Next Article in Journal
Quinoid Redox Mediators and Their Involvement in Environmental Pollution Treatment
Previous Article in Journal
Method for Distinguishing Sargassum and Zostera in the Seto Inland Sea Using Sentinel-2 Data
Previous Article in Special Issue
Biotic Integrity, Water Quality, and Landscape Characteristics of a Subtropical River
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Assessment of Water Quality and Parasitofauna, and a Biometric Analysis of the Prussian Carp of the Romanian Lentic Ecosystem in Moara Domnească, Ilfov County

Faculty of Land Reclamation and Environmental Engineering, University of Agronomic Sciences and Veterinary Medicine of Bucharest, 59 Marasti Blvd., District 1, 011464 Bucharest, Romania
Faculty of Agriculture, University of Agronomic Sciences and Veterinary Medicine of Bucharest, 59 Marasti Blvd., District 1, 011464 Bucharest, Romania
National Research and Development Institute for Soil Science, Agrochemistry and Environment—ICPA, 61 Marasti Blvd., District 1, 011464 Bucharest, Romania
Authors to whom correspondence should be addressed.
Water 2023, 15(22), 3978;
Submission received: 27 September 2023 / Revised: 3 November 2023 / Accepted: 6 November 2023 / Published: 16 November 2023
(This article belongs to the Special Issue Water Quality and Ecological Risk Assessment in Surface Waters)


The aim of this study was to perform a morphometric analysis of Prussian carp individuals collected from the Moara Domnească pond in Ilfov County, Romania. This was accompanied by a physico-chemical characterization of the water, which was sampled from the same location. Consequently, we analyzed a total of 60 Prussian carp individuals that were caught in April (N = 32) and May 2023 (N = 28). For the Carassius gibelio in the study site, we provided, for the first time, data on the weight–length relationships (WLRs), the Fulton K condition factor and the biometric features, including the total length (TL), standard length (SL), head length (HL), snout length (SNL), body depth (BD), girth and ratios for the TL/HL, as well for the TL/BD and TL/SL. A negative growth type was estimated for both the samples of C. gibelio. Based on other studies and fishing reports, besides C. gibelio, there are another 11 species that belong to the Cypriniformes order, and there are two teleost members of the Perciformes order (Lepomis gibbosus and Perca fluviatilis)—these were also associated with this habitat. Water samples were collected from 12 sampling points (SP), which were subjected to physico-chemical characterizations that consisted of an assessment of the turbidity (T), pH, electrical conductivity (EC), chloride (Cl), total hardness (TH), oxygen regime parameters, phosphate phosphorus (P-PO43−), nitrate nitrogen (N-NO3), nitrite nitrogen (N-NO2) and ammonium nitrogen (N-NH4+) levels. In addition, considering their characteristics (high toxicity, non-biodegradability, long-range atmospheric transport and bioaccumulation potential), a number of organochlorine pollutants (OCPs) were identified. The total hardness values presented a very significant correlation with conductivity (r = 0.5974 ***) and with pH (r = 0.5854 ***). The results allowed for the water samples to be placed into the quality classes established by legislation, and they were also discussed in relation to the fishes’ requirements.

1. Introduction

Water quality in aquatic systems is influenced by interactions between parameters such as water hardness, nutrient levels, dissolved oxygen, etc., which may have profound effects on the lives of aquatic organisms. For instance, the pH may enhance the toxicity of certain chemical species; more precisely, an increase of pH favors ammonia toxicity, while a decrease in acidic values generates the solubilization of heavy metals and the formation of toxic hydrogen sulfide [1].
In addition, the eutrophication generated by high levels of nitrogen and phosphorus-containing species from different sources favors the proliferation of phytoplankton, benthic algae and macrophytes, which influence the dissolved oxygen levels and, consequently, aquatic organisms’ lives [2].
The physico-chemical parameters of water [3,4] were monitored for the purpose of evaluating the ecosystem’s health; furthermore, this type of work is being sustained by many research studies.
Additionally, though monitoring the water quality of fishponds is strongly recommended since productivity is related to it [5], it is also recommended to alleviate the pollution effects generated inclusively by fish feeding [6,7]. To achieve this goal, many methods were used to determine the main water quality parameters; however, besides classical laborious approaches, there are also smart solutions based on implementing the Internet of Things (IoT) in fisheries. These assure quick data acquisition and help farmers to make fast decisions—either to prevent certain unexpected situations or to increase productivity [8,9]. Also, a geospatial spectral analysis of water and statistical modeling were reported as alternatives to save time and money [10].
Considering the nutrient concentration of water, the Moara Domnească freshwater pond (Ilfov County, Romania) is known for having high trophic resources, including a varied fish community [11,12]. Weight–length relationships and Fulton’s condition factor are essential tools in fishery research, and they are estimated to provide data on the growth types and the states of the well-being of fish individuals in their habitats [13,14,15].
Furthermore, considering both the limited data in the literature regarding the characterization of water from the Moara Domnească pond [16,17] and the continuation of our set guidelines that were mentioned previously [18], in this study, we will focus on the following: (i) evaluating certain biometric indices of Carassius gibelio, (ii) assessing water quality parameters and (iii) analyzing if common gibel carp individuals find optimum life conditions in the Moara Domnească pond (a freshwater resource used for recreational purposes, mainly fishing).

2. Materials and Methods

2.1. Description of the Site and Sampling Points

Located in the northeast of Bucharest, south of the national highway of Bucharest–Constanța, the Moara Domnească pond belongs to the Pasărea Lake chain, which opens in the Argeș River. This freshwater lake is used for the irrigation of neighboring agricultural areas and for pisciculture [19,20].
The Pasărea is 48 km long and has a sinuous course (Figure 1). This makes the drainage of water difficult, but the conditions do favor the emergence and development of marsh vegetation. Due to the low slope (1‰), water runoff is marked by average annual flows of less than 1 m3 s−1, and the water velocity is maintained at especially low values. Along its path, a series of 24 lakes have developed. These are mainly used for fish farming and irrigation, and, together, they cover an area of 434 hectares.
Ponds are usually named after the places where they are situated, examples include the following: Tunari (21 ha); Crețuleasca (25 ha); Ștefănești (12 ha); Boltașul (25 ha); Afumați I, II, III and IV (41 ha); Moara Domnească (13 ha); Găneasa I, II and III (22 ha); Cozieni (20 ha); Pustnicul I, II and III (32 ha); Brănești I, II, III and IV (60 ha); Vadul Anei (33 ha); and Fundeni I, II and III (134 ha) [21].
The sampling points (SPs) and coordinates are shown in Table 1 and are depicted in Figure 2. The distance between the SPs from the same row was around 13 m, and the distance between each group of the three SPs was approximatively ten times higher.
Figure 1. Course of the Pasărea River from its origin to its discharge [22].
Figure 1. Course of the Pasărea River from its origin to its discharge [22].
Water 15 03978 g001

2.2. Sampling and Analytical Methods

2.2.1. Fish Inventory and Biometric Analysis

Shortly after their capture, the Carassius gibelio specimens were subjected to rapid cooling [24], and they were then transported to the laboratory in order to record their biometric characteristics, as well as to investigate for the presence of parasites when placed under a Krüss Optronic stereomicroscope.
The morphometric analysis for each Carassius gibelio individual was based upon the values of the following 14 features: total and standard length (TL, SL); total weight (TW); head length (HL); snout length (SNL); body depth (BD); girth (G); SL in % of TL; HL in % of TL; SNL in % of TL; BD in % of TL; and the respective ratios for TL/HL, TL/BD and TL/SL [25,26].
An inventory of the fish species living in the Moara Domnească pond was made using the data provided by previous studies, gray literature (fishing websites including photos of the catches) and also personal surveys that took place during April–May 2023.
The scientific classification of each fish species was conducted as per FishBase [27], and both English and Romanian common names were provided. The specimens belonging to C. gibelio were identified following Kottelat and Freyhof [28]. The parasites of Prussian carp and other fish species were also mentioned for the studied ecosystem. Overall, 60 Prussian carp individuals were measured (total length, TL ± 0.1 cm; standard length, SL ± 0.1 cm) and weighed (total body weight, TW ± 0.01 g) (Figure 3). A precision balance model PS 2100.R2.M (Radwag, Poland) was used.
The weight–length relationships (WLRs) were expressed as TW = aTLb and TW = aSLb, as well as through linear regression (Log TW = Log a + b Log TL; Log TW = Log a + b Log SL) (intercept a—the rate of change of weight with length of fish, slope b—information about the type of growth) [4,29,30]. A positive allometric type of growth was suggested when b > 3; meanwhile, when b < 3, a negative allometric or hypoallometric growth type was exhibited [31].
The coefficient of determination (r2) and confidence intervals (CI95%) for parameters a and b were calculated by the least-square method using PAST (Paleontological Statistics) [32], version 4.04 and MS Excel 2010.
The Fulton’s condition factor (K) of Prussian carps in their habitat was computed using the equation K = (TW/TL3) × 100 in order to assess the well-being of the fishes [4,15,33].

2.2.2. Water Collection

A sampling campaign was conducted in May 2023, which consisted of the collection of water samples from 12 sampling points (SP) located on the Moara Domnească pond (Figure 2). In order to ensure accuracy in the water quality assessment, three samples were collected from each sampling point (coded SP1, …, SP12), whereby each one was analyzed in duplicate. The overall dataset comprised 36 water samples. The reported values for each SP were the average of their determinations.
Samples were collected by manual grab as this is the approach commonly performed for lake monitoring studies [34] when working from a 0.5 m depth with polyethylene bottles (which were previously rinsed three times with sampling water). All labeled samples were transported in a cold box (4 °C) to the laboratory. Before analysis, the samples were allowed to stay in the laboratory until they reached room temperature.

2.2.3. Analyses and Instrumentation

The determined physico-chemical parameters and persistent organochlorine pollutants (POPs), analytical methods and instrumentation used are depicted in Table 2.
The pH, electrical conductivity (EC) and dissolved oxygen (DO) of each water sample were measured in situ; meanwhile, the remaining parameters were determined within 24–48 h of collection. Chloride was quantified by Mohr’s method of titration with a silver nitrate (AgNO3) solution when using potassium chromate (K2CrO4) solution as the indicator. The total hardness (TH) was determined via titration with disodium ethylenediamine tetraacetate (Na2EDTA) when using Eriochrome Black T for endpoint detection. Chemical oxygen demand (CODMn) was achieved by redox titration when using potassium permanganate (KMnO4) solution in an acidic medium. The biochemical oxygen demand (BOD) was assessed by the polarographic method. Nutrient phosphorus and nitrogen species were assessed by spectrophotometric means as follows: phosphate-phosphorus (P-PO43−) when using the molybdenum blue method (λ = 720 nm); nitrate nitrogen (N-NO3) when using 2,4-phenoldisulphonic acid in a basic medium (λ = 420 nm); nitrite nitrogen (N-NO2) when using Griess reagent (λ = 540 nm); and ammonium nitrogen (N-NH4+) when using Nessler reagent (λ = 420 nm).
Appropriate calibrations of the devices employed in analyses were performed before determinations to ensure the quality of the analytical procedures and correctness of the obtained data.
The persistent organochlorine pollutants (POPs), more specifically organochlorine pesticides (OCPs), were assessed by the gas chromatography technique. Thus, the study focused on the four isomers of hexachlorocyclohexane (HCH)—α, β, γ, δ (their sum is expressed as ∑HCH)—and 4,4′-dichlorodiphenyltrichloroethane (4,4′-DDT), as well as its main metabolites: 4,4′-dichlorodiphenyltrichloroethylene (4,4′-DDE) and 4,4′-dichlorodiphenyldichloroethane (4,4′-DDD) (of which the sum is expressed as ∑DDT). Among all of the 12 sampling points used in this study, 6 were selected to determine the levels of the abovementioned OCPs.
A volume of 1 L of water was extracted twice with 150 mL of hexane in a separatory funnel. The organic extract was treated with an anhydrous sodium sulphate, which was then reduced to an appropriate volume. The separation and identification of the target compounds (those mentioned above) were performed with a non-polar stationary phase capillary column (DB-5MS with 60 m × 0.25 mm × 0.25 μm) that had a programmed temperature (from 80 °C to 330 °C at 20 °C min−1). The injector temperature was 210 °C, and the carrier gas was helium 6.0 at 1 mL min−1. The injection volume was 1 μL in splitless mode. All of the solvents were of a high-purity grade for the purpose of chromatography.

3. Results and Discussion

3.1. Biometric Analysis of Carassius gibelio

The morphometric characteristics for Carassius gibelio (N = 32, April 2023 and N = 28, May 2023, sex combined) are shown in Table 3.
The weight–length relationships (Table 4) revealed a negative allometric type of growth (b < 3) for each Prussian carp sample, which could mean that C. gibelio grows faster in length than in weight in the study site [31]. The values were within the expected range of 2.5–3.5 for slope b. Fulton’s condition factor indicated favorable growth conditions for the Prussian carp in the Moara Domnească Lake as its K was higher than 1 [35].
However, the obtained data should be interpreted with caution since it is known that the fish condition and parameters of WLR in fishes could be influenced by several factors, such as sample size, the size of the captured specimens, environmental conditions or sampling methods [36,37].
Compared to FishBase, where the common total length for C. gibelio is 20 cm, the mean values for the TL registered in the present samples were 22.12 ± 1.32 cm and 23.89 ± 2.26 cm for April and May 2023, respectively. According to the same database, the BD of Prussian carp represent 29.8% of TL, and the HL is 19.3% of the TL. Present research reports for BD are 29.23% TL ± 0.94 (April) and 27.36% TL ± 1.53 (May). For HL, the values are 21.72% TL ± 2.04 (April) and 21.33% TL ± 0.89 (May). In comparison, certain authors [38] have registered populations of Carassius auratus gibelio with values of HL ranging from 22.9 to 30.7% of the body length, while others [39] have noted an average HL value of 29.3% of the standard length in a study regarding the plastic features of C. gibelio from Uzbekistan. Moreover, the mean value of the HL % in SL was 29.1 for gibel carp from southern Iraq [40]. Prior to the study, the standard length by FishBase was estimated as 85.9% TL, while the present survey instead found average values of 80.26%TL ± 7.71 (April) and 80.75%TL ± 2.16 (May) (Table 3).

3.2. Ichthyofauna of the Moara Domnească Pond

A checklist comprising 14 fish species was compiled for the ichthyofauna of the studied ecosystem (Table 5). Of these, only 4 species were personally caught and identified, namely Prussian carp Carassius gibelio (present and previous surveys), Lepomis gibbosus, Pseudorasbora parva and Rutilus rutilus [19,41] (Table 5).

3.3. Parasites Recorded in Fishes from Moara Domnească Pond

The parasitological examination of the C. gibelio from the Moara Domnească pond revealed a single individual of Philometroides sanguineus (Nematoda: Philometridae), which was observed in the caudal fin of a fish (TL = 21 cm, SL = 15 cm, TW = 167.74 g) from the sample of April 2023 (Figure 4).
The nematode P. sanguineus was registered before in Romanian specimens of Prussian carp [47,48,49].
No parasitic infection was observed in the Prussian carps caught in May 2023. The second ectoparasite species of C. gibelio from the Moara Domnească pond was the copepod Lernaea cyprinacea, which was registered in a study dated June 2008 [19]. In the same sampling month, this lernaeid was also noticed in Lepomis gibbosus, Pseudorasbora parva and Rutilus rutilus; meanwhile, in July 2008, the monogenean Paradiplozoon sp. was found in the gills of Rutilus rutilus [41] (Table 5).

3.4. Water Quality Analyses and Interpretation

An overview of the data concerning the main physico-chemical parameters is depicted in Table 6 and Table 7; meanwhile, associations with the quality classes for the surface water is presented in Figure 5.

3.4.1. Results concerning Turbidity

An increase in turbidity is related to a decrease in water transparency, which results in the water becoming cloudy or turbid. This behavior is generated mainly by particulate and organic matter, the presence of algae and even microscopic organisms. Thus, it may affect fish life and other aquatic organisms by reducing access to food supplies or impacting the development of visual systems and the behavioral ecology of fish (navigation, mating, territoriality, etc.) [51]. In the case of our study, the nephelometric assessment of this parameter provided an average value of 8.35 ± 1.29 NTU (Table 7), which is considered low [52]. In addition, a similar study developed in the same pond conducted by our team in September 2022 indicated much higher values of 51.77 to 72.40 NTU (58.43 NTU, as average) [18]. Since the previous study was conducted in autumn (September 2022), the high values are explainable due to the algae proliferation that occurs during summer and autumn [53], which is in contrast with this study as it was conducted in spring (May 2023).

3.4.2. Results concerning pH, EC and TH, as Well as the Correlations between Them

(a) Determination of the pH values provided values in the range 7.25–8.11, with an average of 7.39 ± 0.23 (Table 6 and Table 7). These values agreed with the Romanian legislation regarding surface waters [50]. For aquatic life, the pH parameter is particularly important since high values increase the toxicity of ammonia and low values increase the toxicity of certain metals. In addition, pH values lower than 6 favor the formation of toxic hydrogen sulfide [54]. Furthermore, a desirable range for fish production is between 6.5–9.0, with each fish species having an optimum pH level [8]. The effects of the pH values outside the optimum range are related with poor fish growth, a decrease in fish reproduction and even poor phytoplankton growth [55,56].
(b) The EC parameter was determined by the dissolved ions that were able to conduct electricity. In our study, the EC ranged between the values of 1043–1164.67 μS cm−1 (Table 6 and Table 7), which characterize a desirable range for most fish species [55]. Previously published data regarding the water quality from ponds in Romania indicated lower EC values for the Comana pond (645–705 μS cm−1) [49], Tătaru pond (653–760 μS cm−1) [57] and even for the Moara Domnească pond (683.66–724.33 μS cm−1) [18]. Moreover, higher EC values were also found for the Bițina pond (801–851 μS cm−1) [28], and the Brănești pond (1105–1156 μS cm−1) also had similar values [58]. EC values between 301–1160 μS cm−1 have also been reported for several urban ponds in Lisbon, Portugal [59].
(c) The TH parameter is associated with the presence of divalent cations, mainly Ca2+ and Mg2+. These chemical species are essential minerals required in the biological processes that occur in aquatic organism bodies. Therefore, it is important to manage the TH values, especially in aquaculture. In addition, the optimum Ca2+ and Mg2+ levels for different types of aquariums were obtained from the literature [8].
The TH values for the Moara Domnească pond were between 20.22–22.45 mg CaO L−1 (Table 6 and Table 7). In considering the fact that, for TH, there is no limit value or safe interval imposed by legislation, the reference interval must be related to the aquatic organisms’ requirements. Consequently, values between 28–84 mg CaO L−1 were considered optimum for aquaculture [55]. However, the TH values determined were slightly lower than this range, as well as slightly higher than those already reported [18].
(d) Correlations between the pH, EC and TH.
The analysis of the results indicated that EC (μS cm−1) presents a very significant correlation with TH (mg CaO L−1), correlation coefficient being r = 0.5974 ***. Between the pH and TH values (mg CaO L−1), a very significant correlation with r = 0.5854 *** was also evidenced (Figure 6).
The coefficient of correlation (r) and the confidence intervals (CI99%) for the parameters were calculated using MS Excel 2019. The p-values were obtained in an Excel regression analysis, an F test and an ANOVA test at a level of statistical significance set at α = 0.01.

3.4.3. Results concerning Chloride Levels

Chloride ions are naturally found in waters in various concentrations; however, in the past decade, its levels have increased as a consequence of growing populations, industrial developments, agricultural practices and environmental pollution. The presence of chloride ions in lakes is considered benign, but concentrations over 100 mg L−1 may result in ecological impacts. In addition, chloride levels may vary cyclically due to climatic conditions [60].
Based on the chloride concentrations assessed for the Moara Domnească pond (73.28–82.96 mg L−1) (Table 6 and Table 7), as well as the standards according to Romanian legislation [50], the water from this pond was associated with the III quality class (Figure 5). These results are explainable considering the location of the pond in the vicinity of a livestock farm as the high chloride levels could possibly be related with the leachate derived from animal waste [61].

3.4.4. Results concerning Oxygen Regime

DO is a parameter that is essential for aquatic life, and it is given by the amount of oxygen dissolved in water [62]. DO is strongly influenced by temperature and algae growth. Also, during daylight hours, DO increases as algae synthesize it by photosynthesis. Conversely, at night, DO decreases as aerobic organisms consume oxygen [63].
The levels of DO determined for the water samples from the Moara Domnească pond were between 7.71–8.95 mg O2 L−1, which, according to the legislation, correspond to a II quality class for surface water (Table 6 and Table 7, as well as Figure 5). It may be considered that these values are optimum for aquatic organisms since warm water fish need at least 5 mg of O2 L−1, whilst cold water fish require 6–7 mg O2 L−1 [64]. Also, values below 3–4 mg O2 L−1 will favor an increase in anaerobic bacteria activity, which will generate methane and hydrogen sulfide [1].
COD and BOD are important parameters that are often monitored and discussed in aquaculture. For instance, BOD measures the amount of oxygen consumed by bacteria and other microorganisms during the oxidation of organic matter. Furthermore, if the organic matter is chemically oxidized, then the time of the analysis is shortened and, as was the case in this study, appears as the COD parameter, whose value is higher than that of BOD. On the basis of the performed analyses, the CODMn and BOD values ranged between 11.14–14.82 mg of O2 L−1 and 3.25–4.29 mg of O2 L−1, respectively (Table 6 and Table 7). In addition, based on the average values of CODMn and BOD, the water was in the III and II quality classes for surface water quality (Figure 5).
Considering that an assessment of the oxygen regime parameters is time consuming, certain authors have provided some useful models that are able to predict them on the basis of other easily available data [62].

3.4.5. Results concerning Nutrient Levels

Phosphorus and nitrogen are encountered in environments that have incorporated different chemical species, and they are also natural parts of aquatic systems. In addition, these elements support the growth of algae and other aquatic plants, and their excessive amounts are responsible for eutrophication, in which the consequences mainly entail dissolved oxygen levels.
Due to their importance and effects, phosphorus and nitrogen levels in surface waters are regulated by Romanian legislation [50], and this is achieved by setting each concentration range within one of the associated quality classes.
When referring to the phosphate phosphorus levels in the water from the Moara Domnească pond, the analyses indicated high values between 2.326–2.579 mg P L−1 (Table 6 and Table 7). These values meant that the waters would fall under the V quality class (Figure 5). Furthermore, the phosphate levels between the levels of 0.08 and 0.10 mg L−1 may enhance periodic algal bloom [8].
Having in view the effects on fish, an increase in the phosphorus levels in water induces a decrease in cyprinid body weight [65].
In aquatic ecosystems, nitrogen is found predominantly under nitrite (NO2), nitrate (NO3) and ammonium (NH4+) forms, and elevated concentrations are mainly responsible for eutrophication, which spoils the living environment of fish. In addition, the conversion order of ammonium → nitrite → nitrate requires oxygen, which therefore results in an oxygen reduction and thus a further impact on fishes.
The nitrite, nitrate and ammonium nitrogen concentrations assessed in the Moara Domnească pond were between 0.107–0.132 mg N L−1, 0.257–0.584 mg N L−1 and 7.438–8.456 mg N L−1, respectively (Table 6 and Table 7). Other than the nitrate nitrogen levels, on the basis of which the water was associated with the I quality class, the other forms were elevated such that the water quality was in the IV and V quality classes, respectively (Figure 5). Also, according to the literature [8], the optimum nitrite range for fish is 0–0.2 mg L−1, harmful values are higher than 0.5 mg L−1, whilst values over 1.6 mg L−1 are considered lethal.
The obtained results evidenced high levels of nutrients (phosphorus and nitrogen, as well as nitrite and ammonium), and we could assume that these high concentrations may be due to the fertilizers applied on agricultural land, which were in vicinity of the pond and may have undergone possible contamination with the animal wastes from the livestock farm situated nearby. Furthermore, the algae bloom and eutrophication of the pond was clearly highlighted in the images taken in the autumn of 2022 (Figure 7).
It was noted that the levels of ammonia in the spring (current study) were 5 times higher than that in the autumn [18]; however, this is explained by the fact that the snow from the adjacent areas melted and drained into the pond.
High nitrite levels were found to have accumulated in the tissues of the gills, spleens, muscles and brains of the fish, and the oxidization of hemoglobin to methemoglobin, which affects blood properties, was also observed [66]. In addition, the degree of tolerance to nitrite levels was found to vary as follows: the largemouth and smallmouth bass tolerated high levels, while the cool water fish were sensitive even to low levels of nitrite [67].
In the pond water, ammonia was present in two forms: un-ionized ammonia (NH3) and ammonium (NH4+). Between them, an equilibrium was present that was driven by the pH and temperature. For instance, at a pH under 8, un-ionized ammonia was found at especially low levels. The ammonium form, which is less toxic, was predominant at low pH and temperature levels [68]. The negative impact of ammonia on fish was reviewed by Eddy [69].
A useful approach for decreasing nitrite, nitrate and ammonium levels in fishponds is to add, into the pond water, nitrifying and denitrifying bacteria at the optimum concentration of 0.60 mg L−1 [70].

3.4.6. Results concerning the Assessment of Several OCPs

Before 1989, Romania was one of the countries with the highest levels of pesticide production, use and export. Considering that the Moara Domnească pond is located near agricultural plots, where—though there were many in years past, there is still a great deal currently—cereals, crops, fruits and vegetables are grown, it is easy to presume that the extensive use of pesticides was necessary. Furthermore, even if OCP use was banned globally now, it was intensively used in the past at enormous scales. Thus, given their continuing persistence in air, water, soil and sediment, as well as in aquatic and terrestrial organisms [71,72], it has been challenging to assess their levels in pond water.
The results (Table 8) indicated undetectable amounts of α-HCH and δ-HCH; however, in one sampling point (SP5) for β-HCH, a concentration of 0.023 μg L−1 was found. In the case of the γ-HCH isomer (lindane), concentrations between 0.008 and 0.018 μg L−1 (Table 8) were determined. This was somewhat of an expected situation considering that the γ-HCH isomer (lindane) is the only isomer in the group with pesticidal properties [73,74], while the others are actually by-products of lindane production. From the HCH isomers, only γ-HCH (lindane) exhibited detectable levels at all of the sampling points as its contribution to ∑HCH was 43.90% for SP5 and 100% for the rest of sampling points. In addition, considering the standards imposed by Romanian [50] and European legislations [75], the lindane levels determined were found to be below the imposed limit (Table 8).
To our knowledge, the present study is the second investigation that has dealt with OCP level quantification for water that has been collected from the Moara Domnească pond. The first one, which was conducted in 2008–2009 [16], reported higher levels of HCH (more specifically, it reported ∑HCH values at a maximum value of 0.104 μg L−1).
Regarding ∑DDT, the analyses indicated a concentration of 0.049 μg L−1 for SP5, which was higher than the limit value (Table 8) but lower than the values reported previously by Lutai and Tudor [16].
The presence of lindane in all of the sampling points was somewhat explainable, considering that γ-HCH is more soluble in water than in other organochlorine compounds; in addition, it also tends to remain in the water column [76]. The main sources of γ-HCH to surface waters are agricultural run-off and point source discharges [77]. However, the OCP levels did not emphasize a recent contamination, rather an accumulation of residues from the time when they were used intensively in agriculture.

4. Conclusions

This study was undertaken as a continuation of our previous findings and concerns related to water quality monitoring (which plays a substantial role in maximizing fish productivity). Furthermore, maintaining balanced levels of the quality parameters is crucial for aquatic organism life.
In this research, the physico-chemical parameters that decide water quality, the associations of them with acceptable or imposed limits and their impact on fish life was discussed. In addition, the biometric characteristics of the Carassius gibelio specimens from the Moara Domnească pond were determined. A negative growth type was estimated for both of the samples (April and May 2023) of Carassius gibelio, thus meaning that the Prussian carp individuals grew faster in length than in weight. With the Fulton condition factor being higher than 1, a favorable growth was indicated for the C. gibelio inhabiting the Moara Domnească Lake. The values of the biometric features were found to be similar with those published before in the scientific literature for Prussian carp.
Based on the present study data and on previous personal data, the ichthyoparasifauna of this ecosystem were found to include the nematode P. sanguineus, the copepod L. cyprinacea and the monogenean Paradiplozoon sp.
Also, so as to seek a better knowledge of the fish species that exist in this pond, a checklist comprising 14 of the fish species identified in the studied ecosystem was drawn up.
The physico-chemical parameters were used to place the water within the quality classes established by Romanian legislation. Consequently, the chloride levels (76.49 mg L−1) were found to fall in the III quality class. On the basis of oxygen regime parameters, the water was associated with II (DO and BOD) and III (COD) classes. Other than nitrate nitrogen (which was found to be so low that the water was in the I quality class), the other nutrient species (nitrite, ammonium, phosphate) were assessed at high concentrations, thereby suggesting massive algae blooms that were otherwise observed in a previous monitoring study. Furthermore, the pH, TH and EC parameters were found at levels that were considered optimum for aquatic life and aquaculture. The TH values respectively presented very significant correlations with the EC (r = 0.5974 ***) and pH (r = 0.5854 ***). In addition, certain quality parameters (pH, EC, TH and chloride) for SP12 were found to be higher in comparison to those found in other sampling points. We believe that this discrepancy was related to the position of SP12 as near the water edge, where building materials/cement-based materials for the rehabilitation of a zone nearby were stored.
Among the investigated OCP levels, the presence of γ-HCH in all of the sampling points was highlighted; meanwhile, the DDE and DDT levels were evidenced at SP5 only. Relying on the present study results regarding OCP levels in water, it may be concluded that their presence is related with past agricultural practices, which explains the amount of evidence showing their persistence in the environment.
Hence, maintaining fishpond water parameters at optimum values must be discussed from two perspectives: keeping the environment clean and increasing pond productivity.

Author Contributions

Conceptualization, M.A.S., G.V.S., M.-M.S.-B. and R.M.M.; methodology, G.V.S., M.-M.S.-B. and R.M.M.; software, R.M.M. and M.A.S.; validation, M.A.S., M.P. and A.V.; formal analysis, G.V.S., R.M.M. and M.-M.S.-B.; investigation, M.P., M.A.S., G.V.S. and A.V.; writing—G.V.S. and M.-M.S.-B.; writing—review and editing, M.A.S.; visualization, R.M.M. and A.V.; supervision, G.V.S.; project administration, M.A.S.; funding acquisition, M.A.S. All authors have read and agreed to the published version of the manuscript.


This paper is part of the project “Hybrid environmental engineering lab for hands-on skills practice” acronym HybridPraxisLab—project code 2021-0017/14.07.2021, contract number 1059/15.06.2022, funded by internal projects carried out by University of Agronomic Sciences and Veterinary Medicine of Bucharest—Romania. The APC was funded by research project 1059/15.06.2022.

Institutional Review Board Statement

This article does not contain any studies that were performed by any of the authors with human participants or laboratory animals. The fish were legally purchased from commercial fishermen who conduct standard fishing activities in natural aquatic ecosystems.

Data Availability Statement

The data are contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.


  1. New Mexico State University. Available online: (accessed on 21 August 2023).
  2. Camargo, J.; Alonso, A. Ecological and toxicological effects of inorganic nitrogen pollution in aquatic ecosystems: A global assessment. Environ. Int. 2006, 32, 831–849. [Google Scholar] [CrossRef] [PubMed]
  3. Pinto, I.; Nogueira, S.; Rodrigues, S.; Formigo, N.; Antunes, S.C. Can zooplankton add value to monitoring water quality? A case study of a meso/eutrophic Portuguese reservoir. Water 2023, 15, 1678. [Google Scholar] [CrossRef]
  4. Mogane, L.K.; Masebe, T.; Msagati, T.A.M.; Ncube, E. A comprehensive review of water quality indices for lotic and lentic ecosystems. Environ. Monit. Assess. 2023, 195, 926. [Google Scholar] [CrossRef]
  5. Tumwesigye, Z.; Tumwesigye, W.; Opio, F.; Kemigabo, C.; Mujuni, B. The effect of water quality on aquaculture productivity in Ibanda District, Uganda. Aquac. J. 2022, 2, 3. [Google Scholar] [CrossRef]
  6. Pfeffer, E. The pollution of water by fish feeding. Dtsch. Tierarztl. Wochenschr. 1990, 97, 273–275. [Google Scholar]
  7. Kong, W.; Huang, S.; Yang, Z.; Shi, F.; Feng, Y.; Khatoon, Z. Fish feed quality is a key factor in impacting aquaculture water environment: Evidence from incubator experiments. Sci. Rep. 2020, 10, 187. [Google Scholar] [CrossRef]
  8. Akhter, F.; Siddiquei, H.R.; Alahi, M.E.E.; Mukhopadhyay, S.C. Recent Advancement of the Sensors for Monitoring the Water Quality Parameters in Smart Fisheries Farming. Computers 2021, 10, 26. [Google Scholar] [CrossRef]
  9. Manoj, M.; Dhilip Kumar, V.; Arif, M.; Bulai, E.-R.; Bulai, P.; Geman, O. State of the art techniques for water quality monitoring systems for fish ponds using IoT and underwater sensors: A Review. Sensors 2022, 22, 2088. [Google Scholar] [CrossRef]
  10. Elsayed, S.; Ibrahim, H.; Hussein, H.; Elsherbiny, O.; Elmetwalli, A.H.; Moghanm, F.S.; Ghoneim, A.M.; Danish, S.; Datta, R.; Gad, M. Assessment of water quality in Lake Qaroun using ground-based remote sensing data and artificial neural networks. Water 2021, 13, 3094. [Google Scholar] [CrossRef]
  11. Iorgu, E.I.; Popa, O.P.; Adam, C.; Pârvu, C.; Ban-Calefariu, C.; Ruști, D.; Stan, M.; Aioanei, F.; Stavrescu, M.-M.; Tudor, V.; et al. Agroecosistems fauna diversity and their role in bioconservation—A case study: Moara Domnească experimental centre. In Annual Zoological Congress of “Grigore Antipa” Museum; Murariu, D., Adam, C., Chişamera, G., Iorgu, E., Popa, L.O., Popa, O.P., Eds.; “Grigore Antipa” National Museum of Natural History: Bucharest, Romania, 2009; Volume 87. [Google Scholar]
  12. Bălan, V.; Tudor, V.; Marin, D.I.; Mihalache, M.; Dobrin, I.; Iacomi, B.; Armeanu, I.; Aioanei, F.; Stavrescu, M.-M.; Şchiopu, E.; et al. Multidisciplinarity research on pilot agroecosystem under conditions of climate change. Sci. Papers Ser. A Agron. 2010, LIII, 484–491. [Google Scholar]
  13. Jin, S.; Yan, X.; Zhang, H.; Fan, W. Weight–length relationships and Fulton’s condition factors of skipjack tuna (Katsuwonus pelamis) in the western and central Pacific Ocean. PeerJ 2015, 3, e758. [Google Scholar] [CrossRef] [PubMed]
  14. Zhang, H.; Xie, S.; Wang, S. Weight–length relationship and condition factor of gibel carp (Carassius auratus gibelio var. CAS V) at different growth stages and feed formulations. Fishes 2023, 8, 439. [Google Scholar] [CrossRef]
  15. Khan, M.S.; Hayat, S.; Ishtiaq, A.; Malik, A.; Akhtar, M.N.; Khan, G.Z.; Khan, Z.I.; Ali, Q. Length-Weight relationships of Cyprinus carpio from the Indus River at Chashma Lake, District Mianwali, Punjab, Pakistan. J. Wildl. Biodivers. 2020, 4, 72–80. [Google Scholar] [CrossRef]
  16. Lutai, C.; Tudor, L. Level of pollution with organochlorine pesticides in Lake Moara Domnească. Sci. Papers Ser. A Agron. 2010, LIII, 161E–166E. [Google Scholar]
  17. Enache, I.; Radu, M.; Diacu, E. Evaluation of longitudinal and temporal differentiation in heavy metals concentrations as priority hazardous substances in the water of Moara Domnească Lake. In Proceedings of the INCD Ecoind—International Symposium SIMI 2011, Bucharest, Romania, 16–18 November 2011. [Google Scholar]
  18. Sandu, M.A.; Virsta, A.; Vasile Scăețeanu, G.; Iliescu, A.-I.; Ivan, I.; Nicolae, C.G.; Stoian, M.; Madjar, R.M. Water quality monitoring of Moara Domnească Pond, Ilfov County, using UAV-based RGB imaging. Agrolife Sci. J. 2023, 12, 191–201. [Google Scholar] [CrossRef]
  19. Stavrescu-Bedivan, M.M.; Popa, O.P.; Aioanei, F.T.; Popa, L.O. Infestation of the pumpkinseed Lepomis gibbosus (Teleostei: Cyprinidae) by the copepod Lernaea cyprinacea (Crustacea)—Some ecological aspects. Trav. Mus. Natl. Hist. Nat. Grigore Antipa 2011, LIV, 63–68. [Google Scholar] [CrossRef]
  20. Hanganu, E.; Orza, O.; Bosoc, S.; Bălăceanu, C.; Suciu, G. Statistical Approach on Floods Features Based on Long Term Data Series Analysis. In Proceedings of the Air and Water—Components of the Environment, Cluj-Napoca, Romania, 18 March 2022. [Google Scholar]
  21. Cocoş, O. Managementul Apei în Municipiul București; Ars Docendi: Bucharest, Romania, 2006; pp. 68–103. [Google Scholar]
  22. Google Maps. Available online:,26.0332269//@44.4644361,26.2635744,12.04z/data=!4m5!4m4!1m1!4e1!1m0!3e2?entry=ttu (accessed on 18 October 2023).
  23. Google Earth Pro Desktop Version. Available online: (accessed on 18 October 2023).
  24. AVMA (American Veterinary Medical Association). AVMA Guidelines for the Euthanasia of Animals; American Veterinary Medical Association: Schaumburg, IL, USA, 2020; p. 64. [Google Scholar]
  25. Salini, J.P.; Milton, D.A.; Rahman, M.J.; Hussain, M.G. Allozyme and morphological variation throughout the geographic range of the tropical shad, hilsa Tenualosa ilisha. Fish. Res. 2004, 66, 53–69. [Google Scholar] [CrossRef]
  26. Aager, P.; Bagge, O.; Hansen, O.; Hoffman, E.; Holden, M.J.; Kesteven, G.L.; Knudsen, H.; Raitt, D.F.S.; Saville, A.; Williamns, T. Methods of resource investigation and their application. In Manual of Fisheries Science; Holden, M.J., Raitt, D.F.S., Eds.; FAO: Rome, Italy, 1974; Available online: (accessed on 19 August 2023).
  27. FishBase. Available online: (accessed on 19 August 2023).
  28. Kottelat, M.; Freyhof, J. Handbook of European Freshwater Species; IUCN: Cornol, Switzerland; Berlin, Germany, 2007; 646p. [Google Scholar]
  29. Froese, R. Cube law, condition factor and weight-length relationships: History, meta-analysis and recommendations. J. Appl. Ichthyol. 2006, 22, 241–253. [Google Scholar] [CrossRef]
  30. Stavrescu-Bedivan, M.-M.; Madjar, R.M.; Sion, I.A.; Scăeţeanu Vasile, G. Assessment of water quality and biometric analysis for common carp Cyprinus carpio L.; 1758: A case study from Bițina pond (Ilfov County, Romania). Agrolife Sci. J. 2022, 11, 191–200. [Google Scholar] [CrossRef]
  31. Karachle, P.K.; Stergiou, K.I. Morphometrics and allometry in fishes. In Morphometrics; Wahl, C.M., Ed.; IntechOpen: Rijeka, Croatia, 2012; pp. 65–68. [Google Scholar] [CrossRef]
  32. Hammer, Ø.; Harper, D.A.T.; Ryan, P.D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 2001, 4, 9. Available online: (accessed on 19 August 2023).
  33. Nehemia, A.; Maganira, J.D.; Rumisha, C. Length-Weight relationship and condition factor of tilapia species grown in marine and fresh water ponds. Agric. Biol. J. N. Am. 2012, 3, 117–124. [Google Scholar] [CrossRef]
  34. Kianpoor Kalkhajeh, Y.; Jabbarian Amiri, B.; Huang, B.; Henareh Khalyani, A.; Hu, W.; Gao, H.; Thompson, M.L. Methods for Sample Collection, Storage, and Analysis of Freshwater Phosphorus. Water 2019, 11, 1889. [Google Scholar] [CrossRef]
  35. Șimșek, E.; Kale, S. Length-Weight Relationship and Condition Factor of Prussian Carp (Carassius gibelio, Bloch, 1782) from Asi River. J. Agri. Prod. 2022, 3, 69–77. [Google Scholar] [CrossRef]
  36. Stavrescu-Bedivan, M.M.; Scăeţeanu Vasile, G.; Madjar, R.M.; Manole, M.S.; Staicu, A.C.; Aioanei, F.T.; Plop, E.F.; Tobă, G.L.; Nicolae, C.G. Interactions between fish well-being and water quality: A case study from Morii Lake area, Romania. Agric. Agric. Sci. Procedia 2016, 10, 328–339. [Google Scholar] [CrossRef]
  37. Famoofo, O.O.; Abdul, W.O. Biometry, condition factors and length-weight relationships of sixteen fish species in Iwopin fresh-water ecotype of Lekki Lagoon, Ogun State, Southwest Nigeria. Heliyon 2020, 6, e02957. [Google Scholar] [CrossRef]
  38. Boroń, A.; Boroń, S. Meristic and biometric parameters of silver crucian carp—Carassius auratus gibelio (Bloch, 1783) from Zegrzyński Dam Reservoir. Acta Acad. Agricult. Tech. Olst. 1996, 22, 3–13. [Google Scholar]
  39. Khalimova, N.T.; Kanatbaeva, T.S.; Ummatova, M.E.; Yuldashov, M.A.; Kamilov, B.G. Morphological characteristics of carp (Carassius gibelio) in the conditions of pond fish farming in Uzbekistan. IOP Conf. Ser. Earth Environ. Sci. 2023, 1142, 012072. [Google Scholar] [CrossRef]
  40. Jawad, L.A.; Al-Faisal, A.; Al-Mukhtar, M. Presence of the non-indigenous fish species, Gibel Carp (Carassius gibelio) (Family: Cyprinidae) in Basrah Province Freshwater Systems, Iraq. Water Res. Manag. 2012, 2, 41–44. [Google Scholar]
  41. Stavrescu-Bedivan, M.M. Some aspects of branchial parasitism in Rutilus rutilus (Linnaeus, 1758) from Moara Domnească Lake. Rom. J. Biol.-Zool. 2013, 58, 93–99. [Google Scholar]
  42. Pescuit Info. Available online: (accessed on 19 August 2023).
  43. Despre Pescuit. Available online: (accessed on 19 August 2023).
  44. Pescuitul. Available online: (accessed on 19 August 2023).
  45. Stavrescu-Bedivan, M.M.; Popa, O.P.; Popa, L.O. Infestation of Lernaea cyprinacea (Copepoda: Lernaeidae) in two invasive fish species in Romania, Lepomis gibbosus and Pseudorasbora parva. Knowl. Managt. Aquatic Ecosyst. 2014, 414, 12. [Google Scholar] [CrossRef]
  46. Balti. Available online: (accessed on 19 August 2023).
  47. Cojocaru, C.D. Fish Parasites of Romania—An Update. In Proceedings of the Sixth International Symposium on Aquatic Animal Health, Global Strategies for a changing environment, Tampa, FL, USA, 5–9 September 2010. [Google Scholar] [CrossRef]
  48. Stavrescu-Bedivan, M.M.; Scăețeanu Vasile, G. A note on the presence of philometrid fish parasites in Romania, with emphasis on Philometroides sanguineus (Rudolphi, 1819). Agrolife Sci. J. 2020, 9, 313–318. [Google Scholar]
  49. Stavrescu-Bedivan, M.-M.; Săndulescu, E.M.; Croitoru, C.M.; Madjar, R.M.; Boiu-Sicuia, O.-A.; Vasile Scăețeanu, G. A preliminary evaluation of water quality related to aquatic organisms in Comana pond. Agrolife Sci. J. 2023, 10, 178–188. [Google Scholar] [CrossRef]
  50. Order 161/2006 for the Approval of the Normative Concerning the Classification of Surface Water Quality to Establish the Ecological Status of Water Bodies. Official Journal of Romania 2006, 511 Bis. Available online: (accessed on 27 August 2023).
  51. Newport, C.; Padget, O.; Burt de la Perera, T. High turbidity levels alter coral reef fish movement in a foraging task. Sci. Rep. 2021, 11, 5976. [Google Scholar] [CrossRef]
  52. A Monitor’s Guide to Water Quality. Available online: (accessed on 27 August 2023).
  53. Zeng, Y.; Chang, F.; Wen, X.; Duan, L.; Zhang, Y.; Liu, Q.; Zhang, H. Seasonal Variation in the Water Quality and Eutrophication of Lake Xingyun in Southwestern China. Water 2022, 14, 3677. [Google Scholar] [CrossRef]
  54. Sallenave, R. Understanding Water Quality Parameters to Better Manage Your Pond; Guide W-104; Cooperative Extension Service; College of Agricultural, Consumer and Environmental Sciences: Urbana, IL, USA, 2012. [Google Scholar]
  55. Stone, N.M.; Thomforde, H.K. Understanding Your Fish Pond Water Analysis Report; Cooperative Extension Program; University of Arkansas at Pine Bluff, United States Department of Agriculture: Washington, DC, USA, 2004; pp. 1–4. [Google Scholar]
  56. Alatorre-Jacome, O.; Garcia-Trejo, F.; Rico-Garcia, E.; Soto-Zarazua, G.M. Aquaculture water quality for small-scale producers. In Aquaculture and the Environment—A Shared Destiny; Sladonja, B., Ed.; IntechOpen: Rijeka, Croatia, 2011; Volume 7, pp. 125–140. [Google Scholar] [CrossRef]
  57. Vasile Scăețeanu, G.; Madjar, R.M.; Silvestro, A.; Dulea, C.; Stavrescu-Bedivan, M.-M. Water quality monitoring and assessment of the ecological status of Tătaru fish pond, Călărași County. In Proceedings of the International Multidisciplinary Scientific GeoConference SGEM Vienna Green 2019, Palais Niederösterreich, Austria, 9–12 December 2019. [Google Scholar] [CrossRef]
  58. Stavrescu-Bedivan, M.-M.; Vasile Scăețeanu, G.; Madjar, R.M.; Matei, P.B.; Tobă, G.F. Comparative study of length-weight relationship, size structure and Fulton’s condition factor for Prussian carp from different Romanian aquatic ecosystems. Agrolife Sci. J. 2015, 4, 132–139. [Google Scholar]
  59. Rodrigues, A.; Calheiros, C.S.C.; Teixeira, P.; Galvão, A. Water quality assessment of urban ponds and remediation proposals. Hydrology 2022, 9, 114. [Google Scholar] [CrossRef]
  60. Dugan, H.A.; Bartlett, S.L.; Burke, S.M.; Doubek, J.P.; Krivak-Tetley, F.E.; Skaff, N.K.; Summers, J.; Farrell, K.; McCollough, I.; Morales-Williams, A.; et al. Salting our freshwater lakes. Proc. Natl. Acad. Sci. USA 2017, 114, 4453–4458. [Google Scholar] [CrossRef]
  61. Hudak, P. Chloride/bromide ratios in leachate derived from farm-animal waste. Environ. Pollut. 2003, 121, 23–25. [Google Scholar] [CrossRef]
  62. Tao, H.; Bobaker, A.; Ramal, M.M.; Yaseen, Z.H.; Hossain, M.S.; Shahid, S. Determination of biochemical oxygen demand and dissolved oxygen for semi-arid river environment: Application of soft computing models. Environ. Sci. Pollut. Res. 2019, 26, 923–937. [Google Scholar] [CrossRef]
  63. Erez, J.; Krom, M.D.; Neuwirth, T. Daily oxygen variations in marine fish ponds, Elat, Israel. Aquaculture 1990, 84, 289–305. [Google Scholar] [CrossRef]
  64. Hudson, H. Lake Notes. Common Lake Water Parameters. Available online: (accessed on 5 August 2023).
  65. Jeppesen, E.; Jensen, J.P.; Søndergaard, M.; Lauridsen, T.; Landkildehus, F. Trophic structure, species richness and biodiversity in Danish lakes: Changes along a phosphorus gradient. Freshw. Biol. 2000, 45, 201–218. [Google Scholar] [CrossRef]
  66. Kim, J.-H.; Kang, Y.J.; Lee, K.M. Effects of nitrite exposure on the hematological properties, antioxidant and stress responses of juvenile hybrid groupers, Epinephelus lanceolatusEpinephelus fuscoguttatus ♀. Antioxidants 2022, 11, 545. [Google Scholar] [CrossRef]
  67. Durborow, R.M.; Crosby, D.M.; Brunson, M.V. Nitrite in Fish Ponds; SRAC Publication: Leland, MS, USA, 1997; Volume 462, Available online: (accessed on 8 August 2023).
  68. Hargreaves, J.; Tucker, C. Managing Ammonia in Fish Ponds; SRAC Publication: Leland, MS, USA, 2004; Volume 4503, Available online: (accessed on 8 August 2023).
  69. Eddy, F.B. Ammonia in estuaries and effects on fish. J. Fish Biol. 2005, 67, 1495–1513. [Google Scholar] [CrossRef]
  70. Han, D.; Hu, Z.; Li, D.; Tang, R. Nitrogen removal of water and sediment in grass carp aquaculture ponds by mixed nitrifying and denitrifying bacteria and its effects on bacterial community. Water 2022, 14, 1855. [Google Scholar] [CrossRef]
  71. Bouwman, H.; Viljoen, I.; Quinn, L.; Polder, A. Halogenated pollutants in terrestrial and aquatic bird eggs: Converging patterns of pollutant profiles, and impacts and risks from high levels. Environ. Res. 2013, 126, 240–253. [Google Scholar] [CrossRef]
  72. Tzanetou, E.N.; Karasali, H. A comprehensive review of organochlorine pesticide monitoring in agricultural soils: The silent threat of a conventional agricultural past. Agriculture 2022, 12, 728. [Google Scholar] [CrossRef]
  73. Environmental Protection Agency. Available online: (accessed on 13 August 2023).
  74. Sandu, M.; Virsta, A. Environmental toxicity of lindane and health effect. J. Environ. Prot. Ecol. 2015, 16, 933–944. [Google Scholar]
  75. Directive 2008/105/EC of the European Parliament and the Council of 16 December 2008 on the Environmental Quality Standards in the Field of Water Policy. Official Journal of the European Union. Available online: (accessed on 27 August 2023).
  76. Sang, S.S.; Petrovic, S.; Cuddeford, V. Lindane—A Review of Toxicity and Environmental Fate; World Wildlife Fund (WWF) Canada: Ottawa, ON, Canada, 1999. [Google Scholar]
  77. Donald, D.B.; Block, H.; Wood, J. Role of ground water on hexachlorocyclohexane (lindane) detections in surface water in western Canada. Environ. Toxicol. Chem. 1997, 16, 1867–1872. [Google Scholar] [CrossRef]
Figure 2. Location of the sampling points from the Moara Domnească pond [23].
Figure 2. Location of the sampling points from the Moara Domnească pond [23].
Water 15 03978 g002
Figure 3. Biometric analysis of Carassius gibelio.
Figure 3. Biometric analysis of Carassius gibelio.
Water 15 03978 g003
Figure 4. Philometroides sanguineus in the caudal fin of Carassius gibelio.
Figure 4. Philometroides sanguineus in the caudal fin of Carassius gibelio.
Water 15 03978 g004
Figure 5. Quality classes for the surface water according to legislation [50], as well as to the assigned quality classes for water from the Moara Domnească pond.
Figure 5. Quality classes for the surface water according to legislation [50], as well as to the assigned quality classes for water from the Moara Domnească pond.
Water 15 03978 g005
Figure 6. Correlations between the conductivity and pH values with the total hardness of the water samples from the Moara Domnească pond (N = 36 samples, α = 0.01 and p < 0.01).
Figure 6. Correlations between the conductivity and pH values with the total hardness of the water samples from the Moara Domnească pond (N = 36 samples, α = 0.01 and p < 0.01).
Water 15 03978 g006
Figure 7. Algae bloom in the Moara Domnească pond. (Original images taken on 12 September 2022).
Figure 7. Algae bloom in the Moara Domnească pond. (Original images taken on 12 September 2022).
Water 15 03978 g007
Table 1. Sampling point coordinates.
Table 1. Sampling point coordinates.
Sampling Points (SP)Latitude, NorthLongitude, East
SP144°29′45.12048″ N26°14′45.1644″ E
SP244°29′45.65904″ N26°14′45.80844″ E
SP344°29′46.62996″ N26°14′46.74156″ E
SP444°29′48.685952″ N26°14′35.58336″ E
SP544°29′49.00812″ N26°14′37.09536″ E
SP644°29′49.84728″ N26°14′38.24304″ E
SP744°29′54.609″ N26°14′33.2142″ E
SP844°29′54.86892″ N26°14′31.32672″ E
SP944°29′55.36392″ N26°14′29.42808″ E
SP1044°29′58.20684″ N26°14′24.5238″ E
SP1144°29′59.90388″ N26°14′26.6244″ E
SP1244°29′59.63964″ N26°14′25.332″ E
Table 2. Determined parameters, analytical methods and instrumentation.
Table 2. Determined parameters, analytical methods and instrumentation.
ParameterAbbreviationUnitAnalytical MethodsInstrumentation
Turbidity T NTU Nephelometry HI88703-02 Turbidity Meter, Hanna Instruments, Smithfield, RI, USA
pH pH pH units Potentiometry inoLab pH 720, WTW, Munich, Germany
Electrical conductivity EC μS cm−1 Conductometry SensION7, Hach, Loveland, CO, USA
Chloride Cl mg L−1 Mohr’s method Digital burette, Eppendorf, Hamburg, Germany
Total hardness TH mg CaO L−1 Complexometry -
Dissolved oxygen DO mg O2 L−1 Polarographic method HI98193 Dissolved Oxygen/
BOD/OUR/SOUR Meter, Hanna Instruments, USA
Chemical oxygen demand CODMn mg O2 L−1 Redox titration -
Biochemical oxygen demand BOD mg O2 L−1 Polarographic method HI98193 Dissolved Oxygen/
BOD/OUR/SOUR Meter, Hanna Instruments, USA + BOD Sensor, Velp Scientifica, Usmate Velate, Italy + FTC90E Incubator BOD, Velp Scientifica, Italy
Phosphate phosphorus P-PO43− mg P L−1 Spectrophotometry Metertech SP830 Plus
spectrophotometer, Metertech Inc., Taipei, Taiwan
Nitrite nitrogen N-NO2 mg N L−1
Nitrate nitrogen N-NO3 mg N L−1
Ammonium nitrogen N-NH4+ mg N L−1
Organochlorine pesticides OCPs μg L−1 Gas chromatography 8890 Gas Chromatograph (GC) System equipped with electron capture detector (ECD), Agilent Technologies, Santa Clara, CA, USA
Table 3. Biometric features of the Prussian carp from the Moara Domnească pond, Ilfov County, Romania (April–May 2023) (n = sample size; SD = standard deviation; TL and SL = total and standard length, in cm; TW = total weight, in grams; HL = head length, in cm; SNL = snout length, in cm; BD = body depth, in cm; and G = girth, in cm).
Table 3. Biometric features of the Prussian carp from the Moara Domnească pond, Ilfov County, Romania (April–May 2023) (n = sample size; SD = standard deviation; TL and SL = total and standard length, in cm; TW = total weight, in grams; HL = head length, in cm; SNL = snout length, in cm; BD = body depth, in cm; and G = girth, in cm).
Morphometric MeasurementsSampling Season
April 2023 (n = 32)May 2023 (n = 28)
Min.–Max.Mean ± SDMin.–Max.Mean ± SD
TL 19.9–25.5 22.12 ± 1.32 18–26.9 23.89 ± 2.26
SL 15.8–20 17.75 ± 1.04 14–22 19.29 ± 2.02
TL/SL 1.08–1.56 1.25 ± 0.12 1.15–1.29 1.24 ± 0.03
SL in % of TL 63.92–92.96 80.26 ± 7.71 77.78–86.61 80.75 ± 2.16
HL 4.2–5.2 4.79 ± 0.29 3.8–5.8 5.1 ± 0.49
HL in % of TL 18.49–26.24 21.72 ± 2.04 19.92–23.4 21.33 ± 0.89
SNL 1.1–1.7 1.31 ± 0.13 1.1–1.7 1.49 ± 0.15
SNL in % of TL 5.53–6.67 5.88 ± 0.25 4.68–8.89 6.25 ± 0.97
BD 5.7–7.2 6.48 ± 0.35 4.7–7.7 6.54 ± 0.75
BD in % of TL 28.12–32.38 29.23 ± 0.94 25–31.7 27.36 ± 1.53
G 13–16 14.86 ± 0.82 10–16.5 14.52 ± 1.53
TW 125.85–234.07 184.27 ± 26.94 82.84–267.51 196.26 ± 46.79
TL/HL 3.81–5.42 4.61 ± 0.44 4.27–5.02 4.69 ± 0.19
TL/BD 3.09–3.56 3.42 ± 0.11 3.21–4 3.65 ± 0.2
Table 4. Weight–length relationships (WLRs) (n = sample size; b = slope; r2 = coefficient of determination; and CI95% of b = confidence intervals for slope), condition factor K (Min.–Max. and Mean ± SD) and the type of growth, tg (A− = allometric negative) for the Prussian carp from the Moara Domnească pond, Ilfov County, Romania.
Table 4. Weight–length relationships (WLRs) (n = sample size; b = slope; r2 = coefficient of determination; and CI95% of b = confidence intervals for slope), condition factor K (Min.–Max. and Mean ± SD) and the type of growth, tg (A− = allometric negative) for the Prussian carp from the Moara Domnească pond, Ilfov County, Romania.
Sampling Seasonnbr2WLRsCI95% of bKtg
April 2023322.33050.8169TW = 0.1338TL2.33051.961–2.7461.41–1.94A−
LogTW = 2.3305TL − 0.8735
2.45830.8852TW = 0.1547SL2.45832.128–2.7881.68 ± 0.13
Log TW = 2.4583LogSL − 0.8106
May 2023282.7430.9292TW = 0.0325TL2.7432.438–3.0481.23–1.65A−
LogTW = 2.743LogTL − 1.4877
2.40590.8874TW = 0.1586SL2.40592.060–2.7511.44 ± 0.11
LogTW = 2.4059SL − 0.7997
Table 5. Records of the fish species and their parasites that inhabit the Moara Domnească pond, Ilfov County, Romania (as obtained from scientific publications and gray literature).
Table 5. Records of the fish species and their parasites that inhabit the Moara Domnească pond, Ilfov County, Romania (as obtained from scientific publications and gray literature).
Fish Species, Scientific Classification, Common Name and Romanian Common NameParasite Species, Scientific Classification and Common NameReferences
Alburnus alburnus (Cypriniformes: Leuciscidae), Bleak/oblet * N/A[42]
Carassius auratus gibelio * (Cypriniformes: Cyprinidae), Prussian carp//silver Prussian carp/Gibel carp, caras argintiuLernaea cyprinacea
(Copepoda: Lernaeidae)
Carassius gibelio (Cypriniformes: Cyprinidae), Prussian carp,
caras argintiu
Philometroides sanguineus
(Nematoda: Philometridae)
present study
Ctenopharyngodon idella * (Cypriniformes: Xenocyprididae), Grass carp, cosaș N/A[41]
Cyprinus carpio (Cypriniformes: Cyprinidae), Common carp, carp * N/A[43]
Cyprinus carpio var. specularis (Cypriniformes: Cyprinidae), Mirror carp, salontă * N/A[44]
Hypophthalmichthys nobilis syn. Aristichthys nobilis * (Cypriniformes: Xenocyprididae), Bighead carp, novac N/A[41]
Hypophthalmichthys molitrix * (Cypriniformes: Xenocyprididae), Silver carp, sânger N/A[41]
Lepomis gibbosus * (Perciformes: Centrarchidae), pumpkinseed, regina L. cyprinacea[19,45]
Pelecus cultratus (Cypriniformes: Leuciscidae), Sichel, săbioară * N/A[46]
Perca fluviatilis (Perciformes: Percidae), European perch, biban *N/A[46]
Pseudorasbora parva * (Cypriniformes: Gobionidae), Stone moroko, murgoi bălțat L. cyprinacea[19,45]
Rutilus rutilus * (Cypriniformes: Leuciscidae), Roach, babușcă L. cyprinacea[19]
Paradiplozoon sp.
(Platyhelminthes: Diplozoidae)
Scardinius erythrophthalmus * (Cypriniformes: Leuciscidae), Rudd, roșioară N/A[41]
Squalius cephalus syn. Leuciscus cephalus * (Cypriniformes: Leuciscidae), Chub, clean N/A[41]
Notes: N/A = data not available; * as mentioned in original source.
Table 6. Determined values ± the standard deviation (SD) for the water quality parameters at the 12 sampling points (SP) of the Moara Domnească pond.
Table 6. Determined values ± the standard deviation (SD) for the water quality parameters at the 12 sampling points (SP) of the Moara Domnească pond.
Sampling PointTpHECTHClOxygen Regime DataNutrient Data
NTUμS cm−1mg CaO L−1mg L−1mg O2 L−1mg P L−1mg N L−1
SP17.62 ± 0.0577.38 ± 0.0491043 ± 9.8520.35 ± 0.1382.96 ± 0.898.30 ± 0.03111.14 ± 0.403.59 ± 0.0262.533 ± 0.0830.107 ± 0.0080.307 ± 0.0337.869 ± 0.235
SP27.83 ± 0.1917.25 ± 0.0151065 ± 11.1420.94 ± 0.1378.42 ± 0.858.68 ± 0.02012.10 ± 0.403.43 ± 0.0252.444 ± 0.0940.110 ± 0.0020.323 ± 0.0237.986 ± 0.235
SP36.80 ± 0.0157.26 ± 0.0151064.67 ± 12.5020.57 ± 0.1377.07 ± 0.318.12 ± 0.10812.31 ± 0.893.55 ± 0.0362.338 ± 0.0530.112 ± 0.0030.257 ± 0.0298.378 ± 0.179
SP48.37 ± 0.0597.33 ± 0.0121047 ± 16.0920.52 ± 0.2276.93 ± 0.128.10 ± 0.02513.32 ± 0.333.82 ± 0.0302.406 ± 0.0950.108 ± 0.0090.311 ± 0.0308.025 ± 0.591
SP56.93 ± 0.1267.38 ± 0.0211078.67 ± 14.2220.22 ± 0.1374.74 ± 0.428.75 ± 0.06012.15 ± 0.584.02 ± 0.0172.364 ± 0.0760.115 ± 0.0050.323 ± 0.0357.595 ± 0.136
SP67.42 ± 0.0537.27 ± 0.0291067 ± 3.6120.67 ± 0.2676.64 ± 0.088.88 ± 0.01512.42 ± 0.823.74 ± 0.0462.410 ± 0.0630.106 ± 0.0070.380 ± 0.0358.260 ± 0.179
SP78.68 ± 0.0217.35 ± 0.0101072.33 ± 4.7321.39 ± 0.1375.94 ± 0.128.85 ± 0.04512.90 ± 0.333.91 ± 0.0202.397 ± 0.1720.114 ± 0.0030.499 ± 0.0758.299 ± 0.359
SP87.53 ± 0.0157.31 ± 0.0061082 ± 3.0020.57 ± 0.2674.56 ± 0.088.95 ± 0.06811.99 ± 0.583.25 ± 0.0512.554 ± 0.0450.114 ± 0.0050.388 ± 0.0377.986 ± 0.423
SP98.60 ± 0.0207.34 ± 0.0211074 ± 5.5720.57 ± 0.1375.87 ± 0.148.60 ± 0.02512.53 ± 0.333.85 ± 0.0462.326 ± 0.0840.118 ± 0.0030.584 ± 0.0547.790 ± 0.412
SP109.53 ± 0.0317.38 ± 0.0261075.67 ± 4.9322.29 ± 0.1375.63 ± 0.098.65 ± 0.07614.82 ± 0.513.68 ± 0.0472.541 ± 0.0760.114 ± 0.0050.549 ± 0.0247.438 ± 0.179
SP1111.25 ± 0.0457.36 ± 0.0061074.33 ± 5.0321.92 ± 0.1373.28 ± 0.078.05 ± 0.06214.07 ± 0.423.86 ± 0.0462.448 ± 0.0670.117 ± 0.0020.423 ± 0.0817.751 ± 0.311
SP129.65 ± 0.1338.11 ± 0.0321164.67 ± 4.1622.45 ± 0.1375.83 ± 0.317.71 ± 0.07011.99 ± 0.584.29 ± 0.0932.579 ± 0.0950.132 ± 0.0020.288 ± 0.0358.456 ± 0.587
Table 7. Ranges, means, standard deviations and the assignment of water quality classes for the samples collected from the Moara Domnească pond.
Table 7. Ranges, means, standard deviations and the assignment of water quality classes for the samples collected from the Moara Domnească pond.
Mean Value ± SDLimits/Quality Classes *
TNTU6.80–11.258.35 ± 1.29NA
pHpH unit7.25–8.117.39 ± 0.236.50–8.50
ECμS cm−11043–1164.671075.69 ± 30.43NA
THmg CaO L−120.22–22.4521.04 ± 0.78NA
Clmg L−173.28–82.9676.49 ± 2.43III
DOmg O2 L−17.71–8.958.47 ± 0.40II
CODMnmg O2 L−111.14–14.8212.64 ± 1.005III
BODmg O2 L−13.25–4.293.75 ± 0.28II
P-PO43−mg P L−12.326–2.5792.445 ± 0.087V
N-NO2mg N L−10.107–0.1320.114 ± 0.007IV
N-NO3mg N L−10.257–0.5840.386 ± 0.107I
N-NH4+mg N L−17.438–8.4567.986 ± 0.317V
Notes: SD = standard deviation; * = according to [50]; and NA = not available.
Table 8. Determined levels of the OCPs.
Table 8. Determined levels of the OCPs.
Sampling Pointα-HCHβ-HCHγ-HCH
μg L−1
SP5 <LOQ0.0230.018<LOQ0.0410.015<LOQ0.0340.049
Range -LOQ–0.0230.008–0.018-0.008–0.041LOQ–0.015-LOQ–0.034LOQ–0.049
Mean value ± SD --0.0115 ± 0.0036-0.0153 ± 0.0126----
Limits * --0.020-0.042 0.0100.025
Notes: SD = standard deviation; LOQ = limit of quantification (0.001 μg L−1); and * = according to [50,75].
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Sandu, M.A.; Madjar, R.M.; Preda, M.; Vîrsta, A.; Stavrescu-Bedivan, M.-M.; Vasile Scăețeanu, G. Assessment of Water Quality and Parasitofauna, and a Biometric Analysis of the Prussian Carp of the Romanian Lentic Ecosystem in Moara Domnească, Ilfov County. Water 2023, 15, 3978.

AMA Style

Sandu MA, Madjar RM, Preda M, Vîrsta A, Stavrescu-Bedivan M-M, Vasile Scăețeanu G. Assessment of Water Quality and Parasitofauna, and a Biometric Analysis of the Prussian Carp of the Romanian Lentic Ecosystem in Moara Domnească, Ilfov County. Water. 2023; 15(22):3978.

Chicago/Turabian Style

Sandu, Mirela Alina, Roxana Maria Madjar, Mihaela Preda, Ana Vîrsta, Mala-Maria Stavrescu-Bedivan, and Gina Vasile Scăețeanu. 2023. "Assessment of Water Quality and Parasitofauna, and a Biometric Analysis of the Prussian Carp of the Romanian Lentic Ecosystem in Moara Domnească, Ilfov County" Water 15, no. 22: 3978.

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