Introduction

A significant source of heavy metal contamination of the environment is mining and processing of metal ores (Dudka and Adriano 1997; Navarro et al. 2008; Singh et al. 2005). Excessive buildup of heavy metals in agricultural soils around mining areas either by aerial depositions or by irrigation is likely to induce a corresponding elevation of metals in the harvested crops (Nan et al. 2002). The accumulation of the metals in the crops either can be from the contaminated soils and water via plant roots or can be through direct deposition of contaminants from the atmosphere onto plant surfaces (McBride 2003). Since a major route for heavy metal exposure to human is through soil–crop–food pathway, cultivation of crops on contaminated soil can potentially lead to the uptake and accumulation of trace metals in the edible plant parts and then exert potential risk to the local inhabitants (Gupta and Gupta 1998; Adriano 2001; McLaughlin et al. 1999; Järup 2003; Monika and Katarzyna 2004; McBride 2007).

Earlier studies show that heavy metal pollution of mined areas caused health damage to the local inhabitants (Kachenko and Singh 2006; Liu et al. 2005; Pruvot et al. 2006). It is known that serious systemic health problems like malfunction of organs and chronic syndromes may be caused by ingestion of relatively low doses of toxic heavy metals over a long period (Oliver 1997). Thus, information about heavy metal concentrations in food products and their dietary intake is very important for assessing their risk to human health (Zhuang et al. 2009a, b).

As per the statistical data provided by the Food and Agriculture Organization (FAO) (2004), approximately 30% of the dietary energy supply and 20% of the dietary protein intake around the world are provided by rice. As rice is the main staple food in India, especially in the region of our study area, heavy metals in rice may have a major contribution to the total metal intake. Therefore, there is an essentiality for the study of heavy metal levels in rice from the copper mining areas of East Singhbhum. The objectives of this study were to investigate the concentrations of metals in rice (Oryza sativa L.) samples in the mining areas of Singhbhum Copper Belt in India and to assess the associated potential health risk to local inhabitants through consumption of this crop. The hazard quotient (HQ) and hazard index (HI) were used to assess non-carcinogenic health risks from individual metals and the combined health risk from all the studied metals, respectively.

Materials and methods

Site description

The study was carried out in the vicinity of mining and processing industries in the Singhbhum Copper Belt of Jharkhand, which is rich in mineral resources and copper, iron, uranium, gold, and kyanite.

Sampling and analysis

Samples were collected from 14 locations in the environs of the copper mining and processing facilities (Fig. 1). At each site, approximately 500 g of rice was collected in April 2016. All the samples were collected in triplicates. Rice grain samples were washed with deionized water. The rice grain without hull was oven-dried at 70 °C for 72 h and then ground with an agate mortar to fine powder. A microwave-assisted digestion procedure was used (Schuhmacher et al. 1993). Of homogenized samples, 0.5 g was digested under pressure in Teflon vessels with 6 mL of nitric acid and 2 mL of hydrogen peroxide. On completion of the digestion and after adequate cooling, solutions were filtered and made up to 50 mL with 1% nitric acid. Each rice sample was collected along with the corresponding soil and water samples for correlation purposes. Each soil sample with about 1 kg was taken from the depths of 0–15 cm, which represented the plow layer. Then, the soil samples were packed and sealed in polyethylene bags and brought to the laboratory, where they were air-dried at room temperature (25–30 °C). The coning and quartering method was employed for mixing the samples, and about 100 g of the sample was taken. The samples were then transferred into a porcelain dish and dried in an oven at 110 °C for 24 h. The samples were powdered in a dry mortar–pestle and sieved through standard sieve of 200 mesh size. The samples were then subjected to digestion in microwave by the method 3052 as given by USEPA (1996) for total metal concentration. Aliquots were preserved for the analysis of metals. Water samples for metal analysis were collected, filtered with a Millipore filter paper (pore size 0.45 lm), and preserved by lowering the pH to >2 by addition of 6 N ultrapure nitric acid (Radojevic and Bashkin 1999).

Fig. 1
figure 1

Map of the study area with sampling locations

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Determination of metals

Concentrations of As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Se, V, and Zn were determined in rice, water, and soil samples using inductively coupled plasma-mass spectrometry (ICP-MS, PerkinElmer Elan DRC-e). A calibration blank and an independent calibration verification standard were analyzed every 15 samples to confirm the calibration status of the ICP-MS. Matrix interference (blank) was <1% for all elements. Triplicates of sample analysis yielded relative percent differences of <5%.

Quality assurance and quality control

Standard reference material (SRM) of rice flour (SRM 1568b) supplied by the National Institute of Standards and Technology (NIST), USA, was used for validation of the analytical procedure. The heavy metal contents found in the SRMs were in good agreement with the certificate values, which confirmed the feasibility of the analytical protocols in the determination of heavy metals in rice (Table 1). The accuracy and precision were checked by analyzing the certified reference material under the same conditions. The percentage of recovery varied from 92.5 to 105.9.

Table 1 Heavy metal concentration in certified reference material
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Statistical analysis of the data

All statistical analyses were done using SPSS 16.0 for Windows software package. Prior to statistical analyses, all datasets were checked for normality of distribution using the curve of accumulated frequency (Miller and Miller 1989). As recurrently encountered in environmental samples, the distributions of the concentrations of the metals were not elucidated by normal distribution and were better represented by the log-normal distribution. Thus, the central tendency is represented by the geometric mean (Wayne 1990). The correlation analysis was conducted by Pearson correlation. Correlation is significant at the 0.01 level (two tailed).

Principal component analysis

Principal component analysis (PCA) is a variation reduction technique in which a number of measured parameters can be transformed into a small number of artificial variables known as principal components (PC). The number of extracted PC is equal to the number of variables included in the analysis; however, normally, PCs having eigenvalues >1 are taken into consideration as they account for the maximum variance in the observed parameter (Huang et al. 2014; Kolsi et al. 2013). Varimax rotation is applied to all the extracted PC to reduce the contribution of the variables which are not significant (Closs and Nichol 1975). Each variable considered for PCA has a weight factor associated with it. This weight factor, also referred to as PC score, is the correlation between the original variable and a factor. A PC score close to ±1 indicates a strong correlation between the given variable and the factor.

Metal pollution index

To examine the overall heavy metal concentrations in the rice samples of a location a, metal pollution index (MPI) was computed (Eq. 1). This index was obtained by calculating the geometrical mean of concentrations of all the metals in the rice (Usero et al. 1997).

$$ \mathrm{MPI}\ \left(\mathrm{mg}/\mathrm{kg}\right)={\left({\mathrm{Cf}}_1\times {\mathrm{Cf}}_2\times \dots \dots {\mathrm{Cf}}_n\right)}^{1/ n} $$
(1)

where Cf n  = concentration of metal n in the sample.

Estimated daily intake of heavy metals through rice consumption

The daily intake of metals depends on both the metal concentration in food and the daily food consumption. In addition, the body weight of the human can influence the tolerance of pollutants. The estimated daily intake (EDI) is a concept introduced to take into account these factors. The EDI value for an adult was calculated with the Eq. (2) according to Song et al. 2009 as follows:

$$ \mathrm{EDI}=\left({C}_{\mathrm{metal}}\times {W}_{\mathrm{rice}}\right)/{B}_{\mathrm{w}} $$
(2)

where C metal is the concentration of the heavy metals in the rice samples, W rice stands for the daily average consumption of rice in this region, and B w represents the body weight. Based on the National Sample Survey of India (NSSO 2014), the average daily intake of rice was 199 g per day. The average value of B w used was 52 kg for Indian man (Jain et al. 1995; Dang et al. 1996).

Risk from the intake of metals through ingestion: target hazard quotient

Non-carcinogenic health risks from consumption of rice by the local inhabitants were assessed based on the target hazard quotient (THQ) (Eq. 3). THQ values were calculated by comparing the estimated daily intake (EDI) dose (EDI) (milligrams per kilogram body weight per day) of a chemical to a reference dose (RfD, milligrams per kilogram per day).

$$ \mathrm{THQ}=\mathrm{EDI}/{\mathrm{R}}_{\mathrm{f}}\mathrm{D} $$
(3)

If the ratio is less than 1, the exposed population is unlikely to experience obvious adverse effects. The method of estimating risk using THQ was provided in the US EPA Region III risk-based concentration table (USEPA 2007).

Health risk assessment of multiple metals

The HI was used to estimate total non-carcinogenic health risks of multiple metals on the assumption of dose additivity (Eq. 4).

$$ \mathrm{HI}=\mathrm{THQ}1+\mathrm{THQ}2+.\dots +\mathrm{THQ} n $$
(4)

where THQi is the target hazard quotient of an individual element of metals, HI is the total hazard index for all the 12 metals studied in the present study, and n is 12.

Although dose additivity usually requires that all components act by the same mechanism, the HI is commonly also used as a screening tool for components with the same critical target without regard to mechanism and even for components with different target organs. The HI method does not take into account interactions among the components of the mixture and thus may underestimate the health hazard if the interactions are greater than additive or overestimate the health hazard if the interactions are less than additive (Wilbur et al. 2004).

Results and discussion

Distribution of metals in rice

A wide range of metal concentrations were observed in the rice samples collected from the 14 locations around the copper mining areas of East Singhbhum (Table 2). The average concentration of triplicate samples of each location is given in the table. The geometric mean concentration (mg kg−1, dry weight basis) of As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Se, V, and Zn was 0.065, 0.008, 0.011, 0.281, 4.56, 49.8, 7.3, 0.461, 0.792, 0.033, 0.045, and 27.3, respectively. The metals generally ranked in the order Fe > Zn > Mn > Cu > PB > Ni > Cr > As > V > Se > Co > Cd. The concentrations of metals in the rice samples exceeded the limits of Indian standards for food (Awasthi 2000) for Ni and Pb (1.5 and 2.5 mg kg−1, respectively) in one sample each. In 29% of the samples, the concentration of Zn exceeded the Indian limit of 50 mg kg−1. All the other metal concentrations in rice samples were well within the limits of Indian standards for food.

Table 2 Heavy metal concentrations of rice samples in milligram per kilogram collected from mining areas of Singhbhum Copper Belt (each value is average of 3 samples)
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Table 3 provides the concentrations of metals in the rice samples of the present study compared with those found in some previous studies. The studies represented the areas with strong anthropogenic influence like industrial and mining activities. The metal concentrations in the rice samples varied largely among the different studies, which may be attributed to geology of the area, concentration of metals in the soil and irrigating water, and the anthropogenic activities of the area. However, the metal concentrations of the rice samples in the present study were in range of the other studies.

Table 3 Comparison of metals (mg kg−1) in rice with other studies and Indian guidelines for food
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On analyzing the data, it can be observed that the concentration of the metals varied spatially. The highest concentration of Cd, Co, Cr, Mn, and Ni was observed at Kuldiha, which is located in close proximity to a railway line and a busy roadway. Terenga, which is under both copper mining and vehicular influences, recorded the highest levels of Pb and Zn. Cu concentration was found to be highest at Badia, which is very near to a closed copper mine.

Heavy metal concentrations in water and soil

Water and soil samples were collected from the same locations as that of rice samples for correlation studies. The statistical data along with the standards are presented in Table 4. The comparison of the metal concentrations with the average concentration of metals in the shale (Turekian and Wedepohl 1961) depicted that the levels of almost all the metals exceeded the values. As the study area is characterized by the presence of metal-bearing formations and mining and industrial operations, it is expected to have elevated levels of metals in the soil (Forstner and Wiltman 1983; Preston and Chester 1996). However, the concentrations of metals do not exceed the irrigation water standards for metals as given by FAO (1985).

Table 4 Concentration of heavy metals in water and soil (total metals) collected from mining areas of Singhbhum Copper Belt
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Correlation between heavy metal levels in soil and water with rice

To assess the association between the different matrices, i.e., rice, water, and soil, correlations of the metals were determined and Table 5 presents the coefficients between the different matrices. The results suggested a strong association of As, Cu, Pb, and Zn between the rice samples and corresponding soil samples while a strong correlation for Fe, Se, and V was observed between the rice and water samples. Positive correlations of metals in crops with soils and irrigating water have been reported by many workers (Adams et al. 2004; Liu et al. 2016; Wang et al. 2003; Khan et al. 2008; Singh and Kumar 2006). Since the uptake mechanism of metals includes both adsorption (from soil) and absorption (from water) and takes place from the roots, the uptake of metals through roots depends on the presence of metal concentration in water as well as in the soil. However, the other studied metals, Cd, Co, Cr, Mn, and Ni, were correlated with neither water nor soil.

Table 5 Pearson correlation coefficients of heavy metals between rice and different matrices
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Principal component analysis

Principal component analysis (PCA) was carried out on the heavy metal data of the rice samples in order to better understand their interrelationships and to explore the reduction of the experimental variables. The purpose of PCA is to reduce the dimensionality of the dataset, since a few of the new components explain the major part of the variation of the data. It also helps in assigning source identity to each one of the PCs (Miller and Miller 2000; Yuan et al. 2013, 2014).

The PC loadings, the cumulative percentages for rotated matrix, and the factor scores are presented in Table 6. There were three components with eigenvalues >1, which accounted for 79.1% of the total variance. The loading factors indicate that the first principal component (PC1) was associated with Cd, Co, Cr, Mn, and Ni explaining 36.5% of variance. The factor seems to be associated with air deposition. A number of studies have reported the increase of metals in the cereals and vegetables due to the atmospheric dust deposition (Harrison and Chirgawi 1989; Camazano et al. 1994; Voutsa et al. 1996; Pandey and Pandey 2009; Feng et al. 2011; Sharma et al. 2008). The second component explained 26.6% of variation in the data and is underlined by significant factor loading for As, Cu, Pb, and Zn while the third factor having high loadings for Fe, V, and Se explicated 16% of the variance. The PC2 and PC3 can be related to the concentration of the metals in the soil and irrigating water, respectively. This suggests that the water is more conducive for Fe, V, and Se for their occurrence in the rice samples while for As, Cu, Pb, and Zn, soil environment is more contributing for their uptake in the rice samples of the study area. This is in also correspondence to the correlation studies which deduced similar inference. However, the concentration of metals in soil, water, or air of an area in turn may be dependent on geogenic sources and anthropogenic sources like mining, industrial, and vehicular activities (Yuan et al. 2013; Giri and Singh 2016).

Table 6 Principal component loadings (varimax normalized) for the metals in the rice samples collected from mining areas of Singhbhum Copper Belt
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Metal pollution index and human exposure to metals through rice

MPI is suggested to be a reliable and precise method for metal pollution monitoring in dietary components. It has been used to study the difference of total metals between the locations or species (Singh et al. 2010; Usero et al. 1997). In the present study, MPI is used to evaluate the variation of total metals among the locations (Fig. 2). The highest MPI was calculated at Terenga followed by Benashol. Both the locations are in the close proximity to copper mining and processing industries and a highway.

Fig. 2
figure 2

Metal pollution Index in different locations

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Estimation of daily intake of heavy metals by consuming rice

To evaluate the health risk associated with heavy metal contamination of rice grown in the vicinity of copper mining areas of East Singhbhum, estimated dietary intake (EDI) and target hazard quotients (THQs) were calculated with an assumption that local population consumes the local rice. The comparison of EDI with provisional tolerable daily intake (PTDI) as provided by the Joint FAO/WHO Expert Committee on Food Additives (JECFA) (1999, 2003) for all the studied metals is shown in Table 7. In the present study, it is to be noted that the RfD of 3, 5, and 5 μg/kg day−1 for Cr, Se, and V, respectively, established by USEPA (2011) is taken as the PTDI value because of their unavailable PTDI data. The PTDI and RfD values for arsenic as established by WHO and EPA are for inorganic arsenic. However, the arsenic content estimated in the rice samples was total arsenic, which includes both organic and inorganic types. It is estimated that 35% of the total arsenic in rice is inorganic (WHO Environmental Health Criteria 224 2001). Since there is no standard value for total arsenic intake for an adult, we have calculated the EDI and HQ for As taking this factor into consideration.

Table 7 Estimated dietary intake (EDI) of heavy metals by consuming locally grown rice from mining areas of Singhbhum Copper Belt
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From the EDI of the studied heavy metals through the consumption of rice, it can be suggested that the consumption of average amounts of the rice from the study area does not pose a health risk for the consumers as the values obtained are below the FAO/WHO (JECFA 1999) limits for heavy metal intake based on the body weight of an average adult. The present study showed that the contributions of the local rice consumed in the area to daily intake of As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Se, V, and Zn were 4.1, 3.06, 0.008, 35.8, 3.5, 2.8, 19.9, 35.3, 84.9, 2.51, 3.42, and 10.4%, respectively, of PTDI as suggested by JECFA. Lower dietary intake was observed for Cd, Co, Cu, Se, and V as compared to other metals. Maximum EDI was depicted for Pb followed by Cr and Ni. The EDI value of Pb is high in all the locations studied, and highest EDI was calculated for Terenga, which was about 5.5 times more than the tolerable intake. The other metals whose EDI was exceeding the PTDI at some of the locations were Cr, Fe, Ni, and V. Thus, perennial intake of these contaminated cereals is likely to induce adverse health effects arising largely from Pb, Ni, and Cr exposure at some of the locations.

Risk assessment of heavy metals by consuming rice

THQ has been recognized as a useful parameter for evaluation of risk associated with the consumption of metal-contaminated food crops (Rupert et al. 2004; Sridhara Chary et al. 2008; Zhuang et al. 2009a, b). The THQ values of heavy metals by consuming rice for an individual adult are shown in Table 8. The results show that the mean THQ values for the metals are below 1, highest being for the Pb (0.87). The THQ for the metals from rice consumption is in decreasing order of Pb > Cu > Cr > Zn > As > Fe > Mn > Co > Ni > V > Cd > Se. However, at some of the locations, the THQ values for Co, Cr, Cu, Fe, Pb, V, and Zn are higher than 1, which showed that the inhabitants around the copper mining areas are experiencing relatively high health risk. The local consumers may also be at risk due to the combination of several heavy metals. The HI values for all the locations varied from 2.24 to 12.7 with a geometric mean value of 3.09 indicating an appreciable risk on the human body. Pb, Cu, and Cr are the key components contributing to potential non-carcinogenic risk while V, Cd, and Se are the least contributing. Heavy metals have a toxic impact, but detrimental impacts become apparent only when long-term consumption of contaminated food occurs. Thus, a prolonged consumption of unsafe concentrations of heavy metals through cereals may lead to the chronic accumulation of heavy metals in the kidney and liver of humans causing disruption of numerous biochemical processes, leading to cardiovascular, nervous, kidney, and bone diseases. It is therefore suggested that regular monitoring of heavy metals in dietary components should be performed in order to prevent excessive buildup of these heavy metals in the human food chain.

Table 8 Target hazard quotients (THQs) of heavy metals by consuming locally grown rice from mining areas of Singhbhum Copper Belt
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It must be noted that our assessment only considered the intake of toxic elements via rice consumption. In fact, humans are also exposed to heavy metals through the dietary intake of other food items such as wheat, vegetables, fruit, fish, meat, eggs, milk, and water (Wang et al. 2005; Zheng et al. 2007; Chary et al. 2008; Sipter et al. 2008). Furthermore, there are other pathways for exposure to metals such as dust inhalation, dermal contact, and ingestion (for children) of metal-contaminated soils (Grasmück and Sholz 2005; Hellström et al. 2007). Therefore, the overall potential health risks due to heavy metals to local residents are far beyond the scope of our present study.

Conclusion

From the estimation of metal concentration in the rice samples collected from this study, we obtained a better knowledge regarding the impact of the copper mining and processing operations on the environment and the potential risk to human health. The concentrations of all the metals in the rice grains were below the national MAL except for Pb, Ni, and Zn at some locations. The metal concentration in the corresponding soil samples exceeded the average shale values for almost all the metals. The results furthermore suggested a strong association of the As, Cu, Pb, and Zn between the soil and the rice samples and Fe, Se, and V between water and soil samples. PCA suggests that the source of metals in the rice can be attributed to soil, irrigating water, and atmospheric dust deposition. High potential health risks of heavy metal exposure from rice consumption were depicted based on the estimated daily intake (EDI) of heavy metals and the target hazard quotient (THQ). The estimations of daily intakes of heavy metals for local adults were higher than the tolerable daily intakes provided by WHO at some of the locations for Cr, Fe, Ni, and V indicating potential health risk for the local inhabitants through consumption of contaminated food crops. The local consumers are also likely to be at risk due to the synergistic effect of the metals. The HI values for all the locations varied from 2.24 to 12.7 with a geometric mean value of 3.09 indicating an appreciable risk on the human body. Thus, the local inhabitants due to long-term consumption of the rice grown in vicinity of the copper mining area may bear a potentially serious health risk, which would further elevate if other locally grown food items are also considered. Relevant data for this are still limited, and the implications for human health should be identified by a further detailed study.