3.1. Drinking Water Quality
The mean concentrations of heavy metals in water samples from water bodies in the study area are presented in
Table 2 below. It was found out that 13% of the parameters measured in this study were found to be below the detection limit (10 μg/L respectively for As, Hg, Mn, Pb and Cd).
Table 2.
Mean concentrations (μg/L) of heavy metals in filtered water samples from the study area. Data includes the minimum and maximum values from each sampling site. GSA-175A/WHO guideline for drinking water is shown for reference.
Table 2.
Mean concentrations (μg/L) of heavy metals in filtered water samples from the study area. Data includes the minimum and maximum values from each sampling site. GSA-175A/WHO guideline for drinking water is shown for reference.
Sampling Point | As (μg/L) | Mn (μg/L) | Pb (μg/L) | Cd (μg/L) | Hg (μg/L) |
---|
Mean (SD) | Min–Max | Mean (SD) | Min–Max | Mean (SD) | Min–Max | Mean (SD) | Min–Max | Mean (SD) | Min–Max |
---|
River Achofe | 1246 (464.9) | 15–2851 | 490 (91.6) | 124–580 | 22 (4.80) | 10–28 | 21 (2.87) | 17–32 | 43 (14.9) | <10–64 |
River Agonaben | 89 (19.0) | 14–120 | 234 (54.4) | 36–300 | 120 (47.4) | 25–200 | 396 (161.1) | 19–974 | 55 (23.3) | <10–94 |
River Adeyie | 184 (39.1) | 43–248 | 222 (35.1) | 100–313 | 69.3 (26.8) | 19–134 | 321 (50.5) | 115–341 | 55 (26.2) | <10–120 |
River Bremansu | 59 (28.5) | <10–140 | 259 (51.6) | 151–451 | 165 (38.1) | 13–214 | 53.1 (25.4) | 10–145 | 98 (43.2) | <10–154 |
River Subri | 325 (100.8) | 100–700 | 53 (24.7) | 19–133 | 20 (14.5) | <10–80 | 231 (31.6) | 134–329 | 63 (17.1) | 25–94 |
River Asuobenkasa | 112 (35.5) | 43–248 | 191 (38.1) | 79–313 | 170 (35.8) | 30–226 | 391 (81.8) | 47–420 | 472 (107.9) | 19–520 |
River Asuman | 15 (6.29) | 10–40 | 26 (11.9) | <10–74 | 122 (19.3) | 40–127 | 21 (6.39) | <10–44 | 246 (96.5) | 74 -480 |
Borehole at Abekoase | 20 (1.49) | 18–24.5 | 103.2 (23.3) | 50–147 | 70 (21.2) | 10–140 | 137 (38.1) | 10–241 | 25 (7.38) | 10–42 |
River Huni | 75.1 (19.4) | 54–101 | 57.7 (24.7) | 19–150 | 66.9 (39.5) | 10–221 | 21 (21.2) | <10–109 | 448 (91.4) | 100–520 |
River Ateberebe | 133.9 (39.2) | 125–300 | 31 (7.01) | 12–60 | 226 (56.2) | <10–329 | 126 (55.8) | 21–341 | 83 (28.6) | <10–127 |
GS 175-1/WHO Guideline values | 10 | 400 | 10 | 3 | 10 |
The mean concentrations of As ranged from 15 to 2451 μg/L. The mean As concentration in river Achofe was compared to the GS 175-1/WHO guideline, and it was found to have exceeded the required GS 17-1/WHO permissible guideline value [
24], which is consistent with findings reported by [
25]; the highest concentration of As (≥12,200 μg·L
−1) measured in [
24] corresponded to abandoned mine shafts in Konongo. This indicates that As pollution of the water bodies in the study area is extremely high. The high As contamination in the study area may be due to the dissolution of As in the arsenopyrite ores or indiscriminate discharge of mine effluents rich in As and other toxic chemicals into the environment [
25]. Mean concentrations of As, Pb, Cd and Hg found in most of the water bodies sampled in this study exceeded GS 175-1/WHO guideline values; this assertion is consistent with findings made by [
24]. A graphical comparison of the levels of the various chemicals in the water samples from the study area is presented in
Figure 2 below. In the case of the As, Pb and Cd levels in water samples from the study area, it was found out that 100% of them exceeded the GS 175-1/WHO permissible guidelines [
24]; while for Mn levels in the water samples, 10% of them were found to have exceeded GS 175-1/WHO permissible guidelines. Out of the 200 water samples analysed for Hg, it was found out that 100% of the samples exceeded the GS 175-1/WHO permissible guidelines values.
The source of these elevated levels of As, Cd, Mn, Pb and Hg in water samples from the study area is attributable to artisanal small-scale gold mining (ASGM). For instance, though As and Cd are naturally occurring metals, they are also associated with gold-bearing ores in the study area and are often found at elevated concentrations near gold mining sites [
19,
26,
27,
28]. In the Tarkwa area, elevated Cd concentrations may be the result of mining and processing of zinc and chalcophilic metals [
19]. Some geologic formations, such as the Birimain and Tarkwanian rock systems found in the mining area of Tarkwa, contain high concentrations of Pb, as well as Mn [
19]. Pb and Hg also derives from industrial discharges or mine drainage [
19,
28]. The weathering of ore tailings can lead to the leaching of heavy metals into other media such as water, soil, and sediment [
7].
The pH values of water samples from the study area ranged from 5.59 to 7.45 with a mean value of 7.003, which shows that the water samples are neutral,
i.e., they are neither acidic nor basic (as shown in
Table 3 below), as 60% of the samples had pH values of 6.43 out of the 200 samples from the study area.
Figure 2.
A graph comparing the levels of heavy metals in water samples from the study area with GS 175—1/WHO Permissible guideline values.
Figure 2.
A graph comparing the levels of heavy metals in water samples from the study area with GS 175—1/WHO Permissible guideline values.
Table 3.
Descriptive statistics for physic-chemical parameter for water samples from the study area.
Table 3.
Descriptive statistics for physic-chemical parameter for water samples from the study area.
Parameter | Range | Mean | Standard Deviation | GS 175/1-WHO Guideline Values |
---|
pH | 5.59–7.45 | 6.43 | 0.195 | 6.5–8.5 |
Turbidity | 0.60–8.94 | 1.746 | 2.071 | 5 |
Colour | 2.500–12.00 | 5.900 | 2.859 | 15 |
TDS | 17.2–219.00 | 79.23 | 43.58 | 1000 |
Sodium | 1.80–191.0 | 34.76 | 33.52 | 200 |
Magnesium | 0.80–144.0 | 47.11 | 35.76 | 150 |
Calcium | 0.50–112.0 | 27.68 | 25.76 | 200 |
Potassium | 1.40–23.80 | 6.896 | 5.058 | 30 |
Bicarbonate | 1.00–95.00 | 13.94 | 23.74 | 0.3 |
Sulphate | 1.30–88.0 | 17.69 | 23.04 | 250 |
Chloride | 1.50–50.0 | 7.923 | 9.514 | 250 |
Nitrate-Nitrogen | 0.013–9.72 | 2.224 | 3.330 | 10 |
Nitrite-Nitrogen | 0.001–0.220 | 0.0220 | 0.049 | 3 |
E. Cond | 1.140–97.80 | 26.91 | 30.088 | |
Alkalinity | 4.80–558 | 141.81 | 157.026 | |
Hardness | 0.001–9.50 | 1.789 | 2.576 | |
Phosphate | 0.001–9.50 | 1.586 | 2.645 | |
TSS | 1.0–4.500 | 1.323 | 0.893 | |
Pearson product correlation moment at
p < 0.05 or
p < 0.01 two-tailed revealed that there was no significant relationship between pH and major ions such as Na, Mg, K, Ca and SO
4 in water samples from the Tarkwa mining area (refer to
Table 4 below). However, a positive significant relationship existed between TSS and Turbidity, K and SO
4, as well as between Mg and SO
4 at
p < 0.01 significant level two-tailed.
The correlation matrix in
Table 5 below shows significant inter-metal relationships (
p < 0.05 and
p < 0.01). The Cd-Mn correlation is recognized as the weakest, with a correlation coefficient
r = 0.446. Significant strong correlations (
r > 0.5) were found between As-Cd, Pb-As, Pb-Cd, Pb-Mn, As-Zn, Mn-Cd, Hg-Mn and two more toxic metals, Hg-Pb. To explain the differences in correlations between the trace metals in each of the compartments, physical, chemical and biological processes occurring permanently in an aquatic environment (internal processes) as well as discharging of pollutants and other anthropogenic activities (external processes) and their effects on the partitioning and behaviour of heavy metals in that aquatic system must be taken into consideration.
Other studies by different scientists have found a high degree of variability in concentrations of As, Hg, Mn, Cd and Pb in surface water from rivers or streams sediments in certain mining communities in Ghana. For example, the highest As concentration (3137 μg·L
−1) was reported by [
29,
30]. Also, [
31] reported the highest mean concentration of Hg (4600 µg·L
−1) in water samples from the Tarkwa mining area. A comparison of Hg levels in water samples from this study with Hg levels from ASGM sites in other countries has revealed that Hg levels reported in the water are amongst the highest worldwide (see
Figure 3 below).
Table 4.
Pearson’s Product-Moment Correlation Coefficients for major ions in water samples from the study area.
Table 4.
Pearson’s Product-Moment Correlation Coefficients for major ions in water samples from the study area.
| pH | Col | Turb. | TSS | TDS | Cond. | Alkalinity | Na | Mg | Ca | K | SO4 |
---|
pH | 1 | | | | | | | | | | | |
Col | −0.176 | 1 | | | | | | | | | | |
Turb | 0.065 | −0.267 * | 1 | | | | | | | | | |
TSS | 0.024 | −0.113 | 0.352 ** | 1 | | | | | | | | |
TDS | 0.161 | 0.095 | −0.029 | 0.175 | 1 | | | | | | | |
Cond | 0.176 | −0.234 | −0.038 | 0.047 | 0.147 | 1 | | | | | | |
Alkalinity | −0.018 | 0.116 | −0.104 | 0.071 | 0.040 | −0.181 | 1 | | | | | |
Na | 0.017 | 0.156 | −0.033 | 0.098 | −0.037 | −0.126 | 0.047 | 1 | | | | |
Mg | −0.034 | 0.065 | −0.140 | −0.128 | −0.006 | −0.158 | −0.293 * | 0.231 | 1 | | | |
Ca | 0.058 | 0.057 | −0.146 | 0.241 | 0.037 | 0.142 | 0.480 ** | 0.205 | 0.386 ** | 1 | | |
K | 0.043 | −0.067 | 0.420 ** | 0.575 ** | 0.119 | 0.016 | 0.055 | 0.064 | 0.058 | 0.091 | 1 | |
SO4 | 0.139 | −0.055 | 0.386 ** | 0.564 ** | −0.015 | −0.070 | −0.001 | 0.145 | 0.349 ** | 0.168 | 0.650 ** | 1 |
Figure 3.
A graph comparing Hg levels in water samples from ASGM sites in this study with other ASGM sites in Ghana as well as other countries with active ASGM sites [
6,
28,
29,
30,
32,
33,
34,
35,
36,
37].
Figure 3.
A graph comparing Hg levels in water samples from ASGM sites in this study with other ASGM sites in Ghana as well as other countries with active ASGM sites [
6,
28,
29,
30,
32,
33,
34,
35,
36,
37].
The correlation analysis was conducted to determine the relationship between metal concentration in water and the influence of physico-chemical parameters of water on metal concentrations. The pH and TDS are a major concern in this study since they are the vital factors in metal solubility and control metals speciation and thus their distribution within dissolved fractions [
38]. In this study, concentrations of As and Cd were found to be negatively correlated (
Table 5). According to Mackie [
38] and Elzahabi and Yong [
39], the solubility of heavy metals in water normally increases under acidic condition (pH
< 4). However, the influence of pH on metal solubility is not obvious as the pH values in the present study ranged from 5.59 to 7.45.
Strong As-Cd, As-Pb, As-Mn, As-Hg metal-metal correlations suggest rivers in the Tarkwa mining area are highly polluted with mining effluents. Several factors might have caused this increase; notably among them are most of the ASGM activities such as washing of ores and amalgamation of gold ores with mercury takes place at these sites, remaining inputs are from improper disposal of mine tailings from the tailings dam via the emergency spill way [
40].
Table 5.
Pearson Product-Moment Correlation Coefficients between water quality parameters in Tarkwa Nsuaem Municipality, Ghana (n = 200).
Table 5.
Pearson Product-Moment Correlation Coefficients between water quality parameters in Tarkwa Nsuaem Municipality, Ghana (n = 200).
| pH | Turb. | TDS | Cond. | As | Cd | Co | Mn | Pb | Hg |
---|
pH | 1 | | | | | | | | | |
Turb | −0.237 ** | 1 | | | | | | | | |
TDS | 0.400 ** | −0.169 | 1 | | | | | | | |
Cond | 0.415 ** | −0.182 | 0.993 ** | 1 | | | | | | |
As | −0.367 ** | 0.146 | −0.316 ** | −0.367 ** | 1 | | | | | |
Cd | −0.534 ** | 0.037 | −0.357 ** | −0.373 ** | 0.664 ** | 1 | | | | |
Co | −0.280 | −0.038 | 0.020 | 0.016 | 0.079 | 0.614 ** | 1 | | | |
Mn | 0.038 | 0.016 | −0.045 | −0.040 | 0.625 * | 0.446 ** | −0.122 | 1 | | |
Pb | 0.091 | −0.061 | −0.057 | −0.033 | 0.829 * | 0.603 * | −0.134 | 0.703 * | 1 | |
Hg | 0.035 | −0.014 | −0.0156 | −0.128 | 0.701 ** | −0.003 | 0.004 | 0.553 * | 0.634 * | 1 |
3.2. Socio-Economic Study
The socio-economic study focused on assessing the perceptions of residents of water quality in their area due to gold mining. Answers to the questions asked during the socioeconomic survey have been presented in
Table 1 above. Of the interviewed families, 59% were males and the remaining percentage females.
Education is a key determinant of household formation, structure, socio-economic status and value judgment in every endeavor. The few residents with tertiary education (8.4%) as compared to the national figure of 40% in Tarkwa mining area means that they would have limited employment opportunities with mining companies operating within these areas. This is due to the fact that mining operations require a highly skilled labor force [
40]. The resulting effect of this is the upsurge in the artisanal small-scale mining which requires no skills. Hence from
Table 1 above, 45% of respondents in the study area have no formal education, 29% have their highest educational level to be Junior High School, only 3.4% have attained university education to the bachelors’ level, 5% having diploma, and the remaining 18% have their highest education to the senior school level [
15,
29].
Out of the 250 residents interviewed in the study area, 83.5% of them said their levels of economic activities, which mainly involved farming, were encouraging before the advent of mining activities while 10.5% had a contrary view. Of the 8% of the respondents in the study area who hold either a diploma or first degree from the university, only 2% are employed by the mining companies. This is because most mining companies employ large numbers of expatriate staff to fill positions which could be occupied by Ghanaians. This observation is consistent with studies made by [
41,
42].
The socio-economic dependencies of people in the study area on the mining industry is evident in the fact that 15% of the respondents had relatives who are mine workers; out of the 15% of the respondents who claim they had relatives’ employed by the mining industry; 10% of these mine workers have been laid off by the mining industries they have resorted to illegal mining as a way of meeting their livelihood needs.
For 83.5% of the respondents, farming was their main source of occupation before they ceded their lands to mining companies. This had resulted in a high unemployment rate of 65%, which should be considered as the main cause of many social problems affecting the livelihood of the people.
The majority of the respondents (90%) have some environmental concerns regarding gold mining activities in the study area. When the respondents were asked to be specific with their environmental concerns, 93% of the respondents said mining has polluted the water bodies in their communities. It was significant to note that 82% of the respondents could not drink from their traditional sources of water due to pollution (
Table 1 above). 87% of the respondents said they have lost their farmlands as a result of expanding mining activities in the region. This can have a significant impact on livelihoods, particularly in Tarkwa where the average daily wage is less than $1 U.S. per day.
A stepwise logistic regression was used to estimate explanatory factors influencing the perception of residents on the socio-economic effects of mining in the study area. The responses to questions asked in the survey as well as the categories to which the question belong in logistic regression analysis have been summarized in
Table 6 below.
Table 6.
Results of the logistic regression analysis.
Table 6.
Results of the logistic regression analysis.
Perception of Water Quality (Model) | Predictors | Category | Β | p-Value | Odds Ratios |
---|
Highly polluted water bodies | Education | No formal education | 3.12 | 1.21 | 4.46 |
Junior High School | 0.92 | 0.14 | 2.51 |
Senior High School | 0.78 | 0.14 | 2.17 |
Diploma | 0.23 | 0.72 | 1.25 |
University degree | 0.04 | 0.94 | 1.25 |
Household income | Less than $1 dollar a day vs more | 1.82 | 0.16 | 2.17 |
Taste of drinking water | Deteriorated water quality | 1.93 | 0.02 | 6.92 |
Familiarity environmental problem | If mining activities continues, water bodies would be polluted | 1.63 | 0.010 | 5.97 |
Stoppage of surface mining improves livelihood of residents of mining communities | Education | No formal education | 1.33 | 0.052 | 3.79 |
Junior High School | 1.18 | 0.009 | 3.25 |
Senior High School | 0.04 | 0.840 | 1.04 |
Diploma | 0.03 | 0.941 | 1.01 |
University degree | 0.04 | 0.840 | 1.04 |
Household income | Less than $1 a day vrs more | 1.63 | 0.003 | 5.11 |
The results of the stepwise logistic regression revealed that, for the perception “highly polluted water bodies”, three predictors were most significant in explaining the opinion of residents of the study area interviewed in this survey (
Table 6). These three predictors are education, household income and familiarity with environmental problems. Out of the five education-level groups, those with no formal education were about six times more likely to consider water bodies in the Tarkwa municipality as highly polluted than the Junior and Senior High graduates taken as reference category. This result is not consistent with the assertion that the more highly educated people are more likely to show concern to environment issues [
41]. The second predictor was household income levels. The results showed that people living on less than 1 $ a day were more concern with their environment than those who live above this mark. This observation is consistent with the findings of [
41]. The third predictor was familiarity with environmental problems associated with mining.
The few respondents who believed that the water quality had stayed the same or deteriorated compared to the period before the commencement of mining activity in the study area were six times more likely to perceive the water as being highly polluted than those who said that the quality water had improved since mining operation took place in the area. From
Table 1 above, 68% of people in the study area are of the view that, if mining activities of ASGM should stop or are properly regulated by the agencies mandated by law, pollution of water bodies in the area would cease. That is, making heavy metal contamination of surface waters in Tarkwa a serious issue which requires an urgent attention [
43]. It is also observed in
Table 1 above that 83% of the respondents observed that the taste of drinking water in the study area was bad, and as such classified as highly polluted. The outcome of the perception study agrees with the chemical data in
Table 2 above, indicating strong pollution levels of water bodies in the study area. There was a highly significant correlation between the three predictors and the perception model.