Trace element pollution tracking in the complex multi-aquifer groundwater system of Al-Hassa oasis (Saudi Arabia) using spatial, chemometric and index-based techniques

Abba, S.I.; Yassin, Mohamed A.; Shah, Syed Muzzamil Hussain; Egbueri, Johnbosco C.; Elzain, Hussam Eldin; Agbasi, Johnson C.; Saini, Gaurav; Usaman, Jamilu; Khan, Nadeem A.; Aljundi, Isam H.

doi:10.1016/j.envres.2024.118320

Cited by 9 publications

(2 citation statements)

References 107 publications

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“…Al-Omran et al [8] analyzed the quality of the groundwater, while Dirks et al [9] provided further information on the geological aspects of the Umm Er Radhuma aquifer. In their recent studies, Abba, Yassin, Jibril, et al [10] and Abba, Yassin, Shah, et al [11] utilized advanced methodologies, such as machine learning and spatial mapping, to examine trace-element pollution and nitrate concentrations, respectively. These studies provided state-of-the-art findings on groundwater susceptibility and pollution in Al-Hassa.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of the Subsurface of Al-Hassa Oasis Using Gravity Geophysical Data

Khogali,

Chavanidis,

Kirmizakis

et al. 2024

Applied Sciences

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Al-Hassa city, located in Eastern Saudi Arabia, boasts the world’s largest oasis and the most expansive naturally irrigated lands. Historically, a total of 280 natural springs facilitated significant groundwater discharge and irrigation of agricultural land. Furthermore, the water in certain springs formerly had a high temperature. The spatial variability of the water quality was evident. At the same time, Al-Hassa Oasis is situated on the northeastern side of the Ghawar field, which is the largest conventional onshore oil field in the world in terms of both reserves and daily output (approximately 3.8 mmb/d). The aforementioned traits suggest an intricate subsurface that has not yet been publicly and thoroughly characterized. Due to the presence of significant cultural noise caused by agricultural and nearby industrial activities, a robust, easy-to-use, and accurate geophysical method (gravity) was used to cover an area of 350 km2, producing the 3D subsurface model of the study area. A total of 571 gravity stations were collected, covering the whole Al-Hassa Oasis and parts of the nearby semi-urban areas. The gravity data were corrected and processed, and a 3D inversion was applied. The resulting 3D geophysical subsurface modeling unveiled an intricate subterranean configuration and revealed lateral variations in density, indicating the presence of a potential salt dome structure, as well as fracture zones that serve as conduits or obstacles to the flow of the subsurface fluids. This comprehensive modeling approach offers valuable insights into the subsurface dynamics of the broader study area, enhancing our understanding of its qualitative tectonic and hydraulic features and their impacts on the area’s natural resources, such as groundwater and hydrocarbons.

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Section: Introductionmentioning

confidence: 99%

Reconstruction of the Subsurface of Al-Hassa Oasis Using Gravity Geophysical Data

Khogali,

Chavanidis,

Kirmizakis

et al. 2024

Applied Sciences

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“…The availability of clean water is a fundamental human right, and a sufficient supply of purified and secure potable water is essential to human health (Bhatt et al, 2024 ). Therefore, freshwater is one of the most necessary resources available on the planet since it is required for all living forms to provide the fundamental needs of ecological variety and sustainable development (Abba et al, 2024 ; Egbueri et al, 2023 ). Yet, over the past decade, the water crisis has evolved into a worldwide issue (Tokatli et al, 2024 ; Ustaoğlu et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Toxicological risk assessment using spring water quality indices in plateaus of Giresun Province/Türkiye: a holistic hydrogeochemical data analysis

Karadeniz,

Ustaoğlu,

Aydın

et al. 2024

Environ Geochem Health

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Water scarcity is a growing concern due to rapid urbanization and population growth. This study assesses spring water quality at 20 stations in Giresun province, Türkiye, focusing on potentially toxic elements and physicochemical parameters. The Water Quality Index rated most samples as "excellent" during the rainy season and "good" during the dry season, except at stations 4 (40° 35′ 12″ North/38° 26′ 34″ East) and 19 (40° 44′ 28″ North/38° 06′ 53″ West), indicating "poor" quality. Mean macro-element concentrations (mg/L) were: Ca (34.27), Na (10.36), Mg (8.26), and K (1.48). Mean trace element values (μg/L) were: Al (1093), Zn (110.54), Fe (67.45), Mn (23.03), Cu (9.79), As (3.75), Ni (3.00), Cr (2.84), Pb (2.70), Co (1.93), and Cd (0.76). Health risk assessments showed minimal non-carcinogenic risks, while carcinogenic risk from arsenic slightly exceeded safe limits (CR = 1.75E−04). Higher arsenic concentrations during the rainy season were due to increased recharge, arsenic-laden surface runoff, and human activities. Statistical analyses (PCA, PCC, HCA) suggested that metals and physico-chemical parameters originated from lithogenic, anthropogenic, or mixed sources. Regular monitoring of spring water is recommended to mitigate potential public health risks from waterborne contaminants.

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Towards sustainable industrial development: modelling the quality, scaling potential and corrosivity of groundwater using GIS, spatial statistics, soft computing and index-based methods

Agbasi,

Abu,

Egbueri

2024

Environ Dev Sustain

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Trace element pollution tracking in the complex multi-aquifer groundwater system of Al-Hassa oasis (Saudi Arabia) using spatial, chemometric and index-based techniques

Cited by 9 publications

References 107 publications

Reconstruction of the Subsurface of Al-Hassa Oasis Using Gravity Geophysical Data

Reconstruction of the Subsurface of Al-Hassa Oasis Using Gravity Geophysical Data

Toxicological risk assessment using spring water quality indices in plateaus of Giresun Province/Türkiye: a holistic hydrogeochemical data analysis

Towards sustainable industrial development: modelling the quality, scaling potential and corrosivity of groundwater using GIS, spatial statistics, soft computing and index-based methods

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