Long-Term Trends in Water Quality Indices in the Lower Danube and Tributaries...

[Full title: Long-Term Trends in Water Quality Indices in the Lower Danube and Tributaries in Romania (1996–2017)]

International Journal of Environmental Research and Public Health Article Long-Term Trends in Water Quality Indices in the Lower Danube and Tributaries in Romania (1996–2017) Rodica-Mihaela Frîncu 1,2 Citation: Frîncu, R.-M. Long-Term Trends in Water Quality Indices in the Lower Danube and Tributaries in Romania (1996–2017) Int. J. Environ Res. Public Health 2021 , 18 , 1665 https://doi.org/10.3390/ ijerph 18041665 Academic Editor: Soon-Jin Hwang Received: 21 December 2020 Accepted: 5 February 2021 Published: 9 February 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations Copyright: © 2021 by the author Licensee MDPI, Basel, Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/) 1 National Institute for Research and Development in Chemistry and Petrochemistry—ICECHIM, 202 Splaiul Independentei, 060021 Bucharest, Romania; icechim.calarasi@gmail.com; Tel.: +40-21-315-3299 2 INCDCP ICECHIM Calarasi Branch, 2 A Ion Luca Caragiale St., 910060 Calarasi, Romania Abstract: The Danube River is the second longest in Europe and its water quality is important for the communities relying on it, but also for supporting biodiversity in the Danube Delta Biosphere Reserve, a site with high ecological value. This paper presents a methodology for assessing water quality and long-term trends based on water quality indices (WQI), calculated using the weighted arithmetic method, for 15 monitoring stations in the Lower Danube and Danube tributaries in Romania, based on annual means of 10 parameters for the period 1996–2017. A trend analysis is carried out to see how WQIs evolved during the studied period at each station. Principal component analysis (PCA) is applied on sub-indices to highlight which parameters have the highest contributions to WQI values, and to identify correlations between parameters. Factor analysis is used to highlight differences between locations. The results show that water quality has improved significantly at most stations during the studied period, but pollution is higher in some Romanian tributaries than in the Danube. The parameters with the highest contribution to WQI are ammonium and total phosphorus, suggesting the need to continue improving wastewater treatment in the studied area. The methodology and the results of the study may be very useful instruments for specialists and decision makers in updating river basin management plans and prioritising intervention measures Keywords: Danube; water quality index (WQI); Romania; principal component analysis (PCA); nutrients pollution; long-term monitoring; municipal wastewater; sewerage pollution 1. Introduction The Water Framework Directive (WFD) [ 1 ] of the European Union (EU) has the aim to achieve good ecological status in all EU water bodies and to protect them from pollution, which affects aquatic ecosystems and human health. It was adopted in year 2000 and it introduced a holistic approach in the management of water resources, based on river basin management plans [ 2 ]. Surface waters provide important ecosystem services, including drinking and irrigation water, hydropower, navigation etc. Contamination of water bodies intended for abstraction of drinking water may pose significant public health risks, leading to infectious diseases [ 3 – 5 ], while the ingestion of nitrates leads to methemoglobinemia, blocking oxygen transport to the cells, particularly in infants [ 6 ]. Recent studies investigate the link between nitrates and reproductive and developmental disorders [ 7 ], while nitrites may be linked with increased risk of childhood brain tumors [ 8 ] and different types of cancer in adults [ 6 ]. Water pollution may originate in point sources (municipal and industrial discharges of treated or untreated wastewater) or diffuse sources, like run-off from agricultural land [ 9 ]. After treatment in the drinking water supply facilities, tap water may be contaminated in the distribution network with algal toxins and heavy metals from pipes corrosion, which are directly linked with a wide range of diseases. Moreover, some of the products used for water disinfection may have negative health impacts if not removed [ 10 ]. This has lead Int. J. Environ. Res. Public Health 2021 , 18 , 1665. https://doi.org/10.3390/ijerph 18041665 https://www.mdpi.com/journal/ijerph

Int. J. Environ. Res. Public Health 2021 , 18 , 1665 2 of 16 in many regions of Europe to distrust in drinking tap water and a preference for bottled water [ 5 ]. In the European Union (EU), Council Directive 98/83/EC, also known as the Drinking Water Directive (DWD), has the purpose of ensuring that safe water is provided to citizens in order to protect public health [ 11 ]. In 2020, the EU Council has approved a proposal to revise the directive, updating quality standards and including endocrine disruptors and pharmaceuticals in the monitoring programme [ 12 ]. Several countries have applied for derogations from the DWD, including Italy, Romania, and Hungary [ 13 , 14 ]. According to the WFD, water quality assessment includes chemical status and ecological status of water bodies based on parameters that are monitored on a regular basis by national authorities and reported to the EU The Danube River is the second longest in Europe and its catchment area includes 19 countries, making it the most international river in the world [ 15 ]. Millions of people are relying on the Danube as drinking water source [ 16 , 17 ] and its contamination can have serious impacts on human health [ 18 , 19 ]. Monitoring data is used to assess water quality and identify pollution sources, so that river management plans can include the most appropriate measures to control and reduce pollution, and to ensure sustainable water use. The progress towards the goals and the effectiveness of the measures are assessed by the evolution of water quality in time. In this respect, nutrients were for a long period the main focus of monitoring and research [ 20 ], particularly because municipal wastewater treatment was non-compliant in many areas of the Danube Basin [ 21 , 22 ]. Advanced scientific programmes with international teams of experts were dedicated to modelling nutrient discharges in the Danube and international agreements and regulations were adopted to reduce nutrient pollution [ 23 – 26 ]. In recent years, a much stronger focus is on ecological assessment [ 27 , 28 ] and on emerging micropollutants, like pharmaceuticals and endocrine disruptors, which may have a strong environmental impact even in small amounts [ 29 ]. There are several scientific studies that analyzed individual parameters in the Danube and how they evolved over longer periods of time [ 30 , 31 ] and a number of studies that looked at the complex interaction between parameters [ 32 ] and between parameters and their drivers [ 33 – 39 ]. However, to support authorities and decision makers, and to communicate information to the public, an overall assessment of water quality can be a very useful tool Water quality indices (WQI) have been developed in order to assess the adequacy of using the water for specific purposes, by aggregating several monitored parameters into a single number that can be used by specialists and authorities to define water policy and prioritize management measures. WQI is one of the 25 indicators that can be used to assess the environment, called environmental quality indices, EQI [ 40 ]. WQI calculations are based on monitoring data of selected parameters and weights that often require expert opinion. Thorough reviews of existing methods and their limitations have already proved that applying different methods on the same dataset may lead to different results [ 41 – 43 ]. A possible solution in this case, which has the advantage that it does not need expert judgement for ranking parameters, is to calculate WQI based on limit values from national water quality standards [ 44 , 45 ]. The weighted arithmetic method used in this study is based on the National Sanitation Foundation-Water Quality Index and has already been applied with good results in different river basins [ 44 , 46 ], as well as for the Danube in Romania in the area of Galati for a period of 4 years [ 17 ]. When assessing long-term trends in water quality, the most important challenge is the availability of consistent monitoring data for the studied area [ 47 , 48 ]. In this respect, the Third Joint Danube Survey, organized in 2013 by the International Commission for the Protection of the Danube River (ICPDR), has found chemical monitoring data to be consistent and reliable throughout the Danube River Basin, while ecological monitoring methodologies still need to be harmonized and further developed [ 15 ].

Int. J. Environ. Res. Public Health 2021 , 18 , 1665 3 of 16 The present paper is part of a larger study on long-term trends in main water quality parameters in the Lower Danube [ 49 ]. The parameters belong to the core physical and chemical monitoring plans and were selected according to data availability at the studied sites. In the first phase, each parameter was analyzed individually to see how it evolved during the period 1996–2017 [ 50 ]. However, not all parameters are equally important for ecosystems balance, so, in order to assess the overall water quality and long-term trends, it is useful to calculate an index that includes several parameters and gives them corresponding weights. WQI was calculated using the weighted arithmetic method for 15 locations in the Lower Danube and Romanian tributaries based on annual means of 10 parameters for a period of 22 years (1996–2017), and their trends in time were assessed using Spearman rank correlation. Principal component analysis (PCA) indicates parameters’ contributions to the index and correlations between them, while factor analysis highlights differences between locations. The present paper shows how WQI reflects chemical water quality in the Lower Danube and tributaries during the period 1996–2017 and is a step towards modelling future trends The main aims of the study are: - to assess water quality in the Lower Danube and 6 Romanian tributaries during the period 1996–2017, based on WQI, using 10 core physical-chemical parameters; - to analyze long-term water quality trends during the study period at the selected locations; - to identify correlations between parameters and compare water quality at different locations using advanced statistical methods To the best of the author’s knowledge, this is the first study to analyze WQIs in the Lower Danube and Romanian tributaries for a long period of time 2. Materials and Methods 2.1. Study Area The Danube River originates in the Black Forest Mountains, in Germany, and flows through 10 European countries before draining into the Black Sea. It is the second longest river in Europe, after the Volga, and has a high social and economic importance, serving as drinking water source, navigation way, hydropower generator, and performing a large number of ecosystem services, including provisioning, regulating and cultural services [ 51 ]. 97.4% of Romania’s territory is located in the Danube basin, representing 29% of its catchment area. Efforts to monitor and reduce pollution are coordinated by the ICPDR, located in Vienna [ 21 ]. In this respect, the TransNational Monitoring Network (TNMN) was established in 1996, and currently national authorities in charge of monitoring water quality in the Danube basin submit their data to the ICPDR to be included in its database [ 52 ]. The TNMN was revised in 2007 in order to meet the requirements of the WFD [ 21 ]. National authorities also support their own research, so in Romania several studies were published related to this topic [ 20 , 53 , 54 ]. The study area is in the Southern part of Romania and includes 9 monitoring stations located on the Danube, from Bazias to the Black Sea (1071 km), and 6 monitoring stations located on the Romanian tributaries Jiu, Olt, Arges, Ialomita, Siret, and Prut, close to their mouths, where they drain into the Danube. The locations of the monitoring stations are presented in Figure 1 , and more details regarding their geographic coordinates, position along the river, size of catchment area and discharge are included in Table 1 . The CORINE land cover map in Figure 1 shows that the study area consists mainly of plain non-irrigated agricultural land in the South, and forests in the mountain areas in the North. The map legend is included in the Supplementary Materials (S 3) The selected monitoring stations are part of the TNMN. The study includes only stations that are monitored by the National Administration “Romanian Waters”, who reports data to the ICPDR. Sampling and analysis of water samples is carried out according to international standards, by national River Basin Management authorities. For some

Int. J. Environ. Res. Public Health 2021 , 18 , 1665 4 of 16 stations monitoring data are available for the period 1996–2017, the others were obtained for the period 2007–2017 (Gruia, Jiu, Olt, Ialomita) Figure 1. CORINE land cover map of the study area in the Lower Danube with representation of the sampling points (01–15), Danube—black, tributaries—blue [ 55 ]. Table 1. Characteristics of monitoring locations [ 56 ]. Location Name TNMN Location Code Longitude Latitude River Position along the River (km) Catchment Area (km 2 ) Annual Mean Discharge m 3 /s 01 Bazias RO 1 21.38397 44.81610 Danube 1071.0 570,896 5515 * 02 Gruia RO 18 22.68355 44.27018 Danube 851.0 577,085 - 03 Pristol RO 2 22.67613 44.21418 Danube 834.0 580,100 5383 * 04 Jiu RO 19 23.84549 43.84188 Jiu 9.0 10,046 93 ** 05 Olt RO 20 24.79680 43.74400 Olt 3.0 24,050 185 ** 06 Oltenita RO 3 26.61905 44.05605 Danube 432.0 676,150 6107 * 07 Arges RO 9 26.59900 44.14500 Arges , 0.0 12,550 73 ** 08 Chiciu RO 4 27.26771 44.12757 Danube 375.0 698,600 6113 * 09 Ialomita RO 21 27.66468 44.63477 Ialomit , a 24.0 10,309 48 ** 10 Siret RO 10 28.00940 45.41474 Siret 0.0 42,890 235 ** 11 Prut RO 11 28.20300 45.46900 Prut 0.0 27,480 110 * 12 Reni RO 5 28.23190 45.46324 Danube 132.0 805,700 6731 * 13 Chilia RO 6 29.55336 45.40635 Danube 18.0 817,000 3421 * 14 Sulina RO 7 29.52966 45.18338 Danube 0.0 817,000 1308 * 15 Sf Gheorghe RO 8 29.60945 44.88462 Danube 0.0 817,000 1761 * * average based on TNMN monitoring data. ** [ 57 ]. 2.2. Data Monitoring data were retrieved from the ICPDR Water Quality Database [ 52 ], with the permission to use them for scientific studies. The analyzed parameters are pH, dissolved oxygen (DO), biochemical oxygen demand (BOD 5 ), chemical oxygen demand (COD-Cr), ammonium nitrogen (NH 4 + -N), nitrates nitrogen (NO 3 − -N), total phosphorus (TP), total suspended solids (TS), chlorides (Cl − ), and sulfates (SO 4 2 − ). The parameters were selected as most commonly used and relevant for chemical assessment of water quality, according to literature [ 41 , 42 , 45 ], depending also on data availability For some of the stations, the database includes values from three river sections: left bank (L), middle (M), and right bank (R). In this study only data from the middle point (M) were analyzed, because they were available for all stations. Only monitoring stations operated by Romanian authorities were included in this study Data were downloaded into an excel table and were further processed using Microsoft Excel and the open source R Statistical Software, version 3.6.2. Annual means were calculated for each parameter at each location. The number of observations for each location are presented in Table 2 , and the annual mean values are presented in Figure 2 .

Int. J. Environ. Res. Public Health 2021 , 18 , 1665 5 of 16 Table 2. Number of values of selected parameters available at each location for the period 1996–2017 pH DO BOD 5 COD-Cr NH 4 N NO 3 N TP TS Cl − SO 4 − 01 Bazias 332 319 356 298 379 379 348 378 317 314 02 Gruia 170 138 137 137 170 170 168 169 136 136 03 Pristol 384 351 417 332 457 457 426 452 350 347 04 Jiu 130 131 131 131 131 131 131 131 131 130 05 Olt 125 120 121 122 126 127 123 114 114 96 06 Oltenita 242 242 241 237 241 241 230 241 242 238 07 Arges 241 240 240 235 240 240 229 240 241 237 08 Chiciu 392 470 457 303 490 490 442 484 327 218 09 Ialomita 125 133 127 132 168 167 167 156 108 79 10 Siret 266 341 342 232 359 359 326 350 252 188 11 Prut 269 344 345 229 365 365 326 352 252 187 12 Reni 372 449 449 314 464 465 439 461 333 235 13 Chilia 252 252 249 226 258 258 233 258 249 208 14 Sulina 247 246 244 223 252 251 227 252 245 189 15 Sf Gheorghe 246 245 243 220 251 251 225 251 243 186 Figure 2. Boxplot representation of mean annual values for analyzed parameters at each sampling location during the period 1996–2017. Parameters are BOD 5 (mg O 2 /L), Cl − (mg/L), COD-Cr (mg O 2 /L), DO (mg O 2 /L), NH 4 + -N (mg N/L), NO 3 – -N (mg N/L), pH, SO 4 2 − (mg/L), TP (mg P/L), TS (mg/L) 2.3. Water Quality Index Method The purpose of the study was to calculate a water quality index taking into account a series of physical–chemical parameters that are relevant for an overall assessment of Danube water during the period 1996–2017, in order to compare the analyzed stations and see how quality has evolved during this period Water quality indices (WQI) were calculated using the weighted arithmetic water quality Index method, with Equation (1) [ 42 ]: WQI = ∑ Q i · W i ∑ W i (1)

Int. J. Environ. Res. Public Health 2021 , 18 , 1665 6 of 16 where Q i is the quality rating scale of parameter i and W i is the weight corresponding to parameter i, calculated using Equations (2) and (3), respectively Q i = 100 · V i − V 0 S i − V 0 (2) W i = K S i (3) V i is the annual mean of parameter i, V 0 is the ideal value of the parameter and S i is the standard limit value for parameter i. V 0 = 0 for all parameters, except pH, for which the ideal value is 7 and DO with ideal value 14.6 mg O 2 /L. S i values are Romanian standard values for quality class II [ 58 ], because the Danube water quality is generally considered to belong to this class [ 17 ]. K is a proportionality constant, calculated using Equation (4): K = 1 ∑ 1 S i (4) The limit values S i and the calculated weights W i are presented in Table 3 . Table 3. Limit values (Si) and calculation of unit weights for selected parameters No. Parameter Unit S i 1/S i W i = K/S i 1 pH - 7.5 0.1333 0.6172 2 Dissolved Oxygen (DO) mg O 2 /L 7 0.1429 0.6613 3 Biochemical Oxygen Demand (BOD 5 ) mg O 2 /L 5 0.2000 0.9258 4 Chemical Oxygen Demand (COD-Cr) mg O 2 /L 25 0.0400 0.1852 5 Ammonium Nitrogen (NH 4 + -N) mg N/L 0.8 1.2500 5.7865 6 Nitrates Nitrogen (NO 3 − -N) mg N/L 3 0.3333 1.5431 7 Total Phosphorus (TP) mg P/L 0.4 2.5000 11.5730 8 Total Suspended Solids (TS) mg/L 750 0.0013 0.0062 9 Chlorides (Cl − ) mg/L 50 0.0200 0.0926 10 Sulfates (SO 4 2+ ) mg/L 120 0.0083 0.0386 ∑ 1/S i = 4.6292 K = 1/ ( ∑ 1/S i ) = 0.216020491 ∑ W i = 21.4294 According to this method, water quality is assessed by WQI values as seen in Table 4 . Table 4. Water quality rating by calculated water quality indices (WQI) values [ 44 ]. WQI Water Quality 0–25 Excellent 26–50 Good 51–75 Poor 76–100 Very poor Over 100 Critical 2.4. Statistical Analysis Trend analysis at each monitoring location was carried out by calculating Spearman rank correlation coefficients, ρ , between WQI values and the year for which they were calculated Principal component analysis (PCA) was applied on sub-indices (Qi · Wi/ ∑ W i ) with the aim of finding out which parameters have the strongest influence on the WQI and how parameters are correlated. Because parameters usually have different units of measure and orders of magnitude, data are scaled and centered before analysis to avoid wrong interpretation. In this case, parameters are scaled during WQI calculation, but then are weighed, so PCA was carried out with and without centering and scaling data, to compare both situations.

Int. J. Environ. Res. Public Health 2021 , 18 , 1665 7 of 16 Factor analysis is a statistical method that can be applied on datasets that contain both numerical and categorical variables or factors. It was applied on sub-indices according to their locations in order to highlight the differences between the monitoring stations All results and graphics were generated using Microsoft Excel and the open source R Statistical Software, version 3.6.2. and RStudio Version 1.2.5033 3. Results 3.1. Water Quality Index The calculated WQIs for each location and year are included in Table 5 and represented in scatterplots in Figure 3 . Out of the 260 WQI calculated values at 15 monitoring stations, 23 values (8.2%) are between 0 and 25 (“excellent” water quality), 186 values (66.4%) are between 26 and 50 (“good”), 23 (8.2%) between 51 and 75 (“poor”), 9 (3.2%) between 76 and 100 (“very poor”), and 17 (6.0%) over 100 (“critical”) It can be noticed that in Jiu and Olt tributaries WQI values are similar to those in the Danube at Gruia and Pristol, or lower, so these two rivers do not have a negative impact on Danube water quality. Values at Gruia and Pristol are also similar, so the Timok river, draining from Serbian territory between these stations, does not appear to have a negative impact on the Danube either Table 5. Calculated WQI at each location for the period 1996–2017 Location WQI 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 01 Bazias 30 38 40 29 49 41 32 30 35 41 29 02 Gruia - - - - - - - - - - - 03 Pristol 34 37 38 32 45 34 31 29 30 53 31 04 Jiu - - - - - - - - - - - 05 Olt - - - - - - - - - - - 06 Oltenita 38 34 39 38 44 42 51 37 30 31 33 07 Arges 202 99 115 113 167 132 129 176 117 87 172 08 Chiciu 42 41 45 36 34 38 44 35 64 50 40 09 Ialomita - - - - - - - - - - - 10 Siret 95 84 70 43 40 55 45 - 74 91 57 11 Prut 51 59 70 40 47 49 51 - 74 65 50 12 Reni 32 40 44 37 32 35 34 - 41 65 46 13 Chilia 34 40 47 37 26 37 33 - 43 39 40 14 Sulina 36 38 43 36 30 37 35 - 43 40 41 15 Sf Gheorghe 38 38 48 36 28 34 31 - 27 39 41 Location WQI 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 01 Bazias 34 58 54 28 27 32 26 24 26 - 22 02 Gruia 26 56 53 32 28 31 26 23 26 24 21 03 Pristol 28 60 57 33 30 32 25 22 25 - 21 04 Jiu 46 30 25 28 36 32 27 27 23 27 31 05 Olt 36 - 37 32 27 15 19 26 19 15 16 06 Oltenita 33 27 26 29 29 25 25 26 26 - 33 07 Arges 196 152 124 110 169 217 140 95 - - 157 08 Chiciu 40 31 35 36 28 26 23 25 27 - 26 09 Ialomita 68 - - - 87 247 81 84 51 68 70 10 Siret 39 36 33 36 33 36 35 30 25 - 32 11 Prut 44 30 36 33 29 34 29 31 25 - 27 12 Reni 33 29 28 - 29 33 32 31 - 30 28 13 Chilia 33 31 29 28 26 31 31 31 - - 29 14 Sulina 33 32 29 28 24 31 30 30 - - 26 15 Sf Gheorghe 38 32 28 27 28 28 31 30 - - 27 - data not available.

Int. J. Environ. Res. Public Health 2021 , 18 , 1665 8 of 16 Figure 3. Scatterplot of WQI values at each location during the period 1996–2017 (blue line = limit between “good” and “poor” water quality, WQI = 50) The next two tributaries, Arges and Ialomita, are heavily polluted; however, water quality is similar upstream and downstream from their mouths, so their impact upon the Danube is also limited, because their flow is much smaller than the Danube flow In the Arges River, water quality was “very poor” or “critical” the whole period, with only three values under 100, in 1997, 2005 and 2014. This river receives insufficiently treated municipal sewerage waters from Bucharest, the capital city of Romania, through Dambovita River, Arges tributary. According to the National Institute for Statistics, Bucharest had a population of about 2.15 mil. inhabitants in January 2020 [ 59 ]. Until 2011 Bucharest wastewater treatment plant, Glina, only had mechanical step, and since 2011 it has advanced biological treatment for half of the incoming flow. Further upgrading is in progress Ialomita river is also more polluted than the Danube. The smallest WQI value was 51 in 2015, so all the values range from “poor” to “critical”. Ialomita also has the highest calculated value in the whole dataset, 247, in 2012. It is affected by sewerage from Slatina and Tandarei municipalities and has Prahova and Teleajen rivers as tributaries, bringing wastewater from industrial areas In Siret and Prut tributaries, water quality is worse than in the Danube, but better than in Arges and Ialomita. Their impact upon the Danube is also limited. Siret River had high WQI values related to high ammonium concentrations in 1996 and 1997, which could be related to sewerage pollution. The high value in 2005 corresponds to high total phosphorus values and high discharge, so it could be related to increased run-off during that year At Chilia, Sulina and Sf. Gheorghe, the three arms of the Danube forming the delta, all WQI values were “good” during the entire period, with only one “excellent” value at Sulina, in 2011 Results show that WQI values are higher in some Romanian tributaries, particularly, Arges River, than in the Danube. In order to assess WQI trends in time, a statistical test is required.

Int. J. Environ. Res. Public Health 2021 , 18 , 1665 9 of 16 3.2. WQI Trend Analysis Trends were assessed using the Spearman rank correlation coefficients between calculated WQI values and the respective year. Positive coefficients indicate an increasing trend in time, negative coefficients indicate a decreasing trend The results of the WQI trend analysis for each monitoring station are presented in Table 6 . Table 6. Trend analysis of WQI values during the period 1996–2017 Location No. of Values Spearman Correlation Coefficient ρ p Value Significant ( p < 0.05) Trend 01 Bazias 21 − 0.5403 0.0126 Yes ↓ decrease 02 Gruia * 11 − 0.8273 0.0031 Yes ↓ decrease 03 Pristol 21 − 0.5623 0.0090 Yes ↓ decrease 04 Jiu * 11 − 0.3818 0.2484 No 05 Olt * 10 − 0.8061 0.0082 Yes ↓ decrease 06 Oltenita 20 − 0.7987 1.7525 e − 05 Yes ↓ decrease 07 Arges 20 0.0842 0.7239 No 08 Chiciu 21 − 0.7429 0.0002 Yes ↓ decrease 09 Ialomita * 7 − 0.4524 0.2675 No 10 Siret 19 − 0.8752 0 Yes ↓ decrease 11 Prut 20 − 0.8195 6.7316 e − 06 Yes ↓ decrease 12 Reni 19 − 0.6123 0.0063 Yes ↓ decrease 13 Chilia 19 − 0.6105 0.0065 Yes ↓ decrease 14 Sulina 19 − 0.6667 0.0024 Yes ↓ decrease 15 Sf Gheorghe 19 − 0.5246 0.0228 Yes ↓ decrease * data available for the period 2007–2017 At 12 out of 15 monitoring stations WQI values have decreased in the analyzed period (1996–2017), which means that water quality has improved. For 3 Romanian tributaries the test result was not significant: Jiu, Arges, Ialomita Trend analysis shows that in the rivers with the highest WQIs (Arges and Ialomita) water quality has not improved, so this should be an alert for Romanian authorities managing these catchments to take stronger measures to reduce pollution 3.3. Principal Component Analysis WQIs were calculated based on 10 parameters and, in order to know which measures need to be taken to improve water quality, it is useful to know which parameters have the strongest influence on WQI values, and if there are correlations between them In the first step, principal component analysis (PCA) was applied on sub-indices (Qi · Wi/ ∑ W i ) without scaling and centering to find the parameters with highest variations after weighing. The result is shown in Figure 4 , where arrows represent parameters, and the length of the arrow is proportional to the influence of the parameters on the WQI In Figure 4 it can be seen that ammonium dominates the first component (Dim 1), which explains 90.8% of the variance of the dataset. The second important parameter is total phosphorus, dominating the second component (Dim 2), which explains 8.6% of the variance. These two components explain 99.4% of the variance, so the dimensions of the dataset could be reduced from 10 to 2, without losing any important information.

Int. J. Environ. Res. Public Health 2021 , 18 , 1665 10 of 16 Figure 4. Principal Component Analysis on WQI sub-indices without scaling and centering for the period 1996–2017 This type of analysis reflects very well the weight of the parameters in the WQI, but it is difficult to see eventual correlations between them. For this reason, in the second phase, data were scaled and centered before PCA, so that relationships between parameters could be studied. The PCA coefficients, eigen values and variances for WQI sub-indices are presented in Table 7 . Table 7. Results of principal component analysis (PCA) on centered and scaled sub-indices used in WQI calculation Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7 Dim.8 Dim.9 Dim.10 NH 4 + -N 0.741 − 0.416 0.060 − 0.029 0.262 − 0.200 0.159 − 0.182 − 0.160 − 0.286 BOD 5 0.686 − 0.269 − 0.181 0.221 − 0.250 0.334 0.439 0.058 0.064 0.033 Chlorides 0.649 0.513 0.323 − 0.114 0.067 0.065 0.088 − 0.307 − 0.176 0.239 COD-Cr 0.528 0.611 0.203 0.113 − 0.259 − 0.374 0.117 0.029 0.256 − 0.083 DO 0.428 − 0.584 0.005 − 0.158 − 0.590 − 0.106 − 0.255 − 0.153 − 0.033 0.050 NO 3 − -N 0.615 − 0.003 − 0.454 − 0.525 0.121 − 0.192 0.094 0.252 − 0.017 0.130 pH − 0.325 0.315 − 0.777 0.295 − 0.111 − 0.175 0.032 − 0.198 − 0.144 0.015 Sulfates 0.742 0.154 − 0.340 − 0.060 0.208 0.257 − 0.287 − 0.195 0.270 − 0.072 Solids 0.705 0.460 0.012 0.140 − 0.150 0.162 − 0.237 0.283 − 0.262 − 0.134 Total P 0.617 − 0.401 0.050 0.512 0.267 − 0.201 − 0.156 0.114 0.026 0.210 Eigenvalue 3.814 1.716 1.110 0.749 0.715 0.507 0.477 0.389 0.290 0.234 Explained variance % 38.14 17.16 11.10 7.49 7.15 5.07 4.77 3.89 2.90 2.34 Cumulative variance % 38.14 55.30 66.40 73.88 81.03 86.10 90.87 94.76 97.66 100.00 In Dim 1 ammonium and sulfates have the highest coefficients, but TS, BOD 5 , chlorides, TP and nitrates also have important contributions. In Dim 2 COD-Cr and dissolved oxygen are dominant. Dim 1 explains 38.14% of the variance and Dim 2 17.16%. The first five components explain together 81.03% of the variance, so the dataset could be reduced from 10 to 5 dimensions without losing important information Figure 5 is a graphical representation of parameters’ contributions to the first two principal components (Dim 1 and Dim 2), where arrows are parameters (variables), arrow lengths are proportional with parameters’ coefficients in the first two components, narrow angles between arrows indicate positive correlations, opposed arrows indicate negative correlations, and right angles indicate no correlation From Figure 5 it can be seen that there are strong positive correlations between chlorides and TS, ammonium and TP, and sulfates and nitrates. A negative correlation is between pH and DO, and no correlation between DO and solids. The negative correlation between pH and DO is induced by the calculation formula, because reference values ( V 0 ) are zero for all parameters, except for pH (7) and DO (14.6 mg O 2 /L), so DO sub-indices are inverted. When PCA is applied to annual means, there is a positive correlation between pH and DO [ 50 ].

Int. J. Environ. Res. Public Health 2021 , 18 , 1665 11 of 16 Figure 5. Graphical representation of first two principal components (Dim 1 and Dim 2) of WQI sub-indices PCA for the period 1996–2017 The strong correlation between ammonium and TP, as well as the fact that ammonium has the highest variation in the dataset, are indications that the most important pollution factor is insufficiently treated municipal sewerage water 3.4. Factor Analysis In order to identify which stations are more affected by pollution, PCA results were represented according to sampling locations in Figure 6 , where parameters are represented by symbols, and locations by ellipses Figure 6. Graphical representation of WQI sub-indices PCA by location (1996–2017) Water quality in the Ialomita River, the large ellipse in the upper right region, is most different from the stations on the Danube river, which are close to each other and to the axes. By ellipse position, in Ialomita COD-Cr, chlorides and TS have a strong influence on WQI The Arges River, the green ellipse in the lower right region, is also very different from the Danube, as it was already seen in WQI values, but in this case ammonium and

Int. J. Environ. Res. Public Health 2021 , 18 , 1665 12 of 16 total P are dominant. This river receives municipal wastewater from Bucharest through its tributary, Dambovita River, which is severely affected by sewerage pollution, as was demonstrated by Ionescu et al. [ 60 ]. Factor analysis further clarifies the differences between the studied locations. Apart from Ialomita and Arges, tributaries Siret and Prut, draining close to the Danube Delta, are also more polluted than the Danube, but much less than the first two Water quality in the first group of stations from Romania (Bazias, Pristol, and Gruia) is very similar to the last three stations, right before discharge into the Black Sea (Chilia, Sulina, and Sf. Gheorghe), as they are positioned close to each other, in the same area of the chart 4. Discussion The Danube has gone through significant alterations compared to the 19 th century, due to human activities, with respect to water quality, sediment discharge, hydrology and morphology [ 15 , 32 , 39 ], impacting also species diversity in the river, as well as in the coastal region of the Black Sea where the Danube drains. The main sources of nutrients and organic pollution are insufficiently treated municipal sewerage water, industrial wastewater, manure from non-complying animal farms, and run-off from agricultural land where artificial fertilizers are not properly managed. In the 1980 s, nutrients discharges led to eutrophication in the Black Sea, resulting in a large number of algal blooms, which caused oxygen depletion, affecting fish populations and their diversity [ 20 ]. This situation has changed during recent years in the Middle Danube, diatoms population indicating a shift towards an oligotrophic state [ 32 ]. This study calculated WQIs at 15 monitoring stations in the Lower Danube and tributaries in Romania for the period 1996–2017, by applying the weighted arithmetic method, using 10 parameters and limit values for water quality class II, as defined by Romanian legislation WQI results have shown that, during the studied period, pollution has decreased in the Lower Danube Basin at most of the analyzed locations. According to the applied method classification, water quality was “good” during most of the period at all stations in the Danube River. WQIs are lower for Romanian tributary Olt, which appears to be less polluted than the Danube. Jiu River WQI values are close to those for the Danube, while Siret and Prut were higher in the first part of the analyzed period but rank as “good” since 2007. A study carried out during the period 2013–2016 in Reni area has highlighted seasonal variations of WQIs, with better water quality during the winter season, but the calculated values ranked the water quality as ”poor”, mostly because some heavy metals were included in the index to account for industrial pollution [ 17 ]. Arges and Ialomita Rivers rank as “poor”, “very poor”, or “critical” during the entire period. Similar results were obtained for the Timok River, a Danube tributary from Serbia, using the Water Pollution Index for the period 1995–2009. In the case of Timok River, water quality got worse towards the end of the analysis period [ 61 ]. Trend analysis shows that water quality has improved significantly at 12 stations, while for Jiu, Arges and Ialomita Rivers there is no significant trend. The Third Joint Danube Survey, organized by ICPDR in 2013, found that, in general, Danube water quality has improved compared to previous surveys (in 2001 and 2007) with respect to nutrients, and that total nitrogen concentrations have decreased significantly, while total phosphorus only showed a slight decrease in the lower Danube [ 62 ]. PCA analysis of WQI sub-indices shows that the highest variance was given by ammonium and total phosphorus, indicating discharges of insufficiently treated municipal wastewater. Apart from nutrients concentrations, which allow an assessment based on chemical analysis, diatoms communities can offer valuable information on land use by reflecting environmental responses occurring in time to pressures such as urban agglomerations [ 16 ].

Int. J. Environ. Res. Public Health 2021 , 18 , 1665 13 of 16 Urban sewerage is the main source of pollution of the Danube river and the most reliable safety indicator for drinking water is microbiological analysis. A thorough study on fecal pollution along the Danube during the Third Joint Danube Survey (2013) found that it was mostly of human origin and that contaminants were at critical levels after human agglomerations like Vienna, Budapest, Belgrade, and at strong or excessive levels at Kelheim, Russenski Lom, and Arges [ 18 ]. However, microbiological data are not available for long-term studies at the locations analyzed in the present study Factor analysis further confirmed that in Arges and Ialomita Rivers water quality is significantly lower than in the Danube, but values at Danube’s mouths (Chilia, Sulina, and Sf. Gheorghe) are similar to those at the first three stations in Romania (Bazias, Pristol, and Gruia), so the tributaries do not appear to have a strong impact on the Danube. The same conclusion has resulted from the Third Joint Danube Survey, which was carried out in 2013 [ 62 ]. Recent research has shown that mixtures of pollutants can pose environmental and public health threats even if each compound is below the allowed limit [ 29 ], so new methods are being developed for the ecological assessment of water bodies, including bioassays [ 63 ], diatoms analysis [ 16 ], antibiotic resistant genes [ 64 ] etc. In this respect, developing a water pollution index, that would aggregate several measured parameters, could provide a more accurate picture than screening of individual parameters [ 65 ]. The method applied in the present study could be of real use in developing such an index, because its flexibility allows it to be adapted according to available monitoring data. Source-pressure indices have already been used successfully in the development of an integrity model that can serve as a decision tool for authorities in the management of water bodies [ 2 ]. 5. Conclusions This study presents a complex methodology for assessing water quality, applied at 15 monitoring stations in the Lower Danube and Romanian tributaries for the period 1996–2017. At most locations WQIs decrease over time, indicating that water quality has improved, but some Romanian tributaries are more polluted than the Danube and still require efforts to improve wastewater treatment from urban agglomerations WQI values depend on the chosen calculation method even when applied on the same set of parameters, so it would not be appropriate to compare the results of the present analysis to values obtained in other works, but the improvement of individual parameters and reduced nutrients discharge are confirmed by several other studies, however for shorter periods, until 2009 The applied methodology was useful for aggregating several parameters into one number to assess water quality and for identifying long-term trends at different locations, as well as for comparing locations in terms of pollution Sources of pollution of Ialomita and Arges Rivers require further attention, and efforts should be intensified to improve water quality in these rivers. The study can be useful to decision makers in defining intervention measures and prioritizing them, as well as for assessing the effects of the measures already taken and communicating them to the public Supplementary Materials: The following are available online at https://www.mdpi.com/1660-4 601/18/4/1665/s 1 , Table S 1: Annual Means, Table S 2: WQI Sub-indices for PCA, Document S 3: CORINE Land Cover Legend, Document S 4: R-code Funding: The author has received no funding for this study Institutional Review Board Statement: Not applicable Informed Consent Statement: Not applicable Data Availability Statement: Data used in this study were retrieved from the Danube River Basin Water Quality Database, available online: https://www.icpdr.org/wq-db/ (accessed on 2 January 2020) [ 52 ].

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T, Significant alteration, Human origin, Human activities, Seasonal variation, Strong influence, Statistical analysis, Intervention measures, Long term trend, Dissolved oxygen, Chemical analysis, Heavy metal, Water quality, Principal component analysis, Microbiological analysis, Negative correlation, Positive correlation, Factor analysis, Species diversity, Long-term monitoring, Wastewater treatment, Water quality assessment, Organic pollution, Spearman rank correlation, Total phosphorus, Water quality index, Variance, Total nitrogen, Sampling location, Nitrate, Strong correlation, Trend Analysis, Chloride, Water quality index (WQI), Microbiological data, Spearman rank correlation coefficient, Strong positive correlation, Water quality Indices, Municipal wastewater, Black Sea, Nutrients concentration, Romania, Oxygen depletion, No Correlation, Drinking water source, Dataset, Annual mean, TP, Ecological Assessment, Monitoring data, Measured parameter, Urban agglomeration, Algal bloom, Water Framework Directive, Water quality index method, Sustainable water use, Calculation method, Danube Delta, Danube River, Danube River Basin, Fecal pollution, Calculation formula, Sources of Pollution, Pollution Factor, Monitoring station, Water pollution index, Danube Delta Biosphere Reserve, Nutrients Pollution, Dim 1, Dim 2, BOD5, Water quality trend, Urban sewerage, River basin management plan.