Lower Danube Water Quality Quantified through WQI and Multivariate Analysis

Assessing the Efficiency of a Drinking Water Treatment Plant Using Statistical Methods and Quality Indices

This study presents the efficiency of a drinking water treatment plant from Constanța, Romania. Individual and aggregated indices are proposed and built using nine water parameters for this aim. The analysis of individual indices permits the detection of the period of malfunctioning of the water treatment plant with respect to various parameters at various sampling points. In contrast, the cumulated indices indicate the overall performance of the treatment plant during the study period, considering all water parameters. It was shown that the outliers significantly impact the values of some indices. Comparisons between the simple average and weighted average indices (built taking into account the importance of each parameter) better reflect the impact on the water quality of some chemical elements that might harm people's health when improperly removed.

Linking multivariate statistical methods and water quality indices to evaluate the natural and anthropogenic geochemical processes controlling the water quality of a tropical watershed

The improvement of water management requires monitoring techniques that accurately evaluate water quality status and detect the effects of land use changes on water chemistry. This study aimed to evaluate how multivariate statistical methods and water quality indices can be applied together to evaluate the processes controlling water chemical composition and the overall water quality status of a tropical watershed. Thirty-four water samples were collected in the Formoso River basin, located on the border of the Amazon Forest. Water parameters were measured in situ using a multiparameter and in the lab using spectroscopic and volumetric techniques. The water quality dataset was interpreted through principal component analysis, multivariate linear regression, and water quality indices. Statistical methods allowed us to identify the sources and geochemical processes controlling water quality chemistry, which were carbonate dissolution, runoff/erosion, nutrient input due to anthropogenic activities, and redox reactions in flooded zones. They were also used to create linear functions to evaluate the effects of land use changes on the geochemical processes controlling water chemistry. Conversely, the water quality indices provide information about the overall condition of the water. The Weight-Arithmetic Quality Index correctly evaluates water suitability for its multiple uses, according to the Brazilian guidelines. Conversely, the Ontario Water Quality Index is not suitable to evaluate the water quality of tropical rivers, since the usual higher water temperature and the low oxygen contents associated with tropical environments result in biased water quality evaluations by this index.

Assessing and forecasting water quality in the Danube River by using neural network approaches

The health and quality of the Danube River ecosystems is strongly affected by the nutrients loads (N and P), degree of contamination with hazardous substances or with oxygen depleting substances, microbiological contamination and changes in river flow patterns and sediment transport regimes. Water quality index (WQI) is an important dynamic attribute in the characterization of the Danube River ecosystems health and quality. The WQ index scores do not reflect the actual condition of water quality. We proposed a new forecast scheme for water quality based on the following qualitative classes very good (0-25), good (26-50), poor (51-75), very poor (76-100) and extremely polluted/non-potable (>100). Water quality forecasting by using Artificial Intelligence (AI) is a meaningful method of protecting public health because of its possibility to provide early warning regarding harmful water pollutants. The main objective of the present study is to forecast the WQI time series data based on water physical, chemical and flow status parameters and associated WQ index scores. The Cascade-forward network (CFN) models, along with the Radial Basis Function Network (RBF) as a benchmark model, were developed using data from 2011 to 2017 and WQI forecasts were produced for the period 2018-2019 at all sites. The nineteen input water quality features represent the initial dataset. Moreover, the Random Forest (RF) algorithm refines the initial dataset by selecting eight features considered the most relevant. Both datasets are employed for constructing the predictive models. According to the results of appraisal, the CFN models produced better outcomes (MSE = 0.083/0,319 and R-value 0.940/0.911 in quarter I/quarter IV) than the RBF models. In addition, results show that both the CFN and RBF models could be effective for predicting time series data for water quality when the eight most relevant features are used as input variables. Also, the CFNs provide the most accurate short-term forecasting curves which reproduce the WQI for the first and fourth quarters (the cold season). The second and third quarters presented a slightly lower accuracy. The reported results clearly demonstrate that CFNs successfully forecast the short-term WQI as they may learn historic patterns and determine the nonlinear relationships between the input and output variables.

To a Green Economy across the European Union

"Is the green economy a real solution to the present economic development?" represents the main question of the present research. The paper deals with assessing the impact of innovation on the green economy and quality of life through policies promoted at the European Union (EU) level. The objectives of the paper are to assess the impact level of the policy implementation across the Member States through the research and development (R&D) process and to identify models for the development of the green economy in Europe. The applied methods are empirical and analytical based on the study of literature, econometric modeling, pivoting econometric models, trend synthesis, prioritization, and staging of policy experimentation in the Member States through data processing and statistical programs. The results consist in obtaining development models for the green economy at the EU and national levels. In conclusion, the paper contributes to the early identification of vulnerabilities that may affect the development of European strategic projects in relation to events and security breaches occurring at the EU level at any given time.

Which Are the Best Site and Stand Conditions for Silver Fir (Abies alba Mill.) Located in the Carpathian Mountains?

Silver fir (Abies alba Mill.) is one of the most valuable and productive tree species across European mountains, that accomplish multiple economic, protective and ecologic functions. Alongside spruce (Picea abies (L.) Karst) and beech (Fagus sylvatica L.), silver fir is a characteristic species for the Romanian Carpathians. Although silver fir tree is recommended for the diversification of forests in order to increase the resistance to climate change, it is very sensitive to climatic excesses, especially those that proceed rapidly. Therefore, the aim of this study is to investigate both the environmental conditions and stand characteristics of fir from five mountain ranges of the Romanian Carpathians. The study is based on data recorded over a period of 10 years (1990–2000). As such, a total of 77,251 stands that occupy 211,954 hectares have been investigated in regard to silver fir behaviour. MATLAB scripts were used for analysing consistent data volumes as well as the impact of eight factors on the silver fir productivity (altitude, field aspect, field slope, soil type, participation percentage, road distance, structure and consistency). Our analysis has revealed that higher silver fir productivity is found at altitudes of up to 1200 m, on mid and upper slopes, on NW field aspects, on eutric cambisols and dystric cambisols, with a 10–20% participation in stand composition and in relatively-even aged stands with a full consistency. This study offers valuable insights for forest managers that require comprehensive information in adopting effective strategies to enhance forest resilience under climate change.

Investigation of water quality and its spatial distribution in the Kor River basin, Fars province, Iran

As one of the largest rivers in the southwest of Iran, the Kor River plays an important role in local economy and ecosystem. However, the rapid development of industry has caused significant pollution in this river in recent years. Despite of a number of studies reported on this river regarding water pollution, few have conducted a comprehensive investigation of a wide range of water quality parameters to map the current pollution status. This study focuses on 21 water quality parameters around the industrial centers of the Kor River basin with samples taken from 25 stations. With the measured parameters, the interpolation maps of each parameter were determined using the Kriging method, and the water quality was quantified using the Water Quality Index (WQI) method. The results showed that the WQI values were between 28 and 73, showing more pollution around the factories than in the upstream areas. The results of the principal component analysis (PCA) indicated that BOD, COD, NO₃^-, and coliforms were the most important parameters among the 21 parameters affecting the water quality. Linear regression results suggested that the best parameters for determining coliforms and WQI values were BOD, and Cr, PO₄^3-, Hg and Zn levels, respectively, with R² greater than 0.87. These results can also simplify the prediction of coliforms and WQI using only a few parameters. We further found that flatter regions generally had more pollution, primarily due to pollutant accumulation as a result of water stagnation.

Priority Pollutants Monitoring and Water Quality Assessment in the Siret River Basin, Romania

The Integrated Water Resources Management regulations aim to ensure a good status of surface water quality and its sustainable use. Water quality monitoring of various water users supports the identification of pollution sources and their environmental impacts. The priority pollutants generated by wastewater discharges from municipal, industrial wastewater treatment plants or agricultural areas are of great interest due to their eco-toxicological effects and bio-accumulative properties. The aim of this study was to monitor the priority organic and inorganic pollutants from the Siret River basin, in Romania, with the purpose of assessing the surface water quality status and evaluating it by the Water Quality Index (WAWQI) method. The monitoring of inorganic priority pollutants (e.g., As, Cd, Hg, Ni, Pb) and organic priority pollutants (e.g., Naphthalene, Anthracene, Phenanthrene, Fluoranthene, Benzo(a)anthracene, Benzo(b)fluoranthene, Benzo(k)fluoranthene, Benzo(a)pyrene, Benzo(ghi)perylene, Indeno(1,2,3-cd)pyrene, α, β, and γ-Hexachlorocyclohexane, and Di-2-ethyl-hexyl-phthalate) was conducted within the Siret River basin, during the period 2015–2020. With this purpose, 21 sampling points (18 river sections and 3 lakes) were considered to assess the water quality. The results of this study proved that the water quality within the Siret River basin is generally classified in the 2nd or 3rd class. The spatial distribution of the water quality index values, using ARCGIS, also highlighted the fact that the water quality is mostly unsuitable for drinking water supplies, being influenced by the quality of its main tributaries, as well as by the effluent of wastewater treatment plants.

A Forecasting and Prediction Methodology for Improving the Blue Economy Resilience to Climate Change in the Romanian Lower Danube Euroregion

European Union (EU) policy encourages the development of a blue economy (BE) by unlocking the full economic potential of oceans, seas, lakes, rivers and other water resources, especially in member countries in which it represents a low contribution to the national economy (under 1%). However, climate change represents a main barrier to fully realizing a BE. Enabling conditions that will support the sustainable development of a BE and increase its climate resiliency must be promoted. Romania has high potential to contribute to the development of the EU BE due to its geographic characteristics, namely the presence of the Danube Delta-Black Sea macrosystem, which is part of the Romanian Lower Danube Euroregion (RLDE). Aquatic living resources represent a sector which can significantly contribute to the growth of the BE in the RLDE, a situation which imposes restrictions for both halting biodiversity loss and maintaining the proper conditions to maximize the benefits of the existing macrosystem. It is known that climate change causes water quality problems, accentuates water level fluctuations and loss of biodiversity and induces the destruction of habitats, which eventually leads to fish stock depletion. This paper aims to develop an analytical framework based on multiple linear predictive and forecast models that offers cost-efficient tools for the monitoring and control of water quality, fish stock dynamics and biodiversity in order to strengthen the resilience and adaptive capacity of the BE of the RLDE in the context of climate change. The following water-dependent variables were considered: total nitrogen (TN); total phosphorus (TP); dissolved oxygen (DO); pH; water temperature (wt); and water level, all of which were measured based on a series of 26 physicochemical indicators associated with 4 sampling areas within the RLDE (Brăila, Galați, Tulcea and Sulina counties). Predictive models based on fish species catches associated with the Galati County Danube River Basin segment and the “Danube Delta” Biosphere Reserve Administration territory were included in the analytical framework to establish an efficient tool for monitoring fish stock dynamics and structures as well as identify methods of controlling fish biodiversity in the RLDE to enhance the sustainable development and resilience of the already-existing BE and its expansion (blue growth) in the context of aquatic environment climate variation. The study area reflects the integrated approach of the emerging BE, focused on the ocean, seas, lakes and rivers according to the United Nations Agenda. The results emphasized the vulnerability of the RLDE to climate change, a situation revealed by the water level, air temperature and water quality parameter trend lines and forecast models. Considering the sampling design applied within the RLDE, it can be stated that the Tulcea county Danube sector was less affected by climate change compared with the Galați county sector as confirmed by water TN and TP forecast analysis, which revealed higher increasing trends in Galați compared with Tulcea. The fish stock biodiversity was proven to be affected by global warming within the RLDE, since peaceful species had a higher upward trend compared with predatory species. Water level and air temperature forecasting analysis proved to be an important tool for climate change monitoring in the study area. The resulting analytical framework confirmed that time series methods could be used together with machine learning prediction methods to highlight their synergetic abilities for monitoring and predicting the impact of climate change on the marine living resources of the BE sector within the RLDE. The forecasting models developed in the present study were meant to be used as methods of revealing future information, making it possible for decision makers to adopt proper management solutions to prevent or limit the negative impacts of climate change on the BE. Through the identified independent variables, prediction models offer a solution for managing the dependent variables and the possibility of performing less cost-demanding aquatic environment monitoring activities.

Assessment of Heavy Metal Pollution Levels in Sediments and of Ecological Risk by Quality Indices, Applying a Case Study: The Lower Danube River, Romania

It is a well–known fact that heavy metal pollution in sediments causes serious problems not only in the Danube basin, but also in the large and small adjacent river streams. A suitable method for assessing the level of heavy metals and their toxicity in sediments is the calculation of pollution indices. The present research aims to assess heavy metal pollution in the Lower Danube surface sediments collected along the Danube course (between 180 and 60 km) up to the point where the Danube River flows into the Danube Delta Biosphere Reserve (a United Nations Educational, Scientific and Cultural Organization—UNESCO, protected area). In addition, this monitored area is one of the largest European hydrographic basins. Five heavy metals (Cd, Ni, Zn, Pb, Cu) were analyzed in two different seasons, i.e., the autumn of 2018 and the spring of 2019, using the Inductively Coupled Plasma Mass Spectrometry (ICP– MS) technique. Our assessment of heavy metal pollution revealed two correlated aspects: 1. a determination of the potential risks of heavy metals in sediments by calculating the Potential Ecological Risk Index (RI), and 2. an evaluation of the influence of anthropogenic activities on the level of heavy metal contamination in the surface sediments, using three specific pollution indices, namely, the Geo–Accumulation Index (Igeo), the Contamination Factor (CF), and the Pollution Load Index (PLI). The results of this pioneering research activity in the region highlighted the presence of moderate metal (Ni and Cd) pollution and a low potential ecological risk for the aquatic environment.

Exploring Artificial Intelligence Techniques for Groundwater Quality Assessment

Freshwater quality and quantity are some of the fundamental requirements for sustaining human life and civilization. The Water Quality Index is the most extensively used parameter for determining water quality worldwide. However, the traditional approach for the calculation of the WQI is often complex and time consuming since it requires handling large data sets and involves the calculation of several subindices. We investigated the performance of artificial intelligence techniques, including particle swarm optimization (PSO), a naive Bayes classifier (NBC), and a support vector machine (SVM), for predicting the water quality index. We used an SVM and NBC for prediction, in conjunction with PSO for optimization. To validate the obtained results, groundwater water quality parameters and their corresponding water quality indices were found for water collected from the Pindrawan tank area in Chhattisgarh, India. Our results show that PSO–NBC provided a 92.8% prediction accuracy of the WQI indices, whereas the PSO–SVM accuracy was 77.60%. The study’s outcomes further suggest that ensemble machine learning (ML) algorithms can be used to estimate and predict the Water Quality Index with significant accuracy. Thus, the proposed framework can be directly used for the prediction of the WQI using the measured field parameters while saving significant time and effort.

Determination of Changes in the Quality of Surface Water in the River—Reservoir System

Assessing the changing parameters of water quality at different points in the river–reservoir system can help prevent river pollution and implement remedial policies. It is also crucial in modeling water resources. Multivariate statistical analysis is useful for the analysis of changes in surface water quality. It helps to identify indicators that may be responsible for the eutrophication process of a reservoir. Additionally, the analysis of the water quality profile and the water quality index (WQI) is useful in assessing water pollution. These tools can support and verify the results of a multivariate statistical analysis. In this study, changes in water quality parameters of the Turawa reservoir (TR), and the Mała Panew river at the point below the Turawa reservoir (bTR) and above the Turawa reservoir (aTR), were analyzed. The analyzed period was from 2019 to 2020 (360 samples were analyzed). It was found that TN, NO2-N, and NO3-N decreased after passing through the Turawa reservoir. Nevertheless, principal component analysis (PCA) and redundancy analysis (RDA) showed that NO2-N and NO3-N contribute to the observed variability of the water quality in the river-reservoir system. PCA showed that pH and PO4-P had a lower impact on the water quality in the reservoir than nitrogen compounds. Additionally, RDA proved that the values of the NO3-N and NO2-N indicators obtained the highest values at the aTR point, PO4-P at the bTR, and pH at the TR. This allows the conclusion that the Turawa reservoir reduced the concentration of NO2-N and NO3-N in comparison with the concentration of these compounds flowing into the reservoir. PCA and RDA showed that both parameters (NO2-N and NO3-N) may be responsible for the eutrophication process of the Turawa reservoir. The analysis of short-term changes in water quality data may reveal additional sources of water pollution. High temperatures and alkaline reaction may cause the release of nitrogen and phosphorus compounds from sediments, which indicates an increased concentration of TP, PO4-P, and Norg in the waters at the TR point, and TP, PO4-P, and NH4-N concentrations at the bTR point. The water quality profile combined with PCA and RDA allows more effective monitoring for the needs of water management in the reservoir catchment area. The analyzed WQI for water below the reservoir (bTR) was lower than that of the reservoir water (TR), which indicates an improvement in water after passing through the reservoir.

Investigating Water Quality Data Using Principal Component Analysis and Granger Causality

This work investigates the inter-relationships among stream water quality indicators, hydroclimatic variables (e.g., precipitation, river discharge), and land characteristics (e.g., soil type, land use), which is crucial to developing effective methods for water quality protection. The potential of using statistical tools, such as Principal Component (PC) and Granger causality analyses, for this purpose is assessed across 10 watersheds in the Eastern United States. The PC analysis shows consistency across the ten locations, with most of the variation explained by the first two PCs, except for the least developed watershed that presents three PCs. Results show that stronger Granger causality relationships and correlation coefficients are identified when considering a lag of one day, compared to longer lags. This is mainly due to the watersheds’ limited size and, thus, their fast hydrological response. The strongest Granger causalities are observed when water temperature and dissolved oxygen concentration are considered as the effect of the other variables, which corroborates the importance of these two water properties. This work also demonstrates how watershed size and land use can impact causalities between hydrometeorological variables and water quality, thus, highlighting how complex these relationships are even in a region characterized by overall similar climatology.

Chemometrics Applied in the Development of a Water Quality Indicator System for the Brazilian Amazon

The objective of this work was to develop a method to calculate the water quality index (WQI) using chemometric methods. The study was carried out at the Curuá-Una hydroelectric power plant in the state of Pará in the Brazilian Amazon. Eight collection stations in four periods (winter, intermediate I, summer, and intermediate II) and 29 parameters were selected. Multivariate analysis was applied to the results to verify the quality of the data and to select the most sensitive variables to be used as indicators for the WQI calculation. The 10 parameters selected were E _h, dissolved oxygen, total dissolved solids, chlorophyll a, phosphate, Ba, Ca, Fe, Na, and Sn. The WQI proposed herein was effective in the classification of water quality for the periods analyzed.

Drinking Water Quality Mapping Using Water Quality Index and Geospatial Analysis in Primary Schools of Pakistan

Primary school children in the developing world often lack potable water, which may result in a high burden of water-related diseases and poor school performance. The present study aimed to characterize the drinking water quality in primary schools of Pakistan. We used a multistage random-sampling method to select 425 primary schools from selected districts of Sindh province. Standard methods were used to characterize water quality. The results were compared with maximum acceptable values recommended by the Pakistani National Environmental Quality Standards (Pak NEQS) and the World Health Organization (WHO) drinking water quality standards. Groundwater (62%) and surface water (38%) were identified as two major drinking water sources in the selected schools with varying levels of water quality. Among all parameters, dissolved oxygen (DO), pH, and nitrate remained within WHO and Pak NEQS water quality standard limit, while total dissolved solids (TDS) (33%), electrical conductivity (EC) (46%), chloride (34%), turbidity (27%), and hardness (11%) samples exceeded standard limits. All dissolved salts had moderate to strong positive correlations with TDS and EC values. Based on a water quality index (WQI), 74% of primary schools had access to excellent or good quality drinking water compared to 26% of schools that had access to either poor, very poor or unsuitable drinking water. Among all three regions, more schools from South Sindh had either poor, very poor or unsuitable drinking water (33%) compared to the Central (25%) and North zones (15%). Hence, water from these locations is unfit for human consumption and these schools need better water quality management plans to stop the spread of water-related diseases in primary school children.

A Comparative Approach to a Series of Physico-Chemical Quality Indices Used in Assessing Water Quality in the Lower Danube

Water quality indices are suitable tools used for assessing water quality because of their capacity to reduce a large number of water quality indicators into one value which defines the water quality class. In this study, Water Quality Index (WQI), Water Pollution Index (WPI) and Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) were applied in order to evaluate the seasonal and spatial variation of the water quality in the Romanian Lower Danube sector. Fourteen physico-chemical parameters, i.e., pH, DO, BOD5, COD, N-NH4+, N-NO3−, N-NO2−, N-total, P-total, SO42−, Cl−, Fe-total, Zn2+ and Cr-total, were monitored along the Danube course (on a distance of about 120 km), during the four seasons between the autumn of 2018 and the summer of 2019 in order to calculate the three indices mentioned above. Indices results showed that the water analysed was ranked into different water quality classes, although the same dataset was used. These differences were due to the contribution of each parameter taken into account in the calculation formula. Thus, the WQI scores were mostly influenced by those parameters whose maximum allowable concentration was low (e.g., heavy metals, N-NO2−), while the WPI and CCME-WQI scores were influenced by those parameters which exceeded the maximum allowable concentration (BOD5, DO, COD, N-NO3−, N-NO2−). Based on the WQI results, the water was ranked into quality classes II and III. WPI and CCME-WQI assessed water only in quality class II, with one exception in the case of CCME-WQI when water was ranked into quality class III. The temporal assessment identified the seasons in which the water quality was lower, namely summer and autumn. The variation of the indices values between the sampling stations demonstrates the existence of pollution sources in the study area. Moreover, the indices results illustrated the contribution of the main tributaries (Rivers Siret and Prut) to the Danube River water quality. The appropriate applicability of the three indices was also discussed in this study.

Use of water quality index and multivariate statistical methods for the evaluation of water quality of a stream affected by multiple stressors: A case study

The Sürgü Stream, located in the Euphrates River basin of Turkey, is used for drinking water source, agricultural irrigation and rainbow trout production. Therefore, water quality of the stream is of great importance. In this study, multivariate statistical techniques (MSTs) and water quality index (WQI) were applied to assess water quality of the stream affected by multiple stressors such as untreated domestic sewage, effluents from fish farms, agricultural runoff and streambank erosion. For this, 16 water quality parameters at five sites along the stream were monitored monthly during one year. Most of parameters showed significant spatial variations, indicating the influence of anthropogenic activities. All parameters except TN (total nitrogen) showed significant seasonal differences due to high seasonality in WT (water temperature) and water flow. The spatial variations in the WQI were significant (p < 0.05) and the mean WQI values ranged from 87.6 to 95.3, indicating "good" to "excellent" water quality in the stream. Cluster analysis classified five sites into three groups, that is, clean region, low polluted region and very clean region. Stepwise temporal discriminant analysis (DA) identified that pH, WT, Cl^-, SO₄^2-, COD (chemical oxygen demand), TSS (total suspended solids) and Ca²⁺ are the parameters responsible for variations between seasons, and stepwise spatial DA identified that DO (dissolved oxygen), EC (electrical conductivity), NH₄-N, TN (total nitrogen) and TSS are the parameters responsible for variations between the regions. Principal component analysis/factor analysis revealed that the parameters responsible for water quality variations were mainly associated with suspended solids (both natural and anthropogenic), soluble salts (natural) and nutrients and organic matter (anthropogenic).

The assessment of the annual effective dose due to ingestion of radionuclides from drinking water consumption: calculation methods

In the present paper the different ways of assessing the annual effective dose due to ingestion of radionuclides by drinking water consumption were examined and exemplified. On a set of 10 samples the gross alpha activity, the gross beta activity, the concentration of 210Po, 210Pb, 238U, 232Th and, 226Ra were measured. The highest annual effective dose values assessed by relying on the investigated sample set were found by using the rationale according to which all the gross alpha and beta activity is due to the alpha and beta radionuclide, with the highest effective dose coefficient, namely 210Po and 210Pb/228Ra, respectively.

Assessing the Groundwater Quality in the Liwa Area, the United Arab Emirates

Last period groundwater quality raises big concerns all over the world since it is a limited source of drinkable water and for agricultural and industrial use. While the suitability of the groundwater of Liwa aquifer (Abu Dhabi Emirate) for agricultural use has been previously partially studied, not all the water parameters have been taken into account. Therefore, in this paper, we propose the study of 42 concentrations series of 19 groundwater parameters. We test the hypothesis that the water parameters series recorded at different locations are similar and group the samples in clusters. The main parameters that determine the differences between the clusters are determined by Principal Component Analysis (PCA). Finally, we use a quality index for assessing the water suitability for drinking. The conclusions emphasize the necessity of using more than one technique to evaluate water quality for different purposes and to cross-validate the results.

Development of Flood Risk and Hazard Maps for the Lower Course of the Siret River, Romania

The Siret River is one of the most important tributaries of the Danube River in the Romanian territory. With a total length of 596 km in this territory, the confluence section of the Siret River with the Danube represents an area where major floods have occurred over time. In the last 50 years, over 20 floods have been recorded in the lower area of the Siret River; and the most recent important flood, which happened in 2010, had a negative impact on the local population, the environment, and the economy. Although it is a buffer zone, the Danube River has a significant impact on the discharge rate of the Siret River. Since few studies have been conducted on the prediction of flooding in the lower area of the Siret River, the present study aims at presenting the most important steps to be taken for designing risk and hazard maps for floods, which could be further applied to other rivers. The confluence of the Siret River with the Danube, a distance of 35 km upstream, was chosen as a study area. Techniques of topographic and bathymetric measurements were combined in order to design the risk and hazard maps for floods in this area and to improve the digital terrain model of the minor riverbed for the studied river area. The 1D hydrodynamic model of the HEC-RAS software was used in this research for developing the flood risk and flood hazard maps. The hazard and risk maps were generated based on 10%, 5%, and 1% flood scenarios, which are in accordance with Floods Directive 2007/60/EC; no historical data were available for the 0.1% scenarios. Thus, in a flood scenario that can occur every 100 years, about 9500 inhabitants are vulnerable at a medium flood risk. In this scenario, over 19.5 km of road infrastructure, about 16.5 km of railways, eight cultural heritage indicators, and three environmental indicators may be affected.

Water Quality Index of Suceava River in Suceava City Metropolitan Area

The water quality of rivers worldwide is of persistent interest due to its impact on human life. Five streamwater quality parameters of Suceava River were monitored in 2019 upstream and downstream of Suceava city, Romania: dissolved oxygen, specific conductivity, pH, oxidation-reduction potential, and temperature. Data was recorded at a high temporal frequency, every hour, and produced Water Quality Index (WQI) time series of similar resolution. Our additive WQI has variants with particular advantages. Water quality of Suceava city exhibits a diurnal cycle. Upstream, WQI values indicate a quasi-permanent good water quality; downstream, the water quality oscillates around the average WQI value because of the various sources of water contaminants, especially the wastewaters from the wastewater treatment plant. Parameters from this point source of pollution are taken into account to explain the decaying streamwater quality towards the end of 2019. WQI is useful for detecting time intervals when water self-purification events have a high chance of occurrence.

Human Risk from Exposure to Heavy Metals and Arsenic in Water from Rivers with Mining Influence in the Central Andes of Peru

Water pollution by heavy metals is one of the leading environmental concerns as a result of intense anthropogenic pressure on the aquatic environment. This constitutes a significant limitation to the human right of access to drinking water. In this context, the risk to humans from exposure to heavy metals and arsenic in water from rivers subject to mining influence in the Central Andes of Peru was assessed. Water samples were collected from seven rivers at 63 sampling sites, and concentrations of Cu, Fe, Pb, Zn, and As were determined using flame atomic absorption spectrophotometry. Cluster analysis was used to group 21 sampling sites into four groups with similar chemical characteristics, and principal component analysis was used to simplify the complex relationship between the toxic elements by generating two main components with a total percentage of variation of 86%. Fe, Zn, and As had higher percentages of contribution in the Mantaro, Cunas, and Chia rivers. The hazard quotient was highest for children and adults. The hazard index for ingestion of all the studied heavy metals and As was higher than the threshold value (HIing > 1). HIing in 43% of the rivers indicated that the adult population is at risk of non-carcinogenic effects, and HIing in 14% of the rivers revealed a very high health risk. The risk of cancer by ingestion for children varied from medium to high risk and for adults from low to high risk.

Improving prediction of water quality indices using novel hybrid machine-learning algorithms

River water quality assessment is one of the most important tasks to enhance water resources management plans. A water quality index (WQI) considers several water quality variables simultaneously. Traditionally WQI calculations consume time and are often fraught with errors during derivations of sub-indices. In this study, 4 standalone (random forest (RF), M5P, random tree (RT), and reduced error pruning tree (REPT)) and 12 hybrid data-mining algorithms (combinations of standalones with bagging (BA), CV parameter selection (CVPS) and randomizable filtered classification (RFC)) were used to create Iran WQI (IRWQI_sc) predictions. Six years (2012 to 2018) of monthly data from two water quality monitoring stations within the Talar catchment were compiled. Using Pearson correlation coefficients, 10 different input combinations were constructed. The data were divided into two groups (ratio 70:30) for model building (training dataset) and model validation (testing dataset) using a 10-fold cross-validation technique. The models were evaluated using several statistical and visual evaluation metrics. Result show that fecal coliform (FC) and total solids (TS) had the greatest and least effect on the prediction of IRWQI_sc. The best input combinations varied among the algorithms; generally variables with very low correlations displayed weaker performance. Hybrid algorithms improved the prediction power of several of the standalone models, but not all. Hybrid BA-RT outperformed the other models (R² = 0.941, RMSE = 2.71, MAE = 1.87, NSE = 0.941, PBIAS = 0.500). PBIAS indicated that all algorithms, with the exceptions of RT, BA-RT and CVPS-REPT, overestimated WQI values.

Influencing Factors Analysis of Taiwan Eutrophicated Reservoirs

Treatment cost and quality of domestic water are highly correlated with raw water quality in reservoirs. This study aims to identify the key factors that influence the trophic state levels and correlations among Carlson trophic state index (CTSI) levels, water quality parameters and weather factors in four major reservoirs in Taiwan from 2000 to 2017. Weather (e.g., air temperature, relative humidity, total precipitation, sunlight percentage and cloud cover) and water quality parameters (e.g., pH, chemical oxygen demand, suspended solids (SS), ammonia, total hardness, nitrate, nitrite and water temperature) were included in the principal component analysis and absolute principal component score models to evaluate the main governing factors of the trophic state levels (e.g., CTSI). SS were washed out by precipitation, thereby influencing the reservoir transparency tremendously and contributing over 50% to the CTSI level in eutrophicated reservoirs (e.g., the Shihmen and Chengchinghu Reservoirs). CTSI levels in the mesotrophic reservoir (e.g., Liyutan Reservoir) had strong correlation with chlorophyll-a and total phosphorus. Results show that rainfall/weather factors were the key driving factors that affected the CTSI levels in Taiwan eutrophicated reservoirs, indicating the need to consider basin management and the impacts of extreme precipitation in reservoir management and future policymaking.

Farmers’ Attitudes to the Pricing of Natural Resources for Sustainability: GAP-Şanlıurfa Sampling of Turkey

This research aims to evaluate farmers’ attitudes to the pricing of natural resources, mainly water and soil resources, in GAP-Şanlıurfa-Turkey. It will also define farmers’ willingness to accept pricing for the sustainability of resources and explore the potential factors that contribute to such willingness. The data comes from a sample of 1105 farmers in Şanlıurfa who were chosen by a simple random sampling method and participants were interviewed face to face by questionnaires. The logistic regression is used for analysis. The results indicate that 40% of the farmers have a positive attitude to the pricing of resources for protection and sustainability. The most affecting factors are the location of the farmers, the number of agricultural manpower at the household, land amount, ownership status, income derived from agriculture, and livestock. The average willingness-to-pay amount was calculated in USD as $48.8/ha. Where public finance is insufficient to meet the demands, the willingness-to-pay amount may be used in co-investments. In this way, both the financing problem can be overcome and the ownership rate of the users can be increased. Therefore, the results could be helpful for decision- and policy-makers to develop strategies for the sustainability of resources for GAP-Turkey and areas with similar socioeconomic characteristics.