International Journal of Research and Innovation in Applied Science (IJRIAS) |Volume VII, Issue XI, November 2022|ISSN 2454-6194

Contamination and Risk Assessment of Heavy Metals

in Stream Sediments of Bambui Area, Western
Cameroon
Lemnyuy Prosper Yiika1*, Ndema Mbongué Jean-Lavenir2,3, Mboudou Germain Marie Monespérance2, Emmanuel
Eseya Mengu2, Bewah Emilien Bih2,4
1
Department of Geology, Mining and Environmental Science, University of Bamenda, Bamenda, Cameroon
2
Department of Geology, University of Buea, Buea, Cameroon
3
Department of Earth Sciences, University of Yaoundé I, Yaoundé, Cameroon
4
University of the West of Scotland-London Campus
*Corresponding Author

Abstract: The Bambui area is located at the foot of the Bamenda pollution [7]. The presence of trace elements in the sediments
Mountains, which is part of the Cameroon Volcanic Line (CVL). is widely reported, and both abiotic and biological processes
The study area consists of different rock types such as alkali influence the distribution and circulation of sediments [8].
basalt, trachyte, rhyolite and ignimbrite with a granitic basement
of the Pan Africa Fold belt. The main objective of this work was Heavy metals come from geogenic and anthropogenic
to assess the contamination and risk pose by heavy metals in sources in the natural environment. In general, anthropogenic
stream sediments of Bambui area. Heavy metal in the sources such as industrial wastewater, household waste, sewage
representative stream sediment samples collected were and municipal wastewaters are rich in high levels of metals
determined using inductively coupled plasma-mass spectrometer such as As, Cd, Cr, Co, Cu, Fe, Hg, Mn, Ni, Pb, and Zn [9] at
(ICP-MS). Elevated levels of Co, Cr, Cu, Mn, Ni, Pb, Th, V, Zn,
La, Fe and Ti could be attributed to geological and anthropogenic
high concentration, it is highly toxic and harmful to human
metal input sources in the area. Assessment of contamination health. Heavy metals are harmful when their bioaccumulation
factor, degree of contamination, modified degree of exceeds that released into various environmental
contamination, enrichment factor, ecological risk factor and compartments. Heavy metals entering sediments have a
potential ecological risk index showed that the sediments had a low negative impact on river ecosystems due to their high toxicity,
to high ecological risk index. Pollution load index (0.78-1.60), geo- resistance to degradation and bioaccumulation [10]. Because
accumulation load index (1.73-5641.91) and anthropogenic metal river sediments are one of the most important environmental
input (0-5.25) indicate heavy metal contamination in the study indicators, they can be used to measure pollution levels in
area. Geological origin, agricultural practices, municipal waste natural waters. Stream sediment can be used to measure the
disposal and animal manure were identified as the major sources
of heavy metals in the stream sediments of the study area.
pollution levels in natural waters [4]. Today, metal pollution
has become a major problem in many rapidly developing
Keywords: Bambui area, Heavy metal, stream sediment, risk countries such as Cameroon [11], [12]. Disposal of household
assessment, contamination, Cameroon waste, biosolids and raw waste water from various industrial
I. INTRODUCTION and agricultural resources into open water and rivers has
created a dire situation in Cameroon [11], [5], [13], [12]. Waste

H eavy metals pollution from municipal solid waste,

agricultural practices, sewage, road traffic emissions and
other human inputs has become a serious problem due to their
disposal, biosolids and intense agricultural practices are the
main anthropogenic activities that have contributed to increase
levels of heavy metals in soils and stream sediments of urban
long-term accumulation and toxicity. Heavy metals are town and cities in Cameroon. Bambui area is one of the most
environmental pollutants that affect various environmental rapidly emerging municipalities in Bamenda, Cameroon with
compartments in nature. The environment is exposed to a a-lot of small factories for soap production, food processing,
variety of pollutants through the aquatic environments and mechanical workshops, oil exchange services, traffic releases,
affects the ecosystems. This is because they are often major intense agricultural practices, wastewater irrigation and the
reservoirs of many pollutant [1], [2]. Heavy metals used of animal manure (excreta) which generates huge amounts
contamination poses is a serious threat to the aquatic system of waste and heavy metals [11], [14]. This waste is drained into
due to its toxicity, profusion, abundance, environmental soils or water systems that supply the wetlands and flat areas
persistence and subsequent levels of accumulation in the mostly used for the cultivation of crops and vegetables
aquatic environment [3], [4], [5]. Sediment is widely used as an consumed all over the country. These crops and vegetables
environmental indicator to assess metal contamination in the have the potential of metal uptake from waste water irrigation
watercourse [4], [6]. In generally, sediments provide useful and soils in the study area which is detrimental to the
information about the state of environmental and geochemical population through food chain. The purpose of this study is to

www.rsisinternational.org Page 64
International Journal of Research and Innovation in Applied Science (IJRIAS) |Volume VII, Issue XI, November 2022|ISSN 2454-6194
assess the metal levels, as well as the degree of metal
contamination in stream sediments based on pollution
indicators.
II. GEOLOGICAL SETTING
A. Regional Geology
The Bambui area is located at the southwestern part of the Pan-
African fold belt in Cameroon. This fold belt is linked to the
Trans-Saharan belt of western Africa and to the Brasiliano
Orogen of NE Brazil (Fig. 1a). The Pan-African fold belt is
incompletely covered by basalts and trachytes of Tertiary to
Recent age. In Cameroon, the Pan-African fold belt consists of
3 domains [15], [16] (Fig. 1b): The Yaoundé domain (YD) is
located at the northern edge of the Congo craton (Fig 1b). It is
generally made of Neoproterozoic epicontinental or passive
marginal deposits [17. This domain was latterly recrystallized
during the 630–610 Ma amphibolite- to granulite-facies
metamorphism during nappe tectonics, which thrust the
Archean Congo craton southward [18], [19], [20]. The
Yaoundé domain is associated with high-K calc-alkaline
granitoids harmonious with a subduction-related magmatism
[15], [27]. The Adamawa-Yade domain (AYD) which contains
the present study area, is located between the Tchollire-Banyo
shear zone to the north and the Sanaga shear zone to the south
(Fig. 1b). It consists of Archean and Paleoproterozoic crust Fig. 1(a) Reconstitution map of the Pan-African NE Brazilian
[21], [22], the northern part of the Central African Orogen [21], and West African domains showing the continuity between the
[23]. The Adamawa-Yade domain is associated with Sergipano and North Equatorial ranges after [29]. 1(b)
Neoproterozoic metasedimentary rocks intruded by 640–560 Geological map of Cameroon showing the Pan-African fold
Ma syn-, late- and post-collisional calc-alkaline, high-K calc belt [18]. CCSZ = Central Cameroon Shear Zone, SSZ =
alkaline to shoshonite and alkaline granitoids [24]. The Sanaga Shear Zone, TBSZ = Tchollire-Banyo Shear Zone,
emplacement of these granitoids is controlled by 640–610 Ma RLSZ = Rocher du Loup Shear Zone, ASZ = Adamawa-Yade
crustal thickening episode, and 610–585 Ma wrench tectonic Shear Zone, GGSZ = GodeGormaya Shear Zone, MNZ = Mayo
regime [25] .The Northwestern Cameroon Domain (NWC) is Nolti Shear Zone. Insert showing the location of Cameroon in
located west of the Tchollire-Banyo Shear Zone (TBSZ Fig. the pre-drift reconstruction [30]. PA = Patos shear zone, KF =
1b), it is considered as an early 750–650 Ma continental Kandi Fault, PSZ = Pernambuco shear zone, (YD) Yaoundé
magmatic arc [26]. The domain includes Neoproterozoic domain, (AYD) Adamawa Yade domain, (WCD) Northern
volcano-sedimentary schists and orthogneisses intruded by domain and plutonic complexes that extend from the Gulf of
Pan-African pre-, syn-late, and post-tectonic granitoids that Guinea to the interior of northern Central Africa [32], [33], [34]
derived from partial melting of the lower crust [27], [28]. Mount Oku and Mount Bamenda are among the main volcanic
B. Local Geology massifs along the CVL and sits on a basement of Pan-African
granite-gneisses (~ 600 Ma), migmatites and biotite diorites
The Bambui area is located in the southwestern part of the [35], [36], (Fig. 2a, 2b). The CVL is characterized by alignment
Pan-African fold belt in Cameroon. The southwestern part of of oceanic and continental volcanic massifs, and orogenic
the Pan-African fold belt consists of Mount Oku, Mount plutonic complexes. The continental sector of the CVL is made
Bamenda and Mount Bamboutos [31] (Fig. 2a, b). The Bambui up of massifs amongst which is Bamenda Mountains. The
area lies between the Bamenda Mountains (at the foot of the Bamenda Mountains is one of the most important volcanoes of
Bamenda Mountains) and the Oku Mountains (Fig. 2b) which the Cameroon Line in North-West Cameroon [37], [31].
are part of the Cameroon volcanic line (CVL). The Cameroon Mounts Bamenda (600 km2) is the fourth largest massif in
volcanic line is a late Cretaceous alignment with recent volume in the continental sector of the plutonic-volcanic
intraplate volcanic massifs Cameroon line and lies mid-way between Mount Bamboutos to
the southwest and Mount Oku to the northeast [31]. Bamenda
Mountains consists of volcanic rocks (alkali basalt, trachyte,
rhyolite, and ignimbrite) with age ranging from 22 Ma to
present and granito-gneissic basement of Pan-African ages
[31], [37]. Bamenda Mountains is characterized by two elliptic
calderas that include Santa-Mbu and Lefo caldera [38]. The
radiometric dating gives ages ranging from the current to 17.4

www.rsisinternational.org Page 65
International Journal of Research and Innovation in Applied Science (IJRIAS) |Volume VII, Issue XI, November 2022|ISSN 2454-6194
Ma for the basaltic lavas and from 18.98 Ma to 27.40 Ma for been changed into woodland by the planting of eucalyptus trees
the felsic lavas [38]. Bambui area is a continuation of the to reduce the erosion rate and reduce the risk of landslides.
Bamenda highlands that stretches through Sabga dorsal to the
Sample Collection and Analysis
Boyo hill. Bambui area is made up of four villages; Bambili,
Bambui, Kedjom-Ketinguh and Kedjom-Keku. Most of the Fieldwork was carried out in December 2020 where the four
population in these villages depends on agriculture for villages that make up the Bambui area were sampled. This four
livelihood as many villagers practice intense agriculture with villages are potential areas of pollution due to anthropogenic
the used of phosphate fertilizers, animal manure, activities such as intensive agricultural practices, settlements,
agrochemicals, pesticides, fungicides and waste water bathing and laundry sites refuse, indiscriminate dumped of
irrigation for high crop yield without the knowledge of the garbage, batteries and abandoned car parts, municipal and
negative impact into the environment. The study area is also household solid waste into streams and the use of
recognised for indiscriminate dumped of municipal and agrochemicals in crop cultivation on the wetlands and farms
household solid waste, effluents into stream and rivers without located along stream and river valleys which might be sources
proper treatment, runoff from mechanical workshop and oil of heavy metals in the study area. A total of 25 sediment
exchange services into streams and river which are detrimental samples (about 30Kg each) were collected and quartered
to benthic organisms and human health. manually. Panning was carefully done at the spot for at least
15-20 minutes to collect the concentrates and transferred into a
polythene bag immediately following the procedure used by
[5]. Spacing between the samples was fixed at 450m. Locations
of sampling sites were determined using Global Positioning
System (GPS). Sediment samples were collected from a depth
of 0–50 cm deep to avoid high contents of Fe-Mn oxide and
humus with the use of an auger and stainless-steel shovel.
Among the quartered 25 samples, fourteen sediment
concentrate (about 50g each) were selected (spacing of 1km
between them): five samples each from Kejom-Keku (KK) and
Bambili (BB) and two samples each from Kejom-Ketinguh
(KG) and Bambui (BU). The samples were air-dried in a dry
and dust-free place at room temperature in the laboratory of The
University of Bamenda. The dried sediment samples were
ground manually to a fine powder in an agate mortar using a
pestle and sieved through a 2mm sieve to remove any large
Fig. 2 Location of the Bambui area within the Cameroon organic matter or gravel. The powder samples are then place in
Volcanic Line (CVL): a) Main volcanic systems of the a polythene bags for chemical analysis. The fine sediment
Cameroon Volcanic Line; b) Location of the study area samples (1.5g) was digested with aqua regia (0.6 ml
between the Mount Bamenda and Mount Oku concentrated HNO3 and 1.8 ml concentrated HCl) for 1 hours
at 90ºC. The sample is then cooled and diluted to 10 ml with
III. MATERIALS AND METHODS deionized water and homogenized [50]. The digested samples
Description of Study Area were then analysed for heavy metals content by Inductively
Coupled Plasma-Mass Spectrometer (ICP-MS- Aqua Regia)
Bambui area is found in the grass field zone of Cameroon; after digestion of the samples. Digestion and ICP-MS analysis
precisely in Tubah Sub-division of the Northwest Region of of samples were done at Activation Laboratories (ACTLABS)
Cameroon. Bambui area is located between latitudes 4°50’ and Ltd., Ontario in Canada. The quality assurance procedures and
5°20’N and longitudes 10°35’ and 11°59’E. Bambui area is precautions were ensured for the reliability of the results.
bordered to the North by Belo in Boyo Division, to the North Precautions were taken to avoid contamination during drying,
West by Bafut, to the South and South East by Ndop and grinding, sieving and storage. Glass wares were washed with
Balikumbat in Ngoketungia Division and to the West by liquid soap and rinsed properly and reagents were of analytical
Nkwen in Bamenda III Sub-division. Bambui area is grades. Deionized water was used throughout the study. The
characterized by the humid tropical climate of highland with analysis of blanks, randomisation of sample numbers and the
two seasons, rainy season ranging from mid-March to October use of in-house reference materials and sample triplicates,
with an average precipitation of 1670mm of rain and a short dry provided a measure of trueness of analytical results and
season which starts from November to February with average precision. Laboratory controls of known concentrations and
precipitation of 80mm, favourable for agricultural practices. spiked samples of known concentrations were employed for
The area is usually well drained due to the abundant rivers and quality control.
streams that drain into river Mezam. The vegetation is mostly
grassland with shrubs and has been modified. Most zones have

www.rsisinternational.org Page 66
International Journal of Research and Innovation in Applied Science (IJRIAS) |Volume VII, Issue XI, November 2022|ISSN 2454-6194
Data Analysis sediments cannot be clearly explained by geogenic influence
alone, but have other anthropogenic origins. The high average
Descriptive and multivariate statistical techniques were used to
content of these metals may be due to intense weathering of the
evaluate the sediment data set. Correlation analysis was used to
source rocks, agricultural practices, household and industrial
evaluate relationships between the variables and factor analysis
emission, phosphate fertilizer, municipal waste and sewage
(FA) was used to differentiate natural and anthropogenic
sludge in the Bambui area [40], [5]. The content of metals such
origins of metals in sediment samples of the study area. Data
as Ga (3 – 14 ppm), Hg (0.02 – 0.07 ppm), Al (0.53-1.82 ppm),
collected was analysed using Statistical Package for Social
Sc (2.9-6.7ppm) are very low (except Ba: 17 – 304 ppm;
Science (SPSS 16.0) software and Microsoft Office Excel.
average = 92.21 ppm which is slightly elevated). The low mean
I. RESULTS AND DISCUSSION content of theses metals is due to leaching of the source rocks
during weathering [5]. The results indicate that metal levels
A. Heavy Metals Content in Stream Sediments found in sediments of Bambui area can negatively impact
The concentrations of heavy metals in sediments from the aquatic ecosystems and human health through the food chain.
Bambui area are shown in Table II. The sediments contain high The Pearson’s correlation matrix (Table III) mostly show
level of Mn (690– 2550 ppm, average = 1580 ppm), La (77– positive correlations meanwhile, Ba/Th, Ga/Th, Al/Ti and
1780 ppm average = 442 ppm), Cr (79 – 589 ppm average = Th/Zn show negative correlation. Generally positive
273.9 ppm), V (59 – 157 ppm average = 103 ppm) and Th (18.8
correlation between elements (r 2 > 0.5) could be attributed to
– 200 ppm average = 98 ppm) far exceeds the recommendation
parent rock (geologic unit), agricultural activities (used of
for the Upper Continental Crust proposed by [39]. The phosphate fertilizers, pesticides, fungicide and animal manure)
concentration of some transition metals such as Cu (7.40– 168 and municipal solid waste which are probably governed by the
ppm, average = 32.5 ppm), Ni (20.02 – 146 ppm average = same or similar physicochemical processes [41]. The strong
54.12 ppm), Zn (41 – 147 ppm, average = 93.21 ppm) is
correlations between some elements observed in this study
moderate while the values of Pb (11 – 63.1 ppm average = signify that these elements come from the same source. The
29.13 ppm), Co and (11.9 – 39.5 ppm, average = 23.91 ppm)
correlation of Co/Cr, Ni, V, Cr/Ni, Mo/Ni, Zn, Ni/V, Co/Fe,
are low and also elevated compared to the contents of the Upper
Mo/Fe, V/Fe is a reflection of their transition metal relationship
Continental Crust. The high content of these metals may be due and could be related to a mafic origin in relation with basaltic
to intense weathering of the source rocks, agricultural practices, rocks in the study area [31], [37]. The correlations between
residential development, garbage, waste water irrigation and
Ga/Zn indicate the relationship between chalcophile elements
industrial emission in Bambui area. Comparing the average
in the study area. The correlations between Ba/Th, Ga/Th and
values of metals in various samples, it can be seen that the
Ga/ Al may be associated with an acidic origin, probably due
concentration of Mn, Th, La, Cr, V, Pb and Zn concentrations to the presence of rhyolite, Ignimbrites and trachyte [31], [37]
are higher than those of other metals (Fig 3). This shows that in the research area.
the high concentration of Mn, Th, La, Cr, V, Pb and Zn in the

Table IV. Heavy Metals Concentrations (In Pmm) In Stream Sediments Of Bambui Area

Elements BB01 BB02 BB03 BB04 BB05 BU01 BU02 KG01 KG02 KK01 KK02 KK03 KK04 KK05
Al 1.07 1.42 0.71 1 1.74 0.53 2.28 2.17 1.82 1.15 1.23 0.58 0.7 0.6
Fe 8.96 10.5 7.66 7.32 9.51 7 13.2 5.99 7.9 12.8 7.38 11.2 7.38 6.32
Ti 1.35 1.3 2.03 1.63 1.63 1.47 2.16 1.38 1.54 1.66 0.702 2.2 1.81 1.42
Ba 182 304 35.8 85.1 99.7 27.5 44.8 48.9 166 105 52.2 40.8 17 28.2
Co 20.3 22.2 31 20.4 39.5 16.5 36.7 27.6 17 28 14.1 30.9 18.6 11.9
Cr 244 299 106 513 589 159 593 517 191 122 188 79 147 87
Cu 10.6 10.9 10.8 28.1 13.7 7.8 17.5 10.7 22 168 14.5 10.2 123 7.4
Ga 10 11 4 7 11 4 14 13 9 13 9 8 6 3
Hg 0.02 0.04 0.02 0.02 0.01 0.01 0.02 0.01 0.03 0.02 0.07 0.03 0.02 0.02
Mn 2160 2550 2360 1260 1310 1310 1400 932 1930 1910 1500 1470 690 1340
Mo 3.5 8.5 2.2 1.7 4 0.9 8.5 3.4 4 4.4 5.2 1.6 2.6 1
Ni 65 67.9 33.8 44.6 146 32.3 114 66.6 47.2 31.6 26.5 37.6 24.4 20.2
Pb 33.9 35.2 31.2 23.2 54.1 16.3 9.3 10.5 11.3 43 55.1 6.6 63.1 15
Sc 3.9 5.9 6 5.2 7.5 4.7 6.7 2.9 5.9 4.2 7.1 4.2 3.1 3.9
Th 30.5 25.1 200 20.9 79 200 52.5 55.5 23.7 126 200 18.8 140 200

www.rsisinternational.org Page 67
International Journal of Research and Innovation in Applied Science (IJRIAS) |Volume VII, Issue XI, November 2022|ISSN 2454-6194
V 84 88 87 84 157 85 147 111 73 144 72 146 105 59
Zn 121 147 59 79 98 49 126 97 109 124 100 97 58 41
La 231 149 1010 126 267 739 88 197 77 393 1780 85 392 649

Fig. 3 Enrichment/depletion plot of heavy metals in stream sediments of Bambui area

Table V. Correlation matrix of metals in stream sediments of Bambui area

Variables Ba Co Cr Cu Ga Hg Mn Mo Ni Pb Sc Th V Zn La Al Fe Ti
Ba 1
Co -0.05 1
Cr 0.10 0.52 1
Cu -0.08 0.01 0.25 1
Ga 0.38 0.53 0.58 0.18 1
Hg 0.22 -0.37 -0.28 -0.10 0.05 1
Mn 0.70 0.04 -0.27 -0.14 0.08 0.27 1
Mo 0.55 0.31 0.42 0.01 0.73 0.37 0.36 1
Ni 0.24 0.72 0.81 -0.27 0.60 -0.29 -0.00 0.51 1
Pb 0.08 -0.03 -0.07 0.48 0.02 0.28 -0.02 0.13 0.02 1
Sc 0.20 0.31 0.34 -0.34 0.17 0.39 0.32 0.49 0.50 0.19 1
Th -0.55 -0.33 -0.48 0.13 -0.58 0.11 -0.13 -0.34 -0.46 0.31 0.01 1
V -0.13 0.86 0.39 0.31 0.59 -0.32 -0.20 0.25 0.58 0.05 0.11 -0.32 1
Zn 0.72 0.35 0.34 0.06 0.86 0.30 0.48 0.81 0.46 0.02 0.29 -0.66 0.36 1
La -0.35 -0.37 -0.39 -0.07 -0.38 0.57 0.03 -0.13 -0.42 0.40 0.27 0.81 -0.41 -0.36 1
Al 0.27 0.44 0.75 -0.13 0.81 -0.02 -0.02 0.68 0.70 -0.15 0.34 -0.48 0.33 0.62 -0.33 1
Fe 0.27 0.60 0.13 0.32 0.61 0.03 0.33 0.59 0.39 -0.01 0.27 -0.38 0.71 0.68 -0.36 0.26 1
Ti -0.30 0.62 0.04 0.14 -0.02 -0.53 -0.06 -0.10 0.18 -0.33 -0.09 -0.23 0.57 -0.09 -0.52 -0.06 0.46 1

r2= 0.50-0.69 (moderate correlation), r2= 0.70-0.79 (strong correlation) and r 2= 0.80-0.99 (very strong correlation)

B. Factor Analysis contamination (natural, anthropogenic, or both) on a specific

region. In the evaluation of the factors, varimax rotation with
The factor analysis method is applied to determine the latent
Kaiser normalization that maximizes the variances was used.
factors that control the distribution of metals and the sources of

www.rsisinternational.org Page 68
International Journal of Research and Innovation in Applied Science (IJRIAS) |Volume VII, Issue XI, November 2022|ISSN 2454-6194
According to the [42] criterion, the factors with eigenvalues >1 C. Evaluation of Heavy Metal Contamination
were taken into consideration for evaluations. Table VI
In this study, various pollution indicators were used to
presents a five factors model cumulatively accounting for
determine the level of heavy metal contamination in stream
80.08% in the study area.
sediments of Bambui area.
The elements in factor groups with the factor loading scores >
1) Contamination Factor (CF), Degree of Contamination (Cd)
0.50 were interpreted to identify their possible sources. Five
and Modified Degree of Contamination (mCd)
factors identified in Bambui area include: (i) Factor 1 (Cr, Ga,
Ni, Al) accounts for 20.31% of the total variability contains Ga The contamination factor (CF) is calculated using the
and Ni, and two lithophile element (Al and Cr). The high method presented by [43], [44]. CF is defined as:
loading of the elements by this factor indicates high mobility Concentration of metal in the sample
within the environment rich in lithophile and Al may be CF = Background concentration of metal
associated with alumina silicate minerals in the source rock of
the study area. (ii) Factor 2 (Co, V, Fe, Ti) accounts for The CF values are interpreted as follows: low contamination
15.99%% of the total variability is made up of transition metals. at CF < 1; moderate contamination at 1 < CF < 3, considerable
These factors may be related to the presence of basic minerals contamination at 3 < CF < 6, and very high contamination at
in the parent rock (Basalt). (iii) Factor 3 (Cu, Pb) which CF > 6, according to [44]. Table VII presents the results of CF
accounts for 10.42% of the total variability, consists of of heavy metals in stream sediments of the study area. Ba, Cu,
predominantly by chalcophile elements and is a sulphide phase. Ga, Hg, Sc and Al are slightly contaminated (CF < 1), Co, Cr,
(iv) Factor 4 (Hg, Sc Th, La) accounts for 11.81% of the overall Cu (BB04), Hg (KK02), Mn, Mo, Ni, Pb, V, Zn and Fe are
variability. It is primarily a silicate phase, as it is saturated with moderately contaminated (CF = 1 - 3). Cr (BB04, BU02,
lithophile elements. Factor 4 also reflect a lithological KG01), Mo (KK01, KK02, BU02 and BB02) and Ti (BB01 and
controlled. (v) Factor 5 (Ba, Mn, Mo, Zn) account for 21.54% BB02) are considerably contaminated (CF = 3 - 6) and Th, La
of the total variability reflects the enriched environment of and Ti are highly contaminated CF > 6) within the study area.
transition metal associations. The strong loading of Ba (0.91) Degree of Contamination (Cd) is a cumulative indicator
may be due to the presence of feldspars in the rocks (rhyolite, calculated as the sum of individual contamination factors (Cd).
Ignimbrites and trachyte) of the study area. It represents the sum of all the CF values for all the sampling
Table III. Varimax Rotated Factor Analysis With Kaiser (1958) sites. This was suggested by [45] as follows:
Normalization
𝐶𝑑 = ∑8𝑖=1 𝐶𝐹 .
Variable F1 F2 F3 F4 F5
The Cd is expressed as Cd < 6 = low, 6 < Cd < 12 =
Ba 0.118 -0.200 -0.052 -0.171 0.915 moderate, 12 < Cd 24 = considerably high, and Cd > 24 = high.
Co 0.445 0.832 -0.068 -0.013 0.026 The Cd varies between 28.50 (KK03) to 100.01 (KK02) in the
Cr 0.908 0.116 -0.123 -0.117 -0.024 study area (Table VIII). It is in the order: KK02 > BB03 >
BU01 > KK01 > KK05 > KK04 > BB05 > BU02 > BB03 >
Cu -0.214 0.220 0.794 -0.153 0.006
KG01 > BB01 > BB04 > KG02 > KK03 and the value ranges
Ga 0.708 0.256 0.240 -0.153 0.461 from 28.50 to 100. The entire study area is characterised by a
Hg -0.090 -0.327 0.101 0.536 0.423 high Cd (Cd > 24) in stream sediments.
Mn -0.269 0.081 -0.282 0.178 0.759 Modified degree of contamination helps in the assessment
Mo 0.516 0.145 0.153 0.187 0.657 of overall heavy metal contamination in the sediment samples.
Ni 0.402 -0.158 0.009 0.133
It can be used to better estimate the value. It is calculated using
0.810
the following formula:
Pb 0.029 -0.077 0.768 0.471 0.073
∑𝐶𝐹
Sc 0.357 0.197 -0.260 0.655 0.301 mCd = n
Th -0.411 -0.145 0.135 0.582 -0.466 Where CF is the contamination factor and n is the number
V 0.374 0.821 0.234 -0.130 -0.022 of elements analyzed [45]. mCd values are interpreted as
Zn 0.435 0.176 0.107 -0.090 0.838 follows: mCd< 1.5 is nil to low, 1.5 <mCd< 2 is low, 2 <mCd<
4 is moderate, 4 <mCd< 8 is high, 8 <mCd< 16 is very high, 16
La -0.257 -0.304 0.074 0.872 -0.204
<mCd< 32 is extremely high and mCd> 32 is ultra-high
Al 0.858 0.064 -0.046 -0.079 0.232 contamination. mCd varies between 1.58 (KK03) to 5.56
Fe 0.110 0.747 0.156 -0.032 0.526 (KK02) in the study area (Table IX). The mCd in the study area
Ti -0.124 0.783 -0.090 -0.356 -0.193
follow the order: KK02 > BB03 > BU02 > KK01 > KK05 >
KK04 > BB05 > BU02 > BB02 > KG01 > BB01 > BB04 >
Eigenvalue 6.793 3.578 2.227 1.963 1.484
KG02 > KK03 with values comprised between 1.58 to 5.56.
Varibility (%) 20.31 15.994 10.422 11.814 21.542 Sample BB04, KK02 and KK03 are in the category of low
Cumulative % 20.316 36.310 46.732 58.546 80.089 contamination (mCd<1.5 - 2) accounting for 21.42% of the

www.rsisinternational.org Page 69
International Journal of Research and Innovation in Applied Science (IJRIAS) |Volume VII, Issue XI, November 2022|ISSN 2454-6194
study area. Sample BB01, BB02, BB05, BU01, BU02, KG01, industrial effluents, untreated sewage, municipal waste, storm
KK01 and KK04 are moderately contaminated (mCd = 2 - 4) water runoff with road site deposits, and vehicle of the studied
accounting for 57.14% of the study area and sample BB03 and area [5], [13], [12].
KK02 are classified as highly contaminated (mCd = 4 - 8)
The Geo-accumulation index (Igeo) is introduced by
accounting for 14.28% of the study area, indicating serious
[49] to assess the level of metal accumulation in the soils and
anthropogenic input of heavy metals in stream sediments of the
have been used by several researchers for various studies [44].
study area. This study suggests that proper focus should be
It is mathematically expressed as follows:
taken on monitoring the point sources of metals entering the
stream bed from nearby villages, and reduction of urban 𝐶𝑛
Igeo = log2
domestic sewage discharge, municipal waste and industrial 1.5×𝐵𝑛

effluent. Where, Cn = measured concentration of metal ion in sample;

Bn = background concentration value for metals [39] and 1.5
2) Pollution Load Index (PLI), Geo-accumulation Index (Igeo)
represents the background matrix correction factor due to
and Enrichment Factors (EF)
lithogenic effects. [49] classified Igeo into five groups as
The Pollution Load Index (PLI) is used to comprehensively follows: Igeo < 0 = practically unpolluted, 0 < Igeo < 1 =
assess the pollution effect of study metals. It is widely used to unpolluted to moderately polluted, 1 < Igeo < 2 = moderately
assess the degree of heavy metal pollution in sediments [45]. polluted, 2 < Igeo < 3 = moderately to strongly polluted, 3 <
PLI is given according to [46] by the relation: Igeo < 4 = strongly polluted, 4 < Igeo < 5 = strongly to
extremely polluted and Igeo > 5 = extremely polluted.
PLI = (CF1 × CF2 ×.....× CFn)1/n
According to [49] classification, Ba, Co, Cr, Ga, Hg, Ni, Sc, V,
Where n is the number of metals (18 in the present study),
Zn and Al are in the class of practically unpolluted (Igeo < 0)
CF is the contamination factor. If PLI > 1, the sediments are
in the study area (Table V; Fig. 4). Unpolluted to moderately
polluted, if PLI < 1, there is no pollution and the PLI = 0
polluted (Igeo = 0 - 1) refers to Mn, Mo, Pb (BB05, KK02 and
indicates perfection [47], [48].
KK04), Th and Fe. La and Ti are moderately polluted (Igeo =
PLIs calculated for each sample are shown in Table X. 1 - 2) in the study area. Moderately to strongly polluted (Igeo =
The PLI follow the trend: KK01 > BB05 > KK02 > BB02 2 - 3) is attributed to Ti (KK01, KK03, KG02, BUO2, BB03
>BU02 >BB01 > BB03 > KG02 >KK04 > KG01 > BB04 > and BB05), strongly polluted (Igeo = 3 - 4) for La (BU01,
BU01 > KK03 > KK05 in the study area. The PLI across the KK01, KK04 and KK05) and Th (BB03, BU01, KK01, KK02,
study area varies from 0.78 in sample KK05 to 1.60 in sample KK04 and KK05) while strongly to extremely polluted (Igeo =
KK01. Sample BU01, KK03 and KK05 with PLI values 0.86, 4 - 5) in La (BB03 and KK02). Moderately to extremely
0.85 and 0.78 are less than 1 indicating low pollution within polluted in Mn, Mo, Pb, Th, Fe, Ti and La in the study area is
this sampling sites accounting for 21.43% of the study area, due to anthropogenic activities such as, grazing, urbanization,
while the rest of the sampling sites had PLI values greater than pigment, glazes, industrialization, indiscriminate dumped of
one (PLI >1) indicating that the study area is polluted, household effluents in streams, assorted electronic waste
suggesting inputs from anthropogenic sources. The high level deposition, municipal waste, application of phosphate
of PLI values observed eleven sampling sites accounting for fertilizers, animal excreta, and sewage sludge discharge in
78.57% of the study area might be due to the effects of the various streams and rivers.
various industrial and human activities such as discharge of
Table XI. Contamination factor, Degree of contamination, Modified degree of contamination and pollution load index of heavy metals in stream sediments of
Bambui area

Elements Ba Co Cr Cu Ga Hg Mn Mo Ni Pb Sc Th V Zn La Al Fe Ti Cd mCd PLI

BB01 0.29 1.17 2.65 0.38 0.57 0.40 2.79 3.18 1.38 1.99 0.28 2.90 0.87 1.81 7.45 0.13 2.54 5.40 36.19 2.01 1.21
BB02 0.49 1.28 3.25 0.39 0.63 0.80 3.29 7.73 1.44 2.07 0.42 2.39 0.91 2.19 4.81 0.17 2.97 5.20 40.44 2.25 1.45
BB03 0.06 1.79 1.15 0.39 0.23 0.40 3.05 2.00 0.72 1.84 0.43 19.05 0.90 0.88 32.58 0.09 2.17 8.12 75.83 4.21 1.14
BB04 0.14 1.18 5.58 1.00 0.40 0.40 1.63 1.55 0.95 1.36 0.37 1.99 0.87 1.18 4.06 0.12 2.07 6.52 31.37 1.74 1.05
BB05 0.16 2.28 6.40 0.49 0.63 0.20 1.69 3.64 3.11 3.18 0.54 7.52 1.62 1.46 8.61 0.21 2.69 6.52 50.96 2.83 1.55
BU01 0.04 0.95 1.73 0.28 0.23 0.20 1.69 0.82 0.69 0.96 0.34 19.05 0.88 0.73 23.84 0.07 1.98 5.88 60.35 3.35 0.86
BU02 0.07 2.12 6.45 0.63 0.80 0.40 1.81 7.73 2.43 0.55 0.48 5.00 1.52 1.88 2.84 0.28 3.74 8.64 47.34 2.63 1.43
KG01 0.08 1.60 5.62 0.38 0.74 0.20 1.20 3.09 1.42 0.62 0.21 5.29 1.14 1.45 6.35 0.27 1.70 5.52 36.87 2.05 1.08
KG02 0.27 0.98 2.08 0.79 0.51 0.60 2.49 3.64 1.00 0.66 0.42 2.26 0.75 1.63 2.48 0.22 2.24 6.16 29.18 1.62 1.11
KK01 0.17 1.62 1.33 6.00 0.74 0.40 2.46 4.00 0.67 2.53 0.30 12.00 1.48 1.85 12.68 0.14 3.63 6.64 58.64 3.26 1.60
KK02 0.08 0.82 2.04 0.52 0.51 1.40 1.94 4.73 0.56 3.24 0.51 19.05 0.74 1.49 57.42 0.15 2.09 2.81 100.10 5.56 1.41

www.rsisinternational.org Page 70
International Journal of Research and Innovation in Applied Science (IJRIAS) |Volume VII, Issue XI, November 2022|ISSN 2454-6194
KK03 0.07 1.79 0.86 0.36 0.46 0.60 1.90 1.45 0.80 0.39 0.30 1.79 1.51 1.45 2.74 0.07 3.17 8.80 28.50 1.58 0.85
KK04 0.03 1.08 1.60 4.39 0.34 0.40 0.89 2.36 0.52 3.71 0.22 13.33 1.08 0.87 12.65 0.09 2.09 7.24 52.89 2.94 1.10
KK05 0.05 0.69 0.95 0.26 0.17 0.40 1.73 1.00 0.43 0.88 0.28 19.05 0.61 0.61 20.94 0.07 1.79 5.68 55.58 3.09 0.78

The Enrichment Factors (EF) of heavy metals are commonly

used to assess anthropogenic contamination. EF values were
calculated using the following equation [44].
(me/Fe2O3)sample
EF =
(me/Fe2O3)samplebackground
where (me/Fe2O3) sample) is the measured ratio of metal
between iron oxide and (me/Fe2O3) sample background)
unpolluted background ratio of metal of iron oxide. Five
contamination categories were defined on the basis of the EF,
which are EF < 1, EF < 2, 2 < EF <5, 5 < EF < 20 and EF = 20
< EF < 40 corresponding to no enrichment, minimal
enrichment, moderate enrichment, significant and severe Fig. 4 Geo- accumulation index of heavy metals in stream sediments of
Bambui area.
enrichment [44]. The EFs of heavy metals in the study area are
shown in (Table VXII, Fig. 5). Lanthanum has the highest EF
value (27.46) in sample KK02 (Kedjom-Keku), Th (10.64) is
the second most abundant enriched element in sample KK05
and Ti (3.74) is the third most enriched element in sample BB03
(Bambili) in the study area. Ba, Co, Cr, Cu, Ga, Hg, Ni, Pb, Sc,
V, Zn, Al and Fe show no enrichment (EF < 1) in all samples
of the study area, except Cr (BB01 - BB05), Cu (KK01 and
KK05), Mn, Mo, Pb (BB04, KK02 and KK04), Th and Ti
(KK03) respectively, which was in the category of minimal
enrichment (EF = 1 - 2). Cr in sample KG01 (Kedjom-
Ketinguh), La in sample (BB01, BB05, KG01, BU01, BB03, Fig. 5 Enrichment factor of heavy metals in stream sediments of Bambui area
KK01 and KK05), Ti in sample (BB03, BB04, KG01, KK04 3) Potential Ecological Risk Factor (Er), Potential Ecological
and KK05) and Th in sample (BB03, KG01, KK04 and KK05) Risk Index (PERI) and Anthropogenic Metal Input.
are generally moderately to significant enriched (EF = 2 - 20).
La in sample KK02 (Kedjom-Keku) and Th in sample KK05are The potential ecological risk factor (Er) is an index that
severely enriched (EF = 20 to more than 40). Moderately to quantitatively expresses the potential ecological risk associated
severely enrichment of Cr, La, Th and Ti in the study area with a given single contaminant [45]. It is calculated as follows:
indicates its origin from the anthropogenic sources such as Er = Tri × CFi
agricultural practices, domestic waste water, effluents from
sewage sludge, garbage, assorted electronic wastes, household Where Tri is toxic response factor of a metal (Pb = 5, Zn = 1,
wastes, urban and agricultural runoff, fossils fuel, atmospheric Cd = 30, Cr = 2, Ni = 5, Hg = 40, Cr = 2, Cd = 30, As = 10 and
deposition, animal excreta, pesticides, fungicides, herbicides Cu = 5).CFi is the contamination factor. Five terminologies are
and municipal wastes dumped in to streams and rivers of the used to define Er based on [46]. These are: Er< 40, low
study area. This finding suggests that stream sediments in the potential ecological risk; 40 <Er<80, moderate potential
study area are more likely to contain reasonable amount of ecological risk; 80 <Er< 160, considerable potential ecological
heavy metals. risk; 160 <Er< 320, high potential ecological risk and Er≥ 320,
very high ecological risk.
Table V. Geo- accumulation index of heavy metals in stream sediments of Bambui area

Element Ba Co Cr Cu Ga Hg Mn Mo Ni Pb Sc Th V Zn La Al Fe Ti
BB01 -2.36 -0.35 0.82 -1.98 -1.39 -1.9 0.89 1.08 -0.11 0.41 -2.42 0.95 -0.79 0.26 2.31 -3.51 0.75 1.84
BB02 -1.62 -0.22 1.11 -1.94 -1.25 -0.9 1.13 2.36 -0.05 0.46 -1.83 0.67 -0.72 0.54 1.68 -3.1 0.98 1.79
BB03 -4.7 0.25 -0.81 -1.95 -2.71 -1.9 1.02 0.41 -1.06 0.29 -1.8 3.66 -0.74 -0.76 4.44 -4.1 0.53 2.43
BB04 -3.45 -0.34 1.89 -0.58 -1.9 -1.9 0.11 0.04 -0.66 -0.13 -2.01 0.4 -0.79 -0.34 1.43 -3.61 0.46 2.1
BB05 -3.23 0.6 2.09 -1.61 -1.25 -2.9 0.17 1.27 1.05 1.08 -1.48 2.32 0.11 -0.03 2.52 -2.81 0.84 2.12
BU01 -5.08 -0.65 0.24 -2.42 -2.71 -2.9 0.17 -0.87 -1.12 -0.64 -2.16 3.66 -0.77 -1.03 3.99 -4.52 0.4 1.97

www.rsisinternational.org Page 71
International Journal of Research and Innovation in Applied Science (IJRIAS) |Volume VII, Issue XI, November 2022|ISSN 2454-6194
BU02 -4.38 0.5 2.1 -1.26 -0.9 -1.9 0.26 2.36 0.69 -1.45 -1.64 1.73 0.01 0.32 0.92 -2.42 1.31 2.52
KG01 -4.25 0.08 1.9 -1.97 -1.01 -2.9 -0.31 1.04 -0.08 -1.28 -2.85 1.81 -0.39 -0.05 2.08 -2.49 0.17 1.88
KG02 -2.49 -0.61 0.46 -0.93 -1.54 -1.32 0.73 1.27 -0.57 -1.17 -1.83 0.59 -0.99 0.11 0.72 -2.74 0.57 2.03
KK01 -3.15 0.11 -0.17 2 -1.01 -1.9 0.71 1.41 -1.15 0.75 -2.32 3 -0.01 0.3 3.07 -3.41 1.27 2.14
KK02 -4.16 -0.88 0.44 -1.53 -1.54 -0.1 0.36 1.65 -1.41 1.11 -1.56 3.66 -1.01 0 5.25 -3.31 0.47 0.9
KK03 -4.52 0.25 -0.8 -2.04 -1.71 -1.32 0.33 -0.04 -0.9 -1.95 -2.32 0.25 0 -0.05 0.87 -4.39 1.08 2.55
KK04 -5.78 -0.48 0.09 1.55 -2.12 -1.9 -0.75 0.65 -1.53 1.3 -2.76 3.15 -0.47 -0.79 3.07 -4.12 0.49 2.27
KK05 -5.05 -1.12 -0.66 -2.5 -3.12 -1.9 0.2 -0.58 -1.8 -0.76 -2.42 3.66 -1.3 -1.29 3.8 -4.34 0.25 1.92

Table VXIII. Enrichment factor of heavy metals in stream sediments of Bambui area

Element Ba Co Cr Cu Ga Hg Mn Mo Ni Pb Sc Th V Zn La Al Fe Ti
BB01 0.11 0.46 1.04 0.15 0.23 0.16 1.10 1.25 0.54 0.79 0.11 1.14 0.34 0.71 2.94 0.05 0.40 2.13
BB02 0.16 0.43 1.09 0.13 0.21 0.27 1.11 2.60 0.49 0.70 0.14 0.80 0.30 0.74 1.62 0.06 0.33 1.75
BB03 0.03 0.83 0.53 0.18 0.11 0.18 1.40 0.92 0.33 0.85 0.20 8.78 0.41 0.41 15.01 0.04 0.89 3.74
BB04 0.07 0.57 2.69 0.48 0.19 0.19 0.78 0.75 0.46 0.66 0.18 0.96 0.42 0.57 1.96 0.06 0.65 3.14
BB05 0.06 0.85 2.38 0.18 0.23 0.07 0.63 1.35 1.15 1.18 0.20 2.79 0.60 0.54 3.20 0.08 0.12 2.42
BU01 0.02 0.48 0.87 0.14 0.12 0.10 0.85 0.41 0.35 0.48 0.17 9.61 0.44 0.37 12.02 0.03 0.34 2.97
BU02 0.02 0.57 1.72 0.17 0.21 0.11 0.48 2.07 0.65 0.15 0.13 1.34 0.41 0.50 0.76 0.07 0.44 2.31
KG01 0.05 0.94 3.31 0.23 0.44 0.12 0.71 1.82 0.84 0.36 0.12 3.11 0.67 0.85 3.75 0.16 0.46 3.25
KG02 0.12 0.44 0.93 0.35 0.23 0.27 1.11 1.62 0.45 0.30 0.19 1.01 0.34 0.73 1.11 0.10 0.27 2.75
KK01 0.05 0.45 0.37 1.65 0.20 0.11 0.68 1.10 0.19 0.70 0.08 3.31 0.41 0.51 3.50 0.04 0.15 1.83
KK02 0.04 0.39 0.98 0.25 0.25 0.67 0.93 2.26 0.27 1.55 0.24 9.11 0.36 0.71 27.46 0.07 0.56 1.34
KK03 0.02 0.56 0.27 0.11 0.14 0.19 0.60 0.46 0.25 0.12 0.09 0.56 0.47 0.46 0.86 0.02 0.78 2.77
KK04 0.01 0.51 0.76 2.10 0.16 0.19 0.43 1.13 0.25 1.78 0.11 6.38 0.52 0.41 6.05 0.04 0.98 3.46
KK05 0.03 0.38 0.53 0.15 0.10 0.22 0.97 0.56 0.24 0.49 0.16 10.64 0.34 0.34 11.69 0.04 0.60 3.17

The potential ecological risk index (PERI) is the sum of all the between 30.02 to 90.69, indicating low potential ecological risk
ecological risk factors of the metals under study, taking into index within the study area.
account the cumulative effects of the metals under study [45].
The anthropogenic metal input in tailings is derived from the
It is calculated thus:
following formula:
PERI = (Er1 + Er2 + Er3 … + Ern) 𝑋−𝑋1
Anthropogenic Input = × 100
𝑋1
Where Er is the ecological risk factor and n is the number of
elements studied. The following terminologies have been used Where, x represents the average concentration of the metal in
for the PERI: PEERI < 150, low ecological risk; 150 <PERI < tailings, and x1 is the average background concentration of the
300, moderate ecological risk; 300 < PERI < 600, considerable metal. When anthropogenic input is <1, men they is not
ecological risk and PERI ≥ 600, very high ecological risk. The contribution from human activities and when anthropogenic
Er and the PERI in the study area are presented in Table XIVI input is >1, means it is from human activities within the
and Fig 6. The Er in Bambui area showed low potential ecosystem. The results of anthropogenic metal input show a
ecological risk factor (Er< 40) for Co, Cr, Cu, Hg, Mn, Ni, Pb, variation from -1.73 for Co in sample KG01 (Kedjom-
V and Zn. Hg with value 56 in sample KK02 (Kedjom-Keku) Ketinguh) to 5641.94 for La in sample KK02 (Kedjom-Keku)
have moderate Er (Er 40-80). Hence sediments of the presents in the study area. Anthropogenic contribution to the study area
study show low PERI. Moderate Er refers to Hg (KK02) in the refers to Co, Cr, Mn, Mo, Ni, Pb, Th, Zn, La, Fe and Ti (Table
study area. This could be attributed to agricultural practices, XVII; Fig 7). This may be attributed to the leaching of metals
pesticides, fungicides, agricultural and domestic runoff and from garbage, solid waste heaps, domestic and agricultural
municipal wastes deposited into streams. The PERI across the runoff, municipal wastes, phosphate fertilizers, fungicides,
study area varies between 30.01 in sample BU01 (Bambui) to pesticides, assorted electronic wastes water irrigation in the
90.69 in sample KK02 (Kedjom-Keku) within the study area study area [50], [4], [41], [5], [13], [12]. These anthropogenic
(Table XVI; Fig 6). The PERI is in the order: KK02 > KK01 > activities can generate heavy metals in stream sediment and
BB05 > BB02 > KK04 > BU02 > BB04 > BB01 > KG02 > water bodies that pollute the aquatic ecosystem within the study
KK03 > BB03 > KG01 > KK05 > BU01 and the values vary area. Therefore, proper focus should be taken to monitor the

www.rsisinternational.org Page 72
International Journal of Research and Innovation in Applied Science (IJRIAS) |Volume VII, Issue XI, November 2022|ISSN 2454-6194
sources of metals entering the river and stream bodies from discharge, used of phosphate fertilizers, industrial effluent and
nearby towns and villages and reduction of urban domestic municipal waste.
Table XVIIII. Potential Ecological Risk Factor And Pollution Ecological Risk Index Of Heavy Metals In Stream Sediments Of Bambui Area

Samples Co Cr Cu Hg Mn Ni Pb V Zn PERI
BB01 5.87 5.30 1.89 16.00 2.79 6.91 9.97 1.73 1.81 52.27
BB02 6.42 6.50 1.95 32.00 3.29 7.22 10.35 1.81 2.19 71.74
BB03 8.96 2.30 1.93 16.00 3.05 3.60 9.18 1.79 0.88 47.68
BB04 5.90 11.15 5.02 16.00 1.63 4.74 6.82 1.73 1.18 54.17
BB05 11.42 12.80 2.45 8.00 1.69 15.53 15.91 3.24 1.46 72.50
BU01 4.77 3.46 1.39 8.00 1.69 3.44 4.79 1.75 0.73 30.02
BU02 10.61 12.89 3.13 16.00 1.81 12.13 2.74 3.03 1.88 64.20
KG01 7.98 11.24 1.91 8.00 1.20 7.09 3.09 2.29 1.45 44.24
KG02 4.91 4.15 3.93 24.00 2.49 5.02 3.32 1.51 1.63 50.96
KK01 8.09 2.65 30.00 16.00 2.46 3.36 12.65 2.97 1.85 80.04
KK02 4.08 4.09 2.59 56.00 1.94 2.82 16.21 1.48 1.49 90.69
KK03 8.93 1.72 1.82 24.00 1.90 4.00 1.94 3.01 1.45 48.77
KK04 5.38 3.20 21.96 16.00 0.89 2.60 18.56 2.16 0.87 71.61
KK05 3.44 1.89 1.32 16.00 1.73 2.15 4.41 1.22 0.61 32.77

Fig. 6 Er and PERI of heavy metals in stream sediments of Bambui area

Table XVIIIIII. Anthropogenic Metal Input Of Heavy Metals In Stream Sediments Of Bambui Area.

Sample Ba Co Cr Cu Ga Hg Mn Mo Ni Pb Sc Th V Zn La Al Fe Ti
BB01 -70.83 17.34 165.22 -62.14 -42.86 -60.00 178.71 218.18 38.30 99.41 -72.14 190.48 -13.40 80.60 645.16 -86.87 153.82 440.00
BB02 -51.28 28.32 225.00 -61.07 -37.14 -20.00 229.03 672.73 44.47 107.06 -57.86 139.05 -9.28 119.40 380.65 -82.58 197.45 420.00
BB03 -94.26 79.19 15.22 -61.43 -77.14 -60.00 204.52 100.00 -28.09 83.53 -57.14 1804.76 -10.31 -11.94 3158.06 -91.29 117.00 712.00
BB04 -86.36 17.92 457.61 0.36 -60.00 -60.00 62.58 54.55 -5.11 36.47 -62.86 99.05 -13.40 17.91 306.45 -87.73 107.37 552.00
BB05 -84.02 128.32 540.22 -51.07 -37.14 -80.00 69.03 263.64 210.64 218.24 -46.43 652.38 61.86 46.27 761.29 -78.65 169.41 552.00
BU01 -95.59 -4.62 72.83 -72.14 -77.14 -80.00 69.03 -18.18 -31.28 -4.12 -66.43 1804.76 -12.37 -26.87 2283.87 -93.50 98.30 488.00
BU02 -92.82 112.14 544.57 -37.50 -20.00 -60.00 80.65 672.73 142.55 -45.29 -52.14 400.00 51.55 88.06 183.87 -72.02 273.94 764.00
KG01 -92.16 59.54 461.96 -61.79 -25.71 -80.00 20.26 209.09 41.70 -38.24 -79.29 428.57 14.43 44.78 535.48 -73.37 69.69 452.00
KG02 -73.40 -1.73 107.61 -21.43 -48.57 -40.00 149.03 263.64 0.43 -33.53 -57.86 125.71 -24.74 62.69 148.39 -77.67 123.80 516.00
KK01 -83.17 61.85 32.61 500.00 -25.71 -60.00 146.45 300.00 -32.77 152.94 -70.00 1100.00 48.45 85.07 1167.74 -85.89 262.61 564.00
KK02 -91.63 -18.50 104.35 -48.21 -48.57 40.00 93.55 372.73 -43.62 224.12 -49.29 1804.76 -25.77 49.25 5641.94 -84.91 109.07 180.80
KK03 -93.46 78.61 -14.13 -63.57 -54.29 -40.00 89.68 45.45 -20.00 -61.18 -70.00 79.05 50.52 44.78 174.19 -92.88 217.28 780.00
KK04 -97.28 7.51 59.78 339.29 -65.71 -60.00 -10.97 136.36 -48.09 271.18 -77.86 1233.33 8.25 -13.43 1164.52 -91.41 109.07 624.00
KK05 -95.48 -31.21 -5.43 -73.57 -82.86 -60.00 72.90 0.00 -57.02 -11.76 -72.14 1804.76 -39.18 -38.81 1993.55 -92.64 79.04 468.00

www.rsisinternational.org Page 73
International Journal of Research and Innovation in Applied Science (IJRIAS) |Volume VII, Issue XI, November 2022|ISSN 2454-6194
[3] Fernandes, C. Fontaínhas-Fernandes, A. Cabral, D. Salgado, MA.
(2008). Heavy metals in water, sediment and tissues of Liza saliens
from Esmoriz-Paramos lagoon, Portugal. Environ Monit Assess
136:267–275.
[4] Islam, MK. (2015). Heavy metal pollution in surface water and
sediment: a preliminary assessment of an urban river in a
developing country. Ecol Ind 48:282–291.
[5] EkoaBessa, AZ. El-Amier, YA. Doumo, EPE. Ngueutchoua, G.
(2018). Assessment of sediments pollution by trace metals in the
Moloundou swamp, southeast Cameroon. Annual Research &
Review in Biology.;1-13.
[6] Harikumar, PS. Nasir, UP. Rahman, MPM. (2009). Distribution of
Heavy Metals in the Core Sediments of a Tropical Wetland System.
International Journal of Environmental Science and Technology, 6,
225-232. http://dx.doi.org/10.1007/BF03327626
[7] Rahman, MS and Islam MR. (2010). Adsorption of Cd (II) ions
fromsynthetic waste water using maple sawdust. Energy Sour Part
A: Recovery Util Environ Eff. 32(3):222– 231.
[8] Moiseenko, TI. Dinu, MI. Gashkina, NA. Kremleva, TA. (2019).
Aquatic environment and anthropogenic factor effects on
distribution of trace elements in surface waters of European Russia
and Western Siberia. Environmental Research Letters 14, 065010.
Fig. 7 Histogram of anthropogenic input of heavy metals in stream sediments [9] Larsen, WE. Gilley, JR. Linden, DR (1975). Consequences of
of Bambui area. Waste Disposal on Land. Soil Water Conserve, 2, 68.
[10] He, Z. Li, F. Dominech, S. Wen, X. Yang, S. (2019). Heavy metals
V. CONCLUSIONS of surface sediments in the Changjiang (Yangtze River) Estuary:
distribution, speciation and environmental risks. Journal of
This study aimed at investigating the contamination and Geochemical Exploration, vol. 198, pp. 18–28.
risk pose by heavy metals in stream sediments of the study area. [11] Asongwe, GA and Yerima, BPK. (2016). Heavy Metal Status in
Urban and Peri-Urban Wetland Soils under Vegetable Cultivation
The mean concentrations of Co, Cr, Cu, Mn, Ni, Pb, Th, V, Zn, in the Bamenda Municipality Cameroon. Greener Journal of Soil
La, Fe and Ti are higher than the upper continental crust values. Science and Plant Nutrition, 3(1):001-013.
The strong positive correlations between some elements signify [12] Noa Tang, SD. Messina, TR. Onana, VL. EkoaBessa, AZ. Ndjigui,
that these elements come from the same activities or geologic PD. (2021). Risk assessment of trace metals in Mefou River
sediments, West-Africa. Heliyon.
unit. The correlation of Co/Cr, Ni, V, Cr/Ni, Mo/Ni, Zn, Ni/V; [13] Mandeng, EPB. Bidjeck, LMB. EkoaBessa, AZ. Ntomb, YD.
is a reflection of their transition metal relationship. The PLI and Wadjou, JW. Doumo, EPE. Bitom, LD. (2019). Contamination and
Igeo showed that stream sediments are highly polluted by La, risk assessment of heavy metals, and uranium of sediments in two
Th and Ti and moderately polluted by Mn, Mo, Pb and Fe. Er watersheds in AbieteToko gold district, Southern Cameroon.
Heliyon 5: e02591.
and PERI have low values in stream sediments of the study area [14] Njoyim, EBT. Donfack, A. Mofor, NA. Tchiaze, FG. (2016).
indicating low potential ecological risk index. Anthropogenic Phytoextraction of cadmium by beans and sweet potatoes from
input in the study area refers to Co, Cr, Mn, Mo, Ni, Pb, Th, soils. International Journal of Research and Reviews in Applied
Zn, La, Fe and Ti. This is due to leaching of metals from Sciences, 28 (2): 65-70.
[15] Toteu, SF. Penaye, J. PoudjomDjomani, Y. (2004). Geodynamic
garbage, solid waste heaps, animal and human excreta, Evolution of the Pan-African Belt in Central Africa with Special
unplanned urbanization and haphazard industrialization, Reference to Cameroon. Canadian Journal of Earth Sciences, 41,
domestic and agricultural runoff, household effluents, 7385.
municipal wastes, phosphate fertilizers, fungicides, pesticides, [16] Mbassa, BJ. Njonfang, E. Benoit, M. Kamgang, P. Gregroire, M.
Duchene, S. (2012). Mineralogy, geochemistry and petrogenesis of
sewage sludge, fossils fuel, atmospheric deposition, assorted the recent magmatic formations from Mbengwi, a continental sector
electronic wastes, plastics disposal and waste water irrigation. of the Cameroon Volcanic Line (CVL), Central Africa. Mineralogy
and Petrology, 106, 217–242.
ACKNOWLEDGMENT [17] Nzenti, JP. Barbey, P. Macaudiere, J. Soba, D. (1988). Origin and
evolution of the late Precambrian high-grade Yaound´e gneisses
This study is a part of the first author MSc thesis research (Cameroon). Precambr. Res. 38, 91–109.
work at the University of Buea. The authors extend their sincere https://doi.org/10.1016/0301-9268(88)90086-1
gratitude to editors and reviewers of this manuscript and their [18] Ngako, V. Affaton, P. Njonfang, E. (2008). Pan-African tectonics
proposals and comments important to improving the quality of in northwestern Cameroon: implication for the history of western
Gondwana. Gondwana Res. 14 (3), 509–522.
this paper. [19] Li, XH. Chen, Y. Tchouankou´e JP. Liu, CZ. Li. J. Ling, XX. Tang,
GQ. Liu, Y. (2017). Improving geochronological framework of the
REFERENCES Pan-African orogeny in Cameroon: New SIMS zircon and monazite
[1] Ferreira da Silva, M. Fontes, MPF. Pacheco, AA. Lima, HN. Santos, U-Pb age constraints. Precambr. Res. 294, 307–321.
JZL. (2020). Risk assessment of trace elements pollution of Manaus [20] Bineli, BT. Bondje, L.M.N.B. Mvondo, H. Mama, LNY.
urban rivers. Science of The Total Environment 709, 134471. Molotouala, CA, McFarlane, C. (2020).Rutile LA-ICP-MS U-Pb
[2] Liu, S. Liu, Y. Yang, D. Li, C. Zhao, Y. Ma, H. Luo, X. Lu, S. geochronology and implications for tectono metamorphic evolution
(2020). Trace elements in shellfish from Shenzhen, China: in the Yaound´e Group of the Neoproterozoic Central African
Implication of coastal water pollution and human exposure. Orogeny. J. Afr. Earth Sc. 171, 103939.
Environmental Pollution 263, 114582. [21] Ganwa, AA. Klötzli, US. Hauzenberger, C. (2016). Evidence for
Archean inheritancein the pre-Panafrican crust of Central

www.rsisinternational.org Page 74
International Journal of Research and Innovation in Applied Science (IJRIAS) |Volume VII, Issue XI, November 2022|ISSN 2454-6194
Cameroon: Insight from zircon internal structure and LA-MC-ICP- continental lithospheres: Constraints, controversies and models,
MS U-Pb ages. J. Afr. Earth Sci. 120, 12–22. Earth Sci. Rev., 139, 168–194.
[22] Tchakounte, JN. Eglinger, A. Toteu, SF. Zeh, A. Nkoumbou, C. [35] Njilah, IK. Temdjim, R. Nzolang, C. Ghogomu, RT. Tchuitchou, C.
Mvondo-Ondoa, J. Penaye, J. Wit, M. Barbey, P. (2017). The Rose, HN. Ajonina, HN. (2007) Geochemistry of Tertiary-
Adamawa-Yad´ e domain, a piece of Archaean crust in the Quaternary lavas of Mt. Oku North-West Cameroon.
Neoproterozoic Central African Orogenic belt (Bafia area, RevistaFacultad de Ingenieria. 40:59-75.
Cameroon). Precambr. Res. 299, 210–229. [36] Bate Tibang, E. E., Suh, C. E., Cottle, J., Ateh, K. I., Tiabou, A. F.,
[23] Loose, D and Schenk, V. (2018). 2.09 Ga old eclogites in the Nche, L. A et al (2017). Petrology and geochronology of felsic
EburnianTransamazonian orogen of southern Cameroon: volcanics in the Sabga area (Bamenda Highlands): Implications for
significance for Palaeoproterozoic plate tectonics. Precambrian age variation along the Cameroon Volcanic Line. Journal of
Research 304 1-11. Geosciences, 62, 231–246.
[24] HamdjaNgoniri, A. Soh, TL. Ganno, S. Ngnotue, T. Chen, Z. Li H, [37] Kamgang, P. Njonfang, E. Chazot, G. Tchoua, F. (2007).
Ayonta KP, Nzenti JP (2021) Geochronology and petrogenesis of Géochimie et géochronologie des laves felsiques des Monts
the PanAfrican granitoids from Mbondo-Ngazi Tina in the Bamenda (Lignevolcanique du Cameroun). C.R.
Adamawa-Yade Domain, Central Cameroon. International Journal Geoscience.339.659-666.
of Earth Sciences, 110, 2221–2245. doi: 10.1007/s00531-021- [38] Gountie, DM. Nono, A. Njonfang, E. Kamgang, P. Zangmo, TG.
02071-3. Kagou, DA. Nkouathio, DG. (2009). Le volcanismeignimbritique
[25] Fozing, EM. Kw´ekam, M. Kou´emo, JT. Njanko, T. Njonfang, E. des montsBambouto et Bamenda (Ligne du Cameroun, Afrique
(2021). Kinematic analysis of the Dschang granitic pluton (West- Centrale): Signification dans la formation des caldeiras. Bulletin de
Cameroon): Implications to the PanAfrican deformation of the l’institutscientifique. 2011, n°33, P.1-15.
Central African fold belt in Cameroon during the postcollisional [39] Rudnick, RL. Gao, S. (2014). Composition of the continental crust.
history of western Gondwana. Precambr. Res. 359, 106231. Vol 3, pp. 1-64.
[26] Shellnutt, JG. Pham, NHT. Yeh, MW. Lee, TY. (2020). Two series [40] Singh, H. Singh, D. Singh, SK. Shukla, DN. (2017). Assessment of
of Ediacaran collision related granites in the Gu´era Massif, river water quality and ecological diversity through multivariate
SouthCentral Chad: Tectonomagmatic constraints on the terminal statistical techniques, and earth observation dataset of rivers
collision of the eastern Central African Orogenic Belt. Precambr. Ghaghara and Gandak, India. Int J River Basin Manag, pp 1–14.
Res. 347, 105823. doi:10.1080/15715124.2017.1300159.
[27] Toteu, SF. Penaye, J.PoudjomDjomani, Y. (2004).Geodynamic [41] Otari, M and Dabiri, R. (2015). Geochemical and environmental
Evolution of the Pan-African Belt in Central Africa with Special assessment of heavy metals in soils and sediments of Forumad
Reference to Cameroon. Canadian Journal of Earth Sciences, 41, Chromite mine, NE of Iran. J Min Environ, 6:251-261.
7385. [42] Kaiser HF. (1958). The varimax criterion for analytic rotation in
[28] BouyoHouketchang, M. Penaye, J. Njel, UO. Moussango, IAP. factor analysis. Psychometrika, 23:187-200.
Sep, NJP. Nyama, AB. Wassouo, WJ, Abate, EJM. Yaya, F. [43] Kamunda, C. Mathuthu, M. Madhuku, M. (2016). Health risk
Mahamat, A. Ye, H. Wu, F. (2016). Geochronological, assessment of heavy metals in soils from Witwatersrand gold
geochemical, and mineralogical constraints of emplacement depth mining basin, South Africa. Int J Environ Res Public Health
of TTG suite from the Sinassi Batholith in the central African Fold 13(7):663. https:// doi.org/10.3390/ijerp h1307 0663.
belt (CAFB) of northern Cameroon: implications for [44] Abhijit M. and Ramkrishna M. (2017). Geochemical contamination
tectonomagmatic evolution. J. Afr. Earth Sci. 116, 9–41. in the mine affected soil of RaniganjCoalfielde a river basin scale
[29] Castaing, C. Triboulet, C. Feybesse, JL. Chèvremont, P. (1994). assessment. Geosci Frontiers, 30:1-14.
Tectonometamorphic Evolution of Ghana, Togo, Benin in the Light [45] Hakanson L. (1980). An ecological risk index for aquatic pollution
of the Pan-African/Braziliano Orogeny. Tectonophysics, 218, 323- control. Sedimentology Approach Water Res 14(8):975–1001.
342. [46] Tomlinson, DL. Wilson, JG. Harris, CR. Jeffrey, DW. (1980).
[30] Caby, R. Sial, AN. Arthaud, M. Vauchez, A. (1991). Crustal Problems in the assessment of heavy-metal levels in estuaries and
evolution and the Brasiliano orogeny in northeast Brazil, in: the formation of a pollution index. HelgolMeeresunters, 33(1):566.
Dallmeyer and Lécorché (Ed), The west African orogens and https:// doi.org/10.1007/bf024 14780.
circum-Atlantic correlatives, Springer-Verlag, pp. 373-397. [47] Khan, MZH. Hasan, MR. Khan, M, Aktar, S. Fatema, K. (2017).
[31] Kamgang, P. Njonfang, E. Nono, A. GountieDedzo, M. Tchoua, Distribution of heavy metals in surface sediments of the Bay of
FM. (2010). Petrogenesis of a silicic magma system: geochemical Bengal Coast. J Toxicol, 2017: 9235764-9235764. DOI:
evidence from Bamenda Mountains, NW Cameroon, Cameroon 10.1155/2017/9235764.
Volcanic Line. J Afr Earth Sci 58: 285–304. [48] Tariq, J. Nasir, A. Azhar, M. (2018) .Heavy metals contamination
[32] Fitton, JG. (1987). The Cameroon line, West Africa: A comparison and ecological risk assessment in surface sediments of Namal Lake,
between oceanic and continental alkaline volcanism, in Alkaline Pakistan. Pol. J. Environ. Stud, 27:681-688. DOI:
Igneou Rocks, edited by J. G. Fitton and B. Upton, pp. 273–291, 10.15244/pjoes/75815.
Geol. Soc. of London, London, U. K. [49] Müller, G. (1981). The heavy metal content of the sediments of the
[33] Ngounouno, IB. Deruelle, Demaiffe D. (2000). Petrology of the Neckar and its tributaries: An inventory. Chemische Zeitung,
bimodal Cenozoic volcanism of the Kapsiki plateau (northernmost 105:157-164.
Cameroon, Central Africa), J. Volcanol. Geotherm. Res., 102, 21– [50] Bai, J. Cui, B. Chen, B. Zhang, K. Deng, W. Gao, H. Xiao, R.
44. (2011). Spatial distribution and ecological risk assessment of heavy
[34] Njome MS, MJ de Wit (2014). The Cameroon Line: Analysis of an metals in surface sediments from a typical plateau lake wetland,
intraplate magmatic province transecting both oceanic and China. Ecol. Model., 222 (2): 301-306

www.rsisinternational.org Page 75