Abstract

Principal Component Analysis (PCA) was applied to surface water quality data to identify pollution sources and assess their contribution to variations in water quality. Surface water samples were collected from 5 distinct sampling points along the Mati River in Albania, during the period from September 2024 to October 2025. 14 physico-chemical water quality parameters were analyzed: Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD₅), Chemical Oxygen Demand (COD), Total Dissolved Solids (TDS), pH, conductivity, temperature, sulphates, nitrogen forms, chloride, phosphates (PO₄³⁻), and total phosphorus (P-tot). Water quality was assessed based on site and season, with water quality classes determined and pollution issues identified. Pearson correlation and PCA were employed to evaluate the relationships between all parameters. PCA results indicated that the first 3 principal components (PC1, PC2, and PC3) accounted for approximately 96% of the total variance, effectively representing the underlying structure of the water’s physicochemical properties. The analysis revealed that water quality along the Mati River is primarily influenced by 3 main factors: mineralization, organic pollution, and nutrient loading. PCA demonstrates that the primary determinants of water quality variability are chemical variables associated with pollution and mineralization. In contrast, biological and physical parameters play a secondary role in differentiating locations. The analysis reveals that the strength of these variables and their contributions to the principal components facilitate the identification of key influencing factors and provide clear interpretations of the environmental conditions.