preliminary groundwater quality assessment in the central region of ghana

ORIGINAL ARTICLE

Preliminary groundwater quality assessment in the central regionof Ghana

Samuel Y. Ganyaglo • Shiloh Osae • Samuel B. Dampare •

Joseph R. Fianko • Mohammad A. H. Bhuiyan • Abass Gibrilla •

Edward Bam • Elikem Ahialey • Juliet Osei

Received: 10 July 2010 / Accepted: 20 July 2011 / Published online: 7 August 2011

� Springer-Verlag 2011

Abstract Insufficient knowledge of the hydrogeochem-

istry of aquifers in the Central Region of Ghana has

necessitated a preliminary water quality assessment in

some parts of the region. Major and minor ions, and trace

metal compositions of groundwater have been studied with

the aim of evaluating hydrogeochemical processes that are

likely to impair the quality of water in the study area. The

results show that groundwater in the area is weakly acidic

with mean acidity being 5.83 pH units. The dominant

cation in the area is Na, followed by K, Ca, and Mg, and

the dominant anion is Cl-, followed by HCO3- and SO4

2-.

Two major hydrochemical facies have been identified as

Na–Cl and Na–HCO3, water types. Multivariate statistical

techniques such as cluster analysis (CA) and factor anal-

ysis/principal component analysis (PCA), in R mode, were

employed to examine the chemical compositions of

groundwater and to identify factors that influenced each.

Q-mode CA analysis resulted in two distinct water types as

established by the hydrochemical facies. Cluster 1 waters

contain predominantly Na–Cl. Cluster 2 waters contain

Na–HCO3 and Na–Cl. Cluster 2 waters are fresher and of

good quality than cluster 1. Factor analysis yielded five

significant factors, explaining 86.56% of the total variance.

PC1 explains 41.95% of the variance and is contributed by

temperature, electrical conductivity, TDS, turbidity,

SO42-, Cl-, Na, K, Ca, Mg, and Mn and influenced by

geochemical processes such as weathering, mineral disso-

lution, cation exchange, and oxidation–reduction reactions.

PC2 explains 16.43% of the total variance and is charac-

terized by high positive loadings of pH and HCO3-. This

results from biogenic activities taking place to generate

gaseous carbon dioxide that reacts with infiltrating water to

generate HCO3-, which intend affect the pH. PC3 explains

11.17% of the total variance and is negatively loaded on

PO43- and NO3

- indicating anthropogenic influence. The

R-mode PCA, supported by R-mode CA, have revealed

hydrogeochemical processes as the major sources of ions in

the groundwater. Factor score plot revealed a possible flow

direction from the northern sections of the study area,

marked by higher topography, to the south. Compositional

relations confirmed the predominant geochemical process

responsible for the various ions in the groundwater as

mineral dissolution and thus agree with the multivariate

analysis.

Keywords Groundwater quality � Mineral dissolution �Multivariate analysis � Central Region � Ghana

Introduction

The chemical composition of groundwater is controlled by

many factors, including composition of precipitation, min-

eralogy of the aquifers, climate, topography, and anthropo-

genic activities. These factors combine to create diverse

water compositions that vary temporally and spatially.

Additionally, these factors may lead to contamination of

groundwater with diverse constituents, resulting in severe

environmental and socio-economic problems (Kumar and

Riyazuddin 2008). Hydrochemical characterization of such

systems requires knowledge of the major and minor ion

S. Y. Ganyaglo (&) � S. Osae � S. B. Dampare �J. R. Fianko � A. Gibrilla � E. Bam � E. Ahialey � J. Osei

National Nuclear Research Institute, Ghana Atomic Energy

Commission, P. O. Box LG 80, Legon-Accra, Ghana

e-mail: [email protected]

S. B. Dampare � M. A. H. Bhuiyan

Department of Earth Sciences,

Okayama University, Okayama, Japan

123

Environ Earth Sci (2012) 66:573–587

DOI 10.1007/s12665-011-1266-7

compositions of the groundwater and trace metals contents in

the groundwater. This will enable recharge and discharge

areas as well as flow paths to be defined.

The most reliable source of water supply in the Central

Region of Ghana is groundwater. Groundwater in this

region is used for domestic, agriculture, and industrial

purposes. Prior to its development, the inhabitants depen-

ded heavily on streams and rivers as their major source of

water supply. This led to outbreak of water borne and water

related diseases such as diarrhea, dysentery, cholera, and

guinea worm as the streams and rivers are often polluted.

The development of groundwater in the rural communities

coupled with health education led to the eradication of

most of these diseases in the region. Pipe schemes exist but

are concentrated only in the urban and peri-urban areas of

the region. The cost involved in extending this facility to

the rural areas is often high, making groundwater the most

cost effective and efficient water supply scheme in the rural

communities in the region. Even in the urban centers,

people are becoming aware of the use of groundwater as

the safest source of water supply. A few individuals and

industries have boreholes in their homes and premises,

respectively. In spite of the tremendous increase in cov-

erage of groundwater usage, its continuous development is

being threatened by numerous problems, including (1) high

salinity (high EC) in some of the boreholes especially in

the coastal areas, leading to their abandonment and (2)

insufficient knowledge of the aquifer systems, which is

likely to result in over exploitation of the resource. It thus

becomes difficult to put in place good management prac-

tices that will ensure long term sustainability of boreholes,

which tap various aquifers in the area.

In an attempt to address the problems outlined, a pre-

liminary investigation into the hydrogeochemistry of the

groundwater in the various aquifers in the study area has been

initiated. The major objective is to establish the hydro-

chemical facies in the area and the possible sources of vari-

ous ions in the groundwater. This is to be achieved through

groundwater sampling, analysis of major cations, anions,

minor ions, and trace metals. The major ion study is to

establish the hydrochemical facies in the area and possible

sources of the various ions in the groundwater. The study is to

help understand the hydrogeochemical characteristics of the

various aquifer systems for effective management measures

to be undertaken to mitigate poor groundwater quality in the

area. Interpretative techniques include graphical method,

multivariate, and compositional diagrams.

The study area

The study area lies within latitudes 5�180N–5�44.630N and

longitudes 1�4.070W–1�37.260W. It comprises parts of

Asikuma Odoben Brakwa, Assin, Twifo Praso, and Agona

areas of the region. Very few communities located along

the coast were investigated. Notably among them were

Afutuakwa and Brofoyedur (Fig. 1). The area is drained by

4 major streams. These are Narkwa, Kakuum, Amissah,

Ayensu, and the Pra in the Twifo praso areas. The drainage

patterns are described as dendritic (WRCS 2008). Most of

the streams end up in lagoons, wetlands, and finally into the

sea. The topography is generally undulating with steeper

hills inland. Vegetation in the coastal areas is made of

grassland and shrubs, few scattered trees, and semi-decid-

uous forest to secondary forest. In the northern parts of the

region, secondary forest can be found. Tall trees exhibit

deciduous characteristics during the dry season.

The climate, like the rest of the country, is dominated by

the movement of the Inter Tropical Convergence Zone

(ITCZ). The ITCZ is the region where the hot, dry, and dusty

harmattan air mass from the Sahara in the north meets the

cool, moist monsoon air from the South Atlantic. The ITCZ

is characterized by vigorous frontal activity and its move-

ment controls the amount and duration of rainfall. Normally,

from December to February, the front lies across the Gulf of

Guinea and the dry harmattan prevails over the whole

country. Between March and November, the ITCZ moves

across Ghana in a complex fashion crossing some areas

twice, which results in a distinctly bimodal rainfall pattern.

Two rainfall seasons (major and minor) thus occur in the

Central Region. The major one is from April to June, and the

minor is experienced from September to November.

The soils in the study area are made of four main groups

namely forest ochrosols, forest ochrosols and oxysols in-

tergrades, tropical black earth, and forest lithosols. The

forest ochrosols have a high nutrient value and are suitable

for both tree and food crops, such as cocoa, coffee, citrus,

maize, cassava, pineapple, and vegetables. The forest

ochrosols and oxysols integrates have lesser nutrients

compared with the forest ochrosols but have similar tex-

ture. This type of soil also supports true crops such as

cocoa and all food crops. The tropical black earth is thick,

sticky, and dark in color containing a mixture of high

percentage of magnesium, calcium, and lime. During the

rainy season, these soils become thick and sticky but

become compact and hard and crack up during the dry

periods. The soils are potentially suitable for rice, cotton,

and sugarcane especially when artificial irrigation is

applied. The tropical black earth exists along the coastal

areas and lagoons. The forest lithosols are found between

Nyanyano and Winneba. These soils are also referred to as

rocky soils due to underlying hardpan and mostly have

poor nutrient value. They can, however, support the culti-

vation of vegetables. Crops such as sugarcane, maize, and

pineapple are grown along the valleys. These soils cover a

wide area of the savanna belt of the area. Major fertilizers

574 Environ Earth Sci (2012) 66:573–587

123

employed in the area include nitrogen, phosphorus, and

potassium.

Geology

The Central Region of Ghana is underlain by the Early

Proterozoic Birimian rocks and associated Eburnean

granitoids (Leube et al. 1990). Traditionally, the Birimian

was divided into two main series:

1. A lower series of mainly sedimentary origin and

termed the sedimentary basin or previously called the

Lower Birimian.

2. An upper series of greenstones mainly metamorphosed

basic and intermediate lavas and pyroclastic rocks,

phyllites, and greywackes (Junner 1935, 1940). The

upper series is termed the volcanic belt or the Upper

Birimian.

The sedimentary basin is composed of phyllites, schists,

tuffs, and greywackes. At the transition zone between the

sedimentary basin and the volcanic belt are chemical sed-

iments namely chert, Mn-rich rock, Ca–Fe–Mg carbonates,

carbon-rich rock, and sulfide rich rock (Leube et al. 1990;

Hirdes et al. 1992; Taylor et al. 1992). It is now widely

accepted that the Lower Birimian and Upper Birimian

formed quasi-contemporaneously, as lateral facies equiva-

lents (Leube et al. 1990).

The Eburnean granitoids comprise the sedimentary

basin granitoids (previously called the Cape Caost type;

G1) and the volcanic belt granitoids (previously called the

Dixcove type; G2). Rock types in the sedimentary basin

granitoids include quartz diorites, tonalities, trondhjemites,

granodiorites, adamellites, and granites. Quartz diorites are

rare. The main ferromagnesian mineral is biotite that is

commonly accompanied by muscovite.

The belt granitoids comprise quartz diorite, tonalite,

trondhjemite, granodiorite, admellite, and to a minor extent

granite. The characteristic mafic mineral is hornblende

accompanied by increasing amount of biotite. The major

felsic components include the plagioclase feldspars, which

commonly alter to saussurite and sericite [KAl2(Si3Al)

O10(OH,F)].

Hydrogeology

The various rock types lack primary porosities and per-

meabilities. Groundwater occurrence is by weathering and

development of secondary porosities and permeabilities in

the form of fractures. The fractures include joints, faults,

folds, and shear zones. Appreciable amount of water is

obtained when these fractures are interconnected.

The thickness of the weathered zone in the study area

varies from 2 to 90 m with the average thickness being

33 m (WRCS 2008). The weathered depth is thickest in the

sedimentary basin and volcanic belts than in the granitoids.

Shallow weathered aquifers occur in the granitoids, and

their shallow nature exposes them to evaporation that may

concentrate dissolved salts in the aquifer zones to increase

salinity. Transmissivity in these aquifers is quite low and

may result in sluggish movement. The transmissivity

Fig. 1 Location map of the

study area

Environ Earth Sci (2012) 66:573–587 575

123

ranges from 24 9 10-5 to 45 9 10-5 m2/s (3,000 well

drilling program—Community Water and Sanitation

Agency). Groundwater is sometimes obtained from both

the weathered zone and fractured fresh rock, and there are

instances where water is obtained from only the weathered

zone. In cases where the weathered layer is very thin,

groundwater occurs only in the fractured fresh rock. It thus

follows that aquifer units may be continuous or discon-

tinuous, depending on the prevailing geological structures

in the area. Borehole yields range between 0.1 and 10 l/s

(Armah 2000). Borehole depth varies from near surface,

15 m to about 100 m with a mean depth of 39 m (Armah

2000). Recharge takes place by direct infiltration of rainfall

and to a lesser extent from influence of streams and rivers.

Materials and methods

Samples were collected from 14 boreholes between 2nd

and 5th May 2009. Sampling was done once, in the rainy

season. Samples were collected in 500 ml polyethylene

containers after filtering through 0.45 lm filters on acetate

cellulose with a hand operated vacuum pump. Filtrate for

metal analyses were acidified with 1% HNO3 to keep metal

ions in solution (Clark and Fritz 1997). All samples were

stored in ice chest at a temperature of 4�C and later

transferred to the laboratory for analysis.

In the field, parameters such as pH, temperature, con-

ductivity, and total dissolved solids (TDS) were measured

using a multi-purpose pH-conductivity meter. The pH

electrode was calibrated against pH 4, 7, and 9.2. Prior to

taking readings, pumping was carried out until the meter

readings were stable for each parameter. Alkalinity (as

HCO3-) was determined by field titration with 1.6 N

H2SO4 to pH 4.5 using HACH Digital multi Sampler

Model AL-DT.

In the laboratory, Cl- concentrations were obtained by

titrimetric method. SO42- and PO4

3- were obtained

employing Ultraviolet (UV) spectrometric technique. The

atomic absorption spectrometer was used in determining

the major cations (Ca and Mg) and trace metals (Fe, Mn,

Zn, Cu, Cd, Pb, Co, Cr, and Ni). Na and K were determined

using the flame photometer. The charge balance error for

the analysis ranges from ±2 to ±10%.

Conventional hydrochemical techniques such as the

Piper trilinear diagram and multivariate statistical analysis

(MVA) were used in interpreting the data obtained. The

MVA techniques employed included cluster analysis (CA)

and factor analysis using principal component analysis

(PCA) as extraction method. CA is an unsupervised pattern

recognition technique that uncovers intrinsic structure or

underlying behavior of a dataset without making a priori

assumption about the data, in order to classify the objects

of the system into clusters based on their similarities

(Kumar and Riyazuddin 2008; Hussain et al. 2008). The

main aim of CA is grouping objects (sampling stations)

into classes (clusters), so that objects within a class are

similar to each other but different from those in other

classes. There are two major categories of CA: hierarchical

and nonhierarchical. Hierarchical cluster analysis (HCA) is

the most common approach in which clusters are formed

sequentially, starting with the most similar pair of objects

and forming higher clusters step by step. The process of

forming and joining clusters is repeated until a single

cluster containing all samples is obtained. The result is

displayed as a dendrogram and provides a visual summary

of the clustering process, presenting a picture of the groups

and its proximity with a dramatic reduction in dimension-

ality of the original data. In this study, hierarchical cluster

analysis was performed using Ward’s method on the nor-

malized dataset and the squared Euclidean distance as a

measure of similarities between samples. Cluster analysis

was carried out in two modes, Q and R as mentioned by

Hussain et al. (2008). The statistical software SPSS version

16 was used in the analysis of the data. The Q-mode HCA

was used to classify the cases (samples), measured by the

variables, into statistically defined groups, whereas the

R-mode HCA was used to establish relationships between

the variables (analyzed parameters).

Factor analysis (FA) is designed to transform the ori-

ginal variables into new uncorrelated variables called fac-

tors, which are linear combinations of the original

variables. The FA is a data reduction technique and sug-

gests how many varieties are important to explain the

observed variances in the data. Principal components

method (PCA) is used for extraction of different factors.

The axis defined by PCA is rotated to reduce the contri-

bution of less significant variables (Najafpour et al. 2008).

This treatment provides a small number of factors that

usually account for approximately the same amount of

information as the original set of observations. The FA can

be expressed as:

Fi ¼ a1x1jþ a2x2jþ � � � þ am x mj ð1Þ

where Fi is the factor, a is the loading, x is the measured

value of variable, i is the factor number, j is the sample

number, and m is the total number of variables. The factor

scores can be expressed as:

Zij ¼ a1f 1jþ a2f 2jþ � � � þ am fmjþ eij ð2Þ

or where Z is the measured variable, a is the factor loading,

f is the factor score, the residual term accounting for errors

other source of variation. The hydrochemical data were

subjected to principal component analysis for identification

of the sources of the various ions in the groundwater.

Varimax rotation was used to maximize the sum of the


123

variance of the factor coefficients which better explained

the possible groups/sources that influenced the water sys-

tems (Bhuiyan et al. 2010 in press).

The factor scores from the R-mode PCA were used with

ArcGIS to determine the spatial variations of the dominant

processes influencing the groundwater hydrochemistry in

the area. Discussions on the interpolation techniques, such

as kriging, and integrated use of factor analysis and kriging

methods in the analysis of hydrochemical data have been

provided in literature (e.g., Yidana et al. 2010; Bhuiyan

et al. 2010).

Results and discussion

A statistical summary of hydrochemical parameters mea-

sured in the 14 groundwater samples is presented in

Table 1. Detailed hydrochemical parameters are presented

in Tables 2 and 3 in appendix. The pH varies from 5.17 to

6.28 pH units with mean of 5.83 and standard deviation of

0.36. The pH is thus slightly acidic. The electrical con-

ductivity (EC) values range from 30.6 to 3360 lS/cm.

Relatively low EC values occur inland whereas high EC

values are observed along the coast. High electrical con-

ductivities along the coast are usually attributed to seawater

intrusion, but Armah (2002) has indicated that dissolution

of minerals in the rocks is also a possible source of

salinization in the area. The TDS varies from 43 to

1,386 mg/l. Freeze and Cherry (1979) describe ground-

water with TDS value less than 1,000 mg/l as fresh water

and those with TDS values more than 1,000 mg/l as saline.

Clearly, saline water bodies exist in the study area.

The major cations (Na?, K?, Ca2?, and Mg2?) are gen-

erally low inland and high toward the coast. The dominant

cation in the study area is Na?, followed by K?, Ca2? and

Mg2?, as illustrated in the box plot (Fig. 2). Na? varies from

42 to 641 mg/l with a mean of 132.21 mg/l. The dominant

anion is Cl-, followed by HCO3- and SO4

2-. Cl- varies

from 49.98 mg/l to 899.72 mg/l with a mean of 182.09 mg/l.

HCO3- varies from 18.06 to 182.02 mg/l with a mean of

91.19 mg/l while SO42- varies from 0.52 to 115.52 mg/l

with a mean of 17.77 mg/l. The trace metals Co, Zn, Cd, Cr,

and Pb are below detection limits of the equipment. Mn

varies from 0.002 to 0.117 mg/l with a mean of 0.027 mg/l

below World Health Organization (WHO) 2004 recom-

mended limit of 0.4 mg/l. Cu varies from 0.004 to 0.021 mg/l

with a mean of 0.01 mg/l far below WHO recommended limit

of 2 mg/l. Nickel varies from 0.015 to 0.056 mg/l with a

mean value of 0.033 mg/l. The WHO guideline value for

nickel in groundwater is 0.02 mg/l. There is generally a

slight elevation of nickel above WHO recommended value

in the study area. Iron ranged between\0.006 and 1.18 mg/l.

Elevated Fe concentration occurs in borehole C36 at

Brofoyedur.

Table 1 Statistical summary of

parameters determined in 14

groundwater samples

Detection limit Minimum Maximum Mean Standard deviation

pH 5.17 6.28 5.83 0.36

Temp 25.9 30.6 28.35 1.09

Cond 102.7 3,360 526.29 878.24

TDS 43 1,386 220.15 361.88

Na 42 641 132.21 161.53

K 11 49 21.86 10.67

Ca 3.6 73.5 12.41 18.73

Mg 0.7 30.3 4.86 7.9

HCO3- 18.06 182.02 91.19 55.01

SO42- 0.52 115.52 17.77 32.14

Cl- 49.98 899.72 182.09 229.15

NO3- 0 11.4 1.43 3.29

PO42- 0.01 0.14 0.03 0.04

Fe \0.006 \0.006 1.18 0.37 0.55

Mn 0.002 0.117 0.027 0.03

Cu 0.004 0.021 0.01 0.005

Ni 0.015 0.056 0.033 0.011

Co \0.005 \0.005 \0.005 \0.005 \0.005

Zn \0.001 0.002 0.007 0.0045 0.004

Cd \0.002 \0.002 \0.002 \0.002 \0.002

Cr \0.006 \0.006 \0.006 \0.006 \0.006

Pb \0.01 \0.01 \0.01 \0.01 \0.01


123

Hydrochemical facies

The major ion concentrations of the groundwaters are

plotted on the Piper (1944) trilinear diagram (Fig. 3). Two

main hydrochemical facies are identified, which are NaCl

and NaHCO3 water types. The NaCl water types dominate

the area.

Multivariate statistical analysis (MVA)

Q-mode cluster analysis was performed on the water

chemistry data to group the samples in terms of water

quality. On the basis of the visual assessment of the

rescaled distance in the dendrogram, with the phenon line

set at 15, two distinct hydrochemical clusters were iden-

tified (Fig. 4). Cluster 1 comprises C03 and C36 and has

higher TDS values. TDS for C03 is 578 mg/l and that for

C36 is 1,386 mg/l. Cluster 1 has the highest average TDS

of 982 mg/l. The averages of the chemical parameters in

the two clusters is presented in the stiff diagram (Fig. 5). It

is obvious from Fig. 5a that the dominant cation for cluster

1 is Na? and the dominant anion is Cl- indicating that the

most predominant water type is NaCl. These waters occur

along the coast. Cluster 2 comprises C09, C10, C11, C12,

C19, C20, C21, C25, C26, C28, and C31. It has an average

TDS of 93 mg/l. It contains NaCl and Na–HCO3 waters

(Fig. 5b). It occurs inland. On the basis of TDS, following

the approach of Freeze and Cherry (1979), cluster 2 waters

are fresher and of good quality than those of cluster 1.

Elevated concentrations of Na? and Cl- in cluster 1 may

be explained by the influence of geochemical processes,

sea water intrusion due to its close proximity to the Gulf of

Guinea, marine aerosols, and concentration of salts in the

soil zone. The stiff diagrams for individual clusters confirm

the overall chemical facies revealed by the Piper (1944)

trilinear diagram (Fig. 3).

Cluster analysis in R-mode was performed on pH,

temperature, TDS, conductivity, calcium, magnesium,

potassium, sodium, chloride, sulfate, bicarbonate, nitrate,

phosphate, copper, iron, manganese, turbidity, total sus-

pended solids (TSS), and nickel. Two distinct groups of

clusters were revealed (Fig. 6). Cluster 1 includes pH,

conductivity, TDS, HCO3-, turbidity, SO4

2-, Cl-, Na?,

K?, Ca2?, Mg2?, and Mn2?. Cluster 2 consists of tem-

perature, TSS, PO43-, NO3

-, Fe, Cu, and Ni. Generally,

the field parameters and the major ions form cluster 1,

implying geochemical constituents, while the minor ions

and the trace metals form cluster 2. Cluster 2 also shows

geochemical variables.

R-mode factor analysis was applied to obtain correla-

tions among the hydrochemical constituents of ground-

water samples (Mondal et al. 2010) using principal

component analysis (PCA) as the extraction method. TheTa

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123

variables used for the factor analysis were temperature

(temp), conductivity (cond), TDS, turbidity (turb), SO42-,

Cl, Na, K, Ca, Mg, Mn, HCO3-, PO3

-, NO3-, TSS, Ni, Fe,

and Cu. It generated five significant factors with eigen

value [1, explaining 86.56% of total variance of the data

set. The five factors with different factor loadings indicate

that five different contributions are involved in the chem-

ical composition of groundwater in the study area. The

various factor loadings are presented in Tables 4 and 5.

Factor 1 (PC1) explains 41.95% of the total variance and

has high positive loadings for temp, cond, TDS, Turb,

SO42-, Cl-, Na?, K?, Ca2?, Mg2?, and Mn. These

parameters retain high scores in C03 and C36 (Table 5)

and are considered the most important parameters in these

samples. Factor 1 may be influenced by seawater intrusion

due to its proximity to the Gulf of Guinea or mineralization

of dissolved ions as a result of chemical weathering. The

contribution of the major cations (Na, K, Ca, and Mg) may

also be due to cation exchange processes at soil water

interface and Na-bearing minerals (Singh et al. 2005),

K-bearing minerals, Ca-bearing minerals, and Mg-bearing

minerals. Major anions (Cl-, SO42-) may result from

Table 3 Trace metal concentrations of groundwater in parts of the Central Region of Ghana

Community Fe Mn Cu Ni Co Zn Cd Cr Pb

Afutuakwa \0.006 0.038 0.008 0.038 \0.005 \0.001 \0.002 \0.006 \0.01

Katayiase 0.043 0.027 0.004 0.015 \0.005 \0.001 \0.002 \0.006 \0.01

Assin Nkran 0.003 0.004 0.009 0.026 \0.005 \0.001 \0.002 \0.006 \0.01

Kwameattah 0.023 0.017 0.011 0.056 \0.005 \0.001 \0.002 \0.006 \0.01

Dawumako Assin 0.037 0.047 0.021 0.025 \0.005 0.007 \0.002 \0.006 \0.01

Atonsu 0.04 0.057 0.006 0.027 \0.005 0.003 \0.002 \0.006 \0.01

Assin Dompim 0.016 0.002 0.006 0.041 \0.005 \0.001 \0.002 \0.006 \0.01

Twifo Praso 0.042 0.008 0.019 0.037 \0.005 \0.001 \0.002 \0.006 \0.01

Twifo Moseaso \0.006 0.003 0.009 0.02 \0.005 \0.001 \0.002 \0.006 \0.01

Twifo Aboabo \0.006 0.01 0.005 0.041 \0.005 \0.001 \0.002 \0.006 \0.01

Twifo Mokwa 0.129 0.032 0.007 0.028 \0.005 0.002 \0.002 \0.006 \0.01

Twifo Agona 0.042 0.005 0.019 0.025 \0.005 0.007 \0.002 \0.006 \0.01

Maabasa 0.111 0.005 0.004 0.049 \0.005 \0.001 \0.002 \0.006 \0.01

Brofoyedur 1.184 0.117 0.011 0.035 \0.005 \0.001 \0.002 \0.006 \0.01

Fig. 2 Box and whisker plot of

major chemical constituents in

the groundwater of the study

area


123

atmospheric input, aerosols, and dissolution of NaCl salts

concentrated in the soil zone.

PC2, which explains 16.43% of the variance, has high

positive loadings for pH and HCO3-, and moderate posi-

tive loadings for TDS. The parameters loaded highly on

PC2 and may have resulted from biogenic activities in the

soil zone generating gaseous carbon dioxide that reacts

with infiltrating water to generate HCO3-, which in turn

affects the pH of the groundwater. These parameters are

importantly distributed in samples C03, C09, C12, and C25

(Table 5).

Factor 3 (PC3), explaining 11.17% of the variance, has

moderate negative loadings for PO43- and NO3

- and high

positive loading for Fe. The high positive loading for Fe

suggests a natural geochemical process like dissolution of

iron bearing minerals such as pyrite, biotite, pyroxenes, and

iron rich olivine (fayalite). These are some of the

commonest minerals in the rocks of the area. High scores

of PC3 occur in samples C12, C21, C28, C31, and C36 as

indicated in Tables 4 and 5.

Factor 4 (PC4), explaining 9.86% of the variance, is

contributed by TSS, Mn, and Ni, with a high positive

loading for TSS, moderate positive loading for Mn

and moderate negative loading for Ni. The high positive

loading for TSS and moderate positive loading for Mn are

Fig. 3 Piper plot of water samples from parts of the Central Region

Fig. 4 Dendogram for Q-mode cluster analysis for samples collected

in parts of the Central Region

Fig. 5 a Stiff diagram for cluster 1. b Stiff diagram for cluster 2

Fig. 6 Dendogram for 20 variables from cluster analysis in R-mode


123

indicative of geochemical processes in the area. The effect

of this component is significant in C09, C19, and C28

(Tables 4, 5).

Factor 5 (PC5) explains 7.16% of the total variance and

has a high positive loading for copper (Cu). The effect of

PC5 is more significant in C12, C21, C25, C29, and C36.

PO43- is also weakly and positively loaded on PC5. PC5

may have resulted from the breakdown of Cu bearing

minerals in the area. It may also have resulted from

anthropogenic activities such as application of Cu and

PO43- fertilizers for agricultural activities in the area.

The first two factors were plotted against each other

(Fig. 7). In Fig. 7a, the tight clustering and high positive

loadings of temp, EC, TDS, turb, SO42-, Cl-, Na?, K?,

Ca2?, and Mg2? show that groundwater chemistry is

controlled by different geochemical processes, such as

Table 4 Loadings of

experimental variables on the

first five factors for groundwater

data set in the Central Region

Values in bold are significant

PC 1 PC 2 PC 3 PC 4 PC 5

pH 0.258 0.923 0.082 -0.035 0.038

Temp 0.704 -0.435 -0.125 0.138 0.123

Cond 0.898 0.420 0.050 0.085 -0.004

TDS 0.889 0.436 0.048 0.088 -0.003

HCO3- 0.317 0.921 0.073 0.001 0.021

TSS -0.096 -0.193 0.231 0.830 -0.206

Turb 0.791 0.054 0.392 -0.109 0.230

PO43- -0.084 -0.059 -0.720 -0.215 0.488

NO3- 0.298 -0.299 -0.648 0.446 -0.199

SO42- 0.724 0.424 0.217 -0.056 -0.143

Cl- 0.963 -0.079 -0.037 -0.101 -0.023

Na 0.938 0.298 -0.017 0.096 0.089

K 0.858 -0.053 0.126 0.257 -0.147

Ca 0.868 0.279 0.143 -0.287 -0.106

Mg 0.799 0.363 0.080 -0.342 -0.130

Fe 0.362 0.011 0.832 0.159 0.169

Mn 0.613 0.329 0.304 0.543 -0.031

Cu -0.008 0.027 0.058 -0.045 0.890

Ni 0.113 -0.368 0.257 -0.531 -0.263

Eigenvalues 7.970 3.121 2.123 1.873 1.360

% of variance 41.948 16.426 11.174 9.858 7.157

Cumulative % 41.948 58.375 69.548 79.407 86.564

Table 5 Factor scores for the

R-mode factor analysis

Significant values are in bold

type face

Sampling sites PC 1 PC 2 PC 3 PC 4 PC 5

C03 1.541 0.758 -1.771 -0.208 -0.869

C09 0.166 1.023 -0.038 0.615 -0.777

C10 -0.178 -0.828 -1.803 0.078 0.456

C11 -0.066 -0.836 0.225 -0.767 0.285

C12 -0.735 1.848 0.660 0.345 0.785

C19 0.316 -1.504 -0.100 2.474 -0.678

C20 -0.355 -1.497 0.537 -1.301 -1.020

C21 -0.610 -0.958 0.787 -0.212 1.124

C25 -0.873 0.683 -1.099 -0.269 0.776

C26 -0.455 0.531 -0.225 -0.607 -1.138

C28 -0.721 0.498 1.136 1.492 -0.312

C29 -0.189 -0.199 -0.446 -0.181 1.894

C31 -0.588 0.295 0.775 -1.078 -1.324

C36 2.748 0.186 1.363 -0.379 0.799


123

weathering, dissolution, oxidation–reduction, and ion

exchange involving major ions. In Fig. 7b, C03 and C36

plot in the first quadrant with higher TDS depicting a

discharge area. C10, C11, C19, C20, C21, and C29 plot

mostly in the third quadrant with considerable lower TDS

whereas C09, C12, C25, C26, C28, and C31 plot in the

fourth quadrant with lower TDS suggesting recharge areas.

Possible flow paths from the northern sections of the region

toward the Gulf of Guinea in the south is inferred and

shown in Fig. 7b. The PC1 versus PC2 plot also revealed

three main groupings. Group 1 comprises SO42-, TDS, EC,

Mn, Mg, Ca, Na, turb, K, Cl, and temp (Fig. 7a). Group 2

comprises pH and HCO3- whereas group 3 comprises Cu,

Fe, PO43-, NO3

-, TSS, and Ni. R-mode CA (Fig. 6)

retains two main clusters as compared to three by R-mode

PCA. In R-mode CA, there are sub-clusters of which

members include pH and HCO3. This is distinguished by

the R-mode PCA as a separate group (Fig. 7a). The members

of cluster 1 of R-mode CA are almost similar to that of PC1.

This implies that the CA results largely agree with the PCA

results. The R-mode PCA and CA yielded the following

physicochemical and elemental associations in the

groundwater data set: SO42-–Cl–Na?–K?–Ca2?–Mg2?

–Mn2?, HCO3-–pH, and PO4

3-–NO3-–Fe–Cu–Ni. These

are derived mainly from geochemical processes. R-mode

factor analysis for the sampling sites also revealed three

clusters (Fig. 7b) while the Q-mode CA reveled two clusters

(Fig. 4). Sub-clusters C09, C12, C26, C28, and C31 of

cluster 2 of Q-mode CA have emerged from R-mode PCA as

a separate cluster.

Factor scores were used with geographical coordinates

of the sampling locations to generate interpolation surfaces

that spatially indicate the strength of the major hydro-

chemical processes in the study area. Figures 8, 9, 10, 11,

12 represent the factor score maps for factors 1–5. In

Fig. 8, the area has factor scores ranging between -0.91

and 2.30. About 30% of the entire area has positive factor

scores and covers the southern to southeastern sections of

the study area. More than 75% has scores in the range of

-0.51 to C2.30. This suggests that chemical weathering of

the rocks and seawater intrusion have moderate to high

effects on the hydrochemical data in the study area. Gen-

erally, the effects of factor 1 decrease from southeastern to

northwestern parts of the study area. Since the highest

impact of factor 1 occurs at areas close to Gulf of Guinea, it

is anticipated that the saline water or seawater intrusion is

probably the dominant process and overrides the effect of

mineral weathering in these areas.

In Fig. 9, factor scores range from -1.65 to 1.31 and

about 90% are within the range -0.54 to 1.31 reflecting a

moderate to high impact on the hydrochemical dataset in

the area. High impact is observed in the northwestern

corner, northeastern section, and southeastern corner.

Decomposition of organic matter in the soil zone to gen-

erate carbon dioxide gas that in turn reacts with infiltrating

water to form HCO3- that gets into the groundwater zone

may account for the high impact.

Fig. 7 Plots of first two principal component loadings, PC1 versus

PC2 for all analyzed parameters (a, b) Fig. 8 Factor score map for factor 1


123

In Fig. 10, the factor scores range between -0.61 and

0.16. About 30% of the study area has positive factor

scores and covers the northwestern, northern, part of

northeastern, and part of the southwestern sections showing

a moderate to high localized impact in the area. The

geology of these areas is the sedimentary basin granitoids.

Typical rock types have already been mentioned in section

under geology. The main ferromagnesian mineral present is

biotite. Weathering of biotite may release iron (Fe) in the

water and accounts for the high localized impact.

Factor score map for factor 4 is developed in Fig. 11 and

factor scores range between -0.16 and 0.16. About 60%

have positive factor scores and cover the northern and

northwestern sections of the study area. Factor 4 is thus

pervasive in the study area and generally increases from

southern sections of the study area to the northern and

northwestern sections. This factor reflects a geochemical

component involving chemical breakdown of minerals in

the rocks resulting in the release of ions in the

groundwater. Manganese occurrences have been reported

in rocks of the study area (http://www.ghana-mining.org).

Dissolution of this mineral alongside others such as the

feldspars, micas, pyroxenes, and amphiboles accounts for

the pervasiveness of factor 4 in the area.

In Fig. 12, factor scores range between -0.22 and 0.10.

Ten percent of the study area has positive factor scores and

covers southwestern and northeastern corners of the study

area. Factor 5 has a moderate impact in the study area.

These areas have the sedimentary basin and its associated

granitiods as the dominant geology. Typical rock types

include schist, granite, and gneiss. These rocks are reported

to be associated with transition metals such as Cu, Ni, Co,

and Zn (Melcher 1995). Dissolution of copper bearing

minerals may therefore account for the moderate impact of

factor 4. The moderate impact could also be due to appli-

cation of Cu and PO43- fertilizers in the area for boosting

crop yield.

Fig. 9 Factor score map for factor 2

Fig. 10 Factor map for factor 3




123

http://www.ghana-mining.org

Geochemical evolution of the groundwaters

Compositional relations have been used to assess the origins

of solutes and the processes that result in water compositions

(Rao 2008). The chemical data of the groundwater samples

are plotted on the Ca2??Mg2? versus HCO3- diagram

(Fig. 13). Majority of the data fall below the equiline (1:1),

which suggests that an excess of alkalinity in the water has

been balanced by alkalies (Na??K?). Similarly, sample

points fall below the equiline in a plot of Ca2??Mg2? versus

total cation (Fig. 14). This depicts an increasing contribution

of alkalies to the major ions caused by silicate weathering

(Devadas et al. 2007).

In Fig. 13, it is realized that few samples occur above the

equiline, suggesting that silicate weathering may not be the

only source of the ions in the water. In Fig. 15, a plot of Na?

against Cl- shows that most of the samples exhibit Na?–Cl-

ratio equal to one, and a few of them exhibit a ratio greater

than one. Rao (2008) indicated that a ratio equal to one

implies dissolution of NaCl and a ratio greater than one

reflects a release of Na? from silicate weathering. The

geology of the study area does not provide evidence for

occurrence of NaCl deposits. In fact, the area is a crystalline

basement terrain of which rock types are schist, granitoids,

phyllites, and volcanic rocks. NaCl in the groundwater is

therefore suspected to be concentrated in the soil zone as a

result of silicate weathering. Na rich feldspar (albite) is the

major component of the rocks in the area. Hence, the source

of Na? in the aquifers could be attributed to the breakdown of

albite, as illustrated in Eq. 3. The source of Cl- may be from

atmospheric input. Concentration of these ions in the soil

zone as a result of evaporation may be responsible for the

NaCl waters in the area.

2NaAlSi3O8 þ 2CO2 þ 11H2OAlbiteð Þ

! Al2Si2O5 OHð Þ4þ2Naþ þ 4H4SiO4

Kaoliniteð Þð3Þ

Cl- ? SO42- against Na? ? K? are normally plotted

to infer a common source for these ions from the dissolu-

tion of soil salts (Sarin et al. 1989). In Fig. 16, even though

there are not much data with higher concentrations of

Na? ? K? and Cl- ? SO42-, there is a general increase of

Na? ? K? with Cl- ? SO42- that seem to suggest dis-

solution of these salts in the soil zone. More data are,

however, required to confirm this accession.

In a plot of Ca2??Mg2? versus SO42- ? HCO3

- ?

CO32- (Fig. 17), a deficiency of Ca2??Mg2? relative to

SO42- ? HCO3

- ? CO32- is observed. Therefore, Na?

must balance the excess of negative charge of SO42- and

HCO3- ? CO3

2- ions. Higher concentration of Na? in the

groundwater may be an index of ion exchange process (Rao

2008).

Fisher and Mullican (1997) stated that if ion exchange is

the only controlling process of groundwater composition,

the relation between Ca2? ? Mg2? - SO42- - HCO3

-

? CO32- and Na?–Cl- should show negative linear trend

with a slope of unity, considering the participation of

cations in the ion exchange reaction. In Fig. 18, the sam-

ples show a trend of Ca2? ? Mg2? - SO42- - HCO3

-

? CO32- and Na?–Cl- with a negative slope of less than

Fig. 13 Relationship of Ca2??Mg2? with HCO3- ? CO3

2- in the

Central Region groundwaters

Fig. 14 Relationship of Ca2??Mg2? with TC

Fig. 15 Relation of Na? and Cl- in the Central Region groundwa-

ters. The solid line denotes 1:1 ratio


123

unity, but they spread above and below the linear trend.

This suggests that the controlling of groundwater quality

depends not only on the involvement of ion exchange

process but also involvement of other processes. Other-

wise, the spreading of sample points above and below the

linear trend should not be expected.

The possible geochemical processes controlling the

groundwater chemistry in the area include silicate or rock

weathering, dissolution of NaCl and cation exchange. The

dominant geochemical processes may be silicate weath-

ering and dissolution of NaCl salt in the soil zone.

The sources of Ca2? in the groundwater could be due to

weathering of calcic plagioclase feldspars (anorthite) that is

also a component of the granitoids, schist, and phyllites.

Mg2? may be leached from biotite [K(Mg,Fe)3(AlSi3)

O10(OH,F)2], hornblende [(Ca,Mg,Fe,Na,Al)7–8(AlSi)8

O22(OH)2], and the pyroxenes (R2[Si2O6]) where R is Ca,

Mg, and Fe2?. Weathering of hornblende and pyroxenes

could also produce Ca2? and Na? in groundwater. K? in

groundwater may be due to plagioclase feldspar alteration

into sericite ([KAl2(Si3Al)O10(OH,F)]) as captured in sec-

tion under geology. Break down of biotite may also yield

K? in groundwater. HCO3- is suspected to be generated

from the soil zone by reaction of CO2 generated from

decomposition of organic matter and infiltrating water.

SO42- concentrations are low; its source is therefore

attributable to atmospheric input. Cl-/(CO32- ? HCO3

-)

ratios after ‘‘(Shamugan et al. 2006 Table 6)’’ were

determined in the area. Shamugan et al. (2006) stated that

groundwater with Cl-/(CO32- ? HCO3

-) [200 is attrib-

uted to seawater intrusion. Values obtained ranged between

0.33 and 6.75 (Table 7) ruling out a possible seawater

intrusion in the area. However, further work is required to

confirm some of these deductions.

Conclusion

The preliminary groundwater quality assessment in the

Central Region has revealed that groundwater is generally

weakly acidic with mean acidity being 5.83 pH units.

Electrical conductivity (EC) ranges from 30.6 to 3,360 lS/

cm. High EC is observed from inland toward the coast.

TDS varies from 43 to 1,386 mg/l. TDS greater than

1,000 mg/l indicates saline water bodies occur in the area.

The major dominant cation in the area is Na? followed

by K?, Ca2?, and Mg2?. The dominant anion is Cl- fol-

lowed by HCO3- and SO4

2-. Two major hydrochemical

facies have been identified, which are NaCl and Na–HCO3

water types. Cluster analysis in the Q-mode revealed two

distinct clusters with samples of cluster 1 showing saline

water characteristics and occurring along the coast. The

rest of the clusters represent fresher and good quality water

that occurs inland. R-mode cluster analysis indicated that

the major source of ions in groundwater is that of

hydrogeochemical variables. Factor analysis yielded five

significant factors explaining 86.56% of the total variance.

PC1 explains 41.95% of the variance and is contributed by

temperature (temp), conductivity (cond), TDS, turbidity

(Turb), SO42-, Cl-, Na?, K?, Ca2?, Mg2?, and Mn2?. PC1

loadings describe hydrogeochemical variables originating

from mineralogical compositions of rocks that haveFig. 16 Relationship between Cl- ? SO4

2- and Na??K? in the

groundwaters of the Central Region

Fig. 17 Relationship between Ca2??Mg2? versus SO42- ? HCO3

-

? CO32- in Central Region groundwaters

Fig. 18 Relationship between Ca2? ? Mg2? - SO42- - HCO3

- ?

CO32- against Na?–Cl- in parts of the Central Region groundwaters


123

weathered. PC2 termed as water quality indicator factor

explains 16.43% of the total variance and characterized by

high positive loadings of pH, HCO3-, and moderate posi-

tive loading of TDS. PC3 contains moderate negative

loadings of PO43- and NO3

- and explains 11.17% of the

total variance. Factor score plot revealed a possible flow

direction from the northern section of study area of higher

topography toward the Gulf of Guinea in the south. Gen-

erally, the factor analysis shows that groundwater chem-

istry is controlled by different geochemical processes,

which include weathering, mineral dissolution, and ion

exchange.

Compositional relations confirmed the predominant

geochemical process responsible for the various ions in the

groundwater as silicate weathering and thus agree with the

PC1 loadings. Dissolution of NaCl salt concentrated in the

soil zone and cation exchange have minimal influence on

the hydrochemistry of groundwater in the area. Chemical

break down of minerals in the various aquifers is the main

controlling process influencing the hydrochemistry of the

study area.

Acknowledgments The authors would like to thank the Govern-

ment of Ghana, the Ghana Atomic Energy Commission (GAEC) and

the International Atomic Energy Agency (IAEA) for providing funds

in support of this work which is ongoing. We are also grateful to Nash

Bentle, Eunice Agyeman, and John Senu for assisting in the labora-

tory analysis.

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Cl-/(CO32- ? HCO3

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Description

\0.05 Fresh groundwater

0.05–1.30 Slightly contaminated groundwater

1.30–2.80 Moderately contaminated groundwater

2.80–6.60 Injuriously contaminated groundwater

6.60–15.50 Highly contaminated groundwater

[200.00 Seawater intrusion

Table 7 Cl-/(CO32- ? HCO3

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ID Cl-/(CO32- ? HCO3

-)

C03 3.55

C09 1.43

C10 2.94

C11 2.84

C12 0.33

C19 6.46

C20 6.75

C21 2.94

C25 0.68

C26 1.15

C28 0.60

C29 3.02

C31 1.20

C36 6.13


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