An integrated approach for assessing surface water quality: Case of Beni Haroun dam (Northeast Algeria)

Spatio-temporal evolution of eutrophication and water quality in the Turawa dam reservoir, Poland

The objectives of the article are: to assess spatio-temporal evolution of eutrophication and water quality of the Turawa dam reservoir, located in south-western Poland on the Mała Panew River; to identify location and relationship between potential sources of physicochemical pollution related to the progressing process of eutrophication; and to determine trophic status and water quality indices of the selected research object. The analysis (Mann-Whitney U test, PCA, HCA, Spearman correlation matrix) showed a high susceptibility of the reservoir to eutrophication processes, especially due to the influence of dangerous loads of compounds emerging from areas with high tourist intensity and pollutants flowing from the Mała Panew River. The parameters deteriorating the ecological status were TP, DO, BOD₅, and COD. Considering the cumulative results of water quality indices for the period 1998-2020, the average water quality was in classes II or III. A noticeable deterioration appeared in water quality for the years 2016-2020, which proves the progressing eutrophication in the Turawa reservoir. In 1998-2020, the reservoir was classified as eutrophic or mesoeutrophic based on the calculated three trophic status indices. This article would help in developing a strategy for dealing with water blooms, a reliable system for monitoring pressures causing eutrophication, and optimal technologies for the reconstruction of multifunctional reservoirs.

Groundwater quality evaluation based on water quality indices (WQI) using GIS: Maadher plain of Hodna, Northern Algeria

In a semi-arid region of Maadher, central Hodna (Algeria), groundwater is the main source for agricultural and domestic purposes. Anthropogenic activities and the presence of climate change's effects have a significant impact on the region's groundwater quality. This study's goals were to use water quality indices to evaluate the groundwater's quality and its suitability for drinking and irrigation, as well as to identify contaminated wells using a geographic information system (GIS) and the spatial interpolation techniques of ordinary kriging and inverse distance weighting (IDW). The results reveal that all water samples exceeded the World Health Organization's standards for nitrate ions and had alarming concentrations of calcium, chlorine, and sulfate (WHO). According to Piper's diagram, the groundwater hydrochemical facies is composed of the elements sulfate-chloride-nitrate-calcium (SO₄^2--Cl^-NO₃^--Ca²⁺ water type). The majority of samples fall into the poor water category, slightly more than 10% fall into the very poor water category, and less than 10% fall into the good to the excellent quality category, per the water quality indices, which classify samples in a similar manner. According to irrigation water indices, every sample is suitable for irrigation. Depending on the direction of groundwater flow, the spatial distributions of Ca²⁺, Na⁺, Mg²⁺, SO₄^2-, and Cl^- show that their concentrations are high north of the area and relatively low south of Maadher village (Fig. 3). Nitrate concentrations are high in the majority of samples, particularly those close to the Bousaada wadi. In most samples, particularly those close to the Bousaada wadi, nitrate levels are high. Various water quality models were described, and GIS spatial distribution maps were created using standard kriging and inverse distance weighting (IDW) techniques through selected semi-variograms predicted against measurements. To determine the origin of mineralization and the chemical processes that take place in the aquifer-which include the precipitation and dissolution of dolomite, calcite, aragonite, gypsum, anhydrite, and halite-the groundwater saturation index was calculated.

A decision fusion method based on classification models for water quality monitoring

Monitoring of water quality is one of the world's main intentions for countries. Classification techniques based on support vector machines (SVMs) and artificial neural network (ANN) has been widely used in several applications of water research. Water quality assessment with high accuracy and efficiency with innovational approaches permitted us to acquire additional knowledge and information to obtain an intelligent monitoring system. In this paper, we present the use of principal component analysis (PCA) combined with SVM and ANN with decision templates combination data fusion method. PCA was used for features selection from original database. The multi-layer perceptron network (MLP) and the one-against-all strategy for SVM method have been widely used. Decision templates are applied to increase the accuracy of the water quality classification. The specific classification approach was employed to assess the water quality of the Tilesdit dam in Algeria as a study area, defined with a dataset of eight physicochemical parameters collected in the period 2009-2018, such as temperature, pH, electrical conductivity, and turbidity. The selection of the excellent parameters of the used models can be improving the performance of classification process. In order to assess their results, an experiment step using collected dataset corresponding to the accuracy and running time of training and test phases, and robustness to noise, is carried out. Various scenarios are examined in comparative study to obtain the most results of decision step with and without feature selection of the input data. From the results, we found that the integration of SVM and ANN with PCA yields accuracy up than 98%. The combination by decision templates of two classifiers SVM and ANN with PCA yields an accuracy of 99.24% using k-fold cross-validation. The combination data fusion enhanced expressively the results of the proposed monitoring framework that had proven a considerable ability in surface water quality assessment.

Adaptive-weight water quality assessment and human health risk analysis for river water in Hong Kong

The water quality of Hong Kong's four water control zones (Tolo Harbour and Channel, Port Shelter, Victoria Harbour, and Junk Bay) is of vital importance and has attracted much attention. This study aims to more objectively and comprehensively assess the water quality and its health impact based on the four-year monitoring data of 21 parameters collected from four zones. First, physicochemical characteristics of the water system were investigated based on multivariate statistical approaches, including Kruskal-Wallis test, hierarchical cluster analysis, and Mann-Kendall test. Then, water quality levels over space and time and the element sources were analyzed using adaptive-weight water quality index (AWQI) method, and factor analysis, respectively. Finally, the potential harm of trace elements for humankind was identified based on the health risk assessment model. The results revealed that (1) the values of more than half of the water quality parameters exhibited significant interannual changes, and the values of all parameters distinctly varied over space; (2) The water quality status in four water control zones showed a steady and long-term improvement trend from 2016 to 2019; (3) The sources of pollution elements impacting water quality status were related to the comprehensive influence of human activities and natural processes; (4) The carcinogenic risks of all trace elements were negligible or acceptable, while Mn and As may cause noncarcinogenic harm to humankind.

Assessing Surface Water Quality for Irrigation Purposes in Some Dams of Asir Region, Saudi Arabia Using Multi-Statistical Modeling Approaches

The dam is a crucial water source for both consumption and irrigation in the Asir region of the Kingdom of Saudi Arabia. The current study evaluates surface water quality at the dam and examines the effects of some physicochemical and trace metals on irrigation using multiple statistical approaches. The physicochemical parameters and trace metals of the dam water were measured at 27 sites in the Asir region. Three sites were generated using the K-mean clustering technique; the first group included five sites, the second group contained 20 sites, and the third group added 2 sites. The chemical facies of the surface water were Na-K-SO42−. The surface water had low levels of Zn (0.19 mg/L), Cd (0.10 mg/L) and Pb (0.22 mg/L) except for B (38.50 mg/L), excessive concentrations of Na (2090.65 mg/L), K (535.72 mg/L), SO42− (208.11 mg/L) and Cl− (105.96 mg/L), while pH varied between 6.0 and 8.5 except at a few sites. The EC (electrical conductivity) values were within the standard for irrigation purposes. The results of irrigation water indices such as magnesium absorption ratio (MAR), magnesium hazard (MH), Kelly’s ratio (KR), and soluble sodium percentage (SSP) indicate that dam water is mostly fit for irrigation except for sodium percentage (Na%) and sodium adsorption ratio (SAR). The irrigation water quality index (IWQI) values revealed that 51.85% of samples had a high suitability class and 11.11% of samples had moderate suitability, whereas the rest were low suitability for irrigation use. The redundancy analysis (RDA) biplot revealed that water pH, DO, TH, and SO42− were increased with decreased EC and salt level. The generalized linear model (GLM) model found that salt had a negative effect on the amount of Fe, Ni, Se, and Al concentrations. The study recommends that proper protective measures, including acceptable criteria for different water parameters, are required to reduce the potential influence of physicochemical parameters and metals on irrigation water in agricultural fields.

A Surrogate Water Quality Index to assess groundwater using a unified DEA-OWA framework

In this paper, we introduce a new approach, based on a unified framework incorporating Data Envelopment Analysis (DEA) and Ordered Weighted Averaging (OWA), for assessing water quality in contextual settings that involve a large number of hydrochemical parameters. In order to enhance discrimination among water sources, the DEA model is adopted with data-driven input variables, called "surrogate optimistic closeness values," computed through an aggregation procedure that includes the observed values of the hydrochemical parameters with OWA weights. The proposed DEA-OWA methodology has been employed to assess the quality of 51 water samples, collected from irrigation wells in Sereflikochisar Basin, Turkey, by means of 19 hydrochemical parameters. Using different subjectivity levels, the Surrogate Water Quality Indices (SWQIs) that are produced are proven effective in enhancing discrimination among the water sources while enabling a more robust water quality-based ranking. The k-means analysis has been used for clustering the water quality of the wells into Excellent, Good, Permissible, and Unsuitable rather than using pre-set boundaries. Only one water source has been identified as Excellent, whereas 17.65%, 45.10%, and 35.29% of the sampled wells, respectively, are categorized with Good, Permissible, and Unsuitable water quality. Inferred from wells' location, the results suggest that the groundwater might be drastically affected by saline water intrusion from Lake Tuz. The latter conclusion has been corroborated through a Tobit regression analysis.

Evaluation of Groundwater Using an Integrated Approach of Entropy Weight and Stochastic Simulation: A Case Study in East Region of Beijing

Groundwater is an important source of water in Beijing. Hydrochemical composition and water quality are the key factors to determine the availability of groundwater. Therefore, an improved integrated weight water quality index approach (IWQI) combining the entropy weight method and the stochastic simulation method is proposed. Through systematic investigation of groundwater chemical composition in different periods, using a hydrogeochemical diagram, multivariate statistics and spatial interpolation analysis, the spatial evolution characteristics and genetic mechanism of groundwater chemistry are discussed. The results show that the groundwater in the study area is weakly alkaline and low mineralized water. The south part of the study area showed higher concentrations of total dissolved solids, total hardness and NO3--N in the dry season and wet season, and the main hydrochemical types are HCO3--Ca and HCO3--Ca-Mg. The natural source mechanism of the groundwater chemical components in Chaoyang District includes rock weathering, dissolution and cation exchange, while the human-made sources are mainly residents and industrial activities. Improved IWQI evaluation results indicate that water quality decreases from southwest to northeast along groundwater flow path. The water quality index (WQI) method cannot reflect the trend of groundwater. Sensitivity analysis indicated that the improved IWQI method could describe the overall water quality reliably, accurately and stably.

Using Remote Sensing and Multivariate Statistics in Analyzing the Relationship between Land Use Pattern and Water Quality in Tien Giang Province, Vietnam

This study was carried out to understand how land use patterns influence surface water quality in Tien Giang Province using remote sensing and statistical approaches. Surface water quality data were collected at 34 locations with the frequency of four times (March, June, September, and November) in 2019. Water quality parameters were used in the analysis, including pH, temperature, electrical conductivity (EC), total suspended solids (TSS), dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), ammonium (N-NH4+), nitrite (N-NO2−), nitrate (N-NO3−), sulfate (SO42−), orthophosphate (P-PO43−), chloride (Cl−), total nitrogen (TN), total phosphorus (TP), and coliform. The relationship between land use patterns and water quality was analyzed using geographic information techniques (GIS), remote sensing (RS), statistical approaches (cluster analysis (CA), principal component analysis (PCA), and Krustal–Wallis), and weighted entropy. The results showed water quality was impaired by total suspended solids, nutrients (N-NH4+, N-NO2−, P-PO43−), organic matters (BOD, COD), and ions (Cl− and SO42−). Kruskal–Wallis analysis results showed that all water quality parameters in the water bodies in Tien Giang Province were seasonally fluctuated, except for BOD and TN. The highest levels of water pollutants were found mostly in the dry season (March and June). The majority of the land in the study area was used for rice cultivation (40.64%) and residential (27.51%). Water quality in the study area was classified into nine groups corresponding to five combined land use patterns comprising residential–aquaculture, residential–rice cultivation, residential–perennials, residential–rice–perennial, and residential–rice–perennial crops–aquacultural. The concentrations of the water pollutants (TSS, DO, BOD, COD, N-NH4+, N-NO2−, Cl−, and coliform) in the locations with aquaculture land use patterns (Clusters 1 and 2) were significantly larger than those of the remaining land use patterns. PCA analysis presented that most of the current water quality monitoring parameters had a great impact on water quality in the water bodies. The entropy weight showed that TSS, N-NO2−, and coliform are the most important water quality parameters due to residential–aquaculture and residential–rice cultivation; EC, DO, N-NH4+, N-NO2−, Cl−, and coliform were the significant variables for the land use type of residential–perennial crops; N-NO2−, P-PO43−, and coliform for the land use pattern of residential–rice cultivation–perennial crops) and N-NH4+, N-NO2−, Cl−, and coliform for the land use pattern of residential–rice cultivation–perennial crops–aquaculture. The current findings showed that that surface water quality has been influenced by the complex land use patterns in which residential and rice cultivation may have major roles in causing water impairment. The results of the water quality assessment and the variation in water properties of the land use patterns found in this study provide scientific evidence for future water quality management.

Temporal and spatial variations of surface water quality in the Nile River of Damietta Region, Egypt

Temporal/spatial variations of surface water quality were examined for the Nile River in the Damietta region where it serves as the major source of water for the inhabitants of Damietta Governorate. A total of 32 water quality parameters were monitored at six sampling sites for 12 months from February 2016 to January 2017. Higher values of chemical oxygen demand (COD), biological oxygen demand (BOD), heavy metals, and nutrients were observed upstream. About ~ 70% of the total variance in observations was explained by five main influences using factor analysis. The first factor (24.6% of the variance) was indicative of the mixed sources of natural and anthropogenic inputs. The second (nutritional) and the third (organic) factors were mainly controlled by the discharges from agricultural and domestic sources, respectively. Human activities and natural processes controlled the fourth and fifth factors. Only 11 parameters (K, temperature, COD, HPC, total hardness, DO, NO₂, Na, TDS, Cl, and EC) were necessary for distinguishing temporal variations according to Discriminant analysis (DA). Seven parameters (BOD, PO₄, SiO₃, Al, Turbidity, Fe, and Chlorophyll-a) were the most important variables responsible for spatial variations. Using the results we developed a water quality index (WQI) using only those parameters identified as important. All water quality parameters were below the permissible limits except for turbidity according to the World Health Organization standards, BOD and COD according to the Egyptian regulations. The calculated WQI values ranged between 12.73 and 33.73. According to these values, the Nile River Damietta branch represents a good to an excellent source of drinking water for entering secondary treatment.