A modeling approach to the efficient evaluation and analysis of water quality risks in cold zone lakes: a case study of Chagan Lake in Northeast China

Due to the influence of complex regional climate, water quality perturbation factors of lakes in cold regions are complicated, and the uncertainty of each factor needs further study. This study coupled two algorithms (clustering and EM) to establish a water quality uncertainty model of Chagan Lake, a typical cold region lake in China. A BN model containing nine influencing factors (including water temperature (WT), total phosphorus (TP), total nitrogen (TN), etc.) was established and optimized, and sensitivity analysis was also performed. The results indicate that the water quality status of the lake is class III and 27.47% risk of exceeding the standard. The water quality of the lake is more susceptible to disturbance during the freezing period (WT < 1 degrees C). TP is the most sensitive factor for water quality disturbance in the lake followed by chemical oxygen demand (COD), TN, and fluoride (F). Parameter control result displays, and the multifactor synergistic control scheme could reduce the water quality risk of the lake by 36.47%. This study demonstrates that our proposed method can be used to predict both sudden water quality events and the overall trend of water quality fluctuation, which is important for rapid water quality evaluation and management decisions.

Automated summary

Due to the complex regional climate, the water quality perturbation factors of lakes in cold regions are complicated and require further study. This study used clustering and EM algorithms to establish a water quality uncertainty model for Chagan Lake, a typical cold region lake in China. The results show that the lake has a class III water quality status with a 27.47% risk of exceeding the standard. The study also identified the most sensitive factors for water quality disturbance in the lake.