Environmental Implications of the Soil-to-Groundwater Migration of Heavy Metals in Mining Area Hotspots

Abstract

Groundwater contamination was studied at several hotspot sites in the Majdanpek copper mining area (Serbia). These sites include a milling facility, a metallurgical wastewater treatment plant, a heavy vehicle service area, and a waste disposal site. In addition to Cu, high concentrations of As and heavy metals (Cd and Pb) were detected in groundwater and soil at the same sampling points. Mining operations and heavy vehicle transport activities have been identified as the main sources of pollution. The migration of metals from soil to groundwater, expressed as a concentration ratio, were the highest for Co and the lowest for Mn. The environmental implications of groundwater pollution were studied using the heavy metal pollution index (HPI), Nemerov pollution index (NPI), hazard index (HI), and incremental lifetime cancer risk (ILCR). HPI and NPI show the high potential of groundwater to have adverse environmental effects. HPI ranges in the following descending order of metals: Cd > Pb > As > Mn > Ni > Cr > Hg > Cu > Zn. NPI exceeds the threshold of 0.7 in 66.7% of the samples. Potential human exposure to the studied groundwater may cause severe health problems in adults, with HI ranging from 0.61 to 5.45 and ILCR from 1.72 × 10⁻⁴ to 1.27 × 10⁻³. Children were more susceptible to non-carcinogenic risk than adults, with HI ranging from 0.95 to 8.27. However, the results indicated that children were less prone to carcinogenic risks, with ILCR ranging from 5.35 × 10⁻⁵ to 3.98 × 10⁻⁴. Arsenic is the most contributing element to both risks. This research imposes the need for enhanced groundwater monitoring at hotspots in the mining area and the adoption of remediation plans and measures.

1. Introduction

Groundwater is one of the most important drinking water sources essential to every aspect of human life, including household, agriculture, and industry [1,2]. Thus, groundwater quality significantly influences human well-being, food security, and environmental sustainability [3]. One of the most prominent threats to groundwater quality is heavy metals because of their high toxicity, poor biodegradability, and strong accumulation capabilities [4,5].

Naturally, heavy metals in groundwater are present as a result of rock and mineral weathering, volcanic action, organic decay, and atmospheric deposition [6,7,8]. However, groundwater is being adversely affected by human activity at an accelerating rate. Moreover, over the past few decades, anthropogenic activities, such as rapid urbanization, industrial development, untreated industrial discharges, the overuse of fertilizers in agriculture, and traffic, have become the primary causes of heavy metal groundwater contamination worldwide [9,10,11]. Heavy metals pose a particular threat in areas where mining activities such as surface mining, deep mining, or auxiliary projects occur [12,13,14]. The formation of mining wastewater rich in heavy metals such as arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), manganese (Mn), zinc (Zn), and cobalt (Co) is predominantly a result of the weathering, dissolution, leaching, and erosion of extensive rock waste and tailings, facilitated by rainfall and surface runoff [15,16,17,18]. The penetration of contaminated mining wastewater from the vadose zone into the aquifer has subsequently led to groundwater pollution in areas characterized by mining activities.

Human exposure to heavy metals can occur through multiple routes, such as inhalation, skin exposure, and ingestion, but the primary route of heavy metal intake from groundwater is through drinking water [19,20]. Consuming water with high heavy metal concentrations can result in numerous harmful health effects. Exposure to high levels of arsenic in drinking water can lead to diabetes, skin lesions, and chronic bronchitis. Moreover, arsenic is also a known carcinogen, linked to tumors in the bladder, kidneys, lungs, and liver [21,22]. Furthermore, Cr(VI) and Cd are known carcinogens. Chronic Cr(VI) exposure can cause lung and gastric cancers, while prolonged Cd exposure can lead to pneumonia and cardiovascular disorders [23,24]. Pb poisoning manifests through heart disease, hypertension, and chromosomal damage, whereas elevated Hg levels can cause brain and lung damage, and hypothyroidism by blocking thyroid hormone production [21,25]. Therefore, evaluating groundwater quality is essential for maintaining public health and efficiently managing groundwater resources.

Over the past few decades, different quantitative indices for assessing heavy metal pollution in groundwater have been employed, including the pollution index (PI) [26,27], heavy metal pollution index (HPI) [3,28], and Nemerow pollution index (NPI) [29,30]. Moreover, it is crucial to determine heavy metal sources and conduct quantitative evaluations of the associated health risks. In recent years, receptor models, such as hierarchical cluster analysis (HCA) [31], factor analysis (FA) [32], principal component analysis (PCA) [33,34], absolute principal component score-multiple linear regression (APCS-MLR) [35], and positive matrix factorization (PMF) [36,37], have been used to determine the source and distribution of environmental contaminants. While receptor models have effectively examined pollution in soil, sediment, and air, there has been limited application of these methods in identifying the sources of pollution in groundwater. Among receptor models, PMF stands out as a powerful method for identifying primary pollution sources, even without prior knowledge regarding their profiles [38]. Additionally, by providing quantitative data on each pollutant’s contribution from various sources, this approach helps prioritize pollution control measures [26,27]. Multiple studies have indicated that PMF yields more precise results than other receptor models [6,37,39]. Hence, this study used the PMF methodology to identify groundwater pollution sources.

This study investigates heavy metal pollution in the groundwater of the Majdanpek mine in eastern Serbia. The study area is well known for its decades-long history of copper mining, which significantly contaminates water resources with heavy metals [40,41]. Using the Majdanpek mine as a case study, this study implemented an extensive methodology to assess the health risks caused by heavy metals in groundwater at hotspots, identify likely pollution sources, and establish groundwater pollution levels. The aims of this study were to (1) explore heavy metal content in groundwater and their migration characteristics from soil to groundwater, (2) determine possible sources of groundwater heavy metals by combining Pearson correlation analysis and the PMF methodology, (3) analyze environmental implications through various heavy metal pollution indices, and (4) determine non-carcinogenic and carcinogenic health risks that heavy metals in groundwater pose to people’s health.

There is a lack of comprehensive work in the literature addressing heavy metal contamination hotspots in copper mining sites. Thus, this study holds significance for the scientific community as it aims to broaden existing knowledge regarding a subject that has not received enough attention, providing new insights and perspectives into the subject matter.

2. Materials and Methods

2.1. Study Area

The study area is located within the Majdanpek copper mining region (Figure 1). The Majdanpek mine is situated in eastern Serbia, southeastern Europe, close to the city of Majdanpek. Holding considerable reserves, the Majdanpek deposit is one of the most important copper reserves in Serbia and the world [42]. Mining operations at the Majdanpek copper mine began in 1955 and are segmented into two regions: the North Revir and South Revir open pits. The North Revir predominantly hosts lead and zinc ores alongside minor deposits of porphyry copper. Conversely, South Revir exhibits a substantial presence of porphyry copper deposits and sulfide deposits rich in copper and gold, accompanied by numerous contact skarns [43]. The Majdanpek deposit, with a length of approximately 5 km and an average width of 300 m, exhibits a distinctive feature of low-grade copper mineralization (>0.1% Cu) reaching depths exceeding 1 km. This mineralization primarily occurs in the form of stockworks within the metamorphic aureole surrounding andesitic dykes. The overall copper reserves within the Majdanpek ore are estimated to be 800 megatons, with copper content ranging between 0.4% and 0.8% and gold content in the range of 0.25–1.0 g/t [42,43].

The study area is characterized by hilly topography (Figure S1), mostly covered by woods. Geologically, Eastern Serbia is situated within the Carpathian-Balkan belt, which is renowned as one of the earliest mining regions globally. Furthermore, the Majdanpek mining region represents a part of the Timok Magmatic Complex, which is integral to the Carpathian-Balkan belt, spanning around 60 km in length and 20 km in width. Within the Timok Magmatic Complex, the geological composition comprises Proterozoic metamorphic rocks forming the base, overlaid by Paleozoic metamorphic and sedimentary strata intruded by Hercynian granitoid. Notably, xenoliths from these formations are evident within the volcanic sequence of the Timok Magmatic Complex. Mesozoic formations predominantly comprise carbonate units dating back to the Triassic, Jurassic, and Lower Cretaceous periods. Moreover, within the Timok Magmatic Complex, Cretaceous volcanic formations dominate. During the Upper Cretaceous period, the Timok Magmatic Complex underwent three distinct stages of volcanic activity, giving rise to andesitic lava, andesitic pyroclastic rocks, and sporadic occurrences of dacite [40,41,43].

2.2. Sampling and Laboratory Analyses

Nine groundwater samples were collected from boreholes within the study area (Figure 1 and Table S1 from the Supplementary Materials). Groundwater sampling was conducted in accordance with the ISO 5667-11 and EPA/540/S-95/504 protocols. One-liter polyethylene bottles were used for sampling. A low-flow submersible pump (Geotech model Geosub) operating at a flowrate of 100 mL/min was used for the sampling process. Using the low-flow approach helps minimize disruption to the groundwater system, thereby enhancing the accuracy of the sampling process. To minimize the impact of stagnant water, the monitoring wells were pumped out for 10 min before sample collection. Prior to sampling, bottles were first washed with deionized water and then with the water to be sampled. After pre-cleaning the bottles, each sample was filtered using 0.45 μm Millipore filters. Afterward, the samples were acidified using concentrated nitric acid (Merck, Darmstadt, Germany) to pH < 2 to prevent the hydrolysis, precipitation, and bacterial contamination of heavy metals. The samples were then sealed, carefully labeled, and stored in a portable refrigerator at 4 °C during transport from the sampling site to the laboratory for instrumental analysis of heavy metals. The concentrations of 10 heavy metals, including As, Cd, Cr, Cu, Hg, Ni, Pb, Mn, Zn, and Co, were determined using inductively coupled plasma mass spectrometry (ICP-MS, iCAP Qc, Thermo Scientific, Waltham, MA, USA), as prescribed by the American Public Health Association [44].

The soil was collected at the same locations as the groundwater. At the boreholes, composite soil samples of 0–15 m depth were taken. At each location, 6 kg soil containing 15 subsamples (400 g each) was collected in a previously marked polyethylene bag. The sample portions were stored at 4 °C until further analysis in the laboratory. The soil samples were then dried for 24 h at a temperature of 60 °C, then crushed in a mortar, and passed through a 2.0 mm sieve, to remove coarse debris, gravel, and sand. Then, the soil samples were digested using the Aqua Regia methodology, after which the samples were cooled, diluted with deionized water, and filtered. Finally, the concentration of heavy metals was determined using ICP-MS.

2.3. Quality Assurance and Quality Control

To ensure the reliability and precision of the analytical data, instruments were calibrated, blank samples were measured, and spiked samples were analyzed. In addition, quality control measures involved replicate analyses and determination of recovery rates for spiked samples.

All instruments underwent regular calibration using calibration standards prepared from certified stock solutions through dilution. For the ICP-MS analysis, the standard solution MES-21-5 for multi-element analysis, obtained from Accustandard Inc. (New Haven, CT, USA), was used. Six standards that covered the anticipated range of heavy metal concentrations in the groundwater/soil samples were used to generate calibration curves. For every heavy metal, linearity was confirmed by a correlation coefficient greater than 0.996. Moreover, replicate samples, including field triplicates and laboratory duplicates, were used to determine the precision of the method, which was found to be within ±5% of the relative standard deviation between replicates. To evaluate accuracy, recovery studies were performed by spiking known concentrations of analytes into groundwater samples and using laboratory blanks for every three samples. Regarding the accuracy of soil analysis, standard reference material (NIST SRM Montana Soil 2711a, Gaithersburg, MD, USA), including laboratory blanks for every two samples, was used. All analytes showed average recoveries between 95% and 104%.

2.4. Ecological Risk Indices

For the estimation of the ecological risk posed by heavy metals, the pollution index (PI), Nemerow pollution index (NPI), and heavy metal pollution index (HPI) were used.

The PI is calculated on the basis of heavy metal concentration (C_i, μg/L) and the standard value of heavy metal in groundwater (S_i, μg/L) as follows:

$$PI=\frac{C_i}{S_i}$$

(1)

This study applied the drinking water standards set by the World Health Organization to S_i.

The NPI is calculated on the basis of the maximum (${{PI}_{ }}_{max}^{ }$) and the average (${{PI}_{ }}_{ave}^{ }$) value of PI across all heavy metals as follows:

$$NPI=\sqrt{\frac{{PI}_{max}^2+{PI}_{ave}^2}{2}}$$

(2)

The HPI is calculated based on the heavy metal concentration (C_i, μg/L) and the standard value of the heavy metal in groundwater (S_i, μg/L) as follows:

$$HPI=\frac{\sum _{i=1}^n\left[\left(\frac{C_i}{S_i}\times 100\right)\times \left(\frac{1}{S_i}\right)\right]}{\sum _{i=1}^n\frac{1}{S_i}}$$

(3)

where n represents the number of investigated heavy metals.

The groundwater contamination evaluation criteria for the PI, NPI, and HPI indices are shown in Table S2 from the Supplementary Materials.

2.5. Positive Matrix Factorization

Sources of heavy metal pollution in groundwater were identified using the positive matrix factorization (PMF) methodology, as proposed by the United States Environmental Protection Agency [45]. The method involves decomposing sample concentration data using both least squares and iterative methodologies to identify the optimal contribution value and component spectrum data by minimizing the objective function Q [46]. The method is based on the following equations [2]:

$$X_{ij}={\sum }_{k=1}^p{g}_{ik}{f}_{kj}+e_{ij}$$

(4)

where the original dataset matrix (X_ij) is derived by considering the number of factors (p), source profile matrix (f_kj), source contribution matrix (g_ik), and residual matrix (e_ij), where i, k, and j denote the number of samples, factors, and heavy metals, respectively.

To determine the minimum value of the objective function Q, the following equation is used:

$$Q=\sum _{i=1}^n\sum _{j=1}^m{\left(\frac{e_{ij}}{u_{ij}}\right)}^2$$

(5)

where u_ij denotes the uncertainty of the heavy metal j in sample i. The uncertainty u_ij is calculated based on the heavy metal concentration (c), method detection limit (MDL), and error fraction (ef).

If the concentration of the heavy metal is greater than MDL, the uncertainty u_ij is calculated as follows:

$$u_{ij}=\sqrt{{\left(ef\times c\right)}^2+{\left(0.5\times MDL\right)}^2}$$

(6)

In contrast, u_ij is calculated using the following equation:

$${ u}_{ij}=\frac{5}{6}\times MDL$$

(7)

All calculations were executed using the PMF 5.0 software package [45].

2.6. Health Risk Assessment

Based on the health risk assessment method outlined by the USEPA [47], this study examined the implications of heavy metal exposure on health risks, including non-carcinogenic and carcinogenic effects, across two population groups (adults and children) within the study region. This analysis involved two primary exposure routes: direct ingestion and dermal contact with polluted water. Human health risk assessment involves several steps.

The first step involves the calculation of the chronic daily intake (CDI, mg/kg×day) for both exposure routes, as follows:

$${CDI}_{ ing}^{ }=\frac{C\times IR\times EF\times ED}{BW\times AT}$$

(8)

$${CDI}_{ derm}^{ }=\frac{C\times SA\times Kp\times ET\times EF\times ED\times CF}{BW\times AT}$$

(9)

where C represents the heavy metal concentration (mg/L), IR represents the ingestion rate (2.5 L/day for adults, and 0.78 L/day for children), EF represents the annual exposure frequency (365 days/year for ingestion exposure route and 350 days/year for dermal exposure route, for both population groups), ED represents the exposure duration (30 years for adults, 6 years for children), BW represents the average human body weight (for adults 70 kg, for children 15 kg), AT represents the average time of exposure (10,950 days for adults, and 2190 days for children for non-carcinogenic risk; 25,550 days for both adults and children for carcinogenic risk), SA represents the skin exposure area (18,000 cm² for adults and 6600 cm² for children), ET represents the exposure time (0.58 h/day for adults and 1 h/day for children), K_p represents the dermal permeability coefficient (0.001 cm/h for arsenic, cadmium, copper, mercury, and manganese, 0.002 cm/h for chromium, 0.0004 cm/h for cobalt, 0.0006 cm/h for zinc, 0.0001 cm/h for lead, and 0.0002 cm/h for nickel), and CF is a unit transfer factor (0.001 L/cm³ for both adults and children).

The next step involves calculating the hazard index (HI) to assess the non-carcinogenic risk. It represents the sum of the hazard quotient via ingestion (HQ_ing) and dermal exposure (HQ_derm) as follows:

$${HQ}_{ ing}^{ }=\frac{{CDI}_{ ing}^{ }}{{RfD}_{ing}}$$

(10)

$${HQ}_{ derm}^{ }=\frac{{CDI}_{ derm}^{ }}{{RfD}_{derm}}$$

(11)

$$HI=\sum {(HQ}_{ing}+{HQ}_{derm})$$

(12)

where RfD_ing and RfD_derm denote the reference dose of each heavy metal in mg/(kg×day via the ingestion and dermal exposure routes [48], respectively (Table S3 from the Supplementary Materials).

When HI > 1 and HQ > 1, there is a non-negligible non-carcinogenic risk. Contrarily, no non-carcinogenic risk is present [8].

The assessment of carcinogenic risk is quantified through the incremental lifetime cancer risk (ILCR), which encompasses the cumulative carcinogenic risk posed by both ingestion (CR_ing) and dermal exposure (CR_derm) routes.

$${CR}_{ing}={CDI}_{ing}\times {SF}_{ing}$$

(13)

$${CR}_{derm}={CDI}_{derm}\times {SF}_{derm}$$

(14)

$$ILCR=\sum ({CR}_{ing}+{CR}_{derm})$$

(15)

where SF_ing and SF_derm represent the slope factors of carcinogenic heavy metals (arsenic, chromium, and lead) for the ingestion and dermal exposure routes [48], respectively (Table S4 from the Supplementary Materials).

The ILCR values exceeding 10⁻⁴ indicate unacceptable levels of carcinogenic risk, whereas those falling between 10⁻⁶ and 10⁻⁴ signify tolerable risk. The ILCR values below 10⁻⁶ indicate negligible carcinogenic risk [49].

2.7. Data Analysis

Statistical analysis of the heavy metals was performed using Minitab v. 17 software (Minitab Inc., State Collage, PA, USA), including data normalization using the Ryan–Joiner normality test. All graphs were generated using Origin Pro v. 2021 graphing software (OriginLab, Northampton, MA, USA). Positive matrix factorization was conducted using USEPA [45] PMF v. 5.0 software, while the graphical representation of the study area was created using qGIS v. 3.30 software (qGIS, London, England, UK). Graphical representation of the heavy metal sources coupled with the Pearson correlation matrix was conducted using R software v. 4.3.2 (The R Foundation for Statistical Computing, Vienna, Austria).

3. Results and Discussion

3.1. Migration of Toxic Metals from Soil to Groundwater

An overview of the descriptive statistics (minimum, maximum, mean, median, standard deviation (SD), coefficient of variation (CV), skewness, and kurtosis) for ten heavy metals in groundwater and soil is presented in Table S5 from the Supplementary Materials. The World Health Organization’s drinking water guidelines [50] and UCC soil background values [51] are also included in the table. Figure 2 illustrates box plots of heavy metal concentrations in groundwater.

The concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, Mn, Zn, and Co in groundwater were in the range of 2.7–20.1 μg/L, 0.7–6.2 μg/L, 4.2–34.6 μg/L, 57.0–2934 μg/L, 0.05–0.29 μg/L, 16.0–46.0 μg/L, 4.1–47.8 μg/L, 23.0–51.0 μg/L, 88.0–495 μg/L, 14.0–105 μg/L, respectively. The concentrations of Cr, Hg, Ni, Mn, and Zn were within the WHO drinking water guidelines in all samples. However, As, Cd, Cu, and Pb concentrations exceeded WHO drinking water limits. Moreover, the levels of As, Cd, Cu, and Pb in groundwater exceeded the guidelines of 10 μg/L, 3 μg/L, 2000 μg/L, and 10 μg/L in 55.6%, 22.2%, 22.2%, and 66.7% of the samples, respectively. Extremely large coefficients of variation were observed for Cu (CV = 114.5%), Cd (CV = 79.6%), and Pb (CV = 83.2%), implying that the content of these heavy metals is highly influenced by human activities. Moreover, the Ryan–Joiner normality test showed that Cd, Cu, and Pb concentrations did not follow a normal distribution, further confirming the anthropogenic impact on the origin of these heavy metals.

Groundwater heavy metal pollution around copper mines represents a serious global problem. It has been documented that various areas in the vicinity of copper mines throughout the world exhibit elevated levels of heavy metals in groundwater (see

Table 1. Groundwater heavy metal concentrations from prior studies conducted in the vicinity of copper mines.

Copper Mine, Country	The Average Heavy Metal Concentration (μg/L)										Reference
	As	Cd	Cr	Cu	Hg	Ni	Pb	Zn	Mn	Co
Dabaoshan, China	1.05	0.11	/	7.49	/	/	2.7	16.9	27.1	1540	[52]
O’Kiep, South Africa	10	70	80	628,000	6000	/	/	/	39,000	/	[53]
Dexing, China	1.3	0.08	2.54	3.43	0.024	/	1.24	23.8	/	/	[54]
Khetri, India	/	/	/	690	/	190	20	1450	580	150	[55]
Weilasituo, China	462	/	/	15.9	/	7.1	11.77	63.7	9.4	0.78	[56]
Klein Aub, Namibia	25	/	/	2	2	/	/	134	26	/	[57]
Majdanpek, Serbia	11.2	2.36	16.5	953	0.16	28.8	16.3	235	37.9	51.8	This study
WHO (2022)	10	3	50	2000	6	70	10	5000	80	/	[50]

). For instance, groundwater within the Khetri copper mine in India exhibited average concentration of Ni, Pb, and Mn approximately 3, 2, and 7 times higher than the permissible WHO values. The observed concentrations were significantly higher than in the groundwater of the Majdanpek region. Moreover, heavy metal concentrations in the groundwater of the Weilasituo Zn-Cu-Ag mine in China and the O’Kiep copper mine in South Africa were notably higher than those observed in the Majdanpek region. Severe contamination with As was found in the groundwater of the Weilasituo Zn-Cu-Ag mine in China, where average As concentration reached 462 μg/L. Groundwater in the vicinity of closed O’Kiep copper mine in South Africa showed extremely high Cu, Cd, and Hg concentrations. The authors reported a substantial average Cu concentration of 628,000 μg/L. A similar situation was observed for Cd and Hg, where the average concentrations in groundwater were 70 μg/L and 6000 μg/L, respectively. However, compared with the Dabaoshan copper mine or Dexing copper mine, both located in Southeastern China, the groundwater within the Majdanpek copper mine exhibited As, Cd, Cu, Pb, and Zn concentrations several orders of magnitude higher. Moreover, the average concentration of Cu, Zn, and Mn in the groundwater of the Majdanpek region was several times higher in comparison with concentrations obtained within the Klein Aub closed copper mine in Namibia. However, Klein Aub’s groundwater exhibited elevated As concentrations.

Figure S2 from the Supplementary Materials shows box plots of heavy metal concentrations in soil. Elevated concentrations of heavy metals were also observed in the soil at the same sampling locations. Specifically, As, Cd, Cu, Hg, Pb, and Zn exceeded the corresponding background values in all samples. Furthermore, Cr and Co concentrations in soil surpassed their background values in 44.4% of the samples, whereas 11.1% of the samples exceeded the Ni background value. Only the concentration of Mn in the soil was within its background value. These findings indicate heavy contamination of soil with heavy metals and further confirm that anthropogenic impact plays a crucial role in the origin of these heavy metals.

In order to determine the heavy metal migration pattern from the soil to the groundwater, the ratio of respective concentrations of heavy metals in the groundwater and soil was calculated for each sampling location. The results are depicted in Figure 3. The concentration ratio values were descending in the order of Co > Cu > Cd > Zn > Ni > As > Pb > Cr > Hg > Mn. These findings indicate that Co exhibits the highest migration rate to groundwater, whereas Mn exhibits the slowest migration rate. The primary determinants influencing the migration dynamics of heavy metals are likely the physical and chemical attributes of the soil and metals, alongside the environmental conditions.

It is generally known that the migration capacity of heavy metals in the soil depends on their electronegativity (χ). Higher electronegativity leads to a stronger attraction of electrons from the soil by the metal, and lower mobility [58]. In this way, the better migration ability of Cd (χ = 1.7), Zn (χ = 1.6) and Ni (χ = 1.8) compared with As (χ = 2.0) and Pb (χ = 2.2) can be explained. Moreover, upon entering the soil, more than 90% of Cr is rapidly absorbed and immobilized, resulting in a minimal amount of soluble Cr [59], which can explain its low migration rate. The precedence of Zn and Cd over Pb in concentration ratios is due to Zn and Cd being more readily released from soil compared with Pb under similar environmental conditions [60]. The lowest migration rate of Mn is likely a result of its low soil concentration, well below the background level, as indicated by its subsequent low concentration in groundwater. Co and Cu demonstrated the highest migration rates, likely due to significant soil contamination caused by anthropogenic activities in the study area, thereby enhancing the migration degree of these metals. Additionally, soil properties, including organic matter content, influence the migration potential of heavy metals. Generally, Co exhibits a lower affinity for organic matter compared with Cd, Pb, or Ni, resulting in a higher migration rate for Co [61].

The highest concentration ratio was observed at the GW8 sampling point for most heavy metals, including Co, Cu, Zn, Ni, As, Pb, Cr, and Mn. Only Cd and Hg exhibited the highest concentration ratios at sampling point GW1. Conversely, the lowest concentration ratios were observed at sampling point GW5 for Zn, Ni, Pb, Cr, Hg, Mn, Cu, and Cd, while sampling point GW1 displayed the lowest ratios for As and Co. Most of the heavy metals showed the highest migration capacity at sampling point GW8 in the vicinity of the secondary grinding facility, where flotation is used. Of all the recycled wastewater originating from mineral processing facilities, flotation wastewater has been identified as the primary source of severe environmental degradation, which is attributed to its prevalent contamination by suspended particles, organic pollutants, and heavy metals [62]. Wastewater from the flotation process penetrates the groundwater system via infiltration mechanisms within the soil, resulting in groundwater heavy metal pollution.

3.2. Multivariate Analysis/Pattern Recognition

Pearson correlation analysis was utilized to examine the correlation between heavy metals, followed by positive matrix factorization to ascertain the principal sources of groundwater pollution. The results are presented in Figure 4.

The positive matrix factorization method identified three factors that represent pollution sources.

Factor 1 accounted for 35.3% of the overall pollution and was mainly explained by Cd (64%), followed by Zn (41%) and Hg (36%). The primary cause of the high levels of Cd is the widespread combustion of fossil fuels, such as natural gas and gasoline, which releases Cd into the environment [63]. Furthermore, the deterioration of automotive parts such as tires and brake pads intensifies the accumulation of Zn and Cd [64]. Prior research has demonstrated that human activities such as fuel burning and exhaust emissions are the main sources of Hg emissions. Later, Hg is introduced into the soil and water through atmospheric deposition [27]. It has already been reported that the machines used to carry out mining activities in the Majdanpek copper mine produce a large amount of dust and exhaust gases [65]. Considering that intense mining activities in the study region have resulted in significant transportation requirements involving mostly heavy vehicles, Factor 1 can be explained as a heavy vehicle transport pollution source.

Factor 2 contributed 22.4% to the overall pollution and was mainly characterized by Mn (42%) and Ni (39%). The parent materials of the Earth’s crust and pedogenic processes are considered to be the primary origins of Ni and Mn [66]. The content of Mn and Ni in groundwater was within the WHO drinking water guidelines, suggesting their natural origin. Therefore, Factor 2 can be considered a natural pollution source.

Factor 3, which accounted for 42.3% of the total pollution, was strongly weighted by Cu (83%), followed by Co (51%), Cr (48%), and Pb (48%). During mining activities, including the processing of copper ore, leachate from tailings, and the discharge of process wastewater, large amounts of Cu reach the soil and surface waters, also reaching groundwater through infiltration [13]. Furthermore, elevated Co, Cr, and Pb concentrations around copper mines have been reported worldwide [67,68,69]. In addition, the high coefficient of Pb, Cd, and Cu variation further confirms the anthropogenic influence on the level of these metals in groundwater. Moreover, the flotation tailing pond within the Majdanpek mine is about 390 ha in size [70]. Furthermore, the accumulation pool in the Open Pit South Revir has 156,000 m³ of 4 m thick sludge [71]. It is known that significant amounts of Cu and other heavy metals are found in the tailings and sludge from Cu mining [72]. These metals can leach into the groundwater, usually as a result of precipitation, leading to its contamination. Thus, Factor 3 can be interpreted as pollution from mining activities.

It is important to emphasize that other anthropogenic activities, such as agricultural practices or the improper disposal and infiltration of domestic and municipal wastewater, can also contribute to the contamination of groundwater with heavy metals [2,9,73]. Nonetheless, given that the groundwater was sampled at hotspots in close proximity to the Majdanpek mine open pits, tailings, overburdens, or transportation routes, these sources are considered to have a minor impact on the heavy metal content in the groundwater. Therefore, it is considered that the three listed factors are the main factors affecting groundwater quality in the investigated area.

3.3. Potential Environmental Implications

3.3.1. Ecological Risk Indices

The summarized results of the computed pollution indices, comprising PI, NPI, and HPI, are presented in

Table 2. Statistics of the calculated pollution index (PI), Nemerow pollution index (NPI), and heavy metal pollution index (HPI) of groundwater samples.

Pollution Indices	PTE	Min	Max	Mean
PI	As	0.27	2.01	1.12
	Cd	0.24	2.08	0.79
	Cr	0.08	0.69	0.33
	Cu	0.03	1.47	0.48
	Hg	0.01	0.05	0.03
	Ni	0.23	0.66	0.41
	Pb	0.41	4.78	1.63
	Mn	0.29	0.64	0.47
	Zn	0.02	0.10	0.05
NPI	/	0.32	3.47	1.35
HPI	/	21.2	146	75.0

. In the determination of the PI, Co was excluded from consideration owing to the absence of established regulatory thresholds for its concentration in drinking water. The PI values for Cr, Hg, Ni, Mn, and Zn were lower than 0.7 for all samples, indicating no pollution. However, As, Cd, Cu, and Pb exhibited much higher PI values. The PI values of As ranged between 0.27 and 2.01, with a mean value of 1.12. The majority of the samples (66.7%) fell into the warning to high pollution level category. The PI for Cd was in the range of 0.24–2.08, averaging 0.79, while the PI for Cu ranged between 0.03 and 1.47, with a mean value of 0.48. One-third of the samples exhibited PI values above the 0.7 threshold for Cd, with one sample each belonging to the warning of high, light, and moderate pollution level categories. Similar findings were observed with regard to Cu, where one-third of the samples also surpassed the threshold of 0.7, which is within the warning to light pollution level category. The highest PI values exhibited Pb, ranging from 0.41 to 4.78, with a mean of 1.63. The majority of samples (66.7%) exceeded the no-pollution threshold of 0.7, falling within the light to heavy pollution categories. The comprehensive NPI and HPI ranged from 0.32 to 3.47, with a mean value of 1.35, and 21.2 to 146, with a mean value of 75.0, respectively. According to the NPI results, three samples fell into the category of no pollution (NPI < 0.7), five into the category of light pollution (1 < NPI ≤ 2), and one into the category of heavy pollution (NPI > 3). Regarding the HPI results, all of the samples exceeded the no pollution threshold of 15, with one sample belonging to the medium pollution level category (15 ≤ HPI ≤ 30) and the remaining eight to the high pollution level category (HPI > 30). Moreover, 33.3% of the samples exhibited an HPI value greater than 100, which is extremely dangerous water for drinking due to significant levels of heavy metal contamination [3].

Based on the pollution index results, it can be concluded that the groundwater within the Majdanpek mine is considerably polluted with heavy metals. In addition, the highest pollution levels are characterized for sampling sites associated with the Filtration unit, dams, or vehicle service area (Figure S3 from the Supplementary Materials), indicating an anthropogenic impact on the groundwater quality in the investigated area. Elevated pollution levels can have negative consequences on human health, highlighting the importance of taking action to mitigate and prevent pollution, particularly by addressing the impacts of mining activities.

3.3.2. Potential Human Exposure

Health risk assessment results comprising non-carcinogenic and carcinogenic health risks posed by heavy metals in groundwater for two population groups (adults and children) are presented in

Table 3. The results of the non-carcinogenic (HI) and carcinogenic (ILCR) health risk assessment for adults and children.

Health Risk Index	Heavy Metal	Adults			Children
		Min	Max	Mean	Min	Max	Mean
HI	As	0.33	2.41	1.34	0.48	3.55	1.98
	Cd	0.04	0.31	0.12	0.07	0.59	0.22
	Cr	0.07	0.58	0.28	0.13	1.09	0.52
	Cu	0.05	2.65	0.86	0.08	3.92	1.27
	Hg	0.01	0.04	0.02	0.01	0.06	0.03
	Ni	0.03	0.08	0.05	0.04	0.12	0.08
	Pb	0.04	0.49	0.17	0.06	0.71	0.24
	Sb	0.01	0.02	0.01	0.01	0.03	0.02
	Zn	0.01	0.06	0.03	0.02	0.09	0.04
	Co	0.03	0.19	0.09	0.04	0.27	0.14
	Total	0.61	5.45	2.97	0.95	8.27	4.54
ILCR	As	6.28 × 10−5	4.65 × 10−4	2.59 × 10−4	1.85 × 10−5	1.37 × 10−4	7.63 × 10−5
	Cd	6.66 × 10−5	5.84 × 10−4	2.21 × 10−4	1.95 × 10−5	1.71 × 10−4	6.47 × 10−5
	Cr	4.24 × 10−5	3.50 × 10−4	1.67 × 10−4	1.54 × 10−5	1.27 × 10−4	6.07 × 10−5
	Pb	5.33 × 10−7	6.22 × 10−6	2.12 × 10−6	1.55 × 10−7	1.81 × 10−6	6.18 × 10−7
	Total	1.72 × 10−4	1.27 × 10−3	6.50 × 10−4	5.35 × 10−5	3.98 × 10−4	2.02 × 10−4

and Figure 5.

The non-carcinogenic risk for adults (HIa) and children (HIc) varied from 0.61 to 5.45 and 0.95 to 8.27, respectively. The risk of non-carcinogenic effects was more than 1.5 times higher in children, with HIa and HIc values averaging 2.97 and 4.54, respectively. Common hand-to-mouth activities characterized in children could lead to a greater risk compared with adults. Furthermore, the higher non-carcinogenic risk observed in children may be attributed to their lower body weight [49]. Ingestion through drinking water was the main exposure route, and As was the main health risk contributor for both population groups (Figure S4 from the Supplementary Materials). Moreover, As accounted for 51.1% of the non-carcinogenic risk in adults and 49.2% in children. The second-highest contributing heavy metal was Cu, with relative contributions of 21.2% and 20.4% for adults and children, respectively. These results suggest that special attention should be paid to As and Cu contamination in groundwater within the study area. Furthermore, both HIa and HIc values exceeded the USEPA’s threshold of 1 in 88.9% of the samples, indicating that the inhabitants of the area under study are at substantial non-carcinogenic health risk.

The carcinogenic risk for adults (ILCRa) ranged between 1.72 × 10⁻⁴ and 1.27 × 10⁻³, while the carcinogenic risk for children (ILCRc) varied from 5.35 × 10⁻⁵ to 3.98 × 10⁻⁴. Unlike the non-carcinogenic risk, to which children were more susceptible, adults were more vulnerable to the carcinogenic risk, with the average ILCRa value (6.50 × 10⁻⁴) approximately three times higher than the average ILCRc value (2.02 × 10⁻⁴). These findings can also be explained by the lower body weight of the children [6]. Moreover, the carcinogenic risk for adults was higher than the permissible limit of 1.0 × 10⁻⁴ in all samples, whereas the carcinogenic risk for children exceeded this limit in 66.7% of the samples. The heavy metal that most contributed to the carcinogenic risk was also As, followed by Cd, Cr, and Pb (Figure S4 from the Supplementary Materials). The same contributing trend was observed for both the population groups. Therefore, carcinogenic risk in the study area is non-negligible, and there is a great possibility of developing various adverse health effects in the population, including skin, lung, and kidney cancer [64].

Figure 5 represents the HI and ILCR values for adults and children at each sampling point. It is observed that HIa and HIc values are within the permissible limit of 1 only at sampling point GW1, representing a well in the Debeli Lug settlement close to the Filtration unit. The highest non-carcinogenic health risk was observed at sampling points GW3, GW7, and GW8 for both population groups. Sampling point GW3 represents the borehole near the Filtration unit, while sampling points GW7 and GW8 represent the borehole near the heavy vehicle service area and the borehole near the secondary grinding facility, respectively. Regarding the carcinogenic health risk, the lowest values for both adults and children were also observed at sampling point GW1. Apart from sampling points GW3 and GW8, the highest carcinogenic health risk was observed at sampling point GW4, which corresponds to a borehole close to the Filtration unit.

The health risk assessment results imply that the population of the study area is at substantial non-carcinogenic and carcinogenic risk. Therefore, it is imperative to implement adequate controls and remedial actions to protect public health. The results also showed that mining activities, along with heavy vehicle transportation, are the most responsible for health risks in the study area. The best way to reduce health risks is to effectively control the sources of pollution. The primary measure to mitigate the risks associated with soil and groundwater pollution from the copper mining industry is to prevent waste generation. This can be achieved by improving ore processing efficiency, thereby reducing the volume of waste generated. For instance, ore grinding to the appropriate grain size improves ore processing efficiency, while the volume of slag can be reduced by the addition of specific fluxes [74]. However, if pollution of soil and groundwater is detected, surface capping and encapsulation are among the most commonly employed remediation methods [75]. Surface capping involves overlaying the polluted soil with a waterproof layer, preventing rainwater from leaching contaminants deeper into the soil and reaching groundwater. This method prevents groundwater contamination, but it does not decontaminate the soil. By mixing contaminated soils with concrete, asphalt, or lime, encapsulation renders the contaminants immobile, thus preventing the surrounding areas from contamination. This method effectively prevents pollutants from leaching into the groundwater and surrounding soil [75,76]. Moreover, measures to reduce the health risks and pollution of groundwater from heavy metals must be focused on treating wastewater from the Filtration unit. The current method for wastewater treatment from the Filtration unit involves gravity thickening [77]. After the treatment, wastewater is discharged into the river Veliki Pek. Previous research has shown that this river is characterized by elevated concentrations of heavy metals [41,77]. Therefore, addressing this issue is essential to reducing environmental pollution. Adsorption is seen as an economical and reliable wastewater treatment method, achieving up to 99.9% removal efficiency [78]. The quality of the wastewater in the filtration plant can be improved by using various adsorbents, such as silica gel or zeolite.

Furthermore, even though heavy vehicles play a vital role in maintaining the efficiency and continuity of mining operations, addressing this pollution source is critical in preventing future contamination. A strategy to mitigate the pollution from heavy machinery is to replace the machinery with belt conveyors, where possible. Also, continuous monitoring of the groundwater within the area is necessary, with special attention paid to As and Cu concentration levels. Through the approaches outlined, it becomes possible to mitigate pollution and thereby protect people’s health.

4. Conclusions

This study revealed that soil at hotspots in the Majdanpek copper mining area is severely polluted with heavy metals. The migration of heavy metals from soil into the groundwater resulted in elevated Cd, As, Cu, and Pb concentrations found in the groundwater. The increased concentrations of certain metals investigated are linked to different anthropogenic activities, mainly mining operations and heavy vehicle transportation. However, natural processes have also been identified as the source of heavy metals. Severe heavy metal groundwater pollution was observed according to ecological risk assessment. Moreover, as evidenced by the PI, NPI, and HPI values, the groundwater mostly belonged to the light to heavy pollution categories. The health risk assessment results revealed that the ingestion of groundwater polluted with heavy metals poses substantial non-carcinogenic and carcinogenic risks. Moreover, children were more susceptible to non-carcinogenic risk, whereas adults were more susceptible to carcinogenic risk. These findings emphasize the need for the implementation of adequate controls and remedial actions to protect public health, and continuous groundwater and soil monitoring.