Next Article in Journal
Dissolved Organic Matter in Continental Hydro-Geothermal Systems: Insights from Two Hot Springs of the East African Rift Valley
Next Article in Special Issue
Water Quality and Hydrogeochemical Characteristics of Some Karst Water Sources in Apuseni Mountains, Romania
Previous Article in Journal
Urban Water Pricing and Private Interests’ Lobbying in Small Rural Communities
Previous Article in Special Issue
Testing Different Membrane Filters for 16S rRNA Gene-Based Metabarcoding in Karstic Springs
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Quality and Health Risk Assessment Associated with Water Consumption—A Case Study on Karstic Springs

Ana Moldovan
Maria-Alexandra Hoaghia
Eniko Kovacs
Ionuț Cornel Mirea
Marius Kenesz
Răzvan Adrian Arghir
Alexandru Petculescu
Erika Andrea Levei
1 and
Oana Teodora Moldovan
INCDO-INOE 2000, Research Institute for Analytical Instrumentation, 67 Donath Street, 400293 Cluj-Napoca, Romania
Faculty of Materials and Environmental Engineering, Technical University, 103-105 Muncii Boulevard, 400641 Cluj-Napoca, Romania
Faculty of Horticulture, University of Agricultural Sciences and Veterinary Medicine, 3-5 Manastur Street, 400372 Cluj-Napoca, Romania
Department of Geospeleology and Paleontology, Emil Racovita Institute of Speleology, Calea 13 Septembrie 13, 050711 Bucharest, Romania
Emil Racovita Institute of Speleology, Clinicilor 5, 400006 Cluj-Napoca, Romania
Author to whom correspondence should be addressed.
Water 2020, 12(12), 3510;
Submission received: 13 November 2020 / Revised: 4 December 2020 / Accepted: 10 December 2020 / Published: 14 December 2020
(This article belongs to the Special Issue Research on Karst Eco-Hydrology and Sediment)


In rural areas without centralized water supply systems, inhabitants often use groundwater of unknown quality as drinking water, without understanding the possible negative consequences on their health. Karstic spring waters from Dobrogea region in Romania were assessed for their potential to be used as drinking water source, according to their quality and seasonal variation. The physico-chemical parameters of waters were compared with the guideline values for drinking water established by the World Health Organization and the Directive 98/83/EC. The nitrate and Cr concentrations exceeded the guideline value in the springs from Southern Dobrogea, but met the quality criteria in those from Northern Dobrogea, thus, to be used as drinking water, the karstic springs located in Southern Dobrogea require treatment for nitrates removal. Heavy metals pollution indices showed low to medium cumulative heavy metal pollution in all springs, while the human health risk assessment by oral exposure indicated possible noncarcinogenic risks of nitrates, both for adults and children in springs from South Dobrogea. A rigorous monitoring of the water quality before human consumption is recommended for all four studied water sources.

1. Introduction

Groundwater represents a significant freshwater source and a critical natural resource, and, in many areas, it constitutes the primary source of drinking water. Groundwater or aquifer vulnerability depends on the properties of the layers situated above the saturated zone to attenuate the pollutants’ effects, by retention or neutralization by chemical reactions [1]. The infiltration of the pollutants is predominantly diffuse in karst areas and their discharge is concentrated (e.g., natural springs or tube wells), deteriorating the quality of drinking water [2]. The quality of groundwater is superior to surface water, but it is influenced by various drivers such as anthropogenic pollution (urbanization, industrial sources, sanitation, intensive agricultural practices) and natural processes (geological substrates, recharge water quality) [3,4].
Contamination with nutrients (especially phosphates and nitrates) in agricultural and urban areas, release of Ni, Cr, and Mn by bedrock weathering and seawater intrusion along coastal aquifers by overexploitation were the main factors and mechanisms controlling the distribution of major and trace elements in groundwaters used for irrigation and drinking purposes in Attica region, Greece [5]. The quality and quantity of karstic springs is also influenced by the aridity level and droughts [6]. The major factors influencing the evolution of groundwater chemistry in the Jinta Basin, China were found to be rock weathering, evaporative concentration of salt under water–rock interaction and precipitation, whereas the dissolution of carbonate had little influence [7]. The increase of as concentration following continuous exchange with the shallow aquifer and the high concentration of K and nitrates following substantial anthropogenic soil use were the main contamination mechanisms of the groundwater in Sant’eufemia Plain (Calabria, Italy) [8,9,10].
Among the wide variety of pollutants impacting groundwater, heavy and trace metal contamination raises serious concerns, the majority having toxic effects on the aquatic ecosystem and on human health when their concentrations exceed the maximum permissible limits or when it accumulates over long periods of time [11,12,13]. Cadmium (Cd), lead (Pb), chromium (Cr), copper (Cu), iron (Fe), nickel (Ni), and zinc (Zn) are the most prevalent heavy metals that humans are exposed to and are harmful even at low concentrations. For instance, Cd affects the kidney, skeleton, skin, muscles, and can cause cancer [14,15]. Pb is also highly toxic, causing high blood pressure, headache, abdominal pain, lung, and stomach cancers [16,17]. Toxic metals negatively influence the nervous, circulatory, and immune systems, causing cognitive disorders, behavioural issues, and liver disorders [18,19]. Nitrate represents another important groundwater pollutant, especially in rural areas, deriving mainly from agricultural practices, waste and manure disposal, and lack of sewage management. The exposure to high nitrate levels can generate lethal diseases such as methemoglobinemia in infants and cancer in adults [20].
There are two main routes through which pollutants from water reach the human body: ingestion and dermal absorption. For the assessment of possible noncarcinogenic health risks, the Chronic Daily Intake (CDI), the Hazard Quotient (HQ), the Hazard Index (HI), and Total Hazard Index (THI) are most commonly applied [14,17,18]. In addition, several indices such as Heavy metal Pollution Index (HPI) and Heavy metal Evaluation Index (HEI) have been developed and used to assess the pollution of groundwater [20,21,22,23,24].
At European level around 90% of the population has access to public water supply and sanitation, but in Romania only 69.4% of the population has access to the public water supply system. While the water distribution network reaches 98.7% of the urban population, only 35.3% of the rural population is connected to the public network [25]. In rural areas without a water supply system, households are using untreated and often groundwater of unknown quality groundwater pumped from tube wells without understanding the possible consequences on their health. In Dobrogea area, characterized by aridity, drought, and porous environment, the karstic springs are considered the main drinking water sources, due to the mean annual precipitation (350–450 mm). In this context, assessing and monitoring groundwater quality is needed in order to ensure that rural populations have access to a good quality and safe drinking water, as specified by the EU Water Framework Directive (WFD 2000/60/EC), Groundwater Directive (GWD 2006/118/EC), and the values recommended by the World Health Organization (WHO) guideline for drinking purposes [26,27,28]. This study presents the seasonal variation of karstic spring waters quality from the Dobrogea region in Romania, used as drinking water sources by the local population. The water quality was assessed using the Water Quality Index (WQI), HEI, and HPI. The health risk through ingestion and dermal routes was assessed using CDI, HQ, HI, and THI.

2. Materials and Methods

2.1. Description of the Study Area

Four karstic springs (GW1—General Praporgescu (Praporgescu), GW2—Cloșca (Closca), GW3—Șipote (Sipote), and GW4—Tufani) located in four remote rural areas from south-eastern Romania were selected for this study (Figure 1, Table 1). The GW1 and GW2 springs are situated in Northern Dobrogea, while GW3 and GW4 are situated in Southern Dobrogea. Approximately 90% of the population in the studied localities uses GW1-GW4 water sources for drinking and other household needs. The long-term use of unknown quality groundwaters as drinking water could induce potential health risks.
Praporgescu spring (GW1, 45°0′52.99″ N, 28°22′42.93″ E) belongs to the Danube basin at the limit with Taita basin, the last also including the Closca spring (GW2, 45°0′40.04″ N, 28°28′9.70″ E). GW1 is situated in the eastern part of the General Praporgescu village, while GW2 in the western part of the Closca village. Sipote spring (GW3, 44°2′31.39″ N, 27°57′45.76″ E) is situated in the south-eastern part of Sipote village, while Tufani spring (GW4, 43°59′21.22″ N, 28°0′28.93″ E) is located at almost 8 km south-east from Sipote, both springs belong to the Urluia basin. The main anthropic activities in the surrounding areas of the springs are related to agricultural practices (pastoral, arable, and mixed farming). The local population uses water from these four karstic springs as the main source of drinking water for human and livestock consumption, albeit the water quality is not assessed and monitored by the local or national authorities.
Northern Dobrogea is characterized by faulted limestones and dolomitic limestones, but the karst landscape is isolated (e.g., small ridges) and disrupted by the loess cover, the most important karst features being represented by cliffs (e.g., Somova, Mahmudia, Murighiol). Southern Dobrogea is represented by a platform region composed of three main karst units (Jurassic, Cretaceous, and Eocene), characterized by a semiburied plateau, the main karst feature being represented by large closed depressions filled with loess and dry valleys [29]. From hydrogeological perspective, Northern Dobrogea belongs to the North Dobrogea Orogen Karst, where the carbonate rocks are represented mostly by Triassic dolomitic limestones (Figure 2), and is characterized by a low hydraulic gradient of the aquifer accumulations and a dry pluviometric regime (250–530 mm). These factors determine relatively reduced discharges of the karstic springs [30]. In Southern Dobrogea, the geological settings sustained the development of a karstic aquifer of platform type (Figure 2), represented mostly by almost horizontal limestone layers (which can impose a slow underground flow), thick layers of carbonate successions and vertical faults, low precipitations, and few temporary water courses (Valea Baciului, Urluia, Cerchez). This karstic aquifer is formed by two superposed aquifers represented by the upper aquifer (developed in the Sarmatian limestones) and the lower aquifer (represented by a unitary karstified limestone and dolomitic complex in the Jurassic and Barremian limestone formations) [31]. The main difference between Northern and Southern Dobrogea groundwaters is the main drainage direction. The northern region is characterized by East–South drainage direction, while the southern region, by two superposed aquifers (upper and lower). Both regions are affected by drought and low precipitation input, slow underground water flow, and scarce superficial flow.
The number of inhabitants that use GW3 and GW4 as drinking water sources are 3-fold higher, while the flow rates are one order of magnitude lower than of GW1 and GW3. The human induced alteration of the groundwater flow by intensive usage in the case of GW3 and GW4 might contribute to the increase of pollutants concentration and water quality decline of these springs by the mobilization of naturally occurring trace elements from the aquifer materials through which the water flows [33].
From a climatic perspective, Dobrogea represents one of the most arid regions of Romania, where drought can affect large areas, especially during summer. Records for the period 1961–2009 from nine meteorological stations located in Dobrogea showed a rainfall variation between 257 and 535 mm, while the annual average temperature ranged between 10.1 and 11.8 °C [34]. For the present study, data from the European Climate Assessment and Dataset [35] and data from two representative meteorological stations (Tulcea and Constanta) for Northern and Southern Dobrogea were used. The average rainfall for the sampling period (September 2019–August 2020) reached 299 mm for Northern Dobrogea and 229 mm for Northern and Southern Dobrogea were used. The average rainfall for September 2019–August 2020 period reached 298.9 mm for Northern Dobrogea and 229.2 mm for Southern Dobrogea (Figure 3). The annual mean temperature for Northern Dobrogea was 13.5 °C, with a minimum temperature of −3.2 °C and a maximum of 28.3 °C, and a mean annual temperature of 14.3 °C, with a minimum −2.6 °C and a maximum of 28.1 °C for Southern Dobrogea (Figure 4). At global scale, Dobrogea has a semiarid climate, according to the Köppen–Geiger climate classification [36]. Moreover, giving a different Köppen–Geiger climate classification at a regional scale [37], Northern Dobrogea is characterized by a warm continental climate, while Southern Dobrogea is situated in a warm oceanic climate.
In Table 2, the mean temperatures and precipitation, according to the Tulcea and Constanța meteorological stations for the studied period are presented.
Land use of the territorial functional regions is presented in Figure 5. In Northern Dobrogea, the territorial administrative unit (TAU) Cerna (including General Praporgescu village) covers a surface of ≈200 km2, the largest area being covered by nonirrigated arable land (≈122 km2), broad-leaved forest (≈27 km2), and natural grasslands (≈22 km2), while, in the TAU of Horia (including Closca village), with a surface of ≈40 km2, the largest area is covered by nonirrigated arable land (≈26 km2) and pastures (≈5 km2) [38]. In Southern Dobrogea, the Deleni (including Sipote village) and Independența (including Tufani vilage) TAUs cover 170 km2, respectively, 190 km2. The main functional areas are represented by nonirrigated arable land (122 km2, respectively, 140 km2), pastures (27 km2, respectively, 20 km2), and broad-leaved forest (10 km2, respectively, 20 km2) [38]. In all the studied areas, the urban fabric had low occupancy surface: 6 km2 in Cerna TAU, 7 km2 in Horia TAU and 3 km2 in Deleni and Independența TAUs [38]. Thus, the main anthropogenic activities in the area that could negatively impact the karstic waters are agriculture, animal husbandry and household activities by diffuse infiltration of the fertilizers and manure. Industrial or commercial units, mineral extractions sites, vineyards, mixed forest lands, inland marshes, and water bodies are functional areas category with a surface occupancy rate of less than 3%.

2.2. Field Sampling and Analytical Procedures

The water samples were collected from karstic springs GW1-GW4 in four seasons (autumn 2019, winter 2019, spring 2020, and summer 2020). The samples were stored in precleaned polyethylene bottles and kept at 4 °C during transportation. The field-based water parameters such as temperature, pH, electrical conductivity (EC), and dissolved oxygen (DO) were measured in situ using a Hanna HI 9829 Multiparameter (Hanna, Woonsocket, RI, USA). The instrument was calibrated on-site using HI 9828-25 (Hanna, Woonsocket, RI, USA) standard solution for pH and EC and HI 7040-L (Hanna, Woonsocket, RI, USA) standard solution for DO.
In the laboratory, total alkalinity and bicarbonates were determined by titration with 0.1 N HCl against bromocresol green. For the anions and metals determination, the water samples were filtrated through 0.45 μm cellulose acetate membrane filters. Anions (F, Cl, NO2, NO3, SO42−) were measured using a 761 IC compact ion chromatograph (Metrohm, Herisau, Switzerland), equipped with a Metrosep column 5–100/4 column and a Metrosep A Supp 4/5 mm guard column. A total of 20 µL of sample were injected with flow rate of 0.7 mL/min. Dissolved organic carbon (DOC) was measured by catalytic combustion followed by nondispersive infrared detection using a Multi N/C 2100 S analyzer (Analytic Jena, Jena, Germany) after filtering the samples through 0.45 μm polytetrafluoroethylene membrane filters. Turbidity (TU) was measured using a Turb 555 IR (WTW, Weilheim, Germany) turbidimeter. For the metal determination, 50 mL of filtered water were acidulated with 0.5 mL 63% HNO3. The major elements (Fe, Na, Mg, K, and Ca) concentration was measured by inductively coupled plasma atomic emission spectrometry (ICP-OES) using an Optima 5300 DV Spectrometer (Perkin Elmer, Waltham, MA, USA), while trace elements (Cu, Zn, Ni, Cr, Co, Cd, and Pb) concentration was determined by inductively coupled mass spectrometry (ICP-MS), using an ELAN DRC II Spectrometer (Perkin Elmer, Waltham, MA, USA). Based on the Ca and Mg concentrations, the total hardness (TH) as equivalent CaCO3 was calculated. All reagents were of analytical grade.
The analytical data quality was ensured through the use of calibration standards, procedural blank measurements, and duplicate samples. The accuracy of anions determination was tested by analyzing the concentration of anions from IC 1 Multi-element standard (Certipur Merck, Darmstadt, Germany), while the accuracy for metals determination was tested using NIST 1643e freshwater certified reference material (National Institute of Standards and Technology (NIST), Canada). The mean recoveries were found to range from 89% to 100% for anions and 94% to 102% for metals. Ultrapure water from a Millipore system (Molsheim, France) was used for all the dilutions.

2.3. Data Treatment and Statistical Analysis

2.3.1. Water Quality Index (WQI)

The WQI represents a mathematical tool used in assessing the composite influence of specific chemical indicators on the quality status of different types of water [39]. The WQI is computed in four steps, calculated using the following Equations (1)–(4) [39]:
WQI = i = 1 n S I i
S I i = W i × q i
W i = w i i = 1 n w i
q i = C i S i × 100
where SIi represents the subindex of the ith indicator, Wi is the relative weight, qi represents the quality rating for each chemical indicator, wi is the weight of each element (pH 0.11, EC 0.0004, F 0.666, Cl 0.004, SO42− 0.004, and NO3 0.02), n, Ci, and Si represent the total number (n = 6), the concentration, and the guideline value (according to drinking water quality guidelines established by the World Health Organization (WHO) and according to the Directive 98/83/EC [25,40]. In this study, a total of six chemical parameters were considered, with guideline values according to WHO and the Directive 98/83/EC for F (1.5 mg L−1), pH (6.5–9.5), EC (2500 µS cm−1), Cl (250 mg L−1), NO3 (50 mg L−1), and SO42− (250 mg L−1). The WQI classifies the water quality as excellent (WQI < 50), good (WQI = 50–100), poor (WQI = 100–200); very poor (WQI = 200–300) and unsuitable for drinking (WQI > 300) [39].

2.3.2. Heavy Metal Pollution Index (HPI) and Heavy Metal Evaluation Index (HEI)

The HPI estimates the overall combined effect of individual heavy metals concentration of the quality of water sources [41]. The HPI was calculated using Equation (5) based on the concentration of Cd, Cr, Cu, Ni, Pb, Zn, and Fe and the values established by the WHO guideline and by the Directive 98/83/EC [28,40].
HPI = i = 1 n Q i W i i = 1 n W i
where, Qi is a subindex calculated according to Equation (6) and Wi is the unit weightage (the ratio between k (k = 1) and the guideline values) of the ith parameter and n is the total number of the considered chemical elements (n = 7) [37].
Q i = i = 1 n C i I i S i I i × 100
where, Ci, Ii, and Si are the concentration of the ith element, the ideal, and the standard values, according to the Directive 98/83/EC and WHO guidelines regarding the drinking water quality [28,40]. The used factors are indicated in Table 3. Waters with HPI < 100 are characterized as unpolluted, while with HPI > 100 are classified polluted with heavy metals, considered not suitable for drinking purposes [41].
The HEI presents the overall groundwater quality with respect to heavy metals content, and is computed using Equation (7) [43]:
H E I = i = 1 n H c S i
where, Hc is the determined value of the ith parameter and Si represents the guideline value, according to WHO and the Directive 98/83/EC [28,40] (Table 2). The HEI classifies the water as low (HEI < 1), medium (1 < HEI < 2), and highly polluted (HEI > 2) [43].

2.3.3. Human Health Risk Assessment

Health risk assessment allows the noncarcinogenic health risk level determination following the consumption of inadequate water [14]. In this research, the populations were divided into two age groups: children (0–21 years) and adults (21–72 years). The human health risk was determined for oral and dermal exposure [18,44]. The chronic daily intake (CDI) of the elements was computed using Equations (8) and (9) [45,46,47,48]:
CDI o r a l = C W × I R × E F × E D B W × A T
where, CW is the metal or ion concentration (µg L−1) [45], IR is the ingestion rate (2.2 L day−1 for adults/1 L day−1 for children) [47,48], EF is the exposure frequency (365 days year−1) [45], ED is the exposure duration (70 years for adults/10 years for children) [47,48], BW is the average body weight (70 kg for adults/25 kg for children) [47,48], and AT is the average time of the exposure (25,500 days for adults/3650 days for children) [45]:
CDI d e r m a l = C W × S A × K p × E T × E F × E D × C F B W × A T
where, CW is the metal or ion concentration (µg L−1) [46], SA is the skin surface area (18,000 cm2 for adults/6600 cm2 for children) [46,48], Kp is the permeability coefficient (1 × 10−3 cm h−1 for NO3, Cd, Cr, Cu, Ni, and Fe, 6 × 10−4 cm h−1 for Zn, 1 × 10−4 cm h−1 for Pb) [47,48], ET is the exposure time (0.58 h event−1 for adult/1.00 h event−1 for children [47,48], EF is the exposure frequency (350 days year−1) [47,48], ED is the exposure duration (30 years for adults/6 years for children) [47,48], CF is the conversion factor (1/1000 L cm−3), BW is the average body weight (70 kg for adults/25 kg for children) of the consumers [47,48], and AT is the average time of the exposure (10,950 days for adults/2190 days for children) [47,48].
In order to evaluate the noncarcinogenic health risks, the hazard quotient (HQ) was calculated, for both oral and dermal pathways, using Equation (10). According to the United States Environmental Protection Agency (U.S. EPA), HQ results higher than 1.0 indicate the presence of noncarcinogenic risk [45,48].
HQ = C D I R f D
where, the RfD is the reference dose (µg kg−1 day−1) equal to 300/45 for Fe, 40/12 for Cu, 1.4/0.42 for Pb, 300/60 for Zn, 20/5.4 for Ni, 3/0.015 for Cr, 0.5/0.005 for Cd [49], and 1600/1100 for NO3 (oral and dermal exposure) [50].
The overall potential for noncarcinogenic effects of more than one element is defined as the sum of the calculated HQ for each element and expressed as the hazard index (HI). If HI is lower than one (HI < 1.0), no chronic risks are assumed to occur and if HI is higher than one (HI > 1.0), possible chronic risk generated by water ingestion could appear [47].
Assuming that the risk is cumulative, the total noncarcinogenic risks following exposure to contaminated drinking water is evaluated using the total hazard index (THI) calculated according to Equation (11) [18]:
THI = HI o r a l + HI d e r m a l

3. Results and Discussion

3.1. Physico-Chemical Parameters of the Spring’s Water

The temporal variations of the water quality parameters were slightly influenced by the seasonality of rainfall and temperature (Table 4). The pH of GW1 and GW2 were circumneutral, with values in the range of 7.2–7.8, while GW3 and GW4 were slightly alkaline. Generally, higher pH was found in summer and spring than in autumn and winter, however the seasonal variations were low. In general, the pH indicates the water capacity to react with the acidic or alkaline materials present in water [51]. The average EC was slightly higher in GW3 and GW4 than in the other two springs, with the highest values in autumn and spring in all springs. The mean values of the EC in all four karstic springs were within the values recommended by the WHO guideline for drinking purposes and by the Directive 98/83/EC [28,40]. Generally, DO increases with the decrease of temperature. In the case of karstic waters, the water temperature is more or less constant, and thus the DO values are less season-dependent [52,53]. During the whole year, the water temperature was relatively constant for GW1 (12.9–15.8 °C), GW2 (14.2–15.5 °C), and GW4 (12.5–15.5 °C) and slightly seasonal variations of DO were observed in these springs. In the case of GW3, the water temperature varied widely (4.7–20.4 °C), the highest DO (10.1 mg L−1) being measured in winter, when the water temperature was 4.7 °C, and the lowest in summer (7.8 mg L−1), when the water temperature was 20.4 °C. The average concentration of DOC was comparable in GW1, GW3, and GW4, and slightly lower in GW2. In all springs, the highest DOC was measured in summer and the lowest in winter, although the seasonal variation was low. TU is caused by suspended organic (decomposed organic matter or living organisms) and inorganic (silt, clay) particles and its increase may indicate a fast transport pathway connecting potentially contaminated surface water with the groundwater. The average TU of GW3 and GW4 were one order of magnitude higher than of GW1 and GW2. TU of GW1 and GW2 was very low and no seasonal variation was noticed, while the TU of GW3 and GW4 varied with the season, being the highest in summer and spring, respectively. The TU in all springs met the recommended drinking water standards, according to the Directive 98/83/EC [28]. The concentration of metals and anions higher than the guideline values alters the quality of water used as drinking water sources [54,55]. With some exceptions, the seasonal variations of the chemical parameters were low. The concentration of Cl was comparable in the four studied springs. The Cl concentration in GW1 and GW2 was the highest in summer and the lowest in spring, while in GW3 and GW4 was the highest in autumn and the lowest in spring (GW3) and summer (GW4), respectively.
The nitrate and fluoride concentrations were much higher in GW3 and GW4 than in GW1 and GW1, while the sulphates were higher in GW1 and GW2 than in the other two springs. The highest concentration of F was measured in GW4, reaching the maximum in autumn, while the highest SO42− with very low seasonal variations was measured in GW1. The concentrations of nitrites were below the detection limit in all the samples and seasons. The maximum concentration of NO3 was determined in GW3 in autumn (268 mg L−1) and the lowest in GW2 in summer (23.1 mg L−1). However, important seasonal variation for NO3 was not observed in any of the studied springs.
The NO3 in GW3 and GW4 exceeded the guideline value (50 mg L−1), while GW1 and GW2 were below the guideline value. High levels of NO3 in the drinking water in the dry season as well as medium to high-risk levels for infants, children, and adults were reported in Ghana [56,57]. A positive correlation between Cr and NO3 occurs due to bedrock geology and intensive agricultural practices [5]. The HCO3 was the dominant anion with a mean concentration of 506 mg L−1, for GW1, 525 mg L−1 for GW2, and 501 mg L−1 for GW3. The highest value of HCO3 was determined in autumn in GW4 (683 mg L−1), a value that exceeded the recommended value for drinking water purposes by the Directive 98/83/EC (500 mg L−1) [28]. The variations of temperature and precipitation regime influence the water quality of karstic springs. Karstic springs water is also influenced by the dissolution of minerals and weathering processes in the bedrock. Agricultural practices alter the quality of karstic springs as well, due to the use of fertilizers based on nitrogen compounds. Mainly, nitrates exceed the guideline values according to the WHO guidelines [36] and to the Directive 98/83/EC [28].
The K, Ca, and Mg concentrations in the spring water of the study area did not differ substantially between the sampling seasons: the average values for K varied between 1.73 (GW1) and 5.80 mg L−1 (GW4), for Ca between 34.2 (GW4) and 109 mg L−1 (GW2), and for Mg, between 37.2 (GW2) and 67.1 mg L−1 (GW3). These elements may originate from the dissolution of bedrock minerals such as dolomitic limestone and limestone [5]. The highest variation of Na was observed in GW4, where the concentration in autumn was almost twice the concentration in summer. Based on the TH values, the GW1 and GW2 are classified as very hard (TH < 180), GW3 as hard (121 < TH < 180), and GW4 as moderately hard (61 < TH < 120) waters [58]. Hard water may cause unpleasant taste, scale formations in pipes, and reduces the lather-forming ability of soap, and it may have a potential impact on human health [59]. Calcium and magnesium are essential minerals with important roles in numerous biologic and cellular functions in the human organism [60,61]. Both the high and low levels of these elements in water are associated with health problems. The low levels of Ca or Mg in drinking water may favor osteoporosis, higher incidence of fractures, and disturbed bone development in children, while very high levels of Ca and Mg may increase the risk of renal lithiasis and arthritis. Additionally, there is evidence that suggests a protective or beneficial effect of water Ca and Mg against various neurological, cardiovascular, and metabolic diseases [61,62,63].
The concentrations of Fe, Cu, Zn, Ni, Co, Cd, and Pb were lower than the guideline values set by the Directive 98/83/EC [28] in all springs and seasons. The highest concentration of Fe was obtained in GW3, of Cu and Ni in GW2 in winter, while of Zn in GW1 in autumn. Cr was the metal with the highest variation among all karstic springs in all seasons, the highest values being determined in GW4, where in autumn exceeded the guideline value (50 mg L−1). For GW1 and GW2, the highest concentration of Cr was determined in winter and the lowest in spring, while GW3 and GW4, in autumn, respectively, in winter. Opposite to Cr, the Co, Cd, and Pb had very low seasonal variations in all springs. Co and Cd concentrations had values higher than the detection limits only in winter and spring, and Pb in spring. Comparing springs, GW3 had the highest metal concentration, but regarding the temporal distribution of the metal concentration, GW1 and GW3 had the highest concentration of metals in winter, GW2, in summer and GW4, in autumn.
The Piper diagram allows the visualization of the spring’s chemistry and the identification of the water’s geochemical facies [64]. According to the Piper diagram, GW1, GW2, and GW3 are classified as Ca-Mg-HCO3 type, while GW4 of Na-HCO3 type which indicates the lateral movement of the water from the karstic dolomitic limestones (Figure 6). Studies in the Attica region, Greece indicated the presence of diverse processes between groundwater and seawater besides the dissolution of carbonate minerals [4]. In the Jinta Basin, China the groundwaters are of Mg-Na-SO42− and Na-Mg-SO42−-Cl and Mg-SO42−HCO3 types due to the evaporative crystallization and rock weathering (evaporation of salt, dissolution of silicate rock, and sulphate minerals) [6]. In Italy (Calabria), four main water types were identified: Ca-HCO3, Na-Cl, Ca-SO42−, and Ca- HCO3, due to the interactions between water and rocks [8].
The Total Ionic Salinity (TIS) plot (Figure 7) indicated that the studied samples have different TIS values ranging between 15 and 25 meq L−1. The lowest values were obtained for the Ca-Mg-HCO3 type (sample GW3) in summer and the highest for GW2 in spring. The Na-HCO3 type (GW4) in spring has the highest TIS value.
The groundwater dynamics depend on the tectonic, structural, and morphological settings in which the carbonate rocks are developed, while karst aquifers are directly influenced by the recharge, circulation, and discharge of underground waters [13]. Due to their enhanced permeability, karst aquifers are vulnerable to pollution as runoff can easily reach through conduits into the springs, especially during precipitation. Nutrients (nitrogen, phosphate) and metals are frequently present in surface runoff. Agricultural activities are the main sources of diffuse water pollution in the Dobrogea region. Our data showed that the Southern Dobrogean spring waters pollution with nitrates and Cr is of anthropogenic nature. The local sources of nitrates come from fertilizers used on arable lands, especially natural manure used by the residing population. Besides, due to the lack of centralized sanitation, nitrates infiltrate the porous geological substrates. Cr is presumably also derived from anthropogenic activities, such as agriculture, sewage sludge. There are no mining deposits or industrial sites in the region.

3.2. Water Quality Indices

The WQI (Table 5) ranged between 24.1 and 89.8 indicating good to excellent water quality. Generally, the WQI was comparable between seasons, GW1 and GW2 having excellent quality (WQI < 50), while GW3 and GW4 good quality (50 < WQI < 100) in all four seasons. The highest WQI was obtained in GW4 (89.8), followed by GW3 (65.8) in autumn.
In several studies conducted in Romania on groundwater sources, the WQI indicated various water quality ranging from poor to excellent. The water quality degradation was attributed to high NO3, Cu, Ni, Pb, and Zn concentrations that resulted from anthropogenic activities (agriculture, house holding, and industry) [23,64].
Some studies on groundwater from Asia (Bokaro district, India) and Africa (Bonkoukou, Niger) indicated high values for the WQI (>150), showing inadequacy for drinking purposes or poor quality, due to the anthropogenic activities (agricultural waste, untreated domestic waters), but also due to natural processes (rock weathering, intrusion of sea water) [65,66,67,68]. In Europe, studies on karstic waters in the Megara basin, Greece indicated generally good water quality (WQI < 100) with few cases of poor water quality [5]. In Bodrum Peninsula, Turkey, karst waters were characterized by very poor quality with WQI values around 250, due to the high amount of total hardness, turbidity, and chemical oxygen demand [69]. The WQI of groundwaters used in groundwater resources form the Heihe drainage area (China) ranged between 45 and 729 indicating that the water quality ranges from excellent to very poor and unsuitable. Waters with low quality are characterized by high amounts of TDS, Ca, Mg, Na, Cl, SO42−, and NO3, due to the weathering of rocks and evaporation processes [7].
The heavy metal pollution status is indicated by the cumulative concentrations of the studied metals (Cd, Cr, Cu, Ni, Pb, Zn, and Fe). The HPI ranged between 89.2 and 196 (Table 5) indicating no heavy metals pollution for samples GW1 and GW2 in summer, autumn, and spring and pollution in the winter season. Heavy metal pollution was found for GW3 and GW4 in all seasons. The highest value was obtained in autumn (HPI = 196) in GW4, due to the increased amount of Cr; GW4 being characterized with the highest HPI values, in all seasons. Different studies on groundwaters from the northern part of Romania indicated no pollution, with HPI values ranging between 6.0 and 98. The high HPI values that were attributed to Fe, Cu, and Pb resulted from mining activities [23]. Another study reported no heavy metal pollution of groundwaters in the central part of Romania, although the guideline value for Cd was slightly exceeded, due to the industrial activities developed in the nearby proximity of the groundwater sources [70]. In several other studies, HPI was also successfully used for the assessment of drinking water contamination with heavy metals [71,72,73].
The HEI values indicated medium pollution with Cd, Cr, Cu, Ni, Pb, Zn, Fe in sample GW4, in spring, the computed value of HEI being higher than the unity, due to the high Cr amount, while the rest of the samples were characterized with low pollution (HEI < 1.0). The highest value was obtained in GW4 in all seasons, followed by GW3 in autumn and winter. The lowest values were obtained in spring and autumn in samples GW1 and GW2. Studies conducted in Africa indicated medium and high degree of pollution (HEI = 14.3–64.2), due to anthropogenic activities (leachate percolation from landfill sites) and lithogenic factors [72]. In Seini town, northwestern Romania low degree of heavy metal pollution of groundwater due to mining activities was reported by Dippong et al. [23].

3.3. Exposures and Health Risk Assessments

The long-term consumption of contaminated groundwater could cause potential health risks. To assess the noncarcinogenic health risks following the long-term exposure to contaminated water use, the health risk assessment model recommended by the US EPA was used. The chronic noncarcinogenic risk following oral and dermal exposure was determined based on the average nitrate and metal (Fe, Cu, Zn, Pb, Ni, Cr, Cd) concentrations present in each of the drinking water sources. As shown in Table 6, the chronic daily intake (CDI) is much higher for oral exposure than for dermal exposure for all the elements.
The GW4 and GW3 had higher oral CDI than GW2 and GW1 for both, adults and children, while the dermal CDI were comparable for the studied water sources. The oral CDI for nitrates was the highest both for adults and children. The CDI for Cr, Zn, and Ni were higher than for Cd, Pb, and Cu, in all samples, indicating that both, adults and children, have significant exposure to these compounds.
The highest value of HQoral (Table 7) was obtained for nitrates, while the highest HQdermal for Cr. The HQoral for the NO3 was higher than the unity in the case of GW3 and GW4 for both, adults and children, with the maximum values recorded in autumn (GW3) and summer (GW4), suggesting a possible health risk. The HQdermal for NO3 was lower than the unity in each season in all water sources for adults and also for children. In GW1 and GW2 both the HQoral and HQdermal were below 1 (HQoral < 1.0 and HQdermal < 1.0) for every parameter, indicating no health risks [73].
The HQoral and HQdermal for Cr had the lowest value in GW1 in spring, and the highest, in GW4 in autumn for Cr and in winter for Cd, for both, adults and children. For the other metals, HQoral and HQdermal were much lower than the unity, indicating no health risk. Narsimha et al. (2018) [74] reported that a high content of NO3 could generate a significant noncancer risk caused by a lifetime consumption of groundwater sampled from Telangana State, south of India. Noncarcinogenic risk induced by Cr and Cd was reported for the groundwater from the Aosta Valey, Italy, where the value of HQ for the heavy metals was higher than unity [75].
The obtained HIoral values were higher than unity in the case of GW3 and GW4, in all seasons, and lower than unity for GW1 and GW2. The highest HIoral was in the case of GW3 in autumn, being followed by the HIoral for GW4 in summer. All HIdermal values were lower than unity for all the water sources in all seasons.
The THI was the highest in GW3, followed by GW4. In both springs, the THI above 1 indicated possible noncarcinogenic risk, both for adults and children. In GW1 and GW2 the THI was below 1, suggesting the suitability of water for human consumption without any possible health risks. Although the average THI was below 1, in the case of GW1, in winter, the THI value was higher than the unity for children, suggesting that children may exhibit possible health risks by consumption of this water. The THI had a low variation among seasons in the case of GW1 and GW2, and varied widely in the case of GW3 and GW4 (Figure 8).
The contribution of the metal and nitrate concentration to the noncarcinogenic health risk (Figure 9) decreased in the order NO3 > Cr> Cd > Ni > Pb > Zn > Cu > Fe. Thus, the most important measure, especially in cases of GW3 and GW4, is the removal of nitrates before human use, together with creating a safety area around the source reception basin that could limit the nitrates infiltration from the surface.
Similarly to the South Dobrogea springs, the groundwater from the North-Western part of Romania was characterized by a high concentration of NO3 and a low concentration of metals, but with a THI that indicates an intolerable noncarcinogenic risk (THI >1.0) [23].
Many studies are focused on technologies for drinking water purification to reduce contamination. In this regard, various high-efficiency filters [76], absorbing materials [77], and permeable reactive barriers [78] have been introduced to effectively remove different metals and ions from groundwater and supply safe potable water for human consumption.
In the case of karstic waters, the remediation techniques are more challenging than in the case of other types of water resources, due to the high total ionic saturation, various flow pathways of the aquifer, the porosity of the bedrock, and the interaction between contaminants and the aquifer material [79,80]. In the case of sources with a high flow used by high number of population groups in-situ methods of remediation are feasible, but in the case of water sources with low sources used by small rural communities, like those from Dobrogea region, low cost methods would be more feasible. The most used methods for the nitrates and toxic metals removal are reverse osmosis (RO), electrodialysis (ED), ion exchange (IE), and biological remediation (BR) [79,81]. A possible treatment option could be a RO system with polyamide and cellulose triacetate membrane [79,81]. The ED technology based on the transfer of ions through a membrane by application of direct electric current has similar performance and operation costs with that of RO [81,82]. Compared to the RO and ED techniques, IE was found to be five times more economical and having similar removal efficiency (≈90%) [81]. Even so, one of the biggest disadvantages of IE is the exhaustion of the resins, a process influenced by the nature of the exchange resin and the contaminants [83,84]. The simultaneous removal of nitrates, Cr, and Cd could be achieved by bioremediation technics, using various microalgae, like Chlorella vulgaris and Spirulina spp., with high contaminants removal efficiency (>75 %) [85].

4. Conclusions

Karstic springs are the primary source for drinking water in Dobrogea region, given the low annual values of average precipitation (350–450 mm). However, the extremely porous environment does not protect the water source from the anthropogenic impact, allowing the infiltration of pollutants. Under those conditions, the protective cover (e.g., soil, loess) is a major factor in preventing the dispersion of the pollutants in the karst network. Moreover, the drought and aridity in this region can play an important role on the chemistry of waters in karst aquifers. Additionally, the different geological settings from north to south can imply changes in permissiveness of different pollutants (e.g., the karstic area of northern Dobrogea is more reduced than southern Dobrogea, since a large karst area can be more vulnerable to contaminants). With the exception of nitrates in Sipote and Tufani springs, the physico-chemical parameters met the quality guidelines for drinking waters set by WHO and by the Directive 98/83/EC. Samples are characterized as Ca-HCO3 type in all seasons and Na-HCO3 type in winter and spring. Additionally, the water quality index showed excellent water quality of Praporgescu and Closca and good quality of Sipote and Tufani springs. However, the presence of low to medium metal pollution (mainly with Cr) was indicated by the metal pollution indices, especially in Sipote and Tufani springs. The total hazard index indicated the presence of noncarcinogenic risks following consumption of water from Sipote and Tufani springs, both for adults and children, possible risks for children in the case of Praporgescu spring and no risks in the case of Closca spring. The highest share in the risk was given by nitrates, followed by Cr and Cd. With some exception, the seasonal variation of chemical parameters was low, the total hazard index had the highest value in autumn and summer. The results indicate the influence of natural (dissolution of rocks and weathering) and anthropogenic factors (agricultural practices) on the quality of karstic springs. The present study would recommend a rigorous monitoring of water quality and the ban of using Sipote and Tufani springs as drinking water without proper treatment, especially for nitrates removal. Additionally, the presence of pathogenic bacteria should be assessed in these waters before consumption.

Author Contributions

Conceptualization, A.M., E.A.L. and O.T.M.; methodology, M.-A.H. and E.K.; sampling, I.C.M., M.K. and R.A.A.; analysis, M.-A.H., A.M. and E.K.; writing—original draft preparation, A.M., M.-A.H., I.C.M. E.A.L.; writing—review and editing, E.K., E.A.L. and O.T.M.; visualization, I.C.M., A.P., A.M. and M.-A.H., funding acquisition, O.T.M. All authors have read and agreed to the published version of the manuscript.


This research leading to these results has received funding from the EEA grants 2014–2021, under Project GROUNDWATERISK, contract no. 4/2019.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Adams, B.; Foster, S.S.D. Land-surface zoning for groundwater protection. Water Environ. J. 1992, 6, 312–319. [Google Scholar] [CrossRef]
  2. Doerfliger, N.; Jeannin, P.; Zwahlen, F. Water vulnerability assessment in karst environments: A new method of defining protection areas using a multi-attribute approach and GIS tools (EPIK method). Environ. Geol. 1999, 39, 165–176. [Google Scholar] [CrossRef] [Green Version]
  3. Kazakis, N.; Voudouris, K. Groundwater vulnerability and pollution risk assessment of porous aquifers to nitrate: Modifying the DRASTIC method using quantitative parameters. J. Hydrol. 2015, 525, 13–25. [Google Scholar] [CrossRef]
  4. Wu, J.; Sun, Z. Evaluation of shallow groundwater contamination and associated human health risk in an alluvial plain impacted by agricultural and industrial activities, Mid-west China. Expo. Health 2016, 8, 311–329. [Google Scholar] [CrossRef]
  5. Gamvroula, D.; Alexakis, D.; Stamatis, G. Diagnosis of groundwater quality and assessment of contamination sources in the Megara basin (Attica, Greece). Arab. J. Geosci. 2013, 6, 2367–2381. [Google Scholar] [CrossRef]
  6. Alexakis, D.; Tsakiris, G. Drought impacts on karstic spring annual water potential. Application on Almyros (Heraklion Crete) brackish spring. Desalin. Water Treat. 2010, 16, 1–9. [Google Scholar] [CrossRef] [Green Version]
  7. Feng, J.; Sun, H.; He, M.; Gao, Z.; Liu, J.; Wu, X.; An, Y. Quality Assessments of Shallow Groundwaters for Drinking and Irrigation Purposes: Insights from a Case Study (Jinta Basin, Heihe Drainage Area, Northwest China). Water 2020, 12, 2704. [Google Scholar] [CrossRef]
  8. Vespasiano, G.; Cianflone, G.; Cannata, C.B.; Apollaro, C.; Dominici, R.; De Rosa, R. Analysis of groundwater pollution in the Sant’Eufemia Plain (Calabria—South Italy). Ital. J. Eng. Geol. Environ. 2016, 2. [Google Scholar] [CrossRef]
  9. Vespasiano, G.; Cianflone, G.; Romanazzi, A.; Apollaro, C.; Dominici, R.; Polemio, M.; De Rosa, R. A multidisciplinary approach for sustainable management of a complex coastal plain: The case of Sibari Plain (Southern Italy). Mar. Pet. Geol. 2019, 109, 740–759. [Google Scholar] [CrossRef]
  10. Vespasiano, G.; Notaro, P.; Cianflone, G. Water-mortar interaction in a tunnel located in southern Calabria (Southern Italy). Environ. Eng. Geosci. 2018, 24, 305–315. [Google Scholar] [CrossRef]
  11. Wagh, V.M.; Panaskar, D.B.; Mukate, S.V.; Gaikwad, S.K.; Muley, A.A.; Varade, A.M. Health risk assessment of heavy metal contamination in groundwater of Kadava River Basin, Nashik, India. Model Earth Syst. Environ. 2018, 4, 959–980. [Google Scholar] [CrossRef]
  12. Cadar, O.; Miclean, M.; Cadar, S.; Tanaselia, C.; Senila, L.; Senila, M. Assessment of heavy metals in cow’s milk Rodnei mountains area, Romania. Environ. Eng. Manag. J. 2015, 14, 2523–2528. [Google Scholar] [CrossRef]
  13. Ayotte, J.D.; Szabo, Z.; Focazio, M.J.; Eberts, S.M. Effects of human-induced alteration of groundwater flow on concentrations of naturally-occurring trace elements at water-supply wells. Appl. Geochem. 2011, 26, 747–762. [Google Scholar] [CrossRef] [Green Version]
  14. Nyambura, C.; Hasim, N.O.; Chege, M.W.; Tokonami, S.; Omonya, F.W. Cancer and non-cancer health risks from carcinogenic heavy metal exposures in underground water from Kilimambogo, Kenya. Groundw. Sustain. Dev. 2020, 10, 100315. [Google Scholar] [CrossRef]
  15. Jaishankar, M.; Tseten, T.; Anbalagan, N.; Mathew, B.B.; Beeregowda, K.N. Toxicity, Mechanism and Health Effects of Some Heavy Metals. Interdiscip. Toxicol. 2014, 7, 60–72. [Google Scholar] [CrossRef] [Green Version]
  16. Muhammad, S.; Shah, M.T.; Khan, S. Health risk assessment of heavy metals and their source apportionment in drinking water of Kohistan region, northern Pakistan. Microchem. J. 2011, 98, 334–343. [Google Scholar] [CrossRef]
  17. Cao, X.; Lu, L.; Wang, C.; Zhang, M.; Yuan, J.; Zhang, A.; Song, S.; Baninla, Y.; Khan, K.; Wang, Y. Hydrogeochemistry and quality of surface water and groundwater in the drinking water source area of an urbanizing region. Ecotoxi. Environ. Safe 2019, 186, 109628. [Google Scholar] [CrossRef]
  18. Hossain, M.; Patra, P.K. Contamination zoning and health risk assessment of trace elements in groundwater through geostatistical modelling. Ecotoxicol. Environ. Saf. 2020, 189, 110038. [Google Scholar] [CrossRef]
  19. Chowdhury, H.; Mazumder, M.A.J.; Al-Attas, O.; Husain, T. Heavy metals in drinking water: Occurrences, implications, and future needs in developing countries. Sci. Total Environ. 2016, 569, 476–488. [Google Scholar] [CrossRef]
  20. Deepanjau, M.; Navindu, C. Nitrate pollution of groundwater and associated human health disorders. Indian J. Environ. Health 2000, 42, 28–39. [Google Scholar]
  21. Saha, N.; Rahman, M.S.; Ahmed, M.B.; Zhou, J.L.; Ngo, H.H.; Guo, W. Industrial metal pollution in water and probabilistic assessment of human health risk. J. Environ. Manag. 2017, 10, 100315. [Google Scholar] [CrossRef] [PubMed]
  22. Rezaei, A.; Hassani, H.; Hayati, M.; Jabbari, N.; Barzegar, R. Risk assessment and ranking of heavy metals concentration in Iran’s Rayen groundwater basin using linear assignment method. Stoch. Environ. Res. Risk Assess. 2017, 32, 1317–1336. [Google Scholar] [CrossRef]
  23. Dippong, T.; Mihali, C.; Hoaghia, M.A.; Cical, E.; Cosma, A. Chemical modeling of groundwater quality in the aquifer of Seini town—Someș Plain, Northwestern Romania. Ecotoxicol. Environ. Saf. 2019, 168, 88–101. [Google Scholar] [CrossRef] [PubMed]
  24. Islam, A.R.M.T.; Siddiqua, M.T.; Zahid, A.; Tasnim, S.S.; Rahman, M.M. Drinking appraisal of coastal groundwater in Bangladesh: An approach of multi-hazards towards water security and health safety. Chemosphere 2020, 255, 126933. [Google Scholar] [CrossRef]
  25. National Institute of Statistics 2018. Water Distribution and Wastewater Disposal. 2018. Available online: (accessed on 30 September 2020).
  26. Water Framework Directive, Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 Establishing a Framework for Community Action in the Field of Water Policy, Official Journal of the European Communities. Available online: (accessed on 30 September 2020).
  27. Groundwater Directive, Directive 2006/118/CE on the European Parliament and of the Council of 12 December 2006, on the Protection of Groundwater against Pollution and Deterioration, Official Journal of the European Communities. Available online: (accessed on 30 September 2020).
  28. Council Directive 98/83/EC of 3 November 1998 on the Quality of Water Intended for Human Consumption. Available online: (accessed on 30 September 2020).
  29. Onac, B.P.; Goran, C. Karst and Caves of Romania: A Brief Overview. In Caves and Karst Systems of Romania; Ponta, G.M.L., Onac, B.P., Eds.; Springer: Cham, Switzerland, 2019; pp. 21–35. [Google Scholar]
  30. Orășeanu, I. Hydrogeological regional classification of the Romanian karst. Theor. Appl. Karst. 1993, 6, 7–86. [Google Scholar]
  31. Țenu, A.; Davidescu, F.; Eichinger, L.; Voerkelius, S. Quality evaluation of groundwaters in Southern Dobrogea (Romania). Theor. Appl. Karst. 1997, 10, 63–77. [Google Scholar]
  32. Ghenea, C.; Bandrabur, T.; Ghenea, A. Atlas of Romania: The Underground and Mineral Waters Map; Sheet V-2; Romanian Academy, Institute of Geography: Bucharest, Romania, 1981. [Google Scholar]
  33. Moldovan, O.T.; Baricz, A.; Szekeres, E.; Kenesz, M.; Hoaghia, M.A.; Levei, E.A.; Mirea, I.C.; Năstase-Bucur, R.; Brad, T.; Chiciudean, I.; et al. Testing Different Membrane Filters for 16S rRNA Gene-Based Metabarcoding in Karstic Springs. Water 2020, 12, 3400. [Google Scholar] [CrossRef]
  34. Bandoc, G.; Prăvălie, R. Climatic water balance dynamics over the last five decades in Romania’s most arid region, Dobrogea. J. Geogr. Sci. 2015, 25, 1307–1327. [Google Scholar] [CrossRef]
  35. Klein Tank, A.M.G.; Wijngaard, J.B.; Können, G.P.; Böhm, R.; Demarée, G.; Gocheva, A.; Mileta, M.; Pashiardis, S.; Hejkrlik, L.; Kern-Hansen, C.; et al. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int. J. Climatol. 2002, 22, 1441–1453. [Google Scholar] [CrossRef]
  36. Beck, H.; Zimmermann, N.; McVicar, T. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci. Data 2018, 5, 180214. [Google Scholar] [CrossRef] [Green Version]
  37. Peel, M.C.; Finlayson, B.L.; McMahon, T.A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 2007, 11, 1633–1644. [Google Scholar] [CrossRef] [Green Version]
  38. © European Union, Copernicus Land Monitoring Service 2020, European Environment Agency (EEA). Available online: (accessed on 3 December 2020).
  39. Sener, S.; Sener, E.; Davraz, A. Evaluation of water quality using water quality index (WQI) method and GIS in Aksu River (SW-Turkey). Sci. Total Environ. 2017, 584, 131–144. [Google Scholar] [CrossRef] [PubMed]
  40. WHO. Guidelines for Drinking-Water Quality, 4th ed.; Incorporating first addendum; World Health Organization: Geneva, Switzerland, 2017; Available online: (accessed on 30 September 2020).
  41. Singh, D.D.; Thind, P.S.; Sharma, M.; Sahoo, S.; John, S. Environmentally sensitive elements in groundwater of an industrial town in India: Spatial distribution and human health risk. Water 2019, 11, 2350. [Google Scholar] [CrossRef] [Green Version]
  42. Kumar, M.; Nagdev, R.; Tripathi, R.; Singh, V.B.; Ranjan, P.; Soheb, M.; Ramanathan, A.L. Geospatial and multivariate analysis of trace metals in tubewell water using for drinking purpose in the upper Gangetic basin, India: Heavy metal pollution index. Groundw. Sustain. Dev. 2019, 8, 122–133. [Google Scholar] [CrossRef]
  43. Rezaei, A.; Hassani, H.; Hassani, S.; Jabbari, N.; Far, S.B.; Rezaei, M.S. Evaluation of groundwater quality and heavy metal pollution indices in Bazman basin, southeastern Iran. Groundw. Sustain. Dev. 2019, 9, 100245. [Google Scholar] [CrossRef]
  44. Bodrud-Doza, M.; Islam, S.M.D.; Rume, T.; Quraishi, S.B.; Rahman, M.S.; Bhuiyan, M.A.H. Groundwater quality and human health risk assessment for safe and sustainable water supply of Dhaka City dwellers in Bangladesh. Groundw. Sustain. Dev. 2020, 10, 100374. [Google Scholar] [CrossRef]
  45. Giri, S.; Singh, A.K. Human health risk assessment via drinking water pathway due to metal contamination in the groundwater of Subarnarekha River Basin, India. Environ. Monit. Assess. 2015, 187, 63. [Google Scholar] [CrossRef]
  46. Mukherjee, I.; Singh, U.M.; Singh, R.P.; Kumari, D.; Jha, P.K.; Mehta, P. Characterization of heavy metal pollution in an anthropogenically and geologically influenced semi-arid region of east India and assessment of ecological and human health risks. Sci. Total Environ. 2020, 705, 135801. [Google Scholar] [CrossRef]
  47. U.S. Environmental Protection Agency. USEPA Baseline Human Health Risk Assessment Vasquez Boulevard and I-70 Superfund Site Denver CO. 2001. Available online: (accessed on 30 September 2020).
  48. USEPA. Risk Assessment Guidance for Superfund Volume I: Human Health Evaluation Manual (Part E). 2004. Available online: (accessed on 30 September 2020).
  49. Mohammadi, A.A.; Zarei, A.; Majidi, S.; Ghaderpoury, A.; Hashempur, Y.; Saghi, M.H.; Alinejad, A.; Yousefi, M.; Hosseingholizadeh, N.; Ghaderpoori, M. Carcinogenic and non-carcinogenic health risk assessment of heavy metals in drinking water of Khorramabad, Iran. MethodsX 2019, 6, 1642–1651. [Google Scholar] [CrossRef]
  50. Wu, B.; Zhang, Y.; Zhang, X.; Cheng, S. Health Risk from Exposure of Organic Pollutants Through Drinking Water Consumption in Nanjing, China. Bull. Environ. Contam. Toxicol. 2010, 84, 46–50. [Google Scholar] [CrossRef]
  51. Ahmed, A.N.; Othman, F.B.; Afan, H.A.; Ibrahim, R.K.; Fai, C.M.; Hossain, M.S.; Ehteram, M.; Elshafie, A. Machine learning methods for better water quality prediction. J. Hydrol. 2019, 578, 124084. [Google Scholar] [CrossRef]
  52. Mahler, B.J.; Bourgeais, R. Dissolved oxygen fluctuations in karst spring flow and implications for endemic species: Barton Springs, Edwards aquifer, Texas, USA. J. Hydrol. 2013, 505, 291–298. [Google Scholar] [CrossRef]
  53. Rajwa-Kuligiewicz, A.; Bialik, R.; Rowiński, P. Dissolved oxygen and water temperature dynamics in lowland rivers over various timescales. J. Hydrol. Hydromech. 2015, 63, 353–363. [Google Scholar] [CrossRef] [Green Version]
  54. Hassan, S.H.A.; Gurung, A.; Kang, W.; Shin, B.; Rahimnejad, M.; Jeon, B.; Kin, J.R.; Oh, S. Real-time monitoring of water quality of stream water using sulfur-oxidizing bacteria as bio-indicator. Chemosphere 2019, 223, 58–63. [Google Scholar] [CrossRef] [PubMed]
  55. Jha, M.K.; Shekhar, A.; Jenifer, M.A. Assessing groundwater quality for drinking water supply using hybrid fuzzy-GIS-based water quality index. Water Res. 2020, 179, 115867. [Google Scholar] [CrossRef]
  56. Miyittah, M.K.; Tulashie, S.K.; Tsyawo, F.W.; Sarfo, J.K.; Darko, A.A. Assessment of surface water quality status of the Aby Lagoon System in the Western Region of Ghana. Heliyon 2020, 6, e04466. [Google Scholar] [CrossRef]
  57. Egbi, C.D.; Anornu, G.K.; Ganyaglo, S.Y.; Appiah-Adjei, E.K.; Li, S.L.; Dampare, S.B. Nitrate contamination of groundwater in the Lower Volta River Basin of Ghana: Sources and related human health risks. Ecotoxic. Environ. Safe 2020, 191, 110227. [Google Scholar] [CrossRef]
  58. Rout, C.; Sharma, A. Assessment of drinking water quality: A case study of Ambala cantonment area, Haryana, India. Int. J. Environ. Sci. 2011, 2, 2. [Google Scholar]
  59. Vetrimurugan, E.; Elango, L.; Rajmohan, N. Sources of contaminants and groundwater quality in the coastal part of a river delta. Int. J. Environ. Sci. Technol. 2013, 10, 473–486. [Google Scholar] [CrossRef] [Green Version]
  60. Moisa, C.; Cadar, O.; Barabas, R.; Vicas, L.G.; Hoaghia, M.A.; Levei, E.A.; Jurca, C.; Berce, C. Influence of magnesium compounds on sodium, potassium and calcium levels in different mice organs. Farmacia 2019, 67, 2. [Google Scholar] [CrossRef]
  61. Blaine, J.; Chonchol, M.; Levi, M. Renal control of calcium, phosphate, and magnesium homeostasis. Clin. J. Am. Soc. Nephrol. 2015, 10, 1257–1272. [Google Scholar] [CrossRef] [PubMed]
  62. Kozisek, F. Regulations for calcium, magnesium or hardness in drinking water in the European Union member states. Regul. Toxicol. Pharmacol. 2020, 112, 104589. [Google Scholar] [CrossRef] [PubMed]
  63. Sengupta, P. Potential health impacts of hard water. Int. J. Prev. Med. 2013, 4, 866–875. [Google Scholar] [PubMed]
  64. Manoj, K.; Ghosh, S.; Padhy, P.K. Characterization and classification of hydrochemistry using graphical and hydrostatical techniques. Res. J. Chem. Sci. 2013, 3, 32–42. [Google Scholar]
  65. Hoaghia, M.A.; Cadar, O.; Hognogi, G.G.; Levei, E.; Moisa, C.; Roman, C. Quality and human health risk assessment if metals and nitrogen compounds in drinking water from an urban area near a former non-ferrous ore smelter. Anal. Lett. 2019, 52, 1268–1281. [Google Scholar] [CrossRef]
  66. Verma, P.; Singh, P.K.; Sinha, R.R.; Tiwari, A.K. Assessment of groundwater quality status by using water quality index (WQI) and geographic information system (GIS) approaches: A case study of the Bokaro district, India. Appl. Water Sci. 2020, 10, 27. [Google Scholar] [CrossRef] [Green Version]
  67. Adamou, H.; Ibrahim, B.; Salack, S.; Adamou, R.; Sanfo, S.; Liersch, S. Physico-chemical and bacteriological quality of groundwater in a rural area of Western Niger: A case study of Bonkoukou. J. Water Health 2020, 1, 18. [Google Scholar] [CrossRef]
  68. Baba, M.E.; Kayastha, P.; Huysmans, M.; Smedt, F.D. Evaluation of the groundwater quality using the water quality index and geostatistical analysis in the Dier al-Balah Governorate, Gaza Strip, Palestine. Water 2020, 12, 262. [Google Scholar] [CrossRef] [Green Version]
  69. Koc, C. Water quality index for measuring drinking water quality of Bodrum Peninsula—Turkey. Ohu J. Eng. Sci. 2018, 7, 694–702. [Google Scholar]
  70. Alexakis, D.E. Meta-evaluation of water quality indices. Application into groundwater resources. Water 2020, 12, 1890. [Google Scholar] [CrossRef]
  71. Zakhem, B.A.; Hafez, R. Heavy metal pollution index for groundwater quality assessment in Damascus Oasis, Syria. Environ. Earth Sci. 2015, 73, 6591–6600. [Google Scholar] [CrossRef]
  72. Boateng, T.K.; Opoku, F.; Akoto, O. Heavy metal contamination assessment of groundwater quality: A case study of Oti landfill site, Kumasi. Appl. Water Sci. 2019, 9, 33. [Google Scholar] [CrossRef] [Green Version]
  73. Chen, G.; Wang, X.; Wang, R.; Liu, G. Health risk assessment of potentially harmful elements in subsidence water bodies using a Monte Carlo approach: An example from the Huainan coal mining area, China. Ecotoxicol. Environ. Saf. 2019, 171, 737–745. [Google Scholar] [CrossRef] [PubMed]
  74. Narsimha, A.; Veskatayogi, S.; Geeta, S. Hydrogeochemical data on groundwater quality with special emphasis on fluoride enrichment in Munneru river basin (MRB), Telangana State, South India. Data Brief 2018, 17, 339–346. [Google Scholar] [CrossRef]
  75. Tiwari, A.K.; De Maio, M. Assessment of risk to human health due to intake of chromium in the groundwater of the Aosta Valley region, Italy. Hum. Ecol. Risk Assess. 2017, 23, 1153–1163. [Google Scholar] [CrossRef]
  76. Marsidi, N.; Abu Hasan, H.; Abdullah, S.R.S. A review of biological aerated filters for iron and manganese ions removal in water treatment. J. Water Process. Eng. 2018, 23, 1–12. [Google Scholar] [CrossRef]
  77. Neag, E.; Moldovan, A.; Băbălău-Fuss, V.; Török, A.; Cadar, O.; Roman, C. Kinetic, Equilibrium and Phytotoxicity Studies for Dyes Removal by Low Cost Natural Activated Plant-Based Carbon. Acta Chim. Slov. 2019, 66, 850–858. [Google Scholar] [CrossRef]
  78. Faisal, A.A.H.; Sulaymon, A.H.; Khaliefa, Q.M. A review of permeable reactive barrier as passive sustainable technology for groundwater remediation. Int. J. Environ. Sci. Technol. 2018, 15, 1123–1138. [Google Scholar] [CrossRef]
  79. Kalhor, K.; Ghasemizadeh, R.; Rajic, L.; Alshawabkeh, K. Assessment of groundwater quality and remediation in karst aquifers: A review. Groundw. Sustain. Dev. 2019, 8, 104–121. [Google Scholar] [CrossRef]
  80. Moldovan, O.T.; Skoglund, R.O.; Banciu, H.L.; Dinu Cucos, A.; Levei, E.A.; Persoiu, A.; Lauritzan, S.E. Monitoring and risk assessment for groundwater sources in rural communities of Romania (GROUNDWATERISK). Res. Ideas Outcomes 2019, 5, e48898. [Google Scholar] [CrossRef] [Green Version]
  81. Sharma, A.S.K.; Sobti, R.C. Nitrate Removal from Ground Water: A Review. J. Chem. 2012, 9, 154616. [Google Scholar]
  82. Mohsenipour, M.; Shahid, S.; Ebrahimi, K. Removal Techniques of Nitrate from Water. Asian J. Chem. 2014, 26, 7881–7886. [Google Scholar] [CrossRef]
  83. Duan, S.; Tong, T.; Zheng, S.; Zhang, X.; Li, S. Achieving low-cost, highly selective nitrate removal with standard anion exchange resin by tuning recycled brine composition. Water Res. 2020, 173, 115571. [Google Scholar] [CrossRef] [PubMed]
  84. Labarca, F.; Borquaez, R. Comparative study of nanofiltration and ion exchange for nitrate reduction in the presence of chloride and iron in groundwater. Sci. Total Environ. 2020, 723, 137809. [Google Scholar] [CrossRef] [PubMed]
  85. Kumar, K.S.; Dahms, H.U.; Won, E.J.; Lee, J.S.; Shin, K.H. Microalgae—A promising tool for heavy metal remediation. Ecotoxic. Environ. Safe 2015, 113, 329–352. [Google Scholar] [CrossRef] [PubMed]
Figure 1. (A) Dobrogea map with the location of the four karstic springs. (B) Representation of the village’s administrative boundaries and the sampling points GW1—Praporgescu and GW2—Closca; (C) Representation of the village’s administrative boundaries and the sampling points GW 3—Sipote and GW4—Tufani.
Figure 1. (A) Dobrogea map with the location of the four karstic springs. (B) Representation of the village’s administrative boundaries and the sampling points GW1—Praporgescu and GW2—Closca; (C) Representation of the village’s administrative boundaries and the sampling points GW 3—Sipote and GW4—Tufani.
Water 12 03510 g001
Figure 2. Hydrogeological map of Dobrogea (modified after Ghenea) [32].
Figure 2. Hydrogeological map of Dobrogea (modified after Ghenea) [32].
Water 12 03510 g002
Figure 3. Monthly average precipitation in Northern and Southern Dobrogea (Tulcea and Constanta meteorological stations) between September 2019 and August 2020.
Figure 3. Monthly average precipitation in Northern and Southern Dobrogea (Tulcea and Constanta meteorological stations) between September 2019 and August 2020.
Water 12 03510 g003
Figure 4. Daily mean temperature from Northern and Southern Dobrogea (Tulcea and Constanta meteorological stations) between September 2019 and August 2020.
Figure 4. Daily mean temperature from Northern and Southern Dobrogea (Tulcea and Constanta meteorological stations) between September 2019 and August 2020.
Water 12 03510 g004
Figure 5. Land use categories in the vicinity of the karst springs from Northern and Southern Dobrogea.
Figure 5. Land use categories in the vicinity of the karst springs from Northern and Southern Dobrogea.
Water 12 03510 g005
Figure 6. Piper diagram of the studied karstic spring waters.
Figure 6. Piper diagram of the studied karstic spring waters.
Water 12 03510 g006
Figure 7. Correlation diagram of HCO3 and Ca+Mg+Na.
Figure 7. Correlation diagram of HCO3 and Ca+Mg+Na.
Water 12 03510 g007
Figure 8. Seasonal variations of THI (SUM = summer, AUT = autumn, WIN = winter, SPR = spring).
Figure 8. Seasonal variations of THI (SUM = summer, AUT = autumn, WIN = winter, SPR = spring).
Water 12 03510 g008
Figure 9. Contribution of Fe, Cu, Ni, Cr, Cd, and nitrates to the mean values of THI.
Figure 9. Contribution of Fe, Cu, Ni, Cr, Cd, and nitrates to the mean values of THI.
Water 12 03510 g009
Table 1. Geology and geographic particularities of the tested springs area [29].
Table 1. Geology and geographic particularities of the tested springs area [29].
AreaNorthern DobrogeaSouthern Dobrogea
(m a.s.l.)
GeologySandstone, conglomerate limestonesSandstone, limestoneLimestone, marl limestone, clays, dolomitesLumashelic limestone, Nubeculoria limestone, clays, diatomites
SoilCarbonate chernozemsChernozems, alluvial soilsRendzinas, rocksAlluvial soils
Surface useArable land/forest/soft loamArable land/forest/soft loamArable landArable land/soft loam
No. of inhabitants181101566362
Water flow
Table 2. Average temperatures and precipitation in Northern (Tulcea) and Southern Dobrogea (Constanta).
Table 2. Average temperatures and precipitation in Northern (Tulcea) and Southern Dobrogea (Constanta).
ParametersMeteorological StationAutumnWinterSpringSummer
Temperatures (°C)Northern Dobrogea14.24.3112.323.3
Southern Dobrogea16.35.5211.923.5
Precipitations (mm)Northern Dobrogea11449.234.791.7
Southern Dobrogea47.272.750.159.2
Table 3. The values of coefficients (Si, Ii, guideline value, and Wi) used in HPI computation.
Table 3. The values of coefficients (Si, Ii, guideline value, and Wi) used in HPI computation.
Heavy MetalSi * (µg L−1)Ii * (µg L−1)Wi
* Si—standard value or guideline values and Ii—ideal values according to WHO and Kumar et al. [40,42].
Table 4. Physico-chemical parameters of the four studied karstic springs (SUM = summer, AUT = autumn, WIN = winter, SPR = spring, DWSi = drinking water standards according to the Directive 98/83/EC [28], and Si = guideline values according to WHO [40]) from Dobrogea region, Romania.
Table 4. Physico-chemical parameters of the four studied karstic springs (SUM = summer, AUT = autumn, WIN = winter, SPR = spring, DWSi = drinking water standards according to the Directive 98/83/EC [28], and Si = guideline values according to WHO [40]) from Dobrogea region, Romania.
µS cm−1mg L−1mg L−1mg L−1NTUmg L−1mgL−1mg L−1mg L−1mgL−1mg L−1mg L−1mg L−1mg L−1mg L−1µg L−1µg L−1µg L−1µg L−1µg L−1µg L−1µg L−1
Table 5. The WQI, HPI, and HEI results calculated for all four seasons.
Table 5. The WQI, HPI, and HEI results calculated for all four seasons.
Table 6. Chronic daily intake (CDI, µg kg−1days−1) for adults and children.
Table 6. Chronic daily intake (CDI, µg kg−1days−1) for adults and children.
Fe1.103.00 × 10−33.94 × 10−11.07 × 10−36.30 × 10−11.72 × 10−33.94 × 10−11.07 × 10−3
Cu4.09 × 10−21.86 × 10−45.07 × 10−22.30 × 10−43.80 × 10−21.73 × 10−44.56 × 10−22.07 × 10−4
Pb6.38 × 10−32.90 × 10−66.61 × 10−33.00 × 10−66.53 × 10−32.97 × 10−67.01 × 10−33.18 × 10−6
Zn1.22 × 10−15.53 × 10−56.63 × 10−22.89 × 10−53.86 × 10−21.75 × 10−53.52 × 10−21.60 × 10−5
Ni1.36 × 10−16.16 × 10−41.64 × 10−17.45 × 10−48.23 × 10−23.74 × 10−44.87 × 10−22.21 × 10−4
Cr1.50 × 10−16.81 × 10−41.05 × 10−14.77 × 10−48.02 × 10−13.64 × 10−31.285.82 × 10−3
Cd1.38 × 10−26.26 × 10−51.41 × 10−26.40 × 10−51.31 × 10−25.94 × 10−51.30 × 10−25.90 × 10−5
NO38.51 × 1023.868.31 × 1023.786.83 × 1033.10 × 1012.46 × 1031.12 × 101
Fe1.405.32 × 10−35.00 × 10−11.90 × 10−38.00 × 10−13.04 × 1035.00 × 10−11.90 × 10−3
Cu5.20 × 10−23.29 × 10−46.44 × 10−24.08 × 10−44.83 × 10−23.06 × 10−45.79 × 10−22.07 × 10−4
Pb8.10 × 10−35.13 × 10−63.00 × 10−65.32 × 10−69.00 × 10−38.30 × 10−38.90 × 10−35.63 × 10−6
Zn1.55 × 10−19.79 × 10−58.08 × 10−25.11 × 10−54.90 × 10−23.10 × 10−54.47 × 10−22.83 × 10−5
Ni1.72 × 10−11.09 × 10−32.08 × 10−11.32 × 10−31.05 × 10−16.62 × 10−46.18 × 10−23.91 × 10−2
Cr1.90 × 10−11.20 × 10−31.33 × 10−18.44 × 10−41.026.45 × 10−31.631.03 × 10−2
Cd1.72 × 10−21.11 × 10−41.79 × 10−21.13 × 10−41.66 × 10−21.05 × 10−41.65 × 10−21.04 × 10−4
NO31.08 × 1036.841.06 × 1036.688.67 × 1035.49 × 1013.12 × 1031.97 × 101
Table 7. Average hazard quotient (HQ), hazard index (HI), and total hazard index (THI) for adults and children.
Table 7. Average hazard quotient (HQ), hazard index (HI), and total hazard index (THI) for adults and children.
HQFe3.67 × 10−36.67 × 10−51.31 × 10−32.38 × 10−52.10 × 10−33.81 × 10−51.31 × 10−32.38 × 10−5
Cu1.02 × 10−31.55 × 10−51.27 × 10−31.92 × 10−59.51 × 10−41.44 × 10−51.14 × 10−31.73 × 10−5
Pb2.13 × 10−54.83 × 10−82.20 × 10−55.01 × 10−82.18 × 10−54.95 × 10−82.34 × 10−55.30 × 10−8
Zn4.06 × 10−49.22 × 10−72.12 × 10−44.81 × 10−71.29 × 10−42.92 × 10−71.17 × 10−42.66 × 10−7
Ni6.79 × 10−31.14 × 10−48.20 × 10−31.38 × 10−44.12 × 10−36.93 × 10−52.43 × 10−34.09 × 10−5
Cr5.00 × 10−24.54 × 10−23.50 × 10−23.21 × 10−22.67 × 10−12.43 × 10−14.27 × 10−13.88 × 10−1
Cd2.76 × 10−21.25 × 10−22.82 × 10−21.28 × 10−22.61 × 10−21.19 × 10−22.60 × 10−21.18 × 10−2
NO35.32 × 10−13.51 × 10−35.20 × 10−13.43 × 10−34.272.82 × 10−21.631.08 × 10−2
HI 6.21 × 10−16.16 × 10−25.94 × 10−14.82 × 10−24.572.83 × 10−12.096.16 × 10−2
THI 0.6830.6424.852.50
HQFe4.67 × 10−31.18 × 10−41.67 × 10−34.22 × 10−52.67 × 10−36.75 × 10−51.67 × 10−34.22 × 10−5
Cu1.30 × 10−32.74 × 10−51.61 × 10−33.40 × 10−51.21 × 10−32.55 × 10−51.45 × 10−33.05 × 10−5
Pb2.70 × 10−58.54 × 10−82.80 × 10−58.86 × 10−82.77 × 10−58.75 × 10−82.97 × 10−59.39 × 10−8
Zn5.16 × 10−41.63 × 10−62.69 × 10−48.52 × 10−71.63 × 10−45.17 × 10−71.49 × 10−44.71 × 10−7
Ni8.62 × 10−32.02 × 10−41.04 × 10−22.44 × 10−45.23 × 10−31.23 × 10−43.09 × 10−37.24 × 10−5
Cr6.35 × 10−28.03 × 10−24.44 × 10−25.62 × 10−23.40 × 10−14.30 × 10−15.42 × 10−16.86 × 10−1
Cd3.50 × 10−22.22 × 10−23.58 × 10−22.27 × 10−23.32 × 10−22.10 × 10−23.30 × 10−22.09 × 10−2
NO36.76 × 10−16.22 × 10−36.60 × 10−16.08 × 10−35.424.99 × 10−22.071.91 × 10−2
HI 7.89 × 10−11.09 × 10−17.54 × 10−18.53 × 10−25.85.01 × 10−12.667.27 × 10−1
THI 0.8980.8406.303.38
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Moldovan, A.; Hoaghia, M.-A.; Kovacs, E.; Mirea, I.C.; Kenesz, M.; Arghir, R.A.; Petculescu, A.; Levei, E.A.; Moldovan, O.T. Quality and Health Risk Assessment Associated with Water Consumption—A Case Study on Karstic Springs. Water 2020, 12, 3510.

AMA Style

Moldovan A, Hoaghia M-A, Kovacs E, Mirea IC, Kenesz M, Arghir RA, Petculescu A, Levei EA, Moldovan OT. Quality and Health Risk Assessment Associated with Water Consumption—A Case Study on Karstic Springs. Water. 2020; 12(12):3510.

Chicago/Turabian Style

Moldovan, Ana, Maria-Alexandra Hoaghia, Eniko Kovacs, Ionuț Cornel Mirea, Marius Kenesz, Răzvan Adrian Arghir, Alexandru Petculescu, Erika Andrea Levei, and Oana Teodora Moldovan. 2020. "Quality and Health Risk Assessment Associated with Water Consumption—A Case Study on Karstic Springs" Water 12, no. 12: 3510.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop