Background. There is increasing global concern over the health effects of heavy metals arising from various anthropogenic activities, especially mining. Mining activities in developing countries are often carried out at an artisanal level using a variety of extraction methods with human health and environmental consequences.
Objectives. The broad objective of this study is to assess the chemical forms, distribution pattern, and health risks due to mining and processing techniques at a gold mining site in Igun, Osun State, Nigeria.
Methods. Samples were collected from 28 active mine pits and sequentially extracted using standard methods. Extracts were analyzed for metals using inductively coupled plasma optical emission spectrometer (ICP/OES), while health risk was assessed using United States Environmental Protection Agency (USEPA) and Dutch methods. Chemical speciation of heavy metals and health risk assessment was calculated using mobile phase fraction summation.
Results. Metals were exclusively present in the residual fractions, indicating that these metals are strongly bound to the resistant components of the soil matrix. The percentage in the residual fraction ranged from 9.41% (tin) to 99.42% (aluminium). The heavy metals geoaccumulation index for the site ranged from 0 (no contamination) to 6 (extremely contaminated). The cancer risk ranged from 6.17E-13 to 7.77E-05 and 2.73E-12 to 4.64E-04 for adults and children, respectively.
Discussion. Cancer risk and non-cancer risk (hazard index) assessment showed that arsenic poses a higher risk in adults and children compared to other metals through the dermal exposure route.
Metals such as lead (Pb), cadmium (Cd), mercury (Hg), zinc (Zn), and chromium (Cr) are known for their persistent behavior in the environment with consequent environmental, human and animal damage.1,2 Some of these metals are known to act as human mutagens and carcinogens and are associated with various human ailments such as cardiovascular, nervous system, blood and bone diseases, kidney failure, gingivitis, and tremors, among others.3,4 These metals are easily released into the environment via anthropogenic activities, e.g. metal plating facilities, mining, and agricultural activities.5
Gold mining is often associated with positive economic benefits such as job creation and increased standard of living; however, mining activities may also have negative impacts on the environment and human health. Recently, gold mining activities in two villages in Zamfara State in northwestern Nigeria resulted in the death of over 100 children. This is the first documentation of an outbreak of childhood lead poisoning associated with artisanal gold mining in Nigeria.6 Artisanal gold mining is dangerous to human health, as heavy metals, mainly Hg, Pb, and arsenic (As) are often released into the environment.
The Igun gold mine is located in the southwestern schist belt of Nigeria. Gold is known to occur with pyrite, pyrrhotite and minor chalcopyrite, galena, sphalerite, magnetite and ilmenite.7 There have been reported cases of gold mining at the Igun secondary mine which have resulted in the release of acid mine drainage (AMD).8,9 AMD is water with a low pH, high electrical conductivity, elevated concentrations of iron, aluminum and manganese and raised concentrations of other toxic heavy metals. The acid produced dissolves salts and mobilizes heavy metals from mine workings. Dark, reddish-brown water and pH values as low as 2.5 persist at the site.10
Previous studies at the site only assessed the levels of heavy metals without attention to the chemical forms, distribution pattern, pollution indexing, and human health risk of classified toxic metals by the International Agency for Research on Cancer (IARC).11 The main objective of this study is to assess the chemical forms, distribution pattern, and health risks due to mining and processing techniques adopted by workers at the Igun artisanal gold mining site.
Study Area, Sample Collection and Preparation
The study site is located in the Atakumosa Local Government Area of Osun State. The sampling location is shown in Figure 1. At the site, the sluicing extraction technique was adopted (gravity concentration technique). Sluices use water to wash ore or alluvium down a series of angled platforms. As water washes sediment down a sluice, gold particles sink and are captured by material covering the bottom of the sluice. Sluices are usually inclined at a 5 to 15 degree angle. As moving water travels down a sluice, it generates greater force and keeps gold particles from easily sinking.12 Soil samples were collected from 28 hand-dug pits which were pooled into 7 sampled areas based on pit closeness and grouping of artisanal miners operating at the site. Figure 2 shows the mining pits, artisans, and the processed sample. Soil samples were collected at different depths from which sand was collected and processed for gold extraction by the artisans. The samples were stored in sealed polyethylene bags, labeled, and then transported to the laboratory. Soil samples were air-dried, grinded using mortar and pestle, and then sieved through a 1-mm mesh sieve to remove refuse and small stones. The samples were then transferred into a zip-lock bag for further analysis.
Reagents and Chemicals
All reagents were analytical grade and were obtained from Sigma Aldrich, South Africa. The reference material (BCR-277R) was purchased from the European Community Bureau of Reference, IRMM, Belgium.
Sequential Extraction Procedure
Sequential extraction of metals in collected soil samples was performed using a modified 4-stage extraction procedure previously applied in similar studies.13,14 A schematic presentation of the extraction procedure is shown in Figure 3. Soil samples were (1) acid soluble/exchangeable fraction (F1, exchangeable metal and carbonated associated fractions); (2) reducible fraction (F2, fraction associated with iron (Fe) and manganese (Mn) oxides); (3) oxidizable fraction (F3, bound to organic matter) and (4) residual fraction (F4). Samples were rinsed twice with 10 mL deionized water, centrifuged, and decanted between each extraction step. Extracts were analyzed for trace and heavy metals using an inductively coupled plasma optical emission spectrometer (ICP/OES) running on a smart analyzer equipped with a CETAC ASX-520 auto sampler. Working and calibration standards were prepared daily by proper dilution from 50 mgL−1 of trace elements and 10,000 mgL−1 of calcium (Ca), potassium (K), niacin (Na) and phosphorus (P) in 10% nitric acid. External calibration curves were constructed for the quantification of the elements, while instrument calibration was checked with icalizing solution supplied by Spectro Genesis.
Contamination Assessment Using the Geoaccumulation Index (Igeo)
A common approach for estimating the degree of enrichment of metal concentrations above background or baseline concentrations in soil, sediment, and dust is to calculate the geoaccumulation index (Igeo), as proposed by Muller.15 This method assesses the degree of metal pollution in terms of seven enrichment classes based on the increasing numerical values of the index. This index is calculated as follows:
Where Cn is the concentration (μgg−1) of the element in the enriched samples, and Bn is the background or pristine concentration of the element (μgg−1), i.e. the background content of the element in the crust.16,17 A factor of 1.5 is introduced to minimize the effect of possible variations in background values which may be attributed to lithologic variations in the sediments.18 Muller15 proposed the following descriptive classes for increasing Igeo values: >5 = extremely contaminated, 4–5 = strongly to extremely contaminated, 3–4 = strongly contaminated, 2–3 = moderately to strongly contaminated, 1–2 = moderately contaminated, 0–1 = uncontaminated to moderately contaminated and 0 = uncontaminated.
Quality Control and Statistical Analysis
For each batch of 10 field samples, a procedural blank, certified reference material, and sample triplicate were processed. In order to check for the accuracy of the sequential extraction procedure, reference sediment materials BCR-277R were extracted using the above procedure and analyzed in triplicate. All statistical analyses were carried out using the Statistical Package for the Social Sciences (SPSS) Software. Analysis of variance (ANOVA) and Pearson's correlation index were used to test for significant differences (95% confidence level).
Health Risk Assessment Model
Daily exposure dose and exposure point concentration
The model used for health risk assessment for estimating human exposure to metals in this study is based on criteria of the United States Environmental Protection Agency19 (USEPA) and the Dutch National Institute of Public Health and Environmental Protection.20 The health risk assessment focused on two separate sets of people, children and adults. Exposure to metals can occur through three main paths: (a) ingestion of atmospheric particulates due to their deposition, (b) direct inhalation of atmospheric particulates through the mouth and nose, and (c) dermal absorption of trace elements in particles adhered to exposed skin. Exposure calculation for daily estimation was achieved using the following equations:
Where D (mg kg−1 day−1) is the dose contacted through ingestion (Ding), inhalation (Dinh), dermal contact (Ddermal) and inhalation of vapour (Dvapour). For this study, ingestion rate (IngR) was 200 mg day−1 for children and 100 mg day−1 for adults.21 The ingestion rate (IngR) was 7.6 m3 for children and 20 m3 for adults,20 and the exposure frequency (EF) was 180 days per year−1.13,22 The exposure duration (ED) was 6 years for children and 24 years for adults (USEPA, 2001). Average body weight (BW) was 15 kg for children and 70 kg for adults.23 Averaging time (AT) for non-carcinogens was ED × 365 days and AT for carcinogens was 70 × 365 = 25,550 days. Skin surface area (SA) was 2800 cm2 for children and 3300 cm2 for adults.24 The skin adherence factor (SL) was 0.2 mg cm−2 d−1 for children and 0.7 mg cm−2 d−1 for adults, the dermal absorption factor (ABS) was 0.03 for As and 0.001 for Cd; no values were available for other elements, therefore 1.0% was used. The particle emission factor (PEF) was 1.36 × 109 m3 kg−1. The exposure-point concentration, μg g−1 (C) in Equations 2–5 is an estimate of reasonable maximum exposure4,14,22,24,25 and was calculated as the upper limit of the 95% confidence limit for the mean using Equation 5. Also, this was based on the assumption that the mobile fractions (F1 + F2 + F3) of the trace metals are potentially labile and bioavailable due to physical activities once they enter the complex internal environment of the human body.14
Where X is the arithmetic mean of the log-transformed data, s is the standard deviation of the log-transformed data, H is the value from the H-statistic table26 and n is the number of samples.
The values for SFo (oral slope factor, mg kg−1 day−1), RfDo (reference dose), RfCi (inhalation reference concentration, mg m−3), GIABS (gastrointestinal absorption factor) and IUR (inhalation unit risk (μg m−3)), were obtained from the USEPA website.27 The hazard index (HI) was estimated to be equal to the sum of HQs. HI was used to assess the overall potential for non-carcinogenic effects from different pathways. Risk surpassing 1 × 10−4 is viewed as unacceptable, risk below 1 × 10−6 is not considered to pose significant health effects, while risk in the range of 1 x 10−4 to 1 × 10−6 is generally considered acceptable depending on the circumstances of exposure.24,28 However, it should be noted that in some countries, 1 × 10−5 is considered to be acceptable risk.29
Uncertainty in Risk Assessment
Uncertainty is pervasive in risk assessment, especially when it arises due to lack of precise knowledge, variability in environmental systems, and variability of individual systems and characteristics.31,32 It should be noted that there were no studies on health risk modeling of heavy metals in soil or dust in Nigeria before the previous study by Olujimi et al.5 Therefore, the model parameters might contain some uncertainty due to different geographical locations and human genetic variation. Additionally, the number of Nigerians living with cancer and cancer-related diseases has been on the rise, with approximately 100,000 new cases recorded annually.32,33 Consequently, parameters for toxicity of heavy metals were derived from USEPA guidelines and the relevant literature (Equations 2–7). In adopting the models for dermal absorption, mechanisms via metal-ions complex formation (with carboxylic (-COOH), amine (-NH2) and thio (-SH) of proteins) provided the basis on which the assumption proposed by the USEPA and other literature was based.34,35
Results and Discussion
The recovery efficiencies for all of the metals ranged between 92.05% and 101.01% for ICP-MS (inductively coupled plasma-mass spectrometer) with a deviation of less than 10% in all cases. The drift in instrumentation measurement ratio was also minimal at 0.89 to 1.01.
Chemical Partitioning and Distribution of Metals in Soil
Chemical partitioning and concentrations of the four fractions for the 16 metals obtained by the sequential extraction procedure are presented in Table 1. Eight of these metals: aluminum (Al), As, copper (Cu), Fe, Mn, nickel (Ni), antimony (Sb) and Se were exclusively present in the residual fractions, indicating that these metals are strongly bound to the resistant components of the soil matrix. Barium (Ba) and Cd were also present mainly in the residual fraction with mean proportions of 45.27–94.48% and 45.95–78.82%, respectively. These results are in line with previously published data.36 In all of the soil samples, tin (Sn) was present as mobile extractable Sn ranging from 69.13–90.59%. With the exception of sample 7, all other samples showed high proportions of mobile Pb, ranging from 56.69–79.80%. The major contribution to the mobile fraction of these metals (Sn and Pb) in the soils was from the oxidizable fraction of the soil, representing 56.16–81.63% and 45.32–73.55% of the sample, respectively.
The total metal concentration and fractional percent as presented in Tables 1 and 2 shows that the general distribution pattern for the pooled mine pits is Al > Fe > Sn > silicon (Si) > Se > Pb > Mn > cobalt (Co) > As > Ba > Zn > Cr > Cd > Sb. The order reported in this study differs from the distribution pattern reported in other studies in Niger and Zamfara states in Nigeria and Malaysia.37–39 The mean concentration of Pb, Zn, Cd, and Cu at the mine site was lower than values reported in tailings and soil around a Pb-Zn mine in Spain.40 In addition, the mine pit trace metal concentrations could be arranged in the order of IGUN 5 > IGUN 3 > IGUN2 > IGUN 4 > IGUN 6 >IGUN 1 > IGUN 7. The levels and distribution of Pb differs from previous values reported for gold mine pits in Luku, Niger State.41
The mobility sequence of the trace metals is based on the sum of acid-extractable, reducible and oxidizable fractions for all of the soil samples (Table 2). The mobility sequence is in the order of Sn (82.17%) > Pb (58.01%) > Si (36.01%) > Mn (35.92%) > Cd (34.82%) > Co (24.54%) > Zn (20.25%) > Ba (19.12%) > Cu (17.57%) > Se (16.42%) > Sb (13.81%) > Cr (12.48%) > Ni (11.99%) > Fe (6.31%) > Al (1.42%). Notably, Sn and Pb were more than 50% present in mobile phases and therefore could be considered to be more readily mobilized and bioavailable in the soil samples.
The geoaccumulation index (Igeo) for the 16 elements investigated is presented in Table 3. The mean Igeo levels ranged from −8.24 (Si) to 8.58 (Sn) while the Igeo class ranged from 0 (uncontaminated) to 6 (extremely contaminated). Al, Ba, Cu, Fe, Si and Zn were found in the uncontaminated class, while Ni ranged from uncontaminated to moderately contaminated. Cr and Co ranged from uncontaminated to strongly contaminated, while As, Cd, Pb and Sn were found to be extremely contaminated at the site. As, Cd, Pb, Cr and Co are included in the IARC list of probable carcinogenic compounds. The negative Igeo values obtained for some of the metals (Table 3) is an indication of relatively low levels of contamination of these metals in the soil samples and the background variability factor (1.5) used in the Igeo equation.42
Non-cancer and Cancer Profiling Using Mobile Fraction Concentrations for Adults
The results of the carcinogenic and non-carcinogenic risk assessment for children and adults using the summation of mobile fractions are presented in Table 3 and 4, while Figures 4 and 5 show the percentage contribution of each metal to both the cancer and non-cancer effects. For the non-cancer effects for adults, dermal exposure to Ni (2.30E-01) was the major exposure route, followed by the ingestion route for Co (1.44E-01), Al (1.22E-01) and As (1.02E-01), respectively. The non-cancer distribution pattern for the ingestion route was: Co > Al > As > Mn > Ni > Cr > Cd > Pb >Cu > Zn; dermal route: Ni > As > Cr > Al > Co > Cd > Pb > Mn > Cu > Zn, while the distribution pattern for the inhalation route was Al > Co > Ni > Mn > As > Cd > Cr. The hazard index summation for the sites using mobile fractions (F1+F2+F3) shows that arsenic poses a higher risk of non-cancer effects among the studied elements (Table 2; Figure 4). This is followed by Co and Al, respectively, while Zn poses the lowest risk. The sum (∑)HI for all of the metals and all routes is 5.16E-01. Individually and collectively, the values were lower than 1, and this is an indication that the soil poses no non-cancer threat to adults. The range reported for the non-cancer effects in the present study is consistent with previous studies in China and Nigeria,5,14,24 but lower than values reported by Zheng et al.22,25 and Shi et al.29 Arsenic contributed 34% of the non-cancer effects (Figure 4), while other major contributors are Co, Al and Mn with contributions of 29%, 26% and 6%, respectively. It is important to note that the overall non-cancer effect for all routes of exposure and all metals were in the 50th percentile of the acceptable limit of 1. This is an indication of potential side effects, as some miners might be more affected depending on their underlying health conditions and particular body chemistries.
The carcinogenic risk (CR) for As, Cd, Co, Cr, Ni and Pb was considered. The cancer risk ranged from 6.17E-13 to 7.77E-05 for Cd and As, respectively, while ∑CR for all the metals and routes was 1.34E-04. The percentage contribution for each metal to CR is presented in Figure 5. This shows that As contributed 58%, while Pb and Cr contributed 25% and 17%, respectively. The metals distribution pattern reported in this study differs from previous work on dust reported by Shi et al.29 and Olujimi et al.5 The range for all of the metals is within the acceptable limit for no probable cancer effect, at 1.0E-04 to 1.0E-06. However, it is noteworthy that the ∑CR for the metals considering all routes (1.34E-04) was higher than the 10-5 risk factor acceptable by some authorities.29
Non-cancer and Cancer Profiling Using Mobile Fraction Concentrations in Children
Children are highly susceptible to environmental pollutants due to their unique developmental stage. As presented in Table 4, the non-cancer distribution pattern for the studied metals was Co > Al > As > Mn > Cr > Ni > Cd > Pb > Cu > Zn; As > Cr > Al > Ni > Cd > Pb > Mn > Cu > Zn; and Mn > Al ≈ Co ≈ Ni > As > Cd > Cr for the ingestion, dermal and inhalation routes, respectively. The distribution pattern for the ∑HI of the metals for all routes was Co > Al > As > Mn > Cr > Cd > Cu > Pb > Zn. Individually, Co, Al, and As exceeded the acceptable limit of 1 for non-cancer effects in children. This suggests that these metals could cause or contribute to non-cancer effects in children. This finding is similar to the trend reported by Zheng et al.22,25 and Li et al.43 However, the values reported here are higher than findings elsewhere.5,24,29,42 The non-cancer effects due to mining activities may be more significant in children because the mine site is less than 100 m from the village and children usually follow their parents to the site. As shown in Figure 4, Co had a 34% contribution to the HI, followed by Al, As and Mn at 29%, 26% and 7% respectively. The overall non-cancer effect (∑HI) was 3.97 for all metals, considering all routes of exposure. This is about 4 times higher than the acceptable limit of 1 and suggests that children who are exposed to the mining site have a risk of non-cancer effects.
The carcinogenic risk for children ranged from 2.73E-12 to 4.64E-04 for Ni and As, respectively. The ∑CR for the six studied metals was 8.97E-04. As depicted in Figure 5, As presented the highest risk for cancer development at 52%, followed by Pb and Cr at 34% and 14% respectively. The impacts from other metals were negligible. Furthermore, the cancer risk assessment showed that Cd, Co, Ni and Pb were generally below the acceptable limit range of 1.0E-04 to 1.0E-06, while As and Cr were within the stipulated range. The risk factor for these two metals was higher than the regulated limits in other countries.29 In addition, the ∑CR (8.97E-04) for the site indicates that there is probability of cancer development due to exposure to these metals over all routes of exposure.
The impact of anthropogenic heavy metal pollution at the Igun mine site was evaluated using geoaccumulation indices (Igeo) and human health risk. The indices varied at each location, suggesting that the soil ranged from uncontaminated to extremely contaminated with respect to the metal analyzed. The chemical forms of soil samples at the Igun mine site differed slightly from each other, with most of the metals exclusively present in the residual fractions, while Sn was mainly present in the mobile form. Health risk assessment of the metals indicated that the dermal route was the major form of exposure for both adults and the children and presented the most probable route for risk of cancer development. Non-cancer effects were more likely in children due to Al, Co and As, as the values for these metals and metalloid exceeded acceptable limits. The present study confirms that both adults and children are at risk of cancer development, with children having a higher risk considering all exposure routes.
This work was funded in part by a grant from Pure Earth. The authors also want to thank Cape Peninsula University of Technology, Cape Town for support given during the analysis.
Competing Interests. The authors declare no competing financial interests.