Simulation and control of the cyanobacterial bloom biomass in a typical plateau lake based on the logistic growth model: A case study of Xingyun Lake

• Published in: Ecological informatics Vol. 82; p. 102779
• Main Authors: Wu, Chenhui, Jiang, Cuiling, Ju, Maosen, Pan, Zhengguo, Li, Zeshun, Sun, Lei, Geng, Hui
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-09-2024; Elsevier
• Subjects: Biomass simulation; Cyanobacterial bloom prevention and control; Cyanobacterial blooms; Logistic growth model; Plateau lakes; Cyanobacterial blooms; Cyanobacterial bloom prevention and control; Logistic growth model; Biomass simulation; Plateau lakes
• ISSN: 1574-9541
• DOI: 10.1016/j.ecoinf.2024.102779

The simulation and early warning of cyanobacterial blooms in lakes are of great significance. Controlling the growth of cyanobacteria in plateau lakes is challenging due to the unique geographical environment, climatic conditions, and impact of anthropogenic activities. Therefore, conducting simulations and early warning is crucial to effectively control cyanobacterial blooms in plateau lakes. This study aimed to investigate Xingyun Lake, a representative plateau lake in China, using the logistic growth model to analyze cyanobacterial growth patterns and assess the effects of control projects, along with the influence of meteorological and environmental factors. Moreover, the study proposed a method for establishing control curves and ranges for managing cyanobacterial blooms. The results demonstrated that the chlorophyll-a concentration in the effluent decreased by an average of 97.74% compared with that in the influent after implementing the integrated “deep-well pressure algal control” and “ecological purification for algae-water separation” processes in Xingyun Lake. The total annual decrease in chlorophyll-a was approximately 3.40 times the lake's total chlorophyll-a content. The growth of cyanobacteria in Xingyun Lake followed a logistic pattern during the blooming period, before and after implementing control projects (from 2018 to 2022), with the overall growth trend from 2010 to 2022 aligning with the logistic growth model. The study identified lower temperatures and precipitation, reduced nitrogen and phosphorus loads, and a higher nitrogen-to‑phosphorus ratio as the main environmental factors inhibiting cyanobacterial growth. Establishing logistic control curves for cyanobacterial blooms and sustaining the algal control project before the transition point effectively reduced the maximum chlorophyll-a concentration and attenuated cyanobacterial growth rate throughout the year. This study offered novel perspectives for preventing and controlling cyanobacterial blooms, offering practical guidance for lake management, especially in plateau regions.
•Case study of cyanobacteria management in plateau lakes was performed.•The effectiveness of cyanobacterial control project was evaluated.•The growth of cyanobacteria followed a logistic pattern during the bloom period.•Temperature, precipitation, and total phosphorus influenced cyanobacterial growth.•The cyanobacterial bloom control curves and ranges were established.