Research Journal of Applied Sciences, Engineering and Technology 4(4): 256-261, 2012
ISSN: 2040-7467
© Maxwell Scientific Organization, 2012
Submitted: June 09, 2011
Accepted: August 08, 2011
Published: February 15, 2012
Trace Element Assessment of Stream Sediments Around the Aluminium
Smelting Company in Ikot-Abasi, South-Eastern Nigeria
1
Azubuike S. Ekwere and 2Anthony A. Elueze
1
Department of Geology, University of Calabar, Nigeria
2
Department of Geology, University of Ibadan, Nigeria
Abstract: A study to assess the trace element chemistry in the vicinity of a recently established aluminium
smelting complex in Ikot Abasi, south-eastern Nigeria was carried out, using stream sediments as sampling
media. Twenty three trace elements; Ag, Cu, Mn, Mo, Ni, Pb, Zn, As, B, Bi, Co, Cr, Ga, La, Sb, Sc, Sr, Th,
V, W, Y and Zr, were analysed for in the sediments. Results from analyses showed that concentration levels
of these elements were within limits of geogenic sourcing. Correlation and factor analyses indicated
associations of chemical species to be related to the adjoining geologic suites; Precambrian basement and
Cretaceous sediments. Comparatively the sediments exhibit geochemical characteristics consistent with
sediments from other parts of the Niger Delta. The deductions provide a baseline data set for future monitoring
around the smelting site.
Key words: Factor analysis, geogenic, Ikot Abasi, Nigeria, sediments, trace elements
INTRODUCTION
metals from garbage/solid waste dumps and animal and
human excreta (Förstner and Wittman, 1981). However
the highest concentrations of potentially toxic elements
are usually in the vicinities of mining and smelting sites.
Instances abound where significant concentrations of
heavy metals and/or toxic substances are found in the
vicinities of industrial sites. Zachmann and Block (1993)
reported high concentrations of toxic elements, notably
lead, in soils and stream sediments in the vicinity of a lead
smelting complex in the Harz Mountains in Germany.
Employing soils and water as sampling media, Yukselen
(2002) reported increased levels (above permissible
limits) of Cu and As in soils and Cr, Ni and Cu in waters
around a copper mining and smelting site in Cyprus.
Abrahim (2002) have reported significant enrichment in
Cd, Cu, Pb and Zn in the upper sediments from the highly
industrial Tamaki estuary area of Auckland, New
Zealand. Similarly, Manjunatha et al. (2001) reported
enriched levels of Al, Fe, Ni and Co in soils, Suspended
Particulate Matter (SPM) and bottom sediments near
Karwar in the south-western coast of India resulting from
operations of paper mills, ferro-alloy units and mining of
manganese deposits.
The aim of this study is to determine the background
concentration levels of trace elements in sediments around
an aluminium smelting complex in Ikot-Abasi area of
south-eastern Nigeria. This will provide a platform for
future monitoring in the vicinity of this recently
established outfit.
Advancement in industrialization and urbanization
has shown from studies to lead to degradation of most
components of the natural environment. This may be
recognized in significant concentrations of potentially
toxic elements in various components of the ecosystem.
These elements have been classified by Wood (1974);
Förstner and Wittman (1981) as:
C
C
C
Non-critical elements (e.g., Na, K, Ca, Mg, Fe, Rb,
Sr, Br, Al, Si, Li, P, N, O etc)
Toxic but very insoluble or rare (REEs, Ti, Zr, W,
Ta, Re, Ba, Ru, Ir, Rh, Os, Ga, La etc)
Very toxic and relatively accessible (Cu, Se, Hg, Ni,
Pd, Ag, Pb, Zn, Cd, Sb, Sn etc)
These elements exhibit varying compositions in soils
and sediments, which in turn governs their availability in
plant and animal tissues. Therefore objectionable levels of
theses elements pose severe hazards to human and animal
health. This has brought to fore the need for periodical
monitoring of components of the environment, especially
in areas of recent industrial development and
urbanization. However this is practicable if a baseline data
is available. Assessment of background levels of
potentially toxic elements in components of the
environment around sites of recent industrial activities
and urbanization, are evident in the works of Edet et al.
(2003) and Elueze et al. (2009).
Heavy metals in the environment have many sources:
geologic weathering, industrial processing of ores and
metals, use of metals and metal compounds, leaching of
Location of Study Area: The study area lies within IkotAbasi area of south-eastern Nigeria, delimited by
longitudes 7º32! and 7º35! E and latitudes 4º32! and 436!
Corresponding Author: Azubuike S. Ekwere, Department of Geology, University of Calabar, Nigeria
256
Res. J. Appl. Sci. Eng. Technol., 4(4): 256-261, 2012
Fig.1: Map of south-eastern Nigeria and sample location within the study area
N, covering an area of about 4.8 km2, Fig. 1. Drainage in
the area is by the Imo River and associated creeks (Jaja
and Esene) with a number of tributaries giving a
dendritic drainage pattern. The area belongs to the humid
tropical climate with annual means of temperature,
relative humidity and precipitation of 26ºC, 85% and
3,855 mm, respectively (Ekwere, 2004).
The area belongs to the flat-lying part of the
Cenozoic Niger Delta basin with dominance of alluvial
and beach sands. These represent depositions of materials
derived from the continental crust with sources such as
the Cameroon Volcanic ridge, Calabar Flank and the
Anambra basin. The topmost exposed sedimentary
sequence is the Benin Formation (Oligocene-Recent) of
the Niger Delta and is made up of continental sands and
gravels (Short and Stauble, 1967; Edet and Okereke,
2001).
METHODOLOGY
Sediment samples were collected from 38 locations
in the vicinity of the aluminium smelting complex in Ikot
Abasi area, south-eastern Nigeria in April, 2003 (Fig. 1).
Three samples were also collected from a control site
(3 km from the complex, outside the mapped area). The
sediments collected by hand scooping and subsequently
257
Res. J. Appl. Sci. Eng. Technol., 4(4): 256-261, 2012
Table 1: Statistical summary of trace elements in sediments
Control
Element Mean (ppm) SD
(Mean) ppm
range (ppm)
Ag
0.20
0.13
0.1-0.5
0.1
Cu
9.19
7.14
3-37
8
Mn
140.87
64.35
64-309
147
Mo
1.23
0.80
0.5-3.2
1.0
Ni
15.73
6.58
6-28
11.5
Pb
18.82
5.13
9-29
8
Zn
65.90
62.68
31-306
117
As
6.88
5.98
3-22
7
B
9.64
8.06
3-27
9
Ba
51.78
27.34
34-110
90
Bi
1.16
0.93
0.7-2.1
1.5
Co
3.13
2.51
1-12
6
Cr
40.06
14.54
10-67
51
Ga
15.83
6.96
5-25
20
La
30.21
19.91
16-106
44
Sb
1.63
4.26
4-8
6
Sc
6.40
3.12
1.3-12.8
11.3
Sr
18.92
10.46
6-55
20
Th
5.97
4.52
2-22
8
V
56.39
23.82
27-96
79
W
3.10
2.00
1-6
5
Y
7.78
5.33
3-19
19
Zr
18.50
11.35
7-47
23
samples shows no remarkable difference. Similarly a
comparison with average shale composition, soil and
continental crust trace element composition and data from
other published researches, reveals levels that could be
reckoned to natural or geogenic processes.
A noticeable higher concentration of some elements
e.g, Cu, Pb, Ni, As, V, Co etc, is observed in some sample
locations (active estuaries) within the study area. This
reflects inputs of mixing in tidal flush zones within the
study area. A mixing process with mobilization by
solubilisation from suspended particles may lead to
enhanced concentration in such locations (Azmatullah and
Ekwere, 1985). Processes of solubilisation and cation
exchange for suspended particulate matter impregnated
with metal loads, could lead to increased concentrations
at locations prone to anthropogenic activities. This is
noticed for concentration levels for locations in proximity
to local population centres. However the mean content of
most of the trace elements in the stream sediments is
largely a function of the mean metal content of the rocks
that provides the clastics to the drainage basin.
stored in clean labelled polythene bags prior to treatment
and analysis. The sediment samples were air dried and
disaggregated in a porcelain mortar using a rubber-end
pestle. A nylon sieve was used to obtain <63 :m fraction
(active fraction) of the sediments for further chemical
analysis. About 0.5 g of samples was subjected to
geochemical analysis. Trace element analysis was carried
out by Induced Coupled Plasma-Atomic Absorption
Spectrometry (ICP-AAS) at the Activation Laboratories
Ltd., Canada.
Correlation: Correlation coefficients were determined for
the raw data set as a means of deciphering the existence
or non-existence of relationship between variables. This
further provided information on the degree of such
relationship where they existed. A positive or negative
value of r expresses a relationship, whereas a zero
coefficient implies that there is no relationship (Borrego,
2000). Samples showing r>0.7 are considered to be
strongly correlated, whereas r of 0.5-0.7 shows moderate
correlation at a significance level p< 0.05. Assessment of
the correlation matrix (Table 3), shows significant
positive correlation between the following set of
elements; Cu-Co, Sr; Mo-As, Ag; Ni-Pb, Cr, Sc, Ga;BCo, Y; Co-Sr, Y and Sc-V, W, Zr. Moderate correlations
existed among a varied array of the elements. Correlation
coefficients were strongest among Cr-Ga, V (0.93, 0.87),
Cr-Ni, Pb (0.85, 0.83) and V-Sc (0.84).
RESULTS AND DISCUSSION
Trace element analysis results for the sediments are
as presented in Table 1. This presents the mean, standard
deviation and ranges of values in comparison to the mean
values from the control site. Global averages as well as
data of sediments of similar provenance for some of the
elements are used for comparison (Table 2). Comparison
of the concentration levels with those of the control
Table 2: Comparison of trace element levels (ppm) in various geologic settings (a- mean, b- range, NR-not reported)
2a
3a
4a
5a
6a
7a
8b
9a
Element Present studya 1a
Cu
9.19
4.5
26
3
30
28.10
23
NR
14-60
51
Mn
140.87
NR
760
720
1000
188.30
NR
250.98
NR
NR
Mo
1.23
NR
1.90
1.7
1.2
NR
NR
NR
NR
NR
Ni
15.73
68
34
49
50
39.6
9.2
NR
10-72
46
Pb
18.82
20
29
16
35
25.2
22
16.41
15-40
30
Zn
65.90
95
60
127
90
53.3
18
647.602
26-1416
115
Ba
51.78
NR
568
445
500
NR
NR
NR
NR
NR
Co
3.13
NR
12
13
8
38.0
NR
NR
NR
NR
Cr
40.06
NR
NR
NR
NR
NR
3
16.71
NR
NR
1: Average shale composition Turekian and Wedepohl (1961); 2: Average soil values Ure and Berrow (1982); 3: Average continental rock values
Martin and Whitfield (1983); 4: Average soil values Martin and Whitfield (1983); 5: Cross River sediments Azmatullah and Ekwere (1985); 6: Calabar
river sediments Akpan et al. (2002); 7: Bight of Bonny sediments Ntekim et al. (1992); 8: Qua-Iboe River sediments Ekwere et al. (1992); 9: Fossil
Rhine sediments Förstner and Muller (1981)
258
Res. J. Appl. Sci. Eng. Technol., 4(4): 256-261, 2012
Table 3: Correlation matrix for trace elements from the study area
Ag
Cu
Mn
Mo
Ni
Pb
Zn
Ag
1
0.60
0.52
0.83
0.48
0.70 - 0.17
Cu
1
0.53
0.33
0.43
0.50
0.43
Mn
1
0.47
0.16
0.30
0.03
Mo
1
0.13
0.44 - 0.21
Ni
1
0.85
0.07
Pb
1
- 0.16
Zn
1
As
B
Ba
Bi
Co
Cr
Ga
La
Sb
Sc
Sr
Th
V
W
Y
Zr
Ag
La
Sb
Sc
Sr
Ag
0.00
0.67
0.49
0.47
Cu
0.14
0.37
0.11
0.84
Mn
0.19
0.25
0.17
0.50
Mo
- 0.04
0.68
0.28
0.23
Ni
0.13
0.54
0.71
0.56
Pb
0.30
0.61
0.71
0.49
Zn
- 0.03
- 0.02
-0.26
0.65
As
- 0.07
0.41
0.15
- 0.07
B
- 0.07
0.42
0.27
0.56
Ba
0.21
0.56
0.48
0.66
Bi
0.42
0.52
0.38
- 0.21
Co
0.29
0.45
0.43
0.77
Cr
0.07
0.57
0.77
0.17
Ga
- 0.09
0.61
0.75
- 0.02
La
1
- 0.10
0.20
0.09
Sb
1
0.63
0.31
Sc
1
0.10
Sr
1
Th
V
W
Y
Zr
As
0.38
- 0.06
0.38
0.71
- 0.23
0.04
- 0.15
1
Th
0.14
- 0.01
0.21
0.14
0.06
0.27
- 0.32
0.09
00.3
0.05
0.53
0.13
0.16
0.09
0.87
- 0.02
0.39
- 0.17
1
B
0.63
0.06
0.42
0.58
0.28
0.42
0.20
0.30
1
Ba
0.36
0.52
0.44
0.32
0.54
0.55
0.38
0.28
0.50
1
1
V
0.43
- 0.011
- 0.00
0.35
0.64
0.65
0.52
0.09
0.12
0.22
0.32
0.17
0.87
0.95
- 0.08
0.62
0.84
- 0.12
0.16
1
Bi
0.12
- 0.11
0.13
0.41
- 0.06
0.15
- 0.19
0.46
00.4
0.28
- 0.01
1
W
0.18
0.41
0.18
0.23
0.38
0.27
0.55
0.17
0.54
0.52
0.12
0.39
0.10
0.09
- 0.02
0.43
0.29
0.51
- 0.10
- 0.10
1
Co
0.54
0.76
0.50
0.25
0.66
0.64
0.27
- 0.14
0.52
0.72
0.12
0.34
1
Y
0.27
0.43
0.39
0.16
0.41
0.56
0.08
0.09
0.26
0.72
0.38
0.74
0.24
0.09
0.69
0.32
0.52
0.39
0.53
0.16
0.26
1
Cr
0.51
0.16
0.04
0.39
0.85
0.83
- 0.37
- 0.06
0.12
0.29
0.23
0.14
0.93
1
Ga
0.48
- 0.08
0.01
- 0.04
0.70
0.72
- 0.49
0.14
0.11
0.23
0.42
0.29
00.7
- 0.09
1
Zr
0.48
- 0.13
0.39
0.41
0.49
0.50
- 0.33
0.30
0.27
0.26
0.31
0.22
0.56
0.67
0.13
0.45
0.83
- 0.04
0.42
0.74
0.22
0.28
1
presumed to exist within a set of multivariate observation,
(Davis, 1986). This provides a clearer picture of the
structure of the system that produced the data. The
success if this interpretation technique is evident in the
works of; Olorunfemi (1984), Elueze and Olade (1985),
Adams et al. (2001), Edet et al. (2003) and Ekwere
(2004). Results of factor analysis of the data set are as
shown in Table 4.
Results from the R-mode factor analysis reveals a
four factor grouping that is consistent with geological and
environmental processes as responsible for variance of the
data set. These account for about 71% of the total
Although there is a correlation between Pb-Cu-Ag, the
well known geochemical association between As, Pb, Zn,
Cu and Ag (Levinson, 1974) was not visible for all five
metals. This probably is due to the fact that the study area
is a sink for variety of sources of trace elements.
Factor analysis: R-mode factor analysis is a multivariate
statistical technique that is widely used to aid
interpretation of geochemical data and enhance subtle but
significant single-element anomalies (Garret and Nichol,
1969). In other words, factor analysis has the simple
objective of revealing an underlying relation that is
259
Res. J. Appl. Sci. Eng. Technol., 4(4): 256-261, 2012
Table. 4: Factor loadings for the analysed trace elements
Element
Factor 1
Factor 2
Factor 3
Ag
0.766368
- 0.051478
0.355884
Cu
0.526826
- 0.699030
- 0.024855
Mn
0.458677
- 0.3687643
0.260950
Mo
0.601906
0.039254
0.693165
Ni
0.778027
0.095430
- 0.428063
Pb
0.876785
0.070982
- 0.223256
Zn
- 0.060596
- 0.792945
- 0.088507
As
0.258166
0.108340
0.783279
B
0.567585
- 0.405700
0.362764
Ba
0.698977
- 0.4019663
- 0.035198
Bi
0.333142
0.297275
0.220097
Co
0.707798
- 0.495924
- 0.281348
Cr
0.721737
0.440116
- 0.269498
Ga
0.663302
0.610713
- 0.032300
La
0.227154
- 0.043056
- 0.486343
Sb
0.775469
0.084230
0.317715
Sc
0.791092
0.385763
- 0.188461
Sr
0.520802
- 0.756253
- 0.096040
Th
0.280494
0.258598
- 0.272122
V
0.657473
0.651072
- 0.053074
W
0.443510
- 0.416484
0.108381
Y
0.605049
- 0.233614
- 0.355773
Zr
0.634988
0.482079
0.042088
Eigenva
l8.29000
24.18100
2.463000
% Total
36.05000
18.18000
10.71000
variance
Cumul.
l8.29200
12.4740
14.93700
Eigenva
Cumul. % 36.05000
54.2400
64.94000
CONCLUSION
Factor 4
0.425464
0.275429
0.347639
0.187300
- 0.024314
0.185504
- 0.444525
- 0.143000
0.057175
- 0.329029
- 0.381357
0.122612
0.099701
- 0.011833
0.073920
- 0.234757
- 0.203164
0.074362
0.140996
- 0.087110
- 0.572423
- 0.090327
- 0.141670
1.439000
6.250000
The mean concentrations of Cu, Mn, Ni, Pb, Zn, Co
and Cr are similar to values obtained in other locations
within the Niger Delta region and so can be considered
the background for future work in the area. Comparison
with concentration levels of global averages and
sediments of similar provenance, indicate no
contamination with respect to these elements. However
some locations display elevated concentrations and are
resultant of organo-metallic complexing in some of the
active estuaries. Statistical deductions by correlation
matrix and factor analysis show that the sources of the
elements are natural largely from the weathering of
Precambrian basement, Cretaceous sedimentary rocks as
well as sulphide lodes from the Lower Benue trough. The
data set is the first in the area, thus will provide a
reference material for future monitoring with regards to
metallic pollution in the vicinity of smelting industry.
ACKNOWLEDGMENT
16.37600
The authors wish to acknowledge the support
rendered by the Aluminium Smelting Company, Ikot
Abasi for providing access to their facilities. Also
appreciated are the contributions of Dr. Uyime. E. John of
the University of Birmingham (UK) during both the field
exercise and manuscript preparation.
71.19000
variance. Factors with loadings greater than 0.40 are
considered for explanation of each factor loading.
Factor 1 (Ag, Cu, Mn, Mo, Ni, Pb, B, Ba, Co, Cr, Sb,
Sc, Sr, V, W, Y, Zr), accounts for about 36% of total data
variance, constituting of trace and some of the rare earth
elements. This factor reflects background values of metal
in the rocks that produced sediments to the basin and is
termed a petrologic factor. It accounts for the background
variation in the data related to major differences in
bedrock geochemistry, the adjoining Precambrian and
Cretaceous rocks.
Factor 2 (Cr-Ga-V-Zr) accounts for about 18% of
total data variance and related to the geochemical
processes producing the sediments. This is determined by
the availability of this chemical species usually in the clay
fractions of the sediments. This factor appears erratic in
distribution and may reflect in part the contributions of
irregular occurrence of pegmatite bodies in the adjoining
Precambrian Oban massif.
Factor 3 (Mo, As) and Factor 4 (Ag) appear erratic in
distribution with percentages of data variance of 10.71
and 6.25 respectively. These factors also show geologic
control on its composition with a presumed chalcophilic
rock input especially for Mo-As association. This can be
attributed to the sulphide lodes of the lower Benue trough
which serves as a source of sediments into the basin.
Interpretation of geochemical data through factor
analysis shows that the distribution of trace elements is
influenced by lithological and environmental controls.
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