IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v11y2014i9p9306-9324d40030.html
   My bibliography  Save this article

Simulation and Evaluation of Pollution Load Reduction Scenarios for Water Environmental Management: A Case Study of Inflow River of Taihu Lake, China

Author

Listed:
  • Ruibin Zhang

    (State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210046, China)

  • Xin Qian

    (State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210046, China)

  • Wenting Zhu

    (State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210046, China)

  • Hailong Gao

    (State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210046, China)

  • Wei Hu

    (State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210046, China)

  • Jinhua Wang

    (State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210046, China)

Abstract

In the beginning of the 21st century, the deterioration of water quality in Taihu Lake, China, has caused widespread concern. The primary source of pollution in Taihu Lake is river inflows. Effective pollution load reduction scenarios need to be implemented in these rivers in order to improve the water quality of Taihu Lake. It is important to select appropriate pollution load reduction scenarios for achieving particular goals. The aim of this study was to facilitate the selection of appropriate scenarios. The QUAL2K model for river water quality was used to simulate the effects of a range of pollution load reduction scenarios in the Wujin River, which is one of the major inflow rivers of Taihu Lake. The model was calibrated for the year 2010 and validated for the year 2011. Various pollution load reduction scenarios were assessed using an analytic hierarchy process, and increasing rates of evaluation indicators were predicted using the Delphi method. The results showed that control of pollution from the source is the optimal method for pollution prevention and control, and the method of “Treatment after Pollution” has bad environmental, social and ecological effects. The method applied in this study can assist for environmental managers to select suitable pollution load reduction scenarios for achieving various objectives.

Suggested Citation

  • Ruibin Zhang & Xin Qian & Wenting Zhu & Hailong Gao & Wei Hu & Jinhua Wang, 2014. "Simulation and Evaluation of Pollution Load Reduction Scenarios for Water Environmental Management: A Case Study of Inflow River of Taihu Lake, China," IJERPH, MDPI, vol. 11(9), pages 1-19, September.
    • Handle: RePEc:gam:jijerp:v:11:y:2014:i:9:p:9306-9324:d:40030
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/11/9/9306/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/11/9/9306/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Szu-Ping Cheng & Ru-Yih Wang, 2004. "Analyzing Hazard Potential of Typhoon Damage by Applying Grey Analytic Hierarchy Process," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 33(1), pages 77-103, September.
    2. Rowe, Gene & Wright, George, 1999. "The Delphi technique as a forecasting tool: issues and analysis," International Journal of Forecasting, Elsevier, vol. 15(4), pages 353-375, October.
    3. Norman Dalkey & Olaf Helmer, 1963. "An Experimental Application of the DELPHI Method to the Use of Experts," Management Science, INFORMS, vol. 9(3), pages 458-467, April.
    4. Ruibin Zhang & Xin Qian & Xingcheng Yuan & Rui Ye & Bisheng Xia & Yulei Wang, 2012. "Simulation of Water Environmental Capacity and Pollution Load Reduction Using QUAL2K for Water Environmental Management," IJERPH, MDPI, vol. 9(12), pages 1-18, December.
    5. Singh, Kunwar P. & Basant, Ankita & Malik, Amrita & Jain, Gunja, 2009. "Artificial neural network modeling of the river water quality—A case study," Ecological Modelling, Elsevier, vol. 220(6), pages 888-895.
    6. Kannel, Prakash Raj & Lee, S. & Lee, Y.-S. & Kanel, S.R. & Pelletier, G.J., 2007. "Application of automated QUAL2Kw for water quality modeling and management in the Bagmati River, Nepal," Ecological Modelling, Elsevier, vol. 202(3), pages 503-517.
    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. Qiankun Liu & Jingang Jiang & Changwei Jing & Jiaguo Qi, 2018. "Spatial and Seasonal Dynamics of Water Environmental Capacity in Mountainous Rivers of the Southeastern Coast, China," IJERPH, MDPI, vol. 15(1), pages 1-21, January.

    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. Prommer, Lisa & Tiberius, Victor & Kraus, Sascha, 2020. "Exploring the future of startup leadership development," Journal of Business Venturing Insights, Elsevier, vol. 14(C).
    2. Prianto Budi Saptono & Gustofan Mahmud & Intan Pratiwi & Dwi Purwanto & Ismail Khozen & Muhamad Akbar Aditama & Siti Khodijah & Maria Eurelia Wayan & Rina Yuliastuty Asmara & Ferry Jie, 2023. "Development of Climate-Related Disclosure Indicators for Application in Indonesia: A Delphi Method Study," Sustainability, MDPI, vol. 15(14), pages 1-25, July.
    3. Di Zio, Simone & Bolzan, Mario & Marozzi, Marco, 2021. "Classification of Delphi outputs through robust ranking and fuzzy clustering for Delphi-based scenarios," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    4. Alyami, Saleh. H. & Rezgui, Yacine & Kwan, Alan, 2013. "Developing sustainable building assessment scheme for Saudi Arabia: Delphi consultation approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 43-54.
    5. Georg Aichholzer, 2002. "Das ExpertInnen-Delphi: methodische Grundlagen und Anwendungsfeld ‘Technology Foresight‘ (The Expert Delphi: Methodology and Application in 'Technology Foresight')," ITA manu:scripts 02_01, Institute of Technology Assessment (ITA).
    6. Hayes, Tom, 2007. "Delphi study of the future of marketing of higher education," Journal of Business Research, Elsevier, vol. 60(9), pages 927-931, September.
    7. Haarhaus, Tim & Liening, Andreas, 2020. "Building dynamic capabilities to cope with environmental uncertainty: The role of strategic foresight," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    8. Ribeiro, Barbara E. & Quintanilla, Miguel A., 2015. "Transitions in biofuel technologies: An appraisal of the social impacts of cellulosic ethanol using the Delphi method," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 53-68.
    9. Yeh, Duen-Yian & Cheng, Ching-Hsue, 2015. "Recommendation system for popular tourist attractions in Taiwan using Delphi panel and repertory grid techniques," Tourism Management, Elsevier, vol. 46(C), pages 164-176.
    10. Hsin-Ke Lu & Sung-Chun Tsai & Peng-Chun Lin & Kuo-Chung Chu & Alexander N. Chen, 2020. "Toward a New Real-Time Approach for Group Consensus: A Usability Analysis of Synchronous Delphi System," Group Decision and Negotiation, Springer, vol. 29(2), pages 345-370, April.
    11. Torres-Sibille, Ana del Carmen & Cloquell-Ballester, Vicente-Agustín & Cloquell-Ballester, Víctor-Andrés & Artacho Ramírez, Miguel Ángel, 2009. "Aesthetic impact assessment of solar power plants: An objective and a subjective approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 986-999, June.
    12. Laura Studen & Victor Tiberius, 2020. "Social Media, Quo Vadis? Prospective Development and Implications," Future Internet, MDPI, vol. 12(9), pages 1-22, August.
    13. Volkmar, Gioia & Fischer, Peter M. & Reinecke, Sven, 2022. "Artificial Intelligence and Machine Learning: Exploring drivers, barriers, and future developments in marketing management," Journal of Business Research, Elsevier, vol. 149(C), pages 599-614.
    14. Kawamoto, Carlos Tadao & Wright, James Terence Coulter & Spers, Renata Giovinazzo & de Carvalho, Daniel Estima, 2019. "Can we make use of perception of questions' easiness in Delphi-like studies? Some results from an experiment with an alternative feedback," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 296-305.
    15. Meijering, Jurian Vincent & Tobi, Hilde, 2018. "The effects of feeding back experts’ own initial ratings in Delphi studies: A randomized trial," International Journal of Forecasting, Elsevier, vol. 34(2), pages 216-224.
    16. Paméla Baillette & Bernard Fallery, 2016. "La méthode du Delphi argumentaire, une innovation managériale dans le cadre d'un projet complexe," Post-Print hal-02160359, HAL.
    17. Förster, Bernadette & von der Gracht, Heiko, 2014. "Assessing Delphi panel composition for strategic foresight — A comparison of panels based on company-internal and external participants," Technological Forecasting and Social Change, Elsevier, vol. 84(C), pages 215-229.
    18. Torres Sibille, Ana del Carmen & Cloquell-Ballester, Víctor-Andrés & Cloquell-Ballester, Vicente-Agustín & Darton, Richard, 2009. "Development and validation of a multicriteria indicator for the assessment of objective aesthetic impact of wind farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 40-66, January.
    19. Ebrahim Aghazadeh & Hasan Yildirim & Murat Kuruoglu, 2022. "A Hybrid Fuzzy MCDM Methodology for Optimal Structural System Selection Compatible with Sustainable Materials in Mass-Housing Projects," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    20. Fritschy, Carolin & Spinler, Stefan, 2019. "The impact of autonomous trucks on business models in the automotive and logistics industry–a Delphi-based scenario study," Technological Forecasting and Social Change, Elsevier, vol. 148(C).

    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:11:y:2014:i:9:p:9306-9324:d:40030. 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.