating bed treating wastewater treatment plant effluents: effects of influent nitrogen forms and sediments. Environ. Sci. Pollut. Res. 25 (19), 18793–18801. https://doi.org/10.1007/ s11356-018-2111-2. Defo, C., Kaur, R., Bharadwaj, A., Lal, K., Kumar, P., 2017. Modelling approaches for simulating wetland pollutant dynamics. Crit. Rev. Environ. Sci. Technol. 47 (15), 1371–1408. https://doi.org/10.1080/10643389.2017.1350126. Di Luca, G.A., Mufarrege, M.M., Hadad, H.R., Maine, M.A., 2019. Nitrogen and phosphorus removal and Typha domingensis tolerance in a floating treatment wetland. Sci. Total Environ. 650, 233–240. https://doi.org/10.1016/j.scitotenv.2018.09.042. Dunne, E.J., Coveney, M.F., Hoge, V.R., Conrow, R., Naleway, R., Lowe, E.F., Battoe, L.E., Wang, Y., 2015. Phosphorus removal performance of a large-scale constructed treatment wetland receiving eutrophic lake water. Ecol. Eng. 79, 132–142. https://doi.org/ 10.1016/j.ecoleng.2015.02.003. Gao, H.L., Shi, Q.Y., Qian, X., 2017. A multi-species modelling approach to select appropriate submerged macrophyte species for ecological restoration in Gonghu Bay, Lake Taihu, China. Ecol. Model. 360, 179–188. https://doi.org/10.1016/j. ecolmodel.2017.07.003. Gao, L., Zhou, W.L., Wu, S.Q., He, S.B., Huang, J.C., Zhang, X., 2018. Nitrogen removal by thiosulfate-driven denitrification and plant uptake in enhanced floating treatment wetland. Sci. Total Environ. 621, 1550–1558. https://doi.org/10.1016/j. scitotenv.2017.10.073. Gao, X., Wang, Y., Sun, B., Li, N., 2019. Nitrogen and phosphorus removal comparison between periphyton on artificial substrates and plant-periphyton complex in floating treatment wetlands. Environ. Sci. Pollut. Res. 26 (21), 21161–21171. https://doi.org/ 10.1007/s11356-019-05455-w. Garcia Chance, L.M., White, S.A., 2018. Aeration and plant coverage influence floating treatment wetland remediation efficacy. Ecol. Eng. 122, 62–68. https://doi.org/ 10.1016/j.ecoleng.2018.07.011. Gargallo, S., Martín, M., Oliver, N., Hernández-Crespo, C., 2017. Biokinetic model for nitrogen removal in free water surface constructed wetlands. Sci. Total Environ. 587-588, 145–156. https://doi.org/10.1016/j.scitotenv.2017.02.089. Geng, Y., Han, W., Yu, C., Jiang, Q., Wu, J., Chang, J., Ge, Y., 2017. Effect of plant diversity on phosphorus removal in hydroponic microcosms simulating floating constructed wetlands. Ecol. Eng. 107, 110–119. https://doi.org/10.1016/j.ecoleng.2017.06.061. Güsewell, S., 2005. Responses of wetland graminoids to the relative supply of nitrogen and phosphorus. Plant Ecol. 176 (1), 35–55. https://doi.org/10.1007/s11258-0040010-8. Hammer, D.A., Knight, R.L., 1994. Designing constructed wetlands for nitrogen removal. Water Sci. Technol. 29 (4), 15–27. https://doi.org/10.2166/wst.1994.0148. Hantush, M.M., Kalin, L., Isik, S., Yucekaya, A., 2013. Nutrient dynamics in flooded wetlands. I: model development. J. Hydrol. Eng. 18 (12), 1709–1723. https://doi.org/ 10.1061/(ASCE)HE.1943-5584.0000741.

Y. Wang et al. / Science of the Total Environment 694 (2019) 133633 Headley, T.R., Tanner, C.C., 2012. Constructed wetlands with floating emergent macrophytes: an innovative stormwater treatment technology. Crit. Rev. Environ. Sci. Technol. 42 (21), 2261–2310. https://doi.org/10.1080/10643389.2011.574108. Headley, T., Tanner, C.C., Council, A.R., 2006. Application of Floating Wetlands for Enhanced for Stormwater Treatment: A Review. Auckland Reg Counc Tech Publ. Hermans, C., Hammond, J.P., White, P.J., Verbruggen, N., 2006. How do plants respond to nutrient shortage by biomass allocation? Trends Plant Sci. 11 (12), 610–617. https:// doi.org/10.1016/j.tplants.2006.10.007. Hu, M.-H., Yuan, J.-H., Yang, X.-E., He, Z.-L., 2010. Effects of temperature on purification of eutrophic water by floating eco-island system. Acta Ecol. Sin. 30 (6), 310–318. https://doi.org/10.1016/j.chnaes.2010.06.009. Keizer-Vlek, H.E., Verdonschot, P.P.M., Verdonschot, R.C.M., Dekkers, D., 2014. The contribution of plant uptake to nutrient removal by floating treatment wetlands. Ecol. Eng. 73, 684–690. https://doi.org/10.1016/j.ecoleng.2014.09.081. Khorashadi Zadeh, F., Nossent, J., Sarrazin, F., Pianosi, F., van Griensven, A., Wagener, T., Bauwens, W., 2017. Comparison of variance-based and moment-independent global sensitivity analysis approaches by application to the SWAT model. Environ. Model. Softw. 91, 210–222. https://doi.org/10.1016/j.envsoft.2017.02.001. Koerselman, W., Meuleman, A.F.M., 1996. The vegetation N:P ratio: a new tool to detect the nature of nutrient limitation. J. Appl. Ecol. 33 (6), 1441–1450. https://doi.org/ 10.2307/2404783. Li, X., Guo, R., 2017. Comparison of nitrogen removal in floating treatment wetlands constructed with Phragmites australis and Acorus calamus in a cold temperate zone. Water Air Soil Pollut. 228 (4), 132. https://doi.org/10.1007/s11270-017-3266-z. Li, W., Li, Z., 2009. In situ nutrient removal from aquaculture wastewater by aquatic vegetable Ipomoea aquatica on floating beds. Water Sci. Technol. 59 (10), 1937–1943. https://doi.org/10.2166/wst.2009.191. Li, X.-N., Song, H.-L., Li, W., Lu, X.-W., Nishimura, O., 2010. An integrated ecological floating-bed employing plant, freshwater clam and biofilm carrier for purification of eutrophic water. Ecol. Eng. 36 (4), 382–390. https://doi.org/10.1016/j. ecoleng.2009.11.004. Liu, C., Shan, Y., Lei, J., Nepf, H., 2019. Floating treatment islands in series along a channel: the impact of island spacing on the velocity field and estimated mass removal. Adv. Water Resour. 129, 222–231. https://doi.org/10.1016/j.advwatres.2019.05.011. Lucke, T., Walker, C., Beecham, S., 2019. Experimental designs of field-based constructed floating wetland studies: a review. Sci. Total Environ. 660, 199–208. https://doi.org/ 10.1016/j.scitotenv.2019.01.018. Lynch, J., Fox, L.J., Owen Jr., J.S., Sample, D.J., 2015. Evaluation of commercial floating treatment wetland technologies for nutrient remediation of stormwater. Ecol. Eng. 75, 61–69. https://doi.org/10.1016/j.ecoleng.2014.11.001. Marimon, Z.A., Xuan, Z., Chang, N.B., 2013. System dynamics modeling with sensitivity analysis for floating treatment wetlands in a stormwater wet pond. Ecol. Model. 267, 66–79. https://doi.org/10.1016/j.ecolmodel.2013.07.017. McAndrew, B., Ahn, C., 2017. Developing an ecosystem model of a floating wetland for water quality improvement on a stormwater pond. J. Environ. Manag. 202 (1), 198–207. https://doi.org/10.1016/j.jenvman.2017.07.035. McAndrew, B., Ahn, C., Spooner, J., 2016. Nitrogen and sediment capture of a floating treatment wetland on an urban stormwater retention pond-the case of the rain project. Sustainability 8 (10), 972. https://doi.org/10.3390/su8100972. Ning, D., Huang, Y., Pan, R., Wang, F., Wang, H., 2014. Effect of eco-remediation using planted floating bed system on nutrients and heavy metals in urban river water and sediment: a field study in China. Sci. Total Environ. 485–486, 596–603. https:// doi.org/10.1016/j.scitotenv.2014.03.103. Pavlineri, N., Skoulikidis, N.T., Tsihrintzis, V.A., 2017. Constructed floating wetlands: a review of research, design, operation and management aspects, and data meta-analysis. Chem. Eng. J. 308, 1120–1132. https://doi.org/10.1016/j.cej.2016.09.140. Pianosi, F., Wagener, T., 2015. A simple and efficient method for global sensitivity analysis based on cumulative distribution functions. Environ. Model. Softw. 67, 1–11. https:// doi.org/10.1016/j.envsoft.2015.01.004. Pianosi, F., Wagener, T., 2018. Distribution-based sensitivity analysis from a generic inputoutput sample. Environ. Model. Softw. 108, 197–207. https://doi.org/10.1016/j. envsoft.2018.07.019. Poorter, H., Nagel, O., 2000. The role of biomass allocation in the growth response of plants to different levels of light, CO2, nutrients and water: a quantitative review. Funct. Plant Biol. 27 (12), 1191. https://doi.org/10.1071/pp99173_co.

11

Saad, R.A.B., Kuschk, P., Wiessner, A., Kappelmeyer, U., Muller, J.A., Koser, H., 2016. Role of plants in nitrogen and sulfur transformations in floating hydroponic root mats: a comparison of two helophytes. J. Environ. Manag. 181, 333–342. https://doi.org/ 10.1016/j.jenvman.2016.06.064. Schwammberger, P.F., Lucke, T., Walker, C., Trueman, S.J., 2019. Nutrient uptake by constructed floating wetland plants during the construction phase of an urban residential development. Sci. Total Environ. 677, 390–403. https://doi.org/10.1016/j. scitotenv.2019.04.341. Spangler, J.T., Sample, D.J., Fox, L.J., Owen, J.S., White, S.A., 2019. Floating treatment wetland aided nutrient removal from agricultural runoff using two wetland species. Ecol. Eng. 127, 468–479. https://doi.org/10.1016/j.ecoleng.2018.12.017. Tan, B.C., He, H., Gu, J., Li, K.Y., 2019. Effects of nutrient levels and light intensity on aquatic macrophyte (Myriophyllum aquaticum) grown in floating-bed platform. Ecol. Eng. 128, 27–32. https://doi.org/10.1016/j.ecoleng.2018.12.011. Tanner, C.C., Headley, T.R., 2011. Components of floating emergent macrophyte treatment wetlands influencing removal of stormwater pollutants. Ecol. Eng. 37 (3), 474–486. https://doi.org/10.1016/j.ecoleng.2010.12.012. Tara, N., Arslan, M., Hussain, Z., Iqbal, M., Khan, Q.M., Afzal, M., 2019. On-site performance of floating treatment wetland macrocosms augmented with dye-degrading bacteria for the remediation of textile industry wastewater. J. Clean. Prod. 217, 541–548. https://doi.org/10.1016/j.jclepro.2019.01.258. Tessier, J.T., Raynal, D.J., 2003. Use of nitrogen to phosphorus ratios in plant tissue as an indicator of nutrient limitation and nitrogen saturation. J. Appl. Ecol. 40 (3), 523–534. https://doi.org/10.1046/j.1365-2664.2003.00820.x. Van de Moortel, A.M.K., Meers, E., De Pauw, N., Tack, F.M.G., 2010. Effects of vegetation, season and temperature on the removal of pollutants in experimental floating treatment wetlands. Water Air Soil Pollut. 212 (1–4), 281–297. https://doi.org/10.1007/ s11270-010-0342-z. Wang, C.-Y., Sample, D.J., 2013. Assessing floating treatment wetlands nutrient removal performance through a first order kinetics model and statistical inference. Ecol. Eng. 61, 292–302. https://doi.org/10.1016/j.ecoleng.2013.09.019. Wang, C.-Y., Sample, D.J., 2014. Assessment of the nutrient removal effectiveness of floating treatment wetlands applied to urban retention ponds. J. Environ. Manag. 137, 23–35. https://doi.org/10.1016/j.jenvman.2014.02.008. Wang, D., Bai, S., Wang, M., Xie, Q., Zhu, Y., Zhang, H., 2012. Effect of artificial aeration, temperature, and structure on nutrient removal in constructed floating islands. Water Environ. Res 84 (5), 405–410. https://doi.org/10.2175/ 106143012x13347678384684. Wang, C.-Y., Sample, D.J., Bell, C., 2014. Vegetation effects on floating treatment wetland nutrient removal and harvesting strategies in urban stormwater ponds. Sci. Total Environ. 499, 384–393. https://doi.org/10.1016/j.scitotenv.2014.08.063. Wang, C.Y., Sample, D.J., Day, S.D., Grizzard, T.J., 2015. Floating treatment wetland nutrient removal through vegetation harvest and observations from a field study. Ecol. Eng. 78, 15–26. https://doi.org/10.1016/j.ecoleng.2014.05.018. West, M., Fenner, N., Gough, R., Freeman, C., 2017. Evaluation of algal bloom mitigation and nutrient removal in floating constructed wetlands with different macrophyte species. Ecol. Eng. 108, 581–588. https://doi.org/10.1016/j.ecoleng.2017.07.033. Xiang, W., Peng, Y., 2015. Remediation of eutrophic water by Oenanthe javanica ecofloating bed in Chongqing in winter. Chin. J. Environ. Eng. 9 (11), 5393–5398. https://doi.org/10.12030/j.cjee.20151141. Xu, B., Wang, X., Liu, J., Wu, J., Zhao, Y., Cao, W., 2017. Improving urban stormwater runoff quality by nutrient removal through floating treatment wetlands and vegetation harvest. Sci. Rep. 7 (1), 7000. https://doi.org/10.1038/s41598-017-07439-7. Yang, Z., Zheng, S., Chen, J., Sun, M., 2008. Purification of nitrate-rich agricultural runoff by a hydroponic system. Bioresour. Technol. 99 (17), 8049–8053. https://doi.org/ 10.1016/j.biortech.2008.03.040. Zhang, L., Sun, Z., Xie, J., Wu, J., Cheng, S., 2018. Nutrient removal, biomass accumulation and nitrogen-transformation functional gene response to different nitrogen forms in enhanced floating treatment wetlands. Ecol. Eng. 112, 21–25. https://doi.org/ 10.1016/j.ecoleng.2017.12.021. Zhou, X., Wang, G., 2010. Nutrient concentration variations during Oenanthe javanica growth and decay in the ecological floating bed system. J. Environ. Sci. 22 (11), 1710–1717. https://doi.org/10.1016/S1001-0742(09)60310-7.