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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Quality and Contamination".

Deadline for manuscript submissions: closed (20 September 2024) | Viewed by 4885

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Dr. Qianqian Liu

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Guest Editor

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

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Published Papers (3 papers)

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Research

13 pages, 1137 KiB

Open AccessArticle

Determining the Fluxes and Relative Importance of Different External Sources and Sinks of Nitrogen to the Israeli Coastal Shelf, a Potentially Vulnerable Ecosystem

by Tal Ben Ezra, Anat Tsemel, Yair Suari, Ilana Berman-Frank, Danny Tchernov and Michael David Krom

Water 2024, 16(18), 2585; https://doi.org/10.3390/w16182585 - 12 Sep 2024

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Abstract

While the biogeochemical properties of the Israeli coastal shelf (ICS) are similar to adjacent pelagic waters, the external sources of inorganic nitrogen (N) are very different. The main source of ‘new’ N to the pelagic zone is deep winter mixing, with minor contributions from atmospheric deposition and eddy diffusion across the nutricline. For the ICS, major N sources include offshore water advection (260 × 10⁶ mol N y−¹), atmospheric input (115 × 10⁶ mol N y−¹), and riverine input (138 × 10⁶ mol N y−¹), which primarily consists of treated wastewater and stormwater runoff. Direct pollutant discharge from sewage outfalls and submarine groundwater discharge are relatively minor. Key N sinks are new production (420 × 10⁶ mol N y−¹) and sediment deposition and uptake (145 × 10⁶ mol N y−¹). Inputs of nitrate and ammonium were similar and dominant in winter. Unlike temperate shelves, where riverine input is dominant, here it was only slightly higher than atmospheric input, with net N advection onto the shelf being significant. External N inputs did not change net primary production (NPP) by more than ~30% or affect dominant pico and nanophytoplankton genera, except in localized patches. This study offers baseline values for future climate and environmental change assessments. Full article

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

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16 pages, 4192 KiB

Open AccessArticle

Using Machine Learning Models for Short-Term Prediction of Dissolved Oxygen in a Microtidal Estuary

by Mina Gachloo, Qianqian Liu, Yang Song, Guozhi Wang, Shuhao Zhang and Nathan Hall

Water 2024, 16(14), 1998; https://doi.org/10.3390/w16141998 - 15 Jul 2024

Viewed by 1171

Abstract

This paper presents a comprehensive approach to predicting short-term (for the upcoming 2 weeks) changes in estuarine dissolved oxygen concentrations via machine learning models that integrate historical water sampling, historical and upcoming 2-week meteorological data, and river discharge and discharge metrics. Dissolved oxygen is a critical indicator of ecosystem health, and this approach is implemented for the Neuse River Estuary, North Carolina, U.S.A., which has a long history of hypoxia-related habitat degradation. Through meticulous data preprocessing and feature selection, this research evaluates the predictions of dissolved oxygen concentrations by comparing a recurrent neural network with four other models, including a Multilayer Perceptron, Long Short-Term Memory, Gradient Boosting, and AutoKeras, through sensitivity experiments. The input predictors to our prediction models include water temperature, turbidity, chlorophyll-a, aggregated river discharge, and aggregated wind based on eight directions. By emphasizing the most impactful predictors, we streamlined the model-building processes and built a hindcast system from 2015 to 2019. We found that the recurrent neural network model was most effective in predicting the dissolved oxygen concentrations, with an R² value of 0.99 at multiple stations. Different from our machine learning hindcast models that used observed upcoming meteorological and discharge data, an actual forecast system would use forecasted meteorological and discharge data. Therefore, an actual operational forecast may have lower accuracy than the hindcast, as determined by the accuracy of the predicted meteorological and discharge data. Nevertheless, our studies enhance our understanding of the factors influencing dissolved oxygen variability and set the basis for the implementation of a predictive tool for environmental monitoring and management. We also emphasized the importance of building station-specific models to improve the prediction results. Full article

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

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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

Cited by 16 | Viewed by 2713

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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