Abstract: To identify environmental factors significantly associated with cyanobacterial bloom intensity and to overcome the problems, such as unreasonable selection of dependent variables, low spatio-temporal precision in the present researches, a multi-factor linear regression model was developed for the study on cyanobacterial bloom in the Taihu Lake, using cyanobacterial bloom intensity scale as dependent variable, and monitoring indices of water quality, hydrology, and weather as independent variables. Based on the data of covering areas and aggregation intensities of the blooms, a 7-level scale was designed to indicate bloom intensity, thus making the dependent variable macroscopic and avoiding the problem of lacking microscopicity in using only analogous indices, like chlorophyll a concentration, to represent bloom intensity. The temporal precision of this data set reached as high as twice a day for sampling, and the spatial precision as high as the spatial scale of a certain patch of water in a bay of the Taihu Lake.  Thus, the dependent variable becomes somewhat macroscopic, while the values of independent variables correspond strictly to the value of the dependent variable on the basis of a high spatial-temporal precision. This model was applied to the study on cyanobacterial bloom in Lake Taihu Lake of China. Analysis using the model reveals that the intensity grade of cyanobacterial bloom in the Dagongshan water area of Taihu Lake had was significantly and positively related to air temperature and nitrate concentration in water and negatively to wind speed, humidity, and electrical conductivity. The above conclusions conform to the mainstream conclusion in this research field, which manifests the effectiveness of this identification model.

Key words: cyanobacterial bloom, environmental factor, intensity grade, model, Taihu Lake