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Dr. Qianqian Liu

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Physics and Physical Oceanography, University of North Carolina Wilmington, Wilmington, NC, USA
Interests: hydrodynamic modeling; coupled biophysical modeling; machine learning models; harmful algal blooms

Special Issue Information

Dear Colleagues,

Coastal water quality has been affected by multiple factors, including the worldwide increase in nutrients, pathogens, and chemical contaminants from land-based sources; changes in temperature, salinity, and sea level; and invasive species. Given the importance of coastal water quality in human life and other living species, coastal water quality protection and management will benefit our current and future generations. Coastal water quality modeling is not only a great tool used to support coastal water management, but also a great tool to help us understand the related processes. The rapid development of supercomputation and machine learning models has great potential to advance coastal water quality modeling. This Special Issue focuses on the advancement and application of coastal water quality modeling by including (but not limited to) biogeochemical, statistical, and machine learning models.

Dr. Qianqian Liu
Guest Editor

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Keywords

coastal water quality
model
water management
advancement
application
biogeochemical models
statistical models
machine learning models

Published Papers (1 paper)

Research

34 pages, 8943 KiB

Open AccessArticle

Toward Decontamination in Coastal Regions: Groundwater Quality, Fluoride, Nitrate, and Human Health Risk Assessments within Multi-Aquifer Al-Hassa, Saudi Arabia

by Mohamed A. Yassin, Sani I. Abba, Syed Muzzamil Hussain Shah, Abdullahi G. Usman, Johnbosco C. Egbueri, Johnson C. Agbasi, Abid Khogali, Husam Musa Baalousha, Isam H. Aljundi, Saad Sha. Sammen and Miklas Scholz

Water 2024, 16(10), 1401; https://doi.org/10.3390/w16101401 - 14 May 2024

Viewed by 667

Abstract

Contamination in coastal regions attributed to fluoride and nitrate cannot be disregarded, given the substantial environmental and public health issues they present worldwide. For effective decontamination, it is pivotal to identify regional pollution hotspots. This comprehensive study was performed to assess the spatial as well as indexical water quality, identify contamination sources, hotspots, and evaluate associated health risks pertaining to nitrate and fluoride in the Al-Hassa region, KSA. The physicochemical results revealed a pervasive pollution of the overall groundwater. The dominant water type was Na-Cl, indicating saltwater intrusion and reverse ion exchange impact. Spatiotemporal variations in physicochemical properties suggest diverse hydrochemical mechanisms, with geogenic factors primarily influencing groundwater chemistry. The groundwater pollution index varied between 0.8426 and 4.7172, classifying samples as moderately to very highly polluted. Similarly, the synthetic pollution index (in the range of 0.5021–4.0715) revealed that none of the samples had excellent water quality, with various degrees of pollution categories. Nitrate health quotient (HQ) values indicated chronic human health risks ranging from low to severe, with infants being the most vulnerable. Household use of nitrate-rich groundwater for showering and cleaning did not pose significant health risks. Fluoride HQ decreased with age, and children faced the highest risk of fluorosis. The hazard index (HI) yielded moderate- to high-risk values. Nitrate risks were 1.21 times higher than fluoride risks, as per average HI assessment. All samples fell into the vulnerable category based on the total hazard index (THI), with 88.89% classified as very high risk. This research provides valuable insights into groundwater quality, guiding water authorities, inhabitants, and researchers in identifying safe water sources, vulnerable regions, and human populations. The results highlight the need for appropriate treatment techniques and long-term coastal groundwater management plans. Full article

(This article belongs to the Special Issue Research on Coastal Water Quality Modelling)

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