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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: 25 November 2024 | Viewed by 15645

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Dr. Tianxiang Wang

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Guest Editor

School of Ocean Science and Technology, Dalian University of Technology, Panjin, China
Interests: nitrogen; phosphorus; sediment; ice-covered; model; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Alex Neumann

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Guest Editor

Department of Physical and Environmental Sciences at Scarborough, University of Toronto, Toronto, ON, Canada
Interests: environmental and ecological modeling; water resources; water and wastewater treatment; statistical and mechanistic modeling; data-driven decision making

Special Issue Information

Dear Colleagues,

A good ecological environment is the foundation for sustainable development; human activities have introduced unpredictable impacts on the ecological environment in the course of social and economic development, thereby presenting challenges for its effective management. Fluctuations in water quality emerge as a pivotal aspect of the ecological environment, intricately intertwined with water resources, aquatic ecosystems, and human activities. Accurate prediction of water quality stands as a catalyst for enhancing ecological management capabilities. This necessitates the exploration of comprehensive water quality models. This Special Issue aims to delve into methodologies pertaining to water quality, encompassing water resources, aquatic ecosystems, and the assessment of water environment carrying capacities, alongside case studies in representative regions. Our pursuit encompasses several dimensions: first, the refinement of mechanistic models through the assimilation of regional parameters acquired via on-site investigations or experiments; second, the development of nonlinear models tailored to special regions; third, the exploration of integrated water quality models grounded in extensive datasets, combing machine learning techniques. Ultimately, our aspiration rests upon the synergistic integration of conventional surveys, empirical studies, and smart technologies, thus propelling the advancement of comprehensive water quality modeling.

Dr. Tianxiang Wang
Dr. Alex Neumann
Guest Editors

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Keywords

environmental model
assessment method
statistic model
mechanism model
regional indicators
machine learning
water resource environmental model
assessment method
statistic model
mechanism model
regional indicators
machine learning
water resource

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Research

21 pages, 7579 KiB

Open AccessArticle

A River Water Quality Prediction Method Based on Dual Signal Decomposition and Deep Learning

by Yifan Bai, Menghang Peng and Mei Wang

Water 2024, 16(21), 3099; https://doi.org/10.3390/w16213099 - 29 Oct 2024

Viewed by 477

Abstract

Traditional single prediction models struggle to address the complexity and nonlinear changes in water quality forecasting. To address this challenge, this study proposed a coupled prediction model (RF-TVSV-SCL). The model includes Random Forest (RF) feature selection, dual signal decomposition (Time-Varying Filtered Empirical Mode Decomposition, TVF-EMD, and Sparrow Search Algorithm-Optimized Variational Mode Decomposition, SSA-VMD), and a deep learning predictive model (Sparrow Search Algorithm-Convolutional Neural Network-Long Short-Term Memory, SSA-CNN-LSTM). Firstly, the RF method was used for feature selection to extract important features relevant to water quality prediction. Then, TVF-EMD was employed for preliminary decomposition of the water quality data, followed by a secondary decomposition of complex Intrinsic Mode Function (IMF) components using SSA-VMD. Finally, the SSA-CNN-LSTM model was utilized to predict the processed data. This model was evaluated for predicting total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH₃-N), dissolved oxygen (DO), permanganate index (COD_Mn), conductivity (EC), and turbidity (TB), across 1, 3, 5, and 7-d forecast periods. The model performed exceptionally well in short-term predictions, particularly within the 1–3 d range. For 1-, 3-, 5-, and 7-d forecasts, R² ranged from 0.93–0.96, 0.79–0.87, 0.63–0.72, and 0.56–0.64, respectively, significantly outperforming other comparison models. The RF-TVSV-SCL model demonstrates excellent predictive capability and generalization ability, providing robust technical support for water quality forecasting and pollution prevention. Full article

(This article belongs to the Special Issue Application of Smart Technologies in Integrated Water Quality Modeling)

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30 pages, 6125 KiB

Open AccessArticle

Spatial Mapping and Prediction of Groundwater Quality Using Ensemble Learning Models and SHapley Additive exPlanations with Spatial Uncertainty Analysis

by Shilong Yang, Danyuan Luo, Jiayao Tan, Shuyi Li, Xiaoqing Song, Ruihan Xiong, Jinghan Wang, Chuanming Ma and Hanxiang Xiong

Water 2024, 16(17), 2375; https://doi.org/10.3390/w16172375 - 24 Aug 2024

Viewed by 1237

Abstract

The spatial mapping and prediction of groundwater quality (GWQ) is important for sustainable groundwater management, but several research gaps remain unexplored, including the inaccuracy of spatial interpolation, limited consideration of the geological environment and human activity effects, limitation to specific pollutants, and unsystematic indicator selection. This study utilized the entropy-weighted water quality index (EWQI), the LightGBM model, the pressure-state-response (PSR) framework and SHapley Additive exPlanations (SHAP) analysis to address the above research gaps. The normalized importance (NI) shows that NO₃⁻ (0.208), Mg²⁺ (0.143), SO₄²⁻ (0.110), Cr⁶⁺ (0.109) and Na⁺ (0.095) should be prioritized as parameters for remediation, and the skewness EWQI distribution indicates that although most sampled locations have acceptable GWQ, a few areas suffer from severely poor GWQ. The PSR framework identifies 13 indicators from geological environments and human activities for the SMP of GWQ. Despite high AUROCs (0.9074, 0.8981, 0.8885, 0.9043) across four random training and testing sets, it was surprising that significant spatial uncertainty was observed, with Pearson correlation coefficients (PCCs) from 0.5365 to 0.8066. We addressed this issue by using the spatial-grid average probabilities of four maps. Additionally, population and nighttime light are key indicators, while net recharge, land use and cover (LULC), and the degree of urbanization have the lowest importance. SHAP analysis highlights both positive and negative impacts of human activities on GWQ, identifying point-source pollution as the main cause of the poor GWQ in the study area. Due to the limited research on this field, future studies should focus on six key aspects: multi-method GWQ assessment, quantitative relationships between indicators and GWQ, comparisons of various spatial mapping and prediction models, the application of the PSR framework for indicator selection, the development of methods to reduce spatial uncertainty, and the use of explainable machine learning techniques in groundwater management. Full article

(This article belongs to the Special Issue Application of Smart Technologies in Integrated Water Quality Modeling)

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14 pages, 4534 KiB

Open AccessArticle

Prediction of Dissolved Oxygen Factor at Oncheon Stream Watershed Using Long Short-Term Memory Algorithm

by Heesung Lim, Hyungjin Shin, Jaenam Lee, Jongwon Do, Inhyeok Song and Youngkyu Jin

Water 2024, 16(17), 2363; https://doi.org/10.3390/w16172363 - 23 Aug 2024

Viewed by 599

Abstract

Rapid urbanization and industrialization have caused water quality issues in urban rivers. Appropriate measures based on water quality monitoring systems and prediction methods are needed for water quality management. While South Korea has operated a water quality monitoring system that measures various environmental factors and has accumulated water quality data, a water quality prediction system is not in place. This study suggests a water quality prediction method based on a long short-term model using water quality and meteorological monitoring data. Additionally, we present a derived input set of the prediction model that can improve the prediction model performance. The prediction model’s performance was evaluated by the coefficient of determination under various conditions, such as the hyperparameters, temporal resolution of input data, and application of upstream and downstream data. As a result, using the temporal resolution of the input data as hourly data improved predictions by an average of 25.6% over three days of the prediction period compared to daily data. Meanwhile, it was analyzed that the hyperparameters and using upstream and downstream data have a minor effect on the model performance. The results of this study underscore the crucial role of the number, duration, and temporal resolution of available monitoring data in water quality management. Full article

(This article belongs to the Special Issue Application of Smart Technologies in Integrated Water Quality Modeling)

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25 pages, 24325 KiB

Open AccessArticle

The Investigation of the Response Mechanism of SST and Chlorophyll to Super Typhoon “Rey” in the South China Sea

by Shichao Wang, Jun Song, Junru Guo, Yanzhao Fu, Yu Cai and Linhui Wang

Water 2024, 16(4), 603; https://doi.org/10.3390/w16040603 - 18 Feb 2024

Viewed by 1442

Abstract

As one of the most significant disturbance sources in the upper marine environment of the South China Sea, tropical cyclones (typhoons) serve as a typical research subject for investigating the energy transfer process between the ocean and atmosphere. Utilizing satellite remote sensing data and focusing on Typhoon Rey No. 22’s transit event in 2021, this study quantitatively analyzes typhoon-induced energy input through heat pumping and cold suction at both surface and subsurface levels of the ocean. Additionally, it explores the response characteristics and feedback mechanisms of sea surface temperature (SST) and chlorophyll-a concentration (Chl-a) in the South China Sea to typhoon events. The research results show that the SST variation along the typhoon track displayed an asymmetric pattern, with a more pronounced warming on the right side and a cold anomaly lasting for 3–5 days on the left side. The subsurface warm anomaly dominated on the right side, showing a maximum temperature difference of 1.54 °C, whereas Ekman suction-induced upwelling led to cooling effects both at the subsurface and surface level on the left side, resulting in a maximum temperature difference of −3.28 °C. During the typhoon event, there was a significant decrease in sea surface heat flux, reaching 323.36 W/m², accompanied by corresponding changes in SST due to processes such as upwelling, seawater mixing, and air–sea heat transfer dynamics where anomalies arising from oceanic dynamic processes and exchange through sea surface heat flux contributed equally. Furthermore, strong suction-induced upwelling during the typhoon influenced chlorophyll concentration within the central and western regions of the South China Sea (13.5° N–16.5° N, 111° E–112.5° E), resulting in significant enhancement and reaching its peak value at approximately 0.65 mg/L. The average chlorophyll concentration increased by approximately 0.31 mg/L. Full article

(This article belongs to the Special Issue Application of Smart Technologies in Integrated Water Quality Modeling)

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22 pages, 3133 KiB

Open AccessArticle

Dynamic Successive Assessment of Water Resource Carrying Capacity Based on System Dynamics Model and Variable Fuzzy Pattern Recognition Method

by Xinguo Sun, Anbang Peng, Suduan Hu, Yi Shi, Lu Lu and Aorui Bi

Water 2024, 16(2), 304; https://doi.org/10.3390/w16020304 - 16 Jan 2024

Viewed by 1220

Abstract

The water resource carrying capacity (WRCC) system comprises multiple complex and non-linear interactions related to society, economy, water resources, and the water environment. A comprehensive comprehension of its internal mechanisms is essential for the continual enhancement of the regional WRCC. This study concentrates on the temporal and spatial variability of the WRCC to investigate a method for dynamic successive assessment. Firstly, the pressure–state–response (PSR) framework is used to develop a systematic and causal indicator system. Then, the variable fuzzy pattern recognition (VFPR) model and an analytic hierarchy process—entropy (AHP-E) model are combined to successively and dynamically assess WRCC. The proposed method is applied to the dynamic successive assessment of WRCC in Hebei Province, and it is obtained that the poor water resource carrying capacity in Hebei Province is mainly due to the basic attribute of the decision on the water resource shortage, but Hebei Province actively adopts a variety of measures to save water and pressurize mining, which has made the province’s water resource carrying capacity tend to become better gradually. Simultaneously, a system dynamics model (SD) for water resource carrying capacity was established based on an analysis of the model structure. Moreover, three scenarios were designed, including existing continuation, high-efficiency water saving, and cross-regional water transfer. Subsequently, each scenario is further categorized into high- and low-speed economic development and population growth schemes. Afterward, simulations and predictions were conducted for a total of six schemes spanning from 2023 to 2030. The results indicate that if the current development model is adopted, the water resource carrying capacity will continue to maintain low levels. It was concluded that the high-speed development of the economy and population, the efficient water conservation, and the interbasin transfer scenario (scenario 2 with high speed) are the best choices for the sustainable development of water resources and social economy in Hebei. Full article

(This article belongs to the Special Issue Application of Smart Technologies in Integrated Water Quality Modeling)

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18 pages, 17792 KiB

Open AccessArticle

Rationality Research on Pumping Station Location Based on MIKE Model: A Case Study of the Wanfu River Re-Navigation Project

by Song Han, Xinyan Yu, Wei Zhang, Guoqing Sang, Yuyu Liu and Shiguo Xu

Water 2023, 15(24), 4207; https://doi.org/10.3390/w15244207 - 6 Dec 2023

Viewed by 1457

Abstract

The site selection of hydraulic structures is crucial to the successful implementation of water conservancy projects. Reasonable or not, site selection has a direct impact on the functioning of hydraulic structures, engineering safety, and environmental impact. In this paper, the proposed Wanfu River Guanqiao Ship Lock and Pumping Station engineering is utilised as the object. The MIKE model is executed to simulate both the impact of Guanqiao Ship Lock operation on the water quality of the pumping station intake as well as the effects of pumping station operation on the navigable water level in order to analyse and demonstrate the reasonableness of the pumping station’s location. According to the water quality monitoring data of the last three years, the entropy weight method coupled with the comprehensive pollution index method was used to evaluate the water quality of the Wanfu River. A one-dimensional hydrodynamic water quality model was constructed by applying MIKE11, which reveals the change rule of water quality and also demonstrates the safety of navigable water levels. The MIKE21 two-dimensional water quality model, which intuitively displays the spatial and temporal patterns of change of each indicator, was constructed. The results show the following: (1) The evaluation results of the entropy weight method coupled with the comprehensive pollution index method indicate that the water quality of the Wanfu River is Class III, which meets the water intake standard. (2) Concentrations of the indicators are higher in the abundant water period than in the dry water period, in which the water quality is Class IV in June and July. (3) There is no impact of the pump station operating on navigable water levels. Full article

(This article belongs to the Special Issue Application of Smart Technologies in Integrated Water Quality Modeling)

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17 pages, 3931 KiB

Open AccessArticle

Utilizing the Sobol’ Sensitivity Analysis Method to Address the Multi-Objective Operation Model of Reservoirs

by Haixia Wang, Ying Zhao and Wenyuan Fu

Water 2023, 15(21), 3795; https://doi.org/10.3390/w15213795 - 30 Oct 2023

Cited by 5 | Viewed by 1438

Abstract

The operation of reservoirs has significantly influenced the river ecological system. Upholding the ecological integrity of rivers during reservoir operations has been the focus of research over the years. When the Dahuofang reservoir project started, focus moved to ecological goals to address the Biliuhe reservoir’s environmental issues. The water strategy limits usage for various purposes and outlines the diversion route, complicating Biliuhe operations. In this study, to comprehend the effects of individual water level guidelines and their combined influence on these goals, the Sobol’ sensitivity analysis was introduced as an initial measure to tackle the optimization challenge. The results show that removing the insensitive water levels during specific periods of reservoir scheduling lines and beginning with sensitive water levels for local optimization to identify viable solutions, and then moving to wider optimization, significantly enhances the search efficiency, solution quality, and operational speed compared with an exhaustive search without any preceding steps. This sensitivity analysis technique is crucial for fine-tuning multi-objective reservoir operations. Full article

(This article belongs to the Special Issue Application of Smart Technologies in Integrated Water Quality Modeling)

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12 pages, 4516 KiB

Open AccessArticle

Characteristics of Zinc Adsorption onto Biochars Derived from Different Feedstocks

by Jiajia Liu, Fei Wang and Wangqi Xu

Water 2023, 15(21), 3789; https://doi.org/10.3390/w15213789 - 29 Oct 2023

Cited by 1 | Viewed by 1746

Abstract

Human activities such as the discharge of urban sewage, garbage, and industrial waste have seriously affected the quality of groundwater sources for human consumption. The potential for using biochar as a reactive medium in a permeable reactive barrier (PRB) was explored for Zn-contaminated groundwater treatment in this study. Four different types of biochar produced from wood, coconut shell, rice straw, and fruit shell were used. The production temperature of these biochars were 600 °C, 550 °C, 500 °C, and 500 °C, respectively. The samples were coded with the initials of the biochar source and the production temperature as WD600, CS550, RS500, and FS500. The results of various batch adsorption studies show that equilibrium solution pH has a great effect on the maximum adsorption capacity in the pH range of 2–7. The adsorption of Zn on biochars follows the Freundlich model and fits well with the pseudo-second-order model. The fixed-bed column test data were well fitted to the Dose–Response model. The adsorption capacities of WD600, CS550, RS500, and FS500 were 24.91, 15.87, 9.25, and 46.71 mg/g, respectively. The removal rate of FS500 can reach a maximum of 98.87%. FS500 is considered to be a potential reaction medium for treating Zn-contaminated groundwater in a PRB system. This work provides a new option for converting biomass waste into an adsorbent for zinc in wastewater. The results of this study are expected to provide a solid theoretical basis for the further application of biochar in groundwater pollution remediation. Full article

(This article belongs to the Special Issue Application of Smart Technologies in Integrated Water Quality Modeling)

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15 pages, 2290 KiB

Open AccessArticle

Determination of Heavy Metals and Health Risk Assessment in Tap Water from Wuhan, China, a City with Multiple Drinking Water Sources

by Zufan Liu, Shiyong Tao, Zuyou Sun, Yilin Chen and Jing Xu

Water 2023, 15(21), 3709; https://doi.org/10.3390/w15213709 - 24 Oct 2023

Cited by 4 | Viewed by 2707

Abstract

The health issues of urban tap water are of great concern in the context of sustainability challenges to the environmental quality of water and the security of the water supply. In this work, tap water from the main urban areas in Wuhan and surface water from the Yangtze River and the Hanjiang River were collected during summer (June) and winter (December), 2022. The concentrations of 10 heavy metals including Fe, Al, Mn, Co, Ni, Cu, Se, Cd, Cr and Pb were determined for water quality evaluation and health risk assessment. The results demonstrated that almost all of the tap water samples contained metal concentrations below the Chinese national standard limits for drinking water (GB 5749-2022). The risk of heavy metals in tap water to human health was evaluated, and the results showed that the total carcinogenic risk (TCR) was in the range of 10⁻⁶ and 10⁻⁴ and the hazard index (HI) was much lower than one in both summer and winter. The current tap water in Wuhan is generally in a relatively safe state and will not cause acute hazards or chronic diseases in the short term, but the long-term cancer risk is still noteworthy. The heavy metal pollution index (HPI) showed that the overall water quality of urban drinking water sources in Wuhan has been satisfactory, despite its slightly polluted state in winter. Pipeline corrosion was considered as one of the important sources of heavy metals in Wuhan tap water, which can explain, to a certain extent, the increase in the heavy metal concentrations of tap water outlets relative to the finished water reported by waterworks, such as Fe, Ni, Cd and Pb. This study has implications for the formulation of better urban water supply security management strategies and associated sustainability challenges. Full article

(This article belongs to the Special Issue Application of Smart Technologies in Integrated Water Quality Modeling)

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23 pages, 4079 KiB

Open AccessArticle

Influencing Factors and Nutrient Release from Sediments in the Water Level Fluctuation Zone of Biliuhe Reservoir, a Drinking Water Reservoir

by Weijia Li, Shiguo Xu, Xiaoqiang Chen, Dongning Han and Baoquan Mu

Water 2023, 15(20), 3659; https://doi.org/10.3390/w15203659 - 19 Oct 2023

Cited by 5 | Viewed by 2486

Abstract

Significant amounts of nitrogen and phosphorus in sediments will be released into the overlying water during the flood season in the water level fluctuation zone (WLFZ) of reservoirs that undergo periodic drying and flooding. This will result in water quality deterioration of the reservoir. In order to clarify the distribution characteristics and release behavior of nitrogen (N) and phosphorus (P) from sediments in the WLFZ of a reservoir, this study analyzed the sediment distribution characteristics and potential exchange flux sediment–water interface(SWI) through field investigations and sediment core incubation experiments. And the main factors affecting the release of N and P through the incubation experiments in sediments of the WLFZ in the reservoir were determined. Our findings indicated that the sediment in the WLFZ serves as the primary source of NH₄⁺-N and acts as a sink for NO₂-N in the overlying water of sediment. The concentration of NH₄⁺-N in the interstitial water of sediments is the key factor that affects the water quality of Biliuhe Reservoir. Total nitrogen content of surface sediments in the WLFZ of Biliuhe Reservoir ranges from 1052.52 ± 49.39 to 3520.54 ± 30.31 mg/kg. High concentrations of N pollution are the primary increased risk of eutrophication in Biliuhe Reservoir during summer. The sediment N and P release flux of BLH1 located in the main stream is 1.67 ± 1.06 and 12.32 ± 2.42 mg·(m²·d)⁻¹, respectively, which is smaller than that of BLH2 (3.27 ± 2.15 and 15.19 ± 2.36 mg·(m²·d)⁻¹, respectively), BLH3 (4.24 ± 1.74 and 17.02 ± 2.47 mg·(m²·d)⁻¹, respectively) and BLH4 (7.78 ± 2.03 and 20.56 ± 2.38 mg·(m²·d)⁻¹, respectively) located in the tributary. It indicates that the water conveyance project located in BLH1 has an impact on nutrient scouring of sediments in the WLFZ at this site. The main water environment factor affecting the release of N and P in the sediment of the WLFZ is dissolved oxygen (DO). And the Pearson correlation coefficients between TN and TP with DO were −0.838 and −0.777, respectively (p < 0.05). At the same time, the diffusion of nutrients in the sediments can be effectively inhibited by maintaining a certain DO concentration in the overlying water. Full article

(This article belongs to the Special Issue Application of Smart Technologies in Integrated Water Quality Modeling)

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