Environ Monit Assess (2014) 186:3531–3539 DOI 10.1007/s10661-014-3636-6
Source identification study of heavy metal contamination in the industrial hub of Unnao, India Ashish Kr. Dwivedi & Padma S. Vankar
Received: 8 August 2013 / Accepted: 13 January 2014 / Published online: 2 February 2014 # Springer International Publishing Switzerland 2014
Abstract India's Unnao region is home to many leather-treatment facilities and related industries. Industrial and agricultural waste leads to heavy metal contamination that infiltrates groundwater and leads to human health hazards. This work measured the amount of heavy metal in groundwater at specific sites near the industrial facilities in Unnao and identified potential sources of contamination as anthropogenic or lithogenic. Groundwater samples were taken from 10 bore well sites chosen for depth and proximity to industry. Data obtained from sample sites was interpreted using a multivariate statistical analytical approach, i.e., principal component analysis, clustering analysis, and correlation analysis. The results of the multivariate analysis showed that cadmium, copper, manganese, nickel, lead, and zinc were correlated with anthropogenic sources, while iron and chromium were associated with lithogenic sources. These findings provide information on the possible sources of heavy metal contamination and could be a model for assessing and monitoring heavy metal pollution in groundwater in other locales. This study analyzed a selection of heavy metals chosen on the basis of industries located in the study area, which might not provide a complete range of information about the sources and availability of all heavy metals. Therefore, an extended investigation on heavy metal fractions will be developed in further studies. A. K. Dwivedi (*) : P. S. Vankar 204 A Southern Block, Facility for Ecological and Analytical Testing (FEAT), Indian Institute of Technology, Kanpur 208 016, India e-mail:
[email protected]
Keywords Heavy metals . Principal component analysis . Correlation matrix . Cluster analysis . India . Unnao . Kanpur-Unnao
Abbreviations/symbols CA Cluster analysis Cd Cadmium Co Cobalt CM Correlation matrix Cr Chromium Cu Copper GPS Global positioning satellite Hg Mercury km2 Square kilometer ml Milliliter Mn Manganese mg/L Milligrams per liter mS/cm Milli-Siemens per centimeter Ni Nickel Pb Lead PCA Principal component analysis ppt Parts per thousand TDS Total dissolved solids Zn Zinc
Introduction Kanpur-Unnao, India's eigth largest metropolis, is a hub of multinational leather industries and considered to be
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one of the Ganga Plain's pollution hot spots. Due to urban expansion and industrialization, the amount of fertile farmland in the Unnao region has rapidly shrunk in the last three decades. Meanwhile, environmental pollution caused by discharge from tanneries, along with chemical fertilizers and pesticides, municipal and animal waste, sewage irrigation and sludge application, has created a potential hazard to human health. Many researchers have carried out work on heavy metal pollution in the Unnao–Kanpur region. Ansari et al. (1998, 1999, 2000) and Srinivasa Gowd et al. (2010) carried extensive research work to highlight pollution status of Unnao and Kanpur. The Ministry of Water Resources, Government of India constituted a committee under the Chairmanship of Director, NIH, Roorkee, to find the facts about the present state of Unnao's water bodies/ groundwater and to estimate how much of Unnao's groundwater is affected so far. Identification of particular tanning units (Table 1) which are responsible for causing pollution to Unnao's water bodies has been summarised in combined report of the Government of India. Preparation of inception report was based on the survey and findings of CGWB, CWC, IITR, CPCB, and UPPCB for submitting it to the Ministry of Water Table 1 Details of existing micro and small enterprises and artisan units in the Unnao District (MSME 2012) Sample no.
Type of industry
Number of units
1
Agro-based
1,878
2
Soda water
7
3
Cotton textile
150
4
81
5
Woolen, silk and artificial thread-based clothes Jute and jute-based
6
Ready-made garments and embroidery
716
7
Wood/wooden-based furniture
717
8
Paper and paper products
341
9
Leather-based
1,310
10
Chemical/chemical-based
551
11
Rubber, plastic and petro-based
247
12
Mineral-based
345
13
Metal-based (steel-fabricated)
758
14
Engineering units
201
15
155
16
Electrical machinery and transport equipment Repairing and servicing
17
Others
1,751
20
2,369
Resources (MoWR) by 1 June 2012 (Ministry of Water Resources 2013). The current study was carried out as a selective assessment of groundwater pollution in the Unnao industrial area. The aims of the study were as follows: 1. Find out average regional concentrations of specific heavy metals chosen on the basis of an industrial profile of the area, 2. Characterize these pollutants as originating from natural and/or anthropogenic sources, and 3. Spot sources causing contamination in groundwater used for consumption and irrigation purposes. There are two main sources of heavy metal in groundwater: 1. Lithogenic sources, or parent rocks, the origins of the natural background level of heavy metal concentrations and 2. Anthropogenic contamination, which includes agrochemicals, organic soil amendments, animal manure, mineral fertilizer, and sewage sludge. Generally, more heavy metal in water originates from anthropogenic sources than natural. More than 50 % of the cadmium, cobalt, chromium, copper, mercury, nickel, lead, tin, zinc, and organic carbon found in the groundwater, sediment, and soil of this region can be traced back to anthropogenic sources.
Materials and methods Water sampling and heavy metal determination Ten sampling sites were selected in the industrial region of Unnao, covering an area of 20 km2. The sampling sites—preexisting bore wells composed of metal pipes 20 cm in diameter—were selected on the basis of depth (approximately 200–300 ft deep) and proximity to industries (within 100 m) that have been producing treated and untreated effluent for the past few decades (Vankar and Dwivedi 2009). Bore wells were equipped with hand or mechanized pumps. Water samples were drawn with the help of these pumps and collected in sterilized 250-ml plastic containers. The geologic sequence of soil types was the same for each site, i.e., clay, sandy loam, and loam. Clay
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layers were not analyzed for soil sampling. Each sampling site was geotagged as shown in Fig. 1. Basic parameters for water samples such as pH, conductivity (milli-Siemens per centimeter (mS/cm)), resistivity (ohm), total dissolved solids (parts per thousand (ppt)), and salinity (ppt) were analyzed by CyberScan Eutech PCD 300 global positioning satellite (GPS) locations, and refined maps were prepared using a Magellan Triton 300 handheld GPS instrument and VantagePoint software (both are products of MiTAC International, USA). The content of total heavy metals in the water samples was determined after acid digestion with dilute nitric acid (HNO3) (Huamain 2005), followed by analysis on inductive coupled plasma mass spectrometer (ICP-MS) for copper, iron, manganese, nickel, cadmium, chromium, lead, and zinc. Hexavalent chromium was analyzed by a UV-VIS spectrometer (Thermogram Cooperation Helios).
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groups, clustering the samples with similar heavy metal content. CA was formulated according to the Ward algorithmic method. Outcomes were shown in a dendrogram, which illustrated the hierarchical arrangement of resulting clusters, and values of the distances between clusters (squared Euclidean distance) were represented. A correlation matrix (CM) was used to identify the relationship between the sampled elements (Richard and Gregory 1985). Correlation coefficient was calculated in the forms of matrix (Samuel et al. 2000).
Results Each water sample from Unnao was analyzed for the following characteristics: pH, conductivity (mS/cm), resistivity (ohm), amount of total dissolved solids (ppt), and salinity (ppt). Results are shown in Table 2.
Statistical analysis
Basic parametric analysis and total heavy metal content of groundwater samples
A multivariate statistical analytical approach, i.e., principal component analysis, clustering analysis, and correlation analysis, was adopted for the interpretation of geochemical data obtained from Unnao (Einax and Soldt 1999; Oliva and Espinosa 2007; Tuncer et al. 1993). The data obtained by sampling was treated statistically using SPSS software (version 15.0 for Windows, IBM, USA). Principal component analysis (PCA) was used to infer the hypothetical source of heavy metals (natural or anthropogenic). The components of the PCA were rotated by a varimax rotation. Cluster analysis (CA) was applied to identify different geochemical
As previously stated, the groundwater analysis considered pH, conductivity (ms/cm), resistivity (ohm), total dissolved solids (TDS, ppt), and salinity (ppt) as basic parameters (Table 3). Among these factors, maximum values were found as 7.04 in U6 (pH), 5.750 in U4 (conductivity), 1837.00 in U1 (resistivity), 365.90 in U1 (TDS), and 3.365 in U4 (salinity). Similarly, minimum values were 6.66 in U2 and U9 (pH), 0.544 in U1 (conductivity), 173.90 in U4 (resistivity), 1.245 in U2 (TDS), and 0.265 in U1 (salinity). The mean value of the total heavy metal content in the water samples followed a descending order:
Fig. 1 Sampling site in the industrial area of Unnao
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Table 2 Basic parametric description and GPS location for collected water samples Sites
Nomenclature
Location
pH
Conductivity (mS/cm)
Resistivity (ohm)
TDS (ppt)
Salinity (ppt)
Garhi
U1
26.56696°N 80.52700°E
6.80
0.544
1837
365.9
0.265
King's Tannery
U2
26.56430°N 80.52413°E
6.66
1.855
539.5
1.245
0.958
Magarwara-1
U3
26.51527°N 80.43169°E
6.79
3.573
279.6
2.408
2.058
Magarwara-2
U4
26.51315°N 80.43112°E
6.84
5.750
173.9
3.867
3.365
Maswasi
U5
26.50972°N 80.43875°E
6.68
3.269
303.9
2.216
1.885
Nehrubag-1
U6
26.51322°N 80.41833°E
7.04
2.340
427.2
1.60
1.318
Nehrubag-2
U7
26.51278°N 80.41879°E
6.95
1.864
536.1
1.255
0.962
Banergee Hospital
U8
26.51283°N 80.42528°E
6.83
4.034
248.2
2.713
2.329
Patrol tanki
U9
26.51367°N 80.42741°E
6.66
4.554
219.9
3.056
2.635
Sariaya
U10
26.50881°N 80.43875°E
6.70
2.504
399.5
1.682
1.413
zinc>manganese>iron>lead>copper>nickel>cadmium>chromium>hexavalent chromium. Interestingly, the maximum value found in the samples for each heavy metal was above the maximum permissible value as set by the USEPA (2009), as presented in Table 4. Out of the eight metals, only total chromium content (0.032 milligrams per liter (mg/L)) was near to the EPA's permissible level of 0.03 mg/L. Lead and manganese were found at higher concentrations than the permissible limit.
component model, which accounted for 62 % of all the data variation. In the rotated component matrix, the first principal component (PC1, variance of 46 %) included nickel, manganese, copper, cadmium, lead, and zinc, while the second principal component (PC2, variance of 16 %) was made up of iron, chromium, and hexavalent chromium.
Table 4 Total heavy metal content of collected water samples Elements (in mg/L)
Max.
Min.
Mean
Median
Maximum permissible valuea (mg/L)
Cu
0.231
0.018
0.079
0.069
0.02
Fe
2.191
0.000
0.551
0.406
0.30
Mn
2.772
0.000
0.622
0.155
0.05
Table 3 Basic parameters for collected water samples
Ni
0.120
0.005
0.058
0.055
0.01
Parameters
Cd
0.074
0.015
0.037
0.036
0.01
Cr
0.032
0.002
0.015
0.013
0.03
Principal component analysis The PCA results for heavy metal content are presented in Table 5. Heavy metals were grouped into a two-
Max
Min
Median
Mean
pH
7.04 (U6)
6.66 (U2)
6.79
6.79
Pb
0.895
0.086
0.432
0.356
0.01
Conductivity
5.75 (U4)
0.54 (U1)
2.88
3.02
Zn
2.710
0.069
0.917
0.500
0.30
1,837.00 (U1)
173.90 (U4)
351.70
496.48
Cr(VI)
0.064
0.003
0.014
0.011
0.03
365.90 (U1)
1.24 (U2)
2.31
38.59
3.36 (U4)
0.26 (U1)
1.64
1.71
Resistivity TDS Salinity
a
Maximum permissible concentration as defined by USEPA (2009)
0.453
−0.066
0.014
0.741
−0.064
0.216
−0.009
0.838
−0.010
PC2
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Discussion
0.693
0.017 −0.474
0.326 0.761
−0.478 Cr(VI)
Zn
0.079 0.716 0.206 Pb
0.698
0.177 −0.942
0.676 −0.184
−0.925 Cr
0.904
Cd
0.814 0.071 0.839
0.888 −0.166 Ni
Mn 73.023
82.845
91.551
96.679
99.646
99.997
100.000
0.993 11.032
9.822
8.706
5.129
2.966
0.352
0.003
0.884
0.784
0.462
0.267
0.032
0.000
3
4
5
6
7
8
9
Extraction method: principal component analysis; rotation method: varimax
0.708
0.834 −0.050 0.096
0.695 −0.133 Cu
Fe 61.991
45.192 4.067 45.192
1.512 16.799 61.991
46.099
61.991 1.430 15.892 2
4.149 46.099 46.099 4.149 46.099 1
% of variance Cumulative % % of variance Cumulative % Total % of variance Cumulative % Total
1.430 15.892
PC1
PC2
PC1
Anthropogenic sources
Total
Component Initial eigenvalues
Table 5 Principal component analysis
Extraction sums of squared loadings
Rotation sums of squared loadings
Elements Component matrix
Rotated component matrix
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In the first principal component analysis, nickel, manganese, copper, cadmium, and zinc (in descending order) were found in amounts greater than 0.5 mg/L, confirming that these metals were from anthropogenic sources. Since nickel, manganese, copper, cadmium, and zinc are not found naturally in this region so enhanced concentration points forward towards industrial activities. After a survey of the industries found in Unnao, several nickel- and lead-processing units were found to have been in operation for over 20 years, and aging machinery with inadequate controls could be sources of hazardous concentrations of nickel and lead. Traces of other heavy metals like manganese, cadmium, copper, and zinc were probably the by-products of nearby steel- and wood-processing units and tanneries. Chromium was found in lower concentrations than other heavy metals. One reason could be existing remediation efforts by industries and pollution-regulating authorities. Since this area was notorious for chromium contamination over the past few decades, the government has instituted stringent regulations on the treatment of this heavy metal. Atmospheric deposition may also be an anthropogenic source of heavy metal contamination in the area (Huamain et al. 1999). The heavy metals in the atmosphere might come from metal smelting and refining, and manufacturing and waste incineration processes which are widespread throughout the study area. There is a large-scale chemical industrial base in the study area, basically represented by the sites U3, U4, U6, U7, U8, U9, and U10 (Fig. 1). Since non-ferrous metals such as manganese, copper, lead, zinc, and nickel are products of this industrial zone, it is a reasonable argument to make. Heavy metals deposited on the soil surface are subsequently incorporated into the soil and can eventually leach into groundwater, contributing to overall concentrations of heavy metal. Lithogenic sources PC2 could be considered as a natural component, because the variability of heavy metal concentrations appeared to be products of the study area's soil system. Moreover, iron and chromium levels were lower than those found for other elements. This result suggests that
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the distribution of iron and a certain amount of chromium and hexavalent chromium has lithogenic origins, and therefore these three heavy metals were included in the second principal component. Initially, it was supposed that the chromium levels would be present in much higher concentrations than other metals such as lead and nickel, but principal component analysis showed that PC1 heavy metals were found at more alarming rates. As another part of the PCA component matrix, iron, manganese, lead, and zinc were represented almost equally in both principal components. After being rotated, manganese, lead, and zinc showed a deflection towards PC2 (Table 5). Since these four metals shared equal places in both PC1 and PC2, they display a relationship with both groups and seem to have both natural and anthropogenic sources. However, iron, manganese, and zinc in groundwater could also have lithogenic sources as they formed a number of soluble and/or insoluble salts. According to the prevailing pedogenic processes, while lead may have been a matter of long-term industrial discharge behaving as both natural and anthropogenic, to support this argument, many authors have investigated possible heavy metal sources in different countries. Micó et al. (2006) reported that copper, cadmium, and lead constituted an anthropogenic component, whereas the remaining elements (cobalt, chromium, iron, manganese, and nickel) appeared to be associated with parent rocks. Facchinelli et al. (2001) also drew conclusions about two groups of heavy metals wherein copper and zinc were associated with specific agronomic practices and lead was derived from car exhaust, and all three kinds of metals were related to human activities. By contrast, chromium, cobalt, and nickel were sourced from parent rocks.
In this study, it seems reasonable to conclude that copper, nickel, lead, manganese, zinc, and cadmium have an anthropogenic origin, whereas the remaining elements (iron and chromium) appear to be associated with lithogenic sources. Since the concerned area has been industrialized for about 50 years while stringent controls against chromium pollution have been in place for only the past 5 years, it can be argued that the observed chromium content in groundwater may be low but it is still present in lower soil horizons which appeared in groundwater analysis, hence lithogenic along with iron. Component matrix and rotated component matrix 2D It is interesting to note that if the PCA results were plotted (PC1 vs PC2), a different conclusion may be considered. The principal component plots suggest two associative groups: (1) copper, cadmium, and nickel and (2) iron and lead. Chromium appears by itself while zinc and manganese may or may not be associated. Clustering analysis Results shown in Fig. 3a reveal two major clusters: (1) chromium and hexavalent chromium and (2) nickel and copper. These two groups are the closest to each other in terms of concentration. Subclusters include cadmium with both chromiums, and lead with nickel, copper, and cadmium. On the other hand, the concentrations of zinc, manganese, and iron are only distantly related to each other. This cluster analysis suggested that zinc, manganese, and iron may have different origins, i.e., zinc and manganese arise from anthropogenic sources
Table 6 Correlation matrix of total heavy metal contents
Cu Fe Mn Ni Cd Cr Pb Zn Cr(VI)
Cu
Fe
Mn
Ni
Cd
Cr
Pb
1.000
0.072
0.589
0.600
0.201
1.000
0.223
−0.063
1.000
0.690 1.000
−0.678
−0.017
0.350
−0.272
−0.107
0.135
0.314
0.234
−0.159
0.661
−0.606
0.069
0.671
−0.137
0.501
−0.931
0.105
0.605
−0.262
1.000
−0.532
0.075
0.369
−0.445
−0.638
0.49
0.320
−0.020
1.000
−0.258
1.000
−0.19 1.000
Zn
Cr(VI)
1.000
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Fig. 2 Individual concentrations of heavy metal with maximum and minimum values on corresponding geosite
as supported by the PCA, while iron may be lithogenic in origin. Similarly, a clustering analysis performed for the geosites (Fig. 3b) resulted in a dendrogram identifying close geochemical groups. Two geosite groups were determined to be most similar in terms of heavy metal pollution. Group 1 consisted of U6, U4, and U2, while group 2 included U1 and U10. U3, U7, U5, U8, and U9 were the subcluster component which proved that the clustered groups were in accordance with the real distribution of samples. Overall, clustering analysis showed similar results, enabling the identification of heavy metal sources as lithogenic or anthropogenic. Multivariate analysis reconfirmed that the elements studied come from two different sources. Besides, clustering analysis by sample cases verified the fact that the part rich in
Fig. 3 Cluster analysis of heavy metals and geosites
iron and chromium was mainly affected by heavy metals originating from natural sources, while parts 1 and 2 (rich in copper, nickel, lead, manganese, and cadmium) were influenced by human activities. Parts 1, 2, and 3 are most close to distantly related heavy metals. Correlation analysis Results obtained from the PCA were confirmed by the correlation analysis (Table 6). Anthropogenic metals such as copper are positively correlated with manganese, nickel, zinc, and cadmium, while lead is positively correlated with manganese, nickel, and cadmium but more significantly with iron. This correlation of lead with iron demonstrated by the PCA study signifies that lead became lithogenic due to several
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lead-processing units operating from the last two decades while chromium and hexavalent chromium were found to be negatively correlated with every heavy metal, conferring their low and independent lithogenic source. These results indicate that there existed some original relationship between confined pairs of heavy metals and suggest the possibility of two different heavy metal sources. There is also a chance that heavy metals can bear some synergies, resulting in potential compound pollution (Covelo et al. 2007). These findings about relationships of total heavy metal contents could be applied to explain the behavior and the fate of heavy metals effectively and efficiently in the groundwater.
Conclusions The results obtained in this work measured the amount of heavy metal in groundwater from specific industrial sites in Unnao, identified potential sources of the contamination, and analyzed the possible causes and effects of heavy metals (Figs. 1 and 2). The PCA performed on eight heavy metals identified two principal components which controlled their variability in groundwater samples. Nickel, manganese, copper, cadmium, lead, and zinc (PC1) were thought to have an anthropogenic component due to their highlevel presence in all samples. PC2, which included iron and lead, seemed to be controlled by the lithogenic system (Table 5). Clustering analysis resulted in the same groupings. There were two main clusters of elements: the first included elements that had previously been interpreted as natural elements (iron and chromium) and the second cluster contained the anthropogenic elements nickel, manganese, copper, cadmium, lead, and zinc (Fig. 3). In the correlation analysis, anthropogenic metals such copper, manganese, nickel, zinc, and cadmium were significantly correlated while naturally derived metals (iron and chromium) were poorly correlated (Table 6). In this report, the involved parameters were a few heavy metals chosen on the basis of existing industries in the study area. This does not provide a complete range of information about the sources and availability of all heavy metals. Therefore, an extended investigation on heavy metal fractions will be developed in further studies. This study could be a model for
Environ Monit Assess (2014) 186:3531–3539
assessing and monitoring heavy metal pollution in groundwater in other locations.
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