1. Introduction

2. Materials and Methods

3. Results

3.1. Data Analysis

In the following, the statistical and numerical approaches’ results will be provided.

Table 2. Results of the parameters from the IARC list analyzed from the water samples taken in July 2022.

Table 2. Results of the parameters from the IARC list analyzed from the water samples taken in July 2022.

Station	Aluminium (µg/L)	Arsenic (µg/L)	Beryllium (µg/L)	Cadmium (µg/L)	Chromium (µg/L)	Phosphate - P-PO4 (mg/L)
P1	14.468	0.002	<LOD (< 0.5 ng/L)	0.008	1.027	0.080
P2	19.670	0.001	<LOD (< 0.5 ng/L)	0.004	0.764	0.040
P3	46.890	0.002	<LOD (< 0.5 ng/L)	0.021	1.280	0.070
P4	15.704	0.002	<LOD (< 0.5 ng/L)	0.016	1.605	0.140
P5	38.868	0.002	<LOD (< 0.5 ng/L)	0.016	1.239	0.090
P6	17.214	0.001	<LOD (< 0.5 ng/L)	0.008	1.123	0.070
P7	24.168	0.001	<LOD (< 0.5 ng/L)	0.038	1.291	0.050
P8	8.665	0.001	<LOD (< 0.5 ng/L)	0.004	0.771	0.070
P9	28.889	0.001	<LOD (< 0.5 ng/L)	0.020	2.318	0.080
P10	32.650	0.002	<LOD (< 0.5 ng/L)	0.019	1.694	0.080

3.4. Numerical Analysis

For a numerical simulation and an evaluation of the distribution of the concentrations of the studied metals in the investigation area, we considered the possibility of using dedicated software programs, often used in the literature [1,6,23]. HEC-RAS is a program that allows, in the new version, to solve diffusion-type equations in the 2D case, such as the equations below[18]:

$${\displaystyle \frac{\partial H}{\partial t}}+{\displaystyle \frac{\partial H}{\partial x}}+{\displaystyle \frac{\partial H}{\partial y}}=r$$

(1)

$${\displaystyle \frac{\partial p}{\partial t}}+{\displaystyle \frac{\partial }{\partial x}}\left({\displaystyle \frac{p^2}{h}}\right)+{\displaystyle \frac{\partial }{\partial y}}\left({\displaystyle \frac{pq}{h}}\right)=-{\displaystyle \frac{{\eta }^{2}pg\sqrt{p^2+q^2}}{h^2}}-gh{\displaystyle \frac{\partial H}{\partial x}}+pf+{\displaystyle \frac{\partial }{\rho \partial x}}\left(h{\tau }_{xx}\right)+{\displaystyle \frac{\partial }{\rho \partial y}}\left(h{\tau }_{xy}\right)+A(x,y)$$

$${\displaystyle \frac{\partial p}{\partial t}}+{\displaystyle \frac{\partial }{\partial x}}\left({\displaystyle \frac{pq}{h}}\right)+{\displaystyle \frac{\partial }{\partial y}}\left({\displaystyle \frac{q^2}{h}}\right)=-{\displaystyle \frac{{\eta }^{2}pg\sqrt{p^2+q^2}}{h^2}}-gh{\displaystyle \frac{\partial H}{\partial y}}+qf+{\displaystyle \frac{\partial }{\rho \partial x}}\left(h{\tau }_{xy}\right)+{\displaystyle \frac{\partial }{\rho \partial y}}\left(h{\tau }_{yy}\right)+A(x,y)$$

where $H(x,y,t)=z(x,y)+h(x,y,t)$ is the height of the surface (in m), $z=z(x,y)$ is the elevation of the cell in the Cartesian coordinate system,$(x,y)$, $h$ - is the water depth (in m), $p=hu$ and $q=hv$ are the specific flows along the axes $x$ and respectively $y$; $u$ and $v$ and are the specific speeds along the axes $x$ nd respectively $y$; $g$ - is the gravitational acceleration (in $m/s^2$), $\eta$ is the Manning roughness coefficient (in $s/m^3$),$\rho$ - is the density (in $kg/m^3$), iar ${\tau }_{x,x},{\tau }_{x,y},{\tau }_{y,y}$ - represents the components of the flow tensor and $f$ is the Corriolis parameter (in $s^{-1}$). These equations can be solved within Hec-Ras using finite element calculation [32,38,39]). The term $A(x,y)$ represents the attenuation term - correlated with the transfer phenomenon. In the context of the HEC-RAS bidimensional model, an additive terms can be significant in order to obtain a correlation for concentration variability by two reasons: a) model complexity: an additive term can account for additional physical processes or factors that affect concentration variability but are not captured by the primary terms of the model; b) accuracy and precision: additive term can improve the accuracy and precision of the model by fine-tuning the representation of the transport and diffusion of substances in water which is the main aim; c) calibration and validation: These terms can be crucial for the calibration and validation of the model, ensuring that the simulated data matches observed data as closely as possible.

The modeler in HEC-RAS allowed the development of geometric data either by primary drawing in the river system scheme using the Geometric Data Window (Figure 3) or by developing a scheme in HEC-RAS Mapper (HEC-RAS Mapper). Thus the river system diagram is a diagram of how the water flow system is connected together. In the present case, 2D models were considered because the existence of weirs in the river bed make the evaluated concentrations differ and there are differences between areas related to the two banks. The calibration of the hydraulic model was first performed upstream of the study area, more precisely in the Braila sector for Danube river, and later downstream, at the mouth of the Ceatal branch in the Reni - Tulcea area. The calibration of the model in the HEC-RAS software was made by alternate values of the Manning roughness coefficients, between 0.025 and 0.03, and by applying the rating curve data from the Tulcea hydrometric station [13,19]. In this respect, by considering the disadvantage of the static method is that the simulations cannot be calibrated with field data, was used the dynamic methodology has the advantage of introducing a much more complex input data set, including calibration data, which gives a higher confidence in the results obtained.

In general, the time of concentration (TC) is a parameter in runoff estimation, used for the study and design of different types of hydrographic systems (Pierfranco Costabile, Masih Zolghadr). Any error in the TC calculation leads to an inaccurate flux estimate, which can lead to over- or under-dimensioning of the results. Therefore, choosing the correct time estimation method is of great importance.

Figure 11. representation the graph showing the monthly evolution of the Danube flow in the monitored area - for the non-stationary study.

Figure 11. representation the graph showing the monthly evolution of the Danube flow in the monitored area - for the non-stationary study.

In the present case, we considered unsteady modeling using the data on the monthly flow of the Danube throughout the year 2022, with a sampling rate of 1 hour, an average rate of 1 week and a graphic evaluation with a rate of 1 month. In this way, we considered the model data to be the closest to reality. Also, I entered the monthly flow data for the Siret and Prut rivers according to https://www.hidro.ro/prognoze/.

Considering the ratio between the flows of the tributaries and that of the Danube, the contribution related to the transport attributed to the tributaries is significant only in the areas close to the confluence points, manifesting a dilution along the main flow.

For each chemical species, a number of simulations were carried out, the scenarios being selected based on the comparison between the measured values and those evaluated within the 2D used models.

Figure 12 shows the results of the 2D diffusion models for the two measurement campaigns - from July (Figure 12.a) and from October (Figure 12.b) respectively for iron. Thus, in Figure 12.a, because the flow of the Danube is higher, the flood zones that are recorded near the right bank are also considered [1,32,39]. The measured values and those obtained after the evaluation are in agreement. Thus, during the summer, the values obtained are systematically higher - an observation made in other previous studies [2,34]. The most interesting aspect is given by the possibility of predicting the downstream values, although the distances between the monitoring points cover a distance of 57.8 km. The quality of fit between the measured and evaluated values is in good agreement (R² = 0.8715).

Figure 13 shows the results of the 2D diffusion models for the two measurement campaigns - from July (Figure 13.a) and from October (Figure 13.b) respectively for chromium. Thus, in Figure 13.a, the evaluated and measured values have a constant trend with minimal variations, a maximum being recorded next to the monitoring points P4 and P9 respectively (Figure 13.b). this aspect may be related to the industrial activities near the two points. Similarly, during the autumn campaign, the tendency to keep a constant value is respected with a tendency to reduce values downstream. The measured values and those obtained after the evaluation are in accordance with the measured values (R² > 0.80). This is relatively easy to understand, considering the fact that there are values that tend to maintain a constant trend along the studied 57.8 km.

Figure 14 shows the results of the 2D diffusion models for the two measurement campaigns - from July (Figure 14.a) and from October (Figure 14.b) respectively for Cadmium. In figure 14.a, the evaluated and measured values have a constant trend with minimal variations, a maximum being recorded near the monitoring point P7 (Figure 14.a). This aspect may be related to the industrial activities near this point. Similarly, during the autumn campaign, the tendency to maintain a constant value is respected. The measured values and those obtained after the evaluation are in accordance with the measured values (R² > 0.67).

This is relatively easy to understand, considering the fact that there are values that tend to maintain a constant trend along the studied 57.8 km.

Figure 15 shows the results of the 2D diffusion models for the two measurement campaigns - from July (Figure 15.a) and from October (Figure 15.b) respectively for arsenic. Thus, in Figure 15.a, the evaluated and measured values have a constant tendency, however, being maximum near the monitoring points P5 and P9, respectively (Figure 15.a). Similarly, during the autumn campaign, the tendency to keep a constant value is respected with a tendency to reduce values downstream. The measured and evaluated values are in agreement, the values from the autumn campaign being significantly higher than those during the summer. This was also observed by other authors [1,32].

4. Discussion

References

1. Popescu, F.; Trumić, M.; Cioabla, A.E.; Vujić, B.; Stoica, V.; Trumić, M.; Opris, C.; Bogdanović, G.; Trif-Tordai, G. Analysis of Surface Water Quality and Sediments Content on Danube Basin in Djerdap-Iron Gate Protected Areas. Water 2022, 14, 2991. [Google Scholar] [CrossRef]
2. Popa, P.; Murariu, G.; Timofti, M.; Georgescu, L.P. MULTIVARIATE STATISTICAL ANALYSES OF DANUBE RIVER WATER QUALITY AT GALATI, ROMANIA. Environ. Eng. Manag. J. 2018, 17, 1249–1266. [Google Scholar] [CrossRef]
3. Fang, T.; Lu, W.; Cui, K.; Li, J.; Yang, K.; Zhao, X.; Liang, Y.; Li, H. Distribution, Bioaccumulation and Trophic Transfer of Trace Metals in the Food Web of Chaohu Lake, Anhui, China. Chemosphere 2019, 218, 1122–1130. [Google Scholar] [CrossRef] [PubMed]
4. Hakanson, L. An Ecological Risk Index for Aquatic Pollution Control.a Sedimentological Approach. Water Research 1980, 14, 975–1001. [Google Scholar] [CrossRef]
5. Simionov, I.-A.; Călmuc, M.; Iticescu, C.; Călmuc, V.; Georgescu, P.-L.; Faggio, C.; Petrea, Ş.-M. Human Health Risk Assessment of Potentially Toxic Elements and Microplastics Accumulation in Products from the Danube River Basin Fish Market. Environmental Toxicology and Pharmacology 2023, 104, 104307. [Google Scholar] [CrossRef] [PubMed]
6. Teodorof, L.; Ene, A.; Burada, A.; Despina, C.; Seceleanu-Odor, D.; Trifanov, C.; Ibram, O.; Bratfanof, E.; Tudor, M.-I.; Tudor, M.; et al. Integrated Assessment of Surface Water Quality in Danube River Chilia Branch. Applied Sciences 2021, 11, 9172. [Google Scholar] [CrossRef]
7. Fishes-08-00387.Pdf.
8. Matache, M.L.; Marin, C.; Rozylowicz, L.; Tudorache, A. Plants Accumulating Heavy Metals in the Danube River Wetlands. J Environ Health Sci Engineer 2013, 11, 39. [Google Scholar] [CrossRef] [PubMed]
9. Georgescu, P.-L.; Moldovanu, S.; Iticescu, C.; Calmuc, M.; Calmuc, V.; Topa, C.; Moraru, L. Assessing and Forecasting Water Quality in the Danube River by Using Neural Network Approaches. Science of The Total Environment 2023, 879, 162998. [Google Scholar] [CrossRef] [PubMed]
10. Bąk, Ł.; Szeląg, B.; Sałata, A.; Studziński, J. Modeling of Heavy Metal (Ni, Mn, Co, Zn, Cu, Pb, and Fe) and PAH Content in Stormwater Sediments Based on Weather and Physico-Geographical Characteristics of the Catchment-Data-Mining Approach. Water 2019, 11, 626. [Google Scholar] [CrossRef]
11. Iticescu, C.; Georgescu, P.-L.; Arseni, M.; Rosu, A.; Timofti, M.; Carp, G.; Cioca, L.-I. Optimal Solutions for the Use of Sewage Sludge on Agricultural Lands. Water 2021, 13, 585. [Google Scholar] [CrossRef]
12. Sun, C.; Zhang, Z.; Cao, H.; Xu, M.; Xu, L. Concentrations, Speciation, and Ecological Risk of Heavy Metals in the Sediment of the Songhua River in an Urban Area with Petrochemical Industries. Chemosphere 2019, 219, 538–545. [Google Scholar] [CrossRef] [PubMed]
13. Simionov, I.-A.; Cristea, D.S.; Petrea, Ștefan-M. ; Mogodan, A.; Jijie, R.; Ciornea, E.; Nicoară, M.; Turek Rahoveanu, M.M.; Cristea, V. Predictive Innovative Methods for Aquatic Heavy Metals Pollution Based on Bioindicators in Support of Blue Economy in the Danube River Basin. Sustainability 2021, 13, 8936. [Google Scholar] [CrossRef]
14. Calmuc, M.; Calmuc, V.; Arseni, M.; Topa, C.; Timofti, M.; Georgescu, L.P.; Iticescu, C. A Comparative Approach to a Series of Physico-Chemical Quality Indices Used in Assessing Water Quality in the Lower Danube. Water 2020, 12, 3239. [Google Scholar] [CrossRef]
15. Dong, D.; Zhao, X.; Hua, X.; Liu, J.; Gao, M. Investigation of the Potential Mobility of Pb, Cd and Cr(VI) from Moderately Contaminated Farmland Soil to Groundwater in Northeast, China. Journal of Hazardous Materials 2009, 162, 1261–1268. [Google Scholar] [CrossRef] [PubMed]
16. Liu, Y.; Xu, Z.; Hu, X.; Zhang, N.; Chen, T.; Ding, Z. Sorption of Pb(II) and Cu(II) on the Colloid of Black Soil, Red Soil and Fine Powder Kaolinite: Effects of pH, Ionic Strength and Organic Matter. Environmental Pollutants and Bioavailability 2019, 31, 85–93. [Google Scholar] [CrossRef]
17. Resz, M.-A.; Roman, C.; Senila, M.; Török, A.I.; Kovacs, E. A Comprehensive Approach to the Chemistry, Pollution Impact and Risk Assessment of Drinking Water Sources in a Former Industrialized Area of Romania. Water 2023, 15, 1180. [Google Scholar] [CrossRef]
18. Takić, L.; Mladenović-Ranisavljević, I.; Vasović, D.; Đorđević, L. The Assessment of the Danube River Water Pollution in Serbia. Water Air Soil Pollut 2017, 228, 380. [Google Scholar] [CrossRef]
19. Banescu, A.; Arseni, M.; Georgescu, L.P.; Rusu, E.; Iticescu, C. Evaluation of Different Simulation Methods for Analyzing Flood Scenarios in the Danube Delta. Applied Sciences 2020, 10, 8327. [Google Scholar] [CrossRef]
20. Zhang, W.; Long, J.; Zhang, X.; Shen, W.; Wei, Z. Pollution and Ecological Risk Evaluation of Heavy Metals in the Soil and Sediment around the HTM Tailings Pond, Northeastern China. IJERPH 2020, 17, 7072. [Google Scholar] [CrossRef]
21. Da Silva Júnior, R.O.; Almeida, H.P.; Da Silva, M.S.; França, A.C.; Balleroni, E.; Dos Santos, N.; Vilela, P.H.; De Melo, A.M.Q.; Guimarães, J.T.F. Methodological Approach for an Online Water Quality Monitoring System in an Iron Ore Tailing Dam. Water 2023, 15, 3663. [Google Scholar] [CrossRef]
22. Pham Van, C.; Chua, V. Numerical Simulation of Hydrodynamic Characteristics and Bedload Transport in Cross Sections of Two Gravel-Bed Rivers Based on One-Dimensional Lateral Distribution Method. International Journal of Sediment Research 2020, 35, 203–216. [Google Scholar] [CrossRef]
23. Wang, H.; Yuan, W.; Zeng, Y.; Liang, D.; Deng, Y.; Zhang, X.; Li, Y. How Does Three Gorges Dam Regulate Heavy Metal Footprints in the Largest Freshwater Lake of China. Environmental Pollution 2022, 292, 118313. [Google Scholar] [CrossRef] [PubMed]
24. Calmuc, V.A.; Calmuc, M.; Arseni, M.; Topa, C.M.; Timofti, M.; Burada, A.; Iticescu, C.; Georgescu, L.P. Assessment of Heavy Metal Pollution Levels in Sediments and of Ecological Risk by Quality Indices, Applying a Case Study: The Lower Danube River, Romania. Water 2021, 13, 1801. [Google Scholar] [CrossRef]
25. Iticescu, C.; Georgescu, L.P.; Murariu, G.; Topa, C.; Timofti, M.; Pintilie, V.; Arseni, M. Lower Danube Water Quality Quantified through WQI and Multivariate Analysis. Water 2019, 11, 1305. [Google Scholar] [CrossRef]
26. Saeed, O.; Székács, A.; Jordán, G.; Mörtl, M.; Abukhadra, M.R.; Eid, M.H. Investigating the Impacts of Heavy Metal(Loid)s on Ecology and Human Health in the Lower Basin of Hungary’s Danube River: A Python and Monte Carlo Simulation-Based Study. Environ Geochem Health 2023, 45, 9757–9784. [Google Scholar] [CrossRef] [PubMed]
27. Cordeli, A.N. Bioaccumulation of Metals in Some Fish Species from the Romanian Danube River: A Review. 2023.
28. Simionov, I.-A.; Călmuc, M.; Iticescu, C.; Călmuc, V.; Georgescu, P.-L.; Faggio, C.; Petrea, Ş.-M. Human Health Risk Assessment of Potentially Toxic Elements and Microplastics Accumulation in Products from the Danube River Basin Fish Market. Environmental Toxicology and Pharmacology 2023, 104, 104307. [Google Scholar] [CrossRef]
29. "; Dunarea de Jos"; University of Galati, Romania; Arseni, M. ; Roșu, A.; “Dunarea de Jos” University of Galati, Romania; Murariu, G.; “Dunarea de Jos” University of Galati, Romania; Georgescu, L.P.; “Dunarea de Jos” University of Galati, Romania; et al. The Role of River Channel Roughness for Water Level Modeling during the 2005 Year Flood on Siret River Using HEC-RAS Model. Ann_UGAL_Math_Phys_Mec 2019, 42, 68–76. [Google Scholar] [CrossRef]
30. Water Directive.
31. Barrero-Moreno, M.C.; Diaz-Vargas, C.A.; Restrepo-Parra, E. Computational Simulation of Filters Used in the Removal of Heavy Metals Using Rice Husks. Agriculture 2021, 11, 146. [Google Scholar] [CrossRef]
32. Zolghadr, M.; Rafiee, M.R.; Esmaeilmanesh, F.; Fathi, A.; Tripathi, R.P.; Rathnayake, U.; Gunakala, S.R.; Azamathulla, H.M. Computation of Time of Concentration Based on Two-Dimensional Hydraulic Simulation. Water 2022, 14, 3155. [Google Scholar] [CrossRef]
33. Maxim, C.; Filote, C.; Cojocaru, D.C. COMPARATIVE STUDY OF PHYSICAL INDICATORS AND THOSE OF THEIR REGIME OF OXYGEN, ON WATER QUALITY OF THE GREAT ùOMUZU RIVER, IN THE YEAR 2009. 2011.
34. Iticescu, C.; Georgescu, L.P.; Murariu, G.; Topa, C.; Timofti, M.; Pintilie, V.; Arseni, M. Lower Danube Water Quality Quantified through WQI and Multivariate Analysis. Water 2019, 11, 1305. [Google Scholar] [CrossRef]
35. Crişan, V.-E.; Dincă, L.; Bragă, C.; Murariu, G.; Tupu, E.; Mocanu, G.D.; Drasovean, R. The Configuration of Romanian Carpathians Landscape Controls the Volume Diversity of Picea Abies (L.) Stands. Land 2023, 12, 406. [Google Scholar] [CrossRef]
36. Crişan, V.-E.; Dincă, L.; Bragă, C.; Murariu, G.; Tupu, E.; Mocanu, G.D.; Drasovean, R. The Configuration of Romanian Carpathians Landscape Controls the Volume Diversity of Picea Abies (L.) Stands. Land 2023, 12, 406. [Google Scholar] [CrossRef]
37. Murariu, G.; Dinca, L.; Tudose, N.; Crisan, V.; Georgescu, L.; Munteanu, D.; Dragu, M.D.; Rosu, B.; Mocanu, G.D. Structural Characteristics of the Main Resinous Stands from Southern Carpathians, Romania. Forests 2021, 12, 1029. [Google Scholar] [CrossRef]
38. Jin, Y.; Zhou, Q.; Wang, X.; Zhang, H.; Yang, G.; Lei, T.; Mei, S.; Yang, H.; Liu, L.; Yang, H.; et al. Heavy Metals in the Mainstream Water of the Yangtze River Downstream: Distribution, Sources and Health Risk Assessment. IJERPH 2022, 19, 6204. [Google Scholar] [CrossRef] [PubMed]
39. Costabile, P.; Costanzo, C.; Ferraro, D.; Macchione, F.; Petaccia, G. Performances of the New HEC-RAS Version 5 for 2-D Hydrodynamic-Based Rainfall-Runoff Simulations at Basin Scale: Comparison with a State-of-the Art Model. Water 2020, 12, 2326. [Google Scholar] [CrossRef]
40. Luo, K.; Liu, H.; Yu, E.; Tu, Y.; Gu, X.; Xu, M. Distribution and Release Mechanism of Heavy Metals in Sediments of Yelang Lake by DGT. Stoch Environ Res Risk Assess 2020, 34, 793–805. [Google Scholar] [CrossRef]
41. Ha, M.; Schleiger, R. Environmental Science.
42. Githaiga, K.B.; Njuguna, S.M.; Gituru, R.W.; Yan, X. Water Quality Assessment, Multivariate Analysis and Human Health Risks of Heavy Metals in Eight Major Lakes in Kenya. Journal of Environmental Management 2021, 297, 113410. [Google Scholar] [CrossRef]
43. Feng, W.; Wang, T.; Zhu, Y.; Sun, F.; Giesy, J.P.; Wu, F. Chemical Composition, Sources, and Ecological Effect of Organic Phosphorus in Water Ecosystems: A Review. Carbon Res. 2023, 2, 12. [Google Scholar] [CrossRef]
44. Xu, M.; Wang, R.; Yang, X.; Yang, H. Spatial Distribution and Ecological Risk Assessment of Heavy Metal Pollution in Surface Sediments from Shallow Lakes in East China. Journal of Geochemical Exploration 2020, 213, 106490. [Google Scholar] [CrossRef]
45. Bąk, Ł.; Szeląg, B.; Sałata, A.; Studziński, J. Modeling of Heavy Metal (Ni, Mn, Co, Zn, Cu, Pb, and Fe) and PAH Content in Stormwater Sediments Based on Weather and Physico-Geographical Characteristics of the Catchment-Data-Mining Approach. Water 2019, 11, 626. [Google Scholar] [CrossRef]
46. Pavel, A.; Durisch-Kaiser, E.; Balan, S.; Radan, S.; Sobek, S.; Wehrli, B. Sources and Emission of Greenhouse Gases in Danube Delta Lakes. Environ Sci Pollut Res 2009, 16, 86–91. [Google Scholar] [CrossRef] [PubMed]
47. Feng, W.; Wang, T.; Zhu, Y.; Sun, F.; Giesy, J.P.; Wu, F. Chemical Composition, Sources, and Ecological Effect of Organic Phosphorus in Water Ecosystems: A Review. Carbon Res. 2023, 2, 12. [Google Scholar] [CrossRef]
48. Bărbulescu, A.; Barbeş, L.; Dani, A. Statistical Analysis of the Quality Indicators of the Danube River Water (in Romania). In Frontiers in Water-Energy-Nexus—Nature-Based Solutions, Advanced Technologies and Best Practices for Environmental Sustainability; Naddeo, V., Balakrishnan, M., Choo, K.-H., Eds.; Advances in Science, Technology & Innovation; Springer International Publishing: Cham, 2020; pp. 277–279. ISBN 978-3-030-13067-1. [Google Scholar]
49. Hakanson, L. An Ecological Risk Index for Aquatic Pollution Control.a Sedimentological Approach. Water Research 1980, 14, 975–1001. [Google Scholar] [CrossRef]
50. Ultsch, A.; Lötsch, J. Euclidean Distance-Optimized Data Transformation for Cluster Analysis in Biomedical Data (EDOtrans). BMC Bioinformatics 2022, 23, 233. [Google Scholar] [CrossRef] [PubMed]
51. Hemming, K.; Eldridge, S.; Forbes, G.; Weijer, C.; Taljaard, M. How to Design Efficient Cluster Randomised Trials. BMJ 2017, j3064. [Google Scholar] [CrossRef] [PubMed]
52. Staples, L.; Ring, J.; Fontana, S.; Stradwick, C.; DeMaio, J.; Ray, H.; Zhang, Y.; Zhang, X. Reproducible Clustering with Non-Euclidean Distances: A Simulation and Case Study. Int J Data Sci Anal 2023. [Google Scholar] [CrossRef]
53. Feio, M.J.; Hughes, R.M.; Callisto, M.; Nichols, S.J.; Odume, O.N.; Quintella, B.R.; Kuemmerlen, M.; Aguiar, F.C.; Almeida, S.F.P.; Alonso-EguíaLis, P.; et al. The Biological Assessment and Rehabilitation of the World’s Rivers: An Overview. Water 2021, 13, 371. [Google Scholar] [CrossRef] [PubMed]
54. Liu, Y.; Xu, Z.; Hu, X.; Zhang, N.; Chen, T.; Ding, Z. Sorption of Pb(II) and Cu(II) on the Colloid of Black Soil, Red Soil and Fine Powder Kaolinite: Effects of pH, Ionic Strength and Organic Matter. Environmental Pollutants and Bioavailability 2019, 31, 85–93. [Google Scholar] [CrossRef]
55. Brunner, G. HEC-RAS Verification and Validation Tests.
56. Calmuc, M.; Calmuc, V.; Arseni, M.; Topa, C.; Timofti, M.; Georgescu, L.P.; Iticescu, C. A Comparative Approach to a Series of Physico-Chemical Quality Indices Used in Assessing Water Quality in the Lower Danube. Water 2020, 12, 3239. [Google Scholar] [CrossRef]
57. Antohi, V.M.; Ionescu, R.V.; Zlati, M.L.; Iticescu, C.; Georgescu, P.L.; Calmuc, M. Regional Regression Correlation Model of Microplastic Water Pollution Control Using Circular Economy Tools. IJERPH 2023, 20, 4014. [Google Scholar] [CrossRef]