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Evaluation of algal species distributions and prediction of cyanophyte cell counts using statistical techniques

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Abstract

Safe drinking water sources are crucial for human health. Consequently, water quality management, including continuous monitoring of water quality and algae at sources, is critical to ensure the availability of safe water for local residents. This study aimed to construct statistical prediction models considering probability distributions relevant to cyanophyte cell counts and compare their prediction performance. In this study, water quality parameters at Juam Lake and Tamjin Lake, representative water sources in the Yeongsan and Seomjin rivers, South Korea, were investigated. We used a water quality monitoring network, algae alert system, and hydraulic and hydrological data measured every 7 days from January 2017 to December 2022 from the Water Environment Information System of the National Institute of Environmental Research. Using data for 2017–2021 as a training set and data for 2022 as a test set, the performances of seven models were compared for predicting cyanophyte cell counts. Environmental factors associated with algae in water sources were observed based on the monitoring data, and a prediction model appropriate for the cyanophyte distribution was generated, which also included the risk of toxicity. The extreme gradient boosting with the random forest model had the best predictive performance for cyanophyte cell counts. The study results are expected to facilitate water quality management in various water systems, including water sources.

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Data availability

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors thank the water quality team and algae team of the Yeongsan River Environment Research Center at the National Institute of Environmental Research for their assistance with sampling and analysis and Editage (http://www.editage.co.kr) for their English language editing service.

Funding

This work was supported by the National Institute of Environment Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea (grant number [NIER-2022-01-01-044]). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Yeongsan River Environment Research Center, National Institute of Environmental Research, 5, Cheomdangwagi-ro 208beon-gil, Buk-gu, Gwangju, 61011, Republic of Korea

    Seong-Yun Hwang, Jong-Hwan Park, Won-Seok Lee, Hyeon-Su Chung, Mi-Sun Son & Don-Woo Ha

  2. Watershed Pollution Load Management Research Division, National Institute of Environmental Research, 42, Hwangyeong-ro, Seo-gu, Incheon, 22689, Republic of Korea

    Byung-Woong Choi

  3. Freshwater Bioresources Culture Research Division, Nakdonggang National Institute of Biological Resources, 137, Donam 2-gil, Sangju-si, Gyeongsangbuk-do, 37242, Republic of Korea

    Dong-Seok Shin

  4. Water Environmental Engineering Research Division, National Institute of Environmental Research, 42, Hwangyeong-ro, Seo-gu, Incheon, 22689, Republic of Korea

    Kyung-Lak Lee

  5. Education Planning Division, National Institute of Environmental Human Resources Development, 42, Hwangyeong-ro, Seo-gu, Incheon, 22689, Republic of Korea

    Kang-Young Jung

Authors
  1. Seong-Yun Hwang
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Contributions

Conceptualization, Seong-Yun Hwang and Kang-Young Jung; data curation, Seong-Yun Hwang and Kang-Young Jung; formal analysis, Seong-Yun Hwang and Kyung-Lak Lee; funding acquisition, Jong-Hwan Park and Dong-Seok Shin; investigation, Hyeon-Su Chung, Mi-Sun Son, and Don-Woo Ha; methodology, Seong-Yun Hwang; project administration, Seong-Yun Hwang, Won-Seok Lee, and Dong-Seok Shin; resources, Byung-Woong Choi; software, Kyung-Lak Lee, Seong-Yun Hwang, and Kang-Young Jung; Supervision, Jong-Hwan Park, Won-Seok Lee, and Dong-Seok Shin; validation, Seong-Yun Hwang, Byung-Woong choi, and Kang-Young Jung; visualization, Kyung-Lak Lee, Seong-Yun Hwang, and Kang-Young Jung; roles/writings—original draft, Seong-Yun Hwang; writing—review and editing, Seong-Yun Hwang and Kang-Young Jung. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Kang-Young Jung.

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Responsible Editor: Lotfi Aleya

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Seong-Yun Hwang is the first author of this study.

Highlights

- Statistical distribution of variables predicting water quality or algae-related variables warrant consideration

- Redundancy analysis can investigate seasonal or environmental distributions of algae

- Water quality management is enhanced by predicting cyanophyte cell counts

Appendix

Appendix

See Table 13

Table 13 Abbreviations
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Hwang, SY., Choi, BW., Park, JH. et al. Evaluation of algal species distributions and prediction of cyanophyte cell counts using statistical techniques. Environ Sci Pollut Res 30, 117143–117164 (2023). https://doi.org/10.1007/s11356-023-30077-8

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  • DOI: https://doi.org/10.1007/s11356-023-30077-8

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