IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v24y2022i12d10.1007_s10668-021-02033-4.html
   My bibliography  Save this article

Assessment of water quality trends in Deepor Beel, Assam, India

Author

Listed:
  • Ritabrata Roy

    (National Institute of Technology Agartala)

  • Mrinmoy Majumder

    (National Institute of Technology Agartala)

Abstract

Deepor Beel is the only Ramsar Site of Assam, India. This biodiversity-rich lake supports many aquatic and terrestrial species, including some globally threatened species. The lake is important to the livelihood of the local population as it provides opportunities for fishing and collection of other wetland biological resources. As a consequence, quality of water in this lake is important both economically and environmentally. In the present study, therefore, water quality of Deepor Beel was assessed with different Water Quality Indices in each month for 3 years. Qualitative and quantitative water quality trends of the lake were assessed from those data. The overall water quality of the lake was evaluated as fair (NSF WQI 55-76) with some deterioration during summer (NSF WQI 55-69). The lake was found to be somewhat turbid (11-34 NTU) and eutrophic (Nitrate 1.46–5.75 ppm, Phosphate 0.08–0.47 ppm) throughout the study period. This study also indicated a slight gradual deterioration of the lake every year during summer and post monsoon. Water quality of Deepor Beel was found to deteriorate with time from this study. Better management plan of this lake should be implemented for its sustenance.

Suggested Citation

  • Ritabrata Roy & Mrinmoy Majumder, 2022. "Assessment of water quality trends in Deepor Beel, Assam, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(12), pages 14327-14347, December.
    • Handle: RePEc:spr:endesu:v:24:y:2022:i:12:d:10.1007_s10668-021-02033-4
    DOI: 10.1007/s10668-021-02033-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-021-02033-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10668-021-02033-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jin Li, 2017. "Assessing the accuracy of predictive models for numerical data: Not r nor r2, why not? Then what?," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-16, August.
    Full references (including those not matched with items on IDEAS)

    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. Rufino, Marta M. & Albouy, Camille & Brind'Amour, Anik, 2021. "Which spatial interpolators I should use? A case study applying to marine species," Ecological Modelling, Elsevier, vol. 449(C).
    2. Shinyoung Kwag & Daegi Hahm & Minkyu Kim & Seunghyun Eem, 2020. "Development of a Probabilistic Seismic Performance Assessment Model of Slope Using Machine Learning Methods," Sustainability, MDPI, vol. 12(8), pages 1-22, April.
    3. Nithin Isaac & Akshay K. Saha, 2024. "Forecasting Hydrogen Vehicle Refuelling for Sustainable Transportation: A Light Gradient-Boosting Machine Model," Sustainability, MDPI, vol. 16(10), pages 1-24, May.
    4. Daniel S. Maynard & Lalasia Bialic-Murphy & Constantin M. Zohner & Colin Averill & Johan Hoogen & Haozhi Ma & Lidong Mo & Gabriel Reuben Smith & Alicia T. R. Acosta & Isabelle Aubin & Erika Berenguer , 2022. "Global relationships in tree functional traits," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    5. Fort, Hugo, 2020. "Making quantitative predictions on the yield of a species immersed in a multispecies community: The focal species method," Ecological Modelling, Elsevier, vol. 430(C).
    6. Siwei Li & Jingjing An & Yaqiu Li & Xiagu Zhu & Dongdong Zhao & Lixian Wang & Yonghui Sun & Yuanzhao Yang & Changhao Bi & Xueli Zhang & Meng Wang, 2022. "Automated high-throughput genome editing platform with an AI learning in situ prediction model," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    7. Fort, Hugo, 2018. "On predicting species yields in multispecies communities: Quantifying the accuracy of the linear Lotka-Volterra generalized model," Ecological Modelling, Elsevier, vol. 387(C), pages 154-162.
    8. Ahmad Al-Buenain & Mohamed Haouari & Jithu Reji Jacob, 2024. "Predicting Fan Attendance at Mega Sports Events—A Machine Learning Approach: A Case Study of the FIFA World Cup Qatar 2022," Mathematics, MDPI, vol. 12(6), pages 1-25, March.

    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:spr:endesu:v:24:y:2022:i:12:d:10.1007_s10668-021-02033-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.