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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 June 2024) | Viewed by 12877

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Special Issue Editors

Dr. Jun Song Kim

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Guest Editor

Department of Civil and Environmental Engineering, Hankyong National University, Anseong-si, Republic of Korea
Interests: river hydraulics; contaminant transport; water quality modeling; hyporheic exchange; remote sensing; data-driven modeling

Dr. Dong-Kyun Kim

E-Mail Website
Guest Editor

International Center for Water Security & Management (i-WSSM), Hwaseong-si, Republic of Korea
Interests: plankton dynamics in aquatic ecosystems; water quality prediction and forecast; ecological modeling and risk assessment in management strategies

Dr. Donghae Baek

E-Mail Website
Guest Editor

Korea Environment Institute, Sejong, Republic of Korea
Interests: river hydraulics; estuarine environment; mixing in turbulent flows; numerical analysis; remote sensing; environmental assessment

Special Issue Information

Dear Colleagues,

Water quality is a crucial factor in the assessment of aquatic ecosystems and public health, considering biological diversity, habitat degradation, sustainable water use, and waterfront activities. Thus, the precise modeling and prediction of spatio-temporal variability in contaminant concentrations is critically important for effective water resource management.

The fate and transport of contaminants can generally be analyzed via the advection–dispersion–reaction process. In natural water bodies such as rivers and streams, however, the dispersive behavior of contaminants cannot be adequately explained using conventional advection–dispersion models due to vegetation, dead zones, and flow recirculation around structures, which delay transport and thereby induce non-Fickian mixing. Also, flow exchange at water–sediment interfaces, known as hyporheic flow, exerts significant control on non-Fickian transport. Water quality substances undergo reactions depending upon surrounding environmental conditions, leading to changes in their concentrations. Therefore, we must advance our understanding of complicated interplays between hydrogeological and biochemical factors for the accurate prediction of water quality in aquatic environments.

This Special Issue welcomes contributions related to the modeling and prediction of diverse water quality issues like harmful algal bloom, eutrophication, microplastics, suspended sediments, and accidental pollution spills with not only the development of advanced mathematical and numerical models but also machine-learning- and remote-sensing-based models.

Dr. Jun Song Kim
Dr. Dong-Kyun Kim
Dr. Donghae Baek
Guest Editors

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Keywords

contaminant transport
water quality modeling
non-Fickian mixing
hyporheic exchange
numerical simulation
remote sensing
data-driven modeling

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Published Papers (10 papers)

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Research

17 pages, 8688 KiB

Open AccessArticle

Scenario-Based Modeling on Chlorophyll-a in Uiam Reservoir of Korea According to Variation of Dam Discharge

by Dong Yeol Lee and Kyong Oh Baek

Water 2024, 16(15), 2120; https://doi.org/10.3390/w16152120 - 26 Jul 2024

Viewed by 605

Abstract

This study analyzes quantitative algae mitigation, using chlorophyll-a as an indicator, through waterbody management techniques such as pulses released from upstream dams, employing a three-dimensional numerical model. Numerical simulations focused on algae reduction through dam operations by investigating nine scenarios based on Chuncheon Dam, Soyang Dam, and Uiam Dam, located in the upper and lower reaches of Uiam Reservoir of Korea. These scenarios, aligned with actual dam operation manuals, aimed to differentiate the impact of each dam’s operation by decreasing water residence time for Uiam Reservoir. The Uiam Reservoir, smaller than the upstream Chuncheon Dam and Soyang River Dam, is significantly influenced by their discharge rates. During summer, temperature differences exceeding 7 °C between discharges from Chuncheon Dam and Soyang Dam inflowed into the right side and the left side, respectively, of the reservoir, leading to poor mixing, which was further hindered by islands within the reservoir. Consequently, due to the influence of the different base water temperatures of the Bukhan River and Soyang River and the topographical characteristics, the impact range varied depending on the operation of each dam, and the amount of algae mitigation differed at each point. In emergency situations where algae blooms proliferate rapidly, appropriate dam operations in water bodies with large dams upstream and downstream, like Uiam Reservoir, can be effective in mitigating algae at specific regions of the reservoir. Full article

(This article belongs to the Special Issue Contaminant Transport Modeling in Aquatic Environments)

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27 pages, 7434 KiB

Open AccessArticle

Dual Domain Decomposition Method for High-Resolution 3D Simulation of Groundwater Flow and Transport

by Hao Deng, Jiaxin Li, Jixian Huang, Yanhong Zou, Yu Liu, Yuxiang Chen, Yang Zheng and Xiancheng Mao

Water 2024, 16(13), 1864; https://doi.org/10.3390/w16131864 - 28 Jun 2024

Viewed by 799

Abstract

The high-resolution 3D groundwater flow and transport simulation problem requires massive discrete linear systems to be solved, leading to significant computational time and memory requirements. The domain decomposition method is a promising technique that facilitates the parallelization of problems with minimal communication overhead by dividing the computation domain into multiple subdomains. However, directly utilizing a domain decomposition scheme to solve massive linear systems becomes impractical due to the bottleneck in algebraic operations required to coordinate the results of subdomains. In this paper, we propose a two-level domain decomposition method, named dual-domain decomposition, to efficiently solve the massive discrete linear systems in high-resolution 3D groundwater simulations. The first level of domain decomposition partitions the linear system problem into independent linear sub-problems across multiple subdomains, enabling parallel solutions with significantly reduced complexity. The second level introduces a domain decomposition preconditioner to solve the linear system, known as the Schur system, used to coordinate results from subdomains across their boundaries. This additional level of decomposition parallelizes the preconditioning of the Schur system, addressing the bottleneck of the Schur system solution while improving its convergence rates. The dual-domain decomposition method facilitates the partition and distribution of the computation to be solved into independent finely grained computational subdomains, substantially reducing both computational and memory complexity. We demonstrate the scalability of our proposed method through its application to a high-resolution 3D simulation of chromium contaminant transport in groundwater. Our results indicate that our method outperforms both the vanilla domain decomposition method and the algebraic multigrid preconditioned method in terms of runtime, achieving up to 8.617× and 5.515× speedups, respectively, in solving massive problems with approximately 108 million degrees of freedom. Therefore, we recommend its effectiveness and reliability for high-resolution 3D simulations of groundwater flow and transport. Full article

(This article belongs to the Special Issue Contaminant Transport Modeling in Aquatic Environments)

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22 pages, 3327 KiB

Open AccessArticle

Application of Oversampling Techniques for Enhanced Transverse Dispersion Coefficient Estimation Performance Using Machine Learning Regression

by Sunmi Lee and Inhwan Park

Water 2024, 16(10), 1359; https://doi.org/10.3390/w16101359 - 10 May 2024

Cited by 1 | Viewed by 876

Abstract

The advection–dispersion equation has been widely used to analyze the intermediate field mixing of pollutants in natural streams. The dispersion coefficient, manipulating the dispersion term of the advection–dispersion equation, is a crucial parameter in predicting the transport distance and contaminated area in the water body. In this study, the transverse dispersion coefficient was estimated using machine learning regression methods applied to oversampled datasets. Previous research datasets used for this estimation were biased toward width-to-depth ratio (

W / H

) values ≤ 50, potentially leading to inaccuracies in estimating the transverse dispersion coefficient for datasets with

W / H

> 50. To address this issue, four oversampling techniques were employed to augment the dataset with

W / H

> 50, thereby mitigating the dataset’s imbalance. The estimation results obtained from data resampling with nonlinear regression method demonstrated improved prediction accuracy compared to the pre-oversampling results. Notably, the combination of adaptive synthetic sampling (ADASYN) and eXtreme Gradient Boosting regression (XGBoost) exhibited improved accuracy compared to other combinations of oversampling techniques and nonlinear regression methods. Through the combined ADASYN–XGBoost approach, it is possible to enhance the transverse dispersion coefficient estimation performance using only two variables,

W / H

and bed friction effects (

U / U^{*}

), without adding channel sinuosity; this represents the effects of secondary currents. Full article

(This article belongs to the Special Issue Contaminant Transport Modeling in Aquatic Environments)

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14 pages, 2698 KiB

Open AccessArticle

Comparison between Hyperspectral and Multispectral Retrievals of Suspended Sediment Concentration in Rivers

by Sung Hyun Jung, Siyoon Kwon, Il Won Seo and Jun Song Kim

Water 2024, 16(9), 1275; https://doi.org/10.3390/w16091275 - 29 Apr 2024

Viewed by 1285

Abstract

Remote sensing (RS) is often employed to estimate suspended sediment concentration (SSC) in rivers, and the availability of hyperspectral imagery enhances the effectiveness of RS-based water quality monitoring due to its high spectral resolution. Yet, the necessity of hyperspectral imagery for SSC estimation in rivers has not been fully validated. This study thus compares the performance of hyperspectral RS with that of multispectral RS by conducting field-scale experiments in shallow rivers. In the field experiments, we measured radiance from a water body mixed with suspended sediments using a drone-mounted hyperspectral sensor, with the sediment and riverbed types considered as controlling factors. We retrieved the SSC from UAV imagery using an optimal band ratio analysis, which successfully estimated SSC distributions in the sand bed conditions with both multispectral and hyperspectral data. In the vegetated bed conditions, meanwhile, the prediction accuracy decreased significantly due to the temporally varying bottom reflectance associated with the random movement of vegetation caused by near-bed turbulence. This is because temporally inhomogeneous bottom reflectance distorts the relationship between the SSC and total reflectance. Nevertheless, the hyperspectral imaging exhibited better prediction accuracy than the multispectral imaging, effectively extracting optimal spectral bands sensitive to back-scattered reflectance from sediments while constraining the bottom reflectance caused by the vegetation-covered bed. Full article

(This article belongs to the Special Issue Contaminant Transport Modeling in Aquatic Environments)

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16 pages, 4685 KiB

Open AccessArticle

An Investigation of Contaminant Transport and Retention from Storage Zone in Meandering Channels

by Sung Hyun Jung, Inhwan Park and Jaehyun Shin

Water 2024, 16(8), 1170; https://doi.org/10.3390/w16081170 - 20 Apr 2024

Viewed by 978

Abstract

Contaminant trapping by recirculation zones occurring at the apex of natural meandering channels induces a long tail in the contaminant cloud, thereby complicating the prediction of mixing behaviors. Thus, the understanding of the interaction between solute trapping and recirculating flow is important for responding to and mitigating water pollution accidents. In this research, the EFDC model was employed to reproduce three-dimensional flow structures of recirculating flow at the channel apex and investigate the influence on contaminant mixing. To investigate the contaminant transport characteristics from the storage zone in meandering channels, simulations were conducted using various discharge values to assess the impact of storage zone development on the concentration–time curves. The analysis of the relationship between the storage zone size and mixing behaviors indicates that an increase in discharge could result in a shorter tail and larger longitudinal dispersion even with the larger storage zone size. On the other hand, the enlarged recirculation zone size contributes to reducing transverse dispersion, evidenced by flatter dosage curves under lower flow rate conditions. These findings suggest that the increase in longitudinal dispersion with a larger flow rate is primarily caused by the reduction in transverse dispersion resulting from the formation of the recirculation zone. Full article

(This article belongs to the Special Issue Contaminant Transport Modeling in Aquatic Environments)

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23 pages, 16173 KiB

Open AccessArticle

Surrogate-Based Uncertainty Analysis for Groundwater Contaminant Transport in a Chromium Residue Site Located in Southern China

by Yanhong Zou, Muhammad Shahzad Yousaf, Fuqiang Yang, Hao Deng and Yong He

Water 2024, 16(5), 638; https://doi.org/10.3390/w16050638 - 21 Feb 2024

Cited by 4 | Viewed by 2099

Abstract

Numerical modeling is widely acknowledged as a highly precise method for understanding the dynamics of contaminant transport in groundwater. However, due to the intricate characteristics of environmental systems and the lack of accurate information, the results are susceptible to a significant degree of uncertainty. Numerical models must explicitly consider related uncertainties in parameters to facilitate robust decision-making. In a Chromium Residue Site located in southern China (the study area), this study employed Monte Carlo simulation to assess the impact of variability in key parameters uncertainty on the simulation outcomes. Variogram analysis of response surface (VARS), global sensitivity analysis, and an XGBoost (version 2.0.0)-based surrogate model was employed to overcome the substantial computational cost of Monte Carlo simulation. The results of numerical simulation indicate that the contaminant is spreading downstream towards the northern boundary of contaminated site near Lianshui River, threatening water quality. Furthermore, migration patterns are complex due to both downstream convection and upstream diffusion. Sensitivity analysis identified hydraulic conductivity, recharge rate, and porosity as the most influential model parameters, selected as key parameters. Moreover, uncertainty analysis indicated that the variability in key parameters has a minimal impact on the simulation outcomes at monitoring wells near the contaminant source. In contrast, at wells positioned a considerable distance from the contaminant source, the variability in key parameters significantly influences the simulation outcomes. The surrogate model markedly mitigated computational workload and calculation time, while demonstrating superior precision and effectively capture the non-linear correlations between input and output of the simulation model. Full article

(This article belongs to the Special Issue Contaminant Transport Modeling in Aquatic Environments)

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20 pages, 9212 KiB

Open AccessArticle

Case Study of Contaminant Transport Using Lagrangian Particle Tracking Model in a Macro-Tidal Estuary

by Bon-Ho Gu, Seung-Buhm Woo, Jae-Il Kwon, Sung-Hwan Park and Nam-Hoon Kim

Water 2024, 16(4), 617; https://doi.org/10.3390/w16040617 - 19 Feb 2024

Cited by 1 | Viewed by 1360

Abstract

This study presents a comprehensive analysis of contaminant transport in estuarine environments, focusing on the impact of tidal creeks and flats. The research employs advanced hydrodynamic models with irregular grid systems and conducts a detailed residual current analysis to explore how these physical features influence the movement and dispersion of contaminants. The methodology involves simulating residual currents and Lagrangian particle trajectories in both ‘Creek’ and ‘No Creek’ cases, under varying tidal conditions. The results indicate that tidal creeks significantly affect particle retention and transport, with notable differences observed in the dispersion patterns between the two scenarios. The ‘Creek’ case demonstrates enhanced material retention along the creek pathways, while the ‘No Creek’ case shows broader dispersion, potentially leading to increased sedimentation in open sea areas. The discussion highlights the implications of these findings for sediment dynamics, contaminant transport, and estuarine ecology, emphasizing the role of tidal creeks in modulating flow and material transport. The research underlines the necessity of incorporating detailed environmental features in estuarine models for accurate contaminant transport prediction and effective estuarine management. This study contributes to a deeper understanding of estuarine hydrodynamics and offers valuable insights for environmental policy and management in coastal regions. Full article

(This article belongs to the Special Issue Contaminant Transport Modeling in Aquatic Environments)

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32 pages, 11786 KiB

Open AccessArticle

The Influence of Regional Groundwater Flow and a Neighbouring River on the Behaviour of an Aquifer Thermal Energy Storage System

by Qais H. M. Al-Madhlom, Sanaa A. Jassim and Riyadh H. M. Muttaleb

Water 2024, 16(4), 548; https://doi.org/10.3390/w16040548 - 9 Feb 2024

Viewed by 1013

Abstract

One promising solution for mitigating CO₂ emissions in arid regions is to use Aquifer Thermal Energy Storage (ATES) systems in cooling and heating systems. However, ATES systems need to be subjected to geohydrological investigations before their installation to ensure high performance. Two geohydrological properties are considered: regional groundwater flow and the influence of neighbouring rivers. This study considers a hypothetical ATES system within the city of Hilla, Iraq. MODFLOW 6.1 software was used to simulate the influence of the two properties. The simulation tested two locations situated at 75 m and 300 m from the river. Each location was explored using three flow rates: 10 m³/d, 50 m³/d, and 100 m³/d. The results indicate that the temperature change in the warm and cold wells increases proportionally with time of operation and rate of flow. For example, the temperature of the middle layer (for 10 m³/d operation) changes from 29 °C (after one year) to 34 °C (after twenty years operation), while it changes from 34 °C (one year) to 35 °C (twenty years) under 100 m³/d operation. Another result is that the available regional groundwater flow has a negligible influence on the storage system, while the neighbouring river has a high influence on the stored energy when the distance between them is 75 m or less. The paper recommends the installation of ATES systems at least 300 m from the bank of a river. Full article

(This article belongs to the Special Issue Contaminant Transport Modeling in Aquatic Environments)

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22 pages, 7905 KiB

Open AccessArticle

Improvement of the Two-Dimensional Routing Procedure for Observing Dispersion Coefficients in Open-Channel Flow

by Donghae Baek, Il Won Seo, Jun Song Kim, Sung Hyun Jung and Yuyoung Choi

Water 2024, 16(2), 365; https://doi.org/10.3390/w16020365 - 22 Jan 2024

Viewed by 1417

Abstract

The dispersion coefficients are crucial in understanding the spreading of pollutant clouds in river flows, particularly in the context of the depth-averaged two-dimensional (2D) advection–dispersion equation (ADE). Traditionally, the 2D stream-tube routing procedure (2D STRP) has been the predominant method for determining both the longitudinal and transverse dispersion coefficients of the 2D ADE under transient concentration conditions. This study aims to quantitatively analyze and address the limitations of the 2D STRP using hypothetically generated data. The findings of these evaluations revealed that the existing 2D STRP failed to accurately reproduce reliable results when the tracer clouds reached wall boundaries. This limitation prompted the development of the 2D STRP-i, which effectively resolves this drawback. The newly developed routing-based observation method, 2D STRP-i, enables the reliable estimation of dispersion coefficients, considering the effect of the wall boundary. The results indicated that the existing 2D STRP yielded 2D dispersion coefficients with relative errors ranging from 40% to 200%, while 2D STRP-i consistently yielded relative errors of 3% to 5% on average. When applied to tracer test data obtained through remote sensing, the 2D STRP-i demonstrated its ability to accurately observe temporal concentration distributions, even when wall boundaries have a significant impact on contaminant transport. Full article

(This article belongs to the Special Issue Contaminant Transport Modeling in Aquatic Environments)

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30 pages, 19970 KiB

Open AccessArticle

Prediction of the Turbidity Distribution Characteristics in a Semi-Enclosed Estuary Based on the Machine Learning

by Nam-Hoon Kim, Dong Hyeon Kim and Sung-Hwan Park

Water 2024, 16(1), 61; https://doi.org/10.3390/w16010061 - 23 Dec 2023

Cited by 2 | Viewed by 1416

Abstract

This study addresses the critical challenge of predicting sediment behavior in a semi-enclosed estuary, where the interplay between artificial freshwater discharge and seawater significantly impacts turbidity. Such environments are characterized by complex hydrodynamic interactions that lead to cycles of sediment settling and resuspension, influenced by tidal forces. To tackle this problem, we employed machine learning, leveraging its capability to analyze and predict complex non-linear phenomena. Our approach involved extensive transect observations conducted over two years, encompassing 11 ebb tide and 9 flood tide cycles. These observations were crucial for training the machine learning model, ensuring it captured the nuanced dynamics of sediment behavior under varying hydrodynamic conditions. The necessity of this research lies in its potential to enhance our understanding of sediment dynamics in estuaries, a vital aspect for environmental management and engineering projects. The findings demonstrate a promising alignment between the machine learning model’s predictions and the theoretically assumed sediment behavior, highlighting the model’s effectiveness in deciphering and predicting turbidity patterns in these challenging environments. Full article

(This article belongs to the Special Issue Contaminant Transport Modeling in Aquatic Environments)

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