Numerical modeling on toxin produced by predominant species of cyanobacteria within the ecosystem of Lake Kasumigaura, Japan

Abstract

The paper investigates the predominant algal (Microcystis aeruginosa, Microcystis viridis, Microcystis ichthyoblabe, and Mirocystis wasenbergii) bloom, their transition behaviors and toxin produced as Microcystin-Leucine+Arginine (MC-LR), Microcystin-Arginine+Arginine (MC-RR) and Microcystin-Tyosine+Arginine (MC-YR) by cyanobacteria within the ecosystem of Lake Kasumigaura, Japan by using hydrodynamic ecosystem coupled model. Integrating the famous Monod functions’ concept the second mode of toxin production and Grover et al., 2011 river reservoirs toxin modeling theoretical notion assumes that the rate of toxin production by cyanobacteria depends on proportional to blue green algal composition and their abundance. Conceptualizing these two novel idea we have developed a three dimensional numerical model and to elucidate the structure of algal species and prediction of toxin production within the lake ecosystem. The simulation results compare the toxin (MC-LR, MC-RR and MC-YR) production of some stations in the Lake Kasumigaura with the observational data in the month of July, August and September for 2005, 2006 and 2007.The simulation result shows that there are some dominant species (Microcystis aeruginosa and Mycrocystis viridis are highly toxic) with regard to toxin produce in July, August and September in 2005. But in 2006 and 2007 there is no toxin produced by cyanobacteria (dominant species by Microcystis ichthyoblabe is toxic/nontoxic) in the ecosystem of Lake Kasumigaura. Because of timing and duration of the cyanobacteria bloom, making scum or colony and dying depends on the selecting parameters i.e. light intensity, temperature, water depth, wind direction, buoyancy and N: P ratio etc. But in some cases, toxin production depends on the Microcystis species toxic and non-toxic characteristics. The numerical modeling was calibrated by tuning toxin decay coefficient and other parameters for achieving a good agreement between the observations and the predictions.