Stochastic modeling of chlorophyll-a for probabilistic assessment and monitoring of algae blooms in the Lower Nakdong River, South Korea

• Published in: Journal of hazardous materials Vol. 400; p. 123066
• Main Authors: Kim, Kue Bum, Jung, Min-Kyu, Tsang, Yiu Fai, Kwon, Hyun-Han
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 05.12.2020
• Subjects: Algae blooms; Chlorophyll - analysis; Chlorophyll A; Chlorophyll-a concentration; Environmental Monitoring; Eutrophication; Latent state; Phosphorus - analysis; Probabilistic approach; Republic of Korea; Rivers; Stochastic modeling; Republic of Korea; Algae blooms; Latent state; Stochastic modeling; Probabilistic approach; Chlorophyll-a concentration
• DOI: 10.1016/j.jhazmat.2020.123066
• ISSN: 0304-3894

[Display omitted] •The MHMM effectively clusters the intra-seasonal and inter-annual variability of chlorophyll-a.•The model enables us to understand the spatio-temporal evolutions of algal blooms.•The relationships between hydrologic conditions and chlorophyll-a concentrations were evident.•Effectively infer the conditional likelihood of the eutrophication state for the following month.•The self-transition likelihood of staying in the current state is substantially higher. Eutrophication is one of the critical water quality issues in the world nowadays. Various studies have been conducted to explore the contributing factors related to eutrophication symptoms. However, in the field of eutrophication modeling, the stochastic nature associated with the eutrophication process has not been sufficiently explored, especially in a multivariate stochastic modeling framework. In this study, a multivariate hidden Markov model (MHMM) that can consider the spatio-temporal dependence in chlorophyll-a concentration over the Nakdong River of South Korea was proposed. The MHMM can effectively cluster the intra-seasonal and inter-annual variability of chlorophyll-a, thereby enabling us to understand the spatio-temporal evolutions of algal blooms. The relationships between hydro-climatic conditions (e.g., temperature and river flow) and chlorophyll-a concentrations were evident, whereas a relatively weak relationship with water quality parameters was observed. The MHMM enables us to effectively infer the conditional probability of the eutrophication state for the following month. The self-transition likelihood of staying in the current state is substantially higher than the likelihood of moving to other states. Moreover, the proposed modeling approach can effectively offer a probabilistic decision-support framework for constructing an alert classification of the eutrophication. The potential use of the proposed modeling framework was also provided.