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

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

doi:10.1016/j.heliyon.2023.e19784

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

Heliyon. 2023 Sep 1;9(9):e19784. doi: 10.1016/j.heliyon.2023.e19784. eCollection 2023 Sep.

Authors

Mohammed Benaafi¹, S I Abba¹, Bassam Tawabini², Ismail Abdulazeez¹, Billel Salhi¹, Jamilu Usman¹, Isam H Aljundi^{1

3}

Affiliations

¹ Interdisciplinary Research Center for Membranes and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
² College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
³ Department of Chemical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.

Abstract

The intrusion of seawater (SWI) into coastal aquifers is a major concern worldwide, affecting the quantity and quality of groundwater resources. The region of Saudi Arabia that lies along the eastern coast has been affected by SWI, making it crucial to accurately identify and monitor the affected areas. This investigation aimed to map the degree of seawater intrusion in a complex aquifer system in the study area using an integrated clustering analysis approach. The study collected 41 groundwater samples from wells penetrating multi-layered aquifers, and the samples were analyzed for physicochemical properties and major ions. Clustering analysis methods, including Hierarchical Clustering Analysis (double-clustering) (HCA-DC), K-mean (KMC), and fuzzy k-mean clustering (FKM), were employed to evaluate the spatial distribution and association of the groundwater properties. The results revealed that the analyzed GW samples were divided into four clusters with varying degrees of SWI. Clusters A, B, C, and D contained GW samples with very low (f_sea of 1.9%), high (f_sea of 14.9%), intermediate (f_sea of 7.9%), and low (f_sea of 5.2%) degrees of SWI, respectively. FKM clustering exhibited superior performance with a silhouette score of 0.83. Additionally, the study found a direct correlation between the degree of SWI and increased concentrations of boron, strontium, and iron, demonstrating SWI's impact on heavy metal levels. Notably, the boron concentration in cluster B, which endured high SWI, exceeded WHO guidelines. The study demonstrates the value of clustering analysis for accurately monitoring SWI and associated heavy metals. The findings can guide policies to mitigate SWI impacts and benefit groundwater-dependent communities. Further research can help develop effective strategies to mitigate SWI effects on groundwater quality and availability.

Keywords: Arabian gulf; Clustering analysis; Groundwater; Seawater intrusion; Sustainable groundwater management.