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Performance assessment of horizontal and vertical surface flow constructed wetland system in wastewater treatment using multivariate principal component analysis
Ecological Engineering 116 (2018) 121–126
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Performance assessment of horizontal and vertical surface flow constructed wetland system in wastewater treatment using multivariate principal component analysis
T
⁎
Rashmi Verma , Surindra Suthar School of Environment & Natural Resources, Doon University, Dehradun 248001, Uttarakhand, India
A R T I C LE I N FO
A B S T R A C T
Keywords: Dairy wastewater Wetland plants HHV Principal component analysis Typha
This study aimed to compare the horizontal flow (HFCW) and vertical flow (VFCW) constructed wetland systems in treating dairy wastewater (DWW) and simultaneously harvesting plant biomass from units. The HFCW and VFCW were designed at lab-scale using cattail (Typha angustifolia) and changes in DWW parameters: pH, EC, TSS, NO3-N, NH4-N, PO4−3, SO4−2, Na, K, BOD5, COD and heavy metals (Fe, Cr and Ni) were investigated for 9 months. A setup without plant stand acted as control. The VFCW outperformed to HFCW in terms of removal of NH4-N, PO4−3, BOD5, COD, and heavy metals while NO3-N and SO4−2 showed high removal in HFCW. The principal component analysis (PCA) identified three major components from the 9 major variables accounted for 80.05 and 86.68 of the datasets in HECW and VFCW, respectively. The degree of variance suggested the high performance of VFCW than HFCW. The PCA showed slight variations in functioning of both systems in terms of interdependences of organic and inorganic pollution abetments. The biomass yield of Typha showed great variations between HFCW and VFCW system and relatively the VFCW produced more Typha biomass. The high heating value (HHV) calculated on the basis of proximate and ultimate results indicates that Typha biomass can be used as potential feedstock for renewable energy operations. The Typha based VFCW for dairy wastewater treatment can targets multiple purposes: nutrient capture, habitat restoration, bioenergy, carbon offsets, and water quality credits.
1. Introduction The wastewater from animal farm operations and runoff from agricultural lands contributes a large quantity of nutrients, sediment, and biochemical oxygen demand (BOD5) to any receiving water body (Kadlec and Wallace, 2009). Being rich in nitrogen and phosphorus nutrient species, these types of wastewaters directly feed the algal blooms, which in later phase lead to depletion of dissolved oxygen, fish habitat damage and threaten the recreation of system (Zhang et al., 2005). The conventional wastewater treatment systems fail to reduce the negative impacts of nutrient pollution effectively as these offers limitation, in terms of operation cost and maintenance (Metcalf and Eddy, 2004). Nowadays, the major emphasis of scientific community is on developing a low cost solution to abate the nutrient problems at its source of origin (Schaafsma et al., 2000). Constructed wetland (CW) technology is a novel approach for on-site wastewater treatment mainly characterized by pollutant removal capacity, simplicity, low construction/operation and maintenance costs, low energy demand, process stability, and reduced sludge production (Vymazal et al., 1998; ⁎
Vymazal, 2010; Kadlec and Wallace, 2009; Gikas and Tsihrintzis, 2012). The CWs are differentiated into several types based on set of criteria such as, presence/absence of free-water-surface, types of macrophytes used, and direction of flow of water in system, etc. (Kadlec and Knight, 1996). The use of CWs serves to improve water quality, habitat enhancement, and aesthetic improvement in ornamental ponds and lakes. It has been reported that pollutant removal rate is substantially higher in vertical flow CWs when compared to horizontal flow CWs (Vymazal, 2010). Macrophytes form the essential component of wetlands, which help to stabilize and oxidize sediments (Brix, 1994; Kadlec and Wallace, 2009). It has been shown that a planted wetland system has a higher efficiency of pollutant removal than that without plants (Brix, 1994). Different plant species are used for this purpose; however, various species of genus Typha are frequently used for purification purpose in constructed wetlands. The species Typha offers competitive performance in organic matter retention, nutrient removal, and pathogen reduction (Brix, 1994). Adaptability to different environmental condition and water-load, specifically the high growth rate in a short period
Corresponding author. E-mail address: [email protected] (R. Verma).
https://doi.org/10.1016/j.ecoleng.2018.02.022 Received 27 July 2017; Received in revised form 9 February 2018; Accepted 24 February 2018 0925-8574/ © 2018 Elsevier B.V. All rights reserved.
Ecological Engineering 116 (2018) 121–126
R. Verma, S. Suthar
are few important features that makes Typha as an excellent candidate for wastewater treatment in CWs (Martın and Fernández, 1992). It seems to be a competitive emergent aquatic plant, which converts the available water nutrients into energy biomass for renewable operations. Previous reports have suggested the potential of Typha based CWs in treatment of various kinds of wastewaters: municipal wastewater (Ye and Li, 2009), acid mine drainage (Nivala et al., 2007; Yalcuk and Ugurlu, 2009), industrial wastewater (Calheiros et al., 2009), agricultural and storm runoff (Hammer, 1989), effluent from livestock operations (Schaafsma et al., 2000; Dipu et al., 2010), etc. Typha offers efficient accumulation of nutrients from wastewater and converting it into a valuable biomass resource. Moreover, the harvested biomass can serve as a source of biomass for bio-energy potential (Sheng and Azevedo, 2005). To our best knowledge, no comprehensive report on comparative assessment of working of Typha-based CWs with different modes of flow i.e. horizontal flow and vertical flow has not been studied yet by previous researchers. This study aimed to investigate the removal efficiency of HFCW and VFCW in treating wastewater generate from a dairy industry using lab-scale CWs under ambient conditions. The performance of CWs was compared using multivariate PCA analysis and harvested biomass was analysed for proximate, ultimate and biochemical characteristics and bio-energy potential of harvested was also estimated. The need for the study arose from increased wastewater generation versus constant and/or degrading wastewater treatment facilities due to urbanization and financial constraints. This would help to withstand the dual objective of nutrient removal from wastewater and converting that to energy rich biomass.
Table 1 Typical dairy run-off inflow characteristics (n = 36). Parameter
Unit
Min
Max
Range
Mean
SD
pH EC NO3 -N NH4-N PO4−3 SO2−4 totNa totK BOD5 COD TSS totCr totFe totNi
– µS mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L
6.54 1.34 28.7 52.8 22.4 702 127 63.2 702 1421 421 0.019 1.40 0.616
7.76 3.21 45.1 68.7 39.7 842 182 86.5 842 1962 492 0.034 2.47 1.211
1.22 1.87 16.3 15.7 17.3 71 55 23.3 140 541 71 0.015 1.07 0.595
7.31 2.46 38.6 62.3 32.6 455.5 157.9 74.7 770.7 1676 455.5 0.026 2.03 0.963
0.25 0.43 4.26 4.28 4.42 21.15 14.7 6.23 39.03 165.4 21.1 0.004 0.32 0.161
2.3. Outlet water quality analysis The sampling and further analysis of wastewater was done weekly for the period of nine months, began in the first week of August 2013 and continued until the end of April 2014. Collected wastewater samples were properly stored and analyzed immediately for different physicochemical parameters. The pH was measured using digital pH meter (Metrohm, Swiss-made). The electrical conductivity (EC) was determined by a digital conductivity meter (Remi, India). The major wastewater nutrients: NO3−N, PO43−, and SO42− were analysed spectrophometrically by following the standard protocols as described in APHA-AWWA-WPFC (1994). The biochemical oxygen demand (BOD5) and chemical oxygen demand (COD) in water sample was determined by APHA-AWWA-WPFC (1994). The total content of cations i.e. sodium (totNa), potassium (totK), and calcium (totCa) in raw and treated water were determined using Flame photometry (APHAAWWA-WPFC, 1994). The total content of heavy metals (totCr, totFe and totNi) was analysed by using Atomic absorption spectrophotometer (Thermo Fisher. Model iCE 3000 Series AA System). Meteorological data (ambient temperature and precipitation) for the study duration were procured from local station of Indian Meteorological Department (IMD), Dehradun (Uttarakhand, India). All chemicals and reagents used in analytical work were of AR grade (purity up to 99%).
2. Material and method 2.1. Description of HFCW and VFCW The study was conducted at Doon University campus (30°16′ N, 78° 2′ E), Dehradun (Uttarakhand), India. Two continuous flow system i.e. horizontal and vertical flow pilot plants (HFCW and VFCW, respectively) were constructed. The dimensions of each unit of CW was: 0.5 m in diameter and 1.5 m in height for VFCW and 0.75 m in length, 0.25 m in breadth and 0.5 m in height for HFCW. Different depths of sand, gravel and boulders were filled into each types of CW unit as substrates. In both, HFCWs and VFCWs, filter layers consist of bottom layer of boulders to a depth of 0.05 m, above it a sand layer of 0.1 m, a composite layer of gravel and sand of 0.1 m and topmost layer of gravel of 0.1 m. The purpose of filter materials was to collect water and provide the maximum support and surface area during the operation.
2.4. Harvesting of Typha biomass from CWs and chemical analysis The initial and final dry weight of biomass (g) was determined by harvesting at least three specimen of complete plant stand from each treatment set-up at an interval of three months (October 2013, January 2014 and April 2014). To access the growth characteristic, plant length, root length and root volume were determined after derooting the plant from CW bed. The plant’s root length and individual plant heights were measured using a scale. Root volume was determined by drainage: the water on the surface of the washed roots was absorbed; then the roots were placed in a container (with an overflow pipe) that was full of water. The root volume was equal to the volume of overflow (Liu et al., 2012). The harvested undried Typha biomass was further analysed for its biochemical parameters (total non-structural carbohydrate, total protein and chlorophyll). Ash (%), moisture (%), volatile matter (%), and fixed carbon (%) were determined using methods as described in ASTM manual (ASTM, 1982). The total non-structural carbohydrate (TNC) concentration, which was defined as the sum of soluble sugar and starch concentration (Sharma et al., 2008) was estimated using standard methodology described by Loomis and Shull (1937). The protein content was measured by Lowry et al. (1951) method. The chlorophyll pigments were measured spectrophometrically using the method of Martin et al. (2003). Also, the harvested biomass was powdered and
2.2. Characteristics of inflow and operations of CW units Young specimen of T. angustifolia were collected locally from natural marshy land during May 2013 and used as plant stand in constructing HFCW and VFCW in experimental station of lab. The specimen of Typha of approximately same age and weight were selected and then rooted in the bed of our CWs at a density of twenty-seven plants per m2. Initially, the tape water was used for acclimatization of plant stand in CWs and after two months of appropriate growth of plant stand, the systems were used for further dairy wastewater treatment operations. The raw wastewater replaced the fresh water as the influent into these CWs. The influent flow rate was 25–30 L/day maintained throughout the study period. The design hydraulic loading rate in CWs ranged between 288 and 345 L/m2 day, while the theoretical hydraulic retention time was about 1 L/m2 day. The water sampling frequency was once in a week. All units were fed with dairy wastewater effluent collected from dairy outlet located nearby to the university campus. The components and characteristics of the wastewater are presented in Table 1. Wastewater flow rates were adjusted manually at the inlet of different units using gate valves. 122
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attributed to the design specification of CW and biological mechanisms in rhizosphere. was quite less to the present reported results. Sedimentation (Meuleman et al., 2003), filtration (Karim et al., 2004), hydrolysis (Michell and McNevin, 2001), oxidation/reduction (CasellesOsorio et al., 2011), bacterial metabolism (aerobic/anaerobic/anoxic) (Caselles-Osorio et al., 2011) are considered as major mechanisms for removal of BOD and COD in the planted CWs. The solids in the effluent are the parts of the non-trapped influent solids, surplus sludge, and plant litter solids generated during the process of mineralization. The average removal of TSS was 72.6% in HFCW, significantly higher than the VFCW (55%) during this study (Table 2). TSS removal is affected by multitude of factors such as: filter material, hydraulic retention time, temperature, microbial community, etc. (Karim et al., 2004). Vegetation dynamics together with substratum results in low velocity of wastewater, hence favours aerobic and anaerobic microbial degradation (Kadlec and Knight, 1996; Vymazal, 2010). The results indicate that HFCWs offer adequate sedimentation, filtration, bacterial decomposition, and adsorption to wetland media which further lower down the TSS load in effluent. The inefficiency of removing TSS in VFCW may be due to the rapid seepage of wastewater in the system (Meuleman et al., 2003).
analyzed for elemental composition by using CHNS Elemental analyzer at the end of experiment (9 months). 2.5. Statistical analyses One-way ANOVA was used to analyze the differences among wastewater dilutions for different characteristics of the treated water in different set-ups. PCA was performed to assess variance among different components guarding system functioning. A Tukey’s test was also performed to identify the homogeneous type of the data sets. The relationship between removal rate and WW dilution factor was measured using regression analysis. In the present study, PCA was used to investigate the processes, which influences the removal of wastewater parameters by examining associations defined by one or more variable loadings on factors. A loading weight close to ± 1 indicates a strong correlation between a variable and the factor and variables that exhibited a loading weightage > 0.5 were considered significant. PCA was performed using Varimax rotation with Kaiser Normalization. SPSS® statistical package (Window Version13.0) and STATISTICA® (Window Version 7) were to perform all statistical analysis of datasheets. All statements reported in this study are at the p < 0.05 levels. 3. Result and discussion
3.1.2. Wastewater nutrients and salts (NO3−N, NH4-N, PO43−, SO42−, totNa and totK) Substantial variability has been observed in the removal of different nutrients (NO3−N, NH4-N, PO43−, SO42−, totNa, totK) from dairy wastewater during the study period. Average removal of NO3−N was recorded 62.9% (HFCW) and 47.5% (VFCW), remarkably higher than that of control (Table 2). The results of NO3−N removal in HFCW was higher than reported data by Yousefi and Mohseni-Bandpei, (2010). Likewise, average removal of NH4-N in CW was 53.1% (HFCW) and 66.2% (VFCW), significantly higher than values of control (41%) setup (Table 2). Different species of nitrogen are potentially transformed in wetlands by the processes like NH3 volatilization, nitrification, denitrification, nitrogen fixation, plant and microbial uptake, mineralization (ammonification), NO3 reduction to NH4+ (nitrate-ammonification), anaerobic ammonia oxidation (ANAMMOX), fragmentation, sorption, desorption, burial, leaching, etc. (Kadlec and Knight, 1996; Nurk et al., 2005). The NO3 removal is limited by the lack of oxygen in the filtration bed and this consequently lowers the nitrification processes (Vymazal, 2010). Redox potential, evapotranspiration (ETP) rates and hydraulic retention times (HRT) and plant stand, etc. are few governing factors for overall dynamics of total N, NH4-N, and NOy export in CW system. The VFCWs remove successfully NH4– N, but very limited denitrification takes place in such kind of systems.
3.1. Wastewater treatment performance by HFCW and VFCW setups The results of CWs and control setup performance in terms of removal of major wastewater pollutants from dairy wastewater is presented in Table 2. All the three treatment setups showed statistically (ANOVA) significant (p < 0.05) difference for values of all studied parameters of wastewater at the end of the experimentations. 3.1.1. BOD5, COD and TSS The BOD5 which is considered as major pollutants in wastewater reduced significantly in both VFCW and HFCW as compared removal in control setup. Comparatively, the average removal results were higher in VFCW (82.8%) than HFCW (73%). Similarly, the VFCW showed more reduction in COD (82.8%) significantly higher than HFCW (71.9%) and control (50.7%) setups. Results validate that VFCW outperformed HFCW concerning BOD5 and COD removals, suggesting a high rate of oxygenation in VFCW. The results of BOD5 and COD removal corroborated with the findings of previous researchers, who have reported high efficacy of CWs in organic load removals (Vymazal 2010). A study by Yalcuk and Ugurlu (2009), reported comparatively low removal of COD in HF (27.3%) and VFCW (35.7%) system, which could be
Table 2 Efficacy of different constructed wetland system in terms of wastewater pollutant removal (mean ± SD, n = 36). Parameter
Influent
HFCW
VFCW
ControlA
F valueB
p value
pH EC (µS) NO3-N (mg/L) NH4-N (mg/L) PO4−3 (mg/L) SO2−4(mg/L) totNa (mg/L) totK (mg/L) BOD5 (mg/L) COD (mg/L) TSS (mg/L) totCr (mg/L) totFe (mg/L) totNi (mg/L)
7.31 ± 0.25 2.46 ± 0.43 38.6 ± 4.26 62.3 ± 4.28 32.6 ± 4.42 829.5 ± 81.4 157.9 ± 14.7 74.7 ± 6.23 770.7 ± 39.03 1676.2 ± 165.4 455.5 ± 21.15 0.026 ± 0.004 2.03 ± 0.32 0.96 ± 0.16
7.88 ± 0.22 1.63 ± 0.36 14.2 ± 3.96 (62.9) C 29.2 ± 5.12 (53.1) 16.4 ± 2.49 (49.4) 437.6 ± 41.56 (47.1) 81.7 ± 13.88 (47.9) 51.5 ± 5.48 (30.8) 207.1 ± 28.31(73.0) 433.9 ± 57.5 (73.9) 123.9 ± 31.1 (72.6) 0.014 ± 0.00 (47.3) 0.70 ± 0.14 (65.5) 0.338 ± 0.06 (64.8)
7.91 ± 0.25 1.48 ± 0.35 20.3 ± 3.60 (47.5) 21.0 ± 5.00 (66.2) 13.1 ± 2.88 (59.7) 483.5 ± 2.88 (41.4) 89.2 ± 15.38 (43.2) 58.3 ± 5.76 (21.8) 131.7 ± 32.24 (82.8) 279.6 ± 62.1 (83.2) 204.2 ± 25.02 (55.0) 0.013 ± 0.00 (48.2) 0.63 ± 0.16 (68.9) 0.27 ± 0.07 (71.7)
7.57 ± 0.22 2.22 ± 0.43 25.1 ± 4.01 (35.1) 36.7 ± 3.38 (41.0) 21.2 ± 3.30 (35.1) 689.5 ± 62.5 (16.7) 130.2 ± 11.1(17.4) 68.1 ± 5.63 (8.7) 393.8 ± 22.2 (48.8) 820.7 ± 42.1 (50.7) 256.1 ± 21.7 (43.6) 0.018 ± 0.00 (29.3) 1.17 ± 0.17 (42.0) 0.67 ± 0.13 (30.5)
24.2 37.9 73.8 108.5 71.3 242.4 136.5 81.6 865.4 964.8 238.4 35.8 121.5 177.0
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A B C
Wthout Typha plantation. One-way ANOVA. Values in parenthesis represent removal (%).
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Average PO43− removal was 49.4 and 59.7% in HFCW and VFCW, respectively. The results were in accordance with those reported by earlier workers (Yalcuk and Ugurlu 2009; Vymazal, 2010). The removal of phosphorus in CW system is often limited due to a low sorption capacity of the filtration materials (gravel, crushed rock) in CW beds (Vymazal, 2010). The key factors controlling the phosphorous removal in CW have been identified as redox potential, pH, and temperature. Phosphorus transformations mechanisms in CWs includes: adsorption, desorption, precipitation, dissolution, plant and microbial uptake, fragmentation, leaching, mineralization, sedimentation (peat accretion), burial, etc. (Kadlec and Knight, 1996; Sakadevan and Bavor, 1998). The average removal of SO42− was recorded 47% (HFCW) and 41.4% (VFCW) from dairy wastewater (Table 2). The SO42− removal from wastewater certainly attributes to the activity of sulphate reducing bacteria (SRB) in CWs (Stein et al., 2007). The HFCW columns allows anaerobic processes, including SRB activity, to proceed at rates similar to or greater than unplanted control columns (Stein et al., 2007). The biosynthesis of S-amino acids was supposed as a leading mechanism in the integration of pathways providing carbon building blocks and reduced sulphur (Datko and Mudd, 1984). Immobilization inside the planted wetlands, simultaneous chemical and/or biological oxidation, and intensive dissimilatory sulphate reduction also triggers the sulphate reduction (Wiessner et al., 2010). The totNa and totK are considered essential cationic species responsible for wastewater salinity. The HFCW offered average removal of 47.9 and 30.8% for totNa and totK, respectively while in the VFCW removal was recorded 43.2% for totNa and 21.8% for totK (Table 2). The cations are the main physiological substances required for a normal growth in plants thus absorption of these, through root zone system, plays a prime role in removal of such substances from wastewater during treatment through planted CWs. Sedimentation and filtration were supposed to be other mechanisms responsible for totNa and totK reduction in CWs.
root volume showed a proportionate increase during the operation duration (Table 3). The plant height was directly related to morphologic feature denoting habitat adaptability. The deep penetration of roots in substratum compensates the CW with more O2 supply in deeper layers, thus helping in rapid vegetative growth in plant stands and microbially-mediated nutrient biotransformation (Liu et al., 2012). The results of biochemical analysis of undried Typha biomass are presented in Table 3. The physiological health of a plant can be measured by analysing the pigments (chlorophyll) and energy molecules that indicates the plant productivity. The content of total chlorophyll (mg/g) ranged between 2.59 ± 0.01 and 3.21 ± 0.09 in the HFCW and 2.67 ± 0.03 and 3.20 ± 0.01 in VFCW, not significantly different (p > 0.05) than each other. Results indicated that chlorophyll content decrease in winter, perhaps due to low availability of sunlight during this period that makes abrupt changes in plant physiology in CWs. As temperature lowers, there occur massive reduction in transpiration and photosynthetic activity (Sharma et al., 2008). The effect of temperature decrease is not only relevant for chlorophyll content, but also for synthesis of carbohydrate and protein content in plant biomass also (Tursun et al., 2011). It has been found that total non-structural carbohydrate concentration (TNC) was statistically significant among different sampling months for both: HFCW (F = 149.5; p < 0.05) and VFCW (F = 285.8; p < 0.05). However, the concentration of TNC was more or less similar in HFCW and VFCW setups (Table 3). The average TNC concentration (mg/g) ranged between 194.9 and 259.8 and 191.5 and 260.7 for HFCW and VFCW, respectively. The total protein content in Typha biomass showed significant variations among different harvesting points in HFCW (F = 38.34; p < 0.05) and VFCW (F = 50.12; p < 0.05). The maximum protein content (mg/g) was recorded in plant stand harvested during summer period i.e. 2.17 ± 0.07 in HFCW and 2.13 ± 0.03 in VFCW. A study by Dinka and Szeglet (1999), depicted the seasonal influence on the concentration of TNC. The possible reason behind these disparities perhaps could be the translocation of carbohydrate into older rhizomes and formation of new lateral shoots during the growth season of a plant (Dinka and Szeglet (1999). The elemental analysis of Typha biomass revealed: C = 52.2%; H = 1.21; N = 0.72%; S = 0.10% and O = 45.77%. On the basis of standard equations proposed for calculating the High Heating Value (HHV), the energy value of Typha biomass ranged between 15.2 and 18.5 MJ/Kg, seems well in accordance with reports as published by Lakshman (1984) and Ciria et al. (2005). Typha attracted the attention of biomass-based renewable energy industries mainly due to having properties like low ash content, good growth characteristics, and having ability of baling with other heterogeneous energy materials. It has been reported in earlier cited literature that cattail (Typha spp.) is attractive in biomass production for bioenergy due to its relatively high energy content potential (Ciria et al., 2005; Fedler and Duan, 2011; Dipu et al., 2010). Evidently, cattails growing in nutrient-rich dairy wastewater also lead to greater growth and higher biomass yields thus combining the rapid nutrient capture and high energy biomass yield (Woo and Zedler, 2000). Biomass produced from this study could be utilised as fuel in a small household. The results of HHVs of this study are in accordance with reported values 17.1 to 19.5 MJ/kg (Lakshman, 1984) and 17.6 to 18.9 MJ/kg (Pratt et al., 1988), suggesting the suitability of Typha as bio-tool to stabilize the rural wastewater runoffs and to produce a non-woody substance for domestic fuel supply at low-coast basis.
3.1.3. Total heavy metals (Cr, Ni, Fe) in outlet water The average removal of Cr, Fe and Ni was recorded respectively 47.3%, 65.5%, and 64.8% in the HFCW and, 47.3%, 65.5% and 64.8% in the VFCW (Table 2). The concentrations of Cr, Ni and Fe in the discharged water was significantly lower (p < 0.01) than the inlet water in CWs, indicating significant role of planted CWs in heavy metal removal from wastewater. Heavy metal complexation, chelation, precipitation and rhizofiltration in the rhizosphere of Typha-based CWs could be responsible for heavy metals reduction from wastewater (Maddison et al., 2009). Studies by Goulet and Pick (2001) and Maddison et al. (2009) revealed that biotic and abiotic factors such as sedimentation, flocculation, adsorption, cation and anion exchange, microbial activity, etc. also plays an important role in metal removal during treating water through CW system. Results suggested that that overall removal of the major wastewater pollutants from dairy wastewater was markedly higher in the VFCW than HFCW, indicating the suitability of VFCW in designing a low-cost onsite wastewater mechanism for rural communities. Our findings are well in agreement with reports of previous researchers (Yalcuk and Ugurlu 2009). 3.2. Typha growth dynamics and plant biomass analysis as feedstock for bio-energy The survival of plant in CWs is directly related to its resistance toward pollution level in wastewater and this marks such plant species as an important candidate for CW setup (Fedler and Duan, 2011). The growth characteristics of Typha, harvested from VFCW and HFCW are presented in Table 3. Typha showed statistically (ANOVA) significant variations among different harvesting months for biomass quantity generated during the period in HFCW (F = 141.2; p < 0.05) and VFCW (F = 51.7; p < 0.05). The maximum biomass (g) was witnessed in the VFCW if compared with values in HFCW. Plant height, root length and
3.3. Extractions of multivariate PCA Reports suggested that multivariate PCA as a prime most technique among different statistical tools to minimize the variability in data set (Weber et al., 2008) and to identify the source of cause/change. It explains the variance and compositional patterns within a large datasets and thus provides results that are reasonable, objective, reliable and scientific. It summarizes a multivariate dataset by minimizing initial information loss, simplifying data structure, and convert original 124
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Table 3 Growth and bio-chemical characteristics of Typha plant. Data are the mean ± SD of three replicates (n = 3). Final Biomass (g)
Plant Height (m)
Root length (cm)
Root Volume (cm3)
Total Carbohydrate (mg/g)
Total Protein (mg/g)
Chl a (mg/g)
Chl b (mg/g)
Total Chlorophyll (mg/g)
HFCW Oct 13 Jan 14 Apr 14
660.6 ± 25.7 817.4 ± 17.3 1141.7 ± 53.5
1.34 ± 0.12 1.82 ± 0.06 2.27 ± 0.14
38.1 ± 5.90 53.2 ± 6.13 54.8 ± 5.02
351.0 ± 15.7 371.8 ± 17.2 426.5 ± 28.1
228.3 ± 3.12 194.9 ± 4.10 259.8 ± 6.06
42.5 ± 3.21 30.5 ± 2.51 49.2 ± 2.08
1.94 ± 0.06 1.84 ± 0.03 2.17 ± 0.07
1.26 ± 0.15 0.75 ± 0.03 1.00 ± 0.05
3.21 ± 0.09 2.59 ± 0.01 3.17 ± 0.02
VFCW Oct 13 Jan 14 Apr 14
681.6 ± 13.1 952.2 ± 20.0 1397.4 ± 148.8
1.45 ± 0.09 1.94 ± 0.11 2.42 ± 0.09
35.9 ± 5.36 54.5 ± 3.30 44.4 ± 2.96
355.5 ± 12.0 388.9 ± 5.79 414.5 ± 24.3
229.6 ± 0.80 191.5 ± 4.86 260.7 ± 3.66
44.9 ± 2.00 31.5 ± 1.52 49.5 ± 3.05
1.90 ± 0.04 1.89 ± 0.01 2.13 ± 0.03
1.30 ± 0.10 0.78 ± 0.03 1.07 ± 0.03
3.20 ± 0.06 2.67 ± 0.03 3.20 ± 0.01
Table 4 Total variance and rotated component matrix for different parameter in HFCWs and VFCWs (Factor loadings exceeding 0.5 are indicated with bold fonts). Component
Initial Eigen-values
Extraction sums of squared loadings –Cumulative%
Total
Variance%
Cumulative%
HFCW 1 2 3 4 5 6 7 8 9
4.171 1.873 1.161 0.783 0.371 0.296 0.168 0.098 0.081
46.344 20.811 12.898 8.695 4.117 3.290 1.867 1.084 0.895
46.344 67.155 80.053 88.748 92.865 96.155 98.021 99.105 100.00
VFCW 1 2 3 4 5 6 7 8 9
5.103 1.606 1.092 0.518 0.327 0.151 0.107 0.071 0.024
56.704 17.850 12.134 5.761 3.628 1.682 1.184 0.792 0.265
56.704 74.554 86.688 92.449 96.077 97.759 98.943 99.735 100.00
Rotation sums of squared loadings Total
Variance%
Cumulative%
46.344 67.155 80.053
3.172 2.630 1.403
35.240 29.226 15.587
35.240 64.466 80.053
56.704 74.554 86.688
4.109 2.095 1.598
45.653 23.282 17.753
45.653 68.935 86.688
Sub-group-I
Sub-group-II
Component matrix
Rotated component matrix
Component matrix
Rotated component matrix
Element
PC 1
PC 2
PC 3
PC 1
PC 2
PC 3
Element
PC 1
PC 2
PC3
PC 1
PC 2
PC 3
BOD5 COD SO2−4 NO3 -N pH TSS EC NH4-N PO4−3
0.927 0.921 0.887 0.732 0.285 0.605 0.388 0.496 0.545
0.097 −0.049 −0.183 0.533 0.861 −0.620 −0.470 −0.291 0.338
−0.010 0.054 −0.130 −0.235 −0.044 0.211 0.326 −0.710 0.655
0.912 0.782 0.762 0.658 0.599 0.037 −0.033 0.554 0.218
−0.004 −0.423 0.502 0.623 0.373 0.877 0.688 0.618 0.215
0.206 −0.182 0.191 0.181 −0.585 0.155 −0.050 0.385 0.861
BOD COD SO2−4 NO3−N pH TSS EC NH4−N PO4−3
0.872 0.767 0.889 0.882 0.495 0.510 −0.218 0.938 0.869
−0.321 −0.423 0.048 −0.046 0.229 0.730 0.808 0.051 0.282
0.211 0.392 −0.332 −0.280 0.766 0.068 0.126 −0.067 −0.306
0.618 0.428 0.937 0.896 0.080 0.488 −0.163 0.851 0.932
0.654 0.769 0.152 0.205 0.877 0.225 −0.097 0.403 0.136
−0.314 −0.382 −0.036 −0.119 0.329 0.714 0.824 0.007 0.201
Extraction method: PCA. Rotation method: Varimax with Kaiser Normalization.
dataset in HFCW and 86.68% variance of the normalized dataset in VFCW. In HFCW, the first component (PCI) contributes 35.2% variance and reveals strong associations between the BOD, COD, NO3−, TSS and PO43− for both component matrix and rotated component matrix. The second component (PCII) supported variance of 29.2% of total variance and offers positive correlation among pH and NO3− for both matrixes. In case of third component (PCIII) contributes 15.5% variance and main statistically correlated variables was PO43− in both component and rotated component matrix. In case of VFCWs, three PCs were extracted which offers variance of 45.6% (PCI), 23.2% (PCII) and 17.7% (PCIII). The positive correlation was presented between BOD, COD, TSS, NH4N, PO43−, SO42− and NO3-N. The high variance in VFCWs when compared to HFCWs depicted a more efficient wastewater treatment facility at VFCWs.
variables into new, uncorrelated variable called as principal components (linear combination of studied variables) (Helena et al., 2000). Eigen values greater than 1 were taken as a criterion for extraction of the principal components required to explain the sources of variance in the data. These principal components (PCs) are orthogonal variable (Eigen value > 1) obtained by multiplying original correlated variables with Eigen vector, which is a list of coefficients (loading or weightings) (Ligang et al., 2011). In the present normalized dataset, the PCA was performed on correlation matrix of different variables, followed by Varimax rotation. The results of PCA analysis with nine variables, as studied for the HFCW and VFCW are shown in Table 4. Based on the Eigen values and varimax rotation three factors, explaining the most of the variability present in the dataset, were identified. These three PCs explained 80.05% variance of the normalized 125
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4. Conclusion
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The CWs can answer multi-fold problems related with wastewater and energy supply at community level. Comparative basis, the functioning of two types of CWs: HFCW and VFCW were investigated in terms of removal of major wastewater pollutant from daily wastewater. Results indicated that VFCW setup perform better than HFCW in terms of removal of NH4-N, PO43−, BOD5, COD and heavy metals from wastewater. A high quality of biomass of Typha was also produced from CWs which showed the presence of high contents of energy molecules (total carbohydrates, and protein), suggesting its suitability as feedstock for thermal- as well as chemical-based renewable energy production ventures. The PCA predicted a total of three components explaining all most variability in datasets in both types of CWs. Results suggested that Typha-based CW can be developed at community level for multiple purposes: nutrient capture, habitat restoration, bioenergy feedstock production, carbon offsets, water quality credits, etc. for small communities in rural and sub-urban areas. References American Society for Testing and Materials (ASTM), 1982. Compilation of ASTM Standard Definitions. ASTM, Philadelphia, PA. APHA-AWWA-WPFC, 1994. Standard Methods for the Examination of Water and Wastewater, 15th ed. American Public Health Association, Washington, DC. Brix, H., 1994. Functions of macrophytes in constructed wetlands. Water Sci. Technol. 29, 71–78. Calheiros, C.S.C., Rangel, A.O.S.S., Castro, P.M.L., 2009. Treatment of industrial wastewater with two-stage constructed wetlands planted with Typha latifolia and Phragmites australis. Bioresour. Technol. 100, 3205–3213. Caselles-Osorio, A., Villafañe, P., Caballero, V., Manzano, Y., 2011. Efficiency of Mesocosm-scale constructed wetland systems for treatment of sanitary wastewater under tropical conditions. Water Air Soil Poll. 220, 161–171. Ciria, M.P., Solano, M.L., Soriano, P., 2005. Role of macrophyte Typha latifolia in a constructed wetland for wastewater treatment and assessment of its potential as a biomass fuel. Biosyst. Eng. 92, 535–544. Datko, A.H., Mudd, S.H., 1984. Sulphate uptake and its regulation in Lemna paucicostata (Hegelin 6746). Plant Physiol. 75, 466–473. Dipu, S., Anju, A., Kumar, V., Thanga, S.G., 2010. Phytoremediation of dairy effluent by constructed wetland technology using wetland macrophytes. Glob. J. Environ. Res. 4, 90–100. Dinka, M., Szeglet, P., 1999. Carbohydrate nutrient and content in rhizome of Phragmites australis from different habitats of Lake Fertő/Neusiedler See. Limnologica 29, 47–59. Fedler, C.B., Duan, R., 2011. Biomass production for bioenergy using recycled wastewater in a natural waste treatment system. Resour. Conser. Recycl. 55, 793–800. Gikas, G., Tsihrintzis, V., 2012. A small-size vertical flow constructed wetland for onsite treatment of household wastewater. Ecol. Eng. 44, 337–343. Goulet, R., Pick, F., 2001. The effects of cattails (Typha latifolia l.) on concentrations and partitioning of metals in surficial sediments of surface-flow constructed wetlands. Water Air Soil Poll. 132, 275–291. Hammer, D.A. (Ed.), 1989. Constructed Wetlands for Wastewater Treatment. Municipal, Industrial, and Agricultural. Lewis Publishers, Chelsea, MI, USA, pp. 831. Helena, B., Pardo, R., Vega, M., Barrado, E., Fernandez, J.M., Fernandez, L., 2000. Temporal evolution of ground water composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis. Water Res. 34, 807–816. Kadlec, H.R., Knight, R.L., 1996. Treatment Wetlands. Lewis Publisher, FL, USA. Kadlec, R.H., Wallace, S.D., 2009. Treatment Wetlands, 2nd ed. Taylor and Francis Group, Boca Raton, USA. Karim, M.R., Manshadia, F.D., Karpiscak, M.M., Gerba, C.P., 2004. The persistence and removal of enteric pathogens in constructed wetlands. Water Res. 38, 1831–1837. Lakshman, G., 1984. A study to evaluate the potential of cattail as an energy crop. Saskatchewan Research Council, SRC Technical Report 162, Saskatoon, Saskatchewan. Ligang, X., Hailin, Y., Jianmin, L., Zhang, Q., Jiahu, J., Weiming, L., Zhandong, S., 2011. Analysis on affected factors of treatment efficiency of rural sewage removal with
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Performance assessment of horizontal and vertical surface flow constructed wetland system in wastewater treatment using multivariate principal component analysis
T
⁎
Rashmi Verma , Surindra Suthar School of Environment & Natural Resources, Doon University, Dehradun 248001, Uttarakhand, India
A R T I C LE I N FO
A B S T R A C T
Keywords: Dairy wastewater Wetland plants HHV Principal component analysis Typha
This study aimed to compare the horizontal flow (HFCW) and vertical flow (VFCW) constructed wetland systems in treating dairy wastewater (DWW) and simultaneously harvesting plant biomass from units. The HFCW and VFCW were designed at lab-scale using cattail (Typha angustifolia) and changes in DWW parameters: pH, EC, TSS, NO3-N, NH4-N, PO4−3, SO4−2, Na, K, BOD5, COD and heavy metals (Fe, Cr and Ni) were investigated for 9 months. A setup without plant stand acted as control. The VFCW outperformed to HFCW in terms of removal of NH4-N, PO4−3, BOD5, COD, and heavy metals while NO3-N and SO4−2 showed high removal in HFCW. The principal component analysis (PCA) identified three major components from the 9 major variables accounted for 80.05 and 86.68 of the datasets in HECW and VFCW, respectively. The degree of variance suggested the high performance of VFCW than HFCW. The PCA showed slight variations in functioning of both systems in terms of interdependences of organic and inorganic pollution abetments. The biomass yield of Typha showed great variations between HFCW and VFCW system and relatively the VFCW produced more Typha biomass. The high heating value (HHV) calculated on the basis of proximate and ultimate results indicates that Typha biomass can be used as potential feedstock for renewable energy operations. The Typha based VFCW for dairy wastewater treatment can targets multiple purposes: nutrient capture, habitat restoration, bioenergy, carbon offsets, and water quality credits.
1. Introduction The wastewater from animal farm operations and runoff from agricultural lands contributes a large quantity of nutrients, sediment, and biochemical oxygen demand (BOD5) to any receiving water body (Kadlec and Wallace, 2009). Being rich in nitrogen and phosphorus nutrient species, these types of wastewaters directly feed the algal blooms, which in later phase lead to depletion of dissolved oxygen, fish habitat damage and threaten the recreation of system (Zhang et al., 2005). The conventional wastewater treatment systems fail to reduce the negative impacts of nutrient pollution effectively as these offers limitation, in terms of operation cost and maintenance (Metcalf and Eddy, 2004). Nowadays, the major emphasis of scientific community is on developing a low cost solution to abate the nutrient problems at its source of origin (Schaafsma et al., 2000). Constructed wetland (CW) technology is a novel approach for on-site wastewater treatment mainly characterized by pollutant removal capacity, simplicity, low construction/operation and maintenance costs, low energy demand, process stability, and reduced sludge production (Vymazal et al., 1998; ⁎
Vymazal, 2010; Kadlec and Wallace, 2009; Gikas and Tsihrintzis, 2012). The CWs are differentiated into several types based on set of criteria such as, presence/absence of free-water-surface, types of macrophytes used, and direction of flow of water in system, etc. (Kadlec and Knight, 1996). The use of CWs serves to improve water quality, habitat enhancement, and aesthetic improvement in ornamental ponds and lakes. It has been reported that pollutant removal rate is substantially higher in vertical flow CWs when compared to horizontal flow CWs (Vymazal, 2010). Macrophytes form the essential component of wetlands, which help to stabilize and oxidize sediments (Brix, 1994; Kadlec and Wallace, 2009). It has been shown that a planted wetland system has a higher efficiency of pollutant removal than that without plants (Brix, 1994). Different plant species are used for this purpose; however, various species of genus Typha are frequently used for purification purpose in constructed wetlands. The species Typha offers competitive performance in organic matter retention, nutrient removal, and pathogen reduction (Brix, 1994). Adaptability to different environmental condition and water-load, specifically the high growth rate in a short period
Corresponding author. E-mail address: [email protected] (R. Verma).
https://doi.org/10.1016/j.ecoleng.2018.02.022 Received 27 July 2017; Received in revised form 9 February 2018; Accepted 24 February 2018 0925-8574/ © 2018 Elsevier B.V. All rights reserved.
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are few important features that makes Typha as an excellent candidate for wastewater treatment in CWs (Martın and Fernández, 1992). It seems to be a competitive emergent aquatic plant, which converts the available water nutrients into energy biomass for renewable operations. Previous reports have suggested the potential of Typha based CWs in treatment of various kinds of wastewaters: municipal wastewater (Ye and Li, 2009), acid mine drainage (Nivala et al., 2007; Yalcuk and Ugurlu, 2009), industrial wastewater (Calheiros et al., 2009), agricultural and storm runoff (Hammer, 1989), effluent from livestock operations (Schaafsma et al., 2000; Dipu et al., 2010), etc. Typha offers efficient accumulation of nutrients from wastewater and converting it into a valuable biomass resource. Moreover, the harvested biomass can serve as a source of biomass for bio-energy potential (Sheng and Azevedo, 2005). To our best knowledge, no comprehensive report on comparative assessment of working of Typha-based CWs with different modes of flow i.e. horizontal flow and vertical flow has not been studied yet by previous researchers. This study aimed to investigate the removal efficiency of HFCW and VFCW in treating wastewater generate from a dairy industry using lab-scale CWs under ambient conditions. The performance of CWs was compared using multivariate PCA analysis and harvested biomass was analysed for proximate, ultimate and biochemical characteristics and bio-energy potential of harvested was also estimated. The need for the study arose from increased wastewater generation versus constant and/or degrading wastewater treatment facilities due to urbanization and financial constraints. This would help to withstand the dual objective of nutrient removal from wastewater and converting that to energy rich biomass.
Table 1 Typical dairy run-off inflow characteristics (n = 36). Parameter
Unit
Min
Max
Range
Mean
SD
pH EC NO3 -N NH4-N PO4−3 SO2−4 totNa totK BOD5 COD TSS totCr totFe totNi
– µS mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L
6.54 1.34 28.7 52.8 22.4 702 127 63.2 702 1421 421 0.019 1.40 0.616
7.76 3.21 45.1 68.7 39.7 842 182 86.5 842 1962 492 0.034 2.47 1.211
1.22 1.87 16.3 15.7 17.3 71 55 23.3 140 541 71 0.015 1.07 0.595
7.31 2.46 38.6 62.3 32.6 455.5 157.9 74.7 770.7 1676 455.5 0.026 2.03 0.963
0.25 0.43 4.26 4.28 4.42 21.15 14.7 6.23 39.03 165.4 21.1 0.004 0.32 0.161
2.3. Outlet water quality analysis The sampling and further analysis of wastewater was done weekly for the period of nine months, began in the first week of August 2013 and continued until the end of April 2014. Collected wastewater samples were properly stored and analyzed immediately for different physicochemical parameters. The pH was measured using digital pH meter (Metrohm, Swiss-made). The electrical conductivity (EC) was determined by a digital conductivity meter (Remi, India). The major wastewater nutrients: NO3−N, PO43−, and SO42− were analysed spectrophometrically by following the standard protocols as described in APHA-AWWA-WPFC (1994). The biochemical oxygen demand (BOD5) and chemical oxygen demand (COD) in water sample was determined by APHA-AWWA-WPFC (1994). The total content of cations i.e. sodium (totNa), potassium (totK), and calcium (totCa) in raw and treated water were determined using Flame photometry (APHAAWWA-WPFC, 1994). The total content of heavy metals (totCr, totFe and totNi) was analysed by using Atomic absorption spectrophotometer (Thermo Fisher. Model iCE 3000 Series AA System). Meteorological data (ambient temperature and precipitation) for the study duration were procured from local station of Indian Meteorological Department (IMD), Dehradun (Uttarakhand, India). All chemicals and reagents used in analytical work were of AR grade (purity up to 99%).
2. Material and method 2.1. Description of HFCW and VFCW The study was conducted at Doon University campus (30°16′ N, 78° 2′ E), Dehradun (Uttarakhand), India. Two continuous flow system i.e. horizontal and vertical flow pilot plants (HFCW and VFCW, respectively) were constructed. The dimensions of each unit of CW was: 0.5 m in diameter and 1.5 m in height for VFCW and 0.75 m in length, 0.25 m in breadth and 0.5 m in height for HFCW. Different depths of sand, gravel and boulders were filled into each types of CW unit as substrates. In both, HFCWs and VFCWs, filter layers consist of bottom layer of boulders to a depth of 0.05 m, above it a sand layer of 0.1 m, a composite layer of gravel and sand of 0.1 m and topmost layer of gravel of 0.1 m. The purpose of filter materials was to collect water and provide the maximum support and surface area during the operation.
2.4. Harvesting of Typha biomass from CWs and chemical analysis The initial and final dry weight of biomass (g) was determined by harvesting at least three specimen of complete plant stand from each treatment set-up at an interval of three months (October 2013, January 2014 and April 2014). To access the growth characteristic, plant length, root length and root volume were determined after derooting the plant from CW bed. The plant’s root length and individual plant heights were measured using a scale. Root volume was determined by drainage: the water on the surface of the washed roots was absorbed; then the roots were placed in a container (with an overflow pipe) that was full of water. The root volume was equal to the volume of overflow (Liu et al., 2012). The harvested undried Typha biomass was further analysed for its biochemical parameters (total non-structural carbohydrate, total protein and chlorophyll). Ash (%), moisture (%), volatile matter (%), and fixed carbon (%) were determined using methods as described in ASTM manual (ASTM, 1982). The total non-structural carbohydrate (TNC) concentration, which was defined as the sum of soluble sugar and starch concentration (Sharma et al., 2008) was estimated using standard methodology described by Loomis and Shull (1937). The protein content was measured by Lowry et al. (1951) method. The chlorophyll pigments were measured spectrophometrically using the method of Martin et al. (2003). Also, the harvested biomass was powdered and
2.2. Characteristics of inflow and operations of CW units Young specimen of T. angustifolia were collected locally from natural marshy land during May 2013 and used as plant stand in constructing HFCW and VFCW in experimental station of lab. The specimen of Typha of approximately same age and weight were selected and then rooted in the bed of our CWs at a density of twenty-seven plants per m2. Initially, the tape water was used for acclimatization of plant stand in CWs and after two months of appropriate growth of plant stand, the systems were used for further dairy wastewater treatment operations. The raw wastewater replaced the fresh water as the influent into these CWs. The influent flow rate was 25–30 L/day maintained throughout the study period. The design hydraulic loading rate in CWs ranged between 288 and 345 L/m2 day, while the theoretical hydraulic retention time was about 1 L/m2 day. The water sampling frequency was once in a week. All units were fed with dairy wastewater effluent collected from dairy outlet located nearby to the university campus. The components and characteristics of the wastewater are presented in Table 1. Wastewater flow rates were adjusted manually at the inlet of different units using gate valves. 122
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attributed to the design specification of CW and biological mechanisms in rhizosphere. was quite less to the present reported results. Sedimentation (Meuleman et al., 2003), filtration (Karim et al., 2004), hydrolysis (Michell and McNevin, 2001), oxidation/reduction (CasellesOsorio et al., 2011), bacterial metabolism (aerobic/anaerobic/anoxic) (Caselles-Osorio et al., 2011) are considered as major mechanisms for removal of BOD and COD in the planted CWs. The solids in the effluent are the parts of the non-trapped influent solids, surplus sludge, and plant litter solids generated during the process of mineralization. The average removal of TSS was 72.6% in HFCW, significantly higher than the VFCW (55%) during this study (Table 2). TSS removal is affected by multitude of factors such as: filter material, hydraulic retention time, temperature, microbial community, etc. (Karim et al., 2004). Vegetation dynamics together with substratum results in low velocity of wastewater, hence favours aerobic and anaerobic microbial degradation (Kadlec and Knight, 1996; Vymazal, 2010). The results indicate that HFCWs offer adequate sedimentation, filtration, bacterial decomposition, and adsorption to wetland media which further lower down the TSS load in effluent. The inefficiency of removing TSS in VFCW may be due to the rapid seepage of wastewater in the system (Meuleman et al., 2003).
analyzed for elemental composition by using CHNS Elemental analyzer at the end of experiment (9 months). 2.5. Statistical analyses One-way ANOVA was used to analyze the differences among wastewater dilutions for different characteristics of the treated water in different set-ups. PCA was performed to assess variance among different components guarding system functioning. A Tukey’s test was also performed to identify the homogeneous type of the data sets. The relationship between removal rate and WW dilution factor was measured using regression analysis. In the present study, PCA was used to investigate the processes, which influences the removal of wastewater parameters by examining associations defined by one or more variable loadings on factors. A loading weight close to ± 1 indicates a strong correlation between a variable and the factor and variables that exhibited a loading weightage > 0.5 were considered significant. PCA was performed using Varimax rotation with Kaiser Normalization. SPSS® statistical package (Window Version13.0) and STATISTICA® (Window Version 7) were to perform all statistical analysis of datasheets. All statements reported in this study are at the p < 0.05 levels. 3. Result and discussion
3.1.2. Wastewater nutrients and salts (NO3−N, NH4-N, PO43−, SO42−, totNa and totK) Substantial variability has been observed in the removal of different nutrients (NO3−N, NH4-N, PO43−, SO42−, totNa, totK) from dairy wastewater during the study period. Average removal of NO3−N was recorded 62.9% (HFCW) and 47.5% (VFCW), remarkably higher than that of control (Table 2). The results of NO3−N removal in HFCW was higher than reported data by Yousefi and Mohseni-Bandpei, (2010). Likewise, average removal of NH4-N in CW was 53.1% (HFCW) and 66.2% (VFCW), significantly higher than values of control (41%) setup (Table 2). Different species of nitrogen are potentially transformed in wetlands by the processes like NH3 volatilization, nitrification, denitrification, nitrogen fixation, plant and microbial uptake, mineralization (ammonification), NO3 reduction to NH4+ (nitrate-ammonification), anaerobic ammonia oxidation (ANAMMOX), fragmentation, sorption, desorption, burial, leaching, etc. (Kadlec and Knight, 1996; Nurk et al., 2005). The NO3 removal is limited by the lack of oxygen in the filtration bed and this consequently lowers the nitrification processes (Vymazal, 2010). Redox potential, evapotranspiration (ETP) rates and hydraulic retention times (HRT) and plant stand, etc. are few governing factors for overall dynamics of total N, NH4-N, and NOy export in CW system. The VFCWs remove successfully NH4– N, but very limited denitrification takes place in such kind of systems.
3.1. Wastewater treatment performance by HFCW and VFCW setups The results of CWs and control setup performance in terms of removal of major wastewater pollutants from dairy wastewater is presented in Table 2. All the three treatment setups showed statistically (ANOVA) significant (p < 0.05) difference for values of all studied parameters of wastewater at the end of the experimentations. 3.1.1. BOD5, COD and TSS The BOD5 which is considered as major pollutants in wastewater reduced significantly in both VFCW and HFCW as compared removal in control setup. Comparatively, the average removal results were higher in VFCW (82.8%) than HFCW (73%). Similarly, the VFCW showed more reduction in COD (82.8%) significantly higher than HFCW (71.9%) and control (50.7%) setups. Results validate that VFCW outperformed HFCW concerning BOD5 and COD removals, suggesting a high rate of oxygenation in VFCW. The results of BOD5 and COD removal corroborated with the findings of previous researchers, who have reported high efficacy of CWs in organic load removals (Vymazal 2010). A study by Yalcuk and Ugurlu (2009), reported comparatively low removal of COD in HF (27.3%) and VFCW (35.7%) system, which could be
Table 2 Efficacy of different constructed wetland system in terms of wastewater pollutant removal (mean ± SD, n = 36). Parameter
Influent
HFCW
VFCW
ControlA
F valueB
p value
pH EC (µS) NO3-N (mg/L) NH4-N (mg/L) PO4−3 (mg/L) SO2−4(mg/L) totNa (mg/L) totK (mg/L) BOD5 (mg/L) COD (mg/L) TSS (mg/L) totCr (mg/L) totFe (mg/L) totNi (mg/L)
7.31 ± 0.25 2.46 ± 0.43 38.6 ± 4.26 62.3 ± 4.28 32.6 ± 4.42 829.5 ± 81.4 157.9 ± 14.7 74.7 ± 6.23 770.7 ± 39.03 1676.2 ± 165.4 455.5 ± 21.15 0.026 ± 0.004 2.03 ± 0.32 0.96 ± 0.16
7.88 ± 0.22 1.63 ± 0.36 14.2 ± 3.96 (62.9) C 29.2 ± 5.12 (53.1) 16.4 ± 2.49 (49.4) 437.6 ± 41.56 (47.1) 81.7 ± 13.88 (47.9) 51.5 ± 5.48 (30.8) 207.1 ± 28.31(73.0) 433.9 ± 57.5 (73.9) 123.9 ± 31.1 (72.6) 0.014 ± 0.00 (47.3) 0.70 ± 0.14 (65.5) 0.338 ± 0.06 (64.8)
7.91 ± 0.25 1.48 ± 0.35 20.3 ± 3.60 (47.5) 21.0 ± 5.00 (66.2) 13.1 ± 2.88 (59.7) 483.5 ± 2.88 (41.4) 89.2 ± 15.38 (43.2) 58.3 ± 5.76 (21.8) 131.7 ± 32.24 (82.8) 279.6 ± 62.1 (83.2) 204.2 ± 25.02 (55.0) 0.013 ± 0.00 (48.2) 0.63 ± 0.16 (68.9) 0.27 ± 0.07 (71.7)
7.57 ± 0.22 2.22 ± 0.43 25.1 ± 4.01 (35.1) 36.7 ± 3.38 (41.0) 21.2 ± 3.30 (35.1) 689.5 ± 62.5 (16.7) 130.2 ± 11.1(17.4) 68.1 ± 5.63 (8.7) 393.8 ± 22.2 (48.8) 820.7 ± 42.1 (50.7) 256.1 ± 21.7 (43.6) 0.018 ± 0.00 (29.3) 1.17 ± 0.17 (42.0) 0.67 ± 0.13 (30.5)
24.2 37.9 73.8 108.5 71.3 242.4 136.5 81.6 865.4 964.8 238.4 35.8 121.5 177.0
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A B C
Wthout Typha plantation. One-way ANOVA. Values in parenthesis represent removal (%).
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Average PO43− removal was 49.4 and 59.7% in HFCW and VFCW, respectively. The results were in accordance with those reported by earlier workers (Yalcuk and Ugurlu 2009; Vymazal, 2010). The removal of phosphorus in CW system is often limited due to a low sorption capacity of the filtration materials (gravel, crushed rock) in CW beds (Vymazal, 2010). The key factors controlling the phosphorous removal in CW have been identified as redox potential, pH, and temperature. Phosphorus transformations mechanisms in CWs includes: adsorption, desorption, precipitation, dissolution, plant and microbial uptake, fragmentation, leaching, mineralization, sedimentation (peat accretion), burial, etc. (Kadlec and Knight, 1996; Sakadevan and Bavor, 1998). The average removal of SO42− was recorded 47% (HFCW) and 41.4% (VFCW) from dairy wastewater (Table 2). The SO42− removal from wastewater certainly attributes to the activity of sulphate reducing bacteria (SRB) in CWs (Stein et al., 2007). The HFCW columns allows anaerobic processes, including SRB activity, to proceed at rates similar to or greater than unplanted control columns (Stein et al., 2007). The biosynthesis of S-amino acids was supposed as a leading mechanism in the integration of pathways providing carbon building blocks and reduced sulphur (Datko and Mudd, 1984). Immobilization inside the planted wetlands, simultaneous chemical and/or biological oxidation, and intensive dissimilatory sulphate reduction also triggers the sulphate reduction (Wiessner et al., 2010). The totNa and totK are considered essential cationic species responsible for wastewater salinity. The HFCW offered average removal of 47.9 and 30.8% for totNa and totK, respectively while in the VFCW removal was recorded 43.2% for totNa and 21.8% for totK (Table 2). The cations are the main physiological substances required for a normal growth in plants thus absorption of these, through root zone system, plays a prime role in removal of such substances from wastewater during treatment through planted CWs. Sedimentation and filtration were supposed to be other mechanisms responsible for totNa and totK reduction in CWs.
root volume showed a proportionate increase during the operation duration (Table 3). The plant height was directly related to morphologic feature denoting habitat adaptability. The deep penetration of roots in substratum compensates the CW with more O2 supply in deeper layers, thus helping in rapid vegetative growth in plant stands and microbially-mediated nutrient biotransformation (Liu et al., 2012). The results of biochemical analysis of undried Typha biomass are presented in Table 3. The physiological health of a plant can be measured by analysing the pigments (chlorophyll) and energy molecules that indicates the plant productivity. The content of total chlorophyll (mg/g) ranged between 2.59 ± 0.01 and 3.21 ± 0.09 in the HFCW and 2.67 ± 0.03 and 3.20 ± 0.01 in VFCW, not significantly different (p > 0.05) than each other. Results indicated that chlorophyll content decrease in winter, perhaps due to low availability of sunlight during this period that makes abrupt changes in plant physiology in CWs. As temperature lowers, there occur massive reduction in transpiration and photosynthetic activity (Sharma et al., 2008). The effect of temperature decrease is not only relevant for chlorophyll content, but also for synthesis of carbohydrate and protein content in plant biomass also (Tursun et al., 2011). It has been found that total non-structural carbohydrate concentration (TNC) was statistically significant among different sampling months for both: HFCW (F = 149.5; p < 0.05) and VFCW (F = 285.8; p < 0.05). However, the concentration of TNC was more or less similar in HFCW and VFCW setups (Table 3). The average TNC concentration (mg/g) ranged between 194.9 and 259.8 and 191.5 and 260.7 for HFCW and VFCW, respectively. The total protein content in Typha biomass showed significant variations among different harvesting points in HFCW (F = 38.34; p < 0.05) and VFCW (F = 50.12; p < 0.05). The maximum protein content (mg/g) was recorded in plant stand harvested during summer period i.e. 2.17 ± 0.07 in HFCW and 2.13 ± 0.03 in VFCW. A study by Dinka and Szeglet (1999), depicted the seasonal influence on the concentration of TNC. The possible reason behind these disparities perhaps could be the translocation of carbohydrate into older rhizomes and formation of new lateral shoots during the growth season of a plant (Dinka and Szeglet (1999). The elemental analysis of Typha biomass revealed: C = 52.2%; H = 1.21; N = 0.72%; S = 0.10% and O = 45.77%. On the basis of standard equations proposed for calculating the High Heating Value (HHV), the energy value of Typha biomass ranged between 15.2 and 18.5 MJ/Kg, seems well in accordance with reports as published by Lakshman (1984) and Ciria et al. (2005). Typha attracted the attention of biomass-based renewable energy industries mainly due to having properties like low ash content, good growth characteristics, and having ability of baling with other heterogeneous energy materials. It has been reported in earlier cited literature that cattail (Typha spp.) is attractive in biomass production for bioenergy due to its relatively high energy content potential (Ciria et al., 2005; Fedler and Duan, 2011; Dipu et al., 2010). Evidently, cattails growing in nutrient-rich dairy wastewater also lead to greater growth and higher biomass yields thus combining the rapid nutrient capture and high energy biomass yield (Woo and Zedler, 2000). Biomass produced from this study could be utilised as fuel in a small household. The results of HHVs of this study are in accordance with reported values 17.1 to 19.5 MJ/kg (Lakshman, 1984) and 17.6 to 18.9 MJ/kg (Pratt et al., 1988), suggesting the suitability of Typha as bio-tool to stabilize the rural wastewater runoffs and to produce a non-woody substance for domestic fuel supply at low-coast basis.
3.1.3. Total heavy metals (Cr, Ni, Fe) in outlet water The average removal of Cr, Fe and Ni was recorded respectively 47.3%, 65.5%, and 64.8% in the HFCW and, 47.3%, 65.5% and 64.8% in the VFCW (Table 2). The concentrations of Cr, Ni and Fe in the discharged water was significantly lower (p < 0.01) than the inlet water in CWs, indicating significant role of planted CWs in heavy metal removal from wastewater. Heavy metal complexation, chelation, precipitation and rhizofiltration in the rhizosphere of Typha-based CWs could be responsible for heavy metals reduction from wastewater (Maddison et al., 2009). Studies by Goulet and Pick (2001) and Maddison et al. (2009) revealed that biotic and abiotic factors such as sedimentation, flocculation, adsorption, cation and anion exchange, microbial activity, etc. also plays an important role in metal removal during treating water through CW system. Results suggested that that overall removal of the major wastewater pollutants from dairy wastewater was markedly higher in the VFCW than HFCW, indicating the suitability of VFCW in designing a low-cost onsite wastewater mechanism for rural communities. Our findings are well in agreement with reports of previous researchers (Yalcuk and Ugurlu 2009). 3.2. Typha growth dynamics and plant biomass analysis as feedstock for bio-energy The survival of plant in CWs is directly related to its resistance toward pollution level in wastewater and this marks such plant species as an important candidate for CW setup (Fedler and Duan, 2011). The growth characteristics of Typha, harvested from VFCW and HFCW are presented in Table 3. Typha showed statistically (ANOVA) significant variations among different harvesting months for biomass quantity generated during the period in HFCW (F = 141.2; p < 0.05) and VFCW (F = 51.7; p < 0.05). The maximum biomass (g) was witnessed in the VFCW if compared with values in HFCW. Plant height, root length and
3.3. Extractions of multivariate PCA Reports suggested that multivariate PCA as a prime most technique among different statistical tools to minimize the variability in data set (Weber et al., 2008) and to identify the source of cause/change. It explains the variance and compositional patterns within a large datasets and thus provides results that are reasonable, objective, reliable and scientific. It summarizes a multivariate dataset by minimizing initial information loss, simplifying data structure, and convert original 124
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Table 3 Growth and bio-chemical characteristics of Typha plant. Data are the mean ± SD of three replicates (n = 3). Final Biomass (g)
Plant Height (m)
Root length (cm)
Root Volume (cm3)
Total Carbohydrate (mg/g)
Total Protein (mg/g)
Chl a (mg/g)
Chl b (mg/g)
Total Chlorophyll (mg/g)
HFCW Oct 13 Jan 14 Apr 14
660.6 ± 25.7 817.4 ± 17.3 1141.7 ± 53.5
1.34 ± 0.12 1.82 ± 0.06 2.27 ± 0.14
38.1 ± 5.90 53.2 ± 6.13 54.8 ± 5.02
351.0 ± 15.7 371.8 ± 17.2 426.5 ± 28.1
228.3 ± 3.12 194.9 ± 4.10 259.8 ± 6.06
42.5 ± 3.21 30.5 ± 2.51 49.2 ± 2.08
1.94 ± 0.06 1.84 ± 0.03 2.17 ± 0.07
1.26 ± 0.15 0.75 ± 0.03 1.00 ± 0.05
3.21 ± 0.09 2.59 ± 0.01 3.17 ± 0.02
VFCW Oct 13 Jan 14 Apr 14
681.6 ± 13.1 952.2 ± 20.0 1397.4 ± 148.8
1.45 ± 0.09 1.94 ± 0.11 2.42 ± 0.09
35.9 ± 5.36 54.5 ± 3.30 44.4 ± 2.96
355.5 ± 12.0 388.9 ± 5.79 414.5 ± 24.3
229.6 ± 0.80 191.5 ± 4.86 260.7 ± 3.66
44.9 ± 2.00 31.5 ± 1.52 49.5 ± 3.05
1.90 ± 0.04 1.89 ± 0.01 2.13 ± 0.03
1.30 ± 0.10 0.78 ± 0.03 1.07 ± 0.03
3.20 ± 0.06 2.67 ± 0.03 3.20 ± 0.01
Table 4 Total variance and rotated component matrix for different parameter in HFCWs and VFCWs (Factor loadings exceeding 0.5 are indicated with bold fonts). Component
Initial Eigen-values
Extraction sums of squared loadings –Cumulative%
Total
Variance%
Cumulative%
HFCW 1 2 3 4 5 6 7 8 9
4.171 1.873 1.161 0.783 0.371 0.296 0.168 0.098 0.081
46.344 20.811 12.898 8.695 4.117 3.290 1.867 1.084 0.895
46.344 67.155 80.053 88.748 92.865 96.155 98.021 99.105 100.00
VFCW 1 2 3 4 5 6 7 8 9
5.103 1.606 1.092 0.518 0.327 0.151 0.107 0.071 0.024
56.704 17.850 12.134 5.761 3.628 1.682 1.184 0.792 0.265
56.704 74.554 86.688 92.449 96.077 97.759 98.943 99.735 100.00
Rotation sums of squared loadings Total
Variance%
Cumulative%
46.344 67.155 80.053
3.172 2.630 1.403
35.240 29.226 15.587
35.240 64.466 80.053
56.704 74.554 86.688
4.109 2.095 1.598
45.653 23.282 17.753
45.653 68.935 86.688
Sub-group-I
Sub-group-II
Component matrix
Rotated component matrix
Component matrix
Rotated component matrix
Element
PC 1
PC 2
PC 3
PC 1
PC 2
PC 3
Element
PC 1
PC 2
PC3
PC 1
PC 2
PC 3
BOD5 COD SO2−4 NO3 -N pH TSS EC NH4-N PO4−3
0.927 0.921 0.887 0.732 0.285 0.605 0.388 0.496 0.545
0.097 −0.049 −0.183 0.533 0.861 −0.620 −0.470 −0.291 0.338
−0.010 0.054 −0.130 −0.235 −0.044 0.211 0.326 −0.710 0.655
0.912 0.782 0.762 0.658 0.599 0.037 −0.033 0.554 0.218
−0.004 −0.423 0.502 0.623 0.373 0.877 0.688 0.618 0.215
0.206 −0.182 0.191 0.181 −0.585 0.155 −0.050 0.385 0.861
BOD COD SO2−4 NO3−N pH TSS EC NH4−N PO4−3
0.872 0.767 0.889 0.882 0.495 0.510 −0.218 0.938 0.869
−0.321 −0.423 0.048 −0.046 0.229 0.730 0.808 0.051 0.282
0.211 0.392 −0.332 −0.280 0.766 0.068 0.126 −0.067 −0.306
0.618 0.428 0.937 0.896 0.080 0.488 −0.163 0.851 0.932
0.654 0.769 0.152 0.205 0.877 0.225 −0.097 0.403 0.136
−0.314 −0.382 −0.036 −0.119 0.329 0.714 0.824 0.007 0.201
Extraction method: PCA. Rotation method: Varimax with Kaiser Normalization.
dataset in HFCW and 86.68% variance of the normalized dataset in VFCW. In HFCW, the first component (PCI) contributes 35.2% variance and reveals strong associations between the BOD, COD, NO3−, TSS and PO43− for both component matrix and rotated component matrix. The second component (PCII) supported variance of 29.2% of total variance and offers positive correlation among pH and NO3− for both matrixes. In case of third component (PCIII) contributes 15.5% variance and main statistically correlated variables was PO43− in both component and rotated component matrix. In case of VFCWs, three PCs were extracted which offers variance of 45.6% (PCI), 23.2% (PCII) and 17.7% (PCIII). The positive correlation was presented between BOD, COD, TSS, NH4N, PO43−, SO42− and NO3-N. The high variance in VFCWs when compared to HFCWs depicted a more efficient wastewater treatment facility at VFCWs.
variables into new, uncorrelated variable called as principal components (linear combination of studied variables) (Helena et al., 2000). Eigen values greater than 1 were taken as a criterion for extraction of the principal components required to explain the sources of variance in the data. These principal components (PCs) are orthogonal variable (Eigen value > 1) obtained by multiplying original correlated variables with Eigen vector, which is a list of coefficients (loading or weightings) (Ligang et al., 2011). In the present normalized dataset, the PCA was performed on correlation matrix of different variables, followed by Varimax rotation. The results of PCA analysis with nine variables, as studied for the HFCW and VFCW are shown in Table 4. Based on the Eigen values and varimax rotation three factors, explaining the most of the variability present in the dataset, were identified. These three PCs explained 80.05% variance of the normalized 125
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4. Conclusion
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