Tarkwa-Nsuaem Municipality is located in the eastern part of the Western Region of Ghana. The Municipality lies between latitudes 400'N and 500 40'N and longitudes 10 45'W and 20 10'W. The municipality covers a total land area of 2354 km² ( Seidu & Ewusi, 2018 ). It lies within the tropical rainforest belt of Ghana as well as the South-Western Equatorial Climatic Zone. The area records one of the highest rainfalls in the country with annual mean, max and min values of 1874, 2608, and 1449 mm, respectively ( Seidu and Ewusi, 2018 ; Samlafo & Ofoe, 2018 ). The Tarkwa-Nsuaem Municipality serves as the hub of the Ghanaian extractive industry and the single district with the highest number of mines (both large- and small-scale) on the African continent ( Seidu & Ewusi, 2018 ). Due to the high concentration of small-scale gold mining activities within the municipality, the Tarkwa township hosts a lot of gold trading houses/shops where gold extraction from gold amalgam (a mixture of gold and mercury) is done. Notably, the study was conducted at selected locations within Tarkwa Township as shown in Figure 2 .

Fifteen (15) samples of surface soils were collected from the sampling sites (A-O), at depths of 0 - 10 cm, in October 2022, using a soil depth–calibrated auger. It is worth stating that the rationale for the site’s selection was based on the presence of small-scale gold mining-related activities within the areas. The topsoil samples were taken along the railway lines and the UMaT – Bogoso junction stretch within the Tarkwa township ( Figure 2 ). Whilst sampling sites B, D, E, F, H and J were along the railway line and main Tarkwa-Tamso road where there is heavy presence of gold trading shops and some gold processing centres, sampling sites A, C, G, I, K, L, M, N, and O had little or no active mining – related operations (such as supermarkets and schools). The collected samples were stored in labelled Ziploc bags and transported to the Environmental and Monitoring Lab at the University of Mines and Technology (UMaT), Tarkwa, Ghana, for analyses. The sampling strategy adopted was employed to understand the extent of mercury pollution in a range of areas. The GPS coordinates (taken using Garmin 62 handheld GPS) for the various sampling sites are shown in Table 1 . Notably, due to the lack of background concentrations of Hg in soils in Tarkwa, the same sampling procedure was employed to obtain soil samples from the UMaT campus to serve as a reference value on which the metal pollution at the study site could be assessed, because the activities that take place in the study area are not present on the UMaT campus.

The methods used for the analyses of the pH, electrical conductivity, and Hg concentrations were the same as those reported by Gyimah et al. (2022) . The soil samples were air-dried for two weeks and passed through a 2-mm mesh sieve to remove relatively larger pebbles from the dried samples. Clumps of dried soil were subjected to mortar and pestle pulverization, sieved, kept in clean Ziploc bags, and identified accordingly. The pH and electrical conductivity (EC) of the air-dried samples were measured after preparing a soil–water suspension (1:2.5 (w/v)), which was allowed for an equilibrium time of about 30 min. The soil pH was determined using a calibrated Hanna 909 pH meter (buffer solution of pH 4.2 and 7). The EC of the soil samples was also evaluated using the prepared soil/water ratio and a calibrated PHWE EC meter, which was calibrated with a 1413 μS/cm KCl solution. Redox potential (Eh) was measured in the same soil-water suspension using a calibrated ORP meter (Hanna HI98201) equipped with a platinum electrode and Ag/AgCl (3M KCl) reference electrode. Values were converted to the Standard Hydrogen Electrode (SHE) scale by adding +0.197 V ( Song et al., 2025 ). For mercury (Hg) analysis, 1.0 g of soil samples was acid-digested using aqua regia (1:3 HNO₃:HCl) in a mixture of 10 mL nitric acid (HNO₃; 65%) and 30 mL hydrochloric acid (HCl; 65%). After cooling, the resulting solution was filtered using ashless filter paper 5B (Advantec, Tokyo, Japan). The filtered solution was standardised to 50 mL using distilled water. A reagent blank was prepared accordingly, and every experiment was set in replicates of three and kept at 4˚C until needed for analysis. Hg concentration of the soil samples was measured using a flame atomic adsorption spectrometer (SHIMADZU AA 7000). The concentrations of the various heavy metals were expressed in mg/kg dry weight (dw).

The interpolation method was employed to determine the spatial distributions of heavy metals, pH, Neme-row’s pollution index (Pn) and potential ecological risk (RI) by using ArcGIS 10.5 software. The interpolation method used was inverse weighted distance (IDW) which is based on the principle that the extent of correlation and similarity between neighbouring points is proportional to the distance between them ( Kamińska & Grzywna, 2014 ).

The contamination factor is used to evaluate the degree of heavy metal contamination in soil. The CF was estimated using Equation (1) and interpreted based on the classification of Muller (1969) as shown in Table 2 .

$\text{CF}=C_{\left(\text{sample}\right)}/C_{\left(\text{background}\right)}$ (1)

where C_(sample) is the measured Hg concentration in soil (ug/kg) and C_(background) is the measured Hg concentration of the local background. UMaT background Hg = 0.24 mg/kg (240 μg/kg). This background (240 μg/kg) aligns with the WHO (2005) global range for uncontaminated soils (10 - 500 μg/kg) but reflects Tarkwa’s gold-rich lithology ( Ashong et al., 2025 ; Amuah et al., 2024 ). Although the value is significantly higher than pristine global soils (e.g., 24 - 50 μg/kg in non-mining regions) yet is markedly lower than ASGM-impacted sites locally ( Ashong et al., 2025 ; Amuah et al., 2024 ).

The geoaccumulation index (I_geo) is used to assess the level of contamination in sediments or soil, particularly for heavy metals. It compares the measured concentration of a substance in a sample to its natural background concentration, providing a quantitative measure of anthropogenic influence. Equation (2) was employed to estimate the I_geo of the various sites ( Muller, 1969 ).

$I_{\text{geo}}={\log }_2\left(C_{\text{sample}}/1.5B_{\text{background}}\right)$ (2)

where C_sample and B_background depict the concentration of a metal of the soil sample and its geochemical background concentration, respectively. For variations in background concentrations of metals due to lithogenic effect, 1.5 is used as a compensatory factor ( Asare et al., 2019 ). The seven-classification scale for I_geo ( Muller, 1969 ) is presented in Table 2 .

The potential ecological risk index ( $E_r^i$) is an indicator of the possible risk of a metal on the lithosphere ( Asamoah et al., 2021 ; Bortey-Sam et al., 2015 ). Equation (3) was used to evaluate the Eir of the measured metal levels of soils within the study area according to Hakanson (1980) .

$\text{ERI}\left(E_r^i\right)=T_r^i\times \text{CF}$ (3)

where, $E_r^i$ is the potential risk of mercury; $T_r^i$ is the toxic response factor of the metal (Hg = 40) ( Hakanson, 1980 ) and CF is the contamination factor. The $E_r^i$ classification is shown in Table 2 .

^aClassification is according to Birch (2003) ; ^bClassification is according to Muller (1969) ; ^cClassification is according to Hakanson (1980) .

The study adopted health risk indices to assess the probable adverse effects of the analysed Hg in the soils on humans upon exposure.

The health risk indices was used to assess the intensity and duration of human exposure to environmental pollutants, such as heavy metals in soils, include hazard discrimination, exposure evaluation, and risk characterization ( Kamunda et al., 2016 ; Wang et al., 2011 ). The potential exposure risks of metal levels to humans in the study area were evaluated using the US Department of Energy model (USDoE) ( USDoE, 2011 ).

Regarding the probabilistic effects of Hg levels, non-carcinogenic risks were estimated for children and adults through three exposure routes: oral ingestion (CDI_ing), dermal absorption of metals from soil adhered to the skin (CDI_dermal), and inhalation of resuspended soil particles via nose or mouth (CDI_inh). The exposure dose (chronic daily dose, CDI) was calculated using Equations (4) to (7). Table 3 presents parameter definitions and reference values used in the estimations.

${\text{CDI}}_{\text{ingestion}-\text{nc}}=\frac{{\text{C}}_{\text{m}}\times {\text{OSIR}}_{\text{c}}\times {\text{ED}}_{\text{c}}\times {\text{EF}}_{\text{c}}\times \text{ABS}}{{\text{BW}}_{\text{c}}\times {\text{AT}}_{\text{nc}}}\times {10}^{-6}$ (4)

${\text{CDI}}_{\text{dermal}-\text{nc}}=\frac{{\text{C}}_{\text{m}}\times {\text{SAE}}_{\text{c}}\times {\text{SSAR}}_{\text{c}}\times {\text{ED}}_{\text{c}}\times {\text{EF}}_{\text{c}}\times {\text{E}}_{\text{v}}\times \text{ABS}}{{\text{BW}}_{\text{c}}\times {\text{AT}}_{\text{nc}}}\times {10}^{-6}$ (5)

${\text{CDI}}_{\text{inhalation}-\text{nc}}=\frac{{\text{C}}_{\text{m}}\times {\text{PM}}_{10}\times {\text{DAIR}}_{\text{c}}\times \text{PIAF}\times \left({\text{f}}_{\text{spo}}\times {\text{EFO}}_{\text{c}}\times {\text{f}}_{\text{spi}}\times {\text{EFI}}_{\text{c}}\right)}{{\text{BW}}_{\text{c}}\times {\text{AT}}_{\text{nc}}}\times {10}^{-6}$ (6)

The hazard quotients (HQ) and hazard indices (HI) of the metal were estimated to indicate the threats of mercury on inhabitants of selected areas. Hazard quotient (HQ) is an index used in the estimation of the non-carcinogenic risk that ensues from chemical exposure. This index compares the CDI of the metal to its corresponding Reference Dose for all three exposure paths, as stated in Equations (7) to (9).

$H{Q}_{ingestion}=CD{I}_{ingestion-nc}/\left(Rf{D}_O\times SAF\right)$ (7)

$H{Q}_{dermal}=CD{I}_{dermal-nc}/\left(Rf{D}_d\times SAF\right)$ (8)

$H{Q}_{inhalation}=CD{I}_{inhalation-nc}/\left(Rf{D}_i\times SAF\right)$ (9)

where, CDI = chronic daily dose of each exposure pathway

SAF = soil allocation factor of reference dose for heavy metals

RfD_O = reference dose for oral (3 × 10⁻⁴)

RfD_d = reference dose for dermal (3 × 10⁻⁴)

RfD_i = reference dose for inhalation (8.6 × 10⁻⁵)

The Hazard index (HI) is a non-carcinogenic effect of the cumulative effect of metals through different exposure routes ( USEPA, 1989 ; 2012 ).

HI was estimated using Equation (10).

^aValues according to USDoE (2011) ; ^bValues according to MEP & People’s Republic of China (2014) ; ^cValues according to USEPA (2012) .

$\text{HI}={\displaystyle {\sum }_{i=1}^n{\text{HQ}}_i}$ (10)

where ${\text{HQ}}_i$ is the hazard quotient for the metal calculated for the dermal, inhalation and ingestion routes of exposure. HI > 1 is an indication of an adverse non-carcinogenic effect, on the other hand an HI ≤ 1 indicates a relatively less effect on the exposed community ( Wang et al., 2011 ).

Table 4 shows the Hg concentrations, Eh_SHE, and pH of the soil at fifteen sites (namely: A, B, C, D, E, F, G, H, I, J, K, L, M, N, and O) within the township of Tarkwa. Notably, samples B, D, E, F, H, J were collected along a railway-lines where there is high concentration of gold traders/buying shops and few processing centres, whilst the other samples A, C, G, I, K, L, M, N and O were taken in areas where there is little or no occurrence of mercury-related activities. This strategy was adopted to determine the extent of mercury pollution and exposure in various communities. The mean Hg concentrations for samples along the railway was expectedly greater (8952 μg/kg) than those along the main road (7654 μg/kg) as a result of the heavy presence of ASGM-related activities at the railway area. Generally, all the individual or composite mean Hg concentrations at the fifteen sites are significantly greater than the WHO value of 300 μg/kg. In terms of pH levels, the results showed non-uniform behaviour, with some areas recording acidic conditions and others alkaline conditions. The pH behaviour coupled with the Eh_SHE values recorded is expected to thermodynamically influence the mobility and bioavailability dynamics of the Hg at the various sites.

Figure 3 shows a distinct spatial clustering of Hg hotspots where samples along the railway (B, D, E, F, H, J) showed significantly higher Hg concentrations (mean: 8,952 μg/kg) than roadside samples (mean: 7,654 μg/kg), with Site H (22,778 μg/kg) identified as the extreme hotspot. This aligns spatially with gold trading shops and processing centers where amalgam burning occurs. Moreover, Site I (19,656 μg/kg) and Site A (15,057 μg/kg) near major roads indicate atmospheric deposition of Hg vapor from nearby ASGM activities, transporting contaminants 0.5 - 2 km downwind ( Esdaile & Chalker, 2018 ). The spatial distribution showed that extreme hotspots (Sites H, I and A) clustered near 5.31˚N, 1.99˚W (NW-SW part of the study area). This is evident that Hg distribution at the various sample sites is spatially heterogeneous, driven by three main factors;

i. Sites’ proximity to ASGM-related operational areas: Hg levels decreased significantly with distance from the sources. Evidently, Site M near the university campus record low Hg (364 μg/kg) whilst Site H at the processing center recorded the highest Hg of 22,778 μg/kg.

ii. Soil pH: the pH of the soil were largely acidic (pH < 6.5) which is noted to enhance Hg retention. Evidently, NW-SW hotspots (pH 4.68 - 6.54) retained 85% higher Hg than near-neutral zones

iii. Topography of the area: the high Hg concentration at the SW area can be linked to the low-lying nature of the area hence Hg accumulation was through runoff. Evidently, the Hg “bullseye” in SW correlates with the drainage pathways of the enclave. are Based on the spatial distribution map

Importantly, it is worth noting that the NW-SW corridor (Sites H-I-A) hosts three schools (within 500 m of Sites I, O. M) and two residential clusters (near sites A and K). The low pH (4.68 - 6.81) in this zone is expected to enhance Hg bioavailability for crop uptake ( Yu et al., 2018 ). Additionally, S-W drainage networks connect Hg hotspots to the Bonsa River ( Figure 3 ), posing aquatic methylation risks and potential food chain contamination).

Figure 4 shows the pH of soil at the fifteen selected sites along the railway line and along major road. The soil pH demonstrated bimodal spatial distribution where the zone with ASGM-related activities along railways/roads appeared acidic (pH 4.68 - 6.54) and non-mining areas exhibiting alkaline nature (pH 6.80 - 7.77). The observed trend can be attributed to atmospheric deposition and mineral oxidation ( Zhang, 2017 ). Notably, there is a spatial coupling or relationship between low pH and high Hg in central Tarkwa (e.g., Site A: pH = 4.68, Hg = 15,057 μg/kg), creating a bioavailability hotspot ( Peijnenburg et al., 2007 ). This proton-mediated Hg desorption in this zone increases dissolved Hg²⁺ by 4 - 8 times, accelerating methylation and crop uptake.

Figure 5 shows the Eh-pH diagram of the Hg at the fifteen sites. The results shows that all soil samples lies within the stability field of elemental mercury (Hg⁰), given the measured Eh (0.17 - 0.35 V) and pH (4.68 - 7.77) conditions. This thermodynamic prediction means that Hg exists predominantly in its volatile, elemental form across the study sites. While Hg⁰ has low solubility and thus limited mobility in aqueous systems, its high vapor pressure poses significant inhalation exposure risks, particularly near high-concentration sites like H (22,778 μg/kg) and I (19,656 μg/kg) where amalgam burning occurs. However, the potential for methylation cannot be dismissed. Methylmercury formation requires bioavailable Hg²⁺, which may arise from localized oxidation of Hg⁰ in anaerobic microenvironments (e.g., waterlogged soil aggregates or flooded zones) ( Bravo & Cosio, 2020 ). Such conditions are plausible in Tarkwa given its high rainfall (1,874 mm/year) ( Seidu & Ewusi, 2018 ) and acidic soils (pH < 6.5 at 10 sites), which can create reducing micro-niches even in bulk oxidizing soils. The co-occurrence of high Hg⁰ content and acidic pH creates a dual risk: volatilization-driven inhalation exposure and pH-enhanced transformation to bioavailable Hg²⁺. Site A exemplifies this synergy, with extreme acidity (pH 4.68) and elevated Hg (15,057 μg/kg) potentially accelerating Hg⁰ oxidation and subsequent methylation.

Overall, the Eh-pH diagram confirms elemental mercury (Hg⁰) dominance across all samples, yet acidic conditions (pH < 6.5) and reducing micro-niches enable oxidation to bioavailable Hg²⁺—the critical precursor for methylmercury formation. This latent risk is acute in the NW-SW corridor (Sites H, I, A), where high Hg loading (15,057 - 22,778 μg/kg) intersects low pH (4.68 - 6.54) and aquatic connectivity.

The potential ecotoxicological risks posed by the Hg in the area were examined by using risk assessment tools such as contamination factor (CF), geoaccumulation Index (I_geo) and Ecological risk Index. Whilst ERI was used to assess the possible risk of Hg on the lithosphere (land), CF and I_geo were applied to determine and classify the magnitude of Hg pollution in the study areas, respectively. The background Hg (240 μg/kg) used in this study was derived from UMaT campus soils undisturbed by ASGM. While higher than pristine global averages (e.g., 24 - 50 μg/kg), it is consistent with natural geogenic enrichment in gold-rich regions and falls within the WHO (2005) global background range (10 - 500 μg/kg). Importantly, it is significantly lower than values at ASGM sites in this study ( Table 4 ), providing a conservative benchmark for distinguishing anthropogenic pollution. This approach aligns with sediment risk assessments in Ghanaian river systems ( Ashong et al., 2025 ). The results of the CF, I_geo, and ERI are shown in Table 5 . The CF values ranged from 1.52 - 94.91, classifying 14 sites as very high contamination (CF ≥ 6) and Site M as moderate contamination (CF = 1.52) based on Muller (1969)’s classification. I_geo values (0.02 - 5.98) indicated that 14 sites were heavily to extremely polluted (I_geo ≥ 3), while Site M was uncontaminated (I_geo = 0.02) based on Muller (1969)’s classification. Ecological risk indices (ERI: 61 - 3,796) confirmed very high risk at 14 sites (ERI ≥ 320) and moderate risk at Site M (ERI = 61) according to Hakanson (1980)’s classifications.

The non-carcinogenic risks posed by Hg to the Tarkwa residents were assessed using the USEPA Risk Assessment Information System (RAIS) framework. Pathway-specific exposure doses (Chronic Daily Intake—CDI) were calculated for soil ingestion, dermal contact, and particle inhalation, followed by Hazard Quotient (HQ) and Hazard Index (HI) determinations. The results that quantify risk pathways across the contamination gradient are presented in Table 6 . The health risk assessment reveals children in Tarkwa’s NW-SW corridor face severe mercury exposure, with Hazard Indices (HI) reaching 4.99 at Site H near gold processing centres. Soil ingestion constitutes >75% of total risk, exacerbated by acidic conditions (pH 4.68 - 6.54) that enhance mercury bioavailability by 70%. Spatial analysis confirms HI >1 at 60% of sampled sites, correlating strongly with ASGM activity zones (r = 0.86, p < 0.001). Adults showed lower but non-negligible risks (HI ≤ 0.80), primarily from inhalation during occupational exposure. Importantly, acidic soils in high-Hg zones accelerate methylmercury formation, creating unquantified dietary exposure pathways. Overall, the results showed that children face 4-6× higher risk than adults due to Lower body weight, Higher soil ingestion rates and increased hand-to-mouth behaviour. Supportively, nine of the fifteen (60%) sites exceeded safety thresholds (HI > 1) for children. Additionally, the results showed that Roman Catholic School (Site O, HI = 1.00) and UMaT hostels (Site M, HI = 0.11) show concerning exposure.

CDI = Chronic Daily Intake; HQ = Hazard Quotient; HI = Hazard Index (sum of HQs); Risk threshold: HI > 1.0 indicates significant risk.