Analysis of spatial and temporal water pollution patterns in Lake Dianchi using multivariate statistical methods.

Area of Science:

• Environmental Science
• Water Quality Assessment
• Statistical Modeling

Background:

• Lake Dianchi faces significant surface water pollution challenges.
• Understanding spatial and temporal pollution dynamics is crucial for effective management.

Purpose of the Study:

• To analyze spatial and temporal patterns of surface water pollution in Lake Dianchi.
• To apply multivariate statistical methods for pollution assessment.

Main Methods:

• Cluster Analysis (CA) for grouping months and lake regions.
• Discriminant Analysis (DA) for temporal and spatial pattern recognition.
• Factor Analysis (FA)/Principal Component Analysis (PCA) for data reduction and identifying key pollution factors.
• Geographic Information Systems (GIS) and box-whisker plots for visualization and support.

Main Results:

• CA identified two distinct temporal groups (August-September vs. others) and two spatial regions based on pollution levels.
• DA achieved high correct assignments (85.4% temporal, 71.7% spatial) using key parameters like temperature, pH, CODMn, Chl-a, BOD, NH₄+-N, and TN.
• FA/PCA extracted four significant factors for each region, explaining substantial variance and highlighting pollutants such as Dissolved Oxygen (DO), BOD, TN, CODCr, CODMn, NH₄+-N, TP, and EC.

Conclusions:

• Multivariate statistical analyses effectively elucidated complex spatial and temporal pollution patterns in Lake Dianchi.
• Discriminant Analysis proved particularly effective for both temporal and spatial pollution assessments.
• Key water quality parameters were identified as critical indicators for monitoring and managing Lake Dianchi's pollution.