Ensuring high water quality in Algeria, particularly in Annaba, is crucial for the well-being of its population and the sustainable development of its diverse ecosystems. The study focuses on the Cheffia Dam, Oued El Aneb, and Treat boreholes as crucial sources of drinking water. The water quality index (WQI) is used to assess water quality based on various physico-chemical parameters. The research spans from January to December 2021, analyzing 16 parameters, such as temperature, pH, conductivity, turbidity, total hardness, calcium, magnesium, sodium, potassium, chloride, nitrate, sulfate, phosphate, and iron, this results in a total of 36 samples and 576 analyses. Principal component analysis (PCA) is employed to delve into interrelationships between variables, revealing distinct characteristics for each site. This study, the first of its kind, provides a comprehensive 1-year evaluation of water quality in Annaba. The collected data serve as a valuable resource for future water management decisions, highlighting both temporal and spatial variations. The current study indicates that the analysis of water parameters, adherence to standards, and the application of WQI reveal that the water sources are generally good quality throughout the year with excellent water quality in autumn. However, challenges such as elevated turbidity in Cheffia dam water necessitate targeted interventions.

  • First-of-its-kind assessment of water quality in Annaba, focusing on key water sources.

  • Seasonal variations highlight excellent water quality in autumn.

  • Water quality index offers a standardized measure for accessible assessment.

  • Principal component analysis (PCA) reveals unique characteristics of water sources.

  • Model for sustainable water management, emphasizing the importance of monitoring and analysis.

Water quality is vital for human health, ecosystems, and well-being due to various reasons. Clean drinking water is crucial for survival and preventing waterborne diseases, especially in vulnerable groups (Kumar et al. 2022). Agriculture relies on water quality to ensure safe and healthy crops, while polluted water can harm aquatic ecosystems (Syafrudin et al. 2021). Impaired water quality also impacts tourism, fisheries, and industries, leading to economic losses (Russ et al. 2022). Treating poor water quality is expensive and requires advanced technologies (Palansooriya et al. 2020). Preserving water quality is essential for environmental conservation and protecting endangered species (Valera et al. 2019). Moreover, water quality affects recreational activities, drinking water scarcity, and sustainable development goals (Chen et al. 2020). To ensure a healthier future, collective efforts are necessary to prevent pollution, promote responsible water use, and implement effective water management practices. Water quality assessment involves various methods tailored to measure specific parameters and contaminants present in the water (Altenburger et al. 2019). These methods include analyzing physical characteristics like temperature, turbidity, color, and odor using handheld meters or observation (Bwadi et al. 2021). Chemical analysis determines concentrations of substances like pH, dissolved oxygen, nutrients, heavy metals, and pesticides through spectrophotometry and colorimetric tests. Biological assessments study organisms like macroinvertebrates and algae to gauge overall aquatic ecosystem health (Kotalik et al. 2021). Microbiological tests detect harmful microorganisms, while bioassays expose living organisms to assess water toxicity (do Nascimento et al. 2023). Remote sensing uses satellite imagery for larger water bodies, and real-time monitoring with automated sensors provides immediate data on water quality (Zhuang et al. 2022). Biosensors, isotope analysis, and traditional sampling with laboratory analysis also contribute to comprehensive water quality understanding, facilitating better water resource management and protection decisions (Bieroza et al. 2023).

A water quality index (WQI) is a numerical rating used to assess and communicate the overall quality of water in a specific area. It considers multiple water quality parameters, such as physical, chemical, and biological characteristics, to calculate a single value representing water quality. The index typically ranges from 0 to 100, with higher values indicating better water quality (Uddin et al. 2021). It simplifies complex data for easy understanding by the public and decision-makers, enables comparisons over time and across locations, communicates potential risks to human health and the environment, and aids policymakers in prioritizing actions to improve water quality. However, the WQI's simplicity may not capture all water quality nuances, and it should be used in conjunction with comprehensive monitoring and analysis while considering local conditions and assessment objectives (Giri 2021).

Scientific research on the WQI is extensive and widespread (Wong et al. 2020). Researchers and environmental agencies worldwide have worked to develop, refine, and validate various WQI models (Uddin et al. 2021). They have explored its effectiveness in assessing water quality and its applicability in different regions and water bodies. Studies have focused on validating and calibrating WQI models, comparing multiple approaches, and evaluating its role in decision-making and resource allocation (Akdogan & Güven 2023). Long-term studies using WQI have helped identify water quality trends over time and understand the impacts of environmental policies and climate change (Vatanpour et al. 2020). Researchers have also investigated the connection between WQI values and ecosystem health and human health risks. As scientific knowledge advances, the WQI continues to be a valuable tool for water quality management and is expected to gain more significance in addressing environmental challenges and promoting sustainable water resource management.

In the last two decades, numerous studies have demonstrated the deterioration of groundwater quality in various countries, as highlighted by researchers such as Jeong (2001), Moon et al. (2004), Adimalla (2019), and Gaikwad et al. (2020a, 2020b); Asmamaw & Debie (2023). Developing nations encounter challenges in accessing safe drinking water, contributing to multiple public health issues, as noted by Abedin et al. (2019). Additionally, unattended agricultural activities and domestic effluents exacerbate the degradation of water quality, as emphasized by Khan et al. (2021). Effective water quality management is vital for comprehensive integrated water resource management (Xiang et al. 2021). Unfortunately, declining rainfall, irregular seasonal patterns, desertification, and environmental degradation worsen the water problem (Singh et al. 2021). Therefore, regular and continuous monitoring of water quality is urgently required to address and preserve it (Yan et al. 2022). Contaminated water poses serious threats to public health and ecosystems, causing waterborne diseases and environmental degradation (Panaskar et al. 2016; Mukate et al. 2018; Wagh et al. 2018; Nabi et al. 2019). As environmental pollutants continue to emerge, improving water quality has become a critical concern for water resource management experts to protect human health and aquatic life (Ngqwala & Muchesa 2020).

Algeria, as many other countries, has not been spared from issues related to surface and groundwater, as well as their quality. Several studies have been conducted in arid or semi-arid regions to address these water challenges as highlighted by Kouadri et al. (2021) and Bouderbala (2021).

In North-East of Algeria, especially Annaba Province, which is currently facing significant challenges due to water shortages and intermittent water distribution (Berredjem et al. 2023). The water demand was estimated at 98 million cubic metres (MCM) by the year 2021, with the potential to reach 148 MCM by the year 2070 (Berredjem et al. 2023). In the face of this water shortage, some researchers have directed their focus toward investigating the deterioration of water quality as highlighted by Attoui et al. (2016) and Hafsi & Boutaghane (2022). The research aimed to assess water quality in North-East Algeria using a temporal monitoring system extends from January to December 2021 for physico-chemical parameters in raw water obtained from the Cheffia Dam, Oued El Aneb, and Treat boreholes, where the water of these three sources is not treated, as the WQI and PCA are being used in order to ensure that the drinking water supplied to the vast majority of the population through the distribution network met the necessary potability criteria to protect public health, or the transition to water treatment when the WQI indicates poor water quality to unsuitable for consumption, as well as assisting in identifying appropriate treatment systems.

Study area

The study area, located in the northeastern region of Algeria, encompasses the province of Annaba (Figure 1). This city is home to approximately 720,203 residents and covers an expansive area of 1,439 km2, equivalent to 0.06% of the national territory. Annaba is bordered by the towns of Skikda to the west, El Tarf to the east, and Guelma to the south, boasting a stunning 80 km coastline along the Mediterranean Sea. Annaba experiences a Mediterranean climate, characterized by humid winters and hot summers. The region receives an annual rainfall ranging from 650 to 1,000 mm, with peak rainfall occurring in December and January, typically ranging from 90 to 120 mm, and an average temperature fluctuating between 14 and 34 °C. The study area also encompasses modern farming practices, covering approximately 43,850 ha. These practices include the cultivation of cereals, vegetables, fruit, and fishing activities. In terms of irrigation, only 13.27% of the area is irrigated, with 72% relying on groundwater sources, and the remaining supply coming from the Cheffia Dam. The city's economy features highly concentrated industries, including steel production, chemical fertilizers, and tomato processing, as documented in studies by Achouri et al. (2017) and Berredjem et al. (2023).

One of Annaba's prominent features is its dense hydrographic network, highlighted by Lake Fetzara, an expansive freshwater body spanning 18,670 ha, and the Seybouse River, stretching over 127.5 km2.

The available water resources in the city of Annaba are estimated at approximately 92.6 MCM/year. These resources are distributed among various sources, including surface waters from the Cheffia Dam (44 MCM/year) and the Mexa Dam (21 MCM/year), as well as hillside reservoirs (7 MCM/year). Groundwater also plays a significant role, sourced from wells, springs (0.6 MCM/year), and boreholes (20 MCM/year).

Sampling and data collection

The city of Annaba faces the challenge of limited local water resources, relying heavily on neighboring regions such as El Tarf, Skikda, and Guelma. Our study focuses on three distinct locations within Annaba (Table 1). The selection of these sites (Figure 2) is based on their critical role in supplying drinking water to the region. Factors considered include hydrological significance, urbanization levels, and accessibility to watercourses.
Table 1

Lists of sampling points

SiteMunicipalityX (UTM)Y (UTM)
Site 1 Cheffia dam Sidi Amar 386,363.11 m E 4,076,696.16 m N 
Site 2 Oued El Aneb borehole Oued El Aneb 365,405.01 m E 4,082,630.50 m N 
Site 3 Treat borehole Treat 359,810.80 m E 4,084,693.35 m N 
SiteMunicipalityX (UTM)Y (UTM)
Site 1 Cheffia dam Sidi Amar 386,363.11 m E 4,076,696.16 m N 
Site 2 Oued El Aneb borehole Oued El Aneb 365,405.01 m E 4,082,630.50 m N 
Site 3 Treat borehole Treat 359,810.80 m E 4,084,693.35 m N 

UTM, Universal Transverse Mercator.

Figure 2

Sampling sites. (a) Cheffia Dam, (b) Oued El Aneb borehole, (c) Treat borehole, and (d) Water sampling (photographs by Guenouche, 2021).

Figure 2

Sampling sites. (a) Cheffia Dam, (b) Oued El Aneb borehole, (c) Treat borehole, and (d) Water sampling (photographs by Guenouche, 2021).

Close modal

In total, 36 samples across the study area were collected. The experimental setup for studying the water sample contents involved the use of 1.5-L mineral water bottles (Figure 2(d)). These bottles were thoroughly rinsed with distilled water to eliminate any potential residues. Each bottle was meticulously labeled to ensure precise traceability of the collected samples. Water samples were collected using sterile gloves, ensuring the preservation of sample integrity and preventing any external contamination. After collection, the samples were stored in a cooler at a constant temperature of 4 °C, following the procedures recommended by Rodier et al. (1996). This storage method ensures the stability of the chemical properties of the samples until their subsequent analysis. Sampling was carried out systematically, with a monthly frequency throughout the year 2021, specifically in the morning around 10 am, thus covering all seasons and weather conditions of the year. This methodical approach ensures the representativity of the collected samples, providing a solid foundation for the in-depth analysis of water content over time.

Water analyses

Water analysis is a critical aspect of environmental monitoring and quality control (QC), involving the determination of various chemical and physical parameters. The laboratory analyses encompassed 36 samples for 16 parameters, totaling 576 analyses. Three main analytical methods, namely electrochemical, volumetric, and spectrophotometric, are employed for comprehensive water analysis.

The electrochemical method relies on redox reactions at an electrode in a solution, utilizing voltammetry for measuring electrical parameters and analyzing chemical compounds. This method is implemented through a multiparameter instrument like the HACH HQ4100 for parameters such as pH, temperature, conductivity, salinity, and turbidity.

The volumetric method, as described by Genin et al. (2007), involves precise measurement of reactive solution volumes to determine substance concentration. In water analysis, it is applied using the Paillaise spectrophotometer or HACH DR3900 for measuring calcium and magnesium concentrations, as well as total hardness (TH).

Spectrophotometric analysis, based on the absorption or transmission of light by substances in solution, plays a crucial role in determining chemical concentrations. For potassium and sodium analysis, a photometric method is employed, measuring light absorption by these ions using a photometer. The variation in absorption is directly proportional to the concentrations of potassium and sodium in the solution. Moreover, Table 2 provides information on various water parameters, their respective units, and the corresponding analysis methods and instruments used.

Table 2

Materials and analytical methods

ParametersUnityAnalysis methodsMaterial used
pH    
Temperature °C Electrochemical  
Conductivity μs/cm Multiparameter – HACH HQ4100 
Salinity mg/L Conductimeter – HACH 54650 
Turbidity NTU Turbidimeter – HACH 2100 
Calcium mg/L Volumetric Paillaise spectrophotometer – HACH DR3900 
Magnesium mg/L 
Total hardness mg/L 
Chloride mg/L 
Nitrate mg/L Spectrophotometric 
Nitrite mg/L 
Sulfate mg/L 
Phosphate mg/L 
Iron mg/L 
Potassium mg/L Photometric 
Sodium mg/L 
ParametersUnityAnalysis methodsMaterial used
pH    
Temperature °C Electrochemical  
Conductivity μs/cm Multiparameter – HACH HQ4100 
Salinity mg/L Conductimeter – HACH 54650 
Turbidity NTU Turbidimeter – HACH 2100 
Calcium mg/L Volumetric Paillaise spectrophotometer – HACH DR3900 
Magnesium mg/L 
Total hardness mg/L 
Chloride mg/L 
Nitrate mg/L Spectrophotometric 
Nitrite mg/L 
Sulfate mg/L 
Phosphate mg/L 
Iron mg/L 
Potassium mg/L Photometric 
Sodium mg/L 

Quality assurance and QC

Calibrating and adjusting measurement instruments play a crucial role in ensuring the reliability and accuracy of analytical data in laboratory settings. In this study, we conducted a calibration and adjustment process at the laboratory level for four instruments from the HACH range: the HACH HQ4100 Multiparameter, HACH 54650 Conductometer, HACH 2100 Turbidimeter, and HACH DR3900 Spectrophotometer. For each of these instruments, we utilized standard calibration solutions of known concentrations to fine-tune the measured values according to the manufacturer's instructions. Emphasizing preventive maintenance of components such as electrodes and measurement cells is essential to ensure accurate results. Meticulously documenting all performed calibrations is crucial for maintaining measurement traceability and ensuring compliance with established quality standards and laboratory best practices. This process contributes to enhancing the credibility of analyses conducted in scientific environments.

WQI for drinking

The term ‘Water Quality Index (WQI)’ refers to a scoring method aimed at assessing the overall impact of various water quality parameters on the suitability of water for human consumption (Mitra et al. 2006). The process of calculating the WQI involves three distinct steps:

In the initial stage, the nine parameters [pH, electrical conductivity (CE), calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), chloride (Cl), sulfate (SO4), and nitrate (NO3)] serve as key indicators of water quality. These parameters primarily correspond to the different minerals present in water. It is important to note that water with elevated levels of solids might potentially lead to constipation or exert laxative effects (Hajji et al. 2018). Recognizing the pivotal role of parameters such as chlorides, sulfates, and nitrates in water quality assessment, the highest weight of 5 has been attributed to them (Vasanthavigar et al. 2010; Srinivasamoorthy et al. 2014). Meanwhile, other parameters like potassium, sodium, magnesium, and calcium have been assigned weightings ranging from 2 to 4, based on their respective significance in determining water quality. Refer to Table 3 for detailed weight assignments.

Table 3

The weight (wi) and relative weight (Wi) of each chemical parameter

ParameterAlgerian standards (JORADP 2011)Weight (wi)Relative weight (Wi)
pH 6.9 < pH < 9.0 0.105 
Electrical conductivity (EC) (μS/cm) 2,800 0.105 
Calcium (mg/l) 200 0.05 
Magnesium (mg/l) 150 0.05 
Sodium (mg/l) 200 0.08 
Potassium (mg/l) 12 0.05 
Chloride (mg/l) 500 0.132 
Sulfate (mg/l) 400 0.132 
Nitrates (mg/l) 50 0.132 
  Σwi = 32 Σwi = 0,836 
ParameterAlgerian standards (JORADP 2011)Weight (wi)Relative weight (Wi)
pH 6.9 < pH < 9.0 0.105 
Electrical conductivity (EC) (μS/cm) 2,800 0.105 
Calcium (mg/l) 200 0.05 
Magnesium (mg/l) 150 0.05 
Sodium (mg/l) 200 0.08 
Potassium (mg/l) 12 0.05 
Chloride (mg/l) 500 0.132 
Sulfate (mg/l) 400 0.132 
Nitrates (mg/l) 50 0.132 
  Σwi = 32 Σwi = 0,836 

Moving on to the second step, the computation of the relative weight (Wi) necessitates the application of the following equation:
formula
(1)
Wi signifies the relative weight, where wi is the weight of an individual parameter, and n represents the total parameter count. The computed values for each parameter's relative weight (Wi) are presented in Table 3.
During step three, the scale of quality assessment (qi) is established based on the concentration of each parameter within the water samples. This assessment scale is aligned with relevant standards, following World Health Organization (WHO) recommendations. Subsequently, it is scaled by a factor of 100, as computed by the following formula:
formula
(2)
where qi represents the quality score; Ci stands for the concentration of the chemical parameter in each water sample, measured in mg/L. Si signifies the standard for drinking water pertaining to each specific chemical parameter, given in mg/L, as defined by both the WHO guidelines (WHO 2011) and Algerian standards (JORADP 2011). During this phase, the SI values for sub-indices of all chemical parameters are computed by multiplying the relative weight and the quality rating.
formula
(3)
In the final step, WQI was calculated by summing all subsets of all groundwater samples by the following equation:
formula
(4)

The sub-index for each parameter is SIi, and the number of parameters is n. The calculated WQI has been divided into five categories shown in Table 4.

Table 4

Water quality classification (Tiwari et al. 2018; Ibrahim 2019)

WQI rangeType of water
<50 Excellent water 
50–100 Good water 
100–200 Poor water 
200–300 Very poor water 
>300 Unfit for drinking 
WQI rangeType of water
<50 Excellent water 
50–100 Good water 
100–200 Poor water 
200–300 Very poor water 
>300 Unfit for drinking 

Principal component analysis

Another commonly used approach involves statistical methods such as PCA. It is a statistical method utilized for dimensional reduction of data while retaining the highest quantity and quality of the input data. It aggregates the information being dispersed in many dimensions into a smaller dimension which are independent of each other. PCA is a commonly used method for selecting independent variables and discarding highly correlated or redundant variables. The PCA involves five main steps as follows:

  • 1.
    The original data matrix is listed as given by:
    formula
where xij is a matrix containing originally measured data, n is the number of monitoring data, and p is the water quality parameter.
  • 2.
    The originally measured data are standardized to mitigate the impact of dimension with z score standardization:
    formula
where zij represents the standardized variable, xj is the mean value for the jth parameter, and sj is the standard deviation for the jth parameter. It's crucial to work with a sizable dataset to ensure that correlations manifest as distinct variables during the PCA process. Consequently, conducting a Kaiser–Meyer–Olkin (KMO) test becomes essential to gauge the adequacy of the sample for analysis. Additionally, the homogeneity of datasets must be evaluated through the application of the Bartlett test.

  • 3.
    Correlation coefficient matrix is determined based on the following equation:
    formula
  • 4.
    The eigenvalues and eigenvectors are then calculated as follows:
    formula
where ui is eigen vectors and is the standardized parameter.

formula
  • 5.

    The principal components (PCs) were derived using the equation as follows, where λi represents the eigenvalue. The outcomes obtained through PCA encapsulate diverse attributes present within the dataset. However, achieving this outcome is improbable when the initial parameters are interdependent or heavily correlated.

This analysis condenses the original variables into a more compact set of novel variables, leading to a minor loss of information from the original parameters.

The new personal computers undergo a process known as varimax rotation, resulting in the creation of varimax factors. This technique aims to reduce the dimensionality of the original data and identify key variables for straightforward interpretation (Ul-Saufie et al. 2010). By examining component loadings, significant variables are categorized, enabling source identification. Notably, a factor loading of 0.75 is indicative of a robust and significant factor, while loadings falling within the range of 0.75–0.5 range are considered moderate, and those within 0.5–0.3 are deemed weak.

The significance of each component is determined by eigenvalues, indicating the extent of variance explained by individual components within the sample. Components with eigenvalues of at least 1 are retained to interpret the variation in surface water. In cases where multiple variables are chosen for a PC, a multivariate correlation analysis is employed to decide which variable to include. Highly correlated variables are regarded as duplicative, and only the variable with the highest loading is retained. Conversely, if highly loaded variables display no correlation, each one is deemed important and consequently incorporated.

General characteristics

The samples were collected from three different sites (Cheffia Dam, Oued El Aneb borehole, and Treat borehole) during 12 months of 2021. The maximum/minimum and analytic results for each parameter are summarized in Table 5. The values are compared against recommended standards such as Algerian potability standards (AS) and WHO guidelines.

Table 5

Summary of the analyzed water quality parameters

ParametersCheffia dam
Oued El Aneb borehole
Treat borehole
Algerian standardsWHO (2011) 
MinMaxMeanMinMaxMeanMinMaxMean
pH 7.52 8.51 8.02 7.17 7.97 7.53 7.23 7.81 7.43 6.9–9 6.9–8.5 
T (°C) 16.3 20.9 18.66 16.6 21.3 19.19 17.01 21.3 19.1 25 °C 25 °C 
EC (μS/cm) 412 669 505.25 1,061 1,530 1348.25 998 1,354 1155.58 2,800 1,000 
Salinity 294 478 362.29 783.21 1,159 1011.85 757 1,027 866.86 – – 
Turbidity (NTU) 10.5 54 28.05 0.41 3.35 1.006 0.33 1.33 0.63 
NO3 (mg/L) 6.33 36.54 17.36 7.32 24.52 12.22 6.25 33.73 16.14 50 50 
NO2 (mg/L) 0.018 0.091 0.047 0.019 0.152 0.09 0.011 0.171 0.07 0.2 
K (mg/L) 1.33 38.02 14.83 3.52 18.33 7.55 2.42 13.11 7.1075 12 12 
Na (mg/L) 18.54 98.3 50.75 23.33 123.5 61.96 9.51 98.45 47.95 200 200 
SO4 (mg/L) 44.07 309.8 152.58 44.09 175.5 88.66 45 118.54 78.65 400 400 
PO4 (mg/L) 0.046 1.64 0.246 0.029 0.087 0.06 0.01 0.073 0.04  
Ca+2 (mg/L) 46.76 352.7 105.48 46.49 221.24 107.14 25.65 159.2 66.8 200 200 
Mg+2 (mg/L) 1.76 184.68 44.62 3.04 103.4 52.62 21 96.5 53.67 150 50 
TH (mg/L) 2.4 261 131.87 4.24 443 179.12 3.72 405 131.19 200 200 
Cl (mg/L) 34.46 283.6 113.39 49 673.55 217.92 51 319.05 187.27 500 250 
Fe (mg/L) 0.021 0.391 0.14 0.013 0.124 0.05 0.013 0.102 0.05 0.3  
ParametersCheffia dam
Oued El Aneb borehole
Treat borehole
Algerian standardsWHO (2011) 
MinMaxMeanMinMaxMeanMinMaxMean
pH 7.52 8.51 8.02 7.17 7.97 7.53 7.23 7.81 7.43 6.9–9 6.9–8.5 
T (°C) 16.3 20.9 18.66 16.6 21.3 19.19 17.01 21.3 19.1 25 °C 25 °C 
EC (μS/cm) 412 669 505.25 1,061 1,530 1348.25 998 1,354 1155.58 2,800 1,000 
Salinity 294 478 362.29 783.21 1,159 1011.85 757 1,027 866.86 – – 
Turbidity (NTU) 10.5 54 28.05 0.41 3.35 1.006 0.33 1.33 0.63 
NO3 (mg/L) 6.33 36.54 17.36 7.32 24.52 12.22 6.25 33.73 16.14 50 50 
NO2 (mg/L) 0.018 0.091 0.047 0.019 0.152 0.09 0.011 0.171 0.07 0.2 
K (mg/L) 1.33 38.02 14.83 3.52 18.33 7.55 2.42 13.11 7.1075 12 12 
Na (mg/L) 18.54 98.3 50.75 23.33 123.5 61.96 9.51 98.45 47.95 200 200 
SO4 (mg/L) 44.07 309.8 152.58 44.09 175.5 88.66 45 118.54 78.65 400 400 
PO4 (mg/L) 0.046 1.64 0.246 0.029 0.087 0.06 0.01 0.073 0.04  
Ca+2 (mg/L) 46.76 352.7 105.48 46.49 221.24 107.14 25.65 159.2 66.8 200 200 
Mg+2 (mg/L) 1.76 184.68 44.62 3.04 103.4 52.62 21 96.5 53.67 150 50 
TH (mg/L) 2.4 261 131.87 4.24 443 179.12 3.72 405 131.19 200 200 
Cl (mg/L) 34.46 283.6 113.39 49 673.55 217.92 51 319.05 187.27 500 250 
Fe (mg/L) 0.021 0.391 0.14 0.013 0.124 0.05 0.013 0.102 0.05 0.3  

The variation of hydrochemical parameters in the water samples collected from the three sites has been presented in Figures 3 and 4. These figures serve as comprehensive visual representations, offering insights into the dynamic nature of the hydrochemical composition at each site. In Figure 3, the fluctuations in key parameters such as pH, T°C, CE,S,NO3, NO2, K, Na, SO4 and turbidity are depicted over the monitoring period. While Figure 4 consolidates the monthly variations of, PO4, Ca, Mg, TH, Cl, and Fe for the three study sites.
Figure 3

Temporal variation of pH, T°C, CE,S,NO3, NO2, K, Na, SO4, and turbidity.

Figure 3

Temporal variation of pH, T°C, CE,S,NO3, NO2, K, Na, SO4, and turbidity.

Close modal
Figure 4

Temporal variation of PO4, Ca, Mg, TH, Cl, and Fe.

Figure 4

Temporal variation of PO4, Ca, Mg, TH, Cl, and Fe.

Close modal
Figure 5

Principal component analysis (PCA). (a) Correlation circle represented by the Planes 1–2 and (b) projection of individuals onto the Planes 1–2.

Figure 5

Principal component analysis (PCA). (a) Correlation circle represented by the Planes 1–2 and (b) projection of individuals onto the Planes 1–2.

Close modal

Water quality index

The results of the monthly variation of the WQI during the year 2021 for the three study sites reveal a range of fluctuations. For the dam, the variation spans from 37.3 to 86.3, while for the two wells, it extends from 45.5 to 91.5. These values unequivocally demonstrate that the waters in the study area belong to both the categories of good water and excellent water.

Principal component analysis

The utilization of principal component analysis (PCA) in this study offers a comprehensive understanding of the intricate relationships among physico-chemical variables in the investigated study sites (Meybeck et al. 1996). The PCA employed to analyze 16 measured parameters, allowing for the exploration of both the interrelationships among variables and the spatial distribution of the study sites.

Figure 5 depict the correlation circle of parameters, illustrating the use of the horizontal axis F1 (First component) and the vertical axis F2 (second component), accompanied by theprojection of samples onto the plane defined by the first two factorial axes, namely F1 and F2.

Physico-chemical analysis of three distinct water sources, namely Cheffia Dam (S1), Oued El Aneb Well (S2), and Treat Well (S3), provides valuable insights into their characteristics and potential health implications. The study covers various parameters, and the main findings are interpreted and explained as follows.

Firstly, pH levels reveal a range from 7.17 to 8.51, indicating slightly alkaline conditions. Importantly, these values fall within the recommended range for drinking water (6.5–9.5) and comply with both Algerian drinking water standards and WHO standards. The pH range of 7.2–7.8, considered ideal for maintaining good health according to previous research (WHO 2011; Saleem et al. 2022), is within the observed values. Overall, pH analysis suggests that the water sources are suitable for consumption, in accordance with established standards and guidelines.

Regarding water temperature, recorded values range from 16.3 to 21.3 °C for the three sources. These temperatures fall within a reasonable range and do not exceed 25 °C, which is a positive sign for mitigating the risk of pollution. The conclusions align with previous research findings by Nguefack et al. (2018) highlighting the importance of temperature in assessing water source vulnerability. The observed temperature range supports the overall health of the water sources.

Conductivity and salinity values show considerable variation, indicating significant mineralization. Some measurements exceed Algerian drinking water standards, raising concerns about water suitability for consumption. However, salinity values fall within a range that, with proper management, may not pose significant issues. It is emphasized that considering the local context and balancing mineral content against health guidelines are crucial. Close monitoring and appropriate measures are needed to ensure water remains safe to consume.

Turbidity values range from 0.33 to 54 Nephelometric Turbidity Unit (NTU), with Cheffia Dam water exceeding drinking water standards and WHO recommendations. High turbidity suggests potential issues with sediment and particles, requiring enhanced treatment or protective measures. This observation highlights the need for targeted interventions to address specific challenges associated with Cheffia Dam water.

Nitrate concentrations and other chemical parameters are below recommended guidelines, including nitrite, sodium, sulfate, phosphate, and TH. However, potassium levels exceed recommendations, posing a potential problem. Additionally, calcium content in Cheffia Dam water exceeds AS recommendations, while magnesium content varies but remains within acceptable limits. These results underscore the importance of monitoring specific chemical parameters to ensure comprehensive water quality.

Finally, chloride concentrations vary, with Oued El Aneb Well having the highest level. Iron content is within acceptable limits for S2 and S3 but exceeds AS standards in Cheffia Dam water. It is essential to address elevated chloride and iron levels in Cheffia Dam water to comply with established standards.

Overall, the comprehensive analysis provides a nuanced understanding of water quality in these sources, highlighting both strengths and areas requiring attention to maintain safe drinking water.

Water quality index

Figure 6 illustrates WQI values for three distinct locations: Cheffia Dam (S1), Oued El Aneb Well (S2), and Treat Well (S3) throughout the months of the year. These values are categorized into different water quality types, such as ‘Excellent’ and ‘Good water’.
Figure 6

Water quality index (WQI) trends of the study area.

Figure 6

Water quality index (WQI) trends of the study area.

Close modal

Firstly, the data highlight notable temporal variations in water quality across locations over the months. For example, Cheffia Dam (S1) shows substantial fluctuations in WQI values, ranging from a minimum of 37.29 in February to a maximum of 86.30 in January. This variation signifies a dynamic change in water quality over a year.

Secondly, there is a discernible spatial disparity among the locations. Oued El Aneb Well (S2) consistently shows lower WQI values compared to other locations, particularly evident in February and March. This disparity suggests potential differences in environmental factors or pollution sources affecting water quality at these sites.

Additionally, the data suggest the presence of seasonal trends influencing water quality. Generally, there is a trend where winter months (January, February) exhibit higher WQI values compared to summer months (July, August). This observation could be attributed to seasonal variations in precipitation, temperature, or changes in human activities impacting water quality.

Overall, the majority of months indicate ‘Good’ water quality, suggesting that water is generally acceptable for various uses. However, there are cases of ‘Excellent’ water quality, indicating even better conditions in terms of natural conditions, climate, and human activities, while a few months display lower WQI values, indicating potential concerns.

Cheffia Dam consistently maintains higher WQI values compared to other locations, indicating relatively better water quality. Similarly, Treat Well (S3) generally maintains good water quality, with some months displaying an ‘Excellent’ classification.

In contrast, Oued El Aneb Well (S2) exhibits more fluctuations in WQI values, potentially indicating greater vulnerability or sensitivity to environmental changes or pollution sources.

Comparing WQI results with national and international standards, it can be noted that although some parameters, such as conductivity, exceed recommended limits, the majority of water quality parameters at the three sites meet Algerian and WHO standards, indicating good to excellent water quality. This is consistent with WQI values and water quality throughout. However, it is crucial to continue monitoring and addressing any parameters that may deviate from standards over time.

The figure provides a comprehensive overview of water quality dynamics across different locations and months. Interpretation of these results could guide further research and monitoring efforts to understand underlying factors influencing these variations. It is crucial to address these factors to maintain or improve water quality in the studied areas, potentially requiring targeted interventions or environmental management strategies.

Principal component analysis

The findings, summarized through projection onto a two-dimensional plane capturing 46.18% of the total variance, reveal two notable associations. Axis 1 and Axis 2 contribute 30.13 and 16.05% of the total variance, respectively. The first association, characterized by negative correlations, includes parameters such as conductivity, salinity, Mg, Na, NO2, temperature (T°C), and TH, explaining 30.6% of the total variance. The second association, marked by positive correlations, encompasses pH, turbidity, K, Ca, NO3, PO4, Iron, and SO4, representing 27.14% of the total variance.

The PCA investigation further unveils significant interrelationships, highlighting negative connections between conductivity and salinity, as well as between iron and sulfate. Importantly, each study site exhibits distinctive characteristics, leading to the identification of unique site/variable patterns. Notably, fluctuations in the physical water parameters of specific sites, such as Cheffia Dam, Oued El Aneb, and Treat boreholes, are intricately linked to chemical properties and influenced by spatio-temporal variations.

The analysis of Plans 1–2 concerning individuals reveals three distinct groups. The first group encompasses individuals S1.1–S1.12, located in the positive domain of the factorial axis, representing Cheffia Dam analyses. These values correspond to surface waters and exhibit lower mineralization compared to sites 2 and 3, which are situated in the negative domain. The second group comprises individuals (S2.1, S2.2, S2.3, S3.1, S3.2, S3.3); these individuals represent the first 3 months of both drillings and are characterized by low nutrient levels for both drills. The third group, consisting of the remaining individuals, demonstrates a progressive increase in all parameters.

The application of PCA in this study provides valuable insights into the complex relationships and spatial differentiation within the study sites. The positive and negative associations revealed by the analysis offer a nuanced understanding of how various physico-chemical parameters interact, contributing to the overall variability observed in the water characteristics of the studied locations. This study not only contributes to the understanding of local water quality but also suggests potential avenues for future research, such as exploring the environmental factors influencing the observed patterns and their implications for ecosystem health and management.

Although chemical analyses and WQI are crucial tools for identifying water quality, it is imperative to recognize and understand some limitations in order to achieve a more comprehensive and precise assessment of water quality in the future. These limitations include challenges associated with using a single index, which may simplify the complexity of water quality by overlooking specific nuances for each parameter and the potential subjectivity in establishing weights for different parameters in WQI calculation. The temporal and spatial variability of water conditions can lead to fluctuating and unrepresentative results of the actual water quality. Additionally, the analytical limitations of chemical methods may result in estimation errors, especially in cases of very low or high concentrations. The evolution of water quality standards, the influence of climatic factors, and the neglect of biological aspects are dimensions that require particular attention for a more holistic assessment of water quality in the future.

This comprehensive study underscores the critical importance of maintaining high water quality for the well-being of Annaba's population and the sustainability of its ecosystems. Through an extensive evaluation of water quality from January to December 2021, focusing on the Cheffia dam, Oued El Aneb, and Treat boreholes, valuable insights have been gained using the WQI and various physico-chemical parameters.

The findings indicate excellent water quality in autumn and generally good water quality throughout the year, therefore suitable for consumption, with distinct characteristics observed for each site through principal component analysis PCA. The analysis of water parameters indicates an adherence to standards; however, challenges such as elevated turbidity in the Cheffia dam and fluctuations in certain chemical parameters necessitate targeted interventions for maintaining safe drinking water.

While this study provides valuable insights, it is crucial to acknowledge limitations, such as the use of a single index and potential subjectivity in assigning weights to parameters.

It is essential for practitioners to strengthen monitoring efforts and implement adaptive management strategies to address potential concerns, considering evolving standards, climatic factors, and biological aspects. Administrators should develop adaptive public policies that target specific interventions for each water source, addressing challenges related to turbidity, mineralization, and chemical compounds.

Engineers can contribute by conducting detailed studies on turbidity sources in the Cheffia Dam to guide treatment strategies and exploring innovative approaches to reduce mineralization and address specific chemical parameters using advanced technologies. Longitudinal studies on the effectiveness of management measures implemented following these findings will provide crucial insights for ongoing and sustainable improvement of water quality in Annaba.

This research sets a significant milestone for future water resource management decisions in Annaba, offering valuable insights into temporal and spatial variations and guiding sustainable water management practices. Future research should focus on in-depth seasonal variations, specific turbidity sources, adaptive water resource management, and innovative technologies, aiming for the long-term enhancement of water quality and the preservation of ecosystems.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Abedin
M. A.
,
Collins
A. E.
,
Habiba
U.
&
Shaw
R.
2019
Climate change, water scarcity, and health adaptation in southwestern coastal Bangladesh
.
International Journal of Disaster Risk Science
10
,
28
42
.
Achouri
M.
,
Louhi
A.
&
Belaidi
W.
2017
Study of contamination and accumulation of some heavy metals in agricultural soils and durum wheat of industrial zone in Annaba (northeast Algerian)
.
Journal of Industrial Pollution Control
33
,
645
656
.
Akdogan
Z.
&
Güven
B.
2023
Multi-criteria decision analysis in assessing watershed scale pollution risk: A review of combined approaches and applications
.
Environmental Reviews
31
(
4
),
669
689
.
Altenburger
R.
,
Brack
W.
,
Burgess
R. M.
,
Busch
W.
,
Escher
B. I.
,
Focks
A.
&
Krauss
M.
2019
Future water quality monitoring: Improving the balance between exposure and toxicity assessments of real-world pollutant mixtures
.
Environmental Sciences Europe
31
(
1
),
1
17
.
Attoui
B.
,
Toumi
N.
,
Messaoudi
S.
&
Benrabah
S.
2016
Degradation of water quality: The case of plain west of Annaba (northeast Algeria)
.
Journal of Water and Land Development
31
(
1
),
3
.
Berredjem
A. F.
,
Boumaiza
A.
&
Hani
A.
2023
Simulation of current and future water demands using the WEAP model in the Annaba province, Northeastern Algeria: A case study
.
AQUA – Water Infrastructure, Ecosystems and Society
72
(
9
),
1815
1824
.
Bieroza
M.
,
Acharya
S.
,
Benisch
J.
,
Ter Borg
R. N.
,
Hallberg
L.
,
Negri
C.
&
Kirchner
J. W.
2023
Advances in catchment science, hydrochemistry, and aquatic ecology enabled by high-frequency water quality measurements
.
Environmental Science & Technology
57
(
12
),
4701
4719
.
Bwadi
B. E.
,
Yusuf
M. B.
,
Abdullahi
I.
,
Giwa
C. Y.
&
Audu
G.
2021
Analysis of ground water from selected sources in Jalingo Metropolis, Nigeria
. In:
Water Quality-Factors and Impacts
(
Dunea, D., ed.)
.
IntechOpen
,
London
.
doi: 10.5772/intechopen.96701
.
do Nascimento
L. S.
,
de Oliveira
S. L.
,
da Costa
C. C.
,
Aracati
M. F.
,
Rodrigues
L. F.
,
Charlie-Silva
I.
&
de Andrade Belo
M. A.
2023
Deleterious effects of polypropylene microplastic ingestion in Nile Tilapia (Oreochromis niloticus)
.
Bulletin of Environmental Contamination and Toxicology
111
(
1
),
13
.
Gaikwad
S. K.
,
Kadam
A. R.
,
Ramgir
R. R.
,
Kashikar
A. S.
,
Wagh
V. M.
&
Kandekar
A. M.
2020a
Assessment of the groundwater geochemistry from a part of west coast of India using statistical methods and water quality index
.
Hydro Research
3
,
48
60
.
Gaikwad
S.
,
Gaikwad
S.
,
Meshram
D.
,
Wagh
V.
,
Kandekar
A.
&
Kadam
A.
2020b
Geochemical mobility of ions in groundwater from the tropical western coast of Maharashtra, India: Implication to groundwater quality
.
Environment, Development and Sustainability
22
,
2591
2624
.
Genin
A.
,
Essemiani
K.
,
Lemoine
C.
,
Barbier
E.
&
Logette
S.
2007
Impact of hydrodynamics on the precipitation efficiency–application to HARDTAC reactor
.
Water Science and Technology
56
(
11
),
101
108
.
Guenouche
F. Z.
,
Mesbahi-Salhi
A.
,
Kimour
M. T.
&
Bouslama
Z.
2022
Comparative study of the bacteriological quality of the water intended for consumption in the city of Annaba (North-East Algeria)
.
Asia Life Science
12
(
11
),
5662
.
Hafsi
R.
&
Boutaghane
H.
2022
Water quality evaluation analysis of an urban river based on self-organising maps: Annaba City (Eastern Algeria)
.
International Journal of Hydrology Science and Technology
14
(
1
),
1
13
.
Hajji
S.
,
Ayed
B.
,
Riahi
I.
,
Allouche
N.
,
Boughariou
E.
&
Bouri
S.
2018
Assessment and mapping groundwater quality using hybrid PCA-WQI model: case of the Middle Miocene aquifer of Hajeb Layoun-Jelma basin (Central Tunisia)
.
Arabian Journal of Geosciences
11
,
1
21
.
Ibrahim
M. N.
2019
Assessing groundwater quality for drinking purpose in Jordan: Application of water quality index
.
Journal of Ecological Engineering
20
,
3
.
JORADP
2011
Executive Decree No. 11–125 of March 23, 2011 on the quality of water for human consumption in Algeria
.
Official Journal of People's Democratic Republic of Algeria
18
,
6
9
.
Khan
A. U.
,
Rahman
H. U.
,
Ali
L.
,
Khan
M. I.
,
Khan
H. M.
,
Khan
A. U.
&
Ahmad
I.
2021
Complex linkage between watershed attributes and surface water quality: Gaining insight via path analysis
.
Civil Engineering Journal
7
(
4
),
701
712
.
Kouadri
S.
,
Kateb
S.
&
Zegait
R.
2021
Spatial and temporal model for WQI prediction based on back-propagation neural network, application on EL MERK region (Algerian southeast)
.
Journal of the Saudi Society of Agricultural Sciences
20
(
5
),
324
336
.
Meyberck
M.
,
Friedrich
G.
,
Thomas
R.
&
Chapman
D.
1996
Rivers Water Quality Assessments: A Guide to the Use of Biota, Sediments and Water in Environment Monitoring
. Chapman Edition, 2nd Edition,
E & FN Spon, London, pp. 59–126
.
Mitra
B. K.
,
Sasaki
C.
&
Keijirou
E.
2006
Spatial and temporal variation of groundwater quality in sand dune area of Aomori prefecture in Japan. Meteorological Department, Federal Ministry of Aviation. Paper number 062023, ASAE Annual Meeting. doi: 10.13031/2013.20673
.
Moon
S. K.
,
Woo
N. C.
&
Lee
K. S.
2004
Statistical analysis of hydrographs and watertable fluctuation to estimate groundwater recharge
.
Journal of Hydrology
292
(
1–4
),
198
209
.
Mukate
S.
,
Panaskar
D.
&
Wagh
V.
2018
Impact of anthropogenic inputs on water quality in Chincholi industrial area of Solapur, Maharashtra, India
.
Groundwater for Sustainable Development
7
,
359
371
.
Nabi
G.
,
Ali
M.
,
Khan
S.
&
Kumar
S.
2019
The crisis of water shortage and pollution in Pakistan: Risk to public health, biodiversity, and ecosystem
.
Environmental Science and Pollution Research
26
,
10443
10445
.
Ngqwala
N. P.
&
Muchesa
P.
2020
Occurrence of pharmaceuticals in aquatic environments: A review and potential impacts in South Africa
.
South African Journal of Science
116
(
7–8
),
1
7
.
Nguefack
C. V. S.
,
Ndjouenkeu
R.
&
Ngassoum
M. B.
2018
Qualité de l'eau de la localité de Dschang et impact sur la santé des consommateurs.(Water quality in the locality of Dschang and its impact on consumer health)
.
Afrique Science
14
(
3
),
96
107
.
Palansooriya
K. N.
,
Yang
Y.
,
Tsang
Y. F.
,
Sarkar
B.
,
Hou
D.
,
Cao
X.
&
Ok
Y. S.
2020
Occurrence of contaminants in drinking water sources and the potential of biochar for water quality improvement: A review
.
Critical Reviews in Environmental Science and Technology
50
(
6
),
549
611
.
Rodier
J. C.
,
Geoffray
C.
&
Rodi
L.
1996
L'analyse de l'eau naturelle, eaux résiduaires, eau de mer, 8ème Edition, Dénod, Paris, p. 1383. (Analysis of natural water, wastewater, seawater, 8th Edition, Dénod, Paris, p. 1383.)
.
Russ
J.
,
Zaveri
E.
,
Desbureaux
S.
,
Damania
R.
&
Rodella
A. S.
2022
The impact of water quality of GDP growth: Evidence from around the world
.
Water Security
17
,
100130
.
Saleem
S.
,
Haider
H.
,
Hu
G.
,
Hewage
K.
&
Sadiq
R.
2022
Continuous performance improvement of aquatic centres : A taguchi-based optimization approach towards sustainability
.
Journal of Building Engineering
54
,
104576
.
Singh
C.
,
Jain
G.
,
Sukhwani
V.
&
Shaw
R.
2021
Losses and damages associated with slow-onset events : urban drought and water insecurity in Asia
.
Current Opinion in Environmental Sustainability
50
,
72
86
.
Srinivasamoorthy
K.
,
Gopinath
M.
,
Chidambaram
S.
,
Vasanthavigar
M.
&
Sarma
V. S.
2014
Hydrochemical characterization and quality appraisal of groundwater from Pungar sub basin, Tamilnadu, India
.
Journal of King Saud University-Science
26
(
1
),
37
52
.
Syafrudin
M.
,
Kristanti
R. A.
,
Yuniarto
A.
,
Hadibarata
T.
,
Rhee
J.
,
Al-Onazi
W. A.
&
Al-Mohaimeed
A. M.
2021
Pesticides in drinking water – a review
.
International Journal of Environmental Research and Public Health
18
(
2
),
468
.
Uddin
M. G.
,
Nash
S.
&
Olbert
A. I.
2021
A review of water quality index models and their use for assessing surface water quality
.
Ecological Indicators
122
,
107218
.
Ul-Saufie
A. Z.
,
Yahya
A. S.
&
Ramli
N. A.
2010
Improving multiple linear regression model using principal component analysis for predicting PM10 concentration in Seberang Prai, Pulau Pinang
.
International Journal of Environmental Sciences
2
(
2
),
403
410
.
Valera
C. A.
,
Pissarra
T. C. T.
,
Filho
M. V. M.
,
Valle Júnior
R. F. D.
,
Oliveira
C. F.
,
Moura
J. P.
&
Pacheco
F. A. L.
2019
The buffer capacity of riparian vegetation to control water quality in anthropogenic catchments from a legally protected area: A critical view over the Brazilian new forest code
.
Water
11
(
3
),
549
.
Vasanthavigar
M.
,
Srinivasamoorthy
K.
,
Vijayaragavan
K.
,
Rajiv Ganthi
R.
,
Chidambaram
S.
,
Anandhan
P.
&
Vasudevan
S.
2010
Application of water quality index for groundwater quality assessment: Thirumanimuttar sub-basin, Tamilnadu, India
.
Environmental Monitoring and Assessment
171
,
595
609
.
Vatanpour
N.
,
Malvandi
A. M.
,
Hedayati Talouki
H.
,
Gattinoni
P.
&
Scesi
L.
2020
Impact of rapid urbanization on the surface water's quality : A long-term environmental and physicochemical investigation of Tajan river, Iran (2007–2017)
.
Environmental Science and Pollution Research
27
,
8439
8450
.
Wagh
V. M.
,
Panaskar
D. B.
,
Mukate
S. V.
,
Gaikwad
S. K.
,
Muley
A. A.
&
Varade
A. M.
2018
Health risk assessment of heavy metal contamination in groundwater of Kadava River Basin, Nashik, India
.
Modeling Earth Systems and Environment
4
,
969
980
.
WHO
2011
Guidelines for Drinking-Water Quality
, 4th edn.
World Health Organization Chron
,
Geneva, Switzerland
.
Xiang
X.
,
Li
Q.
,
Khan
S.
&
Khalaf
O. I.
2021
Urban water resource management for sustainable environment planning using artificial intelligence techniques
.
Environmental Impact Assessment Review
86
,
106515
.
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