Variations in the Distribution of Chl-a and Simulation Using a Multiple Regression Model

Area of Science:

• Environmental Science
• Limnology
• Water Quality Monitoring

Background:

• Chlorophyll a (Chl-a) is a key indicator of algal biomass in aquatic ecosystems.
• Meiliang Bay in Lake Taihu, China, is a eutrophic environment requiring monitoring.
• Long-term data is crucial for understanding and managing algal blooms.

Purpose of the Study:

• To investigate the spatiotemporal distribution of Chl-a in Meiliang Bay.
• To develop a statistical model relating Chl-a concentration to key environmental drivers.
• To provide insights for eutrophication management in Lake Taihu.

Main Methods:

• Monthly monitoring of Chl-a concentrations from 2005-2015 at four stations.
• Statistical analysis of Chl-a data and its correlation with environmental variables (TP, DOM, pH, WT, NO3-N, DO, SD).
• Development of a multiple linear regression model to simulate Chl-a dynamics.

Main Results:

• Chl-a concentrations varied widely (2.6 to 330.0 μg/L) with higher levels at the northernmost site.
• An upward trend in annual mean Chl-a was observed at most sites.
• Chl-a showed significant positive correlations with TP, DOM, pH, and WT, and negative correlations with NO3-N, DO, and Secchi depth.

Conclusions:

• A robust multiple linear regression model (R=0.80) was established, integrating TP, DOM, WT, and pH.
• The model effectively simulates spatiotemporal Chl-a dynamics and can be applied to other Lake Taihu regions.
• The findings offer a valuable tool for lake managers to control eutrophication and mitigate surface algal blooms.