Interdisciplinary Evaluation of the Săpânța River and Groundwater Quality: Linking Hydrological Data and Vegetative Bioindicators

Abstract

This study was carried out to fill the present research gap in the study area by assessing water chemistry, potential heavy metal contamination, and the associated health risk evaluation that goes along with it in surface water bodies and groundwater in the NE of Maramureș County, near the Tisa River. The main methods we applied were Piper, Ficklin–Caboi, and Gibbs diagrams for determining the water typology and chemistry, the Overall Water Quality Index (OWQI) and vegetation cover to determine the water quality, a contamination index for analyzing the contamination degree, and a human health risk assessment through water ingestion after exposure of children and adults. This article’s main findings specify that waters were characterized and classified into the CaMgHCO₃⁻ dominant category of water type, with precipitation, agricultural, and domestic inputs, related to the Cl⁻ (mean ranging between 1.01–5.65 mg/L) and NO₃⁻ (mean ranging between 2.23–5.52 mg/L) content. The OWQI scores indicated excellent quality, below the critical value, ranging between 0.70 and 6.57. The applied risk assessment indicated that the daily intake of toxins is higher in the case of children than in adults, up to four and five times. The hazard quotient scores, ranging between 0.00093 and 0.248 for adults and between 0.0039 and 1.040 for children, indicated that if consumed, the studied waters can pose potential negative effects on children.

1. Introduction

Water is among the most fundamental elements of life, being the most prevalent on the surface of the globe. Surface waters are defined as units characterized by biotic and abiotic elements. The biotic elements comprise aquatic fauna and flora, which are strongly influenced by the natural conditions in which they live. The abiotic elements encompass morphological, geological, hydrological, and climatological factors [1]. Water, found in different states, is a limited resource. River systems have played a pivotal role in the development of human societies and the enhancement of the human condition [2,3,4,5,6,7]. Human well-being is impacted by water quality through direct human uses like drinking and irrigation in agriculture and industry, as well as indirect effects like its impact on ecosystem services like aquaculture and fisheries productivity and wetland biodiversity [3,4,5]. Rapid urbanization and other human activities, such as hydrogeochemical processes, water–rock interaction, and evaporation, have an important impact on the geochemical evolution of groundwater [5,6,7,8,9]. According to other studies, contaminants like nitrates, heavy metals, and microbiological infections are common and present major health dangers to the local populace. The rapid expanding human population and hydrological processes (recharge characteristics, possible recharge zones, groundwater origin, pollution sources, and surface water interactions in areas like wetlands, as well as water-stable isotopes) favor the water contamination process [3,4,5,6,7,8,9]. The equilibrium in natural ecosystems may be impacted or even destroyed by human selfishness regarding nature and the living environment, which are exploited carelessly and without supervision [10]. Because water bodies receive untreated wastewater from households, municipal farms, industrial activities, and other sources, most aquatic ecosystems (ponds, lakes, rivers, and streams) are of low quality because of human activity [11]. Investigating natural ecosystems includes looking into the main risks to biodiversity, such as overuse of natural resources, water pollution, land fragmentation, habitat destruction or degradation, and non-native species incursions [12,13].

Aquatic habitats are characterized by biodiversity with higher sensitivity related to the terrestrial ecosystem [13,14,15]. Two amphibian species (Triturus cristatus and Bombina variegata) and one reptilian species (Emys orbicularis) of conservation significance round out the faunal list [15]. The composition and geographic distribution of amphibians (vital and therefore protected species in Romania) echo the negative effects of human pressure on humid environments (pollution, habitat deterioration) [14]. The water level, water flow velocity, and untreated wastewater discharge from draining water with suspended sediment all affect the nutrient concentrations in moving water bodies. For example, the release of phosphorus particles has been demonstrated to induce degradation in the quality of water through the process of sedimentation [16].

Efficient tools, specifically contamination and health risk indices, are based on the monitored and measured chemical parameters in waters and the standard values or reference doses established by authorities through regulations and guidelines at national and international levels, correlated with the water quality and its purpose, applied in order to determine the potential contamination degree and the human health risks. The most common and successful indices used at the global level are represented by the contamination index (CI), heavy metal evaluation index (HMEI), chronic daily index (CDI), hazard quotient (HQ), or the hazard index (HI) [1,2]. They are mandatory to be applied in case of potential contamination. In this framework, the present study area was selected based on the use of water sources and the contamination sources situated near the water sources. The main objective of the current research was to measure and examine the chemical content (13 chemical indicators, 10 trace metals, and 4 major cations) related to water sources sampled from Natural Area Tisza, used with drinking purpose. Based on the obtained concentrations, the water typology was analyzed (Piper, TIS (Total Ionic Salinity), Gibbs, Ficklin–Caboi charts), and the degree of contamination with heavy metals was calculated based on the contamination indices (PI and HMEI). The overall water quality was determined by applying the WQI (Water Quality Index). Likewise, a human health risk evaluation with non-carcinogenic implication was implemented in two scenarios (children and adults) with the help of three risk indices (CDI, HQ, and HI). The obtained results are significant and can be used by policy makers and local authorities in preventing and reducing the contamination and potential risk induced for human health.

The major research question challenged in the current study implies the chemical composition of two different water types, surface water and groundwater, potentially influenced by anthropogenic activities. The site-specific context of this study implies a protected area from the NE part of Maramureș County, Romania, where several sampling points were selected on the Săpânța River, a tributary of the Tisa River, and a groundwater source.

The specific objectives of this study include (a) the determination of the physico-chemical marker distribution and sources concerning the Săpânța River and groundwater sampling points; (b) a water typology analysis based on the Piper and Total Ionic Salinity (TIS) plots; (c) the assessment of the potential metal contamination degree of surface waters and groundwaters, using diverse contamination indices, as well as changes in the quality of waters, which were analyzed by using vegetation parameters as an indicator; (d) the evaluation of the ecological and biological variety related to the Săpânța River and groundwater sampling points, with the aim of implementing conservation regulations, and (e) a human health risk assessment of metals through water ingestion in the cases of children and adults.

The significance of this work implies the assessment of water quality (drinking water source) and public health, emphasizing the scientific contribution of the obtained results given by its interdisciplinary use in water and human protection. Similarly, the findings of this study, also implying the identification of various hazardous metals, are significant in improving the application of environmental risk management in the studied area, aiming for contamination mitigation.

2. Materials and Methods

2.1. Study Area and Sampling

Protected natural areas are designated for the conservation of species and their habitats. Natura 2000 sites are unique in that they encompass extensive surface areas, thereby concentrating both biodiversity values and human communities engaged in diverse activities. The Tisa and Săpânța Rivers are part of the protected area, encompassing a diverse array of habitats and species of common interest. The Săpânța River plays a pivotal role in enhancing biodiversity in a critical section of the protected area. It is situated in the Gutâi Mountains amidst natural beech forests, ensuring optimal water quality. However, the rivers traverse the town of Săpânța, which has a population of approximately 3000, as well as a substantial influx of tourists due to its nationally and internationally recognized objectives, namely the Merry Cemetery and the Peri Monastery. The presence of an anthropogenic disturbance, in the form of a micro-hydropower plant on the Săpânța meadow, in conjunction with the impact of agriculture in the Tisa meadow, has given rise to inquiries regarding the quality of the river and the waters connected to it, including groundwater and the Tisa River.

From a geological point of view, the area belongs to the Maramures Depression [17]. The formations that are included in the Maramures Depression are attributed to the Badenian, Sarmatian, and Pliocene ages. The geological structure consists of Pannonian marls as a substrate of sediments from the Tisa month. The formations attributed to the Pleistocene are represented by the low terrace deposits of the Tisa River. These are formed by alluvial–proluvial material represented by gravel, boulders, and sand. Petrographically, in the mobile deposits, eruptive formations predominate in association with metamorphic and sedimentary rocks, which are responsible for a part of minerals and ions, with the potential to increase conductivity in relation to the surface water of the river.

The Săpânța River is localized in the Săpânța locality, in the NE part of Maramureș County near the Tisa River (Figure 1). The sampling points for all four (4) studied water bodies are presented in Figure 1, which also indicates the land use (mainly agricultural crops, a touristic area, and forest). The sampling points had the following coordinates: SR1 (47°59′16″ N, 23°41′46″ E), SR2 (47°59′3″ N, 23o41′51″ E), SR3 (47°58′55″ N, 23°41′52″ E), and GW (47°58′54″ N, 23°42′3″ E). The sampling strategy was applied after standard procedures, and several physico-chemical analyses were performed on-site.

2.2. Water Quality Analysis

Analysis of the physico-chemical parameters was applied 48 h after the water samples were collected. Samples were collected in sterile polyethylene flasks to avoid contamination, and they were refrigerated until analysis and were based on standard procedures [18]. Samples were collected in triplicate, as monthly measurements in 2024. A conductometer model, INOLAB 740 (WTW, Weilheim in Oberbayern, Germany), was used to measure the EC, and an HI 253 pH meter (Hanna Instruments, Woonsocket, RI, USA) with a mixed pH electrode was used for the pH determinations, in accordance with standard guidelines [19,20]. Turbidity was determined applying titrimetric methods and using a WTW 355 IR portable turbidimeter (WTW, Weilheim in Oberbayern, Germany) [21].

Subsequent to the incorporation of oxalic acid (99.0% purity, purchased by Sigma-Aldrich, St. Louis, MO, USA), the dissolved oxygen (DO) concentration was determined through back titration employing potassium permanganate (99.0% purity, purchased by Merck Darmstadt, Darmstadt, Germany) [22]. In agreement with standard procedures, total alkalinity was measured by acidic titration, while precipitation titration using silver nitrate (≥99.0% purity, purchased by Merck Darmstadt, Darmstadt, Germany) was used to quantify the concentration of Cl⁻ [23,24]. The EDTA titrimetric procedure was applied to determine total hardness (h_t) [25]. The analysis of Al, NH₄⁺, and NO₃⁻ was conducted using a UV–VIS spectrophotometer, model Specord 50 (Analytik Jena, Jena, Germany), which conforms to standard methodologies [26,27,28]. The PO₄³⁻ was determined using the SR EN ISO 6878:2005 method [29]. The SR ISO 6332/1996 protocol was employed to analyze the dissolved Fe content [30], and the SR ISO 8662/2/1997 protocol was used to analyze the Mn content [31].

A 1:1 HNO₃ (65%, supplied by Merck, Darmstadt, Germany) solution was employed to preserve the water samples until an acid pH of 1–2 was attained. The samples were subjected to digestion using a mixture of 30% hydrogen peroxide and 70% concentrated HNO₃ (both supplied by Merck, Darmstadt, Germany). The digested samples were stored at 4 °C after being dissolved in HNO₃. Filtration and acidification with 0.5 mL of 70% HNO₃ per 100 mL of each sample, excluding the mineralization step, were required for samples that were to be examined using the graphite furnace [32,33].

The analysis was conducted using graphite flame absorption atomic spectrometry (GFAAS) on a spectrophotometer, the Analyst 800 model (PerkinElmer, Norwalk, CT, USA), determined using standard procedures [32,33]. Details related to the equipment and calibration are presented in the Supplementary Material.

Based on the physico-chemical and chemical analysis conducted on the water samples, variate plots were constructed, serving different purposes in analyzing the studied water samples.

The total salinity of water was determined based on the Total Ionic Salinity in the form of a TIS plot. TIS shows the salinity of waters expressed in meq/L. It is determined with the help of the SO₄²⁻, Cl⁻, and HCO₃⁻ content. The relation between the total metal load and pH determined in the present study was represented with the help of a Ficklin–Caboi diagram. This plot shows the metal load and pH type, classifying the waters into one of six categories (I. high acid, extreme metal; II. acid, extreme metal; III. near-neutral, extreme metal; IV. high acid, high metal; V. acid, high metal; and VI. near-neutral, high metal). TIS and Ficklin–Caboi plots were obtained with the help of Microsoft Excel 2016 MSO, version 2503 Build.

2.3. Water Typology

The water typology was determined based on the Piper plot. This plot was constructed with the free version 1.30.0.0 of GW_Chart, software approved by the U.S. Geological Survey. A Piper plot contains three different plots (two trilinear plots: one based on the cation content and one on the anions content, as well as one diamond plot which combines the results of both trilinear plots) and indicates the water typology based on the cation (Na, K, and Mg) and anion (CO₃²⁻, HCO₃⁻, SO₄²⁻, and Cl⁻) content measured in the water samples.

Water sources were identified with the help of Gibbs plots and the relation between NO₃⁻/Cl⁻ and Cl⁻ content, using Microsoft Excel 2016 MSO, version 2503 Build. Gibbs plots include two plots, one based on the ratio among the TDS content (mg/L) and the anion ratio (Cl⁻/(Cl⁻ + HCO₃⁻)) content and one based on the ratio between the TDS content (mg/L) and the cation ratio ((K + Na)/(Ca + Na + K)). According to the representation of samples related to the plots, it classifies its origin in one of two categories (water–rock interaction or precipitation dominance). Agricultural inputs and domestic effluents are two other sources into which the waters are classified, based on the relation between the Cl⁻ content and ratio between NO₃⁻ and Cl⁻ content (mmol/L).

2.4. Potential Contamination Degree Evaluation

The overall water quality of waters was determined and studied by applying the OWQI (Overall Water Quality Index) approach. The OWQI converts quantitative data into qualitative data, identifying the degree of quality. The OQWI is based on the measured data, implying the physico-chemical and chemical indicator results (major cations, anions, trace metals), the unit weight, the maximum allowable concentration (MAC), and the ideal value of each indicator. This index is comprised with the help of Equation (1), as indicated by Das et al. [34]:

$$OWQI={\displaystyle \frac{\left[100\times \left(\sum \frac{{MD}_i - ID}{{MAC}_{i }- ID}\right)\right]}{W_i}},$$

(1)

MD_i signifies the average results related to each chemical indicator determined in the water samples, ID is the ideal value, and W_i is the weight of each chemical parameter considered for calculating this tool. After applying Equation (1), the water sample is classified into one of five (5) quality categories (unsuitable for drinking if the OWQI score is below 100; very poor (OWQI <76), poor (51 < OWQI ≤ 75), good (26 < OWQI < 50), and excellent (OWQI ≤25)), characterizing the studied water sample [34].

The potential contamination in relation to heavy metals and As content was determined and evaluated by applying the contamination index (CI) and the heavy metal evaluation index (HMEI). Both index approaches consider the total quality of the water related to the heavy metal and As content. The CI is based on the weighted arithmetic quality average of each studied chemical, while the HMEI represents the ratio between the measured concentration and the MAC of each chemical. Equations (2) and (3) indicate the calculation methods for CI and for HMEI, respectively [35]:

$$CI={\displaystyle \frac{\sum _{i=1}^n{W}_{i }\times q_i}{\sum _{i=1}^n{W}_i}},$$

(2)

$$HMEI={\sum }_{i=1}^i{\displaystyle \frac{{MD}_i}{{MAC}_i}},$$

(3)

Element q_i represents the sub-index of the ith indicator, calculated as the ratio between the obtained result and MAC concerning heavy metal, multiplied by 100. The obtained CI score classifies the water as contaminated (CI >100) or not contaminated (CI ≤100) with the studied heavy metals and As, compared to the critical contamination index value (100) [35]. The HMEI, otherwise, offers an evaluation of the overall quality of water, implying the impact of toxin implications on human health. Based on the HMEI scores, waters are characterized by one of the six quality classes: severely affected (HMEI >6.0), heavily affected (4.0 ≤ HMEI ≤ 6.0), moderately affected (2.0 ≤ HMEI < 4.0), partly affected (1.0 ≤ HMEI < 2.0), clean (0.3 < HMEI < 1.0), and very clean (HMEI ≤0.3) [36].

2.5. Human Health Risk Assessment of Toxins

To quantify the exposure to heavy metals through water ingestion, the lifetime mean daily dose of ingested toxins, the Chronic Daily Index (CDI), was applied and analyzed. In this framework, Equation (4) was applied:

$$CDI={\displaystyle \raisebox{1ex}{$MD\times ED\times EF$}\!\left/ \!\raisebox{-1ex}{$AT\times BW$}\right.}\times IR,$$

(4)

where ED (ED_children = 6 years, ED_adults = 70 years) and EF (EF_children and EF_adults = 365 days) are the exposure duration and exposure frequency, and AT (AT_children = 2190 days, AT_adults = 25550 days) and BW (BW_children = 15 kg, BW_adults = 70 kg) are the average time exposure and body weight. Element IR represents the daily ingestion rate (IR_children = 1.8 L/day, IR_adults = 2.0 L/day) [36].

Non-carcinogenic concerns include teratogenic and genetic consequences and are mostly related to exposure. Two different scenarios were applied in the present study, health risk affecting children and adults, through the ingestion pathway related to heavy metals present in water.

Furthermore, the non-cancer risk of heavy metals, implying potential negative effects, was assessed by calculating two health risk indices: the hazard quotient (HQ) and the hazard index (HI). For their determination, Equations (5) and (6) were applied as follows:

$$HQ={\displaystyle \frac{CDI}{RfD}},$$

(5)

$$HI=\sum {HQ}_{HM1}+{HQ}_{HM2}+\dots +{HQ}_{HMn}$$

(6)

RfD represents the chronic oral reference dose, including the daily oral exposure level of the general population plus a sensitive subgroup that is most likely not at high risk of long-term negative effects. It is specific for each toxin used in the calculation of the CDI. The chronic hazard index (HI), defined as the total of all HQs determined for each heavy metal, was employed to quantify the potential human health risks associated with exposure at multiple HMs. HQ and HI are expressed in mg/kg/day. HQ and HI scores higher than one (HQ and HI ≥1.0) indicate non-carcinogenic risk, while scores lower than one imply no significant non-cancer risk from heavy metals ingested through water consumption [36,37].

The risk assessment methods were applied considering the Sr, Mn, Fe, and Zn concentrations, as well as four specific metals, including Pb, Ni, Cu, and As, which pose significant health risks to humans, especially when exposure occurs over extended periods due to their toxicity at low concentrations, carcinogenicity, and persistency.

3. Results and Discussion

3.1. Analysis of Water Chemistry

The results related to the physico-chemical and chemical indicators measured in the water samples are presented in Table 1 and Table 2 as annual median values.

The electrical conductivity of the Săpânța River varied between 43.3 and 50.0 µS/cm. In comparison, the groundwater, as represented by a well near the river, exhibited a conductivity of 255 µS/cm. This value is well below the legal limit of 2500 µS/cm. The low conductivity values are attributable to two factors. Firstly, there is a low level of dissolved inorganic substances in ionized form [38,39,40,41]. Secondly, there is a dilution effect during atmospheric precipitation, which is also influenced by temperature [38,39,40,41]. The electrical conductivity of water is contingent upon the Cl⁻ and HCO₃⁻ levels, depending on the water’s composition. Evaporation and flooding influence the electrical conductivity of water as well [38,39,40,41].

Table 1. The annual median values of the physico-chemical parameters determined in water samples (SR1–SR3) and groundwater near Săpânța River (GW). The results are expressed as the mean ± standard deviation (n = 12 (monthly measurement in 2024)).

	SI	SR1	SR2	SR3	GW	Mean	Median	Min	Max	SD	Errors	MAC *
EC	μS/cm	50.0 ± 4.8	46.2 ± 4.5	43.3 ± 4.1	255 ± 23	98.6	48.1	43.3	255	104	±8.3	250
pH	-	7.95 ± 0.77	7.98 ± 0.78	7.75 ± 0.63	7.38 ± 0.21	7.77	7.85	7.99	7.98	0.28	±1.15	6.5–9.5
DO	mg/L	9.26 ± 0.89	8.69 ± 0.72	8.55 ± 0.81	7.99 ± 0.68	8.63	8.62	1.09	9.26	0.52	±1.10	-
T	NTU	2.15 ± 0.19	5.52 ± 0.47	3.16 ± 0.30	1.09 ± 0.09	2.98	2.66	1.86	5.52	1.89	±0.25	<5
Ht	°g	2.45 ± 0.15	1.86 ± 0.12	2.78 ± 0.22	11.55 ± 0.96	4.66	2.62	1.01	11.55	4.61	±0.19	>5
Cl−	mg/L	1.01 ± 0.88	2.75 ± 0.16	1.88 ± 0.16	5.65 ± 0.53	2.82	2.32	0.045	5.65	2.01	±0.08	250
NH4+	mg/L	0.045 ± 0.004	0.057 ± 0.005	0.053 ± 0.005	0.066 ± 0.006	0.055	0.055	2.23	0.066	0.009	±0.002	0.5
NO3−	mg/L	2.23 ± 0.17	2.76 ± 0.20	2.88 ± 0.24	5.52 ± 0.49	3.35	2.82	3.84	5.52	1.48	±0.39	50
SO42−	mg/L	3.84 ± 0.29	4.68 ± 0.33	4.89 ± 0.41	5.45 ± 0.26	4.72	4.79	3.84	5.45	0.67	±0.42	250
PO43−	mg/L	0.02 ± 0.001	0.04 ± 0.002	0.03 ± 0.001	0.03 ± 0.002	0.03	0.03	0.02	0.04	0.008	±0.001	0.4
TDS	mg/L	44.2 ± 4.2	47.2 ± 3.9	53.5 ± 5.1	62.9 ± 5.9	52.0	50.6	44.2	62.9	8.3	±4.2	-
At	mmol/L	0.35 ± 0.02	1.22 ± 0.11	1.38 ± 0.09	2.65 ± 0.19	1.40	1.30	0.35	2.65	0.95	±0.05	-

Notes: * MAC based on international regulations, WHO guidelines [42], and national law [43] related to quality of water used as drinking water source.

Table 1. The annual median values of the physico-chemical parameters determined in water samples (SR1–SR3) and groundwater near Săpânța River (GW). The results are expressed as the mean ± standard deviation (n = 12 (monthly measurement in 2024)).

	SI	SR1	SR2	SR3	GW	Mean	Median	Min	Max	SD	Errors	MAC *
EC	μS/cm	50.0 ± 4.8	46.2 ± 4.5	43.3 ± 4.1	255 ± 23	98.6	48.1	43.3	255	104	±8.3	250
pH	-	7.95 ± 0.77	7.98 ± 0.78	7.75 ± 0.63	7.38 ± 0.21	7.77	7.85	7.99	7.98	0.28	±1.15	6.5–9.5
DO	mg/L	9.26 ± 0.89	8.69 ± 0.72	8.55 ± 0.81	7.99 ± 0.68	8.63	8.62	1.09	9.26	0.52	±1.10	-
T	NTU	2.15 ± 0.19	5.52 ± 0.47	3.16 ± 0.30	1.09 ± 0.09	2.98	2.66	1.86	5.52	1.89	±0.25	<5
Ht	°g	2.45 ± 0.15	1.86 ± 0.12	2.78 ± 0.22	11.55 ± 0.96	4.66	2.62	1.01	11.55	4.61	±0.19	>5
Cl−	mg/L	1.01 ± 0.88	2.75 ± 0.16	1.88 ± 0.16	5.65 ± 0.53	2.82	2.32	0.045	5.65	2.01	±0.08	250
NH4+	mg/L	0.045 ± 0.004	0.057 ± 0.005	0.053 ± 0.005	0.066 ± 0.006	0.055	0.055	2.23	0.066	0.009	±0.002	0.5
NO3−	mg/L	2.23 ± 0.17	2.76 ± 0.20	2.88 ± 0.24	5.52 ± 0.49	3.35	2.82	3.84	5.52	1.48	±0.39	50
SO42−	mg/L	3.84 ± 0.29	4.68 ± 0.33	4.89 ± 0.41	5.45 ± 0.26	4.72	4.79	3.84	5.45	0.67	±0.42	250
PO43−	mg/L	0.02 ± 0.001	0.04 ± 0.002	0.03 ± 0.001	0.03 ± 0.002	0.03	0.03	0.02	0.04	0.008	±0.001	0.4
TDS	mg/L	44.2 ± 4.2	47.2 ± 3.9	53.5 ± 5.1	62.9 ± 5.9	52.0	50.6	44.2	62.9	8.3	±4.2	-
At	mmol/L	0.35 ± 0.02	1.22 ± 0.11	1.38 ± 0.09	2.65 ± 0.19	1.40	1.30	0.35	2.65	0.95	±0.05	-

Notes: * MAC based on international regulations, WHO guidelines [42], and national law [43] related to quality of water used as drinking water source.

The pH exhibited fluctuations within the permissible limits, ranging from 7.75 to 7.95 (Table 1), both in the Săpânța River and in the groundwater (7.38). The decrease in water pH is attributable to an increase in the content of carbon dioxide or other gases with acidic reactions, due to organic matter accumulation. An increase in water pH is associated with the alkalinity of the soil type, while significant fluctuations present stress for aquatic species [44,45].

The range of dissolved oxygen (DO) levels in the Săpânța River was between 8.55 and 9.26 mg/L, with a mean value of 8.7 mg/L. The DO level in groundwater (7.99 mg/L) is a function of its oxygen content, which is determined by the degree of organic substances present. The presence of pathogenic microflora in groundwater is a consequence of the water conditions, as well as the atmospheric pressure, water temperature, microorganisms in hydrogeochemical processes, and oxidizable substances. The geographical area of the basin, climate, altitude, temperature, flora, leakages of fertilizers applied on agricultural terrains, soil characteristics of the riverbed, and biological and chemical properties of the water are also factors that influence the oxygen content of groundwater [39,40,44]. The lower value of DO in the groundwater compared to the Săpânța River water may be indicative of an overabundance of bacteria capable of consuming the dissolved oxygen in the water [11,39,40,44]. The levels of DO in water are influenced by various factors, including water temperature, air pressure, aquatic vegetation, phytoplankton, and the presence of oxidizable materials and bacteria [43]. During the monitoring period, DO concentrations increased, concurrently with a decline in the electrical conductivity of the water [44]. A state of deoxygenation induced by environmental eutrophication, characterized by elevated PO₄³⁻ and NH₄⁺ amounts and augmented algae activity, has been associated with diminished DO levels. This phenomenon can be attributed to anaerobic processes, as evidenced by the accumulation of deposits on pond bottoms [11,46].

Turbidity is attributed to suspended particulate matter, including but not limited to inorganic and organic compounds, clay, dissolved substances, and microorganisms. The elevated turbidity level (5.52 NTU) obtained in the present study is attributable to the presence of soil particles derived from runoff, dust, and mineral particulates resulting from mining activities. Additional contributing factors include geological characteristics of the river course and its sedimentary formations, the presence of cyanobacteria and other algae in the Săpânța River water, and the occurrence of in-bloom conditions [39,40]. Water turbidity is associated with a variety of factors, including clay, sludge, fine and inorganic organic matter, algae, soluble colored organic compounds and plankton, and their composition. Turbidity is also related to agricultural and irrigation activities, mainly to the intense use of chemical fertilizers and agricultural pesticides [11].

The water samples exhibited a characteristic low total hardness (H_t), with the groundwater registering 11.55 °g (classified within the hard water range) and the Săpânța River water (soft water) displaying 1.86–2.78 °g (soft water), indicative of low total alkalinity. The composition of the soil in the sampling locations exerted a significant influence on the overall hardness. The low level of total hardness, varying between 1.86 and 2.78 °g (soft water) in Săpânța River, indicated that the geology of the riverbed and human runoff are the primary sources of natural alkalinity with a low content of carbonate and bicarbonate [11,12,13,14,15,16]. Mg and Ca salts are correlated with the total, while the atmospheric pressure, temperature, and pH of water are responsible for the total hardness variation [39,47,48].

Anthropogenic pollution or interaction with rocks and soil can release Cl⁻ (1.01–5.65 mg/L) into water bodies. Water’s salinity is correlated with the presence of Cl⁻, which is salt [49]. The geological structure (soil and rock formations) of the aquifer contains minerals rich in Cl⁻ [48]. Elevated amounts of Cl⁻ can be introduced into groundwater by human activities including industrial discharge, the intense application of specific fertilizers, the use of road salt for deicing that includes chloride compounds, and water–rock interaction [49].

The NH₄⁺ content is very low in all the studied points (0.045–0.066 mg/L), being well below the permissible limit of 0.5 mg/L. This is related to the runoff of organic matter from the soils around the Săpânța River and due to the decomposition of vegetation and phytoplankton fallen in the water [39,40,41,44]. Since NH₃ ions are linked to Mg and Fe and their oxidizing effect is reduced during reduction treatment, their presence may be caused by the level of pollution, contamination with excreta, and incomplete dissolution of nitrogen-based organic substates in the soil. They can be eliminated by a chlorination process. High temperatures and pH levels encourage the production of NH₄⁺ in natural waterways [44].

The NO₃⁻ levels varied between 2.23 and 2.88 mg/L in the river Săpânța and 5.52 mg/L in the groundwater, being well below the MAC (50 mg/L), due to the oxidation process of NH₄⁺ and organic substances with NO₃⁻ formation and to the reduced uptake of NO₃⁻ by aquatic plants [1,49]. The presence of NO₃⁻ in water can be attributed to soil composition, the mineralization of organic pollutants of a proteinaceous nature, and the application of nitrogen-containing fertilizers and pesticides (nitrogen-based fertilizers) in agricultural practices [1]. Low NO₃⁻ levels during warm months could be caused by aquatic plants and phytoplankton absorbing large amounts of nitrogen compounds [1,49].

The concentrations of SO₄²⁻ in the Săpânța River (3.84–4.89 mg/L) and a groundwater point (5.52 mg/L) are related to rocks with high concentrations of sulfur compounds, industrial waste, domestic wastewater discharges, polluted domestic and industrial waters, and water–soil interactions, as well as mineral leaching [49,50,51]. The primary sources of SO₄²⁻ include sulfide dissolution from vegetation and animal decomposition, pyrite, sedimentary rocks, organic matter, minerals in water, gypsum and lignite in soil, and the combustion of organic matter due to acid rain and volcanic activity [49,50,51].

The low PO₄³⁻ concentration (0.02–0.04 mg/L) is caused by inorganic fertilizer leakage from agricultural practices, which promotes algae growth and alters the water’s color and flavor. Eutrophication decreases the DO content of water and poses a threat to the biodiversity of aquatic biota, and it is caused by PO₄³⁻ in water, which is derived from household products containing detergents based on P, industrial processes, and P-containing fertilizers intensively applied in agricultural practices [52].

The level of total dissolved solids (TDS), which ranges from 44.2–53.5 mg/L in the Săpânța River and 62.9 mg/L in the groundwaters, quantifies the total dissolved mineral level in water, and pH specifies the ability of water to react with alkalis or acids [51]. While high levels of TDS and alkalinity are related to strong industrial and agricultural activities, organic matter leaking into the water, and precipitation processes, the low values of both indicators in the water systems are associated with the catchment geology [53].

Temperature, dissolved salts, dissolved cations, and pH can all affect total alkalinity. Weak acids and their conjugate bases provide this essential buffering function, and high alkalinity safeguards the environment, especially against hazards like acid rain and sewage that can change pH levels. Low alkalinity, which is frequently brought on by human activity and geologic influences, indicates low levels of CO₃²⁻, OH⁻, and HCO₃⁻ [39,48].

By dissolving and infiltrating silica salts created by human activity, interchangeable cations among clay and water were found, with Na and K ranging between 1.22 and 3.82 mg/L and 0.76–2.63 mg/L (Table 2). Natural erosion of salt deposits, the dissolution of Na-containing rocks, irrigation and pluvial streams that interact with Na-rich soils, wastewater infiltration, and sewage system effluents are the primary sources of Na [54,55]. Human-caused activities, such as the use of animal manure as fertilizer and human waste, are the source of K, a basic element in aquatic vegetation and fish population nutrition that enters water through mineral dissolution. Because K is less soluble and more resistant to weathering and clay formation, its concentration in water is lower than that of Na [54,55].

Table 2. The annual median values of metals and As determined in water samples (SR1–SR3) and groundwater near Săpânța River (GW). The results are expressed as the mean ± standard deviation (n = 12 (monthly measurement in 2024)).

Metals	SI	SR1	SR2	SR3	GW	Mean	Median	Min	Max	SD	Errors	MAC *
Na	mg/L	1.22 ± 0.11	1.45 ± 0.14	1.56 ± 0.15	3.82 ± 0.35	2.01	1.51	1.22	3.82	1.21	0.17	200
K	mg/L	0.76 ± 0.05	1.63 ± 0.12	1.83 ± 0.16	2.63 ± 0.19	1.71	1.73	0.76	2.63	0.77	0.14	10
Ca	mg/L	5.91 ± 0.48	5.47 ± 0.51	5.33 ± 0.46	4.22 ± 0.39	5.23	5.40	4.22	5.91	0.72	0.50	100
Mg	mg/L	1.22 ± 0.10	0.94 ± 0.77	1.44 ± 0.12	3.89 ± 0.27	1.87	1.33	0.94	3.89	1.36	0.16	50
Fe	μg/L	5.52 ± 0.46	4.22 ± 0.38	5.15 ± 0.47	5.92 ± 0.52	5.20	5.34	4.22	5.92	0.73	0.52	20
Mn	μg/L	7.71 ± 0.68	7.16 ± 0.70	7.03 ± 0.66	6.88 ± 0.61	7.20	7.10	6.88	7.71	0.36	0.68	50
Al	μg/L	16.8 ± 0.12	17.3 ± 0.08	17.9 ± 0.12	18.5 ± 0.08	17.6	17.6	16.8	18.5	0.74	1.69	200
Sr	μg/L	19.5 ± 0.15	23.2 ± 0.20	27.5 ± 0.21	31.1 ± 0.26	25.3	25.4	19.5	31.1	5.05	2.49	7000
Zn	μg/L	11.1 ± 0.09	14.3 ± 0.08	13.5 ± 0.07	34.7 ± 0.15	18.4	13.9	11.1	34.7	10.9	1.80	5000
Cr	μg/L	0.88 ± 0.06	0.95 ± 0.07	0.99 ± 0.08	1.23 ± 0.09	1.01	0.97	0.88	1.23	0.15	0.09	50
Cu	μg/L	2.71 ± 0.12	3.41 ± 0.28	3.22 ± 0.25	5.22 ± 0.43	3.64	3.32	2.71	5.22	1.09	0.35	100
Ni	μg/L	0.88 ± 0.05	1.07 ± 0.07	1.25 ± 0.05	1.37 ± 0.02	1.14	1.16	0.88	1.37	0.21	0.10	20
Pb	μg/L	1.11 ± 0.02	1.37 ± 0.05	1.55 ± 0.09	1.84 ± 0.06	1.47	1.46	1.11	1.84	0.31	0.14	10
As	μg/L	0.39 ± 0.012	0.52 ± 0.02	0.49 ± 0.03	0.22 ± 0.01	0.41	0.44	0.22	0.52	0.14	0.04	10

Notes: * MAC based on international regulations, WHO guidelines [45], and national law [42] related to quality of water used as drinking water source.

Table 2. The annual median values of metals and As determined in water samples (SR1–SR3) and groundwater near Săpânța River (GW). The results are expressed as the mean ± standard deviation (n = 12 (monthly measurement in 2024)).

Metals	SI	SR1	SR2	SR3	GW	Mean	Median	Min	Max	SD	Errors	MAC *
Na	mg/L	1.22 ± 0.11	1.45 ± 0.14	1.56 ± 0.15	3.82 ± 0.35	2.01	1.51	1.22	3.82	1.21	0.17	200
K	mg/L	0.76 ± 0.05	1.63 ± 0.12	1.83 ± 0.16	2.63 ± 0.19	1.71	1.73	0.76	2.63	0.77	0.14	10
Ca	mg/L	5.91 ± 0.48	5.47 ± 0.51	5.33 ± 0.46	4.22 ± 0.39	5.23	5.40	4.22	5.91	0.72	0.50	100
Mg	mg/L	1.22 ± 0.10	0.94 ± 0.77	1.44 ± 0.12	3.89 ± 0.27	1.87	1.33	0.94	3.89	1.36	0.16	50
Fe	μg/L	5.52 ± 0.46	4.22 ± 0.38	5.15 ± 0.47	5.92 ± 0.52	5.20	5.34	4.22	5.92	0.73	0.52	20
Mn	μg/L	7.71 ± 0.68	7.16 ± 0.70	7.03 ± 0.66	6.88 ± 0.61	7.20	7.10	6.88	7.71	0.36	0.68	50
Al	μg/L	16.8 ± 0.12	17.3 ± 0.08	17.9 ± 0.12	18.5 ± 0.08	17.6	17.6	16.8	18.5	0.74	1.69	200
Sr	μg/L	19.5 ± 0.15	23.2 ± 0.20	27.5 ± 0.21	31.1 ± 0.26	25.3	25.4	19.5	31.1	5.05	2.49	7000
Zn	μg/L	11.1 ± 0.09	14.3 ± 0.08	13.5 ± 0.07	34.7 ± 0.15	18.4	13.9	11.1	34.7	10.9	1.80	5000
Cr	μg/L	0.88 ± 0.06	0.95 ± 0.07	0.99 ± 0.08	1.23 ± 0.09	1.01	0.97	0.88	1.23	0.15	0.09	50
Cu	μg/L	2.71 ± 0.12	3.41 ± 0.28	3.22 ± 0.25	5.22 ± 0.43	3.64	3.32	2.71	5.22	1.09	0.35	100
Ni	μg/L	0.88 ± 0.05	1.07 ± 0.07	1.25 ± 0.05	1.37 ± 0.02	1.14	1.16	0.88	1.37	0.21	0.10	20
Pb	μg/L	1.11 ± 0.02	1.37 ± 0.05	1.55 ± 0.09	1.84 ± 0.06	1.47	1.46	1.11	1.84	0.31	0.14	10
As	μg/L	0.39 ± 0.012	0.52 ± 0.02	0.49 ± 0.03	0.22 ± 0.01	0.41	0.44	0.22	0.52	0.14	0.04	10

Notes: * MAC based on international regulations, WHO guidelines [45], and national law [42] related to quality of water used as drinking water source.

Ca varies in the studied waters from 4.22 mg/L to 5.33 mg/L, while Mg varies from 0.94 mg/L to 3.89 mg/L (Table 2). Both originated from a geological origin, the same as K [1,49,52]. Since both Mg and Ca are necessary for human health, their presence in water is usually not a problem, depending on the anion that is present. Cation exchange and geogenic processes that indicate igneous rocks (amphibole, pyroxene, and feldspars) are the sources of Ca in aquifers. The presence of Mg in samples is related to natural processes including mineral alteration processes (olivine, micas, or amphiboles) [56,57].

Fe levels varied between 4.22 μg/L and 5.92 μg/L (Table 2), below the MAC of 200 µg/L, related to the geological conditions and potential contamination sources identified in the study area, namely the historical mining activities (Mn, Fe, Cu ore extraction) and industrial pollution and waste-related effluents from Maramures County [49,58]. Iron is naturally present in most aquatic vegetation, gravel, and soils (weathered formations causing a high organic matter content). It also takes part in photosynthesis as part of the reactive redox Fe–sulfur center, which converts N to NH₃⁻ [49,58,59,60].

The presence of elevated Mn levels (6.88–7.71 µg/L) is becoming increasingly prevalent, and the subsequent accumulation of this substance can exert deleterious effects on the central nervous system [58]. Potential sources are related to mining activities, intense agricultural activities, and industrial processes (cleaning products and alkaline battery production). Anthropogenic activities (such as wasteyard effluents and the use of fungicides) and geological abundance (such as igneous rock minerals, limestone, and metamorphic and dolomitic rocks) may be the source of Mn in water. Microbial activity and DO have an impact on the presence of Mn in various oxidation states in water [58,59].

In the present study, Al ranged between 16.8 and 18.5 µg/L, and it dissolves only in alkaline or acidic waters. Potential sources of Al are the use of Al₂(SO₄)₃ in the water treatment process and the use of aluminum sulfate-based agents in water treatment [59,60].

Geological variables, such as the composition of the subsurface rocks, are related to the Sr level in the studied samples, below the permissible limit of 200 µg/L, reaching 19.5–31.1 μg/L. To maintain proper management and supervision of water quality, this research emphasizes how crucial it is to take the Ca/Sr ratio into account when evaluating the overall impact of Sr in water sources used for consumption [55].

Zn concentrations measured in the samples range from 11.14 to 14.3 µg/L in Săpânța and 34.7 µg/L in groundwater, which is below the maximum acceptable concentration (MAC) of 5000 µg/L (Table 2). In the long run, Zn may rise to levels that are harmful to species living in the sediment, and clay content, phosphorus availability, redox conditions, and organic matter concentration all have an impact [61]. Zinc is a naturally occurring metal that slowly enriches groundwater through interactions with rocks. It is controlled by pH and inorganic carbon content, and it is known to be very mobile in water systems [61,62].

The very toxic heavy metal Cr (0.88–1.23 µg/L) is associated with oxidative stress, cancer, and DNA damage. Anthropogenic sources involve the use of Cr in chemical industries, refractory, metallurgy, leather tanning, and refining, and Cr is a naturally occurring element that usually occurs in volcanic emissions, soil, and erosion [63,64].

The relatively elevated Cu content (2.71–3.22 µg/L in Săpânța and 5.22 µg/L in the groundwater) may be attributed to anthropogenic activities applied in the study area (e.g., agriculture, mining, industry) or natural processes (e.g., breakdown of rocks and rock degradation) [49,50,65]. The geological characteristics of aquifer bedrock and copper–sulfide ores, such as bornite and chalcocite, are indicative of chemical reactions that result in the liberation of copper into aquifers [65]. Cu–sulphide ores and aquifer bedrock’s geological characteristics imply that chemical reactions could take place, releasing Cu into water systems [49,50].

Ni concentrations in samples have been found to reach 0.88–1.37 μg/L (Table 2) at pH values below 7.38–7.75. Soil, depth, and pH can all affect the Ni content. When utilized for irrigation, soil contamination with Ni occurs, affecting plant health and altering the well-functioning of the environment. Rock formations based on Ni and its anthropogenic sources (tannery effluent, mafic and ultramafic rocks) may leak into the groundwater because of interactions with the aquifer and underlying geology [50].

According to Regulation no. 7, 2023 [42], the MAC is 10 µg/L, although the measured Pb concentration values ranged from 1.11 to 1.84 µg/L. Pb, and its compounds may enter groundwater as a result of mining activities. Both natural processes (carbonate rock mineralization, weathering processes) and those generated by humans (oil as fuel, metal industry, burning coal, the application of pesticides and fertilizers in agricultural activities, electronic waste, and mining exploitation) are the primary sources of Pb [9,50].

As, a non-volatile, odorless, and tasteless substance, is a potentially toxic pollutant that often occurs in elevated concentrations in groundwater. Groundwater containing As has been observed to be susceptible to abrupt alterations [50,65]. The measured levels in the Săpânța River for the present study range from 0.39 to 0.52 µg/L, whereas the groundwater sources have values of 0.22 µg/L. Geological factors, prevalent in rural areas, can be a contributing factor to the presence of naturally occurring arsenic in aquifers. The issue is exacerbated by agricultural practices, a lack of awareness, and the challenges associated with accessing clean water alternatives [50,65].

It was observed that the electrical conductivity, total hardness (Săpânța River falls into the soft water category), Cl⁻, NH₄⁺, NO₃⁻, SO₄²⁻, TDS, total alkalinity, and metal content (Na, K, Mg, Fe, Al, Sr, Zn, Cr, Cu, Ni, Pb) were lower in the river samples compared to the groundwater samples. On the other hand, pH, dissolved oxygen, turbidity, and some metals (Ca, Mn, and As) were higher in the river samples compared to the groundwater samples.

Based on the chemical and physico-chemical results, the acidity, metal load, and Total Ionic Salinity were determined by applying a Ficklin–Caboi plot (Figure 2a) and a TIS chart (Figure 2b).

The metal load chart or the Ficklin–Caboi diagram indicates the total metal load content (Ca, Na, Mg, K, Fe, Al, As, Cr, Cu, Mn, Ni, Pb, Zn, and Sr) related to the pH determined in the water samples. The metal load, expressed in mg/L, was calculated as the sum of all studied metals. According to the Ficklin–Caboi diagram, most waters were characterized by near-neutral pH and a high metal load (Figure 2a). The acidity of waters can be related to fertilizer run-off and the direct discharge of wastewater.

All samples were characterized by a salinity lower than 6.0 meq/L, according to the TIS diagram (Figure 2b). It was noticed that the groundwater sampling point (GW) has the highest position in the graphical representation concerning the Cl⁻ + HCO₃⁻ content, while the river sampling points (SR1–SR3) were visibly lower. The difference is correlated with the water origin and specific chemistry, as well as natural processes.

The chemical equilibrium between the anionic (HCO₃⁻, CO₃²⁻, Cl⁻, and SO₄²⁻) and cationic (K, Mg, Ca, and Na) content was represented by a Piper plot (Figure 3). Based on the trilinear Piper chart, waters were characterized and classified into the CaMgHCO₃⁻ dominant category of water type. As it can be observed, many samples are mainly influenced by CO₃²⁻, HCO₃⁻, and Ca ions. According to the cation trilinear diagram represented in Figure 3, the groundwater (GW) sampling point is observed to be slightly apart from the river samples, which cluster together. This fact is observed specifically in the cationic trilinear chart, concerning the Mg, Na, and K concentrations. Generally, groundwaters are rich in Mg, compared to rivers, due to long-term rock–water interaction, the geological composition of aquifers, and longer residence time. The dissolution of Mg-bearing minerals comes from magnetite, dolomite, and ultramafic rocks, as well as olivine, micas, or amphiboles [57]. On the other hand, river water tends to be lower in Mg due to short residence time and dilution by rainfall. Generally, surface processes like runoff and atmospheric deposition have an impact on river water and their chemical composition. Ca is higher in rivers compared to groundwater due to the direct contact of agricultural runoff and urban waste and their frequent interaction with carbonate rocks [1,54].

The development of surface water chemistry is summed up in the original Gibbs [66] article. The primary controlling mechanisms indicated int the chart are water–rock interaction, precipitation, and evaporation. The scatter plot, which plots sodium/(Ca + Na) ratios (x-axis) against TDS on the y-axis, clearly illustrates the impact of these processes. Because they amalgamate waters with different transit periods, rivers are usually in a condition of dynamic disequilibrium with basin sediments and bedrock geochemistry. They also frequently have higher concentrations of Ca and HCO₃⁻ than Na and Cl, respectively. Based on these data, and with the help of Gibbs plots, represented by Figure 4, the potential sources of waters are visually shown. It was observed that, according to the anionic ratio and the cationic ratio plots, generally, samples were characterized by precipitation dominance and not water–rock interaction dominance. This characterization is associated with the low TDS values, as well as the low content of cation and anions, which is related to a geogenic origin, associated with water–rock interaction. The precipitation dominance is reflected by the chemical composition, which is mainly influenced by environmental parameters or processes, specifically atmospheric deposition and rainfall (intensity and amount), with minimal influence from rock interaction or evaporation. Precipitation dominance is maintained when high rainfall dilutes solute concentrations and contributes to low TDS, as well as when a consistent freshwater input exists. Anthropogenic sources, specifically urbanization, mining activities, industrial emissions, and agricultural practices, which occur in the present study, influence the precipitation dominance characterizing the studied water samples. Although impervious surfaces may decrease water–rock interaction by boosting runoff and decreasing infiltration, they can increase the inflow of pollutants via surface contaminants and atmospheric deposition. Even when precipitation predominates, an increase in acid rain alters the ion composition of rainwater, changing the water’s chemical signature.

Numerous factors, such as the geochemical environment (EC and pH), the exchange processes—which may differ from those significant for surface environments—and the solubility and availability of minerals, all affect the concentration of chemical components in natural groundwater. Besides natural processes and phenomena, anthropogenic activities, implying the intense use of chemical substances, use of natural resources, discharge of wastewater, and emitted gases, change and alter all environmental factors and processes. It is imperative to determine the potential contamination sources and their existence and monitorization near the study area to prevent and mitigate the potential impact on the water system in this case. Furthermore, Figure 5 indicates the potential agricultural and domestic inputs, related to the Cl⁻ and NO₃⁻ content. Correlated with the Ficklin–Caboi plot, Figure 5 showed the run-offs associated with the agricultural inputs. This chart focuses mainly on both sources, due to the frequency of their practice in the study area, and in other studies implying water used with the drinking purpose. NO₃⁻ and Cl⁻ are the main and commonly contaminants occurred in high amounts after agricultural and nearby domestic households.

3.2. Analysis of Water Quality and Contamination Level

The overall quality of studied waters was determined and analyzed with the help of the OWQI. In the present study, the OWQI was calculated based on two (2) physico-chemical indicators (pH, and electrical conductivity), and twelve (12) chemical indicators (NH₄⁺, NO₃⁻, Cl⁻, Fe, Mn, Al, Cu, Zn, Pb, Cr, Ni, and As), and the standard values considered to be the maximum allowable concentrations (MAC) established by the international Regulation, WHO Guideline [42], and national Law [43] related to the quality of water used as drinking water source. The unit weight values (W_i) was the ration between one (1.0) and the MAC, and the ideal value (ID) was considered zero (ID = 0) for every chemical indicator, except for pH (ID_pH = 7.0) [34]. The obtained results were presented in

Table 3. Scores related to the overall water quality, and contamination level related to the heavy metals, and As content (indices calculated based on the mean values).

Sampling Points	Overall Water Quality Assessment			Contamination Level Assessment
	Ʃ[q × Wi]	OWQI	Water Quality	CI	HMEI	Contamination Level
SR1	446	1.11	Excellent	6.90	0.43	Low
SR2	280	0.70	Excellent	8.30	0.47	Low
SR3	467	1.16	Excellent	8.90	0.49	Low
GW	2643	6.57	Excellent	9.20	0.53	Low

, indicating that, generally, the studied water samples were characterized by excellent quality (OWQI scores < 7.00).

The obtained scores indicated excellent quality. In the protected area, an excellent water quality, given by the OWQI scores aligns with the biodiversity conservation goals from at least three points of view, specifically: 1. Enhanced habitat quality, 2. Support for aquatic ecosystems, and 3. Indicator of effective management. Fish, amphibians, and invertebrates depend on clean water to survive and reproduce. Protected regions characterized by high OWQI values frequently have diverse aquatic communities, which enhances overall biodiversity. High-quality water is essential for maintaining the integrity of environments such as freshwater lakes, riparian zones, and wetlands. These ecosystems serve as protective barriers against environmental stressors and are essential habitats for a variety of species. Protected areas with successful conservation and management strategies often have excellent OWQI scores, showing that they are effectively reducing pollution and maintaining ecological balance [67].

The mean values of obtained OWQI scores ranged between 0.70 and 6.57. River sampling points SR1 and SR3 present a similar trend, while river sampling point SR2 was characterized by the lowest score, and the groundwater sampling point (GW) was characterized by the highest score, correlated with the highest content of Cl⁻, NH₄⁺, NO₃⁻, SO₄²⁻, Fe, Na, Pb, and Zn, which are correlated with the different geological origins, dissolution processes, and water source types.

Prior studies on alluvial aquifer samples also situated in the northern part of the country indicated the classification of studied waters into three quality levels, based on the WQI scores [3]. The index was based on the pH, NO₂⁻, NO₃⁻, NH₄⁺, EC, Cl⁻, total harness, turbidity, and major cations K, Na, Mg, and Ca. The obtained scores varied between 8.6 and 54, having a mean of 22. Based on these results, waters were classified into the good quality category or the poor and excellent quality categories [3]. These results are higher than the results obtained in the current study, up to eight times, mainly attributed to the higher NH₄⁺ amounts characterizing the studied samples, based on Dippong et al. [3]. A different recent study implemented by Dippong et al. [9] implied the study of surface waters, from the northern part of Romania, by applying the WQI. The index was based on the pH, TDS, EC, total hardness, K, Ca, Mg, Na, Cl⁻, NO₃⁻, SO₄²⁻, and NH₄⁺. The obtained results exceeded the results obtained in the present study, by up to four times. The river samples were classified into two quality categories, good and excellent, with scores ranging between 16.0 and 25.2 [3].

Another different study, at the international level, in Bangladesh (Jamalpur Sadar area), studied surface water and groundwater that were characterized by different quality status. The scores obtained in the case of surface waters ranged between 9.90 and 327, indicating that 60% of samples were unsuitable for drinking and irrigation purposes, 19% had poor quality, 10% had very poor and good quality, and some presented excellent quality status. The high scores were correlated with high electrical conductivity (EC >2000 μS/cm), total dissolved solids (TDS >1300 mg/L), Cl⁻ (Cl⁻ >200 mg/L), and K (K >680 mg/L), as well as low pH (pH <5.0). The groundwater samples had lower quality compared to surface water, due to the higher standard values used for calculating the index [68]. Abdo et al. [69] studied surface water quality in Egypt (Kafr El Zayat and El-Kanater El-Khayria area) by applying this methodology. The obtained scores were below 125, indicating good and poor quality for the studied rivers, mainly due to the high amount of NH₄⁺. The waters were rich in nutrients due to agricultural activities’ runoffs, particularly those that are intensively cultivated and utilize substantial amounts of synthetic fertilizers and urban effluent [69].

The contamination degree with the studied heavy metals was determined and analyzed. In this framework, the CI and HMEI methods were applied. CI and HMEI were calculated for eight heavy metals (Mn, Ni, Zn, Pb, Cu, Cr, Fe, and Sr) and one metalloid (As). Standard values used in the methodology are represented by the WHO Guideline [42] and Romanian Regulation [43], both concerning the quality of water used as a drinking source. Tables S1 and S2 include the water quality classifications and contamination degrees based on the index scores, and the results concerning the contamination with heavy metals and As level characterizing the studied waters are indicated in Table 3. The obtained results indicated low contamination levels for heavy metals and As, their scores being lower than the critical limit of 100. It was observed that the river sampling point SR1 has the lower score, and the highest value was obtained in the case of the groundwater sampling point GW, correlated with the highest concentrations of Zn, Pb, Cu, and Cr determined in GW.

3.3. Water Quality Based on Vegetation Cover near Săpânța River

Water is one of the indispensable elements related to life, plants, and vegetation in particular. Water quality influences and selects plant species, and at the same time, plants, through root absorption processes, absorb water with nutrients, including pollutants, acting as living and active filters contributing to improving the quality of environmental factors, including water. The study of vegetation can contribute to the assessment of water quality, both through the composition and structure of the vegetation and through chemical analyses of the water. Wetlands with over 30% of the surface area covered by tree canopy are defined as forested wetlands (also known as “tree swamps”) [47,56].

Forested wetlands provide critical habitats for a variety of plants and animals [47,56,70], support unique plant communities, and account for a large proportion of the plant diversity in forested landscapes [70,71,72]. They accumulate large amounts of plant biomass and can therefore be significant carbon stores. They also play an important role in regulating water flow, improving water quality, and recharging groundwater.

Forested wetlands are supplied with water in varying proportions from precipitation, groundwater, and surface water runoff, and their soils remain saturated for longer or shorter periods of time [56,73,74,75]. In seasonally saturated forested wetlands surrounded by upland areas, the amount and timing of saturation are largely controlled by precipitation and upland runoff [74]. A thorough understanding of vegetation–hydrology associations is essential for assessing, managing, and restoring the ecological processes that enhance the stability of wetland ecosystems, especially in disturbed or otherwise managed landscapes [75]. It also supports the sustainable management of forest and water resources and promotes the conservation of biodiversity in forested wetlands.

Studies mention the correlation between hydrology, other environmental factors, and plant diversity depending on the wetlands or vegetation types. It is important to gain a comprehensive understanding of the hydrological conditions that contribute to and promote biodiversity in wetland ecosystems. This understanding is crucial for the conservation of forested wetlands, but also for the assessment and maintenance of the quality of abiotic factors. In order to capture the correlation between the water factor and the forested wetlands on the banks of the Săpânța River, each water sampling point was studied through the five following phytocenological surveys: the type of vegetation (characteristic or not for the reference area), the anthropization degree of the vegetation cover expressed by the number of invasive species, the presence and abundance of nitrophilous species, and the presence/abundance of species indicative of chemical factors restrictive to vegetation.

In the four water sampling points, the vegetation is woody, characterized by fragments of warbler forests, which total 95 species of vascular plants. In

Table 4. Floristic composition and structure of the vegetation cover at the water sampling points.

	GW	SR3	SR2	SR1
Number of sample areas	5	5	5	5
Slope inclination (%)	20	5	5	5
Exposition	N-E	N	N	N
Tree height (m)	5–20	5–20	5–20	5–30
Tree diameter (cm)	10–20	10–30	10–30	10–40
Stratification	4	4–5	4–5	5–6
Grassy carpet covering (%)	70–80	80	75	80–100
Sample surface (mp)	1000	1000	1000	1000
Plant species	Ad m (dominance abundance index according to the Braun-Blanquet scale
Salix alba	2	2	2	2
Salix purpurea	−	1	1	2
Salix fragilis	−	−	+	+
Alnus glutinosa	+	2	2	−
Populus alba	+	+	1	1
Populus nigra	−	+	−	1
Aegopodium podagraria	+	+	+	+
Humulus lupulus	−	−	+	+
Fraxinus excelsior	−	+	+	+
Matteucia struthiopteris	−	+	+	−
Rubus caesius	−	+	+	+
Sambucus nigra	+	−	+	+
Clematis vitalba	+	+	+	+
Portulaca oleracea	+	−	−	−
Leucojum vernum	+	−	−	+
Galium odoratum	+	+	+	+
Scilla bifolia	+	+	+	+
Viola reichenbachiana	+	+	+	+
Alliaria petiolata	+	+	−	+
Stellaria aquatica	+	+	−	−
Stellaria nemorum	+	−	−	+
Isopyrum thalictroides	+	+	+	+
Anemone ranunculoides	+	−	−	−
Eupatorium cannabinum	+	−	+	+
Quercus robur	1	−	−	−
Crataegus monogyna	1	−	−	−
Geranium robertianum	+	−	−	+
Ranunculus ficaria	1	1	1	1
Potentilla reptans	−	+	+	+
Veronica anagallis-aquatica	1	−	−	−
Ranunculus cassubicus	+	−	+	+
Ranunculus sardous	+	−	−	+
Chelidonium majus	+	−	−	+
Festuca pratensis	2	+	+	+
Erigeron annuus	−	−	−	±1
Geum urbanu	+	+	+	+
Fragaria vesca	+	+	+	+
Lolium perennum	1	−	−	−
Caltha palustris	1	−	−	−
Cruciata levipes	+	+	+	+
Telekia speciosa	+	+	+	+
Corylus avellana	+	−	+	−
Carpinus betulus	+	−	−	−
Hepatica nobilis	+	−	−	−
Glechoma hederacea	+	+	+	+
Galium molugo	+	+	+	+
Carex pillosa	1	−	−	+
Alisma plantago-aquatica	+	−	−	−
Phragmites australis	3	−	−	−
Cirsium vulgare	+	−	−	−
Rumex aquaticus	+	+	−	−
Urtica dioica	2	−	−	1
Corydalis cava	+	+	+	+
Mercurialis perennis	+	+	+	−
Trifolium repens	+	−	−	+
Rorippa austriaca	+	+	+	+
Crocus heuffelianus	+	+	+	+
Cardamine glanduligera	+	+	−	−
Anemone nemorosa	+	+	+	−
Juncus conglomeratus	+	−	−	+
Scrophularia nodosa	+	−	−	+
Scirpus sylvaticus	1	−	−	−
Chrysosplenium alternifolium	+	+	+	+
Asarum europaeum	+	+	+	−
Primula vulgaris	+	+	+	−
Lycopus europaeus	+	+	+	+
Bidens tripartitus	+	+	+	+
Lythrum salicaria	−	−	−	+
Artemisia vulgarsi	−	−	−	+
Polygonum hydropiper	+	+	+	+
Arctium lappa	−	−	−	+
Equisetum fluviatile	+	+	+	+
Solanum dulcamara	+	−	−	+
Myosotis nemorosa	+	+	+	+
Scutellaria hastifolia	−	−	−	+
Gratiola officinalis	−	+	+	+
Mentha aquatica	1	+	+	−
Stellaria media	+	−	−	+
Cucubalus baccifer	+	+	+	+
Agrostis stolonifera	+	+	+	+
Glyceria fluitans	+	+	−	+
Trifolium pratense	+	−	−	+
Silene alba	−	−	+	+
Chenopodium hybridum	+	−	−	+
Amaranthus retroflexus	−	−	+	+
Chenopodium murale	+	−	−	+
Sambucus ebulus	+	+	−	−
Calystegia sepium	+	−	−	+
Stachys palustris	−	−	+	+
Echinocystis lobata	+	−	−	+
Acer negundo	−	−	+	+
Reynoutria japonica	−	−	−	2
Solidago canadensis	−	−	+	1
Robinia pseudoacacia	+	−	−	1
Erigeron canadensis	−	−	−	+

, the first positions are occupied by the species characteristic of the plant associations Salicetum albae-fragilis, which achieve high dominance abundance indices that define the fen features. Among the 16 species of woody plants, trees, and shrubs, 11 species are typical fen species, totaling 60–90% of the total plant cover. The floristic composition and structure of the marginal stands of the Săpânța River, denote a favorable state of conservation, generally indicating an abiotic substrate with favorable parameters.

The four water sampling points are distinguished by relatively homogeneous plant cover, with many common features. Since this study is focused on the analysis of water quality in the Săpânța River and groundwater, we use elements of the vegetation cover to confirm (or not) the physico-chemical analyses of the mentioned waters. Plants often tolerate increased concentrations of chemical elements depending on numerous factors: phenology, robustness, growth stage, chemical elements, and water chemistry [76].

Although there are no studies for precise quantifications, the absorption of chemical elements from water, with respect to sediments in plant roots depends on a series of factors such as pH, reducing potential, temperature, salinity, organic matter content, and element levels [77,78]. In addition to these factors, others such as seasonal physiological variations, accumulation, and compartmentalization capacity can influence the level of accumulation in plants [79,80]. Certainly, roots are the main route of uptake of chemical elements from the substrate; as a result, they tend to reflect the level of chemical elements in sediments, soil, or water [81].

According to the indicator species theory, there are species with preferences for precise intensities of abiotic factors. As a result, the presence of these species known as indicators can replace or converge with habitat analyses. Floristic changes may be identified through changes in species composition over time and the invasion of fast-growing nitrophytes (N-demanding species) [82,83].

One such category is that of nitrophilic species, faithful to defined concentrations of N salts in the substrate. The analysis of the preferences towards N of the species identified at the sampling points revealed the following situation presented in

Table 5. Distribution of species in the sample plots according to their preferences for N.

Number of Species	N × 3	N4	N5	N6	N7	N8	N9	Nx *	Nec. *
95	3	3	12	8	27	19	9	10	2
GW	3	3	8	7	20	16	5	11	1
SR3	0	2	6	7	13	6	3	6	0
SR2	0	3	5	8	15	6	4	7	0
SR1	0	3	7	6	20	14	6	8	2

Notes: * Categories of indicator species for N; Nx—amphitolerant species; Nec.—species for which preferences towards N are not known.

.

A proportion of 55 species out of the total of 95 are indicator plants for N-rich substrates. The four analyzed points are grouped differently. Points SR1 and SR2 have a lower proportion of nitrophilic plants (Figure 6 and

Table 6. Similarity and distance indices between the four sampling points.

	SR1	SR2	SR3	GW
SR1	0	10.296	11.533	5.099
SR2	10.296	0	3.000	12.728
SR3	11.533	3.000	0	13.892
GW	5.099	12.727	13.892	0

). The cluster analysis highlights a distance index of about 12% between the two groups of samples.

According to this distribution, two of the points include several nitrophilic species: GW and SR3. The presence of these plants is due to an excess of N salts in the superficial horizon of the soils, correlated with the movement of domestic animals and the dumping of garbage, but also the fertilizers used on the agricultural lands in the immediate vicinity. Because N salts have a fertilizing effect on plant species, they absorb nitrates and ammonium salts at the root level, producing natural filtration and preventing the concentration of these salts in river waters, as well as in the groundwater.

In general, the banks of the Săpânța River are accompanied by plant associations characteristic of water edges, with robust phytocenoses, which, through their floristic composition and structure, denote good water quality, both in the river and in the groundwater. However, at the water sampling points, direct observations, respective to qualitative analysis of the component species from the perspective of the indicator species theory highlight subtle changes that occur progressively, especially due to anthropization. As in similar cases mentioned by the bibliographic resources [83], the presence of nitrophilic species in the grassy carpet, even if they have low values of the AD index (abundance dominance according to the Braun–Blanquet scale), indicates an input of N salts. The geological substrate and the soils, respectively, exclude the natural source of nitrates, the input being of anthropic nature, because all the water sampling points are in anthropized areas by the presence of roads traveled by domestic animals and people, by vehicles, but especially by agricultural lands cultivated with annual plants, which require significant quantities of fertilizers. Figure 7 indicates a greater number of nitrophilic species in the vegetation cover at points GW and SR1, the first being located in a marshy area with shallow surface water stagnation, with marshy micro-depression land and in the immediate vicinity of agricultural lands, and the second near the confluence of the Săpânța River into the Tisa, an intensely trafficked area, where waste is dumped and where invasive plant species, also having nitrophilic preferences, have free rein for expansion and are found in high proportions (Table 4, last six positions). Even if the number of nitrophilic plant species is increasing, for now they do not achieve high values of the dominance abundance indices, which signifies slow and transient increases in N salts, as well as a reversibility of the process, to the extent that the use of fertilizers and anthropization will be maintained at controlled levels.

Another aspect of the connection between water quality, environmental factors, and vegetation quality is the presence of indicator species for different heavy metals. Water and soil contamination is increasing in most ecosystems around the world [84,85,86]. Chemical elements can have both natural origin in geochemical processes and human activities (mining, metal processing, fossil fuel burning, use of fertilizers and pesticides) [87,88,89]. Studies indicate that several plant species, such as Trifolium pratense, Amaranthus retroflexus, and Plantago lanceolata, can accumulate quantities of the heavy metals Zn, Ni, Cd, and Pb, respectively. Based on physiological characteristics and bioconcentration values, Trifolium pratense and Plantago lanceolata highlight qualities as bioindicators for heavy metals for numerous contaminants. Trifolium pratense demonstrates adaptability and absorption of Zn, Ni, and Cd, while Plantago lanceolata excels in the accumulation of Zn and Cd. In the vegetal carpet of the stands on the banks of the Săpânța River, such plants appear sporadically, meaning that the incidence of heavy metals is minimal. These results converge with the water analyses.

3.4. Health Risk Assessment for Toxins

All negative effects posed by ingesting toxins (heavy metals and As in the present study case) were considered through health risk assessment from the non-carcinogenic risk perspective. The evaluation was implemented with the help of three methods: the chronic daily intake (CDI), the hazard quotient (HQ), and the hazard index (HI). These methods were applied in two different study cases: exposure of children and exposure of adults to toxin ingestion through water consumption. Non-carcinogenic risk assessment was analyzed after a period of 6 years (children) and 70 years (adults) of water ingestion, with the water containing heavy metals and As. The obtained results are shown in

Table 7. Non-carcinogenic risk for children and adults at each toxin analyzed in river (R1–R3) and groundwater (GW) sampling points.

	Sample Point	SR1	SR2	SR3	GW
HQscore
As	children	0.780000	1.04000 *	0.980000	0.440000
	adults	0.185714	0.247619	0.233333	0.104762
Cu	children	0.008130	0.010230	0.009660	0.015660
	adults	0.001936	0.002436	0.002300	0.003729
Fe	children	0.016560	0.012660	0.015450	0.017760
	adults	0.003943	0.003014	0.003679	0.004229
Ni	children	0.005280	0.006420	0.007500	0.008220
	adults	0.001257	0.001529	0.001786	0.001957
Pb	children	0.011100	0.013700	0.015500	0.018400
	adults	0.002643	0.003262	0.003690	0.004381
Sr	children	0.003900	0.004640	0.005500	0.006220
	adults	0.000929	0.001105	0.001310	0.001481
Zn	children	0.004456	0.005720	0.005400	0.013880
	adults	0.001061	0.001362	0.001286	0.003305
Mn	children	0.006609	0.006137	0.006026	0.005897
	adults	0.001573	0.001461	0.001435	0.001404
HIscore	children	0.83603	1.09951	1.04504	0.52604
	adults	0.19906	0.26179	0.24882	0.12525

Notes: * scores higher than the maximum admissible limit of 1.00 → presence of non-carcinogenic risk at studied toxins.

.

The increasing trend of ingested toxins identified through the obtained daily intake as average CDI scores is As < Ni < Pb < Cu < Fe < Mn < Zn < Sr.

Generally, children are more vulnerable than adults; the daily intake is higher in the case of children than in adults, up to four and five times. Children present an average intake (CDI) of As ranging from 2.64 × 10⁻⁵ to 6.24 × 10⁻⁵ mg/kg/day, followed by CDI_Ni varying between 1.06 × 10⁻⁴ and 1.64 × 10⁻⁴ mg/kg/day; CDI_Pb, 1.33 × 10⁻⁴ and 2.21 × 10⁻⁴ mg/kg/day; CDI_Cu, 3.25 × 10⁻⁴ and 6.26 × 10⁻⁴ mg/kg/day; CDI_Fe, 5.06 × 10⁻⁴ and 7.10 × 10⁻⁴ mg/kg/day; CDI_Mn, 8.26 × 10⁻⁴ and 9.25 × 10⁻⁴ mg/kg/day; CDI_Zn, 1.34 × 10⁻³ and 4.16 × 10⁻³ mg/kg/day; and CDI_Sr, 2.34 × 10⁻³ and 3.73 × 10⁻³ mg/kg/day.

Adults present a lower daily intake of toxins through water ingestion than children, but with a similar trend: CDI_As ranges from 6.29 × 10⁻⁶ to 1.49 × 10⁻⁵ mg/kg/day; CDI_Ni, 2.51 × 10⁻⁵ to 3.91 × 10⁻⁵ mg/kg/day; CDI_Pb, 3.17 × 10⁻⁵ to 5.26 × 10⁻⁵ mg/kg/day; CDI_Cu, 7.74 × 10⁻⁵ to 1.49 × 10⁻⁴ mg/kg/day; CDI_Fe, 1.21 × 10⁻⁴ to 1.69 × 10⁻⁴ mg/kg/day; CDI_Mn, 1.96 × 10⁻⁴ to 2.20 × 10⁻⁴ mg/kg/day; CDI_Zn, 3.18 × 10⁻⁴ to 9.91 × 10⁻⁴ mg/kg/day; and CDI_Sr, 5.57 × 10⁻⁴ and 8.86 × 10⁻⁴ mg/kg/day.

Based on the CDI results, the hazard quotient (HQ) was calculated for each studied heavy metal and As. The obtained scores indicated that if consumed, the studied waters can pose potential negative effects on humans, especially on children. HQ scores were significantly high in the case of As, exceeding the critical unity in river sampling point SR2 and up to score HQ = 1.00 in the case of river sampling point SR3, followed by SR1 (Table 7). As is an element that generally occurs naturally in the Earth’s crust. It can be introduced into surface and groundwater by natural processes and anthropogenic activities. The natural sources of As are represented by different processes, such as geological weathering, the presence of acid sulphate soils, or atmospheric deposition. Different minerals, specifically arsenolite, arsenopyrite, orpiment, or realgar contain As. Through the weathering of these minerals, As is mobilized, reaching and finally enriching the chemical composition of the water systems. In soils rich in FeS minerals, after draining or air exposure, oxidation occurs, favoring the mobilization of As to the surface water and groundwater. On the other hand, As has been observed to enter soil and implicitly water systems as a result of the intense use of herbicides, pesticides, and livestock feed additives containing As. Additionally, As enters into groundwater as a result of irrigation with water that is rich in As. Emissions of As generated by fossil fuel combustion and industrial activities, specifically steel and glass production, contribute to the contamination of air and implicitly water (after atmospheric deposition). Nevertheless, the extraction of different metals releases As as a by-product, which enters groundwater and surface water. A comprehensive understanding of these sources is imperative for evaluating the potential dangers to the environment and human health [69].

The average hazard scores ranged between 5.07 × 10⁻³ for Sr exposure and 8.10 × 10⁻¹ for As exposure through ingestion in the case of children and 1.21 × 10⁻³ for Sr and 1.93 × 10⁻¹ for As in the case of adults. Exposure to As through water ingestion, in the case of children, can pose an impact on health, according to the high HQ scores. The low reference dose and the relatively high amounts of As increased the scores. The average HQ scores obtained for children ranged between 0.44 and 1.04 for As exposure, 8.13 × 10⁻³ and 1.57 × 10⁻² for Cu, 1.27 × 10⁻² and 1.78 × 10⁻² for Fe, 5.28 × 10⁻³ and 8.22 × 10⁻³ for Ni ingestion, 1.11 × 10⁻² and 1.84 × 10⁻² for Pb ingestion. They were between 3.90 × 10⁻³ and 6.22 × 10⁻³ and between 4.46 × 10⁻³ and 1.39 × 10⁻² in the cases of Sr and Zn ingestion, while the hazard calculated for Mn exposure ranged between 5.89 × 10⁻³ and 6.61 × 10⁻³.

The overall non-carcinogenic risk for all studied toxins was determined by applying the hazard index, HI. Results concerning HI for adults ranged between 0.125 and 0.2618, indicating that exposure at all studied heavy metals and As poses no negative effects if consumed through water ingestion. On the other hand, children are more vulnerable and can present non-carcinogenic risk if they consume the studied water sources. HI calculated for children indicated values exceeding the critical value in 50% of samples. HI is high due to the high HQ related to exposure to As.

In order to monitor, determine, analyze, and prevent contamination concerning waters, it is mandatory to follow several steps. A potential assessment model was proposed in this framework, presented with the help of Figure 8.

Being a natural protected area for the conservation of vegetation and flora and fauna species, anthropogenic activities have been limited. Agricultural land use has also been limited. However, during the surveys of the vegetation cover, it was repeatedly observed that local people repeatedly deviate from the rules of not using fertilizers within the perimeter of the protected natural area. Both natural fertilizers, animal manure, and synthetic fertilizers were found, as well as packaging and temporary storage on the edges of fields. At present, water quality analyses, both in the river and in the groundwater, do not show exceedances of nitrogen salts and heavy metals, respectively. Subtle nitrification is evidenced only by the species composition of the plant associations in the area. We propose that in prospective studies, soil, water, and vegetation analyses will be carried out in a network of points, both in the natural vegetation cover, cultivated plants, and in the river in order to determine the source of excess nitrogen, the trend in the concentration of elements in the substrate and in living organisms, and the direction of migration of these elements from abiotic to biotic. Understanding these mechanisms is necessary to inform more restrictive measures in the use of resources in natural areas in order to secure biodiversity resources and the health of inhabitants.

4. Conclusions

The purpose of this study was to assess the river water and groundwater chemistry and quality in Săpânța locality, situated in North Romania. The site-specific context of this study implies several sampling points in the Săpânța River, a tributary of the Tisa River, and a groundwater source. Most water exhibited a substantial metal load and an almost neutral pH level, based on the Ficklin–Caboi diagram. The Piper plot showed the chemical equilibrium between the cationic (K, Mg, Ca, and Na) and anionic (HCO₃⁻, CO₃², Cl⁻, and SO₄²⁻) contents, indicating the typology of the water, which is CaMgHCO₃⁻. A total of twelve (12) chemical indicators (NH₄⁺, NO₃⁻, Cl⁻, Fe, Mn, Al, Cu, Zn, Pb, Cr, Ni, and As), two (2) physico-chemical indicators (pH and electrical conductivity), and the standard values deemed to be the maximum allowable concentrations pertaining to the quality of water used as a drinking water source were used to calculate the OWQI. Both the CI and HMEI approaches were used in this framework. Eight heavy metals (Mn, Ni, Zn, Pb, Cu, Cr, Fe, and Sr) and one metalloid (As) had their CI and HMEI determined. All scores were below the critical limit of 100, indicating lack of contamination with the studied toxins.

Following a period of consistent ingestion of 6 years for children and 70 years for adults, a comprehensive examination of As and eight heavy metals was conducted to ascertain their potential non-carcinogenicity in humans. Results indicated that in general, children are more susceptible than adults, and their daily intake might be up to four or five times more than that of adults. The average daily intake (CDI) of As in children ranges from 2.64 × 10⁻⁵ to 6.24 × 10⁻⁵ mg/kg As, whereas the CDI of As varies from 1.06 × 10⁻⁴ to 1.64 × 10⁻⁴ mg/kg. The increasing trend of ingested toxins identified through the obtained daily intake as average CDI scores is As < Ni < Pb < Cu < Fe < Mn < Zn < Sr. Adults ingest less pollutants through their water consumption each day than children, although the trend is similar. According to the hazard quotient, expressed as HQ scores, the sampled water may have harmful impacts on people, particularly children, if they are ingested. HQ scores were up to HQ = 1.00 in the case of river sampling points SR3 and SR1, respectively, and notably high in the case of As, above the critical unity in river sampling point SR2. Fundamental information about environmental risk management and water pollution control must be provided by applying a pollution and quality evaluation with the use of comprehensive indicators. Using the results, conservation restrictions will be implemented by evaluating the biological and ecological diversity of the river and groundwater inside the study area.

Vegetation analyses were used as a precursor to water analyses. The presence of indicator species, both nitrophilic and those that resist and accumulate heavy metals, constitute warning signals for changes occurring in the composition of the substrate. In the case of the Săpânța River, the interpretation of the floristic composition and the structure of the vegetation cover reflects and indicates a subtle involution from stands with a favorable conservation status, towards vegetation with a nitrophilic accent. In the short and medium term, vegetation, together with the clays in the substrate, acts as a filter for nitrogen salts, so that they are not found in the water. Monitoring indicator species can signal disturbances in soil and water chemistry from exogenous sources, signaling the deterioration of environmental quality, risks associated with human health, and the need to monitor the level of pollution of various kinds.

Based on this study’s results, certain suggestions were made to improve the efficacy of future directions for research and mitigation efforts targeted at reducing metal contamination in drinking water sources. These consist of (1) determining and analyzing the temporal variation of chemical parameters; (2) being concerned with water purification solutions in order to enhance agricultural and livestock practices and acquire a dependable water source; (3) a comprehensive assessment of biological and ecological diversity to apply effective implementation of conservation measures in the Săpânța River and groundwater sampling points.