Improving drainage water quality: Constructed wetlands-performance assessment using multivariate and cost analysis

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Research Article

Improving drainage water quality: Constructed wetlands-performance assessment using multivariate and cost analysis A. El Hawary, M. Shaban ∗ Drainage Research Institute, National Water Research Center, Egypt Received 14 May 2018; received in revised form 2 July 2018; accepted 15 July 2018

Abstract Multivariate analysis of variance followed by multiple comparisons and discriminant function analysis were used to characterize the performance of three plant species (water hyacinth WH, reed RD and duckweed DW) to remove drainage water pollutants using constructed wetlands under the Egyptian conditions. In addition, a simple economic comparison that covers the basic expenses were carried out. During the period from January 2016 to December 2017, input and output water for each treatment cell was monthly sampled and analyzed for five parameters namely; Total Suspended Solids, Dissolved Oxygen, Biochemical Oxygen Demand, Ammonia-N and Phosphate. The results showed wide variation in the removal efficiency of the tested plants according to the monitored pollutants. The efficiencies for DW and WH decrease during the winter season. In the meantime, RD efficiency improves during summer. The cost estimation revealed that the average annual plantation, harvesting and disposal costs estimated for the DW, WH and RD were $6.17, $6.17 and $1.25 per cubic meter, respectively. Consequently, RD plants are recommended as a first priority for treating polluted drainage water. © 2018 National Water Research Center. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Drainage water treatment; Constructed wetlands; Water quality; Water hyacinth; Reed and duckweeds; Multivariate analysis of variance; Discriminant function analysis

∗

Corresponding author. E-mail addresses: [email protected] (A. El Hawary), [email protected] (M. Shaban). Peer review under responsibility of National Water Research Center.

https://doi.org/10.1016/j.wsj.2018.07.001 1110-4929/© 2018 National Water Research Center. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Please cite this article in press as: El Hawary, A., Shaban, M., Improving drainage water quality: Constructed wetlandsperformance assessment using multivariate and cost analysis. Water Sci. (2017), https://dx.doi.org/10.1016/j.wsj.2018.07.001

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1. Introduction Egypt adapted a strong strategy to reuse agricultural drainage water in crop production through direct reuse or mixing with fresh water (NAWQAM, 1999). Around 6.0 BCM of this water are being annually reused in the Nile Delta (DRI, 2011). Egypt produces around 5.0 BCM of domestic wastewater per year which mostly are drained treated/partially treated into the agricultural drains. This process results in a large amount of polluted drainage water that cannot be treated using the conventional treatment methods. Natural/engineered treatment systems could be an economically feasible solution to treat and then reuse theses low quality waters (GEF, 2007). In Egypt, these water treatment systems appear to be promising since they proved to be environmentally attractive and economically feasible (Allam et al., 2014). Although, natural wetlands have usually significant impacts in improving wastewater quality, their performance in removing pollutants has a wide range of variability resulting in extremely uncertain prediction to their responses to wastewater application. However, constructed wetlands are regularly well controlled through tailor-made treatment facilities including the selection of vegetation types, substrata and the flow pattern (hydraulic pathways and retention time) (Kadlec and Wallace, 2009). The process of pollutants removal in such systems combines physical, chemical, and biological interactions including sedimentation, precipitation, adsorption to soil particles, assimilation by the plant tissue, and microbial transformations (Brix, 1993). The vegetative component (macrophytes) in these systems distributes and decreases the flow velocity leading to significant improvement in the sedimentation process of the suspended solids. It also offers great surface area for biofilms. Tissues are occupied by intense communities of photo-synthetic algae, bacteria and protozoa. The algae oxygenate the water and take up nutrients and bacteria degrade organic matters (Pettecrew and Kalff, 1992; Gumbricht, 1993a, b; Somes et al., 1996). Macrophytes also stabilize the surface of the beds, provide good conditions for physical filtration, prevent vertical flow systems from clogging and protect against frost during winter (Brix, 1994). Therefore, the choice of these macrophytes as suitable treatment plants is an extremely important issue in planning for constructed wetlands, as they play an important role in the treatment of the wastewater and its variability. The present study aims at characterizing the performance of three common plant species in Egypt (water hyacinth (WH), reed (RD) and duckweed (DW)) to remove water pollutants using constructed wetland that receives wastewater from Bahr El-Baqar drain as a highly polluted agricultural drain. In Egypt, these plants were tested in different regions under dissimilar environmental conditions (Abou EL-Kheir et al., 2007; Rashed et al., 2007; Allam et al., 2014; El Hawary, 2015). Therefore, this study will combine the separate cells all in one experimental field through plantation under the same setting. Consequently, comparing their pollutants removal efficiencies can be more precise. The study will also provide a simple economic comparison that covers the basic expenses related to plantation, harvesting and disposal costs. 1.1. Investigated plants Water hyacinth (WH) is an excellent solution for wastewater treatment. One hectare of WH plants is potentially capable of removing 160 kg of phenol per 72 h from water polluted with this chemical (Wolverton and McKown, 1976). However, combinations of microorganisms with WH must be seriously considered in developing treatment systems for removing toxic chemicals, such as heavy metals and carcinogenic materials (Wolverton and McDonald, 1976). The results of several studies on wastewater treatment show that the WH plants consume considerable amounts of nitrogen and phosphorus. (Wooten and Dodd, 1976). In Egypt, previous studies showed that the plant can cause a significant reduction in the concentration of some pollutants in the agriculture drainage water. The plant was successfully used in a Passive In-stream Wetland to significantly reduce fecal coliform (FC) levels. However, its ability to reduce phosphorus (PO4) was clearly limited (Rashed et al., 2007; El Hawary, 2015). The use of reed (RD) in constructed wetlands has been intensively studied in many locations (Vymazal, 2002). RD has a good treatment efficiency under different conditions and is able to treat different pollutants in the drainage water (Jenssen et al., 1993). RD contributes to wastewater treatment in many ways: increasing the permeability and porosity of the substrate; creating oxygenated micro-sites within reducing conditions by releasing oxygen from the roots (Tanner, 2000). The unused parts protect the plant roots during the cold period, so the pollutant removal capacity is affected slightly by the seasons (Vymazal, 2000). Please cite this article in press as: El Hawary, A., Shaban, M., Improving drainage water quality: Constructed wetlandsperformance assessment using multivariate and cost analysis. Water Sci. (2017), https://dx.doi.org/10.1016/j.wsj.2018.07.001

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Previous studies evaluated the effectiveness of using RD in drainage water treatment under the Egyptian arid climate. The results showed that the overall treatment efficiency varies depending on the type and behavior of each pollutant. The removal efficiency was relatively high for FC. However, this efficiency was clearly low for biological oxygen demand (BOD) and (PO4) (Rashed et al., 2007; El Hawary, 2015). The use of duckweed (DW) in wastewater treatment started during 1970s (Culley and Epps, 1973; Wolverton and McDonald, 1976). The use of DW to treat wastewater is due to its rapid growth rate and high capacity for nutrient removal from wastewater (Cheng et al. 2002; Ozengin and Elmaci 2007). Applications of DW in wastewater treatment was found to be very effective in the removal of nutrients, soluble salts, organic matter, heavy metals and in eliminating suspended solids, algal abundance and total and fecal coliform densities (Chaudhary and Sharma, 2014). DW treats wastewater directly through nutrient uptake and accumulation in their tissue, and indirectly by creating an environment for nitrifying and denitrifying bacteria through stratification of oxygen in the water column. The utility of DW in wastewater treatment plants has been demonstrated mainly in laboratory and field experiments (Öbek and Hasar, 2002; Shammout et al., 2008). Wastewater treatment using DW is inexpensive, easy to operate and has a good efficiency in treatment. Phytoremediation of contaminated water using DW is promising due to its ability to grow at wide ranges of pH and nutrient levels. However, one of the problems of use in wastewater treatment is that it stops growing in cold seasons (Bonomo et al., 1997). The efficiency of DW as a cost effective natural biological tool in wastewater treatment in general and eliminating concentrations of both nutrients and soluble salts was successfully examined under the Egyptian conditions. Abou EL-Kheir et al. (2007) studied the efficiency of DW in wastewater treatment for wide range of pollutants under local outdoor natural conditions. Also, Allam et al. (2014) studied the potential use of the plant for the removal of chemical oxygen demand (COD) and ammonia. The authors recommended using DW plants as an alternative cost-effective biological tool for the treatment of drainage water.

2. Materials and method 2.1. Experimental design and operation This experiment was conducted within the Lake Manzala Water Research Station, north east of Egypt (Fig. 1). The station is operated by the Drainage Research Institute, the National Water Research Center since 2007. In this station, a pilot scale treatment wetland was constructed to investigate/demonstrate the ability of wetland plants, soils, and their associated microbial assemblages to help improving the water quality of Bahr El-Baqar drain as one of the most polluted drains in Egypt. Historically, the station started as the Lake Manzala Engineered Wetland Project (LMEWP) at the outlet of Bahr El-Baqar drain in the north eastern fringes of the Nile Delta of Egypt. The project aimed at investigating the suitability of using engineered wetlands as a low-cost alternative for treating sanitary sewage from cities, towns and villages, wherever ample land area is available. The station treats an amount of 25,000 m3 /day of drainage water which is polluted with sewage and industrial wastes in order to improve the conditions of Lake Manzala. The work in LMEWP proved the ability of the engineered wetland to treat the drainage water successfully (NIRAS, 2007). With regard to the experimental work, the water is pumped daily from Bahr El-Baqar drain to the sedimentation ponds of LMEWP. After two days in the sedimentation ponds (1.5 m water depth), about 33.75 m3 /day of water discharges by gravity to a manhole through a 10.0 cm pipe. The water is then distributed equally using a 10.0 cm pipe to the surface treatment cells through a 5.0 cm branch pipe. Finally, the water is released uniformly to each cell using a perforated pipe with 5.0 cm diameter and 3.0 m length. The water retention time within the free cells is two days. Table 1 presents the hydraulic parameters for each of those cells. The tested plants are WH (Eichhornia crassipes), RD (Phragmites communis) and DW (Lemna gibba). The selection of the plants was based on their availability and their previous use in constructed wetlands. WH and DW were imported from the nearby water canals to the treatment cells while RD was transplanted directly in the cells. Effluent from the sedimentation ponds flows to the three surface flow cells each with approximate dimensions of 3.0 m × 15.0 m and 50 cm average water depth (Fig. 2). One cell was planted with RD 4.0 plants/m2 , the second cell surface was covered with DW and third cell was covered with WH. Please cite this article in press as: El Hawary, A., Shaban, M., Improving drainage water quality: Constructed wetlandsperformance assessment using multivariate and cost analysis. Water Sci. (2017), https://dx.doi.org/10.1016/j.wsj.2018.07.001

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Fig. 1. Location of Lake Manzala engineered wetland. Table 1 Hydraulic parameters for each free water surface cell. Value

Units

Parameters

11.25 .5 45 22.5 4 2

m3 /day

Flow Depth Area Volume Area per m3 of flow Retention time

m m2 m3 m2 /m3 Day

Fig. 2. Schematic diagram of the treatment cells.

Please cite this article in press as: El Hawary, A., Shaban, M., Improving drainage water quality: Constructed wetlandsperformance assessment using multivariate and cost analysis. Water Sci. (2017), https://dx.doi.org/10.1016/j.wsj.2018.07.001

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The plantation process was carried out taking into account the growth progress of the tested plants especially for the DW where its cell was partially planted in the beginning to consider the relatively fast growth for the DW compared to the other tested plants. This was also reflected in the number of harvests for each plant (every week for DW, every two weeks for WH and every six months for RD). 2.2. Water sampling and laboratory analysis Water samples were collected monthly from the inflow and outflow of each treatment cell during two years (Jan, 2015 to Dec, 2016) of monitoring. Water samples were collected from the designed monitoring locations and delivered to the laboratories for water quality analysis. Samples were analyzed to determine five water quality parameters, including total suspended solids (TSS), dissolved oxygen (DO), biochemical oxygen demand (BOD), ammonium-N (N-NH4) and phosphate (PO4). 2.3. Overall removal efficiency Treatment efficiency was calculated as the percent removal (R) for each parameter and was calculated by R = (1 − Ce /Ci ) × 100

(1)

where Ci and Ce are the influent and effluent concentrations in mg/l. For each WQP, the percent removals were calculated for each month and then averaged to get the overall removal efficiencies. 2.4. Multivariate analysis Multivariate analysis of variance (MANOVA), an extension of the analysis of variance (ANOVA), aims at determining the effect of several independent variables on multiple dependent variables. MANOVA investigates the significant differences between means by dividing the total variance into: component due to true random error (i.e., within- groups) and components due to differences between means. There are three fundamental assumptions for using MANOVA: the dependent variable should be normally distributed within groups; the variances in the different groups are homogenous and the inter-correlations (covariances) for the multiple dependent variables are homogeneous (Field, 2009). For the purpose of this research, MANOVA followed by multiple comparisons (MCs) and discriminant function analysis (DFA) were used to characterize the performance of three plant species to remove water pollutants using constructed wetlands under the Egyptian conditions (weather and pollution level). The previous statistical analyses were employed in two stages. Stage I employed the complete data set collected during the designated study period while Stage II employed the same data but segregated for two seasons (summer and winter). Summer season data includes water quality data for the months from May to October while November to April was considered as the winter season data. This is to deeply characterize the behavioral differences for the three examined plants in seasonal basis. It has to be noted that the MANOVA and DA was employed on the original (experimental) data sets that were standardized through Z-scale transformation. This standardization process gives the values in terms of standard deviation units from the variable’s mean. This is mainly to avoid misclassification due to wide differences in data dimensionality (Liu et al., 2003). 2.5. Economic comparison Assessing costs is a relatively more straightforward approach than assessing values. In constructed wetland systems, the basic costs are mainly for land acquisition, infrastructures, operation and maintenance (O&M). However, for the purpose of this research, a simple economic comparison for the use of the examined plants (WH, RD and DW) was carried out including only plantation, harvesting and disposal costs. Please cite this article in press as: El Hawary, A., Shaban, M., Improving drainage water quality: Constructed wetlandsperformance assessment using multivariate and cost analysis. Water Sci. (2017), https://dx.doi.org/10.1016/j.wsj.2018.07.001

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3. Results and discussions 3.1. Removal efficiency 3.1.1. Biological oxygen demand (BOD mg/l) From the results of the study, it appears that the BOD concentrations were reduced from 20–27 mg/l at the inlets to 2–15 mg/l at outlets of the treatment cells (Fig. 3). This is a typical behavior of wetland systems where the internal decomposition of vegetation litter and other organic materials reproduce BOD concentrations. Therefore, the complete removal of the BOD can not be achieved. Chin (2006) reported that the residual BOD varies between 2.0 to 10 mg/l. The average improvement ratios in the concentration of BOD at the outlets of the treatment cells had a wide range from 33% to 91% with an average equals to 63.1%. On average, RD had the highest treatment efficiency (67.9%) to reduce the BOD followed by DW (61.9%) then WH (59.5%). The reductions in BOD levels between Inlet and Outlets are mainly due to the oxidization process of soluble BOD and trapped materials by the periphytic microorganisms (Chin, 2006). 3.1.2. Dissolved oxygen (DO mg/l) The average concentration of DO was improved after treatment cells from the range of 3–5 mg/l at the inlet to the range of 4–12 mg/l at the outlets of the treatment cells (Fig. 3). The average improvement ratios in the concentration of DO at the outlet of the treatment cells had a wide range from 6.7% to 283.3% and an overall average removal efficiency equals 110.9%. However, the DW had the highest average changes in DO (118.2%), followed by RD (113.9%) then WH (100.5%). 3.1.3. Total suspended solids (TSS mg/l) TSS concentrations at inlet varied within the range of 25–35 mg/l, while the concentrations at the outlets varied within the range of 8–22 mg/l (Fig. 3). These are relatively higher than TSS residual values presented by Chin (2006) that varied between 2 to 10 mg/l. These high values may be attributed to a possible leakage during the regular maintenance process for the treatment cells. Other possible reason is that wetland systems may produce residual background concentrations of TSS as a by-product of decomposing vegetation and other existed natural organics. The average improvement ratios in the concentration of TSS at the outlets of the treatment cells had a range from 15% to 70% and the resulted overall average removal efficiency equals 55.8%. On average, RD had the highest average efficiency in reducing the TSS (63.1%) followed by WH (53.7%) then DW (50.7%). 3.1.4. Ammonia (NH4 mg/l) N-NH4 had the same range of average concentration at inlet and the outlet of the treatment cells (1–3 mg/l) (Fig. 3) which indicates low treatment efficiency of the tested plants (1–24%). The overall treatment efficiency for the tested plants equals 15.6%. WH had the highest average efficiency in removing N-NH4 (16.9%) followed by RD (16.3%) then DW (13.7%). The average retained N-NH4 was 20 (g N-NH4/m2 /year) that is near to the previously reported loads (10–20 g N/m2 /year) as a component of nitrogen (Chin, 2006). 3.1.5. Phosphate (PO4 mg/l) From the results it appears that the PO4 concentration at the intake varies within the range of .8–2.3 mg/l (Fig. 3), while its concentrations at the outlet of the treatment cells vary in the range of .3 to 1.6 mg/l. The overall treatment efficiency of the tested plants in removing PO4 equals 24.2%. Water hyacinth has the highest efficiency in removing PO4 (26.6%) followed by reed (24.2%) then duckweed (21.8%). The average retained phosphate in the treatment cells is 3.4 (g PO4/m2 year) that is clearly high due to the high concentration of PO4 at the input (.8–2.3 mg/l). In general, wetlands can consistently retain phosphorus in the amounts of 1–2 (g P/m2 year). The retention mechanisms include uptake and release by vegetation and microorganisms; sorption and exchange reactions with soils and sediments; chemical precipitation in the water column; and sedimentation and entrainment (Reddy et al. 1999). The results indicate that RD had relatively better pollutants removal efficiencies. This is due to the fact that as water pass between the RD stems, micro-organisms living on the stems breakdown the organic matter in the Please cite this article in press as: El Hawary, A., Shaban, M., Improving drainage water quality: Constructed wetlandsperformance assessment using multivariate and cost analysis. Water Sci. (2017), https://dx.doi.org/10.1016/j.wsj.2018.07.001

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Fig. 3. Box-and-Whisker Plots for input and output water quality for the treatment facilities.

drainage water. Also, the bacteria grow around the RD roots producing the oxygen that improves the treatment process. Table 2 presents a comparison between the treated water quality for each plant cell (outlets) and the related Egyptian standard (Law 48, 1982 and its amendment 2013). Please cite this article in press as: El Hawary, A., Shaban, M., Improving drainage water quality: Constructed wetlandsperformance assessment using multivariate and cost analysis. Water Sci. (2017), https://dx.doi.org/10.1016/j.wsj.2018.07.001

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Table 2 Treated water quality for each plant cell (outlets) and the related Egyptian standard for disposing drainage water to surface brackish water area (Law 48, 1982 and its amendment 2013). Water quality parameters

Unit

TSS DO BOD N-NH4 TP

mg/l mg/l mg/l mg/l mg/l

Outlet water quality (averages)

Egyptian standards

WH

RD

DW

13.3 7.4 9.2 1.1 1.1

10.8 7.9 7.3 1.1 1.1

14.1 7.9 8.6 1.2 1.1

50 4 60 50 10

Table 3 Box’s test of equality of covariance matrices. Box’s M F df1 df2 Sig.

29.760 .522 45 20950.876 .997

Box’s M tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups. Table 4 Multivariate test statistics for the input/output water quality data. Effect Treatments

a b

Pillai’s trace Wilks’ lambda Hotelling’s trace Roy’s largest root

Value F

Hypothesis dfb

Error dfb

Sig.

Partial Eta squared Noncent. parameter Observed powera

.126 .874 .145 .145

15.000 15.000 15.000 5.000

126.000 110.824 116.000 42.000

.984 .984 .983 .319

.042 .044 .046 .126

.370 .371 .373 1.215

5.543 5.106 5.595 6.077

.223 .204 .224 .387

Computed using alpha = .05. Degree of freedom.

3.2. Multivariate analysis 3.2.1. Stage I In this stage, MANOVA followed by MCs and DFA were applied for the complete data set for the WQPs collected during the designated study period. Three basic assumptions were verified before interpreting the results. These are: the normality of the dependent variable within groups (water quality results for Inlets and outlets), homogeneity of the variances and covariances (inter-correlations) in the different groups. The analysis started with the application of the Box’s Test of equality of covariance matrices to examine the null hypothesis that the variance–covariance matrices are the same in all examined groups (treatments). The test statistic (Table 3) was insignificant (p = .997 > .05) indicating that the covariance matrices do not differ significantly and so the homogeneity assumption was met. Table 4 shows the multivariate test statistics including Pillai’s Trace, Wilks’ lambda, Hotelling’s Trace and Roy’s Largest Root (at 95% confidence level). These tests indicate whether the treatment facilities had influences on the measured WQPs or not. The difference between the power of these tests were investigated by many research and it was observed that for small and moderate sample sizes the tests statistics do not differ significantly in terms of power (Olson, 1974, 1976, 1979; Stevens, 1980). For this data, Pillai’s trace, Wilks’ lambda, Hotelling’s Trace and Roy’s Largest Root have significance values more than the .05 (sig. > .05). This means that all of them indicated that the treatment facilities have insignificant influences on improving the WQPs. Please cite this article in press as: El Hawary, A., Shaban, M., Improving drainage water quality: Constructed wetlandsperformance assessment using multivariate and cost analysis. Water Sci. (2017), https://dx.doi.org/10.1016/j.wsj.2018.07.001

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Since the analysis did not achieve a statistically significant result, no further follow-up tests were performed. Nevertheless, the analysis was then repeated for thorough investigation using the same data but segregated into two seasons (summer and winter). 3.2.2. Stage II In this stage, multivariate analysis of variance was employed using the same standardized data but divided for two seasons (summer and winter). Summer season data includes water quality data for the months from May to October while November to April was considered as winter data. This is to characterize the behavioral differences for the three examined plants in seasonal basis. The analysis started with calculating some basic statistics (mean, median, standard deviation, minimum and maximum) for the examined WQPs (Table 5). The well known normality tests namely: Kolmogorov–Smirnov and Shapiro–Wilk tests were then employed to examine the normality of the seasonal data. In cases with small sample sizes, Shapiro–Wilk test is preferred. However, both tests provided similar conclusions as all the tested parameters proved to be normally distributed with significance (Sig.) more than .05 (Table 6). The Box’s test statistic was insignificant (p = .003 < .05) indicating that the covariance matrices are significantly different and so the homogeneity assumption would have been violated. However, since the sample sizes of the examined data were equal with no clear outliers, MANOVA can be considered as fairly robust against the violation of this assumption (Field, 2009). Table 7 presents the MANOVA results obtained for the seasonal data. The results indicate that the Pillai’s trace, Wilks’ lambda, Hotelling’s Trace and Roy’s Largest Root had significance (sig.) less than .05 indicating that the treatment facilities have statistically significant influences on the WQPs. However, this result neither clarifies the nature of these influences nor indicates which parameters are significantly influenced by the differences in the treatment type. Therefore, Levene’s test of equality of variances and tests of Between-subjects Effects were used to figure out the nature of the previous influences as followings: The Levene’s test results (Table 8) indicate that the assumption of equal variances was met for all the examined parameters with larger significance values (sig. > .05). In the mean time, the Between-subjects Effects tests results (Table 9) provide information concerning the univariate influence of the treatment facilities (independent variable) on each of the examined WQPs (dependent variables) separately. The test results showed that the treatment facilities had significant influences on the WQPs: BOD, DO and TSS (sig. < .05). These facilities had insignificant effects on the parameters namely, N-NH4 and PO4 (sig. > .05) confirming the results of the multivariate analysis. Once the influence of the treatment facilities on the examined WQPs was proved, post hoc range tests and pairwise multiple comparisons can be used to test the difference between each pair of means, and yield a matrix where asterisks indicate significantly different groups at an alpha level of 0.05. Recalling Table 8, the WQPs did not violate the equal variances assumption. Thus, the Tukey’s Honestly Significant Difference range test (Tukey (HSD)) was employed. This test is known to have a good statistical power when having equal sample sizes and the population variances are expected to be similar (Toothaker, 1993). Table 10 presents the obtained results from the previous Range tests. It indicates clearly that all treatment facilities with the three plant species (water hyacinth, reed and duckweed) had significant influences in improving the quality parameters (BOD, DO and TSS) of the input flow (inlet) on seasonal basis. For the other parameters (NH4 and PO4), insignificant differences were found. These results can be clearly seen using the Box-and-Whisker Plots (Fig. 4) for input and output water quality for the treatment facilities in seasonal basis. More detailed information is summarized as followings: • BOD (mg/l): For the three treatment facilities, all summer measurements were lower than those taken in winter although BOD levels in both seasons were very close at the inlet. In summer, duckweed showed the best performance then reed and finally the water hyacinth. In winter, reed showed the best performance then water hyacinth and finally the duckweed. • DO (mg/l): For the three treatment facilities, all summer measurements were lower than those taken in winter although DO levels in both seasons were very close at the inlet. In summer, duckweed showed the best performance then reed and finally the water hyacinth. In winter, the performance of the three examined plants was very similar with insignificant differences. Please cite this article in press as: El Hawary, A., Shaban, M., Improving drainage water quality: Constructed wetlandsperformance assessment using multivariate and cost analysis. Water Sci. (2017), https://dx.doi.org/10.1016/j.wsj.2018.07.001

Inlet Sum

Inlet Win

Water hyacinth Sum

Water hyacinth Win

Reed Sum

Reed Win

Duckweed Sum

Duckweed Win

BOD (mg/l)

Mean Median Std. deviation Minimum Maximum

23.17 23.50 2.48 20.00 27.00

22.50 22.50 1.52 21.00 25.00

7.72 7.00 2.82 5.00 13.00

10.67 11.50 2.50 6.00 13.00

5.50 5.00 1.87 4.00 9.00

9.17 10.00 2.99 4.00 13.00

4.83 4.50 2.79 2.00 10.00

12.33 13.00 2.50 8.00 15.00

DO (mg/l)

Mean Median Std. deviation Minimum Maximum

3.73 3.70 .74 3.00 5.00

3.80 3.70 .41 3.30 4.50

9.28 9.30 .98 8.00 10.60

5.57 5.40 1.17 4.00 7.00

10.00 9.95 .71 9.00 11.20

5.70 5.50 1.24 4.10 7.50

10.28 11.10 1.80 8.00 12.00

5.58 5.50 .90 4.50 6.60

TSS (mg/l)

Mean Median Std. deviation Minimum Maximum

31.33 31.50 2.58 28.00 35.00

27.50 27.50 1.87 25.00 30.00

10.50 10.50 1.52 9.00 13.00

16.00 16.50 4.47 8.00 21.00

11.50 11.50 1.05 10.00 13.00

10.17 10.00 1.17 9.00 12.00

11.00 11.00 1.55 9.00 13.00

17.17 20.50 6.37 9.00 22.00

N-NH4 (mg/l)

Mean Median Std. deviation Minimum Maximum

1.00 1.01 .12 .78 1.12

1.75 1.80 .93 .62 2.69

.84 .84 .11 .67 1.00

1.42 1.41 .73 .50 2.20

.85 .85 .09 .71 .97

1.44 1.40 .78 .50 2.30

.85 .86 .12 .65 1.00

1.53 1.49 .83 .53 2.50

PO4 (mg/l)

Mean Median Std. deviation Minimum Maximum

1.56 1.63 .72 .76 2.30

1.41 1.52 .27 .96 1.67

.93 .98 .54 .26 1.54

1.28 1.36 .30 .89 1.63

.94 .85 .43 .50 1.60

1.26 1.28 .30 .90 1.60

.96 .79 .44 .56 1.53

1.31 1.40 .29 .90 1.60

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Parameters

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Table 5 Basic statistics for the examined water quality parameters.

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Table 6 Tests of normality for standardized seasonal data. Kolmogorov–Smirnova

Treatment Season

Statistic

Shapiro–Wilk

Dfb

Sig.

Statistic

Dfb

Sig.

Inlet Sum Inlet Win Water hyacinth Sum Water hyacinth Win Reed Sum Reed Win Duckweed Sum Duckweed Win

.202 .204 .268 .228 .272 .276 .309 .272

12 12 12 12 12 12 12 12

.200* .200* .200* .200* .187 .170 .075 .189

.957 .902 .848 .847 .815 .899 .864 .911

12 12 12 12 12 12 12 12

.794 .389 .152 .148 .080 .367 .204 .443

DO (mg/l)

Inlet Sum Inlet Win Water hyacinth Sum Water hyacinth Win Reed Sum Reed Win Duckweed Sum Duckweed Win

.193 .260 .164 .187 .277 .214 .300 .242

12 12 12 12 12 12 12 12

.200* .200* .200* .200* .166 .200* .097 .200*

.905 .926 .969 .944 .909 .966 .792 .885

12 12 12 12 12 12 12 12

.405 .547 .888 .690 .431 .863 .050 .290

TSS (mg/l)

Inlet Sum Inlet Win Water hyacinth Sum Water hyacinth Win Reed Sum Reed Win Duckweed Sum Duckweed Win

.150 .122 .204 .245 .183 .223 .241 .338

12 12 12 12 12 12 12 12

.200* .200* .200* .200* .200* .200* .200* .030

.979 .982 .902 .916 .960 .908 .913 .721

12 12 12 12 12 12 12 12

.945 .961 .389 .476 .820 .421 .456 .010

N-NH4 (mg/l)

Inlet Sum Inlet Win Water hyacinth Sum Water hyacinth Win Reed Sum Reed Win Duckweed Sum Duckweed Win

.241 .290 .179 .250 .186 .256 .175 .239

12 12 12 12 12 12 12 12

.200* .124 .200* .200* .200* .200* .200* .200*

.881 .821 .983 .866 .961 .865 .964 .894

12 12 12 12 12 12 12 12

.273 .090 .964 .211 .829 .206 .853 .337

PO4 (mg/l)

Inlet Sum Inlet Win Water hyacinth Sum Water hyacinth Win Reed Sum Reed Win Duckweed Sum Duckweed Win

.232 .279 .197 .201 .205 .251 .309 .296

12 12 12 12 12 12 12 12

.200* .159 .200* .200* .200* .200* .076 .108

.842 .874 .889 .923 .921 .888 .799 .869

12 12 12 12 12 12 12 12

.136 .243 .314 .527 .514 .308 .057 .222

BOD (mg/l)

Sum = Summer Season. Win = Winter Season. * This is a lower bound of the true significance. a Lilliefors significance correction. b Degree of freedom.

• TSS (mg/l): For the duckweed and water hyacinth facilities, all summer measurements were much lower than those taken in winter although TSS levels in summer season were clearly higher at the inlet. In contrary, summer and winter measurements were very close for the case of the reed facility. In each season (summer and winter), the performances of the three examined plants were very similar with insignificant differences except the reed facility that was significantly better than duckweed in winter season. Generally speaking, water hyacinth was the best in Please cite this article in press as: El Hawary, A., Shaban, M., Improving drainage water quality: Constructed wetlandsperformance assessment using multivariate and cost analysis. Water Sci. (2017), https://dx.doi.org/10.1016/j.wsj.2018.07.001

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Table 7 Multivariate MANOVA test statistics for the input/output seasonal water quality data. Effect Treatment season

a b

Pillai’s trace Wilks’ lambda Hotelling’s trace Roy’s Largest Root

Value

F

Hypothesis dfb

Error dfb

Sig.

Partial Eta squared

Noncent. parameter

Observed powera

1.040 .199 2.934 2.567

1.501 2.057 2.884 14.668

35.000 35.000 35.000 7.000

200.000 153.868 172.000 40.000

.045 .001 .000 .000

.208 .276 .370 .720

52.544 58.649 100.945 102.675

.987 .993 1.000 1.000

Computed using alpha = .05. Degree of freedom.

Table 8 Levene’s tests of equality of error variances.a

BOD (mg/l) DO (mg/l) TSS (mg/l) N-NH4 (mg/l) PO4 (mg/l) a b

F

dfb

df2b

Sig.

.611 .727 .395 .293 .889

7 7 7 7 7

88 88 88 88 88

.743 .650 .899 .953 .524

Tests the null hypothesis that the error variance of the dependent variable is equal across groups. Degrees of freedom.

Table 9 Tests of between-subjects effects Stage II. Source Treatment season

a b

BOD (mg/l) DO (mg/l) TSS (mg/l) N-NH4 (mg/l) PO4 (mg/l)

Type III sum of squares

dfa

Mean square

F

Sig.

Noncent. parameter

Observed powerb

2140.62 306.94 2830.98 5.61 2.35

7 7 7 7 7

305.803 43.848 404.426 .802 .336

49.762 38.327 41.533 2.334 1.770

.000 .000 .000 .063 .121

348.331 268.287 290.730 16.341 12.387

1.000 1.000 1.000 .781 .638

Degrees of freedom. Computed using alpha = .05.

summer then duckweed and finally reed where in winter, reed showed highest performance then water hyacinth and finally duckweed. • NH4 (mg/l): For the three treatment facilities as well as the inlet, all summer measurements were relatively lower than those taken in winter. However, the differences were statistically insignificant. In each season (summer and winter), the performances of the three examined plants were very similar with insignificant differences. • PO4 (mg/l): For the three treatment facilities, all summer measurements were clearly lower than those taken in winter although PO4 levels in summer season were relatively higher at the inlet. However, the differences were statistically insignificant. In each season (summer and winter), the performances of the three examined plants were very similar with insignificant differences. • In contrary to the previous results, the Box Plots in Fig. 3 may suggest that the two quality parameters N-NH4 and PO4 do have significant differences. Therefore, the nonparametric Kruskal–Wallis tests were also performed to insure whether the results for the parametric and nonparametric tests are similar. The test results (Table 11) proved to be identical to the parametric analysis for all the examined WQPs. When looking to the seasonal data, there were significant differences between the performances of the three plant species in removing the examined water pollutants (BOD, DO and TSS). This finding was further investigated and visualized using a combined groups plot (Fig. 5) obtained after the application of the discriminant function analysis (DFA) using linear combinations of the dependent variables (variates or sometimes called latent variables or factors). Please cite this article in press as: El Hawary, A., Shaban, M., Improving drainage water quality: Constructed wetlandsperformance assessment using multivariate and cost analysis. Water Sci. (2017), https://dx.doi.org/10.1016/j.wsj.2018.07.001

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Table 10 Range tests (multiple comparisons) results in seasonal basis. (I) Treatment Season

(J) Treatment Season

BOD (mg/l) mean difference (I–J) (Tukey (HSD))

DO (mg/l) mean difference (I–J)

TSS (mg/l) mean difference (I–J)

NH4 (mg/l) mean difference (I–J)

PO4 (mg/l) mean difference (I–J)

Inlet Sum

Inlet Win Water hyacinth Sum Water hyacinth Win Reed Sum Reed Win Duckweed Sum Duckweed Win

.67 15.44* 12.50* 17.67* 14.00* 18.33* 10.83*

−.062 −5.550* −1.833 −6.267* −1.967 −6.550* −1.850

3.83 20.83* 15.33* 19.83* 21.17* 20.33* 14.17*

−.7483 .1617 −.4217 .1533 −.4433 .1483 −.5317

.1448 .6300 .2723 .6150 .2967 .5963 .2517

Inlet Win

Water hyacinth Sum Water hyacinth Win Reed Sum Reed Win Duckweed Sum Duckweed Win

14.78* 11.83* 17.00* 13.33* 17.67* 10.17*

−5.488* −1.771 −6.204* −1.904 −6.488* −1.788

17.00* 11.50* 16.00* 17.33* 16.50* 10.33*

.9100 .3267 .9017 .3050 .8967 .2167

.4852 .1275 .4702 .1518 .4515 .1068

Water hyacinth Sum

Water hyacinth Win Reed Sum Reed Win Duckweed Sum Duckweed Win

−2.94 2.22 −1.44 2.89 −4.61*

3.717* −.717 3.583* −1.000 3.700*

−5.50 −1.00 .33 −.50 −6.67*

−.5833 −.0083 −.6050 −.0133 −.6933

−.3577 −.0150 −.3333 −.0337 −.3783

Water Hyacinth Win

Reed Sum Reed Win Duckweed Sum Duckweed Win

5.17* 1.50 5.83* −1.67

−4.433* −.133 −4.717* −.017

4.50 5.83* 5.00 −1.17

.5750 −.0217 .5700 −.1100

.3427 .0243 .3240 −.0207

Reed Sum

Reed Win Duckweed Sum Duckweed Win

−3.67 .67 −6.83*

4.300* −.283 4.417*

1.33 .50 −5.67

−.5967 −.0050 −.6850

−.3183 −.0187 −.3633

Reed Win

Duckweed Sum Duckweed Win

4.33 −3.17

−4.583* .117

−.83 −7.00*

.5917 −.0883

.2997 −.0450

Duckweed Sum

Duckweed Win

−7.50*

4.700*

−6.17*

−.6800

−.3447

Win = Winter. Sum = Summer. * The mean difference is significant at the .05 level. Table 11 Nonparametric Kruskal–Wallis tests − Stage II.a

Chi-Square dfb Asymp. Sig.c a b c

BOD (mg/l)

DO (mg/l)

TSS (mg/l)

N-NH4 (mg/l)

PO4 (mg/l)

37.666 7 .000

40.338 7 .000

31.752 7 .000

11.626 7 .114

9.012 7 .252

Kruskal Wallis test. Degrees of freedom. Asymptotic significance.

This graph plots the variate scores for each dependent (WQPs) grouped by the grouping factor (Treatment Facilities) and the average variate scores for each group (centroids). DFA results show that the differences between the plants performance can be explained in terms of one underlying dimension since only one discriminant function is significant. This function is mainly concerned with the WQPs: BOD, DO and TSS.

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Fig. 4. Box-and-Whisker Plots for input and output water quality for the treatment facilities in seasonal basis.

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Fig. 5. Discriminant function analysis (combined group plot). Table 12 Plantation, harvesting and disposal average annual costs (US Dollars) for the examined plants. Cost item

Duckweed

Water hyacinth

Reed

Plantation process A: Plantation cost

2.8

2.8

11.3

Harvesting and disposal process a: Harvesting cost per run b: Disposal cost per run n: Number of plants harvesting and disposal per year B: Annual cost of harvesting and disposal = (a + b) * n

1.7 1.1 48 136

2.8 2.8 24 136

5.7 2.8 2 17

Total cost per cell

139

139

28

Total cost = A + B (per cubic meter)

6.17

6.17

1.25

Noting the positions of the centroids in Fig. 5, it is clear that Function 1 discriminates the inlet water quality (mainly BOD, DO and TSS) with largest horizontal distances between centroids. However for the same function, the treatment facilities with water hyacinth and duckweed seemed to differ from reed but these differences are not statistically significant. 3.3. Cost comparison The main purpose of the cost comparison in this research was to provide a simple evaluation for the main expenses related to plantation, harvesting and disposal of the examined plants. The detailed information concerning these costs is presented in Table 12. The results indicate that the average annual plantation cost for RD ($11.3) is clearly higher than the other two plants WH and DW ($2.8 for each plant). However, RD had the lowest harvesting and disposal average annual costs ($17.0) compared to the other two plants WH and DW ($136.0 for each plant). It is clear that the harvesting and disposal processes play a vital role in determining the total cost. For DW and WH, a regular harvest is essential to obtain higher removal efficiencies. Therefore, they were harvested every 7 and 15 days Please cite this article in press as: El Hawary, A., Shaban, M., Improving drainage water quality: Constructed wetlandsperformance assessment using multivariate and cost analysis. Water Sci. (2017), https://dx.doi.org/10.1016/j.wsj.2018.07.001

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respectively by removing a two third of the plant while RD was harvested only twice a year. Then the harvested plants were collected and disposed of the site. The average annual plantation, harvesting and disposal costs estimated for the DW. WH and RD were $6.17, $6.17 and $1.25 per cubic meter. 4. Conclusions and recommendations Over the year (Stage I), all treatment facilities with the three plant species (water hyacinth, reed and duckweed) improved to a good extent the quality parameters (BOD, DO and TSS) of the input flow (inlet). For the other parameters (N-NH4 and PO4), slight improvements were obtained. However, these latest parameters had already good quality levels in relation to the Egyptian standard for disposing drainage water to surface brackish water area (Law 48, 1982 and its amendment 2013) presented in Table 2. The tested data were segregated into two seasons (summer and winter (Stage II)) to characterize thoroughly the behavioral differences for the three examined plants in seasonal basis. During summer, duckweed showed the highest efficiency in removing BOD and DO followed by reed while water hyacinth showed the highest efficiency in removing TSS. In winter, reed showed the highest efficiency in removing BOD and TSS followed by water hyacinth. duckweed had the lowest efficiency in removing BOD and TSS. Comparing plants performances in summer to winter indicates that the removal efficiencies for duckweed and water hyacinth decrease during winter due to their limited growth under temperate and cold climatic conditions. In the meantime, the efficiency of reed improves during summer. Under the Egyptian conditions, the spread of water hyacinth is known as a serious trouble for irrigation and drainage canals due to its rapid growth rates. Consequently, intensive harvesting programs, mowing over plants, mulching, adding lime, and pesticides are needed especially in the winter season to improve the pollutants uptake efficiencies. These consequently, increase the system cost for both harvesting and disposal processes (Chin, 2006; Robinson et al., 1976). These factors make water hyacinth is unfavorable (not recommended) for the Egyptian conditions. Also, the main problem of duckweed is the lack of wide rooting systems and thus having a relatively lesser surface area for attached microbial growth. Therefore, it is recommended to use reed as a first priority for treating polluted drainage water due to its relatively higher removal efficiency. Duckweed comes at the second priority. Future work may investigate using combinations for some of these plants together in the same treatments cells. Acknowledgements Great thanks are due to the Drainage Research Institute (DRI) staff and the director Professor Dr. Essam Khalifa for their support with all the required data and information. References Abou EL-Kheir, W., Ismail, G., EL-Nour, F., Tawfik, T., Hammad, D., 2007. Assessment of the efficiency of duckweed (Lemna gibba) in wastewater treatment. Int. J. Agric. Biol., 1560-8530/2007/09-5-681-687 http://www.fspublishers.org. Allam, A., Tawfik, A., El-Saadi, A., Negm, A., 2014. Potentials of using duckweed (Lemna gibba) for treatment of drainage water for reuse in irrigation purposes. Desalin. Water Treat., 1–9, http://dx.doi.org/10.1080/19443994.2014.966760. Bonomo, L., Pastorelli, G., Zambon, N., 1997. Advantages and limitations of duckweed-based wastewater treatment systems. Water Sci. Technol. 35 (5), 239–246. Brix, H., 1993. Wastewater treatment in constructed wetlands: system design, removal processes, and treatment performance. In: Moshiri, A.G. (Ed.), Constructed Wetlands for Water Quality Improvement. CRC Press, Boca Raton, Florida, pp. 9–22. Brix, H., 1994. Functions of macrophytes in constructed wetlands. Water Sci. Technol. J. 29 (4), 71–78. Chaudhary, Ekta, Sharma, Praveen, 2014. Use of duckweed in wastewater treatment. Int. J. Innovative Res. Sci. Eng. Technol. 3 (6), 13622–13624. Cheng, J., Bergmann, B.A., Classen, J.J., Stomp, A.M., Howard, J.W., 2002. Nutrient recovery from swine lagoon water by Spirodela punctata. Bioresour. Technol. 81, 81–85. Chin, D.A., 2006. Water Quality Engineering in Natural Systems, Copyright ©. John Wiley and Sons. Culley, D.D., Epps, E.A., 1973. Use of duckweed for waste treatment and animal feed. J. Water Pollut. Control Fed. 45, 337–347. DRI (Drainage Research Insitute), 2011. Drainage Water Status in the Nile Delta. Drainage Research Institute, National Water Research Center, Cairo, Report No (82).

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