Application of Principal Component Analysis (PCA) in groundwater quality evaluation: A case study of arid region

Abstract

Principal component analysis (PCA) is a commanding tool for assessing groundwater quality. It has potential to reduce data complexity, identify substantial variables, and disclose patterns. Groundwater quality dynamics could be understood well by using PCA which would advance the management and protection strategies of groundwater resources. The study aims to evaluate groundwater quality of an arid region of India. Principal component analysis was done for two seasons for two consecutive years by utilizing Minitab software. Groundwater samples of pre-monsoon 2019 shows that parameters like EC, TDS, TH, sodium, potassium, calcium, magnesium, chloride, fluoride, sulfate, bicarbonate, uranium, and zinc have major contribution in groundwater quality. All parameters come under first principal component (except carbonate and nitrate) in pre-monsoon 2020. While, the principal component analysis of monsoon season of 2019 and 2020 display that all the parameters fall under first principal component with exception of manganese and nitrate for monsoon 2019 and bicarbonate, carbonate, nitrate, EC, TDS, and chromium in monsoon 2020. Henceforth, PCA provides a comprehensive and insightful analysis that aids in effective groundwater quality assessment and management.