Current World Environment

Introduction

Soil heavy metal pollution is worldwide and in terrestrial ecosystems, soils dish up as the most important sink^1,2 and generally considered to be originating either from geogenic or natural sources or the anthropogenic sources such as agrochemicals and mineral fertilizers, industrial wastes, sewage sludge etc.^3,4,5,6 As a result of increasing population, urbanization, agricultural activities, heavy metal pollution in agricultural soils is the matter of concern causing various health impacts^7,8 and their higher concentration in deeper layers can contaminate the groundwater^9,10 due to their mobility in soils. Therefore, it has become important to assess heavy metal pollution in soil.

Study Area

Mewat district a semi-arid region in Haryana (Fig. 1) that covers about 1500 sq. km. geographical area with population of 1,089,406 (2011-Census)¹¹ is one of the major agricultural areas with scarce perennial surface water sources and groundwater is the main source of water for irrigation and domestic use. The major crops grown in the area are: wheat, millet and mustard, vegetable crops like onion, tomato, eggplant, chilly, etc. Mewat receives low annual rainfall i.e. 577 mm, limited to a period of 23-35 days in the year.

Methodology

There are problems of excessive salinity in Nagina block and water level is decreasing in Tauru block¹² and the problem of salinity is also affecting the water quality in semi-arid regions.¹³ The salinity zone in Nagina block is expanding into the fresh water zone towards the Aravali ranges.¹⁴ Keeping this in view, the agricultural soil samples were collected from 3 places namely Ulheta, Karhera/Ghagas and Tauru covering 2 blocks namely Nagina and Tauru. The samples were collected from 5 different depths as 0-30 cm, 30-60cm, 60-90cm, 90-120 cm, 120-150 cm. The soil samples were air dried, grinded and analysed for As, Fe, Mn. Cd, Ni, Cu and Zn on ICP-OES (make: Shimadzu) at Central Analytical Facility, CSSRI (ICAR), Karnal. 

The instrument is based on the emission principle and involves the measurement of the intensity of the characteristic radiations of the element to be determined. Argon serves as sample carrier gas and help in producing plasma by interacting the oscillating magnetic field generated by the radio frequency generator in the coil. The sample is aspirated by nebulizers and injected into the plasma, causing the excited neutral atoms or ions within the sample to emit radiation of specific wavelengths. The emitted radiations are observed by the light tube through the entrance slit of a spectrometer which illuminates the diffraction grating. Radiations are separated into its component wavelengths and are incident on a photo multiplier tube. The photo multiplier tube produces a signal directly proportional to the intensity of the impinged light. This signal is then processed by the measuring system. It used for the estimation of more than 50 elements present in water, soil and plant extract. It measured all the elements selected during calibrating the instrument through the PC in one aspiration.

The soil texture at Ulheta is loamy sand to sandy loam, at Karhera/Ghagas is sandy loam to silty loam and at Tauru is Sandy loam. Principal Component Analysis (PCA), a multivariate assessment technique where large variations are explained but number of variables reduced to group of individuals and this technique has been used to estimate spatial and temporal patterns of heavy metal contamination.¹⁵
 

Figure 1: Study area showing sampling points. Click here to view figure

Results and Discussion

The mean concentration of all the samples for all 3 sites is shown in Table 1 and Fig. 2. The average contents of As, Cd, Ni, Cu, Fe, Mn and Cu were reported 0.0271 ppm, 0.0272 ppm, 0.1182 ppm, 41.1 ppm, 511.5 ppm, 567.9 ppm and 18.8 ppm, respectively in soils of Mewat. Among all the sites, maximum As, Cd and Ni to the tune of 0.0316 ppm, 0.0306 ppm and 0.1528 ppm, respectively were reported from Ulheta followed by 0.0282 ppm, 0.0288 ppm and 0.1192 ppm, respectively at Karhera and minimum at 0.0216 ppm, 0.0222 ppm and 0.0826 ppm, respectively at Tauru.  Generally, it was found that As, Fe, Mn, Cd, Ni, Cu concentrations were found more at the depth range of 30-60cm. 
 

Figure 2: Depth wise distribution of soil heavy metal pollution at different locations. Click here to view figure

Correlation

The correlation matrix of three sampling sites is shown in Tables 2-4. Arsenic (As) has strong/perfect positive correlation with Cd and Ni at all places. At Ulheta strong positive correlation was found between Fe and Cu; Ni and Mn; a high and positive correlation of Mn was also found with As and Cd at Ulheta (Table 2).

At Karhera, good and positive correlation of Fe was observed with As and Cd, while high negative correlation of Fe was found with Cu (Table 3). Fe had a strong positive correlation with Mn at Tauru (Table 4). The variation in correlation may be due to contribution of these heavy metals from different sources.

Table 2: Correlation among heavy metals at Ulheta.

In bold, significant values (except diagonal) at the level of significance alpha=0.050 (two-tailed test)

Table 3: Correlation among heavy metals at Karhera/Ghagas.

In bold, significant values (except diagonal) at the level of significance alpha=0.050 (two-tailed test).

Table 4: Correlation among heavy metals at Tauru.

In bold, significant values (except diagonal) at the level of significance alpha=0.050 (two-tailed test).
.
Depth Distribution of Heavy Metals

Depth wise distribution of heavy metals at all the 3 sites is shown in figures 3-5. At Ulheta, and Tauru upto 30% of the depth, the heavy metal concentration is increasing sharply but after that gradual increase in observed (fig. 6, 7) while at Karhera/Ghagas, concentration of As, Fe, Mn and Cu is found increasing constantly at all depths but trend is not same for Cd, Ni and Zn.

Principal Component Analysis (PCA)

Tables 5,6,7 represent the statistics of PCA which includes the eigenvalue, percentage of variance and cumulative variance. It is observed that only 1 factor at Ulheta (Table 5) and 2 factors (Tables 6 &7) present with eigenvalue greater than 1. In Ulheta factor 1 explains 84.787 % of variance in the data (Table 3), in Karhera factors 1 & 2 explain variance of 61 and 21.656 %, respectively while at Tauru factors 1 & 2 explain variance of 72.876 and 15.967 %, respectively.

Figure 3: Depth wise distribution of heavy metals in soils of Ulheta, Mewat. Click here to view figure

Figure 4: Depth wise distribution of heavy metals in soils of Karhera/Ghagas, Mewat. Click here to view figure

Figure 5: Depth wise distribution of heavy metals in soils of Karhera/Ghagas, Mewat. Click here to view figure

Table 5:Total Variance (Ulheta).

Figure 6: Coordinates of principal component axis based on the heavy etals concentrations and sampling depths at Ulheta. Click here to view figure

Figures 3, 4 &5 shown that the first two principal components provided a general view on the temporal and spatial variations of soil metal contamination. In Ulheta, PC 1 contributing 84.79% was positively affected by Fe, Cu, As, Cd, Ni and Mn and negatively affected by Zn while PC 2 contributing 10.58 % was positively affected by Zn, Fe, Cu, As, Cd and negatively affected by Ni and Mn (fig. 6).

In Karhera, PC 1 contributing 61% was positively affected by all the heavy metals except for Cu while PC 2 contributing 26.66 % was positively affected by Zn, Fe and negatively affected by Cu, As, Cd, Ni and Mn (Fig. 7).

In Tauru, PC 1 contributing 72.88 % was positively affected by As, Cd, Ni, Zn and negatively affected by Cu, Fe and Mn while PC 2 contributing 15.97 % and was positively affected by all the heavy metals (Fig. 8).

Table 6: Total Variance (Karhera/Ghagas)

Figure 7: Coordinates of principal component axis based on the heavy metals concentrations and sampling depths at Karhera/Ghagas. Click here to view figure

Figure 8: Coordinates of principal component axis based on the heavy metals concentrations and sampling depths at Tauru. Click here to view figure

1. LiX Y., LiuL J., Wang Y. G., Luo G. P., Chen X., Yang X. L., etal. Heavy metal contamination of urban soil in an old industrial city (Shenyang) in Northeast China. Geoderma. 2013;192:50–58.
   CrossRef
2. Liu X. M., WuJ. J., XuJ. M. Characterizing the risk assessment of heavy metals and sampling uncertainty analysis in paddy field by geostatistics and GIS. Environ Pollut. 2006;141:257–264.
   CrossRef
3. Zhang Ch. Sh. Using multivariate analyses and GIS to identify pollutants and their spatial patterns in urban soils in Galway, Ireland. Environmental Pollution. 2006;142:501–511.
   CrossRef
4. Jiao X., Teng Y., Zhan Y., WuJ, Lin X, Soil Heavy Metal Pollution and Risk Assessment in Shenyang Industrial District, Northeast China. PLoS ONE. 2015;10(5):e0127736.
   CrossRef
5. Nath S., Rai A., Gurung K., Das M., Pradhan M., Burman S., Haldar P. Comparison of heavy metals level in Grasses and Grasshoppers from Darjeeling Hills. Journal of Hill Research. 2008;21(2):67-69.
6. Wadia D. N. 1998. Geology of  india, 4th edition. Tata McGraw Hill Publication Co. Ltd., New Delhi. 454.
7. Sun Y., Zhou Q., XieX., Liu R. Spatial, sources and risk assessment of heavy metal contamination of urban soils in typical regions of Shenyang, China. J Hazard Mater. 2010;174:455–462.
   CrossRef
8. Xie Y.,Chen T. B.,Lei M.,Yang J.,Guo Q. J.,Song B. Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: accuracy and uncertainty analysis. Chemosphere. 2011;82(3):468–476.
   CrossRef
9. Richards B. K., SteenhuisT. S., Peverly J. H., McBride M. B. Metal mobility at an old, heavily loaded sludge application site. Environ Pollut. 1998;99(3):365–377.
   CrossRef
10. Camobreco V. J., Richards B. K., Steenhuis T. S.,Peverly J. H., McBride M. B. Movement of heavy metals through undisturbed and homogenized soil columns. Soil Science. 1996;161:740–750.
    CrossRef
11. Census 2011. Mewat Data Sheet-Census. Available at: www.census2011.co.in/census/district/226-mewat.html. Accessed 18 July, 2018.
12. Priyanka., Krishan G., Sharma L. M., Yadav B. K., Ghosh N. C. 2016. Analysis of Water Level Fluctuations and TDS Variations in the Groundwater at Mewat (Nuh) District, Haryana (India). Current World Environment. 11(2): 388-398.
    CrossRef
13. Krishan G., Rao M. S., Kumar C. P., Kumar S., Loyal R. S., Gill G. S., Semwal P. Assessment of Salinity and Fluoride in Groundwater of Semi-Arid Region of Punjab, India. Curr World Environ. 2017;12(1):34-41.
    CrossRef
14. Thomas N., Sheler R., Reith B., Plenner S., Sharma L. M., Saiphy S., Basu N., Muste M. M. (2012). Rapid Assessment of the Fresh-Saline Groundwater Interaction in the Semi-arid Mewat District (India). University of Iowa's Winterim Program Development of Resilient and Sustainable Agricultural Watersheds. https://www.iihr.uiowa.edu/intl-perspectives2011-12/home/outcomes/%e2%80%a2paper-for-international-shallow-water-conference-rapid-assessment-of-the-fresh-saline-groundwater-interaction-in-the-semi-arid-mewat-district-india (Assessed on October 06, 2018).          
15. Shine J. P., Ika R. V., Ford T. E. Multivariate statistical examination of spatial and temporal patterns of heavy metal contamination in New Bedford Harbor marine sediments. Environmental Science and Technology. 1995;29:1781–1788.
    CrossRef
16. Minhas P. S., Samra J. S. Waste water use in peri-urban agriculture impacts and opportunities. Tech, Bull. 2/2004, Central Soil Research Institute, Karnal, 132001, India. 2004;74.