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Influence of clogging and resting processes on flow patterns in vertical flow constructed wetlands
STOTEN-24337; No of Pages 9 Science of the Total Environment xxx (2017) xxx–xxx
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Influence of clogging and resting processes on flow patterns in vertical flow constructed wetlands Guofen Hua a,⁎, Jun Kong b, Yuyu Ji a, Man Li a a b
College of Water Conservancy and Hydroelectric Power, Hohai University, Nanjing 210098, PR China College of Harbour, Coastal and Offshore Engineering, Hohai University, Nanjing 210098, PR China
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• Quantitative description of flow patterns during clogging and resting processes. • A resting operation alleviated dead zones and improved hydraulic efficiency. • Models elaborated and validated tracer experiments.
a r t i c l e
i n f o
Article history: Received 27 August 2017 Received in revised form 12 October 2017 Accepted 12 October 2017 Available online xxxx Editor: Jay Gan Keywords: Vertical flow constructed wetland Clogging Flow pattern Tracer test Resting operation
a b s t r a c t Vertical flow constructed wetlands are widely used for removing pollutants from wastewater. Substrate clogging is an operational challenge of constructed wetlands, which can result in impeded water flow and finally a significant decline in the ability of the system to treat the wastewater. The entire clogging process in a vertical flow constructed wetland (VFCW) was quantitatively analyzed by measurements of hydraulic conductivity. Tracer tests and model simulations were carried out to investigate internal flow patterns during the clogging and resting processes. This analysis revealed that hydraulic conductivity gradually decreased with operation time. Further, the distribution time of the flow field was different under different degrees of clogging. Non-uniformity in water flow was primarily observed in the first 400 min after adding the tracer (NaCl) in the early clogging stage, as opposed to the last 400 min in the late clogging stage. Variation in water flow divergence was closely correlated with piston flow; the reaction efficiency was highest in the early stages of clogging. In the later stages, stronger flow mixing was observed. Resting operations can reduce the dispersion of internal flow and improve reaction efficiency. After resting for approximately 15 days, tracer concentration fluctuations decreased and internal flow back-mixing was alleviated. A simulation further described the internal flow pattern and elaborated and validated the tracer experiment. The outcomes of this study will assist in understanding how internal flow behavior varies in response to the clogging process and reveal details of the internal clogging mechanism in VFCWs. © 2017 Elsevier B.V. All rights reserved.
⁎ Corresponding author. E-mail addresses: [email protected], [email protected] (G. Hua).
https://doi.org/10.1016/j.scitotenv.2017.10.113 0048-9697/© 2017 Elsevier B.V. All rights reserved.
Please cite this article as: Hua, G., et al., Influence of clogging and resting processes on flow patterns in vertical flow constructed wetlands, Sci Total Environ (2017), https://doi.org/10.1016/j.scitotenv.2017.10.113
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1. Introduction Vertical flow constructed wetlands (VFCWs) are widely used around the world for removing pollutants from wastewater (Vymazal, 2011). These wetlands consist of an emergent macrophyte community growing in a porous medium (usually gravel or sand), where wastewater is subjected to the appropriate conditions for purification via several physical, chemical, and biological processes (Nivala et al., 2012). However, this purification protocol results in a gradual clogging of the substrate, which causes deterioration in hydraulic conductivity (K), overflow, and finally, poor treatment performance. This is because the rate of fluid flow through a porous medium is directly proportional to the hydraulic conductivity of the medium, and this directly influences the mass transfer rates of natural fluids and contaminant plumes in the substrate (Knowles et al., 2010; Samsó and García, 2013). The hydraulic performance of a constructed wetland directly affects pollutant removal performance. In other words, internal hydraulic characteristics are influenced by the degree of clogging. Currently, a method commonly used to calculate hydraulic conductivity is the constant head test or falling head test, followed by linear interpolation of the hydraulic conductivity values to obtain transverse cross-sections of hydraulic conductivity (Knowles et al., 2011; Ranieri et al., 2013). However, it is challenging to determine the spatial distribution of water flow in a clogged filter medium due to its non-cohesive nature, which considerably limits the number of samples. Another basic tool for investigating hydraulics in constructed wetlands is through hydraulic tracer experiments, where a selected inert tracer is used as an indicator of water flow and movement through the wetland (Musner et al., 2014). Several analyses of subsurface flow constructed wetlands (SFCWs) with conservative tracer tests have been carried out to improve understanding of these complex systems (Muñoz et al., 2006; Chang et al., 2012; Qi et al., 2013). Typically, the main reason for the application of conservative tracer tests is to calculate the hydraulic retention time (HRT) and the dispersion coefficient in hydraulic systems. Instead of using the HRT, the actual flow conditions in a sand filter, e.g., a wetland, can be better defined based on the residence time distribution (RTD) and mean retention time obtained by experimental tracer tests. This analysis is based on determining the probability distribution of material age at the outlet of a reactor. By analyzing the shape of the distribution curve, non-ideally mixed volumes, such as dead zones and short-circuits, may be identified (Ranieri et al., 2013; Ranieri et al., 2015). The advantage of this method is that it does not require prior knowledge of fluid properties. However, tracer experiment methodologies can be relatively expensive and are often impossible to perform in field situations; therefore, mathematical models are increasingly sought after to study the hydraulic characteristics of subsurface environments, including VFCWs. A variety of analytical and numerical models are available to predict water and solute transport processes in porous media. However, analyses of tracer data using different computational methods can result in different interpretations of wetland hydraulics (Bodin et al., 2013). Computational fluid dynamics (CFD) is a powerful approach that provides detailed spatial distribution of flow fields. This approach can also provide 2D or 3D visualization of a system. Recent advancements in CFD, for example, COMSOL multiphysics software, have significantly enhanced the understanding of flow fields in wastewater treatment plants. A robust computational fluid dynamics (CFD) model accounting for both spatial and temporal dynamics of a subsurface vertical flow treatment wetland system was developed by combining fluid transport, solute transport, biokinetics, biofilm development, and biofilm detachment/sloughing using COMSOL Multiphysics (Rajabzadeh et al., 2015). Samsó et al. (2016) also developed a mathematical model using COMSOL Multiphysics and MATLAB to simulate bioclogging effects including variably saturated subsurface flow and overland flow in horizontal flow constructed wetlands (HFCWs).
An appropriate hydraulic design may not only improve pollutant removal efficiency but also reduce costs and achieve optimal treatment and associated engineering benefits. Appropriate engineering design requires a detailed understanding of the flow pattern within a system. In this study, COMSOL Multiphysics™ (version 5.0) was used to simulate water flow during the clogging and resting processes. The results of this study will assist in understanding how internal flow behavior varies in response to clogging processes and reveal the mechanisms behind internal clogging. 2. Materials and methods 2.1. Experimental wetland setup and operation A laboratory-scale sand wetland column composed of Perspex (acrylic glass), 70 cm in height and 30 cm in diameter, was used for constructing a VFCW. The column was filled with fine sand (average particle diameter, dp, of 0.15 mm) to a depth of 50 cm. The bottom of the column was filled with a 5-cm layer of large pebbles. Wastewater flowed onto the top of the columns and was discharged from the bottom through tubes inserted through an outlet. Piezometers were set at depths of 5, 10, 15, 25, 35 and 50 cm to measure each layer's head loss to calculate hydraulic conductivity. Artificial wastewater was prepared by adding starch to tap water to create a source of slowly biodegradable suspended organic solids. The biochemical oxygen demand (BOD) and suspended solids (SS) concentrations were both adjusted to 100 mg/L. Subsequently, (NH4)2SO4, CO(NH2)2 and K2HPO4 were added to the artificial wastewater to provide TN and TP concentrations of approximately 10 mg/L and 2 mg/L, respectively. The column was continuously operated at a constant hydraulic loading rate of 1.0 m/d. A resting operation was not commenced until the wetland column surface displayed evidence of water retention. When water pooled on the surface of the wetland column, we stopped operating the wetland column and allowed it to rest for 15 days. In other words, during the 15 days of resting, the wastewater inside the column was discharged and then nothing was fed to the wetland column. Thus, this process was called the resting period. It is difficult to determine porosity directly since it depends on the volume of water inside the pores, which is not easily measurable. In this study, the porosity was measured based on the drainage volumes of each layer along the length of the column under the saturated sand filter condition. The measured drainage volumes were divided by the volume of each layer of the filter without being filled with sands, and the porosity of each layer was then obtained. 2.2. Tracer test A solution of NaCl (1 g/L) was used as a tracer, because gravel has a low adsorption capacity for NaCl (Langergraber, 2003; Walker, 1998). Furthermore, when the NaCl concentration is between 25 and 12,000 mg/L, it has a linear relationship with conductivity (Langergraber, 2003). The pulse-response method was adopted for the tracer experiment. Briefly, the column was maintained in a saturated state, under the original inflow and outflow rates. Next, 100 mL NaCl solution was rapidly pulsed into the wetland column, at which time the hydraulic conductivity of the outflow was monitored with a YSI probe (Proplus portable multi-parameter water quality meter, USA) every 10 min until the conductivity value returned to its baseline. The recovery rate of the added tracer mass was as high as 95 ± 3%. Tracer tests were conducted to evaluate the flow pattern during the clogging process on the 20th, 80th and 95th days of operation of the experiment. The reasons why we chose those three points were as follows: After 20 days' operation, a more rapid decrease in the hydraulic conductivity occurred when the second tracer experiment was carried out. After 80 days of run time, the third tracer experiment was carried out. During
Please cite this article as: Hua, G., et al., Influence of clogging and resting processes on flow patterns in vertical flow constructed wetlands, Sci Total Environ (2017), https://doi.org/10.1016/j.scitotenv.2017.10.113
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this period, the wetland became covered with an overlying layer of clay and exhibited further decreases in the hydraulic conductivity. Studies have shown that a resting period of 1 to 3 weeks is sufficient to ensure recovery from clogging (Hua et al., 2014a). After 15 days of rest (i.e., 95th day of experiment), a sharp decrease in the hydraulic conductivity was observed. Therefore, in our study, we terminated the resting period after 15 days and then initiated the fourth tracer experiment. The hydraulic conductivity was determined by the Professional Standards Compilation Group of People's Republic of China, Standard for Soil Test Method (GB/T50123-1999). The tracer calculations were based on the methods of Muñoz et al. (2006). Hydraulic retention time (HRT) represents the amount of time that it takes for surface water to flow through the wetland system. The theoretical hydraulic retention time (tm) was calculated as follows: tm ¼
Vξ Q
ð1Þ
where tm is the retention time (h), V is the volume of the wetland system (m3), Q is the volumetric flow rate of water through the wetland system (m3/day), and ξ is the porosity of the medium (−). The mean hydraulic retention time ( tn) was based on the measured wetland hydraulic retention time and was defined as the center of gravity position within the residence time distribution (RTD) curve. This was calculated as follows: P∞ tcðt ÞΔt t n ¼ P0∞ 0 cðt ÞΔt
flow. Accordingly, the Darcy's law (dl) module in COMSOL was used to simulate the internal flow field distribution for the Reynolds number in the range from 1 to 6 in this study. For simplicity, the VFCW was modeled in a two-dimensional form. As in the laboratory experiment, the modeled VFCW was divided into six layers (Fig. 1). Coarse sand was modeled as the substrate. An unstructured triangular mesh, generated by the software, was utilized here. The import function used velocity boundary conditions, while the export function used atmospheric pressure boundary conditions. The experimentally determined porosity values were used and each boundary layer was walled off. The flow field distribution and solute transport were calculated with static and transient models, respectively. Model parameters are shown in Table 1. Tracer experiment modeling focuses on transient solute migration, i.e., the solute distribution in the flow field changes with time. Thus, Darcy's law (dl) and porous media (TDS) modules were coupled to simulate tracer solute migration. A total of 3892 elements were modeled as described in Fig. 1. A sensitivity evaluation was completed for the meshing selection. We tried several types of meshing in COMSOL such as extreme refinement of the grid (15,300 elements), special refinement of the grid (3892 elements) and refinement of the grid (1316 elements). We found that the results produced with extreme refinement of the grid and special refinement of the grid were almost the same but a little better than those produced with refinement of the grid. Thus, to save calculation time, we finally chose special refinement of the grid (3892 elements) for further
ð2Þ
where C(t) is the tracer concentration (mg/L) at time (t), t is the time of sampling (day), and Δt the change in time between samples (day). Volumetric efficiency (e) was calculated as follows (Thackston et al., 1987): e ¼ t n =t m
ð3Þ
where e is the effective volume ratio, tn is the mean hydraulic retention time, and tm is the theoretical hydraulic retention time. Kadlec and Wallace's (2009) method for calculating the number of continuously stirred tank reactors (CSTRs) was used in the analysis of wetland hydraulic characteristics: N¼
t 2n σ2
ð4Þ
where N is the number of CSTRs in series, and σ2 is the variance that can be determined directly from the residence time distribution curve. If n = 1, it denotes completely mixed flow, while n → ∞ denotes plug flow. The hydraulic efficiency (λ) was calculated using an equation derived by Persson et al. (1999), as well as on the basis of effective volume (Alcocer et al., 2012): λ ¼ t p =t n
ð5Þ
1 λ ¼ e 1− N
ð6Þ
2.3. Geometric description and meshing Modeling of VFCW flow was carried out using COMSOL Multiphysics ™ (version 5.0) bidimensional software that uses the finite element method. Water flow in a VFCW substrate can be regarded as flow in a porous medium. Kadlec (1990) noted that in a constructed wetland, the hydraulic gradient and water head are not sufficient to produce turbulent
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Fig. 1. Geometric description and meshing.
Please cite this article as: Hua, G., et al., Influence of clogging and resting processes on flow patterns in vertical flow constructed wetlands, Sci Total Environ (2017), https://doi.org/10.1016/j.scitotenv.2017.10.113
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Table 1 All needed parameters used in the COMSOL model. a. Physical properties used in the COMSOL model. Parameter
Value
Initial conditions
Model diameter (mm) Thickness of the sand layer (mm) Outlet size (mm) Inlet velocity (cm/s)
Sand layer
Export pressure (Kpa) Sand average diameter (dp) (mm)
300 500 4 0.02 (0 day) 0.018 (20th day) 0.0005 (80th day) 0.015 (95th day) 101.3 0.15
Geometric parameters
b. Porosities of different layers during the clogging and resting processes (unit: %).
0–5 cm 5–10 cm 10–15 cm 15–25 cm 25–35 cm 35–50 cm
0
20th day
80th day
95th day
34 33 32 35 35 35
30 23 24 30 30 31
6 13 13 24 25 26
17 18 18 27 27 27
calculations in this study. Each model simulation took approximately 10 min in real time on an 8.00 GB Intel(R) Core(TM) i7-2600 CPU@ 3.40 GHz Computer.
3. Results and discussion 3.1. Hydraulic performance of the wetland column during the clogging and resting periods As shown in Fig. 2, the 20th and 80th days were two evident inflection points for hydraulic conductivity. The 80th day represented the demarcation between the operation and resting processes, namely, when the internal hydraulic conductivity reached its lowest value. At this point a surface clay layer formed along with surface water accumulation. Resting can improve the hydraulic conductivity of the upper layer considerably more than in the lower layer because the rate of oxygen diffusion into the pores created by evaporation was sufficient to oxidize and disperse the flocculant biofilm, which was essential to alleviate bioclogging (Hua et al., 2017). The hydraulic performance of a VFCW system is an important indicator of the degree of clogging. A well-run system is characterized by good hydraulic performance, smooth flow, and the absence of stagnant water on the surface.
In addition, the hydraulic conductivities in the early stages of operation (before the 20th day) were relatively uniform with depth. After 20 days of operation, the mean hydraulic conductivity not only decreased to below 0.03 cm/s but also displayed heterogeneity with depth in the 0 to 15 cm substrate layer. The mean value and variance of the hydraulic conductivity during different periods are provided in Table 2. It was evident that the non-uniformity in hydraulic conductivity gradually increased with time during the first 80 days of operation. Some recovery of hydraulic conductivity was observed during the resting period; this was accompanied by an increase in uniformity of values as described in Table 2. Furthermore, the wetland column displayed a hierarchical structure on the vertical direction. The non-uniformity in hydraulic conductivity in the vertical direction and the hydraulic efficiency had an approximately linear relationship (R2 = 0.8244) (Fig. 3). This may be explained by the existence of hydraulic shortcuts. 3.2. Tracer test results As shown in Fig. 4, the breakthrough curve of the tracer experiment displayed a fluctuation around the peak immediately before operation. At 600 min after this peak, a sharp decrease occurred. On the 20th day of operation, the curve began to display greater fluctuations around the peak, with a bimodal phenomenon at 210 min. Compared with the first tracer experiment, the recovery rate in the second experiment was 5% lower. This suggested that the dead zone at the bottom of the wetland had expanded in area. On the 80th day of operation, the solute peak (0.292 mg/L) was extended over a period of 180 min; a second small peak occurred at 570 min. The peak concentration also decreased, which implied that the area of the dead zone increased with clogging and that part of the tracer solute was adsorbed in the surface clay layer. Further analysis revealed that there was a small fluctuation in solute concentration rather than a constant decline at the 600 min mark. This suggests that part of the tracer matter within the dead zone returned to the mainstream channels after a certain period of time, and eventually reached the outlet. The results of the tracer testing in the current study confirm the internal water mixing and short-circuiting obtained by previous studies (Boog et al., 2014; Pálfy et al., 2017). After resting, the clay blanket layer became thinner due to biomass degradation, which increased hydraulic conductivity. By the 95th day, the curve's fluctuations had decreased considerably compared with the three previous tracer experiments. The peak was observed at 130 min (approximately 45–60 min earlier than in the preceding experiments), corresponding to a NaCl concentration of 0.315 mg/L. No twin peaks were observed on the generally smooth breakthrough curve. This suggests that resting can, to a certain extent, alleviate internal flow backmixing and thus improve hydraulic performance. Tracer experiments are an effective means of studying the hydraulic characteristics of constructed wetlands. The breakthrough curves in the tracer experiments and model (Eqs. 1 through 6) were analyzed to determine the average residence time, peak time, effective volume ratio, flow dispersion, hydraulic efficiency and reaction unit number, to quantitatively evaluate hydraulic characteristics during the clogging process and resting period. As shown in Table 3, the hydraulic characteristics of the system were highly variable. All the values for the effective volume were less than 1 but increased with operation time. This suggested that short circuiting and dead zones existed through the operation, with the largest such Table 2 Comparison of the means and variances of hydraulic conductivity with depth during different periods.
Fig. 2. Hydraulic conductivity performances during the clogging and resting processes.
Mean value Variance
0 day
20th day
80th day
95th day
0.03 0.000
0.02 0.001
0.01 0.003
0.02 0.002
Please cite this article as: Hua, G., et al., Influence of clogging and resting processes on flow patterns in vertical flow constructed wetlands, Sci Total Environ (2017), https://doi.org/10.1016/j.scitotenv.2017.10.113
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Table 3 Hydraulic parameters of flow patterns based on tracer experiments during different operation stages.
0 day 20th day 80th day 95th day a b
Fig. 3. The relationship between hydraulic efficiency and vertical heterogeneity.
zone at the point of clogging (Chazarenc et al., 2003). This was because the saturated condition had a very positive effect on the degree of mixing in vertical flow constructed wetlands (Boog et al., 2014). The mean retention time and hydraulic efficiency were positively correlated with operation time. These results were different from those reported by other studies (Muñoz et al., 2006; Schmid et al., 2004) because those studies focused on horizontal wetlands but were comparable to the values of hydraulic efficiency reported by Boog et al. (2014) in a saturated vertical flow constructed wetland. Vertical wetlands require a long operation period for wastewater filtration. Although this translates to higher contaminant removal rates, the total amount removed is lower, compared to horizontal wetlands (Hua et al., 2010). This was confirmed by the number of CSTRs observed 2.3 at the time of clogging, representing a trough during the operating period. A lower CSTR number indicates a less efficient system. After resting, the mean retention time, hydraulic efficiency, effective volumes and number of CSTRs were comparable to those recorded on the 20th day of the operation day, suggesting that resting alleviated clogging to a certain extent. Based on the model provided by Alcocer et al. (2012), the number of consecutive mixing reactors during the clogging and resting processes remained between 1 and ∞. This implied that after resting, the internal water flow was somewhere between completely mixed flow and plug flow. The dispersion coefficient values
Fig. 4. Tracer tests during different operation stages in vertical flow constructed wetlands.
tm(h)
tn(h)
tp(h)
e(%)
λ1a
λ2b
N
4.40 4.40 4.40 4.40
2.26 2.68 2.85 2.77
2.33 2.50 2.83 2.27
0.52 0.60 0.63 0.58
0.51 0.61 0.64 0.62
0.53 0.57 0.64 0.52
5.2 7.8 2.3 6.1
Results calculated by Eq. 5. Results calculated by Eq. 6.
increased over the operation time. This is likely due to the increase in biomass volume and the decrease in porosity thereby helping to promote hydrological mixing. The other reason may be that the addition of plants to the mesocosm appears to promote an increase in the dispersion coefficient with operation time (Weber and Legge, 2011). This can be explained, as the plants developed a complicated physical network of roots in the wetland media that would effectively increase the amount of hydrological mixing (dispersion coefficient) in the mesocosms. The values of dispersion coefficients obtained in this study lie within the range of reported values from other studies of VFCWs (Garcia et al., 2004; Weber and Legge, 2011). The plants played different roles related to time and space. From time's perspective, the plant roots played opposite roles between the beginning clogging stage and the later clogging stage. The plant roots hindered the water flow at the beginning of clogging when the original effective porosity was relatively higher; while the clogging was becoming more serious, particularly in the root growth layer where the effective porosity gradually decreased, the growing plant roots might open new pore spaces in the substrate, which would increase the hydraulic conductivity and even result in preferential flow and short circuits. From space's perspective, the plant roots were of no real importance from the view of the entire column on the effective porosity (Hua et al., 2014b; Tang et al., 2017). 3.3. Modeling results of hydraulic characteristics 3.3.1. Flow velocity inside the substrate The flow velocity profile along the x- and y-axes inside the wetland column during different operation times and resting periods are described in Fig. 5. The x-axis and y-axis velocity transects are indicated at time points of 0, 20, 80 and 95 days of operation, demonstrating the ability of this model to calculate local (microscopic) velocities in each mesh element. Given the spatial hierarchy in the column, at a depth of 4 cm, the horizontal flow velocity exhibited a high degree of variation at the outlet of the substrate. The maximum flow velocity (0.46 mm/s) was at x = 27.0 cm, while the flow velocities in the middle and lower layers were low. At depths of 28 cm and 48 cm depth, variation in horizontal flow was similar but different from that at a depth of 4 cm. At x = 15 cm, the flow velocities were high but decreased towards the sides. At x = 3 and 15 cm, longitudinal velocity increased with depth. At x = 27 cm, the longitudinal velocities first increased to a peak and then decreased and stabilized. It was evident that the flow velocity at the bottom (opposite to the outlet) gradually decreased, while a rapid increase followed by a sharp decrease was observed near the outlet. Irrespective of the location of the main flow front, the flow velocities decreased with an increase in the degree of clogging. This could be reversed by a resting operation. As expected, a consistently high flow velocity was observed near the outlet. On the other hand, the lower water velocities in the area opposite the outlet region created a dead zone in that area. This zone was characterized by low flow shear stress and accumulation of nutrients favoring microbial growth. This resulted in the proliferation of bacteria and the production of extracellular polymers. Thus, the porosity of this region can be expected to decrease with increasing operation time. The central part of the column was indicative of a typical VFCW mesocosm.
Please cite this article as: Hua, G., et al., Influence of clogging and resting processes on flow patterns in vertical flow constructed wetlands, Sci Total Environ (2017), https://doi.org/10.1016/j.scitotenv.2017.10.113
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Fig. 5. Flow velocities inside the wetland column during different operation times and resting periods.
Please cite this article as: Hua, G., et al., Influence of clogging and resting processes on flow patterns in vertical flow constructed wetlands, Sci Total Environ (2017), https://doi.org/10.1016/j.scitotenv.2017.10.113
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It is difficult to measure the flow velocity inside the wetland column because it is somewhat of a “black box”. So by comparing the simulated results (flow velocity values) with the experimental results (hydraulic conductivities which can be measured) of the different layers that are close to the wetland column side along its depth, the simulated values
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are found to fit the measured data quite well, which indicates that the model works reasonably well. In general, compared with those of Samsó et al. (2016) and Rajabzadeh et al. (2015), our results agreed much more closely with Rajabzadeh's work, which stated that there were three regions in the
Fig. 6. Modeling simulation of tracer transport.
Please cite this article as: Hua, G., et al., Influence of clogging and resting processes on flow patterns in vertical flow constructed wetlands, Sci Total Environ (2017), https://doi.org/10.1016/j.scitotenv.2017.10.113
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mesocosm in terms of water velocity, rather than a moving dead zone, which was described in Samso's study. 3.3.2. Simulation of tracer experiment The simulations of the tracer experiment are described in Fig. 6. At the beginning of the operation and after resting, as indicated in Fig. 6(a), (b) and (c), at time (t) = 20 min, solute migrated to the region y = 40 cm. At this time, solute concentration was uniform along the vertical axis, indicating that it was primarily influenced by water flow and not diffusion. At t = 100 min to 260 min, solute was still transported primarily with water flow downward along the wetland column. At this stage, the solute concentration began to display non-uniformity. Subsequently, a portion of the solute quickly reached the outlet, forming an early peak. Another part of the solute driven by the main water flow front diffused to the bottom opposite to the outlet, where solute was exchanged between the main flow zones. This explained the bimodal characteristic of the experimental breakthrough curve. At t = 337 min to 500 min, solute accumulated and was left stuck at the bottom of the wetland column for a period of time. Eventually, it was transported out, as shown by the tail of the tracer curve. Compared with other operation times, with the occurrence of clogging (Fig. 6c), solute migrated to the region y = 25 cm, with uniform solute distribution with depth. However, the migration of the solution was slow and delayed the exit of the solute, which was consistent with the delayed peak in the tracer curve (Fig. 4). An accumulation of solute was observed at the bottom of the wetland column, which was supported by the longer tail and lower recovery in the model. Both the models and physical tracer experiments showed that the time that the solute spent at the bottom of the column was triple the time it took for the solute to migrate from the top of the column to the bottom. This was the main reason for the presence of tailing in the tracer concentration curve. Furthermore, the model proved that impeded flow occurred in the upper part of the column, whereas diffusion occurred in the lower region (i.e., the diffusion of water gradually increased with depth). Based on analysis of the physical tracer experiment (Fig. 6), it can also be concluded that the concentration of solute was highest in the zone of accumulation where the hydrodynamic shear stress was small. This encouraged rapid growth of microorganisms. Thus, non-uniformity in hydraulic conductivity was observed not only along the vertical axis but also along the horizontal axis on the bottom of the wetland column. Overall, comparing the simulated results and the experimental results in the tracer tests, the simulated dynamic process can be considered to be in good agreement with the experimental process, which further indicates that the model fits well with the actual data. Compared with the work of Langergraber (2003), our tracer results were not consistent with it which found that the measured hydraulic retention time was longer than the theoretical residence time. The reasons for this might be as follows: (1) the wetlands in Langergraber's study were loaded intermittently with wastewater while ours were operated with continuous flow. Thus, the model developed by Langergraber for unsaturated flow used the Richards Equation. In our study, we used Darcy's law in COMSOL because the water flow was saturated laminar flow. (2) In Langergraber's work, immobile pore water and mobile pore water were separately considered while in our study they were not distinguished from one another. It is not easy to find a specific study which is the same with our operation pattern and wetland type (i.e. VFCWs) to compare with since the actual wastewater treatment demand is ever-changing. Thus, although there were a numbers of similar studies in the literature presented above, to our best knowledge, this is the first work to present a variation in water flow pattern combined using of tracer experiments and COMSOL models focusing on the whole clogging and resting process in vertical flow constructed wetland. Furthermore, it is also the first time to use numerical simulation to further interpret the whole process of the trace experiments.
3.3.3. Limitations and future research There was no sensitivity analysis completed in general. The reasons for this were as follows: (1) In this study, we focused on evaluating the influences of the clogging and resting processes on flow patterns; and (2) The parameters used in the simulation process of water flow, such as model diameter, the thickness of sand layer, etc., described in Table 1, were almost all fixed in the actual constructed wetlands except for the parameter of inlet velocity when we practically ran in an existing constructed wetland. Thus, we changed the inlet velocity to investigate flow pattern variation, which was equivalent to a sensitivity analysis on flow rate. However, each factor affecting the treatment function of constructed wetland technology will be studied individually in the near future, which is still necessary before it is to be put into practice. Furthermore, water flow mechanism of short-term studies in a labscale is similar to that of long-term studies in full-scale. Whatever the scale is, the essence of clogging is the available void space being filled by accumulated SS and biofilms, rather than the SS/COD loading that directly determines the clogging occurrence. However, the higher the solids loading, the shorter the clogging time would be. In contrast, when lower SS/COD loading is applied in practice, the clogging time will be longer. Thus, the conclusions obtained in this study still can give insights to full-scale wetlands with a long term operation. Obviously, as an ideal solution, further studies should consider microbiological and plant-related processes. Moreover, more efforts should be made to calibration and validation and other data sets are thus needed to improve the model predictions before the model is used for design purpose. 4. Conclusions In this study, we analyzed the inner flow patterns in VFCWs during the clogging and resting processes with tracer experiments and numerical models. The clogging process was characterized by impeded circulation and a greater proportion of so-called dead zones. Furthermore, the tracer concentration displayed greater fluctuations during the clogging process than during the resting process. The active region expanded with operation time before clogging commenced. The latter was preceded by a peak in the effective volume and hydraulic efficiency. Nonuniformity in hydraulic conductivity with depth was linearly correlated to the hydraulic efficiency. During the early stages, water flow was comparable to piston flow when the reaction efficiency was at its peak. This was followed by an increase in the degree of mixing as clogging proceeded. This was partially reversed by a resting operation, which improved reaction efficiency. Modeling revealed that the tail phenomenon of the breakthrough curve was primarily caused by solute retention at the bottom of the wetland column. The retention time of the solute at the bottom was triple that of the transport time from the top to the bottom of the column. Acknowledgements The authors acknowledge financial support for this study from the Natural Science Foundation of China (51509070), the Fundamental Research Funds for the Central Universities (2015B15214) and the SSSTC project (EG 02-032015). This study was also supported by the China Scholarship Council (No. 201504280002) for 1 year study at EPFL, Switzerland. References Alcocer, D.J.R., Vallejos, G.G., Champagne, P., 2012. Assessment of the plug flow and dead volume ratios in a sub-surface horizontal-flow packed-bed reactor as a representative model of a sub-surface horizontal constructed wetland. Ecol. Eng. 40, 18–26. Bodin, H., Persson, J., Englund, J.E., Milberg, P., 2013. Influence of residence time analyses on estimates of wetland hydraulics and pollutant removal. J. Hydrol. 501, 1–12. Boog, J., Nivala, J., Aubron, T., Wallace, S., Afferden, M., Muller, R.A., 2014. Hydraulic characterization and optimization of total nitrogen removal in an aerated vertical subsurface flow treatment wetland. Bioresour. Technol. 161, 166–174.
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G. Hua et al. / Science of the Total Environment xxx (2017) xxx–xxx Chang, N.B., Xuan, Z.M., Wanielista, M.P., 2012. A tracer study for assessing the interactions between hydraulic retention time and transport processes in a wetland system for nutrient removal. Bioprocess Biosyst. Eng. 35, 399–406. Chazarenc, F., Merlin, G., Gonthier, Y., 2003. Hydrodynamics of horizontal subsurface flow constructed wetlands. Ecol. Eng. 21 (2–3), 165–173. Garcia, J., Chiva, J., Aguirre, P., Alvarez, E., Sierra, J.P., Mujeriego, R., 2004. Hydraulic behaviour of horizontal subsurface flow constructed wetlands with different aspect ratio and granular medium size. Ecol. Eng. 23 (3), 177–187. Hua, G.F., Zhu, W., Zhang, Y.H., 2010. A conceptual approach based on suspended solids to estimate clogging time in constructed wetlands. J. Environ. Sci. Health A Tox. Hazard. Subst. Environ. Eng. 45 (12), 1519–1925. Hua, G.F., Zeng, Y.T., Zhao, Z.W., Cheng, K.B., Chen, G., 2014a. Applying a resting operation to alleviate bioclogging in vertical flow constructed wetlands: an experimental lab evaluation. J. Environ. Manag. 136 (8), 47–52. Hua, G.F., Zhao, Z.W., Kong, J., Guo, R., Zeng, Y.T., Zhao, L.F., Zhu, Q.D., 2014b. Effects of plant roots on the hydraulic performance during the clogging process in mesocosm vertical flow constructed wetlands. Environ. Sci. Pollut. Res. 21, 13017–13026. Hua, G.F., Chen, Q.W., Kong, J., Li, M., 2017. Evapotranspiration versus oxygen intrusion: which is the main force in alleviating bioclogging of vertical-flow constructed wetlands during a resting operation? Environ. Sci. Pollut. Res. 24 (22), 18355–18362. Kadlec, R.H., 1990. Overland Flow in Wetlands: Vegetation Resistance. J. Hydraul. Eng. 116 (5), 691–706. Kadlec, R.H., Wallace, S.D., 2009. Treatment Wetlands. Second edition. Boca Raton, Florida, USA, pp. 965–999. Knowles, P.R., Griffin, P., Davies, P.A., 2010. Complementary methods to investigate the development of clogging within a horizontal sub-surface flow tertiary treatment wetland. Water Res. 44, 320–330. Knowles, P., Dotro, G., Nivala, J., García, J., 2011. Clogging in subsurface-flow treatment wetlands: occurrence and contributing factors. Ecol. Eng. 37 (2), 99–112. Langergraber, G., 2003. Simulation of subsurface flow constructed wetlands - results and further research needs. Water Sci. Technol. 48 (5), 157–166. Muñoz, P., Drizo, A., Hession, W.C., 2006. Flow patterns of dairy wastewater constructed wetlands in a cold climate. Water Res. 40, 3209–3218. Musner, T., Bottacin-Busolin, A., Zaramella, M., Marion, A., 2014. A contaminant transport model for wetlands accounting for distinct residence time bimodality. J. Hydrol. 515, 237–246. Nivala, J., Knowles, P., Dotro, G., García, J., Wallace, S., 2012. Clogging in subsurface-flow treatment wetlands: measurement, modeling and management. Water Res. 46 (6), 1625–1640.
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Contents lists available at ScienceDirect
Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv
Influence of clogging and resting processes on flow patterns in vertical flow constructed wetlands Guofen Hua a,⁎, Jun Kong b, Yuyu Ji a, Man Li a a b
College of Water Conservancy and Hydroelectric Power, Hohai University, Nanjing 210098, PR China College of Harbour, Coastal and Offshore Engineering, Hohai University, Nanjing 210098, PR China
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• Quantitative description of flow patterns during clogging and resting processes. • A resting operation alleviated dead zones and improved hydraulic efficiency. • Models elaborated and validated tracer experiments.
a r t i c l e
i n f o
Article history: Received 27 August 2017 Received in revised form 12 October 2017 Accepted 12 October 2017 Available online xxxx Editor: Jay Gan Keywords: Vertical flow constructed wetland Clogging Flow pattern Tracer test Resting operation
a b s t r a c t Vertical flow constructed wetlands are widely used for removing pollutants from wastewater. Substrate clogging is an operational challenge of constructed wetlands, which can result in impeded water flow and finally a significant decline in the ability of the system to treat the wastewater. The entire clogging process in a vertical flow constructed wetland (VFCW) was quantitatively analyzed by measurements of hydraulic conductivity. Tracer tests and model simulations were carried out to investigate internal flow patterns during the clogging and resting processes. This analysis revealed that hydraulic conductivity gradually decreased with operation time. Further, the distribution time of the flow field was different under different degrees of clogging. Non-uniformity in water flow was primarily observed in the first 400 min after adding the tracer (NaCl) in the early clogging stage, as opposed to the last 400 min in the late clogging stage. Variation in water flow divergence was closely correlated with piston flow; the reaction efficiency was highest in the early stages of clogging. In the later stages, stronger flow mixing was observed. Resting operations can reduce the dispersion of internal flow and improve reaction efficiency. After resting for approximately 15 days, tracer concentration fluctuations decreased and internal flow back-mixing was alleviated. A simulation further described the internal flow pattern and elaborated and validated the tracer experiment. The outcomes of this study will assist in understanding how internal flow behavior varies in response to the clogging process and reveal details of the internal clogging mechanism in VFCWs. © 2017 Elsevier B.V. All rights reserved.
⁎ Corresponding author. E-mail addresses: [email protected], [email protected] (G. Hua).
https://doi.org/10.1016/j.scitotenv.2017.10.113 0048-9697/© 2017 Elsevier B.V. All rights reserved.
Please cite this article as: Hua, G., et al., Influence of clogging and resting processes on flow patterns in vertical flow constructed wetlands, Sci Total Environ (2017), https://doi.org/10.1016/j.scitotenv.2017.10.113
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1. Introduction Vertical flow constructed wetlands (VFCWs) are widely used around the world for removing pollutants from wastewater (Vymazal, 2011). These wetlands consist of an emergent macrophyte community growing in a porous medium (usually gravel or sand), where wastewater is subjected to the appropriate conditions for purification via several physical, chemical, and biological processes (Nivala et al., 2012). However, this purification protocol results in a gradual clogging of the substrate, which causes deterioration in hydraulic conductivity (K), overflow, and finally, poor treatment performance. This is because the rate of fluid flow through a porous medium is directly proportional to the hydraulic conductivity of the medium, and this directly influences the mass transfer rates of natural fluids and contaminant plumes in the substrate (Knowles et al., 2010; Samsó and García, 2013). The hydraulic performance of a constructed wetland directly affects pollutant removal performance. In other words, internal hydraulic characteristics are influenced by the degree of clogging. Currently, a method commonly used to calculate hydraulic conductivity is the constant head test or falling head test, followed by linear interpolation of the hydraulic conductivity values to obtain transverse cross-sections of hydraulic conductivity (Knowles et al., 2011; Ranieri et al., 2013). However, it is challenging to determine the spatial distribution of water flow in a clogged filter medium due to its non-cohesive nature, which considerably limits the number of samples. Another basic tool for investigating hydraulics in constructed wetlands is through hydraulic tracer experiments, where a selected inert tracer is used as an indicator of water flow and movement through the wetland (Musner et al., 2014). Several analyses of subsurface flow constructed wetlands (SFCWs) with conservative tracer tests have been carried out to improve understanding of these complex systems (Muñoz et al., 2006; Chang et al., 2012; Qi et al., 2013). Typically, the main reason for the application of conservative tracer tests is to calculate the hydraulic retention time (HRT) and the dispersion coefficient in hydraulic systems. Instead of using the HRT, the actual flow conditions in a sand filter, e.g., a wetland, can be better defined based on the residence time distribution (RTD) and mean retention time obtained by experimental tracer tests. This analysis is based on determining the probability distribution of material age at the outlet of a reactor. By analyzing the shape of the distribution curve, non-ideally mixed volumes, such as dead zones and short-circuits, may be identified (Ranieri et al., 2013; Ranieri et al., 2015). The advantage of this method is that it does not require prior knowledge of fluid properties. However, tracer experiment methodologies can be relatively expensive and are often impossible to perform in field situations; therefore, mathematical models are increasingly sought after to study the hydraulic characteristics of subsurface environments, including VFCWs. A variety of analytical and numerical models are available to predict water and solute transport processes in porous media. However, analyses of tracer data using different computational methods can result in different interpretations of wetland hydraulics (Bodin et al., 2013). Computational fluid dynamics (CFD) is a powerful approach that provides detailed spatial distribution of flow fields. This approach can also provide 2D or 3D visualization of a system. Recent advancements in CFD, for example, COMSOL multiphysics software, have significantly enhanced the understanding of flow fields in wastewater treatment plants. A robust computational fluid dynamics (CFD) model accounting for both spatial and temporal dynamics of a subsurface vertical flow treatment wetland system was developed by combining fluid transport, solute transport, biokinetics, biofilm development, and biofilm detachment/sloughing using COMSOL Multiphysics (Rajabzadeh et al., 2015). Samsó et al. (2016) also developed a mathematical model using COMSOL Multiphysics and MATLAB to simulate bioclogging effects including variably saturated subsurface flow and overland flow in horizontal flow constructed wetlands (HFCWs).
An appropriate hydraulic design may not only improve pollutant removal efficiency but also reduce costs and achieve optimal treatment and associated engineering benefits. Appropriate engineering design requires a detailed understanding of the flow pattern within a system. In this study, COMSOL Multiphysics™ (version 5.0) was used to simulate water flow during the clogging and resting processes. The results of this study will assist in understanding how internal flow behavior varies in response to clogging processes and reveal the mechanisms behind internal clogging. 2. Materials and methods 2.1. Experimental wetland setup and operation A laboratory-scale sand wetland column composed of Perspex (acrylic glass), 70 cm in height and 30 cm in diameter, was used for constructing a VFCW. The column was filled with fine sand (average particle diameter, dp, of 0.15 mm) to a depth of 50 cm. The bottom of the column was filled with a 5-cm layer of large pebbles. Wastewater flowed onto the top of the columns and was discharged from the bottom through tubes inserted through an outlet. Piezometers were set at depths of 5, 10, 15, 25, 35 and 50 cm to measure each layer's head loss to calculate hydraulic conductivity. Artificial wastewater was prepared by adding starch to tap water to create a source of slowly biodegradable suspended organic solids. The biochemical oxygen demand (BOD) and suspended solids (SS) concentrations were both adjusted to 100 mg/L. Subsequently, (NH4)2SO4, CO(NH2)2 and K2HPO4 were added to the artificial wastewater to provide TN and TP concentrations of approximately 10 mg/L and 2 mg/L, respectively. The column was continuously operated at a constant hydraulic loading rate of 1.0 m/d. A resting operation was not commenced until the wetland column surface displayed evidence of water retention. When water pooled on the surface of the wetland column, we stopped operating the wetland column and allowed it to rest for 15 days. In other words, during the 15 days of resting, the wastewater inside the column was discharged and then nothing was fed to the wetland column. Thus, this process was called the resting period. It is difficult to determine porosity directly since it depends on the volume of water inside the pores, which is not easily measurable. In this study, the porosity was measured based on the drainage volumes of each layer along the length of the column under the saturated sand filter condition. The measured drainage volumes were divided by the volume of each layer of the filter without being filled with sands, and the porosity of each layer was then obtained. 2.2. Tracer test A solution of NaCl (1 g/L) was used as a tracer, because gravel has a low adsorption capacity for NaCl (Langergraber, 2003; Walker, 1998). Furthermore, when the NaCl concentration is between 25 and 12,000 mg/L, it has a linear relationship with conductivity (Langergraber, 2003). The pulse-response method was adopted for the tracer experiment. Briefly, the column was maintained in a saturated state, under the original inflow and outflow rates. Next, 100 mL NaCl solution was rapidly pulsed into the wetland column, at which time the hydraulic conductivity of the outflow was monitored with a YSI probe (Proplus portable multi-parameter water quality meter, USA) every 10 min until the conductivity value returned to its baseline. The recovery rate of the added tracer mass was as high as 95 ± 3%. Tracer tests were conducted to evaluate the flow pattern during the clogging process on the 20th, 80th and 95th days of operation of the experiment. The reasons why we chose those three points were as follows: After 20 days' operation, a more rapid decrease in the hydraulic conductivity occurred when the second tracer experiment was carried out. After 80 days of run time, the third tracer experiment was carried out. During
Please cite this article as: Hua, G., et al., Influence of clogging and resting processes on flow patterns in vertical flow constructed wetlands, Sci Total Environ (2017), https://doi.org/10.1016/j.scitotenv.2017.10.113
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this period, the wetland became covered with an overlying layer of clay and exhibited further decreases in the hydraulic conductivity. Studies have shown that a resting period of 1 to 3 weeks is sufficient to ensure recovery from clogging (Hua et al., 2014a). After 15 days of rest (i.e., 95th day of experiment), a sharp decrease in the hydraulic conductivity was observed. Therefore, in our study, we terminated the resting period after 15 days and then initiated the fourth tracer experiment. The hydraulic conductivity was determined by the Professional Standards Compilation Group of People's Republic of China, Standard for Soil Test Method (GB/T50123-1999). The tracer calculations were based on the methods of Muñoz et al. (2006). Hydraulic retention time (HRT) represents the amount of time that it takes for surface water to flow through the wetland system. The theoretical hydraulic retention time (tm) was calculated as follows: tm ¼
Vξ Q
ð1Þ
where tm is the retention time (h), V is the volume of the wetland system (m3), Q is the volumetric flow rate of water through the wetland system (m3/day), and ξ is the porosity of the medium (−). The mean hydraulic retention time ( tn) was based on the measured wetland hydraulic retention time and was defined as the center of gravity position within the residence time distribution (RTD) curve. This was calculated as follows: P∞ tcðt ÞΔt t n ¼ P0∞ 0 cðt ÞΔt
flow. Accordingly, the Darcy's law (dl) module in COMSOL was used to simulate the internal flow field distribution for the Reynolds number in the range from 1 to 6 in this study. For simplicity, the VFCW was modeled in a two-dimensional form. As in the laboratory experiment, the modeled VFCW was divided into six layers (Fig. 1). Coarse sand was modeled as the substrate. An unstructured triangular mesh, generated by the software, was utilized here. The import function used velocity boundary conditions, while the export function used atmospheric pressure boundary conditions. The experimentally determined porosity values were used and each boundary layer was walled off. The flow field distribution and solute transport were calculated with static and transient models, respectively. Model parameters are shown in Table 1. Tracer experiment modeling focuses on transient solute migration, i.e., the solute distribution in the flow field changes with time. Thus, Darcy's law (dl) and porous media (TDS) modules were coupled to simulate tracer solute migration. A total of 3892 elements were modeled as described in Fig. 1. A sensitivity evaluation was completed for the meshing selection. We tried several types of meshing in COMSOL such as extreme refinement of the grid (15,300 elements), special refinement of the grid (3892 elements) and refinement of the grid (1316 elements). We found that the results produced with extreme refinement of the grid and special refinement of the grid were almost the same but a little better than those produced with refinement of the grid. Thus, to save calculation time, we finally chose special refinement of the grid (3892 elements) for further
ð2Þ
where C(t) is the tracer concentration (mg/L) at time (t), t is the time of sampling (day), and Δt the change in time between samples (day). Volumetric efficiency (e) was calculated as follows (Thackston et al., 1987): e ¼ t n =t m
ð3Þ
where e is the effective volume ratio, tn is the mean hydraulic retention time, and tm is the theoretical hydraulic retention time. Kadlec and Wallace's (2009) method for calculating the number of continuously stirred tank reactors (CSTRs) was used in the analysis of wetland hydraulic characteristics: N¼
t 2n σ2
ð4Þ
where N is the number of CSTRs in series, and σ2 is the variance that can be determined directly from the residence time distribution curve. If n = 1, it denotes completely mixed flow, while n → ∞ denotes plug flow. The hydraulic efficiency (λ) was calculated using an equation derived by Persson et al. (1999), as well as on the basis of effective volume (Alcocer et al., 2012): λ ¼ t p =t n
ð5Þ
1 λ ¼ e 1− N
ð6Þ
2.3. Geometric description and meshing Modeling of VFCW flow was carried out using COMSOL Multiphysics ™ (version 5.0) bidimensional software that uses the finite element method. Water flow in a VFCW substrate can be regarded as flow in a porous medium. Kadlec (1990) noted that in a constructed wetland, the hydraulic gradient and water head are not sufficient to produce turbulent
3
Fig. 1. Geometric description and meshing.
Please cite this article as: Hua, G., et al., Influence of clogging and resting processes on flow patterns in vertical flow constructed wetlands, Sci Total Environ (2017), https://doi.org/10.1016/j.scitotenv.2017.10.113
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Table 1 All needed parameters used in the COMSOL model. a. Physical properties used in the COMSOL model. Parameter
Value
Initial conditions
Model diameter (mm) Thickness of the sand layer (mm) Outlet size (mm) Inlet velocity (cm/s)
Sand layer
Export pressure (Kpa) Sand average diameter (dp) (mm)
300 500 4 0.02 (0 day) 0.018 (20th day) 0.0005 (80th day) 0.015 (95th day) 101.3 0.15
Geometric parameters
b. Porosities of different layers during the clogging and resting processes (unit: %).
0–5 cm 5–10 cm 10–15 cm 15–25 cm 25–35 cm 35–50 cm
0
20th day
80th day
95th day
34 33 32 35 35 35
30 23 24 30 30 31
6 13 13 24 25 26
17 18 18 27 27 27
calculations in this study. Each model simulation took approximately 10 min in real time on an 8.00 GB Intel(R) Core(TM) i7-2600 CPU@ 3.40 GHz Computer.
3. Results and discussion 3.1. Hydraulic performance of the wetland column during the clogging and resting periods As shown in Fig. 2, the 20th and 80th days were two evident inflection points for hydraulic conductivity. The 80th day represented the demarcation between the operation and resting processes, namely, when the internal hydraulic conductivity reached its lowest value. At this point a surface clay layer formed along with surface water accumulation. Resting can improve the hydraulic conductivity of the upper layer considerably more than in the lower layer because the rate of oxygen diffusion into the pores created by evaporation was sufficient to oxidize and disperse the flocculant biofilm, which was essential to alleviate bioclogging (Hua et al., 2017). The hydraulic performance of a VFCW system is an important indicator of the degree of clogging. A well-run system is characterized by good hydraulic performance, smooth flow, and the absence of stagnant water on the surface.
In addition, the hydraulic conductivities in the early stages of operation (before the 20th day) were relatively uniform with depth. After 20 days of operation, the mean hydraulic conductivity not only decreased to below 0.03 cm/s but also displayed heterogeneity with depth in the 0 to 15 cm substrate layer. The mean value and variance of the hydraulic conductivity during different periods are provided in Table 2. It was evident that the non-uniformity in hydraulic conductivity gradually increased with time during the first 80 days of operation. Some recovery of hydraulic conductivity was observed during the resting period; this was accompanied by an increase in uniformity of values as described in Table 2. Furthermore, the wetland column displayed a hierarchical structure on the vertical direction. The non-uniformity in hydraulic conductivity in the vertical direction and the hydraulic efficiency had an approximately linear relationship (R2 = 0.8244) (Fig. 3). This may be explained by the existence of hydraulic shortcuts. 3.2. Tracer test results As shown in Fig. 4, the breakthrough curve of the tracer experiment displayed a fluctuation around the peak immediately before operation. At 600 min after this peak, a sharp decrease occurred. On the 20th day of operation, the curve began to display greater fluctuations around the peak, with a bimodal phenomenon at 210 min. Compared with the first tracer experiment, the recovery rate in the second experiment was 5% lower. This suggested that the dead zone at the bottom of the wetland had expanded in area. On the 80th day of operation, the solute peak (0.292 mg/L) was extended over a period of 180 min; a second small peak occurred at 570 min. The peak concentration also decreased, which implied that the area of the dead zone increased with clogging and that part of the tracer solute was adsorbed in the surface clay layer. Further analysis revealed that there was a small fluctuation in solute concentration rather than a constant decline at the 600 min mark. This suggests that part of the tracer matter within the dead zone returned to the mainstream channels after a certain period of time, and eventually reached the outlet. The results of the tracer testing in the current study confirm the internal water mixing and short-circuiting obtained by previous studies (Boog et al., 2014; Pálfy et al., 2017). After resting, the clay blanket layer became thinner due to biomass degradation, which increased hydraulic conductivity. By the 95th day, the curve's fluctuations had decreased considerably compared with the three previous tracer experiments. The peak was observed at 130 min (approximately 45–60 min earlier than in the preceding experiments), corresponding to a NaCl concentration of 0.315 mg/L. No twin peaks were observed on the generally smooth breakthrough curve. This suggests that resting can, to a certain extent, alleviate internal flow backmixing and thus improve hydraulic performance. Tracer experiments are an effective means of studying the hydraulic characteristics of constructed wetlands. The breakthrough curves in the tracer experiments and model (Eqs. 1 through 6) were analyzed to determine the average residence time, peak time, effective volume ratio, flow dispersion, hydraulic efficiency and reaction unit number, to quantitatively evaluate hydraulic characteristics during the clogging process and resting period. As shown in Table 3, the hydraulic characteristics of the system were highly variable. All the values for the effective volume were less than 1 but increased with operation time. This suggested that short circuiting and dead zones existed through the operation, with the largest such Table 2 Comparison of the means and variances of hydraulic conductivity with depth during different periods.
Fig. 2. Hydraulic conductivity performances during the clogging and resting processes.
Mean value Variance
0 day
20th day
80th day
95th day
0.03 0.000
0.02 0.001
0.01 0.003
0.02 0.002
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Table 3 Hydraulic parameters of flow patterns based on tracer experiments during different operation stages.
0 day 20th day 80th day 95th day a b
Fig. 3. The relationship between hydraulic efficiency and vertical heterogeneity.
zone at the point of clogging (Chazarenc et al., 2003). This was because the saturated condition had a very positive effect on the degree of mixing in vertical flow constructed wetlands (Boog et al., 2014). The mean retention time and hydraulic efficiency were positively correlated with operation time. These results were different from those reported by other studies (Muñoz et al., 2006; Schmid et al., 2004) because those studies focused on horizontal wetlands but were comparable to the values of hydraulic efficiency reported by Boog et al. (2014) in a saturated vertical flow constructed wetland. Vertical wetlands require a long operation period for wastewater filtration. Although this translates to higher contaminant removal rates, the total amount removed is lower, compared to horizontal wetlands (Hua et al., 2010). This was confirmed by the number of CSTRs observed 2.3 at the time of clogging, representing a trough during the operating period. A lower CSTR number indicates a less efficient system. After resting, the mean retention time, hydraulic efficiency, effective volumes and number of CSTRs were comparable to those recorded on the 20th day of the operation day, suggesting that resting alleviated clogging to a certain extent. Based on the model provided by Alcocer et al. (2012), the number of consecutive mixing reactors during the clogging and resting processes remained between 1 and ∞. This implied that after resting, the internal water flow was somewhere between completely mixed flow and plug flow. The dispersion coefficient values
Fig. 4. Tracer tests during different operation stages in vertical flow constructed wetlands.
tm(h)
tn(h)
tp(h)
e(%)
λ1a
λ2b
N
4.40 4.40 4.40 4.40
2.26 2.68 2.85 2.77
2.33 2.50 2.83 2.27
0.52 0.60 0.63 0.58
0.51 0.61 0.64 0.62
0.53 0.57 0.64 0.52
5.2 7.8 2.3 6.1
Results calculated by Eq. 5. Results calculated by Eq. 6.
increased over the operation time. This is likely due to the increase in biomass volume and the decrease in porosity thereby helping to promote hydrological mixing. The other reason may be that the addition of plants to the mesocosm appears to promote an increase in the dispersion coefficient with operation time (Weber and Legge, 2011). This can be explained, as the plants developed a complicated physical network of roots in the wetland media that would effectively increase the amount of hydrological mixing (dispersion coefficient) in the mesocosms. The values of dispersion coefficients obtained in this study lie within the range of reported values from other studies of VFCWs (Garcia et al., 2004; Weber and Legge, 2011). The plants played different roles related to time and space. From time's perspective, the plant roots played opposite roles between the beginning clogging stage and the later clogging stage. The plant roots hindered the water flow at the beginning of clogging when the original effective porosity was relatively higher; while the clogging was becoming more serious, particularly in the root growth layer where the effective porosity gradually decreased, the growing plant roots might open new pore spaces in the substrate, which would increase the hydraulic conductivity and even result in preferential flow and short circuits. From space's perspective, the plant roots were of no real importance from the view of the entire column on the effective porosity (Hua et al., 2014b; Tang et al., 2017). 3.3. Modeling results of hydraulic characteristics 3.3.1. Flow velocity inside the substrate The flow velocity profile along the x- and y-axes inside the wetland column during different operation times and resting periods are described in Fig. 5. The x-axis and y-axis velocity transects are indicated at time points of 0, 20, 80 and 95 days of operation, demonstrating the ability of this model to calculate local (microscopic) velocities in each mesh element. Given the spatial hierarchy in the column, at a depth of 4 cm, the horizontal flow velocity exhibited a high degree of variation at the outlet of the substrate. The maximum flow velocity (0.46 mm/s) was at x = 27.0 cm, while the flow velocities in the middle and lower layers were low. At depths of 28 cm and 48 cm depth, variation in horizontal flow was similar but different from that at a depth of 4 cm. At x = 15 cm, the flow velocities were high but decreased towards the sides. At x = 3 and 15 cm, longitudinal velocity increased with depth. At x = 27 cm, the longitudinal velocities first increased to a peak and then decreased and stabilized. It was evident that the flow velocity at the bottom (opposite to the outlet) gradually decreased, while a rapid increase followed by a sharp decrease was observed near the outlet. Irrespective of the location of the main flow front, the flow velocities decreased with an increase in the degree of clogging. This could be reversed by a resting operation. As expected, a consistently high flow velocity was observed near the outlet. On the other hand, the lower water velocities in the area opposite the outlet region created a dead zone in that area. This zone was characterized by low flow shear stress and accumulation of nutrients favoring microbial growth. This resulted in the proliferation of bacteria and the production of extracellular polymers. Thus, the porosity of this region can be expected to decrease with increasing operation time. The central part of the column was indicative of a typical VFCW mesocosm.
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Fig. 5. Flow velocities inside the wetland column during different operation times and resting periods.
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It is difficult to measure the flow velocity inside the wetland column because it is somewhat of a “black box”. So by comparing the simulated results (flow velocity values) with the experimental results (hydraulic conductivities which can be measured) of the different layers that are close to the wetland column side along its depth, the simulated values
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are found to fit the measured data quite well, which indicates that the model works reasonably well. In general, compared with those of Samsó et al. (2016) and Rajabzadeh et al. (2015), our results agreed much more closely with Rajabzadeh's work, which stated that there were three regions in the
Fig. 6. Modeling simulation of tracer transport.
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mesocosm in terms of water velocity, rather than a moving dead zone, which was described in Samso's study. 3.3.2. Simulation of tracer experiment The simulations of the tracer experiment are described in Fig. 6. At the beginning of the operation and after resting, as indicated in Fig. 6(a), (b) and (c), at time (t) = 20 min, solute migrated to the region y = 40 cm. At this time, solute concentration was uniform along the vertical axis, indicating that it was primarily influenced by water flow and not diffusion. At t = 100 min to 260 min, solute was still transported primarily with water flow downward along the wetland column. At this stage, the solute concentration began to display non-uniformity. Subsequently, a portion of the solute quickly reached the outlet, forming an early peak. Another part of the solute driven by the main water flow front diffused to the bottom opposite to the outlet, where solute was exchanged between the main flow zones. This explained the bimodal characteristic of the experimental breakthrough curve. At t = 337 min to 500 min, solute accumulated and was left stuck at the bottom of the wetland column for a period of time. Eventually, it was transported out, as shown by the tail of the tracer curve. Compared with other operation times, with the occurrence of clogging (Fig. 6c), solute migrated to the region y = 25 cm, with uniform solute distribution with depth. However, the migration of the solution was slow and delayed the exit of the solute, which was consistent with the delayed peak in the tracer curve (Fig. 4). An accumulation of solute was observed at the bottom of the wetland column, which was supported by the longer tail and lower recovery in the model. Both the models and physical tracer experiments showed that the time that the solute spent at the bottom of the column was triple the time it took for the solute to migrate from the top of the column to the bottom. This was the main reason for the presence of tailing in the tracer concentration curve. Furthermore, the model proved that impeded flow occurred in the upper part of the column, whereas diffusion occurred in the lower region (i.e., the diffusion of water gradually increased with depth). Based on analysis of the physical tracer experiment (Fig. 6), it can also be concluded that the concentration of solute was highest in the zone of accumulation where the hydrodynamic shear stress was small. This encouraged rapid growth of microorganisms. Thus, non-uniformity in hydraulic conductivity was observed not only along the vertical axis but also along the horizontal axis on the bottom of the wetland column. Overall, comparing the simulated results and the experimental results in the tracer tests, the simulated dynamic process can be considered to be in good agreement with the experimental process, which further indicates that the model fits well with the actual data. Compared with the work of Langergraber (2003), our tracer results were not consistent with it which found that the measured hydraulic retention time was longer than the theoretical residence time. The reasons for this might be as follows: (1) the wetlands in Langergraber's study were loaded intermittently with wastewater while ours were operated with continuous flow. Thus, the model developed by Langergraber for unsaturated flow used the Richards Equation. In our study, we used Darcy's law in COMSOL because the water flow was saturated laminar flow. (2) In Langergraber's work, immobile pore water and mobile pore water were separately considered while in our study they were not distinguished from one another. It is not easy to find a specific study which is the same with our operation pattern and wetland type (i.e. VFCWs) to compare with since the actual wastewater treatment demand is ever-changing. Thus, although there were a numbers of similar studies in the literature presented above, to our best knowledge, this is the first work to present a variation in water flow pattern combined using of tracer experiments and COMSOL models focusing on the whole clogging and resting process in vertical flow constructed wetland. Furthermore, it is also the first time to use numerical simulation to further interpret the whole process of the trace experiments.
3.3.3. Limitations and future research There was no sensitivity analysis completed in general. The reasons for this were as follows: (1) In this study, we focused on evaluating the influences of the clogging and resting processes on flow patterns; and (2) The parameters used in the simulation process of water flow, such as model diameter, the thickness of sand layer, etc., described in Table 1, were almost all fixed in the actual constructed wetlands except for the parameter of inlet velocity when we practically ran in an existing constructed wetland. Thus, we changed the inlet velocity to investigate flow pattern variation, which was equivalent to a sensitivity analysis on flow rate. However, each factor affecting the treatment function of constructed wetland technology will be studied individually in the near future, which is still necessary before it is to be put into practice. Furthermore, water flow mechanism of short-term studies in a labscale is similar to that of long-term studies in full-scale. Whatever the scale is, the essence of clogging is the available void space being filled by accumulated SS and biofilms, rather than the SS/COD loading that directly determines the clogging occurrence. However, the higher the solids loading, the shorter the clogging time would be. In contrast, when lower SS/COD loading is applied in practice, the clogging time will be longer. Thus, the conclusions obtained in this study still can give insights to full-scale wetlands with a long term operation. Obviously, as an ideal solution, further studies should consider microbiological and plant-related processes. Moreover, more efforts should be made to calibration and validation and other data sets are thus needed to improve the model predictions before the model is used for design purpose. 4. Conclusions In this study, we analyzed the inner flow patterns in VFCWs during the clogging and resting processes with tracer experiments and numerical models. The clogging process was characterized by impeded circulation and a greater proportion of so-called dead zones. Furthermore, the tracer concentration displayed greater fluctuations during the clogging process than during the resting process. The active region expanded with operation time before clogging commenced. The latter was preceded by a peak in the effective volume and hydraulic efficiency. Nonuniformity in hydraulic conductivity with depth was linearly correlated to the hydraulic efficiency. During the early stages, water flow was comparable to piston flow when the reaction efficiency was at its peak. This was followed by an increase in the degree of mixing as clogging proceeded. This was partially reversed by a resting operation, which improved reaction efficiency. Modeling revealed that the tail phenomenon of the breakthrough curve was primarily caused by solute retention at the bottom of the wetland column. The retention time of the solute at the bottom was triple that of the transport time from the top to the bottom of the column. Acknowledgements The authors acknowledge financial support for this study from the Natural Science Foundation of China (51509070), the Fundamental Research Funds for the Central Universities (2015B15214) and the SSSTC project (EG 02-032015). This study was also supported by the China Scholarship Council (No. 201504280002) for 1 year study at EPFL, Switzerland. References Alcocer, D.J.R., Vallejos, G.G., Champagne, P., 2012. Assessment of the plug flow and dead volume ratios in a sub-surface horizontal-flow packed-bed reactor as a representative model of a sub-surface horizontal constructed wetland. Ecol. Eng. 40, 18–26. Bodin, H., Persson, J., Englund, J.E., Milberg, P., 2013. Influence of residence time analyses on estimates of wetland hydraulics and pollutant removal. J. Hydrol. 501, 1–12. Boog, J., Nivala, J., Aubron, T., Wallace, S., Afferden, M., Muller, R.A., 2014. Hydraulic characterization and optimization of total nitrogen removal in an aerated vertical subsurface flow treatment wetland. Bioresour. Technol. 161, 166–174.
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Please cite this article as: Hua, G., et al., Influence of clogging and resting processes on flow patterns in vertical flow constructed wetlands, Sci Total Environ (2017), https://doi.org/10.1016/j.scitotenv.2017.10.113