Abstract
The problem of algal bloom caused by eutrophication has attracted global attention. Many scholars have studied the problem associated with algae bloom, but few have carried out dynamic monitoring, instead focusing on the formation mechanism of cyanobacteria. For our study of the Taihu Lake in China, we used Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat remote sensing image data from 2017 to establish a prediction model. First, we used MODIS data to retrieve the concentration of N, P, and chlorophyll a in water. Then, we applied the analytic hierarchy process (AHP) model to the inversion results to construct the diffusion potential index. Finally, we used C# to compile the cellular automata (CA) model. We found that the distribution of cyanobacteria predicted by our method was consistent with the algal bloom situation of Taihu Lake in 2017. The results showed that the method effectively predicts the dynamic transfer of cyanobacteria from outbreak to diffusion in a short period of time, which can help decision-makers monitor lake health.
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Data availability
The datasets analyzed in the current study are available in the China geospatial data cloud repository, (http://www.gscloud.cn/).
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Acknowledgements
We thank the anonymous reviewers and editors for their thoughtful suggestions and careful work, which helped in improving this paper substantially. In addition, we are also thankful to the Geospatial Data Cloud.
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This research was funded by the National Natural Science Foundation of China (grant number 41961064).
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Conceptualization: Fei Zhao and Sujin Zhang, Ruonan Chen; Methodology and Draft Preparation: Ruonan Chen and Liyun Xiao; Software and Visualization: Guize Luan and Siwen Feng; Formal Analysis: Zhiqiang Xie; Data Curation: Liyun Xiao; Writing-Original: Sujin Zhang; Writing—Review and Editing: Sujin Zhang.
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Zhao, F., Zhang, S., Chen, R. et al. A modified cyanobacteria prediction model based on cellular automata model using N and P concentration reverse data: a case study in Taihu Lake. Environ Sci Pollut Res 29, 34546–34557 (2022). https://doi.org/10.1007/s11356-022-18612-5
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DOI: https://doi.org/10.1007/s11356-022-18612-5