Human health risk assessment due to metals in cow’s milk from Singhbhum copper and iron mining areas, India

Abstract

The concentration of Al, As, Ba, Co, Cr, Cu, Fe, Mn, Ni, Pb, Se and Zn were determined in the milk collected from the locally rearing cows from the vicinity of copper mining areas of East Singhbhum and iron mining areas of West Singhbhum using inductively coupled plasma-mass spectrometry for a risk assessment and source apportionment study. Principal component analysis suggested both natural and anthropogenic activities as causative sources of metals in the milk. The hazard indices ranged from 0.26 to 0.89 with a mean of 0.56 in the iron mining areas and 0.29–1.89 with a mean of 1.17 in the copper mining areas due to ingestion of milk, which indicated that the risk is negligible in the iron mining areas while there is an appreciable risk to the health of consumers of milk in the copper mining areas.

Introduction

Amidst the rapid developmental activities, one of the most serious problems that have emerged for the human society is the metal contamination of the environment. One of the sources of metals in the environment can be attributed to the metal mining and processing industries (Navarro et al. 2008). The metals in the environmental components like soil and water can easily be transferred to the plants, livestock and ultimately human beings through the food chains causing serious human health issues. Not only the plants, but food products of animal origin also depict high levels of metals due to bioaccumulation (Licata et al. 2012). Grazing of the livestock on the contaminated soil and feeding them with contaminated feed and water are the probable sources of metals accumulation in the meat and milk.

Milk is regarded as a nearly complete food for its being a good source of proteins, fats, minerals and vitamins and thus consumed widely by all age group of people worldwide (Malhat et al. 2012). Milk is also an ideal source of macroelements like P, K, Ca and microelements like Fe, Zn, Se, Cu, etc. However, the contamination of milk with toxic metals at very low levels as well as concentration of essential metals at high levels beyond limits both can cause toxicity to the consumers (Li et al. 2005).

Singhbhum craton and shear zones of Eastern India harbors one of the largest deposits of iron and copper, respectively. The regions are under the extensive influence of metal mining and processing industries thus paving the way for metal contamination of the environment and subsequently the food chains. Considering these facts, the present study was undertaken to examine the metal concentrations in the milk of the locally rearing cows. Statistical source apportionment of the metals and human health risk assessment due to consumption of the milk is also addressed in the study.

Materials and methods

Milk samples from locally rearing cows were collected from twelve locations each from the vicinity of copper mining areas from East Singhbhum and iron mining areas from West Singhbhum. The milk samples were procured from the owners of the cows who consented for the use of the milk samples for the study. Sampling was carried out in the month of June 2016 in triplicates. Preservation of the milk samples was carried out using 37% formaldehyde (3 mL per liter of milk) immediately after collection (Douglas 1967). The samples were acid digested using mixture of nitric acid and perchloric acid on hotplate (Richards 1968) and aliquots were preserved for the metal analysis. Concentrations of the metals (Al, As, Ba, Co, Cr, Cu, Fe, Mn, Ni, Pb, Se and Zn) were estimated in the milk samples using inductively coupled plasma-mass spectrometry (ICP-MS, Perkin Elmer Elan DRC-e).

The method of analysis was validated using the standard addition method and thus calculating the recoveries. The recoveries varied from 95.8 to 100% for the studied metals. Calibration standards were regularly analysed to ensure the stability of the instrument.

Principal component analysis (PCA) is a statistical method applied to reduce the number of measured variables into a smaller number of artificial variables that are named principal components (PC). The number of extracted PC that are taken into consideration are the ones which have eigenvalues > 1 and they justify the maximum variance in the dataset (Kolsi et al. 2013). A weight factor is associated with each variable considered for PCA which is referred to PC score. It is the correlation between the original variable and the factor. A PC score close to ± 1 indicates a strong positive or negative correlation between the given variable and the factor.

Estimated daily intake (EDI) was calculated taking the geometric mean concentration of the metals in milk (mg L¹) and per day consumption of 144 mL of milk for Indian population (NSSO 2014). The estimated daily intake (EDI) of metals was calculated with Eq. (1) (Song et al. 2009)

$$\text{EDI}=\frac{{\text{C}}_{\text{metal}}\times {\text{W}}_{\text{milk}}}{{\text{B}}_{\text{w}}}$$ 1

where EDI is the estimated daily intake of metal through milk for an adult (in mg kg⁻¹ d⁻¹), C_metal is the estimated concentration of metal in milk (in mg L⁻¹), W_milk stands for the daily consumption of milk (in L d⁻¹), and B_w is the body weight of an Indian adult (in kg) which is used as 52 kg for the study (Dang et al. 1996).

Target hazard quotient (THQ) as proposed by the US Environmental Protection Agency (US EPA 1989) is the ratio of the estimated daily intake (EDI; mg kg⁻¹ d⁻¹) of a metal to the oral reference dose of that metal (R_fD, mg kg⁻¹ d⁻¹). It can also be defined as the maximum tolerable daily intake of a specific metal that does not result in any adverse health effects (Eq. 2):

$$\text{THQ}=\text{EDI}/{\text{R}}_{\text{fD}}$$ 2

If the value of THQ is greater than unity, there is a probability of harmful health effects for the exposed population. Considering the risk assessment of multiple metals in milk, hazard index (HI) was used which is the sum of all the calculated THQ values of studied metals (Eq. 3). HI > 1 suggests a potential for detrimental effect on human health and advocates further detailed study (USEPA 1989). This approach is based on EPA’s Guidelines for Health Risk Assessment of Chemical Mixtures.

$$\text{HI}=\sum _{i=1}^n{\text{THQ}}_{\text{i}}$$ 3

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

Results and discussion

Distribution of metals in milk

Large variations were observed in the metal concentrations of milk collected from both the study areas (Table 1). The concentrations provided are the mean of the triplicate samples collected from each location. The geometric concentration (in mg L⁻¹) of Al, As, Ba, Co, Cr, Cu, Fe, Mn, Ni, Pb, Se and Zn in copper mining areas of East Singhbhum and iron mining areas of West Singhbhum was 0.507, 0.015, 0.340, 0.033, 0.153, 0.509, 8.843, 0.280, 0.624, 0.129, 0.084, 1.224 and 0.268, 0.010, 0.339, 0.008, 0.058, 0.312, 11.404, 0.549, 0.201, 0.089, 0.036, 1.035, respectively.

Table 1.

Mean metal concentrations (mg L⁻¹) in Cow’s milk collected from East Singhbhum (copper) and West Singhbhum (iron) mining areas, India (each value is average of 3 samples)

S.No.	Location	Al	As	Ba	Co	Cr	Cu	Fe	Mn	Ni	Pb	Se	Zn
East Singhbhum
1	Kuldiha	0.147	0.003	0.021	0.012	0.099	0.186	2.799	0.019	0.117	0.008	0.013	0.370
2	Kutludih	1.305	0.033	0.584	0.036	0.162	1.976	27.830	1.585	0.840	0.503	0.116	1.898
3	Pathergora	0.890	0.027	0.415	0.028	0.147	0.300	7.271	0.457	0.797	0.161	0.088	1.152
4	Rakha	0.338	0.011	0.399	0.023	0.143	0.231	5.743	0.064	0.762	0.039	0.089	1.240
5	Sohada	0.379	0.006	0.443	0.048	0.161	0.178	7.252	0.332	0.725	0.724	0.119	2.013
6	Badia	0.561	0.007	0.555	0.073	0.195	2.319	19.589	1.580	0.978	0.011	0.102	0.901
7	Ghatsila	0.313	0.010	0.441	0.039	0.196	0.270	10.580	0.096	0.568	0.204	0.135	1.779
8	Mahulia	1.106	0.022	0.495	0.033	0.111	0.289	4.363	0.679	0.728	0.454	0.065	0.957
9	Bhatin	0.271	0.031	0.368	0.017	0.160	0.219	4.549	0.054	0.665	0.072	0.058	1.695
10	Mosabani	0.652	0.023	0.215	0.090	0.125	3.250	6.980	0.256	1.750	0.210	0.080	1.258
11	Terenga	0.253	0.016	0.658	0.040	0.205	0.830	11.210	0.315	0.250	0.850	0.110	3.125
12	Kalikapur	1.526	0.042	0.412	0.020	0.169	0.620	30.120	1.789	0.690	0.090	0.180	0.548
	Geomean	0.507	0.015	0.340	0.033	0.153	0.509	8.843	0.280	0.624	0.129	0.084	1.224
West Singhbhum
1	Karampada	0.430	0.003	0.425	0.009	0.127	0.216	37.025	1.162	0.252	0.058	0.041	0.873
2	Chota nagra	0.474	0.038	0.503	0.017	0.019	0.436	5.874	0.352	0.324	0.003	0.062	0.498
3	Bahda	0.219	0.001	0.327	0.003	0.005	0.292	2.100	0.162	0.120	0.168	0.024	1.506
4	Sedal	0.258	0.009	0.254	0.009	0.121	0.214	21.360	0.914	0.214	0.536	0.029	2.698
5	Gua	0.314	0.012	0.334	0.007	0.138	0.313	25.024	1.025	0.193	0.025	0.047	0.547
6	Noamundi	0.387	0.024	0.496	0.014	0.111	0.411	19.240	0.845	0.291	0.036	0.059	0.698
7	Tentarighat	0.125	0.019	0.217	0.013	0.058	0.245	10.250	0.238	0.246	0.057	0.031	1.147
8	Bokna	0.224	0.006	0.229	0.003	0.089	0.309	12.780	0.359	0.156	0.254	0.022	1.028
9	Banker	0.308	0.021	0.347	0.005	0.119	0.358	9.690	0.783	0.183	0.318	0.041	2.147
10	Dangoaposi	0.169	0.007	0.411	0.011	0.093	0.396	13.560	0.665	0.149	0.211	0.036	0.893
11	Bichaikiri	0.254	0.009	0.314	0.009	0.086	0.299	9.142	0.515	0.214	0.254	0.029	0.968
12	Kotgarh	0.269	0.014	0.354	0.007	0.015	0.345	6.548	0.625	0.168	0.069	0.034	1.025
	Geomean	0.268	0.010	0.339	0.008	0.058	0.312	11.404	0.549	0.201	0.089	0.036	1.035

Compared to the Indian standards for food, none of the metals from both the study areas exceeded the limits. While comparing both the study areas, it can be observed that most of the metals have higher concentrations in the milk samples collected from the copper mining areas as compared to the iron mining areas except for Fe and Mn which were found to be higher in the samples collected from iron mining areas.

Principal component analysis

Inter-elemental relationships can be studied by correlation analysis and significant positive correlations indicate coexistence in the environment. However, advanced multivariate statistical tools like principal component analysis (PCA) provide a better picture regarding the source apportionment of the metals (Chabukdhara and Nema 2012). PCA was applied on the elemental data of milk samples from both the study areas to assist the interpretation of the data.

For the data of copper mining area, four principal components (Table 2) with eigenvalue greater than unity collectively explicated for 87.9% of the total variance. Metals such as Al, As, Ba, Cr, Fe, Mn and Se showed high loadings for first and second components (PC1 and PC2) accounted for 46.7% of variance and can be related to the geological formations of the study area. The third factor (PC3) expounds 21.8% of total variance, with Cu, Ni and Co contributing highly to this component. This factor can be attributed to the copper mining and processing activities of the area (Ikenaka et al. 2010; Giri and Singh 2015). The fourth component (PC4) explained 19.5% of variance, with Ba, Pb and Zn being the major contributors. This component seemed to be associated with anthropogenic sources, like fossil fuel combustion, industrial activities (electroplating, manufacturing of alloys, etc.) and vehicular sources (brake linings, catalytic converters, tires, fuel additives) (Giri and Singh 2015; Kennedy and Gadd 2000).

Table 2.

Principal component loadings (varimax normalized) for the metals in the copper mining areas of East Singhbhum, India

Elements	PC1	PC2	PC3	PC4
Al	0.206	0.944	0.111	− 0.089
As	0.067	0.882	− 0.006	0.052
Ba	0.619	0.188	0.016	0.628
Co	0.129	− 0.183	0.947	0.131
Cr	0.865	− 0.206	0.009	0.366
Cu	0.145	0.124	0.923	− 0.049
Fe	0.761	0.565	0.118	− 0.091
Mn	0.634	0.630	0.198	− 0.186
Ni	− 0.110	0.240	0.893	− 0.083
Pb	0.037	0.054	− 0.010	0.921
Se	0.776	0.346	0.049	0.196
Zn	0.199	− 0.234	− 0.003	0.916
Eigenvalues	4.348	2.868	2.309	1.019
% Total variance	23.8	22.8	21.8	19.5
Cumulative variance	23.8	46.6	68.4	87.9

For the concentrations of metals in the milk samples from the iron mining areas, three principal components (Table 3) with eigenvalue greater than 1 were extracted which together elucidated 81.2% of the total variance. The first component has high loadings for Al, As, Ba, Co, Cu, Ni and Se and can be related to the natural sources. It explained 36.3% of the total variance. The second component (PC2) illustrates 25.3% of total variance and is emphasized by major factor loading for Cr, Fe and Mn. This factor can be linked to the iron mining activities of the study area. The third component (PC3) was associated with Pb and Zn and may be ascribed to the vehicular activities of the area.

Table 3.

Principal component loadings (varimax normalized) for the metals in the iron mining areas of West Singhbhum, India

Elements	PC1	PC2	PC3
Al	0.677	0.389	− 0.292
As	0.905	− 0.286	− 0.029
Ba	0.683	0.139	− 0.505
Co	0.813	− 0.031	− 0.152
Cr	0.005	0.860	0.223
Cu	0.566	− 0.391	− 0.362
Fe	0.001	0.967	− 0.111
Mn	0.141	0.932	0.009
Ni	0.858	0.192	− 0.104
Pb	− 0.270	0.089	0.890
Se	0.878	0.203	− 0.369
Zn	− 0.190	0.049	0.924
Eigenvalues	5.371	3.048	1.329
% Total variance	36.3	25.3	19.6
Cumulative variance	36.3	61.6	81.2

Risk assessment of heavy metals due to consumption of milk

To evaluate the risk associated with the human health due to the consumption of milk in the mining areas, Estimated daily intake (EDI), target hazard quotients (THQ) and hazard index (HI) were calculated with a supposition that the local population consumes only the milk from the locally reared cows. The EDI values of metals for an adult through the consumption of milk for both the study areas are shown in Table 4.

Table 4.

Estimated dietary intakes (EDI) of metals by consuming milk collected from East Singhbhum (copper) and West Singhbhum (iron) mining areas, India

Elements	East Singhbhum	West Singhbhum	PTDIa
Al	1.757	0.792	285
As	0.052	0.038	2.14
Ba	1.139	0.972	20
Co	0.104	0.025	500
Cr	0.432	0.226	3
Cu	2.381	0.885	500
Fe	31.34	39.83	800
Mn	1.599	1.764	140
Ni	2.022	0.579	5
Pb	0.736	0.459	3.57
Se	0.264	0.105	5
Zn	3.868	3.237	1000

EDI values, in mg kg body wt⁻¹ d⁻¹

^aPTDI values (in mg kg body wt⁻¹ d⁻¹) of all the metals were based on the data suggested by The Joint FAO/WHO Expert Committee on Food Additives (JECFA 2003). The PTDI values of Cr is based on the reference doses (RfD) of Cr(VI) established by U.S. Environmental Protection Agency (2011)

The values are lower than the provisional tolerable daily intake (PTDI) values as given by FAO/WHO (JECFA 2003) and thus it can be suggested that considering the PTDI, the consumption of milk in both the study areas does not pose health risk for consumers. The THQ values for the metals considering the consumption of milk by an adult are depicted in Table 5. It can be seen from the table that the highest HQ values are obtained for Co followed by Pb in case of the copper mining areas of East Singhbhum. For the iron mining areas of West Singhbhum, the highest HQ values were calculated for the Pb followed by As. However, for none of the metals, the HQ was greater than unity for both the locations and thus can be said that the metals do not pose risk individually to the consumers of the milk.

Table 5.

Target hazard quotients (THQ) of metals by consuming milk collected from East Singhbhum (copper) and West Singhbhum (iron) mining areas, India

S.No.	Location	Al	As	Ba	Co	Cr	Cu	Fe	Mn	Ni	Pb	Se	Zn	HI
East Singhbhum
1	Kuldiha	0.0004	0.0259	0.0003	0.1108	0.0914	0.0129	0.0111	0.0004	0.0162	0.0066	0.0073	0.0034	0.287
2	Kutludih	0.0036	0.3007	0.0081	0.3295	0.1497	0.1368	0.1101	0.0313	0.1163	0.3981	0.0641	0.0175	1.666
3	Pathergora	0.0025	0.2521	0.0057	0.2538	0.1359	0.0208	0.0288	0.0090	0.1104	0.1275	0.0488	0.0106	1.006
4	Rakha	0.0009	0.1031	0.0055	0.2086	0.1320	0.0160	0.0227	0.0013	0.1055	0.0306	0.0491	0.0114	0.687
5	Sohada	0.0010	0.0511	0.0061	0.4394	0.1483	0.0123	0.0287	0.0066	0.1004	0.5730	0.0658	0.0186	1.451
6	Badia	0.0016	0.0632	0.0077	0.6720	0.1799	0.1606	0.0775	0.0313	0.1354	0.0085	0.0564	0.0083	1.402
7	Ghatsila	0.0009	0.0950	0.0061	0.3609	0.1813	0.0187	0.0419	0.0019	0.0786	0.1612	0.0747	0.0164	1.038
8	Mahulia	0.0031	0.2015	0.0069	0.3046	0.1022	0.0200	0.0173	0.0134	0.1007	0.3593	0.0362	0.0088	1.174
9	Bhatin	0.0007	0.2846	0.0051	0.1560	0.1478	0.0152	0.0180	0.0011	0.0920	0.0567	0.0319	0.0156	0.825
10	Mosabani	0.0018	0.2123	0.0030	0.8308	0.1154	0.2250	0.0276	0.0051	0.2423	0.1662	0.0443	0.0116	1.885
11	Terenga	0.0007	0.1477	0.0091	0.3692	0.1892	0.0575	0.0443	0.0062	0.0346	0.6725	0.0609	0.0288	1.621
12	Kalikapur	0.0042	0.3877	0.0057	0.1846	0.1560	0.0429	0.1192	0.0354	0.0955	0.0712	0.0997	0.0051	1.207
West Singhbhum
1	Karampada	0.0012	0.0266	0.0059	0.0785	0.1172	0.0150	0.1465	0.0230	0.0349	0.0459	0.0225	0.0081	0.525
2	Chota nagra	0.0013	0.3505	0.0070	0.1569	0.0171	0.0302	0.0232	0.0070	0.0449	0.0025	0.0345	0.0046	0.680
3	Bahda	0.0006	0.0116	0.0045	0.0305	0.0042	0.0202	0.0083	0.0032	0.0166	0.1326	0.0131	0.0139	0.259
4	Sedal	0.0007	0.0831	0.0035	0.0831	0.1117	0.0148	0.0845	0.0181	0.0296	0.4241	0.0161	0.0249	0.894
5	Gua	0.0009	0.1108	0.0046	0.0646	0.1274	0.0217	0.0990	0.0203	0.0267	0.0198	0.0260	0.0050	0.527
6	Noamundi	0.0011	0.2215	0.0069	0.1292	0.1025	0.0285	0.0761	0.0167	0.0403	0.0285	0.0327	0.0064	0.690
7	Tentarighat	0.0003	0.1754	0.0030	0.1200	0.0535	0.0170	0.0405	0.0047	0.0341	0.0451	0.0172	0.0106	0.521
8	Bokna	0.0006	0.0554	0.0032	0.0277	0.0822	0.0214	0.0506	0.0071	0.0216	0.2010	0.0122	0.0095	0.492
9	Banker	0.0009	0.1938	0.0048	0.0462	0.1098	0.0248	0.0383	0.0155	0.0253	0.2516	0.0227	0.0198	0.754
10	Dangoaposi	0.0005	0.0646	0.0057	0.1015	0.0858	0.0274	0.0536	0.0132	0.0206	0.1669	0.0199	0.0082	0.568
11	Bichaikiri	0.0007	0.0831	0.0043	0.0831	0.0794	0.0207	0.0362	0.0102	0.0296	0.2010	0.0161	0.0089	0.573
12	Kotgarh	0.0007	0.1292	0.0049	0.0646	0.0136	0.0239	0.0259	0.0124	0.0233	0.0546	0.0188	0.0095	0.381

Nevertheless, considering the combined effect of the metals, the HI values for the various locations of the copper mining areas ranged from 0.29 to 1.89 with an average of 1.17 which is greater than 1.0 thus indicating risk to human health by dietary intake of metals through milk. Co, Pb and As are the highest contributors towards the non-carcinogenic risk for the area. The highest HI (1.89) was calculated for the location of Mosabani which is under the influence of copper mining and processing facilities. The next highest HI was estimated for Kutludih (1.67) which is in close proximity of the copper processing industry of Maubhandar. Thus, it can be noted that higher risk were estimated for the locations which are in close vicinity to the mining and processing industries. The detrimental effects of the chronic exposure to the metals through contaminated food become noticeable after a lapse of a considerable time period. The effects may include gastrointestinal problems, nervous disorders, cardiovascular problems, kidney and bone diseases.

For the other study area i.e. iron mining areas the human health risk due to consumption of locally rearing cow’s milk is considerably lower as compared to the copper mining areas even after considering the cumulative effect of the metals. The HI values in the iron mining areas ranged from 0.26 to 0.89 with an average of 0.56 thus indicating negligible risk to the local population.

Conclusion

The results of the investigation suggest that some of the milk samples collected from the mining areas particularly copper mining areas might be unfit for human consumption. Principal component analysis for both the mining areas explained 87.9% and 81.2% of the variance, respectively, indicating both innate and anthropogenic activities as causative sources of metals in the milk of locally rearing cows. The mean hazard index value was calculated to be 0.56 and 1.17 for the iron and copper mining areas, respectively, indicating negligible risk for the iron mining areas and considerable risk for the milk consumers from the copper mining areas. Thus, the study points towards potential contamination of the food chain with metals due to industrialization, urbanization and mining in some of the locations, particularly for the copper mining areas.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest.