- Email: [email protected]
Modelling of vertical subsurface flow constructed wetlands for treatment of domestic sewage and stormwater runoff by subwet 2.0
Ecological Engineering 74 (2014) 8–12
Contents lists available at ScienceDirect
Ecological Engineering journal homepage: www.elsevier.com/locate/ecoleng
Short communication
Modelling of vertical subsurface flow constructed wetlands for treatment of domestic sewage and stormwater runoff by subwet 2.0 Jinhui Jeanne Huang a, * , Xiang Gao a , Gordon Balch b , Brent Wootton b , Sven Erik Jørgensen c , Bruce Anderson d a
State Key Laboratory of Hydraulic Engineering Simulation and Safety, School of Civil Engineering, Tianjin University, Tianjin 300072, PR China Centre for Alternative Wastewater Treatment, Fleming College, Lindsay, ON, K9V 5E6, Canada University of Copenhagen, Nørregade 10DK-1165, Copenhagen, Denmark d Department of Civil Engineering, Queen’s University, Kingston, ON, K7L 3N6, Canada b c
A R T I C L E I N F O
A B S T R A C T
Article history: Received 26 June 2014 Received in revised form 5 October 2014 Accepted 9 October 2014 Available online xxx
With the increasing number of constructed wetlands being built, the modelling of wetland function and performance is valuable. This work examines the efficacy of applying a numeric model (SubWet 2.0) originally designed for horizontal subsurface flow wetlands to model wastewater treatment within vertical subsurface flow constructed wetlands (VSSF-CWs). The treatment efficiencies of two VSSF-CWs with substantially different influent characteristics, one in Canada and one in China, were modelled with SubWet 2.0 and simulated values were then compared to observed values to determine how closely SubWet 2.0 reflects the actual observed performance of these wetlands. The model was calibrated to each wetland with observed data that had been collected prior to the simulations. The correlation coefficient (R) and Nash–Sutcliff coefficient of efficiency (NSE) were used to evaluate the modelling performance for 5-day biochemical oxygen demand (BOD5), ammonium nitrogen, nitrate nitrogen and total phosphorous (TP). The results showed that the modelling performance for TP and BOD5 was better for these parameters than that observed for ammonium nitrogen and nitrate nitrogen for either of the two wetlands. For TP and BOD5, the correlation coefficient R achieved a value of 0.79 for the wetland receiving stormwater and exceeded this value for the Canadian wetland receiving domestic wastewaters. For nitrate nitrogen, the wetland treating domestic waste showed a correlation coefficient R as high as 0.97, while the wetland treating stormwater runoff had a correlation coefficient R of 0.48. For ammonium nitrogen, both wetlands showed low correlation coefficients with values of 0.70 and 0.60 for domestic wastewater and for stormwater runoff, respectively. This study demonstrated that SubWet 2.0 is suitable for the modelling of VSSF-CWs. The two case studies, with substantial differences in the characteristcs of the influents, demonstrated that Subwet 2.0 is a versatile and robust tool for modelling of constructed wetlands. ã 2014 Published by Elsevier B.V.
Keywords: SubWet 2.0 Vertical subsurface flow constructed wetland Domestic sewage Stormwater runoff
1. Introduction A constructed wetland (CW) is an alternative engineered process commonly used for treating contaminated water. CWs embody treatment procesess analogous to those found in natural wetlands; including physical, chemical and biological processes, such as sedimentation, filtration, precipitation, sorption, plant uptake, microbial decomposition and nitrogen transformations (Dan et al., 2011; Faulwetter et al., 2009; Liang et al., 2009).
* Corresponding author. Tel.: +86 22 27403676; fax: +86 22 27403676. E-mail address: [email protected] (J.J. Huang). http://dx.doi.org/10.1016/j.ecoleng.2014.10.027 0925-8574/ ã 2014 Published by Elsevier B.V.
With the increasing number of CWs being built, the modelling of wetland function and performance has also attracted more attention. The main objectives of these modelling studies are to better understand the treatment processes in CWs and to improve the design, management, monitoring and maintenance of CWs. The SubWet 2.0 model is an interesting option that was originally developed for HSSF wetlands. This model has been reviewed and described previously by Jørgensen and Gromiec (2011); Chouinard et al. (2014a,b); Chouinard et al. (2014a,b). A key feature of SubWet 2.0 is the relative ease in calibrating the model to site conditions. This is done by adjusting the rate coefficients (within a defined range) until simulated values match observed values. In this manner, SubWet provides an integrated process response which lessens the need to know specific details about all treatment
J.J. Huang et al. / Ecological Engineering 74 (2014) 8–12
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Table 1 Characteristics of the influents for VFCWI and VFCWII. Observed influents Contents
BOD5 COD Ammonia nitrogen Nitrate nitrogen TN TP
Unit
Mean
Standard deviation
Median
–
VFCWI
VFCWII
VFCWI
VFCWII
VFCWI
VFCWII
Maximum VFCWI
VFCWI
Minimum VFCWI
VFCWII
mg/L mg/L mg/L mg/L mg/L mg/L
15.3 45.1 12.9 12.0 22.6 11.1
11.5 38.3 3.0 0.11 11.3 0.13
6.4 17.4 14.9 18.2 20.9 3.8
4.0 13.2 2.0 0.1 6.9 0.06
16.5 48.1 9.3 3.8 15.7 9.8
11.0 36.7 2.3 0.2 9.7 0.11
32.0 79.1 62 69 76.1 23.2
21.8 72.6 7.6 0.6 29.6 0.31
3.0 21.5 1.1 0.2 2.6 7.0
4.0 13.3 0.5 0.1 3.7 0.05
DO 3.6–8.8, Temp. 9.2–20 C, pH 7.49–7.91 in VFCWI. DO 1.5–5.0, Temp. 4.5–22.4 C, pH 7.59–8.11 in VFCWII.
processes. For example, the ability to calibrate to site conditions allowed SubWet to be successfully applied to natural tundra treatment wetlands, where several of the parameters such as hydraulic loading and bed depth were variable and poorly defined (Chouinard et al., 2014b). The goal of this study was to assess the efficacy of SubWet 2.0 being applied to a vertical subsurface flow constructed wetland (VSSF-CW) in the hope that this model would provide a user-friendly, low cost and reasonably accurate option for the modelling of VSSF-CWs. For this purpose the performance of SubWet 2.0 was assessed by calibrating it to two different VSSFCWs that treated different sources of wastewaters. One of the VSSF-CWs was located in south eastern Canada and treated domestic wastewater, while the other CW was located in China and treated stormwater runoff. 2. Methodology and materials 2.1. Case studies The present study investigated two VSSF-CWs in two locations; one in Canada and one in China. The VSSF-CWI (as CWI) system was built at the Centre for Alternative Wastewater Treatment in Canada (latitude 45.05 , and longitude 78.53 ) to treat domestic sewage. The size of the vertical flow wetland cell was 4.0 m in length, 3.0 m in width and 1.5 m deep. The main matrix of the vertical flow wetland cell was sand. The treatment volume was between 15.5 and 16.6 m3 of waste water per day. The porosity of the matrix was estimated to be 0.35 and the effective volume of the vertical flow wetland bed was 6.3 m3 (in winter, the influent level was decreased to prevent surface freezing which reduced the effective volume of the wetland bed to 3.15 m3). Phragmites spp. was planted in the vertical flow wetland cell. The VSSF-CWII (as CWII) was built at Tianjin University in China (latitude 39.11, and longitude 117.17 ) to treat the water from a nearby stormwater pond. The dimensions of the vertical wetland flow wetland cell were 1.0 m in length, 0.5 m in width and 0.9 m deep. The matrix of the wetland cell consisted of gravel and ceramsite. The system was designed to treat a volume of 0.8–2.7 m3 of stormwater runoff per day. The porosity of the matrix was estimated to be 0.87 and the effective volume of the wetland bed was 0.39 m3. There were no plants in this system.
2.2. Data Water quality monitoring data were collected at the influent and effluent of CWI from February to September of 2013 and the CWII from June to December of 2013. Water samples were analysed for biochemical oxygen demand (BOD5, mg/L), chemical oxygen demand (COD, mg/L), nitrite nitrogen (mg/L), nitrate nitrogen (mg/ L), ammonium nitrogen (mg/L), total Kjeldahl nitrogen (TKN, mg/ L), total nitrogen (TN, mg/L), dissolved oxygen (DO, mg/L), phosphate as PO4 (mg/L), total phosphorus as P (TP, mg/L), pH and temperature ( C) according to standard methods (APHA, 2005). Table 1 summarizes the characteristics of the influents for the main parameters including BOD5, COD, nitrate nitrogen, ammonium nitrogen, TN and TP. The mean and standard deviation of the influent concentrations of CWI are higher than CWII since the source wastewater was pre-treated (septic tank) domestic sewage, while the influent of CWII was stormwater runoff. The mean values of nitrate nitrogen and TP of CWI were more than 100 the values of CWII, while the standard deviations of nitrate nitrogen and TP of CWI were 63 and 180 the values of CWII, respectively. The mean values of TN and ammonium nitrogen for CWI were 2 and 4.3 the values of CWII, while the standard deviations of TN and ammonium nitrogen of CWI were 3.0 and 7.5 the values of CWII, respectively. BOD5 and COD were approximately the same for the two CWs. 2.3. Model description (SubWet 2.0) SubWet was developed by UNEP-DTIE-IETC, and was originally intended for use in the design of horizontal subsurface flow constructed wetlands for the treament of domestic wastewaters. The model employs 25 differential process equations and 16 parameters as described by Jorgensen and Fath (2011). It can simulate the removal of BOD5, nitrate nitrogen, ammonium nitrogen, organic nitrogen and TP in milligrams per liter and the corresponding removal efficiencies in percentage. 2.4. Modelling setup Cold climate wetlands are defined in SubWet 2.0 as being sites with surface temperatures varying between below freezing in
Table 2 The correlation between observed and simulated results in VFCWI and VFCWII. Variable (mg/L)
BOD5 Ammonia-nitrogen Nitrate- nitrogen TP
Correlation coefficient (R)
NSE
P value
VFCWI
VFCWII
VFCWI
VFCWII
VFCWI
VFCWII
0.84 0.70 0.97 0.94
0.79 0.60 0.48 0.98
0.53 0.51 0.93 0.87
0.26 0.33 0.23 0.94
<0.0001 0.002 <0.0001 <0.0001
<0.0001 0.001 0.012 <0.0001
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J.J. Huang et al. / Ecological Engineering 74 (2014) 8–12
Fig. 1. Comparison of observed and simulated effluent values of BOD5, nitrate nitrogen, ammonium nitrogen and TP. The time series on the X-axis refer to the sequencies of the data being collected.
J.J. Huang et al. / Ecological Engineering 74 (2014) 8–12
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Table 3 Calibrated values for the parameters of the models. Parameter Description
kOC
lOO uTO kAC
uTA
kPA kNC
lMA uTN
kPN kDC
lKO uTD lMN
CAF kMPP
Applicable range
Oxidation rate coefficient for organic matter, expressed as BOD5 (1/24 h) 0.05–2.0 Michaelis–Menten constant for influence of oxygen on the oxidation rate of organic matter, expressed as 0.01–2 BOD5 (mg/L) Temperature coefficient for oxidation of organic matter expressed as BOD5 (no units) 1.02–1.06 Max. decomposition rate of organic nitrogen (1/24 h) 0.05–2.0 Temperature coefficient for ammonification (no units) 1.02–1.06 Max. plant uptake rate of ammonium (1/24 h) 0.00–1 Max. nitrification rate (1/24 h) 0.1–2.5 Michaelis–Menten constant for nitrification (mg/L) 0.05–5 Temperature coefficient for nitrification (no units) 1.02–1.09 Max. plant uptake rate of nitrate (1/24 h) 0.00–1 Denitrification rate coefficient (1/24 h) 0.00–5 Michaelis–Menten constant for the influence of oxygen on the nitrification rate (mg/L) 0.01–2 Temperature coefficient for denitrification (no units) 1.05–1.12 Michaelis–Menten constant for denitrification (mg/L) 0.01–1 Inverse phosphorus adsorption capacity (mg/L) 0–100 Max. plant uptake rate of total phosphorus (no units) 0.00–1
winter to up to 22 C in summer. Warm climate wetlands are those which typically range in surface temperatures between 26 C and 34 C. The design input values of the model are used to specify the wetland width, length, depth, precipitation factor, slope, average % particulate matter, hydraulic conductivity and selected flow rate (in cubic meters per day). The types of wetlands (constructed wetland or natural wetland) must also be specified. After doing so, the model will calculate wetland area, volume, hydraulic loading, recommended horizontal flow, recommended flow, flow width, flow length and number of paths. The forcing functions present the operational parameters including length of simulation, volume, water flow, porosity (fraction), average oxygen concentration in five boxes, temperature, the input values of BOD5, nitrate nitrogen, ammonium nitrogen, total phosphorus and organic nitrogen along with the fraction of BOD5, phosphorus, and organic nitrogen as suspended matter. SubWet divides the wetland into 5 equal compartments or boxes and identifies these boxes with the suffixes of A, B, C, D, and E. The starting values (i.e., concentrations) entered into Box A for each of the water quality parameters being modelled should be a value that is slightly lower than the corresponding influent concentration for that water quality parameter stipulated in the previous Forcing Functions section. Subsequent values chosen for Boxes B–E should exhibit a step wise reduction so that the value in Box E reflects what is anticipated to be a reasonable level of treatment. If the length of the simulation chosen is long enough to reach a steady state, then the initial values of these water quality parameters do not need to be precise. The choice of cold climate or warm climate operating mode will determine which set of default parameters the model uses. These values can be used to calibrate SubWet to an individual wetland by comparing the simulated effluent values to the observed effluent values for that wetland. Slight modifications to specific coefficient values will improve the simulation by making the simulated effluent values closer to those of the observed effluent values. Further details outlining the operation of SubWet 2.0 are summarized by Jørgensen and Gromiec (2011) and Chouniard et al. (2014a).
3. Discussion and results 3.1. Statistical analyses The correlation coefficient R and NSE were obtained by using the standard software IBM SPSS Statistical 19. The significant
Default value
Calibration
Cold
Warm CWI
CWII
0.25 0.05
0.5 1.3
0.25 2
0.05 2
1.04 0.9 1.05 0.01 0.09 0.1 1.07 0.001 3.5 0.01 1.07 0.1 0.36 0.001
1.04 0.5 1.04 0.01 0.8 1 1.047 0.01 2.2 1.3 1.09 0.1 1 0.003
1.06 0.05 1.02 0.01 2.5 0.1 1.02 0.005 0.01 1 1.12 0.01 4.693 0.0015
1.06 0.05 1.06 0.001 0.5 0.1 1.02 0.001 1 2 1.12 0.01 3.54 0.001
difference (p < 0.05) was employed to assess the R values. These values are shown in Table 2. 3.2. Calibration and results Fig. 1 compares the observed values and simulated values for BOD5, nitrate nitrogen, ammonium nitrogen and TP. The comparison shows a reasonable agreement between the simulated values and observed values. Table 2 indicates that all correlation coefficients have a significance level above 99% and the R and NSE values of CWI are higher than CWII except for TP. The lowest R and NSE values are 0.48 and 0.23 for nitrate nitrogen, however, they are still statistically significant for correlations between simulated and observed values of CWII. The results demonstrate that SubWet 2.0 can be calibrated to well represent the processes in a VSSF wetland. 3.2.1. Simulation performance of the model for BOD5 The model simulation results indicate that the oxidation rate coefficient for organic matter in CWI is higher than that in CWII as demonstrated in Table 3. It is generally stated that organic matter is primarily removed by biological oxidation in wetlands, which is highly dependent on DO concentrations (Shackle et al., 2000 Zhang et al., 2010). Insufficient DO in the matrix can decrease biological oxidation and impair the removal of organic matter in wetlands. From Table 1 we can see that the concentration of DO in CWI was higher than in CWII, because the DO of domestic sewage for CWI was higher than for CWII. Additionally, the root zone of macrophytes may also result in higher DO in the wetlands (CasellesOsorio and García, 2007) and thus influence microbial transformation rates. 3.2.2. Simulation performance of the model for nitrogen From Table 2 we can see that the simulation performance of ammonium nitrogen and nitrate nitrogen are worse than the simulation performance of BOD5 and TP for the two CWs. Table 3 shows that the Max. nitrification rate coefficient of CWI is larger than for CWII, but the denitrification rate coefficient of CWI is smaller than for CWII. This is due to the fact that the primary form of TN is organic nitrogen and ammonification (mineralization) proceeds slower than nitrification proceeds in CWII. Therefore, less ammonium nitrogen is generated by mineralization that can then be nitrified for CWII. On the contrary, the proportion of the influent concentration of ammonium nitrogen made up 57.08% of TN in CWI
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J.J. Huang et al. / Ecological Engineering 74 (2014) 8–12
and the influent had a higher concentration of DO than CWII (Chang et al., 2012; Vymazal, 2007). From Table 3, we can see that the plant uptake rates of ammonium nitrogen and nitrate nitrogen in CWI are larger than the values for CWII. Plants can play a major role in nitrogen removal when plants are harvested and managed to remain in an accelerated growth phase (Klomjek and Nitisoravut, 2005; Peterson and Teal, 1996). In fact, no plants were planted in CWII, however, there can still be microbial mediated transformation of nitrogen even though plants do not exist, therefore, a very small number 0.001 (the lowest number by default) was used for “plant uptake” to represent the process. 3.2.3. Simulation performance of the model for TP From Table 2, we can see that the R and NSE values for TP of CWII are both slightly higher than for CWI. Table 3 shows that Max. plant uptake rate of total phosphorus of CWII is slightly higher than for CWI. Arias et al. (2001) and Vohla et al. (2011) reported that the plant uptake and subsequent harvesting are the sustainable removal mechanism for phosphorus. With the same problem as the Chapter 3.2.2 about the plant uptake rates of ammonium nitrogen and nitrate nitrogen for CWII, the Max. plant uptake rate of total phosphorus is also a very small number 0.001 (the lowest number by default) for CWII. The model uses the parameter CAF to represent the adsorptive and inverse adsorptive ability of the matrix. From Table 3 we can see that the CAF value of the CWI is slightly higher than the value for the CWII. This indicates that the phosphorus adsorption capacity of the sand is stronger than the blend of gravel and ceramsite in the two CWs. 4. Conclusions The modelling of the two cases here illustrated that SubWet 2.0 model can adequately simulate the treatment of a range of wastewaters (stormwater runoff and domestic wastewater) within vertical subsurface flow constructed wetlands. The modelling of the two cases here illustrated that SubWet 2.0 simulated values were a reasonable reflection of observed treatment values. This study demonstrates that Subwet 2.0 provides a user-friendly and robust option for the modelling of vertical flow subsurface wetlands despite the fact that its original intention was for horizontal subsurface flow wetlands, underscoring the versatility of this wetland model. This study also observed that the high concentrations of DO can improve the removal efficiencies of COD, BOD5 and ammonium nitrogen but is not significant for nitrate nitrogen removal. Vegetation may play a major role in removing nitrogen and TP when plants are managed to remain in an accelerated growth phase. Acknowledgements This research was financially supported by Ministry of Water Resources (201201114) and Ministry of Education (NCET-09-0586).
We would like to express our thanks to the staff working in the Centre for Alternative Wastewater Treatment for their great efforts in collecting data to support this research. References American Public Health Association. APHA, 2005. Standard Methods for the Examination of Water and Wastewater, 21st ed. APHA, Washington, DC. Arias, C.A., Del Bubba, M., Brix, H., 2001. Phosphorus removal by sands for use as media in subsurface flow constructed reed beds. Water Res. 35 (5), 1159–1168. doi:http://dx.doi.org/10.1016/S0043-1354(00)368-7. Caselles-Osorio, A., García, J., 2007. Impact of different feeding strategies and plant presence on the performance of shallow horizontal subsurface-flow constructed wetlands. Sci. Total Environ. 378 (3), 253–262. doi:http://dx.doi.org/ 10.1016/j.scitotenv.2007.02.031. Chang, J.J., Wu, S.Q., Dai, Y.R., Liang, W., Wu, Z.B., 2012. Treatment performance of integrated vertical-flow constructed wetland plots for domestic wastewater. Ecol. Eng. 44, 152–159. doi:http://dx.doi.org/10.1016/j.ecoleng.2012.03.019. Chouinard, A., Balch, G.C., Wootton, B.C., Jørgensen, S.E., Anderson, B.C., 2014a. SubWet 2.0. Modeling the performance of treatment wetlands. In: Jørgensen, S. E., Chang, N.-B., Xu, F.-L. (Eds.), Ecological Modelling and Engineering of Lakes and Wetlands. Elsevier B.V., Amsterdam, The Netherlands doi:http://dx.doi.org/ 10.1016/B978-0-444-63249-4.00021-X. Chouinard, A., Yates, C.N., Balch, G.C., Jørgensen, S.E., Wootton, B.C., Anderson, B.C., 2014b. Management of tundra wastewater treatment wetlands within a lagoon/ wetland hybridized treatment system using the SubWet 2.0 wetland model. Water 6 (3), 439–454. doi:http://dx.doi.org/10.3390/w6030439. Dan, T.H., Quang, L.N., Chiem, N.H., Brix, H., 2011. Treatment of high-strength wastewater in tropical constructed wetlands planted with Sesbania sesban: horizontal subsurface flow versus vertical downflow. Ecol. Eng. 37 (5), 711–720. doi:http://dx.doi.org/10.1016/j.ecoleng.2010.07.030. Faulwetter, J.L., Gagnon, V., Sundberg, C., Chazarenc, F., Burr, M.D., Brisson, J., Stein, 2009. Microbial processes influencing performance of treatment wetlands: a review. Ecol. Eng. 35 (6), 987–1004. doi:http://dx.doi.org/10.1016/j.ecoleng.2008.12.030. Jorgensen, S.E., Fath, B., 2011. Ecological modelling. Applications in Environmental Management and Research, Vol. 23. Elsevier, pp. 23. Jørgensen, S.E., Gromiec, M.J., 2011. Mathematical models in biological waste water treatment – chapter 7.6., 4th ed. In: Jørgensen, S.E., Fath, B.D. (Eds.), Applications in Fundamentals of Ecological Modelling, Environmental Management and Research, Vol. 23. Elsevier, Amsterdam, Netherlands, pp. 414. Klomjek, P., Nitisoravut, S., 2005. Constructed treatment wetland: a study of eight plant species under saline conditions. Chemosphere 58 (5), 585–593. doi:http:// dx.doi.org/10.1016/j.chemosphere.2004.08.073. Liang, W., Deng, J.Q., Zhan, F.C., Wu, Z.B., 2009. Effects of constructed wetland system on the removal of dibutyl phthalate (DBP). Microbiol. Res. 164 (2), 206– 211. doi:http://dx.doi.org/10.1016/j.micres.2006.11.013. Peterson, S.B., Teal, J.M., 1996. The role of plants in ecologically engineered wastewater treatment systems. Ecol. Eng. 6 (1), 137–148. doi:http://dx.doi.org/ 10.1016/0925–8574(95) 55–0. Shackle, V.J., Freeman, C., Reynolds, B., 2000. Carbon supply and the regulation of enzyme activity in constructed wetlands. Soil Biol. Biochem. 32 (13), 1935– 1940. doi:http://dx.doi.org/10.1016/S0038–0717(00) 169–3. Vohla, C., Kõiv, M., Bavor, H.J., Chazarenc, F., Mander, Ü., 2011. Filter materials for phosphorus removal from wastewater in treatment wetlands – a review. Ecol. Eng. 37 (1), 70–89. Vymazal, J., 2007. Removal of nutrients in various types of constructed wetlands. Sci. Total Environ. 380 (1), 48–65. doi:http://dx.doi.org/10.1016/j.scitotenv.2006.09.014. Zhang, L.Y., Zhang, L., Liu, Y.D., Shen, Y.W., Liu, H., Xiong, Y., 2010. Effect of limited artificial aeration on constructed wetland treatment of domestic wastewater. Desalination 250 (3), 915–920. doi:http://dx.doi.org/10.1016/j. desal.2008.04.062.
Contents lists available at ScienceDirect
Ecological Engineering journal homepage: www.elsevier.com/locate/ecoleng
Short communication
Modelling of vertical subsurface flow constructed wetlands for treatment of domestic sewage and stormwater runoff by subwet 2.0 Jinhui Jeanne Huang a, * , Xiang Gao a , Gordon Balch b , Brent Wootton b , Sven Erik Jørgensen c , Bruce Anderson d a
State Key Laboratory of Hydraulic Engineering Simulation and Safety, School of Civil Engineering, Tianjin University, Tianjin 300072, PR China Centre for Alternative Wastewater Treatment, Fleming College, Lindsay, ON, K9V 5E6, Canada University of Copenhagen, Nørregade 10DK-1165, Copenhagen, Denmark d Department of Civil Engineering, Queen’s University, Kingston, ON, K7L 3N6, Canada b c
A R T I C L E I N F O
A B S T R A C T
Article history: Received 26 June 2014 Received in revised form 5 October 2014 Accepted 9 October 2014 Available online xxx
With the increasing number of constructed wetlands being built, the modelling of wetland function and performance is valuable. This work examines the efficacy of applying a numeric model (SubWet 2.0) originally designed for horizontal subsurface flow wetlands to model wastewater treatment within vertical subsurface flow constructed wetlands (VSSF-CWs). The treatment efficiencies of two VSSF-CWs with substantially different influent characteristics, one in Canada and one in China, were modelled with SubWet 2.0 and simulated values were then compared to observed values to determine how closely SubWet 2.0 reflects the actual observed performance of these wetlands. The model was calibrated to each wetland with observed data that had been collected prior to the simulations. The correlation coefficient (R) and Nash–Sutcliff coefficient of efficiency (NSE) were used to evaluate the modelling performance for 5-day biochemical oxygen demand (BOD5), ammonium nitrogen, nitrate nitrogen and total phosphorous (TP). The results showed that the modelling performance for TP and BOD5 was better for these parameters than that observed for ammonium nitrogen and nitrate nitrogen for either of the two wetlands. For TP and BOD5, the correlation coefficient R achieved a value of 0.79 for the wetland receiving stormwater and exceeded this value for the Canadian wetland receiving domestic wastewaters. For nitrate nitrogen, the wetland treating domestic waste showed a correlation coefficient R as high as 0.97, while the wetland treating stormwater runoff had a correlation coefficient R of 0.48. For ammonium nitrogen, both wetlands showed low correlation coefficients with values of 0.70 and 0.60 for domestic wastewater and for stormwater runoff, respectively. This study demonstrated that SubWet 2.0 is suitable for the modelling of VSSF-CWs. The two case studies, with substantial differences in the characteristcs of the influents, demonstrated that Subwet 2.0 is a versatile and robust tool for modelling of constructed wetlands. ã 2014 Published by Elsevier B.V.
Keywords: SubWet 2.0 Vertical subsurface flow constructed wetland Domestic sewage Stormwater runoff
1. Introduction A constructed wetland (CW) is an alternative engineered process commonly used for treating contaminated water. CWs embody treatment procesess analogous to those found in natural wetlands; including physical, chemical and biological processes, such as sedimentation, filtration, precipitation, sorption, plant uptake, microbial decomposition and nitrogen transformations (Dan et al., 2011; Faulwetter et al., 2009; Liang et al., 2009).
* Corresponding author. Tel.: +86 22 27403676; fax: +86 22 27403676. E-mail address: [email protected] (J.J. Huang). http://dx.doi.org/10.1016/j.ecoleng.2014.10.027 0925-8574/ ã 2014 Published by Elsevier B.V.
With the increasing number of CWs being built, the modelling of wetland function and performance has also attracted more attention. The main objectives of these modelling studies are to better understand the treatment processes in CWs and to improve the design, management, monitoring and maintenance of CWs. The SubWet 2.0 model is an interesting option that was originally developed for HSSF wetlands. This model has been reviewed and described previously by Jørgensen and Gromiec (2011); Chouinard et al. (2014a,b); Chouinard et al. (2014a,b). A key feature of SubWet 2.0 is the relative ease in calibrating the model to site conditions. This is done by adjusting the rate coefficients (within a defined range) until simulated values match observed values. In this manner, SubWet provides an integrated process response which lessens the need to know specific details about all treatment
J.J. Huang et al. / Ecological Engineering 74 (2014) 8–12
9
Table 1 Characteristics of the influents for VFCWI and VFCWII. Observed influents Contents
BOD5 COD Ammonia nitrogen Nitrate nitrogen TN TP
Unit
Mean
Standard deviation
Median
–
VFCWI
VFCWII
VFCWI
VFCWII
VFCWI
VFCWII
Maximum VFCWI
VFCWI
Minimum VFCWI
VFCWII
mg/L mg/L mg/L mg/L mg/L mg/L
15.3 45.1 12.9 12.0 22.6 11.1
11.5 38.3 3.0 0.11 11.3 0.13
6.4 17.4 14.9 18.2 20.9 3.8
4.0 13.2 2.0 0.1 6.9 0.06
16.5 48.1 9.3 3.8 15.7 9.8
11.0 36.7 2.3 0.2 9.7 0.11
32.0 79.1 62 69 76.1 23.2
21.8 72.6 7.6 0.6 29.6 0.31
3.0 21.5 1.1 0.2 2.6 7.0
4.0 13.3 0.5 0.1 3.7 0.05
DO 3.6–8.8, Temp. 9.2–20 C, pH 7.49–7.91 in VFCWI. DO 1.5–5.0, Temp. 4.5–22.4 C, pH 7.59–8.11 in VFCWII.
processes. For example, the ability to calibrate to site conditions allowed SubWet to be successfully applied to natural tundra treatment wetlands, where several of the parameters such as hydraulic loading and bed depth were variable and poorly defined (Chouinard et al., 2014b). The goal of this study was to assess the efficacy of SubWet 2.0 being applied to a vertical subsurface flow constructed wetland (VSSF-CW) in the hope that this model would provide a user-friendly, low cost and reasonably accurate option for the modelling of VSSF-CWs. For this purpose the performance of SubWet 2.0 was assessed by calibrating it to two different VSSFCWs that treated different sources of wastewaters. One of the VSSF-CWs was located in south eastern Canada and treated domestic wastewater, while the other CW was located in China and treated stormwater runoff. 2. Methodology and materials 2.1. Case studies The present study investigated two VSSF-CWs in two locations; one in Canada and one in China. The VSSF-CWI (as CWI) system was built at the Centre for Alternative Wastewater Treatment in Canada (latitude 45.05 , and longitude 78.53 ) to treat domestic sewage. The size of the vertical flow wetland cell was 4.0 m in length, 3.0 m in width and 1.5 m deep. The main matrix of the vertical flow wetland cell was sand. The treatment volume was between 15.5 and 16.6 m3 of waste water per day. The porosity of the matrix was estimated to be 0.35 and the effective volume of the vertical flow wetland bed was 6.3 m3 (in winter, the influent level was decreased to prevent surface freezing which reduced the effective volume of the wetland bed to 3.15 m3). Phragmites spp. was planted in the vertical flow wetland cell. The VSSF-CWII (as CWII) was built at Tianjin University in China (latitude 39.11, and longitude 117.17 ) to treat the water from a nearby stormwater pond. The dimensions of the vertical wetland flow wetland cell were 1.0 m in length, 0.5 m in width and 0.9 m deep. The matrix of the wetland cell consisted of gravel and ceramsite. The system was designed to treat a volume of 0.8–2.7 m3 of stormwater runoff per day. The porosity of the matrix was estimated to be 0.87 and the effective volume of the wetland bed was 0.39 m3. There were no plants in this system.
2.2. Data Water quality monitoring data were collected at the influent and effluent of CWI from February to September of 2013 and the CWII from June to December of 2013. Water samples were analysed for biochemical oxygen demand (BOD5, mg/L), chemical oxygen demand (COD, mg/L), nitrite nitrogen (mg/L), nitrate nitrogen (mg/ L), ammonium nitrogen (mg/L), total Kjeldahl nitrogen (TKN, mg/ L), total nitrogen (TN, mg/L), dissolved oxygen (DO, mg/L), phosphate as PO4 (mg/L), total phosphorus as P (TP, mg/L), pH and temperature ( C) according to standard methods (APHA, 2005). Table 1 summarizes the characteristics of the influents for the main parameters including BOD5, COD, nitrate nitrogen, ammonium nitrogen, TN and TP. The mean and standard deviation of the influent concentrations of CWI are higher than CWII since the source wastewater was pre-treated (septic tank) domestic sewage, while the influent of CWII was stormwater runoff. The mean values of nitrate nitrogen and TP of CWI were more than 100 the values of CWII, while the standard deviations of nitrate nitrogen and TP of CWI were 63 and 180 the values of CWII, respectively. The mean values of TN and ammonium nitrogen for CWI were 2 and 4.3 the values of CWII, while the standard deviations of TN and ammonium nitrogen of CWI were 3.0 and 7.5 the values of CWII, respectively. BOD5 and COD were approximately the same for the two CWs. 2.3. Model description (SubWet 2.0) SubWet was developed by UNEP-DTIE-IETC, and was originally intended for use in the design of horizontal subsurface flow constructed wetlands for the treament of domestic wastewaters. The model employs 25 differential process equations and 16 parameters as described by Jorgensen and Fath (2011). It can simulate the removal of BOD5, nitrate nitrogen, ammonium nitrogen, organic nitrogen and TP in milligrams per liter and the corresponding removal efficiencies in percentage. 2.4. Modelling setup Cold climate wetlands are defined in SubWet 2.0 as being sites with surface temperatures varying between below freezing in
Table 2 The correlation between observed and simulated results in VFCWI and VFCWII. Variable (mg/L)
BOD5 Ammonia-nitrogen Nitrate- nitrogen TP
Correlation coefficient (R)
NSE
P value
VFCWI
VFCWII
VFCWI
VFCWII
VFCWI
VFCWII
0.84 0.70 0.97 0.94
0.79 0.60 0.48 0.98
0.53 0.51 0.93 0.87
0.26 0.33 0.23 0.94
<0.0001 0.002 <0.0001 <0.0001
<0.0001 0.001 0.012 <0.0001
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Fig. 1. Comparison of observed and simulated effluent values of BOD5, nitrate nitrogen, ammonium nitrogen and TP. The time series on the X-axis refer to the sequencies of the data being collected.
J.J. Huang et al. / Ecological Engineering 74 (2014) 8–12
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Table 3 Calibrated values for the parameters of the models. Parameter Description
kOC
lOO uTO kAC
uTA
kPA kNC
lMA uTN
kPN kDC
lKO uTD lMN
CAF kMPP
Applicable range
Oxidation rate coefficient for organic matter, expressed as BOD5 (1/24 h) 0.05–2.0 Michaelis–Menten constant for influence of oxygen on the oxidation rate of organic matter, expressed as 0.01–2 BOD5 (mg/L) Temperature coefficient for oxidation of organic matter expressed as BOD5 (no units) 1.02–1.06 Max. decomposition rate of organic nitrogen (1/24 h) 0.05–2.0 Temperature coefficient for ammonification (no units) 1.02–1.06 Max. plant uptake rate of ammonium (1/24 h) 0.00–1 Max. nitrification rate (1/24 h) 0.1–2.5 Michaelis–Menten constant for nitrification (mg/L) 0.05–5 Temperature coefficient for nitrification (no units) 1.02–1.09 Max. plant uptake rate of nitrate (1/24 h) 0.00–1 Denitrification rate coefficient (1/24 h) 0.00–5 Michaelis–Menten constant for the influence of oxygen on the nitrification rate (mg/L) 0.01–2 Temperature coefficient for denitrification (no units) 1.05–1.12 Michaelis–Menten constant for denitrification (mg/L) 0.01–1 Inverse phosphorus adsorption capacity (mg/L) 0–100 Max. plant uptake rate of total phosphorus (no units) 0.00–1
winter to up to 22 C in summer. Warm climate wetlands are those which typically range in surface temperatures between 26 C and 34 C. The design input values of the model are used to specify the wetland width, length, depth, precipitation factor, slope, average % particulate matter, hydraulic conductivity and selected flow rate (in cubic meters per day). The types of wetlands (constructed wetland or natural wetland) must also be specified. After doing so, the model will calculate wetland area, volume, hydraulic loading, recommended horizontal flow, recommended flow, flow width, flow length and number of paths. The forcing functions present the operational parameters including length of simulation, volume, water flow, porosity (fraction), average oxygen concentration in five boxes, temperature, the input values of BOD5, nitrate nitrogen, ammonium nitrogen, total phosphorus and organic nitrogen along with the fraction of BOD5, phosphorus, and organic nitrogen as suspended matter. SubWet divides the wetland into 5 equal compartments or boxes and identifies these boxes with the suffixes of A, B, C, D, and E. The starting values (i.e., concentrations) entered into Box A for each of the water quality parameters being modelled should be a value that is slightly lower than the corresponding influent concentration for that water quality parameter stipulated in the previous Forcing Functions section. Subsequent values chosen for Boxes B–E should exhibit a step wise reduction so that the value in Box E reflects what is anticipated to be a reasonable level of treatment. If the length of the simulation chosen is long enough to reach a steady state, then the initial values of these water quality parameters do not need to be precise. The choice of cold climate or warm climate operating mode will determine which set of default parameters the model uses. These values can be used to calibrate SubWet to an individual wetland by comparing the simulated effluent values to the observed effluent values for that wetland. Slight modifications to specific coefficient values will improve the simulation by making the simulated effluent values closer to those of the observed effluent values. Further details outlining the operation of SubWet 2.0 are summarized by Jørgensen and Gromiec (2011) and Chouniard et al. (2014a).
3. Discussion and results 3.1. Statistical analyses The correlation coefficient R and NSE were obtained by using the standard software IBM SPSS Statistical 19. The significant
Default value
Calibration
Cold
Warm CWI
CWII
0.25 0.05
0.5 1.3
0.25 2
0.05 2
1.04 0.9 1.05 0.01 0.09 0.1 1.07 0.001 3.5 0.01 1.07 0.1 0.36 0.001
1.04 0.5 1.04 0.01 0.8 1 1.047 0.01 2.2 1.3 1.09 0.1 1 0.003
1.06 0.05 1.02 0.01 2.5 0.1 1.02 0.005 0.01 1 1.12 0.01 4.693 0.0015
1.06 0.05 1.06 0.001 0.5 0.1 1.02 0.001 1 2 1.12 0.01 3.54 0.001
difference (p < 0.05) was employed to assess the R values. These values are shown in Table 2. 3.2. Calibration and results Fig. 1 compares the observed values and simulated values for BOD5, nitrate nitrogen, ammonium nitrogen and TP. The comparison shows a reasonable agreement between the simulated values and observed values. Table 2 indicates that all correlation coefficients have a significance level above 99% and the R and NSE values of CWI are higher than CWII except for TP. The lowest R and NSE values are 0.48 and 0.23 for nitrate nitrogen, however, they are still statistically significant for correlations between simulated and observed values of CWII. The results demonstrate that SubWet 2.0 can be calibrated to well represent the processes in a VSSF wetland. 3.2.1. Simulation performance of the model for BOD5 The model simulation results indicate that the oxidation rate coefficient for organic matter in CWI is higher than that in CWII as demonstrated in Table 3. It is generally stated that organic matter is primarily removed by biological oxidation in wetlands, which is highly dependent on DO concentrations (Shackle et al., 2000 Zhang et al., 2010). Insufficient DO in the matrix can decrease biological oxidation and impair the removal of organic matter in wetlands. From Table 1 we can see that the concentration of DO in CWI was higher than in CWII, because the DO of domestic sewage for CWI was higher than for CWII. Additionally, the root zone of macrophytes may also result in higher DO in the wetlands (CasellesOsorio and García, 2007) and thus influence microbial transformation rates. 3.2.2. Simulation performance of the model for nitrogen From Table 2 we can see that the simulation performance of ammonium nitrogen and nitrate nitrogen are worse than the simulation performance of BOD5 and TP for the two CWs. Table 3 shows that the Max. nitrification rate coefficient of CWI is larger than for CWII, but the denitrification rate coefficient of CWI is smaller than for CWII. This is due to the fact that the primary form of TN is organic nitrogen and ammonification (mineralization) proceeds slower than nitrification proceeds in CWII. Therefore, less ammonium nitrogen is generated by mineralization that can then be nitrified for CWII. On the contrary, the proportion of the influent concentration of ammonium nitrogen made up 57.08% of TN in CWI
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and the influent had a higher concentration of DO than CWII (Chang et al., 2012; Vymazal, 2007). From Table 3, we can see that the plant uptake rates of ammonium nitrogen and nitrate nitrogen in CWI are larger than the values for CWII. Plants can play a major role in nitrogen removal when plants are harvested and managed to remain in an accelerated growth phase (Klomjek and Nitisoravut, 2005; Peterson and Teal, 1996). In fact, no plants were planted in CWII, however, there can still be microbial mediated transformation of nitrogen even though plants do not exist, therefore, a very small number 0.001 (the lowest number by default) was used for “plant uptake” to represent the process. 3.2.3. Simulation performance of the model for TP From Table 2, we can see that the R and NSE values for TP of CWII are both slightly higher than for CWI. Table 3 shows that Max. plant uptake rate of total phosphorus of CWII is slightly higher than for CWI. Arias et al. (2001) and Vohla et al. (2011) reported that the plant uptake and subsequent harvesting are the sustainable removal mechanism for phosphorus. With the same problem as the Chapter 3.2.2 about the plant uptake rates of ammonium nitrogen and nitrate nitrogen for CWII, the Max. plant uptake rate of total phosphorus is also a very small number 0.001 (the lowest number by default) for CWII. The model uses the parameter CAF to represent the adsorptive and inverse adsorptive ability of the matrix. From Table 3 we can see that the CAF value of the CWI is slightly higher than the value for the CWII. This indicates that the phosphorus adsorption capacity of the sand is stronger than the blend of gravel and ceramsite in the two CWs. 4. Conclusions The modelling of the two cases here illustrated that SubWet 2.0 model can adequately simulate the treatment of a range of wastewaters (stormwater runoff and domestic wastewater) within vertical subsurface flow constructed wetlands. The modelling of the two cases here illustrated that SubWet 2.0 simulated values were a reasonable reflection of observed treatment values. This study demonstrates that Subwet 2.0 provides a user-friendly and robust option for the modelling of vertical flow subsurface wetlands despite the fact that its original intention was for horizontal subsurface flow wetlands, underscoring the versatility of this wetland model. This study also observed that the high concentrations of DO can improve the removal efficiencies of COD, BOD5 and ammonium nitrogen but is not significant for nitrate nitrogen removal. Vegetation may play a major role in removing nitrogen and TP when plants are managed to remain in an accelerated growth phase. Acknowledgements This research was financially supported by Ministry of Water Resources (201201114) and Ministry of Education (NCET-09-0586).
We would like to express our thanks to the staff working in the Centre for Alternative Wastewater Treatment for their great efforts in collecting data to support this research. References American Public Health Association. APHA, 2005. Standard Methods for the Examination of Water and Wastewater, 21st ed. APHA, Washington, DC. Arias, C.A., Del Bubba, M., Brix, H., 2001. Phosphorus removal by sands for use as media in subsurface flow constructed reed beds. Water Res. 35 (5), 1159–1168. doi:http://dx.doi.org/10.1016/S0043-1354(00)368-7. Caselles-Osorio, A., García, J., 2007. Impact of different feeding strategies and plant presence on the performance of shallow horizontal subsurface-flow constructed wetlands. Sci. Total Environ. 378 (3), 253–262. doi:http://dx.doi.org/ 10.1016/j.scitotenv.2007.02.031. Chang, J.J., Wu, S.Q., Dai, Y.R., Liang, W., Wu, Z.B., 2012. Treatment performance of integrated vertical-flow constructed wetland plots for domestic wastewater. Ecol. Eng. 44, 152–159. doi:http://dx.doi.org/10.1016/j.ecoleng.2012.03.019. Chouinard, A., Balch, G.C., Wootton, B.C., Jørgensen, S.E., Anderson, B.C., 2014a. SubWet 2.0. Modeling the performance of treatment wetlands. In: Jørgensen, S. E., Chang, N.-B., Xu, F.-L. (Eds.), Ecological Modelling and Engineering of Lakes and Wetlands. Elsevier B.V., Amsterdam, The Netherlands doi:http://dx.doi.org/ 10.1016/B978-0-444-63249-4.00021-X. Chouinard, A., Yates, C.N., Balch, G.C., Jørgensen, S.E., Wootton, B.C., Anderson, B.C., 2014b. Management of tundra wastewater treatment wetlands within a lagoon/ wetland hybridized treatment system using the SubWet 2.0 wetland model. Water 6 (3), 439–454. doi:http://dx.doi.org/10.3390/w6030439. Dan, T.H., Quang, L.N., Chiem, N.H., Brix, H., 2011. Treatment of high-strength wastewater in tropical constructed wetlands planted with Sesbania sesban: horizontal subsurface flow versus vertical downflow. Ecol. Eng. 37 (5), 711–720. doi:http://dx.doi.org/10.1016/j.ecoleng.2010.07.030. Faulwetter, J.L., Gagnon, V., Sundberg, C., Chazarenc, F., Burr, M.D., Brisson, J., Stein, 2009. Microbial processes influencing performance of treatment wetlands: a review. Ecol. Eng. 35 (6), 987–1004. doi:http://dx.doi.org/10.1016/j.ecoleng.2008.12.030. Jorgensen, S.E., Fath, B., 2011. Ecological modelling. Applications in Environmental Management and Research, Vol. 23. Elsevier, pp. 23. Jørgensen, S.E., Gromiec, M.J., 2011. Mathematical models in biological waste water treatment – chapter 7.6., 4th ed. In: Jørgensen, S.E., Fath, B.D. (Eds.), Applications in Fundamentals of Ecological Modelling, Environmental Management and Research, Vol. 23. Elsevier, Amsterdam, Netherlands, pp. 414. Klomjek, P., Nitisoravut, S., 2005. Constructed treatment wetland: a study of eight plant species under saline conditions. Chemosphere 58 (5), 585–593. doi:http:// dx.doi.org/10.1016/j.chemosphere.2004.08.073. Liang, W., Deng, J.Q., Zhan, F.C., Wu, Z.B., 2009. Effects of constructed wetland system on the removal of dibutyl phthalate (DBP). Microbiol. Res. 164 (2), 206– 211. doi:http://dx.doi.org/10.1016/j.micres.2006.11.013. Peterson, S.B., Teal, J.M., 1996. The role of plants in ecologically engineered wastewater treatment systems. Ecol. Eng. 6 (1), 137–148. doi:http://dx.doi.org/ 10.1016/0925–8574(95) 55–0. Shackle, V.J., Freeman, C., Reynolds, B., 2000. Carbon supply and the regulation of enzyme activity in constructed wetlands. Soil Biol. Biochem. 32 (13), 1935– 1940. doi:http://dx.doi.org/10.1016/S0038–0717(00) 169–3. Vohla, C., Kõiv, M., Bavor, H.J., Chazarenc, F., Mander, Ü., 2011. Filter materials for phosphorus removal from wastewater in treatment wetlands – a review. Ecol. Eng. 37 (1), 70–89. Vymazal, J., 2007. Removal of nutrients in various types of constructed wetlands. Sci. Total Environ. 380 (1), 48–65. doi:http://dx.doi.org/10.1016/j.scitotenv.2006.09.014. Zhang, L.Y., Zhang, L., Liu, Y.D., Shen, Y.W., Liu, H., Xiong, Y., 2010. Effect of limited artificial aeration on constructed wetland treatment of domestic wastewater. Desalination 250 (3), 915–920. doi:http://dx.doi.org/10.1016/j. desal.2008.04.062.