Groundwater Contaminant Identification and Modelling Using Principal Component Analysis and Geostatistical Techniques

Abstract

Groundwater which constitutes high percent of the global fresh water is the most important source of drinking water, which when polluted, have acute effects on its users. Consequently, the quality and pollution of groundwater is a health concern in the world. The target of this research is to evaluate the quality of groundwater around the Niger Delta Basin Development Authority in order to identify and analyze the distribution of the critical contaminants that affect the overall quality of groundwater water around the study area. About hundred (100) boreholes spread to cover the study area were sampled. The water samples were analyzed using standard procedures for assessing drinking water qualities in order to understand the existing condition of groundwater within the study area. Statistical analysis of the groundwater quality data was done using average weighted index method to compute the water quality index, factor analysis using principal component method to identify the groundwater contaminants affecting the overall groundwater quality and geospatial analysis using kriging interpolation method to evaluate the spatial distribution of the selected groundwater contaminants. From the principal component analysis, result revealed that; nitrate, total dissolved solids, concentration of iron, total suspended solids and turbidity were the most important contaminants affecting the quality of the groundwater. Result of geospatial analysis using kriging interpolation revealed that; the water quality parameters showed relatively strong degree of spatial dependency which made it possible to generate the spatial distribution map for the selected water quality parameters.

Keywords: Geospatial analysis, Kriging interpolation, water quality index, Principal component analysis.

DOI: 10.7176/JNSR/9-24-05

Publication date: December 31^st 2019