The present study is a continuation of our work on evaluation of heavy metal contamination of water resources in the West Bokaro coalfield area by using a HPI and GIS technique (Tiwari et al. 2015). In this study, we evaluate the distribution of some selected heavy metals (Al, As, Ba, Cr, Cu, Fe, Mn, Ni, Se and Zn) in the groundwater of the West Bokaro coalfield using a heavy metal pollution index (HPI) and geographic information system (GIS). The information of the heavy metal contamination is very useful for utilization of groundwater resources that may also help in future water resource planning for the mining area. The concentrations of most of the metals are usually very low in unaffected environments and mostly derived from the mineralogy and weathering (Karbassi et al. 2007). Main anthropogenic sources of heavy metal contamination are mining, disposal of untreated and partially treated effluents containing toxic metals as well as metal chelates from different industries and indiscriminate use of heavy metal-containing fertilizer and pesticides in agricultural fields (Ammann et al. 2002; Nouri et al. 2008). Mining and related activities are causing water pollution and threatens the quality and quantity of surface and groundwater resources (Tiwary 2001; Verma and Singh 2013; Singh et al. 2013c). There are no proper water management plans in most of the mines in India, major part of mine water is discharged into the open channels without any treatment or beneficial use. Mine water can vary greatly in the concentration of contaminants present, and in some cases it may even meet the drinking water specification.

Groundwater is an important water source for the agricultural purposes, industrial sectors and majorly used as potable water in India (Singh et al. 2014; Chandra et al. 2015). However, availability of clean and potable drinking water emerged as one of the most serious developmental issue in many parts of India including Jharkhand in recent years (Tiwari and Singh 2014). Water quality indices are one of the most effective tools to communicate information on the quality of any water body (Singh et al. 2013a). The heavy metal pollution index (HPI) is a technique of rating that provides the overall quality of water with respect to the heavy metals. In recent past many researchers have used various water quality indices for assessing quality of water (Kumar et al. 2012; Prasanna et al. 2012; Yankey et al. 2013; Mahato et al. 2014; Varghese and Jaya 2014; Selvam et al. 2015; Singh et al. 2015; Yadav et al. 2015; Jahanshahi and Zare 2015; Gautam et al. 2015; Singh and Kamal 2015; Singaraja et al. 2015; Krishan et al. 2016 and several others).

Materials and Methods

The West Bokaro coalfield lies between 23°41′ to 23°52′ N latitude and 85°24′ to 85°41′ E longitude (Fig. 1). The geology of the study area consists of Barakar Formation, Barren Measures, Mahadeva, Metamorphics, Panchet, Raniganj and Talchir Formation (Fig. 1). The topography of the region lies between 229 and 660 m above the sea level. The hypsometric curve of the region shows that about 61.8 % of the area has elevation between 229 and 350 m and 36.6 % between 350 and 400 m and small area i.e. 1.6 % above 400 m. Previous studies have also been conducted on role of different factor in other kind of slope in mines (Vishal et al. 2010; Pradhan et al. 2014). The average annual rainfall of the district is 1418 mm and more than 85 % of annual rainfall occurs during the four monsoon months (June to September). Details about climate, drainage and geology of the West Bokaro coalfield are described elsewhere (Tiwari et al. 2015).

Fig. 1
figure 1

Sampling locations and geological map of the West Bokaro coalfield

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Total sixty-six groundwater samples were collected from thirty-three locations during the post-monsoon (November, 2012) and the pre-monsoon (May, 2013) seasons in pre-washed 100 mL narrow mouth polyethylene bottles for analysis of the heavy metals (Fig. 1). Concentration of heavy metals (Al, As, Ba, Cr, Cu, Fe, Mn, Ni, Se and Zn) in acidified groundwater samples were determined by using inductively coupled plasma-mass spectroscopy (ICP–MS, Perkin Elmer, Model: ELAN DRCe). Details about preparation of samples for heavy metals analysis and the accuracy of the analysis and calibration verification standard procedures are described elsewhere (Tiwari et al. 2015).

Principal component analysis (PCA) technique extracts eigenvalues and eigenvectors from the covariance matrix of original variables. Principal component analysis is a powerful pattern recognition tool that attempts to explain the variance of a large dataset of inter-correlated variables with a smaller set of independent variables (Simeonov et al. 2003). Factor analysis further reduces the contribution of less significant variables obtained from PCA. The new group of variables known as varifactors (VF) is extracted through rotating the axis along with the variables obtained from PCA (Vega et al. 1998).

The method described in our previous study (Tiwari et al. 2015) has been followed for calculation and classification of HPI value. Briefly, three classes have been demarcated as low (<15 HPI), medium (<15–30 HPI) and high (>30 HPI) values as suggested by Edet and Offiong (2002). The concentration limits [i.e. highest permissive value for drinking water (S i ) and maximum desirable value (I i ) for each parameter] were taken from the Indian drinking water specifications (BIS 2003, IS:10500). The HPI model (Mohan et al. 1996) is given by Eq. (1)

$${\text{HPI}} = \frac{{\mathop \sum \nolimits_{i = 1}^{n} W_{i} Q_{i} }}{{\mathop \sum \nolimits_{i = 1}^{n} W_{i} }}$$
(1)

where, Q i is the sub-index of the ith parameter. W i is the unit weightage of ith parameter, and n is the number of parameters considered. The sub index (Qi) of the parameter is calculated by Eq. (2)

$$Q_{i} = \mathop \sum \limits_{i = 1}^{n} \frac{{\left\{ {M_{i} \left( - \right)I_{i} } \right\}}}{{\left( {S_{i} - I_{i} } \right)}}$$
(2)

where M i is the monitored value of heavy metal of ith parameter, I i is the ideal value of the ith parameter and S i is the standard value of the ith parameter. The sign (−) indicates numerical difference of the two values, ignoring the algebraic sign.

Geographic information system (GIS) is most effective tool to provide better information of the consumers, policy makers and this helps for taking quick decision. For the spatial distribution map at first the corresponding toposheets are geo-referenced to the projection UTM. Datum: WGS—84 and Zone—45 N in GIS 10.2 environment. These toposheets were digitized in GIS platform to generate the base map of the West Bokaro coalfield. The spatial distributions of HPI value were done with the help of spatial analyst module in ArcGIS software. Inverse distance weighted (IDW) interpolation technique was used for spatial modelling.

Results and Discussion

The statistical results of the heavy metals analysis for the post- and pre-monsoon seasons are presented in Table 1. The average concentration of Al, As, Ba, Cr, Cu, Fe, Mn, Ni, Se and Zn of the entire sample in the post-monsoon season was found as 35.4, 0.32, 54.5, 0.8, 1.2, 476, 28.6, 9.4, 0.7 and 9.3 µg L−1 respectively. The mean concentration of Al, As, Ba, Cr, Cu, Fe, Mn, Ni, Se and Zn in the pre-monsoon season was found 90, 0.5, 79, 2.8, 4.1, 880, 53, 12.3, 0.9 and 27.2 µg L−1 respectively. In the post-monsoon season, the concentrations of the metals were found lower as compared to the pre-monsoon season.

Table 1 Seasonal variation in metal concentration in the groundwater of the West Bokaro coalfield and comparison with drinking water guidelines values of WHO (2006), BIS (2003) and USEPA (2009) (unit: µg L−1)
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After the comparison of result with the drinking water guidelines of WHO (2006), USEPA (2009) and Indian standards (BIS 2003), it was observed that Al, Fe, Mn, and Ni in the post- and pre-monsoon seasons were found in higher concentration than the level of drinking water in different sampling location (Table 1). This is interesting to note that variations of seasonal input to groundwater are mainly due to the hydrological regime and mining activities. The concentrations of Fe ranged from 308 to 895 µg L−1 (average 476 µg L−1) in the post-monsoon season and 491–1321 µg L−1 (average 880 µg L−1) in the pre-monsoon season, exceeding the desirable limit of 300 µg L−1 in all the groundwater samples in both seasons (Fig. 2a, b). Fe and Mn are common metallic elements found in the earth’s crust (Kumar et al. 2010). Excess Fe in water is mostly accumulated through the weathering of rocks and industrial effluents discharge. The observed high value of Fe in the groundwater may be attributed to mine effluent and leachate water from overburden dumps of coal mines of the area. Previous studies by Tripathy (2010) and Mahato et al. (2014) indicate that coal mining and other mining related operations releases Fe in the environment. Excess iron causes endemic water quality problem in India (Singh et al. 2013b). The concentration of Mn ranged from 1.3 to 139 µg L−1 in the post-monsoon and 2.6 to 191 µg L−1 in the pre-monsoon, exceeding the desirable limit of 100 µg L−1 in 9 % and 24 % groundwater samples in the post-monsoon and pre-monsoon season respectively. High concentrations of Mn in the groundwater are due to the geological formation of the area (Senapaty and Behera 2012). The concentration of Ni ranged from 0.5 to 36 µg L−1 (average 9.4 µg L−1) in the post-monsoon season and 1.0 to 44 µg L−1 (average 21 µg L−1) in the pre-monsoon season, exceeding the desirable limit of 20 µg L−1 in 18 % and 52 % groundwater samples in the post-monsoon and pre-monsoon respectively. The Al, As, Cr, Cu, Ba, Se, and Zn concentration was higher in the pre-monsoon as compared to post-monsoon season irrespective of the locations.

Fig. 2
figure 2

Concentration contour showing spatial distribution for Fe in groundwater throughout the study area in the a post-monsoon season, b pre-monsoon season

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Generally, the concentrations of the metals are higher in the pre-monsoon, while the lowest concentration was found in the monsoon and post-monsoon seasons which represent the heavy rainfall (Giri et al. 2012; Tiwari et al. 2015). The overall decrease in the relative concentration of metals from summer to monsoon is due to heavy local precipitation and seepage which not only dilutes the metals but also aids in their migration (Ramesh et al. 1995). It has been observed that water chemistry in mineralised areas with strong wet to dry rainfall patterns demonstrates a marked seasonality, with all solute concentrations decreasing during the rainy season owing to dilution through rainfall recharge. The fluctuations are due to the seasonality in groundwater flow, with high recharge and discharge during the rains and little to no further groundwater discharge by the middle dry season (Heyden and New 2004). This type of hydrologic response is expected to be the primary reason for the higher concentration of metals observed in the pre-monsoon season as compared to the post-monsoon season (Tiwari et al. 2015). The recharge of the groundwater in the monsoon, leads to the dilution of the heavy metals thus reducing its concentration. Similar observation have been made in the studies where monsoon is predominant pattern of rainfall (Dhakate and Singh 2008; Giri et al. 2012).

Principal component analysis (PCA) was adopted to assist the interpretation of elemental data. The PCA of groundwater showed that the variables were correlated to two principal components in which 62.7 % of the total variance was justified. Two factor with eigenvalues >1 have been extracted from the principal factor matrix after varimax rotation. The first factor (PC-I) seemed to be associated to the earth’s crust and the geological formation of the area. The first component with 46.7 % of variance comprises Cu, Al, As, Ba, Mn, Cr, and Zn with high loadings (Table 2). The second component (PC-II) contributed Ni, Fe and Se and account for 16 % of the total variance. This factor can be attributed to the mining activities of the area. Mine effluent and leachate water from overburden dumps material of coal are the sources of these metals (Tiwari 2001; Bhuiyan et al. 2010).

Table 2 Principal component loadings (varimax normalized) for the metals in groundwater of the West Bokaro coalfield
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In order to calculate the HPI of the groundwater, the mean concentration value of the selected metals (Al, As, Ba, Cr, Cu, Fe, Mn, Ni, Se and Zn) have been taken into account. The values of HPI was to be found in the range of 3–17 (mean 7) in the post-monsoon and 4–32 (mean 12) in the pre-monsoon season. The higher values of HPI have been found at sites 7 and 9 in the post-monsoon season and at sites 1, 2, 7, 9, 14, 17, 26, 28 in the pre-monsoon season. The higher values of HPI may be attributed to weathering of rocks and mining activities. Lower HPI values in the post-monsoon season indicate pollutants dilution affect of rain water. The HPI values of the samples within study area were found below the critical pollution index (100) in both seasons. However, considering the classes of HPI, all the locations for both seasons fall under the low class (HPI < 15), medium class (HPI 15–30) and high class (HPI > 30) (Fig. 3a, b). In the post-monsoon season the percentage (%) of HPI classes varied from low class (94 %) to medium class (6 %). However, in the pre-monsoon season the percentage (%) of HPI classes ranged from low class (79 %), medium class (18 %) and 3 % of groundwater samples belong to high class.

Fig. 3
figure 3

Heavy metal pollution index class map of the West Bokaro coalfield area in the a post-monsoon season, b pre-monsoon season (after Edet and Offiong 2002)

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In conclusion, the concentrations of dissolved metals (Al, As, Ba, Cr, Cu, Fe, Mn, Ni, Se and Zn) and HPI values in the groundwater of the West Bokaro coalfield area, demonstrated great seasonality. Seasonal variations in the HPI values showed that 21 % samples in the pre-monsoon season and 6 % samples in the post-monsoon season are not suitable for direct consumption. High HPI were observed at one location (Hesagara) near to mining area. All the other locations fall under low to medium classes of HPI in both seasons. Concentrations of the Al, Fe, Mn, and Ni are exceeding the desirable limits in many groundwater samples in both seasons and require suitable treatment before its utilization. The higher values of metals in the groundwater samples are attributed to the geological and mining sources.