Evaluation of Groundwater Quality Using Multivariate Statistical Techniques, in Dashen Area, North Eastern Nigeria

Ishaku, J. M.; Ankidawa, B. A.; Pwalas, A. J. D.

doi:10.9734/bjast/2016/24069

Cited by 5 publications

(5 citation statements)

References 23 publications

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“…It also aids in characterizing water quality in order to identify potable issues and makes protecting efforts more convenient [7]. The physicochemical parameters (pH, EC, TDS, BOD, COD, TH, SO4, NO3, Turbidity, Pb and PO4) were used to calculate the WQI in this study [17]. The Water Quality Index (WQI) was calculated using the Weighted Average Water Quality Index (WAWQI) method [10] which involved:…”

Section: Calculation Of Water Quality Indexmentioning

confidence: 99%

Groundwater Quality Assessment in Aba (Abia State) Using WQI and GIS Techniques

Kanu,

Ugwoha,

Udeh

et al. 2023

AJEE

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Groundwater is considered as the most important source of freshwater for human consumption and activities in the urban environment. The assessment of groundwater quality is critical to avoid the negative effects of polluted groundwater on human health, agriculture, and the economy. In this study, the quality of groundwater in Aba (Abia State), Nigeria was assessed. Thirty-two water samples were taken from sixteen boreholes during the rainy and dry seasons, and analysed for pH, Electrical Conductivity, Total Hardness, Turbidity, Temperature, BOD5, COD, Pb, Cd, Cr, NH3, TDS, SO4, NO3 and PO4. The Weighted Average Water Quality Index (WAWQI) approach was used to compute the Water Quality Index (WQI). Geostatistical analysis methods such as Ordinary Kriging, Empirical Bayesian Kriging (EBK), Inverse Distance Weighting (IDW) and Spline interpolation methods were compared in the spatial evaluation of variable concentrations using ArcGIS. In the dry season, 100% of the borehole locations had Water Quality Index values above 100 which is Unsuitable for drinking purpose. For the rainy season, 87.5% of the borehole locations had WQI values above 100 which is Unsuitable for drinking purpose, 6.25% had WQI which was of very poor quality and 6.25% had WQI values within the range of good water quality between 26 and 50. With a mean error of 0.372483, RMSE of 0.5515, and RMSSE of 1.030492 for the dry season and a mean error of 0.05625, RMSE of 10, and RMSSE of 0.986448 for the rainy season, the EBK interpolation approach was the best fit model for the WQI determination. WQI values for rainy season were generally lower than the WQI values of the dry season, attributable to surface water run-off during the rains.

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Section: Calculation Of Water Quality Indexmentioning

confidence: 99%

Groundwater Quality Assessment in Aba (Abia State) Using WQI and GIS Techniques

Kanu,

Ugwoha,

Udeh

et al. 2023

AJEE

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“…Control and preservation of water quality, resources that are under threat from anthropogenic activities is priority for many researchers around the world [12], [18], [19], [20], [21]. The temperature values recorded for water show slight fluctuations observed from one source to another due to: exposure, sampling time, depth of water table.…”

Section: Issnmentioning

confidence: 99%

Wells and Boreholes Physicochemical Water Quality Evaluation in the Atakpamé Commune Under Agriculture and Municipal Wastewater Impact .

2021

IJESRT

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Most of the drinking water supplies of Atakpamé town is assured by wells and boreholes. The quality of this water is threatened by human activities. This study is to evaluate the impact of agricultural techniques and wastewater management on the evolution of the physicochemical quality of well and borehole water, for an understanding of the acquisition of groundwater mineralization in this commune. The water was collected and analyzed and, the results submitted to statistical processing using the software XLSTAT 2018.6 shows that the most downgrading physicochemical parameters are pH, conductivity and nitrates, compared to World Health Organization (WHO) standards. Factor analysis and correlation between the parameters explain the source of nitrate pollution which is linked to the farming and other human activities. The hydro chemical facies represented in the Piper diagram using the diagram software are in majority chlorinated, sodipotassic. Qgis software led to the spatial representation of the nitrate pollution. This pollution is high in Abotèssè, Aféyé-Kpota, Idiotsè and Ikotadi localities. The Hierarchical Ascendant Classification (HAC) of sampled water point gives four important homogeneous subgroups for future sampling selecting a point while representing the subgroup under surveillance.

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“…esri.com/en-us/home). The interpolation, through IDW has been widely used to map the spatial distribution of water elements [2,64,65]. The IDW method uses the existing values that are around the area to estimate the concentration of the non-sampled sites.…”

Section: Spatial Variability Of the Physicochemical Parameters (Php)mentioning

confidence: 99%

Multivariate and Spatial Analysis of Physicochemical Parameters in an Irrigation District, Chihuahua, Mexico

Prieto‐Amparán

Rocha-Gutiérrez

Ballinas-Casarrubias

et al. 2018

Water

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Water quality is relevant due to the complexity of the interaction of physicochemical and biological parameters. The Irrigation District 005 (ID005) is one of the most important agricultural region in Chihuahua, México; for that reason, it was proposed to investigate the water quality of the site. Water samples were collected in two periods: Summer (S1) and Fall (S2). The samples were taken from 65 wells in S1, and 54 wells in S2. Physicochemical parameters (PhP) such as Arsenic (As), Temperature, Electrical Conductivity (EC), Oxide Reduction Potential (ORP), Hardness, pH, Total Dissolved Solids (TDS), and Turbidity were analyzed. The data were subjected to statistical principal component analysis (PCA), cluster analysis (CA) and spatial variability tests. In both seasons, the TDS exceeded the Mexican maximum permissible level (MPL) (35% S1, 39% S2). Turbidity exceeded the MPL in S1 (29%) and in S2 (12%). Arsenic was above the MPL for water of agricultural use in 9% (S1) and 13% (S2) of the wells. The PCA results suggested that most variations in water quality in S1 were due to As, pH and Temperature, followed by EC, TDS and Hardness; while in S2 to EC, TDS and Hardness, followed by As and pH.

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Evaluation of Groundwater Quality Using Multivariate Statistical Techniques, in Dashen Area, North Eastern Nigeria

Cited by 5 publications

References 23 publications

Groundwater Quality Assessment in Aba (Abia State) Using WQI and GIS Techniques

Groundwater Quality Assessment in Aba (Abia State) Using WQI and GIS Techniques

Wells and Boreholes Physicochemical Water Quality Evaluation in the Atakpamé Commune Under Agriculture and Municipal Wastewater Impact .

Multivariate and Spatial Analysis of Physicochemical Parameters in an Irrigation District, Chihuahua, Mexico

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