Integrating Multi-Index and Health Risk Assessment to Evaluate Drinking Water Quality in Central Romania

Abstract

Chemical contaminants in drinking water represent a widespread threat to human health, making water quality monitoring an essential mitigation measure. This study aimed to assess the quality of drinking water by conducting comprehensive multi-year seasonal monitoring at seven distribution points in central Romania, determining the spatial and temporal trends of relevant physical parameters (pH and electrical conductivity) and chemical contaminants (NO₂, NO₃, NH₄, Cl, and SO₄). The pollution degree was evaluated using the pollution index and the overall pollution assessment index. The principal component analysis attributed over 60% of water quality variance to NO₂, NO₃, and NH₄ pollution, linked to incomplete nitrification or external loading, such as agricultural practices. Additionally, a human health risk assessment was performed according to U.S. EPA guidelines, calculating the chronic daily intake, hazard quotient, and hazard index for nitrogen compounds via oral and dermal exposure pathways for both adults and children. The results showed significant seasonal fluctuations in nitrogen compounds and electrical conductivity. The pollution indices classified the water bodies across a spectrum from “light” to “significant” pollution degrees. The health risk assessment revealed that NO₃ was the primary risk driver, with hazard index values exceeding the threshold of one in specific locations and seasons, indicating potential adverse health effects, particularly for children.

1. Introduction

Global access to safe and uncontaminated drinking water for personal domestic use is a widely recognised human right and constitutes a primary indicator of public health [1,2]. The WHO reports that unsafe drinking water is a major source of infectious diseases [3]. Children are more susceptible to normal levels of toxins due to higher relative concentrations (the effect of low body weight and higher water intake) [4]. In agricultural regions, elevated health risks are associated with nitrate in groundwater [5,6,7]. Sulphate and chloride determine the corrosion and leaching of lead and copper in drinking water [8]. Electrical conductivity (σ) reflects the cumulative effect of all dissolved ions [9,10]. The interference of these parameters promotes contamination and accelerates infrastructure deterioration in distribution networks [11]. The quality of water in distribution systems depends on the source parameters, seasonal climatic fluctuations, and the integrity of the distribution infrastructure [12,13].

Nitrogen compounds, specifically nitrite (NO₂), nitrate (NO₃), and ammonium (NH₄), are a class of global contaminants originating from agricultural runoff, livestock farming, wastewater discharge, or industrial effluents, and are used as indicators of anthropogenic pollution [14,15,16]. The mobility of these compounds in the soil allows them to readily leach into groundwater and surface water [17]. Their seasonal variability is linked to fertiliser application in spring or to heavy rainfall causing contaminants to flow into aquifers [18], and should be included in the risk assessment.

In drinking water derived from surface water, nitrate concentrations usually do not exceed 10 mg/L, except for areas exposed to runoffs, sewage effluents, and industrial wastes. However, exposures to higher nitrate concentrations in drinking water (>50 mg/L) were recorded in 15 European countries, affecting about 10 million people [19].

Due to their direct and indirect health impacts, the presence of nitrogen species in drinking water must be minimised. Consumption of drinking water polluted with nitrite, nitrate, or ammonium is linked to several classes of diseases. Cumulated nitrogenous chemical contamination, via combined routes, produces a spectrum of non-malignant and malignant, dose-dependent diseases distinct from microbial water-borne infections [20]. Examples include methaemoglobinaemia, where nitrite oxidises haemoglobin, causing ischemia, in particular in infants <6 months [21]; undesirable outcomes in pregnancy, where chronic nitrate intake is associated with foetal growth restriction, pre-term delivery, and neural tube defects [22,23]; thyroid dysfunction due to deficient iodide absorption in the presence of nitrate, which promotes hypothyroidism in children and healthy pregnant women [24,25]; promotion of type-2 diabetes [26] via oxidative stress pathways [27]; elevated methaemoglobin levels and cardiovascular stress, causing ischemic disease and disturbed blood pressure [28]; and glioma and gastrointestinal cancers, as suggested by statistical associations [29,30]. A recent study found that nitrate levels in tap water, even below legal limits, can pose health risks to bottle-fed infants when combined with disinfection by-products, highlighting the need for integrated risk assessments [31]. Likewise, a multi-route exposure study demonstrated that dermal absorption during showering contributes to the total daily intake of nitrosamine precursors, significantly increasing bladder cancer risk [32].

Infants are the most vulnerable age class to nitrogenous species exposure. High nitrate levels can cause methaemoglobinaemia, a potentially fatal condition in infants, while the endogenous conversion of nitrate to nitrite generates N-nitroso compounds, classified as carcinogens causing gastro-intestinal tract cancers [33,34]. According to the U.S. Environmental Protection Agency (EPA), the maximum contaminant level for nitrate in drinking water is 10 mg/L, measured as nitrate-nitrogen (NO₃⁻-N); for nitrite, it is 1 mg/L, measured as nitrite-nitrogen (NO₂⁻-N) [35]. The World Health Organization (WHO) guideline value for nitrate is 50 mg/L of nitrate ions (NO₃⁻), which is equivalent to approximately 11.3 mg/L as N; for nitrite, it is 3 mg/L of nitrite ions (NO₂⁻), equivalent to approximately 0.92 mg/L as N [36]. Nitrate levels in tap water, combined with disinfection by-products, can result in N-Nitrosodimethylamine (a potent carcinogen) through chloramination, as pointed out by multi-source and multi-route exposure. Therefore, water contamination and health impacts require integrated risk assessments [37]. Moreover, a review revealed that thresholds for nitrate in public drinking water offer protection against infant methaemoglobinaemia, but not against the risk of specific cancers and congenital disabilities, although it is a precursor in the pathway of N-nitroso compound formation [33]. Consequently, reference thresholds are not sufficient to assess cumulative health risks, in particular among sensitive subjects.

Despite the remarkable health benefits of the chlorination of municipal drinking waters, epidemiological studies have revealed an association between disinfection by-products, in particular trihalomethanes consumed with chlorinated drinking water, and an increased risk of bladder cancer [38]. Some other products of the residual chlorine’s reactions with the organic matter and bromide are trihalomethanes, haloacetic acids, chlorophenols, chloral hydrate, and haloacetonitriles, suspected of causing DNA damage and cancers [39]. Sulphate ingestion through drinking water was widely reported to cause acute (osmotic) diarrhoea [40].

The evaluation of water quality is increasingly based on pollution indices, which integrate different metrics into a single, comprehensible value. The pollution index (PI) and the overall pollution assessment (OPA) discriminate pollution categories, reflecting the progressive degradation of water bodies. These indices characterise the environmental state. The consequent risk to human health can be assessed by determining the hazard quotient (HQ) and hazard index (HI) in order to quantify carcinogenic and non-carcinogenic risks [41]. The HQ is the ratio between the concentration of a contaminant and the reference dose considered safe for human health. The HI is used when assessing the effects synergistic contaminants and is calculated as the sum of individual HQ values. An Italian study determined the HQ and HI in relation to oral and dermal exposure to contaminated groundwater by grouping the populations into three groups: children, adults, and workers [7]. Çankaya et al. performed seasonal sampling for NO₃ and NO₂, and their HQ and HI for children and adults, separately [42]. Golaki et al. demonstrated combined nitrate and nitrite HQ and HI in all age groups based on 12 months of data [43]. The HQ and HI have some important limitations: the aggregate exposure to a chemical from all relevant sources, and not only from a specific source, should be assessed, otherwise the risk might be underestimated. Goumenou [44] developed an alternative approach simulating an aggregated exposure: the source-related HQ (HQs) for a specific source compared to the reference values and adjusted by a correction factor. Some studies combine heavy metal pollution indices and their uptake in fish with human health risk indices for children and adults through the ingestion and skin absorption of water [45]. Similarly, an index-based approach was applied to evaluate pollution in drinking water sources and associated health risks for nitrogenous-derived compounds and a series of other contaminants [46].

The indexing of multi-annual pollution of drinking water based on spatial–temporal trend analysis and consequent health risk assessments for Transylvania (Romania), based on the mentioned methodologies, is scarce. Therefore, this study presents a comprehensive, multi-year seasonal monitoring of seven water distribution points in central Romania by determining the spatial and temporal trends of relevant physical parameters and chemical contaminants. Then, we classify the pollution degree using established pollution indices, and conduct a human health risk assessment to quantify the potential non-carcinogenic risks associated with the nitrogen compound exposure for both adult and child populations. The novelty of this study lies in its integrated methodological approach and the geographic and temporal data gaps it fills, offering the first holistic evaluation of long-term drinking water pollution and its health implications for the communities (adults vs. children) in the studied areas of central Romania. More specifically, this is among the first studies in Transylvania to integrate long-term, seasonally resolved monitoring and age-specific health risk assessment within a unified methodological framework. The dataset, which spans multiple years and has been largely inaccessible for this region, facilitates the discernment of emerging contamination patterns that cannot be revealed by short-term studies. This research has a significant scientific and societal relevance across multiple domains. For public health, it provides evidence regarding drinking water contamination risks affecting a large number of residents. From an environmental management perspective, it supplies comprehensive, actionable baseline data for water quality monitoring. Methodologically, it establishes a replicable analytical framework within an Eastern European context. The findings also support regulatory compliance by supporting the alignment with international and national water quality standards. The final goal of the study was to simultaneously cover the following methodological gaps identified in the literature: multi-annual record, seasonal resolution, spatial–temporal data, and estimated nitrogen age-specific non-carcinogenic health risk. The obtained data and risk calculation results can directly guide intervention priorities and policies.

2. Materials and Methods

2.1. Area of Study and Sampling Procedure

The present study was conducted in seven different locations (Agnita, Alma, Medias, Dumbraveni, Tarnaveni, Iernut, and Sura Mare localities) in Sibiu County, Romania. Sibiu County is located in central Romania, in the southeastern region of Transylvania. The area under consideration encompasses approximately 5432 km², constituting about 2.3% of Romania’s total territory. The topography, geology, and climate of the region are significantly impacted by its geographical location, which is situated at the intersection of the Transylvanian Plateau and the Southern Carpathians. Sibiu County is characterised by its pronounced spatial heterogeneity, both in terms of morphology and climate. This heterogeneity results from the county’s position as a transitional zone between the Transylvanian Plateau and the Southern Carpathians. The landscape is discernibly segmented into plateau and hills, valleys, and high mountains. From a geological perspective, the convergence of sedimentary plateau rocks and crystalline mountain massifs creates a diverse array of landforms. These landforms dictate the potential for various resource opportunities in the region [47].

The geology of Sibiu County is intricate as a consequence of its location at the southern periphery of the Transylvanian Basin and the northern periphery of the Southern Carpathians. In the plateau and hilly regions, sedimentary rocks predominate, including limestones, marls, sandstones, and clays, which are associated with the Transylvanian Basin and the inter-Carpathian depressions [48].

From a climatological perspective, the topography exacerbates variations in temperature, precipitation, and snow cover duration, all of which determine the potential land uses. The annual average air temperature varies considerably, within a range of approximately 9.4 to 4.3 °C. Precipitation also varies significantly across the landscape, ranging from approximately 650 mm in lower depressions to over 1300 mm in mountainous regions [47].

A seasonal monitoring campaign was conducted from seven water distribution points, starting from January 2022 to October 2024, four times per year, covering all four seasons (winter—January, spring—April, summer—July, and autumn—October). The sampling points are Agnita (SP1), Alma (SP2), Medias (SP3), Dumbraveni (SP4), Tarnaveni (SP5), Iernut (SP6), and Sura Mare (SP7) (Figure 1).

Samples were collected in sterile polyethylene containers from the water system distribution after the water was pumped for up to half an hour to prevent stagnation. The sampling procedure followed strict guidelines for sampling drinking water from distribution networks [49]. To exclude contamination, containers were rinsed with each water sample, eliminating the air after filling. The samples were then transported to the laboratory in opaque cooling boxes at 4 °C under controlled conditions. They were prepared for the proposed analysis on the same day. Good homogenisation was applied before all determinations. Filtration was performed prior to the anion’s measurements using 0.45 µm filters.

2.2. Instrumentation Analysis

A comprehensive analysis of chemicals was performed immediately after sampling. Key physicochemical indicators (pH and electrical conductivity) were determined utilising an InoLab Multi 740 multiparameter (WTW, Gotha, Germany). After the homogenisation and filtration, NO₂, NO₃, and NH₄ were measured through UV–VIS spectrophotometry using Specord 200 Plus equipment (Analytik Jena, Jena, Germany). The chloride content (Cl) was determined through titration, while SO₄ was measured by UV–VIS spectrophotometry. To ensure analytical accuracy, check standards and blank samples were regularly measured. The uncertainty and accuracy of methods were 1.0% and 0.004 (pH), 2.9% and 9.1 µS/cm (electrical conductivity), 7.6% and 1.4 × 10⁻⁴ mg/L (NO₂), 8.9% and 0.02 mg/L (NO₃), 9.0% and 1.5 × 10⁻³ mg/L (NH₄), and 12% and 0.63 mg/L (Cl). Pearson correlation and PCA were applied in order to determine the correlations between chemical indicators and pollution index scores.

2.3. Statistical Analysis

Data were processed with the help of XLSTAT, an Excel Add-in software extension (2016 MSO, 2508 version, 32-bit). Data included two replicates per measurement. The minimum, maximum, median, and standard deviation values were calculated based on the obtained results. Data followed a normal distribution. Two different statistical techniques were applied: Pearson correlation and principal component analysis (PCA). Pearson correlation was applied for all chemical indicators and pollution index scores. The significance level was 0.05 (α = 0.05). The aim of applying this method was to determine the correlation status between the mentioned data.

PCA is a multivariate statistical analysis method used to determine and observe clusters, trends, and outliers. In statistical analysis, PCA locates hyperplanes, planes, and lines in K-dimensional space that best represent data points using the least squares method [50]. In this study, the significance level applied to the PCA was 5%. The PCA type was correlation (Pearson) and the rotation method was Varimax.

2.4. Pollution Degree Estimation

To estimate the pollution degree, two different paths were followed: 1. the assessment of the pollution with nitrogen compounds (NO₂, NO₃, and NH₄); 2. the assessment of the overall pollution degree. Therefore, two different methods were applied: the pollution index (PI) and the overall pollution assessment index (OPA), respectively. The pollution index was applied individually for NO₂, NO₃, and NH₄ concentrations by using Equations (1)–(3):

$${PI}_{{NO}_2}={\displaystyle \frac{ {NO}_{2C}-{TH}_C}{{TH}_C}},$$

(1)

$${PI}_{{NO}_3}={\displaystyle \frac{{NO}_{3C}-{TH}_C}{{TH}_C}},$$

(2)

$${PI}_{{NH}_4}={\displaystyle \frac{{NH}_{4C}-{TH}_C}{{TH}_C}},$$

(3)

Here, PI_x represents the pollution index for each compound; NO_2C, NO_3C, and NH_4C are the concentrations of the nitrogen compounds determined in the water samples; TH_C represents the concentration threshold value for each component. The TH_C value for NO₂ and NH₄ is 0.5 mg/L, and the value for NO₃ is 50 mg/L, based on the guidelines for drinking water quality [36,51]. According to the calculation results, the samples were assigned one of the five pollution classes: very significant pollution degree (PI_x > 3.0), significant pollution degree (2.0 < PI_x < 3.0), moderate pollution degree (1.0 < PI_x < 2.0), light pollution degree (0 < PI_x < 1.0), and no pollution (PI_x < 0) [52,53].

The overall, cumulative level of pollution with Cl, SO₄, NO₂, NO₃, and NH₄ was assessed based on the OPA index (Equation (4)).

$$OPA={\displaystyle \frac{\sum _{i=1}^{i=n}\left[\left(\frac{C}{{TH}_c}\times 100\right)\times W_i\right]}{\sum _{i=1}^{i=n}{W}_i}},$$

(4)

Here, C and TH_C are the concentration values and thresholds, respectively, for each contaminant in the water samples, while W_i represents the weight of each parameter, calculated as the ratio between one and TH_C. One of the following pollution classes can be associated to the obtained score: high pollution degree (OPA > 30), medium pollution degree (15 < OPA < 30), and low pollution degree (OPA < 15) [54].

2.5. Evaluating the Exposure at Potential Toxic Agents in Human Populations

The present investigation employed the technique recommended by the U.S. Environmental Protection Agency (EPA) to conduct a comprehensive health risk evaluation. This approach constitutes a type of assessment that integrates water pollution with its risk to human health. It characterises the extent of harm caused to the human body by the exposure to water pollution through various pathways, such as oral intake and dermal contact [55]. The objective of the risk assessment is to develop recommendations and preventive measures for the protection of human health. Also, the chronic daily intake (CDI) was determined for children and adults for two contamination routes: water oral intake and skin contact (Equations (5) and (6)).

$${CDI}_{OI}={\displaystyle \frac{C\times IR\times ED\times EF}{AT\times BW}},$$

(5)

$${CDI}_{SC}={\displaystyle \frac{C\times SA\times ED\times EF\times ET}{AT\times BW}}\times K_p,$$

(6)

$${HQ}_{OI/SC}={\displaystyle \frac{{CDI}_{OI/SC}}{R_{DO OI/SC}}}$$

(7)

$${HI}_{OI}=\sum {HQ}_{{NO}_2}+{HQ}_{{NO}_3}$$

(8)

Here, according to the USEPA guidelines and handbooks [56,57] and Zakir et al. [58], CDI_OI and CDI_SC represent the chronic daily intake through ingestion (oral intake) and skin contact routes, respectively, expressed as mg/kg × day. C represents the concentration (mg/L) of the considered compound with potentially negative effects on human health, in this case, NO₃ and NO₂. IR, ED, and EF are the ingestion rates (children: 1.8 L/day; adults: 2.2 L/day), exposure duration (children: 6 years; adults: 70 years), and exposure frequency (children and adults: 365 days/year). AT is the average time (children: 2190 days; adults: 25,550 days) and BW is the average body weight (children: 15 kg; adults: 70 kg). SA is the skin surface area (children: 6600 cm²; adults: 18,000 cm²) and K_p is the permeability coefficient (children and adults: 0.01). ET represents the exposure time (children: 1.0 h/day; adults: 0.58 h/day) [58].

The chronic daily intake is used further in the hazard quotient methods in order to quantify the health risk posed by toxin ingestion and skin contact. In this context, two methods were used: the hazard quotient (HQ) and the hazard index (HI). The HQ is applied for determining the hazard generated by each compound, while the HI is used to determine the cumulative impact of toxins (Equations (7) and (8)). The HQ is based on the ratio between the CDI and the reference dose (R_DO). The R_DO is specific for each toxin and it is expressed as mg/kg x day. A score exceeding 1 (HQ > 1.0; HI > 1.0) indicates that the threshold has been surpassed, and that the contaminant concentration poses a risk to the human health [58].

3. Results and Discussion

3.1. Assessment of Physicochemical Parameters in Water

The content and trend of NO₂, NO₃, NH₄, Cl, and SO₄, the electrical conductivity (σ), as well as the pH were determined in seven drinking water sources (SPA1–SPA7) during four different seasons from 2022 to 2024. Descriptive statistics of the chemical indicators (mean, standard deviation, minimum, maximum, and median values) are presented in

Table 1. Descriptive statistics of chemical parameters (mean values determined in each sample, and the minimum, maximum, and median values of all results determined during the sampling campaigns; expressed as µS/cm for electrical conductivity and mg/L for anions) determined in the water samples (SPA1–SPA7) in four seasons (January, April, July, and October) of three different years (2022–2024).

2022	MAC *	SPA1	SPA 2	SPA 3	SPA4	SPA5	SPA6	SPA7	Min	Max	Median
2022
pH	6.5–9.5	7.25 ± 0.25	8.60 ± 0.08	7.35 ± 0.17	6.90 ± 0.08	7.70 ± 0.07	7.43 ± 0.05	7.40 ± 0.08	6.80	8.70	7.40
σ **	250	74.3 ± 8.30	1023 ± 48.2	400 ± 144	786 ± 35.1	904 ± 305	302 ± 61.8	740 ± 42.8	65.0	1268	703
NO2	0.5	0.030 ± 0.0	0.030 ± 0.0	0.030 ± 0.0	0.030 ± 0.0	0.006 ± 0.003	0.004 ± 0.0	0.545 ± 0.35	0.004	0.80	0.03
NO3	50	1.73 ± 0.45	3.10 ± 1.10	3.75 ± 1.37	0.49 ± 0.002	4.39 ± 0.97	5.23 ± 1.75	7.05 ± 0.67	0.49	7.98	3.72
NH4	0.5	0.05 ± 0.0	0.05 ± 0.0	0.05 ± 0.0	0.06 ± 0.014	0.01 ± 0.004	0.01 ± 0.002	0.49 ± 0.18	0.007	0.70	0.05
SO4	250	2.50 ± 0.43	45.0 ± 0.0	28.9 ± 13.9	104 ± 8.77	23.2 ± 7.84	20.1 ± 7.32	24.0 ± 0.0	1.88	113	22.1
Cl	250	3.07 ± 1.08	15.2 ± 0.0	41.1 ± 9.2	27.9 ± 1.47	197 ± 73.5	36.9 ± 10.7	8.13 ± 0.0	2.13	281	32.8
2023
pH	6.5–9.5	7.30 ± 0.22	8.65 ± 0.24	7.43 ± 0.05	7.00 ± 0.14	7.68 ± 0.12	7.38 ± 0.05	7.43 ± 0.24	6.80	8.80	7.40
σ	250	72.3 ± 4.92	1056 ± 12.8	423 ± 98.6	838 ± 10.4	897 ± 178	334 ± 58.3	760 ± 22.8	66.0	1113	729
NO2	0.5	0.030 ± 0.0	0.030 ± 0.0	0.030 ± 0.0	0.030 ± 0.0	0.004 ± 0.001	0.004 ± 0.0	0.299 ± 0.09	0.002	0.43	0.03
NO3	50	1.71 ± 0.14	3.91 ± 0.23	4.05 ± 0.38	0.49 ± 0.005	4.43 ± 0.89	5.75 ± 1.23	6.38 ± 1.56	0.49	8.59	4.10
NH4	0.5	0.05 ± 0.0	0.05 ± 0.004	0.05 ± 0.0	0.05 ± 0.004	0.01 ± 0.003	0.01 ± 0.003	1.07 ± 0.65	0.007	1.64	0.05
SO4	250	61.4 ± 0.85	54.8 ± 17.3	126 ± 16.6	26.9 ± 3.17	21.8 ± 2.33	139 ± 0.0	24.0 ± 0.0	19.8	139	30.4
Cl	250	1.78 ± 0.92	534 ± 733	42.36 ± 10.5	25.89 ± 0.50	174 ± 32.1	37.56 ± 10.2	5.75 ± 0.89	0.71	1053	31.8
2024
pH	6.5–9.5	7.28 ± 0.15	8.60 ± 0.01	7.43 ± 0.36	7.03 ± 0.05	7.45 ± 0.11	7.26 ± 0.06	7.48 ± 0.05	6.90	8.60	7.40
σ	250	76.7 ± 9.50	1045 ± 1.00	449 ± 125	811 ± 15.0	1066 ± 476	301 ± 44.9	754 ± 11.1	67.0	1516	740
NO2	0.5	0.030 ± 0.0	0.030 ± 0.0	0.030 ± 0.0	0.030 ± 0.0	0.004 ± 0.0	0.004 ± 0.0	0.323 ± 0.16	0.004	0.49	0.03
NO3	50	2.06 ± 0.33	3.49 ± 0.85	4.33 ± 1.09	0.58 ± 0.10	3.74 ± 0.79	5.38 ± 1.34	5.98 ± 0.66	0.49	7.37	3.87
NH4	0.5	0.06 ± 0.02	0.05 ± 0.008	0.05 ± 0.0	0.05 ± 0.0	0.01 ± 0.004	0.01 ± 0.002	1.28 ± 0.23	0.006	1.53	0.05
SO4	250	4.3 ± 0.0	44.5 ± 0.0	62 ± 0.0	80.2 ± 0.0	19.6 ± 7.09	18.7 ± 1.97	20.0 ± 0.0	4.30	80	20.4
Cl	250	40.5 ± 55.9	524 ± 732	63.7 ± 0.0	406 ± 565	130 ± 1.06	30.2 ± 7.16	7.0 ± 0.0	0.96	1041	34.4

Note: * Maximum Allowable Concentration specified by the Romanian Legislation [51], and International Guideline of the World Health Organization [36]; ** σ = electrical conductivity expressed as µS/cm.

.

The pH values varied within the safe range specified by the Romanian and international guidelines, with a higher trend observed at the SPA2 sampling source and generally lower values at SPA4 (Figure 2a–c). There was a slightly increasing trend (probably due to the CO₃ input and treatment adjustments) during 2022–2024 in all samples, except for SPA6, which showed a significant decrease (possibly due to pollution caused by acid rain and organic acids). The mean values of pH in 2022 varied between 6.80 and 8.70, in 2023 varied between 6.80 and 8.80, and in 2024 ranged from 6.90 to 8.60 (Table 1). Comparable values, ranging between 6.70 and 7.99, were reported in a study conducted in Malaysia analysing tap water from 20 selected locations [59].

The electrical conductivity (σ) of the studied drinking water sources exhibited distinct seasonal patterns and significant spatial variability throughout the monitoring period. The mean electrical conductivity (σ) varied between 65.0 and 1268 µS/cm in 2022, between 66.0 and 1113 µS/cm in 2023, and between 67.0 and 1516 µS/cm in 2024. Generally, the highest values were obtained in the summer season and the lowest in the rainy season. This correlates with the higher precipitation volume in the rainy season, which is affected by the evaporation caused by high temperatures and lower precipitation levels in the summer season. SPA2 was characterised by the highest σ values, and SPA1 by the lowest (Figure 2d–f). SPA1 is situated at the highest altitude, near the Carpathian Mountains, which is reflected in the lowest contaminant concentration values compared to the rest of the samples. The EU Drinking Water Directive established a specification for electrical conductivity of 2500 µS/cm [60]. The results obtained are under the parametric value set by the directive.

The Cl content exceeded the threshold established by the Romanian and international guidelines for drinking water (250 mg/L) up to four times in SPA2, three times in SPA4, and once in SPA5. Typically, Cl contamination is generated only by anthropogenic activities [61]. Cl varied between 2.13 and 281 mg/L in 2022, between 0.71 and 1053 mg/L in 2023, and between 0.96 and 1041 mg/L in 2024 (Table 1). There were seasonal variations, generally with a higher Cl content in the autumn. The variability between samples is determined by the primary water source and its water–rock interaction (geogenic source), as well as by the agricultural and industrial activities. Conversely, a study analysing the quality of various drinking water sources in Nigeria reported that Cl concentrations in municipal water were within the recommended limit [62].

The SO₄ content was lower in all samples, ranging from 1.88 to 133 mg/L in 2022, from 19.8 to 139 mg/L in 2023, and from 4.30 to 80 mg/L in 2024. SPA1 was characterised by the lowest concentrations (1.89–2.88 mg/L 2022, 0.93–4.11 mg/L in 2023, and 4.3 mg/L in 2024), with the highest trend in the winter season. SPA4 had the highest SO₄ content, with an average of 104 mg/L in 2022, 126 mg/L in 2023, and 80 mg/L in 2024. All of the values obtained were below the taste threshold of 250 mg/L set by the WHO and by the EPA [36,63].

Concerning the nitrogen species content, the waters were characterised by elevated concentrations of NO₂ and NH₄ that were up to three times higher than the 0.5 mg/L threshold limit established by the Romanian and international guidelines related to drinking water quality. High values were predominantly observed in SPA7, with an upward trend in the autumn and winter seasons for NO₂ (Figure 2j), and in the spring and winter seasons for NH₄ (Figure 2k). In 2022, the trend varied between 0.004 and 0.80 mg NO₂/L, and between 0.007 and 0.70 mg NH₄/L. In 2023, the trend ranged from 0.02 to 0.43 mg NO₂/L, and from 0.007 to 1.64 mg NH₄/L. In 2024, the trend ranged from 0.04 to 0.49 mg NO₂/L, and from 0.006 to 1.53 mg NH₄/L. It was observed that, in the autumn season, NO₂ reached the highest concentrations in all three years, while NH₄ reached the highest concentrations in winter. The results are one to two orders of magnitude above the ones reported in municipal tap water in Denmark [64], indicating an unusual degree of nitrification instability within the distribution network. NH₄ (from various sources or derived from disinfectants) may be nitrified to NO₂ and finally NO₃ [64].

Nitrate varied between 0.50 and 8.6 mg/L, below the threshold established by the Romanian and WHO guidelines of 50 mg/L [36,51]. SPA4 was characterised by the lowest content (<1.0 mg/L), while SPA7 was characterised by the highest. The mean concentrations ranged between 0.49 and 7.98 mg/L in 2022, between 0.49 and 8.59 in 2023, and between 0.49 and 7.37 mg/L in 2024, with no significant variations between the monitored years. In a study conducted in Denmark, no strong seasonal variation was reported in distributed drinking water nitrate concentrations, which aligns with the findings of the current study [64]. Nitrification (or agricultural/groundwater loading) frequently results in NO₃, which is more stable. High levels of NO₃ in drinking water are known to be harmful to human health, particularly for young children and expectant mothers [64].

Changes in water samples over the years are related to the different sources of water used in the supply network. These sources have specific geological conditions and water–rock interactions, resulting in different chemical compositions. It has been noticed that climate variability with different effects has occurred in recent years, with trends of lower precipitation amounts [65,66]. It can thus be concluded that the natural dilution process is caused by a decrease in precipitation and an increase in evaporation during the summer months. This ultimately results in an increase in salts, ions, electrical conductivity, and SO₄, Cl, and nitrogen compounds. Moreover, the low-flow conditions of water with a low CO₂ content have been shown to increase the pH. SPA2 and SP4 were characterised by the highest electrical conductivity, which is associated with high Cl and SO₄ contents. Both areas are known for agricultural communities. The intensive agricultural practices, such as excessive and improper farming (e.g., animal husbandry and crop production), are significant sources of high Cl and SO₄ ion levels. This is due to the intensive use of fertilisers (both chemical and natural) and irrigation, which increases soil salinity. Additionally, aquifers and surface waters are used as sources for the network supply, and thus contribute to the overall levels of these ions. Furthermore, both areas are characterised by a rural topography, each equipped with individual septic tanks and obsolete sewer systems. This configuration fosters the occurrence of wastewater leakage, which is characterised by a high concentration of detergents and faecal matter.

3.2. Spatiotemporal Analysis of Water Pollution

After analysing the concentrations against the reference values from the drinking water quality guidelines, the pollution degree was calculated by determining the following: (1) the overall pollution assessment based on the OPA index; (2) the individual pollution assessment based on the pollution index (PI) by contaminant (NO₂, NO₃, and NH₄).

For the overall water pollution, the OPA index was applied for Cl, SO₄, NO₂, and NO₃, and their cumulative content. The results are presented in Figure 3. Water pollution was determined after applying the OPA index. Samples were integrated into two categories of pollution, with scores varying from season to season. In the winter season, SPA7 was characterised by high pollution from 2022 to 2024 (OPA scores ranged between 128 and 185, exceeding the limit value of 30 up to six times). The high score is directly corelated to the high content of NO₂ and NH₄, which exceeded (up to 1.5 times) the threshold concerning the drinking water quality (Figure 3a). SPA1 tends to be included into the medium pollution category in winter 2024 (OPA = 12.7), with similar scores (OPA = 7.96) obtained in 2022 and 2023, indicating a low pollution degree. In the summer season (July), scores were lower than in winter, but still higher than the threshold limit (up to five times) in SPA7 (OPA: 47.9–165). Samples SPA1–SPA6 were characterised by a low pollution degree, with scores ranging between 7.96–8.28 (SPA1–SPA4) and 1.05–1.79 (SPA5 and SPA6) (Figure 3c). In the rainy seasons (April and October), some scores were higher than in the summer and winter seasons (Figure 3b,d). SPA4 presented the highest score of the study (10.9 in April, 2022 and 8.35 in October 2022). A low pollution degree was indicated by the OPA scores for the majority of samples in both seasons. An exception was SPA7, characterised by a high pollution level (OPA_April: 130–162; OPA_October: 57.7–153), which was correlated to the high amount of NO₂ and SO₄ in October, and NH₄ and NO₂ in April.

Due to the low fluctuations of NO₂ and NH₄ contents, samples SPA1–SPA4 were characterised by a similar score trend.

An individual assessment by contaminant was necessary for observing the correlation of the OPA index results to the nitrogen compounds. Therefore, the pollution index (PI) was applied based on individual contaminant species. The PI was calculated for NO₂, NO₃, and NH₄. The results (mean values) are presented in

Table 2. PI scores considering the average contents of NO₃, NO₂, and NH₄ obtained over three years for samples SPA1–SPA7.

	2022			2023			2024
	PINO3	PINO2	PINH4	PINO3	PINO2	PINH4	PINO3	PINO2	PINH4
SPA1	0.965 **	−0.940 *	−0.900 *	0.966 **	−0.940 *	−0.900 *	0.959 **	−0.940 *	−0.877 *
SPA2	0.938 **	−0.940 *	−0.900 *	0.922 **	−0.940 *	−0.894 *	0.945 **	−0.940 *	−0.892 *
SPA3	0.925 **	−0.940 *	−0.900 *	0.919 **	−0.940 *	−0.900 *	0.913 **	−0.940 *	−0.899 *
SPA4	0.990 **	−0.940 *	−0.884 *	0.990 **	−0.940 *	−0.897 *	0.988 **	−0.940 *	−0.900 *
SPA5	0.912 **	−0.989 *	−0.980 *	0.911 **	−0.993 *	−0.975 *	0.925 **	−0.992 *	−0.982 *
SPA6	0.895 **	−0.992 *	−0.979 *	0.885 **	−0.992 *	−0.980 *	0.892 **	−0.992 *	−0.957 *
SPA7	0.859 **	0.090 **	−0.017 *	0.872 **	−0.402 *	1.133 ***	0.880 **	−0.355 *	1.564 ***

Note: * clean status; ** light pollution level status; *** moderate pollution level status.

. It was noticed that the studied samples were not contaminated with NO₃ based on the PI_NO3 scores. The PI_NO3 score ranges throughout all monitoring periods were 0.859–0.990 (2022), 0.872–0.990 (2023), and 0.880–0.988 (2024). According to the PI_NO2 results, the majority of samples were characterised by a clean status from the perspective of NO₂ content, except for SPA7 in 2022, which presented a high pollution level status (Table 2). On the other hand, a moderate pollution status was found in 2023 and 2024, which was related to the mean NH₄ content. SPA7 was characterised as moderately polluted, and the rest of the samples had a clean status.

Correlations between the chemical parameter concentrations and OPA scores were determined according to the Pearson correlation and PCA representations. Generally, positive corelations were determined between the OPA scores and NO₂ and NH₄ concentrations, and between the NO₂ and NH₄ amounts (

Table 3. Pearson correlation of the chemical parameter concentrations (mean values) obtained in April/spring 2022 with the OPA scores.

Variables	OPA	EC	pH	Cl−	NH4	NO2	NO3	SO4
OPA	1	0.264	−0.115	−0.340	1.000	1.000	0.694	0.030
EC	0.264	1	0.507	−0.014	0.274	0.252	0.155	0.326
pH	−0.115	0.507	1	0.067	−0.126	−0.102	0.411	−0.569
Cl	−0.340	−0.014	0.067	1	−0.347	−0.333	−0.156	0.035
NH4	1.000	0.274	−0.126	−0.347	1	0.999	0.678	0.052
NO2	1.000	0.252	−0.102	−0.333	0.999	1	0.712	0.005
NO3	0.694	0.155	0.411	−0.156	0.678	0.712	1	−0.541
SO4	0.030	0.326	−0.569	0.035	0.052	0.005	−0.541	1

Note: Values in bold are different from 0, with a significance level alpha = 0.05.

; Supplementary Tables S1 and S2). This suggested that the high OPA scores were linked to the high content nitrogen compounds due to a shared source.

The PCA exhibited a total variance that exceeded 50%. This finding indicates that the initial two principal components (PC1 and PC2) account for the bulk of the variation in water chemistry across seasons (Figure 4a,b and Figure S1a–d). While not exceedingly elevated, this value is sufficient to discern salient patterns within the dataset. In 2022, PCA1 was 47.3% in spring and 37.5% in summer, and PCA2 was around 17% in both seasons (Figure 4a,b). It was noticed that, in both seasons, NH₄ and NO₂ had the highest positive contribution to the PCA1, OPA scores, and SPA7 observations, which was characterised by the highest amount of nitrogen compounds and scores. The negative loading (low degree) in PC1 was represented by the pH level in the summer season, while in the spring, the pH level exhibited a positive loading but low variability. An examination of the pH loading according to season showed that the pH level exhibits considerably low influence or variability in the summer.

In the case of PCA2, the EC, SO₄, and Cl had the highest positive loadings but low variability in both seasons, indicating that PCA2 captures a salinity/mineralisation gradient.

In 2023 and 2024, as indicated in the Supplementary Material (Supplementary Figure S1a–h), the highest positive loadings in PCA1 were represented by the nitrogen compounds and OPA scores, which were the highest in SPA7. PCA1 reliably detects a nitrogen pollution gradient, suggesting that SPA7 remains a significant source of nitrogen enrichment, likely attributable to stable or recurring sources such as agricultural inputs or the presence of wastewater or septic system leakage, which is a concern. This finding confirms that nitrogen remains the predominant factor contributing to variations in water chemistry over extended periods, a conclusion that is supported by the analysis of multiple years of data, although the variability or influence degree of the indicators is low.

Differences were observed for NO₃ (positive loadings for PCA2 in the autumn season of 2023 and summer season of 2024) and SO₄ (negative loadings for PCA1 and PCA2 in the summer season of 2024 and positive loadings in winter of 2023 and 2024, and negative loadings for PCA2 in autumn 2024), but were characterised by a low variability. The negative SO₄ loadings are consistent with dilution during high-flow events, potential SO₄ reduction in warm, low-oxygen zones, and strong SO₄ runoff dominance [67]. The variations obtained in 2023 were between 12% and 17% for PCA2, and 48–51% for PCA1; in 2024, PCA1 varied from 15% to 17%, and PCA2 from 44% to 48% (Supplementary Figure S1a–d).

PCA counting for PC2 and PC3 presented a lower variability compared to the first analysis (ranging between 36.9% and 46.4%) (Figure 4c,d and Supplementary Figure S1e–h). In the spring season of 2022, the variability in indicators was low. Among this, the highest positive loading related to PC2, with a contribution of 22.9%, was represented by the pH, followed by NO₃, Cl, and EC; SO₄ had a negative loading, and NO₂ and NH₄ presented no variability, being close to zero. Regarding PC3 (17.9% variability), positive loading characterised all parameters except for pH and NH₄. In the summer season (44.7% total variability), there were slight differences, except for NO₃, which fell into negative loading (PC3) (Figure 4d). In the winter season of 2023, SO₄ and NO₂ had a negative loading for PC2, and NO₂, NH₄, NO₃, and Cl for PC3 (Figure S1e). In the autumn of 2023, positive loading characterised all chemical parameters in PC2, and pH and nitrogen compounds in PC3, but with low variability (Figure S1f). In the winter season of 2024, pH and NO₃ had low variability and negative loading in PC3 (Figure S1g). The summer season of 2024 was characterised by a very low variability, the lowest in all of the applied PCAs (Figure S1h). Generally, the highest variability was obtained in the cases of EC, Cl, SO₄, pH, and NO₃, potentially caused by hydrological changes in water sources, such as dissolution, runoff, or ion inputs with geogenic and anthropogenic origins.

3.3. Risk Characterisation

Given the pollution status exhibited by the pollution scores, and the exceedance of national and international thresholds for drinking water quality, human health risk assessment was performed. This analysis converts qualitative data into quantitative data, determining if the exposure to contaminated water poses risks to human health.

The first studied pathway was oral ingestion. The results are presented in

Table 4. Hazard quotient (HQ), chronic daily intake (CDI), and hazard index (HI) scores (mean, minimal, and maximum values) obtained between 2022 and 2024 during four seasons in two study cases (oral ingestion exposure at children and at adults).

Children				Adults
HQNO3					HQNO3
	average	min	max	average	min	max
2022	0.27575	0.00480	0.00048	0.0722	0.0096	0.1567
2023	0.2863	0.0365	0.64455	0.075	0.0096	0.1688
2024	0.2743	0.0365	0.5527	0.0718	0.0096	0.1448
HQNO2					HQNO2
2022	0.11563	0.00480	0.96240	0.0303	0.0013	0.2521
2023	0.07311	0.0024	0.5112	0.0191	0.0006	0.1339
2024	0.0788	0.0048	0.5880	0.0206	0.0013	0.154
CDINO3 (mg/kg × day)					CDINO3 (mg/kg × day)
2022	0.44119	0.00288	0.9574	0.11555	0.0153	0.2507
2023	0.4581	0.0584	1.0313	0.11998	0.0153	0.2701
2024	0.4389	0.0584	0.8844	0.11494	0.0153	0.2316
CDINO2 (mg/kg × day)					CDINO2 (mg/kg × day)
2022	0.01156	0.00048	0.09624	0.00302	0.000126	0.0252
2023	0.00731	0.00024	0.05112	0.00191	6.286 × 10−5	0.01339
2024	0.00788	0.00048	0.0588	0.00206	0.000126	0.0154
HI					HI
2022	0.39138	0.07252	1.5608	0.1025	0.01899	0.4087
2023	0.3594	0.07252	1.1557	0.0941	0.01899	0.3027
2024	0.3531	0.07252	1.0703	0.0925	0.01899	0.2803

, and the trends are presented in Figure 5a–c and Supplementary Figures S2 and S3a–i. For children, the CDI through water ingestion presented a minimum value of 0.00024 mg NO₂/kg × day in 2023. A maximum value of 1.0313 mg NO₃/kg × day was obtained in the same year, while the average value was 0.4461 mg NO₃/kg × day and 0.00892 mg NO₂/kg × day (Figure 5a–c; Supplementary Figure S2a–i). For adults, the average CDI score was 0.1168 mg NO₃/kg × day and 1.049 × 10⁻⁴ mg NO₂/kg × day. The minimum score was obtained in 2023 (CDI = 6.286 × 10⁻⁵ mg NO₂/kg × day), while the maximum score was obtained in 2023 with a value of 0.2701 mg NO₃/kg × day (Table 4; Figure S3a–i).

Furthermore, the HQ corresponding to the oral intake of NO₂ in children had a minimum value of 0.0024 in 2023, while the HQ_NO3 minimum was 0.0365. The maximum score was obtained in 2022 (HQ_NO2 = 0.9624). This score is concerning due to its proximity to the threshold limit of 1.0, which indicates the presence of carcinogenic risk. The average scores for this index were 0.2788 for the HQ related to NO₃ intake, and 0.0892 for the HQ implying the ingestion of NO₂ (Figure 5a–c; Supplementary Figure S3a–i).

The hazard to adults was related to the minimum and maximum HQ values obtained for NO₂ oral intake (HQ_min = 0.0006 and HQ_max = 0.2521) in 2023. The average HQ score was 0.073 for NO₃ and lower for NO₂ (HQ = 0.0233).

Observation of the HQ scores indicated that the threshold limit of 1.0 was not exceeded, suggesting that the studied water sources would be safe for drinking. However, the HI scores, which provide an overall assessment of hazard (cumulative impacts of NO₂ and NO₃), presented health risks related to the exposure through the oral ingestion pathway. A study assessing the potential human health risks associated with NO₃ and F⁻ reported that the permissible threshold was exceeded in HQ_NO3 in 40% of samples for children, while 26% of the water samples posed a high risk for adults [68].

The HI scores exceeded the limit in the scenarios implicating children (Figure 5a–c). In general, the limit was exceeded in most seasons, except for summer 2022, all seasons of 2023, and winter, spring, and summer in 2024. The highest values were obtained in samples from SPA7 in all seasons of all three years, indicating a risk to human health if ingested. The descending trend concerning the highest HI scores characterising the samples was SPA7 > SPA6 > SPA5 > SPA3 > SPA2 > SPA1 > SPA4 in winter, SPA7 > SPA2 > SPA6 > SPA5 > SPA1 > SPA3 > SPA4 in spring, SPA7 > SPA3 > SPA6 > SPA5 > SPA2 > SPA1 > SPA4 in summer, and SPA7 > SPA5 > SPA6 > SPA3 > SPA1 > SPA2 > SPA4 in autumn of 2022. In 2023, in the autumn season, the highest scores trend was SPA7 > SPA6 > SPA3 > SPA5 > SPA2 > SPA1 > SPA4, and in 2024 it was SPA7 > SPA6 > SPA3 > SPA5 > SPA1 > SPA4 > SPA2. The scenario involving adults was characterised by the highest HI scores, ranging from 0.142 to 0.409, both of which were obtained in SPA7 in 2022. In 2023, the highest HI scores ranged from 0.171 to 0.303 (SPA7), and in 2024 they ranged from 0.177 to 0.280 (SPA7) (Supplementary Figure S3a–i). In a study conducted in Suihua, China, the assessment of NO₃ in rural groundwater revealed significant health risks across different age and gender groups. The HI values were highest in infants, reaching a maximum of 32.03, followed by children, with a maximum of 17.98. This was followed by adult women, with a maximum of 12.45, and finally adult men, whose maximum was 10.81 [69]. Thus, the results from the current study and data from the scientific literature demonstrate that even moderate nitrate enrichment in drinking water can cause the HI to exceed the acceptable level for the most vulnerable age groups.

Nitrogen species in high amounts can have negative effects and even cause diseases (like methaemoglobinaemia or cancer), especially in vulnerable subpopulations such as infants, small children, and already unhealthy persons [70].

The last studied and assessed exposure pathway was dermal contact for children and adults (

Table 5. Hazard quotient (HQ) scores (mean, minimal, and maximum values) obtained between 2022 and 2024 in all seasons in the two study cases (dermal contact exposure in children and adults).

Dermal Contact Exposure in Children				Dermal Contact Exposure in Adults
	average	min	max	average	min	max
2022
Winter	0.024192	0.002704	0.042819	0.008200212	0.000916479	0.014514028
Spring	0.019381	0.002687	0.039233	0.006569254	0.000910867	0.013298291
Summer	0.019807	0.002687	0.03644	0.006713807	0.000910867	0.012351886
Autumn	0.01777	0.002687	0.044022	0.007077238	0.000910867	0.014921767
2023
Winter	0.019925257	0.002687234	0.028676699	0.006753886	0.000910867	0.009720284
Spring	0.019181124	0.002687234	0.035000251	0.006501654	0.000910867	0.011863721
Summer	0.020819162	0.002687234	0.031948834	0.007056885	0.000910867	0.01082941
Autumn	0.024337239	0.002742413	0.047421119	0.008249376	0.000929571	0.016073912
2024
Winter	0.019617829	0.003581139	0.034514673	0.00664968	0.001213866	0.011699129
Spring	0.019022681	0.002703787	0.03438776	0.006447948	0.000916479	0.011656111
Summer	0.018228887	0.002687234	0.027589666	0.006178882	0.000910867	0.009351822
Autumn	0.024467436	0.003785302	0.040667168	0.008293507	0.00128307	0.013784586

). The CDI and HQ were determined for the N-NO₃ component. The results showed no exceedances of the threshold limit (HQ < 1.0), suggesting that dermal contact with the studied water samples does not pose a significant risk to either children or adults.

The minimum score for the CDI was 0.000484 mg N-NO₃/kg x day during the entire observation study concerning the health impact assessment scenario on children (Figure 6; Supplementary Figure S4a–c). For adults, the lowest score was 0.000164 mg N-NO₃/kg × day, and the maximum score was 0.00289 mg N-NO₃/kg × day, which was obtained in 2024 (Supplementary Figure S5a–c). In Dilwarpur Mandal, India, an assessment of groundwater hydrochemistry during pre- and post-rainy seasons revealed significant health risks associated with NO₃⁻ exposure through dermal contact. The analysis showed that the HQ for dermal exposure to NO₃ exceeded the threshold limit of 1.0 across various age groups, indicating potential non-carcinogenic health effects [71].

The hazard posed by the dermal contact pathway in children was represented by maximum and minimum values of 0.0474 and 0.00269, respectively, as well as an average score of 0.0205, all of which indicate safety. HQ scores were lower for adults, indicating that children are more vulnerable to N-NO₃ exposure through dermal contact. The average HQ score was 0.00704, with a maximum score of 0.0161. The minimum HQ score was 0.000911 (Supplementary Figure S6a–c).

Generally, the deterministic human health risk assessment showed that children may be exposed to non-carcinogenic risks of infection caused by NO₂, NO₃, and NH₄, particularly through oral ingestion.

4. Conclusions and Perspectives

The current study characterised health risk by combining hydrochemical monitoring, pollution indices, and health-based exposure metrics across seasons, locations, and population groups. Although the hydrochemical conditions were generally acceptable, recurrent elevations of nitrogen species, particularly NO₂ and NH₄, were observed at specific locations. SPA7 was identified as the most critical site. These exceedances, together with pronounced seasonal variability, suggest ongoing pressure from site-specific sources and hydrological conditions.

According to the pollution indices, contamination levels are not uniform across the study area. While most sites exhibited low pollution levels throughout the monitoring period, some samples repeatedly showed elevated pollution levels, particularly during the wet and cold seasons. Occasional medium pollution levels at other sites shows that deterioration can occur episodically, even in otherwise low-impact environments. These findings emphasise the importance of considering both spatial heterogeneity and seasonal dynamics when assessing water quality. The health risk assessment concluded that the probability of adverse health effects varies substantially by age group, location, and season. For adults, exposure levels to nitrogen compounds generally remain below health-based reference values, resulting in a low likelihood of measurable health impacts under current conditions. In contrast, children consistently exhibit higher exposure levels due to their lower body weight and higher intake rates. This increases the probability of health impacts, particularly during seasonal exposure peaks. Although acute toxicity events are unlikely, the recurrence and persistence of seasonal peaks increase the probability of cumulative or subclinical health effects. Dermal exposure pathways pose a negligible health risk to all population groups, confirming ingestion as the dominant exposure route. NO₂ poses the highest concern due to its association with methaemoglobinaemia in infants and its potential contribution to longer-term health effects in children. Consequently, even moderate exposure levels may result in moderate health consequences for sensitive populations. While NO₃ exposure is largely within regulatory limits, it presents low immediate consequences under current conditions but remains relevant for chronic exposure scenarios.

The holistic risk assessment indicates that the probability of pervasive acute health consequences is minimal. However, local and seasonal factors pose a moderate risk to vulnerable populations, particularly children. While the impacts are generally not severe, they justify proactive management. The application of a risk-based framework that accounts for likelihood, consequences, and uncertainty is key to effectively protecting public health and sustainably managing drinking water resources.

It is recommended that these findings be taken into consideration in order to emphasise the necessity of continued surveillance, targeted management of high-risk sites, and preventive strategies guided by a risk-based approach. It is imperative that regulatory compliance is maintained and vulnerable populations are protected through ongoing monitoring, consideration of seasonal risks, and adaptive management. Future research should extend the assessment framework to include additional contaminants, such as heavy metals. Furthermore, risk estimates should be refined by considering combined exposures and population-specific vulnerabilities.