Groundwater Quality Assessment Using Statistical and Spatiotemporal Techniques: A Case Study of an Onshore Hydrocarbon-Producing Region, Niger Delta

Abstract

Groundwater quality remains a key environmental concern in hydrocarbon-producing regions such as the Niger Delta, where it serves as a primary source of water for domestic and industrial use. Understanding the interaction between natural groundwater processes and hydrocarbon presence is essential for effective monitoring and sustainable management. This study presents an assessment of groundwater quality in an onshore hydrocarbon processing facility, with emphasis on spatial variability, controlling factors, and comparison with regulatory thresholds including EGASPIN (2018) standards. Groundwater data from ten monitoring wells and four reference wells were analysed across wet and dry seasons for physicochemical parameters, hydrocarbons (TPH and benzene), dissolved gases, and trace metals. The analysis applied descriptive statistics, Background Threshold Values (BTVs), Water Quality Index (WQI), multivariate techniques (PCA and AHCA), regression modelling, and spatiotemporal analysis using GWSDAT. Results indicate spatial variability, with monitoring locations exhibiting higher turbidity, dissolved solids, hydrocarbon indicators, and redox-sensitive metal relative to reference wells. Hydrocarbon concentrations were confined to specific locations rather than distributed across the entire site, indicating limited subsurface spread. WQI values indicate that groundwater quality varies across monitoring locations, with some areas not meeting potable use standards, while reference wells remain within acceptable limits. Multivariate analysis identified salinity-related processes and sub-surface conditions as key drivers of groundwater variability, while regression modelling showed that turbidity and manganese account for approximately 97% of WQI variation. The results show that groundwater quality is mainly controlled by natural processes, with some variation across the study area. The study demonstrates the effectiveness of combining statistical and spatiotemporal methods for groundwater quality assessment in hydrocarbon-producing regions.