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Major ion chemistry and suitability of groundwater resources for different utilizations in mica mining areas, Jharkhand, India

Geochemical Transactions volume 26, Article number: 5 (2025) Cite this article

Abstract

Groundwater resources in mica mining areas of Jharkhand are vital for local communities, agriculture, and domestic utilization. The study investigates the major ion chemistry of groundwater in the mica mining regions, focusing on key physicochemical parameters such as pH, electrical conductivity (EC), total dissolved solids (TDS), and concentrations of major cations (Ca²⁺, Mg²⁺, Na⁺, K⁺) and anions (HCO₃⁻, Cl⁻, SO₄²⁻, NO₃⁻, F−). Groundwater samples from the study area were collected before the monsoon season, during the monsoon season, and after the monsoon season. The hydro-chemical analysis reveals that groundwater in the mica mining zones exhibits elevated levels of dissolved ions, with NO₃⁻, F−, Ca²⁺, Mg²⁺ and total hardness exceeding permissible limits set by Bureau of Indian Standards (BIS) for drinking purposes at some locations. Water Quality Index (WQI) assessments suggest that a significant proportion of groundwater samples fall into the “good” to “very good” category for drinking and about 29% of the samples fall under the “poor” category. The groundwater was generally suitable for irrigational use with exception of a few due to high salinity. The principal component analysis revealed rock weathering as a dominant source of ions along with anthropogenic sources like mining and agriculture contributing minorly to the ionic load. The predominant hydro-chemical facies identified were Ca-Mg-HCO3 and Ca-Mg-Cl-SO4 types. Both carbonate and silicate weathering play an important role in the geochemical signature of the groundwater in the area. The study implicates the potential health impacts of using the groundwater as drinking water without treatment at a few locations owing to high fluoride, nitrate and dissolved solids. The study also highlights the need for sustainable water management practices and regular monitoring of groundwater quality to mitigate the anthropogenic impacts on groundwater resources.

Introduction

Groundwater provides drinking water to 50% of the world’s population and accounts for more than 40% of agricultural water [1]. Groundwater accounts for about 85% of rural and 50% of urban water supplies in India, making it critical to agriculture and economic development [2]. Natural processes like water-rock interactions, as well as human activities like agriculture, urbanisation, and mining, alter the chemical composition and quality of groundwater. Nevertheless, excessive extraction, contamination, and climate change substantially impact the groundwater quality and availability, especially in mining and industrial locations [3]. Common contaminants that endanger human health and agricultural production include arsenic, iron, manganese, fluoride, and nitrates [4]. These concerns highlight the importance of hydrochemical analysis and continual monitoring to assess groundwater quality for potable, agricultural, and industrial purposes [5].

Groundwater’s ion chemistry, including cations (Ca²⁺, Mg²⁺, Na⁺, K⁺) and anions (HCO₃⁻, Cl⁻, F− SO₄²⁻, NO₃⁻), reflects its flexibility and the geochemical processes that impact its composition. Mineral dissolution, cation exchange, and redox reactions are all geogenic variables that have an impact on groundwater chemical profiles [6]. The primary ions have an impact on groundwater’s physicochemical properties, including total dissolved solids (TDS), electrical conductivity (EC), pH, and total hardness, all of which serve as indicators of water quality. Changes in ionic content can make groundwater unfit for consumption, irrigation, and industrial usage.

Amongst the anthropogenic activities, mining is one of the major causes affecting the groundwater quality and this has been extensively studied worldwide [7,8,9,10]. Most research focuses on heavy metals, leaving gaps in thorough hydro-chemical assessments of non-metallic contaminants. Consequently, studies pertaining to groundwater quality in the mica mining areas are also scarce despite the fact that mica mining can be a major sources of groundwater contamination, primarily by the leaching of fluoride ions into aquifers. Nevertheless, Fluoride contamination of groundwater has been extensively studied in many states of India [11,12,13]. Fluoride contamination may be attributed to geological factors such as silicate weathering and carbonate dissolution in areas with granitic rocks, which are exacerbated by mica mining [14, 15]. Owing to its high solubility in water, nitrate is another widespread pollutant of the groundwater, predominantly in agricultural dominated areas [16,17,18]. The main cause of the increase of nitrate in groundwater is anthropogenic activities, which comprise the extensive use of nitrogen-based fertilizers for agriculture. Other factors include landfill leachate, irrigation with wastewater, and industrial effluent discharge [19, 20].

Geochemical modelling and statistical analysis like principal component analysis (PCA) are critical for monitoring water quality and finding pollution sources by analysing ionic concentrations and interactions [12]. For instance, in mining areas, the findings aid in better understanding of the mining impacts and developed mitigation methods [21, 22]. This highlights the importance of systematic research on groundwater major ion chemistry in mining environment. A water quality index (WQI) has also proved as an important tool to compare the quality of groundwater and their management [23]. It depicts the composite impact of different water quality parameters and communicates water quality information to the public and legislative policy-makers to shape strong policy and implement the water quality programs by the government [24].

Mica, an electrically and thermally insulating silicate material, is widespread in India. Jharkhand, Bihar, Rajasthan, and Andhra Pradesh are major producers of mica, which is an essential component of the worldwide supply chain. However, the negative influence of mica mining on groundwater quality is escalating and significantly an environmental concern. Mica mining and processing affect the groundwater by accelerating ion and trace element migration into surrounding aquifers, altering their chemical composition and leaving them unsuitable for consumption and domestic use [25]. A recent study used machine learning techniques to decode geochemical fingerprints, finding that mica mining sites have higher fluoride concentrations in groundwater, which could pose health hazards to local residents [26].

Mica is extensively produced in Jharkhand, a mineral-rich region in eastern India, encompassing the Great Mica Belt. Mining leaches mica-containing rocks into groundwater, releasing ions and potentially toxic trace elements [27, 28]. Also, the area is having ample of agricultural activities. Owing to the sparce environmental studies in the mica mining areas [14, 28, 29], a detailed long-term water quality record and regular monitoring of parameters affecting water quality are necessary to analyse the effects of anthropogenic activities on the water systems and management of water resources in the area. Also, seasonal variations in groundwater quality are frequently disregarded, which limits our understanding of how monsoon oscillations affect pollutant concentrations. Furthermore, the use of advanced statistical approaches such as PCA and geographic modelling is still limited, making it difficult to identify contamination sources accurately. Addressing these gaps is critical to effective groundwater management and policy formulation. With this backdrop, this study aims to (i) assess spatial and temporal variations in the major ions concentrations of groundwater, (ii) identify the predominant geochemical processes affecting groundwater composition, (iii) evaluate the water quality index to evaluate environmental risks related to ion contamination, (iv) assess the quality of the groundwater resources for drinking and agricultural uses.

Materials and methods

The methodology for investigating the major ion chemistry of groundwater involves a systematic process including site selection, sample collection, laboratory analysis, and data interpretation.

Study area and site selection

The mica-producing region of Jharkhand is an important mineral-rich location in the Great Mica belt, India. The region is well-known for its large deposits of mica, and it plays a vital role in India’s mining industry, supplying high-quality mica to domestic and international markets. This study centres on the districts of Koderma, Giridih, and Hazaribagh, which form the core of Jharkhand’s mica mining region. The mica mining belt in Jharkhand mostly includes the districts of Koderma, Giridih, and parts of Hazaribagh, which are located between latitudes 23°15’N and 24°30’N and longitudes 85°30’E and 86°30’E (Fig. 1). The mica mining region of Jharkhand has undulating landscape, with elevations ranging from 200 to 500 m above sea level. The region is part of the Chotanagpur Plateau, which is made up of Precambrian rocks like granite, gneiss, and schist, all of which are rich in minerals. The study area is underlain by wide range of geological formations, ranging in age from Archeans to Recent. Phyllite-mica schist, muscovite-biotite schist, granite-gneiss and intrusive granites are the main litho-units in the area. Dolerite, quartz pegmatite and quartzite are also occasionally found at some places, while alluviums are restricted along the river courses [30].

Fig. 1
figure 1

Land-use land cover map of the study area with the sampling locations of groundwater

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The hydrogeology of the mica mining zone is significantly influenced by the underlying geology and mining processes. Groundwater in the region is primarily found in fractured and worn zones of crystalline rocks [30]. Aquifers are typically shallow, with depths ranging from 10 to 50 m, and are mostly replenished by monsoon rain.

Groundwater sampling design

A systematic random sampling was carried out in accordance with the standard guideline to ensure comprehensive representation of the study region encompassing a wide range of land use patterns and geological conditions. The samples were collected so as to represent the entire study area covering the areas of different land uses. Samples were collected form mining areas, agricultural zones, urban areas, areas with extensive vehicular load and also from areas with no visible anthropogenic influence. Figure 1 depicts the land use land cover of the study area along with the 37 sampling locations. To investigate seasonal variations (i.e., Pre-monsoon, Monsoon, and Post-monsoon), 37 groundwater samples were taken from wells, boreholes, and hand pumps during each season. Groundwater samples were collected by pumping water from wells, tubewells, and handpump for more than twenty minutes to remove any standing water. The samples were collected in cleaned and pre-rinsed high-density polyethylene vials. At each location, polyethylene bottles were rinsed with sample water prior to sample collection. The depth of water level ranges from 3 to 10 m during pre-monsoon and 2 to 4 m below ground level during monsoon and post-monsoon seasons.

Laboratory analysis of major ions

Analysis of ground water samples was done as per Standard Methods for water quality parameters [31]. The pH was assessed using a digital pH meter with reference and glass electrodes calibrated at pH 4.0, 7.0, and 10.0. The electrical conductivity (EC) was assessed using a digital benchtop meter calibrated with a reference solution of analytical grade KCl at 0.5 M concentration. Bicarbonate was estimated by the acid titration method [31]. Major anions (F−, Cl−, NO3− and SO42−) and cations (Na+, K+, Ca+ 2, and Mg+ 2) were quantitatively assessed in the laboratory by using an ion chromatograph (Metrohm 930 Compact IC Flex for anions and Metrohm 883 Basic Ion Chromatography Plus for cations). All the standards were diluted using ultrapure MilliQ water. The ionic concentrations of major cations and anions were obtained in mg/L.

Quality assurance

The validation of the analytical procedures was carried out by proper calibration of the instruments and ascertaining their precision and linearity. A 3-point calibration with known standards of the ions was adapted for calibration of the Ion Chromatograph. A R2 value close to one was confirmed before the analyses of the samples. Some of the samples were analysed in triplicates. For some of the ions like F–, SO42− and NO3–, both spectrophotometric and ion chromatographic methods were used to analyse some selected samples. The results from these methods were compared and the deviations were found to be within 5% which indicated reliability between the methods suggesting an acceptable quality assurance.

Hydro-chemical data analysis

Ion balance error (IBE)

The Ion Balance Error was determined to verify the dependability of the chemical data in analysing the accuracy of the results. The electrical neutrality principle argues that negatively charged ions (anions) must balance positively charged ions (cations). In an accurate and reliable analysis, the milliequivalents of main cations and anions should be approximately equal. The anion-cation balance method presented here to determine the reliability of major ion analyses of groundwater assumes that major ions comprise most of the total dissolved solids in a groundwater sample, and requires that all major ion concentrations be measured. The charge difference (in %) was found < 10% for the groundwater samples of the study area. The percent charge difference i.e. the Ion Balance Error (IBE) is calculated with the following formula:

$$\:\varvec{I}\varvec{B}\varvec{E}=100\times\:\left(\frac{\sum\:\varvec{c}\varvec{a}\varvec{t}\varvec{i}\varvec{o}\varvec{n}\varvec{s}-\sum\:\varvec{a}\varvec{n}\varvec{i}\varvec{o}\varvec{n}\varvec{s}}{\sum\:\varvec{c}\varvec{a}\varvec{t}\varvec{i}\varvec{o}\varvec{n}\varvec{s}+\sum\:\varvec{a}\varvec{n}\varvec{i}\varvec{o}\varvec{n}\varvec{s}}\right)$$

Water quality index

Water quality indices serve as effective tools for assessing the overall quality of water sources permitted for potable use [12, 32]. In the present study, ten variables were used to evaluate groundwater WQI; pH, TDS, F−, Cl−, NO3−, SO42−, Ca2+, Mg2+, Na+ and TH, to assess its aptness for drinking uses. According to their importance in determining the quality of the water, each metric was given a weight factor ranging from 2 to 5 [33]. A weight of 5 was assigned to TDS, F−, Cl−, NO3−, 4 to pH and SO42−, 3 to TH and 2 to Ca2+, Mg2+ and Na+; respectively. The WQI was calculated using following equation [33]:

$$\:{W}_{i}=\:{w}_{i}/\sum\:_{i-1}^{n}{w}_{i}$$
(1)

where Wi represents the relative weight, wi is the weight of each water quality parameter, and n signifies the number of parameters. Then, a quality rate (qi) scale is made for each parameter by dividing the amount of that parameter in water samples of each location by the standard for that parameter’s drinking water quality and then multiplying the result by 100:

$${q_i} = {\text{ }}{C_i}/{S_i} \times 100$$
(2)

where, qi = quality rating, Ci = concentration (mgL− 1) of each water quality parameter in each water sample and Si = standard (mgL− 1) for each water quality parameter as per the Bureau of Indian standards (BIS) [34]. The threshold established by World Health Organization [35] was utilised as the norm for Na, since BIS does not offer a guideline for this element. To calculate the water quality index, the SI is initially established for each water quality parameter, which is subsequently utilised to compute the water quality index (WQI) according to the following equations:

$$S{I_i} = {\text{ }}{W_i} \times {\text{ }}{q_i}$$
(3)
$$WQI{\text{ }} = {\text{ }}\sum S{I_i}$$
(4)

where, the SIi = sub-index of ith parameter, qi = rating based on the concentration of ith parameter and n = number of parameters. If the WQI number is less than 50, the water quality is considered excellent. If the WQI calculated is from 50 to 100, it is good while it is poor, if it is from100 to 200. If the WQI is between 200 and 300, very poor water quality is reflected. Nevertheless, if WQI surpasses 300, water is considered unfit for drinking [33].

Statistical and geochemical analysis

The relationships between ions were analysed using bivariate scatter plots to infer geochemical processes. Piper diagrams and Gibb’s diagrams were used to classify groundwater types based on ionic composition and identify dominant hydrochemical facies. The concentration ranges, means, standard deviations, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) of ions are evaluated using SPSS-20. PCA and HCA are the two important statistical tools intended for source evaluation of ions or elements in various environmental components. PCA is a method which decreases the variation in data by converting the number of measured variables to smaller number of artificial variables depicted as principal components (PCs). The extracted variables with eigenvalue greater than 1 are taken up for analysis since they attribute for maximum data variance and thus each PC is linked to a source of the variables (ions in the present study) [36]. To reduce the role of trivial variables, varimax rotation is applied to the extracted PCs [37]. To assess the rationality of PCA, the Bartlett’s test of sphericity and Kaiser–Meyer–Olkin (KMO) test are needed. KMO values > 0.5, and appropriate significance of Bartlett’s test (p < 0.05) are essential to confirm the validity of PCA [38]. HCA is used along with PCA for source apportionment studies in which the measured parameters are classified based on grouping of the parameters [39]. The assemblages of the cluster analysis depend on common properties and correlations shared by the variables [40] and thus it is easy to identify the mutual attributes of the clusters and subsequently assigning them the probable sources. Centroid clustering method was used for the HCA.

Assessment of groundwater quality for drinking and irrigational uses

The pH, TDS, and ion concentrations were assessed against water quality standards established by organisations such as the World Health Organisation [35], and the Bureau of Indian Standards [34] to determine the aptness of groundwater for drinking, while various indices were employed to evaluate its appropriateness for irrigation. The concentrations of the ions were taken as meq/L for the calculation of the indices. The indices used were:

  1. (a)

    Sodium Adsorption Ratio (SAR): The sodium adsorption ratio (SAR) can be estimated by the formula:

$$SAR = N{a^ + }/{\left[ {\left( {C{a^{2 + }} + M{g^{2 + }}} \right)/2} \right]^{0.5}}$$
  1. (b)

    Percent Sodium (%Na): The percent sodium (%Na) in the water samples is calculated by the equation:

$$\begin{gathered} Na\% = N{a^ + } + {K^ + }/ \hfill \\\left( {C{a^{2 + }} + M{g^{2 + }} + N{a^ + } + {K^ + }} \right) \times 100 \hfill \\ \end{gathered} $$
  1. (c)

    Residual Sodium Carbonate (RSC): This index is used to assess the impact of bicarbonate and carbonate ions on soil and is evaluated as:

$$RSC = \left( {C{O_3}{{^ - }_{}} + {\text{ }}HC{O_3}^ - } \right)-\left( {C{a^{2 + }} + {\text{ }}M{g^{2 + }}} \right)$$
  1. (d)

    Magnesium Hazard (MH): The magnesium hazard (MH) value can be calculated by the following formula:

$$MH = M{g^{2 + }}/\left( {C{a^{2 + }} + {\text{ }}M{g^{2 + }}} \right) \times 100$$
  1. (e)

    Kelley index (KI): Kelley index is also significant in evaluating the suitability of water quality for irrigation purposes.

$$KI = N{a^ + }/\left( {C{a^{2 + }} + M{g^{2 + }}} \right)$$

Geographical information system (GIS)

GIS is widely used in environmental research for graphical representation due to its reliability in collecting and organising data of environmental concerns and precisely establishing their spatial associations [41]. Inverse distance weighting (IDW) is one of the important interpolation tools in the GIS environment for spatial distribution mapping of environmental quality parameters [42]. Therefore, IDW interpolation has been used in a GIS environment to produce distribution maps of selected ionic concentrations, total dissolved solids and computed water quality indices.

Results and discussions

The physico-chemical parameters of the analysed groundwater samples of all the three seasons, including statistical measures and comparison with the drinking water quality standards [34, 35] are given in Table 1. The table represents the statistical summary of the analysed water quality parameters i.e. pH, electrical conductivity, major anions and cations of the groundwater samples from 37 locations collected in 3 seasons (pre-monsoon, monsoon and post-monsoon seasons). Except for NO3− and Mg+ 2 no significant seasonal fluctuations in anion and cation concentrations were identified. This was determined statistically using One-way ANOVA test. Significant seasonal variation was found for nitrate (significance value = 0.048) which may be attributed to significantly higher values during the monsoon season. For, Mg also the seasonal variation was significant (significance value = 0.022) with considerable higher concentrations during the post monsoon season. For pH, TDS and other ions, the seasonal variation was not significant. The results also revealed that the TDS, F–, Cl–, SO42–, NO3–, Ca+ 2 and Mg+ 2 surpassed the BIS for drinking water [34] at some locations for all the seasons. Table 2 depicts the contribution of the major ions in percentage towards the total dissolved solids. The largest contribution is made by HCO3− irrespective of the seasons, followed by Cl− and Ca+ 2. Amongst the ions, least influence in the TDS was from F− and K+. Nitrate also has a moderate contribution (6% pre-monsoon and 6.3% post-monsoon), but it nearly doubles (11%), which might be attributed to the increase in agricultural activities. The analysed groundwater is further evaluated in terms of its major ion chemistry and suitability for domestic, and irrigation uses.

Table 1 Statistical summary of the analyzed groundwater quality parameters and comparison with the BIS (2012) and WHO (2017) drinking water guideline values
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Table 2 Dissolved ions contribution towards the TDS in the study area
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Suitability for drinking and domestic consumption

Koderma Mica belt groundwater parameters are compared to the Indian Drinking Water Standard [34] and World Health Organisation [35] drinking water recommendations to determine groundwater suitability for drinking and public health (Table 1). The pH values of the groundwater samples of the study area varied from 5.5 to 8.0, indicating that the groundwater was slightly acidic to alkaline nature (Table 1). About 32% of the groundwater samples during the pre-monsoon season and post-monsoon season and 35% of the samples in the monsoon were below the acceptable limit (6.5), signifying that samples were not suitable for direct drinking and domestic consumption. The value of TDS in the groundwater samples during three seasons varied between a minimum of 68 mg/L to a maximum of 1307 mg/L in the study area (Fig. 2), with the majority of the samples being classified as freshwater with TDS below 1000 mg/L. About 14% of the samples, considering all the seasons, exceeded 1000 mg/L, falling under brackish water. Considering the TDS in accordance with the BIS acceptable limit of 500 mg/L, around 46% of samples during the pre-monsoon and post-monsoon and 41% of the samples in the monsoon exceeded the acceptable limit restraining these samples for direct use for drinking and domestic purposes (Table 1). The total hardness (TH) varied between 26 mg/L (minimum) to 830.3 mg/L (maximum) in the groundwater samples during the three seasons. About 54% of the groundwater samples collected in the pre-monsoon season and post-monsoon season and 57% of the collected samples in the monsoon exceeded the BIS acceptable limit of 200 mg/L in the study area limiting the groundwater from some locations of the study area to be used for drinking. In case of drinking extremely hard water for extended periods, this may lead to an increased incidence of urolithiasis (kidney stones), prenatal mortality, some forms of cancer, anencephaly and cardio-vascular disorders [43, 44].

Fig. 2
figure 2

Spatio-temporal variation of total dissolved solids (TDS) in the groundwater of the study area

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Among the cations, Ca2+ and Mg2+ concentrations varied from a minimum of 7.4 mg/L and 1.5 mg/L to a maximum of 249.7 mg/L and 69.2 mg/L in water samples during three seasons, respectively. Considering Calcium, around 32% of the samples in the pre-monsoon and monsoon and 35% of the samples during the post-monsoon season exceeded the BIS acceptable limits of 75 mg/L, while for Magnesium; 22, 5.4 and 32% of the samples were above the BIS acceptable limits of 30 mg/L during the pre-monsoon, monsoon and post-monsoon seasons, respectively, in the study area. The sodium concentrations in the water samples from the study area were below this limit of mg/L as provided by World Health Organization across all three seasons.

F− concentration in groundwater samples from the three seasons ranged from 0.2 mg/L to 3.7 mg/L. In the study area, 32% of pre-monsoon, 22% of monsoon, and 38% of post-monsoon samples exceeded the BIS acceptable level of 1.0 mg/L (Fig. 3). High concentration of F− may be attributed to the abundance of mica minerals like muscovite and biotite and their mining in the area. Mica minerals are known to be associated with fluoride generically [45, 46]. Mining activities exposes the mica-containing rocks and releases the constituent ions to the environment, subsequently enhances the leaching of potentially toxic elements like fluoride into the groundwater. A high concentration of F− in drinking water may cause dental fluorosis and skeletal fluorosis [47, 48]. However, F− values in the range of 0.8–1.0 mg/L are beneficial for the calcification of dental enamel, especially for children below 8 years [49]. During the three seasons, Cl− concentrations in groundwater samples ranged from 2.4 mg/L to 415.2 mg/L, above the BIS permissible limit of 250 mg/L in three pre-monsoon samples and two post-monsoon and monsoon samples, respectively. A high concentration of Cl− can impart a salty taste to the water and can cause high blood pressure in long-time consumers [50]. SO42− concentrations in the groundwater samples varied from 0.1 mg/L (minimum) to 485.7 mg/L (maximum) in the study area (Table 1) and only one sample had a value above the BIS acceptable limit of 200 mg/L during each of the three seasons. Higher sulphate values are related to gastrointestinal disorders [51]. NO3− concentrations in water samples ranged between 0.2 mg/L to 378.3 mg/L across the three seasons, with 27% of pre- and post-monsoon samples and 43% of monsoon samples exceeding the BIS acceptable level of 45 mg/L (Fig. 4). Excessive NO3− in drinking water can be causative of methaemoglobinaemia in infants and goiter, gastric cancer, hypertension, and birth malformations in adult consumers [52].

Fig. 3
figure 3

Spatio-temporal variation of Fluoride concentrations in the groundwater of the study area

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Fig. 4
figure 4

Spatio-temporal variation of Nitrate concentrations in the groundwater of the study area

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Impact of land use changes on nitrate contamination in Mica mining areas

Mica mining in Jharkhand has dramatically altered the land use, leading to nitrate contamination in groundwater, mostly as a result of deforestation, soil degradation, and inadequate waste management and increased agricultural activities. Agriculture is also practiced extensively in some parts of the study area which is evident from the land use map (Fig. 1) of the area. The spatial distribution map of nitrate during the monsoon season (Fig. 4) also depicts high nitrate in the areas which have high agricultural activities as per land use. Thus, nitrate can be attributed to the agricultural activities of the area i.e., the widespread application of the nitrogen-based fertilizers like urea, calcium ammonium nitrate, etc. used to enhance the agricultural production [53]. It can also be noted that substantially higher concentration of nitrate in the groundwater of the study area was found in the monsoon season which is the main agricultural season of the area marked with heavy rainfall. Paddy which needs ample of water for its growth is the main crop of the area cultivated in the monsoon season and is associated with application of fertilizers. Also, extensive deforestation for mining diminishes vegetation cover, resulting in heightened soil erosion and reduced nitrate absorption by plants. Expanding settlements near mining areas, frequently devoid of adequate sewage facilities, also exacerbate organic nitrate contamination. These cumulative alterations compromise groundwater quality and present significant health hazards, such as methemoglobinemia.

Water quality index

The estimated Water Quality Index (WQI) of the groundwater samples ranged from 20 to 152 (with a mean value of 74), 18 to 239 (with a mean value of 29) and 19 to 161 (with a mean value of 76) in the pre-monsoon, monsoon and post-monsoon seasons, respectively. In the pre-monsoon, monsoon, and post-monsoon seasons, 38%, 32%, and 35% of the samples had WQI values below 50, indicating very good water quality, while 35%, 41%, and 32% had WQI values between 50 and 100 (Fig. 5), indicating good water quality. However, around 27%, 27% and 33% of the samples in the pre-monsoon, monsoon and post-monsoon seasons, respectively, had WQI values in the range of 100–200, revealing poor category for water in accordance with WQI classification suggesting their unsuitability for the drinking consumption. The calculated WQI and spatial distribution map (Fig. 5) reveal that a higher percentage of samples were in poor class in the pre-monsoon as compared to post-monsoon and Monsoon seasons. Furthermore, pertaining to WQI, one groundwater sample (26) belonged to a very poor class in the monsoon season.

Fig. 5
figure 5

Water Quality Index (WQI) values of the groundwater for the three seasons in the study area

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Suitability of irrigation practices

Sodium adsorption ratio (SAR)

SAR is a significant index to evaluate the water suitability for irrigation purposes. The values of EC and Na+ are key to classifying the water quality for irrigation uses. High EC in water creates saline soil, while high Na + content creates alkaline soil [54]. Saline class is categorized based on the EC concentration, such as EC = < 250 µS cm− 1 (low), EC = 250–750 µS cm− 1 (medium), EC = 750–2,250 µS cm− 1 (high) and EC = 2,250–5,000 µS cm− 1 (very high). However, alkaline class is orderly based on SAR value, such as SAR < 6 (low), SAR 6–12 (medium), SAR 12–18 (high) and SAR > 18 (very high) [54]. The SAR value varied between a lowest of 0.1 to a highest of 3.9 during the three seasons, recommending that water belongs to a low alkaline class (Table 3) and can be used for irrigation without any alkali hazard in the study area. The EC concentration ranged from 94 µS/cm to 1592 µS/cm across the three seasons, with 43% of pre- and post-monsoon samples and 38% of monsoon samples being high saline (Table 3; Fig. 6). These waters are suitable for irrigation of salt-tolerant and semi-tolerant crops in areas with adequate drainage conditions.

Table 3 Statistical summary of the analyzed groundwater quality parameters for irrigation uses
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Fig. 6
figure 6

US Salinity diagram for classification of groundwater of the study area for irrigational purpose

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Percent sodium (%Na)

The evaluation of irrigation water quality using percent sodium (%Na) is also significant [55]. High Na+ in irrigation water deflocculates and reduces soil permeability [56]. The water samples with %Na up to 60% can be used for irrigational purposes as per Indian Standard [57]. %Na varied from 4.6 to 74.7%, considering the three seasons in the study area (Table 3). Figure 7 shows that 35 pre-monsoon samples and 36 monsoon and post-monsoon samples were excellent to good and could be utilised for irrigation without risk. The remaining samples exceeded the BIS guideline and can only be used for irrigation after treatment.

Fig. 7
figure 7

Plot of sodium percent versus electrical conductivity (after Wilcox 1955) of the groundwater of the study area

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Residual sodium carbonate (RSC)

The excess HCO3− and CO3− over alkaline earths (Ca2++Mg2+) is expressed as RSC and impacts irrigation water suitability [58]. RSC readings over 2.5 meq/L indicate unsuitability for irrigation, whereas RSC values over 5 meq/L indicate plant growth injury. Table 3 shows that the computed RSC values for the three seasons ranged from − 3.8 to 2.1, indicating that the water samples were suitable for irrigation.

Magnesium hazard (MH) and Kelley index (KI)

Important indicators of water quality agricultural suitability include magnesium hazards. Soil quality and agricultural yields suffer from Mg ion overabundance. Higher than 50% MH values indicate soil damage and unsuitability for irrigation [59, 60]. In the present study, MH values ranged from 1.3 to 69.1% considering the three seasons (Table 3). About 19% of the samples in the post-monsoon seasons had values greater than 50% (Fig. 8), which makes them unsuitable for irrigation purposes in the area. However, water samples collected in the pre-monsoon and monsoon seasons had values below the recommended limit (50%) and were suitable for irrigation. The Kelley index is also significant in evaluating the suitability of water quality for irrigation purposes. If KI is greater than 1.0, Na+ ions are present and water is inappropriate for irrigation [61, 62]. In the present study, KI values ranged between 0.0 and 2.9, considering the three seasons (Table 3) and about 16%, 11% and 5.4% of samples during the pre-monsoon, monsoon and post-monsoon seasons were inappropriate for irrigation in the area considering the Kelly Index.

Fig. 8
figure 8

Spatio-temporal variation of magnesium hazard of the groundwater in the study area

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Correlations and principal component analysis

The study (Table 4) found significant correlations between pH and HCO3−; EC with Cl−, HCO3−, SO42−, NO3−, Ca²⁺, Mg²⁺, Na⁺, and K⁺; Cl− with HCO3−, NO3−, Ca²⁺, Mg²⁺, Na⁺, and K⁺; HCO3− with Ca²⁺, Mg²⁺, and Na⁺; and SO42− with Ca²⁺, Mg²⁺. Overall, the diverse results showed that the significant correlation may be associated with the same sources; however, it must be used in conjunction with other statistical analysis approaches.

Table 4 Correlation coefficient matrix of the groundwater quality (#n = 111) parameters
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The inter-element correlation in the groundwater of the Mica mining area was analysed, and the Principal Component Analysis (PCA) results are presented in Table 5. Principal Component Analysis (PCA) was employed to facilitate the understanding of elemental data and to discern the group of ions with a mutual origin. The quantity of important main components is determined according to the Kaiser Criterion, which stipulates that only those with an Eigenvalue over 1 are considered [63]. Three principal components (PC) were extracted explaining 75.9% of the total variance.

Table 5 Principal component loadings of the groundwater samples (#n = 111) in the study area*
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The analysis revealed that the first component (PC-1) is strongly associated to the geogenic factors in particular with the dissolution of minerals like feldspar, dolomite, gypsum and carbonate rocks, contributing Electrical Conductivity (EC), Chloride (Cl−), Bicarbonates (HCO₃⁻), Sulphate (SO42−), Calcium (Ca²⁺), and Magnesium (Mg²⁺), to the groundwater. This indicates that water-rock interaction is a key natural process regulating the ion chemistry of the groundwater of the study area. The second component showed high loadings for Nitrate (NO3⁻), Sodium (Na⁺) and Potassium (K⁺), suggesting anthropogenic influences, predominantly agricultural activities like application of fertilizers as the sources of these ions. The widespread application of the nitrogen-based fertilizers like urea, calcium ammonium nitrate, potassium nitrate, etc. used to enhance the agricultural production [53]. Excessive use of fertilizers can prove detrimental to the groundwater. NO3– is highly soluble in water and has low retention in the soil matrix; therefore, it can easily find its way into the subsurface water through leaching. Hence, a large proportion of the fertilizers get leached into the groundwater due to rainfall infiltration, leading to nitrate contamination [52]. The third component (PC-3) has high loading for pH, fluoride (F⁻), and bicarbonates (HCO₃⁻) which may be attributed to the weathering of the fluoride bearing minerals like biotite and muscovite which are abundant in the study area which is further augmented by an alkaline pH [46, 50]. The factor can also be associated with the mica mining activities of the area, which enhances the exposure of the mica minerals to the environment, further propagating the leaching of the associated elements. The results of the PCA are in good agreement with the Hierarchical Cluster Analysis represented by dendogram depicting similar sources of the ions (Fig. 9).

Fig. 9
figure 9

Dendrogram plot elaborating Hierarchical clusters based on the major groundwater quality parameters

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Hydrogeochemical interpretation

As depicted by PCA, rock weathering is the most important source of ions in the groundwater of the Koderma mica mining area. However, for detailed geochemical interpretation, several plots, such as Piper plot, Gibb’s plot and a number of bivariate scatter plots, were prepared and analyzed.

Piper analysis

The trilinear diagram is an essential geochemical approach that plays a significant role in identifying water types in an area and the relationship between different dissolved ions in water chemistry [64]. The Piper diagram shows that 51%, 46%, and 57% of groundwater samples in the pre-monsoon, monsoon, and post-monsoon seasons fall into the HCO3− zone and 27%, 32%, and 24%, respectively, belong to no dominance zones in the anion facies. However, around 19% of the samples in the pre-monsoon and monsoon seasons and 16% of the samples during the post-monsoon season fall into Cl− zone, while one sample in each of the three seasons fall in the SO42− zone considering the anion facies (Fig. 10). The cation facies split groundwater samples into the Ca2+ zone (43%, 57%, and 35%) and the no dominance zone (41%, 32%) and 59%, respectively. Nevertheless, the remaining samples from the three seasons belong to the Na+ + K+ zone (Fig. 10). The diamond-shaped graph of the Piper diagram shows that the groundwater of the study area fell into four zones. Almost 38%, 35% and 51% of the groundwater samples in the pre-monsoon, monsoon and post-monsoon season were dominated by Ca-Mg-HCO3 water type, while 46%, 54%, and 43% of the samples were dominated by Ca-Mg-Cl-SO4 water types in the study area, respectively. Furthermore, four groundwater samples of the pre-monsoon, three samples of the monsoon and one sample of the post-monsoon were dominated by Na-HCO3-Cl, while Na-Cl was dominated in two groundwater samples during the pre-monsoon and one sample during the monsoon and post-monsoon seasons, respectively (Fig. 10). The Piper diagram suggested the groundwater samples of the study area have variable composition during the three seasons.

Fig. 10
figure 10

Piper’s trilinear diagram showing the hydrochemical facies of the groundwater of study area

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Mechanisms controlling groundwater chemistry

In general, lithological factors such as the weathering of carbonate, sulphide, and silicate minerals and the dissolution of evaporites are the chief sources of the dissolved ions in groundwater. Gibb’s diagram is largely used to assess the effective sources of dissolved ions in water, i.e. rock weathering, precipitation, and evaporation occurrence [65]. Gibbs’s diagrams represent the ratio of Cl− + NO3−/(Cl− + NO3− + HCO3−) and Na+ + K+/(Na+ + K+ + Ca2+) as a function of TDS values. The data plot on Gibbs’s diagram (Fig. 11a&b) shows that the rock weathering processes are the primary driving force controlling the groundwater chemistry of the Koderma Mining area during the three seasons. Additionally, the mean value of Na+/Cl− and K+/Cl− ratios for the groundwater samples were 3.0 and 0.1 in the pre-monsoon season, 2.7 and 0.1 in the monsoon season and 2.6 and 0.09 in the post-monsoon season, respectively. These values were higher than marine aerosol ratios (Na+/Cl− = 0.86 and K+/Cl− = 0.0176), signifying a limited atmospheric precipitation contribution and revealing that high concentrations of major ions in the water samples were due to weathering of rock-forming minerals. Positive correlations between Cl− and Na+ (0.576), Cl− and EC (0.911), and Na+ and EC (0.730) show that some of the Cl− and Na+ came from anthropogenic sources.

Fig. 11
figure 11

Gibbs plot showing the ratios of (a) Na++K+/(Na++K+ +Ca2+) and (b) Cl− + NO3−/ (Cl− + NO3− + HCO3−) as a function of TDS values

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The scatter plot of Ca2+ + Mg2+ against HCO3− reveals that the majority of samples from the three seasons clustered near the 1:1 line at lower concentrations (Fig. 12a), suggesting that carbonate weathering, primarily of calcite and dolomite, significantly contributes to the release of these ions into the area’s groundwater [66]. However, samples at medium to higher concentrations suggest that the extra Ca2+ + Mg2+ was probably due to reverse ion exchange processes and non-carbonate sources such as gypsum weathering which is evident from the scatter plot of Ca2+ + Mg2+ versus HCO3− + SO42− which displays larger number of groundwater samples of three seasons close to the 1:1 line than the previous plot (Fig. 12b). This suggested the dominance of calcite, dolomite, and gypsum mineral weathering in the study area. However, some points deviate from the theoretical 1:1 trend at higher concentrations. 22% of the samples in the pre-monsoon and monsoon seasons and 30% of the samples during the post-monsoon fall above the equiline (Fig. 12b), signifying an excess of Ca2+ + Mg2+ which can be attributed to weathering of silicate mineral (e.g. CaMgSiO6 + 4 H+ = Ca2+ + Mg2+ + 2SiO2 + 2H2O) and probably reverse ion-exchange phenomenon [67,68,69]. Furthermore, a few samples of three seasons fall below the equiline, suggesting a significant input from the non-carbonate source and suggesting the excess of HCO3− + SO42− to be balanced by the alkalies (Na+ + K+) in the area [70]. The scatter plot of Ca2+ + Mg2+ versus TZ+ (total cations) shows that samples of the three seasons fall below the equiline; the departure is more noticeable at the higher concentrations (Fig. 12c), reflecting an increasing contribution of sodium and potassium ions with increasing dissolved solids. Moreover, the scatter plot of Na+ + K+ versus TZ+ shows that sodium and potassium significantly contribute to the total cations at the higher concentration range (Fig. 12d). Besides, the scatter plot of Ca2+ + Mg2+ versus Na+ + K+ displays that some of the groundwater samples fall close to equiline during three seasons, suggesting the influence of silicate mineral weathering, while samples falling much above the equiline during three seasons demonstrate the result of carbonate mineral dissolution in the study area (Fig. 12e).

Fig. 12
figure 12

Scatter plot between (a) Ca2+ + Mg2+ versus HCO3− + SO42−, (b) Ca2+ + Mg2+ versus HCO3−, (c) Ca2+ + Mg2+ versus total cations (TZ+), (d) Na+ + K+ versus TZ+, (e) Ca2+ + Mg2+ versus Na+ + K+, (f) Mg2+ / Na+ versus Ca2+ / Na+ relating carbonate and silicate end members (mM)

Full size image

The data plotted on Mg2+/Na+ versus Ca2+/Na+ (Fig. 12f) also suggested that the silicate and carbonate mineral weathering controlled the solute acquisition processes in the groundwater of the study area [71]. This is also evident from the good correlation of Ca2+ with HCO3− (0.627), Mg2+ (0.565), and SO42− (0.510) during the three seasons, indicating that the carbonate and gypsum mineral weathering were primary sources of these ions in the groundwater of the area. Nevertheless, good correlations of Ca2+ with Na+ (0.549) and HCO3− with Na+ (0.536) suggested that silicate weathering also contributed some portion of calcium and bicarbonate concentrations in the water samples.

The groundwater quality analysis of the Koderma mica mining area reveals the unsuitability of the groundwater for drinking purposes at some of the locations due to high TDS and elevated concentrations of fluoride and nitrate, which is attributed to both geogenic and anthropogenic causes. The water quality indices, PCA and HCA findings support the results. High fluoride in drinking water is associated to nervous disorders along with skeletal and dental fluorosis [72, 73]. High nitrate in the drinking water may be causative of several health risks, the most dreaded being methemoglobinemia (blue baby syndrome) in infants. Apart from this, excessive nitrate in the drinking water may cause hypertension, gastric cancer, goiter, thyroid disorder, respiratory problems and multiple sclerosis in the adult population [74,75,76].

The study strongly advocates for implementing effective groundwater management strategies to address the groundwater quality issues related to fluoride and nitrate contamination in the study area aiming to safeguard the health of the local population. Cost-effective defluoridation techniques are needed to be used in locations with high fluoride in the groundwater. Enforcing regulations on mining activities is also suggested. Additionally, the study recommends the judicial use of nitrogen fertilizers and the adoption of advanced agricultural practices in the region. Need-based adequate use of fertilizers according to the crops is suggested so that the infiltration of the excess fertilizers into the groundwater can be avoided. Public awareness regarding the adverse effects of fluoride and nitrate is also required. The groundwater sources with high fluoride and nitrate should be marked and restricted to be used as source of drinking water. Continuous monitoring of the groundwater in the area especially community- based monitoring is also suggested to alleviate long-term effects. Detailed studies correlating the health issues with the groundwater contamination in the area and advanced geochemical and isotopic studies for detailed insights into sources of contaminants can be taken up as future research. The result of the present investigation would be helpful for policymakers and mining authorities for groundwater quality management in the area. The study might also be useful for groundwater management of other mica mining area in India and worldwide with similar quality issues.

Conclusions

The groundwater in mica mining regions of Jharkhand has unique chemical properties shaped by geological formations and anthropogenic operations predominantly mining and agriculture. Groundwater samples were acidic to mildly alkaline, according to the water quality investigation. The alkaline groundwater shows no substantial seasonal variation in dissolved ions, indicating a stable chemical composition across the three seasons. The predominant ions, comprising calcium (Ca²⁺), magnesium (Mg²⁺), bicarbonates (HCO₃⁻), and sulphates (SO₄²⁻), govern the water chemistry, establishing hydrochemical facies of the Ca-Mg-HCO3 and Ca-Mg-Cl-SO4 classifications. These indicate the effects of mineral dissolution from mica-dominant lithologies. Although the majority of groundwater comply with the acceptable limits for drinking water according to WHO and BIS guidelines, localised pollution by fluoride (F⁻), nitrate (NO₃⁻), and high total dissolved solids (TDS) in 31%, 32% and 44% of the samples, respectively poses potential health risks if used untreated. Long-term use of this groundwater may lead to health issues like fluorosis, hypertension, thyroid disorders, gastrointestinal problems in adults. Excessive NO3− in drinking water can be causative of methaemoglobinaemia in infants and birth malformations. Seasonal fluctuations in the ion concentrations were not evident except for nitrate which increased considerably in the monsoon season and magnesium which elevated during post monsoon. The calculated Water Quality Index and groundwater samples showed that the region’s groundwater was suitable for drinking and domestic use, with exceptions due to low pH, high total hardness, and high total dissolved solids concentrations. Groundwater is typically appropriate for irrigation, as evidenced by favourable Sodium Absorption Ratio (SAR) and Residual Sodium Carbonate (RSC) readings. Nonetheless, elevated salt and sodium levels in 41% and 4% of the locations pose threats to soil degradation and diminished agricultural output. According to the estimated indices, most water samples in the region were appropriate for irrigation, except for magnesium concerns. An increased magnesium hazard at about 50% of the locations during post monsoon season limits the groundwater use as irrigation water, and requires additional management. The study strongly advocates effective groundwater management strategies to address the groundwater quality issues related to fluoride and nitrate contamination in the study area and protect the local populace from the related health issues. The result of the present investigation would be helpful for policymakers and mining authorities for groundwater quality management in the area.

Data availability

The data is provided as tables. For any other information corresponding author may be contacted.

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Acknowledgements

The authors are grateful to the Department of Science and Technology for supporting the study through the DST-Women Scientist Scheme-A (WOS-A) (Grant No. SR/WOS-A/EA-28/2018). The authors also wish to thank the Director and the Water Research Management Laboratory of the CSIR-Central Institute of Mining and Fuel Research, Dhanbad, for providing the necessary laboratory facilities for the research.

Funding

This work was funded by Department of Science and Technology, Government of India, India. (Grant No. SR/WOS-A/EA-28/2018).

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Authors and Affiliations

  1. Department of Environmental Science, Central University of South Bihar, Gaya, 824236, India

    Soma Giri

  2. School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

    Ashwani Kumar Tiwari

  3. Department of Environmental Studies, Lakshmibai College, University of Delhi, Delhi, 110052, India

    Mukesh Kumar Mahato

  4. CSIR-Central Institute of Mining and Fuel Research, Barwa Road, Dhanbad, 826001, India

    Abhay Kumar Singh

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  1. Soma Giri
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  2. Ashwani Kumar Tiwari
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  3. Mukesh Kumar Mahato
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Contributions

The data used in this manuscript is a part of DST-Women Scientist Scheme-A (WOS-A) (Grant No. SR/WOS-A/EA-28/2018) project work carried out at CSIR-Central Institute of Mining and Fuel Research. The principal investigator, S.G., was responsible for field data collecting, groundwater sampling, laboratory analysis, data interpretation, and the conceptualisation and preparation of a draft article. M.K.M. supported in data acquisition and geochemical interpretation, A.K.T. helped with preparation of various thematic maps using GIS and edited the manuscript, and A.K.S. contributed to data analysis and preparation of the final manuscript.

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Correspondence to Soma Giri.

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Two of the co-authors (Dr. Abhay Kumar Singh and Dr. Ashwani Kumar Tiwari) are the guest editors of the special issue to which the manuscript is being submitted.

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Giri, S., Tiwari, A.K., Mahato, M.K. et al. Major ion chemistry and suitability of groundwater resources for different utilizations in mica mining areas, Jharkhand, India. Geochem Trans 26, 5 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12932-025-00099-x

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Keywords

Geochemical Transactions

ISSN: 1467-4866

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