Spatial Prediction of Heavy Metal Pollution for Soils based on ANN and Kriging Model

By A. Gandhimathi¹ * and T. Meenambal²
April 2012

  1. Associate Professor, Department of Civil Engineering, Kumaraguru College of Technology (KCT), Coimbatore, Tamil Nadu, India *Corresponding Author
  2. Professor, Department of Civil Engineering, Government College of Technology (GCT), Coimbatore, Tamil Nadu, India
Abstract
The concentration of five soil heavy metals (Cr, Pb, and As) was measured in 121 sampling sites in Coimbatore, Tamil Nadu, India regions known as centres of pollution due to the chemical and metallurgical activities. The soil samples were collected from locations where the ground is not sliding and the probability of alluvial deposits is small. The concentration of heavy metals was measured by using Atomic Absorption spectrometer. Kriging and ANN techniques were used to develop the model to predict the constituents of the heavy metal in the soils. In some locations, the concentration for the investigated heavy metals exceeds the concentration admitted by the guideline. The highest concentration of lead (8.9 ppm) was found in Ukkadam Lake. The highest concentration of chromium was found in Ganapathi (3.6 ppm). The highest concentration of Arsenic (5.4 ppm) was found in Sidco Industrial Estate. The maximum admitted concentrations in the sensitive areas revealed to be exceed from five to twenty times.

Keywords: Kriging, ANN, Soil Pollution, Heavy metals (Cr, Pb, and As), Coimbatore

Figure 1 · Location Map of Study Area
Study Area

Introduction

Heavy metal contamination of soil results mainly due to as mining [1], smelting procedures [2] and agriculture [3] as well as natural activities. Chemical and metallurgical industries are the most important sources of heavy metal contamination in the environment [4]. There are so many metal-based industries located in Coimbatore in an unorganized manner and is the second largest industrial centre in Tamil Nadu, India. 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 and around Coimbatore.

The metals are classified as “heavy metals” if they have a specific gravity of more than 5 g/cm³. There are more than sixty heavy metals. Heavy metals get accumulated in soils and plants causing negative influence on photosynthesis, gaseous exchange, and nutrient absorption of plants resulting reductions in plant growth, dry matter accumulation and yield [5, 6]. In small concentrations, the traces of the heavy metals in plants or animals are not toxic [7]. Lead, cadmium and mercury are exceptions; they are toxic even in very low concentrations [8].

The main goal of the present research was to assess the heavy metals distribution in some Coimbatore areas, known as chemical or metallurgy industry centres.

Study Area

The study area (Figure 1) is located in the southern part in the state of Tamil Nadu, India. 121 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.

Figure 2 · Kriging Model for Lead
Figure 2
Figure 3 · Kriging Model for Chromium
Figure 3
Figure 4 · Kriging Model for Arsenic
Figure 4

Materials 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 were analyzed by Atomic Absorption Spectro photometry. Atomization and Quality assurance was guaranteed through double determinations and use of blanks for correction of background and other sources of error.

The GLOBEC Kriging Software Package – EasyKrig 3.0 was used for creating the prediction model [9]. The prediction models are depicted in Figures 2 to 4. The soils with potential risk of heavy metal pollution were located in isolated spots mainly in the northern part of the study region.

An artificial Neural Network technique is used to develop a model to predict the constituents of the heavy metal in the soils [10] such as lead, chromium, arsenic. The developed neural network model consist of 2 input neurons for latitude and longitude, 6 hidden layers consisting of 10 to 20 neurons in each layer for training the data and 1 neuron to predict the constituents of the heavy metal in the soils. The architecture of the model is depicted in Figures 5 to 7.

Figure 5 · Neural Network Architecture - Lead
Figure 5
Figure 6 · Neural Network Architecture - Chromium
Figure 6
Figure 7 · Neural Network Architecture - Arsenic
Figure 7

Results and Discussion

Kriging Model

The heavy metal from various localities including wetland soil sample were collected, analyzed and the results were reported. The metals analyzed were Cr, Pb and As.  Lead Pb concentration varies from 0 to 8.9 ppm with a maximum 8.9 ppm at Ukkadam Lake. Reason for maximum Pb at Ukkadam Lake is due to discharging of sewage water into lake. Cr concentration ranged between 0 - 3.6 ppm. Maximum concentration was in Ganapathy because of the concentration of foundry industry. As ranged between 0 – 5.4 Maximum at Sidco Industrial Estate and Singanallur because of the concentration of electroplating industry. It is observed that maximum heavy metal pollution near the industrial, traffic junction and the legendary 'go-slow' of automobiles is the order of the day and in localities of large population concentration and relatively small areas under poor conditions of sanitation.

Kriging model was used to predict the heavy metal at the unknown point. From the model of heavy metals we can conclude that the residential areas are uncontaminated with Cr and moderately contaminated with Pb. Heavy metal accumulation in few prominent wetlands of 10 localities was analyzed. Pb is maximum in Velangulam Lake Ukkadam, and at the Sungam Lake.

ANN Model

The feed forward three layered back propagation network architecture is used to develop ANN model. The input layer consist of two nodes which represents latitude and longitude which used to predict the response and the output layer consist of one node which represents the constituents of the heavy metal in the soils such as lead, chromium, arsenic. The number of hidden layer and neurons in the hidden layer has been determined by training several networks.

The surveyed, predicted and error percentage of heavy metal is tabulated in Table 1.

Comparison of Kriging Model and ANN Model

Unlike an ANN model where spatial variability of particular metal deposition is captured through the nonlinear input – output mapping via a set of connection weights, kriging uses nearby sample points to predict the particular metal concentration at a particular location. Kriging and ANNs thus work in different frameworks. ANN resembles a parametric nonlinear global fitting model, whereas kriging works like a nonparametric local fitting model that restricts the mapping of the model to a local neighborhood of data points.

In kriging, the prediction of an unknown value at a location is obtained by linearly weighting the data points near to that particular location using the variogram structure of the attribute. In the present study, several kriging techniques–simple kriging (SK), ordinary kriging (OK), kriging with drift function (KD) and kriging with an external drift function (KED)–were used. Although the basic mechanisms of these techniques are the same, there are some fundamental differences. For example, unlike SK, OK and KD, the KED technique used the particular metal variable as secondary information to predict particular metal. Therefore, secondary information of the particular metal variability to detect particular metal is easily incorporated in the kriging model.

For Kriging, training and calibration datasets were merged to form a single dataset, based on which a kriging model was developed. The kriging models were tested on the same prediction datasets as those used for the ANN models.

The neural network model was developed and tested on the prediction dataset. The performance of the kriging techniques was also evaluated on the same prediction dataset as used in the ANN. The following test statistics were used to assess model performance. Mean error is a measure of bias, which also shows on average whether a model underestimates or overestimates the grades. A negative sign indicates overestimation and a positive sign indicates underestimation. Mean absolute error measures the mean absolute deviation of actual minus predicted values, which is a measure of accuracy.

Kriging Interpolation

The use of Geostatistics in general and Kriging in particular was a useful tool to estimate the pollutants distribution in a contaminated site and also to give both the advantages and disadvantages associated with the use of Kriging.

Advantages of Kriging

Disadvantages of Kriging

Artificial Neural Network

Advantages of ANN Model

Disadvantages of ANN Model

Conclusion

Table 1 · Surveyed, Predicted and Error Percentage of Heavy Metal
Station Output (Pb) Error % Output(Cr) Error % Output(As) Error %
Latitude Longitude Surveyed Predicted Surveyed Predicted Surveyed Predicted
1 10° 52’27.96”N 77° 0’27.39”E 0.579 0.5858 -1.1739 0.65 0.6566 -1.0193 1.39 1.4041 -1.0128
2 10° 52’35.41”N 77° 0’15.12”E 0.12 0.1222 -1.8339 0.56 0.5483 2.0863 0.967 0.9855 -1.9091
3 11° 08’29.54”N 77° 1’51.76”E 0.032 0.0316 1.3125 0.41 0.4228 -3.1253 0.541 0.5544 -2.4762
4 11° 05’22”N 76° 52’31”E 0.41 0.4183 -2.0244 0.341 0.3506 -2.8289 0.321 0.3113 3.0137
5 11° 02’18.78”N 76° 8’39.81”E 2.44 2.5243 -3.4551 3.5 3.3944 3.0165 0.349 0.3529 -1.1063
6 11° 01’9.92”N 76° 57’45.09”E 2.12 2.0970 1.0847 3.62 3.5566 1.7523 0.321 0.3281 -2.2216
7 11° 03’28.2”N 76° 9’31.38”E 0.76 0.7445 -1.9312 2.341 2.4338 2.9355 0.211 0.2157 2.0692
8 11° 01’4.77”N 76° 57’56.82E 2.89 2.9295 -1.3665 0.23 0.2369 -3.0035 0.191 0.1931 -1.1124
9 11° 0’2.82”N 76° 58’5.38”E 3.2 3.1685 0.9831 0.0231 0.0233 -1.0031 0.876 0.9029 -3.0741
10 11° 15’25.01N 76° 57’49.84”E 2.39 2.3316 2.4446 0.015 0.0149 0.8998 0.0275 0.0269 2.1236
11 11° 14’0.84’’N 77° 06’22.97”E 1.234 1.2812 -3.8241 0.012 0.0124 -3.0081 0.006 0.0062 -2.6241
12 11° 03’47.27”N 76° 8’57.01”E 0.89 0.8889 0.1236 0.032 0.0320 0.1236 0.045 0.0445 1.0231
13 11° 12’53.01”N 77° 06’14.99”E 0.432 0.4416 -2.2301 0.012 0.0122 -2.0231 0.023 0.0225 2.0351
14 11° 10’26.6”N 77° 03’28.78”E 1.45 1.4116 2.6449 0.019 0.0186 2.2669 0.0468 0.0478 -2.1175
15 11° 02’44.”N 76° 56’48.97”E 7.3 7.5914 -3.9914 0.02 0.0206 -2.9914 0.0102 0.0099 3.1129
16 11° 0’40.26”N 76° 57’12.45”E 1.237 1.2631 -2.1081 0.43 0.4424 -2.8898 0.0184 0.0188 -2.0833
17 11° 1’34.79”N 76° 57’2.86”E 0.31 0.3160 2.0351 0.02 0.0194 -3.9654 0.0348 0.0341 -2.2301
18 11° 1’33.79”N 76° 57’29.6”E 1.34 1.3684 -2.1175 0.45 0.4641 -3.1275 0.0098 0.0095 2.6449
19 11° 9’ 51.14”N 76° 58’54.88” 0.01 0.0097 3.1129 0.008 0.0079 1.0029 0.069 0.0718 -3.9914
20 11° 1’02”N 76° 6’06.43”E 0.012 0.0122 -2.0833 0 0.0000 0 0.005 0.0051 -2.1081
21 11° 0’3.42”N 77° 03’2.44”E 0.023 0.0228 1.0435 0 0.0000 0 0.002 0.0020 2.4135
22 11° 0’28.75”N 76° 57’3.31”E 6.02 6.1405 -2.0017 0.067 0.0692 -3.2231 0.003 0.0029 2.9917
23 11° 0’34.57”N 76° 57’9.89”E 0.004 0.0041 -2.5201 0.56 0.5746 -2.5987 5.68 5.8805 -3.5301
24 11° 0’57.56”N 76° 57’49.84”E 0.001 0.0010 0 3.568 3.4966 2.0001 6.12 6.0596 0.9871
25 10° 58’59.16”N 77° 01’24.24”E 2.15 2.1237 1.2247 0.025 0.0242 3.0047 0.014 0.0138 1.2247
26 10° 59’57.29”N 76° 58’20.89”E 0 0.0000 0 0.001 0.0010 0 0.025 0.0245 1.9233
27 11° 1’30.5”N 77° 01’18.94”E 0 0.0000 0 0.78 0.7600 2.5642 4.67 4.7163 -0.9912
28 10° 58’57.29”N 76° 58’21.89”E 0 0.0000 0 0.612 0.6351 -3.7812 3.98 3.9419 0.9561
29 10° 58’17.75”N 76° 59’19.7”E 0 0.0000 0 0.003 0.0029 3.1281 0.001 0.0010 0
30 10° 59’55.36”N 76° 57’37.89”E 0.001 0.0010 0 0.009 0.0092 -2.7697 4.89 4.9926 -2.0991
31 11° 56’37”N 76° 56’18.44”E 0.002 0.0019 2.9981 0.004 0.0038 3.9001 0 0.0000 0
32 11° 57’47.16”N 76° 56’49”E 0 0.0000 0 0.001 0.0010 0 0.45 0.4455 0.9991
33 11° 54’34.79”N 76° 57’11.59”E 0.002 0.0020 0 2.98 3.0490 -2.3145 6.65 6.8052 -2.3331
34 11° 2’44”N 76° 56’48.44”E 1.82 1.8747 -3.0055 1.002 1.0321 -3.0055 0.011 0.0113 -2.8811
35 11° 5’0.4”N 76° 56’0.67”E 8.376 8.2838 1.1012 0.029 0.0281 3.0012 0.007 0.0068 2.3112
36 11° 3’48.26”N 76° 58’58.73”E 0.89 0.8711 2.1236 0.012 0.0119 1.1236 0.004 0.0041 -2.1261
37 11° 4’12.48”N 76° 52’56.2”E 0.568 0.5795 -2.0176 0.022 0.0227 -3.3176 0.0679 0.0697 -2.6171
38 11° 8’27.19”N 76° 01’1.92”E 0 0.0000 0 0.081 0.0802 0.9876 0.026 0.0257 0.9765
39 11° 12’0.84’’N 77° 10’22.97”E 0 0.0000 0 0.004 0.0040 0 0.002 0.0020 0.8891
40 11° 14’19.4”N 76° 57’31.78”E 0.231 0.2334 -1.0433 0.065 0.0657 -1.0433 0.456 0.4608 -1.0433
41 10° 57’4.86” 76° 58’16.9”E 1.15 1.1740 -2.0871 0.005 0.0051 -2.8795 7.93 8.0955 -2.0871
42 10° 59’37.95” 76° 57’38.86”E 8.56 8.4460 1.3312 0.087 0.0842 3.1612 7.63 7.5284 1.3312
43 11° 02’13.0” 76° 57’1.92”E 2.58 2.6575 -3.0039 0.003 0.0031 -3.5039 0.029 0.0299 -3.0039
44 10° 56’30.36’’N 76° 53’52.93”E 0.002 0.0020 0 0.09 0.0870 3.2927 0 0.0000 0
45 11° 2’37.32”N 76° 57’2.86”E 0.78 0.7879 -1.0128 0.0011 0.0011 0 0.002 0.0020 1.0351
46 11° 1’28.98”N 76° 54’14.26”E 0.011 0.0112 -1.9091 0.007 0.0073 -3.9091 5.89 5.9980 -1.8339
47 11° 0’35.43”N 76° 57’0.20”E 0.021 0.0215 -2.4762 0.002 0.0021 -3.4762 1.34 1.3224 1.3125
48 11° 0’40.26”N 76° 57’12.45”E 0.27 0.2619 3.0137 0.05 0.0485 2.9637 1.002 1.0223 -2.0244
49 11° 4’43.63”N 77° 0’7.11”E 1.59 1.6076 -1.1063 0.028 0.0290 -3.7163 1.28 1.3242 -3.4551
50 11° 1’52.76”N 76° 59’.59.32”E 6.14 6.2764 -2.2216 0.001 0.0010 0 0.0826 0.0817 1.0847
51 10° 59’23.22”N 76° 59’3.67”E 1.59 1.5571 2.0692 4.2 4.1064 2.2292 0.155 0.1580 -1.9312
52 10° 59’57.29”N 76° 58’20.89”E 4.12 4.1658 -1.1124 3.21 3.2821 -2.2476 3.76 3.8114 -1.3665
53 10° 53’2.99”N 77° 00’3.42"E 0.135 0.1392 -3.0741 0.013 0.0134 -3.2741 0.505 0.5000 0.9831
54 10° 49’4.8”N 77° 01’35”E 0.89 0.8711 2.1236 0.067 0.0657 2.0136 0.098 0.0956 2.4446
55 11° 05’22”N 76° 52’31”E 0.546 0.5516 -1.0183 0.013 0.0135 -3.9183 0.088 0.0889 -1.0183
56 11° 01’4.77”N 76° 57’56.82E 0.134 0.1299 3.0746 0.043 0.0418 2.7346 1.2 1.1631 3.0746
57 10° 57’37.14”N 76° 52’5.6E 1 1.0222 -2.2201 0.12 0.1212 -1.0198 0.056 0.0572 -2.2201
58 11° 4’35.16”N 76° 57’56.82E 3.21 3.1688 1.2835 0.021 0.0202 3.8352 0.056 0.0542 3.1831
59 11° 3’46.24”E 76° 54’28”E 0 0.0000 0 0.004 0.0039 2.9132 0.001 0.0010 0
60 10° 58’10.10”N 76° 51’35.3E 0 0.0000 0 2.98 2.8807 3.3312 0 0.0000 0
61 11° 0’21.67”N 77° 07’32.80E 2.87 2.8093 2.1135 0.04 0.0391 2.3511 0.561 0.5666 -1.0065
62 10° 55’25.46”N 77° 17’10.87E 2.134 2.1885 -2.5547 0.987 1.0220 -3.5447 0.78 0.7877 -0.9876
63 11° 0’40.26”N 76° 57’12.45”E 0.112 0.1143 -2.0893 2.87 3.0116 -4.9321 5.45 5.5586 -1.9934
64 10° 55’36.98”N 76° 58’53.64E 2.34 2.2954 1.9043 2.98 2.9063 2.4743 0.78 0.7653 1.8829
65 11° 8’29.54”N 77° 1’51.76”E 2.23 2.2816 -2.3145 0.023 0.0236 -2.4567 0.076 0.0783 -3.0042
66 10° 54’7.31’’N 76° 59’45.55”E 1.2 1.1780 1.8333 0.1 0.0972 2.8223 0.78 0.7879 -1.0107
67 11° 4’54.83”N 76° 54.5’0.3”E 8.2 8.1245 0.9212 0.025 0.0245 2.1299 0.98 0.9997 -2.0102
68 10° 58’40.3”N 76° 57’38.56”E 2.89 2.9187 -0.9935 0.034 0.0351 -3.3415 0.38 0.3862 -1.6343
69 10° 57’14.81”N 76° 59’36.02”E 5.12 4.9663 3.0021 0.076 0.0743 2.2245 0.72 0.7323 -1.7117
70 10° 57’4.86”N 76° 58’16.99”E 4.56 4.6103 -1.1022 2.89 2.8966 -0.2299 4.567 4.4299 3.0018
71 10° 59’01”N 76° 57’36.62”E 8.912 9.0110 -1.1111 0.92 0.9211 -0.1189 1.23 1.2437 -1.1111
72 10° 58’24.28”N 76° 57’54.92”E 7.654 7.4840 2.2213 0.675 0.6538 3.1367 0.987 0.9651 2.2213
73 10° 59’44.51”N 76° 59’ 0.95”E 9.27 8.9816 3.1111 1.04 1.0180 2.1109 7.56 7.3248 3.1111
74 10° 58’49.22”N 76° 55’19.88”E 1.543 1.5585 -1.0065 0.067 0.0691 -3.1165 6.67 6.7371 -1.0065
75 11° 59’37.63”N 77° 1’12.9”E 1.67 1.6865 -0.9876 0.054 0.0529 2.0611 5.89 5.9482 -0.9876
76 11° 1’48.88”N 77° 07’12.11”E 2.982 3.0414 -1.9934 0.54 0.5508 -1.9934 6.78 6.9152 -1.9934
77 11° 0’5.88”N 76° 56’46.10”E 3.5 3.4341 1.8829 0.89 0.8712 2.1129 2.12 2.0801 1.8829
78 10° 59’31.54”N 76° 56.5’21.13”E 2.38 2.4515 -3.0042 0.387 0.3986 -3.0042 3.12 3.2137 -3.0042
79 11° 0’40.26”N 76° 57’12.45”E 1.87 1.8889 -1.0107 0.245 0.2475 -1.0107 1.23 1.2424 -1.0107
80 11° 00’10.6”N 76° 56’38.04”E 0.98 0.9997 -2.0102 0.17 0.1734 -2.0102 0.45 0.4590 -2.0102
81 10° 59’31.54”N 76° 56.5’21.13”E 2.3 2.3376 -1.6343 0.19 0.1966 -3.4873 0.012 0.0117 2.1135
82 10° 57’43.89”N 76° 55’41.65”E 5.82 5.9196 -1.7117 0.034 0.0347 -2.1711 0.005 0.0051 -2.5547
83 10° 58’53.91”N 77° 5’18.69”E 5.42 5.2573 3.0018 2.345 2.2702 3.1886 3.89 3.9713 -2.0893
84 11° 1’24.75”N 76° 57’25.22”E 0 0.0000 0 0.005 0.0049 1.9932 0.345 0.3384 1.9043
85 11° 4’00.39”N 77° 5’18.69”E 0.002 0.0020 0 0 0.0000 0 0.789 0.8073 -2.3145
86 11° 0’12.55”N 77° 4’18.33”E 0 0.0000 0 0 0.0000 0 0.003 0.0029 1.8333
87 11° 04’00.39”N 77° 5’18.69”E 0 0.0000 0 0.002 0.0021 -3.3337 0.002 0.0020 0
88 11° 05’45.10”N 76° 59’47”E 0.002 0.0020 0 0.005 0.0051 -2.1255 0.001 0.0010 0
89 11° 7’3.86”N 76° 56’7.2”E 0.19 0.1861 2.0526 0.389 0.3750 3.5926 2.53 2.4384 3.6216
90 10° 56’22.66”N 76° 44’47.8”E 0 0.0000 0 0.007 0.0068 2.4412 0.008 0.0081 -1.3222
91 10° 59’24.02”N 76° 50’31.36”E 0 0.0000 0 0 0.0000 0 0 0.0000 0
92 10° 59’29.55”N 76° 48’ 15.77”E 0 0.0000 0 0 0.0000 0 0.005 0.0050 0.8823
93 11° 0’48.17”N 77° 2’29.9”E 0.678 0.6984 -3.0147 0.832 0.8499 -2.1479 0.0535 0.0546 -2.0911
94 11° 1’49.17”N 77° 2’ 29.9”E 0.654 0.6382 2.4153 0.567 0.5491 3.1535 0.798 0.7883 1.2153
95 11° 1’18.08”N 77° 10’39”E 0.003 0.0029 1.9981 0 0.0000 0 0.566 0.5604 0.9981
96 10° 56’22.66”N 76° 44’47.8”E 0.002 0.0020 0 0 0.0000 0 0 0.0000 0
97 10° 59’00.59”N 76° 56’23.85”E 0.001 0.0010 0 0.008 0.0082 -2.6518 0 0.0000 0
98 11° 4’00.39”N 77° 5’18.69”E 0 0.0000 0 0.021 0.0218 -3.6631 0 0.0000 0
99 11° 5’37.47”N 76° 46’31”E 0.0001 0.0001 0 0.001 0.0010 0 0 0.0000 0
100 11° 0’48.17”N 77° 2’29.9”E 0 0.0000 0 0 0.0000 0 0 0.0000 0
101 11° 16’13.15”N 76° 59’56.74”E 0 0.0000 0 0 0.0000 0 0 0.0000 0
102 11° 20’52.26”N 77° 10’7.07”E 0.003 0.0029 1.8812 0 0.0000 0 0 0.0000 0
103 11° 03’36.47”N 77° 15’57.97”E 0 0.0000 0 0 0.0000 0 0 0.0000 0
104 11° 01’11.73”N 77° 04’14.34”E 0.005 0.0051 -1.6522 0.002 0.0019 3.1456 0.002 0.0020 0
105 11° 59’49.16”N 77° 05’33.98”E 0.003 0.0031 -3.1987 0.001 0.0010 0 0.001 0.0010 0
106 10° 48’45.42”N 76° 51’28.61”E 0 0.0000 0 0 0.0000 0 0 0.0000 0
107 11° 30’18.07”N 77° 14’14.49”E 0 0.0000 0 0.003 0.0031 -3.1129 0 0.0000 0
108 11° 00’48.11”N 76° 58’9.86”E 0.054 0.0546 -1.1111 0 0.0000 0 0 0.0000 0
109 10° 57’48.23”N 76° 52’9.32”E 0.001 0.0010 0 0.001 0.0010 0 0 0.0000 0
110 10° 56’23.48”N 76° 56’35.4”E 0 0.0000 0 0.002 0.0021 -2.9822 0 0.0000 0
111 11° 00’39.45”N 76° 58’3.6”E 0.053 0.0543 -2.4431 0.002 0.0019 3.1577 0.002 0.0020 0
112 10° 53’43.69”N 76° 53’35.4”E 0 0.0000 0 0 0.0000 0 0 0.0000 0
113 10° 49’7.88”N 77° 03’19.65”E 0.002 0.0020 0 0.001 0.0010 0 0.001 0.0010 0
114 10° 47’4.09”N 77° 03’31.03”E 0.023 0.0225 2.2985 0.104 0.1028 1.1976 0.202 0.1994 1.2977
115 11° 01’41.52”N 76° 57’40.6”E 0.031 0.0304 1.9888 0.002 0.0019 3.6712 0.032 0.0317 1.0891
116 11° 01’24.7”N 76° 57’25.27”E 0.057 0.0583 -2.3333 0.003 0.0031 -3.1191 0.011 0.0112 -1.6666
117 11° 05’35.02”N 76° 56’41.27”E 0.006 0.0062 -3.1265 0.001 0.0010 0 0.021 0.0212 -1.1344
118 11° 02’54.9”N 76° 58’14.1”E 0 0.0000 0 0 0.0000 0 0 0.0000 0
119 11° 07’6.69”N 77° 02’32.6”E 0.071 0.0703 0.9876 0 0.0000 0 0 0.0000 0
120 11° 10’54.47”N 77° 04’47.64”E 0 0.0000 0 0 0.0000 0 0 0.0000 0
121 11° 12’5.7”N 77° 4.39’30”E 0.032 0.0316 1.1111 0.001 0.0010 0 0 0.0000 0

References

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  2. Brumelis, G., Brown, D.H., Nikodemus, O., Tjarve, D., 1999.The monitoring and risk assessment of Zn deposition around a metal smelter in Latvia. Environmental Monitoring and Assessment, 58(2), 201-212.
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  5. Devkota, B., Schmidt, G.H., 2000. Accumulation of heavy metals in food plants and grasshoppers from the Taigetos Mountains, Greece. Agriculture, Ecosystems and Environment, 78(1), 85-91.
  6. Baker, A.J.M., 1981. Accumulator and excluders: Strategies in response of plant to heavy metals. J. Plant Nutr. 3, 643-654.
  7. de Vries, W., Romkens, P.F., Schutze, G., 2007.Critical soil concentrations of cadmium, lead, and mercury in view of health effects on humans and animals. Reviews of Environmental Contamination and Toxicology, 191, 91-130.
  8. Galas-Gorchev., 1991. H. Dietary Intake of Pesticide Residues: Cadmium, Mercury and Lead. Food Add. Cont. 8, 793-806.
  9. Gandhimathi, A., Meenambal, T., 2011. Spatial Prediction of Heavy Metal Pollution for Soils in Coimbatore, India based on universal kriging. International Journal of Computer Applications, 29(10), 52-63.
  10. Gandhimathi, A., Meenambal, T., 2012. Analysis of Heavy Metal for Soil in Coimbatore by using ANN Model. European Journal of Scientific Research, 68(4), 462-474.

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