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Statistical Modeling of Phosphorus Removal in Horizontal Subsurface Constructed Wetland

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Abstract

A horizontal subsurface flow constructed wetland (HSSF-CW) was constructed to improve the water quality of an artificial lake in Beijing wildlife rescue and rehabilitation center, Beijing, China. Multiple Regression Analysis (MRA) and Artificial Neural Networks (ANNs) including Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were used to model the treatment performance of total phosphorus (TP). In order to increase the model efficiency, input parameters were selected as influent TP concentration, hydraulic retention time, wastewater temperature, month of the year, porosity, area, precipitation and evapotranspiration based on the methods of principal component analysis (PCA) and redundancy analysis (RDA). Genetic algorithm and cross-validation were utilized to find the optimal network architecture and parameters for ANNs. The overall performance of the models was validated using different datasets from the case study spanning 3 years. The results implied that modeling using adequate but crucial parameters can provide an efficient and robust tool for predicting performance. By comparing the three models in terms of model fitness when applied to the prediction, ANNs seemed to be more efficient than MRA in modeling of the areal TP removal and RBF (R2: 0.829, p = 0.000) produced the most accuracy and efficiency indicating strong potential for modeling the TP treatment processes in HSSF-CW systems.

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References

  • Akratos CS, Papaspyros JN, Tsihrintzis VA (2009) Total nitrogen and ammonia removal prediction in horizontal subsurface flow constructed wetlands: use of artificial neural networks and development of a design equation. Bioresour Technol 100:586–596

    Article  CAS  PubMed  Google Scholar 

  • Arias CA, Brix H (2005) Phosphorus removal in constructed wetlands: can suitable alternative media be identified? Water Sci Technol 51:267–274

    CAS  PubMed  Google Scholar 

  • Bodyanskiy Y, Lamonova N, Pliss I, Vynokurova O (2005) An adaptive learning algorithm for a wavelet neural network. Expert Syst 22:235–240

    Article  Google Scholar 

  • Braskerud B (2002) Factors affecting phosphorus retention in small constructed wetlands treating agricultural non-point source pollution. Ecol Eng 19:41–61

    Article  Google Scholar 

  • Cui LJ, Zhang Y, Zhao XS, Li W, Zhang MY, Wang YF, Li SN (2011) Pollutants removal in subsurface constructed wetland based on the first-order kinetic model. China Environ Sci 31:1697–1704

    CAS  Google Scholar 

  • Dunne EJ, Culleton N, O’Donovan G, Harrington R, Daly K (2005) Phosphorus retention and sorption by constructed wetland soils in Southeast Ireland. Water Res 39:4355–4362

    Article  CAS  PubMed  Google Scholar 

  • Gómez Cerezo R, Suárez ML, Vidal-Abarca MR (2001) The performance of a multi-stage system of constructed wetlands for urban wastewater treatment in a semiarid region of SE Spain. Ecol Eng 16:501–517

    Article  Google Scholar 

  • Greenway M (2005) The role of constructed wetlands in secondary effluent treatment and water reuse in subtropical and arid Australia. Ecol Eng 25:501–509

    Article  Google Scholar 

  • Hamed MM, Khalafallah MG, Hassanien EA (2004) Prediction of wastewater treatment plant performance using artificial neural networks. Environ Model Softw 19:919–928

    Article  Google Scholar 

  • Hamoda MF, Al-Ghusain IA, Hassan AH (1999) Integrated wastewater treatment plant performance evaluation using artificial neural networks. Water Sci Technol 40:55–65

    Article  Google Scholar 

  • Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall, Inc

  • Kadlec RH (2000) The inadequacy of first-order treatment wetland models. Ecol Eng 15:105–119

    Article  Google Scholar 

  • Kadlec RH, Knight RL (1996) Treatment Wetlands. CRC Press, Boca Raton

    Google Scholar 

  • Kadlec RH, Wallace SD (2009) Treatment Wetlands. CRC Press, Boca Raton

    Google Scholar 

  • Keppel G, Zedeck S (1989) Data analysis for research designs. Worth Pub, New York

    Google Scholar 

  • Knight RL, Payne VWE, Borer RE, Clarke RA, Pries JH (2000) Constructed wetlands for livestock wastewater management. Ecol Eng 15:41–55

    Article  Google Scholar 

  • Krishna K, Murty MN (1999) Genetic K-means algorithm. Syst, Man, Cybern, Part B: Cybern, IEEE Transact 29:433–439

    Article  CAS  Google Scholar 

  • Laffaille P, Baisez A, Rigaud C, Feunteun E (2004) Habitat preferences of different European eel size classes in a reclaimed marsh: a contribution to species and ecosystem conservation. Wetlands 24:642–651

    Article  Google Scholar 

  • Laiho R, Vasander H, Penttilä T, Laine J (2003) Dynamics of plant–mediated organic matter and nutrient cycling following water–level drawdown in boreal peatlands. Global Biogeochemical Cycles 17

  • Langergraber G (2007) Simulation of the treatment performance of outdoor subsurface flow constructed wetlands in temperate climates. Sci Total Environ 380:210–219

    Article  CAS  PubMed  Google Scholar 

  • Liu J, Savenije HHG, Xu J (2003) Forecast of water demand in Weinan City in China using WDF-ANN model. Phys Chem Earth, Parts A/B/C 28:219–224

    Article  Google Scholar 

  • Maltais-Landry G, Maranger R, Brisson J, Chazarenc F (2009) Greenhouse gas production and efficiency of planted and artificially aerated constructed wetlands. Environ Pollut 157:748–754

    Article  CAS  PubMed  Google Scholar 

  • Mitsch W, Gosselink J (2000) Wetlands, 3rd edn. John Wiley & Sons, New York

    Google Scholar 

  • Naz M, Uyanik S, Yesilnacar MI, Sahinkaya E (2009) Side-by-side comparison of horizontal subsurface flow and free water surface flow constructed wetlands and artificial neural network (ANN) modelling approach. Ecol Eng 35:1255–1263

    Article  Google Scholar 

  • Pastor R, Benqlilou C, Paz D, Cardenas G, Espuña A, Puigjaner L (2003) Design optimisation of constructed wetlands for wastewater treatment. Resour, Conserv Recycl 37:193–204

    Article  Google Scholar 

  • Reed SC, Brown D (1995) Subsurface flow wetlands: a performance evaluation. Water environment research:244–248

  • Reed SC, Crites RW, Middlebrooks EJ (1995) Natural systems for waste management and treatment. McGraw-Hill, Inc

  • Rousseau DP, Vanrolleghem PA, Pauw ND (2004) Constructed wetlands in Flanders: a performance analysis. Ecol Eng 23:151–163

    Article  Google Scholar 

  • Sarle WS (2001) Neural Network FAQ, Part 2 of 7: learning, periodic posting to the Usenet newsgroup comp. ai. neural-nets. Retrieved September

  • Sharpley A, Kleinman P (2003) Effect of rainfall simulator and plot scale on overland flow and phosphorus transport. J Environ Qual 32:2172–2179

    Article  CAS  PubMed  Google Scholar 

  • Šmilauer P (2003) Cano Draw for Windows 4.1, Software manual, Microcomputer Power. New York

  • Song Z, Zheng Z, Li J, Sun X, Han X, Wang W, Xu M (2006) Seasonal and annual performance of a full-scale constructed wetland system for sewage treatment in China. Ecol Eng 26:272–282

    Article  Google Scholar 

  • Talebizadeh M, Moridnejad A (2011) Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models. Expert Syst Appl 38:4126–4135

    Article  Google Scholar 

  • Tanner CC (1994) Treatment of dairy farm wastewaters in horizontal and up-flow gravel-bed constructed wetlands. Water Sci Technol 29:85–93

    CAS  Google Scholar 

  • Tomenko V, Ahmed S, Popov V (2007) Modelling constructed wetland treatment system performance. Ecol Model 205:355–364

    Article  Google Scholar 

  • Vohla C, Alas R, Nurk K, Baatz S, Mander Ü (2007) Dynamics of phosphorus, nitrogen and carbon removal in a horizontal subsurface flow constructed wetland. Sci Total Environ 380:66–74

    Article  CAS  PubMed  Google Scholar 

  • Vymazal J (2002) The use of sub-surface constructed wetlands for wastewater treatment in the Czech Republic: 10 years experience. Ecol Eng 18:633–646

    Article  Google Scholar 

  • Vymazal J (2009) The use constructed wetlands with horizontal sub-surface flow for various types of wastewater. Ecol Eng 35:1–17

    Article  Google Scholar 

  • Wang W, Gelder PHV, Vrijling JK, Ma J (2006) Forecasting daily streamflow using hybrid ANN models. J Hydrol 324:383–399

    Article  Google Scholar 

  • Wynn TM, Liehr SK (2001) Development of a constructed subsurface-flow wetland simulation model. Ecol Eng 16:519–536

    Article  Google Scholar 

  • Yager RR, Filev DP (1994) Generation of fuzzy rules by mountain clustering. J Intell Fuzzy Syst 2:209–219

    Google Scholar 

  • Zhang CB, Wang J, Liu WL, Zhu SX, Ge HL, Chang SX, Chang J, Ge Y (2010) Effects of plant diversity on microbial biomass and community metabolic profiles in a full-scale constructed wetland. Ecol Eng 36:62–68

    Article  Google Scholar 

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Acknowledgments

This study was funded by National Nonprofit Institute Research Grant of Chinese Academy of Foerestry “Dynamic mechanism of phosphorus removal in subsurface constructed wetland” (CAFINT2013C13). We are grateful to all members of the research team for their helpful comments and advice.

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Correspondence to Lijuan Cui.

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Li, W., Cui, L., Zhang, Y. et al. Statistical Modeling of Phosphorus Removal in Horizontal Subsurface Constructed Wetland. Wetlands 34, 427–437 (2014). https://doi.org/10.1007/s13157-013-0509-7

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