Assessing Water Quality Variability Using Principal Component Analysis: A Study of Wells in Kerala, India
Jazal K T
Department of Farm Machinery and Power Engineering, KCAEFT, Tavanur, India.
Akansha V R
Department of Soil and Water Conservation Engineering, KCAEFT, Tavanur, India.
Devika Guguloth
Department of Farm Machinery and Power Engineering, KCAEFT, Tavanur, India.
Mahantesh Ganigi
Department of Farm Machinery and Power Engineering, KCAEFT, Tavanur, India.
Deepthi. T.V
Department of Basic Engineering and Applied Sciences, KCAEFT, Tavanur, India.
Ramdas E.R
Department of Processing and Food Engineering, KCAEFT, Tavanur, India.
Vaisak Venu
Department of Basic Engineering and Applied Sciences, KCAEFT, Tavanur, India.
Sruthi. P.S *
Department of Processing and Food Engineering, KCAEFT, Tavanur, India.
Sruthy. P.B *
Department of Processing and Food Engineering, KCAEFT, Tavanur, India.
*Author to whom correspondence should be addressed.
Abstract
Aims: To identify the major factors influencing groundwater quality across different wells in Kerala using Principal Component Analysis (PCA), and to evaluate spatial and seasonal variations in water quality parameters including conductivity, nitrogen, pH, total coliforms, and total dissolved solids. The study aims to provide actionable insights for improved water resource management.
Study Design: Cross-sectional, observational, analytical study based on statistical analysis using PCA.
Place and Duration of Study: Groundwater samples were collected from multiple wells across districts in Kerala, India. Data were obtained from the Central Pollution Control Board (CPCB) under the National Water Quality Monitoring Programme (NWMP), 2022.
Methodology: Sixteen parameters, including temperature, pH, conductivity, total coliforms, nitrogen, biochemical oxygen demand (BOD), total dissolved solids (TDS), and fluoride, were used to obtain and standardize data on groundwater quality. In order to reduce dimensionality and identify important principle components (PCs), PCA was carried out using R software. A detailed analysis of the first four components (PC1–PC4), which accounted for 68.4% of the total variance, was conducted. To evaluate regional variations and dominating trends in water quality, each well was given a score based on the PCs.
Results: PC1 (33.33%) showed increased conductivity and total coliform levels along with an overall pattern of declining water quality. PC2 (21.06%) emphasized pH and nitrogen-related variance, with Punalur and Malappuram wells exhibiting higher nutrient levels. PC4 (11.06%) represented localized anomalies, while PC3 (14%) concentrated on pH fluctuation. For instance, the Kannur Municipality well revealed high levels of contamination, whereas the Vytilla (Ernakulam) well had low water quality. These trends indicate the effects of industrial runoff, sewage, and agriculture.
Conclusion: Principal Component Analysis successfully identified the main variables and geographical trends affecting Kerala's groundwater quality. In order to ensure sustainable and secure groundwater resources, the study emphasizes the necessity of focused water quality management through enhanced sanitation, pollution prevention, and localized monitoring techniques.
Keywords: Principal Component Analysis (PCA), water quality, groundwater, contamination, nutrient levels, pH variations, coliforms, total dissolved solids, wastewater management, water monitoring