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An innovative method based on Gaussian cloud distribution and sample information richness for eutrophication assessment of Yangtze’s lakes and reservoirs under uncertainty

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Abstract

The precise assessment of a water body’s eutrophication status is essential for making informed decisions in water environment management. However, conventional approaches frequently fail to consider the randomness, fuzziness, and inherent hidden information of water quality indicators. These would result in an unreliable assessment. An enhanced method was proposed for the eutrophication assessment under uncertainty in this study. The multi-dimension gaussian cloud distribution was introduced to capture the randomness and fuzziness. The Shannon entropy based on various sample size and trophic levels was proposed to maximize valuable information hidden in the datasets. Twenty-seven significant lakes and reservoirs located in the Yangtze River Basin were selected to demonstrate the proposed method. The sensitivity and consistency were used to evaluate the accuracy of the proposed method. Results indicate that the proposed method has the capability to effectively assess the eutrophication status of lakes and reservoirs under uncertainty and that it has a better sensitivity since it can identify more than 33–50% trophic levels compared to the traditional methods. Further scenario experiments analysis revealed that the sample information richness, i.e., sample size and the number of trophic levels is of great significance to the accuracy/robustness of the method. Moreover, a sample size of 60 can offer the most favorable balance between accuracy/robustness and the monitoring expenses. These findings are crucial to optimizing the eutrophication assessment.

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Data Availability

Data that support the findings of this study will be made available from the corresponding author on reasonable request.

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Funding

This work was supported by the National Natural Science Foundation of China (52300244) and the Major Project of the National Social Science Foundation of China (21&ZD166).

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Authors and Affiliations

  1. China National Environmental Monitoring Centre, Beijing, 100012, China

    Nan Zang

  2. Chinese Academy for Environmental Planning, Beijing, 100043, China

    Nan Zang, Guozhi Cao, Yanxue Xu, Zesheng Xu & Xiafei Zhou

  3. Sinosoft Company Limited, Beijing, 100089, China

    Yu Feng

  4. Center for Water and Ecology, State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China

    Yunjie Liao

Authors
  1. Nan Zang
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  2. Guozhi Cao
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  3. Yanxue Xu
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  4. Yu Feng
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  5. Zesheng Xu
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  6. Xiafei Zhou
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  7. Yunjie Liao
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Contributions

Nan Zang: conceptualization, methodology, formal analysis, data curation, writing—original draft, writing—review and editing, visualization, software, validation, investigation. Guozhi Cao: resources, writing—review and editing. Yanxue Xu: investigation, visualization, resources. Yu Feng: investigation. Zesheng Xu: investigation. Xiafei Zhou: investigation. Jiaxun Guo: investigation, visualization. Yunjie Liao: conceptualization, methodology, formal analysis, writing—original draft, writing—review and editing, visualization, validation, supervision.

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Correspondence to Yunjie Liao.

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Zang, N., Cao, G., Xu, Y. et al. An innovative method based on Gaussian cloud distribution and sample information richness for eutrophication assessment of Yangtze’s lakes and reservoirs under uncertainty. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33307-9

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