Ecological and Health Risk Assessment of Nitrates and Heavy Metals in the Groundwater of the Alluvial Area of the Danube River near Kostolac Basin, Serbia

Abstract

Water-quality indicators and trace elements were measured in the alluvial area of the Kostolac Basin, Serbia. The results revealed that the groundwater was naturally neutral, with a mean conductance of 920.10 μs/cm. The primary factors contributing to the decline in the groundwater quality in the researched area were electrical conductivity (EC), NO₃⁻, SO₄²⁻, Cd, and Pb; the water samples were deemed unfit for human consumption and the water was classified as having impaired quality. A Pearson correlation matrix, a principal component analysis (PCA), and a cluster analysis (CA) were applied to identify the pollution source and factors controlling the groundwater quality and the results indicated that both natural and anthropogenic factors influenced the groundwater of the studied area. The Nemerow pollution index indicated medium to high pollution levels along with the degree of contamination. A health risk evaluation was conducted to determine the non-carcinogenic risks posed by nitrates and heavy metals from oral consumption and skin contact in the researched area. Nearly all monitoring sites had hazard quotients (HQs) below 1, suggesting that potential concerns might be negligible. However, children showed the highest exposure levels, with HQs for nitrates at 2.26 and for lead (Pb) at 2.515. No carcinogenic risk from oral lead (Pb) exposure was found in any sample.

1. Introduction

Water is essential for environmental sustainability as well as human health and wellbeing. In the years ahead, numerous challenges will obstruct the global goal of ensuring that humans have access to enough water of adequate quality. According to forecasts, 50% of the global population may face a water shortage by 2025 [1]. Developing nations rely heavily on groundwater as a source of safe drinking water. The quality of groundwater is influenced by both human activities and natural processes [2,3]. Groundwater contamination, particularly with sulfates and nitrates, is increasing due to atmospheric deposition, bacterial production, acid rain, and emissions from household, municipal, and industrial wastewater. International laws and guidelines regulate these ions due to their adverse effects on human health at high concentrations [4,5]. Moreover, urbanization and industrial activities expose groundwater systems to elevated levels of trace metals. Although some of these metals are essential for human health, using low-quality water for irrigation reduces crop yield and can lead to various health problems [6]. According to the World Health Organization (WHO), about 80% of human diseases are related to water-related risk factors. Groundwater serves as a crucial natural resource for drinking and agriculture [7]. However, densely populated areas with intensive land use are particularly prone to groundwater contamination. Almost all activities that release waste or chemicals into the environment, whether intentionally or accidentally, can pollute groundwater. The remediation of contaminated groundwater is costly and time-consuming. The lack of safe and reliable drinking-water sources may lead to desertification, forced migration, hunger, diseases, and domestic or regional conflicts. It is important to note that the impact of water scarcity in developing countries is more challenging than in the developed parts of the world. To create a sustainable water infrastructure, the focus should be on conservation, the protection of water sources, and limitation of pollution. Desalination is considered to be one of the most effective ways to increase water supplies and provide clean and affordable water to millions of people. Nevertheless, it is important to recognize that addressing the global water crisis requires a multifaceted approach. In addition to desalination, other measures should be implemented, such as centralized governance, education, improvements in water catchment and harvesting technologies, irrigation, agricultural practices, distribution infrastructure, prevention of leakage, wastewater reclamation, pollution treatment and prevention, investment in innovative technology, and transboundary water cooperation [8,9].

Evaluation of the health risks associated with groundwater pollution is vital as it links environmental pollution to human health [10,11,12]. The process of evaluating risks to human health typically involves four steps: hazard identification, exposure assessment, dose–response assessment, and risk characterization. Multivariate statistical techniques are employed as powerful tools for data reduction and chemical data interpretation in order to explore variations in groundwater chemistry [13,14].

The Danube River, an international waterway and the second-largest river in Europe, has a total length of 2778 km. It holds significant importance for Serbia, flowing through the country for 558 km from Bezdan in the northwest to Djerdap in the southeast. The section near Kostolac, in Northeast Serbia, is about 17 km long (Kostolac–Kovin). This area is home to industrial and power-generation plants, Kostolac thermal power plants, and farmsteads near the Danube that engage in farming, livestock rearing, and seed production, along with a network of local roads with heavy traffic.

The primary objectives of this investigation were twofold: (1) to analyze groundwater quality in terms of dissolved elements (temperature; pH; electrical conductivity; cations such as Na⁺, K⁺, Ca²⁺, and Mg²⁺; anions like Cl⁻, SO₄²⁻, NO₃⁻, and PO₄³⁻; and trace elements including Pb, As, Cd, Hg, and Zn) using multivariate techniques; and (2) to assess water quality following water standards and guidelines relevant to human health. Numerous groundwater studies have shown that groundwater is being used at rates that globally exceed the natural recharge rates. This paper presents a case study of the pollution of groundwater in the Danube River alluvial area near the Kostolac Basin, Serbia, revealing elevated levels of heavy metals and nitrates and highlighting the need for region-specific studies and remediation strategies. The review results may be used as guidelines for global efforts to protect drinking-water supplies from life-threatening contaminants. Furthermore, these findings help to bridge the knowledge gaps regarding the contamination status, distribution, and sources of chemical pollutants in the surface and groundwater of these areas, ultimately contributing to improved water-management practices.

2. Materials and Methods

2.1. Geographical Location

The Kostolac coal basin covers the area between the Danube River in the north, the Resava River and the town of Svilajnac in the south, the Morava River in the west, and the Golubac Mountains in the east (Figure 1a). Coal deposits have been explored and confirmed in areas of Kostolac as well as in the villages of Drmna, Klenovnik, Ćirikovac, and Poljana. This basin spans an area of 100 km². The Kostolac Basin represents one of the most economically significant deposits of lignite, which is an essential energy resource in Serbia. The extensive lignite exploitation supports the operation of two thermal power plants, namely, “Kostolac A” and “Kostolac B”. The coal-fired power stations of Kostolac contribute to approximately 14% of the annual electricity production in the Republic of Serbia [15]. The Kostolac power plants and mines are situated in the northeastern part of Serbia, approximately 90 km east of Belgrade [15].

2.2. Sampling and Analytical Procedure

Water samples were collected from 12 monitoring sites (Figure 1) between 29 November and 8 December 2017. Before sampling, groundwater wells were washed and pumped for 15 min to ensure the removal of stagnant water and ensure representative samples were obtained. The samples were collected during the morning, with three repetitions per site, and the average data were used to ensure reliability. Brown glass bottles were utilized for sample collection to prevent contamination, and the samples were subsequently transported to the laboratory in cooled containers within 4 h to maintain the sample integrity. The water samples were filtered using a Whatman No. 40 filter paper and tested according to the methods specified by the standard methods [16,17].

The well water was utilized for both drinking and irrigation purposes.

In this study, 12 well samples were collected and analyzed using the standard procedures for 21 parameters, including temperature (T), pH, electrical conductivity (EC), cations (Na⁺, K⁺, Ca²⁺, and Mg²⁺), anions (Cl⁻, NO₃, SO₄²⁻, and PO₄³⁻), and trace elements (Cd, Hg, Pb, As, and Zn) [16,17]. An InoLab720 Multi 720 WTW pH meter was used for the pH measurements. In the first 24 h, the nitrate concentration in the samples was measured with the standard method using a spectrophotometer at a 220 nm wavelength. On-site measurements included EC, pH, and T, while the elemental analysis was conducted using an atomic emission spectrometer (Varian SpectrAA 20-plus). Water samples were digested using concentrated HNO₃ for trace metal determination, as described by APHA. Instrument calibration was performed using multielement standard solutions obtained from a certified supplier, ensuring traceability and accuracy. Calibration curves were generated for each analyzed element, with correlation coefficients (R²) greater than 0.999. Recovery rates for the quality control samples were maintained within 95–105% to ensure the accuracy of the analytical procedures. Additionally, reagent blank analyses were conducted to check for any potential contamination, and duplicate samples were analyzed to confirm the precision of the results.

All chemicals used in the analyses were of analytical grade and the glassware was washed with 2% HNO₃ before use. Optimizing instrument settings and conducting a sensitivity assessment were integral to the instrument performance evaluation process.

Chemical parameters were expressed in mg/L or μg/L (

Table 1. Statistical summary of the analyzed parameters in groundwater from the alluvial area of the Danube River near Kostolac Basin, Serbia.

Parameters	Minimum	Maximum	Mean	Std. Deviation
pH	7.15	7.74	7.4233	0.0597
EC, μS/cm	417	2030	912.33	169.852
Ca, mg/L	53	79	70.25	1.92
Mg, mg/L	15	21	17.67	0.51
Na, mg/L	11.2	15.1	13.04	0.335
K, mg/L	1.2	12	3.396	0.99
Cl−, mg/L	19.34	174.5	49.94	16.86
SO42−, mg/L	10.86	478.5	120.30	49.56
NO3−, mg/L	0.14	90.40	16.48	9.98
PO4 3−, mg/L	0.08	0.1	0.088	0.005
Total N, mg/L	0.45	90.40	17.06	9.94
Cd, μg/L	2	3.5	2.65	0.147
Pb, μg/L	39	87	59.25	3.75
Zn, μg/L	65	1940	501.09	201.65
As, μg/L	2.9	5.7	4.09	0.33
Hg, μg/L	0.30	0.42	0.351	0.011

), except for pH (units) and EC (μS/cm).

2.3. Pollution Evaluation Indices

The Heavy Metal Evaluation Index (HEI), Degree of contamination (Cd) and Nemerow pollution index (NI) were investigated to assess the drinking suitability of the groundwater. HEI provides an insight into the overall quality of the groundwater concerning trace metals and is calculated as follows:

HEI = ƩHc/Hmac

(1)

where Hc is the monitored value, and Hmac is the maximum admissible concentration of the ith parameter. The degree of heavy metal index was classified into three divisions as follows: HEI ≤ 10: low, HEI (10–20): medium and HEI > 20: high [18].

The degree of contamination (Cd) indicates the overall effects of each contaminant and is calculated using the following equation

Cd = ƩC

(2)

where, Cfi = CAi/Ci Ni-1, Cfi = Contamination factor; CAi = analytical value of the ith component, CNi = upper permissible concentration of the ith component. Here, CNi is considered as the Maximum Allowable Concentration (MAC). The pollution level was divided into three categories: Cd < 1: low, Cd (1–3): medium and Cd > 3: high [19].

The Nemerow Pollution Index was applied to determine the extent of heavy metal pollution in the groundwater at the sampling sites in the study area [20,21], and it is calculated as follows:

PI_N =√ Pi_ave² + Pi_max²/2

(3)

where Pi_ave and Pi_max are the mean and maximum of the single pollution indices for individual trace elements. The pollution in the samples was classified into five levels based on the corresponding PI_N values: PI_N < 0.7—safety domain; 0.7 ≤ PI_N < 1.0—precaution domain; 1.0 ≤ PI_N < 2.0-slightly polluted domain; 2.0 ≤ PI_N < 3.0—moderately polluted domain and PI_N > 3.0—seriously polluted domain.

2.4. Human Health Risk Assessment

Most of the residents in the study area use wells to extract groundwater for daily use. As such, a health risk assessment is essential for a comprehensive quality assessment of drinking water. The human health risk models including carcinogenic and non-carcinogenic ones raised by US EPA have proved successful and adopted worldwide [22].

Human health risk due to ingestion of groundwater with various ions-non-carcinogenic risk or chemical toxicity was assessed using hazard index (HI) which is based on the following equations [22]. The calculations for the daily exposure dose of contaminants via various exposure pathways [20,21,22,23].

CDIOral = (CW × IR × EF × ED)/(BW × AT)
CDIDermal = (CW × SA × Kp × ET × EF × ED × CF)/(BW × AT)

(4)

Potential non-carcinogenic risks for exposure to contaminants of concern were evaluated by comparing the estimated contaminant intakes from each exposure route (oral, dermal) with the reference dose (RfD) as defined by USEPA, resulting in the calculation of the Hazard Quotient (HQ) [24,25,26,27]. If the value of HQ exceeds 1, there is an unacceptable risk of adverse non-carcinogenic health effects, while an HQ value less than 1 indicates an acceptable level of risk [24,25,26,27].

HQ = CDI/RfD

(5)

where, HQ = Hazard Quotient (unitless) and RfD is the reference dose (μg/Kg/day) obtained from the risk-based concentration table. To assess the overall potential for non-carcinogenic effects posed by more than one chemical, the HQs calculated for each chemical are summed and expressed as the Hazard Index (HI).

HI = HQ₁ + HQ₂+ HQ₃ +……H_n

(6)

If HI does not exceed unity (HI < 1), no chronic risks are assumed to occur at the site. If HI > 1, it implies a potential non-carcinogenic health risk [28].

Carcinogenic risks were estimated as the incremental probability of an individual developing cancer over a lifetime due to exposure to a potential carcinogen [28]. The following linear dose carcinogenic risk equations, were used for each exposure route.

Cancer risk CR = CDI × CSF

(7)

where “Risk” stands for cancer risk, and “SF” is the slope factor of contaminants (mg/kg/day). Risk in the range of 1 × 10⁻⁶ to 1 × 10⁻⁴ is typically considered acceptable [29,30].

2.5. Multivariate Statistical Analysis

Multivariate statistical methods such as a principal component analysis (PCA), correlation analysis, and cluster analysis (CA) were applied using SPSS (version 22.0) software for Windows to evaluate the analytical data. The principal component analysis identified potential heavy-metal sources. The exploratory factor analysis was performed using the varimax rotation technique, which reduced the number of variables with high loadings on each component, thereby simplifying the interpretation of the PCA results [31]. The principal components (PCs) were employed to pinpoint the most significant values across the entire dataset, enabling data reduction with a minimal loss of baseline information. According to [32], this method reduces the influence of the new set of variables derived from the PCA and the less significant components. A Pearson correlation coefficient was utilized to validate the PCA results and to understand the variations in the components that could be explained by their interactions [33]. The cluster analysis was implemented [34] to identify groups of samples with similar heavy-metal contents, thus providing evidence to support the PCA results. The between-group distance was considered to determine the distance between clusters with similar metal concentrations, and the CA was performed using the average linkage method [35]. The use of these statistical methods allowed for a comprehensive analysis of the groundwater data, identifying underlying patterns and sources of contamination. The PCA helped to reduce the complexity of the dataset by highlighting the most influential factors, while the correlation analysis provided insights into the relationships between different parameters. The cluster analysis grouped the sampling sites based on similarities in heavy-metal content, further supporting the identification of pollution sources.

3. Results

The analysis of the samples included 21 parameters, comprising temperature (T), pH, electrical conductivity (EC), cations (Na⁺, K⁺, Ca²⁺, and Mg²⁺), anions (Cl⁻, NO₃⁻, SO₄²⁻, and PO₄³⁻), and trace elements (Cd, Hg, Pb, As, and Zn). Careful standardization, procedural blank measurements, and duplicate samples ensured the quality of the analytical data. The laboratory also participates in a regular national program of analytical quality control. The basic statistics of the water-quality dataset are summarized in Table 1. The statistical summaries of the parameters analyzed using box and whisker plots are shown in Figure 2. Box and whisker plots are particularly useful when examining summaries of numerical data. They facilitate the identification of potential outliers and abnormal distributions and enable a quick and easy comparison of distributions by displaying the overall range, spread, and center, thus helping to visualize the numerical summary. The recommended mean values of the World Health Organization, European Parliament and Council of the European Union 2006, and MWQ Serbia are presented in

Table 2. Comparison of water-quality values from the present study and the recommended guidelines.

Parameters	Range	MWQ Serbia	EU Limit	WHO
pH	7.15–7.74	6.5–8.5	6.5–<9.5	-
Eh	417–2030 μS/cm	1500 μS/cm	2500 μS/cm	1500 μS/cm
Na+	11.20–15.10 mg/L	150 mg/L	150 mg/L	50 mg/L
K+	1.20–12 mg/L	12 mg/L		12 mg/L
Ca2+	53.0–79.0 mg/L	200 mg/L		75 mg/L
Mg2+	15.0–21.0 mg/L	50 mg/L		30 mg/L
Cl−	19.34–174.50 mg/L	150 mg/L	187 mg/L	250 mg/L
SO42−	10.86–478.50 mg/L	200 mg/L	187 mg/L	250 mg/L
NO3−	0.14–90.40 μg/L	50 μg/L	37.5 μg/L	10 μg/L
Cd	2.0–3.5 μg/L	3 μg/L	3 μg/L	3 μg/L
As	2.90–5.70 μg/L	10 μg/L	7.5 μg/L	10 μg/L
Hg	0.30–0.42 μg/L	-	1.0 μg/L	1.0 μg/L
Zn	65–1940 μg/L	3000 μg/L	-	5000 μg/L
Pb	39.0–87.0 μg/L	10 μg/L	10 μg/L	10 μg/L

Note(s): Data in bold are concentrations in samples that exceeded the recommended values for drinking water. WHO: World Health Organization; EC: European Commission

.

The results of the correlations among the investigated groundwater samples are presented in

Table 3. Results of the Pearson correlation analysis.

	Eh	NO3−	Cl−	SO42−	PO43−	Na	K	Ca	Mg	Cd	As	Hg	Pb	Zn
Eh
NO3−	0.976 **
Cl−	0.983 **	0.999 **
SO42−	0.999 **	0.983 **	0.988 **
PO43−	0.167	0.159	0.154	0.171
Na	0.78	0.068	0.076	0.083	0.601
K	0.75	0.395	0.389	0.356	0.361	0.524
Ca	0.691	−0.185	−0.177	−0.150	−0.600	0.019	−0.028
Mg	0.693	−0.059	−0.086	−0.118	−0.281	−0.460	−0.600	0.076
Cd	0.510	0.357	0.391	0.491	0.127	0.222	0.029	0.066	−0.464
As	0.218	0.247	0.242	0.227	−0.651	−0.305	0.145	0.422	0.200	−0.072
Hg	0.697	0.622	0.638	0.684	−0.119	−0.345	−0.116	0.132	0.126	0.483	0.186
Pb	−0.046	−0.169	−0.126	−0.068	−0.375	−0.045	−0.269	0.385	−0.397	0.783 *	0.013	0.251
Zn	−0.434	−0.318	−0.350	−0.414	0.349	0.169	0.139	−0.390	0.297	−0704 **	−0.157	−0.608	−0.715 **

Note(s): ** Significance at the 0.01 probably level; * Significance at the 0.05 probably level.

. The factor loadings, cumulative percentage, and percentage of variance explained by each factor are listed in

Table 4. Varimax-rotated principal component analysis of groundwater samples (bold values indicate strong loadings (0.7)).

Variable	PC1	PC2	PC3	PC4
EC	0.980	0.140	0.101	−0.027
pH	0.970	0.120	0.105	−0.011
Nitrates	0.985	−0.014	0.100	−0.004
Sulfates	0.984	0.114	0.104	−0.024
Chlorides	0.984	0.027	0.108	−0.005
Phosphates	0.137	-0.170	0.431	−0.826
Ca	−0.184	0.297	0.057	0.767
Mg	0.004	−0.439	0.769	0.115
Na	−0.31	0.011	0.816	−0.262
K	0.325	−0.0237	0.852	0.115
Cd	0.371	0.839	0.182	−0.140
As	0.280	−0.145	−0.069	0.671
Hg	0.697	0.409	−0.374	0.076
Pb	−0.170	0.940	−0.011	0.192
Zn	−0.337	−0.887	0.097	−0.244
Eigenvalues
% Total variance	39.728	18.815	15.181	13.908
% Cumulative variance	39.728%	58.542%	73.723%	87.630%

. The component plot in the rotated space of the principal component analysis is shown in Figure 3, while the result obtained using HCA in the Q-mode is shown in Figure 4.

The results of the groundwater pollution evaluation indices are presented in

Table 5. Results of groundwater pollution evaluation indices. Note(s): The high values are in red bold.

Sample	HEI	Degree of Heavy Metals	Cd	Pollution Category for Cd	NI	Pollution Degree for NI
Gw 1	9.16	Low	4.16	High	1.75	Polluted to medium pollution
Gw 2	7.57	Low	2.60	Medium	1.53	Polluted to medium pollution
Gw 3	11.82	Medium	6.82	High	10.62	Severe pollution
Gw 4	8.44	Low	3.44	High	1.48	Polluted to medium pollution
Gw 5	11.05	Medium	5.84	High	1.43	Polluted to medium pollution
Gw 6	7.41	Low	2.28	Medium	1.51	Polluted to medium pollution
Gw 7	11.45	Medium	6.45	High	5.18	Severe pollution
Gw 8	8.44	Low	3.44	High	5.74	Severe pollution
Gw 9	8.90	Low	3.90	High	5.90	Severe pollution
Gw 10	9.57	Low	4.56	High	4.81	Severe pollution
Gw 11	12.62	Medium	7.53	High	14.85	Severe pollution
Gw 12	10.49	Medium	5.39	High	6.50	Severe pollution

and Figure 5. The values of the reference dosage (RfD) used in this study were based on documents from the United States Environmental Protection Agency, shown in

Table 6. Parameters of exposure assessment of oral ingestion and dermal absorption pathways.

Parameters	Unit	Oral Values	Dermal Values
CDI, chronic daily intake	μg/kg	-	-
CW, conc. of trace metal	μg/L	-	-
IR, ingestion rate	L/day	2.2 (adult) 1 (child)	- -
EF, exposure frequency	Days/year	365	350
ED, exposure duration	Year	70 (adult) 10 (child)	30 (adult) 6 (child)
ET, exposure time	h/event	-	0.58 (adult) 1.0 (child)
BW, body weight	kg	70 (adult) 25 (child)	70 (adult) 25 (child)
AT, average time	Days	25,550 (adult) 3650 (child)	Non-carcinogenic effects = 10,950 (adult) 2190 (child) Carcinogenic effects 25,550
SA, skin surface area	cm2	-	18,000 (adult) 6600 (child)
Kp, permeability coefficient	cm/hr	-	Cd 0.001, Pb 0.0004, As 0.001, Hg 0.001, and Zn 0.0006
CF, conversion factor	L/cm3	-	1/1000

and

Table 7. The toxicity responses of trace metals for the oral and dermal reference dose (RfD) and oral slope factor (SF).

Elements	Oral RfD (μg/kg/day)	Oral SF (mg/kg/day)	Dermal RfD (μg/kg/day)
Cd	0.5	0.38	0.005
As	0.3	1.5	0.00125
Hg	0.01	n.d.	0.0021
Pb	3.5	0.0085	0.0042
Zn	300	n.d.	60
NO3−	1.6	n.d.	n.d.

. The chronic human health risks of groundwater (HQ and HI) through oral and dermal exposure routes for both adults and children are summarized in

Table 8. HQ and H values of adults and children for oral exposure pathway.

Sample	Cd	As	Hg	Pb	Zn	Nitrate	HI	Chronic Risk according to USEPA
Gw 1 adults	0.13	0.58	0.037	1.20	0.072	0.019	2.032	Medium
Gw 1 children	0.16	0.74	0.047	1.514	0.092	0.0093	2.562	Medium
Gw 2 adults	0.19	0.60	0.04	1.21	0.0068	1.77	3.820	Medium
Gw 2 children	0.24	0.76	0.051	1.543	0.009	2.26	4.863	High
Gw 3 adults	0.16	0.335	0.0314	0.876	0.2026	0.0028	1.605	Medium
Gw 3 children	0.20	0.43	0.04	1.115	0.259	0.0035	2.0475	Medium
Gw 4 adults	0.189	0.304	0.042	1.460	0.0078	0.072	2.074	Medium
Gw 4 children	0.24	0.39	0.373	1.860	0.010	0.0913	2.964	Medium
Gw 5 adults	0.140	0.597	0.0377	1.189	0.0825	0.0073	2.054	Medium
Gw 5 children	0.18	0.76	0.048	1.515	0.105	0.0093	2.618	Medium
Gw 6 adults	0.189	0.388	0.440	1.210	0.0068	1.775	4.009	High
Gw 6 children	0.26	0.49	0.056	1.543	0.009	2.260	4.618	High
Gw 7 adults	0.132	0.340	0.0305	0.890	0.198	0.00275	1.593	Medium
Gw 7 children	0.17	0.43	0.04	1.120	0.252	0.0035	2.016	Medium
Gw 8 adults	0.201	0.305	0.035	1.460	0.0078	0.0720	2.081	Medium
Gw 8 children	0.256	0.39	0.044	1.870	0.011	0.0915	2.663	Medium
Gw 9 adults	0.207	0.330	0.034	1.550	0.0082	0.00175	2.150	Medium
Gw 9 children	0.264	0.42	0.043	1.971	0.011	0.0230	2.732	Medium
Gw 10 adults	0.157	0.370	0.0335	1.730	0.0103	0.0147	2.316	Medium
Gw 10 children	0.22	0.47	0.047	2.20	0.0132	0.01875	2.970	Medium
Gw 11 adults	0.198	0.470	0.040	1.980	0.0613	0.0743	2.824	Medium
Gw 11 children	0.252	0.590	0.051	2.515	0.078	0.0945	3.580	Medium
Gw 12 adults	0.201	0.597	0.0346	1.750	0.0205	0.0630	2.670	Medium
Gw 12 children	0.256	0.76	0.044	2.230	0.026	0.0803	3.396	Medium

and

Table 9. HQ and H values of adults and children for dermal exposure pathway.

Sample	Cd	As	Hg	Pb	Zn	HI	Chronic Risk according to USEPA
Gw 1 adults	0.018	0.0029	0.0024	0.0018	0.00098	0.026	Low
Gw 1 children	0.023	0.005	0.0042	0.0032	0.00174	0.037	Low
Gw 2 adults	0019	0.0029	0.0026	0.0018	0.00093	0.027	Low
Gw 2 children	0.033	0.00028	0.00362	0.00235	0.0049	0.042	Low
Gw 3 adults	0.0157	0.0016	0.0020	0.00133	0.0028	0.0235	Low
Gw 3 children	0.028	0.00028	0.0036	0.00235	0.0049	0.042	Low
Gw 4 adults	0.019	0.0048	0.0027	0.0022	0.0001	0.029	Low
Gw 4 children	0.034	0.0026	0.0048	0.0039	0.00019	0.045	Low
Gw 5 adults	0.014	0.0029	0.0024	0.0018	0.0013	0.023	Low
Gw 5 children	0.0245	0.0051	0.0042	0.0032	0.002	0.039	Low
Gw 6 adults	0.033	0.0033	0.0051	0.0032	0.00016	0.045	Low
Gw 6 children	0.019	0.0018	0.0029	0.0018	0.0001	0.0255	Low
Gw 7 adults	0.0132	0.0016	0.0020	0.0013	0.0027	0.021	Low
Gw 7 children	0.024	0.0028	0.0036	0.00235	0.0048	0.038	Low
Gw 8 adults	0.020	0.00146	0.00225	0.0022	0.00011	0.0261	Low
Gw 8 children	0.0356	0.0026	0.004	0.00392	0.00018	0.0463	Low
Gw 9 adults	0.0210	0.00158	0.0022	0.0024	0.00012	0.073	Low
Gw 9 children	0.037	0.0028	0.0039	0.0042	0.0002	0.048	Low
Gw 10 adults	0.0157	0.00176	0.0024	0.0026	0.00014	0.0226	Low
Gw 10 children	0.0278	0.0031	0.00422	0.0046	0.00025	0.040	Low
Gw 11 adults	0.020	0.00223	0.0026	0.0030	0.00089	0.029	Low
Gw 11 children	0.035	0.0039	0.0046	0.0053	0.0015	0.050	Low
Gw 12 adults	0.0173	0.0029	0.0023	0.0027	0.00028	0.0254	Low
Gw 12 children	0.036	0.005	0.00398	0.0047	0.0005	0.050	Low

, while the carcinogenic risk of lead via an oral exposure pathway was calculated for both adults and children and the results are presented in

Table 10. Cancer risk from lead (Pb) among adults and children for oral exposure pathway.

Cancer Risk Value for Pb Carcinogenic Risk according to USEPA
No. of Sample	Adults	Children	Adults	Children
Gw 1	3.57× 10−5	4. × 10−5	Acceptable level	Acceptable level
Gw 2	3.60× 10−5	4.6× 10−5	Acceptable level	Acceptable level
Gw 3	2.61× 10−5	3.32× 10−5	Acceptable level	Acceptable level
Gw 4	4.34× 10−5	5.54× 10−5	Acceptable level	Acceptable level
Gw 5	3.54× 10−5	4.51× 10−5	Acceptable level	Acceptable level
Gw 6	3.60× 10−5	4.59× 10−5	Acceptable level	Acceptable level
Gw 7	2.65× 10−5	3.40× 10−5	Acceptable level	Acceptable level
Gw 8	4.31× 10−5	5.56× 10−5	Acceptable level	Acceptable level
Gw 9	4.61× 10−5	5.86× 10−5	Acceptable level	Acceptable level
Gw 10	5.15× 10−5	6.55× 10−5	Acceptable level	Acceptable level
Gw 11	5.90× 10−5	7.50× 10−5	Acceptable level	Acceptable level
Gw 12	5.21× 10−5	6.63× 10−5	Acceptable level	Acceptable level

.

4. Discussion

4.1. Characteristics of Water Chemistry

Table 1 provides a summary of the statistical information regarding the water-quality data collection. The majority of groundwater samples exhibited a very consistent temperature range of 5.7 to 6.9 °C. The pH of water is one of the most crucial factors in determining its safety for drinking. The pH values of the groundwater samples varied between 6.9 and 7.4, indicating that the water was naturally neutral. Electrical conductivity (EC) is a key indicator used to classify drinking-water quality. Higher EC values correspond with increased concentrations of dissolved solids and ionic strength in the groundwater [35]. Our samples demonstrated positive correlations between EC and both cations and anions. The investigated water samples had an average EC of 920.10 μs/cm, ranging from 417 to 2030 μs/cm. Although the average value was within permissible limits, 16% of the samples exceeded the suggested limits [36,37,38] (Table 2).

The tests used to assess water quality detect naturally occurring cations and anions. The groundwater displayed the following relative abundances of major cations: Ca > Mg > Na > K (mg/L). The relative abundance of anions was SO₄²⁻ > Cl⁻>NO₃⁻ > PO₄³⁻. The study area exhibited a broad concentration range of calcium, with the highest amount among all the cations. Sixty percent of the sampling sites had calcium concentration levels higher than 70 mg/L in general, which is above the WHO-recommended limits. The concentrations of other cations, which are essential to human health, were found to be within their maximum water quality (MWQ) limits. The phosphates and chlorides present were below the acceptable limits.

The findings indicated that the average concentrations of sulfate and nitrate did not exceed the standard determinations set by the MWQ, EU, and WHO [36,37,38], except for two samples in which the values exceeded the suggested criteria. Sulfate in groundwater mainly originates from mineral dissolution, atmospheric deposition, and human activities such as mining and fertilizer use. Sulfate, a common salt necessary for life in certain amounts, is not harmful in the same way as heavy metals, pesticides, or other toxic substances. However, high levels of sulfate in drinking water can cause laxative effects and impair taste [39]. Nitrate is one of the most common pollutants in water. The World Health Organization (WHO) established a guideline value of 50 mg/L for nitrate (as nitrate ion), based on epidemiological studies showing no adverse health effects at concentrations below this value [38]. The primary form of nitrogen pollution was NO₃⁻. Significant correlations would be expected between nitrate and the major cations found in chemical fertilizers if fertilizer use was the main source of nitrate contamination. However, no discernible relationship was found between nitrate and potassium, calcium, or magnesium, indicating that agriculture was not the primary source of the nitrate pollution (Table 3). Furthermore, the regions with elevated nitrate concentrations were primarily industrial districts. High levels of sulfates and nitrates are present in the section of the Danube River passing through this region due to industrial discharges from the Kostolac Basin, although the overall levels are lower than those in globally contaminated surface waters around urban and industrial areas [40]. Significant variations in the EC, sulfate, and zinc values (Figure 2) were primarily caused by geochemical processes such as ion exchange, evaporation, sediment dissolution, and rainwater infiltration, in addition to human sources [41,42]. Heavy metals such as cadmium (Cd) and lead (Pb) have several detrimental effects on human health, including DNA damage, cancer, and central nervous system injuries, as noted in [43]. The levels of Cd and Pb in the samples (67% and 100%, respectively) exceeded the MWQ, EU, and WHO guidelines, indicating a significant impact on the disease burden of the local population in the studied area. The highest trace metal pollution levels of lead (87 μg/L) and cadmium (3.5 μg/L) were found in locations where there were large-scale, uncontrolled metal inputs from human activities such as TEKO Kostolac. The results of the chemical analyses compared with the World Health Organization, the EU Directive, the Water Act, and the Regulations on the Monitoring of Water Quality introduced by the Government of the Republic of Serbia showed that levels of metals like arsenic (As), mercury (Hg), and zinc (Zn) were below or equal to the acceptable limits. This suggested that the groundwater was generally of adequate chemical quality for household or agricultural use, and the water quality at the tested locations could be categorized as class II. Conversely, the water quality fell into class III/IV (polluted) due to the quantities of EC, nitrates, total nitrogen, sulfates, and metals (Pb and Cd) exceeding the MWQ, EU, and WHO criteria. The ranges and means of the nitrates and metal concentrations in the samples from groundwater were compared with data from other countries to evaluate the water quality of the present study area. Generally, except for Pb, Zn, and nitrates, for which high concentrations were observed, the concentrations of the other parameters in the Serbian water were similar or slightly lower than other regions of world [4,8,9,10,17,19,21,22,23]. It should be noted that comparisons between the studies were relative and the reported means and ranges were all based on the methodology of a particular study. Increased pollution control efforts are sorely needed. Industry and agriculture produce a large diversity of pollutants, forming severe threats to groundwater quality. The protection of groundwater quality by effective regulation and strict enforcement is urgently required in all sectors as adequate practices are still rare.

4.2. Statistical Treatment

The source materials of the groundwater were identified using a PCA, a CA, and a Pearson correlation matrix. According to [44], a correlation analysis reveals the relationship between the heavy-metal characteristics of water samples and can provide data regarding the sources of these metals. A high correlation coefficient indicates a strong positive relationship between two variables at a p-value of less than 0.05, while a value close to zero suggests no relationship. References [45,46] explain that a moderate correlation is indicated by an R-value between 0.5 and 0.7, while values of R > 0.7 are considered to be highly correlated. Significant positive and negative correlations between the measured parameters were revealed by the Pearson correlation values presented in Table 3. There was a significant relationship between Pb and Cd, suggesting that they had a similar geological behavior as well as similar sources.

With a Kaiser–Meyer–Olkin test result of 0.684, appropriate sampling adequacy was indicated. To improve the explanation of the potential factors influencing heavy metals, a varimax rotation was employed to maximize the sum of the variance of the factor loadings. Figure 3 displays a scree plot of the eigenvectors as a function of the factor number. Four major components were identified using the Kaiser criterion, which accounted for eigenvalues greater than 1. These components explained 87.63% of the total variance. The scree plot was used to determine the number of PCs. Table 4 lists the component loadings, cumulative percentages, and percentage of variations explained by each factor. Figure 3 displays the component plot in the rotated space of the principal component analysis.

PCA 1, which accounted for 17.89% of the total variance, showed significant correlations for EC, pH, Cl⁻, NO₃⁻, and SO₄²⁻. All these components had levels higher than the baseline except chloride, suggesting that they may have been impacted by human activity rather than natural sources. The anthropogenic toxic pollution sources represented by Component PCA 2 (Cd and Pb) most likely originated from industrial effluents. Component 3 had substantial loadings for Na, K, and Mg and accounted for 15.181% of the total variation. Significant positive correlations (p < 0.01) were found between them in the samples, suggesting that these ions originated from the mineral part of the groundwater (natural sources). The last component featured strong loadings of calcium and phosphate. The Pearson correlation matrix supported the hypothesis for PCA 4 that the behavior of calcium in groundwater may be influenced by other mechanisms. Moreover, a high negative loading by phosphates (with values not exceeding the safe water criteria defined by the MWQ, EU, and WHO [36,37,38]) suggested that this source did not make a major contribution to the other parameters.

Based on the PCA analyses and the similarity of the groundwater-quality features, HCA divided the sample locations into three clusters. A total of eight samples (1, 4, 5, 8, 9, 10, 11, and 12) made up the first cluster (A), which was further separated into two subgroups—A-1 (1, 5, 9, and 10) and A-2 (4, 8, 11, and 12); two samples each made up the second cluster, B (2 and 6) and the third cluster, C (3 and 7) (Figure 4). The hydrochemistry of the A-1 group was dictated by mineralization processes controlled by the lithological composition of the groundwater. Component 3 (all cations) of the PCA dominated in group CA-1, while component PC 2, which had higher indicated contents of Pb and Cd due to human activities (scattered point sources—industrial effluent), was represented in group A-2. Group B samples had higher levels of Eh (2030 μg/L), SO₄ (478.50 mg/L), and NO₃ (90.40 mg/L) than the other groups. Additionally, they were contaminated with similar pollutants to subgroup A-2. Conversely, the samples from group C had an extremely high loading of Zn (1940 μg/L), which was likely the result of anthropogenic activities or the cation exchange properties of groundwater wells, but these values did not exceed the guidelines set by MWQ, EU, and WHO [36,37,38] for safe water.

4.3. Pollution Level

Table 5 and Figure 5 provide the results of the pollution evaluation levels. The heavy-metal evaluation index had a mean value of 9.041 and it ranged from 7.41 to 12.62. About 41.70 percent of the sample sites exhibited a medium level of pollution, while low and high levels of contamination were found at 58.3% and 0% of the sample sites, respectively. The level of metal pollution at the sampling sites was assessed using the degree of contamination. The Cd results had a mean value of 4.71, with a range from 2.28 to 7.53. The results for the Cd pollution degree showed that 33.3% of the sampling sites fell into the medium pollution category and 66.7% into the high pollution rate category. To determine the extent to which various heavy metals contaminated the groundwater, the Nemerow pollution index was further implemented. Our samples had an NI ranging from 1.43 to 14.85, with a mean value of 5.11. As seen in Table 5, it was found that nearly 58.3% of the sites experienced high pollution levels (severe pollution), with the remaining 41.7% having medium pollution levels. Mercury and lead were identified as the most significant contributors to pollution.

4.4. Human Health Risk

The World Health Organization (WHO) has ranked arsenic, lead, mercury, and cadmium among the top 10 substances of great risk to people worldwide due to their significant toxicity, persistence in the ecosystem, and tendency for bioaccumulation [47]. The United States Environmental Protection Agency (USEPA) defines a human health risk assessment as the process of evaluating the potential for adverse health impacts from human exposure to chemicals in a contaminated environment. This assessment is performed using the following four steps [48]:

Step 1: Hazard Identification—Contaminants are assumed to impact on human health, regardless of whether they meet the toxicity criteria or not. In this paper, eleven specifications were divided into two categories: non-carcinogenic factors such as nitrate (NO₃⁻), Cd, As, Hg, Zn, and Pb; and carcinogenic effects (only Pb was considered in this work because the Pb levels in 100% of the samples exceeded the MWQ, EU, and WHO recommendations). We only considered the non-carcinogenic risk of nitrogen because, currently, there is insufficient evidence to link nitrogen exposure to human cancer [49].

Step 2: Dose–Response Assessment—This step uses the reference dose (RfD) to determine the precise relationship between the contaminant exposure dose and its effects. The RfD values used in this study are listed in Table 6 and Table 7.

Step 3: Exposure Assessment—According to [50], this step involves assessing exposure aspects such as exposure pathways, body parameters, frequency of exposure, and duration of exposure, either quantitatively or qualitatively. There are three potential routes of exposure: direct ingestion of drinking water, inhalation, and skin absorption. In this study, groundwater exposure was quantified using an oral exposure pathway and dermal absorption. By comparing the estimated contaminant exposures from each pathway (oral and dermal) with the RfD using Equation (4), potential non-carcinogenic concerns were identified. This article focused on nitrogen exposure from a drinking-water intake route because skin absorption is considered to be less significant (less than one-thousandth of the drinking-water intake) [51,52,53]. Table 8 and Table 9 summarize the chronic human health risks associated with groundwater exposure through oral and dermal routes for both adults and children. It was found that the HQ values for Cd, As, Hg, and Zn were less than 1, indicating that these elements might not pose serious hazards when present alone. However, the highest HQ oral values for Pb and nitrates were found to be 1.776 and 2.260 for adults and 2.515 and 2.260 for children, respectively. According to USEPA (Table 8), most of the samples indicated a medium category of chronic risk for both adults and children. Table 9 provides additional results of dermal HI for adults and children, showing that dermal exposure is typically less hazardous than oral exposure. In both the adult and the child populations, 100% of the samples were associated with low levels of chronic risk. In all cases (adults and children), sampling site Gw 6 exhibited the highest potential chronic risk.

Carcinogenic Risk—Equation (6) and Table 7 illustrate how the carcinogenic risk (CR) associated with Pb exposure was calculated as the total daily intake [mg/(kg day)−1] multiplied by the cancer slope factor (SF). As noted earlier, the only carcinogenicity risk established in this study originated from lead. Based on the guidelines suggested by some authors [54,55], all of the samples fell into the low-risk category concerning the carcinogenic risk of lead exposure via the oral pathway (Table 10 provides estimates for both adults and children).

5. Conclusions

Multivariate statistical and health risk analyses of water-quality parameters were applied to determine the source of contaminants and their effects on human health in the alluvial area of the Danube River near the Kostolac Basin, Serbia. A total of 21 parameters, including temperature (T), pH, electrical conductivity (EC), cations (Na⁺, K⁺, Ca²⁺, and Mg²⁺), anions (Cl⁻, NO₃⁻, SO₄²⁻, and PO₄³⁻), and trace elements (Cd, Hg, Pb, As, and Zn), in water samples collected from 12 groundwater wells were analyzed. The levels of nitrates (90.40 μg/L), Pb (87 μg/L), and cadmium (3.5 μg/L) in 17%, 67%, and 100% of the samples, respectively, exceeded the Serbian Water Act criteria (MWQ), European Parliament and Council of the European Union (EU), and the World Health Organization (WHO) groundwater-quality guidelines, indicating a significant impact on the disease burden of the local population in the studied area. There was a significant relationship between Pb and Cd, suggesting that they had a similar geological behavior as well as similar sources. These samples were categorized as class III/IV, based on the Serbian Water Act criteria, indicating that they required sanitation due to unacceptable quality. A principal component analysis (PCA), a cluster analysis (CA), and a Pearson correlation matrix identified the dominant and potential sources of contaminants in the groundwater wells, including natural geogenic processes and industrial activities. According to the HEI results, around 50% of the samples exhibited a medium level of contamination, while 83% and 58.3% of the samples showed a high level of pollution regarding Cd and Ni, respectively. Pb and nitrates reported the highest HQ oral values for adults (1.980 and 1.776, respectively) and children (2.515 and 2.260, respectively). Generally, there is a lower risk associated with dermal exposure than oral exposure. As the HQ values at other sites were less than 1, it was considered probable that any potential problems could be disregarded. However, HQ values greater than 1 for lead and nitrates at some sample sites raised concerns regarding both chronic and carcinogenic health risks. According to the lead carcinogenic risk via the oral exposure pathway, there was a minimal risk for both adults and children associated with all of the samples.

Most groundwater pollution is anthropocentric and can be prevented through intensive health education. If the improvement of groundwater quality and future scenarios are not considered, this problem may become even more serious. Therefore, this study provides relevant information to residents and public authorities concerned with drinking-water quality and the potential effects of contamination. This article also provides an insight into the groundwater contamination issues that are affecting low- and middle-income countries and countries with emerging economies in the Eastern Hemisphere. Researchers from Europe, North America, and other high-income countries often do not grasp the extent of groundwater contamination from geogenic and anthropogenic sources in other regions. The co-authors of this paper anticipate that this article will facilitate an understanding of the origins and extent of groundwater contamination and its consequences, providing examples of possible approaches to the remediation of groundwater contamination and the protection of groundwater quality. In the future, “big data” analyses, drone surveys, and molecular and stable isotope analysis technologies should be applied to groundwater research.