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Redalyc.Spatial Prediction of Heavy Metal Pollution for Soils in Coimbatore, India Based on Universal Kriging International Journal of Combinatorial Optimization Problems and Informatics E-ISSN: 2007-1558 [email protected] International Journal of Combinatorial Optimization Problems and Informatics México Gandhimathi, A.; Meenambal, T. Spatial Prediction of Heavy Metal Pollution for Soils in Coimbatore, India based on universal kriging International Journal of Combinatorial Optimization Problems and Informatics, vol. 4, núm. 2, mayo- agosto, 2013, pp. 31-52 International Journal of Combinatorial Optimization Problems and Informatics Morelos, México Available in: http://www.redalyc.org/articulo.oa?id=265229633004 How to cite Complete issue Scientific Information System More information about this article Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Journal's homepage in redalyc.org Non-profit academic project, developed under the open access initiative © International Journal of Combinatorial Optimization Problems and Informatics, Vol. 4, No. 2, May-Aug 2013, pp. 31-52. ISSN: 2007-1558. Spatial Prediction of Heavy Metal Pollution for Soils in Coimbatore, India based on universal kriging A.Gandhimathi, Department of Civil Engg, Kumaraguru College of Technology, Dr.T.Meenambal Department of Civil Engg, Government College of Technology, Coimbatore, Tamil Nadu, India. ABSTRACT Coimbatore is a fast growing city, Manchester of Tamil Nadu, India. In Coimbatore Industry effluents and wastes being discharged randomly on soil, river, lake and road side without any treatment. They pollute productive soil, natural water system as well as ground water. Assessment of heavy metal content in soil and wetland from various localities of Coimbatore, Tamil Nadu was undertaken. Heavy metal pollution generally a non-stationary variable, the technique of universal kriging is applied in preference to ordinary kriging as the interpolation method. Topsoil samples (0-20 cm) were taken at various locations with reference to latitude and longitude. The concentration of heavy metal As, Hg, and Cd were analyzed in the Atomic Absorption spectrometer. Universal Kriging model was developed with suitable empirical semivariogram model. The model having the least error was selected by comparing the observed water-table values with the values predicted by empirical semivariogram models. It was determined that the presence of As is high at SIDCO. Presence of Hg is high in Sanganur road. Presence of Cd is high at Sanganur and townhall were because of electroplating industries. The aim of this analysis is to investigate the level, causes of heavy metal contamination in soil and prediction of heavy metal at various locations in the vicinity of industries and around Coimbatore city. Keywords: Spatial analysis, Heavy metals, Geo-accumulation, Universal kriging, Semivariogram, Soil pollution. Received Aug 16, 2011 / Accepted Oct 16, 2012 Editorial Académica Dragón Azteca (EDITADA.ORG) Gandhimathi and Meenambal / Spatial Prediction of Heavy Metal Pollution for Soils in Coimbatore, India based on universal kriging. IJCOPI Vol. 4, No. 2, May-Aug 2013, pp. 31-52. EDITADA. ISSN: 2007- 1558. 1. Introduction There are so many metal-based industries located in Coimbatore in an unorganized manner and is the second largest industrial centre in Tamil Nadu. The major industries include textile, dyeing, electroplating, motor and pump set, foundry and metal casting industries. According to the present situation, about 4500 textiles, 1200 electroplating industries, 300 dyeing units and 100 foundries are present in Coimbatore district. Industrial waste water and effluent are being discharged randomly on soil, into canal and river along road side or in the vicinity of industry operations without any treatment in Coimbatore district of Tamil Nadu. They pollute productive soils, natural water system as well as ground water. Industrial effluents and municipal waste contain medium amount of heavy metals such as As, Hg, and Cd. Apart from these industries, unorganized sets of sewers numbering 21,000 (Somasundaram, 2001) are running through various zones and finally discharging into the sewage farm located in Ukkadam, which has been used for irrigating the nearby fields. To adopt any type of remedial measures, it is necessary to determine the heavy metal load in the contaminated soil. Against this background information, it is necessary to analyze the heavy metal concentration in and around Coimbatore, Tamil Nadu. 130 Soil samples (three replicates) were collected at surface level (0–20 cm in depth) were collected from various locations to cover industrial, commercial, residential areas and wetland area. Heavy metal pollution generally a non-stationary variable, the technique of universal kriging is applied in preference to ordinary kriging as the interpolation method. Kriging is a technique of making optimal, unbiased estimates of regionalized variables at un-sampled locations using the structural properties of the semivariogram and the initial set of data values (David 1977). Kriging takes into consideration the spatial structure of the parameter and hence score over other methods like arithmetic mean method, nearest neighbor method, distance weighted method, and polynomial interpolation. Also, kriging provides the estimation variance at every estimated point, which is an indicator of the accuracy of the estimated value. This is considered as the major advantage of kriging over other estimation techniques. Kriging has been used in soil science Bardossy and Lehmann 1998; Araghinejad and Burn 2005; and atmosphere science Merino et al. 2001. In this paper, application of kriging to interpolate the heavy metal concentration, as observed in the part of Coimbatore, Tamil Nadu, India, has been shown. 32 Gandhimathi and Meenambal / Spatial Prediction of Heavy Metal Pollution for Soils in Coimbatore, India based on universal kriging. IJCOPI Vol. 4, No. 2, May-Aug 2013, pp. 31-52. EDITADA. ISSN: 2007- 1558. 2. Methodology Although details on the kriging techniques are well documented (Isaaks and Srivastava 1989), a brief account of the relevant methods used is prescribed here. The first step in kriging is to calculate the experimental semivariogram using the following equation. ---- (1) Where γ*(h) = estimated value of the semi variance for lag h; N(h) is the number of experimental pairs separated by vector h; z(xi) and z(xi +h) = values of variable z at xi and xi+h, respectively; xi and xi+h = position in two dimensions. Experimental semivariogram were calculated for June and September period from the year 1985 to 1990 using the computer program (in FORTRAN language) written by Kumar (1996). A lag distance of 5km and a tolerance of 2.5km were used for the calculation of semivariogram. The experimental semivariogram were fitted with various theoretical models like spherical, exponential, Gaussian, linear and power by the weighted least square method. The theoretical model that gave minimum standard error is chosen for further analysis. The adequacy of the fitted models was checked on the basis of validation tests. In this method, known as jackknifing procedure, kriging is performed at all the data points, ignoring, in turn, each one of them one by one. Differences between estimated and observed values are summarized using the cross-validation statistics: mean error (ME), mean squared error (MSE), and kriged reduced mean error (KRME), and kriged reduced mean square error (KRMSE). If the semivariogram model and kriging procedure adequately reproduce the observed value, the error should satisfy the following criteria. ---- (2) ---- (3) ---- (4) ---- (5) 33 Gandhimathi and Meenambal / Spatial Prediction of Heavy Metal Pollution for Soils in Coimbatore, India based on universal kriging. IJCOPI Vol. 4, No. 2, May-Aug 2013, pp. 31-52. EDITADA. ISSN: 2007- 1558. Where, z*(xi), z(xi) and are the estimated value, observed value and estimation variance, respectively, at points xi . N is the sample size. As a practical rule, the MSE should be less than the variance of the sample values and KRMSE should be in the range 1±2√2/N. In all interpolation techniques, interpolated value of z at any point x0 is given as the weighted sum of the measured values i.e. ---- (6) Where, λi is the weight for the observation z at location xi. In kriging, the weights λi are calculated by equation (7) so that z*(x0) is unbiased and optimal (minimum squared error of estimation). ---- (7) Where, μ = Lagrange multiplier γ (xi, xj) = semivariogram between two points xi and xj The minimum squared error estimation is also a measure for the accuracy of estimates, which is known as estimation variance, or kriging variance, and is given by ---- (8) Where, μ is the Lagrange multiplier. 34 Gandhimathi and Meenambal / Spatial Prediction of Heavy Metal Pollution for Soils in Coimbatore, India based on universal kriging. IJCOPI Vol. 4, No. 2, May-Aug 2013, pp. 31-52. EDITADA. ISSN: 2007- 1558. 3. Study Area The study area (Fig. 1) is located in the southern part in the state of Tamil Nadu, India India Fig. 1. Location map of study area 130 locations were selected in the study area to collect the soil samples for analysis. To avoid contamination of the sample was thoroughly clean, Black polythene bag was used in the collection of soil samples. To clean black polythene bags were dried at lower temperature. The soil samples were collected at random by digging the soil to about 1 meter at the specific refuse dumps. 35 Gandhimathi and Meenambal / Spatial Prediction of Heavy Metal Pollution for Soils in Coimbatore, India based on universal kriging. IJCOPI Vol. 4, No. 2, May-Aug 2013, pp. 31-52. EDITADA. ISSN: 2007- 1558. 4. Material and Methods The collected soil samples were air-dried and sieved into coarse and fine fractions. Well- mixed samples of 2 g each were taken in 250 ml glass beakers and digested with 8 ml of aqua regia on a sand bath for 2 hours. After evaporation to near dryness, the samples were dissolved with 10 mL of 2% nitric acid, filtered and then diluted to 50 mL with distilled water. Heavy metal concentrations of each fraction was analyzed by Atomic Absorption Spectro photometry using GBC Avanta version 1.31 by flame Atomization.
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