Formulation of Water Quality Prediction Model considering Several Species of Phytoplankton - Collections | Kyushu University Library

＜departmental bulletin paper＞
Formulation of Water Quality Prediction Model considering Several Species of Phytoplankton

Creator	Author PID L001464 Creator Name 増田, 慎也 Masuda, Shinya Affiliation Affiliation Name 九州大学大学院生物資源環境科学府生産環境科学専攻地域環境科学講座水環境学研究室 Laboratory of Drainage and Water Environment, Division of Regional Environment Science, Department of Bioproduction Environmnetal Sciences, Graguate School of Bioresource and Bioenvironmental Sciences, Kyushu University
	Author PID K002875 Creator Name 原田, 昌佳 Harada, Masayoshi Affiliation Affiliation Name 九州大学大学院農学研究院生産環境科学部門地域環境科学講座水環境学研究室 Laboratory of Drainage and Water Environment, Division of Regional Environment Science, Department of Bioproduction Environmnetal Sciences, Faculty of Agriculture, Kyushu University
	Author PID K001582 Creator Name 平松, 和昭 Hiramatsu, Kazuaki Affiliation Affiliation Name 九州大学大学院農学研究院生産環境科学部門地域環境科学講座水環境学研究室 Laboratory of Drainage and Water Environment, Division of Regional Environment Science, Department of Bioproduction Environmnetal Sciences, Faculty of Agriculture, Kyushu University
	Author PID K003070 Creator Name 丸居, 篤 Marui, Atsushi Affiliation Affiliation Name 九州大学新キャンパス計画推進室 New Campus Planning Office, Kyushu University
Language	Japanese
Publisher	九州大学大学院農学研究院
Publisher	Faculty of Agriculture, Kyushu University
Date	2009-10-29
Source Title	Science bulletin of the Faculty of Agriculture, Kyushu University
Vol	64
Issue	2
First Page	135
Last Page	145
Publication Type	Version of Record
Access Rights	open access
JaLC DOI	https://doi.org/10.15017/16506

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PISSN	1347-0159
NCID	AA11577672
Record ID	16506
Peer-Reviewed	Unrefereed
Subject Terms	Geo-Environment Science
Subject Terms	地水環境学
Notes	Because of eutrophication, a bloom of phytoplankton called 'aoko' is causing serious problems in Okubo Pond in the Obaru River basin of the Itoshima area of Fukuoka Prefecture. Therefore, a water quality prediction model was built to understand the features of phytoplankton. This model was based on a completely mixed system, and in the model, phytoplankton was separated into four groups: green algae, blue green algae, diatom/dinoflagenates, and cryptophyte algae. Results calculated with the model agreed with observational results. Also, the model parameters connected to phytoplankton corresponded approximately to anamnestic cases. Next, with the model, we considered the characteristics　involved in the growth of phytoplankton.　The result indicated that features of phytoplankton　related to temperature and nutrients varied with species. Also was found that the seasonal　prevalence of phytoplankton was multiply influenced by temperature and nutrients.
Notes	本研究では，福岡県糸島地域の富栄養化貯水池である大久保池を対象に，有機汚濁・富栄養化関連項目およびFluoroProbeによる藻類綱別Chl.alphaに関する水質観測と，ワンボックス型低次生態系モデルを用いた数値計算により，藻類綱ごとに植物プランクトンの季節的消長について検討した．まず生態系モデルでは，植物プランクトンを緑藻類，藍藻類，珪藻類/渦鞭毛藻，クリプト藻類の４種類に分類し，増殖項における水温，水中光強度，栄養塩濃度の各要因による制御因子を藻類ごとにモデル化することで，藻類綱の季節的消長や種間競争を考慮した．計算結果と観測結果に若干のずれは見られるものの，概ね良好な再現結果が得られた．ついで，水温，光，栄養塩濃度の各制御因子の経時変化をもとに，各環境因子の観点から各藻類綱の増殖特性を検討した．その結果，①藻類綱によって水温による制御因子は季節的に異なり，藍藻類は低水温期に，藍藻類，珪藻類/渦鞭毛藻とクリプト藻類は高水温期に，さらに緑藻類は両時期に水温による増殖制御の影響を多大に受ける．②栄養塩濃度については，藻類綱と季節に関係なくリン制限が強く，とくに緑藻類と藍藻類に対するその影響は10月以降に強まる．③藻類綱の季節的消長に対する光の直接的影響は小さい．
Created Date	2010.03.02
Modified Date	2022.11.11