عنوان مقاله [English]
Background and Objectives: Groundwater is considered as one of the most important sources of fresh water which is available of human. Groundwater is used for various goals such as drinking, sanitation, agriculture and industry. Therefore Studying, understanding and protecting it looks a necessary work in arid and semi-arid areas like Iran. Shiramin region with an aquifer with approximate area of 34 km2 is one the marginal plains of Urmia lake. Deep understanding of the dominant hydrogeochemical processes on aquifer system is very important in the management of groundwater resources. Graphics methods are among the usual methods in the recognition of the dominant processes on the groundwater, but lack of use of some chemical parameters such as (nitrate, arsenic, etc.), including its limitations. Multivariate statistical analysis methods (Factor analysis, Clustering analysis) has been used as a complementary method along with graphics method to identify factors affecting the quality of underground water, finding the source of contamination and to classifying the similar samples. Using Geographic Information System (GIS) along with mentioned methods leading to better insight and easier understanding in groundwater issues. The aim of this study is to identify the effective factors on the quality of water resources of Shiramin region, finding their sources, and hydrogeochemical analysis of them using multivariate statistical analysis, graphical and GIS methods.
Materials and Methods: In order to investigate groundwater of Shiramin area, 18 samples of groundwater from all over the plain with regard to the best distribution, were collected and analyzed. For interpretation and representing the data, multivariate statistical analysis (principal component analysis (PCA) and hierarchical cluster analysis (HCA)), correlation matrix, graphical and GIS methods were used.
Results: Content of nitrate is higher and fluoride is less than the standard value. Factor analysis resulted in the extraction of four factors: (first component: carbonate component, the second component: component of rain-fed and the third component: from sulfoflouride, the fourth component, nitrate component). Hierarchical cluster analysis has resulted in the extraction of two clusters named HCA1 (first cluster) and HCA2 (second cluster). Samples related to first cluster in the upstream of plain illustrate better quality and samples related to second cluster are located in the downstream of plain with higher EC and lower quality.
Conclusion: Using multivariate statistical analysis methods in identifying Effective factors on water quality and clustering them is very effective. Graphical methods and GIS are also effective for understanding the Hydrogeochemical processes.
yes, no, yes.