Water quality assessment and source identification of Daliao River Basin using multivariate statistical methods

Area of Science:

• Environmental Science
• Water Quality Management
• Statistical Analysis

Background:

• The Daliao River Basin's water quality is crucial for ecological health and human use.
• Understanding temporal and spatial variations is essential for effective pollution control.

Purpose of the Study:

• To analyze water quality data from 2003-2005 in the Daliao River Basin.
• To identify temporal and spatial variations in water quality.
• To pinpoint potential pollution sources and their characteristics.

Main Methods:

• Multivariate statistical methods including Cluster Analysis (CA), Discriminant Analysis (DA), and Principal Component Analysis (PCA).
• Hierarchical CA classified months into three periods and sampling sites into three groups (A, B, C).
• DA identified significant parameters for temporal and spatial group distinction, while PCA elucidated pollution sources.

Main Results:

• CA grouped 12 months into three periods and 18 sites into three groups.
• DA achieved high accuracy (84.5% temporal, 73.61% spatial) in distinguishing groups using specific parameters.
• PCA identified five latent pollution sources: oxygen consuming organic, toxic organic, heavy metal, fecal, and oil pollution.
• Seasonal analysis revealed distinct pollution patterns for different site groups and periods, including non-point source pollution.

Conclusions:

• Multivariate statistical methods effectively characterized temporal and spatial water quality variations.
• The study identified key pollution sources and seasonal pollution dynamics in the Daliao River Basin.
• Findings provide valuable insights for targeted water quality management and pollution source control strategies.