Surface water quality analysis using multivariate statistical techniques: a case study of Fars Province rivers, Iran

Area of Science:

• Environmental Science
• Hydrogeochemistry
• Water Quality Assessment

Background:

• River water quality is crucial for public health and ecosystem stability.
• Long-term monitoring and multivariate statistical analysis are essential for understanding complex water quality dynamics.
• Identifying pollution sources and assessing health risks are key to effective water resource management.

Purpose of the Study:

• To analyze physicochemical parameters in 34 rivers over 17 years (1997-2014).
• To classify riverine water quality sub-regions and identify contamination sources.
• To assess potential health risks associated with drinking water quality for children and adults.

Main Methods:

• Multivariate statistical methods, including cluster analysis and principal component analysis (PCA).
• Analysis of physicochemical parameters over a 17-year period.
• Hazard quotient (HQ) and hazard index (HI) calculations for health risk assessment.

Main Results:

• Cluster analysis identified six riverine water quality sub-regions (SP, HP, P, MP, LP, NP).
• Pollution sources identified include natural pollutants, municipal wastes, industrial effluents, geochemical formations, and agricultural runoff.
• Chloride (Cl-) and sodium (Na+) were identified as major contributors to health risks (HI), particularly for children.

Conclusions:

• Hydrochemical characteristics are influenced by both natural processes and anthropogenic activities.
• Minimizing pollution from agricultural, domestic, and industrial sources is essential for providing hygienic river water.
• Effective water quality management strategies are needed to mitigate health risks and ensure sustainable water resources.