Spatial variation assessment of groundwater quality using multivariate statistical analysis(Case Study: Fasa Plain, Iran)
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Abstract:
Groundwater is considered as one of the most important sources for water supply in Iran. The Fasa Plain in Fars Province, Southern Iran is one of the major areas of wheat production using groundwater for irrigation. A large population also uses local groundwater for drinking purposes. Therefore, in this study, this plain was selected to assess the spatial variability of groundwater quality and also to identify main parameters affecting the water quality using multivariate statistical techniques such as Cluster Analysis (CA), Discriminant Analysis (DA), and Principal Component Analysis (PCA). Water quality data was monitored at 22 different wells, for five years (2009-2014) with 10 water quality parameters. By using cluster analysis, the sampling wells were grouped into two clusters with distinct water qualities at different locations. The Lasso Discriminant Analysis (LDA) technique was used to assess the spatial variability of water quality. Based on the results, all of the variables except sodium absorption ratio (SAR) are effective in the LDA model with all variables affording 92.80% correct assignation to discriminate between the clusters from the primary 10 variables. Principal component (PC) analysis and factor analysis reduced the complex data matrix into two main components, accounting for more than 95.93% of the total variance. The first PC contained the parameters of TH, Ca2+, and Mg2+. Therefore, the first dominant factor was hardness. In the second PC, Cl-, SAR, and Na+ were the dominant parameters, which may indicate salinity. The originally acquired factors illustrate natural (existence of geological formations) and anthropogenic (improper disposal of domestic and agricultural wastes) factors which affect the groundwater quality.
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Key words:
- Groundwater /
- Iran /
- Multivariate statistical methods /
- Pollution
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Table 1. The ID of sampling wells
ID Sampling site ID Sampling site 1 Jangalkari 12 Qanatno 2 Tonbakan 13 Kamal Abad 3 Toureh 14 Kheir Abad 4 Baniyan 15 Firuzemard 5 Kahnekoyeh 16 Dastjeh 6 Shomal Fasa 17 Baghe Jafari 7 Kushk Qazi 1 18 Sahraroud 8 Kushk Qazi 2 19 Saad Abad 9 Rahmat Abad 20 Ghiyas Abad 10 Harom 21 Soghad 11 Chaghad 22 Cheshme Abnarak 
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Table 2. Descriptive statistics of groundwater quality parameters ranges and their comparison with the Iranian standard for drinking water
Variable Iranian permissible limit N Minimum Maximum Mean Std. Deviation EC (µmhos/cm) 1 400 110 333 52 00 1 441.43 843.99 Cl- (ppm) 400 110 0.15 22.50 4.89 3.73 TH (ppm as CaCO3) 500 110 166 2 000 570.79 367.80 SAR (-) - 110 0.11 4.17 1.56 0.92 K+ (ppm) 12 110 0.01 0.32 0.07 0.05 Na+ (ppm) 200 110 0.15 17.39 3.77 2.80 Mg2+ (ppm) 30 110 0.20 22.50 5.30 3.85 Ca2+ (ppm) 300 110 2.00 27.00 6.11 4.44 Cations (ppm) - 110 3.72 57.66 15.26 9.40 SO42- (ppm) 400 110 0.19 33.92 5.47 6.14 Valid N (listwise) 110
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Table 3. Spatial clustering of sampling wells
Groups EC Cl- TH SAR K+ Na+ Mg2+ Ca2+ Cations SO42- No. of wells 1 1 154.7 4.0 445.4 1.4 0.06 3.1 4.1 4.8 12.1 3.4 18 2 2 731.8 9.1 1 135 2.1 0.14 6.8 10.7 12.0 29.7 14.6 4
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Table 4. Independent sample test
Levene's test for equality of variances t-test for equality of means Variable F sig. t df Sig.
(2-tailed)Mean difference
(LP-HP)Std. error difference EC Equal variances assumed 4.215 0.053 -8.462 20 0.000 -1 577.222 186.374 Cl- Equal variances assumed 1.047 0.318 -4.777 20 0.000 -5.122 1.072 TH Equal variances assumed 0.547 0.468 -8.305 20 0.000 -689.589 83.031 SAR Equal variances not assumed 7.445 0.013 -1.219 3.380 0.150 -0.626 0.514 K+ Equal variances assumed 0.079 0.782 -7.410 20 0.000 -0.077 -0.010 Na+ Equal variances not assumed 9.556 0.006 -2.570 3.293 0.037 -3.750 1.460 Mg2+ Equal variances assumed 0.862 0.364 -6.679 20 0.000 -6.595 0.987 Ca2+ Equal variances not assumed 19.526 0.000 -2.545 3.073 0.041 -7.193 2.826 Cations Equal variances assumed 2.731 0.114 -9.022 20 0.000 -17.613 1.952 SO42- Equal variances not assumed 10.417 0.004 -3.891 3.138 0.014 -11.186 2.875
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Table 5. Resulted coefficients of LDA method
Variable EC Cl- TH SAR K+ Na+ Mg2+ Ca2+ Cations SO42- LDA coefficient -0.423 -0.215 -0.388 0.000 -0.297 -0.144 -0.351 -0.298 -0.430 -0.345
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Table 6. Classification matrix obtained from LDA of spatial variation of the groundwater in the Fasa Plain
Predicted cluster determined by LDA Actual cluster Cluster 1 Cluster 2 Cluster 1 95.60 4.40 Cluster 2 10.00 90.00 Total accuracy 92.80
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Table 7. Results of KMO and Bartlett's tests
Kaiser-Meyer-Olkin measure of sampling adequacy 0.623 Bartlett's Test of Sphericity Approx. Chi-Square 919.709 df 45 Sig. 0.000
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Table 8. Total variance explained with two principal components
Component Initial eigenvalues Extraction sums of squared loadings Rotation sums of squared loadings Total Variance
(%)Cumulative
(%)Total Variance
(%)Cumulative
(%)Total Variance
(%)Cumulative
(%)1 7.960 79.600 79.600 7.960 79.600 79.600 5.839 58.393 58.393 2 1.633 16.327 95.928 1.633 16.327 95.928 3.753 37.534 95.928 3 0.284 2.837 98.765 4 0.084 0.842 99.607 5 0.010 0.253 99.860 6 0.004 0.098 99.958 7 0.017 0.035 99.993 8 0.001 0.007 100.000 9 2.774E-6 2.774E-5 100.000 10 3.456E-9 3.456E-8 100.000 Extraction method: Principal component analysis
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Table 9. Rotated component matrixa
Component Variable 1 2 EC (µmhos/cm) 0.828 0.559 Cl- (ppm) 0.516 0.799 TH (ppm as CaCO3) 0.943 0.329 SAR 0.013 0.954 K+ (ppm) 0.883 0.445 Na+ (ppm) 0.339 0.932 Mg2+ (ppm) 0.697 0.638 Ca2+ (ppm) 0.976 0.008 Cations (ppm) 0.851 0.525 SO42- (ppm) 0.970 0.187 Extraction method: Principal component analysis
Rotation method: Varimax with Kaiser Normalizationa
a. Rotation converged in 3 iterations.
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Table 10. Correlation matrixa of studied variables
Variable EC Cl- TH SAR K+ Na+ Mg2+ Ca2+ Cations SO42- EC 1.000 Cl- 0.878 1.000 TH 0.966 0.757 1.000 SAR 0.535 0.694 0.317 1.000 K+ 0.978 0.787 0.977 0.462 1.000 Na+ 0.798 0.903 0.622 0.908 0.713 1.000 Mg2+ 0.946 0.908 0.879 0.542 0.883 0.804 1.000 Ca2+ 0.804 0.490 0.917 0.068 0.875 0.354 0.616 1.000 Cations 0.998 0.860 0.975 0.509 0.982 0.779 0.930 0.833 1.000 SO42- 0.904 0.609 0.972 0.226 0.944 0.514 0.771 0.961 0.923 1.000 a. Determinant = 1.87E-024
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