Ibuprofen and iohexol removal in saturated constructed wetland mesocosms

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Ibuprofen and iohexol removal in saturated constructed wetland mesocosms Yang Zhang a,b,∗ , Tao Lv b , Pedro N. Carvalho b,∗∗ , Liang Zhang b , Carlos A. Arias b , Zhanghe Chen a , Hans Brix b a b

College of Life Science, South China Normal University, Guangzhou 510631, PR China Department of Bioscience, Aarhus University, 8000 Aarhus C, Denmark

a r t i c l e

i n f o

Article history: Received 30 January 2016 Received in revised form 22 May 2016 Accepted 23 May 2016 Available online xxx Keywords: Emerging organic contaminants Pharmaceuticals Iodinated X-ray contrast media (ICM) Phytoremediation Hydraulic loading rate

a b s t r a c t The removal of pharmaceuticals by constructed wetlands (CWs) has been gaining more interest in the last decade. However, it is still unclear which are the key factors influencing the removal process. The aim of the present study is to investigate the removal efficiency of ibuprofen (IBU) and iohexol (IOH) by saturated CW mesocosms, depending on type of mesocosm (planted with Typha latifolia, Phragmites australis, Iris pseudacorus, Juncus effusus, Berula erecta or unplanted control), season (summer and winter), hydraulic loading rate (HLR) (0.7, 1.8, 3.4, 6.9 and 13.8 cm d−1 ) and initial spiking concentration (10 and 100 ␮g L−1 ). The results show that the presence of IBU and IOH had no influence on the monitored water parameters, while the type of mesocosm (different plant species and unplanted control) and season were the main factors explaining the differences observed in pH, dissolved oxygen (DO), oxygen saturation (SAT), total nitrogen (TN), ammonium (NH4 -N) and phosphate (PO4 -P). IBU was more efficiently removed (>80%) by mesocosms planted with J. effusus and B. erecta under low HLR and low initial spiking concentration in summer. For IOH, higher removal efficiency (>80%) was achieved by B. erecta-planted mesocosms under low HLR and high initial spiking concentration. Data on IBU removal from mesocosms could be fitted by the first-order kinetic model, with removal rate constants ranging from 0.2 to 4.0 d−1 . For IOH, however, different kinetic models were applied but none could sufficiently describe the removal rate. Regression analysis on IBU demonstrated that 64% of the variation in removal efficiency could be explained by temperature, NH4 -N and DO. In contrast, IOH removal correlation with any of the variables studied only accounted for 10.6% of the removal variation observed. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Pharmaceuticals are extensively used to prevent and alleviate the effects of disease and illness in humans and animals. As pharmaceuticals are not completely metabolised in the body, large amounts of pharmaceuticals and their metabolites have been introduced into the aquatic environment through sewage, which carries the excreta of individuals who have used these chemicals and agricultural runoff comprising livestock manure. The insufficient removal of pharmaceuticals by conventional wastewater treatment plants (WWTPs) (Jelic´ et al., 2012) have made sewage and

∗ Corresponding author at: College of Life Science, South China Normal University, Guangzhou, 510631, PR China. ∗∗ Corresponding author at: Department of Bioscience, Aarhus University, 8000 Aarhus C, Denmark. E-mail addresses: [email protected] (Y. Zhang), [email protected] (P.N. Carvalho).

agricultural runoff the main source of these compounds to the environment. Consequently, concentration levels of pharmaceuticals at the scale of ng L−1 –␮g L−1 have been detected in different water bodies around the globe (Nikolaou et al., 2007). These concentration levels of pharmaceuticals have been reported by recent environmental risk assessments to possibly exceed the predicted no-effect concentration, resulting in adverse effects on fish and algae growth (Hernando et al., 2006). Hence, pharmaceuticals have become public concerns because of their potential adverse effect on biota and presence in drinking-water sources. Therefore, removal of pharmaceuticals from contaminated water has become the target of many organisations, such as the EU Water directives (Kunz et al., 2015) and the National Natural Science Foundation of China (National Natural Science Foundation of China, 2014). Constructed wetlands (CWs), as a robust and low-cost technology for treating wastewater, have been proven to be efficient in also removing pharmaceuticals (Verlicchi and Zambello, 2014). Published research about removal of pharmaceuticals by CWs can

http://dx.doi.org/10.1016/j.ecoleng.2016.05.077 0925-8574/© 2016 Elsevier B.V. All rights reserved.

Please cite this article in press as: Zhang, Y., et al., Ibuprofen and iohexol removal in saturated constructed wetland mesocosms. Ecol. Eng. (2016), http://dx.doi.org/10.1016/j.ecoleng.2016.05.077

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be found for several scales of systems (microcosm, mesocosm, pilot and full-scale systems), covering a range from primary, secondary or tertiary treatment steps, as well as for hybrid systems, either working with artificial or real wastewater (Verlicchi and Zambello, 2014). Despite the extensive list of publications dealing with the removal of pharmaceuticals by CWs, as shown by the comprehensive reviews available, questions still exist on the role of filling media, plant species, wastewater type, system design and environmental parameters on the removal of pharmaceuticals by CWs (Li et al., 2014; Verlicchi et al., 2013). A key issue in improving the removal of pharmaceuticals by CWs is to develop an understanding of their kinetics. CW practitioners widely use the P-K-C* kinetic model for CW sizing (Kadlec and Wallace, 2008). Regarding pharmaceuticals, however, the more commonly used model is the first-order kinetic model (Matamoros and Bayona, 2006). However, zero-order and second-order kinetic models have also been used in CWs (Mitchell and McNevin, 2001; Pérez et al., 2014). Studies on pharmaceutical degradation kinetics in CWs are scarce, and previous research has mainly focused on the effects of single factors, such as plant species, season etc., while the potential synergy between factors has been neglected. In the present study, ibuprofen (IBU) and iohexol (IOH) were selected as model compounds. IBU is a non-steroidal antiinflammatory drug frequently used by humans and is known to be easily biodegradable and widely studied, allowing for better comparison of results. IOH is not a pharmaceutical with a therapeutic effect, but a diagnostic iodinated contrast agent used in high doses in hospitals. It is excreted from the body by urine in a nonmetabolized form. The aim of the present study is to assess the effects of plant species, season, HLR, and the concentration of IBU and IOH on the removal efficiency and removal kinetics of these compounds in saturated CW mesocosms. 2. Materials and methods 2.1. Reagents and materials Commercial products of IBU and IOH were purchased from a local pharmacy for the artificial influent preparation. The exact concentrations of IBU and IOH in the commercial products were analysed prior to the experiment. All the reagents and materials used for the analytical work are described in the Supplementary material. Characteristics of IBU and IOH are shown in Table S1. 2.2. Experimental setup The experimental setup was established outdoors at an experimental field station close to Aarhus, Denmark, under a glass roof providing protection against rain and snow. Similarly sized plants from the five species Typha latifolia L., Phragmites australis (Cav.) Trin. ex Steud., Iris pseudacorus L., Berula erecta (Huds.) Coville and Juncus effusus L. were selected (fresh biomass 120 ± 10 g) and planted in the mesocosms after being rinsed carefully. Each mesocosm, a 6 L pot, was filled with substrate in the following order: coarse gravel (1900 g) at the bottom, followed by quartz sand (5700 g, particle size 0.5–1 mm with average porosity of 37%) and coarse gravel (1700 g) on top to avoid light exposure. A geotextile was placed in between the bottom coarse gravel and the sand layer to maintain the draining system. The surface area of each mesocosm was 0.36 m2 . The mesocosms simulated water-saturated CWs conditions, where the water flowed from the top surface through the substrate to the bottom collection device. Water drip of the mesocosms upper outlet by hydrostatics (Fig. S1). Eighteen mesocosms (5 plant species and 1 unplanted control, in triplicate), together with a 350 L influent water storage tank and an Ø 16 mm PE pipe

fitted with 0.5 L h−1 pressure-compensated drippers for each mesocosm constituted a working line in the experiment (Fig. 1). Three working lines were used: one spiked with IBU, one spiked with IOH, and one without pharmaceuticals was set up as a control line. The artificial influent was prepared with tap water, “Pioner Grøn” N: P: K nutrients (total-N, 19.3 mg L−1 ; NO3 -N11.9; NH4 -N, 7.4; P, 2.0 mg L−1 ; Mg, 3.0 mg L−1 ; K, 15.4 mg L−1 ; and S, 3.9 mg L−1 ) (Brøste Group, Denmark) and acetic acid (12 mg L−1 TOC). The whole system was acclimatized for one month before the start of the experimental period. The HLR was adjusted by a timer-controlled pump at five different levels (0.7, 1.7, 3.4, 6.9 and 13.8 cm d−1 ). Two spiking concentrations for IBU and IOH were used: 10 ␮g L−1 and 100 ␮g L−1 .The experiment was performed covering two seasons, summer and winter, between July 2014 and March 2015. 2.3. Sampling strategy For each HLR tested, a stabilization period of three complete hydraulic cycles was followed, after which performance was assumed to be representative for the particular HLR. The influent volume of each mesocosm was calculated from the measured total volume consumed in the input tank per sampling time. The effluent volume of each mesocosm was measured by sample weight. For each working line, approximately 1 L of water was sampled from the influent (storage tank, n = 3) and effluent of each mesocosm (n = 1). For each water sample, 500 mL was transferred to an amber bottle for IBU or IOH analysis, and 40 mL was transferred to a poly ethylene bottle for analysis of total organic carbon (TOC), total nitrogen (TN), ammonium (NH4 -N), nitrate (NO3 -N), and phosphate (PO4 -P). The remaining amount was used for the in-situ measurements of pH, water temperature, dissolved oxygen (DO), oxygen saturation (SAT), and electrical conductivity (EC). The samples were stored in portable refrigerators until arrival at the lab, where samples were acidified to pH2 using hydrochloric acid and kept at 5 ◦ C until analysis. Air temperature and relative air humidity (RH) were registered every 30 min throughout the experimental period. Furthermore, leaf chlorophyll content (n = 3) was measured randomly for each planted mesocosm in summer. In winter, all plants wilted except for J. effusus. The plant aerial tissue (100 g FW from each planted mesocosm) was collected for IBU and IOH analysis at the end of the summer and winter; only J. effusus material was collected in winter. Roots were not sampled to avoid mesocosm destruction. Each mesocosm’s substrate was sampled at the end of the summer and winter periods using a syringe to collect 10-cm cores with Ø 0.5 cm (10 g FW) for pharmaceutical analysis. Substrate collection was a compromise between collecting acceptably representative samples while simultaneously avoiding the destruction of the mesocosms. The plant aerial tissue (n = 1) and the substrate samples (n = 1) from each mesocosm were freeze-dried and stored at −8 ◦ C until analysis of IBU and IOH, which took place within 1 month. 2.4. Analytical methodology Detailed information on the measurement of environmental variables, and the IBU and IOH analyses are found in the Supplementary material. Briefly, the environmental variables were monitored continuously with loggers and the water quality measured using standard methods. The IBU and IOH were analysed by a high performance liquid chromatography (HPLC) system equipped with a diode array detector (DAD) (Ultimate 3000, Thermo Scientific, Denmark). For water samples, prior to the HPLC analysis, solid phase extraction (SPE) was conducted as

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Fig. 1. Schematics of a working line in the experiment.

a pre-concentration step. Plant tissue was extracted by ultrasonication with methanol:acetone (95:5, v/v), and the extract was further clean-up with activated carbon prior to the HPLC injection. Mesocosm substrate was extracted by ultrasonication with methanol:acetone (95:5, v/v), dried and re-dissolved (methanol:water (1:1, v/v)) prior to HPLC injection. Full details as well as the analytical figures of merit of the methodology are presented in Table S2.

3. Results

2.5. Data analysis Evapotranspiration was calculated based on the balance of the measured influent and effluent of each mesocosm using the following formula: Evapotranspiration =

vin −vout × 100% vin

For estimation of the removal efficiency (%), the water loss due to evapotranspiration was considered and calculated based on the effective mass balance of IBU and IOH between the influent and effluent of each mesocosm using the following formula: Removal efficiency(%) =

Cin × Vin −Cout ×Vout × 100% Cin × Vin

Data were fitted to the zero-, first- and second-order kinetic degradation models as follow: Cout − Cin = −kt





In Cout ⁄Cin = −kt 1 Cout

coefficients analysis using SPSS was conducted to test the fit of the three kinetic models. A principal component analysis (PCA) using SPSS and a standard least squares test using JMP were applied to identity the most important environmental variables affecting IBU and IOH removal efficiency. All the data were tested for normal distribution prior to statistical analysis. Homogeneity of variance was tested using Levene’s test.

= −kt⁄

where Vin and Vout represent the volume of the influent and effluent; Cin and Cout are the concentrations of IBU and IOH from the influent and effluent, respectively; t represents real hydraulic retention time (HRT); and k is the kinetic removal rate constant. HRT was calculated taking into account the evapotranspiration. Fresh plant biomass in each mesocosm was estimated by subtracting the weight of each unplanted mesocosm from the weight when planted. 2.6. Statistical analysis Statistical analysis was carried out using SPSS software (IBM SPSS, IL, USA) and JMP (SAS Institute, NC, USA). Analysis of variance (ANOVA) was used to identify significant differences in the following: pH, water temperature, DO, SAT, EC, TOC, TN, NO3 -N, NH4 -N, PO4 -P and chlorophyll content between working lines, seasons and types of mesocosms; IBU and IOH removal efficiencies between types of mesocosms, seasons, HLRs and initial spiking concentrations; and removal rate constants between types of mesocosms, seasons, and initial spiking concentrations. Pearson correlation

3.1. General monitoring measurements The presence of a plant in the mesocosms influenced the monitored water parameters in the water samples, revealing significant differences (p < 0.05) for pH (F-ratio = 43), DO (F-ratio = 24), SAT (F-ratio = 33), TN (F-ratio = 234), NH4 -N (F-ratio = 345), PO4 -P (Fratio = 97) and chlorophyll content (F-ratio = 143) (Fig. 2 and Fig. S2). Additionally, significant seasonal differences (p < 0.05) were observed in water temperature (F-ratio = 2515), EC (F-ratio = 135), DO (F-ratio = 106), SAT (F-ratio = 77), TN (F-ratio = 130), NH4 -N (Fratio = 334) and PO4 -P (F-ratio = 99). Regardless of the HLR and spiking concentration applied, however, statistical analysis showed no significant differences (p > 0.05) between the working lines for any of the monitored water parameters. 3.2. Pharmaceutical removal efficiency The removal efficiency of IBU and IOH was significantly affected by mesocosm type, HLR and initial spiking concentrations (p < 0.05) (Fig. 3, Table 1). Regarding the seasonal influence, a significant difference (p < 0.05) was only observed for IBU removal efficiency. Additionally, significant interaction effects up to the level of fourway interactions were observed on the removal efficiency for both IBU and IOH. Mesocosm type and the initial spiking concentration explained most of the variation in the IBU and IOH removal efficiencies, respectively. Significant differences in the removal efficiency of IBU and IOH between planted and unplanted mesocosms, and even between plant species were observed (Fig. 3). For IBU, all the planted mesocosms generally had higher removal efficiencies than the unplanted mesocosms. Additionally, J. effusus and B. erecta tended to have higher removal efficiencies than the other mesocosm types in all the treatments. For IOH, the removal efficiency in the unplanted mesocosm was not significantly different from the removal efficiencies in the planted ones, even though B. erecta generally had higher removal efficiency than the other mesocosm types (Fig. 3). Seasonal differences in removal efficiency were only observed for IBU, which had higher removal efficiency in summer. As expected, the removal efficiencies increased with decreasing HLR for both IBU and IOH (Fig. 3). Concerning the initial spiking concentrations, higher IBU removal efficiency was observed at the low initial spiking concentration in all the mesocosms. In contrast, for IOH, the

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Fig. 2. Box plots of the monitored water parameters in the mesocosms (planted with Typha latifolia, Phragmites australis, Iris pseudacorus, Juncus effusus, Berula erecta or unplanted control) during the summer and winter periods. The bottom and top of the box represent the 25th and the 75th percentiles, respectively. The line within the box represents the median. Error bars below and above the box indicate the 10th and 90th percentiles, respectively. Solid circles represent data outliers. The three boxes within each colour group represent control (no chemical spiking), iohexol and ibuprofen working lines from left to right, respectively.

mesocosms exhibited higher removal efficiencies when exposed to the high initial spiking concentration. 3.3. Pharmaceutical removal kinetics The first-order kinetic model was applied to determine the removal rate constants of IBU and IOH in the experiment. All the concentrations used for calculating the kinetics, as well as the HRT,

were corrected for evapotranspiration. Table 2 shows the estimated first-order removal rate constants of IBU for the different mesocosm types, seasons and initial spiking concentrations. The Pearson correlation coefficients revealed moderate to strong correlations between the first-order removal rate constants and HRT, demonstrating a good fit between the first-order kinetic model and IBU removal. It should be noted that when the effluent concentration values were lower than the limit of detection (LOD), the calculated

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Fig. 3. Removal efficiency of ibuprofen and iohexol in the different mesocosms according to mesocosm type (planted with Typha latifolia, Phragmites australis, Iris pseudacorus, Juncus effusus, Berula erecta or unplanted control), season, hydraulic loading rate and initial spiking concentration.

Table 1 Results (F-ratios) of the analysis of variance (ANOVA) on the effects of mesocosm type (T; planted with Typha latifolia, Phragmites australis, Iris pseudacorus, Juncus effusus, Berula erecta or unplanted control), season (S; summer and winter), HLR (H; 0.7, 1.7, 3.4, 6.9 and 13.8 cm d−1 ), initial spiking concentration (C; 10 and 100 ␮g L−1 ) and their interactions on ibuprofen (IBU) and iohexol (IOH) removal efficiency. Removal efficiency

IBU IOH

Main factors

Interactions

T

S

H

C

S×T

S×H

S×C

T×H

T×C

H×C

S×T×H

S×T×C

S×H×C

T×H×C

S×T×H×C

346 41

149 2

201 78

303 438

19 29

5 2

21 14

11 3

20 21

30 48

5 2

5 16

22 0.5

3 3

2 3

Figures in bold indicate significant difference (p < 0.05).

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Table 2 Estimated first-order removal rate constants (k) and the half-life (t½ ) of ibuprofen in summer and winter during the experiment. Types

Season

Initial conc. (␮g L−1 )

k (d−1 )

Pearson cor. coef.

p-value

t½

Typha

Summer

10 100 10 100 10 100 10 100 10 100 10 100 10 100 10 100 10 100 10 100 10 100 10 100

n.a. 1.7 1.5 0.6 n.a. 1.1 0.9 0.3 n.a. 2.7 1.8 1.2 n.a. 4.0 n.a. 1.7 n.a. 4.0 3.8 2.7 0.7 0.2 0.2 0.2

n.a. 0.997** 0.850* 0.876** n.a. 0.937** 0.858** 0.877** n.a. 0.951** 0.987* 0.916** n.a. 0.959** n.a. 0.770** n.a. 0.943* 0.850 0.924** 0.642 0.833** 0.876** 0.833**

n.a. 0.003 0.015 0.000 n.a. 0.000 0.000 0.004 n.a. 0.000 0.013 0.000 n.a. 0.002 n.a. 0.006 n.a. 0.016 0.15 0.000 0.062 0.01 0.000 0.001

n.a. 0.4 0.5 1.1 n.a. 0.6 0.8 2.5 n.a. 0.3 0.4 0.6 n.a. 0.2 n.a. 0.4 n.a. 0.2 0.2 0.3 1.0 4.0 3.0 3.0

Winter Phragmites

Summer Winter

Iris

Summer Winter

Berula

Summer Winter

Juncus

Summer Winter

Unplanted mesocosms

Summer Winter

* **

Correlation is significant at the 0.05 level. Correlation is significant at the 0.01 level.

removal was not used for determining the kinetics to avoid artefacts. For IBU, the removal rate constants ranged from 0.2 to 4.0 d−1 . Significant differences were found for the main effects of mesocosm type, season and initial spiking concentration, with B. erecta and J. effusus mesocosms, the summer period and the low initial spiking concentration showing significantly higher removal rate constants (p < 0.05). The estimated removal rate constants are consistent with the recorded removal efficiencies. However, no significant interaction effects between the factors were observed for the removal rate constants. Regarding IOH, the first-order kinetic model was not able to describe the observed removal of pharmaceuticals in the mesocosms. Further analysis revealed mixed behaviour (Table S3), with the zero-order kinetic model fitting IOH removal in winter at the low initial spiking concentration, and the second-order kinetic model fitting IOH removal in winter at the high initial concentration. None of the models explored could adequately describe the removal process of IOH in all the mesocosms.

3.4. Mass distribution of the pharmaceutical compounds Besides the determination of the pharmaceuticals in the water phase, plant aerial tissue and substrate were also sampled for IBU and IOH analysis. The IBU and IOH concentrations measured in the plant aerial tissue and substrate, respectively, were lower than the limit of detection (1 and 0.07 ␮g g−1 DM) for both compounds at the end of summer and winter periods (Table S2). Based on the mass balance of each compound spiked into the systems during each season and considering the limit of quantification, substrate sorption was estimated to account for <0.4%. Similarly, phytoaccumulation measured in the aerial part accounted for <0.17% of the mass of each pharmaceutical compound spiked into the systems. Additionally, based on the previously found translocation factor and biomass distribution ratios (Zhang et al., 2016), the accumulation in roots was estimated, resulting in a potential total phytoaccumulation <0.35%.

3.5. Regression analysis To better understand the removal processes of IBU and IOH in the present study, a regression analysis was performed to assess the effects of the different variables (pH, water temperature, EC, DO, SAT, PO4 -P, NH4 -N, NO3 -N, TN, TOC, air temperature, RH, evapotranspiration rate and HRT) on removal efficiency. The observed ranges of these variables are shown in Table S4. Table 3 shows the variables converted to four major principle components for the IBU working line and five components for the IOH and control working lines. For IBU, the first principle component explained 31.6% of the variation with high loadings for water temperature, air temperature and the concentration of NH4 -N. The second principle component explained an additional 18.7% of the variation, with a high loading for DO. All the four principle components together explained 74.2% of the variation, with most of the variation explained by water temperature, air temperature, and concentration of NH4 -N and DO. For IOH, the first principle component explained 32.5% of the variation with high loadings for the concentrations of TN, PO4 -P and NH4 -N. The second principle component explained an additional 23% of the variation, with a high loading for DO. All the five principle components together explained 83.8% of the variation, with most of the variation explained by the concentration of TN, PO4 -P, NH4 -N and DO. For the control working line, the first principle component explained the largest percentage of the variation (31.9%), with high loadings for the concentration of NH4 -N and air temperature. The second principle component explained an additional 18.9% of the variation, with a high loading for DO. All the five principle components together explained 77.7% of the variation, with most of it explained by the concentration of NH4 -N, DO and air temperature. To identify which variables were correlated with the removal efficiency of IBU and IOH in the mesocosms, the principle components extracted from all the variables were used as independent variables in a multiple regression analysis, where the dependent variables were the removal efficiencies of IBU and IOH (Table 4). The first two principle components in the IBU working line explained 64% of the variation in IBU removal efficiency; these principal components were positive correlated with water temperature, air

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Table 3 Principal component analysis (PCA) of the 14 variables in the experiment from the different mesocosms in the control, ibuprofen and iohexol working lines. Control

Eigenvalue Proportion of variance (%) Cumulative proportion of variance (%) pH Water temperature Electric conductivity Dissolved oxygen Oxygen saturation Total organic carbon Total nitrogen Phosphate Ammonium Nitrate Air temperature Relative humidity Evapotranspiration Hydraulic retention time

Ibuprofen

Iohexol

PC1

PC2

PC3

PC4

PC5

PC1

PC2

PC3

PC4

PC1

PC2

PC3

PC4

PC5

4.468 31.9 31.9 0.085 −0.727 −0.244 −0.112 −0.615 0.338 0.705 0.664 0.757 0.289 −0.776 0.735 −0.736 −0.299

2.646 18.9 50.8 0.156 0.571 0.377 −0.858 −0.525 0.054 0.443 0.451 0.384 −0.027 0.564 −0.507 0.078 −0.096

1.456 10.4 61.2 0.149 −0.098 0.329 0.179 0.131 0.697 0.195 −0.053 0.100 0.470 −0.042 −0.143 0.251 0.651

1.304 9.3 70.5 0.737 −0.054 −0.307 0.251 0.244 −0.331 0.207 0.230 0.271 −0.345 0.024 −0.194 0.043 0.321

1.009 7.2 77.7 −0.162 0.211 −0.209 0.254 0.409 −0.136 0.337 −0.013 0.259 0.526 0.206 −0.078 0.039 −0.372

4.431 31.6 31.6 0.310 −0.800 −0.466 0.217 −0.454 0.058 0.735 0.591 0.812 0.465 −0.833 0.723 −0.427 −0.237

2.623 18.7 50.4 0.179 0.416 0.406 −0.873 −0.646 0.357 0.471 0.507 0.363 0.057 0.406 −0.405 0.023 −0.063

1.979 14.1 64.5 0.821 −0.054 0.171 0.371 0.422 0.019 0.223 0.162 0.257 0.105 0.017 −0.329 0.512 0.658

1.360 9.7 74.2 0.057 0.288 −0.136 −0.008 0.250 −0.741 0.251 −0.152 0.230 0.509 0.295 −0.123 −0.085 −0.369

4.550 32.5 32.5 0.493 −0.670 −0.195 −0.082 −0.635 0.533 0.791 0.833 0.853 0.103 −0.666 0.601 −0.504 −0.105

3.218 23.0 55.5 0.162 0.663 0.405 −0.896 −0.497 0.195 0.438 0.372 0.309 −0.191 0.706 −0.704 0.146 −0.159

1.738 12.4 68.0 0.578 −0.095 0.381 0.301 0.274 0.148 0.171 0.054 0.149 0.063 −0.055 −0.192 0.610 0.769

1.166 8.3 76.2 0.504 0.209 −0.544 0.134 0.362 −0.385 0.143 0.137 0.166 −0.267 0.134 0.006 0.117 −0.324

1.054 7.5 83.8 0.080 0.100 0.166 0.019 0.080 −0.386 0.149 −0.048 0.157 0.878 0.081 0.008 −0.101 −0.135

The bold type represents variables with high loadings.

Table 4 Multiple regression analysis of the principle components extracted from the 14 variables on ibuprofen (IBU) and iohexol (IOH) removal efficiency. Regression parameters

PC1 PC2 PC3 PC4 a b *

Response variable: IBU removal efficiencya

Regression parameters

Estimate

Std. Error

t Ratio

P

−0.27 −0.33 0.004 −0.15

0.02 0.03 0.03 0.07

−12.68 −11.86 0.12 −2.08

<0.0001* <0.0001* 0.903 0.039

PC1 PC2 PC3 PC4 PC5

Response variable: IOH removal efficiencyb Estimate

Std. Error

t Ratio

P

0.1 −0.16 0.06 −0.06 0.03

0.04 0.04 0.07 0.07 0.07

2.66 −3.83 0.95 −0.81 0.45

0.0086* 0.0002* 0.3414 0.4195 0.6568

R2 = 0.65, adjustedR2 = 0.64 F ratio = 76.5, p < 0.0001. R2 = 0.14, adjustedR2 = 0.106, F ratio = 4.7, p = 0.0006. Represents significant correlation.

temperature and DO, and negative correlated with concentration of NH4 -N. In the IOH working line, the first two principle components explained 10.6% of the variation in the removal efficiency of IOH and were positively correlated with concentrations of TN, PO4 -P, NH4 -N and DO. 4. Discussion In the present study, the CW mesocosms were not affected by the presence of IBU and IOH as assessed by the water quality parameters and plant chlorophyll content. Some studies have reported phytotoxic effects of IBU and IOH on wetlands plants (Carvalho et al., 2014; Zhang et al., 2016) but at much higher concentration levels than those used in this study. Matamoros et al. (2007) did not observe any adverse effects on the removal of total suspended solids (TSS), biochemical oxygen demand (BOD5 ) and NH4 -N in vertical flow CWs and sand filters when the systems were exposed to 13 pharmaceuticals and personal care products (PPCPs) at ␮g L−1 concentration levels. This is probably because the low concentration level of added pharmaceuticals would not be expected to stress the plant and/or microorganisms in the system. Hence, the commonly found concentrations of IBU (0.02–48 ␮g L−1 ) and IOH (3.36–5.42 ␮g L−1 ) in raw sewage, primary effluent and secondary effluent (Echeverría et al., 2013; Voulvoulis et al., 2015; Weigel et al., 2004) are not expected to have any adverse effects on a full-scale CW. The presence of plants in the mesocosms generally had a strong influence on the water quality parameters, especially on nutrient removal (PO4 -P, TN and NH4 -N). The most important functions of plants in CWs are widely accepted to be their physical structure, which provides surface area to facilitate microbial growth,

the release of oxygen and root exudates to the rhizosphere, and nutrient uptake (Brix, 1997). In addition, the removal performance differed significantly between summer and winter. The lower temperature in winter probably inhibited the use of nutrients by the microbial community and also the N and P uptake by the senescent plants. The effluent DO in the unplanted mesocosms was significant higher than in the influent water storage tank in both summer and winter, which could be attributed to the technical setup inducing turbulent flow, both by the dripper systems used to load the mesocosms and also by the effluent collection system. Nevertheless, the effluent DO was higher in T. latifolia and P. australis mesocosms in winter than in the unplanted mesocosms, confirming the important role of the plants in releasing oxygen at the root level. The pharmaceuticals may ionize in aquatic systems, depending on pH, which can influence the treatment. However, in the present study, the pH in the influent and effluent ranged from 6.8 to 8.5 and was stable throughout the experiment. The state of IBU (ionized) and IOH (molecular state) did not change within the range of pH recorded in the experiment. Regarding removal efficiency, both IBU and IOH were eliminated efficiently (>80%) in the mesocosms, if considering typical HRT in the CWs (>1d). High removal efficiency of IBU has been achieved previously in planted subsurface flow CWs (Ávila et al., 2010). Comparing the removal efficiency of IBU with other types of treatment systems, similar removal efficiencies have been obtained in traditional WWTPs (88–93%) (Santos et al., 2007). For IOH, to the best of our knowledge, there is no previous report on IOH removal efficiency in CWs. Removal efficiency for IOH has been inconsistent in the literature for different type of treatments. In advanced oxidation processes, efficient removal of IOH has been observed using

Please cite this article in press as: Zhang, Y., et al., Ibuprofen and iohexol removal in saturated constructed wetland mesocosms. Ecol. Eng. (2016), http://dx.doi.org/10.1016/j.ecoleng.2016.05.077

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UV (Wang et al., 2016), while limited efficiency occured with chlorination (0.5% in 5 min; (Wang et al., 2016)) or ozonation (around 30%; (Bahr et al., 2007)). For conventional biological processes, both lack of removal (Ryu et al., 2014) and up to 90% removal (Kormos et al., 2011) have been found in full-scale WWTPs (Kormos et al., 2011; Ryu et al., 2014). Additionally, high removal efficiencies of IBU (around 100%) and variable ones for IOH (13–80%, depending on the plant species) were achieved in a 24-day batch hydroponic culture study using the same plants in this study (Zhang et al., 2016). The performance of the CW mesocosms were influenced by all the factors studied: type of mesocosm (different plant species and unplanted control), season, HLR and initial spiking concentration. Previous studies have mainly focused on single factors. For example, temperature was linked to variable IBU removal efficiencies in subsurface flow CWs (Ávila et al., 2013). Fewer studies have focused on the interaction effects between environmental factors, like temperature and redox potential (Hijosa-Valsero et al., 2010), or plant species and HRT (Matamoros and Salvadó, 2012). The first-order kinetic model has been the most commonly used one to predict degradation of organic micro-contaminants in several different systems, including CWs. (2006) found that several PPCPs could be fitted by both a zero- and first-order kinetic areal based removal model in a subsurface flow CW planted with P. australis, including IBU, which was fitted by a first-order removal model (kA 0.051m day−1 ). The observed removal rate constants for IBU in the present study are similar or higher than those obtained in hydroponic studies by Dordio et al. (2011), 0.768 d−1 for Typha, and also consistent with the rate constants of 0.17–0.38 d−1 found for the same plant species in a previous study (Zhang et al., 2016). For IOH, to the best of our knowledge, studies on the removal kinetics in CWs are non-existent. Using a slow sand filter, however, Escolà Casas and Bester (2015) found that the first-order removal rate constant of IOH was 0.004 d−1 (0.101 h−1 ) and concluded that biodegradation could contribute to the removal of IOH. Zhang et al. (2016) reported that the first-order removal rate constants of IOH in a hydroponic culture were 0.06 and 0.007 d−1 in a system with P. australis and J. effusus in hydroponic culture. In the present study, IOH removal could not be fitted by any of the kinetic models considered, which can probably be attributed to the complex mechanisms involved in the removal of IOH in a CW system and the characteristic of the compound. Organic pollutants are eliminated by CWs through synergistic processes, including volatilization, hydrolysis, photodegradation, substrate sorption, plant uptake and biodegradation (Imfeld et al., 2009; Ávila et al., 2015). In the present study, volatilization and hydrolysis as pathway for IBU and IOH removal from the mesocosms were assumed not to occur due to the experimental setup and the physicochemical characteristics of the compounds at the pH studied. Photodegradation can also be neglected, as the water in the experimental setup was not exposed to light. Substrate sorption was estimated to be below 0.4% of the total mass added to each mesocosm for each season. In fact, IBU and IOH in the substrate were not detectable by our analytical procedure. Typical materials used as substrates in CWs are sand and gravel, which normally function as filters for retaining larger particles and as support for microbial biofilms. The capacity of sand and gravel to retain micro-pollutants such as IOH and IBU by sorption is generally negligible (Dordio and Carvalho, 2013). Plant uptake and accumulation in the plant tissue were estimated by measuring the concentrations of IBU and IOH in the above-ground plant tissue. The concentrations of IBU and IOH in the plant aerial tissues were lower than the LOD, thus total phytoaccumulation was estimated to be lower than 0.35% of the amount of IBU and IOH spiked into the mesocosms. Previously, Zhang et al. (2016) demonstrated that under hydroponic conditions, the same plant species studied could only accumulate up to 1.1% and 5.7% of

the amount of IBU and IOH spiked into a solution at an initial concentration of 10 mg L−1 at the end of a 24-day incubation period. Although phytoaccumulation may seem irrelevant in the present study, it is unclear to what extent plant uptake and internal metabolization may contribute to the removal of the compounds in CWs. An important factor in the present study is the reduced availability of the compounds to the plants at the lower concentrations studied here, and the complex interactions with the substrate, biofilm and plant roots. In conclusion, once measured sorption in the substrate and estimated total phytoaccumulation were accounted for (<0.75%), this study suggests that biodegradation was the main pathway for the removal of IBU and IOH in the saturated CW mesocosms. For IBU, this process has also been suggested in other studies (Matamoros et al., 2012). Plants seem to play a pivotal role for the degradation of IBU and IOH in CWs as removal efficiencies were consistently higher in planted systems compared to non-planted control systems. In addition, there were also differences between the plant species. Season, due to different temperatures and plant activity, will shape the microbial activity and the consequent biodegradation in the CW mesocosms. Attempts have been made to understand the removal mechanisms of different pharmaceuticals in CWs and how this removal is related to environmental parameters such as evapotranspiration, temperature and HRT. A previous study demonstrated that the removal of IBU was positively correlated with DO in a subsurface flow CWs with influent concentration of IBU of 75 ␮g L−1 (Ávila et al., 2010), indicating that aerobic conditions favor the removal of IBU. The present study is in agreement with this observation as the removal of IBU was also positively correlated with DO. In addition, the removal of IBU was also correlated with water temperature and air temperature. The negative correlation with NH4 -N concentration in combination with the positive correlation with the DO concentration suggests that the removal process might also be correlated with nitrification processes or plant uptake of ammonium. IOH removal efficiency was positively correlated to the concentrations of TN, PO4 -P, NH4 -N and DO, but these factors only accounted for 10.6% of the variation observed. Hence, other factors not accounted for in the present work must affect the removal of IOH in CWs.

5. Conclusion The present study aims to investigate the removal of IBU and IOH in water-saturated CW mesocosms to better understand the removal mechanism of these pharmaceuticals by CWs under watersaturated conditions. The mesocosms were not affected by the exposure to IBU and IOH at the ␮g L−1 concentration levels studied. Both IBU and IOH could be efficiently (>80%) removed in the CWs, but the efficiency depended on the plant species used in the CWs, season (only for IBU), HLR and initial spiking concentration. Particularly the presence of plants and the initial spiking concentrations of IBU and IOH contributed most to the differences in removal efficiency. IBU removal was fitted by the first-order kinetic model, whereas the removal of the more recalcitrant IOH could not be adequately described by any of the kinetic models tested. The high removal efficiency of IBU and IOH, together with the low rates of phytoaccumulation and sorption to substrate, suggest that biodegradation in the CW substrate medium was the main removal pathway for both IBU and IOH in the saturated CW mesocosms. In addition, removal of IBU and IOH were stimulated at high temperature and aerobic conditions. Further studies are needed to improve our understanding of the removal pathways of IBU and IOH in CWs.

Please cite this article in press as: Zhang, Y., et al., Ibuprofen and iohexol removal in saturated constructed wetland mesocosms. Ecol. Eng. (2016), http://dx.doi.org/10.1016/j.ecoleng.2016.05.077

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Please cite this article in press as: Zhang, Y., et al., Ibuprofen and iohexol removal in saturated constructed wetland mesocosms. Ecol. Eng. (2016), http://dx.doi.org/10.1016/j.ecoleng.2016.05.077