Groundwater for Sustainable Development 10 (2020) 100296 Contents lists available at ScienceDirect Groundwater for Sustainable Development journal homepage: http://www.elsevier.com/locate/gsd Research paper Assessment of groundwater quality and the main controls on its hydrochemistry in some Voltaian and basement aquifers, northern Ghana Yvonne Sena Akosua Loh a, Bismark Awinbire Akurugu a,b,*, Evans Manu b, Abdul-Samed Aliou a a Department of Earth Science, University of Ghana, Legon, Accra, Ghana b Council for Scientific and Industrial Research -Water Research Institute - (CSIR-WRI), Accra, Ghana A R T I C L E I N F O A B S T R A C T Keywords: Groundwater resources play the single most important role in the delivery of potable water to rural communities Sawla-Tuna-Kalba in northern Ghana, especially during the long dry season and where surface water sources are polluted or non- Hydrochemical analysis existent. This study sought to assess the quality and main controls on groundwater chemistry in parts of Sawla- Groundwater quality Tuna-Kalba District in the Savannah Region of Ghana. Multivariate statistical analysis and conventional Geostatistics hydrochemical plots were employed in the analysis of 112 groundwater samples from the study area. Conven- tional graphical methods, R-mode Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) identified dissolution of silicates and the influence of agrochemicals and domestic wastewaters as the main sources of variations in the hydrochemistry in the study area. Q-mode HCA coupled with Stiff diagrams identified Ca–HCO3 water type in recharge areas, and Mg–Ca–HCO3 water type, which evolves into a Ca–Na–K–HCO3 water type in discharge areas in the groundwater flow regime. Mineral stability diagrams indicate the groundwater is stable in kaolinite, which suggests little or no restricted groundwater flow conditions. Groundwater quality for domestic purposes was assessed using the weighted arithmetic index approach. The computed water quality indices (WQIs) from the data suggest that 94% of the sampled boreholes provide groundwater of “excellent” quality for drinking purposes, whereas 5% and 1% present water of “good” and “poor” quality respectively. Spatial interpolation of the estimated WQIs suggests the quality of the groundwater in the study area is suitable for domestic purposes. The assessment of the groundwater quality for irrigation purposes suggests the water is of “excellent” to “permissible” quality and may be used for irrigation without prior treatment. 1. Introduction instance have been at the forefront in this regard in recent times, especially in hydrochemical studies where they have been used to Groundwater is being adopted as a preferable source of potable characterise groundwater flow regimes, discriminate hydrochemical water supply in arid and semi-arid climates. This is due to its minimal facies, make predictions at unsampled locations, generate statistical treatment requirements, spatial availability and capacity to balance models, identify sources of variations and characterise groundwater large rainfall variability as well as associated water demands during evolution (Adomako, 2011; Jing and Yufei, 2011; Kumar et al., 2011; droughts and when surface water resources exceed their sustainable Yidana et al., 2011, 2012a; Nur et al., 2012; Sarukkalige, 2012; Hassan, levels (Treidel et al., 2012; Bloomfield et al., 2019). A comprehensive 2014; Sharma et al., 2015; Abanyie et al., 2018; Sunkari et al., 2019). In groundwater assessment must reflect the several factors and processes an attempt to assess the main sources of hydrochemical variation and influencing groundwater chemistry in space and time, and is prerequi- suitability of groundwater for drinking purposes, Kumar et al. (2011) site to fully understanding the groundwater system for a proper man- employed factor analysis, hierarchical cluster analysis (HCA) and geo- agement of the resource. In this regard, researchers have adopted several spatial techniques on major physicochemical parameters in the Palar strategies to assess the several sources of variation in groundwater river basin, India. Their study concluded that effluent discharge in the chemistry. Most of these studies are well documented in literature (e.g. Palar river basin degraded groundwater quality in the northeast and Saana et al., 2016; Hwang et al., 2017; Telahigue et al., 2018; Boateng southeast parts of the river basin and groundwater along such areas were et al., 2019; Sunkari et al., 2019). Advanced geostatistical techniques for unsuitable for drinking. HCA when combined with conventional * Corresponding author. Department of Earth Science, University of Ghana, Legon, Accra, Ghana. Tel: þ233202900233. E-mail address: bismarkakurugu@yahoo.com (B.A. Akurugu). https://doi.org/10.1016/j.gsd.2019.100296 Received 25 May 2019; Received in revised form 14 September 2019; Accepted 30 October 2019 Available online 4 November 2019 2352-801X/© 2019 Elsevier B.V. All rights reserved. Y.S.A. Loh et al. G r o u n d w a t e r f o r S u s t a i n a b l e D e v e l o p m e nt 10 (2020) 100296 Fig. 1. Location map of the study area. graphical techniques can help explain the evolution of groundwater and wide array of powerful statistical models and tools to effectively explore the minerals dissolved in it as it moves through the rock matrix. Belkhiri and analyse hydrochemical data. However, employing these techniques et al. (2011) demonstrated this when they employed Q-mode HCA to requires an in-depth understanding of the prevailing environmental distinguish recharge zones from discharge areas in the groundwater flow conditions in the study area such as geology, hydrogeology, topography, regime in the Ain Azel plain, Algeria. Geostatistical techniques offer a weather conditions and dominant anthropogenic activities. Fig. 2. Geological map of the study area showing sampled points. 2 Y.S.A. Loh et al. G r o u n d w a t e r f o r S u s t a i n a b l e D e v e l o p m e nt 10 (2020) 100296 Geostatistical techniques have been employed extensively in Supergroup. These include; the Winneba, Bongo, Dixcove and the Cape groundwater studies in Ghana. For instance, with the aim of unveiling Coast type granites. The Cape Coast and Dixcove granites are seen to be the key factors influencing fluoride concentrations in some parts of present in the current study area. The Cape Coast type granitoids occur Northern Region Ghana, Yidana et al. (2012a) utilised principal only within the Birimian metasediments. This group also includes component analysis (PCA) coupled with cluster analysis to explain the gneisses, which are especially well developed in the metasediments hydrochemistry and factors influencing fluoride enrichment and other (Banoeng-Yakubu et al., 2011). Dixcove-type granitoids are metal- ions of the groundwater in the middle Voltaian aquifers. They also uminous and typically dioritic to granodioritic in composition, and adequately distinguished the various facies in the groundwater flow intrude the Birimian metavolcanic rocks. They are typically hornblende regime using Q-mode HCA. Furthermore, Sunkari et al. (2019) com- bearing and are commonly associated with gold mineralisation where bined multivariate statistics and mass balance techniques to assess the they occur as small plutons within the volcanic belts (Kesse, 1985). The drivers of groundwater chemistry in Ga West, Ghana. Their study granitoids are massive in outcrop, do not have a compositional banding revealed silicate and carbonate weathering, seawater intrusion and or foliation, and are thus generally considered post-deformation anthropogenic activities as the main controls on groundwater chemistry (Banoeng-Yakubu et al., 2011). The Kwahu-Morago Group of the Vol- in the district. Other similar studies in Ghana are well documented in taian Supergroup consist mainly of sandstone, which are white, Helstrup et al. (2007); Banoeng-Yakubo et al. (2009); Yidana et al. medium-grained, cross-bedded, flaggy, and quartzose. (2010); Loh et al. (2016); Abanyie et al. (2018). Hydrogeologically, the study area falls within three main hydro- It follows therefore, that when applied properly, with adequate geological provinces, namely Birimian Province, Voltaian Province, and knowledge of the terrain, geostatistical techniques can be used to Crystalline Basement Granitoid Complex Province (Dapaah-Siakwan satisfactorily characterise groundwater systems. This study adopts and Gyau Boakye, 2000). Groundwater in the Birimian Province occurs similar methodology in addition to conventional and mass balance mainly in the saprolite, saprock and in the fractured bedrock. The most hydrochemical models to unveil the relationships among water param- productive zones in terms of groundwater delivery in the Birimian eters and the main influence on groundwater chemistry from the base- Province comprise the lower part of the saprolite and the upper part of ment aquifers in the Sawla-Tuna-Kalba District in northern Ghana as the saprock, which usually complement each other in terms of perme- well as assesses its suitability for domestic and irrigation purposes. ability and storage (Carrier et al., 2008). The upper, less permeable part of the saprolite can act as a semi-confining layer for this productive zone, 2. Study area while the lower, usually saturated part of the saprolite is characterised by lower secondary clay content, thus creating a zone of enhanced hy- The study area (Fig. 1) is located in the western part of the Savannah draulic conductivity (Banoeng-Yakubu et al., 2011). Generally, areas Region of Ghana. It lies between latitudes 8�400 and 9�400 north and underlain by the Birimian rocks display deeper weathering than areas longitudes 1�500 and 2�450 west. The study area shares common underlain by the granitoids. boundaries with Wa West and Wa East Districts to the North, Bole Dis- Banoeng-Yakubo (1989) identified three types of basement aquifers. trict to the South, West Gonja and North Gonja Districts to the East, and These are the weathered rock aquifers, which are fracture related, the La Cote d’Ivoire and Burkina Faso to the West. It has a total land area of fractured quartz-vein aquifers and the fractured unweathered aquifers. about 4226.9 km2 and a population of 99,863 (Ghana Statistical Service, Banoeng-Yakubu et al. (2011) underscored that in the Birimian Province 2010). aquifers, the saprolitic zone is a combination of the topsoil, the under- Generally, the topography of the study area is undulating, with lying lateritic soil, the highly weathered zone and the moderately elevation ranging between 245 m and 350 m. The Black Volta River runs weathered zone. Successful boreholes drilled through the rocks of the through the study area along the western border and drains the area Birimian and the Tarkwaian range between 35 and 55 m with an average through several tributaries and streams. The area lies within the Tropical of 42 m (Agyekum, 2004). Carrier et al. (2008) also recorded borehole continental climate, and generally is one of the driest parts of the depth in a similar range of 35 and 55 m with an average of 50 m in the country. It experiences a single rainfall season that occurs between early granitoids. Data collated from WRI-CSIR shows that the depth to water May and late October, with the highest rainfall experienced between table falls within 4–37 m with an average of 21 m. There is no August and September (Dickson and Benneh, 1995). Relative humidity well-defined local groundwater flow regime, however, the groundwater is high during the rainy season (65–85%) but may fall to as low as 20% flow within the study area follows the regional flow regime, which is during the dry season. Monthly mean rainfall ranges between 200 mm from north to south and to a large extent toward the south-western parts and 300 mm, with an annual average of about 1100 mm (Sawla-Tuna-- of the study area. The aquifer transmissivity of the productive zones of Kalba District Assembly, 2014). the Birimian Province has been reported by Banoeng-Yakubu et al. (2011) to range between 0.2 m2/d and 119 m2/d, with an average of 2.1. Geology and hydrogeology 7.4 m2/d. The Kwahu-morago Group, which is composed of ‘Yabraso’ sandstone formation, has been identified as good aquifer zone based on The study area is principally underlain by about 80% Birimian rocks available data from drilling projects within the area. The basement rocks with the other 20% being granitoids and Kwahu-Morago Group of the are characterised by little or no primary porosity, and therefore the Voltaian Supergroup (Fig. 2). The Birimian consists of metamorphosed hydrogeology of the area is controlled by secondary permeabilities volcanic and sedimentary rocks, which form five subparallel belts of resulting from weathering and fracturing of the rock which has volcanic rock separated by broad “basins” of sedimentary rocks (Kesse, enhanced the storage and transmissive properties of the rocks to form 1985). Rocks of the Birimian Supergroup have been divided into met- groundwater reservoirs (Darko et al., 2006; Yidana et al., 2012b). asedimentary and metavolcanic rocks (Junner, 1935, 1940; Bates, 1955). The Birimian metasedimentary are made up of greywackes with 3. Methodology turbidite features, phyllites, slates, schists, weakly metamorphosed tuffs and sandstones whiles the Birimian metavolcanics comprise rock types Data of groundwater samples collected from boreholes in the study such as lava flows and dyke rocks of basaltic and andesitic composition. area were obtained from Hydronomics Ghana limited in Accra. Standard Most of these rocks have now been metamorphosed to hornblende protocols for water sampling and storage as prescribed in APHA (2005); actinolite-schists, calcareous chlorite schists and amphibolites (Kesse, USGS (2006) and Canada CCME (2011) were adopted. One hundred and 1985). The Birimian Supergroup is intruded by granitoids of the Ebur- twelve (112) samples collected 500 ml in pre-cleaned sterilized poly nean and ‘Tamnean’ Plutonic Suites. In Ghana, four main types of propylene plastic bottles were stored in cool boxes (about 3 �C) and granitoids are recognized to be associated with the Birimian transported to the Water Research Institute of the Council for Scientific 3 Y.S.A. Loh et al. G r o u n d w a t e r f o r S u s t a i n a b l e D e v e l o p m e nt 10 (2020) 100296 and Industrial Research (WRI-CSIR) laboratory for major and trace Table 1 cations and anions analyses whiles pH, electrical conductivity (EC) and Statistical summary of the concentrations of the physicochemical parameters. Temperature were determined on the field with the aid of a Parameter Mean (X) Std. Dev (s) Minimum Maximum multi-parameter portable meter (PELI 1521) from HANNA Instruments. In the laboratory, the samples were stored at 4 �C in a refrigerator for pH 6.86 0.61 5.87 9.99 about a week before they were analysed. Different methods (i.e. gravi- EC 429 196 101 1040 TH 158.0 47.7 50.0 270.0 metric (TDS), titrimetric (Ca, Total hardness, alkalinity), SPADNS (F ), Alkalinity 178.3 57.5 70.0 366.0 turbidimetric (SO24 ), argentometric (Cl ), atomic absorption spec- Ca2þ 33.6 14.4 9.6 77.8 trometry (As, Mn, Fe, HCO þ 2þ 2þ3 ), flame photometry (K, Na , Mg ), hy- Mg 18.0 6.4 3.4 32.5 þ drazine reduction (NO3 ), Stannous chloride (PO24 )) were used to Na 31.0 17.7 7.5 97.8 Kþ 4.9 1.7 1.4 11.0 analyse the major, minor and trace elements. The dataset was subjected HCO3 217.5 70.2 85.4 447.0 to an internal consistency test using the charge balance error (CBE) to SO2- 4 12.8 14.5 2.5 65.2 test its accuracy (equation (1)). A charge balance error value within Cl 18.8 13.1 3.0 70.5 �5% is generally acceptable and shows that the laboratory analysis of NO3 0.40 0.75 <0.01 5.3 2- the parameters are a good balance of the cations and anions (Appelo and PO4 0.08 0.09 <0.01 0.39 F 0.8 0.4 0.2 2.4 Postma, 2005). In view of this, samples that recoded CBE values above Fe 0.16 0.47 <0.01 4.13 �5% were dropped from the analysis. Mn 0.06 0.07 <0.01 0.29 P P SiO2 59.1 11.6 28.0 87.6 mcjzcj majzaj C B E P P As <0.01 0.01 <0.01 0.08 : : ¼ � 100 (1) mcjzcj þ majzaj Where mc and ma, and zc and za are respectively molar concentrations of spatially interpolated using inverse distance weighting (IDW) technique. major cations and anions, and charges of cations and anions. In order to minimise the errors associated with the spatial prediction, the Similarly, the resulting datasets were subjected to normality test, interpolation was limited to places in the study area with optimal since optimal multivariate statistical analyses assume Gaussian distri- sample-point distribution, such that places with little/no sample points bution of datasets. Datasets that were not normally distributed were log- were excluded from the interpolation. Irrigation quality of the ground- transformed and/or standardised to their z score (equation (2)) values. water on the other hand was assessed using the Wilcox (1955) and x μ United States Salinity Laboratory (USSL, 1954) diagrams. z¼ (2) s 4. Results and discussions Where x, μ and s are respectively the measured value, mean and stan- dard deviation of the parameter. The statistical summaries of the analysed parameters are presented The transformed datasets were subjected to R-mode and Q-mode in Table 1. Most of the major chemical parameters display high ranges of hierarchical cluster analysis (HCA). The Q-mode HCA is used to variance, suggesting variable sources and factors influence and discriminate the spatial associations or evolution of the groundwater contribute to the concentration of these parameters of the groundwater into various types in space and/or time, whereas R-mode is used to in the study area. Generally, concentrations of the major chemical pa- determine and rank the sources of variation in the hydrochemistry. rameters are within the acceptable WHO recommended ranges for Although several similarity/dissimilarity and agglomerative techniques potable water (WHO, 2017). are available in HCA, the squared Euclidean distance and Ward’s However, there are a few cases where some hydrochemical param- agglomeration method were employed in this study, since a combination eters, which mostly occur as outliers, fall outside the acceptable WHO of these two have been identified to yield the best outcomes in HCA (2017) ranges for potable water. One instance is the occurrence of high (Davis, 1986; Yidana et al., 2010, 2012a). For significant and optimal fluoride levels recorded in some parts of Gindabo and Tuna in the central stochastic analysis, parameters with large missing datasets such as F, Fe, sections of the study area, which may be associated with the leaching Mn and As in most cases were excluded from the cluster analysis. and weathering of fluorite and/or the influence of alkaline water types Principal component analysis (PCA) was also applied to the dataset in the area. Alkaline waters have been reported to retard the adsorption to identify the main controls on groundwater chemistry. PCA is a data of fluoride onto the surfaces of clay minerals (Viero et al., 2009). pH dimension reduction technique that reveals the significant components/ presents the least variance among the major chemical parameters and factors to aid interpretation of a large set of data and to visualise the has ranges falling outside (5.87–9.99) the WHO (2017) recommended correlations between the variables and hopefully be able to limit the range of 6.5–8.5, signifying a slightly acidic to slightly alkaline number of variables. PCA was performed using the correlation matrix, groundwater system. which brings the measurements onto a common scale and the principal components sorted in a diminishing order of variance, such that the most important principal components are listed first. To ensure that the 4.1. Assessment of the main sources of variation in groundwater extracted components did not correlate with each other, an orthogonal chemistry (varimax) rotation technique was used. Assertions made in relation to the hydrochemical associations and Results of Pearson correlation (r) analysis are presented in Table 2. processes based on the multivariate statistical techniques were sup- Correlation analysis provides a quick way to visualise the relations be- ported by employing conventional hydrochemical plots and mineral tween two parameters for purposes of drawing inferences. It is obvious stability diagrams to further establish the main controls on groundwater from the results that the main contributors to EC in the study area are 2þ þ 2 chemistry and to identify the most stable mineral phases in the Ca , Na , HCO3 , SO4 , Cl and F since these parameters have sig- groundwater flow system. nificant correlations (�0.50) with EC. Similarly, bicarbonate shows þ 2þ The quality of the groundwater was also assessed for drinking and significant relation with Na and Ca , which is consistent with silicate irrigation purposes. Quality assessment for domestic purposes was based weathering (Freeze and Cherry, 1979), and possibly influenced by on a modified form of the water quality index (WQI) approach (Sahu and precipitation. Sikdar, 2008), which is a weighted arithmetic index method. The R-mode hierarchical cluster analysis (HCA) and principal component resultant water quality indices estimated from this method were analysis (PCA) have been used to further unearth the main chemical processes controlling groundwater chemistry in the study area. The 4 Y.S.A. Loh et al. G r o u n d w a t e r f o r S u s t a i n a b l e D e v e l o p m e nt 10 (2020) 100296 Table 2 Pearson correlation analysis of hydrochemical parameters. pH Ca2þ Mg2þ Naþ Kþ HCO SO2- Cl NO PO2- 3 4 3 4 F Fe Mn SiO2 As EC pH 1.00 Ca2þ 0.30 1.00 Mg2þ 0.11 0.14 1.00 Naþ 0.32 0.51 0.04 1.00 Kþ 0.02 0.35 0.18 0.14 1.00 HCO3 0.42 0.67 0.47 0.69 0.29 1.00 SO2- 4 0.06 0.50 0.00 0.54 0.20 0.15 1.00 Cl 0.07 0.47 0.13 0.39 0.14 0.09 0.61 1.00 NO3 0.08 0.24 0.13 0.21 0.19 0.08 0.35 0.42 1.00 PO2- 4 0.02 0.15 0.23 0.28 0.11 0.31 0.13 0.04 0.17 1.00 F 0.42 0.38 0.01 0.69 0.24 0.53 0.36 0.23 0.02 0.25 1.00 Fe 0.13 0.24 0.00 0.31 0.19 0.17 0.40 0.27 0.15 0.07 0.47 1.00 Mn 0.17 0.28 0.36 0.22 0.30 0.28 0.24 0.31 0.36 0.24 0.25 0.32 1.00 SiO2 0.27 0.40 0.00 0.45 0.10 0.42 0.26 0.16 0.04 0.12 0.44 0.14 0.00 1.00 As 0.04 0.00 0.16 0.01 0.06 0.10 0.05 0.04 0.03 0.07 0.03 0.02 0.01 0.00 1.00 EC 0.34 0.78 0.37 0.76 0.34 0.76 0.58 0.55 0.36 0.35 0.60 0.39 0.39 0.43 0.06 1.00 Fig. 3. Dendrogram from R-mode cluster analysis. dendrogram (Fig. 3) for the R-mode cluster analysis presents the visual association of EC, Ca2þ, HCO3 , Naþ and pH in cluster 1a suggests the associations among the parameters with a phenon line drawn at a dissolution of feldspars in the sandstones found in the study area. The linkage distance of about 22. Although the definition of clusters based reaction of carbon dioxide in the atmosphere and precipitation and on the distance of the phenon line is subjective, it is informed by the possibly within the soil zone results in the formation of carbonic acid, researcher’s understanding of the combining environmental factors such which dissolves such minerals during infiltration, thereby releasing as the geology, hydrogeology and human activities which prevail in the HCO3 and the associated ions. Cluster 1b on the other hand consists of study area and are likely to affect the chemistry of groundwater (Yidana SiO2, Kþ and Mg2þ, at a much shorter linkage distance than cluster 1a, et al., 2012a). The phenon line is drawn such that too many or too few and therefore presents the most similar members of the same cluster clusters are not generated since the interpretation might be difficult, given the shorter linkage distance (Yidana et al., 2011), relative to defeating the purpose of cluster analysis, or some important hydro- cluster 1a and cluster 2. Cluster 1b probably represents silicate mineral chemical processes might be omitted. weathering, especially micas such as biotite which are also present in the Notwithstanding the semi-objectivity, two clusters were generated study area (Sawla Tuna Kalba District Assembly, 2014). which represent two main groundwater associations and/or processes. The second cluster links NO , SO23 4 and Cl . Cluster 2 represents the Cluster 1 consists of Naþ, Ca2þ, EC, HCO3 , pH, K, Mg and SiO2 which influence of agrochemicals and domestic wastewaters, which are com- represents the dominance of rock-water interaction; dominated by mon pollutants of groundwater in agrarian settlements such as the study dissolution of silicate minerals in the rocks of the area. The close area (Han et al., 2016). 5 Y.S.A. Loh et al. G r o u n d w a t e r f o r S u s t a i n a b l e D e v e l o p m e nt 10 (2020) 100296 Table 3a Results of principal component analysis (PCA) revealed two main Final factor loadings for the water quality parameters. components just like the R-mode cluster analysis, and these accounted Component for about 70% of the total variance in the hydrochemistry (Table 3). The factors were extracted based on variables with communalities of 0.5 and 1 2 above (Kaiser, 1960), and variables below the set lower limit such as Kþ, HCO3 .923 -.050 PO24 , As, Mn and Fe were excluded from the analysis. Communalities EC .805 .491 Naþ .782 .328 measure the proportion of each variable’s variance that can be explained Ca2þ .738 .397 by the factors. Therefore, variables which loaded highly with more than SiO2 -.663 -.011 one component such as Mg2þ and pH were dropped, since these vari- Cl .175 .843 ables had duplicate effects and could not be used to explain a particular SO24 .305 .770 unique process in the hydrochemistry. NO3 -.009 .728 The first component which accounts for over 40% of the total vari- ance in groundwater chemistry in the study area has high positive loadings for HCO , EC, Naþ, Ca2þ3 and high negative loading for SiO2. It Table 3b can be deduced from the correlation analysis, HCA and PCA that Communalities of parameters on factor model. Component 1 represents the dissolution of silicates, particularly, the Initial Extraction feldspars in the sandstones underlying the area, thus confirming the EC 1.000 .889 findings from cluster 1a (Fig. 3). The negative loading of SiO2 with Ca2þ 1.000 .702 component 1 also suggests that the silica in the groundwater does not HCO3 1.000 .854 contribute significantly to the electrical conductivity, which is in line Naþ 1.000 .720 SO2 1.000 .686 with the results of the correlation analyses (Table 2). A plot of Na versus 4 Cl 1.000 .741 Cl (Fig. 4) suggests that the dissolution of halite is not the main source of NO3 1.000 .531 Naþ in the groundwater and thus the dissolution of silicate minerals SiO2 1.000 .520 could be the source of this ion in the groundwater. The strong correla- tion between F and Naþ (r ¼ 0.69) as shown in Table 2, possibly sug- gests the dissolution of minerals such as villiaumite and fluorapatite, which are common constituents of the bedrock of the study area (Yidana et al., 2012a). Table 3c Total variance explained. Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative % 1 4.136 51.697 51.697 4.136 51.697 51.697 3.218 40.231 40.231 2 1.426 17.829 69.526 1.426 17.829 69.526 2.344 29.294 69.526 3 .737 9.208 78.733 4 .642 8.028 86.761 5 .504 6.297 93.058 6 .363 4.533 97.592 7 .129 1.608 99.200 8 .064 .800 100.000 Fig. 4. Molar concentrations of Naþ versus Cl suggesting silicate weathering/ion exchange in the hydrochemistry of the study area. 6 Y.S.A. Loh et al. G r o u n d w a t e r f o r S u s t a i n a b l e D e v e l o p m e nt 10 (2020) 100296 Fig. 5. Spatial distribution of the cluster from Q-mode HCA. Fig. 6. Dendrogram from the Q-mode HCA showing the spatial groundwater associations. Component 2 on the other hand loads highly with Cl , SO24 and NO3 study area is composed mainly of agrarian communities, with farming and represents the influence of agrochemicals and domestic wastewa- practices, which involves the use of organic and inorganic fertilizers ters, as these ions are common constituents of such pollutants. Since the such as manure and NPK, groundwater is likely to be polluted by such 7 Y.S.A. Loh et al. G r o u n d w a t e r f o r S u s t a i n a b l e D e v e l o p m e nt 10 (2020) 100296 Fig. 7. Stiff diagrams made from average concentrations of major ions from the three clusters from Q-mode HCA. Fig. 8. Gibbs diagram showing the main sources of variation in groundwater chemistry for the study area. chemicals in the study area (Cobbina et al., 2012). have been designated as recharge and discharge zones respectively, Q-mode HCA resulted in a dendrogram (Fig. 6) showing three main consistent with topographically-driven groundwater flow (Fitts, 2002), spatial associations in the hydrochemistry in the study area. Average whereas cluster 2 samples are considered to represent a transition be- concentrations from the three clusters are presented with Stiff diagrams tween clusters 1 and 3. This is clearly demonstrated by the Stiff diagrams (Fig. 7). Generally, aquifers of the Birimian deliver groundwater of low in Fig. 7. ionic concentrations since their hydrochemistry is mainly influenced by It is apparent from the Stiff diagrams (Fig. 7) that cluster 1 is a silicate mineral weathering (Yidana et al., 2012a). Notwithstanding, the Ca–Mg–HCO3 water type, whereas cluster 2 is Mg–Ca–HCO3 water type three clusters exhibit varying degrees of ionic concentrations and sug- which evolves into a Ca–Na–K–HCO3 water type in cluster 3. The gest a flow pattern in the groundwater system. It is obvious from Fig. 7 dissolution of villiaumite and fluorapatite is plausible when the water that on the average cluster 1 presents the least mineralised groundwater had enough time to interact with the rocks as it evolves towards system. Cluster 1 is mainly composed of samples from around Kong, discharge areas thus increasing the concentration of Na in those areas. Sooma, Jilinkon, Nahari and Grungu, which are generally high elevation Similar water types were identified by Cobbina et al. (2012) in the locations in the study area (Fig. 5). Conversely, cluster 3 presents the Voltaian and Basement aquifers in the Savannah Region. This is in highest mean concentration of the major ions and TDS, and is found at tandem with the assertions made above from Table 3 and Fig. 4, that locations with low elevations such as Babalayurin, Degwiwu, Kalba, and dissolution of silicates plays a significant role, explaining over 40% of generally the south western portions of the study area, whereas cluster 2 the total variance in groundwater chemistry in the study area. occurs between clusters 1 and 3 (Fig. 5). A Gibbs (1970) diagram plotted for all three clusters clearly indicates In recharge areas, the concentration of ions in groundwater is usually rock dominance as the main source of the dissolved ions in groundwater low and similar to the precipitation in the area, but as the water travels from the study area (Fig. 8). However, the hydrochemistry of cluster 1 through the subsurface and dissolves minerals and other materials, its samples is largely influenced by the chemistry of precipitation in the ionic content increases with time. This explains why clusters 1 and 3 area, which is characteristically low in dissolved ions and high in 8 Y.S.A. Loh et al. G r o u n d w a t e r f o r S u s t a i n a b l e D e v e l o p m e nt 10 (2020) 100296 The mineral stability diagrams for NaO–Al2O3–SiO2–H2O and Al2O3–SiO2–H2O are presented in Fig. 10. Both plots indicate that the most stable silicate phase in the aquifers is kaolinite, indicating the incongruent weathering of silicate minerals such as biotite and musco- vite, and suggests that the groundwater in the aquifer is relatively young to intermediate in its flow regime and age. This implies that there is little or no restricted groundwater flow such that the residence time is not long enough to allow for significant silica leaching into the groundwater (Edet and Okereke, 2005; Yidana et al., 2008a,b; Loh et al., 2016). 4.2. Groundwater quality evaluation for domestic use The quality of the groundwater has further been examined for its potability, using the water quality index (WQI) approach, modified after Brown et al. (1972). This approach is a weighted arithmetic approach, which involves a series of steps to arrive at a single value (index), which describes the overall quality of water in space and time. The suitability of groundwater in this study was based on the standards set by the World Health Organisation (WHO, 2017) for domestic drinking water. In the Fig. 9. Durov diagram showing the predominant groundwater types in the first step, water quality parameters of concern are assigned weights (wi) study area. based on their importance and health implications when present in drinking water. F , NO3 and As were assigned a maximum of 5 based on bicarbonate as a result of the CO2-charged rainfall. Although samples their relative importance in drinking water. The rest of the parameters 2þ 2þ þ þ 2 3 from clusters 2 and 3 plot within the rock dominance section, it can be (pH, TDS, TH, Ca , Mg , Na , K , CI , SO4 , PO4 , Fe and Mn) were seen that samples from cluster 3 plot much closer to rock domi- assigned weights of 1–5 also based on their relative importance and nance–evaporation-crystallization line than the rock domi- health implications when present in drinking water. Secondly, the nance–precipitation line. This supports the argument that waters from cluster 3 are more enriched in dissolved ions as a result of the longer Table 4 interaction of the groundwater with the rock matrix. Standards, weights and relative weights used for WQI computation. Generally, groundwater from the study area is clearly a mixed cation Chemical Objective to be met (Si) Weight Relative weight fresh water type dominated by bicarbonate (HCO3>SO4þCl), as Parameter (mg/l) (wi) (Wi) demonstrated by the Stiff diagrams (Fig. 7). Durov (1948) diagram has pH 7.5 4 0.0943 been used to discriminate the hydrochemistry further. From Fig. 9, it is TDS 500 4 0.0755 apparent that most of the groundwater samples (42%) are dominated by TH 200 4 0.0755 2þ Ca–HCO3, implying the dominance of alkaline earths over alkali (thus Ca 200 2 0.0377 2þ Ca þ Mg > Na þ K), whereas 36% and 20% are respectively Mg–HCO Mg 150 2 0.0377 3 Naþ 200 2 0.0377 and Na þ K–HCO3 fresh water types. Cluster 3 samples plot closer to the Kþ 30 2 0.0377 Na þ K field and further away from the Ca and Mg fields, with corre- Cl 250 3 0.0566 sponding high TDS and pH values (Fig. 9), confirming that these waters SO2- 4 250 3 0.0566 have had longer time to interact with the rock matrix and the sur- NO3 50 5 0.0943 PO3- 0.7 4 0.0755 rounding environment. The low pH values mainly contributed from 4F 1.5 5 0.0943 precipitation, as seen in cluster 1 samples, are neutralized as the water Fe 0.3 3 0.0566 interacts and dissolves the rock material as it transits the recharge zones Mn 0.4 4 0.0755 and evolves into a more ionically enriched water in cluster 3. As 0.01 5 0.0943 Fig. 10. Mineral stability diagrams for the (a) Ca–Al–SiO2–H2O system, (b) Na–Al–SiO2–H2O system at 1 atm and 25 �C. 9 Y.S.A. Loh et al. G r o u n d w a t e r f o r S u s t a i n a b l e D e v e l o p m e nt 10 (2020) 100296 Fig. 11. Spatial distribution of water quality indices reclassified after Sahu and Sikdar (2008). relative weight (Wi) of each parameter was computed as a fraction of the total weights computed from the fifteen parameters (Table 4) based on Ciqi¼ � 100 (4) equation (3). Si wi Where Ci and Si are respectively parameter concentration and set WiP (3) wi objective. The sub-index and WQI are determined respectively using equations Lastly, a quality rating scale (qi) was calculated for each parameter (5) and (6). by dividing its concentration by the set objective (WHO standard) and multiplying by 100 (equation (4)). Si¼Wi� qi (5) Fig. 12. Groundwater quality classification for irrigation in the study area (USSL, 1954). 10 Y.S.A. Loh et al. G r o u n d w a t e r f o r S u s t a i n a b l e D e v e l o p m e nt 10 (2020) 100296 Fig. 13. Groundwater quality assessment for the study area based on Wilcox (1955) diagram. Xn This assertion is corroborated by the Wilcox (1955) plot, in which all the WQI¼ Si (6) water samples plot in “excellent to permissible” category. n¼1 It is obvious from Figs. 12 and 13 that the salinity of the groundwater The calculated WQIs are categorised according to Sahu and Sikdar in the study area increases as the water travels from recharge (cluster 1) (2008) as “excellent water” (WQI <50); “good water” (WQI ¼ 50–100); to discharge (cluster 3) areas. Cluster 1 samples are typically low in both “poor water” (WQI ¼ 100–200); “very poor water” (WQI ¼ 200–300); sodium and salinity as a result of the short residence time and interac- and “unsuitable for drinking” (WQI >300). tion with the geology. Cluster 2 samples appear to present the best The computed WQIs have been spatially interpolated based on an balance of sodium and salinity ranges (SAR ¼ 1–5 and EC ¼ 500–900 inverse distance weighting (IDW) technique, which proved to be the μ/cm) (Banoeng-Yakubo et al., 2009) and hence have the best quality for most appropriate interpolation technique as a result of the smallest root irrigation. But as the groundwater interacts and dissolves the rock ma- mean square error associated with it (RMSE ¼ 0.852). The output was trix, alongside getting polluted by anthropogenic sources such as agro- reclassified according to Sahu and Sikdar (2008) (Fig. 11). The esti- chemicals and domestic wastewaters, its quality for irrigation mated WQIs ranged from 16.64 to 132.15, with about 94% of the water deteriorates, as seen in cluster 3 samples (Fig. 12). classified as being “excellent” for drinking purposes, whereas 5% and 1% are considered “good” and “poor” quality respectively (Fig. 11). The 5. Conclusion poor water arises from two samples, STKD_086 and STKD_93 located at Sawla and Soomia, which are characterised by elevated levels of fluo- The key factors controlling groundwater chemistry and quality in ride, arsenic and Fe. These samples generally have high TDS values, and some Voltaian and Basement aquifers in portions of Sawla-Tuna-Kalba the contamination could be attributed to localised leaching of such ions District, northern Ghana have been assessed. Statistical techniques into the groundwater system. such as HCA, PCA and correlation analysis coupled with conventional hydrochemical plots suggests the dissolution of silicate minerals, particularly feldspars and micas in the rocks, as well as the influence of 4.3. Assessment of groundwater quality for irrigation purposes agrochemicals and domestic waste as the main factors controlling groundwater chemistry in the study area. Even though silicate mineral The presence of sodium ion in groundwater is of grave concern due to weathering was identified as the dominant process controlling the its ability to affect the permeability of soil as well as offset the osmotic groundwater chemical composition, the concentration of SiO is low and pressure of plants. An offset in the osmotic pressure of the plants affects 2 did not contribute significantly to the EC. Mineral speciation calcula- the rate of water intake by plant roots and its subsequent use in the þ tions derived from the analysis also suggests that the most stable silicate process of photosynthesis. High concentrations of Na in soil therefore mineral phase is kaolinite, which suggests that groundwater in the area affect the metabolism of the plants and this can translate negatively to is at intermediate stage, and flow is not restricted mainly due to the crop yield (Mohan et al., 2000; Zaidi et al., 2015). Saleh et al. (1999) occurrence and pervasiveness of secondary permeability. concluded that excessively high salinity waters also have the tendency to Q-mode HCA identified three (3) groundwater flow regimes in the affect the ability of the plants to absorb nutrients and water. Fig. 12 is study area, recharge zones (cluster 1) located in high elevated areas, based on the United States Salinity Laboratory (USSL, 1954), which characterised by low TDS, and dominated by Ca–HCO water type; represents a plot of sodium adsorption ratio (SAR) against EC (salinity). 3 transition zones (cluster 2) dominated by Mg–Ca–HCO water type; and All the samples from the study area plotted within the “low sodicity” 3 Ca–Na–K–HCO3 water type (cluster 3) in discharge areas. (S1) region and “low to high salinity” ranges (C1 – C3). About 18% of the Also, groundwater quality for domestic purposes was assessed using samples fell within C1– S1 (low salinity—low sodicity) and are mainly a weighted arithmetic index approach. The computed water quality composed of waters from cluster 1 members of the Q-mode HCA. Sam- indices (WQIs) from the data suggest that 94% of the sampled wells ples from cluster 1 have been categorised as recharge zones, and have provide groundwater of “excellent” quality for drinking purposes, low ionic content. Most of the samples (72%) plotted within the C2–S1 whereas 5% and 1% present water of “good” and “poor” quality region, “medium salinity—low sodicity” irrigation waters, whereas only respectively. The poor quality presented by some of the wells is attrib- 6% of the samples plot within C3–S1. All three regions present water of uted to elevated levels of fluoride, arsenic, Fe and TDS and may have good quality and may be used for irrigation without prior treatment. 11 Y.S.A. Loh et al. G r o u n d w a t e r f o r S u s t a i n a b l e D e v e l o p m e nt 10 (2020) 100296 resulted from the leaching of such ions into the groundwater. The study Durov, S.A., 1948. 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