MLR index-based principal component analysis to investigate and monitor probable sources of groundwater pollution and quality in coastal areas: a case study in East India.

Identifying groundwater contamination sources and supervising groundwater quality conditions are urgently needed to protect the groundwater resources of coastal areas like Contai of India, as communities here are heavily relying on groundwater which deteriorates progressively. So current research aims to address in detail about origins and influencing factors of groundwater contamination, status, and monitoring water quality by employing extremely useful leading technologies like principal component and factor analyses (PCA/FA), groundwater quality index (G), and multiple linear regression (MLR) that helps to simplify complicated works instead of the conventional methods. Eight groundwater quality parameters were evaluated here, such as pH, TH (total hardness), Tur (turbidity), EC (electrical conductivity), TDS (total dissolved solids), Mn (manganese), Fe (iron), and Cl (chloride) for 38 sites. Three principal components with ~ 81% of the total variance were extracted from the PCA/FA analysis. The origin of maximum loadings of each factor is identified as a result of saline water, disintegration and leaching process, organic or else biogenic activities, and lithogenic or otherwise non-lithogenic links through percolating water. G results show that ~ 87% of the samples fall into the good category and ~ 13% of the samples fall into the poor to very poor category. A model consisting of Tur, Fe, EC, Mn, TH, and Cl as independent parameters is more feasible and is proposed to predict G obtained from MLR analysis. This MLR model also suggests that turbidity with the highest beta coefficient (0.820) is a key contributor relative to the entire groundwater class in this affected area. The findings relating to this research may support the designer and officials in monitoring and protecting coastal groundwater resources like selected areas.