Integrated clustering analysis for delineating seawater intrusion and heavy metals in Arabian Gulf Coastal groundwater of Saudi Arabia

Benaafi, Mohammed; Abba, S.I.; Tawabini, Bassam; Abdulazeez, Ismail; Salhi, Billel; Usman, Jamilu; Aljundi, Isam H.

doi:10.1016/j.heliyon.2023.e19784

Cited by 6 publications

(2 citation statements)

References 53 publications

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“…Two criteria were used for the selection of the two clustering methods: (1) HCA and k-means are known algorithms that can withstand outliers giving reasonable clustering solutions and, (2) among other methods, HCA is widely used in hydrogeochemical studies [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53]. To compare the HCA and k-means clustering methods regarding their effectiveness in explaining the groundwater composition of the aquifer in terms of ionic balance outliers, the ionic balance was first calculated according to the following formula [54]:…”

Section: Multivariate Analysis and Hydrogeochemical Diagramsmentioning

confidence: 99%

Hydrogeochemical Characterization of an Intermontane Aquifer Contaminated with Arsenic and Fluoride via Clustering Analysis

Irigoyen-Campuzano,

Barraza-Barraza,

Gutiérrez

et al. 2024

Hydrology

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The controlling hydrogeochemical processes of an intermontane aquifer in central Mexico were identified through multivariate statistical analysis. Hierarchical cluster (HCA) and k-means clustering analyses were applied to Na+, K+, Ca2+, Mg2+, F−, Cl−, SO42−, NO3−, HCO3−, As, pH and electrical conductivity in 40 groundwater samples collected from shallow and deep wells, where As and F− are contaminants of concern. The effectiveness of each hierarchical and k-means clustering method in explaining solute concentrations within the aquifer and the co-occurrence of arsenic and fluoride was tested by comparing two datasets containing samples from 40 and 36 wells, the former including ionic balance outliers (>10%). When tested without outliers, cluster quality improved by about 5.4% for k-means and 7.3% for HCA, suggesting that HCA is more sensitive to ionic balance outliers. Both algorithms yielded similar clustering solutions in the outlier-free dataset, aligning with the k-means solution for all 40 samples, indicating that k-means was the more robust of the two methods. k-means clustering resolved fluoride and arsenic concentrations into four clusters (K1 to K4) based on variations in Na+, Ca2+, As, and F−. Cluster K2 was a Na-HCO3 water type with high concentrations of As and F. Clusters K1, K3, and K4 exhibited a Ca-HCO3, Na-Ca-HCO3, and Ca-Na-HCO3 water types, respectively, with decreasing As and F concentrations following the order K2 > K3 > K1 > K4. The weathering of evaporites and silicates and Na-Ca ion exchange with clays were the main processes controlling groundwater geochemistry. The dissolution of felsic rocks present in the aquifer fill is a likely source of As and F−, with evaporation acting as an important concentration factor.

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Section: Multivariate Analysis and Hydrogeochemical Diagramsmentioning

confidence: 99%

Hydrogeochemical Characterization of an Intermontane Aquifer Contaminated with Arsenic and Fluoride via Clustering Analysis

Irigoyen-Campuzano,

Barraza-Barraza,

Gutiérrez

et al. 2024

Hydrology

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show abstract

“…Seven groundwater samples were collected from shallow coastal aquifers with less than 1.4 m water depth. The aquifers are situated within Quaternary alluvial and Aeolian formations, which comprise layers of sand, gravel, silt, and clay overlain by tertiary carbonate rocks [87,88]. The groundwater samples were collected using 1 L amber glass vials.…”

Section: Pfass In Saudi Groundwatermentioning

confidence: 99%

Comprehensive Review of Global Perspectives on Per- and Polyfluoroalkyl Compounds: Occurrence, Fate, and Remediation in Groundwater Systems

Benaafi,

Bafaqeer

2024

Water

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Groundwater contamination with per- and polyfluoroalkyl compounds (PFASs) has become a growing worldwide environmental issue. The current review comprehensively evaluates the global perspective of PFAS pollution in groundwater. Data from 224 recent research articles covering various land use and source types were reviewed, including industrial facilities, landfills, biosolids applications, and firefighting training sites. The bibliographic analysis shows an exponential increase in publications on PFAS pollution in groundwater in the last five years, with more than 50% coming from the USA, followed by Australia, Canada, China, and Sweden. The recent groundwater PFAS pollution research provides insight into the analytical techniques, absorbing materials, treatment strategies, field tests, and enhanced natural attenuation. Nevertheless, the current review identified significant research gaps in the areas of precursor characterization, subsurface behavior, model validation with field data, and long-term and sustainable solutions. Moreover, a global cross-disciplinary approach is required to reduce and regulate PFASs’ risks to humans and the ecological system. This review presents a case study of PFASs in Saudi Arabian groundwater, revealing elevated levels of PFOA and PFOS and highlighting the need for region-specific studies and remediation strategies. The review results will guide global efforts to protect drinking water supplies from life-threatening contaminants.

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Predicting seawater intrusion in coastal areas using machine learning: A case study of arid coastal aquifers, Saudi Arabia

BinMakhashen,

Benaafi

2024

Groundwater for Sustainable Development

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Integrated clustering analysis for delineating seawater intrusion and heavy metals in Arabian Gulf Coastal groundwater of Saudi Arabia

Cited by 6 publications

References 53 publications

Hydrogeochemical Characterization of an Intermontane Aquifer Contaminated with Arsenic and Fluoride via Clustering Analysis

Hydrogeochemical Characterization of an Intermontane Aquifer Contaminated with Arsenic and Fluoride via Clustering Analysis

Comprehensive Review of Global Perspectives on Per- and Polyfluoroalkyl Compounds: Occurrence, Fate, and Remediation in Groundwater Systems

Predicting seawater intrusion in coastal areas using machine learning: A case study of arid coastal aquifers, Saudi Arabia

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