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A cumulative eutrophication risk evaluation method based on a bioaccumulation model

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  • Xu, Yanhong
  • Peng, Hong
  • Yang, Yinqun
  • Zhang, Wanshun
  • Wang, Shuangling

Abstract

Water pollution problems related to rapid economic development have received considerable attention in recent years, and water eutrophication has become one of the greatest threats to aquatic environments around the world. In this study, we introduce a eutrophication risk assessment method to address the urgent need for simple but effective modeling tools to support environmental management. This method is based on a bioaccumulation model and algal growth dynamics. The water eutrophication risk is expressed by the ratio of the algal concentration in a water body and the critical algal concentration for eutrophication. The combined and cumulative effects of meteorological factors (e.g., temperature and light intensity), water quality factors (e.g., TN, TP, and NH3-N), and biological factors (e.g., algal settlement and normal death) on eutrophication are considered. We examine the advantages of our eutrophication risk assessment model using the seasonal, mesotrophic Xikeng Reservoir (Shenzhen City, China) as a case study and analyze three typical sets of meteorological and water quality conditions. Prior parameter distributions were chosen based on both a literature survey and experimental research. The results indicate that the combined actions of multiple external factors have a long impact on the eutrophication risk of the Xikeng Reservoir. Eutrophication was observed on the 6th day of the simulation time in the Xikeng Reservoir under the influence of higher temperature (T=28.3°C), higher light intensity (I=6037lx), and higher pollutants concentration (CTN=5.40mg/L, CTP=0.391mg/L, CNH3-N=4.18mg/L). These findings indicate that Xikeng Reservoir influence by similar environmental conditions should receive more of our attention to prevent eutrophication. Finally, we highlight the benefits of the new eutrophication risk assessment model, including its simple calculation process, reduced parameter set, and accurate prediction of eutrophication tendency. These features make the model perfectly suited for stakeholders and policy makers who need to make risk management assessment of environmental water, especially for the environmental management of areas where data are unavailable.

Suggested Citation

  • Xu, Yanhong & Peng, Hong & Yang, Yinqun & Zhang, Wanshun & Wang, Shuangling, 2014. "A cumulative eutrophication risk evaluation method based on a bioaccumulation model," Ecological Modelling, Elsevier, vol. 289(C), pages 77-85.
    • Handle: RePEc:eee:ecomod:v:289:y:2014:i:c:p:77-85
    DOI: 10.1016/j.ecolmodel.2014.07.006
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    References listed on IDEAS

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    1. Kuo, Jan-Tai & Hsieh, Ming-Han & Lung, Wu-Seng & She, Nian, 2007. "Using artificial neural network for reservoir eutrophication prediction," Ecological Modelling, Elsevier, vol. 200(1), pages 171-177.
    2. Wanshun Zhang & Yan Wang & Hong Peng & Yiting Li & Jushan Tang & K. Wu, 2010. "A Coupled Water Quantity–Quality Model for Water Allocation Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(3), pages 485-511, February.
    3. Grover, James P. & Roelke, Daniel L. & Brooks, Bryan W., 2012. "Modeling of plankton community dynamics characterized by algal toxicity and allelopathy: A focus on historical Prymnesium parvum blooms in a Texas reservoir," Ecological Modelling, Elsevier, vol. 227(C), pages 147-161.
    4. Arhonditsis, George B. & Qian, Song S. & Stow, Craig A. & Lamon, E. Conrad & Reckhow, Kenneth H., 2007. "Eutrophication risk assessment using Bayesian calibration of process-based models: Application to a mesotrophic lake," Ecological Modelling, Elsevier, vol. 208(2), pages 215-229.
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    Cited by:

    1. Dash, Siddhant & Kalamdhad, Ajay S., 2022. "Systematic bibliographic research on eutrophication-based ecological modelling of aquatic ecosystems through the lens of science mapping," Ecological Modelling, Elsevier, vol. 472(C).
    2. Bai, Jing & Zhao, Jian & Zhang, Zhenyu & Tian, Ziqiang, 2022. "Assessment and a review of research on surface water quality modeling," Ecological Modelling, Elsevier, vol. 466(C).

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