International Journal of Geosciences, 2011, 2, 259-266
doi:10.4236/ijg.2011.23028 Published Online August 2011 (http://www.SciRP.org/journal/ijg)
Geochemical Assessment of Impact of Mine Spoils on the
Quality of Stream Sediments within the Obuasi Mines
Environment, Ghana
Prosper Mackenzie Nude1*, Gordon Foli2, Sandow Mark Yidana1
1Department of Earth Science, University of Ghana, Ghana, Legon-Accra, Ghana
2University for Development Studies, Navrongo Campus, Tamale, Ghana
Received March 28, 2011; revised June 7, 2011; accepted July 23, 2011
Abstract
Stream sediment samples were analyzed for the concentrations of some trace metals in the Obuasi gold min-
ing environment, Ghana. The objectives were to determine the possible impacts of mining operations in the
area on sediments’ trace metal load, and the resulting effects on agriculture and livelihoods. The concentra-
tions of arsenic (As), copper (Cu), lead (Pb), zinc (Zn), iron (Fe), with calcium (Ca) as reference element,
were compared to their respective background concentrations to calculate the enrichment and contamination
factors, and also geo-accumulation and pollution load indices of each trace metal. These were in turn com-
pared to standard tables to determine the status of contamination. Q-mode hierarchical cluster analysis (HCA)
was then applied to the samples for spatial classification. This study suggests probable contribution of min-
ing and associated activities in the Obuasi area to the concentrations of trace metals especially arsenic, in the
stream sediments. Three spatial relationships were revealed based on the concentrations of these trace metals
from the Q-mode HCA. The samples presented generally high concentrations, which were more profound for
samples taken closer to holding pond and tailings dams, and decreased downstream.
Keywords: Enrichment Factor, Contamination Factor, Geo-Accumulation Index, Pollution Load Index, Trace Metals,
Obuasi Gold Mine, Ghana
1. Introduction
The Obuasi gold mine constitutes the single largest gold
mine in Ghana, accounting for over 60% of the total
national production. Gold being one of the major sources
of foreign exchange for the country has been produced
from this mine for over a century. Gold in the Obuasi
area occurs mainly in quartz reefs or as sulphide ores.
The former consist mostly of chalcopyrite, sphalerite,
galena and gold whereas the latter consists of arseno-
pyrite, pyrite, pyrrhotite and gold.
In the Obuasi mining district, evidence of local conta-
mination in association with gold mining activities has
been noted and indications of trace metal contamination
in streams, sediments, and biota abound, and obviously
result from contamination from diverse sources including
mining and municipal discharges [1-4]. Although such
signs of contamination have been obvious overtime, the
geochemical implications of these contaminations in
stream sediments, as well as the severity of such con-
taminations have not been previously evaluated using
relevant evaluation indices. On the other hand, a tho-
rough assessment of the level of contamination of the
major environmental receptors such as soils, biota, water
bodies, and stream sediments is critically required in
order to assist environmental managers and the relevant
stakeholders plan to avert any future epidemics.
For example in the Obuasi area, it is common practice
by the locals to undertake gardening and food crop culti-
vation along banks of streams and other areas where the
sediments or stream overloads might be contaminated
with these trace metals and associated toxins. It is there-
fore critically important that the trace metal contents of
stream sediments in the local area be evaluated to deter-
mine whether or not these sediments are worthy of their
current patronage. Such a research would highlight
possible hotspots and suggest any possible mitigation
measures in order to forestall environmental damage and

P. M. NUDE ET AL. 260
the concomitant effects on biological systems including
humans.
Serious concerns about existence of trace metals in
aquatic environments and their effects on plant and ani-
mal life have been documented, e.g. Zvinowanda et al.
[5], Mohiuddin, [6]; as they are often noted to exhibit
extreme toxicity even at trace levels [7]. In the aquatic
environment, trace metals are redistributed throughout
the water column, deposited or accumulated in sediments
and consumed by biota [8-11].
Trace metals are non-biodegradable and stream sedi-
ments serve as pollutant storage tanks [12] from where
they are able to cause long-term impacts [11,13,14] in
related media. It has also been noted that sediment qua-
lity values are useful to screen the potential for contami-
nants within sediment to induce biological effects and
compare sediment contaminant concentration with the
corresponding quality guideline [15,16]. Sediment quality
values are also useful to rank and prioritize the conta-
minated areas or the chemicals for further investigation
[17].
The assessment of soil enrichment with elements can
be carried out in many ways. The most common ones are
the index of geoaccumulation and enrichment factor [18].
Also of immerse importance are contamination factor
and pollution load index. This research evaluates (1) the
trace metal contents in sediments of the streams, (2)
compares the levels of trace metals contents with geo-
chemical background and toxicological reference values
for stream sediments. In addition, spatial relationships
among all the samples have been established using Q-
mode hierarchical cluster analysis in order to facilitate an
identification of the possible sources of these conta-
minations.
2. Location and Physiography
Obuasi is located between latitude 5.35N and 5.65N and
longitude 6.35N and 6.90N, and in the southern part of
the Ashanti Region, Ghana (Figure 1). The Municipality
covers a land area of about 162.4 km2. The climate is of
the semi-equatorial type with a double maxima rainfall
regime. Mean annual rainfall ranges between 125 and
175 mm. Mean average annual temperature is 25.5oC and
relative humidity is 75% - 80% in the wet season. The
vegetation is predominantly a degraded and semi-deci-
duous forest type [19].
The Kwabrafo, Nyam and Nyankuma streams drain
the area in N-S and NE-SW directions respectively. The
Kwabrafo received effluent from the former Pompora
Treatment Plant (PTP) area and drains through a network
of tailings dam sites, while the Nyankuma drains some
abandoned open pit areas and the Sulphide Treatment
Figure 1. Map of Ghana showing location of Obuasi.
Plant (STP) area (Figure 2) [20]. The stream catchments
were largely developed by the rapid urbanization in
recent years.
3. Materials and Methods
3.1. Analytical Processes
Twenty one (21) representative stream sediment samples
were taken from streams draining through areas related
to both defunct and active mining and mineral processing
and tailings dam facilities. About 250 g of sediment
samples from each sampling point were dried in an oven
at a constant temperature of 90˚C overnight to obtain
sample homogenization and dry weight. Pulverised por-
tions of the samples were digested in aqua regia com-
posed of a mixture of 11.5N HCl and 15.5N HNO3 at
90˚C - 100˚C for one hour.
The solutions were allowed to stand for 30 minutes to
generate supernatant solutions that were filtered for
analysis. Samples were analysed for As, Cu, Pb, Zn, Fe,
and Ca, using the Varian 55B atomic absorption spectro-
meter (AAS). Analyses of replicate, standard and field-
split duplicate samples ensured quality control. Also,
standard solutions made up of 10.0 mg/L concentration
of analytes were spiked by adding equal volumes of
known concentrations of water samples determined at
selected sites; the spiked sample results were then com-
pared with the expected. The achieved results were found
to be within acceptable limits of ±10% of the expected.
3.2 Evaluation Indices
3.2.1. Enrichment Factor (EF)
Enrichment factor is the relative abundance of a che-
mical element in a soil compared with the background

P. M. NUDE ET AL. 261
Figure 2. A sketch map of parts of the Obuasi area (study area) showing the network of streams and sampled locations (After
Foli and Nude [20]).
[21]. It is a convenient method of measuring geoche-
mical trends for comparison between areas [15]. Enrich-
ment factor is also often used in the assessments of
anthropogenic pollution in sediments [6]. The factor, as
outlined by Loska, et al. [18] is presented in Equation 1:
nn
ref ref
CB
EF CB  (1)
where:
n—Content (mg/kg) of the examined element in the
examined environment,
C
ref
C—Content (mg/kg) of the examined element in the
reference environment,
n—Content (mg/kg) of the reference element in the
examined environment,
B
ref —Content (mg/kg) of the reference element in the
reference environment.
B
Five contamination categories on the basis of the
enrichment factor are summarized in Table 1 below:
3.2.2. Geo accumulation In dex (
g
eo
Geo-accumulation index is also used to assess metal pol-
lution in sediment. Geoaccumulation index determines
contamination by comparing current metal contents with
I)

P. M. NUDE ET AL. 262
Table 1. Contamination categories on the basis of enrich-
ment factor.
Enrichment factor (EF) Description
EF < 2 Depletion to minimal enrichment
2 < EF < 5 Moderate enrichment
5 < EF < 20 Significant enrichment
20 < EF < 40 Very high enrichment
EF > 40 Extremely high enrichment
pre-industrial levels [18]. The index quantifies metal
accumulation in sediments for classification as either po-
lluted or unpolluted [22]. The geoaccumulation index Igeo
is as follows (Equation (2)):
2
log 1.5
n
geo
n
C
I
x
B
 (2)
where n is the measured concentration of element n in
the sediment and n
B the geochemical background for
the element n, which is either directly measured in
pre-civilization sediments of the area or taken from lit-
erature [6]. The constant 1.5 compensates for possible
variations of the background values that are due to
lithologic variations, or natural fluctuations of a given
substance in the environment as well as very small an-
thropogenic influences [18]. Seven grade or class of the
geo-accumulation index is summarized as below [6,15,
18] (Table 2).
C is
3.2.3 Pollution Load Index
The pollution load index (PLI) outlined by Kumar and
Edward [12] and Mouhiuddin et al., [6], has also been
used in this study to measure the level of contamination
in sediments. The PLI for a single site is the nth root of n
number multiplying the contamination factors (CF) to-
gether. The contamination factor represents the individ-
ual impact of each trace metal on the sediments [22],
(Equation (3)).
Metal concenteation
B
ackgroundconcenteation ofsamemetal
C
CF C  (3)
This also implies,
n
ref
C
CF C

As in the EF equation (Equation 1), the contamination
factor CF, also referred to as metal ratio, is based on the
mean content of metals from at least five sampling sites
[12, 22].
The PLI for a single site is determined using Equation
(4).
n
n
PLIforsiteCFxCF CF (4)
where,
CF = Contamination factor, and
n = the number of contamination factors and sites, re-
spectively [6]. Four categories of contamination factor
have been distinguished as in Table 3. From the defini-
tion for PLI, range of values and categories may be ex-
pressed in terms of the contamination factor.
4. Reference Element and Background
Values for Measur ed Pa r am e te rs
An element is regarded as a reference element if it is of
low occurrence variability and/or is presented in the en-
vironment in trace amounts. The most common reference
elements are Sc, Mn, Al and Fe. However it is also pos-
sible to apply an element of geochemical nature whose
substantial amounts occur in the environment but has no
characteristic effects such as synergism or antagonism
towards an examined element [18].
In the local area, Fe is perceived as having synergism
with As; Fe was found to be responsible for As fixing in
soils [23]. On the above basis, Ca was considered, with
value quoted from Taylor and McLennan [24] and Loska
et al. [18] as 30,000 mg/kg. Continental crust values as
background for measured parameters such as As, Cu, Pb
and Zn are 1.8 mg/kg, 55 mg/kg, 12.5 mg/kg and 70
mg/kg respectively [6], and for Fe as 35,900 mg/kg [25].
The background values are considered as geogenic con-
tributions while the difference between the measured
Table 2. The seven classes of the geo-accumulation index.
Class Range Interpretation (Quality)
0 Igeo ≤ 0 Practically uncontaminated
1 0< Igeo < 1 Uncontaminated to
moderately contaminated
2 1< Igeo < 2 Moderately contaminated
3 2< Igeo < 3 Moderately to heavily
contaminated
4 3< Igeo < 4 Heavily contaminated
5 4< Igeo < 5 Heavily to very heavily
(extremely) contaminated
6 Igeo ≥ 5 Very heavily (extremely)
contaminated
Table 3. Four categories of contamination based on the c on-
tamination factors.
Contamination factor Description of
contamination level
CF < 1 Low
1 ≤ CF < 3 Moderate
3 ≤ CF < 6 Considerable
6 ≤ CF Very high

P. M. NUDE ET AL.263
metal contents and the background values for the metals
are taken to be contribution from anthropogenic source
[22].
5. Q-Mode Hierarchical Cluster Analysis
Cluster analysis is used to group samples or parameters
into associations or clusters based on perceived field
relationships. It is a multivariate statistical technique
which has gained a wide range of use in the environ-
mental earth sciences. In pollution studies, the method is
useful for highlighting hotspots, and thus facilitates the
resolution of environmental problems [26,27]. When
applied to geochemical datasets, cluster analysis is useful
for revealing surface and groundwater flow regimes, and
assists in identifying recharge areas in the groundwater
flow regime and distinguishing confined from uncon-
fined situations [28] for instance.
Hierarchical cluster analysis (HCA) is a particular type
of cluster analysis whereby the parameters or cases
(samples) are grouped into hierarchical classes based on
similarities/dissimilarities discerned from the distribution
of the datasets. In Ghana, the generality of multivariate
statistical tools have been used severally to study hydro-
chemical trends [29-31] and highlighting the impacts of
seawater intrusion on the salinity of groundwater in
coastal aquifers [32].
In the current study, Q-mode HCA was applied to
dataset with an objective of determining spatial relation-
ships amongst the various sampled locations. Such spa-
tial relationships will assist in determining the probable
sources of these metals in the sediments. In this study the
data on the concentrations of trace metals in the sedi-
ments were used to run the Q-mode HCA. The data for
each parameter were standardized to their corresponding
z- scores using Equation 5.
x
Z



 (5)
where x, μ, and σ are respectively the value, mean, and
standard deviation of the data for each parameter.
Data transformation is a common practice in multi-
variate statistical analyses since most data do not meet
the requirements of normal distribution and homoscedas-
ticity as is required for optimal results. In cluster analysis,
the use of skewed data would affect the computation of
the Euclidean distances and therefore lead to faulty end
results and interpretations.
6. Results and Discussion
Three sets of sediment samples were assessed on the
basis of the EF, CF, Igeo and PLI. The samples were tak-
en from the Kwabrafo Stream (A), Nyankuma Stream
(B), and the Nyam Stream (C) in the study area (as
shown in Figure 2. Figure 3 presents the results of the
EF, CF and PL I computations in a graphical form. The
EF of the various trace metals at all three stations are
high and suggest enrichment of the species far in excess
of the background concentrations; and confirmed by the
CF values or metal ratios, and also PLI.
Among the three locations the Nyankuma stream ap-
pears to suggest the highest levels of anthropogenic im-
pacts on the concentrations of all the trace metals (Table
4). Arsenic has extremely high EF and CF values in the
Nyankuma sample and appears to have significant inputs
from the effects of mining and other anthropogenic ac-
tivities within the catchment areas of this stream. Toxic
concentrations of As in water can cause black foot dis-
ease and skin cancer [33].
High As levels in sediments are injurious to plants and
can be transmitted through the food chain to higher or-
ganisms such as humans. This is true of all the areas. The
EF of Pb in the samples suggests significant enrichment
but the concentration is not critically above the back-
ground levels in the three different areas. Copper and Fe,
similarly show significant enrichment above background
levels but the CFs are moderate to significant. The gold
ore being mined in the Obuasi mine is associated with
arsenopyrite and other sulfides which are often used as
pathfinders for the gold. The concentrations of the trace
metals analyzed in this study reflect significant contribu-
tions from mine tailings and mine waste. Zinc is gener-
ally consistently low in all three locations.
Figure 4 show the Igeo values. Table 2 suggests that
where the geo-accumulation index is higher than 5, the
medium is said to be heavily contaminated with respect
to the trace metal in question. In this respect, the three
media are heavily contaminated with respect to As, indi-
cating a high level of anthropogenic impacts on the
stream sediments. Geo-accumulation indices for all the
Figure 3. Bar charts showing the Enrichment Factor (EF),
Contamination Factor (CF) and Pollution load index (PLI).

P. M. NUDE ET AL. 264
Figure 4. Geoaccumulation Index (Igeo) presented in bar
chart.
other trace metals indicate relatively uncontaminated
states. In Nyam for example, the Igeo, CF and PLI values
for the trace metals are below 0, except As.
The Q-mode HCA resulted in three clusters or field
associations (Figure 5). The samples were clearly dis-
tinguished on the basis of the concentration of the ele-
ments analyzed for, and were sorted out according to
their relative proximities to mine tailings and waste dis-
posal damps. Cluster 3 has the highest average concen-
trations of all the trace metals analyzed for this study
(Figures 6(a) and (b)). Members of this group of sam-
ples were largely taken from the Nyankuma stream and
surrounding areas.
The high concentrations are attributed to leakage from
the holding pond (Figure 2). For example, Sample P20,
which was taken at the portion of the Nyankuma stream
gracing the holding pond, has the highest concentration
of all the trace elements.
Possible contamination of the soils in the neighbor-
hood of the holding pond could result from a possible
interconnection between the stream water and the content
of the pond. Trace-element laden stream water deposits
some of its load in the immediate sediments as the speed
of the water reduces downstream. In that respect, the
concentration of the trace metals would naturally de-
crease with increasing distance from the holdings pond.
P11 has the lowest concentrations of the trace elements,
except Fe. This is attributed to the position of this sample
in relation to the holding pond. The high Fe content of
this sample is owed to the effects of the tailings dam
which is in close proximity to P11.
Cluster 2 has the second highest concentrations of the
trace elements. Members of this association are samples
taken from the Kwabrafo stream and close to the tailings
dam in the northern parts of the study area (Figure 2).
There is an obvious influence of the tailings dam. These
effects are highest in P3 (which has the highest trace
Figure 5. A dendogram from the Q-mode HCA.
Figures 6. (a): Linear plots showing the average concentra-
tions of Ca, As and Fe in the three clusters from the HCA.
(b): Linear plots showing the average concentrations of Cu,
Pb and Zn in the three clusters from the HCA.
element concentration in this group) and P1 (with the
second highest concentrations of trace elements). In this
group, P10 has the lowest concentrations of most of the

P. M. NUDE ET AL.265
trace elements analyzed although the concentrations are
far above background levels. This is attributed to the
location of P10 in relation to the tailings dams which
have significantly registered their effects in cluster 2.
Cluster 1 has the lowest average and individual sample
concentrations of all the trace elements under analysis in
this study. Samples 9, 16, and 17 are conspicuously high
in terms of the trace metals. This is due to their closer
proximities to the tailings dams (in the case of sample P9)
and holding pond (in the case of samples 16 and 17),
compared to the other members of the group.
7. Conclusions
Stream sediments in Obuasi and surrounding areas
within the limits of this study suggest significant concen-
trations of trace elements largely from mining and re-
lated activities in the area. Enrichment factors, pollution
load and geoaccumulation indices calculated from stream
sediment data indicate significant enrichment above
background concentrations of all the trace elements. The
degree of contamination has been observed to decrease
farther downstream and away from the probable con-
tamination sources: tailings dams and holding pond. The
research highlights the possible impacts of mining, mine
waste, and the improper disposal of mine waste on soil
quality and effects on food production. Three sediment
associations have been distinguished on the basis of
Q-mode hierarchical analysis: heavily contaminated se-
diments (where the concentrations of all the trace ele-
ments are critically high) in close proximity to the hold-
ing pond, highly contaminated sediments close to the
tailings dams, and moderately contaminated sediments
where the concentrations of the trace elements are above
background concentrations but where the concentrations
are generally lower than is captured by the first two
clusters or associations.
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