Causes and prediction of cyanobacteria bloom (Aoko) in Lake Shinji

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  • SATO Sachiko
    Shimane Prefectural Institute of Public Health and Environmental Science
  • OHSHIRO Hitoshi
    Shimane Prefectural Institute of Public Health and Environmental Science
  • MANIWA Akira
    Shimane Prefectural Institute of Public Health and Environmental Science
  • SUGAHARA Shogo
    Department of Material Science, Faculty of Science and Engineering, Shimane University
  • KAMIYA Hiroshi
    Shimane Prefectural Institute of Public Health and Environmental Science
  • OHTANI Shuji
    Faculty of Education, Shimane University

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Other Title
  • 宍道湖におけるアオコ発生の環境要因とその事前判別
  • シンジコ ニ オケル アオコ ハッセイ ノ カンキョウ ヨウイン ト ソノ ジゼン ハンベツ

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Abstract

Occasional summer cyanobacteria blooms (Aoko) occur in Lake Shinji which is located in the eastern Shimane Prefecture. To clarify the cause of cyanobacteria bloom occurrence, we conducted discriminant analysis between cyanobacteria bloom occurrence, Cl- concentration, water temperature, NO3-N concentration, PO4-P concentration, duration of sunshine, and the flow rate from the Hii River from 1984 to 2012. We found that three of the most relevant variables are Cl- concentration during the index month, Cl- concentration two months prior to the index month, and water temperature one month prior to the index month. This discrimination is 89.7% correct.<br>　In the present study, “index month” indicates the month in which cyanobacteria blooms occur, and if not, indicates the month of August.<br>　Pearson's correlation coefficient between Cl- concentration during the index month and Cl- concentration one month prior to the index month is high (0.78). Next, we conducted discriminant analysis using Cl- concentration (one month prior), Cl- concentration (two months prior), and water temperature (one month prior) as variables. This discrimination is 86.2% correct.<br>　Our study determined that the analysis of Cl- concentration one month prior to the index month yields similar results to the analysis of Cl- concentration during the index month. Based on our study results, we confirmed that we can predict cyanobacteria bloom occurrence one month prior to bloom occurrence.

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