INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume x, No x, 2012 © Copyright 2010 All rights reserved Integrated Publishing services ISSN 0976 – 4380 Research article Title of the article Author name1, Author name2 1- Author affiliation 2- Author affiliation Author mail id ABSTRACT The content provided is a sample text, replace this text by pasting the authors content in the respective place without any space break, page break or section break. The content provided is a sample text, replace this text by pasting the authors content in the respective place without any space break, page break or section break.The content provided is a sample text, replace this text by pasting the authors content in the respective place without any space break, page break or section break. The content provided is a sample text, replace this text by pasting the authors content in the respective place without any space break, page break or section break. The content provided is a sample text, replace this text by pasting the authors content in the respective place without any space break, page break or section break. Keywords: Provide up to 6 keywords to identify the article. 1. Introduction Drinking water quality is strongly influenced by the quality of the corresponding “parent” natural water from which drinking water derives. Groundwater quality in a region is largely determined by both the natural processes (lithology, groundwater velocity, quality of recharge waters, and interaction with other types of water aquifers) and anthropogenic activities (agriculture, industry, urban development, and increasing exploitation of water resources) (Helena et al., 2000). In recent decades, there has been a rapid increase in nonpoint sources of groundwater pollution i.e. pollution, entering the system over a wide area. Martinez and Albiac (2004) noted that non-point source pollution from agriculture has increased significantly in several parts of the state. Agriculture is now considered to be the dominant source of non-point pollution of groundwater (Jones, 1997). Many different sources and processes can be responsible for the contaminants polluting the groundwater. The commonly found contaminants in groundwater due to agriculture are nitrate, chloride, sodium, calcium, magnesium, ammonia, phosphate and trace elements (George et al., 1987; Burkhart and Kolpin, 1992; Fetter, 1992; Spalding and Exner, 1993; Beke et al., 1993). Detailed hydrochemical research is needed to evaluate the different processes and mechanisms involved in polluting water (Helena et al., 1999). The conventional method like piper and stiff are used to determine the quality of groundwater in minor extent. In recent years, the limitations of the use of conventional methods and increasing number of chemical parameters, a wide range of statistical techniques are now required for proper analysis of data (Ashley and Lloyd, 1978; Dalton and Upschurch, 1978; Usunoff and Guzman, 1989; Olmez et al., 1994; Voudouris et al. 2000; Guler et al., 2002; Reghunath et al, 2002; Hussein, 2004; Mahlknecht et al., 2004). In this view, the Multivariate Statistical analysis technique is very useful tool for interpret the water quality parameters. Submitted on August 2012 published on September 2012 285 Provide the article title here Author name The purpose of the present study is to evaluate the similarities and dissimilarities with specific process that control the quality of groundwater throughout the Principal Component Analysis (PCA). 2. Study area The study area is located at Tamil Nadu, India. The study area is bounded on the north by latitude (Fig. 1). The area forming part of Indian peninsular gneissic complex, with chief rock types of augen gneiss, quartzite and Hornblende Biotite Gneiss (HBG) and miopliocene sandstone and clay. The sedimentary formation represented by sandstones and clay (fig 2) are major in extent. The gneissic rock exhibit secondary porosity developed by fracture and joints. From the groundwater point of view, the Precambrian rocks and quaternary sediments are referred to as crystalline rocks and unconsolidated sediments, respectively; the former have fracture porosity, while the latter have pore porosity. Weathering process is very common in this area. The weathered zone corresponds to unconfined conditions and the fractured zone corresponds to semi – confined to confined conditions to support the groundwater. The study area, experiences relatively less than rainfall (av. 70–100 mm/yr) than the average annual rainfall of the state. This rainfall occurs primarily in the North East monsoon (90%) and small amount received from South East Monsoon season. Figure 1: Study area 3. Methodology 3.1 Sampling International Journal of Geomatics and Geosciences Volume 3 Issue 1, 2012 286 Provide the article title here Author name The 31 representative groundwater samples were collected from dug wells during the pre monsoon season of 2007. The samples were taken in pre – washed IL polythelene bottles filling it to the capacity. Sampling, preservation, and transportation of the water samples to the laboratory were conducted by the standard methods reported by APHA (1998) and Richards (1954). The samples were analyzed for 17 parameters. 3.2 Statistical procedure 3.2.1 Principal Component Analysis (PCA) Multivariate techniques can help to simplify and organize large data sets and to make useful generalizations that can lead to meaningful insight (Laaksoharju et al 1999). Cluster and factor analyses are efficient ways of displaying complex relationships among many objects (Davis 1986). In general, the PCA gives large set of principal components to explain the relationship among the chemical variables. The computer package Statistical Packages like for Social Sciences (SPSS) and STATISTICA 6 have been used to carry out the analysis. The data have been standardized by using standard statistical procedures. The interpretation of the principal components can be simplified, using the certain rotational procedures. In the present study, the varimax normalised rotation of Kaiser (1958) was applied to obtain a simple structure with scores. The scores are obtained called factor scores, which show the intensity of chemical process (factor) described by each principal component (Dalton and Upchurch, 1978). Regionally distributed and lithologically controlled variables are extracted first from the principal component scores and the more local pathway/origin controlled variables are then identified (Lawrence and Upchurch, 1982). Three sets of plots were made from the PCA results. (1) Scatter plots between the component loadings of the three selected principal component scores of Kaiser varimax normalized rotation and (iii) sample – wise areal distribution of the principal component scores on the map area. 4. Results and discussion Table 1: Physico – chemical characteristics of ground waters of study area in pre onsoon season – 2007 S. No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Location Maaplampatti Kil Pungudi Eluvankottai Devakottai Aravayal Kalluppatti Ogampatti Sevarakottai Perichchikovil Tiruppattur Kongaratti Alangudi Variyangaval Devakottai Rasta Iluppaikkudi EC 1900 1750 1280 2500 2200 1500 4300 1410 650 1220 1530 1470 1970 910 1100 TDS 241.62 3951.97 720.11 519.64 817.59 711.92 1163.51 3034.37 784.00 1448.79 1367.00 308.99 1005.34 638.41 241.62 pH 7.87 7.72 8.47 7.26 8.35 8.70 8.60 7.30 7.42 8.05 8.50 8.69 7.90 8.21 8.90 K.Ratio 0.43 0.78 1.80 2.08 0.58 2.31 1.91 2.19 1.14 0.88 0.97 0.59 1.36 1.90 0.43 SSP 45.24 45.49 66.15 71.43 41.29 72.57 67.62 69.29 54.00 47.39 50.20 50.73 57.89 66.39 45.24 RSC 0 0 0.90 0.30 0 2.40 0 0 2.50 0 0.70 0 0.30 3.40 0 International Journal of Geomatics and Geosciences Volume 3 Issue 1, 2012 SAR 0.93 6.88 5.33 4.74 2.48 6.12 7.03 12.87 4.22 4.70 4.88 1.38 5.43 5.36 0.93 Mg hazards 30.43 56.81 25.00 30.77 42.86 40.00 73.53 86.05 44.93 36.11 33.86 40.74 60.00 30.00 30.43 Na% 45.24 45.49 66.15 71.43 41.29 72.57 67.62 69.29 54.00 47.39 50.20 50.73 57.89 66.39 45.24 287 Provide the article title here Author name 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Karaikudi Managiri Ilangudi Siruvayal Sennalkudi Avadapoigai Kunnakudi Kottaiyur Puduvayal Karnakkudi Sirugappatti Ponnambalam Pallattur Chokkanpatti Muttuppattanam Kanadukattan 1370 840 2100 830 2200 1800 2400 430 1880 1300 1740 420 670 1200 500 980 1689.29 286.73 241.94 1349.53 1717.09 352.60 2368.47 1038.82 801.59 959.56 520.65 158.61 280.92 983.99 548.09 2511.26 7.40 8.56 8.74 8.70 8.00 6.90 7.30 8.65 8.84 9.16 8.46 9.71 8.10 7.90 9.09 8.63 0.10 0.50 0.45 0.65 0.85 0.26 0.56 1.00 0.43 0.52 0.71 0.53 3.29 0.73 0.34 0.67 10.94 41.18 39.53 39.77 46.88 27.34 36.56 51.05 31.62 36.21 44.79 48.28 78.00 43.09 27.08 40.42 0 0 0 0 0 0 0 0 0 0 0 0.70 0.90 0 0 0 0.65 1.22 1.03 3.62 4.97 0.73 4.30 4.31 1.63 2.43 2.30 0.92 4.88 3.39 1.26 5.03 57.89 30.00 11.54 51.92 28.82 55.26 63.23 35.48 29.17 11.71 41.51 33.33 18.18 58.88 25.71 64.34 10.94 41.18 39.53 39.77 46.88 27.34 36.56 51.05 31.62 36.21 44.79 48.28 78.00 43.09 27.08 40.42 Unit: Concentration in epm except pH: EC (uS Cm-1) RSC and SAR (meq l-1). Table 2: Physico – chemical characteristics of ground waters of study area in pre monsoon season – 2007 S. No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Location Maaplampatti Kil Pungudi Eluvankottai Devakottai Aravayal Kalluppatti Ogampatti Sevarakottai Perichchikovil Tiruppattur Kongaratti Alangudi Variyangaval Devakottai Rasta Iluppaikkudi Karaikudi Managiri Ilangudi Siruvayal Sennalkudi Avadapoigai Kunnakudi Kottaiyur Puduvayal Karnakkudi Sirugappatti Ponnambalam Pallattur Chokkanpatti Ca 32.1 336.7 66.1 36.1 104.2 42.1 36.1 48.1 76.2 184.4 168.3 32.1 64.1 56.1 32.1 192.4 42.1 46.1 150.3 242.5 34.1 214.4 120.2 102.2 196.4 62.1 20.0 18.0 88.2 Mg 8.5 268.7 13.4 9.7 47.4 17.0 60.8 180.0 37.7 63.2 52.3 13.4 58.4 14.6 8.5 160.5 10.9 3.6 98.5 59.6 25.5 223.7 40.1 25.5 15.8 26.8 6.1 2.4 76.6 Na 23.0 698.3 181.7 124.2 121.9 186.3 298.3 868.0 180.3 289.8 282.9 36.8 249.8 174.3 23.0 50.6 34.5 27.1 232.3 333.5 23.0 377.2 213.7 71.3 131.6 86.0 18.4 83.3 180.6 K 35.2 82.1 27.4 43.0 43.0 45.4 48.1 41.4 10.2 14.5 19.6 46.1 5.5 12.5 35.2 23.5 23.5 20.3 7.8 19.6 16.8 14.5 16.0 9.0 22.7 21.9 23.5 10.9 9.8 HCO3 42.7 250.2 311.2 134.2 494.3 317.3 244.1 951.9 555.3 756.6 805.5 134.2 506.5 433.2 42.7 689.5 109.8 67.1 616.3 903.1 128.1 829.9 256.3 311.2 244.1 109.8 115.9 85.4 390.5 International Journal of Geomatics and Geosciences Volume 3 Issue 1, 2012 Co3 12.0 12.0 6.0 21.0 0.0 21.0 12.0 12.0 9.0 12.0 6.0 12.0 0.0 9.0 12.0 9.6 12.0 9.0 9.0 6.0 6.0 15.0 9.0 12.0 9.0 36.0 9.0 18.0 12.0 So4 0.0 240.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Cl 109.9 2191.4 273.0 219.9 258.9 244.7 588.6 1418.4 198.6 514.2 443.3 102.8 379.4 159.6 109.9 914.9 109.9 102.8 549.6 613.5 184.4 1117.0 514.2 429.1 464.5 234.0 24.8 106.4 425.5 288 Provide the article title here Author name 30 31 Muttuppattanam Kanadukattan 104.2 204.4 21.9 223.7 54.3 437.7 9.4 14.5 79.3 756.6 0.0 12.0 96.1 0.0 223.4 1248.2 Note: All values are in ppm The concentration of sodium, chloride, calcium and bicarbonate content also show a significant difference between the medium and maximum values. The mean values are near the quarter values of the maximum values. It suggests that local contamination inputs to the groundwater system. The wide range of bicarbonate content, from 42.7 to 951.9 ppm (Table 3) is the result of the lateral geological variations of the layers conferring to the groundwater different concentration. Box plot of the chemical concentration show that bicarbonate, calcium, chloride and TDS have the largest dispersions (Figure 2). The enrichment of chloride and TDS from values of 24.8 to 2191.4 is present in the groundwater located eastern side of the study area. The increase in the salt concentration could be associated to different mechanisms like water rock interaction processes. Table 3: Mean and standard deviation of the groundwater sample of pre monsoon season Parameters Ca Mg Na K HCO3 CO3 SO4 Cl EC TDS pH K.Ratio SSP RSC SAR Mg Hazard Na % Mean 101.7 60.5 196.6 24.9 376.6 11.0 10.8 466.9 1495.2 1056.9 8.3 1.0 48.2 -3.5 3.7 41.2 48.2 Minimum 18.0 2.4 18.4 5.5 42.7 0.0 0.0 24.8 420.0 158.6 6.9 0.1 10.9 -34.4 0.7 11.5 10.9 Maximum 336.7 268.7 868.0 82.1 951.9 36.0 240.2 2191.4 4300.0 3952.0 9.7 3.3 78.0 3.4 12.9 86.0 78.0 Std.Dev. 79.8 72.9 195.3 16.7 286.8 6.8 45.9 472.4 783.0 891.0 0.7 0.7 15.1 7.3 2.6 17.7 15.1 Unit: Concentration in epm except pH: EC (uS Cm-1) RSC and SAR (meq l-1). 4.1 Principal component analysis In order to evaluate the most significant parameters in water quality assessment, a sensitivity analysis was performed using FA/PCA methodology. FA/PCA was applied to the all variable in order to compare the compositional patterns between the analyzed water samples and to identify the factors that influence each one. PCA was performed on the physical–chemical data from the study area using STATISTIC 6 code (Statsoft Inc, 2001). Three components with eigenvalues over 1 were obtained (Table 4 and Fig. 3). The eigenvalues determine the number of principal components that are necessary. This number depends on the amount of variation in the data. International Journal of Geomatics and Geosciences Volume 3 Issue 1, 2012 289 Provide the article title here Author name Box & Whisker Plot 3000 2500 2000 1500 1000 500 0 -500 Mean ±SD ±1.96*SD -1000 Ca Mg Na K HCO3 CO3 SO4 Cl TDS Figure 2: Box and Whisker plot for chemical parameters of groundwater sample of pre monsoon season Factor Loadings, PC 1 vs. PC 2 vs. PC 3 Rotation: Varimax Normalized Extraction: Principal components K SO 4 SAR Na SSP % K.Ratio CO 3 EC pH Cl Na TDS Mg Ca Mg Hazard HCO 3 RSC Figure 3: PCA Loading from the study area We assume that in this study one principal component is enough to represent the data in a sufficiently accurate way. According to the eigenvalue-one criterion, three PCs in the study area were selected, which explain 71.32% of the total variance in the water quality data sets (Table 4). This analysis indicates that the most relevant variables defining water quality are related to water dissolved salts (EC, Ca, Na, K, Cl, SO4, HCO3). PC 1 accounts for 36% of the total variance and appears to characterize the water–rock interaction processes. The aquifer matrix dissolution induces high loadings in Ca, Mg, Na, Cl International Journal of Geomatics and Geosciences Volume 3 Issue 1, 2012 290 Provide the article title here Author name and HCO3. This represents the groundwater evolution and the importance of water–rock interaction processes. It emphasizes that groundwater, with a higher residence time, has higher values in bicarbonate, calcium and magnesium, related with the lithology of the aquifer (reference). PC 2 represents 24% of the total variance and shows high loadings for SSP, K Ratio, SAR and Na %. PC 3 is dominated by high loadings in K and SO 4. This fact might be the result of agricultural contamination by the use of pesticides. Table 4: Results of PCA in the study area Parameters Ca Mg Na K HCO3 CO3 SO4 Cl EC TDS pH K.Ratio SSP RSC SAR Mg Hazard Na % Eigenvalue % Total Variance Cumulative PC 1 0.659 0.889 0.896 0.120 0.830 -0.117 0.284 0.873 0.129 0.938 -0.497 0.096 -0.008 -0.052 0.701 0.718 -0.008 6.17 36.28 36.28 PC 2 -0.492 -0.257 0.219 0.111 0.024 0.290 -0.276 -0.189 0.099 -0.124 -0.134 0.936 0.963 0.602 0.648 0.046 0.963 4.15 24.39 60.66 PC 3 0.132 0.228 0.263 0.881 -0.431 0.331 0.691 0.403 0.386 0.289 0.037 0.087 0.171 -0.258 0.157 0.083 0.171 1.81 10.65 71.31 4.2 Pricipal component loadings Scatter plot of the pricipal component loading between PC 1 and PC 2, PC 1 and PC 3, and PC 2 and PC 3 are illustrated in Fig. 4 to 6. This figure can be read for the variation from positive to negative loading of each principal component and also the scatter between the principal components of the different pairs. 4.2.1 Principal Compontent – 1 The TDS, Na, Ca, Mg, Cl, HCO3, SAR and Mg hazards plotting close to one other in the positive region of PC – 1 in fig 4 & 6.PC 1 is taken as salinity factor. Salinity is directly controlled by the concentration of Na and Cl ions, which is derived from the interaction of water and rock, and gets enriched in the groudnwater due to their higher solubility (Reference). Accoring to the classification of WHO (1991) water quality standard 12 groudnwater samples are of brackish type (TDS >1000 ppm) and remaining samples shows the fresh water environment. The samples located in the areas of unconsolidated sediemnts are shows the fresh water and others are in the areas of crystalline rocks. This difference clearly suggests the ligthological control over the TDS concentration and other major ion concentrations. International Journal of Geomatics and Geosciences Volume 3 Issue 1, 2012 291 Provide the article title here Author name Factor Loadings, PC 1 vs. PC 2 Rotation: Varimax Normalized Extraction: Principal components 1.2 Na SSP % K.Ratio 1.0 0.8 PC 2 SAR RSC 0.6 0.4 CO 3 Na 0.2 K EC Mg HazardHCO 3 0.0 TDS Cl Mg pH -0.2 SO 4 -0.4 Ca -0.6 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 PC 1 Figure 4: Scatter plot of principal component loading for (a) PC – 1 vs PC – 2 Factor Loadings, PC 1 vs. PC 3 Rotation: Varimax Normalized Extraction: Principal components 1.0 K 0.8 SO 4 0.6 PC 3 0.4 Cl EC CO 3 NaTDS Mg Na SSP % 0.2 CaSAR Mg Hazard K.Ratio pH 0.0 -0.2 RSC HCO 3 -0.4 -0.6 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 PC 1 Figure 5: Scatter plot of principal component loading for (a) PC – 1 vs PC - 3 4.2.2 Principal Component – 2 Principal component – 2 has higher positive loading of SSP, Na %,K ratio, SAR and RSC (Table 4) and their close plotting indicates that their relationship comes under the single process. This indicate the indirect effect of the agriculture activities. Residential and agricultural activities appear to play an important role in the study area. Since there is no lithological impact on the pollution activity, PC – 2 is considered as a non – lithologically controlled factor. International Journal of Geomatics and Geosciences Volume 3 Issue 1, 2012 292 Provide the article title here Author name Factor Loadings, PC 2 vs. PC 3 Rotation: Varimax normalized Extraction: Principal components 1.0 K 0.8 SO 4 0.6 Cl 0.4 EC PC 3 TDS 0.2 CO 3 Na Mg Na SSP % K.Ratio SAR Ca pH Mg Hazard 0.0 -0.2 RSC HCO 3 -0.4 -0.6 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 PC 2 Figure 6: Scatter plot of principal component loading for (a) PC – 2 vs PC - 3 4.2.3 Principal Component - 3 PC – 3 has a higher positive loading of K and SO4, which is apparent as isolated one in fig. This suggests that the K and SO4 act as independent variables, which represents the pollution effect on the groundwater quality of this region. In figs. 4 and 5 K and SO4 falls on high positive region of PC – 3. The concentration of K exceeds 10 ppm in the 96% of the total groundwater samples in the study area. The higher concentration of K has lithological sources and represents dissolution felspathic group minerals like albite and anorhthite. 4.2.4 Principal Component Scores To identify the influence of relation of processes (factors) on the specific field setting of the samples, principal component scores are plotted with respect to the samples in plots of PC – 1 vs. PC – 2, PC – 1 vs. PC – 3 and PC – 2 vs. PC – 3 (Table 5 and Figs. 7 to 9). Table 5: Results of principal component scores S.No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 PC – 1 -1.060 1.949 -0.340 -0.649 -0.164 -0.414 0.108 2.571 0.275 0.637 0.551 -1.036 0.503 -0.101 -1.208 0.835 PC – 2 -0.325 -1.022 1.041 1.403 -0.519 1.892 1.198 1.704 0.779 -0.116 0.106 -0.120 0.561 1.608 -0.402 -1.887 PC - 3 0.537 3.981 -0.088 1.135 0.088 0.591 1.557 0.132 -1.419 -0.720 -0.787 0.745 -0.925 -1.113 0.478 -0.733 S.No 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 PC – 1 -0.990 -1.211 0.534 0.899 -0.429 1.708 -0.090 -0.501 -0.529 -0.877 -1.232 -0.800 0.224 -0.794 1.630 International Journal of Geomatics and Geosciences Volume 3 Issue 1, 2012 PC – 2 -0.483 -0.594 -0.553 -0.233 -0.931 -0.681 -0.013 -0.890 -0.873 -0.046 -0.330 1.997 -0.255 -1.404 -0.612 PC - 3 -0.030 0.194 -0.914 -0.627 -0.502 -0.439 -0.322 -0.318 0.043 0.790 -0.051 -0.100 -0.603 -0.049 -0.530 293 Provide the article title here Author name An analysis of the above table shows that only 11 samples (7, 9, 10, 11, 13, 16, 19, 20, 22, 29 and 31) have positive principal component scores in PC – 1, PC – 2, PC – 3 (Table 6). These 11 samples are possibly influenced by the factors of lithological influences, but not uniformly, because the positive scores vary among them. Figure 7: A score plot of Principal Component for PC – 1 vs. PC - 2 Figure 8: A score plot of Principal Component for PC – 1 vs. PC - 3 International Journal of Geomatics and Geosciences Volume 3 Issue 1, 2012 294 Provide the article title here Author name Figure 9: A score plot of Principal Component for PC – 2 vs. PC – 3 Table 6: Distribution of groundwater samples in positive principal component scores Positive Principal Component Scores PC – 1 PC – 2 13 samples) (10 samples) 2, 7, 8, 9, 10, 11, 13, 16, 3, 4, 6, 7, 8, 9, 19, 20, 22, 29, 31 11, 13, 14, 28 PC – 1 vs. PC – 2 PC – 1 vs. PC – 3 (12 Samples) (10 samples) 2, 7, 9, 10, 11, 13,16, 19, 20, 22, 1, 7, 10, 11, 16, 19, 20, 22, 29, 31 29, 31 PC – 1 vs. PC – 2, PC – 1 vs. PC – 3, PC – 2 vs. PC – 3 (11 Samples) 7, 9, 10, 11, 13, 16, 19, 20, 22, 29, 31 PC – 3 (12 Samples) 1, 2, 4, 5, 6, 7, 8, 12, 15, 18, 25, 26 PC – 2 vs. PC – 3 (10 samples) 3, 4, 6, 7, 8, 9, 11, 13, 14, 28 5. Conclusion The PCA of 13 chemical variables in the groundwater samples taken as a complementary solution to hydrogeochemcial study led to the identification of three principal components. The results obtained by a scientific evaluation groundwater quality data can be addition by using PCA which leads to the interpretation that the water–rock interaction process is the major mechanism responsible for the groundwater salinity. These results are described as lithologically and non – lithologically controlled factor. 6. References ( Provide full journal name in reference list, do not provide abbreviated journal names) 1. Achuthan Nair,G. Abdalla,I,Mohamed, Mohmoud Mahdy Fadiel, Muttah,A, ALMariami, Jalal,A, Bohjuari and K.Premkumar., (2005), Physico-chemical parameters and correlation coefficients of groundwater of North-East Libya, Pollution research, 24(1), pp 1-6. 2. 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