Paddy Water Environ (2010) 8:325–332 DOI 10.1007/s10333-010-0211-9
ARTICLE
Determination of regression model parameter for constructed wetland using operating data Y. K. Son • C. G. Yoon • H. C. Kim J. H. Jang • S. B. Lee
•
Received: 1 February 2010 / Revised: 14 April 2010 / Accepted: 16 April 2010 / Published online: 5 May 2010 Ó Springer-Verlag 2010
Abstract Seven years of performance data from a free surface flow constructed wetland system receiving agricultural runoff were used to determine treatment performance and to develop regression and wetland design models. Removal rates by the wetland were 21–43.6% for 5-day biochemical oxygen demand (BOD5), 49.0–58.1% for total phosphorous (TP), 24.1–46.0% for total nitrogen (TN), and 57.6–77.8% for total suspended solids (TSS). First-order area-based rate constant (k20) values for BOD5 were 15.48 m/year in the early stage of observation and decreased to 12.00 m/year for the stable period. Similar results were found for TP, for which k20 values were 18.72 m/year in the early stage and 14.92 m/year for the stable period. For TN, k20 values in the early stage (21.32 m/year) were slightly lower than those for the stable period (38.02 m/year). Finally, TSS had values of 132.4 and 172.6 m/year in the early and stable periods, respectively. The low k20 for BOD5 was not important for nonpoint source pollution control in the constructed wetland because these kinds of wetlands mainly focus on nitrogen and phosphorus retention. The wetland area and outlet concentration could be approximately predicted using the first-order kinetic model, but the maturity and hydraulic loading rate should be considered for more accurate prediction. Keywords Constructed wetland Design factor Nonpoint source pollution control Regression analysis
Y. K. Son C. G. Yoon (&) H. C. Kim J. H. Jang S. B. Lee Department of Environmental Science, Konkuk University, Seoul 143-701, Korea e-mail:
[email protected]
Introduction Nonpoint source pollutants are often significant contributors to eutrophication in lakes, reservoirs, and estuaries. Eutrophication is a critical problem impairing surface water quality, and effective control of lake and reservoir eutrophication has received a great deal of interest. Best management practices to control nonpoint source pollution have been considered the primary methods of protecting against eutrophication. Among such practices, wetland restoration and wetland construction have frequently been proposed to combat the eutrophication of aquatic ecosystems and reduce nutrient loads. Quantifying nutrient retention in natural and constructed wetlands is an important step in efforts to enhance water quality (Vymazal et al. 1998; Lund et al. 2000; Spieles and Mitsch 2000, Yoon et al. 2008). Treatment wetlands are engineered systems designed to use natural processes involving wetland vegetation, soils, and their associated microbial assemblages to assist in treating wastewater (Vymazal et al. 1998). Contaminants in wetland are removed through a combination of physical, chemical, and biological processes including sedimentation, precipitation, adsorption to soil particles, assimilation by plant tissue, and microbial transformations and interactions (Moshiri 1993; Reed et al. 1995; Kadlec and Knight 1996; Mitsch and Gosselink 2000; White et al. 2004). The topography of the wetland bottom, including the arrangement and abundance of deep and shallow zones, has also been considered an important factor (Kadlec 2007). Many researchers have used regression models and first-order kinetic equations to simplify and demonstrate wetland performance. However, although simple and widely used, such models fail to adequately characterize the complex processes that occur in a wetland (Kadlec 2000). Current
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design procedures also fail to incorporate atmospheric interactions such as precipitation, evaporation, and transpiration. These variables produce a secondary hydraulic regime that may influence retention times and invalidate steady state theoretical models (Jamieson et al. 2007). While such limitations are known, data availability and applicability have meant that such simple regression and kinetic models have been widely used. In this study, long-term monitoring data were collected from June 2002 to October 2008 and used to investigate the role of a constructed wetland system in treating nonpoint source pollution by developing regression equations and first-order area-based models, and comparing with other researches. These research results may be applied to design and manage of constructed wetland for treating nonpoint source pollution control from paddy field.
Materials and methods Study area and constructed wetland The study site was located in the Seokmoon polder watershed on the west coast of the Korean Peninsula (Fig. 1). This area has a moderate climate, with an average annual temperature of 12.0°C, ranging from 33.5°C in August to -13.1°C in January. Average annual rainfall is 909.6 mm, over 50% of which falls in July and August. The Seokmoon polder watershed encompasses 22,630 ha with 14% agricultural land use and 33% forested lands. The polder area covers 3,740 ha, and a reservoir with an average depth of 3.87 m covers 874 ha. Two large streams, the Dangjin and the Yuk, flow into the Seokmoon estuarine reservoir. In this study, water flowing from Dangjin Stream into Seokmoon reservoir was pumped into the studied wetland system to examine treatment performance. Effluent of paddy field is considered a major nonpoint source pollutant of Dangjin Stream. The constructed wetland studied here was built in 2002 and included four sets of free-surface-flow wetlands; each
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consisting of 0.8 ha of shallow vegetation zone and 0.08 ha of deep retention zone (Fig. 1). Water depths in the wetland and pond areas were maintained at 0.3–0.5 and 2 m, respectively, and the hydraulic retention time was about 2–15 days. Two different wetland systems were examined: a shallow vegetation zone-deep retention zone system (cells 2 and 4), where polluted water first flows into the shallow vegetation zone and then drains to the deep retention zone, and a deep retention zone–shallow vegetation zone system (cells 1 and 3) where water flows into the deep retention zone and then the shallow vegetation zone. Sampling and analytical methods Water flow was continuously measured in each wetland cell during the study period. Spot measurements of inlet and outlet flows of each cell were made using small current meters. Flows were also monitored and recorded using pressure-type water gauges and a data logger to assist spot measurement and verify continuous flow. Water samples of influent and effluent were taken from each wetland cell. Sampling frequency was twice a month during the study period of June 2002 to October 2008. Conventional water quality parameters including 5-day biochemical oxygen demand (BOD5), total nitrogen and phosphorus (TN, TP), and total suspended solids (TSS) were analyzed using standard methods (APHA 1998). The observed data were compared with those from the Treatment Wetland Database (TWDB) using scatter plots and regression models, and simplified design factor equations were developed using a first-order area-based model obtained by non-linear regression (Kadlec and Knight 1996). Regression analysis Knight et al. (2000) and Stone et al. (2004) conducted regression analyses to determine if significant relationships existed between wetland inflow and outflow concentrations. The regression equation was modeled to predict outflow concentration as a function of inflow concentration and hydraulic loading rate: Co ¼ aCib qc ;
Fig. 1 The study area and constructed wetland system
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ð1Þ
where Co is the outflow concentration (mg/l), Ci inflow concentration (mg/l), q the hydraulic loading rate (cm/day), and a, b, and c are regression coefficients. The parameters of such statistical models are referred to as ‘‘rate constants,’’ but there is no a priori reason to believe that these ‘‘constants’’ do not in fact depend on other operational characteristics of the wetland (Kadlec 1997). Therefore, regression models may provide information on the overall performance of wetlands but are typically considered valid only for the range of data used in the model (Stone et al. 2004). In
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this study, the observed data used to develop regression models were compared to TWDB data to relate them to data from other wetlands. The TWDB contains system descriptions and performance data for a large number of pilot and full-scale wetland systems treating a variety of sources, including municipal wastewater, stormwater runoff, industrial wastewater, and agricultural runoff. The database includes most of the entries in the revised North American Database (NADB Version 2) and data from many additional treatment wetlands (US EPA 2000). Moreover, result of developed regression equation was verified with observed data of wetland effluent using scatter plot.
Table 1 Water quality variables (mean ± standard deviation) for the constructed wetland cells between June 2002 and October 2008
First-order area-based model
Cell 3
BOD5 mg/l
where Co is the outflow concentration (mg/l), Ci inflow concentration (mg/l), C* the background concentration in the wetland (mg/l), kT the first-order areal rate constant for removal (m/year), s mean detention time (day), N number of the tanks (unitless), and h water column depth (m). In the equation, wetland was divided into five tanks which represent hydraulic parameter of wetland (N). Model determined temperature dependence of the first-order areal rate constant using a modified Arrhenius equation (Chapra 1997): kT ¼ k20 hðT20Þ ;
ð4Þ
where kT and k20 are area-based first-order rate constants at temperatures T and 20°C, respectively, and h the temperature correction factor. Nonlinear regression was applied to the experimental data to obtain the k20, h, and C* values.
Results and discussion Constructed wetland performance Table 1 presents the mean inlet and outlet concentrations for BOD5, TN, TP, and TSS. The mean inlet and outlet concentrations were as follows: 3.77–5.35 and 2.72– 3.02 mg/l for BOD5, 3.42–4.09 and 1.57–1.83 mg/l for TN, 0.27–0.36 and 0.16–0.23 mg/l for TP, and 12.50–18.00 and 3.50–5.00 mg/l for TSS, respectively.
TP mg/l
TSS mg/l
Cell 1 Inlet
4.32 ± 3.07
3.60 ± 1.77
0.29 ± 0.16
12.50 ± 9.94
Outlet
3.00 ± 3.69
1.57 ± 1.79
0.22 ± 0.18
3.50 ± 6.09
RR
30.6
56.3
24.1
72.0
Cell 2 Inlet Outlet RR
Many pollutants decline exponentially to a background concentration (C*) upon passage through a wetland (Kadlec et al. 2000). Accordingly, a biological reaction is usually described as a first-order reaction. In this study, wetland performance was evaluated by seasonal performance and temperature effect using the following first-order area-based model with tank series philosophy (Kadlec et al. 2000; Kadlec and Wallace 2008): C C kT 365s ln ; ð2Þ ¼ N 1 þ Nh Co C
TN mg/l
3.90 ± 2.72 2.72 ± 3.09 21.5
3.82 ± 1.70 1.60 ± 1.65 58.1
0.36 ± 0.19 0.23 ± 0.26 35.5
18.00 ± 17.83 4.00 ± 3.53 77.8
Inlet
5.35 ± 3.93
3.42 ± 1.64
0.27 ± 0.14
14.00 ± 7.13
Outlet
3.02 ± 3.13
1.64 ± 1.43
0.16 ± 0.13
4.00 ± 4.97
RR
43.6
55.0
39.6
57.6
Cell 4 Inlet
3.77 ± 2.74
4.09 ± 1.83
0.34 ± 0.22
15.00 ± 38.99
Outlet
2.98 ± 3.10
1.83 ± 1.70
0.18 ± 0.16
5.00 ± 8.15
RR
21.0
49.0
46.0
66.7
RR removal rate (%); Number of samples: 91
The above values and standard deviations show variation in the influent and effluent concentrations of the constructed wetlands and could indicate with flowing treatment rate that the constructed wetlands had a high capacity to remove pollutants. The constructed wetlands generally had similar ranges of removal efficiency, with the following values: 21–43.6% for BOD5, 49.0–58.1% for TN, 24.1–46.0% for TP, and 57.6–77.8% for TSS. Performances of the pond–wetland system (cells 1 and 3) and wetland–pond system (cells 2 and 4) were generally in the same range, but the former is considered preferable because it allows incoming suspended solids and associated pollutants to settle. Regression analysis Five-day biochemical oxygen demand Figure 2 shows the observed relationships between BOD5 mass loading and treatment wetland outflow concentration for all of the individual data points. TWDB data are also included for comparison. Fitting of a simple regression model to these data allows for estimation of the average outlet concentration Co based on the inlet concentration Ci and hydraulic loading q: Co ¼ 0:6249Ci1:1012 q0:0562 :
ð5Þ
For observed values, the coefficient of determination (r2) for the regression was 0.53. The inlet and outlet
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Fig. 2 Relationship between inlet BOD5 loading and the outlet concentration
concentrations of BOD5 were 0.18–14.25 and 0.1–16 mg/l, respectively. The trend of the regression curve developed with observed data was similar to that of TWDB data, but the r2 value suggests that caution is needed if applying this equation to long-term prediction. Total nitrogen A regression model for TN was developed here because agricultural runoff, especially farmyard outflow, could contain high nitrogen concentration because of using fertilizer uses. A simple regression model for TN fitted to these data enables estimation of the average outlet concentration Co based on the inlet concentration Ci and hydraulic loading q: Co ¼ 0:2983Ci1:2526 q0:1268 :
ð6Þ
Figure 3 summarizes the observed inlet loading and outlet concentration of TN and plots the regression model with a mean hydraulic loading rate of 8.99 cm/day along with those of Knight et al. (2000) and Stone et al. (2004). The r2, inlet and outlet concentrations for TN were 0.57, 0.88–9.05 mg/l, and 0.17–6.90 mg/l, respectively. Y-intercept of the regression graph developed in this study which means treatment efficiency was compared with two other studies. Estimated graph show less treatment than Knight et al. (2000), but more effective treatment than reported by Stone et al. (2004). The studies of Knight et al. and Stone et al. included summary data from various constructed wetlands treating dairy, cattle, swine, poultry, catfish pond water, and runoff from cattle feed operations. Total phosphorus The TP regression model for inlet mass loading and hydraulic loading rates versus outlet concentration gave r2 values of *0.49 (Fig. 4).
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Influent concentrations ranged from 0.093 to 1.170 mg/l for TP, while effluent concentrations were 0.002– 0.990 mg/l; the regression model was as follows: Co ¼ 0:3857Ci0:9621 q0:2175 :
ð7Þ
The wetland system studied here was constructed in 2002, and TP treatment efficiency could vary with age. The treatment efficiency for phosphorus may be higher during the initial years of operation and decline as a system matures (Kadlec and Knight 1996). Therefore, the low r2 value might reflect variation in inlet and outlet concentrations during the operation period. Total suspended solids Inflow and outflow TSS concentrations were 0.50–99.50 and 0.33–46.00 mg/l, respectively. Figure 5 presents the observed relationship between BOD5 mass loading and treatment wetland outflow concentrations and TSS data from the TWDB. Y intercept of the regression model predicted better treatment than found by Knight et al. (2000), while the scatter plot range was approximately similar to that for TWDB data. The following simple regression model for TSS was developed: Co ¼ 4:2690Ci0:2638 q0:1690 :
ð8Þ
Model verification Developed regression models were verified with observed data. Equations 5, 6, 7, and 8 were used for BOD, TN, TP, and TSS, respectively. Simulation data were plotted versus observed data in Fig. 6. BOD and TN showed similar tendency with observed data, but TN was underestimated in high effluent concentration. TP was underestimated in all conditions therefore use of regression model that developed in this study should be careful and considered that
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Fig. 3 Relationship between inlet TN loading and outlet concentration. Regression models were plotted with a mean hydraulic loading of q = 8.99 cm/day for comparison
Fig. 4 Relationship between inlet TP loading and outlet concentration. The regression models were plotted with a mean hydraulic loading of q = 8.99 cm/day for comparison
Fig. 5 Relationship between inlet TSS loading and outlet concentration. Regression models were plotted with a mean hydraulic loading of q = 8.99 cm/day for comparison
simulation data could lower than observed values. TSS equation was shown poor performance because simple regression equation could not translate resuspension according to wind and rainfall event. Although performance
of developed regression model does not showed high accuracy, these equations were simple and useful tools to predict constructed wetland performance when understand the weak point of developed regression model.
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First-order area-based model Table 2 lists the first-order areal rate constants (k20), background concentrations (C*), and temperature correction factors (h) for this study. Wetland data were obtained for the entire study period and analyzed by nonlinear regression to calculate rate constants of BOD5, TN, TP, and TSS for the four constructed wetland systems. Observed data were divided into two periods, early period (2002– 2004) and stable period (2005–2008), and the rate constants were compared based on the maturity of the constructed wetland. For BOD5, the k20 values were 15.48 m/year in the early stage, but dropped to 12.00 m/year for the stable period. Similar results were found for TP, which had k20
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values of 18.72 m/year in the early period and 14.92 m/year for the stable period. The k20 values for TP in this study were higher than Kadlec and Knight’s (1996) value of 11.9 m/year. During the initial operation of wetlands, phosphorus removal may be inflated because of rapid storage of phosphorus on soil sorption sites and in growing vegetative biomass (Kadlec and Knight 1996). Therefore, we expect that a lower TP removal rate might be maintained after several years and should be monitored. The removal rate constants for TN and TSS increased with maturity. For TN, k20 values were 21.32 m/year in the early stage and slightly higher at 38.02 m/year for the stable period. Values of TSS changed from 132.4 m/year initially to 172.6 m/year for the stable period. Removal of
Fig. 6 Regression model simulation data versus observed data for BOD, TN, TP, and TSS
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Table 2 Rate constant (k20), background concentration (C*), and temperature correction factor (h) for the early period (2002–2004), stable period (2005–2008), and the total period (2002–2008), based on the maturity of the constructed wetland 2002–2004 k20
C*
2005–2008 h
k20
C*
2002–2008 h
K20
C*
h
BOD5
15.48 2.20 1.23
12.00 2.90 0.87
12.32 2.00 1.19
TN
21.32 0.92 1.07
38.02 0.85 1.08
25.52 0.94 1.06
TP
18.72 0.09 0.99
14.92 0.10 0.99
13.44 0.11 0.99
TSS
132.4
6.28 1.13 172.6
4.31 1.07 168.3
5.36 1.14
TN most commonly arises from denitrification (Spieles and Mitsch 2000), by which nitrate is converted into nitrogen gases via the intermediates nitrite, nitric oxide, and nitrous oxide using a carbon source. As most denitrification is accomplished by heterotrophic bacteria, the process greatly depends on carbon availability (Kadlec and Wallace 2008). In the early stage of the wetland, carbon sources may have been insufficient because carbon supply was limited to influent BOD. Therefore, carbon availability could have limited nitrogen removal and the wetland might not have generated the required carbon energy source. After several years, the carbon supply would have been supplemented by plant biomass and carbon accumulation in wetland soils. This carbon accumulation could explain the increased nitrogen removal rate.
Conclusions A wetland system was constructed for treatment of agricultural tailwater in the Seokmoon polder watershed on the west coast of Korea. During 7 years of operation, hydraulic loading was regulated from 500 to 1,000 m3/day, and four water quality parameters were observed. In four independent wetland cells, the observed treatment efficiencies were 21–43.6% for BOD5, 49.0–58.1% for TN, 24.1–46.0% for TP, and 57.6–77.8% for TSS. The overall removal rates indicated that these wetlands were effective in treating nutrients from agricultural runoff. Nonlinear regression analysis was performed to obtain simple regression models and removal rate constants. Moreover, a first-order area-based model was developed from the rate constants and background concentrations. The developed regression curves were compared with regression equations reported by Knight et al. (2000) and Stone et al. (2004). Coefficients of determination (r2) ranged from 0.49 to 0.62. The observed data and TWDB data had similar distributions, but r2 values suggest caution when applying the developed equations to long-term prediction. The computed first-order rate constants (k20) for
TN and TP from this study were higher than those of Kadlec and Knight (1996) (TN 15.4 m/year; TP 11.9 m/year), while k20 values for BOD5 were lower (32.0 m/year) but within the range of reported values. The low k20 for BOD5 was not important for the control of nonpoint source pollution in the constructed wetland because these kinds of wetlands mainly focus on retention of nitrogen and phosphorus (Ham 2005). The wetland area and outlet concentration could be approximately predicted except TSS using the first-order areal-based models developed in this study. However, the maturity and hydraulic loading rate should be considered to obtain more accurate predictions. Acknowledgments This study was supported by the Development Program of Freshwater Reservoir Integrated Water Quality Prediction and Improvement System of the Korea Rural Research Institute.
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