International Journal of Phytoremediation
ISSN: 1522-6514 (Print) 1549-7879 (Online) Journal homepage: http://www.tandfonline.com/loi/bijp20
Phytoremediation of domestic wastewater using a hybrid constructed wetland in mountainous rural area Saloua Elfanssi, Naaila Ouazzani, Lahbib Latrach, Abdessamed Hejjaj & Laila Mandi To cite this article: Saloua Elfanssi, Naaila Ouazzani, Lahbib Latrach, Abdessamed Hejjaj & Laila Mandi (2018) Phytoremediation of domestic wastewater using a hybrid constructed wetland in mountainous rural area, International Journal of Phytoremediation, 20:1, 75-87, DOI: 10.1080/15226514.2017.1337067 To link to this article: https://doi.org/10.1080/15226514.2017.1337067
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Date: 05 January 2018, At: 08:12
INTERNATIONAL JOURNAL OF PHYTOREMEDIATION 2018, VOL. 20, NO. 1, 75–87 https://doi.org/10.1080/15226514.2017.1337067
Phytoremediation of domestic wastewater using a hybrid constructed wetland in mountainous rural area Saloua Elfanssia,b, Naaila Ouazzani
a,b
, Lahbib Latracha,b, Abdessamed Hejjaja, and Laila Mandia,b
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a National Center for Research and Studies on Water and Energy (CNEREE), Cadi Ayyad University, Marrakech, Morocco; bLaboratory of Hydrobiology, Ecotoxicology and Sanitation (LHEA, URAC 33), Faculty of Sciences Semlalia, Marrakech, Morocco
ABSTRACT
KEYWORDS
The purpose of this study is to evaluate the efficiency of hybrid constructed wetlands (HCWs) in a rural mountainous area. The experiment was set up in small rural community named Tidili within the region of Marrakech, Morocco. The wastewater treatment plant was composed of three vertical flow constructed wetlands (VFCWs) working in parallel, followed by two parallel horizontal-subsurface flow constructed wetlands (HFCWs), with hydraulic loading rates of 0.5 and 0.75 m3/m2.d, respectively. The two units were planted with Phragmites australis at a density of 4 plants/m2. Wastewater samples were collected at the inlet of the storage tank and at the outlet of the whole system (VFCWs, HFCWs) stages. The main removal percentages of total suspended solids (TSS), biochemical oxygen demand measured in a 5-day test (BOD5), chemical oxygen demand (COD), total nitrogen, and total phosphorus were respectively 95%, 93%, 91%, 67%, and 62%. The system showed a very high capacity to remove total coliforms, fecal coliforms, and fecal streptococci (4.46, 4.31, and 4.10 Log units, respectively). Artificial neural networks (ANNs) were used to model the quality parameters (TSS, BOD5, COD) and total coliforms and fecal streptococci. Based on the obtained results, the ANN model could be considered as an efficient tool to predict the studied phytoremediation performances using HCWs.
Artificial neural networks; domestic wastewater; hybrid constructed wetland; Phragmites australis; phytoremediation; removal efficiency
Introduction In Morocco, as in all developing countries, sanitation and wastewater treatment are one of the biggest environmental problems in rural areas. This has accentuated in recent years after the great development of access to safe drinking water, thanks to the Rural Water Supply Projects, which increased the rate from 14% to over 90% (Lahbabi and Anouar 2009). According to the National Sanitation Master Plan, about 13 million of the rural population lives in more than 32000 village and small rural agglomerations (PNAR 2013). In these areas, only 11% of rural populations have sanitation facilities (PNAR 2013). This means that the need for drinking water for these inhabitants is very important and the necessity for collecting and treating wastewater within these communities is growing. However, wastewater treatment infrastructure is either poorly developed or non-existent in these rural areas, which causes severe water pollution and infectious diseases. So it is clear that the development of treatment techniques adapted to this socioeconomic context, taking into account both the technical and financial capacities of these small and mountainous localities, is very challenging (Fahd et al. 2007). The phytoremediation technologies, such as constructed wetlands (CWs) for wastewater treatment, could be considered as an ingenious solution for environmental protection and rehabilitation. In addition, they are economical and sustainable
for developing countries (Vymazal 2011). The CWs are successful in removing many pollutants, for instance: organic compounds, suspended solids, pathogens, metals, and nutrients (Kadlec and Wallace 2008; Calheiros et al. 2013; Ranieri et al. 2013; T€ urker et al. 2014). Moreover, CWs are economic, easily operated, and highly efficient (Vymazal 2007). Many natural processes (Adsorption, infiltration and microbial degradation) are involved in these phytoremediation engineered systems related to the role of vegetation, soils, and the associated microbial assemblages to assist in purifying wastewater (Vymazal 2005). Two kinds of CW systems are generally used. The first one is called free water surface and the second is named the subsurface flow (SSF) wetland. Both systems are shallow, but each has its own characteristics and way of working (EPA 2006). The treatment of wastewater by wetland plants was first experimented in Germany by Seidel in the early 1950s (Vymazal 2005). Several authors confirmed that CW phytoremediation could be adapted to treat both domestic and industrial wastewaters (Mandi et al. 1996; Mandi et al. 1998; Abissy and Mandi 1999; Tiglyene et al. 2008; Laaffat et al. 2015). It is possible to use various CW configurations together in order to enhance the efficiency of their treatment, especially for nitrogen. For many industrial, agricultural wastewaters, landfill leachate and storm water runoff, which are difficult to be treated in a single-stage system, the hybrid systems have
CONTACT Laila Mandi
[email protected] National Center for Research and Studies on Water and Energy, Cadi Ayyad University, P.O. Box 511, 40000 Marrakech, Morocco. Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/bijp. © 2018 Taylor & Francis Group, LLC
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S. ELFANSSI ET AL.
been introduced (Vymazal 2005). They could also be used for combined sewer overflow (Avila et al. 2013) or refinery effluent (Wallace and Kadlec 2005). The hybrid systems are constantly composed of vertical flow and horizontal subsurface flow staged in many possible ways. The two systems complement each other and create a balance in the way they work (Vymazal 2007). Moreover, they are efficient to remove the conventional water quality parameters, as well as to eliminate emerging organic contaminants (Hijosa-Valsero et al. 2010). The early research, founded on theoretical bases, recommended that CW phytoremediation could not be adapted to higher altitude locations due to the incapability of plants to survive harsh and freezing conditions of sub-mountainous and mountainous regions. However, an experience on the ground has proved that plants can thrive well and filtration beds do not frost in high-altitude locations (Z€ ust and Sch€ onborn 2003). This evidence was also noticed in USA (Minnesota), Canada, and Northern Japan (Kadlec et al. 2003; Mc Carey et al. 2004; Kato et al. 2013). In the case of Morocco, there has been no research study on CWs in the field scale within the mountainous regions. Modeling is important for predicting hybrid constructed wetland system (HCW) performances and could help to understand the CW’s behavior under mountain climates better. An artificial neural network (ANN) model can simulate the concentration of output pollutants, in a specific area. Neuronbased modeling is used as a substitute to mechanistic models which are difficult to implement and not accurate (Shahaf and Marom 2001). The ANNs are categorized as single and multilayer feed-forward networks (FFNNs), self-organized networks, feedback networks, and recurrent networks. In terms of application as a connection type and learning method, the FFNN is the most used type of network in the domain of modeling and prediction. Its building block has a simple structure (called neuron) because it presents a weighted sum of its inputs and calculates an output by using certain predefined activation functions. These latter functions are needed to introduce nonlinearity into the network. On the other hand, the most
Figure 1. Geographical location of the Tidili wastewater treatment plant.
common choices for the activation functions are the Sigmoidal functions. The number of neurons and the way in which they are interconnected defines the neural system’s architecture. A set of input/output pairs feed the network in order to reproduce the outputs. The objectives of this study are: 1) to evaluate the behavior and the treatment performances of HCWs in a mountain region for removing biochemical oxygen demand measured in a 5-day test (BOD5), chemical oxygen demand (COD), total suspended solids (TSS), total nitrogen (TN), total phosphorus (TP), and bacterial indicators of fecal contamination; 2) to examine the effects of seasonal variations on the performances of such wastewater treatment plants; and 3) to simulate the effectiveness of WWTP using the ANNs.
Materials and methods Presentation of the study site The HCW is built in Tidili, a mountainous region, with 1844 inhabitants, located at latitude 31 290 47.3900 N, longitude ¡7 340 4.0300 E, and altitude 876 m—70 km from Marrakech (Figure 1). The study area is characterized by a humid climate with storms and rainfall as well as snowfall.
Climate data Between 1970 and 2011 the average monthly temperatures ranged from 12 C in January to 29 C in July (Figure 2). The data were recorded in the climatic station of Sidi Rahal and were provided by the River Basin Agency of Tensift (ABHT). In the coldest month, January, the average minimum temperature was 3 C; in the warmest month, July, the average maximum temperature reached 43 C. Between 2005 and 2010, the average annual rainfall was 285 mm, estimated from monthly average precipitations (Table 1) of the Tafrihat climatic station (ABHT 2010).
INTERNATIONAL JOURNAL OF PHYTOREMEDIATION
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Both the constructed wetland systems (VFCW, HFCW) were planted by P. australis at a density of 4 plants/m2. The hydraulic loading rates were 0.5 and 0.75 m3/m2.d, respectively for VFCW and HFCW. Organic loading rates of 194 § 14.66 and 28 § 11.36 g BOD/m2/d were applied respectively to VFCW and HFCW. Sampling
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Figure 2. Monthly average temperature ( C) and evaporation mean (mm) recorded in the resort of Sidi Rahal between (1970–2011).
Hybrid constructed wetland system design Figure 3 describes the flow process line of the treatment plant, which is composed of a lifting station, two submersible pumps, a screen, an electrical panel positioned outside, a PVC PN 10pipe discharge with a diameter of 110 mm, and a total head of 4.02 m which can raise the raw wastewater to a retention basin (length D 6 m, width D 3 m, usable depth D 1.2 m) allowing a batch feeding and finally an overflow Outside diameter (DN) PVC 250. A rotation device is provided in a downstream direction facing the storage basin to alternate the sewage alimentation to the first treatment stage. In its first stage the system is composed of three parallel vertical flow constructed wetlands (VFCWs) with dimensions 13 £ 10 £ 0.9 m (length £ width £ depth), filled by 20 cm of pebble (60/100 mm) followed by 20 cm of coarse gravel (3/20 mm), and 50 cm of fine gravel (2/ 8 mm). Shown in Figure 4a are the gravel layers that are used for the filter layer, transition layer, and drainage layer of the system. The second and third layers are separated by a geomembrane (200 g/m2) to prevent clogging of the drainage layer. Each bed has a slope of 1% directed downstream and is equipped with a PVC outlet fitting 160 mm in diameter to evacuate the percolating water to both vertical flow beds through a PVC pipe of 200 mm diameter. Sequentially, each bed is fed during 2 days and then undergoes a rest period to allow regulation of biomass growth and to preserve aerobic conditions inside the filter (Molle et al. 2008). In its second stage the system is composed of two parallel horizontal subsurface flow constructed wetlands (HFCWs) with dimensions 11 £ 8 £ 0.6 m (length £ width £ depth), filled with fine gravel and coarse gravel (Figure 4b) . Each bed has a slope of 1% directed downstream and equipped with an outlet tip PVC 110 mm in diameter. This is used for discharging the treated water toward a tank where it is temporarily stored and pumped later for irrigation.
After a commissioning period of the vegetation, the wastewater treatment plant was monitored every 2 weeks for 2 years (April 2014 to April 2016) (50 samples). Water samples were taken at the storage tank inlet (after the lift station), and at both the VFCW and HFCW outlets. During the monitoring period, raw and treated wastewaters were collected in plastic bottles for physicochemical analyses and in presterilized glass bottles for sanitary indicators. The samples were transported into the laboratory in an ice chest under temperatures of 4 C in order to preserve the integrity of the samples before analysis. Physicochemical and microbial analysis Physicochemical parameters such as electrical conductivity (EC), potential of hydrogen (pH), and dissolved oxygen (DO) were measured in situ by a multiparameter probe type instrument WTW Multi 340i/set [Wissenschaftlich-Technische Werkstaetten GmbH (WTW) B€ uro–weilheim, Germany]. Water temperature was measured using a Galinstan thermometer. To determine the BOD5, we used the Warburg method; while the COD was analyzed according to the dichromate open reflux method (APHA 1998). The TSS were estimated by membrane filtration (Millipore membrane filter, pore size 0.45 um) (AFRNOR 1997). Ammonium-Nitrogen (NH4C-N) was analyzed by the Indophenol method, the nitritenitrogen (NO2¡-N) by the Diazotation method, and TP by Potassium Peroxodisulfate digestion (AFNOR 1997). Nitrate-Nitrogen (NO3¡-N) was evaluated as NO2¡-N after its reduction through a Cadmium-Copper column according to Rodier (1996). Total Kjeldahl Nitrogen (TKN) was measured by Kjeldahl mineralization and distillation of Ammonium and final acidimetric titration (AFNOR 1997). Finally, TN was evaluated as a summation of NH4C-N, NO2¡-N, NO3—N, and TKN. The removal rates for each pollutant under study were calculated as listed below: Polluant Removal efficiency ð%Þ D ð½polluant inlet ¡ ½polluant outlet 6 ½Polluants inletÞ 100 Fecal coliforms, total coliforms and streptococci were measured using the dilution method for the samples suspected to
Table 1. Rainfall (mm) recorded in the Tafriat station from 2005 to 2010. Year
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Annual rainfall (mm)
2005–2006 2006–2007 2007–2008 2008–2009 2009–2010 Average rainfall (mm)
0 5 1 27 92 25
40 32 11 35 7 25
17 17 58 47 1 28
18 35 15 28 41 27.4
106 5 34 70 70 57
53 28 24 84 53 48.4
2 1 1 95 27 25.2
25 34 2 2 10 14.6
32 39 26 2 9 21.6
8 0 0 14 7 5.8
0 1 0 0 2 0.6
3 9 0 1 19 6.4
304 206 172 405 338 285
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S. ELFANSSI ET AL.
Figure 3. Layout of the hybrid constructed wetlands implemented at the rural commune of Tidili (Morocco).
be highly contaminated; membrane filtration (0.45 mm) was used in the case of treated wastewater (Moroccan Standards 2006). Lactose 2,3,5 Triphenyl Tetrazolium Chloride (TTC) (Panreac, Espa~ na) with Tergitol agar (Himedia, India) media was used to enumerate total and fecal coliforms (Moroccan Standard 08.0.124, 2006). Bile aesculin agar (Biokar Diagnostics, French) media was used for group streptococci counts (Moroccan Standard 03.7.001, 2006). The number of coliforms was expressed as log colony forming units (CFU) per 100 mL (log CFU/100 mL). The bacteriological removal was calculated as the difference between the inlet and the outlet. Artificial neural networks The artificial neural network black-box model was created in “R” software package neuralnet, version 1.33, with a
Feed-Forward Back-Propagation to predict the performance of Tidili plant. The input and output pollutants for the models are [COD, BOD, TSS, total coliform (TC), and fecal streptococci (FS)]. The root mean square error (RMSE) and Nash were used between observed and simulated values to evaluate the performances of the model in both processes (calibration and validation). Statistical analysis Statistical analysis was performed using SPSS 21.0 software (SPSS, Chicago, Illinois, USA, 2012). The means were compared using Turkey’s test at the significance level of a D 0.05. The Box Whiskers method was used to present the data. The performance of the Constructed Wetlands (CWs) was based on the inflow and outflow concentrations and abatements
Figure 4. Dimensions of the VFCWs and HFCWs of the hybrid constructed wetland. (a) Schematic of vertical flow bed. (b) Schematic of horizontal flow bed.
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Figure 5. Influent and effluent values of temperatures and dissolved oxygen concentrations (n D 50).
calculated on the first quartile (Q1), second quartile (Q2; median), and third quartile (Q3). The Person correlation between air temperature, physicochemical and microbiological performances were also reported.
Results and discussion In situ parameters The average temperature of influent and effluent was respectively 14.5 C § 1.60 and 14.43 C § 1.19. In July and August, the temperature reached its highest level (17.55 § 1.69 and 18.27 C § 1.56 respectively). While, its lowest level is recorded in January (8.65 C § 1.26) (Figure 5).The average of DO in the influent and the effluent of the HCW was 0.64 § 0.1 and 2.73 § 0.5 mg/L respectively. The DO was low in raw wastewater and reached the highest value in June 3.5 mg/L in treated water (Figure 5). On the other hand, the values of pH, which is an essential element for water quality, indicated neutral conditions in the treatment system during the study period (mean pH D 7.5 § 0.1), as shown in Table 2. The pH ranged between 7.35 § 0.05 and 7.87 § 0.01 in the raw wastewater and between 7.26 § 0.03 and 7.83 § 0.02 in the treated wastewater. So, their values varied according to seasons: the lowest pH was noticed in autumn and winter (7.50 § 0.01 and 7.44 § 0.03 respectively) and the highest in summer (7.78 § 0.02). The EC average of the HCW effluent (1723 § 43.41 mS/cm) was slightly reduced compared to the values obtained at the influent (1815.32 § 42.68 mS/cm), except in August when the values of outflow were higher than inflow (Table 2). Increased evapotranspiration and/or water assimilation by plant roots may have caused this effect (Hench et al. 2003). Physicochemical and microbial parameters Table 3 shows the average content of physicochemical and microbial parameters of the HCW influent, which is in the range of urban wastewater. A significant difference between the
cold and warm periods was noted. For example, the values for BOD5, COD, TSS, TN, TP were higher in winter than in summer. This could be explained by lower water consumption in cold periods. However, the concentration of bacterial indicators of fecal contamination increased from winter to summer due to bacterial growth enhanced by higher temperatures. Results showed that concentrations of all parameters in influent were highly influenced by the season. According to the Tukey test, four groups were distinguished (Table 3). A statistically significant increase in BOD5, COD, TSS, TN, and TP from the warm to the cold season was observed. However, the concentrations
Table 2. Influent and effluent values (mean § standard deviation) of electrical conductivity and pH samples (n D 3) of the HCW. pH
EC (mS/cm)
Months
Influent
Effluent
Influent
Effluent
Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Apr-15 May-15 Jun-15 Jul-15 Aug-15 Sep-15 Oct-15 Nov-15 Dec-15 Jan-16 Feb-16 Mar-16 Apr-16
7.62 § 0.02 7.7 § 0.01 7.79 § 0.02 7.84 § 0.03 7.87 § 0.01 7.62 § 0.01 7.57 § 0.03 7.4 § 0.04 7.35 § 0.05 7.38 § 0.03 7.43 § 0.02 7.7 § 0.01 7.51 § 0.03 7.62 § 0.03 7.67 § 0.02 7.74 § 0.01 7.77 § 0.01 7.53 § 0.02 7.48 § 0.03 7.41 § 0.04 7.37 § 0.03 7.53 § 0.01 7.61 § 0.02 7.74 § 0.01 7.55 § 0.01
7.51 § 0.01 7.65 § 0.01 7.73 § 0.02 7.81 § 0.01 7.83 § 0.02 7.6 § 0.02 7.5 § 0.04 7.33 § 0.03 7.29 § 0.03 7.31 § 0.02 7.37 § 0.03 7.59 § 0.02 7.4 § 0.03 7.53 § 0.02 7.63 § 0.01 7.67 § 0.01 7.73 § 0.02 7.49 § 0.02 7.4 § 0.03 7.32 § 0.03 7.26 § 0.03 7.27 § 0.01 7.34 § 0.02 7.42 § 0.02 7.37 § 0.01
1823 § 2.68 1831 § 2.22 1753 § 2.12 1737 § 4.32 1725 § 4.22 1726 § 6.41 1841 § 6.36 1847 § 6.17 1850 § 6.11 1855 § 2.67 1842 § 2.54 1818 § 2.32 1842 § 2.52 1854 § 2.12 1771 § 2.22 1757 § 3.32 1773 § 4.22 1747 § 3.15 1863 § 4.12 1870 § 5.42 1881 § 5.33 1861 § 3.41 1852 § 3.22 1826 § 2.17 1838 § 2.12
1748 § 2.42 1749 § 2.16 1646 § 2.09 1623 § 4.27 1757 § 4.12 1643 § 6.33 1754 § 6.29 1761 § 6.12 1767 § 6.05 1763 § 2.62 1759 § 2.40 1734 § 2.23 1739 § 2.33 1740 § 2.25 1636 § 2.34 1619 § 3.25 1720 § 4.16 1633 § 3.05 1746 § 4.09 1752 § 5.37 1758 § 5.21 1772 § 3.27 1764 § 3.12 1744 § 2.20 1754 § 2.32
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S. ELFANSSI ET AL.
Table 3. Physicochemical and microbial parameters (mean § standard deviation) estimated from influent samples of the HCW. Unit
Spring(n D 14)
Summer(n D 12)
Autumn(n D 12)
Winter(n D 12)
mg/L mg/L mg/L mg/L mg/L Log(CFU/100 mL) Log(CFU/100 mL) Log(CFU/100 mL)
281.35 § 33.31bc 532.39 § 25.91b 392.07 § 65.51b 50.53 § 1.57a 5.60 § 0.76b 6.78 § 0.06a 6.90 § 0.05a 6.60 § 0.14b
251.04 § 36.71c 408.80 § 26.05c 280.40 § 45.07c 39.89 § 4.54c 4.39 § 0.47d 6.83 § 0.04a 6.95 § 0.03a 6.65 § 0.26b
325.17 § 47.31b 523.50 § 34.41b 417.05 § 70.18ab 37.51 § 3.55c 4.92 § 0.85c 6.76 § 0.03b 6.89 § 0.04b 6.83 § 0.21a
371.94 § 14.17a 644.40 § 43.45a 464.63 § 19.46a 46.87 § 2.18b 6.41 § 0.37a 6.73 § 0.03b 6.86 § 0.03b 6.38 § 0.20c
Parameter BOD5 COD TSS TN TP FC TC FS
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Notes: BOD5, biological oxygen demand measured in a 5-day test; COD, chemical oxygen demand; TSS, total suspended solid; TN, total nitrogen; TP, total phosphorus; FC, fecal coliforms; TC, total coliforms; FS, fecal streptococci. a. b. c and d; grouping test results of Tukey test.
(Log units) of bacterial indicators decreased from the warm to the cold season. The results showed high average removal rates for BOD5 (93.47%), COD (91.40%), and TSS (94.83%). The obtained results revealed that the concentration of pollutants decreased significantly along the flow path of the hybrid wetland system (Table 4). Organic matter removal efficiency varied according to the season. BOD5 and COD removal rates fluctuated between summer and winter from 96.70% to 90.27%, and from 94.24% to 90.27%, respectively (Table 4). The total reduction in TSS ranged from 90.78% in the cold season to 98.03% in the warm season (Table 4). The decrease in removal rate noticed in the cold season could be attributed to the highest organic loads of raw wastewater during this period. Significant correlations were obtained between temperature and organic matter, nutrients and coliforms (p > 0.05). The removal of COD and BOD5 seemed to be influenced by temperature (r-Pearson D 0.80 and 0.74, respectively). Higher organic matter removal efficiencies coincided with the higher temperatures observed in the warm season. In fact, the studies
done by Hijosa-Valsero et al. (2010); Llorens et al. (2009) suggested that there is no visible difference in TSS removal between seasons. However, the results of this study showed that TSS and temperature were significantly positively correlated (r-Pearson D 0.83), which is in concordance with the results obtained by Garfi et al. (2012). The inlet concentrations of pollutants varied widely between 362 and 683 mg/L for COD (Figure 6a), 177 and 395 mg/L for BOD5 (Figure 6b), and between 221 and 495 mg/L for TSS (Figure 6c). The average values of COD, BOD5, and TSS were 524, 290 and 405 mg/L respectively. The VFCW unit outflow is characterized by median concentrations of 81 mg/L for COD, 65 mg/L for BOD5, and 50 mg/L for TSS. The steep decrease in median content occurred after passage of the HFCW unit to 49, 18, and 20 mg/L, respectively (Figure 6a–c), this gave a greater contribution to pollutant removal, reducing their average content and significantly reducing effluent variability. Both physical and microbial mechanisms played an important role in the removal of COD and BOD5 by HCWs. Due to the physical filtration mechanisms and low porosity of the
Table 4. COD, BOD, and TSS concentrations (mean § standard deviation) estimated from influent and effluent samples (n D 3) of the HCW and removal rate efficiency. BOD5 (mg/L)
COD (mg/L)
TSS (mg/L)
Months
influent
effluent
Removal (%)
influent
effluent
Removal (%)
influent
effluent
Removal (%)
Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Apr-15 May-15 Jun-15 Jul-15 Aug-15 Sep-15 Oct-15 Nov-15 Dec-15 Jan-16 Feb-16 Mar-16 Apr-16
243.00 § 23.50 322.50 § 10.00 177.25 § 20.75 247.25 § 12.87 288.25 § 9.12 386.75 § 7.37 290.50 § 11.5 275.50 § 7.00 355.00 § 2.00 383.75 § 3.00 388.00 § 7.00 259.25 § 5.75 261.75 § 17.25 320.25 § 2.75 244.75 § 3.87 266.75 § 12.75 282.00 § 23.00 394.75 § 5.37 305.75 § 8.75 297.75 § 7.25 361.00 § 12.75 396.99 § 6.33 400.12 § 7.50 272.49 § 4.89 274.87 § 16.67
16.50 § 1.50 18.00 § 1.25 7.00 § 0.50 4.75 § 1.75 6.00 § 1.37 19.75 § 1.00 25.50 § 2.25 24.50 § 0.50 33.75 § 0.75 32.50 § 0.60 30.75 § 1.00 18.00 § 1.25 15.50 § 1.00 15.25 § 1.00 11.50 § 3.50 10.50 § 3.75 9.00 § 3.37 24.75 § 2.75 29.25 § 2.50 29.50 § 1.00 47.00 § 1.00 36.74 § 3.22 34.12 § 1.33 16.42 § 4.78 14.66 § 1.00
93.21 94.42 96.05 98.08 97.92 94.89 91.22 91.11 90.49 91.53 92.07 93.06 94.08 95.24 95.30 96.06 96.81 93.73 90.43 90.09 86.98 90.75 91.47 93.97 94.67
500.72 § 48.42 566.43 § 58.35 400.41 § 33.90 401.64 § 87.73 362.84 § 31.14 450.70 § 87.88 523.67 § 45.14 559.96 § 7.45 653.84 § 26.65 683.61 § 10.44 668.78 § 22.66 559.71 § 24.09 519.75 § 48.37 515.34 § 80.93 446.76 § 27.15 428.31 § 52.26 412.84 § 37.41 534.04 § 50.90 532.00 § 27.86 540.63 § 17.90 571.39 § 28.98 695.19 § 10.44 682.15 § 22.66 568.21 § 24.09 522.27 § 48.37
45.62 § 2.30 49.62 § 2.86 25.33 § 2.00 23.30 § 2.66 19.84 § 2.26 43.24 § 11.65 51.17 § 4.57 54.74 § 7.20 68.57 § 6.58 75.69 § 2.39 75.45 § 2.21 49.65 § 2.92 45.14 § 3.66 36.35 § 2.59 30.06 § 2.18 22.30 § 1.58 20.84 § 3.17 49.57 § 2.81 48.71 § 7.99 56.24 § 2.83 72.93 § 3.78 69.54 § 2.39 69.36 § 2.21 42.77 § 6.17 43.98 § 6.32
90.89 91.24 93.67 94.20 94.53 90.41 90.23 90.22 89.51 88.93 88.72 91.13 91.32 92.95 93.27 94.79 94.95 90.72 90.84 89.60 87.24 90.00 89.83 92.47 91.58
296.39 § 51.24 293.93 § 67.28 221.58 § 21.94 256.67 § 17.68 280.95 § 12.33 478.98 § 2.67 459.52 § 21.81 454.23 § 9.77 466.42 § 29.07 495.09 § 1.06 480.42 § 9.15 405.32 § 50.38 386.16 § 36.96 367.42 § 23.09 260.27 § 22.24 282.51 § 15.44 372.18 § 61.84 470.29 § 2.36 440.42 § 12.95 442.80 § 8.90 471.42 § 4.07 499.76 § 1.06 485.25 § 9.15 405.43 § 50.83 391.12 § 36.96
13.74 § 3.49 12.29 § 1.65 5.49 § 0.88 3.68 § 0.84 2.46 § 0.32 21.26 § 3.16 29.43 § 2.44 32.87 § 0.02 49.00 § 2.28 39.57 § 4.99 40.54 § 0.37 22.83 § 2.13 19.26 § 4.69 14.79 § 1.46 9.62 § 1.15 5.17 § 1.25 5.58 § 1.09 20.09 § 2.37 31.68 § 0.95 30.45 § 3.64 46.83 § 2.27 55.61 § 4.99 49.76 § 0.37 30.14 § 2.13 26.18 § 4.69
95.37 95.82 97.52 98.57 99.12 95.56 93.60 92.76 89.50 92.01 91.56 94.37 95.01 95.97 96.30 98.17 98.50 95.73 92.81 93.12 90.07 88.87 89.75 92.57 93.31
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Figure 6. Box-plot diagrams of COD (a), BOD (b), TSS (c), TN (d), and TP (e) concentrations (mg/L) in the different sampling points of the hybrid constructed wetland. Different letters indicate significant differences at p < 0.05 by Tukey’s test.
gravel media, the organic solids could be percolated and captured in the bed of CWs for a long time, thereby yielding greater biodegradation. Also the sedimentation of suspended solids and the rapid decomposition processes cause the high removal rates for COD and BOD5 (Zhang et al. 2009). The obtained results also revealed that there is a complete removal of TSS during the study period. In conclusion, the TSS removal in CWs is probably the result of physical processes such as sedimentation and filtration (Kadlec 2003). These reductions are generally within the broad ranges reported in the literature for similar CW systems as summarized by Vymazal. (2013). Therefore, it can be noted that the treatment process depends on the aquatic plants used in this wetland system; the root area is of specific importance in the elimination of biodegradable organic content (Noyes 1994) and provides the substrate for the attached bacteria which are responsible for the biodegradation of organic matter. In fact,
they contribute to the high level of organic matter reduction through oxygen transfer by the aerenchymatic tissues of roots and rhizomes (Brix 1994). In SSF wetlands, the anaerobic microbial metabolism removes dissolved organic matter, with some aerobic metabolism localized near the root and at the gravel bed surface (U.S. EPA 2000). Hybrid constructed wetlands are used for increasing the removal efficiency of TN because the various kinds of wetland environments give different redox conditions, which are appropriate for nitrification and denitrification (Vymazal 2011). The results depicted in Table 5 show the variation of nitrate and total nitrogen concentrations during the study period. The average NO3¡-N concentration in the influent was 0.58 § 0.03 mg/L, while the nitrate values were 2.92 § 0.01 and 1.95 § 0.02 mg/L respectively, in the VFCW and HFCW effluents (Table 5). The results indicated that the VFCWs were more efficient than the HFCWs in nitrification. Indeed, the first stage
VF effluent
2.16 § 0.02 2.22 § 0.01 2.21 § 0.02 2.19 § 0.01 2.20 § 0.04 3.18 § 0.04 3.17 § 0.01 3.19 § 0.00 4.17 § 0.01 4.19 § 0.00 4.21 § 0.01 2.24 § 0.01 2.17 § 0.02 2.23 § 0.02 2.22 § 0.03 2.21 § 0.01 2.18 § 0.03 3.16 § 0.04 3.25 § 0.01 3.20 § 0.01 4.22 § 0.00 4.22 § 0.02 4.24 § 0.03 2.22 § 0.01 2.15 § 0.01
Influent
0.45 § 0.04 0.39 § 0.03 0.34 § 0.06 0.32 § 0.03 0.31 § 0.01 0.53 § 0.04 0.62 § 0.01 0.75 § 0.04 0.77 § 0.04 0.78 § 0.02 0.76 § 0.04 0.54 § 0.06 0.47 § 0.04 0.41 § 0.05 0.38 § 0.04 0.35 § 0.02 0.32 § 0.02 0.60 § 0.02 0.72 § 0.03 0.80 § 0.06 0.84 § 0.04 0.92 § 0.02 0.91 § 0.03 0.72 § 0.02 0.51 § 0.01
Months
Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Apr-15 May-15 Jun-15 Jul-15 Aug-15 Sep-15 Oct-15 Nov-15 Dec-15 Jan-16 Feb-16 Mar-16 Apr-16
NO3¡-N (mg/L)
1.14 § 0.05 1.16 § 0.01 1.15 § 0.04 1.17 § 0.02 1.18 § 0.06 2.36 § 0.05 2.22 § 0.04 2.20 § 0.01 3.35 § 0.02 3.30 § 0.02 3.27 § 0.02 1.20 § 0.01 1.16 § 0.02 1.16 § 0.05 1.17 § 0.01 1.18 § 0.04 1.20 § 0.02 2.33 § 0.01 2.28 § 0.02 2.30 § 0.01 3.40 § 0.05 3.28 § 0.02 3.25 § 0.04 1.21 § 0.01 1.19 § 0.03
HF effluent 44.90 § 3.16 36.61 § 1.54 34.99 § 0.33 34.41 § 1.12 34.13 § 0.63 43.70 § 4.22 45.43 § 1.54 46.38 § 0.57 50.57 § 0.35 52.47 § 1.84 50.51 § 0.39 48.62 § 0.25 47.24 § 3.19 38.61 § 1.69 37.02 § 1.03 36.31 § 0.64 35.63 § 0.47 40.87 § 1.98 45.09 § 2.00 45.71 § 0.59 51.24 § 2.30 56.32 § 0.41 60.04 § 0.74 58.71 § 0.62 57.40 § 0.50
Influent 15.94 § 2.41 14.53 § 2.29 13.24 § 2.63 12.60 § 2.44 12.31 § 2.52 18.37 § 0.13 18.90 § 0.06 19.44 § 0.23 20.55 § 0.22 22.27 § 1.35 22.21 § 1.71 18.60 § 0.75 17.77 § 0.97 14.37 § 0.82 12.92 § 0.80 12.10 § 0.91 12.74 § 0.34 17.20 § 0.34 18.07 § 0.23 18.94 § 0.41 20.38 § 0.51 22.25 § 0.60 22.23 § 0.49 18.63 § 0.36 17.82 § 0.51
VF effluent 14.38 § 1.28 11.17 § 0.69 9.49 § 0.85 9.14 § 1.09 9.07 § 1.07 15.37 § 0.61 17.04 § 1.11 17.88 § 0.62 20.19 § 0.42 21.52 § 1.58 20.17 § 1.10 17.10 § 1.16 15.04 § 0.53 10.90 § 0.98 9.83 § 0.49 9.14 § 0.84 8.79 § 0.74 14.37 § 1.57 16.71 § 0.39 17.55 § 0.44 20.19 § 0.42 22.24 § 0.55 20.10 § 0.21 17.14 § 0.63 16.23 § 0.42
HF effluent
TN (mg/L)
67.99 69.49 72.87 73.44 73.43 64.82 62.48 61.45 60.08 58.98 60.06 64.83 68.16 71.76 73.46 74.83 75.34 64.83 62.95 61.62 60.60 60.51 66.52 70.81 71.72
Removal rate (%) 5.57 § 0.17 5.31 § 0.05 4.04 § 0.07 3.98 § 0.03 3.87 § 0.04 5.70 § 0.02 6.15 § 0.02 6.35 § 0.03 6.69 § 0.04 6.77 § 0.04 6.81 § 0.03 5.30 § 0.01 4.72 § 0.01 4.65 § 0.02 4.41 § 0.04 3.97 § 0.06 3.94 § 0.06 4.58 § 0.03 5.31 § 0.08 5.82 § 0.04 6.33 § 0.05 6.90 § 0.03 6.93 § 0.02 5.42 § 0.02 4.80§ 0.04
Influent
Table 5. Nitrate, total nitrogen, and total phosphorus content (mean § standard deviation) in influents and effluents (n D 3) of VFCWs and HFCWs; removal rate efficiency.
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2.51 § 0.11 2.68 § 0.05 2.39 § 0.06 2.02 § 0.05 2.32 § 0.01 3.12 § 0.02 3.25 § 0.05 3.18 § 0.03 2.90 § 0.02 2.87 § 0.03 2.76 § 0.06 2.75 § 0.02 2.73 § 0.02 2.84 § 0.07 2.98 § 0.03 1.68 § 0.04 2.70 § 0.03 3.03 § 0.03 3.28 § 0.05 3.03 § 0.05 2.93 § 0.03 2.94 § 0.06 2.80 § 0.04 2.82 § 0.03 2.81 § 0.02
VF effluent
2.15 § 0.09 2.01 § 0.02 1.04 § 0.00 0.84 § 0.07 0.84 § 0.02 2.23 § 0.06 2.67 § 0.04 2.90 § 0.04 3.37 § 0.02 3.42 § 0.03 3.42 § 0.04 2.07 § 0.01 1.84 § 0.04 1.68 § 0.03 1.23 § 0.02 0.85 § 0.01 0.85 § 0.02 2.10 § 0.02 2.48 § 0.03 2.77 § 0.03 3.16 § 0.09 3.43 § 0.06 3.44 § 0.04 2.10 § 0.03 1.86 § 0.01
HF effluent
TP (mg/L)
61.43 62.20 74.32 79.01 78.31 60.89 56.56 54.41 49.67 49.46 49.78 60.93 61.09 63.85 72.02 78.72 78.45 54.17 53.24 52.43 49.99 50.29 50.36 61.25 61.25
Removal rate (%)
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(VFCWs) provides adequate conditions for nitrification, while the second stage (HFCWs) furnishes adequate conditions for denitrification. The average removal efficiency of TN in winter was 59.93%, with an initial value of 51.19 § 0.71 mg/L. The summer results indicated a better removal of TN as it reached 73.89% with an initial value of 35.41 § 0.84 mg/L (Table 5). TN input concentration was higher than 34 mg/L during the monitoring period; the highest concentration was reached in February with a value of 60.04 § 0.74 mg/L. The high removal of TN is a result of high nitrification in the VFCW stage; nitrate generated in the VFCW effluent is successfully decreased in the HFCW effluent. Moreover, these results are better than those obtained by Lesage et al. (2007) who obtained only 43% removal of TN by HCWs. Furthermore, a seasonal pattern was clearly observed for TN and NO3—N removal efficiency. Significant positive statistical correlations were reported between temperature and TN (r-Pearson D 0.79). It is proved that temperature influences the way in which TN is removed using soil filtration, aquatic plant and microbial decomposition. Nitrogen transformation in wetlands takes place via biological processes; nitrification and denitrification are generally indicated as the principal processes for nitrogen reduction (Reed et al. 1995). The HFCW bed reduces the variability better than the VFCW bed with an average outflow value of 15 § 2.13 mg/L (Figure 6d). For nutrient removal, it is well confirmed that the removal of ammonia (i.e. oxidation of ammonia to nitrate) in horizontal flow is limited, due to the deficiency of oxygen in the horizontal flow bed as a result of permanent waterlogged conditions (Vymazal 2007). Despite the complexity of nitrogen cycle, its removal is controlled by microbial process rather than other mechanisms such as plant uptake and ammonia volatilization (Green et al. 1997). There are two biological processes of nitrogen removal. The first is nitrogen assimilation by living organisms, the second is nitrate reduction. The second process is based on a microbial respiratory activity which is considered as the main process for the removal of nitrogen due to many factors such as temperature, pH, alkalinity, and DO availability. Moreover, through direct uptake, macrophytes may eliminate some nutrients. Close to the roots and rhizomes, ammonium atoms are treated by two successive operations: firstly they are oxidized to nitrite by nitrifying bacteria such as Nitrosomonas; secondly to nitrate by Nitrobacter in aerobic micro sites. So, nitrate will be used as electron sink and finally reduced to dinitrogen gas (Drizo et al. 1997). The variation of TP concentrations during the study period are presented in Table 5. The average removal efficiency of TP in winter was 49.93%, with an initial input value of 6.74 § 0.04 mg/L. The decrease of TP by the HCW system in summer was more significant as it reached 76.80% with an initial inflow
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value of 4.04 § 0.02 mg/L. The removal rates of TP varied according to the seasons, related to the increase in plant growth and microbial activity from the cold to warm season. A positive correlation was noted between TP removal and seasonal variation (r-Pearson D 0.85), which is in agreement with the metrics of Zhao et al. (2011). The VFCWs decreased the median value of TP from 5 § 0.01 to 3 § 0.02 mg/L. The concentration of TP was reduced to 2 mg/L after treatment by the HFCWs (Figure 6e). Phosphorus removal could be done first of all by ligand exchange reactions, where phosphate dislodges water or hydroxyls from the surface of Fe hydrous oxides (Vymazal 2005). This removal could be done also through sorption on substrates, precipitation, plant uptake which is very important in wetlands, and peat/soil accretion (Vymazal 2007). In the present study, VFCWs showed a greater TP removal rate (47.25%) compared to those in HFCWs (22.62%) (Table 5). VFCWs achieve better oxygenation than HFCWs. Thus, the role of vegetation and oxygen in P removal in the VFCW stage was better than the HFCW stage. This concurs with the metrics of Sohair et al. (2013). These removal percentages are better than those obtained by Lesage et al. (2007): 45% of TP, and by Zhai et al. (2011) who obtained a removal of 68.1%. The reduction of TC, FC, and FS by the HCWs were 4.36 § 0.04, 4.27 § 0.04, and 3.91 § 0.04 Log units, respectively (Table 6). The highest removal efficiency of bacterial indicators of fecal contamination was observed in summer with values of 4.80 § 0.02, 4.67 § 0.04, and 4.07 § 0.03 Log units for TC, FC, and FS, respectively. The weakest performance in terms of microbial quality was noted in winter, mainly in December, with a removal of 3.91 § 0.05, 3.88 § 0.06, and 3.46 § 0.05 Log units for TC, FC, and FS, respectively. In addition, removal rates of fecal bacteria declined significantly in the HCWs during the cold season. It is well noticed that the correlation between bacterial indicators and air temperature is significant (r- Pearson D 0.36) for TC, (r-Pearson D 0.50) for FC, and (r- Pearson D 0.53) for FS. As a result, we can say that the coliform removal capacity depends clearly on the temperature in the study area. This finding is comparable with results reported by Kouki et al. (2009) from HCWs in Tunisia who obtained a removal of 4 Log units for FCs and 3 Log units for FS. Furthermore, the average values obtained in the effluent for fecal bacteria, 2.50 § 0.04 Log units/100 ml, were complying with the Moroccan Code of practice for wastewater reuse in irrigation (Moroccan Standards 2006). The removal of coliforms by the HCW system is carried out by macrophytes, which decrease the number of coliforms due to the stimulation of preying microorganisms in the rhizosphere and the secretion of inhibiting metabolites (Kouki et al. 2009). In this study, the high removal rate of coliforms obtained during the warm
Table 6. Average log units removal (mean § standard deviation) of total coliforms, fecal coliforms, and fecal streptococci of the HCW. Season Microbial parameters Log (CFU/100 mL) Total coliforms Fecal coliforms Fecal streptococci.
Spring (n D 14)
Summer (n D 12)
Autumn (n D 12)
Winter (n D 12)
4.62 § 0.03 4.48 § 0.03 4.11 § 0.03
4.80 § 0.02 4.67 § 0.04 4.07 § 0.03
4.13 § 0.07 4.04 § 0.03 4.00 § 0.06
3.91 § 0.05 3.88 § 0.06 3.46 § 0.05
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Figure 7. Schematic of the multi-layer ANN structure.
season is probably due to several phenomena such as the physical filtration through the root of P. australis, UV action, temperature, oxidation, and predation. ANN modeling To simulate the performance level of the Tidili wastewater treatment plant, a multi-layer neural network modeling was utilized for this analysis. The performance of the Tidili plant is assessed over a period of 2 years using this advanced model. The neural network model was created in “R” software package neural net, version 1.33, which offers a platform for the simulation application. The input variables used in this study are quality parameters (TSS, BOD5, and COD) and coliforms (TC and FS). The structure of the model developed in this study as shown in Figure 7 consists of (5) the input layer of neuron, (3) a number of hidden layers, and (5) output layer. The number of input and output neurons is determined by the nature of the problem. The hidden layers act like feature detectors. 50% of input pollutants were chosen from the available data sets and operated to calibrate the designed networks. The
training performance for TSS, BOD5, COD, TC, and FS are presented in Figure 8 by tracing the measured and simulated output pollutants. The observed values of quality parameters and coliforms were in close agreement with their respective simulated values. It is noted that, results of calibration process are reassuring because they match accurately with the target values. Four plots summarize each pollutant as follows: the central tendency of the sample is indicated by a central line in each box that represents the median; the variability around the central tendency is indicated by a box; and the range of the variable around the box is indicated by whiskers. The result of validation performance for the second 50% for input variables are shown in Figure 8. The model results are very acceptable. The experimental output concentrations for TSS, BOD5, COD, TC, and FS are very close to the ANN model output concentrations. The results marked a high correlation coefficient (R-value) between the measured and simulated output variables, exceeding up to 0.9 (Figure 9). These results are in agreement with the metrics
Figure 8. Analysis of Measured and simulated output pollutants (COD, BOD, TSS, TC, and TS) using Box-plot diagrams.
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Figure 9. Correlation coefficient between the measured and simulated values.
of Vijayan et al. (2015), Zidan et al. (2015) who used neural network models to predict CW treatment system performances. Table 7 presents the Nash criterion and the RMSE values obtained for the calibration and validation period for TSS, Table 7. Nash criterion and the RMSE values obtained for the calibration and validation.
BOD5, COD, TC, and FS. The Nash values range between 0.92 and 0.96 for calibration with the neural network model (Table 7); the RMSE values vary between 2.72 for the TSS and 88.24 for the TC. In validation, the criterion values are also very good, ranging between 0.90 and 0.96. The RMSE values are between 3.10 and 76.66.
Formula RMSE Parameters
RMSE D ffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X .Qobs ¡ Qcalc /2 n
TSS BOD5 COD TC FS
2.72* 3.10** 2.86* 3.29** 4.21* 4.86** 88.24* 76.66** 38.02* 42.08**
RMSE, root mean square error; calibration; validation.
Nash Nash D X .Qobs ¡ Qcalc /2 1¡ X .Qobs ¡ Qm /2 0.96* 0.95** 0.92* 0.93** 0.95* 0.94** 0.95* 0.90** 0.96* 0.96**
Conclusion The present work described an investigation of the HCW for domestic wastewater phytoremediation in a rural mountainous region. Wastewater treatment plant has worked remarkably well over the 25 months of monitoring. High average values of removal efficiency were observed >80% for organic matter, >60% for nutrient, and >4 Log units for coliforms, despite the simplicity and thrifty nature of this technology. The difference in the pollutant removal efficiencies between warm and cold seasons was significant. However the pollutant elimination was always higher than 50% for organic load and nutrients, even in the cold season. In addition, the microbial quality obtained by the studied HCWs met the irrigation standards (
