IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v14y2017i7p765-d104463.html
   My bibliography  Save this article

Modeling Nitrogen Dynamics in a Waste Stabilization Pond System Using Flexible Modeling Environment with MCMC

Author

Listed:
  • Hussnain Mukhtar

    (Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan)

  • Yu-Pin Lin

    (Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan)

  • Oleg V. Shipin

    (Environmental Engineering and Management Program, School of Environment, Resources and Development, Asian Institute of Technology, Pathum Thani 12120, Thailand)

  • Joy R. Petway

    (Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan)

Abstract

This study presents an approach for obtaining realization sets of parameters for nitrogen removal in a pilot-scale waste stabilization pond (WSP) system. The proposed approach was designed for optimal parameterization, local sensitivity analysis, and global uncertainty analysis of a dynamic simulation model for the WSP by using the R software package Flexible Modeling Environment (R-FME) with the Markov chain Monte Carlo (MCMC) method. Additionally, generalized likelihood uncertainty estimation (GLUE) was integrated into the FME to evaluate the major parameters that affect the simulation outputs in the study WSP. Comprehensive modeling analysis was used to simulate and assess nine parameters and concentrations of ON-N, NH 3 -N and NO 3 -N. Results indicate that the integrated FME-GLUE-based model, with good Nash–Sutcliffe coefficients (0.53–0.69) and correlation coefficients (0.76–0.83), successfully simulates the concentrations of ON-N, NH 3 -N and NO 3 -N. Moreover, the Arrhenius constant was the only parameter sensitive to model performances of ON-N and NH 3 -N simulations. However, Nitrosomonas growth rate, the denitrification constant, and the maximum growth rate at 20 °C were sensitive to ON-N and NO 3 -N simulation, which was measured using global sensitivity.

Suggested Citation

  • Hussnain Mukhtar & Yu-Pin Lin & Oleg V. Shipin & Joy R. Petway, 2017. "Modeling Nitrogen Dynamics in a Waste Stabilization Pond System Using Flexible Modeling Environment with MCMC," IJERPH, MDPI, vol. 14(7), pages 1-15, July.
  • Handle: RePEc:gam:jijerp:v:14:y:2017:i:7:p:765-:d:104463
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/14/7/765/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/14/7/765/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Soetaert, Karline & Petzoldt, Thomas, 2010. "Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i03).
    2. Møller, Cathrine Christmas & Weisser, Johan J. & Msigala, Sijaona & Mdegela, Robinson & Jørgensen, Sven Erik & Styrishave, Bjarne, 2016. "Modelling antibiotics transport in a waste stabilization pond system in Tanzania," Ecological Modelling, Elsevier, vol. 319(C), pages 137-146.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Littfinski, Tobias & Stricker, Max & Nettmann, Edith & Gehring, Tito & Hiegemann, Heinz & Krimmler, Stefan & Lübken, Manfred & Pant, Deepak & Wichern, Marc, 2022. "A generalized whole-cell model for wastewater-fed microbial fuel cells," Applied Energy, Elsevier, vol. 321(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, W. & O’Neill, E. & Moncaster, A. & Reiner, D. & Guthrie, P., 2019. "Applying Bayesian Model Averaging to Characterise Urban Residential Stock Turnover Dynamics," Cambridge Working Papers in Economics 1986, Faculty of Economics, University of Cambridge.
    2. Hanson, Paul C. & Stillman, Aviah B. & Jia, Xiaowei & Karpatne, Anuj & Dugan, Hilary A. & Carey, Cayelan C. & Stachelek, Joseph & Ward, Nicole K. & Zhang, Yu & Read, Jordan S. & Kumar, Vipin, 2020. "Predicting lake surface water phosphorus dynamics using process-guided machine learning," Ecological Modelling, Elsevier, vol. 430(C).
    3. Hannah Al Ali & Alireza Daneshkhah & Abdesslam Boutayeb & Zindoga Mukandavire, 2022. "Examining Type 1 Diabetes Mathematical Models Using Experimental Data," IJERPH, MDPI, vol. 19(2), pages 1-20, January.
    4. Taffi, Marianna & Paoletti, Nicola & Liò, Pietro & Pucciarelli, Sandra & Marini, Mauro, 2015. "Bioaccumulation modelling and sensitivity analysis for discovering key players in contaminated food webs: The case study of PCBs in the Adriatic Sea," Ecological Modelling, Elsevier, vol. 306(C), pages 205-215.
    5. Lucash, Melissa S. & Marshall, Adrienne M. & Weiss, Shelby A. & McNabb, John W. & Nicolsky, Dmitry J. & Flerchinger, Gerald N. & Link, Timothy E. & Vogel, Jason G. & Scheller, Robert M. & Abramoff, Ro, 2023. "Burning trees in frozen soil: Simulating fire, vegetation, soil, and hydrology in the boreal forests of Alaska," Ecological Modelling, Elsevier, vol. 481(C).
    6. Meier, Laura & Brauns, Mario & Grimm, Volker & Weitere, Markus & Frank, Karin, 2022. "MASTIFF: A mechanistic model for cross-scale analyses of the functioning of multiple stressed riverine ecosystems," Ecological Modelling, Elsevier, vol. 470(C).
    7. Sehjeong Kim & Abdessamad Tridane, 2017. "Thalassemia in the United Arab Emirates: Why it can be prevented but not eradicated," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-13, January.
    8. Lee, Kyoungjae & Lee, Jaeyong & Dass, Sarat C., 2018. "Inference for differential equation models using relaxation via dynamical systems," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 116-134.
    9. Jinyoung Yang & Jeffrey S. Rosenthal, 2017. "Automatically tuned general-purpose MCMC via new adaptive diagnostics," Computational Statistics, Springer, vol. 32(1), pages 315-348, March.
    10. repec:jss:jstsof:33:i09 is not listed on IDEAS
    11. Soetaert, Karline & Petzoldt, Thomas & Setzer, R. Woodrow, 2010. "Solving Differential Equations in R: Package deSolve," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i09).
    12. McCullough, Ian M. & Dugan, Hilary A. & Farrell, Kaitlin J. & Morales-Williams, Ana M. & Ouyang, Zutao & Roberts, Derek & Scordo, Facundo & Bartlett, Sarah L. & Burke, Samantha M. & Doubek, Jonathan P, 2018. "Dynamic modeling of organic carbon fates in lake ecosystems," Ecological Modelling, Elsevier, vol. 386(C), pages 71-82.
    13. Venolia, Celeste T. & Lavaud, Romain & Green-Gavrielidis, Lindsay A. & Thornber, Carol & Humphries, Austin T., 2020. "Modeling the Growth of Sugar Kelp (Saccharina latissima) in Aquaculture Systems using Dynamic Energy Budget Theory," Ecological Modelling, Elsevier, vol. 430(C).
    14. Haas, Marcelo B. & Guse, Björn & Pfannerstill, Matthias & Fohrer, Nicola, 2015. "Detection of dominant nitrate processes in ecohydrological modeling with temporal parameter sensitivity analysis," Ecological Modelling, Elsevier, vol. 314(C), pages 62-72.
    15. Keane, Robert E. & McKenzie, Donald & Falk, Donald A. & Smithwick, Erica A.H. & Miller, Carol & Kellogg, Lara-Karena B., 2015. "Representing climate, disturbance, and vegetation interactions in landscape models," Ecological Modelling, Elsevier, vol. 309, pages 33-47.
    16. Shoya Iwanami & Kosaku Kitagawa & Hirofumi Ohashi & Yusuke Asai & Kaho Shionoya & Wakana Saso & Kazane Nishioka & Hisashi Inaba & Shinji Nakaoka & Takaji Wakita & Odo Diekmann & Shingo Iwami & Koichi , 2020. "Should a viral genome stay in the host cell or leave? A quantitative dynamics study of how hepatitis C virus deals with this dilemma," PLOS Biology, Public Library of Science, vol. 18(7), pages 1-17, July.
    17. Krishna, Shubham & Pahlow, Markus & Schartau, Markus, 2019. "Comparison of two carbon-nitrogen regulatory models calibrated with mesocosm data," Ecological Modelling, Elsevier, vol. 411(C).
    18. Raquel Martins Lana & Maíra Moreira Morais & Tiago França Melo de Lima & Tiago Garcia de Senna Carneiro & Lucas Martins Stolerman & Jefferson Pereira Caldas dos Santos & José Joaquín Carvajal Cortés &, 2018. "Assessment of a trap based Aedes aegypti surveillance program using mathematical modeling," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-16, January.
    19. Littfinski, Tobias & Stricker, Max & Nettmann, Edith & Gehring, Tito & Hiegemann, Heinz & Krimmler, Stefan & Lübken, Manfred & Pant, Deepak & Wichern, Marc, 2022. "A generalized whole-cell model for wastewater-fed microbial fuel cells," Applied Energy, Elsevier, vol. 321(C).
    20. Kankoé Sallah & Roch Giorgi & El-Hadj Ba & Martine Piarroux & Renaud Piarroux & Badara Cisse & Jean Gaudart, 2020. "Targeting Malaria Hotspots to Reduce Transmission Incidence in Senegal," IJERPH, MDPI, vol. 18(1), pages 1-17, December.
    21. Tom Shatwell & Jan Köhler & Andreas Nicklisch, 2014. "Temperature and Photoperiod Interactions with Phosphorus-Limited Growth and Competition of Two Diatoms," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-15, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:14:y:2017:i:7:p:765-:d:104463. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.