Groundwater Hydrochemistry Assessment of North Dhi-Qar Province, South of Iraq Using Multivariate Statistical Techniques

• Published in: IOP conference series. Earth and environmental science 2021-06, Vol.790 (1), p.12075
• Main Authors: Ewaid, Salam Hussein, Mhajej, Kareem Ghafel, Abed, Salwan Ali, Al-Ansari, Nadhir
• Format: Article
• Language: English
• Subjects: Calcium ions; Chemical properties; Cluster analysis; Dhi-Qar; Electrical conductivity; Electrical resistivity; Groundwater; Multivariate analysis; pH effects; principal component analysis; Principal components analysis; Statistical analysis; Statistical methods; Total dissolved solids; Variance; Water analysis; Water quality; Water sampling; Water wells
• ISSN: 1755-1307; 1755-1315
• DOI: 10.1088/1755-1315/790/1/012075

Multivariate statistical techniques including correlation, principal component analysis (PCA), and cluster analysis (CA) were applied in this study to assess the groundwater hydrochemistry of the North Dhi-Qar Province, South of Iraq. The water samples were taken from 16 water wells in the period from January to October 2020 and ten water variables were analyzed, pH, electrical conductivity (EC), total dissolved solids (TDS), Ca +2 , Mg +2 , Na + , K + , Cl − , SO 4 −2 , and HCO 3 − . The results obtained from Spearman’s correlation showed that the positive and negative correlation of P < 0.05 between water variables is different at two-tailed grades. Results from the PCA have shown that approximately 85% of the overall variance has been clarified by the three PCs achieved. The main causes of variation in the hydro-chemical properties of water samples of the wells can therefore be determined. PC 1 represents about 36.75% of the variance and holds a high loading for EC, HCO 3 − , Cl − , K + , and EC. PC2, which explains 35% of the total variance, has high loadings for EC, Na + , TDS, Ca +2 , and SO 4 −2 . PC 3 shows high loadings for pH, which accounts for 13.235% of the variation in the water hydrochemistry. The hierarchical cluster analysis (CA) grouped the 16 sampling wells into three clusters of similar water quality characteristics. In the analysis of space changes in water quality, this research demonstrates the use of multivariate statistical methods for the interpretation of complex data sets. This will thus improve future studies preparation.