Analyzing the algal bloom risk and its relationship with environmental variables in urban landscape water

Abstract

Longjing Lake is an urban landscape lake located in the Chongqing Expo Garden, Chongqing City, China. In order to assess the lake condition for eutrophication, the water quality and phytoplankton community in Longjing Lake was investigated monthly in 2016. A total of 53 genera of phytoplankton belonging to eight phyla were identified. The dominant organisms included Pseudanabaena, Ankistrodesmus and Cryptomonas, with Pseudanabaena being the most dominant, (dominance value = 0.7163). One-way ANOVA showed significantly larger Pseudanabaena abundance but lower biotic indices (Shannon-Wiener index (H), richness index (Dm), evenness index (J) and Simpson diversity index (D)) in June through September compared to other months (p < 0.05). A stepwise discriminant function analysis was employed to develop predictive model for assessing the level of algal bloom risk. The input variables for the model included water temperature (T), chemical oxygen demand (COD) and dissolved oxygen (DO). By measuring the values of T, DO, and COD concentrations, thus, lake managers could understand the temporal variation in phytoplankton biomass, and analyze the risk of algal bloom. Since the model developed in this study use only three easy-to-measure variables, its application can help in rapid assessment of algal bloom risk.