GEO-STATISTICAL ASSESSMENT OF GROUND WATER QUALITY IN DHAMAR BASIN, YEMEN

M. ALLAM, Q.Y. MENG, H. AL-AIZARI, M. ELHAG, J. YANG, M. SAKR, Z. WANG
2020 Applied Ecology and Environmental Research  
Allam et al.: Geo-statistical assessment of ground water quality in Dhamar Basin, Yemen -625 -APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 18(1):625-644. Abstract. Groundwater quality in the Basin of Dhamar (Yemen) has been studied using geographic information system (GIS), principal component analysis (PCA) and correlation technique. A geographic information system is a tool for mapping and analyzing spatial data and is also used to retrieve groundwater quality information. For this study 26
more » ... s were selected, for each well thirteen physicochemical parameters were analyzed including electrical conductivity, Calcium (Ca 2+ ), Magnesium (Mg 2+ ), Sodium (Na + ), potassium (K + ), bicarbonate (HCO3 -), Chloride (Cl -), Sulphate (SO4 2-), Nitrate (NO3 -), Iron (Fe 2+ ), Fluoride (F -) and Total Hardness (TH). The collected groundwater was evaluated for its suitability for both drinking and irrigation purposes. Correlation and PCA have been utilized to analyze the parameters. Cartographic maps of the study area have been generated using multivariate statistical tools and GIS approach Inverse Distance Weighting (IDW) for all the above parameters. The created maps can be used to visualize, analyze, and understand the relationship among all locations. Most of the wells are found to be within the permissible limit except one well and this is due to the anthropogenic pollution received by human activities. Correlation and principal component analysis can help in selecting the most significant parameters to determine the status of water quality. The tools available in the GIS environment supported the study in the integration of data with very different data structures, i..e. point, 2D and 3D, towards spatial modeling of processes relevant to the assessment of groundwater resources.
doi:10.15666/aeer/1801_625644 fatcat:gzv6tlg2bfhh3a2nmqjophyg3m