Innovative lake pollution profiling: unveiling pollutant sources through advanced multivariate clustering techniques.

Area of Science:

• Environmental Science
• Water Quality Management
• Ecosystem Health

Background:

• Lakes are vital drinking water sources, increasingly threatened by anthropogenic pollution.
• Polluted lakes harm ecosystems and human health via waterborne diseases.
• Effective methods are needed to identify polluted lakes and their specific contaminants.

Purpose of the Study:

• To develop a novel multivariate method for identifying polluted lakes and critical pollutants.
• To analyze correlations between physical, chemical, and biological water quality parameters.
• To demonstrate the method's effectiveness using a case study of Indian lakes.

Main Methods:

• Employed advanced hierarchical clustering and multivariate analysis (R-mode and Q-mode).
• Generated rotating component matrices, dendrograms, monoplots, and biplots.
• Conducted a case study on five lakes in Bengaluru and one in Jammu & Kashmir, India.

Main Results:

• Identified conductivity, nitrates, biological oxygen demand (BOD), total coliform (TC), and fecal coliform (FC) as critical contaminants.
• Factor analysis revealed four primary components explaining 85% of variance.
• Successfully identified pollution hotspots and contaminant sources.

Conclusions:

• The proposed method effectively identifies polluted lakes and critical pollutants.
• Recommends source-based pollution control and integrated watershed management for lake restoration.
• Emphasizes continuous monitoring for sustainable lake development and improved water quality.