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• Published: 05 March 2024

Groundwater quality assessment using water quality index and principal component analysis in the Achnera block, Agra district, Uttar Pradesh, Northern India

• Shahjad Ali 1 ,
• Sitaram Verma 2 ,
• Manish Baboo Agarwal 1 ,
• Raisul Islam 3 ,
• Manu Mehrotra 1 ,
• Rajesh Kumar Deolia 4 ,
• Jitendra Kumar 5 ,
• Shailendra Singh 6 ,
• Ali Akbar Mohammadi 7 ,
• Deep Raj 8 ,
• Manoj Kumar Gupta 9 ,
• Phuyen Dang 10 , 11 &
• Mehdi Fattahi 10 , 11  

Scientific Reports volume  14 , Article number:  5381 ( 2024 ) Cite this article

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• Environmental chemistry
• Environmental monitoring

The qualitative and quantitative assessment of groundwater is one of the important aspects for determining the suitability of potable water. Therefore, the present study has been performed to evaluate the groundwater quality for Achhnera block in the city of Taj, Agra, India, where groundwater is an important water resource. The groundwater samples, 50 in number were collected and analyzed for major ions along with some important trace element. This study has further investigated for the applicability of groundwater quality index (GWQI), and the principal component analysis (PCA) to mark out the major geochemical solutes responsible for origin and release of geochemical solutes into the groundwater. The results confirm that, majority of the collected groundwater samples were alkaline in nature. The variation of concentration of anions in collected groundwater samples were varied in the sequence as, HCO 3−  > Cl −  > SO4 2−  > F − while in contrast the sequence of cations in the groundwater as Na > Ca > Mg > K. The Piper diagram demonstrated the major hydro chemical facies which were found in groundwater (sodium bicarbonate or calcium chloride type). The plot of Schoellar diagram reconfirmed that the major cations were Na + and Ca 2+ ions, while in contrast; major anions were bicarbonates and chloride. The results showed water quality index mostly ranged between 105 and 185, hence, the study area fell in the category of unsuitable for drinking purpose category. The PCA showed pH, Na + , Ca 2+ , HCO 3− and fluoride with strong loading, which pointed out geogenic source of fluoride contamination. Therefore, it was inferred that the groundwater of the contaminated areas must be treated and made potable before consumption. The outcomes of the present study will be helpful for the regulatory boards and policymaker for defining the actual impact and remediation goal.

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Introduction.

Quality of life is associated with quality of water we consume. Out of all water resource, groundwater is one of the important drinking water resources 1 , 2 . In the arid and semi-arid regions, especially for the developing countries like India and Bangladesh, the rapid population growth associated with intensive developmental activities results in a severe increase in water demand 1 , 2 , 3 . The day-to-day degradation of groundwater quality has now become one of the serious challenges in the world. Billions of people across the globe are compelled to consume the polluted water due to the scarcity of potable water, and therefore the scarcity of groundwater is an alarming threat to the humans. It has now been well established that groundwater is at higher risk, in terms of its purity 3 , 4 , 5 , 6 . In remote areas, the situation of groundwater is even more miserable, due to over withdrawal of groundwater. The residents of urban areas have to walk several kilometers to fetch potable water 5 , 6 . The government and various non-governmental organizations (NGOs) are working hard enough to provide contaminants free potable water to every individual 7 . It has been reported in previous literatures that, contaminants like heavy metals, pesticides, organic and inorganic pollutant are causing the serious human health disease such as hypertension, hypocalcaemia, kidney stones, gastro-renal discomfort, arterial calcification, thrombosis 8 , 9 , 10 , 11 , 12 . Apart from the availability of heavy metals in drinking water, the presence of nitrogen has also been proven as the strong potential threat to the quality of drinking water 13 , 14 , 15 . With the increasing groundwater pollution, it is essential to analyze groundwater chemical characteristics and evaluate groundwater quality for water supply purpose. In this regard, methods like groundwater quality index (GWQI), the fuzzy comprehensive method and the health risk weight method (HRWM) have been widely used by researchers. Among these methods, the water quality index (WQI) has been more commonly used by international researcher due to its simple calculation, practicality, and versatile applications 16 , 17 , 18 .

GWQI is a mathematical expression that can be used to determine the quality of groundwater in different locations globally. The idea of GWQI has been kept to assess the water quality throughout diverse world-wide areas 19 , 20 . It is an important tool for the decision-makers to choose the best method for pre-remediation goal 21 , 22 , 23 , 24 . As a result, it has become a crucial component in the evaluation of water quality. In India, multiples research in different areas indicated that the sources of drinking water contain heavy metals like cadmium, lead, mercury, arsenic, and manganese. Also, research findings have shown elevated levels of fluoride, which exceed World Health Organization (WHO), limits of 1.5 ppm 25 , 26 , 27 , 28 , 29 , 30 , 31 . In a study on the Ramganga aquifer of Bareilly District in Uttar Pradesh (India), it was found that the groundwater which were extracted from shallow aquifer contain high percentage of zinc and nickel, whereas the samples collected from deep aquifer consists of heavy metals like copper, cobalt, nickel, manganese, cadmium, and zinc 32 . Previous research revealed that the quality of drinkable water in several regions of northern India is unfit for drinking. About 35 districts are reported to have been found variously affected with arsenic toxicity 31 , 32 , 33 .

Considering the above mention highlights, this study was undertaken to achieve the following objectives: (a) the primary aim of the present research work was to explore the level of contamination in one of the unexplored parts of northern India, which has not been marked before in the previous studies, i.e., the city of Taj Mahal, Agra, India, (b) Further, qualitative of groundwater has been estimated by hydro-chemical analysis and GWQI estimation respectively, (c) to use PCA for the determination of the components that influence the discharge of hydro chemical solutes into the groundwater, (d) to investigate the correlation between hydro chemical parameters and their common source of origin.

Materials and methods

Study area and its geology.

The present study was focused on one of the blocks of city of Taj-Mahal, Agra, India. This city is located on the bank of river Yamuna, Uttar Pradesh, India, between 27°11′ N and 78°02’ E (Fig.  1 ). With a rapid pace of population increase, it is one of biggest city in northern India. There are 15 administrative blocks, 904 villages, and six tehsils in the Agra district. With reference to the 2011 India’s Census, the Agra district has over 7 million households, with a population of 44, 18,797 of which 53.52% are males and 46.48% are females. The weather of the sampled area was semi-arid to sub-tropic type, with an average annual precipitation of 687 mm and evaporation of 1466 mm/year. The average temperature range varies from21.9 to 45 °C in hot days and 3.9–32.2 °C in cold days. Annually, the rainfall averages to about 687.2 mm due to the southwest monsoon, and consequently the daily relative humidity ranges from 30 to 100% 34 .

Locations of Achhnera block in Agra, North India.

The study area occupies a part of Indo-Gangetic plain and its major part is underlined by alluvial sediments of quaternary age encompasses primarily a sequence of clay, silt, sand of different grades, gravels and kankar in different magnitude. In this study region, over 90% of the population use groundwater for drinking purposes thus, investigations of quality of groundwater are among the highest priorities. In this study, ArcGIS (version 10.8.2) was used for geographical data processing and visualization. ArcGIS is a product of Esri and more details about the software can be found on their official website ( https://www.esri.com ).

Sample collection and hydro-geochemical analysis

The samples were collected from the selected area via tube wells, hand pumps and wells. All samples were collected in a time interval of one year from February 2022 to January 2023. A total of 50 groundwater samples from 10 villages (5 × 10) were collected and preserved in polypropylene bottles at 4ºC. The sampling locations have been plotted through ArcGIS 10.2 (Fig.  1 ). To stabilize the pH, conductivity and temperature of the sampling area, the hand pumps were used for some time before collection of samples. All chemical used were of analytical grade (Merck Darmstadt, Germany). During the analysis of samples standard methods were used as given in APHA 2012. Concentration of fluoride (F − ), sulphate (SO 4 2− ) and nitrate (NO 3− ) ions were determined by using spectrophotometer. Mohr’s method (AgNO 3 ) was used to determine chloride (Cl − ) content in the samples. Titration and flame photometry method was used to determine hardness, alkalinity, Mg 2+ , Ca 2+ , Na + , and K + ions in the water samples. Total dissolved solids (TDS) and pH were analyzed by multi-parameter kit 35 . The results were counter checked by the calculation of cation and anion balance. The estimated error was less than ± 5% for all the collected samples.

Calculation of the WQI of the samples

The WQI model is an interested tool for assessing groundwater and surface water quality. It uses aggregation techniques that allow conversion of extensive water quality data into a single value or index. Globally, the WQI model has been applied to evaluate water quality according to local criteria. The guidelines laid by WHO for drinking water are illustrated in (Table 1 ).

Calculation of the unit weight (Wn)

The following equations refer to the calculation Wn (Eq.  1 ).

where \(K = \frac{1}{{\sum {Xs} }}\) , Wn: unit weight parameter 36 , 37 ; Xs: suggested standard for parameter, K: Proportionality Constant; n = number of different water quality parameters.

Calculation of groundwater quality rating

Quality rating scale Qn was computed according to WHO guidelines using the relation of Eq. ( 2 ).

Xn—actual concentration of water quality parameters; Xi—ideal value of different water quality parameters (0 for all parameters except pH 7 ppm).

Estimation of Water Quality Index (WQI)

Various researchers have followed the above method to calculate WQI. Generally, the water quality index (WQI) differentiates potable water into different classes, as shown below in Table 2 36 , 38 , 39 , 40 .

Principal component analysis (PCA)

A well-reported statistical approach in ground water research, the principal component analysis. The data clarification is obtained along the hidden factor created by original factors such as water quality indicators by regard at the key source of variance in the data. The hidden parameters from a matrix composed of factor loading (weight of principal variable) and factor score (prediction of sampling location on the principal component axis). The PCA has been executed in this study to determine homologous behavior and common origin of different physicochemical properties of groundwater. Further PCA was carried out to identify the various factors responsible for release of contaminants into groundwater 41 , 42 .

Statistics used and Data analysis

The data was analyzed using SPSS 16.0 (SPSS Inc. Chicago, USA) and Microsoft Excel 2013. Through SPSS 16.0 spearman correlation was calculated to know the inter-relationship between various hydro-chemical solutes 25 . Further Spatial distribution map has been drawn using arc GIS-10.2 by ESR to evaluate the spatial distribution of fluoride from samples collected from different parts of villages.

Result and discussion

Hydrochemistry of groundwater of achhnera block, agra.

The physiochemical properties of groundwater samples have been presented in Table 3 . The alkalinity of the groundwater sample has been found in the range from 187 to 493.8 ppm, with an average value of 343 ppm, which is within the permissible limit of 600 ppm 39 . TDS of the samples was very high from 801 to 2065 ppm with the average value of 1327 ppm, which is higher than prescribed limit 39 . The concentration of chloride ranged from 226 to 814 ppm with the average value of 470 ppm. The concentration of sodium, potassium, sulphate, and nitrate ions were found in the range of 165–680 ppm, 12–67 ppm, 37–114 ppm, and 4.6–11 ppm, respectively. The hardness was observed between the ranges of 155 to 485 ppm with an average value of 320 ppm, and correspondingly, the concentration of calcium and magnesium ions was ranged from 64–160 ppm to 6.8–32 ppm, respectively. The most prominent anion found in underground water samples was HCO 3− , but some samples had Cl − ions as the most prominent anion. Out of all the samples collected, about 50% of them have pH values above the permissible limit of WHO and BIS standards (IS: 10500, 2012) i.e., 6.5–8.5 38 , 39 . The concentration of fluoride in the sampled water was found in the range of 0.910 to 2.46 ppm, with the average value of 1.628 ppm as shown as in Fig.  2 . The result demonstrated that the concentration of fluoride ion was on elevated side, crossing the permissible limit of WHO (1.5 ppm) 38 .

Fluoride concentration in Achhnera block.

In Korea area, Kim et al. studied the co-contamination of arsenic and fluoride in the groundwater of an alluvial aquifer and reported that the concentrations of fluoride ions among the total 50 samples collected, 35 samples have increased level, which indicated that the soil and the rock of that region contain fluoride-rich minerals 43 . In a similar kind of study carried out by Ali et al.2021, also showed the elevated groundwater fluoride in some blocks of the Agra district and it was observed that the concentration of fluoride in the range of0.14 to 4.88 mg/L 3 . Another study carried out by Ansari and Umar (2019), found very much similar results in Unnao, Uttar Pradesh (India), and the concentration of fluoride was reported in the range between 0.06 to 1.83 44 . A very similar study performed by Chaurisiaya et al. (2018) observed the concentration of fluoridebetween0.28 to 2.01 in Varanasi, Uttar Pradesh, India. Similarly, in some other previous research, the concentration of fluoride ions was ranged from 0.32 to 3.5 in Banda, Uttar Pradesh 45 . Tiwari et al. (2016) reported the elevated range of fluoride concentration i.e., between 0.41 and 3.99 in Pratapgarh, Uttar Pradesh, India 46 . Dev and Raju (2014) found the fluoride concentration between0.08 to 6.7 in Sonbadra, Uttar Pradesh 47 . Hence, it may be inferred that the major portion of northern India is endemic to elevated fluoride concentration (Table 4 ).

Geochemical characterization of Achhnera block

For all groundwater samples, the primary dissolved ions were shown through Piper trilinear diagrams and Schoellar diagrams to comprehend the geochemical progression of groundwater. AqQa v1.X, a Rock ware program, was used to plot the diagrams. Separate ternary plots revealed the cations and anions in the piper diagram. Magnesium, calcium, and sodium, potassium was the apex of cation plot while chloride, sulphate, and carbonate, and bicarbonate ions were the apexes of anions plot (Fig.  3 ). The predominant cation present in the samples was sodium. As a result, the water quality of Achhnera region was classified as either Na + /HCO 3− or Na + /Cl − type, and Ca 2+/ HCO 3− type. When fluorite get dissolves in water containing sodium bicarbonate, there is often a moderate correlation between increased fluoride levels due to the presence of bicarbonates 48 . Ionic components of groundwater samples have been displayed in Schoellar diagram (Fig.  4 ). The primary ionic components of groundwater are SO 4 2− , HCO 3− , Cl − , Mg 2+ , Ca 2+ , Na + , and K + , and their concentrations are shown in the semi-logarithmic Schoellar diagram as equivalents per million per kilogram of solution (meq/kg). Each ion's concentration in each sample was shown by points on six evenly spaced lines, and those points were linked by a line.

Piper plots for groundwater samples at Achhnera block, Agra, North India.

Schoeller diagram Achhnera block, Agra, North India.

In one of the studies on Poyang Lake, China, the presence of nitrogen-nitrate was reported as major threat to the lake with the extensive ongoing agricultural practices 49 , 50 . In the study, the multi-methods which include grey correlation analysis, Pearson correlation, mathematical statistics, and human health risk assessment were used for the investigation of spatiotemporal variations and potential risks of nitrogen.

The PCA has been executed in this study to determine homologous behavior and common origin of different physicochemical properties of groundwater. The values of different Principal Components (PCs) can be considered under strong, moderate, and weak loadings, if their value ranges from 1–0.75, 0.75–0.50 to 0.50–0.30, respectively. The application of PCA in the present study is to obtain correlations between the hydro-chemical components of the groundwater samples.

The PCA of the groundwater samples revealed that the variables are inter-correlated with 38.29% of the total variance. As per Kaiser Criterion, the PCs values, whose eigen values were found more than one, can be considered in factor analysis 51 . After varimax rotation, only three PCs values were found more than one, as shown in the scree plot Fig.  5 , and the rest can be ignored as their eigen values have been found less than one. Hence, three principal components have been extracted for the consideration. Table 5 showed the variance in the three PC values which is 38%, 32% and 10.85% reasonable correspondingly; hence, the rest of the components can be ignored. Principal component one (PC-1) comprised TDS, NO 3− , HCO 3− , Na + , TA, and fluoride with moderate to strong loading. Fluoride ion in PCA-1 showing the moderate to strong loading with TDS, NO 3− , HCO 3− , Na + and Ca 2+ , which appeared to be linked with geological origin fluoride in the present block, and their origin has been significantly correlated. The changes in the concentration of fluoride were directly related with the TDS, NO 3− , HCO 3− , Na + , Total Alkalinity, which can be explained due to the evolution of the fluoride from the fluoride bearing minerals present in host rocks and their interaction with groundwater. Therefore, it is concluded that there are no human sources of fluoride in groundwater, indicating that it is obtained geologically. Principal component two (PC-2) includes TH, Ca 2+ and Mg 2+ showing high positive factor loadings while in case of principal component three (PC-3) includes pH, Cl − , moderate to weak loading. Thus, it can be predicted from the PCA that the component one represents the controlling factors, which is responsible for rerelease of fluoride ions, as all sensitive parameters (TDS, NO 3− , HCO 3− , Na + and Ca 2+ ) of groundwater have moderate to strong loading with respect to all other principal components (Fig.  6 ). While other sensitive parameters (TDS, NO 3− , HCO 3− , Na + and Ca 2+ ) of groundwater show moderate to strong loading with respect to all other principal components.

Scree plot of PCA of Achhnera block, Agra, Northern India.

Component plot in rotated space of physico-chemical components of Achhnera block, Agra, North India.

Correlation analysis of Achhnera block

The correlation coefficient data of rural parts of Achhnera block, Agra region, North India are tabulated in Table 6 . Alkalinity was found to increase due to the replacement of fluoride with hydroxide ions. A positive correlation of concentration of hydrogen ion and sodium ion is observed with fluoride ion. The region may be due to high pH. Many researchers have found in their experimental work that there is a strong correlation ship between F − and H + ions as they have a strong tendency of combining and forming HF 52 , 53 , 54 . The concentration of sodium and bicarbonates have shown positive correlation with fluoride, which can be explained due to the high alkalinity in the sampled water, resulting in the dissolution of fluoride in groundwater 51 , 52 , 53 , 54 .

A strong correlation coefficient between different water quality parameters is seen at those places where the climatic conditions are humid like Assam (India). The fluoride content in these water samples are increasing in these arid and semi-arid climate regions because of the slow rate of water percolation through the ground 43 , 55 . Increase in concentration of OH − , HCO 3− , and CO 3 2− results in increase in alkalinity of water sample. Various studies carried out in different regions of the world show that desorption of as ion and F − from metallic oxide surface causes higher pH of the sampled water which is also confirmed by various experimental studies 30 , 43 , 65 . Table 7 provides a result of the impact of these variables on release of two pollutants separately and on the conductive environment for co-occurrence.

WQI and spatial distribution

In the area under investigation, it has been observed that water quality index ranges from 105 to 185, delineated as per the Table 8 . Therefore, ‘special treatment’ is needed in the study area, to qualify in ‘fit water’ category. It was found that the ions like F − , Cl − , Na + and alkalinity were above the permissible limit, resulting high total dissolved solids (TDS) value, which might be the cause of geogenic activities.

Through (Inverse Distance Weighted) IDW methods, the spatial distribution of factor scores was interpolated (Fig.  7 ). The graphical presentation of the WQI of the Achhnera block is illustrated in Fig.  7 a. Based on the measurement of physiochemical aspects of different samples, taken from different locations and the WQI range of the sampled region is shown in the pie chart (Fig.  7 b). From the analysis of the different water quality parameters, it was found that all the calculated values surpass the permissible limits suggested by WHO & BIS 38 , 39 , which results in a high level of TDS values. It is concluded from the experimental results that the high percentage of fluoride in the samples of Achhnera block, Agra district may be due to its geological conditions and the water of this region is unfit for drinking and cannot be used for various other purposes.

( a ) Spatial distribution of WQI in the study area and ( b ) graphical data representation of WQI classifications of Achhnera block, Agra region, Uttar Pradesh, North India.

Comparative study of the rural and urban areas using water quality parameters

It showed the comparative study done on the extent of pollution in drinking water between urban areas and the rural areas of Agra district of northern India. Study on GWQI of urban areas was previously carried out by Ali et al. 56 and current study is based on rural areas of Agra district of northern India (Fig.  8 ). Twelve important water quality parameters were compared in groundwater quality analysis (GWQI). The GWQI of urban area were ranged from 50.01 to 130.62, which reveals that more than half of the urban region was found in the category unfit for drinking (64%), nearly one fourth of the region lies in the poor category range (21.42%) and the remaining region lies in the very poor category range (14.28%). Figure  8 a, showed that no samples lie in the category of good or excellent. It was inferred that the large value of water quality index at urban regions was due to the geogenic as well as with some anthropogenic source (outlet of fertilizer industry).

Bar chart of WQI comparative study with standard deviation of urban ( a ) and rural ( b ) areas of Agra region, Uttar Pradesh, Northern India.

Present study in, Achhnera block, shows that the WQI ranges from 185 to 105, delineated in Fig.  8 b. The analysis of WQI revealed that the sampled area lies in the unfit category for the drinking purpose. Therefore, it is advised that the drinking water should be treated before making it suitable for drinking in Achhnera block of Agra region, Northern India. It can be concluded from the comparative analysis that potable water of the rural areas is comparatively more polluted than that of the urban areas, which may be due to geogenic as well as anthropogenic activities (use of fluoride laden fertilizer in the field, leeches into the groundwater) 13 , 14 , 15 .

Conclusions

The present study has been performed to evaluate the groundwater quality for Achhnera block in the city of Taj, Agra, India, where groundwater is an important water resource. Therefore, this study was designed to the applicability of GWQI, and the PCA to mark out the major geochemical solutes responsible for origin and release of geochemical solutes into the groundwater. This study confirms that, majority of the groundwater samples in the study areas were mostly alkaline in nature. Elevated values of electrical conductivity, total dissolved solid, total hardness, fluoride and chloride in groundwater samples were mainly due to rock water interaction and high rate of evaporation. The results conclude that the water quality index belongs to unfit category for potable use in the study area, hence, almost all sampling tube-wells of the study area fell in the category of unsuitable for drinking purpose. Further, hydrochemistry of groundwater confirms that, most of the collected groundwater samples in the study area were comparatively saltier than freshwater. The Piper diagram concludes that, the major hydro chemical facies found in groundwater were sodium bicarbonate type or calcium chloride type. Finally, the PCA shows the pH, Na + , Ca 2+ and fluoride with high loading, suggests geogenic source of fluoride contamination. Therefore, it is recommended that the water of Achhnera block of Agra region Northern India, should be treated properly before use as potable water. It can be concluded from the comparative analysis that regions of the rural areas are comparatively more polluted than that of urban areas, which may be due to geogenic as well as anthropogenic activities (use of fluoride laden fertilizer in the field, leeches into the groundwater). Complete distribution of physico-chemical characteristics of water is shown in this study which can be used as a tool to improve the water quality for drinking purposes.

Data availability

The datasets generated and analyzed during the current study were available from the corresponding author on reasonable request.

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The authors would like to acknowledge (Anand Engineering College, Agra, Northern India) for granting permission to perform research work.

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Shahjad Ali, Manish Baboo Agarwal & Manu Mehrotra

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Sitaram Verma

Department of Civil Engineering, GLA University, Mathura, India

Raisul Islam

Department of Applied Science (Mathematics), G.L. Bajaj Group of Institutions, Mathura, India

Rajesh Kumar Deolia

Department of Mathematics and Computing, Madhav Institute of Technology and Science, Gwalior, India

Jitendra Kumar

Department of Mechanical Engineering, Anand Engineering College, Agra, India

Shailendra Singh

Department of Environmental Health Engineering, Neyshabur University of Medical Sciences, Neyshabur, Iran

Ali Akbar Mohammadi

Department of Environment Science and Engineering, SRM University-AP, Amaravati, Andhra Pradesh, India

Department of Applied Science, Bundelkhand Institute of Engineering and Technology (BIET), Jhansi, India

Manoj Kumar Gupta

Institute of Research and Development, Duy Tan University, Da Nang, Vietnam

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Conceived and designed the experiments: S.A. and A.A.M. performed the experiments: S.V., R.K.D., R.I., M.B.A., M.M., analyzed and interpreted the data; D.R., J.K., contributed reagents, materials, analysis tools or data: M.K.G. and M.F. wrote the paper: R.I., M.B.A., M.M., M.K.G., P.D. and M.F.

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Ali, S., Verma, S., Agarwal, M.B. et al. Groundwater quality assessment using water quality index and principal component analysis in the Achnera block, Agra district, Uttar Pradesh, Northern India. Sci Rep 14 , 5381 (2024). https://doi.org/10.1038/s41598-024-56056-8

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Research methodology, results and discussion, assessment of cau river water quality assessment using a combination of water quality and pollution indices.

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Cao Truong Son , Nguyen Thị Huong Giang , Trieu Phuong Thao , Nguyen Hai Nui , Nguyen Thanh Lam , Vo Huu Cong; Assessment of Cau River water quality assessment using a combination of water quality and pollution indices. Journal of Water Supply: Research and Technology-Aqua 1 March 2020; 69 (2): 160–172. doi: https://doi.org/10.2166/aqua.2020.122

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This research aims at using a combined water quality index (WQI) and pollution index (PI) to assess and characterize river water quality of Cau River which is one of the longest rivers in the north of Vietnam. Five different water quality and water pollution indices were used including the Water Quality Index (WQI), Comprehensive Pollution Index (CPI), Organic Pollution Index (OPI), Eutrophication Index (EI), and Trace Metal Pollution Index (TPI). The combined water pollution indices show more serious pollution towards the river downstream. In particular, CPI and OPI reveal a high risk of eutrophication. Cluster analysis was applied to classify water monitoring points into different quality groups in order to provide a better understanding of the water status in the river. This study indicates that a combined water quality analysis could be an option for decision making water use purposes while its single index shows the current situation of water quality.

Water is a vital commodity, both to sustain life and for the global economy. However, the quality of global water has rapidly declined for decades due to the impact of both natural and anthropogenic factors ( Vadde et al. 2018 ). Assessing water quality for different water use purposes, such as domestic use, irrigation, conservation and industrial usage, are an important strategy for food safety and human health. Water quality evaluation aims to identify the sources of water pollution and develop a strategy for sustainable water source management, maintaining and promoting human health and other social and economic growth ( Carroll et al. 2006 ). Surface water quality indexes have been developed and introduced worldwide by researchers with various applications of the Nation Sanitation Foundation Water Quality Index (NSFWQI) ( Carroll et al. 2006 ), the Water Quality Index (WQI) ( Gupta et al. 2003 ; Chaturvedi & Bassin 2009 ; Rocha et al. 2015 ; Sener et al. 2017 ), the Comprehensive Pollution Index (CPI) ( Matta et al. 2017 ), the Organic Pollution Index (OPI) ( Mezbour et al. 2018 ), the Trace Metal Pollution Index (TPI) ( Reza & Singh 2010 ), the Eutrophication Index (EI) ( Liu et al. 2011 ) based on the the database of water monitoring parameters. In Vietnam, research on water quality assessment mostly focuses on comparing the concentration of pollutant to the national surface water quality standard ( MONRE 2015 ). The WQI has been used for 10 years ( VEA 2011 ), however, a combination of WQI with other water pollution indices was not applied widely for water quality assessment research.

Vietnam has abundant water resources with up to 2,360 rivers being over 10 km in length. There are 16 river basins in the whole country with an area larger than 2,500 km 2 . The average annual surface flow of Vietnam's river basin ranges from 830 to 840 cubic meters per year and the annual rainfall is around 1,940 mm. Despite the abandance of natural water sources, Vietnam is still considered as a water shortage country due to three main causes. About 72% of the river basins are distributed outside the country, which accounts for 63% of the total national water source (equal to 520–525 billion cubic meter) and the volume of water coming from inside the country area only is 37%, around 310–315 billion cubic meters ( MONRE 2006 ). Vietnam water resources are distributed unequally throughout geographical location and seasons. The Cuu Long River delta basin has the largest water proportion at 60%, the Hong and Thai Binh rivers have nearly a fifth of the national water volume at 16%, and less than a quarter of the total water volume belongs to other rivers. Vietnam also has seasonal precipitation in which the rainfall is concentrated mostly in the summer season and the other seasons have less than a third of the total rainfall. Therefore, many areas suffer drought for several months, especially in the winter ( MONRE 2012 ). The other reason is water pollution. The water quality in Vietnam recently has been declining significantly under the pressure of population and economic growth.

Currently, the average water consumption is 9,560 cubic meter per capita annually. It is slightly lower than the middle water consumption country according to the average level of the International Water Resource Agency (IWRA). However, Vietnam accounts for a half (ca. 4,000 cubic meters) while other sources come from neighboring countries ( MONRE 2012 ). The Vietnamese government has implemented various national water monitoring programs to protect the water environment. From 2008 to 2018, the general water monitoring programs of ten main river basins were established and implemented. In this study, we use a monitoring database in Cau River basin (from 2008 to 2018) to calculate the quality index and pollution index. The results of this study provide clear scientific evidence to regulate the situation of water quality of this river. The case of Cau River water evaluation could propose a solution for the Vietnamese government in applying water quality indices to managing and monitoring water sources in the future.

Cau River is the longest river branch of the Thai Binh River system at 288 km and its basin area covers 6,030 km 2 ( Figure 1 ). The Cau River flows through four provinces including Bac Kan, Thai Nguyen, Bac Ninh and Bac Giang before being discharged into the Thai Binh River to the sea. The river's width changes following the flood season and drought season from 100 to 50 m, respectively. The river's bed is larger and the slope reduces to 0.05%. The downstream river is located in two provinces, Bac Giang and Bac Ninh, with an average elevation of 10–20 m ( MONRE 2006 ).

Cau River watershed in Vietnam.

According to the Center for Environmental Monitoring of Vietnam (CEM) water monitoring report (2018), there are 63 pollution sources discharging into Cau River which are scattered along four provinces: Bac Kan, Thai Nguyen, Bac Giang and Bac Ninh. The upper part is located in Bac Kan Province, and the population density is low at 65.65 persons per square kilometer. This area has ten pollution sources mostly related to agriculture production and residential activities. The middle part of the river flows through Thai Nguyen province, and it has a high population density as well as heavy industrial activities. CEM's water monitoring program at Cau River Basin has identified 16 sources of pollution in this area. The downstream of the river crosses two provinces, Bac Giang and Bac Ninh. These are populated places with diverse economies: agriculture, industrial and craft villages. The total waste water discharge into the river is estimated at 138.192 cubic meters per year ( Table 1 ). The location of these pollution sources is the foundation for the establishment of water monitoring points.

Characteristics of pollution sources of Cau River

Water sampling

Monitoring locations.

A total of 22 monitoring points were selected for sampling of which six points are located in Bac Kan province, seven points in Thai Nguyen Province and the remainder belong to Bac Giang and Bac Ninh Provinces. A description of water sampling location is summarized in Table 2 .

Description of sampling location in Cau River, Vietnam

Monitoring parameters

Water monitoring statistics of 22 points in the period of 2008–2018 from CEM was collected. Each water sample analysis contains 16 parameters which are described in Table 3 . The pH, WT, turbidity and dissolved oxygen (DO) were analyzed by a portable pH meter, WT meter, turbidity meter and DO meter (D–50 Series, Horiba, Co. Ltd), respectively. Ammonium, nitrate, nitrite and phosphates ion concentrations were determined using a spectrophotometer (UV/VIS–EVOLUTION, Model EV0300PC). Heavy metal concentrations were determined using atomic absorption spectrophotometer (AAS) (EPA method). Coliform was analyzed by counting methods (ISO 10304-1:2007).

Surface water parameters in the study

ISO, International Standardization Organization; SMEWW, Standard Method for Examination of Water and Wastewater; EPA, Environmental Protection Agency of the United States.

Water quality and water pollution indices

Water quality index (wqi).

Based on the calculated score of WQI, water quality is classified into five categories:

Level 1: WQI score obtained from 0 to 25: Water is extremely polluted, emergency treatment is required before reuse.

Level 2: WQI score obtained from 26 to 50: water quality is suitable for transportation and equivalent purposes.

Level 3: WQI score obtained from 51 to 75: water quality is suitable for irrigation and equivalent purposes.

Level 4: WQI score obtained from 76 to 90: water quality is suitable for domestic usage.

Level 5: WQI score obtained from 91 to 100: water quality is suitable for domestic water supply.

Comprehensive pollution index (CPI)

CPI is classified into five categories:

Category 1: CPI from 0 to 0.20 (clean);

Category 2: CPI from 0.21 to 0.40 (sub clean);

Category 3: CPI from 0.41 to 1.00 (slightly polluted);

Category 4: CPI from 1.01–2.00 (medium polluted);

In this study, we calculate CPI by using 12 water parameters: COD, BOD, TSS, amonium, phosphates, nitrate, nitrite, coliform, Fe, Cu, Zn and Cd. These parameters were analyzed in the Cau River water monitoring program.

Organic pollution index (OPI)

Eutrophication index (ei).

DIN concentration is calculated by the total concentration volume of nitrate, nitrite, and ammonium, whereas the DIP is calculated by the concentration of phosphate in water. The EI is classified into two categories:

EI < 0: Zero eutrophication

EI > 0: Eutrophication

Trace metal pollution index (TPI)

The TPI value is categorized into two groups:

Group 2: TPI > 1 – pollution

In this study, TPI is calculated based on the average concentration of four trace heavy metals: Fe, Cu, Zn and Cd. These are four monitoring indicators in the general water monitoring program of Vietnam.

Data analysis

T-test analysis was used to evaluate the significant difference of water indices between the rainy season and the dry season in Cau River. ANOVA was used to identify the significant difference between upstream, midstream and downstream. Cluster analysis was applied to evaluate the water quality and pollution level between monitoring points. The points which have similar polluted levels were gathered into one group. The results of cluster analysis could assist managers to conduct better water monitoring and management plans.

Water quality in Cau River

The average values of ten consecutive years of water quality show that TSS and COD exceed the regulated level indicated in Vietnam National Standards QCVN08:2015/BTMT, column A1 – standard for domestic water usage ( Table 4 ) ( MONRE 2015 ). The TSS concentration was double the permission level, ranging from 49.46 to 54.52 mg/L compared to 20 mg/L of regulated value. The COD concentration was also higher ranging from 11.63 to 13.62 mg/L. Other parameters met the regulated levels. The table also shows the changes of water quality in the dry season and the rainy season. In comparison, parameters of TSS, DO and NH 4 + in the rainy season were higher than in the dry season, contrary to other parameters. This result was a consequence of the pollutants diluting due to the increase of river water volume in the monsoon season ( MONRE 2006 ).

Variation of water quality of Cau River between the rainy season and the dry season

QCVN08:A1 = Vietnamese national technical regulation for surface water quality.

Table 5 presents the correlated relationship between monitoring parameters. The analysis shows the strong relations between physical and chemical parameters, especially the chemical parameters such as BOD, COD, NH 4 + , NO 2 – and NO 3 – . The correlations were more significant in the dry season compared to the rainy reason. In a similar study, Jahin et al. (2020) employed multivariate analysis to develop an irrigation water quality index for suface water in Kafr El-Sheikh Governorate and found that the elements in waster have similar dynamics.

Correlation matrix of water parameters in Cau River between the dry season and the rainy season

The water quality index

For overall quality assessment, data from 22 sampling points was calculated ( Table 6 ). The average WQI scores were 67.52 and 69.67 in the dry and rainy season, respectively, indicating sufficient quality for irrigation supply. The WQI decreased from upstream to downstream indicating an accumulation of pollutants from discharge sources. Figure 2 shows that WQIs of Cau River were fairly stable in the rainy season and generally satisfied the quality for irrigation and similar purposes according to WQI's classification. However, the water quality in the dry season highly fluctuated due to geographical locations and the impacts of pollution sources. In the upper and lower part of the river, the WQI scores were mostly higher than the rainy season. Monitoring point number 5 nearly achieved the quality for domestic usage. This was a consequence of the steep river bed and the increase of turbidity in the monsoon period. Monitoring point number 10 was considered as extremely polluted. Water quality at other points in the river were categorized as suitable for transportation. The results show that the water quality index could be used effectively for water supply purposes. Similar research has been comprehensively conducted in many countries such as Egypt ( Jahin et al. 2020 ), Sarayduzu Dam Lake, Turkey ( Kükrer & Mutlu 2019 ), Amazonia Rivers, Brazil ( Medeiros et al. 2017 ), and the Ganga River, India ( Tripsthi & Singal 2019 ).

Water quality indexes of Cau River

SD (standard deviation); (*), (**), (***) indicates level of significance at P -value is 0.1; 0.05 and 0.01.

WQI scores of 22 water monitoring points in Cau River.

Water pollution indices

The results of water pollution indices calculation in both the dry and rainy season are summarized in Table 7 .

Water pollution indices between the dry season and rainy season of Cau River

CPI, Comprehensive Pollution Index; OPI, Organic Pollution Index; EI, Eutrophication Index; TPI, Trace Heavy Metal Index. (*), (**), (***) = significant difference at 0.1, 0.05 and 0.01 levels.

The CPI data show the value of the entire river with no significant difference between dry and rainy seasons. In the dry season, the CPI of Cau River ranged from 0.50 to 1.57 with an average value of 1.08. According to the CPI's classification, this river was slightly and medium polluted. In the rainy season, the CPI of Cau River reached 0.66–1.37 with an average score at 0.96 and its quality was classified as the same level as the dry season.

Although the water quality of each water monitoring point tended to be better during the monsoon period, this difference was not statistically significant. However, the CPIs were different among the monitoring points upstream, midstream and downstream. The midstream had lower CPIs during the monsoon period in comparison to the dry season. We used the ANOVA test to analyze the significant difference levels of CPI in three parts of the river and the t-test to analyze the significant difference between the two seasons of the year. The results showed that the difference of CPI in three parts of the river was not statistically significant.

Similar to the CPI, the OPI in the dry season was higher than the rainy season (1.55 compared to 1.18). This result was significant with α = 0.1 ( P = 0.0538). The OPIs of Cau River could be classified into two groups: Good (0 < OPI < 1) and Polluted (1 < OPI < 4). Regarding spatial location, the OPI of the upstream was 0.92 and 0.63 in the dry and rainy season respectively. These scores were in the good quality category (0 < OPI < 1). However, this difference was not statically significant. In the midstream of the river, the OPI was substantially higher in the rainy season (1.35 compared to 0.79 in the dry season) and this difference was statistically significant with α = 0.01 ( P = 0.007559). The average OPI of the downstream in the dry season was 2.12, which was equal to the extremely polluted level, much higher than the OPI in the rainy season (1.83). However, the difference of OPI between the two seasons was not significant. Similar to the results of the CIP analysis, the OPI of the upstream had the highest quality and the downstream had the worst quality ( Figure 3 ). However, the difference of OPI among three geographical areas was not significant.

Cluster analysis water monitoring points in Cau River.

The EI of the entire river was obtained in the range of about 100–400 for both dry and rainy seasons. This indicates that the river was at high risk of eutrophication. However, the EI in the dry season was much higher than the rainy season and this difference was statistically significant with α = 0.1 ( P = 0.07977). According to geographical location, the EI of all monitoring points was higher than zero and increased from upstream to downstream of the river. There were significantly different average values of EI in both the dry season and the rainy season with α = 0.01 ( P = 0.001376) and α = 0.001 ( P = 0.000101) respectively . Specifically, the EI of the upstream was 128.57 and 85.54; the midstream was 224.49 and 101.64; downstream was 394.79 and 322.84 in the dry season and rainy season respectively. The EI in the dry season was always higher compared to the rainy season. However, this difference was only significant at midstream with α = 0.05 ( P = 0.019287).

Trace heavy metal index (TPI)

In contrast to other pollution indices, the TPIs of Cau River were low which clarified that this river was not polluted by trace heavy metals according to the TPI's classification. The rainy season also had higher TPI compared to the dry season, although this difference was not significant.

The TPI also slightly differed according to geographical locations. It was highest at the downstream and reduced in the upper part of the river. The difference of TPI in the upstream and downstream between the dry and rainy season was significant at α = 0.01 ( P = 0.006916) and α = 0.05 ( P = 0.026554). However, the average values of TPI at upstream, midstream and downstream monitoring points were not significantly different. The results of water pollution indices calculation determined that the level of pollution increased from the upper part to lower part of the river, specifically the CPI, OPI and EI, but not the TPI. The pollution level also tended to be higher in the dry season compared to the rainy season. This environmental status could be explained by the low density of pollution sources in the upstream. The midstream and downstream received more pressure from agricultural and industrial activities and populous areas. In addition, the accumulation of pollutants due to water flow in the downstream also contributed to higher concentrations of pollutants in this part of the river. The better water quality in the rainy season was a consequence of the increase of water volume which led to a higher dilution capacity. All trends of water pollution indices were reflected and are similar to the results of WQI calculations.

Water quality indices and water pollution indices among monitoring points

Cluster analysis was used in order to classify the monitoring points which have similar water quality characteristics. The computation categorized 22 points into five groups as summarized in Table 8 .

Clustering water monitoring points between dry season and rainy season

WQI, Water quality index; CPI, Comprehensive Pollution Index; OPI, Organic Pollution Index; EI, Eutrophication Index; TPI, Trace Heavy Metal Index.

According to Table 8 , cluster analysis shows the order of water quality of which group 1 represents highest quality and group 5 the lowest. Representing the entire river, the polluted points (belonging to groups 4 and 5) accounted for around 13% (3 of 21 points in total). Nevertheless, in the dry season, the number of polluted points in groups 4 and 5 was double and the number in group 1 significantly reduced to six monitoring points. The results of cluster analysis stated that the water quality in monsoon time of Cau River was better compared to the dry season.

Grouping water monitoring points with similar characteristics could assist managers in making decisions related to water use planning for different purposes, such as domestic water supply, aquaculture cultivation, irrigation, or other purposes. Furthermore, the results of water quality clustering also support managers in designing water quality monitoring systems, especially addressing high attention to the serious pollution points. These benefits provide meaningful foundations for sustainable water source management.

This study has suggested a possible combination of quality and pollution indices based on monitoring environmental parameters for river quality assessment. At the current state, the water quality meets the requirements of Vietnam National Environmental Standard QCVN08-(A1 category), the standard for domestic water supply. TSS and COD concentrations are higher than the regulation for domestic water supply. The average concentration of pollutants was lower in the dry season, excluding TSS, DO and NH 4 + . The results of WQI analysis indicate that the water of Cau River achieved the standard for irrigation purposes in both the dry season and rainy season. However, further study on bearing capacity of the river will be needed for water supply purposes.

The water quality indices varied depending on location and monitoring time. In the dry season, the water quality of the upstream was better than other parts of the river, followed by the midstream and downstream respectively.

Pollution Indices calculation indicates that the Cau River is polluted in different geographical conditions. Many locations of the river are contaminated by organic pollution with OIP > 1. The river water was at high risk of eutrophication as EI was above zero. The TPI of Cau River is in the safety level. All pollution indices in the Cau River tend to increase from upstream to midstream, then downstream. Cluster analysis grouped the water monitoring points into five groups with the quality reducing gradually from the first group to the fifth group. The classification of clustering analysis provided meaningful support for water pollution monitoring and appropriate solutions for the treatment of water for water supply.

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Drinking water quality assessment and its effects on residents health in Wondo genet campus, Ethiopia

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Water is a vital resource for human survival. Safe drinking water is a basic need for good health, and it is also a basic right of humans. The aim of this study was to analysis drinking water quality and its effect on communities residents of Wondo Genet.

The mean turbidity value obtained for Wondo Genet Campus is (0.98 NTU), and the average temperature was approximately 28.49 °C. The mean total dissolved solids concentration was found to be 118.19 mg/l, and EC value in Wondo Genet Campus was 192.14 μS/cm. The chloride mean value of this drinking water was 53.7 mg/l, and concentration of sulfate mean value was 0.33 mg/l. In the study areas magnesium ranges from 10.42–17.05 mg/l and the mean value of magnesium in water is 13.67 mg/l. The concentration of calcium ranges from 2.16–7.31 mg/l with an average value of 5.0 mg/l. In study areas, an average value of sodium was 31.23 mg/1and potassium is with an average value of 23.14 mg/1. Water samples collected from Wondo Genet Campus were analyzed for total coliform bacteria and ranged from 1 to 4/100 ml with an average value of 0.78 colony/100 ml.

On the basis of findings, it was concluded that drinking water of the study areas was that all physico–chemical parameters. All the Campus drinking water sampling sites were consistent with World Health Organization standard for drinking water (WHO).

Safe drinking water is a basic need for good health, and it is also a basic right of humans. Fresh water is already a limiting resource in many parts of the world. In the next century, it will become even more limiting due to increased population, urbanization, and climate change (Jackson et al. 2001 ).

Drinking water quality is a relative term that relates the composition of water with effects of natural processes and human activities. Deterioration of drinking water quality arises from introduction of chemical compounds into the water supply system through leaks and cross connection (Napacho and Manyele 2010 ).

Access to safe drinking water and sanitation is a global concern. However, developing countries, like Ethiopia, have suffered from a lack of access to safe drinking water from improved sources and to adequate sanitation services (WHO 2006 ). As a result, people are still dependent on unprotected water sources such as rivers, streams, springs and hand dug wells. Since these sources are open, they are highly susceptible to flood and birds, animals and human contamination (Messeret 2012 ).

The quality of water is affected by an increase in anthropogenic activities and any pollution either physical or chemical causes changes to the quality of the receiving water body (Aremu et al. 2011 ). Chemical contaminants occur in drinking water throughout the world which could possibly threaten human health. In addition, most sources are found near gullies where open field defecation is common and flood-washed wastes affect the quality of water (Messeret 2012 ).

The World Health Organization estimated that up to 80 % of all sicknesses and diseases in the world are caused by inadequate sanitation, polluted water or unavailability of water (WHO 1997 ). A review of 28 studies carried out by the World Bank gives the evidence that incidence of certain water borne, water washed, and water based and water sanitation associated diseases are related to the quality and quantity of water and sanitation available to users (Abebe 1986 ).

In Ethiopia over 60 % of the communicable diseases are due to poor environmental health conditions arising from unsafe and inadequate water supply and poor hygienic and sanitation practices (MOH 2011 ). About 80 % of the rural and 20 % of urban population have no access to safe water. Three-fourth of the health problems of children in the country are communicable diseases arising from the environment, specially water and sanitation. Forty-six percent of less than 5 years mortality is due to diarrhea in which water related diseases occupy a high proportion. The Ministry of Health, Ethiopia estimated 6000 children die each day from diarrhea and dehydration (MOH 2011 ).

There is no study that was conducted to prove the quality water in Wondo Genet Campus. Therefore, this study is conducted at Wondo Genet Campus to check drinking water quality and to suggest appropriate water treated mechanism.

Results and discussions

The turbidity of water depends on the quantity of solid matter present in the suspended state. It is a measure of light emitting properties of water and the test is used to indicate the quality of waste discharge with respect to colloidal matter. The mean turbidity value obtained for Wondo Genet Campus (0.98 NTU) is lower than the WHO recommended value of 5.00 NTU.

Temperature

The average temperature of water samples of the study area was 28.49 °C and in the range of 28–29 °C. Temperature in this study was found within permissible limit of WHO (30 °C). Ezeribe et al. ( 2012 ) reports similar result (29 °C) of well water in Nigeria.

Total dissolved solids (TDS)

Water has the ability to dissolve a wide range of inorganic and some organic minerals or salts such as potassium, calcium, sodium, bicarbonates, chlorides, magnesium, sulfates etc. These minerals produced un-wanted taste and diluted color in appearance of water. This is the important parameter for the use of water. The water with high TDS value indicates that water is highly mineralized. Desirable limit for TDS is 500 mg/l and maximum limit is 1000 mg/l which prescribed for drinking purpose. The concentration of TDS in present study was observed in the range of 114.7 and 121.2 mg/l. The mean total dissolved solids concentration in Wondo Genet campus was found to be 118.19 mg/l, and it is within the limit of WHO standards. Similar value was reported by Soylak et al. ( 2001 ), drinking water of turkey. High values of TDS in ground water are generally not harmful to human beings, but high concentration of these may affect persons who are suffering from kidney and heart diseases. Water containing high solid may cause laxative or constipation effects. According to Sasikaran et al. ( 2012 ).

Electrical conductivity (EC)

Pure water is not a good conductor of electric current rather’s a good insulator. Increase in ions concentration enhances the electrical conductivity of water. Generally, the amount of dissolved solids in water determines the electrical conductivity. Electrical conductivity (EC) actually measures the ionic process of a solution that enables it to transmit current. According to WHO standards, EC value should not exceeded 400 μS/cm. The current investigation indicated that EC value was 179.3–20 μS/cm with an average value of 192.14 μS/cm. Similar value was reported by Soylak et al. ( 2001 ) drinking water of turkey. These results clearly indicate that water in the study area was not considerably ionized and has the lower level of ionic concentration activity due to small dissolve solids (Table 1 ).

PH of water

PH is an important parameter in evaluating the acid–base balance of water. It is also the indicator of acidic or alkaline condition of water status. WHO has recommended maximum permissible limit of pH from 6.5 to 8.5. The current investigation ranges were 6.52–6.83 which are in the range of WHO standards. The overall result indicates that the Wondo Genet College water source is within the desirable and suitable range. Basically, the pH is determined by the amount of dissolved carbon dioxide (CO 2 ), which forms carbonic acid in water. Present investigation was similar with reports made by other researchers’ study (Edimeh et al. 2011 ; Aremu et al. 2011 ).

Chloride (Cl)

Chloride is mainly obtained from the dissolution of salts of hydrochloric acid as table salt (NaCl), NaCO 2 and added through industrial waste, sewage, sea water etc. Surface water bodies often have low concentration of chlorides as compare to ground water. It has key importance for metabolism activity in human body and other main physiological processes. High chloride concentration damages metallic pipes and structure, as well as harms growing plants. According to WHO standards, concentration of chloride should not exceed 250 mg/l. In the study areas, the chloride value ranges from 3–4.4 mg/l in Wondo Genet Campus, and the mean value of this drinking water was 3.7 mg/l. Similar value was reported by Soylak et al. ( 2001 ) drinking water of Turkey.

Sulfate mainly is derived from the dissolution of salts of sulfuric acid and abundantly found in almost all water bodies. High concentration of sulfate may be due to oxidation of pyrite and mine drainage etc. Sulfate concentration in natural water ranges from a few to a several 100 mg/liter, but no major negative impact of sulfate on human health is reported. The WHO has established 250 mg/l as the highest desirable limit of sulfate in drinking water. In study area, concentration of sulfate ranges from 0–3 mg/l in Wondo Genet Campus, and the mean value of SO 4 was 0.33 mg/l. The results exhibit that concentration of sulfate in Wondo Genet campus was lower than the standard limit and it may not be harmful for human health.

Magnesium (Mg)

Magnesium is the 8th most abundant element on earth crust and natural constituent of water. It is an essential for proper functioning of living organisms and found in minerals like dolomite, magnetite etc. Human body contains about 25 g of magnesium (60 % in bones and 40 % in muscles and tissues). According to WHO standards, the permissible range of magnesium in water should be 50 mg/l. In the study areas magnesium was ranges from 10.42 to 17.05 mg/l in Wondo Genet Campus and the mean value of magnesium in water is 13.67 mg/l. Similar value was reported by Soylak et al. ( 2001 ) drinking water of Turkey. The results exhibit that concentration of magnesium in Wondo Genet College was lower than the standard limit of WHO.

Calcium (Ca)

Calcium is 5th most abundant element on the earth crust and is very important for human cell physiology and bones. About 95 % of calcium in human body stored in bones and teeth. The high deficiency of calcium in humans may caused rickets, poor blood clotting, bones fracture etc. and the exceeding limit of calcium produced cardiovascular diseases. According to WHO ( 2011 ) standards, its permissible range in drinking water is 75 mg/l. In the study areas, results show that the concentration of calcium ranges from 2.16 to 7.31 mg/l in Wondo Genet campus with an average value of 5.08 mg/l.

Sodium (Na)

Sodium is a silver white metallic element and found in less quantity in water. Proper quantity of sodium in human body prevents many fatal diseases like kidney damages, hypertension, headache etc. In most of the countries, majority of water supply bears less than 20 mg/l, while in some countries the sodium quantity in water exceeded from 250 mg/l (WHO 1984 ). According to WHO standards, concentration of sodium in drinking water is 200 mg/1. In the study areas, the finding shows that sodium concentration ranges from 28.54 to 34.19 mg/1 at Wondo Genet campus with an average value of 31.23.

Potassium (k)

Potassium is silver white alkali which is highly reactive with water. Potassium is necessary for living organism functioning hence found in all human and animal tissues particularly in plants cells. The total potassium amount in human body lies between 110 and 140 g. It is vital for human body functions like heart protection, regulation of blood pressure, protein dissolution, muscle contraction, nerve stimulus etc. Potassium is deficient in rare but may led to depression, muscle weakness, heart rhythm disorder etc. According to WHO standards the permissible limit of potassium is 12 mg/1. Results show that the concentration of potassium in study areas ranges from 20.83 to 27.51 mg/1. Wondo Genet College with an average value of 23.14 mg/1. Present investigation was similar with reports made by other researchers’ study (Edimeh et al. 2011 ; Aremu et al. 2011 ). These results did not meet the WHO standards and may become diseases associated from potassium extreme surpassed.

Nitrate (NO 3 )

Nitrate one of the most important diseases causing parameters of water quality particularly blue baby syndrome in infants. The sources of nitrate are nitrogen cycle, industrial waste, nitrogenous fertilizers etc. The WHO allows maximum permissible limit of nitrate 5 mg/l in drinking water. In study areas, results more clear that the concentration of nitrate ranges from 1.42 to 4.97 mg/l in Wondo Genet campus with an average value of 2.67 mg/l. These results indicate that the quantity of nitrate in the study site is acceptable in Wondo Genet campus (Table 2 ).

Bacterial contamination

The total coliform group has been selected as the primary indicator bacteria for the presence of disease causing organisms in drinking water. It is a primary indicator of suitability of water for consumption. If large numbers of coliforms are found in water, there is a high probability that other pathogenic bacteria or organisms exist. The WHO and Ethiopian drinking water guidelines require the absence of total coliform in public drinking water supplies.

In this study, all sampling sites were not detected of faecal coliform bacteria. Figure  1 shows the mean values of total coliform bacteria in drinking water collected from the study area. All drinking water samples collected from Wondo Genet Campus were analyzed for total coliform bacteria and ranged from 1 to 4/100 ml with an average value of 0.78 colony/100 ml. In Wondo Genet College, the starting point of drinking water sources (Dam1), the second (Dam2) and Dam3 samples showed the presence of total coliform bacteria (Fig.  1 ). According to WHO ( 2011 ) risk associated in Wondo Genet campus drinking water is low risk (1–10 count/100 ml).

The mean values of total coliform bacteria in drinking water

According to the study all water sampling sites in Wondo Genet campus were meet world health organization standards and Ethiopia drinking water guideline. Figure  2 indicated that mean value of the study sites were under the limit of WHO standards.

Comparison of water quality parameters of drinking water of Wondo Genet campus with WHO and Ethiopia standards

Effect of water quality for residence health’s

Diseases related to contamination of drinking-water constitute a major burden on human health. Interventions to improve the quality of drinking-water provide significant benefits to health. Water is essential to sustain life, and a satisfactory (adequate, safe and accessible) supply must be available to all (Ayenew 2004 ).

Improving access to safe drinking-water can result in tangible benefits to health. Every effort should be made to achieve a drinking-water quality as safe as practicable. The great majority of evident water-related health problems are the result of microbial (bacteriological, viral, protozoan or other biological) contamination (Ayenew 2004 ).

Excessive amount of physical, chemical and biological parameters accumulated in drinking water sources, leads to affect human health. As discussed in the result, all Wondo Genet drinking water sources are under limit of WHO and Ethiopian guideline standards. Therefore, the present study was found the drinking water safe and no residence health impacts.

On the basis of findings, it was concluded that drinking water of the study areas was that all physico–chemical parameters in all the College drinking water sampling sites, and they were consistent with World Health Organization standard for drinking water (WHO). The samples were analyzed for intended water quality parameters following internationally recognized and well established analytical techniques.

It is evident that all the values of sodium (Na), potassium (K), calcium (Ca), magnesium (Mg), chloride (Cl), SO 4 , and NO 3 fall under the permissible limit and there were no toxicity problem. Water samples showed no extreme variations in the concentrations of cations and anions. In addition, bacteriological determination of water from College drinking water sources was carried out to be sure if the water was safe for drinking and other domestic application. The study revealed that all the College water sampling sites were not contained fecal coliforms except the three water sampling sites had total coliforms.

The study was conducted in Wondo Genet College of Forestry and Natural Resources campus, which is located in north eastern direction from the town of Hawassa and about 263 km south of Addis Ababa (Fig.  3 ). It lies between 38°37′ and 38°42′ East longitude and 7°02′ and 7°07′ north latitude. Landscape of the study area varies with an altitude ranging between 1600 and 2580 meters above sea level. Landscape of the study area varies with an altitude ranging between 1600 and 2580 meters above sea level.

Map of study area

The study area is categorized under Dega (cold) agro-ecological zone at the upper part and Woina Dega (temperate) agro-ecological zone at the lower part of the area. The rainfall distribution of the study area is bi-modal, where short rain falls during spring and the major rain comes in summer and stays for the first two months of the autumn season. The annual temperature and rainfall range from 17 to 19 °C and from 700 to 1400 mm, respectively (Wondo Genet office of Agriculture 2011).

Methodology

Water samples were taken at ten locations of Wondo Genet campus drinking water sources. Three water samples were taken at each water caching locations. Ten (10) water samples were collected from different locations of the Wondo Genet campus. Sampling sites for water were selected purposely which represents the entire water bodies.

Instead of this study small dam indicates the starting point of Wondo Genet campus drinking water sources rather than large dams constructed for other purpose. Taps were operated or run for at least 5 min prior to sampling to ensure collection of a representative sample (temperature and electrical conductivity were monitored to verify this). Each sample’s physico–chemical properties of water were measured in the field using portable meters (electrical conductivity, pH and temperature) at the time of sampling. Water samples were placed in clean containers provided by the analytical laboratory (glass and acid-washed polyethylene for heavy metals) and immediately placed on ice. Nitric acid was used to preserve samples for metals analysis.

Analysis of water samples

Determination of ph.

The pH of the water samples was determined using the Hanna microprocessor pH meter. It was standardized with a buffer solution of pH range between 4 and 9.

Measurement of temperature

This was carried out at the site of sample collection using a mobile thermometer. This was done by dipping the thermometer into the sample and recording the stable reading.

Determination of conductivity

This was done using a Jenway conductivity meter. The probe was dipped into the container of the samples until a stable reading will be obtained and recorded.

Determination of total dissolved solids (TDS)

This was measured using Gravimetric Method: A portion of water was filtered out and 10 ml of the filtrate measured into a pre-weighed evaporating dish. Filtrate water samples were dried in an oven at a temperature of 103 to 105 °C for \(2\frac{1}{2}\)  h. The dish was transferred into a desiccators and allowed cool to room temperature and were weighed.

In this formula, A stands for the weight of the evaporating dish + filtrate, and B stands for the weight of the evaporating dish on its own Mahmud et al. ( 2014 ).

Chemical analysis

Chloride concentration was determined using titrimetric methods. The chloride content was determined by argentometric method. The samples were titrated with standard silver nitrate using potassium chromate indicator. Calcium ions concentrations were determined using EDTA titrimetric method. Sulphate ions concentration was determined using colorimetric method.

Microorganism analysis

In the membrane filtration method, a 100 ml water sample was vacuumed through a filter using a small hand pump. After filtration, the bacteria remain on the filter paper was placed in a Petri dish with a nutrient solution (also known as culture media, broth or agar). The Petri dishes were placed in an incubator at a specific temperature and time which can vary according the type of indicator bacteria and culture media (e.g. total coliforms were incubated at 35 °C and fecal coliforms were incubated at 44.5 °C with some types of culture media). After incubation, the bacteria colonies were seen with the naked eye or using a magnifying glass. The size and color of the colonies depends on the type of bacteria and culture media were used.

Statically analysis

All data generated was analyzed statistically by calculating the mean and compare the mean value with the acceptable standards. Data collected was statistically analyzed using Statistical Package for Social Sciences (SPSS 20).

Abbreviations

ethylene dinitrilo tetra acetic acid

Minstor of Health

nephelometric turbidity units

total dissolved solid

World Health Organization

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Authors’ contributions

YM: participated in designing the research idea, field data collection, data analysis, interpretation and report writing; BA: participated in field data collection, interpretation and report writing. Both authors read and approved the final manuscript.

Authors’ information

Yirdaw Meride: Lecturer at Hawassa University, Wondo Genet College of Forestry and Natural Resources. He teaches and undertakes research on solid waste, carbon sequestration and water quality. He has published three articles mainly in international journals. Bamlaku Ayenew: Lecturer at Hawassa University, Wondo Genet College of Forestry and Natural Resources. He teaches and undertakes research on Natural Resource Economics. He has published three article with previous author and other colleagues.

Acknowledgements

Hawassa University, Wondo Genet College of Forestry and Natural Resources provided financial support for field data collection and water laboratory analysis. The authors thank anonymous reviewers for constructive comments.

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The authors declare that they have no competing interests.

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Meride, Y., Ayenew, B. Drinking water quality assessment and its effects on residents health in Wondo genet campus, Ethiopia. Environ Syst Res 5 , 1 (2016). https://doi.org/10.1186/s40068-016-0053-6

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DOI : https://doi.org/10.1186/s40068-016-0053-6

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Water quality index for assessment of drinking groundwater purpose case study: area surrounding Ismailia Canal, Egypt

• Hend Samir Atta   ORCID: orcid.org/0000-0001-5529-0664 1 ,
• Maha Abdel-Salam Omar 1 &
• Ahmed Mohamed Tawfik 2  

Journal of Engineering and Applied Science volume  69 , Article number:  83 ( 2022 ) Cite this article

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The dramatic increase of different human activities around and along Ismailia Canal threats the groundwater system. The assessment of groundwater suitability for drinking purpose is needed for groundwater sustainability as a main second source for drinking. The Water Quality Index (WQI) is an approach to identify and assess the drinking groundwater quality suitability.

The analyses are based on Pearson correlation to build the relationship matrix between 20 variables (electrical conductivity (Ec), pH, total dissolved solids (TDS), sodium (Na), potassium (K), calcium (Ca), magnesium (Mg), chloride (Cl), carbonate (CO 3 ), sulphate (SO 4 ), bicarbonate (HCO 3 ), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), lead (Pb), cobalt (Co), chromium (Cr), cadmium (Cd), and aluminium (Al). Very strong correlation is found at [Ec with Na, SO 4 ] and [Mg with Cl]; strong correlation is found at [TDS with Na, Cl], [Na with Cl, SO 4 ], [K with SO 4 ], [Mg with SO 4 ] and [Cl with SO 4 ], [Fe with Al], [Pb with Al]. The water type is Na–Cl in the southern area due to salinity of the Miocene aquifer and Mg–HCO 3 water type in the northern area due to seepage from Ismailia Canal and excess of irrigation water.

The WQI classification for drinking water quality is assigned with excellent and good groundwater classes between km 10 to km 60, km 80 to km 95 and the adjacent areas around Ismailia Canal. While the rest of WQI classification for drinking water quality is assigned with poor, very poor, undesirable and unfit limits which are assigned between km 67 to km 73 and from km 95 to km 128 along Ismailia Canal.

Introduction

Nowadays, groundwater has become an important source of water in Egypt. Water crises and quality are serious concerns in a lot of countries, particularly in arid and semi-arid regions where water scarcity is widespread, and water quality assessment has received minimal attention [ 3 , 9 ]. So, it is important to assess the quality of water to be used, especially for drinking purposes.

Poor hydrogeological conditions have been encountered causing adverse impacts on threatening the adjacent groundwater aquifer under the Ismailia Canal. The groundwater quality degradation is due to rapid urban development, industrialization, and unwise water use of agricultural water, either groundwater or surface water.

As groundwater quality is affected by several factors, an appropriate study of groundwater aquifers characteristics is an essential step to state a supportable utilization of groundwater resources for future development and requirements [ 11 , 12 ]. It is important that hydrogeochemical information is obtained for the region to help improving the groundwater management practices (sustainability and protection from deterioration) [ 17 ].

Many researchers have paid great attention to groundwater studies. In the current study area, the hydrogeology and physio-hydrochemistry of groundwater in the current study area had been previously discussed by El Fayoumy [ 15 ] and classified the water to NaCl type; Khalil et al. [ 27 ] stated that water had high concentration of Na, Ca, Mg, and K. Geriesh et al. [ 21 ] detected and monitored a waterlogging problem at the Wadi El Tumilate basin, which increased salinity in the area. Singh [ 34 ] studied the problem of salinization on crop yield. Awad et al. [ 7 ] revealed that the groundwater salinity ranges between 303 ppm and 16,638 ppm, increasing northward in the area.

Various statistical concepts were used to understand the water quality parameters [ 24 , 28 , 35 ].

Armanuos et al. [ 4 ] studied the groundwater quality using WQI in the Western Nile Delta, Egypt. They had generated the spatial distribution map of different parameters of water quality. The results of the computed WQI showed that 45.37% and 66.66% of groundwater wells falls into good categories according to WHO and Egypt standards respectively.

Eltarabily et al. [ 19 ] investigate the hydrochemical characteristics of the groundwater at El-Khanka in the eastern Nile Delta to discuss the possibility of groundwater use for agricultural purposes. They used Pearson correlation to deduce the relationship between 13 chemical variables used in their analysis. They concluded that the groundwater is suitable for irrigation use in El-Qalubia Governorate.

The basic goal of WQI is to convert and integrate large numbers of complicated datasets of the physio-hydrochemistry elements with the hydrogeological parameters (which have sensitive effect on the groundwater system) into quantitative and qualitative water quality data, thus contributing to a better understanding and enhancing the evaluation of water quality [ 38 ]. The WQI is calculated by performing a series of computations to convert several values from physicochemical element data into a single value which reflects the water quality level's validity for drinking [ 16 ].

Based on the physicochemical properties of the groundwater, it should be appraised for various uses. One can determine whether groundwater is suitable for use or unsafe based on the maximum allowable concentration, which can be local or international. The type of the material surrounding the groundwater or dissolving from the aquifer matrix is usually reflected in the physicochemical parameters of the groundwater. These metrics are critical in determining groundwater quality and are regarded as a useful tool for determining groundwater chemistry and primary control mechanisms [ 18 ].

The objective of this research is to assess suitability of groundwater quality of the study area around Ismailia Canal for drinking purpose and generating WQI map to help decision-makers and local authorities to use the created WQI map for groundwater in order to avoid the contamination of groundwater and to facilitate in selection safely future development areas around Ismailia Canal.

Description of study area

The study area lies between latitudes 30° 00′ and 31° 00′ North and longitude 31° 00′ and 32° 30′ East. It is bounded by the Nile River in the west, in the east there is the Suez Canal, in the south, there is the Cairo-Ismailia Desert road, and in the north, there are Sharqia and Ismailia Governorates as shown in Fig. 1 . Ismailia Canal passes through the study area. It is considered as the main water resource for the whole Eastern Nile Delta and its fringes. Its intake is driven from the Nile River at Shoubra El Kheima, and its outlet at the Suez Canal. At the intake of the canal, there are large industrial areas, which include the activities of the north Cairo power plant, Amyeria drinking water plant, petroleum companies, Abu Zabaal fertilizer and chemical company, and Egyptian company of Alum. Ismailia Canal has many sources of pollution, which potentially affects and deteriorates the water quality of the canal [ 22 ].

Map of the study area and location of groundwater wells

The topography plays an important role in the direction of groundwater. The ground level in the study area is characterized by a small slope northern Ismailia Canal. It drops gently from around 18 m in the south close to El-Qanater El-Khairia to 2 amsl northward. While southern Ismailia Canal, it is characterized by moderate to high slope. The topography rises from 10 m to more than 200 m in the south direction.

Geology and hydrogeology

The sequence of deposits rocks of wells was investigated through the study of hydrogeological cross-section A-A′ and B-B′ located in Fig. 2 a, b [ 32 ]. Section B-B′ shows that the study area represents two main aquifers that can be distinguished into the Oligocene aquifer (southern portion of the study area) and the Quaternary aquifer (northern portion of the study area). The Oligocene aquifer dominates the area of Cairo-Suez aquifer foothills. The Quaternary occupies the majority of the Eastern Nile Delta. It consists of Pleistocene sand and gravel. It is overlain by Holocene clay. The aquifer is semi-confined (old flood plain) and is phreatic at fringes areas in the southern portion of eastern Nile Delta fringes. The Quaternary aquifer thickness varies from 300 m (northern of the study area) to 0 at the boundary of the Miocene aquifer (south of the study area). The hydraulic conductivity ranges from 60 m/day to 100 m/day [ 8 ]. The transmissivity varies between 10,000 and 20,000 m 2 /day.

a Geology map of the study area. b Hydrogeological cross-section of the aquifer system (A-A′) and geological cross-section for East of Delta (B-B′)

Groundwater recharge and discharge

The main source of recharge into the aquifer under the study area is the excess drainage surplus (0.5–1.1 mm/day) [ 29 ], in addition to the seepage from irrigation system including Damietta branch and Ismailia Canal.

Groundwater and its movements

In the current research, it was possible to attempt drawing sub-local contour maps for groundwater level with its movement as shown in Fig. 3 . Figure 3 shows the main direction of groundwater flow from south to north. The groundwater levels vary between 5 m and 13 m (above mean sea level). The sensitive areas are affected by (1) the excess drainage surplus from the surface water reclaimed areas which located at low lying areas; (2) the seepage from the Ismailia Canal bed due to the interaction between it and the adjacent groundwater system, and (3) misuse of the irrigation water of the new communities and other issues. Accordingly, a secondary movement was established in a radial direction that is encountered as a source point at the low-lying area (Mullak, Shabab, and Manaief). Groundwater movement acts as a sink at lower groundwater areas (the northern areas of Ismailia Canal located between km 80 to km 90) due to the excessive groundwater extraction. The groundwater level reaches 2 m (AMSL). The groundwater levels range between + 15 m (AMSL) (southern portion of Ismailia Canal and study area near the boundary between the quaternary and Miocene aquifers).

Groundwater flow direction map in the study area (2019)

The assessment of groundwater suitability for drinking purposes is needed and become imperative based on (1) the integration between the effective environmental hydrogeological factors (the selected 9 trace elements Fe, Mn, Zn, Cu, Pb, Co, Cr, Cd, Al) and 11 physio-chemical parameters (major elements of the anions and cations pH, EC, TDS, Na, K, Ca, Mg, Cl, CO 3 , SO 4 , HCO 3 ); (2) evaluation of WQI for drinking water according to WHO [ 36 ] and drinking Egyptian standards limit [ 14 ]; (3) GIS is used as a very helpful tool for mapping the thematic maps to allocate the spatial distribution for some of hydrochemical parameters with reference standards.

The groundwater quality for drinking water suitability is assessed by collecting 53 water samples from an observation well network covering the area of study, as seen in Fig. 1 . The samples were collected after 10 min of pumping and stored in properly washed 2 L of polyethylene bottles in iceboxes until the analyses were finished. The samples for trace elements were acidified with nitric acid to prevent the precipitation of trace elements. They were analyzed by the standard method in the Central Lab of Quality Monitoring according to American Public Health Association [ 2 ].

The water quality index is used as it provides a single number (a grade) that expresses overall water quality at a certain location based on several water quality parameters. It is calculated from different water parameters to evaluate the water quality in the area and its potential for drinking purposes [ 13 , 25 , 31 , 33 ]. Horton [ 23 ] has first used the concept of WQI, which was further developed by many scholars.

The first step of the factor analysis is applying the correlation matrix to measure the degree of the relationship and strength between linearly chemical parameters, using “Pearson correlation matrix” through an excel sheet. The analyses are mainly based on the data from 53 wells for physio-chemical parameters for the major elements and trace elements. Accordingly, it classified the index of correlation into three classes: 95 to 99.9% (very strong correlation); 85 to 94.9% (strong correlation), 70 to 84.9% (moderately), < 70% (weak or negative).

Equation ( 1 ) [ 4 ] is used to calculate WQI for the effective 20 selected parameters of groundwater quality.

In which Q i is the ith quality rating and is given by equation ( 2 ) [ 4 ], W i is the i th relative weight of the parameter i and is given by Eq. ( 3 ) [ 4 ].

Where C i is the i th concentration of water quality parameter and S i is the i th drinking water quality standard according to the guidelines of WHO [ 36 ] and Egypt drinking water standards [ 14 ] in milligram per liter.

Where W i is the relative weight, w i is the weight of i th parameter and n is the number of chemical parameters. The weight of each parameter was assigned ( w i ) according to their relative importance relevant to the water quality as shown in Table 2 , which were figured out from the matrix correlation (Pearson correlation, Table 1 ). Accordingly, it was possible assigning the index for weight ( w i ). Max weight 5 was assigned to very strong effective parameter for EC, K, Na, Mg, and Cl; weight 4 was assigned to a strong effective parameter as TDS, SO 4 ; 3 for a moderate effective parameter as Ca; and weight 2 was assigned to a weak effective parameter like pH, HCO 3, CO 3 , Fe, Cr, Cu, Co, Cd, Pb, Zn, Mn, and Al. Equation ( 2 ) was calculated based on the concertation of the collected samples from representative 53 wells and guidelines of WHO [ 36 ] and Egypt drinking water standards [ 14 ] in milligram per liter. This led to calculation of the relative weight for the weight ( W i ) by equation ( 3 ) of the selected 20 elements (see Table 2 ). Finally, Eq. ( 1 ) is the summation of WQI both the physio-chemical and environmental parameters for each well eventually.

The spatial analysis module GIS software was integrated to generate a map that includes information relating to water quality and its distribution over the study area.

Results and discussion

The basic statistics of groundwater chemistry and permissible limits WHO were presented in Table 3 . It summarized the minimum, maximum, average, med. for all selected 20 parameters and well percentage relevant to the permissible limits for each one; the pH values of groundwater samples ranged from 7.1 to 8.5 with an average value of 7.78 which indicated that the groundwater was alkaline. While TDS ranged from 263 to 5765 mg/l with an average value of 1276 mg/l. Sodium represented the dominant cation in the analyzed groundwater samples as it varied between 31 and 1242 mg/l, with an average value of 270 mg/l. Moreover, sulfate was the most dominant anion which had a broad range (between 12 and 1108 mg/l), with an average value of 184 mg/l. This high sulfate concentration was due to the seepage from excess irrigation water and the dissolution processes of sulfate minerals of soil composition which are rich in the aquifer. Magnesium ranged between 11 and 243 mg/l, with an average value of 43 mg/l. The presence of magnesium normally increased the alkalinity of the soil and groundwater [ 10 , 37 ]. Calcium ranged between 12 and 714 mg/l with a mean value of 119 mg/l. For all the collected groundwater samples, calcium concentration is higher than magnesium. This can be explained by the abundance of carbonate minerals that compose the water-bearing formations as well as ion exchange processes and the precipitation of calcite in the aquifer. Chloride content for groundwater samples varies between 18 and 2662 mg/l with an average value of 423 mg/l. Carbonate was not detected in groundwater, while bicarbonate ranged from 85 to 500 mg/l. Figures 5 , 6 , and 7 were drawn to show the extent of variation between the samples in each well.

Piper diagram [ 30 ] was used to identify the groundwater type in the study area as shown in Fig. 4 . According to the prevailing cations and anions in groundwater samples Na–Cl water type in the southern area due to salinity of the Miocene aquifer, Mg–HCO 3 water type in the northern area due to seepage from Ismailia Canal and excess of irrigation water and there is an interference zone which has a mixed water type between marine water from south and fresh water from north.

Piper trilinear diagram for the groundwater samples

Concentration of selected physio-chemical parameters

Concentration of major elements

Concentration of trace element

Concentration for 20 elements by percentage of wells (relevant to their limits of WHO for each element)

a , b WQI aerial distribution for drinking groundwater suitability for WHO ( a ) and Egyptian standards ( b )

Atta, et al. [ 5 ] revealed that the abundance of Fe, Mn, and Zn in the groundwater is due to geogenic aspects, not pollution sources. Khalil et al. [ 26 ] and Awad et al. [ 6 ] revealed that the source of groundwater in the area is greatly affected by freshwater seepage from canals and excess irrigation water which all agreed with the study.

Table 3 and Fig. 8 showed that 100% of wells for EC were assigned at desirable limits. 43.79% of wells for TDS were assigned at the desirable limit and 27.05% of them at the undesirable limits. While pH, 81.25% were assigned at the desirable limit. The percentage of wells for the aerial distribution of cations concentration assigned at desirable limits ranged between 64.6% for K, 85.45% for Mg, 68.73% for Na, and 70.8% for Ca. While the percentage of wells for the aerial distribution of cations concentration assigned at the undesirable limits ranged between 8.3% for Mg, 31.27% for Na, 14.6% for K, and 16.7% for Ca.

The percentage of wells for the aerial distribution of anions concentration assigned at desirable limits ranged between 72.9% for Cl, 66.7% for HCO 3 , and 79.2% for SO 4 . While the percentage of wells for the aerial distribution of anions concentration assigned at the undesirable limit ranged between 4.2% for Cl, 0% for HCO 3 , and 20.8% for SO 4 as shown in Table 3 and Fig. 8 .

Table 3 and Fig. 8 presented the aerial distribution concentration for 8 sensitive trace elements. The percentage of wells assigned at desirable limits ranged between 100% for (Zn, Cr, and Co), 86% for Fe, 27.3% for Mn, 77.4% for Cd, 27.2% for Pb, and 96% for Al, while the percentage of wells assigned at undesirable limits ranged between 0% for (Fe, Zn, Cr, and Co), 50% for Mn, 13.6% for Cd, 36.4% for Pb, and 4% for Al.

Figure 8 summarizes the results of the concentration for the selected 20 elements (11 physio-hydrochemical characteristics, and 9 sensitive environmental trace elements) by %wells relevant to the limits of WHO for each element.

The water quality index is one of the most important methods to observe groundwater pollution (Alam and Pathak, 2010) [ 1 ] which agreed with the results. It was calculated by using the compared different standard limits of drinking water quality recommended by WHO (2008) and Egyptian Standards (2007). Two values for WQI were calculated and drawn according to these two standards. It was classified into six classes relevant to the drinking groundwater quality classes: excelled water (WQI < 25 mg/l), good water (25–50 mg/l), poor water (50–75 mg/l), very poor water (75–100 mg/l), undesirable water (100–150 mg/l), and unfit water for drinking water (> 150 mg/l) as shown in Fig. 9 a, b. Figure 9 a (WHO classification) indicated that in the most parts of the study area, the good water class was dominant and reached to 35.8%, 28.8% was excellent water; 7.5% were poor water, 11.3% very poor water quality, and 13.3% were unfit water for drinking water. Similarly, for Egyptian Standard classification via WQI, the study area was divided into six classes: Fig. 9 b indicated that 35.8% of groundwater was categorized as excellent water quality, 34% as good water quality, 9.4% as poor water, 5.7% as very poor water, 1.9% as undesirable water and 13.3% as unfit water quality. This assessment was compared to Embaby et al. [ 20 ], who used WQI in the assessment of groundwater quality in El-Salhia Plain East Nile Delta. The study showed that 70% of the analyzed groundwater samples fall in the good class, and the remainder (30%), which were situated in the middle of the plain, was a poor class which mostly agreed with the study.

Conclusions and recommendation

This research studied the groundwater quality assessment for drinking using WQI and concluded that most of observation wells are located within desirable and max. allowable limits.

The groundwater in the study area is alkaline. TDS in groundwater ranged from 263 to 5765 mg/l, with a mean value of 1277 mg/l. Sodium and chloride are the main cation and anion constituents.

The water type is Na–Cl in the southern area due to salinity of the Miocene aquifer, Mg–HCO 3 water type in the northern area due to seepage from Ismailia Canal and excess of irrigation water and there is an interference zone which has a mixed water type between marine water from south and fresh water from north.

The WQI relevant to WHO limits indicated that 23% of wells were located in excellent water quality class that could be used for drinking, irrigation and industrial uses, 38% of wells were located in good water quality class that could be used for domestic, irrigation, and industrial uses, 11% of wells were located in poor water quality class that could be used for irrigation and industrial uses, 8% of wells were located in very poor water quality class that could be used for irrigation, 6% of wells were located in unsuitable water quality class which is restricted for irrigation use and 15% of wells were located in unfit water quality which will require proper treatment before use.

The WQI relevant to Egyptian standard limits indicated that 25% of wells were located in excellent water quality class that could be used for drinking, irrigation, and industrial uses, 43% of wells were located in good water quality class that could be used for domestic, irrigation, and industrial uses, 8% of wells were located in poor water quality class that could be used for irrigation and industrial uses, 6% of wells were located in very poor water quality class that could be used in irrigation, 6% of wells were located in unsuitable water quality class which is restricted for irrigation use and 13% of wells were located in unfit water quality which will require proper treatment before use.

The percentage of wells located at unfit water for drinking were assigned in the Miocene aquifer, and north of Ismailia Canal between km 67 to km 73 and from km 95 to km 128.

It is highly recommended to study the water quality of the Ismailia Canal which may affect the groundwater quality. It is recommended to study the water quality in detail between km 67 to 73 and from km 95 to km 128 as the WQI is unfit in this region and needs more investigations in this region. A full environmental impact assessment should be applied for any future development projects to maximize and sustain the groundwater as a second resource under the area of Ismailia Canal.

Availability of data and materials

The datasets generated and analyzed during the current study are not publicly available because they are part of a PhD thesis and not finished yet but are available from the corresponding author on reasonable request.

Abbreviations

World Health Organization

• Water Quality Index

Electrical conductivity

Total dissolved solids

Bicarbonate

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Acknowledgements

The researchers would like to thank Research Institute for Groundwater that provided us with the necessary data during the study.

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Atta, H.S., Omar, M.AS. & Tawfik, A.M. Water quality index for assessment of drinking groundwater purpose case study: area surrounding Ismailia Canal, Egypt. J. Eng. Appl. Sci. 69 , 83 (2022). https://doi.org/10.1186/s44147-022-00138-9

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DOI : https://doi.org/10.1186/s44147-022-00138-9

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