Science of the Total Environment 430 (2012) 174–183

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Comparison of interannual removal variation of various constructed wetland types

María Hijosa-Valsero a,⁎, Ricardo Sidrach-Cardona b, Eloy Bécares a

a Department of Biodiversity and Environmental Management, Faculty of Biologic and Environmental Sciences, University of León, Campus de Vegazana s/n, E-24071 León, Spainb Environment Institute, University of León, C/La Serna 58, E-24007 León, Spain

⁎ Corresponding author at: Instituto de Diagnóstico A(IDAEA), CID, CSIC, C/Jordi Girona 18‐26, 08034 Barce61 00x1305; fax: +34 93 204 59 04.

E-mail addresses: maria.hijosa@idaea.csic.es (M. Hijo(R. Sidrach-Cardona), ebecm@unileon.es (E. Bécares).

0048-9697/$ – see front matter © 2012 Elsevier B.V. Alldoi:10.1016/j.scitotenv.2012.04.072

a b s t r a c t

a r t i c l e i n f o

Article history:Received 5 August 2011Received in revised form 27 April 2012Accepted 28 April 2012Available online 28 May 2012

Keywords:Constructed wetlandsUrban wastewaterTemporal removal changesRDA analysis

Seven mesocosm-scale (1 m2) constructed wetlands (CWs) of different configurations were operated out-doors for thirty-nine months under the same conditions to assess their ability to remove organic matterand nutrients from urban wastewaters. CWs differed in some design parameters, namely the presence ofplants, the species chosen (i.e., Typha angustifolia or Phragmites australis), the flow configuration (i.e., surfaceflow or subsurface flow) and the presence/absence of a gravel bed. It was observed that, in general, removalefficiencies decreased with the aging of the system and that seasonality had a great influence on CWs. A com-parison was made in order to figure out which kind of CW was more efficient for the removal of every pol-lutant in the long term. Planted systems were clearly better than unplanted systems even in winter.Efficiency differences among CWs were not extremely great, especially after a few years. However, sometypes of CWs were more adequate for the removal of certain pollutants. The effect of the aging on themain parameters involved in pollutant removal in CWs (temperature, pH, conductivity, dissolved oxygenconcentration and redox potential) was assessed. The efficiency of CWs should not be evaluated based onshort monitoring periods (1–2 years) after the start-up of the systems, but on longer periods.

© 2012 Elsevier B.V. All rights reserved.

1. Introduction

Constructed wetlands (CWs), used for treatment of urban sewagefrom small communities and of various kinds of industrial wastewa-ters, have been employed and studied for several decades (Kadlecand Wallace, 2009; Vymazal, 2009; Zhang et al., 2009). Their designis a function of the wastewater nature, its pollutant load, the availablearea to build the wetland and the climatic conditions of the site.

The removal efficiency of CWs is usually assessed based on thedata from relatively short experimental or sampling campaign pe-riods (up to 2 years). However, CWs are intended to treat wastewaterduring decades. Therefore, the application of those preliminary data(obtained during a brief period) to the design and/or maintenanceof real CWs can result in an unexpected decrease in the long-termsystem performance. Bulc (2006) monitored the performance of aCW for landfill leachate treatment during seven years. Liikanen et al.(2006) studied seasonal and temporal changes in a boreal CW usedto purify peat mining runoff waters 5 and 15 years after its construc-tion. Mitsch and Wilson (1996) studied the creation and restorationof new wetlands for mitigation of lost wetland habitat and proposeda period of at least 15 years to start judging the success of a CW.

mbiental y Estudios del Agualona, Spain. Tel.: +34 93 400

sa-Valsero), rsidm@unileon.es

rights reserved.

Stefanakis and Tsihrintzis (2009, 2012) assessed during 3 years theperformance of experimental scale CWs treating synthetic wastewa-ter. However, the temporal behaviour of CWs treating raw urbanwastewater could be different from that described by those authors,due to the different nature of the treated waters.

It has been observed that the configuration and nature of aCW affects its performance (García et al., 2004; Hijosa-Valsero et al.,2010a,b). In general, physicochemical parameters (temperature, pH,dissolved oxygen concentration, redox potential, etc.) and the pres-ence of plants (which can modify some of the previous parameters)influence pollutant removal in CWs (Hijosa-Valsero et al., 2011), be-cause many biological removal processes (like microbiological degra-dation, plant uptake, biofilm adsorption, etc.) and abiotic removalprocesses (photodegradation, adsorption, chemical degradation, etc.)are controlled to a lesser or greater extent by these parameters. It isknown that physicochemical parameters in CWs suffer spatial andtemporal changes (Imfeld et al., 2009), even at a monthly or dailyscale (Wießner et al., 2005), thus affecting andmodifying the removalof organic matter.

In this work, the evolution of seven types of mesocosm-scale(1 m2) CWs was assessed during a 39-month period under the sameenvironmental conditions and using the same urban wastewater tofeed all the systems. The monitoring of CWs during such long periodsis not very common in the field of urban wastewater treatment. Thesetreatment systems differed in some design parameters, namely, thepresence or absence of plants, their species (Typha angustifolia orPhragmites australis) and the flow configuration and the presence or

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absence of soil matrix (i.e., floating macrophytes surface flow — FM-SF, free-water surface flow — FW-SF, free-water subsurface flow —

FW-SSF — or conventional horizontal subsurface flow — SSF). Thedifferent behaviour of every CW regarding the removal of chemicaloxygen demand (COD), biological oxygen demand (BOD5), totalsuspended solids (TSS), total Kjeldahl nitrogen (TKN), ammonia nitro-gen (NH4–N), nitrate and orthophosphate was monitored in order todetermine which type of wetland was more efficient in the longterm. In addition, considering that CWs are semi-natural systems,their maturation could lead to important physicochemical changesthroughout time. The effect of time on the main parameters involvedin pollutant removal in CWs (temperature, pH, conductivity, dissolvedoxygen concentration and redox potential) was also assessed.

2. Material and methods

2.1. Description of the CWs

Seven mesocosm-scale CWs were set up in the open air inside thefacilities of the León WWTP, in the northwest of Spain (42°33′35.19″N, 5°33′45.35″W, 807 m a.s.l.). Some climatic values of the site duringthe experimental period are provided in the Supplementary materialsection (Fig. SM1). All CWs consisted of a fibreglass container (80 cmwide, 130 cm long, 50 cm high) with a surface area of approximately1 m2. The CWs differed in their design parameters, which aresummarised in Fig. 1. With the exception of vertical-flow systems,the most commonly used CW-designs were compared. In May 2007,seedlings were collected in nearby wet areas and planted in wetlandsCW1, CW2, CW3, CW5 and CW6 with a density of 50 plants m−2.Wetlands CW1, CW2 and CW3 were planted with T. angustifolia.Wetlands CW5 and CW6 were planted with P. australis. Vegetationcoverage was 100% in all these CWs. Wetlands CW4 and CW7 wereleft unplanted. The aerial part of the plants was harvested in winter2009 and winter 2010. However, the living roots remained insidethe beds and the plants grew again during the subsequent warm pe-riods. Vegetal biomass data at the end of the experiment are availablein Table SM1. Theoretical hydraulic retention time (HRT) values oftanks CW1, CW2, CW3, CW4, CW5, CW6 and CW7 were, respectively,2.1, 3.3, 5.1, 6.1, 2.9, 2.5 and 2.6 days. Actual HRTs measured at the

Fig. 1. Schematic design characteristics of the CWs. Notes: FM: floating macrophytes, FW: freelayer of 30 cm and plant growth was supported by 20 cm long and 10 cm diameter garden-water (FW) over a 25 cm layer of siliceous gravel (d10=4 mm). Systems CW6 and CW7 cons40 cm.

end of the experimental period (October 2010) were 0.50, 0.42,0.96, 0.63, 1.13, 2.21 and 0.73 d, respectively.

León WWTP consists of a primary treatment (screening, sand re-moval, fat removal and primary clarifier) and a secondary treatment(plug-flow activated sludge with nitrification/denitrification and sec-ondary clarifier). Urban wastewater coming from the primary clarifierof the WWTP was conducted to a homogenisation tank of 0.5 m3 inorder to feed all the CWs at a continuous flow rate of 50 L day−1

(input load 50 mm day−1 and a BOD5 load 3 g m−2 day−1).

2.2. Sampling and analytical procedures

The systems started up in May 2007. After a stabilisation period,seven sampling campaigns were carried out: summer 2007 (July–September 2007), winter 2008 (January–March 2008), summer 2008(July–September 2008), winter 2009 (January–March 2009), summer2009 (July–September 2009), winter 2010 (January–March 2010) andsummer 2010 (July–September 2010). Influent and effluent grab sam-ples were collected once a week (n=10 in summer 2007, n=9 inwinter 2008, n=11 in summer 2008, n=10 in winter 2009, n=12in summer 2009, n=7 in winter 2010 and n=10 in summer 2010)at the seven CWs, always on the same day and at the same time.Samples were collected in one-litre amber glass bottles, which weretransported refrigerated (4 °C) to the laboratory, where conventionalwastewater quality parameters (COD, BOD5, TSS, TKN, NH4–N, nitrateand orthophosphate) were analysed within 24 h by using StandardMethods 5220 C, 5210 B, 2450 D, 4500-Norg B, 4500-NH3 C, 4500-NO3

− D and 4500-P E, respectively (APHA-AWWA-WPCF, 2001).Nitrate was only analysed during the three last sampling campaigns(summer 2009, winter 2010 and summer 2010). Physicochemicalparameters (temperature, pH, conductivity, dissolved oxygen andredox potential) weremeasured in situwith probes (WTW,Weilheim,Germany) at two different depths (5 cm below the water surface and5 cm above the bottom of the tank) in each CW and the homogenisa-tion tank.

2.3. Statistical analyses

Experimental results were statistically evaluated using the soft-ware Statistica 7 (StatSoft Inc., Tulsa, OK, USA). The evolution of

-water layer, SF: surface flow, SSF: subsurface flow. Systems CW1 and CW5 had a waternet cylinders (4 cm pore size). Systems CW2, CW3 and CW4 had a 25 cm layer of free-isted of a 45 cm siliceous gravel (d10=4 mm) layer, with an operational water depth of

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removal efficiencies throughout time for every CW was assessed bymeans of a Kruskal–Wallis ANOVA (non-parametric test). Efficiencycomparisons between CWs which only differed in one design param-eter were performed with Mann–Whitney U tests (non-parametrictests). Differences were considered significant when pb0.05. Redun-dancy analyses (RDA) were carried out with the programme Canocofor Windows 4.5 (Ter Braak, 2003) and their graphics were createdwith CanoDraw for Windows 4.1 (Šmilauer, 1999–2003).

3. Results and discussion

3.1. Characteristics of the influent wastewater

Table 1 shows the concentrations of the studied pollutants duringthe seven sampling campaigns. The concentration variation through-out time for every pollutant was assessed with a Kruskal–WallisANOVA. This analysis evidenced that the concentrations of all pollut-ants except orthophosphate were significantly higher in winter 2008(Table 1). This fact could not be avoided, since our influentwastewatercame directly from the primary clarifier and the variability of its pol-lutant load was related to the operational regime of the WWTP.

Regarding wastewater physicochemical parameters, significantlydifferent temperatures were recorded in summer (16.6–19.4 °C) andwinter (11.0–12.2 °C); although in summer 2008 slightly lower tem-perature values than in the other summer periods were measured.As far as redox potential values are concerned, a decline was observedfrom summer 2009 on. However, the rest of measured physicochemi-cal parameters, like pH (7.0–7.5), conductivity (133–608 μS cm−1) ordissolved oxygen concentration (0.3–1.8 mg L−1) showed no clearpatterns (Table 1).

3.2. Temporal evolution of removal efficiencies

To make more reliable comparisons between CWs, pollutant re-moval was calculated as mass removal efficiency, which considersboth influent and effluent concentrations and flows, and thus takesevapotranspiration into account. Evapotranspiration values and efflu-ent concentrations are provided in the SM section (Table SM2 andTable SM3, respectively).

Figs. 2 and 3 show removal efficiencies in every CW throughoutthe seven sampling campaigns (nitrate was only analysed duringthe three last sampling campaigns). However, when doing the statis-tical comparisons (Kruskal–Wallis ANOVA), data were divided intotwo distinct groups (winter and summer), because it was observedthat winter removal efficiencies in CWs were lower than summerones in this temperate region (especially for nutrients), a fact alreadynoticed by other CW researchers (Stefanakis and Tsihrintzis, 2009).Some temporal trends were observed in all the studied systems(Figs. 2 and 3), both in summer and winter (see statistical details in

Table 1Average values and 0.95 confidence intervals for some pollutants and physicochemical parapollutant or parameter) some letters are given between parentheses, which are related toletters, there are no significant differences between campaigns; if they do not have any lette

Summer 2007 Winter 2008 Summer 2008

COD (mg L−1) 101±11 (a) 550±83 (b) 106±28 (a)BOD5 (mg L−1) 56±8 (a) 219±40 (b) 70±19 (a)TSS (mg L−1) 53±5 (abc) 311±52 (b) 55±9 (abc)TKN (mg L−1) 15±2 (ab) 33±6 (c) 20±3 (abc)NH4–N (mg L−1) 11±2 (ab) 19±4 (c) 14±2 (abc)NO3

− (mg L−1) – – –

Orthophosphate (mg L−1) 1.45±0.33 (a) 2.67±0.70 (a) 2.04±0.40 (a)Temperature (°C) 18.6±0.4 (ab) 12.2±0.4 (cd) 16.6±0.6 (ace)pH 7.3±0.1 (ab) 7.1±0.0 (ac) 7.4±0.1 (b)Conductivity (μS cm−1) 608±27 (a) 370±142 (a) 133±12 (b)Dissolved oxygen (mg L−1) 1.3±0.3 (a) 0.7±0.1 (ab) 0.4±0.1 (b)Redox potential (mV) 110±35 (a) −6±56 (ab) −2±52 (ab)

Tables SM4 and SM5). As far as summer results are concerned, a sig-nificant efficiency decrease throughout time was detected in theTypha-planted systems (CW1, CW2, CW3) and the unplanted FW-SSF (CW4) for the removal of all the pollutants. This decrease wasonly recorded for TSS and orthophosphate in the Phragmites-FM-SF(CW5), for COD and orthophosphate in the Phragmites-SSF (CW6),and for COD, TKN and orthophosphate in the unplanted-SSF (CW7)(the removal of the rest of pollutants kept statistically constant overthe years). However, in spite of this decrease, the Phragmites-SSF(CW6) always showed a high performance for the removal of COD(>65%) and orthophosphate (>90%). The unplanted-SSF (CW7)was unable to remove NH4–N. The decline in HRTs throughout time(Section 2.1) could partly explain the efficiency decrease in most wet-lands. It is important to point out that CW6 had the highest HRT at theend of the experiment (and very similar to its initial theoretical HRT),which could be the reason why its performance did not suffer anabrupt drop. Regarding winter performances, all the systems sharedthe same pattern: the removal efficiencies during winter 2009 werelower than those of winter 2008 and winter 2010. This fact is noteasy to explain, since the influent wastewater characteristics in win-ter 2009 a priori do not suggest a greater difficulty for degradation(Table 1). Meteorological data (Fig. SM1) were looked up in orderto find adverse conditions to clarify the worse results obtained inwinter 2009, but the only differentiating parameter was the numberof snow days in January–March (2 days in 2008, 13 days in 2009and 3 days in 2010; these values correspond strictly to the samplingweeks at the experimental site, and because of that they may differfrom Fig. SM1). Although a deeper research is needed to prove this re-lationship, snow could have affected the performance of the systems.A review carried out by Zhang et al. (2009) indicated that CWs func-tion in cold weather, but rates of microbial decomposition may beslow if the system either freezes solid or is under a cover of ice.

Finally, in order to assess the consequences of CW evolution, thecomparison was focused more detailedly on the last year (winterand summer 2010). It was observed that, in the last winter periodmonitored (Table SM6), there were no statistical differences amongCWs for the removal of COD, TSS, nitrate and orthophosphate. In thecase of BOD5, CW3 and CW4 (both FW-SSF) had the highest perfor-mances (but their results were only significantly higher than thoseof the worst system, the Phragmites-FM-SF, CW5). A similar patternwas observed for TKN and NH4–N removal, since the Typha-FW-SSFsystem (CW3) obtained the best removal performances but was onlysignificantly better than the worst system (CW5). As far as the lastsummer is concerned (Table SM6), clearly better CWs were not ob-served either. In this season, no statistical differences among CWsappeared for the removal of COD. The Phragmites-FM-SF (CW5) wasthe best system to remove BOD5; the Typha-FW-SSF (CW3) and thePhragmites-FM-SF (CW5) were the most appropriate for the removalof TSS; the systems planted with P. australis (CW5 and CW6) achieved

meters in the urban wastewater used to feed the CWs. Note: For each line (i.e., for eachthe results of the Kruskal–Wallis ANOVA. If two sampling campaigns share one of ther in common, there are significant differences (pb0.05) between sampling campaigns.

Winter 2009 Summer 2009 Winter 2010 Summer 2010

119±23 (a) 97±23 (a) 138±50 (a) 131±30 (a)55±17 (a) 44±6 (a) 67±12 (a) 74±12 (ab)42±17 (c) 43±4 (c) 53±7 (abc) 49±6 (c)17±2 (ab) 14±1 (b) 21±2 (ac) 20±2 (ac)11±1 (a) 10±1 (a) 14±2 (abc) 16±2 (bc)– 0.39±0.23 (ab) 1.59±0.88 (a) 0.26±0.09 (b)1.41±0.41 (a) 1.45±0.43 (a) 2.13±0.39 (a) 2.29±0.33 (a)11.0±2.8 (de) 19.3±0.7 (b) 11.4±1.2 (de) 19.4±0.4 (b)7.5±0.1 (b) 7.3±0.0 (b) 7.1±0.1 (abc) 7.0±0.0 (c)379±172 (ab) 519±21 (a) 594±59 (a) 587±32 (a)1.8±1.3 (ab) 0.7±0.1 (ab) 1.5±0.7 (a) 0.3±0.1 (b)36±75 (ab) −84±38 (b) −76±25 (b) −100±21 (b)

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Fig. 2. Temporal evolution of the removal of COD, BOD5 and TSS in the studied CWs.

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the highest removal values for TKN, NH4–N and orthophosphate; andthe FW-SSF systems (CW3 and CW4) removed at best nitrate (on thecontrary, the Phragmites-SSF, CW6 released nitrate, which could implya nitrification process). However, the results of these good-performingCWs were only significantly higher than those of the least efficientsystems for every pollutant (Table SM6). All the abovementionedfacts imply that the possible efficiency differences among the variousCW designs tend to disappear with time. This had been observed forPPCP removal in CWs, where detrimental factors related to the aging

of the systems (clogging, matrix saturation, loss of hydraulical con-ductivity, shading of the upper layer) were made responsible for thishomogenization process (Reyes-Contreras et al., 2012).

3.3. Temporal evolution of removal performances and its relationshipwith physicochemical parameters

Many works establish a connection between physicochemical pa-rameters and the removal of pollutants in CWs (Akratos and Tsihrintzis,

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TKN NH4-N Nitrate Ortho-P TKN NH4-N Nitrate Ortho-P

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Fig. 3. Temporal evolution of the removal of nutrients in the studied CWs.

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2007; García et al., 2004; Truu et al., 2009). Some of the variations inphysicochemical parameters may be generated by the organismsinhabiting the CW (Stottmeister et al., 2003). Table SM7 shows themeasured values of physicochemical parameters (temperature, pH,conductivity, dissolved oxygen concentration and redox potential)in the studied mesocosm CWs during the experimental period. Inorder to study the joint time evolution of physicochemical param-eters and removal efficiencies in association with the type of CW-configuration, redundancy analyses (RDA) were performed separately

for summer (Fig. 4) andwinter (Fig. 5). This method has been previous-ly applied to the study of CWs (Hijosa-Valsero et al., 2011). The depen-dent variables in the RDA were the removal efficiency (%) values ofevery pollutant (COD, BOD5, TSS, NH4–N, TKN, nitrate and ortho-phosphate), which are represented by thin arrows in Figs. 4 and 5.The independent variables included in the model were the surface(SUP — superior) and bottom (INF — inferior) values of temperature(°C), pH, conductivity (μS cm−1), dissolved oxygen (mg L−1) andredox potential (mV), represented by the thick arrows named T, pH,

Fig. 4. Ordination diagrams for the Redundancy Analysis (RDA) in summer (2007, 2008, 2009 and 2010). (a) Influence of the studied environmental variables and plant presence onthe removal efficiency of the studied pollutants. (b) Influence of the system age. The samples of every year are distributed according to physicochemical parameters. (c) Effect of thesystem configuration. The samples of every type of constructed wetland are shown.

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Cd, O2 and Eh, respectively, in Figs. 4 and 5. The presence or absence ofplants was used as a factor (qualitative independent variable, represen-ted by triangles in Figs. 4 and 5). Other statistical details about the per-formed RDA can be found in the SM section.

In summer (Fig. 4), the statistical analysis demonstrated that35.7% of the total variation in the removal efficiency data (dependentvariables) could be explained by the chosen independent variables.Fig. 4 (a) indicates that a positive correlation exists between the pres-ence of plants and redox potential values (both in the surface and inthe bottom of the tank) and the removal of orthophosphate, TKN,NH4–N and, to a lesser extent, COD and BOD5. This means that thepresence of plants increases the redox potential (the medium be-comes more aerobic), and that both plants and the redox potentialvalue contribute to the elimination of nutrients directly or indirectly.The amount of oxygen pumped down by plants and released insidethe substrate is low; and it is considered that nutrient uptake by

plants constitutes only a small part of the total percentage removal(Zhang et al., 2009). Nevertheless, both this pumped oxygen and theroot-biofilm interaction can contribute to the creation of aerobic mi-croenvironments, which would be particularly useful for the removalof organic matter (Imfeld et al., 2009; Stottmeister et al., 2003). More-over, there was a negative correlation between the concentration ofdissolved oxygen in the bottom of the tank and the removal of nitrate,which means that an oxygen-rich environment favours the formationof nitrate (i.e., nitrification processes could be taking place). However,it must be remembered that nitrate data were only available for twosummer campaigns (summer 2009 and summer 2010). Nitrogen re-moval in wetlands occursmainly through nitrification–denitrification,and, to a lesser extent, biomass uptake and sedimentation (Bachandand Horne, 2000; U.S. EPA, 2000; Vymazal, 2007). A positive correla-tion existed between conductivity and the removal of TSS, whosemeaning is difficult to explain, since the system with the highest

Fig. 5. Ordination diagrams for the Redundancy Analysis (RDA) in winter (2008, 2009 and 2010). (a) Influence of the studied environmental variables and plant presence on theremoval efficiency of the studied pollutants. (b) Influence of the system age. The samples of every year are distributed according to physicochemical parameters. (c) Effect of thesystem configuration. The samples of every type of constructed wetland are shown.

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conductivity (CW6; Table SM7) was not precisely the best at TSS re-moving (Fig. 2). The short arrows of temperature and pH indicatethat these parameters were not decisive for the removal efficiency insummer. In any case, temperature is an important parameter, becausein summer removal efficiencies are in general better than in winter.The evolution of the systems throughout time can be seen in Fig. 4(b);the samples of the first summer campaign (summer 2007) appear onthe left of the first axis (horizontal axis), an area dominated by highredox potential values. The samples of the following years move clock-wise, so that the last summer campaign samples (summer 2010) appearin the graph in an area dominated by low redox potential values andhigh conductivity. This trend could be attributed to physicochemicalchanges inside CWs. Given that influent conductivity during the firstand the last summer seasons were not very different, the most logicalreason for an electrical conductivity increase throughout time in the

systemwould be a higher evapotranspiration rate, but that was not ob-served in the studied CWs (Table SM2). Another possibility would bethe release of salts and organic matter (especially in planted systems).However, the influence of the nature of the influent wastewater cannotbe underestimated in the case of redox potential (in fact, redox poten-tial values in the influent were lower in summer 2010, see Table 1). InFig. 4(c), the samples are grouped according to the type of CW. Theonly groups showing a marked behaviour are those of the unplantedsystems (CW4 and CW7), which appear in the opposite quadrant tothat of plants.

On the other hand, in winter (Fig. 5) the statistical analysis demon-strated that 21.9% of the total variation in the removal efficiency data(dependent variables) could be explained by the chosen independentvariables. Fig. 5(a) indicates the existence of a negative correlation be-tween plant presence and nitrate removal (that means that nitrate is

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being released by planted systems, which could imply a nitrificationprocess). Nevertheless, nitrate data correspond to an onlywinter cam-paign (winter 2010). There was a positive correlation between redoxpotential values and the removal of orthophosphate, TKN, NH4–Nand COD (an aerobic degradation pathway for these pollutants canbe deduced). There could also be a positive correlation betweenplant presence and redox potential, since these factors/independentvariables point in the same direction. Zhang et al. (2009) stated thatplants can play a key role and make a significant difference in the re-moval of TN and NH4–N, and a much lesser role in the removal of TP,COD and BOD5. A positive correlationwas also found between temper-ature and the elimination of COD, BOD5, TKN and NH4–N. Warm tem-peratures enhance some microbiological processes: microorganismsliving in CWs usually reach their optimal activity at warm tempera-tures (15–25 °C), especially nitrifying and proteolytic bacteria (Truuet al., 2009). Akratos and Tsihrintzis (2007) found positive linearcorrelations between temperature and the removal of COD, BOD5,TKN, N-NH3, total phosphorous and P-PO4

3− in horizontal SSF-CWsfed with synthetic wastewater. According to Bachand and Horne(2000), water temperature was found to affect denitrification rates.For organic pollutants (BOD5 and COD), the temperature dependencewould not be so significant (Steinmann et al., 2003). Moreover, in thepresent study it was observed that a positive correlation between thebottom pH and BOD5 removal existed; another fact difficult to explain,because, apart from the narrow pH-range of the studied wastewater(Table 1 and SM7), the highest pH values were recorded in CW4(Table SM7) and the most efficient system for BOD5 elimination inwinter was CW6 (Fig. 2). Conductivity and the bottom pH valuewere positively correlated with TSS removal, which, apparently, con-tradicts the fact that CW1 and CW7 (the best TSS-removers) did notshow the highest conductivity or pH values (Fig. 2, Table SM7). Thetemporal evolution of winter samples is shown in Fig. 5(b). The firstsampling campaign (winter 2008) appears on the right side of thefirst axis (dominated by high pH values), whereas the last samplingcampaign (winter 2010) moves towards the upper part of the secondaxis (dominated by high conductivity values). Similar to summer sam-ples, this drift could be due to physicochemical changes inside CWs;but changes in the nature of the influent wastewater cannot be dis-carded (conductivity values in the influent were higher in winter2010, see Table 1). Fig. 5(c) shows the distribution of the differenttypes of CWs regarding physicochemical parameters, although noclear groups appeared.

3.4. The importance of the design configuration

In order to study the effect of CWdesign configuration on the elim-ination of organic pollutants, statistical comparisons (Mann–WhitneyU tests) between percentage removal efficiencies of pairs of CWswhich only differed in one design parameter were made. The timeevolution of these pairs of CWs was also monitored. The results ofthese statistical comparisons are given below (see details in TableSM8).

3.4.1. Vegetal speciesThe influence of the plant species chosen was assessed by compar-

ing the Typha-FM-SF (CW1) and the Phragmites-FM-SF (CW5). Duringthe first sampling campaign (summer 2007) the Typha-system (CW1)was the most efficient in removing all the studied compounds. How-ever, at the end of the experimental period, the Phragmites-system(CW5) proved to be significantly better for the removal of TKN andNH4–N during summer (Table SM8a), and this could be due, in part,to its higher HRT at the end of the experimental period.

3.4.2. Plant presenceThe effect of plant presence was studied by comparing the Typha-

FW-SSF (CW3) to its unplanted equivalent (CW4), and the Phragmites-

SSF (CW6) to its unplanted equivalent (CW7), respectively. In thecase of the FW-SSF systems (CW3 and CW4), the planted system(CW3) demonstrated its superiority for the removal of most pollutants,especially TKN, NH4–N and orthophosphate, both in summer and inwinter (Table SM8b). In the case of the conventional horizontal SSF sys-tems (CW6 and CW7), the planted system (CW6)was themost efficientfor the removal of many pollutants, especially TKN, NH4–N and ortho-phosphate, both in winter and summer; the unplanted system (CW7)was the best at removing TSS, because of a continuous presence ofChrysophyceae algae (mixotrophic and heterotrophic species) in theplanted CW6, which caused an increase in the TSS effluent concentra-tion. During summer 2010, it was observed that the unplanted system(CW7) removed nitrate, whereas the planted one (CW6) released thissubstance (which could indicate a nitrification process in the plantedsystem) (Table SM8c). In both pairs of CWs, the planted system had ahigher HRT at the end of the monitoring period than the unplantedsystem.

3.4.3. Gravel matrix or soilless matrixWhen comparing a soilless Typha-FM-SF system (CW1) to a gravel-

bed Typha-FW-SF system (CW2), significant differences were foundduring the first sampling campaigns, pointing to a better performanceof the soilless system (CW1) to remove BOD, TSS, TKN and NH4–N.However, in summer 2010 all significant differences between both sys-tems had disappeared (Table SM8d). When the soilless Phragmites-FM-SF system (CW5)was compared to the Phragmites-SSF system (CW6), itwas observed that the soilless wetland (CW5) was the most efficient toremove TSS, whereas the gravel-system (CW6) coped significantly bet-ter with the removal of TKN, NH4–N and orthophosphate. Nevertheless,those differences in TKN and NH4–N elimination disappeared in sum-mer 2010 and, whereas the soilless system (CW5) removed nitrate,the gravel one (CW6) released this substance (which could indicate anitrification process in the gravel system) (Table SM8e). In comparisonto the soilless system (CW5), the gravel system (CW6)was the best op-tion to eliminate orthophosphate, especially in summer, probably dueto the fact that adsorption onto the substrate (gravel, in this case) isthe main removal mechanism for this compound in CWs. The longerHRT in CW6would have favoured this process. In addition, plant uptake(aminor route for phosphorus removal) reaches amaximum in spring–summer months (Vymazal, 2007). For both pairs of CWs, a homogeni-zation process caused by detrimental factors (thus decreasing removalefficiencies) could be responsible for the disappearance of performancedifferences.

3.4.4. Flow typeThe influence of the flow type was assessed by comparing the

Typha-FW-SF (CW2) and the Typha-FW-SSF (CW3). Although duringthe first years the SSF system (CW3) was significantly more efficientto remove BOD5 and orthophosphate, no differences between thesetwo CWs were found in the last season (summer 2010) (TableSM8f), in spite of the higher HRT recorded in CW3. This could be dueto the abovementioned homogenization process. Other flow configu-rations were contrasted by comparing the unplanted FW-SSF (CW4)to the unplanted SSF (CW7). During the first year and a half (summer2007, winter 2008 and summer 2008), the FW-SSF (CW4) was signif-icantly more efficient to remove TKN and NH4–N (maybe because ni-trogen was being assimilated by the Chlorophyta microscopic algaeliving on its surface), whereas the SSF system (CW7) was better forthe removal of orthophosphate (perhaps by means of an adsorptionprocess on the gravel bed). From that point on, the only differencebetween both CWs was the significantly better removal of BOD5 insummer in the SSF system (CW7) (Table SM8g). This last fact couldbe the consequence of microscopic algae presence on the surface ofthe FW-SSF (CW4), which left the system floating through the efflu-ent, thus contributing to an increase in BOD5 concentrations.

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All these statistical comparisons indicate that planted systems aremore efficient than unplanted systems for the removal of nutrients(TKN, NH4–N and orthophosphate) throughout the whole year (evenin winter, when plants are less active). This fact had already beensuggested by other authors (Tanner, 2001; Vymazal and Kröpfelová,2009; Zhang et al., 2009). As far as to elucidate which vegetal specieswas the most efficient (T. angustifolia or P. australis), the current re-sults cannot clearly give an answer: the former species performed bet-ter at the beginning of the operational period for the removal of mostpollutants, whereas the latter coped better with TKN and NH4–N onlyduring the last summer campaign. This uncertainty is in accordancewith the work of Brisson and Chazarenc (2009), who stated that todate there is no agreement on which species is the most efficient atremoving organic matter and nutrients from wastewater. Regardingthe presence of gravel, the global configuration of the system seemedto be more decisive than the gravel itself. For instance, the soillessTypha-FM-SF (CW1) and the gravel Typha-FW-SF (CW2) presentedno performance differences in the long term. In contrast, the soillessPhragmites-FM-SF (CW5) was worse than the gravel Phragmites-SSF(CW6) for the removal of orthophosphate, which would indicatethat a conventional SSF system (like CW6), provided with gravel andallowing a sufficient contact time between wastewater and the sub-strate, would enhance phosphorous removal via adsorption. Manystatistical efficiency differences between systems with distinct flowtypes disappeared with the aging of the CWs. That was the case of SFvs. SSF (CW2 vs. CW3) or free-water SSF vs. conventional SSF (CW4vs. CW7). The only difference which remained was the better perfor-mance of the unplanted SSF (CW7) over the unplanted FW-SSF(CW4) to remove BOD5 in summer. Efficiency declines related to CWmaturation have been reported in other systems (Blazejewski andMurat-Blazejewska, 1997; Nguyen, 2000; Sun et al., 1999; Tanner etal., 1998). As explained above, we believe that the loss of removal ca-pabilities related to functioning and/or maturation issues (clogging,shading, matrix, saturation, decrease in HRTs, appearance of preferen-tial hydraulic pathways, etc.), tends to eliminate efficiency differencesamong distinct CW configurations.

3.5. Selection of an appropriate treatment system

In order to summarise the results of this work in amore useful wayto facilitate the decision-making of CW operators and designers, itcould be interesting to point out whichwere themost efficient and re-liable systems during the whole monitored period. In summer, thesoilless FM-SF (CW1, CW5) and the Phragmites-SSF (CW6) were themost efficient to remove COD; the Phragmites-FM-SF (CW5) and theunplanted-SSF (CW7) were the best to remove BOD5; the soilless sys-tems (CW1, CW5), the Typha-FW-SF (CW2) and the unplanted-SSF(CW7) were the best option to eliminate TSS; the Phragmites-systems(CW5, CW6) were the most appropriate to eliminate TKN and NH4–N;the FW-SSF (CW3, CW4) and the soilless Phragmites-FM-SF (CW5) re-moved nitrate; and the Phragmites-SSF (CW6) was the most efficientto eliminate orthophosphate. Inwinter the behaviourwas a little differ-ent and it must be remembered that the secondwinter had low perfor-mances. The FW-SSF (CW3, CW4) and the SSF (CW6, CW7) were thebest to reduce COD and BOD5 concentrations; the Typha-FM-SF (CW1)and the unplanted-SSF (CW7) were the most efficient for TSS; theTypha-systems (CW1, CW2, CW3) and the Phragmites-SSF (CW6) elim-inated TKN; the Typha-systems (CW1, CW2, CW3) removed nitrate;and, finally, the Typha-FW-SSF (CW3) and the Phragmites-SSF (CW6)obtained good performances for the removal of orthophosphate (inwinter, no system was able to reduce NH4–N concentrations). There-fore, the combination of several types of CWs in series is proposed toobtain successful removal efficiencies for all the pollutants throughoutthe whole year.

In brief, CW removal efficiency decreases throughout time and sci-entific conclusions should not be drawn based on the first months

after the start-up of a system. In addition, all the studied CWs obtainedCOD, BOD and TSS effluent concentrations below EU discharge limits(125, 25 and 35 mg L−1, respectively) during the last summer, regard-less of their configuration and of plant presence;whichwould indicatethat, at low organic loads and warm temperatures, the system config-uration is not a determinant factor for these pollutants. However, inwinter, the Typha-FW-SSF (CW3) was the most reliable system forthe elimination of most pollutants. Moreover, the presence of plantswas beneficial for nutrients removal, both in summer and winter;and P. australis showed a better performance than T. angustifolia dur-ing summer.

4. Conclusions

− In this work, a wide range of CW configurations were assessedunder the same environmental and load conditions, using realurban wastewater and taking evapotransporation into account tocalculate removal efficiencies. Although some types of CWs weremore efficient for the removal of certain pollutants, efficiency dif-ferences among systems were not extremely great, especiallyonce the treatment wetlands had aged a few years. Accordingly,CW efficiency should not be evaluated based on short monitoringperiods (1–2 years) after the start-up of the systems, but on longerperiods. The efficiency of CWs declined throughout time and as afunction of the specific system configuration. This is a relevant fac-tor to consider when designing a treatment wetland, in order toforesee difficulties and fulfil wastewater discharge regulations.

− In the studied locality (continental Mediterranean climate), re-moval efficiencies were marked by seasonality. The presence ofplants was beneficial both in winter and summer, especially fornutrient elimination.

− This experiment was performed at a small-scale and during a lim-ited period. Further studies should be needed to assess temporalchanges and performance evolution in already-working full-scaleCWs treating urban wastewaters.

Acknowledgements

This study was funded by the Spanish Ministry of Science and Inno-vation (projects CTM2005-06457-C05-03 and CTM2008-06676-C05-03), by the Castilla y León Regional Government (projects LE009A07and LE037A10-2) and by MAPFRE (project AG-180). The authors thankJ.C. Sánchez Sánchez for the maintenance of the systems, Dr. R. ReinosoTapia for his help during the sampling campaign and H. AstiárragaPanizo, M. López Vázquez and P.C. Giloni de Lima for the analyses ofnutrients in the samples. We thank Acciona Agua and Mancomunidadde Saneamiento de León y su Alfoz for their technical support.

Appendix A. Supplementary material

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.scitotenv.2012.04.072.

References

Akratos CS, Tsihrintzis VA. Effect of temperature, HRT, vegetation and porous media onremoval efficiency of pilot‐scale horizontal subsurface flow constructed wetlands.Ecol Eng 2007;29:173–91.

APHA-AWWA-WPCF. Standard methods for the examination of water and wastewater.20th ed. Washington DC: American Public Health Association; 2001.

Bachand PAM, Horne AJ. Denitrification in constructed free-water surface wetlands. II.Effects of vegetation and temperature. Ecol Eng 2000;14:17–32.

Blazejewski R, Murat-Blazejewska S. Soil clogging phenomena in constructed wetlandswith subsurface flow. Water Sci Technol 1997;35(5):183–8.

Brisson J, Chazarenc F. Maximizing pollutant removal in constructedwetlands: shouldwepay more attention to macrophyte species selection? Sci Total Environ 2009;407:3923–30.

Bulc TG. Long term performance of a constructed wetland for landfill leachate treat-ment. Ecol Eng 2006;26:365–74.

183M. Hijosa-Valsero et al. / Science of the Total Environment 430 (2012) 174–183

García J, Aguirre P, Mujeriego R, Huang Y, Ortiz L, Bayona JM. Initial contaminant re-moval performance factors in horizontal flow reed beds used for treating urbanwastewater. Water Res 2004;38:1669–78.

Hijosa-Valsero M, Matamoros V, Sidrach-Cardona R, Martín-Villacorta J, Bécares E,Bayona JM. Comprehensive assessment of the design configuration of constructedwetlands for the removal of pharmaceuticals and personal care products fromurban wastewaters. Water Res 2010a;44:3669–78.

Hijosa-Valsero M, Sidrach-Cardona R, Martín-Villacorta J, Bécares E. Optimization ofperformance assessment and design characteristics in constructed wetlands forthe removal of organic matter. Chemosphere 2010b;81:651–7.

Hijosa-Valsero M, Sidrach-Cardona R, Martín-Villacorta J, Valsero-Blanco MC, BayonaJM, Bécares E. Statistical modelling of organic matter and emerging pollutants re-moval in constructed wetlands. Bioresour Technol 2011;102:4981–8.

Imfeld G, Braeckevelt M, Kuschk P, Richnow HH. Monitoring and assessing processes oforganic chemicals removal in constructed wetlands. Chemosphere 2009;74:349–62.

Kadlec RH, Wallace S. Treatment wetlands. 2nd ed. Boca Raton: CRC Press; 2009.Liikanen A, Huttunen JT, Karjalainen SM, Heikkinen K, Väisänen TS, Nykänen H, et al.

Temporal and seasonal changes in greenhouse gas emissions from a constructedwetland purifying peat mining runoff waters. Ecol Eng 2006;26:241–51.

Mitsch WJ, Wilson RF. Improving the success of wetland creation and restoration withknow-how, time, and self-design. Ecol Appl 1996;6:77–83.

Nguyen LM. Organic matter composition, microbial biomass and microbial activity ingravel-bed constructed wetlands treating farm dairy wastewaters. Ecol Eng 2000;16:199–221.

Reyes-Contreras C, Hijosa-Valsero M, Sidrach-Cardona R, Bayona JM, Bécares E. Tempo-ral evolution in PPCP removal from urban wastewater by constructed wetlands ofdifferent configuration: a medium-term study. Chemosphere 2012;88:161–7.

Šmilauer P. CanoDraw for Windows 4.1 (c); 1999–2003.Stefanakis AI, Tsihrintzis VA. Effect of outlet water level raising and effluent rec-

irculation on removal efficiency of pilot-scale, horizontal subsurface flow con-structed wetlands. Desalination 2009;248:961–76.

Stefanakis AI, Tsihrintzis VA. Effect of loading, resting period, temperature, porousmedia, vegetation and aeration on performance of pilot-scale vertical flow con-structed wetlands. Chem Eng J 2012;181–182:416–30.

Steinmann CR, Weinhart S, Melzer A. A combined system of lagoon and constructedwetland for an effective wastewater treatment. Water Res 2003;37:2035–42.

Stottmeister U, Wießner A, Kuschk P, Kappelmeyer U, Kästner M, Bederski O, et al. Ef-fects of plants and microorganisms in constructed wetlands for wastewater treat-ment. Biotechnol Adv 2003;22:93-117.

Sun G, Gray KR, Biddlestone AJ. Treatment of agricultural wastewater in a pilot scaletidal flow reed bed system. Environ Technol 1999;20:233–7.

Tanner CC. Plants as ecosystems engineers in subsurface-flow treatment wetlands.Water Sci Technol 2001;44(11–12):9-17.

Tanner CC, Sukias JPS, Upsdell MP. Organic matter accumulation during maturation ofgravel-bed constructed wetlands treating farm dairy wastewaters. Water Res1998;32:3046–54.

Ter Braak CJF. Program CANOCO Version 4.5A February 2003. Biometris: quantitativemethods in the life and earth sciences. Wageningen, The Netherlands: Plant Re-search International, Wageningen University and Research Centre; 2003.

Truu M, Juhanson J, Truu J. Microbial biomass, activity and community composition inconstructed wetlands. Sci Total Environ 2009;407:3958–71.

U.S. EPA.Manual: constructed wetlands treatment of municipal wastewaters, EPA/625/R-99/010. Cincinnati: Office of Research and Development; 2000. http://www.epa.gov/owow/wetlands/pdf/Design_Manual2000.pdf (last accessed April 2012).

Vymazal J. Removal of nutrients in various types of constructed wetlands. Sci Total En-viron 2007;380:48–65.

Vymazal J. The use constructed wetlands with horizontal sub-surface flow for varioustypes of wastewater. Ecol Eng 2009;35:1-17.

Vymazal J, Kröpfelová L. Removal of organics in constructed wetlands with horizontalsub-surface flow: a review of the field experience. Sci Total Environ 2009;407:3911–22.

Wießner A, Kappelmeyer U, Kuschk P, Kästner M. Influence of the redox condition dy-namics on the removal efficiency of a laboratory-scale constructed wetland. WaterRes 2005;39:248–56.

Zhang D, Gersberg RM, Keat TS. Constructed wetlands in China. Ecol Eng 2009;35:1367–78.