Geo-informatics Analysis of groundwater quality for drinking and agriculture purposes; case study; Dakhla Depression

صقر, محمد; النجار, أحمد سامى مرسي النجار; علي, الشربينى أحمد أحمد

doi:10.21608/mkwn.2023.217264.1149

Geo-informatics Analysis of groundwater quality for drinking and agriculture purposes; case study; Dakhla Depression

نوع المستند : بحوث علمية محکمة

المؤلفون

¹ كلية الاداب ، جامعة الزقازيق

² كلية الأداب - جامعة الزقازيق

10.21608/mkwn.2023.217264.1149

المستخلص

Abstract:

Groundwater quality in the Dakhla Depression (Egypt) has been studied using Geo-informatics Techniques As (ARC GIS Software), This software is a tool for mapping and analyzing spatial data and is also used to retrieve groundwater quality information. For this study, 39 wells were selected, for each well twelve physicochemical parameters were analyzed including electrical conductivity, Calcium (Ca2+), Magnesium (Mg2+), Sodium (Na+), potassium (K+), bicarbonate (HCO3-), Chloride (Cl-), Sulphate (SO42-), Nitrate (NO3-), Iron (Fe2+), 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 created using GIS by Interpolates a raster surface from points( wells locations which contain attributes Data For all parameters ) using an inverse distance weighted (IDW) technique. and multivariate statistical tools 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 were found to be within the permissible limit. 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 to the assessment of groundwater resources. the analytical results were taken to generate the numerical spatial distribution of the parameters using the geographic information system (GIS) environment (A. ABBASNIA ET AL, 2018) .

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