Spatial Variation in groundwater quality and Health Risk Assessment for Fluoride and Nitrate in Chhotanagpur Plateau, India

Spatial Variation in groundwater quality and Health Risk Assessment for Fluoride and Nitrate in Chhotanagpur Plateau, India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial Variation in groundwater quality and Health Risk Assessment for Fluoride and Nitrate in Chhotanagpur Plateau, India Heena Sinha, Suresh Chand Rai, Sudhir Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2472932/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The evaluation of groundwater quality is extremely important to assess the risk to human health. This study deals with the spatial variation in physico-chemical parameters of groundwater for drinking purposes and human health risk assessment concerning fluoride and nitrate. GIS techniques have been used to determine and delimit zones of pollution. Samples were collected in the post-monsoon season (November 2020) and analyzed for physico-chemical parameters such as pH, TDS, conductivity, cations, and anions. For drinking water quality assessment, analyzed parameters were compared with WHO standards, and Water Quality Index (WQI) was used. Results reveal that the majority of the samples come within the desired limit suggested by WHO. However, in a few samples, EC, TDS, TH, chloride, sulphate, and calcium are higher than the desirable limit, whereas fluoride and nitrate are beyond the maximum permissible limit in some of the samples. To assess health risk, the Hazard quotient (HQ) and total hazard index (THI) were computed. The results indicate that the total non-carcinogenic risk for children, male and female ranges from 0.01 to 7.46 for males, 0.009 to 7.055, and 0.01 to 7.34 for children respectively. Furthermore, 84%, 78%, and 82% of the samples are greater than the recommended limit of THI > 1 for males, females, and children respectively, suggesting detrimental impacts on the health of the residents. Knowledge of spatial variation and anomalous concentration is vital for groundwater management as well as health risk assessment. The findings of this study will be helpful to government officials, policy planners, NGOs, and local communities. Groundwater chemistry Water Quality Index Fluoride Nitrate Health Risk Assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Groundwater is the largest source of drinking water (Gleick 1996 ). The extensive withdrawal of groundwater for economic and domestic purposes has accelerated the rate of depletion of groundwater levels (Das and Mukhopadhyay 2018 ). Anthropogenic activities have led to a decline in groundwater levels (Sinha and Rai 2021 ) as well as resulted in groundwater pollution in several countries and regions (Adimalla et al. 2018 , Li et al. 2017 ). Water quality as a term is used to denote the suitability of water to sustain various uses. Water quality has become an increasing environmental issue globally and demands continuous monitoring of several physicochemical parameters, like anions, cations, and heavy metals (Tiwari et al. 2018 ). As a result of varying geological and lithological factors, each groundwater system develops a unique geochemistry. These factors include rainfall, mineral composition of the aquifer, and soil type (Singh et al. 2018). Human activities also influence the geochemistry of groundwater (Chitradevi and Sridhar 2011). There has been growing literature on the assessment of groundwater quality. Studies conducted in parts of Delhi revealed that except for a few chemicals including chloride and potassium, the concentration of other chemicals is high in the groundwater of Delhi (Gupta and Sarma 2016). In Bist Doab basin groundwater was found unsuitable for domestic use due to chemical leaching from fertilizers, pesticides, and agricultural and industrial wastes (Gautam et al. 2021). Some studies involving the geochemical characterization of groundwater have been carried out in parts of the Chhotanagpur Plateau by Singh et al. 2012 , Prasad et al. 2014 , and Tiwari et al. 2016 . Groundwater quality and human health are deeply intertwined, and the usage of polluted groundwater has negative health ramifications for human beings (Xiaogang et al. 2021). For water quality evaluation, many methods are used. Besides the use of conventional methods for the evaluation of water quality, newer approaches like an assessment of health risks caused due to pollution are also used. Health risk assessment is a key measure that correlates specific environmental pollution with the health of human beings (Rahman et al. 2017). Most of these health risk assessments are based on the US Environment Protection Agency (USEPA). While the genesis of human health assessment (HHRA) has been from environmental risk assessment, it has now evolved independently from the environmental discipline. Human health risk assessments are defined by the US EPA 2022 as “the process to estimate the nature and probability of adverse health effects in humans who may be exposed to chemicals in contaminated environmental media, now or in the future”. Globally environmental researchers have a prime focus on the health risk assessment of humans through the consumption of drinking water (Spayd et al. 2012 ). Such assessment has been carried out in different parts of the world. Studies in parts of South Africa revealed that the groundwater of the Vhembe district has negative implications on health. There is a possible non-carcinogenic risk due to some metals like manganese, zinc, lead, chromium, and cadmium on human health, especially in children. Metals like manganese, copper, and iron pose a carcinogenic risk for children as well as adults (Edokpayi et al. 2018 ). In Ya’an city of Sichuan province, China, groundwater poses carcinogenic as well as a non-carcinogenic risk; the former being much higher. Among the various pollutants, the non-carcinogenic risk is highest due to fluoride in groundwater (Ni et al. 2009 ). Similar studies have been carried out in parts of Dir, Pakistan which reveal that there is a high likelihood that residents of the area are subjected to hazardous pollutants like lead and nickel in the drinking water. The health risk is greater in children and health-deprived people in the area (Ilyas et al. 2017 ). Some studies using HHRA have also been carried out in parts of India; in the Unnao district of UP (Jha et al. 2009 ), Bundelkhand region (Pant et al. 2021 ), Tamil Nadu, and Puducherry (Khan et al. 2021 ). However, such studies have not been carried out in Chhotanagpur Plateau. Some studies involving the geochemistry of groundwater have been carried out in Chhotangapur Plateau. However, the physico-chemical analysis of groundwater quality, suitability for drinking purposes, and human health risk assessment have not been accomplished for the entire Ramgarh and Hazaribagh districts. Thus, the present paper fulfills this research gap. The aims of the present paper are (i) to analyze the spatial variation in physico-chemical characteristics of groundwater, (ii) to assess the suitability of groundwater for drinking purposes, and (iii) to evaluate the human health risk assessment concerning fluoride and nitrate. Further, the study also delineates the areas deemed fit for drinking purposes by delineating groundwater quality zones with the help of indices formulated for assessing water quality. Groundwater monitoring is essential in time and space. It gives an important input for groundwater management. The results of the present study shall help provide holistic information on groundwater quality for drinking purposes and its impact on human health and provide baseline information for future groundwater monitoring. It shall thus, help in estimating the change in chemical quality with time and space. The study shall be highly relevant to policy planners for the proper intervention of groundwater management as well as preventing negative health implications. Materials And Methods Study area The current study is centred on the Ramgarh and Hazaribagh districts (Fig. 1 ), which cover around 5653 sq. km. area of the Chhotanagpur Plateau. The total population of the study area is 2,683,938, out of which 51.6% and 48.4% are males and females, respectively (Census of India 2011). The population in the 0–6 years cohort is 4,16,089, which is around 15.5% of the total population. Most of the people are engaged in mining and agriculture activities. The area consists of surfaces of varying elevations and reveals configurations of varying altitude, dimension, and magnitude. The elevation in the area ranges from 172 to 1049m. The highest peak is Marang Buru (meaning great mountain) hill, attaining a height of 1049m. Rajrappa waterfall is an important waterfall situated in the area. The area is covered with mixed forest comprising both deciduous and evergreen species, in addition to thorny species. The highest average temperature is in May (mean 31.1°C), while the lowest occurs in January (mean 16°C). The rainfall in the study area ranges from 5mm in December to 331mm in July. Sampling To analyze the spatial variation in groundwater quality, samples were collected from 73 different locations (Fig. 1 ) during the post-monsoon season of 2020 in the Ramgarh and Hazaribagh districts. The sampling was carried out by dividing the area into grids of 10 km × 10km dimension, such that the samples adequately represent the entire area. In each grid, at least one sample was collected. Further, more samples were collected in specific areas like urban and mining areas. The samples were collected from hand pumps and bore wells as these are the sources of drinking water in the area. The geographical locations of samples were recorded with the help of the Global Positioning System (GPS). The physical parameters of groundwater which include temperature, pH, and EC were recorded at the time of fieldwork with the help of portable toolkits. Thereafter, the samples were stored in a refrigerated condition at 4° C and then carried to the laboratory of the Hydrological Investigation Department of the National Institute of Hydrology, Roorkee, India for further analysis. Water Quality For Drinking Purposes To assess the quality of water for drinking purposes, each of the parameters was compared with the standards prescribed by WHO for drinking water. Spatial variation maps were also prepared. While each physico-chemical parameter has an individual desirable concentration as per national and international standards, it is not sufficient to comprehend the overall quality of groundwater for drinking purposes. For overall water quality evaluation, many methods are used. Some of the prominently used ones are the factor analysis method, artificial neural network, fuzzy mathematics method, and water quality index method. All of these methods have their advantages and disadvantages (Xiaogang et al. 2021). The water quality index method is regarded as the most appropriate for the analysis of water quality (Sadat-Noori et al. 2014 ). It is a widely used and acceptable technique used for water quality analysis. It is difficult to express the quality of groundwater because a large number of variables determine the water quality. WQI resolves this difficulty by translating a large number of variables to a numerical value, which shall be essential in communicating the status of water resources in terms of their quality. Hence, to get a comprehensive picture of the overall quality of groundwater, WQI has been used in the present study. To calculate the WQI, the following steps were used. Firstly, each of the physico-chemical parameters was assigned a weight from 1 to 5 depending upon their relative significance in drinking water quality. The quality rating scale for n th parameter is calculated using the Eq. ( 1 ) $${Q}_{n}=\frac{{C}_{n}}{{S}_{n}}\times 100$$ 1 Where, C n and S n is the observed value and standard value of n th parameter, respectively. In the present study, quality standard has been assigned as per WHO standard. The unit weight (W n ) for n th parameter is thereafter calculated using Eq. ( 2 ), where, w n is the weight assigned to the n th parameter. $${W}_{n}=\frac{{w}_{n}}{{{\sum }_{n}w}_{n}}$$ 2 Finally, the water quality index (WQI) is calculated using Eq. ( 3 ) $$WQI=\frac{\sum {Q}_{n}{w}_{n}}{\sum {w}_{n}}$$ 3 The details of weight, unit weight, and quality standards have been furnished in Table 1 . Parameters Quality Standard Weight Unit Weight Table 1 Quality Standard, Weight and Unit Weight for calculation of WQI for groundwater samples in Hazaribagh Plateau pH 6.5–8.5 4 0.093 EC 500 4 0.093 TDS 300 5 0.116 TH 200 5 0.116 Fluoride 1.5 5 0.116 Chloride 250 1 0.023 Sulphate 250 3 0.070 Nitrate 50 5 0.116 Bicarbonate 120 3 0.070 Sodium 200 3 0.070 Potassium 12 1 0.023 Magnesium 50 2 0.047 Calcium 75 2 0.047 43 1 Human Health Risk Assessment Fluoride and nitrate have already been declared as non-carcinogenic pollutants (USEPA 2014). Therefore, in the present study, the concentration of fluoride and nitrate ions in the groundwater of the Chhotanagpur Plateau has been taken into consideration to assess the risk to human health. The quantitative risk due to the ingestion of fluoride and nitrate in groundwater is calculated through the exposure dose and hazard quotient for individual ions. Thereafter, the total hazard index is calculated by considering both these ions. Calculation Of Exposure Dose The first step which involves the calculation of exposure dose is carried out using Eq. 4. Where, DE is the exposure dose of fluoride and nitrate through ingestion of water (mg/kg/day) C p is Average concentration of pollutants in groundwater (mg/l) ED is exposure duration (years) IR is the Rate of ingestion (l/day) EF is Exposure frequency (days/year) ABW is average body weight (kg) AET is average age exposure time (years) The standard limit for the calculation of the hazard quotient has been furnished in Table 2 . Table 2 Standard limit of Hazard Quotient assessment Parameters Unit Male Female Children Ingestion rate IR l/day 2.5 2 0.7 Exposure Duration ED Years 64 67 12 Exposure frequency EF Day/year 365 365 365 Average Body weight ABW Kg 65 55 15 Average age exposure time AET days 23360 24455 4380 Estimation Of Hazard Quotient The next step involves the estimation of the hazard quotient (HQ) for each of the ions; fluoride and nitrate, and is computed using Eq. 5 $$HQ=\frac{DE}{RfD} \left(5\right)$$ Where, DE represents exposure dose through ingestion of water (mg/l/day) And, Rfd is the oral reference dose mg/l/day. The RfD for nitrate and fluoride is 1.6 and 0.06mg/kg/day respectively. These values have been collected from the Integrated Risk Information System database (IRIS, US Environmental Protection Agency 2012). In the case of non-carcinogenic risk, two conditions are likely to arise. If the value of HQ is greater than 1, it implies adverse non-carcinogenic effects of concern. On the other hand, if the calculated values of HQ are less than 1, the value is of an acceptable level, suggesting non-carcinogenic effects which have no concern. Calculation Of Total Hazard Index The total Hazard Index (THI) is the overall possibility of non-carcinogenic health effects due to more than one ion. It is calculated as the sum of the computed HQs across different ion, given by Eq. 6. THI=∑HQ (6) Where, THI is Total Hazard Index. In the present study, the THI is considered concerning the sum of HQ for fluoride and nitrate. If the calculated THI is less than 1, it implies no chronic risk for that site. However, if the calculated value of THI is greater than 1, it implies that exposure to groundwater has the likelihood of a negative effect on human health. Groundwater Quality Zoning This aspect was carried out in Arc GIS Software. Firstly, a point map of 73 groundwater samples was prepared. The maps of the spatial distribution of physico-chemical characteristics were prepared in Arc GIS software using the spatial analyst module. The IDW technique was used for this purpose. Similarly, a map of WQI was prepared. The WQI map was then reclassified into various zones depending on the type of drinking water. Then the area of each zone was calculated with the help of map algebra. Similarly, maps showing the spatial variation in human health risks due to fluoride and nitrate were prepared. Result And Discussion Spatial Variation in Physico-Chemical Characteristics of Groundwater The analytical results obtained during physico-chemical analysis were compared with WHO standards to understand their suitability for drinking purposes. Physical Parameters Results reveal that pH lies between 6.3 and 8.1, which is acceptable for drinking purposes (Fig. 2 a). pH in groundwater is less than the desired value suggested by WHO only in Central Saunda (6.3), which indicates that groundwater is slightly acidic. EC in the area ranged between 138 and 1154 µS/cm. Around 32% of the groundwater samples exceed the desirable limit (500 µS/cm). The spatial distribution reveals that EC is low in the northeast and southern part of the study area, whereas it is high in the northwest part of the area (Fig. 2 b). Extremely high EC (> 1000 µS/cm) is reported in 4% of the samples which include Ichat Bazar (1035 µS/, 662mg/l), Barhi (1154 µS/cm, 739mg/l) and Siyarkoni (1103 µS/cm, 706mg/l). The TDS in the area ranges from 88 to 739. Around 36% of the samples report TDS greater than 500, with the highest values being reported in Barhi (739mg/l) (Fig. 2 c). Around 66% of the samples report TH higher than 200mg/l, which is of great concern. Chauparan reports TH greater than 600 (Fig. 2 d) which is more than the maximum permissible limit suggested by WHO. Major Cation Chemistry Calcium concentration in the groundwater of the area ranges from 10 to 161 mg/l. 37% of the samples have calcium values more than the desirable limit (75mg/l) suggested by WHO. Such high concentrations result in kidney stones as well as cardiovascular diseases. The spatial variation reveals higher concentrations in the southern part of the study area in Gola, and central parts in Daru (Fig. 3 a). Concentration of magnesium is more than the desirable limit (50mg/l) in the sites in Hazaribagh (50.4mg/l), Ichakdih (50.57mg/l) and Siyarkoni (84mg/l). The northwestern part has the highest values of magnesium (Fig. 3 b). 95% of the sampling sites have a concentration of magnesium within the desirable limit suggested by WHO. The groundwater of the area contains sodium in desirable quantity (5-105mg/l), making it fit for human consumption. The northwestern part of the area contains a relatively higher concentration of sodium than the rest of the areas (Fig. 3 c). Concentration of potassium in the groundwater is rather low for a large part of the study area. The lowest concentration is observed in the north-west and southern part of the area (Fig. 3 d). Higher than permissible concentrations of potassium have been observed in Pakrih Barwadih (18mg/l), Pahej (12.99mg/l), Ichakdih (12.45mg/l) and Kharanti (26mg/l), which may have a laxative effect on the human. The spatial variation of ammonium in groundwater in the area reveals decreasing values of ammonium from north to south (Fig. 3 f). The northern part of the area, comprising of Hazaribagh district has comparatively higher concentrations of ammonium with the highest value being observed in Jhumra (4.43mg/l). Major Anion Chemistry The concentration of bicarbonate in the study area is within the desirable limit suggested by WHO. The spatial distribution of bicarbonate in the area reveals higher values in the western and northwestern part of the area (Fig. 4 c). The concentration of chloride (mean 71mg/l), is within the desirable limit suggested by WHO in 98% of the samples. Similar concentrations of chloride have also been observed in Dumka and Jamtara districts (Singh et al. 2012 ), and parts of Udaipur in Rajasthan (Bhuiyan and Champati Ray 2017 ). The concentration of chloride is low in the southern and northeastern parts of the area. The higher concentration is observed in only certain pockets in sample 76. (Fig. 4 a) may be attributed to contamination by untreated mine waste effluents. The primary objection to the presence of excessive chlorides in drinking water is that it imparts a salty taste to water. Chlorides in drinking water are not normally detrimental to health, although high concentrations may be harmful to some people suffering from heart or kidney diseases (McKee and Wolf 1963 ). While sulphate alters physical attributes like smell and taste, it also has a detrimental impact on human consumption like cathartic effects. The concentration of sulphate (1-273mg/l) in the groundwater of the area is within the desirable limits as per WHO norms in 98% of the samples. This concentration is spatially diverse; higher concentration can be observed in the southern part of the area (Fig. 4 e). This may be attributed to the weathering of sulphide ores, gypsum, and anhydrite (Todd and Mays 2005), and the presence of coal mining in the area. This reflects the anthropogenic influence on the geochemistry of groundwater. Phosphate is negligible in the majority of the groundwater samples. It ranges from 0.023 to 3.5mg/l, having higher concentrations in the south and southeastern part of the area (Fig. 4 f). The concentration of fluoride ranges from 0.2 to 6.72 mg/l. Fluoride is present beyond the permissible limit (1.5mg/l) suggested by WHO in 70% of the samples. Previous studies conducted in parts of Hazaribagh have also revealed higher concentrations of fluoride (1.89–3.84 mg/l) in groundwater (Kumar and Sadhu 2013 ). The concentration of fluoride is higher in the southeast part, and lowest in the northwestern part of the area (Fig. 4 b). Higher fluoride could be due to the weathering of fluoride-bearing minerals including biotite, fluorite, and apatite, which are present as secondary minerals in granite and granitic gneiss rocks of the area (Singh et al. 2010). In the study area, the concentration of nitrate in groundwater ranged from 0.41 to 273.69 mg/l. 64% of the samples contain nitrate more than the permissible limit suggested by WHO. The spatial distribution of nitrate reveals pockets of high concentration in the northern and central part of the area (Fig. 4 d). Higher concentrations may be due to biological fixation, application of fertilizers and pesticides as well as sewage from industries. Groundwater Quality For Drinking Purposes The analysis of the concentration of individual ions concerning WHO standards is vital, but it is also observed that water is found suitable for one ion and unsuitable for the other. Thus, to understand the overall suitability of water for drinking purposes, the present study analyzes its quality involving a combination of various physical and chemical parameters with the help of WQI. As per WQI, good-quality water dominates the area (Table 3 ). This comprises 4100.38 sq. km. of area, which is around 84% of the total area. Excellent water is observed in around 10% of the area, comprising 42.65 sq km of the area. A similar study conducted in Bist Doab region of Punjab revealed the presence of excellent water in one-third of the area (Gautam et al. 2021). 23% of the samples covering 751.56 sq. km. of the area consist of poor-quality water covering the area of Daru, Tatijhariya, Keredari, Barkagaon, Chalkusa, and Gola. Poor-quality water is present in 0.77% of the area comprising the Chauparan block. The higher concentrations of EC, TH, chloride, fluoride, and nitrate impart poor quality to the groundwater of these regions. Spatial variation of WQI (Fig. 5 ) reveals that the eastern and southern part of the area comprises water suitable for drinking, while the central and northwestern part of the area has a poor-quality of groundwater. Table 3 Classification of groundwater as per Water Quality Index Water Type WQI No. of samples Excellent Water 300 0 Human Health Risk Assessment In countries like India, most people depend on groundwater as a source for drinking purposes. Consequently, the presence of fluoride and nitrate in groundwater poses a non-carcinogenic risk to human health, which has become a serious issue. The residents of the area depend on groundwater for drinking purposes (Heena and Rai 2020). The concentration of fluoride and nitrate are beyond the permissible stipulations of WHO in most of the samples. On account of that, it becomes extremely vital to assess the health risk due to fluoride and nitrate using the universally established criteria for fluoride and nitrate. The present study computes the non-carcinogenic health risks due to nitrate and fluoride for different sections of the population (male, female, and children). Exposure Dose The exposure dose of fluoride and nitrate was calculated for males, females and children. The exposure dose of nitrate in males ranged from 0.015 to 10.52 (mean 1.55) for males, 0.014 to 9.95 (mean 1.47) for females, and 0.01 to 12.7 (mean 1.88) for children (Table 4 ). Higher values of nitrate were observed in Chauparan, while the least exposure of nitrate was noticed at Dadi. The exposure dose of fluoride in males ranged from 0.009 to 0.25 (mean 0.06), 0.0093 to 0.24 (mean 0.05) in females (Table 4 ). The range of exposure dose is 0.01 to 0.31 (mean 0.07) in children, which is similar to that observed in Agra (0.07-0.31mg/kg/day) (Yadav et al. 2019). The highest exposure level of fluoride was observed in Barkagaon, while the least exposure to fluoride was noticed in Churchu. Table 4 Computation of Exposure duration Male Female Children EDF EDN EDF EDN EDF EDN Minimum 0.009 0.015 0.09 0.014 0.01 0.01 Maximum 0.25 10.52 0.24 9.95 0.31 12.7 Mean 0.06 1.55 0.05 1.47 0.07 1.88 Hazard Quotient And Total Hazard Index The hazard quotient for nitrate varied from 0.009 to 6.57 for males (mean 0.97) (Table 5 ), which is lower than observed values in South India (1.71) (Karunanidhi et al. 2019 ). The observed hazard quotient varies from 0.009 to 6.22 for females (mean 0.91) (Table 5 ) and 0.01 to 7.98 for children (mean 1.17), which is higher than values observed in Thoothukudi district, Tamil Nadu (mean 0.9) (Selvam et al. 2021). The calculated values of HQ are greater than 1 in 31.43% of the samples for males and children, while 30% of the samples in females. As the northern and central parts of the area have a higher concentration of nitrate, the health risk is also higher in that area. Nitrate concentrations higher than 11 ppm in the body may be the cause of anoxemia, asphyxia, and blue baby disease. It could have such negative implications that it may even cause death to infants (< 4 months old). Excess nitrate could have equally negative repercussions for older infants and adults, as it can be the cause of gastric cancer (Comly 1945 ; Gilly et al. 1984 ). Table 5 Computation of Hazard Quotient for adult male, females and children Male Female Children HQF HQN HQT HQF HQN HQT HQF HQN HQT Minimum 0.16 0.01 0.01 0.16 0.01 0.01 0.20 1.18 0.01 Maximum 4.31 6.58 7.46 4.07 6.22 7.06 5.23 0.01 9.05 Mean 1.00 0.97 1.89 0.95 0.92 1.79 1.22 7.98 2.30 The hazard quotient for fluoride varied from 0.16 to 4.30 for males (mean 1.01), and 0.15 to 4.07 for females (mean 0.95) (Table 5 ). The hazard quotient calculated for children lies in the range of 0.19 to 5.22 (mean 1.22) (Table 5 ), which is higher than those observed in South India (0.01 to 3.25) (Karunanidhi et al. 2019 ). The hazard quotient is more than the reference dose in 38.5% of the samples in males and children, and 37.14% of the samples in females. This suggests that the majority of the residents of the area are prone to dental and skeletal fluorosis. Previous studies conducted in parts of Hazaribagh reveal the presence of dental fluorosis among children (Kumar and Sadhu 2013 ). This reveals the grim situation of the health of the residents of the area. The presence of fluoride in water in concentrations less than 0.5mg/l creates another problem; it promotes dental carries in children especially in the formative stages of permanent teeth (Bhattacharya 1988), osteoporosis, and growth retardation. In terms of general health, in communities where drinking water is excessively high in fluoride, the most prominent adverse effects are skeletal fluorosis and bone fracture, low IQ, and deformities in growth, especially in infants (Marshall et al. 2004 ). As per USEPA, the Total Hazard Index (THI) should not exceed 1, as it denotes non-carcinogenic risk to human health. The THI ranges from 0.01 to 7.46 (mean 1.89) for males (Table 5 ), which is higher than the Thoothukudi district, Tamil Nadu (1.6). THI in the area ranges from 0.009 to 7.05 (mean 1.79) and 0.01 to 9.05 (mean 2.3) (Table 5 ) for females and children, respectively. Assessment of non-carcinogenic danger based on THI indicates that 83.56%, 78.08%, and 89.04% of the samples surpass the allowable limit for males, females, and children respectively. The vulnerability of total hazard is maximum in children followed by females and then males. Studies conducted in Shanmuganadhi in South India (Karunanidhi et al. 2019 ) also reveals that children are most vulnerable to health risk. Spatial variation in the overall non-carcinogenic risk as depicted by THI is maximum in Chauparan and minimum in Dadi. Lower values are observed in the northwestern part of the study area (Fig. 6 a,b,c). Conclusion The present study brings to light the groundwater quality and assesses its suitability for drinking purposes. For drinking water quality assessment, analyzed parameters were compared with WHO standards, and Water Quality Index (WQI) was used. Results reveal that the majority of the samples come within the desired limit suggested by WHO. However, in a few samples, EC, TDS, TH, chloride, sulphate, and calcium are higher than the desirable limit, whereas fluoride and nitrate are beyond the maximum permissible limit in some of the samples. The physico-chemical characteristics of the groundwater are due to geogenic as well as anthropogenic factors. Results of WQI for the area highlight that the groundwater in the area is of excellent to very-poor quality, with good water being the most predominant one. While the groundwater is termed suitable for drinking purposes, however, poor water and very poor-quality water pose a health risk to the residents of the area. Poor and very poor-quality water is present in around 25% of the total samples. The spatial variation map of WQI reveals that the central and northwestern part of the area has poor and very-poor quality groundwater. The study also assesses the human health risks of fluoride and nitrate. For this purpose, the hazard quotient and total hazard index were computed. The study highlights that non-carcinogenic danger based on THI indicates that 83.56%, 78.08%, and 82.19% of the samples surpass the allowable limit for males, females, and children respectively. Children are most vulnerable to negative health implications. For the good health of the residents of the area, groundwater must require proper remediation strategies before consumption. It is also essential to be mentioned that coal mining areas restrict the suitability of groundwater for drinking purpose and demands special management. Thus, before the problem becomes acute and irreversible, appropriate management strategies need to be implemented so that the water resource of the area is managed sustainably. References Adimalla N, Li P, Venkatayogi S (2018) Hydrogeochemical evaluation of groundwater quality for drinking and irrigation purposes and integrated interpretation with water quality index studies. 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(2021) Human health risk assessment for fluoride and nitrate contamination in the groundwater: a case study from the east coast of Tamil Nadu and Puducherry, India. Environ Earth Sci 80, 724 https://doi.org/10.1007/s12665-021-10001-4 Karunanidhi D, Aravinthasamy P, Subramani T, Jianhua Wu, Srinivasamoorthy K (2019) Potential health risk assessment for fluoride and nitrate contamination in hard rock aquifers of Shanmuganadhi River basin, South India, Human and Ecological Risk Assessment: An International Journal, DOI: 10.1080/10807039.2019.1568859 Khan AF, Srinivasamoorthy K, Prakash R et al. (2021) Human health risk assessment for fluoride and nitrate contamination in the groundwater: a case study from the east coast of Tamil Nadu and Puducherry, India. Environ Earth Sci 80, 724. https://doi.org/10.1007/s12665-021-10001-4 Kumar R, Sadhu, DN (2013) Assessment of Drinking water quality in tribal dominated villages of Barkagaon, Hazaribag, Jharkhand, India. European Scientific Journal, Vol 9(35), pp 331-338. Li P, Tian R, Xue C, Wu J (2017) Progress, opportunities and key fields for groundwater quality research under the impacts of human activities in China with a special focus on western China. Environ Sci Pollut Res 24(15):13224–13234. https ://doi.org/10.1007/s1135 6-017-8753-7 Marshall TA, Levy SM, Warren JJ, Broffitt B, Eichenberger-Gilmore JM, Stumbo PJ (2004) Associations between intakes of fluoride from beverages during infancy and dental fluorosis of primary teeth. J Am College Nutri 23(2):108–116 McKee JE, Wolf HW (1963) Water Quality Criteria. California: State Water Quality Control Board Publication. Ni F, Liu G, Ren H, Yang S, Ye J, Lu X, and Yang M (2009) Health Risk Assessment on Rural Drinking Water Safety-A Case Study in Rain City District of Ya’an City of Sichuan Province. Journal of Water Resource and Protection 2: 128-135. Pant N, Rai SP, Singh R,Kumar S, Saini RK,Purushothaman P, Nijesh P, Rawat Y,Sharma M, Kamaleshwar P (2021). Impact of geology and anthropogenic activities over the water quality with emphasis on fluoride in water scarce Lalitpur district of Bundelkhand region, India. Chemosphere. 279. 10.1016/j.chemosphere.2021.130496. Prasad B, Kumari P, Bano S, Kumari S (2014) Ground water quality evaluation near mining area and development of heavy metal pollution index. Appl Water Sci 4(1):11–17 Ramakrishnaiah CR, Sadashivaiah C, Ranganna G (2009) Assessment of water quality index for the groundwater in Tumkur Taluk, Karnataka state, India. E-J Chem 6(2):523–530 Sinha H, Rai SC (2021) Evaluating geologic and anthropogenic impacts on groundwater level dynamics in Chhotanagpur Plateau, India. Arab J Geosci 14, 1043. https://doi.org/10.1007/s12517-021-07298-7 Sadat-Noori SM, Ebrahimi K, Liaghat AM (2014) Groundwater quality assessment using the water quality index and GIS in saveh-nobaran aquifer, Iran,” Environmental Earth Sciences , vol. 71, no. 8, pp. 3827–3843. Singh AK, Mondal GC, Singh TB, Singh S, Tewary BK, Sinha A (2012) Hydrogeochemical processes and quality assessment of groundwater in Dumka and Jamtara districts, Jharkhand, India. Environ Earth Sci 67(8):2175–2191 Soltan ME (1999) Evaluation of groundwater quality in Dakhla Oasis (Egyptian Western Desert). Environ Monit Assess 57(2):157–168 Spayd SE, Robson MG, Xie R (2012) Importance of Arsenic speciation in population exposed to arsenic in drinking water, Hum. Ecol. Risk. Assess., 18, 1271–91 Tiwari AK, De MaioM, Singh PK, Singh AK (2016) Hydrogeochemical characterization and groundwater quality assessment in a coal mining area, India. Arab J Geosci 9(3):1–17 Tiwari AK, Singh AK, Mahato MK (2018) Assessment of groundwater quality of Pratapgarh district in India for suitability of drinking purpose using water quality index (WQI) and GIS technique. Sustain. Water Resour. Manag. 4, 601–616. https://doi.org/10.1007/s40899-017-0144-1 U.S. EPA (U.S. Environmental Protection Agency), 2022. Human Health Risk Assessment. https://www.epa.gov/risk/human-health-risk-assessment. Vasanthavigar M, Srinivasamoorthy K, Vijayaragavan K, Rajiv Ganthi R, Chidambaram S, Anandhan P, Manivannan R, Vasudevan S (2010) Application of water quality index for groundwater quality assessment: Thirumanimuttar sub-basin, Tamil Nadu, India. Environ Monit Assess 171(1–4):595–609 Xiaogang Fu, Zihan Dong, Shuang Gan, Zhe Wang, Aihua Wei (2021) Groundwater Quality Evaluation for Potable Use and Associated Human Health Risk in Gaobeidian City, North China Plain Journal of Chemistry Article ID 3008567, 15 https://doi.org/10.1155/2021/3008567 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2472932","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":167554051,"identity":"67899b43-a4ff-4801-a8da-2799eb2c9af0","order_by":0,"name":"Heena Sinha","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Heena","middleName":"","lastName":"Sinha","suffix":""},{"id":167554052,"identity":"28bb8706-4e85-4376-9d20-387f62d630f2","order_by":1,"name":"Suresh Chand Rai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYFACxgYGBgOben6GBMYHQC4PH3FaCtISJBsSmA1AWtiIs+nD4QSDAwlsEiA2QS3m7YebP/wwOJwn2Z5jVvk1x06GjYH54aMbeLTInElsk+wxSC/m53ljdlt2WzLQYWzGxjl4tEgwJLYxMxhYM86ckWN2W3IbM1ALD5s0Xi38D5s/MxgwM264kWNWLLmtnggtEokN0gwGzokgLYwftx0mRstDkF/SjCV7nhVLM247zsPGTMgv/OmPP/z4YyPHz5688ePPbdX2/OzNDx/j04ICmHnAJLHKQYDxBymqR8EoGAWjYMQAACnaRSGx8f4hAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Suresh","middleName":"Chand","lastName":"Rai","suffix":""},{"id":167554053,"identity":"130edd24-39b9-4190-9b8b-c06b7d40dd48","order_by":2,"name":"Sudhir Kumar","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sudhir","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2023-01-13 00:44:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2472932/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2472932/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":31687052,"identity":"8721f05a-d166-45e8-8463-94d0493b55d6","added_by":"auto","created_at":"2023-01-17 14:52:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":408086,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of study area and groundwater sampling sites in Chhotanagpur Plateau (modified after Sinha and Rai 2021).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2472932/v1/ae94d25f5e53ef3883206839.png"},{"id":31687841,"identity":"ab3f9647-34f7-4c3d-abac-a2f5cd2743da","added_by":"auto","created_at":"2023-01-17 15:00:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":849777,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of (a) pH (b) EC (c) TDS and (d) TH in Chhotanagpur plateau\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2472932/v1/40a7b417bf64eeb16fbb1378.png"},{"id":31687053,"identity":"990647ef-a8f2-4207-8edc-92878e505386","added_by":"auto","created_at":"2023-01-17 14:52:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1200761,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of cations (a)Ca (b) Ng (c) Na (d) K (e) Li and (f) NH\u003csub\u003e4\u003c/sub\u003e in Chhotanagpur plateau\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2472932/v1/f45b508feacb8750452439d3.png"},{"id":31687050,"identity":"02218d63-11d7-42c4-8621-a06410feddbb","added_by":"auto","created_at":"2023-01-17 14:52:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1267770,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of anions (a)Cl (b) Fl (c) HCO\u003csub\u003e3 \u003c/sub\u003e(d) NO\u003csub\u003e3\u003c/sub\u003e (e) SO4 and (f) PO\u003csub\u003e4\u003c/sub\u003e in Chhotanagpur plateau\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2472932/v1/8b573b7fae2e8fc2aa732fab.png"},{"id":31688330,"identity":"a7b20639-f1ae-4181-b9eb-3ebfd1b78f8f","added_by":"auto","created_at":"2023-01-17 15:08:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":205260,"visible":true,"origin":"","legend":"\u003cp\u003eGroundwater quality in Hazaribagh Plateau as per WQI\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-2472932/v1/e64dcc613e627525d6f3b9c2.png"},{"id":31687051,"identity":"cb20fd5b-7c9b-4780-b36b-aef32738a46a","added_by":"auto","created_at":"2023-01-17 14:52:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":467183,"visible":true,"origin":"","legend":"\u003cp\u003eTotal Hazard Index for (a)male (b) female and (c)children in Chhotanagpur Plateau\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-2472932/v1/3c56ca0e6172903bde6efeda.png"},{"id":31814744,"identity":"e929653b-5d30-4f1b-9012-3e2155fc9003","added_by":"auto","created_at":"2023-01-19 16:14:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4611037,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2472932/v1/a0ed05f7-f75c-413f-99a2-82c898381b40.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial Variation in groundwater quality and Health Risk Assessment for Fluoride and Nitrate in Chhotanagpur Plateau, India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGroundwater is the largest source of drinking water (Gleick \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The extensive withdrawal of groundwater for economic and domestic purposes has accelerated the rate of depletion of groundwater levels (Das and Mukhopadhyay \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Anthropogenic activities have led to a decline in groundwater levels (Sinha and Rai \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) as well as resulted in groundwater pollution in several countries and regions (Adimalla et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Li et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Water quality as a term is used to denote the suitability of water to sustain various uses. Water quality has become an increasing environmental issue globally and demands continuous monitoring of several physicochemical parameters, like anions, cations, and heavy metals (Tiwari et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As a result of varying geological and lithological factors, each groundwater system develops a unique geochemistry. These factors include rainfall, mineral composition of the aquifer, and soil type (Singh et al. 2018). Human activities also influence the geochemistry of groundwater (Chitradevi and Sridhar 2011). There has been growing literature on the assessment of groundwater quality. Studies conducted in parts of Delhi revealed that except for a few chemicals including chloride and potassium, the concentration of other chemicals is high in the groundwater of Delhi (Gupta and Sarma 2016). In Bist Doab basin groundwater was found unsuitable for domestic use due to chemical leaching from fertilizers, pesticides, and agricultural and industrial wastes (Gautam et al. 2021). Some studies involving the geochemical characterization of groundwater have been carried out in parts of the Chhotanagpur Plateau by Singh et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Prasad et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, and Tiwari et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eGroundwater quality and human health are deeply intertwined, and the usage of polluted groundwater has negative health ramifications for human beings (Xiaogang et al. 2021). For water quality evaluation, many methods are used. Besides the use of conventional methods for the evaluation of water quality, newer approaches like an assessment of health risks caused due to pollution are also used. Health risk assessment is a key measure that correlates specific environmental pollution with the health of human beings (Rahman et al. 2017). Most of these health risk assessments are based on the US Environment Protection Agency (USEPA). While the genesis of human health assessment (HHRA) has been from environmental risk assessment, it has now evolved independently from the environmental discipline. Human health risk assessments are defined by the US EPA 2022 as \u0026ldquo;the process to estimate the nature and probability of adverse health effects in humans who may be exposed to chemicals in contaminated environmental media, now or in the future\u0026rdquo;. Globally environmental researchers have a prime focus on the health risk assessment of humans through the consumption of drinking water (Spayd et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Such assessment has been carried out in different parts of the world. Studies in parts of South Africa revealed that the groundwater of the Vhembe district has negative implications on health. There is a possible non-carcinogenic risk due to some metals like manganese, zinc, lead, chromium, and cadmium on human health, especially in children. Metals like manganese, copper, and iron pose a carcinogenic risk for children as well as adults (Edokpayi et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In Ya\u0026rsquo;an city of Sichuan province, China, groundwater poses carcinogenic as well as a non-carcinogenic risk; the former being much higher. Among the various pollutants, the non-carcinogenic risk is highest due to fluoride in groundwater (Ni et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Similar studies have been carried out in parts of Dir, Pakistan which reveal that there is a high likelihood that residents of the area are subjected to hazardous pollutants like lead and nickel in the drinking water. The health risk is greater in children and health-deprived people in the area (Ilyas et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Some studies using HHRA have also been carried out in parts of India; in the Unnao district of UP (Jha et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), Bundelkhand region (Pant et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Tamil Nadu, and Puducherry (Khan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, such studies have not been carried out in Chhotanagpur Plateau.\u003c/p\u003e \u003cp\u003eSome studies involving the geochemistry of groundwater have been carried out in Chhotangapur Plateau. However, the physico-chemical analysis of groundwater quality, suitability for drinking purposes, and human health risk assessment have not been accomplished for the entire Ramgarh and Hazaribagh districts. Thus, the present paper fulfills this research gap. The aims of the present paper are (i) to analyze the spatial variation in physico-chemical characteristics of groundwater, (ii) to assess the suitability of groundwater for drinking purposes, and (iii) to evaluate the human health risk assessment concerning fluoride and nitrate. Further, the study also delineates the areas deemed fit for drinking purposes by delineating groundwater quality zones with the help of indices formulated for assessing water quality.\u003c/p\u003e \u003cp\u003eGroundwater monitoring is essential in time and space. It gives an important input for groundwater management. The results of the present study shall help provide holistic information on groundwater quality for drinking purposes and its impact on human health and provide baseline information for future groundwater monitoring. It shall thus, help in estimating the change in chemical quality with time and space. The study shall be highly relevant to policy planners for the proper intervention of groundwater management as well as preventing negative health implications.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003eStudy area\u003c/h2\u003e\n \u003cp\u003eThe current study is centred on the Ramgarh and Hazaribagh districts (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), which cover around 5653 sq. km. area of the Chhotanagpur Plateau. The total population of the study area is 2,683,938, out of which 51.6% and 48.4% are males and females, respectively (Census of India 2011). The population in the 0\u0026ndash;6 years cohort is 4,16,089, which is around 15.5% of the total population. Most of the people are engaged in mining and agriculture activities. The area consists of surfaces of varying elevations and reveals configurations of varying altitude, dimension, and magnitude. The elevation in the area ranges from 172 to 1049m. The highest peak is Marang Buru (meaning great mountain) hill, attaining a height of 1049m. Rajrappa waterfall is an important waterfall situated in the area. The area is covered with mixed forest comprising both deciduous and evergreen species, in addition to thorny species. The highest average temperature is in May (mean 31.1\u0026deg;C), while the lowest occurs in January (mean 16\u0026deg;C). The rainfall in the study area ranges from 5mm in December to 331mm in July.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eSampling\u003c/h3\u003e\n\u003cp\u003eTo analyze the spatial variation in groundwater quality, samples were collected from 73 different locations (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) during the post-monsoon season of 2020 in the Ramgarh and Hazaribagh districts. The sampling was carried out by dividing the area into grids of 10 km \u0026times; 10km dimension, such that the samples adequately represent the entire area. In each grid, at least one sample was collected. Further, more samples were collected in specific areas like urban and mining areas. The samples were collected from hand pumps and bore wells as these are the sources of drinking water in the area. The geographical locations of samples were recorded with the help of the Global Positioning System (GPS). The physical parameters of groundwater which include temperature, pH, and EC were recorded at the time of fieldwork with the help of portable toolkits. Thereafter, the samples were stored in a refrigerated condition at 4\u0026deg; C and then carried to the laboratory of the Hydrological Investigation Department of the National Institute of Hydrology, Roorkee, India for further analysis.\u003c/p\u003e\n\u003ch3\u003eWater Quality For Drinking Purposes\u003c/h3\u003e\n\u003cp\u003eTo assess the quality of water for drinking purposes, each of the parameters was compared with the standards prescribed by WHO for drinking water. Spatial variation maps were also prepared. While each physico-chemical parameter has an individual desirable concentration as per national and international standards, it is not sufficient to comprehend the overall quality of groundwater for drinking purposes. For overall water quality evaluation, many methods are used. Some of the prominently used ones are the factor analysis method, artificial neural network, fuzzy mathematics method, and water quality index method. All of these methods have their advantages and disadvantages (Xiaogang et al. 2021). The water quality index method is regarded as the most appropriate for the analysis of water quality (Sadat-Noori et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). It is a widely used and acceptable technique used for water quality analysis. It is difficult to express the quality of groundwater because a large number of variables determine the water quality. WQI resolves this difficulty by translating a large number of variables to a numerical value, which shall be essential in communicating the status of water resources in terms of their quality. Hence, to get a comprehensive picture of the overall quality of groundwater, WQI has been used in the present study.\u003c/p\u003e\n\u003cp\u003eTo calculate the WQI, the following steps were used. Firstly, each of the physico-chemical parameters was assigned a weight from 1 to 5 depending upon their relative significance in drinking water quality. The quality rating scale for n\u003csup\u003eth\u003c/sup\u003e parameter is calculated using the Eq. (\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"Equation\" id=\"Equ1\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$${Q}_{n}=\\frac{{C}_{n}}{{S}_{n}}\\times 100$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere, C\u003csub\u003en\u003c/sub\u003e and S\u003csub\u003en\u003c/sub\u003e is the observed value and standard value of n\u003csup\u003eth\u003c/sup\u003e parameter, respectively. In the present study, quality standard has been assigned as per WHO standard.\u003c/p\u003e\n\u003cp\u003eThe unit weight (W\u003csub\u003en\u003c/sub\u003e) for n\u003csup\u003eth\u003c/sup\u003e parameter is thereafter calculated using Eq. (\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), where, w\u003csub\u003en\u003c/sub\u003e is the weight assigned to the n\u003csup\u003eth\u003c/sup\u003e parameter.\u003c/p\u003e\n\u003cdiv class=\"Equation\" id=\"Equ2\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$${W}_{n}=\\frac{{w}_{n}}{{{\\sum }_{n}w}_{n}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eFinally, the water quality index (WQI) is calculated using Eq. (\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"Equation\" id=\"Equ3\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$WQI=\\frac{\\sum {Q}_{n}{w}_{n}}{\\sum {w}_{n}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe details of weight, unit weight, and quality standards have been furnished in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQuality Standard\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit Weight\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eQuality Standard, Weight and Unit Weight for calculation of WQI for groundwater samples in Hazaribagh Plateau\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\u003c/tbody\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5\u0026ndash;8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFluoride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSulphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBicarbonate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnesium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eHuman Health Risk Assessment\u003c/h3\u003e\n\u003cp\u003eFluoride and nitrate have already been declared as non-carcinogenic pollutants (USEPA 2014). Therefore, in the present study, the concentration of fluoride and nitrate ions in the groundwater of the Chhotanagpur Plateau has been taken into consideration to assess the risk to human health. The quantitative risk due to the ingestion of fluoride and nitrate in groundwater is calculated through the exposure dose and hazard quotient for individual ions. Thereafter, the total hazard index is calculated by considering both these ions.\u003c/p\u003e\n\u003ch3\u003eCalculation Of Exposure Dose\u003c/h3\u003e\n\u003cp\u003eThe first step which involves the calculation of exposure dose is carried out using Eq.\u0026nbsp;4.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003cp\u003eWhere,\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eDE\u003c/em\u003e is the exposure dose of fluoride and nitrate through ingestion of water (mg/kg/day)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eC\u003c/em\u003e \u003csub\u003e\u0026nbsp;\u003cem\u003ep\u003c/em\u003e\u0026nbsp;\u003c/sub\u003e is Average concentration of pollutants in groundwater (mg/l)\u003c/p\u003e\n \u003cp\u003eED is exposure duration (years)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eIR\u003c/em\u003e is the Rate of ingestion (l/day)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eEF\u003c/em\u003e is Exposure frequency (days/year)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eABW\u003c/em\u003e is average body weight (kg)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eAET\u003c/em\u003e is average age exposure time (years)\u003c/p\u003e\n \u003cp\u003eThe standard limit for the calculation of the hazard quotient has been furnished in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStandard limit of Hazard Quotient assessment\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChildren\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIngestion rate IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003el/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExposure Duration ED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExposure frequency EF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDay/year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage Body weight ABW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage age exposure time AET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edays\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eEstimation Of Hazard Quotient\u003c/h3\u003e\n\u003cp\u003eThe next step involves the estimation of the hazard quotient (HQ) for each of the ions; fluoride and nitrate, and is computed using Eq.\u0026nbsp;5\u003c/p\u003e\n\u003cdiv class=\"Equation\" id=\"Equa\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$HQ=\\frac{DE}{RfD} \\left(5\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere,\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDE\u003c/em\u003e represents exposure dose through ingestion of water (mg/l/day)\u003c/p\u003e\n\u003cp\u003eAnd, \u003cem\u003eRfd\u003c/em\u003e is the oral reference dose mg/l/day.\u003c/p\u003e\n\u003cp\u003eThe RfD for nitrate and fluoride is 1.6 and 0.06mg/kg/day respectively. These values have been collected from the Integrated Risk Information System database (IRIS, US Environmental Protection Agency 2012).\u003c/p\u003e\n\u003cp\u003eIn the case of non-carcinogenic risk, two conditions are likely to arise. If the value of HQ is greater than 1, it implies adverse non-carcinogenic effects of concern. On the other hand, if the calculated values of HQ are less than 1, the value is of an acceptable level, suggesting non-carcinogenic effects which have no concern.\u003c/p\u003e\n\u003ch3\u003eCalculation Of Total Hazard Index\u003c/h3\u003e\n\u003cp\u003eThe total Hazard Index (THI) is the overall possibility of non-carcinogenic health effects due to more than one ion. It is calculated as the sum of the computed HQs across different ion, given by Eq.\u0026nbsp;6.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTHI=\u0026sum;HQ\u003c/em\u003e (6)\u003c/p\u003e\n\u003cp\u003eWhere, THI is Total Hazard Index.\u003c/p\u003e\n\u003cp\u003eIn the present study, the THI is considered concerning the sum of HQ for fluoride and nitrate. If the calculated THI is less than 1, it implies no chronic risk for that site. However, if the calculated value of THI is greater than 1, it implies that exposure to groundwater has the likelihood of a negative effect on human health.\u003c/p\u003e\n\u003ch3\u003eGroundwater Quality Zoning\u003c/h3\u003e\n\u003cp\u003eThis aspect was carried out in Arc GIS Software. Firstly, a point map of 73 groundwater samples was prepared. The maps of the spatial distribution of physico-chemical characteristics were prepared in Arc GIS software using the spatial analyst module. The IDW technique was used for this purpose. Similarly, a map of WQI was prepared. The WQI map was then reclassified into various zones depending on the type of drinking water. Then the area of each zone was calculated with the help of map algebra. Similarly, maps showing the spatial variation in human health risks due to fluoride and nitrate were prepared.\u003c/p\u003e"},{"header":"Result And Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSpatial Variation in Physico-Chemical Characteristics of Groundwater\u003c/h2\u003e \u003cp\u003eThe analytical results obtained during physico-chemical analysis were compared with WHO standards to understand their suitability for drinking purposes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePhysical Parameters\u003c/h3\u003e\n\u003cp\u003eResults reveal that pH lies between 6.3 and 8.1, which is acceptable for drinking purposes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). pH in groundwater is less than the desired value suggested by WHO only in Central Saunda (6.3), which indicates that groundwater is slightly acidic. EC in the area ranged between 138 and 1154 \u0026micro;S/cm. Around 32% of the groundwater samples exceed the desirable limit (500 \u0026micro;S/cm). The spatial distribution reveals that EC is low in the northeast and southern part of the study area, whereas it is high in the northwest part of the area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Extremely high EC (\u0026gt;\u0026thinsp;1000 \u0026micro;S/cm) is reported in 4% of the samples which include Ichat Bazar (1035 \u0026micro;S/, 662mg/l), Barhi (1154 \u0026micro;S/cm, 739mg/l) and Siyarkoni (1103 \u0026micro;S/cm, 706mg/l). The TDS in the area ranges from 88 to 739. Around 36% of the samples report TDS greater than 500, with the highest values being reported in Barhi (739mg/l) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Around 66% of the samples report TH higher than 200mg/l, which is of great concern. Chauparan reports TH greater than 600 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed) which is more than the maximum permissible limit suggested by WHO.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMajor Cation Chemistry\u003c/h3\u003e\n\u003cp\u003eCalcium concentration in the groundwater of the area ranges from 10 to 161 mg/l. 37% of the samples have calcium values more than the desirable limit (75mg/l) suggested by WHO. Such high concentrations result in kidney stones as well as cardiovascular diseases. The spatial variation reveals higher concentrations in the southern part of the study area in Gola, and central parts in Daru (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Concentration of magnesium is more than the desirable limit (50mg/l) in the sites in Hazaribagh (50.4mg/l), Ichakdih (50.57mg/l) and Siyarkoni (84mg/l). The northwestern part has the highest values of magnesium (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). 95% of the sampling sites have a concentration of magnesium within the desirable limit suggested by WHO. The groundwater of the area contains sodium in desirable quantity (5-105mg/l), making it fit for human consumption. The northwestern part of the area contains a relatively higher concentration of sodium than the rest of the areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Concentration of potassium in the groundwater is rather low for a large part of the study area. The lowest concentration is observed in the north-west and southern part of the area (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Higher than permissible concentrations of potassium have been observed in Pakrih Barwadih (18mg/l), Pahej (12.99mg/l), Ichakdih (12.45mg/l) and Kharanti (26mg/l), which may have a laxative effect on the human. The spatial variation of ammonium in groundwater in the area reveals decreasing values of ammonium from north to south (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). The northern part of the area, comprising of Hazaribagh district has comparatively higher concentrations of ammonium with the highest value being observed in Jhumra (4.43mg/l).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMajor Anion Chemistry\u003c/h3\u003e\n\u003cp\u003eThe concentration of bicarbonate in the study area is within the desirable limit suggested by WHO. The spatial distribution of bicarbonate in the area reveals higher values in the western and northwestern part of the area (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). The concentration of chloride (mean 71mg/l), is within the desirable limit suggested by WHO in 98% of the samples. Similar concentrations of chloride have also been observed in Dumka and Jamtara districts (Singh et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and parts of Udaipur in Rajasthan (Bhuiyan and Champati Ray \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The concentration of chloride is low in the southern and northeastern parts of the area. The higher concentration is observed in only certain pockets in sample 76. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) may be attributed to contamination by untreated mine waste effluents. The primary objection to the presence of excessive chlorides in drinking water is that it imparts a salty taste to water. Chlorides in drinking water are not normally detrimental to health, although high concentrations may be harmful to some people suffering from heart or kidney diseases (McKee and Wolf \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1963\u003c/span\u003e). While sulphate alters physical attributes like smell and taste, it also has a detrimental impact on human consumption like cathartic effects. The concentration of sulphate (1-273mg/l) in the groundwater of the area is within the desirable limits as per WHO norms in 98% of the samples. This concentration is spatially diverse; higher concentration can be observed in the southern part of the area (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). This may be attributed to the weathering of sulphide ores, gypsum, and anhydrite (Todd and Mays 2005), and the presence of coal mining in the area. This reflects the anthropogenic influence on the geochemistry of groundwater. Phosphate is negligible in the majority of the groundwater samples. It ranges from 0.023 to 3.5mg/l, having higher concentrations in the south and southeastern part of the area (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe concentration of fluoride ranges from 0.2 to 6.72 mg/l. Fluoride is present beyond the permissible limit (1.5mg/l) suggested by WHO in 70% of the samples. Previous studies conducted in parts of Hazaribagh have also revealed higher concentrations of fluoride (1.89\u0026ndash;3.84 mg/l) in groundwater (Kumar and Sadhu \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The concentration of fluoride is higher in the southeast part, and lowest in the northwestern part of the area (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Higher fluoride could be due to the weathering of fluoride-bearing minerals including biotite, fluorite, and apatite, which are present as secondary minerals in granite and granitic gneiss rocks of the area (Singh et al. 2010). In the study area, the concentration of nitrate in groundwater ranged from 0.41 to 273.69 mg/l. 64% of the samples contain nitrate more than the permissible limit suggested by WHO. The spatial distribution of nitrate reveals pockets of high concentration in the northern and central part of the area (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). Higher concentrations may be due to biological fixation, application of fertilizers and pesticides as well as sewage from industries.\u003c/p\u003e\n\u003ch3\u003eGroundwater Quality For Drinking Purposes\u003c/h3\u003e\n\u003cp\u003eThe analysis of the concentration of individual ions concerning WHO standards is vital, but it is also observed that water is found suitable for one ion and unsuitable for the other. Thus, to understand the overall suitability of water for drinking purposes, the present study analyzes its quality involving a combination of various physical and chemical parameters with the help of WQI.\u003c/p\u003e \u003cp\u003eAs per WQI, good-quality water dominates the area (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This comprises 4100.38 sq. km. of area, which is around 84% of the total area. Excellent water is observed in around 10% of the area, comprising 42.65 sq km of the area. A similar study conducted in Bist Doab region of Punjab revealed the presence of excellent water in one-third of the area (Gautam et al. 2021). 23% of the samples covering 751.56 sq. km. of the area consist of poor-quality water covering the area of Daru, Tatijhariya, Keredari, Barkagaon, Chalkusa, and Gola. Poor-quality water is present in 0.77% of the area comprising the Chauparan block. The higher concentrations of EC, TH, chloride, fluoride, and nitrate impart poor quality to the groundwater of these regions. Spatial variation of WQI (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) reveals that the eastern and southern part of the area comprises water suitable for drinking, while the central and northwestern part of the area has a poor-quality of groundwater.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of groundwater as per Water Quality Index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWQI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. of samples\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (9.59%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(65.75%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u0026ndash;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(23.29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Poor Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u0026ndash;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1.37%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater Unsuitable for Drinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eHuman Health Risk Assessment\u003c/h3\u003e\n\u003cp\u003eIn countries like India, most people depend on groundwater as a source for drinking purposes. Consequently, the presence of fluoride and nitrate in groundwater poses a non-carcinogenic risk to human health, which has become a serious issue. The residents of the area depend on groundwater for drinking purposes (Heena and Rai 2020). The concentration of fluoride and nitrate are beyond the permissible stipulations of WHO in most of the samples. On account of that, it becomes extremely vital to assess the health risk due to fluoride and nitrate using the universally established criteria for fluoride and nitrate. The present study computes the non-carcinogenic health risks due to nitrate and fluoride for different sections of the population (male, female, and children).\u003c/p\u003e\n\u003ch3\u003eExposure Dose\u003c/h3\u003e\n\u003cp\u003eThe exposure dose of fluoride and nitrate was calculated for males, females and children. The exposure dose of nitrate in males ranged from 0.015 to 10.52 (mean 1.55) for males, 0.014 to 9.95 (mean 1.47) for females, and 0.01 to 12.7 (mean 1.88) for children (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Higher values of nitrate were observed in Chauparan, while the least exposure of nitrate was noticed at Dadi. The exposure dose of fluoride in males ranged from 0.009 to 0.25 (mean 0.06), 0.0093 to 0.24 (mean 0.05) in females (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The range of exposure dose is 0.01 to 0.31 (mean 0.07) in children, which is similar to that observed in Agra (0.07-0.31mg/kg/day) (Yadav et al. 2019). The highest exposure level of fluoride was observed in Barkagaon, while the least exposure to fluoride was noticed in Churchu.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComputation of Exposure duration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEDN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEDN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEDN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eHazard Quotient And Total Hazard Index\u003c/h3\u003e\n\u003cp\u003eThe hazard quotient for nitrate varied from 0.009 to 6.57 for males (mean 0.97) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), which is lower than observed values in South India (1.71) (Karunanidhi et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The observed hazard quotient varies from 0.009 to 6.22 for females (mean 0.91) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and 0.01 to 7.98 for children (mean 1.17), which is higher than values observed in Thoothukudi district, Tamil Nadu (mean 0.9) (Selvam et al. 2021). The calculated values of HQ are greater than 1 in 31.43% of the samples for males and children, while 30% of the samples in females. As the northern and central parts of the area have a higher concentration of nitrate, the health risk is also higher in that area. Nitrate concentrations higher than 11 ppm in the body may be the cause of anoxemia, asphyxia, and blue baby disease. It could have such negative implications that it may even cause death to infants (\u0026lt;\u0026thinsp;4 months old). Excess nitrate could have equally negative repercussions for older infants and adults, as it can be the cause of gastric cancer (Comly \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1945\u003c/span\u003e; Gilly et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1984\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComputation of Hazard Quotient for adult male, females and children\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHQF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHQN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHQT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHQF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHQN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHQT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHQF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHQN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHQT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe hazard quotient for fluoride varied from 0.16 to 4.30 for males (mean 1.01), and 0.15 to 4.07 for females (mean 0.95) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The hazard quotient calculated for children lies in the range of 0.19 to 5.22 (mean 1.22) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), which is higher than those observed in South India (0.01 to 3.25) (Karunanidhi et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The hazard quotient is more than the reference dose in 38.5% of the samples in males and children, and 37.14% of the samples in females. This suggests that the majority of the residents of the area are prone to dental and skeletal fluorosis. Previous studies conducted in parts of Hazaribagh reveal the presence of dental fluorosis among children (Kumar and Sadhu \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This reveals the grim situation of the health of the residents of the area. The presence of fluoride in water in concentrations less than 0.5mg/l creates another problem; it promotes dental carries in children especially in the formative stages of permanent teeth (Bhattacharya 1988), osteoporosis, and growth retardation. In terms of general health, in communities where drinking water is excessively high in fluoride, the most prominent adverse effects are skeletal fluorosis and bone fracture, low IQ, and deformities in growth, especially in infants (Marshall et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs per USEPA, the Total Hazard Index (THI) should not exceed 1, as it denotes non-carcinogenic risk to human health. The THI ranges from 0.01 to 7.46 (mean 1.89) for males (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), which is higher than the Thoothukudi district, Tamil Nadu (1.6). THI in the area ranges from 0.009 to 7.05 (mean 1.79) and 0.01 to 9.05 (mean 2.3) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) for females and children, respectively. Assessment of non-carcinogenic danger based on THI indicates that 83.56%, 78.08%, and 89.04% of the samples surpass the allowable limit for males, females, and children respectively. The vulnerability of total hazard is maximum in children followed by females and then males. Studies conducted in Shanmuganadhi in South India (Karunanidhi et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) also reveals that children are most vulnerable to health risk. Spatial variation in the overall non-carcinogenic risk as depicted by THI is maximum in Chauparan and minimum in Dadi. Lower values are observed in the northwestern part of the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea,b,c).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study brings to light the groundwater quality and assesses its suitability for drinking purposes. For drinking water quality assessment, analyzed parameters were compared with WHO standards, and Water Quality Index (WQI) was used. Results reveal that the majority of the samples come within the desired limit suggested by WHO. However, in a few samples, EC, TDS, TH, chloride, sulphate, and calcium are higher than the desirable limit, whereas fluoride and nitrate are beyond the maximum permissible limit in some of the samples. The physico-chemical characteristics of the groundwater are due to geogenic as well as anthropogenic factors. Results of WQI for the area highlight that the groundwater in the area is of excellent to very-poor quality, with good water being the most predominant one. While the groundwater is termed suitable for drinking purposes, however, poor water and very poor-quality water pose a health risk to the residents of the area. Poor and very poor-quality water is present in around 25% of the total samples. The spatial variation map of WQI reveals that the central and northwestern part of the area has poor and very-poor quality groundwater. The study also assesses the human health risks of fluoride and nitrate. For this purpose, the hazard quotient and total hazard index were computed. The study highlights that non-carcinogenic danger based on THI indicates that 83.56%, 78.08%, and 82.19% of the samples surpass the allowable limit for males, females, and children respectively. Children are most vulnerable to negative health implications. For the good health of the residents of the area, groundwater must require proper remediation strategies before consumption. It is also essential to be mentioned that coal mining areas restrict the suitability of groundwater for drinking purpose and demands special management. Thus, before the problem becomes acute and irreversible, appropriate management strategies need to be implemented so that the water resource of the area is managed sustainably.\u003c/p\u003e"},{"header":"References ","content":"\u003col\u003e\n\u003cli\u003eAdimalla N, Li P, Venkatayogi S (2018) Hydrogeochemical evaluation of groundwater quality for drinking and irrigation purposes and integrated interpretation with water quality index studies. Environ Process 5(2):363\u0026ndash;383\u003c/li\u003e\n\u003cli\u003eBanerjee T, Srivastava RK (2011) Evaluation of environmental impacts of Integrated Industrial Estate\u0026mdash;Pantnagar through application of air and water quality indices. Environ Monit Assess 172:547\u0026ndash;650\u003c/li\u003e\n\u003cli\u003eBhuiyan C, Champati Ray PK (2017) Groundwater Quality Zoning in the Perspective of Health Hazards. Water Resour Manage 31, 251\u0026ndash;267 https://doi.org/10.1007/s11269-016-1522-4\u003c/li\u003e\n\u003cli\u003eComly HH (1945) Cyanosis in infants caused by nitrates in well water. J Am Mwd Assoc 129:12\u0026ndash;114\u003c/li\u003e\n\u003cli\u003eDas N, Mukhopadhyay S (2018) Application of multi-criteria decision-making technique for the assessment of groundwater potential zones: a study on Birbhum district. Environment, Development and Sustainability, West Bengal. https ://doi.org/10.1007/s1066 8-018-0227-7\u003c/li\u003e\n\u003cli\u003eEdokpayi JN, Enitam AN, Mutileni N, Odiyo JO (2018) Evaluation of water quality and human risk assessment due to heavy metals in groundwater around Muledane area of Vhembe District, Limpopo Province, South Africa. Chemistry Central Journal. 12(1): 1-16.\u003c/li\u003e\n\u003cli\u003eGleick PH (1996) Water resources. In: Schneider, S.H. (Ed.), Encyclopedia of Climate and Weather, vol. 2. Oxford University Press, New York, pp. 817e823.\u003c/li\u003e\n\u003cli\u003eGilly G, Corrao G, Favilli S (1984) Concentrations of nitrates in drinking water and incidence of gastric carcinomas. first descriptive study of the Piemonate Region, Italy. Sci Total Environ 34:35\u0026ndash;37\u003c/li\u003e\n\u003cli\u003eIlyas M, Khan S, Khan A, Amin R (2017) Analysis of drinking water quality and health risk assessment- A case study of Dir Pakistan. Journal of Himalayan Earth Sciences 50(1): 100-110.\u003c/li\u003e\n\u003cli\u003eJha SK, Nayak AK, Sharma YK 2009 Fluoride occurrence and assessment of exposure dose of fluoride in shallow aquifers of Makur, Unnao district Uttar Pradesh, India. Environ Monit Assess 156, 561. https://doi.org/10.1007/s10661-008-0505-1\u003c/li\u003e\n\u003cli\u003eKhan AF, Srinivasamoorthy K, Prakash R. et al. (2021) Human health risk assessment for fluoride and nitrate contamination in the groundwater: a case study from the east coast of Tamil Nadu and Puducherry, India. Environ Earth Sci 80, 724 https://doi.org/10.1007/s12665-021-10001-4\u003c/li\u003e\n\u003cli\u003eKarunanidhi D, Aravinthasamy P, Subramani T, Jianhua Wu, Srinivasamoorthy K (2019) Potential health risk assessment for fluoride and nitrate contamination in hard rock aquifers of Shanmuganadhi River basin, South India, Human and Ecological Risk Assessment: An International Journal, DOI: 10.1080/10807039.2019.1568859\u003c/li\u003e\n\u003cli\u003eKhan AF, Srinivasamoorthy K, Prakash R et al. (2021) Human health risk assessment for fluoride and nitrate contamination in the groundwater: a case study from the east coast of Tamil Nadu and Puducherry, India. Environ Earth Sci 80, 724. https://doi.org/10.1007/s12665-021-10001-4\u003c/li\u003e\n\u003cli\u003eKumar R, Sadhu, DN (2013) Assessment of Drinking water quality in tribal dominated villages of Barkagaon, Hazaribag, Jharkhand, India. European Scientific Journal, Vol 9(35), pp 331-338.\u003c/li\u003e\n\u003cli\u003eLi P, Tian R, Xue C, Wu J (2017) Progress, opportunities and key fields for groundwater quality research under the impacts of human activities in China with a special focus on western China. Environ Sci Pollut Res 24(15):13224\u0026ndash;13234. https ://doi.org/10.1007/s1135 6-017-8753-7\u003c/li\u003e\n\u003cli\u003eMarshall TA, Levy SM, Warren JJ, Broffitt B, Eichenberger-Gilmore JM, Stumbo PJ (2004) Associations between intakes of fluoride from beverages during infancy and dental fluorosis of primary teeth. J Am College Nutri 23(2):108\u0026ndash;116\u003c/li\u003e\n\u003cli\u003eMcKee JE, Wolf HW (1963) Water Quality Criteria. California: State Water Quality Control Board Publication.\u003c/li\u003e\n\u003cli\u003eNi F, Liu G, Ren H, Yang S, Ye J, Lu X, and Yang M (2009) Health Risk Assessment on Rural Drinking Water Safety-A Case Study in Rain City District of Ya\u0026rsquo;an City of Sichuan Province. Journal of Water Resource and Protection 2: 128-135.\u003c/li\u003e\n\u003cli\u003ePant N, Rai SP, Singh R,Kumar S, Saini RK,Purushothaman P, Nijesh P, Rawat Y,Sharma M, Kamaleshwar P (2021). Impact of geology and anthropogenic activities over the water quality with emphasis on fluoride in water scarce Lalitpur district of Bundelkhand region, India. Chemosphere. 279. 10.1016/j.chemosphere.2021.130496.\u003c/li\u003e\n\u003cli\u003ePrasad B, Kumari P, Bano S, Kumari S (2014) Ground water quality evaluation near mining area and development of heavy metal pollution index. Appl Water Sci 4(1):11\u0026ndash;17\u003c/li\u003e\n\u003cli\u003eRamakrishnaiah CR, Sadashivaiah C, Ranganna G (2009) Assessment of water quality index for the groundwater in Tumkur Taluk, Karnataka state, India. E-J Chem 6(2):523\u0026ndash;530\u003c/li\u003e\n\u003cli\u003eSinha H, Rai SC (2021) Evaluating geologic and anthropogenic impacts on groundwater level dynamics in Chhotanagpur Plateau, India. Arab J Geosci 14, 1043. https://doi.org/10.1007/s12517-021-07298-7\u003c/li\u003e\n\u003cli\u003eSadat-Noori SM, Ebrahimi K, Liaghat AM (2014) Groundwater quality assessment using the water quality index and GIS in saveh-nobaran aquifer, Iran,\u0026rdquo; \u003cem\u003eEnvironmental\u003c/em\u003e \u003cem\u003eEarth Sciences\u003c/em\u003e, vol. 71, no. 8, pp. 3827\u0026ndash;3843.\u003c/li\u003e\n\u003cli\u003eSingh AK, Mondal GC, Singh TB, Singh S, Tewary BK, Sinha A (2012) Hydrogeochemical processes and quality assessment of groundwater in Dumka and Jamtara districts, Jharkhand, India. Environ Earth Sci 67(8):2175\u0026ndash;2191\u003c/li\u003e\n\u003cli\u003eSoltan ME (1999) Evaluation of groundwater quality in Dakhla Oasis (Egyptian Western Desert). Environ Monit Assess 57(2):157\u0026ndash;168\u003c/li\u003e\n\u003cli\u003eSpayd SE, Robson MG, Xie R (2012) Importance of Arsenic speciation in population exposed to arsenic in drinking water, Hum. Ecol. Risk. Assess., 18, 1271\u0026ndash;91\u003c/li\u003e\n\u003cli\u003eTiwari AK, De MaioM, Singh PK, Singh AK (2016) Hydrogeochemical characterization and groundwater quality assessment in a coal mining area, India. Arab J Geosci 9(3):1\u0026ndash;17\u003c/li\u003e\n\u003cli\u003eTiwari AK, Singh AK, Mahato MK (2018) Assessment of groundwater quality of Pratapgarh district in India for suitability of drinking purpose using water quality index (WQI) and GIS technique. Sustain. Water Resour. Manag. 4, 601\u0026ndash;616. https://doi.org/10.1007/s40899-017-0144-1\u003c/li\u003e\n\u003cli\u003eU.S. EPA (U.S. Environmental Protection Agency), 2022. Human Health Risk Assessment. https://www.epa.gov/risk/human-health-risk-assessment.\u003c/li\u003e\n\u003cli\u003eVasanthavigar M, Srinivasamoorthy K, Vijayaragavan K, Rajiv Ganthi R, Chidambaram S, Anandhan P, Manivannan R, Vasudevan S (2010) Application of water quality index for groundwater quality assessment: Thirumanimuttar sub-basin, Tamil Nadu, India. Environ Monit Assess 171(1\u0026ndash;4):595\u0026ndash;609\u003c/li\u003e\n\u003cli\u003eXiaogang Fu, Zihan Dong, Shuang Gan, Zhe Wang, Aihua Wei (2021) Groundwater Quality Evaluation for Potable Use and Associated Human Health Risk in Gaobeidian City, North China Plain Journal of Chemistry Article ID 3008567, 15 https://doi.org/10.1155/2021/3008567\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Groundwater chemistry, Water Quality Index, Fluoride, Nitrate, Health Risk Assessment ","lastPublishedDoi":"10.21203/rs.3.rs-2472932/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2472932/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe evaluation of groundwater quality is extremely important to assess the risk to human health. This study deals with the spatial variation in physico-chemical parameters of groundwater for drinking purposes and human health risk assessment concerning fluoride and nitrate. GIS techniques have been used to determine and delimit zones of pollution. Samples were collected in the post-monsoon season (November 2020) and analyzed for physico-chemical parameters such as pH, TDS, conductivity, cations, and anions. For drinking water quality assessment, analyzed parameters were compared with WHO standards, and Water Quality Index (WQI) was used. Results reveal that the majority of the samples come within the desired limit suggested by WHO. However, in a few samples, EC, TDS, TH, chloride, sulphate, and calcium are higher than the desirable limit, whereas fluoride and nitrate are beyond the maximum permissible limit in some of the samples. To assess health risk, the Hazard quotient (HQ) and total hazard index (THI) were computed. The results indicate that the total non-carcinogenic risk for children, male and female ranges from 0.01 to 7.46 for males, 0.009 to 7.055, and 0.01 to 7.34 for children respectively. Furthermore, 84%, 78%, and 82% of the samples are greater than the recommended limit of THI\u0026thinsp;\u0026gt;\u0026thinsp;1 for males, females, and children respectively, suggesting detrimental impacts on the health of the residents. Knowledge of spatial variation and anomalous concentration is vital for groundwater management as well as health risk assessment. The findings of this study will be helpful to government officials, policy planners, NGOs, and local communities.\u003c/p\u003e","manuscriptTitle":"Spatial Variation in groundwater quality and Health Risk Assessment for Fluoride and Nitrate in Chhotanagpur Plateau, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-17 14:52:01","doi":"10.21203/rs.3.rs-2472932/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"56209d66-b155-454d-9a16-681ee1cc6f4a","owner":[],"postedDate":"January 17th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-02-01T04:59:19+00:00","versionOfRecord":[],"versionCreatedAt":"2023-01-17 14:52:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2472932","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2472932","identity":"rs-2472932","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}