Abstract
Saudi Arabia struggles to supply its population with enough clean water as being one of the biggest and fastest-growing nations outside its own continent. For this study 49 groundwater samples were collected between June and December of 2021. Different analytical techniques were used for determinations of anions and cations concentration. Using World Health Organization's recommended safe limits to compare results that were collected. Groundwater is affected by aquifer matrix disintegration, water infiltration from irrigation systems, and water–rock interactions, according to the results of the hydrogeochemical study. The anions HCO3−, CO32−, Cl−, NO32−, and F−, as well as the cations Na+, K+, Ca2+, and Mg2+, were also presented in the collected groundwater samples in acceptable concentrations. Using physical characteristics and hydrochemical characterizations is also a commendable approach to regulating the quality of groundwater samples collected for crop agriculture and irrigation. Most of the collected groundwater samples are desired and fit for human and industrial consumption. In addition, application of the statistical analysis concluded that in some locations contaminated surface water may recharge the groundwater. A variety of physical characteristics, including as EC, SAR, RSC, KR, MH, CR, PI, and PS, are widely used to regulate the quality of groundwater samples collected for crops agriculture and irrigation. Piper and Gibbs diagrams for groundwater hydrochemical characterizations were used. The obtained results indicated that the collected groundwater samples are acceptable for crops drinking, agriculture and irrigation.
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The authors acknowledge the Ministry of Education and King Abdulaziz University, Jeddah, Saudi Arabia for their support during this study. This research was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah under grant number IFPIP: 674-135-1443.
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Fallatah, O., Khattab, M.R. Study of hydrogeochemical factors affecting groundwater quality used for land reclamation: application of multivariate statistical analysis. Stoch Environ Res Risk Assess 37, 4719–4735 (2023). https://doi.org/10.1007/s00477-023-02537-7
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DOI: https://doi.org/10.1007/s00477-023-02537-7