Monitoring and Evaluation of Water Quality from Chirita Lake, Romania

Abstract

Water management is a significant challenge, stimulating synergies between scientists and practitioners to create new tools and approaches to streamline decision making in this field. The assessment and monitoring of freshwater quality in surface water bodies are crucial for sustainable and safe water management. The main objectives of this study were to analyze the characteristics and properties of Chirita lake, assess seasonal variations in water quality, determine compliance with national environmental legislation, and perform a comparison with monitoring systems in other European lakes. The study used data that determined water quality indicators for a five-year period, from 2020 to 2024, considering temperature, turbidity, pH, conductivity, alkalinity, hardness, organic matter, nitrates, nitrites, ammonium, and chlorides. The statistical analysis technique based on the Pearson correlation coefficient was used to evaluate the seasonal correlations of water quality parameters in Chirita lake and to extract the essential parameters for assessing seasonal variations in river water quality. The results obtained indicated that the indicators considered important for water quality variation in one season may not be important in another season, except for organic matter and conductivity, which showed a significant contribution to water quality variation throughout the four seasons. This study demonstrated that lake water is classified as first class, according to national regulations. These results provide valuable support for local authorities to develop effective strategies for water quality management and the prevention of eutrophication processes in reservoirs.

1. Introduction

Water constitutes 70% of the earth’s surface and represents an essential resource for socio-economic development, for the support of life, and for the protection of terrestrial and aquatic ecosystems [1]. It is crucial to promote sustainable management of water resources and their efficient and equitable allocation, thus making a direct contribution to economic and social well-being [2]. Globally, surface water quality is facing widespread problems caused by anthropogenic activities, such as industrial and agricultural pollution, natural processes, climate change, and global warming. This has led to serious environmental problems that have prompted the implementation of regular monitoring and assessment programs for water resources and ecosystem sustainability [3,4]. This problem takes on serious dimensions in developing countries, where the transition from centralized to market economies and social changes contribute significantly to the decline in water quality [5].

A number of natural and anthropogenic factors shape the physical–chemical characteristics of lake water quality. Natural factors include topography, precipitation, weathering, geology, hydrographic and atmospheric inputs, and climate variability. Anthropogenic factors refer to disturbances in catchments caused by domestic pollution, municipal and industrial wastewater discharges, and agricultural activities [6].

Water quality monitoring is an effective tool for the assessment and prediction of potential natural hazards and pollution while providing valuable information for planning the sustainable use of water resources [7,8,9]. However, in recent years, water quality in rivers and lakes has degraded, largely due to human activities [1].

The presence of high concentrations of pollutants, including emerging organic compounds, in lake ecosystems compromises water quality and influences its use for drinking, agricultural, recreational, and industrial purposes. Excessive nutrient loading also leads to massive algal blooms, resulting in eutrophication and negative ecological effects on the aquatic environment [6,10,11]. Anthropogenic activities affect water color through the discharge of domestic wastewater and/or industrial effluents into water bodies [12].

Regular monitoring of surface water quality is an important step toward the promotion and achievement of sustainable development goals, such as Goal 6: Clean water and sanitation and Goal 14: Life below water [1,13]. Monitoring water quality in lakes, rivers, and streams is an essential step in the development and implementation of adequate water management, especially when water resources are used for water supply [14,15].

The assessment of water quality indicators has become a constant concern, especially in recent years, largely motivated by freshwater scarcity existent now and anticipated in future [4,16]. At the same time, quality assessment is becoming a key component in integrated water resource management planning.

In recent years, there has been a growing concern about identifying the sources of pollution, especially considering that these produce emerging and priority pollutants. Ongoing threats to water quality have led to complex regulations that are designed to improve resource quality protection. However, the most recent studies show that approximately 50% of freshwater bodies in Europe are experiencing significant pollution; therefore, comprehensive freshwater monitoring and management programs are needed [17,18].

Concordance has been found between quality assessment and frequency of water body monitoring, as reflected in the development of management strategies to control surface water pollution resulting from economic development and anthropogenic processes [15,19].

With its accession to the European Union (EU), Romania has elaborated legislative regulations in this field, and the transposition of the Water Framework Directive (WFD) into Romanian legislation was realized via Order 161/2006 Normative on the classification of surface water quality in order to establish the ecological status of water bodies [20]. The WFD [21] established that by 2015, surface and groundwater bodies must achieve a good status; if there are specific situations requiring derogations, the achievement of this objective may be extended until 2021 or 2027 at the latest [22]. It is important to note that achieving the main objectives of the WFD has been accompanied by numerous challenges. Although progress has been made in ecological flows, the central objective remains far from being achieved. Most countries have not been able to meet the requirements of Article 5 of the WFD regarding economic aspects; hence, there is a pressing need to clarify the methods and approaches used in economic analysis. Climate change is an additional challenge to achieving WFD objectives: the main climate-change-related challenges for water bodies are water demand, decreasing availability of water resources, and increasing incidence of floods. The WFD does not consider climate change risks when meeting targets. Although member states have tried to involve the public and stakeholders through various methods, the impact of these initiatives has been limited, showing that member states have made considerable efforts to comply with the WFD requirements. However, the social, cultural, and economic differences between states have been a key factor in interpreting the main objectives to be achieved [21,22].

This directive implies a unified planning of all water-related activities within a river basin, considering the interconnections between ecological, social, and economic components [15,23].

The WFD and the directives complementing it—Directive 2008/105/EC on environmental quality standards [24], amended by Directive 2013/39/EU adding the list of priority substances, and Directive 2001/39/EU, amended by Directive 2014/80/EU—are the main pieces of legislation for the protection and sustainable use of freshwater resources applied at the European and national levels [18].

Romania continues to face serious problems in terms of management, monitoring, and assessment of surface water quality, largely due to its economy, which is based on an exaggerated consumption of resources, non-compliance with legislative regulations in production, and administrative problems [15].

In Romania, within the framework of environmental management services, the annual environmental monitoring of surface water, groundwater, and marine waters is conducted by 11 Water Basin Administrations and the National Administration Apele Romane [25]. In addition, water quality monitoring and assessment are performed by production units in accordance with the legislation in force [25]. Water resources in Romania are distributed as follows: surface waters comprising rivers and lakes (~90%), and groundwater (~10%) [26]. According to Romania’s National Strategy for Sustainable Development, 2013–2020–2030 horizons, only 57.5% of the total length of qualitatively monitored rivers is suitable for centralized drinking water supply. Out of the total potential resources, only 45.5% are technically usable, primarily because of the resources’ contamination.

For global resource conservation, regular monitoring and assessment of water quality in lakes, rivers, and streams are essential. Water quality monitoring at the European level plays a critical role in assessing compliance with environmental regulations, tracking transboundary pollution, and guiding sustainable water management policies across member states. Although water quality monitoring has received significant attention in the scientific literature since the 1940s, the development of a unified, comprehensive, and practical strategy covering all phases of monitoring processes remains an unsolved challenge [27,28,29]. EU member states apply different approaches to monitoring lakes depending on national priorities, the number and type of lakes, and compliance with EU directives, such as the WFD. While some countries run complex and specific national lake monitoring programs, others include lakes as part of their broader inland water or river monitoring efforts.

Under the WFD, European water quality monitoring has undergone fundamental transformation. Moving away from an exclusive focus on monitoring saprobic conditions, nutrients, and pollutants, a comprehensive and integrated assessment concept is being promoted. In this framework, biological quality elements (BQEs) are considered the priority indicators for assessing the impacts on aquatic ecosystems. In addition, the assessment includes physical–chemical, hydrological, and hydro morphological indicators, relevant both for supporting BQEs and within the context of river basin management plans (RBMPs), reflecting a substantially more holistic approach than previous practices [27,30].

In the context of accelerated urbanization and socio-economic development in Romania, water resources face critical challenges. Regional disparities in freshwater distribution amplify vulnerabilities, especially during periods of drought, while human activities intensify the pressure on water quality. Given the increase in anthropogenic activity and water pollution, there is a growing need for integrated and regular water monitoring to achieve systematic water management. However, traditional in situ water sampling and measurement are time-consuming, laborious, and costly.

The scientific literature reflects a significant number of surface water quality monitoring studies conducted at the national level [7,15,31,32,33,34,35,36].

Although various studies have examined the physical and chemical properties of surface waters, there is a limited understanding of the long-term seasonal variability in reservoirs in Eastern Europe, particularly in Romania. In addition, few studies have integrated statistical correlation analyses with practical water treatment monitoring data. This study addresses this gap by analyzing water quality data from Chirita lake over a five-year period to identify seasonal trends, assess compliance with environmental standards, and support evidence-based decision making in lake management.

In Iasi County, drinking water originates from groundwater and surface water, including rivers, lakes, and reservoirs. The importance of the present study results from the fact that Chirita lake is the main source of drinking water supply for about 70% of the resident population of Iasi County, the quality of surface water thus having a direct impact on human well-being and health [35].

To develop this study, a reliable database was created, which is essential for the description and subsequent monitoring of water quality in the lake.

The main objectives of this study were to (1) monitor and record the specific characteristics and properties of Chirita lake, (2) analyze seasonal and spatial trends using statistical methods, (3) determine compliance in accordance with national legislation for the lake environment, and (4) perform a comparison with monitoring systems in other European lakes.

The study was carried out using a set of classical statistical–mathematical methods, and Pearson correlation coefficient analysis was used as a statistical procedure to evaluate the data obtained in the study. The classification of water from Chirita lake to assess its quality as a source of drinking water was carried out based on monthly and annual values of the determined indicators. Eleven of the eighteen water quality indicators were selected based on continuous data availability. The selected indicators were temperature, pH, turbidity, chemical oxygen uptake, electrical conductivity, nitrates, nitrites, ammonium, chlorides, total alkalinity, and total hardness. The assessment of water quality indicators is essential for obtaining a comprehensive picture that contributes to effective decision making in water resource management. However, this analysis is often hampered by the complexity of the system, the large number of variables involved, and the diversity of the units of measurement, such as the concentrations and levels of importance of each indicator.

The novelty of this study concerns the realization of a systematic analysis of the variations in the seasonal physical–chemical characteristics of the determined surface water quality variables of Chirita lake. Furthermore, monthly and annual values were used to classify the water of Chirita lake according to the national legislation.

The results of this study will allow a better understanding of lake ecosystem dynamics and provide scientific support for the sustainable management of aquatic resources.

2. Materials and Methods

2.1. Study Area

The Chirita lake is located in the north-eastern part of Iasi (47°09′11″ N 27°39′41″ E). It was established in 1964, with an area of 78 ha, as a pre-sedimentation basin of the Prut River used to create a reserve to cover the water needs in case of interruptions in operation (

Table 1. Hydrological characteristics of Chirita lake.

Location	47°09′11″ N 27°39′41″ E
Area	78 ha
Year of commissioning	10 August 1964
Dam length	~300 m
Dam height	~15 m
Total volume	5 × 106 m3
Maximum depth	15 m
Medium volume recorded	3,877,500 m3
Minimum volume recorded	101,200 m3

). The raw water abstraction-pumping complex is located on the right bank of the Prut River, upstream of Tutora. It is a complex consisting of intake no. 1, flow 2000 L/s, caisson no. 2, flow 3000 L/s, the administrative building and the heating plant, the walled transformer station (6/0.4 KV), and 10 mega volt ampere (Mva) 20/6 KV transformers. Water was abstracted from the Prut River through water intakes, which were equipped with rare gratings (with slits arranged at 40 mm). These were intended to retain large solid bodies [37].

The collected water passes through sand separators, in which sand and other solids are removed, and then, water is sent through the rare grates. Finally, the collected water reaches the suction chamber, which is equipped at the inlet with dense gratings (15 mm mesh), from where it is pumped to the Chirita treatment plant and Chirita lake. The Chirita lake is located about 1 km from the Chirita drinking water treatment plant. Water is collected from the lake using a catchment tower. It is equipped with rare grates and a lower vane to purge the accumulated deposits at the base of the intake tower [37].

The pumping and transportation of raw water to Chirita lake is carried out through caisson no.1 with a flow rate of 3600 m³/h and caisson no.2 with a flow rate of 3600 m³/h. Raw water is transported via gravity directly to the treatment plant to be brought to drinking standard quality.

Chirita lake is an important part of the public water system, providing 70% of distributed drinking water (Figure 1).

2.2. Sampling

Particular attention was paid to the collection procedures. The analyzed samples were collected in accordance with the legislation and ISO quality standards in force: ISO 5667-4:1987 [38], ISO 5667-3:2018 [39], and Council Directive (75/440/EEC) [40]. Water samples were collected from inside the Chirita drinking water treatment plant, Iasi, from the raw water adduction pipe every 4 h, including the nights. A careful examination of the available data indicates that the information collection was carried out at similar daily time. The study was based on data selection over a five-year period, with average values calculated for the four seasons (spring, summer, autumn, and winter) for all the investigated indicators. For the analysis of seasonal variations in the indicators, the data were organized into four distinct sets: winter: 1 December–28 February (2020–2024), spring: 1 March–31 May (2020–2024), summer: 1 June–31 August (2020–2024), and autumn: 1 September–31 November (2020–2024). This allowed for differentiated analysis of each seasonal component.

Water samples were collected in 1000 mL bottles, which were previously washed with nitric acid (HNO₃) and rinsed with ultrapure water. The labeled and collected polyethylene bottles were rinsed with water to be sampled. The samples were not preserved, as they were analyzed in the laboratory immediately after collection.

2.3. Reagents Used in the Determinations

Scientific-grade analytical reagents (ARs) Merck (Darmstadt, Germany) and Sigma-Aldrich (Merck, Darmstadt, Germany) were used in this study. The reagents used in the water quality indicator determinations met the characteristics required by internal standards with a purity of 99.9%, and no further modifications were necessary. All reagents were prepared using Milli-Q water prior to determination. Every phase of quality control, including washing and cleaning, was followed. The determination of the analyzed indicators was carried out in the technological flow laboratory of the Chirita drinking water treatment plant, Iasi.

2.4. Methods of Analysis and Equipment

The following equipment was used to determine the physical–chemical indicators. A digital thermometer was used for temperature determination, and the detection limit of the instrument for temperature was −10.00 + 110.00 °C. Electrical conductivity and pH were determined using the HQ40d and HACH multiparameter. A TL 2300 turbidimeter (HACH, Düsseldorf, Germany) was used to determine the turbidity. A DR3900 UV–VIS spectrophotometer (HACH, Germany) was used to determine nitrites, nitrates, and ammonium. The titrimetric method was used to determine the total hardness, organic matter, chlorides, and alkalinity.

Table 2. Instrumental and titrimetric methods in the chemical analysis of surface water.

Characteristics	Indicator Parameter	Unit of Measurement	Method of Analysis	Equipment/ Reagents	Standard Methods
General	Temperature (Twater)	°C	Thermometer
	pH	pH units	Equipment pH/EC	Multiparameter HACH	SR ISO 10523:2012
	Electrical conductivity (EC)	µs/cm−1 at 20 °C	Equipment pH/EC		SR EN 27888:1997
	Turbidity (TB)	NFU	The nephelometric method	Turbidimeter	SR EN ISO 7027-1:2016
	Hardness (TH)	mg/L	Titrimetric EDTA	EDTA, ammonia buffer solution, and Eriochrome Indicator Black-T	SR ISO 6059:2008
	Organic matter (OM)	mg/L	Titrimetric	Sulfuric acid (H2SO4), potassium permanganate (KMnO4) 0.01 N, and oxalic acid (H2C2O5) 0.01 N	SR EN ISO 8467:2001
Majority anions	Chlorides (Cl−)	mg/L	Titrimetric	Silver nitrate (AgNO3), potassium chromate (K2CrO4) 10%	SR ISO 9297:2001
	Nitrates (NO3−)	mg/L	UV–VIS spectrophotometer	Hydrochloric acid (HCl) 1N	SR EN 26777/C91:2006
	Nitrites (NO2−)	mg/L	UV–VIS spectrophotometer	Color reagent	SR ISO 7890-3:2000
	Alkalinity (TA)	mg/L	Titrimetric	Hydrochloric acid 0.1 N and methyl orange 1%	SR ISO 6963:1976
Secondary cations	Ammonium (NH4+)	mg/L	UV–VIS spectrophotometer	Color reagent, sodium dichloroisocyanurate	SR ISO 7150-1:2001

presents the results for water quality indicators, as well as the standards and equipment/reagents used.

The determination of the indicators was carried out using standardized SR ISO methods in accordance with the legislation in force. The values of the analyzed indicators were determined at a temperature of 20 ± 2 °C. The reagents used to determine the indicators were of analytical quality, which ensured reliability and precision in the results obtained.

2.5. Calculation of Standard Deviations and Pearson Correlation Coefficients

To analyze the water samples collected from January 2020 to December 2024, the minimum, maximum, mean, and standard deviation for the 11 indicators were calculated, and the data are presented in Table S1. The monthly variation in the water quality indicators was obtained from daily sampling of six water samples. Six values were calculated as arithmetic means, and the monthly arithmetic mean was calculated by averaging the daily means.

The arithmetic means were calculated using the following formula [41]:

$$M_a={\displaystyle \frac{1}{n}}{\sum }_{i=1}^n{V}_P$$

(1)

where

n = total number of measurements (-);

V_P = value of the analyzed indicators (units according to each indicator presented in Table S1).

The Pearson correlation coefficient was calculated and analyzed for the 11 physical–chemical indicators analyzed in this study, and the correlation results are presented in

Table 3. Pearson correlation matrix among the variables. Significant values are shown in bold.

	TB	Twater	pH	NO2−	NO3−	TH	NH4+	TA	EC	OM	Cl−
TB	1
Twater	0.616647	1
pH	0.051027	−0.18388	1
NO2−	0.166308	0.011781	0.032443	1
NO3−	−0.38077	−0.64827	0.220711	0.193132	1
TH	−0.53907	−0.51229	0.058078	0.119254	0.749928	1
NH4+	−0.05709	0.206755	−0.30183	−0.01317	−0.19565	−0.05412	1
TA	−0.3919	−0.27906	−0.08332	−0.0996	0.417845	0.687539	0.188033	1
EC	−0.51031	−0.39991	0.022131	0.138483	0.680413	0.934466	−0.05779	0.685295	1
OM	0.515487	0.710031	−0.0549	0.05931	−0.49036	−0.4802	0.007914	−0.17215	−0.37811	1
Cl−	−0.03238	0.057421	0.119531	−0.04897	0.150731	0.355858	−0.1701	0.024029	0.342651	−0.196	1

.

Pearson correlation coefficient (PCC) is a bivariate analysis that determines the strength of the direct association between two variables [42,43,44]. To calculate the correlation coefficient, a correlation matrix was constructed, which resulted from the calculation of the coefficients of different pairs of indicators. The correlation coefficients −1 and 1 indicate strongly negative or strongly positive correlations [44,45].

The following formula was used to calculate the Pearson correlation, r [46]:

$$\mathrm{r}={\displaystyle \frac{\mathrm{N}\sum \mathrm{x}\mathrm{y}-\sum \mathrm{x}\sum \mathrm{y}}{\sqrt{[\mathrm{N}\sum {\mathrm{x}}^2-(\sum {\mathrm{X})}^2]·\left[\mathrm{N}\sum {\mathrm{Y}}^2-(\sum {\mathrm{y})}^2\right]}}}$$

(2)

where

r = Pearson r correlation coefficient;

N = number of values in each data set;

∑xy = sum of the products of paired scores;

∑^X = sum of x scores;

∑^Y = sum of y scores;

∑x² = sum of squared x scores;

∑y² = sum of squared y scores.

3. Results and Discussion

3.1. Spatial Variations in the Physical–Chemical Indicators of Water Quality

The results of the analyzed indicators are presented as a horizontal bar chart in Figure 2 and as box plots in Figure 3, both generated using Excel. Detailed numerical values corresponding to the data illustrated in Figure 2 are provided in the Supplementary Material (Table S1). Based on the results presented in Figure 2, the characteristics of the surface water of Chirita lake were emphasized by the mean monthly values of the determined physical–chemical indicators. These indicators are used for surface water quality assessment based on the legislation and quality standards in force [31,47].

Figure 2 depicts the annual average values of the physical and chemical water quality indicators determined in this study for Chirita lake between 2020 and 2024, together with the standard error associated with each average. The selected parameters reflect both the ionic composition (Cl⁻, NH₄⁺, NO₃⁻, NO₂⁻) and the physical and organoleptic characteristics of the water (TB, pH, EC, TA, TH, etc.).

TA recorded the highest values for all parameters analyzed each year, ranging from approximately 9 to 22 mg/L. This indicates a good buffering capacity of the water, which is important for stabilizing the pH and mitigating the acidification effects of contaminants’ precipitation. A relatively low standard error suggests a moderate monthly variation.

The pH values remained within the neutral range (7.8–8.3), which is favorable for aquatic ecosystems and suitable for drinking water.

Electrical conductivity, although not dominant in the graph, showed a relatively constant trend, suggesting stable mineralization of water over time. The chloride values were constant and low. Turbidity and organic matter showed more noticeable variations between the years, with increases in 2021 and 2024. These fluctuations may be associated with events such as surface runoff, bank erosion, or algal blooms.

Turbidity is an important indicator of surface water quality assessment. According to the literature, turbidity is defined as a measure of the opacity of a fluid caused by the presence of suspended particles or colloids [48,49]. Solid particles cause turbidity, which can reduce the effect of disinfection on bacteria, as they can produce interactions with micro-organisms and hinder the disinfectant action.

According to Figure 3a, the annual mean values showed significant variations; namely, the highest value recorded was 24.31 NTU (July 2024), and the lowest value recorded was 2.74 NTU (May 2023). Increased monthly average values were recorded in August 2020, September 2021, September 2023, and May–September 2024. The lowest monthly mean values were recorded in the monthly intervals from January to July 2020 and from January to December 2022, respectively.

Surface water temperature is a key climatic variable. Epilimnion temperature is correlated with atmospheric air temperature and meteorological conditions, influencing several physical processes, such as water turbidity, or it may cause water eutrophication to occur [50,51,52]. Throughout this study, the maximum water temperature of 26.32 °C was recorded in July 2020, and the minimum temperature of 1.08 °C was recorded in February 2021 (Figure 3b).

The results presented in Table S1 demonstrate that water pH was generally stable; the highest value recorded was 9.39 in March 2024, and the lowest value recorded was 7.62 in September 2021. In 2024, the highest monthly mean values were recorded during the study period (7.78–9.39), and the lowest monthly mean values were recorded in September–December 2021 (between 7.62 and 7.96) (Figure 3c).

In general, pH reflects the ability of water to react with alkaline or acidic materials in the aquatic environment [53]. According to recent studies, most bacteria in the aquatic environment reproduce best at pH between 6.5 and 7.5. It is important to emphasize that large fluctuations in pH affect the chemical and biological balance of water resources [12,54]. The highest average monthly pH values were recorded during 2023 (between 7.91 and 8.43) and in 2024, with a maximum value of 9.39, while the lowest values were recorded in 2021 (between 7.62 and 8.21). This analysis showed that the variations in the surface water pH of Chirita lake ranged between 7.62 and 9.39, and the main factors that caused the pH trend increase were changes in the hydrological regime, wastewater emissions, and agricultural pollution [55]. Therefore, all surface water samples collected and determined in the laboratory were pH alkaline.

The determination of electrical conductivity (EC) in water bodies indicates their ability to conduct electric current and is consistent with the number of ionizing compounds in the aquatic environment [44]. The electrical conductivity of water does not pose risks to human health; however, when present in excess, it influences organoleptic characteristics, specifically taste [12,56].

The annual mean values of water conductivity ranged from a maximum of 664.23 µs/cm⁻¹ in February 2021 to a minimum of 397.5 µs/cm⁻¹ in September 2024. The highest values during the study period ranged between 600.22 μS/cm and 664.23 µs/cm⁻¹ in the months of January–April 2021, and the lowest values were recorded during 2024, with values ranging between 397.50 µs/cm⁻¹ and 487 µs/cm⁻¹ (Figure 3d). The determinations carried out revealed a variation. The EC values remained low, and hence, the mineralization was poor for the studied aquifer body, and the lake water was less ionized with a low ionic concentration.

Alkalinity represents the buffering water capacity, i.e., its ability to withstand pH fluctuations [57]. The alkalinity of water is mainly related to the presence of hydroxide, carbonate, and bicarbonate ions. This indicator provides information for studying the corrosive or clogging properties of the analyzed sample.

The annual averages for alkalinity were in the range of 2.90 mg/L and 2.21 in 2021 and 2023, with 2.45 mg/L in 2020, 2.65 mg/L in 2022, and 2.48 mg/L in 2024. The highest monthly mean values were 3.25 mg/L recorded in February 2021 and 2022, while the lowest values were determined in August 2023 (2.21 mg/L) and February 2020 (2.45 mg/L) (Figure 3e).

Water hardness is mainly caused by the presence of cations, such as magnesium and calcium, and anions, such as sulfate, chloride, carbonate, and bicarbonate [53,58]. According to Table S1 (Figure 3f), water hardness recorded an annual maximum value of 11.31 mg/L in the year 2020 and a minimum value of 9.42 mg/L in 2024. The highest monthly averages were determined in the periods January–June 2020 (11.11 mg/L–13.56 mg/L) and March–June 2021 (12.09 mg/L–12.00 mg/L), while the lowest values were recorded in the period June–November 2024 (8.91 mg/L–9.16 mg/L). According to the general guidelines, water hardness is classified as follows: 0–60 mg/L: soft; 61–120 mg/L: moderately hard; 121–180 mg/L: hard; above 180 mg/L: very hard [48]. In terms of total hardness, it can be observed that the samples in this study can be classified as soft water, as they present values below the maximum established for this water category.

The alkalinity and hardness of water correlate with the aquatic environment pH, providing information on abiotic processes and aquatic organisms [53].

The mean annual values for organic matter (OM) for the study period ranged from 2.54 mg/L to 2.82 mg/L. The maximum annual value was 3.82 mg/L for the year 2020, and the minimum value was 2.18 mg/L for the year 2023. The monthly variation in the OM indicator is shown in Figure 3g.

Chloride is generally regarded as an index of water pollution, and an increased concentration of this indicator results in organoleptic changes, such as a salty taste. Chloride concentrations ranged from 23.398 to 36.438 mg/L (Figure 3h). The Cl⁻ concentrations of the samples analyzed were within the acceptable limits of current legislation [59]. The presence of chlorides in lake water may be the result of anthropogenic activities, such as the application of manure-based fertilizers or leaching of chlorides from the soil plant substrate during irrigation processes [60]. The porosity of soil and the permeability of rocks in the area also play a key role in building up chloride concentration [61].

Monitoring nitrite concentrations is of particular interest because of their relatively high toxicity to humans. Nitrite concentrations showed insignificant variations, with average values of 0.011 mg/L in 2023 and 2024, 0.014 mg/L in 2021 and 2022, and 0.40 mg/L in 2020, and a maximum value of 0.186 mg/L in 2020 (Figure 3i). Our findings indicated that the NO₂⁻ concentrations were low for all water samples analyzed. The presence of significant values suggests pollution caused by wastewater discharges and oxygen deficit.

Thus, water from Chirita lake is less likely to cause health problems, and therefore, it meets the standards to serve as a drinking water source. The presence of nitrites in freshwater lakes may lead to the presence of nitrosamines via reaction with organic compounds and may therefore result in carcinogenic effects [62].

The main pathways that increase NO₃⁻ in lake ecosystems are rain, fog, snow, decomposition of organic matter, and fertilizer application in agricultural fields [6,63]. The concentration of nitrates depends on the processes of organic matter leaching into the soil, resulting from atmospheric precipitation, and it is correlated with bio-geochemical processes [12]. The NO₃⁻ concentration showed significant variation during the study. The highest annual mean value was recorded in 2021, with a value of 2.48 mg/L, and the mean value was 0.01 mg/L in 2020. It was found that the highest values were determined in the spring and winter (1.70 mg/L–3.10 mg/L), and the lowest values were reported in the summer and fall months (0.27 mg/L–1.28 mg/L) (Figure 3j).

The NH₄⁺ content varied slightly, with annual averages of 0.033 mg/L in 2024, 0.036 mg/L in 2023, and a maximum average of 0.049 mg/L in 2021. The lowest monthly values were between 0.009 mg/L and 0.014 mg/L in 2020 and 2023, respectively. The highest monthly averages were recorded in months 10–12 and 6 of 2020 (between 0.063 and 0.101 mg/L) and in months 4 and 5 of 2021 (between 0.053 and 0.092 mg/L) (Figure 3k).

By estimating the relationships between water quality variables, this can provide valuable insight into the ecological status of the Chirita reservoir while also reducing the need for periodic sampling for a considerable number of indicators. Strong and persistent correlations between certain variables allow for the development of reliable predictive models, which can increase the accuracy and consistency of water quality assessments. These aspects are essential for efficient surface water resource management, where optimization of monitoring and interventions is crucial for sustainability implementation [64].

The interdependence between water quality indicators stems from both natural bio-geochemical processes and anthropogenic influences. In the present analysis, the correlation observed between turbidity and organic matter may reflect the simultaneous presence of suspended particles and organic matter, which often results from surface runoff during periods of heavy precipitation. Similarly, the relationship between ammonia and nitrate concentrations is determined by the dynamics of the nitrification process, whereby microbial activity converts NH₄⁺ to NO₃⁻ in the presence of oxygen [61,65]. Seasonal variations in pH can influence the solubility and speciation of many components, including metals and nutrients, thereby altering their apparent concentrations and bio-availabilities. In addition, electrical conductivity is strongly associated with total hardness and chloride levels, indicating that mineral content and ionic composition vary in a correlated manner owing to the underlying geological and hydrological factors [65,66]. It is essential to recognize these interconnections, as changes in one parameter often propagate to others, affecting the overall water quality and the requirements for effective treatment prior to human consumption.

3.2. Correlation Analysis

The application of multivariate statistical techniques, such as the Pearson correlation coefficient, allows an efficient interpretation of data matrices to understand the water quality examined [56]. The correlation coefficient is a typical tool used for measuring and determining the correlation between two variables. It is a simple statistical technique that can be used to demonstrate the degree to which one variable is dependent on another.

Values of −1 and +1 represent strong and positive relationships, respectively. A value of zero indicates that there is no linear relationship between variables. A significant positive correlation between the indicators suggests similar behavior, mutual dependence, or a common source [67].

The statistical correlation analysis applied to the data sample in our study proved to be a suitable technique for finding a correlation among the 11 physical–chemical indicators. Thus, this technique can represent a unique step toward surface water quality management.

Thus, variables that recorded a correlation ratio greater than 0.5 were considered correlated based on Evans, J.D. [68] guidelines, where values between 0.4 and 0.59 were considered moderately correlated; values between 0.6 and 0.79 were considered strongly correlated; and values above 0.8 were considered very strongly correlated and different from 0.

The Pearson correlation was calculated to determine whether there was a significant relationship between the physical–chemical indicators determined for Chirita lake from 2020 to 2024 (as presented in Table 3). Water temperature had a significant positive correlation with the standard deviation and a significant positive correlation with OM (r = 0.710031) and NH₄⁺ (r = 0.206755) but a significant negative correlation with NO₃⁻ (r = −0.64827) and TH (r = −0.51229). Water pH was positively correlated with NO₃⁻ (r = 0.22071) and Cl⁻ (r = 0.119531) but negatively correlated with NH₄⁺ (r = 0.220711). NO₂⁻ showed a positive correlation with NO₃⁻ (r = 0.193132) and TH (r = 0.119254). NO₃⁻ showed a significant positive correlation with TH (r = 0.749928) and EC (r = 0.680413) and positively correlated with TA (r = 0.417845). Similarly, TH levels were significantly positively correlated with EC (r = 0.934466) and TA (r = 0.687539). NH₄⁺ was negatively correlated with Cl⁻ (r = −0.1701) but positively correlated with NH₄⁺ (r = 0.188033). TA showed a significant positive correlation with EC (r = 0.685295) and a negative correlation with OM (r = −0.17215). In addition, the EC values were weakly positively correlated with Cl⁻ (r = 0.342651) but weakly negatively correlated with OM (r = −0.37811).

The significant positive correlation between T_water and OM emphasizes the acceleration of bio-geochemical processes under warming conditions, including decomposition of organic matter and reduction in dissolved oxygen, which are eutrophication characteristics.

The significant positive correlation between TH and EC indicates a direct relationship between the metal ion content and total dissolved electrolyte content.

The positive correlation between NO₃⁻ and TH indicates that nitrates may have the same geological source as hardness ions. At the same time, the correlation between turbidity and T_water indicated a significant increase in suspended matter and colloids during warm periods due to algal blooms and accelerated microbial activity at elevated temperatures. These results also emphasize the low-to-moderate contribution of anthropogenic factors to water quality variables in Chirita lake.

3.3. Seasonal Correlation of Water Quality Indicators

Seasonal changes in surface water are an important aspect in the assessment of temporal variations in water quality, mainly due to anthropogenic natural inputs from point and diffuse sources. The data in

Table 4. Correlation matrices (significant values are shown in bold).

Season	TB	Twater	pH	NO2−	NO3−	TH	NH4+	TA	EC	OM	Cl−
Spring
TB	1
Twater	0.1949	1
pH	0.1779	−0.3873	1
NO2−	0.1934	0.1827	0.3044	1
NO3−	0.2963	−0.3627	0.3181	−0.234	1
TH	−0.2829	−0.029	−0.2678	−0.5394	0.5211	1
NH4+	−0.6134	0.3132	−0.1117	0.3081	−0.4783	−0.116	1
TA	−0.6247	−0.2732	−0.1388	−0.4175	0.1819	0.538	0.26709	1
EC	−0.4497	0.0006	−0.3211	−0.4774	0.3466	0.961	0.0092	0.5956	1
OM	0.4118	0.3029	0.2219	0.6446	−0.1936	−0.488	0.0064	−0.8433	−0.462	1
Cl−	−0.2711	0.2148	−0.2723	−0.4017	0.3314	0.75	0.12581	0.7067	0.732	−0.57	1
Season	TB	Twater	pH	NO2−	NO3−	TH	NH4+	TA	EC	OM	Cl−
Summer
TB	1
Twater	0.3397	1
pH	0.7674	0.1308	1
NO2−	0.4036	−0.5412	0.3410	1
NO3−	−0.318	−0.1978	−0.0158	−0.1905	1
TH	−0.568	−0.2726	−0.2653	−0.2868	0.4028	1
NH4+	−0.333	−0.3980	−0.2230	0.3718	0.0258	0.4184	1
TA	−0.078	0.1045	−0.1255	−0.3712	0.0406	0.4648	−0.3671	1
EC	−0.579	−0.0961	−0.2769	−0.3828	0.4924	0.8262	0.3390	0.44152	1
OM	0.147	0.5027	0.3120	−0.4171	−0.1804	0.0093	−0.5012	0.35469	0.1254	1
Cl−	−0.047	0.0177	−0.1425	−0.0795	0.0652	0.4656	0.3527	0.10751	0.0372	−0.3436	1
Season	TB	Twater	pH	NO2−	NO3−	TH	NH4+	TA	EC	OM	Cl−
Autumn
TB	1
Twater	0.5954	1
pH	−0.0231	−0.1148	1
NO2−	0.5358	0.1215	0.0113	1
NO3−	−0.2225	−0.7277	−0.2458	−0.2338	1
TH	−0.5744	−0.6207	−0.3086	−0.2397	0.4995	1
NH4+	−0.0563	−0.0761	−0.6657	−0.0341	0.1975	0.3390	1
TA	−0.3602	−0.0230	−0.7156	−0.2942	0.0551	0.5700	0.5826	1
EC	−0.5682	−0.5384	−0.2611	−0.2709	0.5988	0.8161	−0.0078	0.3839	1
OM	0.3927	0.7004	0.1366	0.2523	−0.7200	−0.5662	−0.3185	−0.1166	−0.4955	1
Cl−	0.2995	0.3113	0.6618	0.0712	−0.3651	−0.6118	−0.5794	−0.8418	−0.4764	0.3056	1
Season	TB	Twater	pH	NO2−	NO3−	TH	NH4+	TA	EC	OM	Cl−
Winter
TB	1
Twater	0.5339	1
pH	−0.1630	0.1985	1
NO2−	0.1837	0.0347	−0.0272	1
NO3−	−0.0455	−0.4100	0.0011	0.4632	1
TH	−0.0901	−0.4254	−0.0631	0.5842	0.8003	1
NH4+	0.0826	−0.1183	−0.2221	−0.2576	0.1719	−0.0264	1
TA	−0.3016	−0.4935	0.0056	0.0606	0.5195	0.7252	0.2356	1
EC	0.0625	−0.2490	−0.0547	0.6415	0.7538	0.9296	0.0821	0.6952	1
OM	0.3029	0.0252	−0.3445	0.2199	0.1231	−0.0696	0.6001	−0.3759	−0.0080	1
Cl−	−0.1586	0.0054	0.0260	0.0398	0.1171	0.2157	−0.2010	0.4140	0.1378	−0.5372	1

Note: 0.4 and 0.59—moderately correlated; 0.6 and 0.79—strongly correlated; above 0.8—very strongly correlated.

show the seasonal correlation matrix of the determined indicators for Chirita lake. In general, the lake water temperature showed relatively moderate-to-acceptable correlations; that is, most of the correlation coefficients were less than 0.70 compared with the other indicators for the four seasons.

In spring, the correlation coefficients between temperature and other indicators were less than or equal to r = 0.3029. Turbidity was moderately positively correlated with OM (r = 0.4118) and weakly positively correlated with T_water (r = 0.1949), pH (r = 0.1779), NO₂⁻ (r = 0.1934), and NO₃⁻(r = 0.2963). NO₂⁻ showed a significant positive correlation with OM (r = 0.6446) but a significant negative correlation with TH (r = −0.5394). NO₃⁻ showed a significant positive correlation with TH (r = 0.5211), and TH was strongly positively correlated with EC (r = 0.961). TA was significantly positively correlated with Cl⁻(r = 0.7067) and EC (r = 0.5956). The significantly positive increase in the correlations between OM, NO₂⁻, and NH₄⁺ may be the result of the accelerated decomposition of organic matter at increased temperatures.

Thus, the correlations changed slightly in summer, with a positive increase in correlations between turbidity and pH (r = 0.7674) and NO₂⁻ (r = 0.4036), T_water and OM (r = 0.5027), pH and NO₂⁻ (r = 0.3044), NO₂⁻ and NH₄⁺ (r = 0.3718), NO₃⁻ and TH (r = 0.4028), TH and NH4+ (r = 0.4184), TA (r = 0.4648), EC (r = 0.8262), and TA and EC (r = 0.44152).

T_water showed a strong correlation with OM (r = 0.7004) and a moderate negative correlation with TH (r =−0.6207) and NO₃⁻ (r = −0.7277), and pH was positively correlated with Cl⁻ (r = 0.6618); similarly, NO₃⁻ showed a positive correlation with TH (r = 0.4995) and EC (r = 0.5988). TH exhibited a strong correlation with EC (r = 0.8161) and a negative correlation with Cl⁻ (−0.6118). NH₄⁺ exhibited a moderate positive correlation with TA (r = 0.5826), which may indicate the presence of two or more ion sources in lake water. Simultaneously, a significant negative correlation between temperature and the other determined indicators was found during fall; the absolute value thus dropped below the threshold of 0.81. It should be mentioned that a moderate positive correlation was observed between temperature and OM in winter. This indicates that OM (r = 0.0252) in the lake is temperature-dependent during the summer, spring, and fall months [69].

In general, TA was positively correlated with EC during all four seasons; however, significant negative correlations were found with Cl⁻ (r = −0.8418) during autumn, strong positive correlations (r = 0.7067) during spring, and moderate negative correlations during winter (r = 0.4140), which may be due to dilution due to precipitation during the rainy summer.

The data in Table 4 show that TH has a significant positive linear relationship with EC (spring: r = 0.961; autumn: r = 0.8161; summer: r = 0.4028; winter: r = 0.9296); therefore, we can state that alkalinity can be a good indirect indicator of water mineralization, reflected by conductivity, in all seasons.

During the four seasons, for NO₃⁻, there was a moderate significant positive correlation with TA, as follows: spring—r = 0.5211; autumn—r = 0.4995; summer—r = 0.4028. During winter, the correlation was strongly significant, with a value of r = 0.8003. For NH₄⁺, a significant variance in the correlation ratio was observed, with a moderate positive value of r = 0.5826 recorded in autumn; in winter, the correlation became weakly negative (r = −0.0264), which may be the result of nitrification processes due to low temperatures in winter [69].

These results also highlight the low-to-moderate contribution of anthropogenic factors to the water quality variables in the lake. Therefore, seasonal changes in surface water quality should be considered when establishing sustainable development policies and objectives.

3.4. Classification of Water in the Chirita Reservoir According to Legal Quality Standards

According to the national legislation in force (NTPA—014 2002 [70] and NTPA—013 2002 [71]), surface water sources are classified based on the treatment processes necessary to produce drinking water. Thus, three categories of surface water were established: A1, A2, and A3. In this study, the values obtained for the determined indicators were correlated with the three categories that refer to the treatment applied for drinking water production.

An objective classification is essential, as it forms the basis for determining the appropriate type of surface water treatment. According to NTPA—013 2002, surface water classified as category A1 necessitates only physical treatment, filtration, and disinfection. In contrast, water categorized as A2 requires both physical and chemical treatment, including pre-chlorination, coagulation–flocculation, sedimentation, filtration, and disinfection through chlorination. Surface water in category A3 demands advanced physical and chemical treatments, which encompass pre-chlorination, intermediate chlorination, coagulation–flocculation, sedimentation, filtration by adsorption with activated carbon and quartz sand, disinfection by ozonation, and final chlorination.

Based on the results in Table S1, it was concluded that in most determinations, the water from Chirita lake was classified as A1 and required physical treatment and simple disinfection. Thus, the following indicators were analyzed during the study: pH, temperature, conductivity, chlorides, nitrates, and ammonium in category A1. In all years (2020–2024), the water pH of Chirita lake fell within the range of pH 7.62–9.39, hence complying with the drinking water standard (A1). EC means that the water has a normal level of dissolved minerals and does not show problems of salinity or excessive mineralization (as presented in

Table 5. Water classification of Chirita lake.

Parameters	UM	Characteristics of Surface Water Used to Produce Drinking Water						Water Classification of Chirita Lake
		A1		A2		A3
		I *	G *	I *	G *	I *	G *	2020	2021	2022	2023	2024
pH	pH units		6.5–8.5		5.5–9.5		5.5–9	A1	A1	A1	A1	A1
Twater	°C	25	22	25	22	25	22	A1	A1	A1	A1	A1
EC	µs/cm−1 at 20 °C		1000		1000		1000	A1	A1	A1	A1	A1
NO3−	mg NO3−/L	50	25	50		50		A1	A1	A1	A1	A1
Cl−	mg Cl−/L		200		200		200	A1	A1	A1	A1	A1
NH4+	mg NH4+/L		0.05	1.5	1	4	2	A1	A1	A1	A1	A1

Note *: I = compulsory value; G = recommended value.

).

Throughout the study period, water from Chirita lake was treated as A2; advanced treatment applied to surface water led to high-quality drinking water and, at the same time, prevented the transmission of diseases through water.

3.5. Comparisons with Monitoring Systems in Other EU Lakes

Water quality monitoring and assessment are essential for maintaining the ecological status of surface water. Regular monitoring programs make it possible to anticipate changes in water composition and manage resources safely. The application of a variety of assessment methods facilitates the interpretation of complex data, provides a clear picture of ecological status and water quality, and helps identify the main factors that can adversely affect aquatic systems [8,72].

In the European Union, the number of lake water quality monitoring and assessment studies is high because of the strict requirements of the Water Framework Directive. The Nordic countries—Finland and Sweden—have implemented and applied extensive monitoring programs at the national level. In Germany, there are no national monitoring programs for the ecological status of lake waters; therefore, water monitoring is carried out at the level of 16 regional states according to their own legislation.

The cited studies investigated the physicochemical characteristics of surface water quality. For example, Falah et al. [73] studied and identified the trends, modifications, and changes in the ecological water quality status over four seasons of Lake Burgas (Lake Vaya) in the Black Sea region of Bulgaria. The results of inorganic and chemical indicators showed low concentrations, even though the study area was industrialized, indicating an unpolluted location for the lake.

In Italy, Frondoni et al. [74] performed a geochemical analysis of Lake Trasimeno using a Pearson correlation matrix to identify the relationships between variables. The study revealed well-defined seasonal trends, particularly the progressive warming of lake water, which was associated with an increase in total dissolved inorganic solids.

In a study carried out by Marković Goran et al. [75] on the evaluation of physical–chemical characteristics of ten lakes in Serbia, it was concluded that most of the studied lakes were within the recommended values, but deviations were observed for organic matter concentration and pH in all samples taken, indicating localized or seasonal deviations.

Comparing the results of these studies, which presented objectives and methodologies similar to those addressed in the present study or found a number of similarities in the long-term variability observed on the basis of the determinations carried out for Chirita lake in agreement with the present studies, the following can be distinguished: the seasonal fluctuation in organic matter, in correlation with water temperature, contributes to eutrophication in similar ways in the lakes studied. In all cases, increasing water temperature was a critical factor in increasing dissolved ion concentrations and altering lake chemistry, emphasizing a consistent pattern related to climate dynamics and anthropogenic pressures at the EU level.

4. Conclusions

In this study, data on the surface water quality of Chirita lake in Iași, Romania, were analyzed for 11 physical and chemical indicators collected between 2020 and 2024 using statistical analysis and Pearson correlation. The results showed that water temperature had moderate-to-weak correlations with other water quality indicators over the four seasons, except OM, with which it had a strong correlation coefficient in the fall (0.7004) and summer (0.5032) seasons. This was due to the increase in water temperature, which led to an increase in biological activities, resulting in water eutrophication. A moderate significant correlation was found between water turbidity and temperature, especially in the fall and winter seasons, which can be attributed to atmospheric conditions and precipitation.

Strong correlations between total water hardness and electrical conductivity were found in winter (0.9296) and fall (0.8161), which can be attributed to seasonal factors, such as low temperatures that slow down chemical reactions and seasonal anthropogenic pollution. The data indicate a moderate-to-significant positive correlation between nitrate content and water hardness; a significantly increased correlation ratio of 0.8001 was found in winter and a moderate correlation ratio of 0.5200 in spring, and the seasonal variation in NO₃⁻ showed an increase during the dormant and growing seasons, respectively.

In general, alkalinity had moderate-to-significant correlations with EC during the four seasons, with a low value in the fall season, which can be attributed to precipitation during the summer season that had diluting effects on alkalinity. Based on statistical analysis, the water of Chirita lake was classified as category A1 (as presented in Table 4), requiring only physical treatment and disinfection. Considering the seasonal variations and occurrence of eutrophication, the water was treated according to the A2 category in the treatment plant, resulting in high-quality drinking water and preventing the transmission of waterborne diseases.

Internationally, lake water quality is deteriorating significantly as a result of rapid urbanization and the irrational use of agricultural fertilizers. Eutrophication remains one of the main environmental problems in both Romania and Europe. Major information deficiencies, particularly in understanding the complex interactions between hydrological, biological, and geochemical processes at large spatial and temporal scales, represent a major obstacle to the implementation of a unique international surface water quality monitoring program with a single database.

The existing international lake water quality monitoring programs are often coordinated at the regional level or through collaboration between countries, organizations, and scientific institutions.

The present study provides results that can be used in the development of local and regional programs to mitigate and combat the eutrophication phenomenon in Chirita lake.