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Journal of Environment Pollution and Human Health, 2023, Vol. 11, No. 3, 51-59
Available online at http://pubs.sciepub.com/jephh/11/3/1
Published by Science and Education Publishing
DOI:10.12691/jephh-11-3-1
Pollution and Ecological Risk Assessment of
Heavy Metals in the Agricultural Soils Around
a Gold Mine in BISSA Village, Burkina-Faso
Yalgado Zakaria Sawadogo1, Telado Luc Bambara1,2,*,
Inoussa Zongo1,3, Karim Kaboré1,4, François Zougmoré1
1Laboratory of Materials and Environment, Physics Department, University Joseph Ki-Zerbo, Ouagadougou, Burkina Faso
2Institute of Sciences and Technology, Ecole Normale Supérieure, Burkina Faso
3National Center of Scientific Research and Technology, Ouagadougou, Burkina Faso
4Physics Department, Virtual University of Burkina Faso, Ouagadougou, Burkina Faso.
*Corresponding author:
Received August 30, 2023; Revised September 30, 2023; Accepted October 07, 2023
Abstract: Mining is considered to be one of the most important sources of environmental pollution when it comes
to heavy metals. Mining causes the release of large quantities of mercury, arsenic and other elements into the
environment and naturally poses a serious threat to the environment. The objective of this study is to assess heavy
metal contamination and the ecological risks of agricultural soils around a gold mine, mainly in Bissa, a village in
the commune of SABCÉ. Twelve (12) soils samples were collected on the surface, in depths of 0 to 15cm. Seven
heavy metals (Cr, Ni, Cu, Zn, As, Hg and Pb) were analyzed by atomic absorption spectrometry. From the
concentrations of these metals and on the basis of the geochemical background described by Wedepohl and Turekian
(1961); the geo-accumulation index (Igeo), the degree of contamination (DC), the pollution load index (PLI), the
risk factor (RF) and the potential ecological risk index (RI) were evaluated. The results revealed that the average
concentrations of metals obtained were classified in descending order Cr>Zn>Cu>As>Ni>Pb>Hg with the
respective values 102.3mg/kg, 58.513mg/kg, 57.133mg/kg, 49.73mg/kg, 38.873mg/kg, 17.943mg/kg and 3.83mg/kg.
Mean concentrations of Cr, Cu, As and Hg exceeded their respective geochemical background values, and only
arsenic was above the WHO/FAO standard. The geo-accumulation index showed that 75% of the soil samples were
heavily polluted with mercury (Hg). The potential ecological risk index showed that 75% of the soil samples
presented a considerable ecological risk, and 8.33% presented a very high ecological risk.
Keywords: soil, heavy metals, pollution, ecological risk, gold mine
Cite This Article: Yalgado Zakaria Sawadogo, Telado Luc Bambara, Inoussa Zongo, Karim Kaboré, and
François Zougmoré, “Pollution and Ecological Risk Assessment of Heavy Metals in the agricultural soils around
a gold mine in BISSA village, Burkina-Faso.” Journal of Environment Pollution and Human Health, vol. 11, no. 3
(2023): 51-59. doi: 10.12691/jephh-11-3-1.
1. Introduction
Rapid urbanization, industrial progress, agricultural
practices, mining activities are the source of the emission
of pollutants such as heavy metals and persistent organic
pollutants in the environment [1,2,3]. Heavy metals are
one of the major pollutants due to these entropic activities
of man [4]. Ubiquitous, persistent and non-biodegradable,
heavy metals easily accumulate in soil, plants and in all
living organisms [5,6]. Accumulated in the soil, through
their toxicity, they modify its biological functions, poison
the fauna and flora and the waters, thus constituting a
potential danger to human health via the food chain
[7,8,9,10].
Among the entropic activities of man, mining is one of
the major sources of environmental contamination by
heavy metals [11]. This is why in recent decades, around
the world, several studies have focused on the assessment
of heavy metals in mining soils or near mining sites.
Relatively high quantities of heavy metals such as
cadmium, chromium, nickel, copper, zinc, arsenic,
mercury or lead have been detected in the soils of several
countries in Africa and the rest of the world: Ivory Coast
[12], Nigeria [13,14], Cameroon [15], South Africa [16],
China [17,18], Spain [19], Iran [20].
Burkina Faso is not exempt from this mining pollution
because the country has experienced a mining boom since
2009 and gold has become its first export product [21,22].
Today, the country is the fifth largest gold producer in
Africa [23]. Consisting mainly of gold mining, the mining
sector represents 43% of exports and globally employs 1.3
million people. This activity is distinguished by a
coexistence between industrial mines and artisanal mines.
The industrial sector had 26 mines, 16 of which operated
52 Journal of Environment Pollution and Human Health
for a production of 50.3 tonnes in 2019. The artisanal
sector, poorly known, had around 800 sites and produced
9.5 tonnes per year [24]. Distributed throughout the
national territory, the Center-North has five (5) industrial
mines, including one in Sabcé where the populations live
mainly from agriculture. In view of the potential emission
of heavy metals by mining activities and its corollary of
associated ecological and health risk, it is therefore
necessary to identify and know the level of contamination
of these metals in the soils around the sites. mining. This
is why our study aims to assess the spatial, quantitative
and qualitative distribution of heavy metals in the
agricultural areas surrounding the mine in order to assess
the associated ecological risks.
2. Materials and Methods
2.1. Study Aera
SABCÉ is a department of Burkina Faso in the province
of Bam in the north-central region, 100 km from the
capital (Ouagadougou). It is located at 13011’52’’ north
latitude and 1031’18’’ west longitude. It is also a rural
commune with wan area of 373 km2 made up of 30
villages, including BISSA.
This region is characterized by a semi-arid and hot climate,
which results in two seasons: a dry season between
October and May, and a rainy season from June to
September. Rainfall is on average 600 mm over 10 years,
but with significant fluctuations. The life of the
inhabitants of SABCE is strongly impacted by the level of
rainfall, as agriculture and livestock are the main sources
of subsistence for the inhabitants.
2.2. Sampling, Preparation and Analysis
2.2.1 Sampling
Soil samples were taken around the mine located in
Bissa. Twelve (12) soils samples were taken from depths
of 0 to 15 cm. Each sampling point was identified using a
GPS. For a given position, the sample is taken from two
different places, then homogenized and then packed in a
sterile plastic collection bag. The samples thus formed are
labelled and numbered from S1 to S12 and transported to
the laboratory.
2.2.2. Preparation and Analysis of Samples.
Soil samples were dried in the sun and then sieved to
remove impurities. This is followed by quartering, which
consists of taking sub-samples representative of the master
samples. Next, the mechanical preparation which
consisted of pulverizing (grinding) and then sieving the
soil samples through a certified 200 mesh (75 micron)
sieve. Samples of one (1) gram were made using a balance
with a capacity of 200 g and an accuracy of 10-4 g, then
digested. The mineralization which consisted in
introducing the test sample into a test tube then adding 2.5
ml of nitric acid (HNO3) and 7.5 ml of hydrochloric acid
(HCl). The mixture thus obtained was placed in a water
bath at 90 ± 5° C, for one hour. The solution obtained was
inverted into a 100 ml volumetric flask, then topped up
with pure water up to the mark, and homogenized by
stirring. Finally, each solution was subjected to
decantation and then sampled for analysis by atomic
absorption.
2.3. Methods
To assess the ecological risks, four (4) pollution indices
(the contamination factor, the degree of contamination, the
pollutant load index and the geo-accumulation index) and
the ecological risk and potential ecological risk indices
were determined.
2.3.1. Contamination Factor, CF
The contamination factor shows the existence or
absence of soil contamination by a given heavy metal. It
also makes it possible to estimate the level of
contamination if it exists [25,26]. CF is expressed as the
ratio between the concentration of the element measured
in the medium and the reference or background
concentration according to [27]. Mathematically, the
contamination factor is given by relation (1)
[17,25,28,29,30]:
n
b
C
CF C
=
(1)
Cn is the metal concentration in the sample and Cb is the
geochemical background value. [27] classified
contamination into four (4) levels [25,26,31]. This
classification is presented in Table 1.
2.3.2. Degree of Contamination (DC)
The degree of contamination (DC) is the sum of the
contamination factors (CF). It allows the estimation of the
a priori polymetallic contamination for each sampling
location. This degree of contamination is expressed by
formula (2) [32,33,34]:
( )
n
i
i
DC CF=
∑
(2)
( )
i
CF
designates the contamination factor of the metal i
considered.
In Table 1, the level of contamination is evaluated
according to DC [27]:
Table 1. Level of contamination according to DC
CF values
DC values
Contamination intensity
CF ≤ 1
DC ≤ 8
Low
1 ≤ CF ≤ 3
8 ≤ DC ≤ 16
Moderate
3 ≤ CF ≤ 6
16 ≤ DC ≤ 32
Considerable
CF ≥ 6
DC ≥ 32
Very high
2.3.3. Pollution Load Index (PLI)
The pollution load index is a powerful tool in the
assessment of heavy metals [35]. Proposed by Tomlinson
(1980), it is an empirical index that comparatively
evaluates the level of heavy metal pollution for each
sampling site [25,29,36]. It is determined by
relation (3) [36]:
Journal of Environment Pollution and Human Health 53
123
nn
PLI CF CF CF CF= × × …×
(3)
In this expression,
is the contamination factor of the
considered metal i and n=7. Its value makes it possible to
give the following interpretations [25,33,37,38]: PLI < 1
(unpolluted soil), PLI = 1 (level of pollution reference)
and PLI > (polluted soil).
2.3.4. Evaluation of Potential Ecological Risk (RI)
The Potential Ecological Risk Index is a comprehensive
method relating all heavy metals to their toxicological
effects. While considering geochemical background heavy
metal content, it also comprehensively considers the
synergy of several elements: contaminant concentrations,
differences in biological toxicity of each element,
ecological effects, and environmental sensitivity related to
pollution. by heavy metals [39,40]. This method is widely
used in the assessment of sediment pollution [41], air [42]
and soil [40,43]. Indeed RI results from the sum of all the
ecological risk factors (Er) linked to each heavy metal.
The ecological risk factor (Er) expresses quantitatively the
ecological danger associated with each metal.
Corroborated by [40, 44], Er and RI are given by the
formulas (4, 5 and 6) of [27]:
.
rr
E T CF=
(4)
n
b
C
CF C
=
(5)
1
r
n
RI E
r
==
∑
(6)
The toxicity response factors (Tr) of the trace elements
studied (As, Cd, Cu, Cr, Hg, Pb, Zn and Ni) are
respectively 10; 30; 5; 2; 40; 5; 1 and 5 [41,45]. The
interpretations of the values of potential ecological risks
according to Hakanson (1980) [27] and by many other
researchers [40,41,44] are presented in table (2):
Table 2. Interpretation of ecological index and potential ecological
risks
Ecological
risk factor Level of
ecological risk
Index of
potential
ecological risk
Level of
potential
ecological risk
Er<40
Low
RI<150
Low
40≤Er<80
Moderate
150≤RI<300
Moderate
80≤Er<160 Considerable 300≤RI<600 Considerable
160≤Er<320
High
RI≥6 00
Extremely high
Er≥320
Extremely
high
2.3.5. Geoaccumulation Index
Established by [45], this index is used to determine the
influence of anthropogenic factors on the levels of a heavy
metal in a given topsoil sample [44,46]. According to
[46,47], it is calculated by relation (7):
2
log 1, 5
n
geo b
C
IC
=
(7)
Cn is the metal concentration, Cb is the geochemical
background value of the metal [48], the constant 1.5 is the
correction factor that compensates for the natural
fluctuations of a given metal while minimizing
anthropogenic impacts [15].
Furthermore, the numerical values of this index are
associated with the seven classes presented in Table 3
[15,49,50].
Table 3. Scale of pollution associated with the values of Igeo
Values
Pollution level
I
geo
< 0
Background concentration
0 ≤ Igeo < 1
Unpolluted
1 ≤ I
geo
< 2
Unpolluted to moderately polluted
2 ≤ I
geo
< 3
Moderately polluted
3 ≤ Igeo < 4
Moderately to highly polluted
4 ≤ I
geo
< 5
Highly polluted
I
geo
≥ 5
Very highly polluted
3. Results and Discussion
3.1. Evaluation of Heavy Metals in Soil
Samples
Seven heavy metals (Cr, Ni, Cu, Zn, As, Hg and Pb)
were analyzed in the 12 soil samples taken from the study
area. The mean, maximum, minimum, standard deviation
and coefficient of variation of concentrations obtained are
recorded in Table 4.
Chromium concentrations in agricultural soils in Sabcé
ranged from 51.46 mg/kg to 222.27 mg/kg with an
average of 102.30 mg/kg. The nickel concentrations were
between 22.65 mg/kg and 55.23 mg/kg with an average
value of 38.87 mg/kg. The average copper concentration
was 57.13 mg/kg, the maximum 107.8 mg/kg and the
minimum 25.04 mg/kg. Zinc concentrations ranged from
37.54 mg/kg to 92.2 mg/kg with an average of 58.51
mg/kg. Arsenic levels fluctuated between 8.98 mg/kg and
194.74 mg/kg with an average value of 49.67 mg/kg. The
mean mercury concentration was 3.83 mg/kg, maximum
5.58 mg/kg and minimum 00 mg/kg. The maximum and
minimum concentrations of lead were respectively 22.74
mg/kg and 12.81 mg/kg and the average value was 17.94
mg/kg. The average concentrations obtained were
classified in descending order: Cr>Zn>Cu>As>Ni>Pb>Hg.
Table 4. Average concentration of heavy metals in the soils of the Bissa area
Cr
Ni
Cu
Zn
As
Hg
Pb
Mean
102.30
38.87
57.13
58.51
49.67
3.83
17.94
Maximum
222.27
55.23
107.88
92.26
194.74
5.58
22.74
Minimum
51.46
22.65
25.04
37.54
8.98
0.00
12.81
Standard deviation
50.87
12.02
30.43
19.15
62.88
1.45
2.96
CV
49.73
30.93
53.27
32.73
126.58
37.99
16.49
54 Journal of Environment Pollution and Human Health
The degree of human influence on metal concentrations
can be estimated by the coefficient of variation [40,51,52].
The coefficient of variation is a statistical quantity which
expresses the dispersion around the mean as a percentage.
Depending on the scale in which its value is entered, the
following interpretations are available [40]: If CV<10%
then the variation is low; if 10%≤CV≤30% then the
variation is moderate; if CV>30% then the variation is
significant or strong.
In this study, the coefficient of variation decreases in
the following order: As (126.57%) > Cu (53.27%) > Cr
(49.73%) > Hg (37.99%) > Zn (32.73%) > Ni (30.93%) >
Pb (16.49%). Most of heavy metal (Cu, Cr, Hg, Zn, and
Ni) show strong variation. Arsenic (As) shows a very
strong variation because CV (As) >75% [53] and lead
which has a moderate variation. The very high variation of
arsenic (As) implies a heterogeneous distribution of this
metal which can be directly attributed to human activities
[53,54]. A number of human activities have the potential
to increase local arsenic concentrations in air, water and
soil. Arsenic (As) is one of the most common pollutants in
soil near gold mines with high concentrations and high
variability [55]. It then happens that the agricultural soils
of Bissa would have been strongly affected by the Bissa
gold mine. The relatively moderate variation of Pb
assumes a relatively stable spatial distribution of Pb which
is moderately dependent on the human factor [56].
3.2. Comparison of Average Concentrations
to Standards
The mean concentrations of heavy metals obtained in
this study were compared to the standards of the
FAO/WHO, and of certain countries such as Nigeria,
South Africa, Holland and Canada. These standards are
listed in Table 5.
Table 5. Comparison of average concentrations of heavy metals with
standards
This
study FAO/
WHO
Holland
(Target
Value)
Nigeria South
Africa Canada
Cr
102.3
100
100
100
0
64
Ni
38.9
50
35
35
91
50
Cu
57.12
100
36
36
16
63
Zn
58.51
300
140
140
200
200
As
49.7
20
29
1
5.8
12
Hg
3.823
-
0.3
0.3
0.93
6.6
Pb
17.94
20
85
85
20
70
Reference
[57]
[58,59]
[60,61]
[62,63]
[64,65]
The mean concentration of chromium obtained during
this study was higher than the standard of FAO/WHO,
Holland, Nigeria, South Africa and Canada. The mean
value of nickel concentration was only slightly above
Dutch and Nigerian standards. The limit concentration
recommended by the FAO/WHO and the Canadian
standard for copper was higher than that the concentration
obtained in this study. The concentrations of zinc and lead
in the studied soils were below all standards (Table 5). It
is therefore deduced that the studied agricultural soils
were not contaminated by Zn and Pb. On the other hand,
the mean concentration of arsenic in the analyzed samples
was above all these standards, which suggests that the
agricultural soils from Bissa were contaminated with
arsenic. The average mercury concentration was only
below the Canadian standard.
The Dutch Soil Quality Standard is considered the most
appropriate guideline indicating all possible exposure
pathways for the protection of humans, plants and animals
[58,59]. The soil is considered unpolluted by a metal if its
concentration is lower than its reference value, it is weakly
to moderately polluted if the level is between the reference
value and the intervention value. On the other hand, if the
content of the metal is above the intervention value, the
soil is considered detrimental to humans, plants and
animals [59]. Considering the mean concentrations of
metals (Table 5), it can be deduced that the sampling site
at Bissa was classified as slightly to moderately polluted
by Cr, Ni, Cu, As and Hg and not polluted by Pb, and
consequently, the site of is in no way detrimental to biota
and humans.
3.3. Comparison of the Mean Concentrations
of Heavy Metals with the Values
Reported by Similar Studies in
other Countries
Table 6. Average concentrations of heavy metals in soils near gold
mines in other countries
This study
Marocco
South Africa
China
Cr
102.3
1.2
278.76
13.34
Ni
38.9
6.2
4.79
24.98
Cu 57.12 58.5 42.51 55.9
Zn
58.51
47.6
51.30
57.5
As
49.7
-
79.40
43.30
Hg
3.823
-
0.09
0.53
Pb
17.94
-
112.06
17.73
References
[66]
[16]
[67]
The data recorded in Table 6 show that the soils from
Bissa were more contaminated with nickel than the soils
of South Africa, Morocco and China. The mean
concentration of lead (17.73) recorded in China in
agricultural soils around a gold mining area in Yitong
County [64], corroborates that of our study. On the other
hand, that reported in South Africa [16] was well above
that of this study. The mean of mercury concentration
recorded in the soils from Bissa was high compared to the
mean concentrations obtained in similar studies carried
out in China [67] and South Africa [16]. The agricultural
soil of Bissa was slightly contaminated in arsenic
compared to the soil of Yitong county, in China [67]
however, it was less contaminated than that of South
Africa. The mean concentration of zinc obtained was
higher than those obtained in South Africa [16] and
Morocco [66] but in line with that recorded in China. The
mean concentration of copper obtained in this study
remained above those in South Africa and Morocco,
however, it was close to that reported in China. As for the
mean concentration of chromium obtained in this study, it
turned out to be well above those obtained in Morocco and
China, but too low compared to the result of South Africa.
Journal of Environment Pollution and Human Health 55
Figure 1. Geoaccumulation index in soil samples.
3.4. Ecological Risk Assessment
3.4.1. Geo-Accumulation Index
The geo-accumulation index (Igeo) of each metal in
each sample is represented in Figure 1.
The geo-accumulation indices of heavy metals were in
the following ranges: -1.391 to 0.719 for Cr; -2.171 to -
0.885 for Ni; -1.431 to 0.676 for Cu; -1.924 to -0.627 for
Zn; -1.119 to 3.320 for As; 1.874 to 3.217 for Hg and -
1.228 to -0.400 for Pb. The average values of the geo-
accumulation indices were arranged in the order: Hg
(2.766)>As (0.553)>Cu (-0.425)>Cr (-0.537)>Pb (-
1.228)>Zn (-1.352) >Ni (-1.458).
The fluctuations of the geo-accumulation index of
heavy metals from the different sampling points are shown
in Figure 1. The values of geo-accumulation indices of Ni,
Zn and Pb were all negative, suggesting that no sample
from the study area was not polluted by Ni, Zn and Pb.
Two (2) samples (S5 and S11) out of twelve (12) had
their Igeo in Cr between 0 and 1, and the ten (10) samples
had their Igeo negative. So all the soil samples from Bissa
were unpolluted in Cr.
In accordance with the values of the Igeo of As, five (5)
samples (S1, S2, S6, S7 and S8) out of twelve (12) were
classified as background concentration, four samples (S3,
S4, S5 and S10) were classified as unpolluted, only one
sample (S9) was unpolluted to moderately polluted and the
last two (S11 and S12) were moderately to highly polluted.
As for mercury (Hg), the pollution was effective in all
the samples but to different degrees: S2 was classified as
unpolluted to moderately polluted; S1, S3, S4, S5, S6, S8,
S9 and S10 were in the moderately polluted class; S7 and
S11 were moderately to highly polluted. This parameter
showed that mercury was the most polluting metal in soils
from Bissa.
3.4.2. Contamination Factor and Degree of
Contamination
The values of the contamination factor (CF), degree of
contamination and pollution load index are recorded in Table 7.
The contamination factor values vary from 0.572 to
2.470 for Cr; from 0.33 to 0.812 for Ni; from 0.556 to
2.397 for Cu; 0.395 to 0.971 for Zn; from 0.691 to 14.98 for
Ace; from 0 to 13.95 for Hg and from 0.6405 to 1.137 for Pb.
Its mean contamination factor values decrease in the
order: Hg (9.569) > As (3.821) > Cu (1.269) Cr (1.137) >
Pb (0.8972) > Zn (0.616) > Ni (0.572).
Maximum values of the Zn and Ni contamination
factors were less than one (1), and suggest that all the
samples from Bissa were low contaminated.
The chromium contamination factors for samples S2,
S3, S4, S5, S6 and S11 were between one (1) and three (3),
implying that they were moderately contaminated. Soil
samples S2, S3, S4, S5, S6, S11 and S12 have copper
contamination factors greater than one (1) and less than
three (3), suggesting moderate contamination.
The S11 and S12 samples had a very high contaminated
in arsenic because they have contamination factors greater
than six (6). The samples S3, S4, S5, S6, S8, S10 had a
moderate arsenic contamination and contamination
intensity of sample S9 was considerable.
All the studied soils were very highly contaminated
with mercury, except for the soil sample S2 which had a
so-called considerable contamination level.
The lead contamination factors of samples S3, S6, S8
and S11 were between one (1) and three (3), therefore
these soils were moderately contaminated.
The values of the degrees of contamination of the soils
studied are recorded in Table 7, and vary from 11.729 to
33.209, with an average of 17.88. In accordance with the
interpretations (Table 1), the samples S2 and S11 were
respectively classified as moderately contaminated and
very highly contaminated and the eight (8) other samples
were in the considerably contaminated class.
3.4.3. Pollution Load Index
The pollution load index varies from 0.910 to 2.364
with an average of 1.347 (Table 7). An analysis of Figure
2 shows that apart from the three (3) samples (S1, S7 and
S10) which were unpolluted, the nine (9) other samples
were polluted. Among the polluted samples, S11 was
considered highly polluted because the PLI value was
between two (2) and three (3). The samples S2, S3, S4, S5,
S6, S8, S9 and S12 were moderately polluted because the
PLI were between one (1) and two (2) [68,69].
56 Journal of Environment Pollution and Human Health
Table 7. Contamination factor, degree of contamination and pollution load index
Soils
Contamination factor
CD PLI
Cr Ni Cu Zn As Hg Pb
S1 0.878 0.437 0.556 0.403 0.691 9.225 0.941 13.130 0.910
S2
1.012
0.646
2.187
0.666
0.798
5.500
0.920
11.729
1.212
S3
1.139
0.812
1.048
0.630
2.665
10.425
1.137
17.856
1.526
S4
1.015
0.487
1.036
0.475
1.838
10.275
0.814
15.940
1.207
S5
1.848
0.584
2.224
0.620
2.179
10.250
0.808
18.512
1.600
S6
1.636
0.749
2.397
0.587
1.407
10.250
1.019
18.046
1.587
S7 0.806 0.420 0.674 0.443 0.749 12.400 0.985 16.476 0.989
S8
0.844
0.459
0.672
0.439
1.055
11.625
1.018
16.111
1.052
S9
0.572
0.367
0.922
0.862
4.264
11.450
0.641
19.077
1.266
S10
0.655
0.333
0.638
0.395
2.416
9.475
0.783
14.694
0.998
S11
2.470
0.791
1.214
0.971
12.811
13.950
1.003
33.209
2.364
S12
0.764
0.773
1.668
0.901
14.980
0.000
0.701
19.787
1.451
Mean
1.137
0.572
1.269
0.616
3.821
9.569
0.897
17.881
1.347
Minimum
0.572
0.333
0.556
0.395
0.691
0.000
0.641
11.729
0.910
Maximum
2.470
0.812
2.397
0.971
14.980
13.950
1.137
33.209
2.364
Figure 2. Histogram of the pollution load index
Figure 3. Variation of RI according to the samples
Journal of Environment Pollution and Human Health 57
Table 8. Ecological risk factor and potential ecological risk
Ecological risk factor
RI
Cr
Ni
Cu
Zn
As
Hg
Pb
Mean 2.273 2.858 6.347 0.616 38.211 382.750 4.486 437.541
Minimum
1.144
1.665
2.782
0.395
6.908
0.000
3.203
167.935
Maximum
4.939
4.061
11.987
0.971
149.800
558.000
5.685
707.056
Precautions to be taken are associated with the value of
PLI [36,70,71]. Thus, they suggest that a more detailed
study is needed to monitor S1 and S11 because 0.5 ≤ PLI
< 1, and that immediate intervention is needed to improve the
pollution status of the other samples because their PLI ≥1.
3.4.4. Potential Ecological Risk
The ecological sensitivity of heavy metals has been
comprehensively assessed using the ecological risk factor
(Er) and the potential ecological risk index (RI). The
variations of the ecological risk factor are shown in Figure
4 and Table 8.
Mean values of ecological risk factors were ranked in
the order: Hg (382.750) > As (38.211) > Cu (6.347) > Pb
(4.486) > Ni (2.858) > Cr (2.273) > Zn (0.616).
The evaluation of ecological risk factors values showed
that mercury was the main contaminant in the area
because it presents a very high ecological risk with an
average Er greater than 320 [54]. The mean ecological risk
factors value of the other metals (As, Cu, Pb, Cr, Zn and
Ni) were all below 40, indicating that these metals had a
relatively low level of ecological risk.
The fluctuations of the potential ecological risk index
(RI) are shown in Figure 3.
The RI values ranged from 167.935 to 707.056,
indicating a level of ecological risk ranging from moderate
to extremally high in the sampling area.
Soil samples S1, S3, S4, S5, S6, S7, S8, S9, and S10
had RI values between 300 and 600, indicating that the
level of ecological risk of these samples was considerable.
The highest value (707.056) of RI was observed in sample
S11, which made it a sample at extremely high ecological
risk because the RI was greater than 600. The samples S2
and S12 indicated a moderate level of ecological risk
because the RI was between 150 and 300.
The ecological risk was proven in the soils studied
around the mine in the village of Bissa, therefore,
corrective measures are urgently needed to mitigate the
pollution [72]. Depollution of its soils could be an
alternative to avoid harmful effects on the health of
populations.
4. Conclusion
The objective of this study was to assess the
concentrations of heavy metals and ecological risks in
agricultural soils around the gold mine in the Bissa village
of the commune of SABCÉ. It shows that the average
concentrations of chromium, arsenic and mercury in the
agricultural soils from Bissa around the mine were higher
than the FAO/WHO standard and other standards in
Africa. In view of these results, the contamination factors
were calculated. On the one hand, the contamination
factor, the geo-accumulation index and the ecological risk
index show that mercury and arsenic were the main
contaminants in the study area. On the other hand, the
evaluation of the ecological impact from the pollution load
index and the potential ecological risk index showed that
the ecological risk was proven in the soils studied around
the mine in the village of Bissa, therefore, remedial
measures are urgently needed to mitigate the pollution.
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