Quantile Regression Illuminates the Heterogeneous Effect of Water Quality on Phytoplankton in Lake Taihu, China

Lake Taihu, a subtropical shallow lake in the Yangtze River Basin, is the third-largest freshwater lake in China. It serves not only as a crucial source of drinking water and an ecological resource but also holds significant economic, tourism, and fisheries value. Phytoplankton, a vital component of aquatic ecosystems, plays a critical role in nutrient cycling and maintaining water structure. Its community composition and concentration reflect changes in the aquatic environment, making it an important biological indicator for monitoring ecological conditions. Understanding the impact of water quality on phytoplankton is essential for maintaining ecological balance and ensuring the sustainable use of water resources. This paper focuses on Lake Taihu, with water samples collected in February, May, August, and November from 2011 to 2019. Using quantile regression, a robust statistical analysis tool, the study investigates the heterogeneous effects of water quality on phytoplankton and seasonal variations. The results indicate significant seasonal differences in water quality in Lake Taihu, which substantially influence phytoplankton, showing weakly alkaline characteristics. When phytoplankton concentrations are low, pondus hydrogenii (pH), chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), water temperature (WT), and conductivity significantly affect them. At medium concentrations, COD, TP, TN, and WT have significant effects. At high concentrations, transparency and dissolved oxygen (DO) significantly impact phytoplankton, while TP no longer has a significant effect. These findings provide valuable insights for policymakers and environmental managers, supporting the prevention and control of harmful algal blooms in Lake Taihu and similar aquatic systems.