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  • Review Article
  • Published: 31 January 2023

Global water resources and the role of groundwater in a resilient water future

  • Bridget R. Scanlon   ORCID: orcid.org/0000-0002-1234-4199 1 ,
  • Sarah Fakhreddine 1 , 2 ,
  • Ashraf Rateb 1 ,
  • Inge de Graaf   ORCID: orcid.org/0000-0001-7748-868X 3 ,
  • Jay Famiglietti 4 ,
  • Tom Gleeson 5 ,
  • R. Quentin Grafton 6 ,
  • Esteban Jobbagy 7 ,
  • Seifu Kebede 8 ,
  • Seshagiri Rao Kolusu 9 ,
  • Leonard F. Konikow 10 ,
  • Di Long   ORCID: orcid.org/0000-0001-9033-5039 11 ,
  • Mesfin Mekonnen   ORCID: orcid.org/0000-0002-3573-9759 12 ,
  • Hannes Müller Schmied 13 , 14 ,
  • Abhijit Mukherjee 15 ,
  • Alan MacDonald   ORCID: orcid.org/0000-0001-6636-1499 16 ,
  • Robert C. Reedy 1 ,
  • Mohammad Shamsudduha 17 ,
  • Craig T. Simmons 18 ,
  • Alex Sun 1 ,
  • Richard G. Taylor 19 ,
  • Karen G. Villholth 20 ,
  • Charles J. Vörösmarty 21 &
  • Chunmiao Zheng   ORCID: orcid.org/0000-0001-5839-1305 22  

Nature Reviews Earth & Environment volume  4 ,  pages 87–101 ( 2023 ) Cite this article

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  • Hydrogeology
  • Water resources

An Author Correction to this article was published on 29 March 2023

This article has been updated

Water is a critical resource, but ensuring its availability faces challenges from climate extremes and human intervention. In this Review, we evaluate the current and historical evolution of water resources, considering surface water and groundwater as a single, interconnected resource. Total water storage trends have varied across regions over the past century. Satellite data from the Gravity Recovery and Climate Experiment (GRACE) show declining, stable and rising trends in total water storage over the past two decades in various regions globally. Groundwater monitoring provides longer-term context over the past century, showing rising water storage in northwest India, central Pakistan and the northwest United States, and declining water storage in the US High Plains and Central Valley. Climate variability causes some changes in water storage, but human intervention, particularly irrigation, is a major driver. Water-resource resilience can be increased by diversifying management strategies. These approaches include green solutions, such as forest and wetland preservation, and grey solutions, such as increasing supplies (desalination, wastewater reuse), enhancing storage in surface reservoirs and depleted aquifers, and transporting water. A diverse portfolio of these solutions, in tandem with managing groundwater and surface water as a single resource, can address human and ecosystem needs while building a resilient water system.

Net trends in total water storage data from the GRACE satellite mission range from −310 km 3 to 260 km 3 total over a 19-year record in different regions globally, caused by climate and human intervention.

Groundwater and surface water are strongly linked, with 85% of groundwater withdrawals sourced from surface water capture and reduced evapotranspiration, and the remaining 15% derived from aquifer depletion.

Climate and human interventions caused loss of ~90,000 km 2 of surface water area between 1984 and 2015, while 184,000 km 2 of new surface water area developed elsewhere, primarily through filling reservoirs.

Human intervention affects water resources directly through water use, particularly irrigation, and indirectly through land-use change, such as agricultural expansion and urbanization.

Strategies for increasing water-resource resilience include preserving and restoring forests and wetlands, and conjunctive surface water and groundwater management.

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A Correction to this paper has been published: https://doi.org/10.1038/s43017-023-00418-9

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B.R.S. conceptualized the review and coordinated input. S.F. reviewed many of the topics and developed some of the figures. A.R. analysed GRACE satellite data and M.S. reviewed this output. Q.G. provided input on water economics. E.J. reviewed impacts of land-use change. S.R.K. provided data on future precipitation changes. L.F.K. provided detailed information on surface water/groundwater interactions. M.M. provided data on water trade. C.J.V. provided input on green and grey solutions. All authors reviewed the paper and provided edits.

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Scanlon, B.R., Fakhreddine, S., Rateb, A. et al. Global water resources and the role of groundwater in a resilient water future. Nat Rev Earth Environ 4 , 87–101 (2023). https://doi.org/10.1038/s43017-022-00378-6

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research paper on ground water

ORIGINAL RESEARCH article

Groundwater quality characterization for safe drinking water supply in sheikhpura district of bihar, india: a geospatial approach.

\nRitesh Kumar

  • School of Ecology and Environment Studies, Nalanda University, Rajgir, India

Groundwater quality due to geogenic factors, aggravated by anthropogenic activities, is a significant threat to human wellbeing and agricultural practices. This study aimed at mapping the spatial distribution of low and high groundwater-contaminated regions in the Sheikhpura district of Bihar for safe drinking and irrigation water availability. To account for spatial distribution, groundwater quality parameters, such as fluoride, iron, total dissolved solids, turbidity, and pH, were analyzed using integrated interpolation, geographical information systems, and regression analysis. A total of 206 dug wells and bore wells were analyzed for in-situ observations in the Sheikhpura district of Bihar, India. The analysis indicated that the periphery south of Chewara and Ariari blocks, i.e., about 9.16% of district area, is affected by fluoride content (1.55–2.32 mg/l) which is highly unsuitable for consumption, as recommended by the WHO and BIS standards. However, the remaining area (90.84%) is within the permissible limit of fluoride content (0.37–1.54 mg/l). In most areas, iron content is beyond WHO permissible limits (>0.1 mg/l), except 3.1% area in the eastern region with 0.06–0.12 mg/l iron, although iron concentrations in groundwater are under the acceptable limit (<0.3 mg/l) as per BIS standard across the district. However, pH and total dissolved solids were within permissible limits. Each of the modeled geospatial maps was validated using a set of 17 in-situ observations. The best-fit model between observed and predicted variables such as fluoride, iron, total dissolved solids, and pH produced a coefficient of determination ( R 2 ) of 0.96, 0.905, 0.91, and 0.906, respectively. The findings of this study provide insights and understanding on groundwater pollution regimes and minimize uncertain causes because of the high spatial distribution of geogenic fluoride and iron occurrence, and will also be helpful to policymakers for better planning, investments, and management to supply potable water in the area.

Introduction

Water, whether on the surface or underground, is the most essential and significant natural resource for sustaining life on Earth and for the sustainable growth of socioeconomic sectors such as irrigation and industrialization. Water, in each form, is an essential component of hydro-geo-ecological and various other metabolic, physiological, and ecological processes of living beings. The “resourcism” and unethical human activities in the Earth's biosphere–hydrosphere–geosphere have created a global water imbalance and crisis which threatens the life of the billion individuals and numerous natural ecosystems ( Mekonnen and Hoekstra, 2016 ; Falkenmark et al., 2019 ). At the global scale, in developed nations, per head water consumption is reported to be 382 l in the USA and 110 l in France ( Grafton et al., 2009 ), whereas, in developing countries like India, it is 150–200 l for drinking and domestic purposes ( CGWA, 2016 ). Even though nearly four billion people are facing severe water scarcity across the world ( Rijsberman, 2006 ; Mekonnen and Hoekstra, 2016 ; Adams et al., 2020 ; Tzanakakis et al., 2020 ), potable water scarcity is projected to affect nearly 10 billion people by 2050 ( Chouchane et al., 2018 ; Boretti and Rosa, 2019 ). Further, at the regional level, water quality deterioration, abstraction, drought, floods, erratic rainfall, etc., affect a large population due to which water scarcity has become a global issue ( Kumar et al., 2010 ; Jain, 2012 ; Jain et al., 2013 ; Boers et al., 2017 ; Ellison et al., 2017 ).

Groundwater aquifers are the primary source of water supply in rural and urban areas, mainly in the arid and semiarid regions worldwide ( Dash et al., 2010 ; Uyan and Cay, 2013 ; Rao et al., 2020 ). Groundwater scarcity in dry seasons draws global attention and perceived risk due to anthropogenic activities, like overexploitation of groundwater for irrigation, industrial, and drinking purposes ( Mekonnen et al., 2015 ; Adimalla et al., 2020b ). Therefore, the abuse of groundwater spawns hazardous impacts, mainly water quality, and quantity, in general ( Ray and Elango, 2019 ). Furthermore, groundwater resources are contaminated via anthropogenic activities and geogenic contaminants bearing rocks and soils ( Saha et al., 2018 ). Consequently, the groundwater crisis is driven by land-use changes, cropping patterns, high water demand, high-yielding crop races, and water availability ( Ellison et al., 2017 ). Inadequate management has further disturbed the global water cycle, likely accelerating groundwater pollution and climate change ( Abbott et al., 2019 ).

Groundwater systems have their unique chemistry and characteristics at each location and depend on various climatic changes, precipitation, surface water, and recharge parameters. Water quality depends mainly on underlying rock's geochemical and lithological composition and subsurface factors ( Magesh and Chandrasekar, 2013 ). Time-sensitive undulation in the source and the configuration of revived water and the hydrological and social variables may generate irregular changes in the parameters dealing with water quality ( Sahoo et al., 2019 ; Ijumulana et al., 2021 , 2022 ). Heavy metals, such as fluoride, arsenic, cadmium, iron, and mercury, and other toxic chemicals either geogenic or discharged from residential areas, industries, and agricultural land contaminate surface and subsurface systems and have been reported to have more than the permissible concentration in drinking water ( WHO, 2004 ; Bhagure and Mirgane, 2011 ; Sahoo et al., 2019 ; Adimalla et al., 2020a ; Ijumulana et al., 2021 , 2022 ). Groundwater quality has been measured using physicochemical properties, such as the concentration of arsenic, fluoride, pH, bicarbonates, chlorine, and total dissolved solids ( Kannel et al., 2007 ; Ijumulana et al., 2020 , 2021 ; Ligate et al., 2021 ). In India, including the present study area, arsenic, fluoride, iron, manganese, chromium, radon, uranium, etc., are geogenic contaminants from mineral deposits in the aquifer and have a significant health concern ( Banerjee et al., 2012 ; Thakur and Gupta, 2015 ; Saha and Sahu, 2016 ; Krishan et al., 2021 ; Sahoo et al., 2022 ). Bihar has severe problems of arsenic, fluoride, iron, nitrate, etc., contamination in groundwater. The majority of the Indo-Gangetic plain is formed by quaternary alluvium deposition (old and new). In the Southern Gangetic Plains of Bihar, groundwater from quaternary aquifers is the principal source for water supply in rural and urban areas through bore wells, dug wells, etc. ( Saha et al., 2007 ). The origin of contaminants is attributed to late quaternary stratigraphy and sedimentation in Middle Ganga Plains ( Shah, 2008 ). Fluoride contamination from underlying parent rocks and soil affects the water quality in the upper layer of alluvium and is a well-known menace ( Saha and Sahu, 2016 ). In the larger scenario, unsustainable and unplanned groundwater exploitation is constructing a negative influence on aquifer systems. It is complex ( Saha et al., 2019 ) and unfit for human consumption and irrigation purposes ( Earle, 2019 ).

India's dependency on groundwater for crop irrigation and drinking water is very high ( Shankar et al., 2011 ). The primary consumers of groundwater are utilizing about 70–90% of annual extrication for irrigation in global agriculture ( Llamas and Martínez-Santos, 2005 ; Kulkarni et al., 2015 ). It is assessed that the contribution of groundwater for irrigation is 62%, and the requirement of rural and urban water consumption is 85 and 50%, respectively, in India ( Saha and Ray, 2018 ; Saha et al., 2019 ). However, the dependency on groundwater utilization in rural Bihar is about 80%. In 2001, the per capita availability of groundwater was 1950 and 1816 cubic m for Bihar and India, respectively, and reported to decrease because of increased population, industrialization, irrigation, etc. Due to the ever-increasing population in India, the annual per capita water availability is projected to shift down from 5,177 cubic m in 1951 to 1,140 cubic m in 2051 ( MoWR, 2015 ). The total water gap across the Sheikhpura district was estimated to be 148.2 million cubic meters (MCM). For water budget, in 2020, it was estimated that groundwater and surface waters were 180.7 and 49.0 MCM, respectively ( TRUAGRICO, 2017 ). Water quantity and quality are inextricably related to water resource management and must be controlled using integrated ways to prevent water pollution.

Fluoride (F) contamination is a severe problem in groundwater across India and the world ( Changmai et al., 2018 ). It has a direct impact on the health of human beings, animals, and plants due to exceeded limits, and it varies in the air (0.1–0.6 μg/l), plant (0.01–42 mg/kg), soils (150–400 mg/kg), rocks (100–2,000 mg/kg), and water (1.0–38.5 mg/l) ( Singh et al., 2018 ). Fluoride concentration is acceptable with < 1.5 mg/l for drinking and irrigation purposes ( WHO, 1997 ). Millions of people are affected by diseases such as skeletal and dental fluorosis in many parts of India, caused by high fluoride contamination. Both lower concentrations of fluoride (0.6 mg/l) and upper concentrations of fluoride (1.2 mg/l) are harmful to health for prolonged consumption ( Singh et al., 2016 ). The fluoride concentration of 1.5–3.0 mg/l can cause dental fluorosis, and the concentration of 3.0–4.0 mg/l causes to stiffens brittle bones, and more than 4.0 mg/l of fluoride concentration can cause crippling fluorosis ( Saxena and Sewak, 2015 ). Besides, fluoride can also affect bruising of the liver, thyroid, and other organs, including deformation in bone and teeth spotting/flaking ( Jiménez-Reyes and Solache-Ríos, 2010 ). Total dissolved solids (TDS) are a useful parameter for deciding safe drinking water quality with a lower range of 500 mg/l to a higher permissible limit of 2,000 mg/l ( Jain et al., 2010 ); however, the World Health Organization (WHO) permits TDS for an extreme concentration of 1,700 mg/l ( WHO, 2008 ). The permissible limit of TDS with <300 mg/l is excellent, 300–600 mg/l good, 600–900 mg/l poor, and > 1,700 mg/l unacceptable ( WHO, 2008 ). However, the TDS concentration varies significantly due to diverse geological locations ( WHO, 2004 ; Magesh and Chandrasekar, 2013 ). The prevalence of high iron (Fe) in drinking water causes severe impacts on human health, such as diabetes, heart diseases, cirrhosis of the liver, liver cancer, and infertility ( Kumar et al., 2017 ). The permissible limit of iron content in drinking water is <0.1 mg/l ( Borah et al., 2010 ; WHO, 2011 ). pH, a crucial constraint in drinking water, varies greatly, and the permissible range is 6.5–9.5 ( Saxena and Ahmed, 2001 ; WHO, 2011 ).

In Bihar, among 38 districts, 13 districts are located beside the Gangetic river are partly impaired by pollution due to the high concentration of arsenic (As >0.05 mg/l), affecting 1,590 habitations ( Singh et al., 2014 ), whereas 11 districts are profoundly affected by fluoride pollution (F > 1.5 mg/l), having a detrimental impact over 4,157 habitations. The iron concentration (Fe > 1 mg/l) was found in 9 districts over 18,673 residences. Previous studies reported fluoride concentrations with respective concentrations of 0.00–1.34 mg/l in Bhagalpur ( Verma et al., 2017 ), 0.10–2.50 mg/l in Rohtas ( Ray et al., 2000 ), and 0.19–14.4 mg/l in Gaya ( Yasmin et al., 2011 ; Ranjan and Yasmin, 2012 ). The fluoride concentration in groundwater in the Sheikhpura district of Bihar is more than 1.5 mg/l, affecting 193 habitations ( PHED, 2009 ), and iron is < 1 mg/l; however, the WHO recommended 0.1 mg/l Fe as the permissible limit for drinking purposes.

Geostatistics has been globally applied as a decision-making tool for groundwater level ( Knotters and Bierkens, 2001 ), contamination analysis ( Gaus et al., 2003 ), groundwater quality analysis ( Yeh et al., 2006 ; Lee and Song, 2007 ; Sakram et al., 2019 ), and storage and reservoir capacity ( Rakhmatullaev et al., 2011 ). In the groundwater pollution modeling, the geostatistical interpolation techniques applied are kriging, inverse distance weighting (IDW), principal component analysis (PCA), etc. ( Chatterjee et al., 2010 ; Machiwal et al., 2011 ; Belkhiri and Narany, 2015 ; Bodrud-Doza et al., 2016 ; Verma et al., 2017 ; Kawo and Karuppannan, 2018 ). Investigating different groundwater quality parameters at every location would be time-consuming and economically inviable. The geospatial interpolation techniques are used significantly in unsampled areas. In this context, two different approaches, deterministic and geospatial methods, are adopted for groundwater pollution studies ( Sarangi et al., 2005 ; Dash et al., 2010 ), and geospatial interpolation techniques are reported and extensively applied in hydrology, hydrogeology, geography, geology, soil science, atmospheric science, etc. ( Diodato and Ceccarelli, 2005 ; Sarangi et al., 2006 ; Tweed et al., 2007 ; Dash et al., 2010 ; Pandian and Jeyachandran, 2014 ; Bodrud-Doza et al., 2016 ; Kawo and Karuppannan, 2018 ; Aher and Deshmukh, 2019 ).

This study aims for adequate information on spatial distribution of groundwater quality for long-term assessment and implementation of groundwater management strategies for irrigation and potable water supply. Therefore, this study proposed spatial mapping and distribution of groundwater quality parameters, such as fluoride, iron, pH, and TDS, using integrated interpolation techniques, geographical information systems, and regression analysis into low and high groundwater contaminated regions in the Sheikhpura district of Bihar for safe drinking and irrigation water supply. Geospatial pattern analysis of different quality parameters is essential for management and monitoring agencies such as Central and State Pollution Control Boards, farmers, agricultural research institutes, the Government of Bihar, and India to implement schemes and policy in different regions.

Materials and Methods

The study area was Sheikhpura, a district in South Bihar, India, which lies between 24 ° 45 ′ and 25 ° 45 ′ North and 85 ° 45 ′ and 86 ° 45 ′ East longitude. The district has six blocks and 360 villages, extending over 609.51 km 2 in size ( Figure 1 ). Groundwater-bearing geological formations in the area have unconsolidated sediments of alluvium plain having hard rock with fissured quartzite formation, as shown in Supplementary Figure 1 ( Rajmohan and Prathapar, 2013 ; IEED, 2019 ). IEED (2019) reported that the geology of the Sheikhpura district is composed of quartzites, phyllite, and schist rocks. Further, several studies documented that the geological formations of quartzites, phyllite, and schist rocks are primary sources of fluoride and iron in groundwater ( Rao and Devadas, 2003 ; Suthar et al., 2008 ; Okofo et al., 2021 ). The area is enriched with old alluvial soil. Most of the area is covered by sand, silt, and clay, with aquifer thickness in the range of 20–190 m because of uneven bed-rock topography across the district ( CGWB, 2013 ; TRUAGRICO, 2017 ). The hydrogeology features comprise unconsolidated formation, i.e., quaternary alluvium and consolidated formation due to hard rocks ( Supplementary Figure 1 ) ( CGWB, 2013 ). The major part is covered with old alluvium, which receives sediments from the Phalgu-Kiul sub-basin of River Ganga. Soils are coarse loamy with dominant subgroups of typic ustifluvents, fine aeric ochraqualfs, fine vertical ochraqualfs, and fine vertical ustochrepts. The major part in the south has fine vertic ochraqualfs and fine vertic ustochrepts in the middle region. In the northern side, soils are coarse loamy typic ustifluvents. The fine aeric ochraqualfs randomly occupy in the west, southeast, and northeast. The maps of soil types and dominant soil subgroups are shown in Figures 2A,B , respectively, and are used for water management strategy. The majority of the area is covered with greenish clay with caliche oxidized and pedocal soil, followed by silt and clay of variegated colors in the northeastern region and sand, silt, and clay unoxidized in the southeastern side. The greenish clay in the northern part occupies a significant area. The minimum area has silt and clay of variegated colors.

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Figure 1 . Village map of Sheikhpura district indicates groundwater sample locations ( N = 206) (source: http://www.sheikhpura.bih.nic.in/ ).

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Figure 2 . Soil maps (A) soil type, (B) dominant soil subgroups (source: NATMO, Kolkata, India).

Research Methods and Data Collection

With a sampling intensity of 10%, 36 villages encompassing all the blocks were randomly selected based on strata of location, population, block, and village size (big, medium, and small). Out of 2,000 bore wells in the area, 206 bore wells were sampled and analyzed. The data on F, Fe, TDS, turbidity, and pH collected by the Ministry of Drinking Water and Sanitation, Government of India, in 2017 under the National Rural Drinking Water Programme were used in this study. The geotagging of these wells was done using the Garmin eTrex Legend navigation system. We prepared a geospatial database of soil types and subgroups ( ICAR, 1998 ) in the geospatial information system (GIS) domain and land use/land cover using satellite data. Geo-coordinates of 206 well locations were imported in the GIS domain, and attributes on F, Fe, TDS, and pH were assigned to prepare geospatial maps using Arc GIS 10.9 software ( Figure 1 ).

Geospatial Modeling

The geospatial approaches such as Kriging and IDW interpolate the spatial variability of point attributes and predict for an unobserved location using nearby known attributes ( Rakhmatullaev et al., 2011 ; Sahoo et al., 2019 ; Ijumulana et al., 2020 , 2021 , 2022 ; Ligate et al., 2021 ). The Kriging interpolation technique is the optimal and widely applicable procedure for estimating unknown values (attributes) using normally distributed known data ( Jager, 1990 ; Dong et al., 2011 ; Wu et al., 2011 ; Ijumulana et al., 2022 ). A linear interpolation method predicts the value of unobserved location attributes based on probabilistic models ( Shyu et al., 2011 ; Ijumulana et al., 2021 , 2022 ) with minimum error ( Mendes and Ribeiro, 2010 ). In the present study, kriging was applied and validated the reliability of different groundwater quality parameters by incorporating geospatial statistical techniques. The water quality parameters, such as F, Fe, TDS, and pH, were characterized as good, moderate, and poor based on the ranges and permissible limits for groundwater quality characterization ( Table 1 ). Under the National Rural Drinking Water Programme, Ministry of Drinking Water and Sanitation, Government of India, in 2017, turbidity was reported almost negligible (<2 NTU) across the Sheikhpura district of Bihar. Therefore, the turbidity data were not considered for integrated interpolation, GIS, and regression analysis. Therefore, except turbidity, each parameter was assigned weights considering their negative impacts. The highest weight of four was assigned to fluoride, then iron (3), TDS (2), and the lowest (1) to pH. Fluoride is more than its permissible limit (> 1.5 mg/l); therefore, it was assigned high weight, and pH was set with the least weight due to its concentration being under the allowable limit (6.5–9.5). A simple overlay analysis considering the ranges and these weights were performed for suitability analysis. The groundwater suitability for irrigation and drinking water supply was characterized, assigned from very good (rank rating = 3), i.e., under the permissible limit, to very poor (rank rating = 1) for more than the allowable limit to each quality parameter ( Islam et al., 2018 ). With criteria for drinking and irrigation purposes, the water quality has been characterized based on each contaminant presented in Table 1 . The validation and adequacy of the model were tested by determining the coefficient of determination ( R 2 ) based on 17 randomly selected point data to establish the relationship between the actual and predicted ranges of pollution parameters.

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Table 1 . Parameterization of groundwater quality assessment.

Results and Discussion

Geospatial mapping of groundwater pollutants.

In this study, groundwater quality parameter limits followed the standards outlined by WHO (1997) and BIS (2012) for drinking and irrigation purposes. Fluoride and iron concentrations exceed the permissible limit, whereas TDS and pH values are under permissible limits. Fluoride level was observed within the permissible limit except in the southeastern region ( Figure 3A ). The northern part is occupied by “Tal” (a low-lying area having soil of silt and clay of variegated colors), contaminated with fluoride ranging between 0.37 and 0.76 mg/l. The southeastern part has slightly higher concentrations of fluoride that range from 1.024 to 1.45 mg/l, which are also within the acceptable limit. The fluoride concentration is acceptable with < 1.5 mg/l for drinking and irrigation purposes, according to the WHO and BIS (Bureau of Indian Standards) standards ( WHO, 1997 ; BIS, 2012 ). Geospatial analyses indicated that the fluoride contamination with permissible limit is higher in most of the area; the moderately affected (1.16–1.54 mg/l) area in the Ariari block covers 6.54% of the total area. The majority of the area is within the fluoride concentration of 1.5 mg/l, which possesses coarse-loamy soils in the northeastern region and fine-loamy soil in the southern region of the Sheikhpura district. Besides this, the lowest range of fluoride from 0.37 to 0.76 mg/l was associated with greenish clay, characterized by caliche oxidized with pedocal soils, and covered almost 76.10% area of the entire district. 91.15% area comes under Sheikhpura, Ghat Kusumbha, Barbigha, and Sheikhpura Sarai, and major portions of Ariari blocks have groundwater with fluoride concentrations in the range of 0.37–1.54 mg/l, i.e., under the WHO-permissible limit of fluoride concentrations, and are suitable for drinking purposes. The maximum fluoride concentration was 1.54–2.32 mg/l in the southeastern region, where groundwater is unsuitable for domestic and drinking purposes. The analysis indicated that the southern peripheral area of Chewara is profoundly affected with fluoride concentrations of 1.55–2.32 mg/l, which covers 5.07% of the total district area. Pedocal soils have high concentrations of fluoride (1.94 mg/l < F <2.32 mg/l) in groundwater ( Figure 3A ) and are unsuitable for drinking purposes. This is the highest level of fluoride spread in ~24.71 km 2 (4.09%) of the total area.

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Figure 3 . Geospatial maps of groundwater quality: (A) fluoride, (B) iron, (C) pH, and (D) TDS based on kriging interpolation of field observations.

Iron is a vital element and occurs naturally in water. The geogenic source of iron in groundwater is due to the underlying quartzite rocks of Sheikhpura. A similar geogenic source of iron in groundwater is reported in the literature ( Rao, 2008 ; Amanambu, 2015 ). In general, the desirable limit of iron is <0.1 mg/l ( WHO, 2011 ) and <0.3 mg/l ( BIS, 2012 ), according to the WHO and BIS standards for drinking purposes. The high concentration of iron in groundwater has a direct impact on health. The categorization of groundwater quality parameters for drinking and irrigation purposes based upon iron is presented in Table 1 . In this regard, the spatial distribution of iron concentration is shown in Figure 3B , indicating that 96.9% of the total area has a concentration of more than 0.1 mg/l. The analysis also revealed that the northern region has 0.23–0.29 mg/l of iron concentration, covering Barbigha, Sheikhpura, and Ghat Kusumbha blocks and a small portion in the Chewara block. Moreover, the west and south regions of the district exhibit a high iron level of 0.20–0.22 mg/l in Sheikhpura Sarai, Ariari, and Chewara blocks. Iron concentrations ranged from 0.13 to 0.18 mg/l in most northeastern blocks, like Sheikhpura, Chewara, and Ghat Kusumbha ( Figure 3B ). The permissible limit (0.00–0.12 mg/l) for drinking purposes was found in pockets of Sheikhpura and Chewara blocks in the northeastern region. Based on the BIS standard, iron concentrations are under the required acceptable limit (<0.3 mg/l).

The pH determines the acidity and alkalinity of groundwater as a significant water quality parameter. The permissible limit of pH ranged from 6.5 to 9.5 as per WHO recommendations ( WHO, 2011 ) and 6.5 to 8.5 according to BIS standards ( BIS, 2012 ) for drinking purposes. Figure 3C depicts the geospatial pattern of pH and is in the desirable limit. It varied from 7.04 to 7.51, under permissible limits recommended by the WHO and BIS standards in drinking water. In the northern area of the district, it ranged from 7.23 to 7.51.

TDS characterizes the total concentration of dissolved substances in groundwater, an important parameter to measure drinking water/groundwater quality. TDS indicate fully dissolved minerals, such as calcium, chlorides, carbonates, bicarbonates, magnesium, silica, and sodium, in groundwater ( Anbazhagan and Nair, 2004 ). The permissible limit <300 mg/l is excellent, and the unacceptable limit is > 1,700 mg/l ( WHO, 2008 ) and 2,000 mg/l according to BIS (2012) standards. In this regard, TDS varied between 271 and 713 mg/l. A slightly higher level of TDS (713 mg/l) was noted in small pockets of Sheikhpura and Ghat Kusumbha blocks ( Figure 3D ). Thus, it is not much of a concern for drinking and irrigation purposes ( Table 1 ). Therefore, this study recommends the development of a practical groundwater management support tool for fluoride-free water for drinking and irrigation purposes. Besides, implementing an efficient and reliable technique should be adopted to achieve groundwater quality under WHO and BIS standard permissible limits.

Model Accuracy

Seventeen observational data points were used to validate the results of Kriging interpolation. The actual observations were evaluated concerning the predicted values. The goodness of fit for an actual concentration of contaminants indicated that the coefficient of determination ( R 2 ) between observed/actual and predicted for F, Fe, TDS, and pH were 0.96, 0.905, 0.91, and 0.906, respectively ( Figures 4A–D ), which shows heterogeneity in the quality parameters ( Table 2 ).

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Figure 4 . Model accuracy assessment between observed and predicted groundwater quality parameters: (A) fluoride, (B) iron, (C) pH, and (D) TDS.

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Table 2 . Relationship between observed vis-à-vis predicted groundwater quality.

Geospatial Modeling for Suitability of Groundwater

Characterization for groundwater quality mapping based on class interval and weights to different layers of F, Fe, TDS, and pH per their significance is shown in a map ( Figure 5 ). Prioritization of groundwater quality was done for planning and conservation of groundwater resources for drinking and irrigation purposes. Only 5.08% area is falling under the “very good” category. Many villages are in Sheikhpura block, whereas four villages, viz., Angpur, Bahuwara, Chakandara, and Chewara villages, are of the Chewara block, and two villages, Kusumbha and Rajauli villages, are of the Ghat Kusumbha block. Significant areas are covered under suitable groundwater conditions in each block, i.e., 55.84% of the total district area has groundwater fit for drinking purposes. Therein, the medium/moderate quality of groundwater is falling under all blocks, which covered 33.95% area of the district, except the Ariari block. The poor, unsuitable groundwater quality covered only 5.13% of the total area. The poor water quality only extended in Arari, Chewara, and Sheikhpura blocks. Nabinagar, Husenabad, Diha, Belchhi, Karki, and Pandhar villages are the most affected in the Arari block. In contrast, Sheikhpura and Eksari villages are affected in the Sheikhpura block, and Mane, Barari, and Chewara are the most affected from the Chewara block. Villages have been categorized under different water quality parameters with corresponding percent area, including the uninhabited village of Sheikhpura district polluted under quality parameters ( Supplementary Table 1 ).

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Figure 5 . Potential groundwater quality map for planning and management of groundwater harvesting and supply.

Relationship Between Soil Types and Groundwater Quality Parameters

Groundwater is vulnerable to contamination under different soil types ( Li et al., 2020 ). Geogenic fluoride contamination is globally observed in shallow aquifers ( Edmunds and Smedley, 2013 ). In the Indo-Gangetic plains, potential fluoride contamination was observed in alluvial aquifers of northwest India, but < 1.5 mg/l of fluoride concentration in groundwater ( Lapworth et al., 2017 ). Therefore, the principal source of contamination might be from underlying rocks and the flow rate in the aquifers ( Saha et al., 2007 ; Saha and Sahu, 2016 ). Overall, it was observed that the groundwater quality parameters exceed the desirable limits for domestic, drinking, and agriculture purposes at several locations in the Sheikhpura district. In Sheikhpura, the high fluoride concentration in the range of 1.55–2.32 mg/l was found in the southeastern region. In addition, the iron concentration is found in the range of 0.13–0.30 mg/l, which is above the permissible limit recommended by the WHO. The geological characteristics which constituted quartzites, phyllite, and schist rocks are responsible for the enrichment of fluoride and iron in groundwater in the Sheikhpura area of Bihar. Both fluoride and iron are likely to indicate geogenic contamination of aquifers in the area ( Saha et al., 2007 ; Saha and Sahu, 2016 ). The pH and TDS are well within the permissible limits, i.e., 7.04–7.51 and 271–713 mg/l, respectively, as per WHO recommendations. The causal relationship between soil types and groundwater contaminants across the district is tabulated ( Table 3 ).

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Table 3 . Summarized relationship between soil types and groundwater quality.

Groundwater contaminant concentrations were randomly distributed among different soil types in the area. There is no noticeable, quantifiable, and significant relationship between soil types and groundwater contaminants. A similar study by Sheehan et al. (2003) highlighted no statistical correlation between groundwater chemistry and toxicity and soil. However, the lack of correlation between soil and groundwater contaminants must not ignore the potential risk assessment to quantify contaminated soils for determining underlying aquifers characteristics, land uses, and groundwater footprint.

Conclusions

Integrated interpolation techniques, geostatistical systems, and regression analysis have proved efficient decision-making tools for groundwater quality analysis, monitoring, and management. In this study, the spatial groundwater quality parameters, such as F, Fe, TDS, and pH, were analyzed across six blocks of the Sheikhpura district of Bihar. Groundwater in Sheikhpura, Arari, and Chewara blocks were contaminated with high fluoride concentrations up to 2.42 mg/l. In addition, iron has also spread with more than the WHO permissible limit (>0.1 mg/l) across all six blocks. Reasons behind the enrichment of fluoride and iron in the groundwater of the Sheikhpura district of Bihar are the underlying geological features composed of quartzites, phyllite, and schist rocks. Besides this, TDS and pH were found under the allowable limit across the district. The spatial analysis of different parameters is conducted based on the Kriging method by estimating unobserved locations relating to the known values. This study also attempted to establish the relationship between soil types and groundwater contaminants; however, no statistical correlation was found for each groundwater quality parameter considered for spatial mapping in the Sheikhpura district. The final groundwater quality map highlighted the area having groundwater quality from “poor” to “very good” across the district. In the interpolated map, the overall groundwater quality of the Arari, Chewara, and Sheikhpura blocks is not appropriate; thus, aquifer quality is relatively low and not acceptable for drinking and irrigation purposes. Thus, spatial mapping of groundwater quality will help policymakers to better operate and manage the groundwater resources through detecting pollutants, demand–supply gap, etc. Overall, proper utility and management of groundwater through canals, channels, and underground movement from safe to affected blocks/zones are recommended for drinking and agricultural purposes to avoid carcinogenic diseases among the population in the future.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found at: All the primary groundwater quality data were obtained from the website of Ministry of Drinking Water and Sanitation, Government of India, surveyed in 2017 under the National Rural Drinking Water Programme and has been duly acknowledged. Map of soil types and soil subgroups published by the National Bureau of Soil Survey and Land Use Planning (ICAR 1998), Nagpur, India. were used.

Author Contributions

RiK: conceptualization, sampling strategy, fieldwork, GIS database creation and analysis, and writing of the first draft of the manuscript. SS: conceptualization, sampling strategy, methodology, GIS analysis supervision, review of results, and editing of the manuscript. RaK: support in GIS analysis, writing, reviewing, and editing of the manuscript. PS: initial conceptualization, review, and editing of the manuscript. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors would like to record their gratitude to SEES lab, Nalanda University for scientific facilities and also like to express their gratitude to the National Rural Drinking Water Programme, Ministry of Drinking Water and Sanitation, Government of India for allowing access to the data on their website. We want to thank Dr. Dharmendra Singh, Assistant Scientist (Environment/Ecology) Haryana Space Applications Centre, Hissar for his timely help during the analysis.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frwa.2022.848018/full#supplementary-material

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Keywords: groundwater pollution, geospatial modeling, fluoride, iron, Bihar

Citation: Kumar R, Singh S, Kumar R and Sharma P (2022) Groundwater Quality Characterization for Safe Drinking Water Supply in Sheikhpura District of Bihar, India: A Geospatial Approach. Front. Water 4:848018. doi: 10.3389/frwa.2022.848018

Received: 03 January 2022; Accepted: 11 February 2022; Published: 24 March 2022.

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Copyright © 2022 Kumar, Singh, Kumar and Sharma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Rakesh Kumar, rakesh.kumar.PhD@nalandauniv.edu.in

† ORCID: Sarnam Singh orcid.org/0000-0003-1829-7941 Rakesh Kumar orcid.org/0000-0001-7264-5682 Prabhakar Sharma orcid.org/0000-0003-0894-0809

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Groundwater quality assessment using water quality index (WQI) under GIS framework

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  • Published: 12 February 2021
  • Volume 11 , article number  46 , ( 2021 )

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research paper on ground water

  • Arjun Ram 1 ,
  • S. K. Tiwari 2 ,
  • H. K. Pandey 3 ,
  • Abhishek Kumar Chaurasia 1 ,
  • Supriya Singh 4 &
  • Y. V. Singh 5  

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Groundwater is an important source for drinking water supply in hard rock terrain of Bundelkhand massif particularly in District Mahoba, Uttar Pradesh, India. An attempt has been made in this work to understand the suitability of groundwater for human consumption. The parameters like pH, electrical conductivity, total dissolved solids, alkalinity , total hardness, calcium, magnesium, sodium, potassium, bicarbonate, sulfate, chloride, fluoride, nitrate, copper, manganese, silver, zinc, iron and nickel were analysed to estimate the groundwater quality. The water quality index (WQI) has been applied to categorize the water quality viz: excellent, good, poor, etc. which is quite useful to infer the quality of water to the people and policy makers in the concerned area. The WQI in the study area ranges from 4.75 to 115.93. The overall WQI in the study area indicates that the groundwater is safe and potable except few localized pockets in Charkhari and Jaitpur Blocks. The Hill-Piper Trilinear diagram reveals that the groundwater of the study area falls under Na + -Cl − , mixed Ca 2+ -Mg 2+ -Cl − and Ca 2+ - \({\text{HCO}}_{3}^{ - }\) types. The granite-gneiss contains orthoclase feldspar and biotite minerals which after weathering yields bicarbonate and chloride rich groundwater. The correlation matrix has been created and analysed to observe their significant impetus on the assessment of groundwater quality. The current study suggests that the groundwater of the area under deteriorated water quality needs treatment before consumption and also to be protected from the perils of geogenic/anthropogenic contamination.

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Introduction

In India, there has been a tremendous increase in the demand for groundwater due to rapid growth of population, accelerated pace of industrialization and urbanization (Yisa and Jimoh 2010 ). The availability and quality of groundwater are badly affected at an alarming rate due to anthropogenic activities viz. overexploitation and improper waste disposal (industrial, domestic and agricultural) to groundwater reservoirs (Panda and Sinha 1991; Kavitha et al. 2019a , 2019b ). Consequently, human health is seriously threatened by the prevailing agricultural practices particularly in relation to excessive application of fertilizers; unsanitary conditions and disposal of sewage into groundwater (Panigrahi et al. 2012 ). The groundwater quality also varies with depth of water, seasonal changes, leached dissolved salts and sub-surface environment (Gebrehiwot et al. 2011 ). According to the World Health Organization (WHO 2017 ), about 80% of all the diseases in human beings are water-borne. Once the groundwater is contaminated, it is difficult to ensure its restoration and proper quality by preventing the pollutants from the source. It, therefore, becomes imperative to monitor the quality of groundwater regularly, and to device ways and means to protect it from contamination. The quality of groundwater is deciphered using various physical, chemical and biological characteristics of water (Diersing and Nancy 2009 ; Panneerselvam et al. 2020a ). It is a measure of health and hygiene of groundwater concerning the need and purpose of human consumption (Johnson et al. 1997 ; Panneerselvam et al. 2020b ).

In recent years, the assessment and monitoring of groundwater quality on a regular basis is being carried out using Geographic Information System (GIS) technique added with the IDW interpolation method and has proved itself as a powerful tool for evaluating and analysing spatial information of water resources (Aravindan et al. 2010 ; Shankar et al. 2010 , 2011a , b ; Venkateswaran et al. 2012 ; Selvam et al. 2013b; Magesh and Elango 2019 ; Balamurugan et al. 2020b ; Soujanya Kamble et al. 2020 ). It is an economically feasible and time-efficient technique for transforming huge data sets to generate various spatial distribution maps and projections revealing trends, associations and sources of contaminants/pollutants. In this work, GIS technique has been used for spatial evaluation of various groundwater quality parameters.

In this study, the physicochemical properties of forty-three groundwater samples collected from wells and hand pumps were determined and compared with international standards of WHO for drinking and domestic uses based on Water Quality Index (WQI). The WQI was first developed by Horton ( 1965 ) based on weighted arithmetical calculation. A number of researchers (Brown et al. 1972 ; GEMS UNEP 2007; Kavitha and Elangovan 2010 ; Alobaidy et al. 2010 ; Shankar and Kawo 2019 ; Bawoke and Anteneh 2020 developed various WQI models based on weighing and rating of different water quality parameters which is derived by the weighted arithmetic method. The WQI is a dimensionless number with values ranking between 0 and 100. The WQI is a unique digital rating expression that expresses overall water quality status viz. excellent, good, poor, etc. at a certain space and time based on various water quality parameters. Thus, the WQI is being used as an important tool to compare the quality of groundwater and their management (Jagadeeswari and Ramesh 2012 ) in a particular region; and is helpful for selecting appropriate economically feasible treatment process to cope up with the concerned quality issues. It depicts the composite impact of different water quality parameters and communicates water quality information to the public and legislative policy-makers to shape strong policy and implement the water quality programs (Kalavathy et al. 2011 ) by the government.

Mineral intractions strongly influence groundwater hydrochemistry in aquifers and disintegration of minerals from various source rocks (Cerar and Urbanc 2013 ; Modibo Sidibé et al. 2019 ). Hydrochemistry of the analysed samples indicates that the mean abundance of major cations is present in order of Na ++  > Ca 2+  > Mg 2+  > K + while major anions in order of \({\text{HCO}}_{3}^{ - }\)  >  \({\text{NO}}_{3}^{ - }\)  > Cl −  >  \({\text{SO}}_{4}^{2 - }\)  > F − . The study shows that the sodium is dominant alkali while calcium and magnesium are the dominant alkaline earth metal leached in the aquafer due to rock water interaction affecting the quality of groundwater. Sodium in aquafer is derived from the weathering of halite and silicate minerals such as feldspar (Khan et al. 2014 ; Mostafa et al. 2017 ). The critical evaluation of Hill-Piper Trilinear diagram reflects Na + -Cl − , mixed Ca 2+ -Mg 2+ -Cl − , Ca 2+ - \({\text{HCO}}_{3}^{ - }\) , mixed Ca 2+ -Na + - \({\text{HCO}}_{3}^{ - }\) , Na + - \({\text{HCO}}_{3}^{ - }\) and Ca 2+ -Cl − type hydro-chemical facies in decreasing order of dominance. The Hydro-chemical characterization of groundwater reveals that the nature of aquifer is controlled by type of water, source and level of contamination (Aghazadeh et al. 2017 ; Brhane 2018 ). Hence, in order to keep the health of any aquaculture system, particularly an aquifer system at an optimal level, certain water quality indicators or parameters must be regularly monitored and controlled. Therefore, the objective of the study is to calculate the WQI of groundwater in order to assess its suitability for human consumption using the GIS interpolation technique and statistical approach in the study area.

Mahoba district is the south-western district of Uttar Pradesh which is adjacent to the state of Madhya Pradesh in south and Hamirpur (UP) in the north. The study area falls under the survey of India (SOI) toposheets no. 54O and 63C lies between latitude N25°01′30″ to N25°39′40″ and longitude E79°15′00″ to E80°10′30″ and covers an area of approximately 2933.59 km 2 . River Dhasan separates the district Mahoba from Jhansi in the west. A certainpart of Jhansi and Banda district has been merged in newly constructed Mahoba district in 1995 (bifurcated from Hamirpur). Mahoba district consists of three tehsils Kulpahar, Charkhari, Mahoba and four blocks Panwari, Jaitpur, Charkhari, Kabrai (Fig.  1 a). Kabrai is the biggest block fromaerial coverage as well as population point of view. Jaitpur is the smallest block from aerial coverage and Charkhari from population point of view. The study area experiences a typical subtropical climate punctuated by long and intense summer, with distinct seasons. The area receives an average annual precipitation of 864 mm mainly from the south-west monsoon. The temperature of the coldest month (January) is 8.3°C while the temperature of the hottest month (May) shoots upto 47.5°C. The entire area under investigation is characterised by highly jointed/fractured Bundelkhand granite (Archean age) with thin soil cover. Physiographically , the area is characterised by Bundelkhand massif terrain and is marked by the occurrence of solitary or clustered hillocks and intervening low relief with undulating plains. Two major physiographic units are: (1) Southern part having high relief with hillocks- This is south of 20°25′ N latitude & maximum altitude is 340 mamsl, reserved forest. Granitoids and intervening pegmatitic veins and numbers of quartz veins are observed. (2) Northern part relatively low relief with lower hillocks- In between 25°25′N and 25°39′N latitude and maximum altitude is 310 mamsl. The area in and around Panwari is mainly covered with thick alluvium, and hard rock is encountered only below 35 mbgl, coverage with seasonal forest. Pedi plain, pediment inselberg and buried pediplains are present.

figure 1

a Study area map depicting the sampling sites. b Geological map of study area

Geological and hydrogeological set-up

The granite, particularly leucogranite, older and younger alluvium consisting of clay, silt, sand and gravel mainly comprises the study area. The geological set-up of the study area indicates that the most dominant lithology is leucogranite covering mainly central and eastern part while recent alluvium covers the northern part (Fig.  1 b). At places, few patches of pink granite have also been recorded which appears enclosed in leucogranite or adjacent to its outcrop.The occurrence of groundwater is highly uncertain and unpredictable in this hilly and rugged terrain as it does not allow percolation and storages underground. The presence of porosity depends on the intensity of weathering and rock fracture which is responsible for groundwater occurrence, its quantity and flow mostly in permeable zones of weathered rock formations and under secondary porosity in the deep fractured zone. Groundwater recharge in the study area is triggered by the depth of overburden 7 m (Jaitpur-Kulpahar area) to 35 m (parts of Mahoba Tahsil and Charkhari block) as well as the intensity of weathering.

Materials and methods

The groundwater samples were collected during pre-monsoon (June 2016) period from the study area according to standard procedures of the American Public Health Association (APHA, 2017). The sampling locations were marked with the help of global positioning system (GPS) as shown in the Fig.  1 a. Samples were collected from the location through hand pump (depth: approx. 40 m) and dug wells (depth: 8–30 m ) as shown in Fig.  2 a–t. The collecting bottles (High-Density Polythene, HDPE) of one-litre capacity each were sterilized under the aseptic condition to avoid unpredictable contamination and subsequent changes in the characteristics of groundwater. Water samples were filtered using Whatman 42 filter paper (pore size 2.5 μm) prior to collection in the bottle. The sample was kept in the ice-box (portable) and brought to NABL accredited (ISO 17,025: 2017) laboratory of Central Ground Water Board (CGWB), Lucknow and Department of Soil Science & Agricultural Chemistry, Banaras Hindu University, Varanasi, UP, India. The samples were stored in a chemical laboratory at temperature 4–5 °C. The samples for metallic parameters were added 2 ml elemental grade nitric acid to obtain the pH 2–3 after acidification. The samples were pre-filtered in the laboratory to carry out the analysis. In the present study, a total of 20 groundwater quality parameters of forty-three samples were analysed as per test standard methods (APHA 2017) in the laboratory except for unstable parameters viz. hydrogen ion concentration (pH), electrical conductivity (EC) and total dissolved solids (TDS) which are determined by portable device (pH-meter, EC-meter and TDS-meter) in situ. Alkalinity (AK), Total hardness (TH), calcium (Ca 2+ ), magnesium (Mg 2+ ), bicarbonate ( \({\text{HCO}}_{3}^{ - }\) ) and chloride (Cl − ) were analysed using volumetric titrations; sodium (Na + ) and potassium (K + ) were analysed using systronics flame photometer model 129; nitrate ( \({\text{NO}}_{3}^{ - }\) ), fluoride (F − ), sulfate ( \({\text{SO}}_{4}^{2 - }\) ), were analysed using shimadzu 1800 spectrophotometer. Prior to analysis of the heavy metals viz. copper (Cu), manganese (Mn), silver (Ag), zinc (Zn), iron (Fe) and nickel (Ni); the groundwater samples were acidified with 1:1 nitric acid and concentrated ten times. The samples were subjected to analysis using Shimadzu 6701 Atomic Absorption Spectrophotometer (AAS) on flame mode with hollow cathode lamps of metal under analysis. The concentration of metal is displayed on the monitor. The standards of the metallic parameters were prepared from National Institute of Standards and Technology (NIST) certified (Certified Reference Materials) CRM as per NABL guidelines of 17,025:2017.

figure 2

a – b Spatial distribution map of pH and EC. c – h Spatial distribution map of TDS, AK, TH, Ca 2+ , Mg 2+ and Na + . i – n : spatial distribution map of K + , \({\text{HCO}}_{3}^{ - }\) , \({\text{SO}}_{4}^{2 - }\) , Cl − , F − and \({\text{NO}}_{3}^{ - }\) . o – t Spatial distribution map of Cu, Mn, Ag, Zn, Fe and Ni

The quality assurance and quality control (QA/QC) procedure of the data has been considered during the study. Approximately half of the volume (500 ml) of samples were specially separated and checked in the laboratory to ensure QA/QC mechanisms. The accuracy of the chemical analysis has been validated by charge balance errors and samples < 5% error were considered.

The inverse distance weighted (IDW) interpolation technique used in this study is now-adays an effective tool for spatial interpolation of groundwater quality parameters leading to the generation of spatial distribution maps (Magesh et al. 2013 ; Kawo and Shankar 2018 ; Balamurugan et al. 2020b ; Sarfo and Shankar 2020 ). The weights were assigned to various parameters at each location based on distance and were calculated, taking into consideration the closest specified locations. The distribution of each groundwater quality parameter has been demarcated in different zones on spatial distribution map viz. acceptable/desirable and permissible limits according to BIS (2012, 2015) and WHO ( 2017 ) for drinking purpose. The statistical analysis and correlation matrix of the analysed groundwater quality parameters have been laid down as shown in Tables 1 and 2 , respectively.

The water quality index (WQI)

The WQI has been determined using the drinking water quality standard recommended by the World Health Organization (WHO 2017 ). The Water Quality Index has been calculated using the weighted arithmetic method, which was originally proposed by Horton ( 1965 ) and developed by Brown et al. ( 1972 ). The weighted arithmetic water quality index (WQI) is represented in the following way:

where n  = number of variables or parameters, W i  = unit weight for the i th parameter, Q i  = quality rating (sub-index) of the i th water quality parameter.

The unit weight ( W i ) of the various water quality parameters are inversely proportional to the recommended standards for the corresponding parameters.

where, W i  = unit weight for the i th parameter, S n  = standard value for i th parameters, K  = proportional constant,

The value of K has been considered ‘1′ here and is calculated using the mentioned equation below:

According to Brown et al. ( 1972 ), the value of quality rating or sub-index ( Q i ) is calculated using the equation as given below:

where V o = observed value of i th parameter at a given sampling site, V i = ideal value of i th parameter in pure water, S n = standard permissible value of i th parameter.

All the ideal values (V i ) are taken as zero for drinking water except pH and dissolved oxygen (Tripathy and Sahu 2005 ). In case of pH, the ideal value is 7.0 (for natural/pure water) while the permissible value is 8.5 (for polluted water). Similarly, for dissolved oxygen, the ideal value is 14.6 mg/L while the standard permissible value for drinking water is 5 mg/L. Therefore, the quality rating for pH and Dissolved Oxygen are calculated from the equations respectively as shown below:

where, V pH  = observed value of pH, V do  = observed value of dissolved oxygen.

If, Q i  = 0 implies complete absence of contaminants while 0 < Q i  < 100 implies that, the contaminants are within the prescribed standard. When Q i  > 100 implies that, the contaminants are above the standards.

The classification of water quality, based on its water quality index (WQI) after Brown et al. ( 1972 ); Chatterjee and Raziuddin ( 2002 ) and Shankar and Kawo ( 2019 ) have been considered here in this study for further reference which is mentioned in Table 3 .

Result and discussion

Groundwater quality parameters.

In this study based on the selected parameters as discussed above the groundwater quality maps have been prepared with the help of ArcGIS software 10.1 as shown in Fig.  2 a–t. In the following lines, the various parameters considered in the study are being discussed: The Bureau of Indian Standard (BIS 2012, 2015) and World Health Organization (WHO 2017 ) of drinking water standards have been considered as a reference in this study.

Hydrogen ion concentration (pH)

It is an important indicator for assessing the quality and pollution of any aquifer system as it is closely related to other chemical constituents of water. The presence of hydrogen ion concentration is measured in terms of pH range. Water, in its pure form shows a neutral pH which indicates hydrogen ion concentration. In the present study, the range of pH varies between 6.81 (minimum) to 8.32 (maximum) which is within the acceptable limit (6.5–8.5, avg: 7.81) indicating the alkaline nature of groundwater (ideal range of pH for human consumption: 6.5–8.5).

Electrical conductivity (EC)

In fact, it is a measure of the ability of any substance or solution to conduct electrical current through the water. EC is directly proportional to the dissolved material in a water sample. The desirable limit of EC for drinking purpose is 750 µS/cm. In this study, the electrical conductivity varies between 286 and 1162 µS/cm. High EC at some sites suggests the mixing of sewage in groundwater as these sites are near dense urbanization.

Total dissolved solids (TDS)

The weight of residue expresses it after a water sample is evaporated to dry state. It includes calcium, magnesium, sodium, potassium, carbonate, bicarbonate, chloride and sulfate. In the present study, it ranges between 280 to 879 mg/l (< 500 mg/l TDS for potable water as per BIS.). The agricultural practices, residential runoff, leaching of soil causing contamination and point source water pollution discharge from industrial or sewage treatment plants are the primary sources for TDS (Boyd 2000 ).

Alkalinity (AK)

It is a measure of the carbonate, bicarbonate and hydroxide ions present in water. The desirable limit of alkalinity in potable water is 200 mg/l, above which the taste of water becomes unpleasant. In the study area, the alkalinity ranges between 50 to 452 mg/l, which is within the permissible limit (600 mg/l).

Total hardness (TH)

It is the amount of dissolved calcium and magnesium in the water. Water moving through soil and rock dissolves naturally occurring minerals and carries them into the groundwater as it is a great solvent for calcium and magnesium. In this study, hardness ranges between 70 to 592 mg/l, which is within the permissible limits (600 mg/l). The high concentration of TH in groundwater may cause heart disease and kidney stone in human beings.

Calcium (Ca 2+ )

It enters into the aquifer system from the leaching of calcium bearing minerals. In the study area, the calcium concentration ranges from 12 to 112 mg/l and is within the permissible limit (200 mg/l). The lesser concentration of Ca 2+ in the groundwater satisfies the chemical weathering and dissolution of fluorite, consequently resulting in an increase of fluoride concentration.

Magnesium (Mg 2+ )

It is an important parameter responsible for the hardness of the water. In the study area, the concentration ranges between 2.4 to 120 mg/l and is present in little excess of the permissible limit (100 mg/l).

Sodium (Na + )

It is a highly reactive alkali metal. It is present in most of the groundwater. Many rocks and soils contain sodium compounds, which easily dissolves to liberate sodium in groundwater. In the study area, it ranges from 48.71 to 244.4 mg/l. The high concentration of Na + indicates weathering of rock-forming minerals i.e., silicate minerals (alkali feldspars) and/or dissolution of soil salts present therein due to evaporation (Stallard and Edmond 1983 ). In the aquifers, the high Na + concentration in groundwater may be related to the mechanism of cation exchange (Kangjoo Kim and Seong-Taekyun 2005).

Potassium (K + )

It is present in many minerals and most of the rocks. Many of these rocks are relatively soluble and releases potassium, the concentration of which increases with time in groundwater. In this study, it varies between 0.87 to 2.7 mg/l.

Bicarbonate ( \({\text{HCO}}_{3}^{ - }\) )

It is produced by the reaction of carbon dioxide with water on carbonate rocks viz. limestone and dolomite. The carbon-dioxide present in the soil reacts with the rock-forming minerals is responsible for the presence of bicarbonate, producing an alkaline environment in the groundwater. In the study area it varies between 36.61 to 536.95 mg/l and is within the permissible limit of 600 mg/l.

Sulfate ( \({\text{SO}}_{4}^{2 - }\) )

It is dissolved and leached from rocks containing gypsum, iron sulfides, and other sulfur bearing compounds. In the present study, it ranges between the 2.23 to 75.17 mg/l, which is well within the acceptable limit of 200 mg/l.

Chloride (Cl − )

In the present study the Cl − ranges between 70.92 to 276.59 mg/l which exceed the permissible limit (250 mg/l). The higher value of chlorine in groundwater makes it hazardous to human health (Pius et al. 2012 ; Sadat-Noori et al. 2014 ).

Fluoride (F − )

In groundwater fluoride is geogenic in nature. It is the lightest halogen, and one of the most reactive elements (Kaminsky et al. 1990 ). It usually occurs either in trace amounts or as a major ion with high concentration (Gaciri and Davies 1993 ; Apambire et al. 1997 ; Fantong et al. 2010 ). The groundwater contains fluorides released from various fluoride-bearing minerals mainly as a result of groundwater-host rock interaction. The study area comprising granite, granitic gneiss etc. is commonly found to contain fluorite (CaF 2 ) as an accessory mineral (Ozsvath 2006 ; Saxena and Ahmed 2003 ) which plays a significant role in controlling the geochemistry of fluoride (Deshmukh et al. 1995 ). In addition to fluorite it is also abundant in other rock-forming minerals like apatite, micas, amphiboles, and clay minerals (Karro and Uppin 2013 ; Narsimha and Sudarshan 2013 ; Naseem et al. 2010 ; Jha et al. 2010 ; Rafique et al. 2009 ; Carrillo-Rivera et al. 2002 ). In the present study, the fluoride concentration ranges from 0.11 to 3.91 mg/l. The concentration of fluoride exceeds the permissible limit (1.5 mg/l) in about 25% of the groundwater samples.

Nitrate ( \({\text{NO}}_{3}^{ - }\) )

Nitrate is naturally occurring ions and is a significant component in the nitrogen cycle. However, nitrate ion in groundwater is undesirable as it causes Methaemoglobinaemia in infants less than 6 months of age (Egereonu and Nwachukwu 2005 ). In general, its higher concentration causes health hazards if present beyond the permissible limit, 45 mg/l (Kumar et al. 2012 , 2014 ). In the study area, its concentration ranges from 86.95 to 210.4 mg/l. It is in excess of the permissible limits throughout the study area. The higher values of nitrate in potable water increases the chances of gastric ulcer/cancer, and other health hazards to infants and pregnant women (Rao 2006 ) also birth malformations and hypertension (Majumdar and Gupta 2000 ). The area under study is granite-gneiss terrain where the atmospheric nitrogen is fixed and added to the soil as ammonia through lightning storms, bacteria present in soil and root of plants. Further, animal wastes, plants and animals remain also undergo ammonification in the soil producing ammonia which undergoes nitrification/ammonia oxidation by Nitrosomonas and Nitrobacter bacteria to form nitrate (Rivett et al. 2008 ; Galloway et al. 2004). Granitic rocks contain nitrogen concentrations up to 250 mg Nkg −1 with ammonium partitioned into the orthoclase feldspar to a greater extent than muscovite or biotite (Boyd et al. 1993 ). Geologic nitrogen (nitrogen contained in bedrock) contribute to the ecosystem with nitrogen saturation (more nitrogen available than required by biota) leading to leaching of nitrogen and consequently elevating nitrate concentrations in groundwater (Dahlgren 1994 ; Holloway et al. 1998 ). Nitrogen released through weathering has a greater impact on soil and water quality. Also, denitrification is significant in modifying the level to which nitrogen released through weathering of bedrock influencing the supply of nitrate in groundwater (McCray et al. 2005 ).

Copper (Cu)

It is a naturally occurring metal in rock, soil, plants, animals, and groundwater in very less concentration. The concentration of Cu may get enriched into the groundwater through quarrying and mining activities, farming practices, manufacturing operations and municipal or industrial waste released. Cu gets into drinking water either by contaminating of well water or corrosion of copper pipes in case of water is acidic. In this study, it ranges between 0 and 0.0078 mg/l, which is within the permissible limit (0.05 mg/l).

Manganese (Mn)

It occurs naturally in groundwater, especially in an anaerobic environment. The concentrations of Mn in groundwater is dependent upon rainfall chemistry, aquifer lithology, geochemical environment, groundwater flow paths and residence time, etc. which may vary significantly in space and time. It may be released by the leaching of the overlying soils and minerals in underlying rocks as well as from the minerals of the aquifer itself in groundwater. In the present study, manganese ranges between 0.005 and 0.221 mg/l, which is within the permissible limit (0.3 mg/l).

It naturally occurs usually in the form of insoluble and immobile oxides, sulfides and some salts. It is rarely present in groundwater, surface water and drinking water at concentrations above 5 µg/litre (WHO 2017 ). In the present study, the silver ranges between 0.000 and 0.021 mg/l, which is within the permissible limit (0.1 mg/l).

Though it occurs in significant quantities in rocks, groundwater seldom contains zinc above 0.1 mg/l. In the present study, the groundwater shows the negligible concentration of Zn (0.0136 mg/l) which is well within the acceptable limit (5 mg/l).

The most common sources of iron in groundwater is weathering of iron-bearing minerals and rocks. The iron occurs naturally in the reduced Fe 2+ state in the aquifer, but its dissolution increases its concentration in groundwater. Iron in this state is soluble and generally does not create any health hazard. If Fe 2+ state is oxidised to Fe 3+ state in contact with atmospheric oxygen or by the action of iron-related bacteria which forms insoluble hydroxides in groundwater. So, the concentration of iron in groundwater is often higher than those measured in surface water. In the present study, the iron ranges between 0.0994 and 0.4018 mg/l, which is within of the permissible limit 1.0 mg/l (BIS 2015).

Nickel (Ni)

The primary source of nickel in groundwater is from the dissolution of nickel ore bearing rocks. The source of nickel in drinking water is leaching from metals in contact such as water supply pipes and fittings. Ni usually occurs in the divalent state, but oxidation states of  +  1,  + 3, or  + 4 may also exist in nature. In the study area, it ranges between 0 and 0.0408 mg/l, and it crosses the permissible limit (0.02 mg/l).

Statistical analysis, correlation matrix and relative weightage

The relative weightage, general statistical analysis and correlation matrix of groundwater quality parameters are tabulated in Tables 4 , 1 and 2 , respectively. The correlation matrix of various 20 groundwater quality parameters, including 6 heavy metals was created and has been analysed using MS Excel 2016 Table 2 . Out of these, eight parameters viz. TDS, EC, Na + , Alkalinity, TH, Ca 2+ , Mg 2+ , \({\text{HCO}}_{3}^{ - }\) are significantly correlated, reflecting more than 0.50 correlation value. Further, TDS vs EC, Na + vs Alkalinity, TH as CaCO 3 − vs Ca 2+ and Mg 2+ , \({\text{HCO}}_{3}^{ - }\) vs Alkalinity and Na + indicates most relevant correlation having a significant impetus on the overall assessment of the quality of groundwater than any other major radicals and physical parameters. However, the majority of quality parameters are positively correlated with each other. A critical analysis of the correlation matrix for the heavy metals indicates that Cu is positively correlated with EC, TDS, Na + , K + , Cl − and \({\text{NO}}_{3}^{ - }\) . Similarly, Mn is positively correlated with pH, EC, TDS and Cu. While, Ag is positively correlated with pH, Ca 2+ , Mg 2+ , K + , TH, Cl − , \({\text{NO}}_{3}^{ - }\) and Mn. Further, Fe is positively correlated with TDS, Mg 2+ , Na + , TH, \({\text{HCO}}_{3}^{ - }\) , \({\text{SO}}_{4}^{2 - }\) , \({\text{NO}}_{3}^{ - }\) , Cu and Ag. Similarly, Ni is positively correlated with pH, EC, TDS, Ca 2+ , K + , \({\text{NO}}_{3}^{ - }\) and Mn.

The higher concentration of Ni, Fe and Mn may trigger the presence of other heavy metals viz. Pb, Cd and Cr which are very sensitive and significant heavy metal and needs to be observed carefully in future for groundwater quality in the study area. The presence of Fe, \({\text{SO}}_{4}^{2 - }\) and \({\text{NO}}_{3}^{ - }\) may trigger the presence of Cd (Chaurasia et al. 2018 ).

Spatial distribution pattern

The spatial distribution pattern of the contour maps of the groundwater quality parameters have been generated as represented in Fig.  2 a–t. The spatial distribution pattern of the pH indicates that the central part along NW–SE across the district with some scattered small patches throughout indicating the presence of alkaline groundwater (Fig.  2 a). In acidic water, fluoride is adsorbed on a clay surface, while in alkaline water, fluoride is desorbed from solid phases; therefore, alkaline pH is more favourable for fluoride dissolution, (Keshavarzi et al. 2010 ; Rafique et al. 2009 ; Saxena and Ahmed 2003 ; Rao 2009 ; Ravindra and Garg 2007 ; Vikas et al. 2009 ). The southern portion of the district in Kabrai Block is having high TDS (> 750 mg/l) in groundwater (Fig.  2 c) due to poor fluxing and highly weathered rock formations. Similarly, EC is mainly highest (> 900 mg/l) in the southern part with small scattered patches in central and NE part of the district (Fig.  2 b). This is in consonance with the higher TDS (significant positive correlation with EC) as evidenced by the correlation matrix of the quality parameters (Table 2 ). The alkalinity map clearly and significantly indicates that it is highest in the central part surrounded by gradually decreasing alkalinity outwards (Fig.  2 d). The bicarbonates trigger the alkalinity in groundwater (Adams et al. 2001 ). The quality of groundwater in a major portion of the study area is alkaline in nature, indicating that the dissolved carbonates are predominantly in the form of bicarbonates. A positive correlation is observed between the alkalinity of groundwater and fluoride (Table 2 ), consequently releasing fluoride in the groundwater. The spatial distribution map of Ca 2+ suggests varying concentration within permissible limit throughout the study area (Fig.  2 f) due to the presence of alkali feldspar in granite. Similarly, Mg 2+ is also distributed unevenly but falls within permissible limit with an exception in NE part of the district (Fig.  2 g). The spatial distribution pattern of TH reflects that the study area is characterized by moderately hard groundwater.

Figure  2 e The Ca 2+ and Mg 2+ ions present in the groundwater are possibly derived from leaching of calcium and magnesium bearing rock-formations in the study area. The fluoride in groundwater shows a negative correlation with Ca 2+ , indicating the high value of fluoride in groundwater in association with low Ca 2+ content. The correlation matrix clearly marks a significant positive correlation among Na + , alkalinity and TDS, which is being reflected from their respective spatial distribution maps (Fig.  2 c, d and h). Na + is highest in the central part (with small patches in the eastern part and insignificantly in the western part) which is in conformity with the alkalinity and TDS spatial distribution patterns. Although, the presence of K + is insignificant and its lower concentration within the permissible limit is covering a major portion of the district due to poor weathering of orthoclase. Its distribution pattern indicates conformity more or less with the TDS and Na + (Fig.  2 c, h, and i). \({\text{HCO}}_{3}^{ - }\) is an important quality parameter showing significant positive correlation (> 0.50) with alkalinity and Na + (Table 2 ) which is also reflected in the spatial distribution pattern of these parameters (Fig.  2 d, h, j). Although sulphate ( \({\text{SO}}_{4}^{2 - }\) ) is an important quality parameter. It is present within the permissible limit in the study.

area (Fig.  2 k). Chloride is slightly in excess in a larger patch, particularly in SE-part of the study area which may cause a health hazard. It is revealed from the spatial distribution map of chloride (Fig.  2 l). This is due to poor fluxing and presence of halite mineral. Fluoride (F − ) is an important quality parameter, especially with respect to the study area where it is present noticeably in scattered patches throughout the district. It is observed that mainly in NE part, the central part and SE part of the district the concentration of fluoride is in excess (2.82 mg/l to 3.91 mg/l) of permissible limit 1.5 mg/l (Fig.  2 m). The higher concentration (> 3.0 mg/l) of fluoride may lead to skeletal fluorosis (Raju et al 2009 ). Several factors viz. temperature, pH, presence or absence of complexing or precipitating ions and colloids, the solubility of fluorine bearing minerals (biotite and apatite), anion exchange capacity of the aquifer (OH − with F − ), size and type of geological formations traversed by groundwater and the contact time during which water remains in contact with the formation are responsible for fluoride concentration in groundwater (Apambire et al. 1997 ). The lithology of fractured rock reveals that it contains more fluoride bearing minerals than massive rocks (Pandey et al. 2016 ). Nitrate (NO 3 − ) in groundwater is mainly anthropogenic in nature which could be due to leaching from waste disposal, sanitary landfills, over-application of inorganic nitrate fertilizer or improper manure management practice (Chapman 1996 ). In this study, it is observed that nitrate is in excess of the permissible limits with varying degree of concentration throughout the district, causing health hazard (Fig.  2 n). The area under study is granite-gneiss terrain where the atmospheric nitrogen is fixed and added to the soil as ammonia through lightning storms, bacteria present in soil and plants roots. Further, animal wastes, plants and animals remain also undergo ammonification in the soil producing ammonia which undergoes nitrification. The high values of nitrate in groundwater samples in the area may be due to unlined septic tanks and unplanned sewerage system that contaminates to the phreatic aquifer (Hei et al. 2020 ). Proper monitoring and concerned regulated effort are consistently required to get the assessment of nitrate impact on human health.

As far as heavy metals concentration in groundwater is concerned, Cu does not mark its noticeable presence (Fig.  2 o). Another, naturally occurring quality parameter is Mn which shows its presence within the permissible limit (Fig.  2 p). Silver and Zinc do not show any remarkable presence in the study area (Fig.  2 q and r). The study reveals a higher concentration of iron in groundwater in the Eastern part of the district due to secondary porosity and where ferrous (Fe 2+ ) ion usually occurs below the water table. The Fe 2+ after converting into Ferric (Fe 3+ ) state, becomes harmful and precipitated. This condition can be avoided naturally by raising the water table through groundwater recharging the affected area (Fig.  2 s). Nickel shows its remarkable presence in smaller patches in different areas (Fig.  2 t) due to the presence of heavy minerals like rutile and apatite.

Water quality index

The water quality index (WQI) map has been prepared using ArcGIS 10.1 on the basis of the selectively chosen quality parameters to decipher the various quality classes viz. excellent, good, poor, very poor and unsuitable at each hydro-station for drinking purpose (Tables 3 and 5 ; Fig.  3 ). The WQI Map of the study area indicates that major portion is having excellent (0–25) quality of groundwater while very poor (75–100) to unsuitable (> 100) quality is prevailing in small pockets in SW part (Fig.  3 ). The map clearly indicates that the quality of groundwater in Panwari Block belongs to excellent to good categories as for as potability for human consumption is concerned.

figure 3

Water quality index map of the study area, District Mahoba

There is gradual variation in groundwater quality from very poor to excellent at the central part and outwards in the Charkhari Block. There is no noticeable change in the quality of groundwater except in the SW part of the Kabari Block. In the Jaitpur block, there is a significant.

variation in the quality class and the SW part (Nanwara, Ajnar and Khama) is characterized by poor, very poor and unsuitable categories (Fig.  3 ). Remaining part of the block falls under good to excellent groundwater quality. Overall, the quality of groundwater belongs to the excellent category in a major portion of the study area and is suitable for drinking as well as domestic uses.

Hydro-chemical facies

The major ions analysed are unevenly distributed and have been plotted on a Hill-Piper Trilinear diagram (Fig.  4 ). This diagram is comprised of two triangles at the base and one diamond shape at the top to represent the major significant cations and anions responsible for the nature of groundwater (Balamurugan et al. 2020a ). The piper diagram is used to categorize groundwater into various six types such as Ca 2+ - \({\text{HCO}}_{3}^{ - }\) type, Na + -Cl − type, mixed Ca 2+ -Mg 2+ -Cl − type, Ca 2+ -Na + - \({\text{HCO}}_{3}^{ - }\) type, Na + - \({\text{HCO}}_{3}^{ - }\) type and Ca 2+ -Cl − type. A critical evaluation of the diagram reflects that 32.56% of the samples fall under Na + -Cl − type, 30.23% of the samples under mixed Ca 2+ -Mg 2+ -Cl − type, 16.28% of the samples under Ca 2+ - \({\text{HCO}}_{3}^{ - }\) type, 13.95% of the samples under mixed Ca 2+ -Na + - \({\text{HCO}}_{3}^{ - }\) type, 4.65% of the samples under Na + - \({\text{HCO}}_{3}^{ - }\) type and 2.33% of the samples under Ca 2+ -Cl − type. Further, the observation reveals that the samples are distributed mainly into Na + -Cl − type, mixed Ca 2+ -Mg 2+ -Cl − type and Ca 2+ - \({\text{HCO}}_{3}^{ - }\) type reflecting higher concentration of sodium and calcium bearing salt/mineral. Hydrochemistry of the analysed samples indicate that the major cations are present in order Na +  > Ca 2+  > Mg 2+  > K + of mean abundance while anions are present in the mean abundance order of \({\text{HCO}}_{3}^{ - }\)  >  \({\text{NO}}_{3}^{ - }\)  > Cl −  >  \({\text{SO}}_{4}^{2 - }\)  > F − (Table 1 ). This reveals that sodium, chloride and bicarbonate dominate the ionic concentration in the groundwater due to action of weathering of minerals like halite and dolomite as well as ion exchange process.

figure 4

Types of groundwater

The outcome of the present research in the hard rock area of the Bundelkhand region of India reveals that the groundwater has been deteriorated due to both geogenic and anthropogenic activities.

The study area is comprised mainly of granite and alkali granite, specifically in extreme southern which is responsible for leaching of fluoride in groundwater.

The thickness of overburden (loose soil and weathered rock) in the northern part of the study area is negligible. Therefore, there is a poor fluxing of groundwater which in turn triggers the concentration of TDS, fluoride and bicarbonate in groundwater.

Anthropogenic activities like unlined septic tanks and unplanned sewerage system have triggered the nitrate concentration in groundwater, particularly in the central and northern part of the study area. The rest of the area is safe and has potable groundwater. In addition, the area under study is granite-gneiss terrain where the atmospheric nitrogen is fixed and added to the soil as ammonia through natural lightning, bacteria present in soil and plants roots. Further, ammonification of animal wastes, plants and animal remains produces ammonia which undergoes nitrification.

Hydro-chemical facies reveal that the nature of groundwater is Na + -Cl − , mixed Ca 2+ -Mg 2+ -Cl − and Ca 2+ - \({\text{HCO}}_{3}^{ - }\) type in the study area.

The high value of WQI has been found, which is due to the higher values of chloride, fluoride, nitrate, manganese, iron, and nickel in the groundwater, which warrants immediate attention.

On the basis of WOI, it is concluded that the groundwater is safe and potable in the study area except for localized pockets in Jaitpur and Charkhari Blocks.

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Acknowledgements

Our thanks go to Central Groundwater Board (CGWB), Lucknow, NR, Region and Department of Soil Science & Agricultural Chemistry, Banaras Hindu University, Varanasi for their valuable support during the chemical analysis of groundwater samples.

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S. K. Tiwari

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H. K. Pandey

Central Groundwater Board, Lucknow, 226021, UP, India

Supriya Singh

Department of Soil Science & Agricultural Chemistry, Banaras Hindu University, Varanasi, 221005, India

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Ram, A., Tiwari, S.K., Pandey, H.K. et al. Groundwater quality assessment using water quality index (WQI) under GIS framework. Appl Water Sci 11 , 46 (2021). https://doi.org/10.1007/s13201-021-01376-7

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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  

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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 ].

figure 1

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.

figure 2

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).

figure 3

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.

figure 4

Piper trilinear diagram for the groundwater samples

figure 5

Concentration of selected physio-chemical parameters

figure 6

Concentration of major elements

figure 7

Concentration of trace element

figure 8

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

figure 9

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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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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New research paper says uranium mining near Grand Canyon threatens regional groundwater

The head frame of the Pinyon Plain Mine, located less than 10 miles from the South Rim of the Grand Canyon within the Baaj Nwaavjo I’tah Kukveni Grand Canyon National Monument, on Sept. 8, 2023. The mine's owner, Energy Fuels Resources, said in late December 2023 that it had begun producing uranium ore at the site that for decades has drawn strong opposition from tribes and environmental groups.

Researchers at the University of New Mexico say uranium mining near the Grand Canyon could pose a greater threat to groundwater than previously shown. In a recent paper , they call for a halt in mining operations.

The scientists say aquifers and other groundwater systems near the canyon are interconnected in ways that aren’t totally understood. As a result, uranium mining and other contaminants could threaten public health and the environment, and impact the Grand Canyon’s springs along with the Havasupai Tribe’s sole water source and other tribal sacred sites.

The Pinyon Plain Mine near the South Rim began producing ore in early 2024 and the researchers warn uranium mining represents a quote “considerable risk of contamination” in parts of the regional aquifer system. The paper continues, "... the authors favor abundant caution and no mining in this sensitive region."

"This is unsurprising for anyone who has looked at the mixing of rivers, but similar processes are more hidden and incompletely understood in groundwater," said lead author Karl Karlstrom in a press release. "Water flows down gradient, and fault pathways control where groundwater ponds in sub-basins. In the Grand Canyon region, these sub-basins are each vented by major springs on tribal or Park lands."

The mine is located within the Baaj Nwaavjo I'tah Kukveni - Ancestral Footprints of the Grand Canyon National Monument declared last summer by President Joe Biden. It banned new uranium mining claims on almost a million acres but the Pinyon Plain Mine was exempt from the designation.

Curtis Moore, senior vice president for marketing and corporate development for Pinyon Plain’s owner, Energy Fuels Resources, says previous studies have shown there are no faults or fractures near the mine, and that it poses no risk to groundwater.

"The UNM report does not reveal any new science or facts," says Moore. "… Scientists know there no faults, fractures, or similar conduits near the Pinyon Plain mine. It is also known that there is no real potential for the mine to even contaminate the perched groundwater zones. The science remains crystal clear: the risk to groundwater is zero."

The Arizona Department of Environmental Quality, which issued the mine’s aquifer protection permit in 2022, says it’s reviewing the study but that the operation is among the most highly regulated uranium mines in the nation.

"Studied, scrutinized, and litigated for over 30 years, the mine has an extensive record," says ADEQ spokesperson Caroline Oppleman. "The record demonstrated, and ADEQ agreed, that adverse impacts to groundwater from the mine are extremely unlikely."

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