Causality analysis and prediction of 2-methylisoborneol production in a reservoir using empirical dynamic modeling. (15th October 2019)

• Record Type: Journal Article
• Authors: Wang, Manna; Yoshimura, Chihiro; Allam, Ayman; Kimura, Fuminori; Honma, Takamitsu
• Is Part Of: Water research. Volume 163(2019)
• Publication Date: 2019-10-15
• Subjects: Empirical dynamic modeling -- Causality -- 2-Methylisoborneol -- Phormidium spp. -- Taste and odor; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2019.114864
• Languages: English
• ISSNs: 0043-1354

Abstract

Abstract: 2-Methylisobornel (MIB) is one of the most widespread and problematic biogenic compounds causing taste-and-odor problems in freshwater. To investigate the causes of MIB production and develop models to predict the MIB concentration, we have applied empirical dynamic modeling (EDM), a nonlinear approach based on Chaos theory, to the long-term water quality dataset of Kamafusa Reservoir in Japan. The study revealed the dynamic nature of MIB production in the reservoir, and determined causal variables for MIB production, including water temperature, pH, transparency, light intensity, and Green Phormidium . Moreover, EDM established that the system is three-dimensional, and the approach found elevated nonlinearity (from 1.5 to 3) across the whole study period (1996–2015). By taking only one or two candidate predictors with varying time lags, multivariate models for predicting MIB production (best model: r = 0.83, p < 0.001, root mean squared error = 3.1 ng/L) were successfully established. The modeling approach used in this study is a powerful tool for causality identification and odor prediction, thus making important contributions to reservoir management. Graphical abstract: Image 1 Highlights: MIB production in Kamafusa Reservoir is a dynamic phenomenon. Empirical dynamic modeling (EDM) determined the system as three-dimension. EDM revealed elevated nonlinearity across the whole study period (1993–2015). Nonlinear causality test using EDM identified causal variables for MIB production. Multivariate models for predicting MIB production were successfully established.