Open AccessQualitative characterization of groundwater sources around Nigeria National Petroleum Cooperation Oil Depot Aba, using multiple linear regressions modelling
Author(s) -
Obinna Chigoziem Akakuru,
Akudinobi B.E.B
Publication year - 2018
Publication title -
international journal of advanced geosciences
Language(s) - English
Resource type - Journals
ISSN - 2311-7044
DOI - 10.14419/ijag.v6i1.8789
Subject(s) - btex , pollutant , environmental science , hydraulic conductivity , aquifer , water quality , xylene , arsenic , groundwater , linear regression , environmental engineering , environmental chemistry , benzene , soil science , chemistry , mathematics , geology , soil water , geotechnical engineering , statistics , ecology , organic chemistry , biology
Qualitative characterization of groundwater sources around NNPC oil depot Aba, using Multiple Linear Regression Modelling has been done to predict the concentration of pollutants (heavy metals and Benzene, Toluene, Ethlybenzene, Xylene (BTEX)) in the study area. This was achieved through water level measurements, grain size, and water sample analyses. Fifty eight (58) water samples were collected within the study area and were subjected to chemical analyses. Eight (8) input parameters for the modelling comprised of the elevation data, depth to water table data, hydraulic head data, hydraulic conductivity data, transmissivity data, aquifer thickness data, and specific yield. The heavy metals and the BTEX were the depended variables, while the input parameters were the independent variables. Multiple Linear Regression (MLR) equations were modeled using MATLAB. The investigation revealed that ionic species of some important water quality concern include Arsenic, Copper, Mercury, Lead (heavy metals); Benzene, Ethlybenzene, and Xylene (organic pollutant). Pre-use treatment becomes a priority in all domestic and industrial application of these water sources. The MLR result revealed different R2: Arsenic (0.77), Copper (0.77), Iron (0.83), Mercury (0.80), Lead (0.61), Benzene (0.74), Toluene (0.84), Ethylbenzene (0.90) and Xylene (0.94), indicating that the predicted values closely tracked the actual values. A total of nine (9) MLR model equations were developed for the prediction of the concentration of pollutants in the study area. the study therefore recommends that it is fundamentally important that standard environmental management and appropriate environmental regulations should be established and enforced within the vicinity of the depot.