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Water, Volume 16, Issue 5 (March-1 2024) – 171 articles

Cover Story (view full-size image): With a focus on assessing hydrological spatial dynamics and the efficacy of ERA5-Land reanalysis rainfall data, this research delves into the intricate variations of exceptional rainfall events in Portugal over 42 hydrological years (1981/1982 to 2022/2023). Validation against rainfall records ensures the reliability of the reanalysis data. Spatial representations reveal notable shifts in exceptional rainfall event distributions, highlighting the evolving hydrological landscape. The severity heat map is a groundbreaking addition, amalgamating key insights on exceptional occurrences and cumulative rainfall. This study enriches our understanding of Portugal's hydrological dynamics, offering crucial insights for risk management and sustainable strategies amid changing rainfall patterns. View this paper
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13 pages, 1974 KiB  
Article
Comparative Analysis of the Disinfection Efficiency of Steel and Polymer Surfaces with Aqueous Solutions of Ozone and Sodium Hypochlorite
by Valentin Romanovski, Andrei Paspelau, Maksim Kamarou, Vitaly Likhavitski, Natalia Korob and Elena Romanovskaia
Water 2024, 16(5), 793; https://doi.org/10.3390/w16050793 - 6 Mar 2024
Cited by 2 | Viewed by 1694
Abstract
Disinfection of surfaces with various functional purposes is a relevant measure for the inactivation of microorganisms and viruses. This procedure is used almost universally, from water treatment facilities to medical institutions and public spaces. Some of the most common disinfectants the World Health [...] Read more.
Disinfection of surfaces with various functional purposes is a relevant measure for the inactivation of microorganisms and viruses. This procedure is used almost universally, from water treatment facilities to medical institutions and public spaces. Some of the most common disinfectants the World Health Organization recommends are chlorine-containing compounds. Sodium and calcium hypochlorites are only used for disinfection of the internal surfaces of water treatment facilities. However, it is known that ozone is a more powerful oxidizing agent. This study compares the effectiveness of inactivating yeast-like fungi Candida albicans, Gram-positive Bacillus subtilis, and Gram-negative bacteria Escherichia coli with aqueous ozone and sodium hypochlorite solutions. This study used ozone solutions in water with a concentration of 0.5–1.5 mg/L and sodium hypochlorite solutions with an active chlorine concentration of 50–150 mg/L. Steel and polymeric plates were used as substrates. Comparison of the CT (concentration by time) criterion at the ratio of LD50 in NaClO to ozonated water shows that the smallest difference, around 100 times, was observed in the inactivation of Candida albicans. The maximum difference is up to 230 times in the inactivation of Bacillus subtilis. Full article
(This article belongs to the Special Issue Water Treatment Technology for Emerging Contaminants)
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<p>Effectiveness of <span class="html-italic">Candida albicans</span> inactivation (<span class="html-italic">Ef</span>) by aqueous ozone solution (<b>a</b>,<b>b</b>) and sodium hypochlorite (<b>c</b>,<b>d</b>), immobilized on metal plates (<b>a</b>,<b>c</b>) and polymer plates (<b>b</b>,<b>d</b>).</p>
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<p>Effectiveness of <span class="html-italic">Bacillus subtilis</span> inactivation by aqueous ozone solution (<b>a</b>,<b>b</b>) and sodium hypochlorite (<b>c</b>,<b>d</b>), immobilized on metal plates (<b>a</b>,<b>c</b>) and polymer plates (<b>b</b>,<b>d</b>).</p>
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<p>Effectiveness of <span class="html-italic">Escherichia coli</span> inactivation by aqueous ozone solution (<b>a</b>,<b>b</b>) and sodium hypochlorite (<b>c</b>,<b>d</b>), immobilized on metal plates (<b>a</b>,<b>c</b>) and polymer plates (<b>b</b>,<b>d</b>).</p>
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<p>Comparison of the C·T criterion for ozonated water and sodium hypochlorite solution.</p>
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13 pages, 2655 KiB  
Article
A Novel IoT-Based Performance Testing Method and System for Fire Pumps
by Shangcong Zhang, Yongfang Li, Xuefei Chen, Ruyi Zhou, Ziran Wu and Taha Zarhmouti
Water 2024, 16(5), 792; https://doi.org/10.3390/w16050792 - 6 Mar 2024
Viewed by 1568
Abstract
Fire pumps are the key components of water supply in a firefighting system. At present, there is a lack of fire water pump testing methods that intelligently detect faulty states. Existing testing approaches require manual operation, which leads to low efficiency and accuracy. [...] Read more.
Fire pumps are the key components of water supply in a firefighting system. At present, there is a lack of fire water pump testing methods that intelligently detect faulty states. Existing testing approaches require manual operation, which leads to low efficiency and accuracy. To solve the issue, this paper presents an automatic and smart testing approach that acquires measurements of the flow, pressure, shaft power and efficiency from smart sensors via an IoT network, so that performance curves are obtained in the testing processes. An IoT platform is developed for data conversion, transmission and storage. The Discrete Fréchet Distance is applied to evaluate the similarities between the acquired performance curves and metric performance curves, to determine the working condition of the fire pump. The weights of the measurement dimensions for distance computation are optimized by the Genetic Algorithm to improve the distinction between normal and faulty performance curves. Finally, the experimental results show that the proposed method can completely detect faulty states and prove its high practicality for real firefighting systems. Full article
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<p>Architecture of the smart fire pump testing system.</p>
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<p>Architecture of the IoT platform.</p>
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<p>Web-based front end of the fire pump monitoring platform.</p>
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<p>Examples of performance curves.</p>
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<p>Complete performance evaluation process.</p>
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<p>Fire pump device employed in the experiments.</p>
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17 pages, 5245 KiB  
Article
Multiscale Spatiotemporal Dynamics of Drought within the Yellow River Basin (YRB): An Examination of Regional Variability and Trends
by Lei Jin, Shaodan Chen and Mengfan Liu
Water 2024, 16(5), 791; https://doi.org/10.3390/w16050791 - 6 Mar 2024
Cited by 2 | Viewed by 1370
Abstract
Drought, as a recurring extreme climatic event, inflicts diverse impacts on ecological systems, agricultural productivity, water resources, and socio-economic progress globally. Discerning the drought patterns within the evolving environmental landscape of the Yellow River Basin (YRB) is imperative for enhancing regional drought management [...] Read more.
Drought, as a recurring extreme climatic event, inflicts diverse impacts on ecological systems, agricultural productivity, water resources, and socio-economic progress globally. Discerning the drought patterns within the evolving environmental landscape of the Yellow River Basin (YRB) is imperative for enhancing regional drought management and fostering ecological conservation alongside high-quality development. This study utilizes meteorological drought indices, the Standardized Precipitation Evapotranspiration Index (SPEI) and the self-calibrating Palmer Drought Severity Index (scPDSI), for a detailed spatiotemporal analysis of drought conditions. It examines the effectiveness of these indices in the basin’s drought monitoring, offering a comprehensive insight into the area’s drought spatiotemporal dynamics. The findings demonstrate the following: (1) SPEI values exhibit distinct fluctuation patterns at varying temporal scales, with more pronounced fluctuations at shorter scales. Drought years identified via the 12-month SPEI time scale include 1965, 1966, 1969, 1972, 1986, 1997, 1999, 2001, and 2006. (2) A modified Mann–Kendall (MMK) trend test analysis of the scPDSI time series reveals a worrying trend of intensifying drought conditions within the basin. (3) Correlation analysis between SPEI and scPDSI across different time scales yields correlation coefficients of 0.35, 0.54, 0.69, 0.76, and 0.62, highlighting the most substantial correlation at an annual scale. Spatial correlation analysis conducted between SPEI and scPDSI across various scales reveals that, within diverse temporal ranges, the correlation peaks at a 12-month time scale, with subsequent prominence observed at 6 and 24 months. This observed pattern accentuates the applicability of scPDSI in the monitoring of medium- to long-term drought phenomena. Full article
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<p>Location of the study area and distribution of meteorological stations.</p>
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<p>SPEI temporal sequences derived from meteorological station data at different time scales (blue signifying SPEI values above 0, red denoting SPEI values below 0): (<b>a</b>) 1-month scale; (<b>b</b>) 3-month scale; (<b>c</b>) 6-month scale; (<b>d</b>) 12-month scale; (<b>e</b>) 24-month scale.</p>
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<p>Assessment of drought frequency in the YRB derived from SPEI values on a 3-month temporal scale.</p>
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<p>Characterization of the distribution of diverse drought frequencies across sub-regions in the YRB.</p>
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<p>Spatial distribution of varied drought intensities, according to the average scPDSI values from the 1961–2017 time series: (<b>a</b>) spring season; (<b>b</b>) summer season; (<b>c</b>) autumn season; (<b>d</b>) winter season; (<b>e</b>) annual mean.</p>
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<p>Trend analysis of the YRB: evaluating scPDSI values from 1961 to 2017.</p>
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<p>Comparative time series analysis of SPEI (blue and red bars) and scPDSI (black solid line) values across various time scales: a study from 1961 to 2017.</p>
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<p>Kriging interpolation analysis of the correlation between meteorological station-based SPEI and scPDSI across various temporal scales: (<b>a</b>) 1-month scale; (<b>b</b>) 3-month scale; (<b>c</b>) 6-month scale; (<b>d</b>) 12-month scale; (<b>e</b>) 24-month scale.</p>
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<p>Analyzing the distribution of correlation coefficients (r) between SPEI and scPDSI across temporal scales in diverse sub-regions of the YRB.</p>
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<p>Temporal dynamics of station-derived versus gridded SPEI values in the YRB, 1961–2017.</p>
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<p>Spatial interpolation analysis of the correlation between station-derived and grid-based SPEI values in the YRB.</p>
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15 pages, 891 KiB  
Article
Evaluating the Effectiveness of the “River Chief System”: An Empirical Study Based on the Water Quality Data of Coastal Rivers in Guangdong Province
by Kun Yang, Jinrui Yao, Yin Huang, Huiyan Ling, Yu Yang, Lin Zhang, Diyun Chen and Yuxian Liu
Water 2024, 16(5), 790; https://doi.org/10.3390/w16050790 - 6 Mar 2024
Cited by 1 | Viewed by 1441
Abstract
The river chief system (RCS) is an innovative reform in China for strengthening the management of rivers and lakes. It is an important means of curbing the current severe water-environment situation. However, the policy impact of the RCS is still inconclusive in the [...] Read more.
The river chief system (RCS) is an innovative reform in China for strengthening the management of rivers and lakes. It is an important means of curbing the current severe water-environment situation. However, the policy impact of the RCS is still inconclusive in the existing literature. Using monthly data spanning from January 2015 to March 2022 from 25 water quality monitoring stations in rivers flowing into the sea across 13 prefecture-level cities in Guangdong Province, this study adopted regression discontinuity to evaluate the policy effects of the RCS on water quality. The results show that after the RCS’s full implementation in Guangdong Province, the concentrations of dissolved oxygen (DO) increased and water quality indicators, such as permanganate index (CODMn), biochemical oxygen demand (BOD), ammonia nitrogen (NH3-N), chemical oxygen demand (COD), and total phosphorus (TP), decreased; NH3-N showed the largest decrease. These findings indicate that the RCS may contribute to a measurable improvement in reducing water pollution. However, no statistically significant changes in pH and total nitrogen (TN) were found, which indicates that the RCS fell short of achieving the policy effect of comprehensive water-pollution control. Therefore, in order to improve the RCS, it is necessary to refine the existing water-quality assessment indicators and to establish an evaluation system centered on the ecological health of rivers and lakes. Additionally, a paradigm shift from an administrative-boundary-based river management model to an overarching, holistic river-basin-based management approach is crucial for actualizing the holistic governance goals of the RCS. Full article
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<p>Monitoring Sections of rivers flowing into the sea in 13 coastal cities of Guangdong Province. Source of Information: Guangdong Provincial Department of Ecological Environment, information disclosure on monitoring of rivers entering the sea in Guangdong Province.</p>
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<p>Third-order polynomial fitted curves for eight water quality indicators in rivers flowing into the sea before and after RCS’s full implementation in 13 coastal cities of Guangdong Province (over 35 periods).</p>
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16 pages, 5646 KiB  
Article
Pressure Pulsation Characteristics on the Bulb Body of a Submersible Tubular Pump
by Jian Wang, Ze Chen, Linghao Li, Chuan Wang, Kangle Teng, Qiang He, Jiren Zhou, Shanshan Li, Weidong Cao, Xiuli Wang and Hongliang Wang
Water 2024, 16(5), 789; https://doi.org/10.3390/w16050789 - 6 Mar 2024
Viewed by 983
Abstract
Submersible tubular pumps are an ideal choice for pump stations that require high flow rates and low lift. These pumps combine the unique features of submersible motors with axial flow pump technology, making them highly efficient and cost-effective. They have found extensive applications [...] Read more.
Submersible tubular pumps are an ideal choice for pump stations that require high flow rates and low lift. These pumps combine the unique features of submersible motors with axial flow pump technology, making them highly efficient and cost-effective. They have found extensive applications in China’s rapidly developing water conservancy industry. In this research, we focus on investigating the pressure pulsation characteristics of the internal bulb body in a specific pump station project in China. To conduct our analysis, we utilize a model of the submersible tubular pump and strategically position 18 monitoring points. These monitoring points cover various sections, including the impeller inlet and outlet, guide vane outlet, as well as the inlet, middle, and outlet sections of the bulb body segment. To calculate the unsteady flow of the system, we employ numerical simulation techniques. By combining the outcomes of model tests, we determine the pressure pulsation characteristics. The comparison of results reveals a remarkable similarity between the efficiency–head curves obtained from the numerical simulation and the model test. While the model test yields slightly higher head results, the numerical simulation indicates slightly higher efficiency values. This finding lends strong support to the reliability of numerical simulation results, which can provide valuable insights for the design and optimization of submersible tubular pumps. Overall, submersible tubular pumps demonstrate their suitability for pump stations with high flow rates and low lift requirements. The study of pressure pulsation characteristics within the bulb body contributes to a better understanding of their performance and facilitates their further application in the field of water conservancy engineering. Full article
(This article belongs to the Special Issue Hydraulics and Hydrodynamics in Fluid Machinery)
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<p>Schematic diagram of the computational domain.</p>
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<p>Schematic diagram of overcurrent component grids.</p>
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<p>Variation curve of the number of grids and the efficiency of the pumping unit.</p>
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<p>Schematic diagram of the test bench.</p>
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<p>Model test components diagram (<b>a</b>) Impeller Model and (<b>b</b>) Guide Vane Model.</p>
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<p>Schematic diagram of pressure measurement section.</p>
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<p>Efficiency head curve.</p>
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<p>Arrangement of pressure pulsation monitoring points.</p>
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<p>Pressure pulsation distribution in inlet section of 0.9 <span class="html-italic">Q</span><sub>d</sub> bulb body.</p>
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<p>Pressure pulsation distribution in inlet section of 1.0 <span class="html-italic">Q</span><sub>d</sub> bulb body.</p>
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<p>Pressure pulsation distribution in inlet section of 1.1 <span class="html-italic">Q</span><sub>d</sub> bulb body.</p>
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<p>Pressure pulsation distribution in middle section of 0.9 <span class="html-italic">Q</span><sub>d</sub> bulb body.</p>
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<p>Pressure pulsation distribution in middle section of 1.0 <span class="html-italic">Q</span><sub>d</sub> bulb body.</p>
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<p>Pressure pulsation distribution in middle section of 1.1 <span class="html-italic">Q</span><sub>d</sub> bulb body.</p>
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<p>Pressure pulsation distribution in outlet section of 0.9 <span class="html-italic">Q</span><sub>d</sub> bulb body.</p>
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<p>Pressure pulsation distribution in outlet section of 1.0 <span class="html-italic">Q</span><sub>d</sub> bulb body.</p>
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<p>Pressure pulsation distribution in outlet section of 1.1 <span class="html-italic">Q</span><sub>d</sub> bulb body.</p>
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27 pages, 7301 KiB  
Article
Calibration of Hybrid-Maize Model for Simulation of Soil Moisture and Yield in Production Corn Fields
by Anthony A. Amori, Olufemi P. Abimbola, Trenton E. Franz, Daran Rudnick, Javed Iqbal and Haishun Yang
Water 2024, 16(5), 788; https://doi.org/10.3390/w16050788 - 6 Mar 2024
Viewed by 1505
Abstract
Model calibration is essential for acceptable model performance and applications. The Hybrid-Maize model, developed at the University of Nebraska-Lincoln, is a process-based crop simulation model that simulates maize growth as a function of crop and field management and environmental conditions. In this study, [...] Read more.
Model calibration is essential for acceptable model performance and applications. The Hybrid-Maize model, developed at the University of Nebraska-Lincoln, is a process-based crop simulation model that simulates maize growth as a function of crop and field management and environmental conditions. In this study, we calibrated and validated the Hybrid-Maize model using soil moisture and yield data from eight commercial production fields in two years. We used a new method for the calibration and multi-parameter optimization (MPO) based on kriging with modified criteria for selecting the parameter combinations. The soil moisture-related parameter combination (SM-PC3) improved simulations of soil water dynamics, but improvement in model performance is still required. The grain yield-related parameter combination significantly improved the yield simulation. We concluded that the calibrated model is good enough for irrigation water management at the field scale. Future studies should focus on improving the model performance in simulating total soil water (TSW) dynamics at different soil depths by including more soil water processes in a more dynamic manner. Full article
(This article belongs to the Section Soil and Water)
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<p>Map of Nebraska (<b>upper right</b>), the county (<b>lower right</b>), and farmer’s field (<b>left</b>).</p>
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<p>(<b>a</b>) Monthly total precipitation in 2019, 2020, and average of 1999 to 2018 growing seasons, (<b>b</b>) cumulative precipitation and grass-reference evapotranspiration (ETo) in 2019, 2020, and average of 1999 to 2018 growing seasons.</p>
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<p>Simulated yield versus observed yield using the model default parameter values for 2019 and 2020.</p>
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<p>Flowchart describing the MPO procedure.</p>
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<p>Multi-parameter optimization of GAM and PSImax for each of the calibration fields in 2019 and 2020.</p>
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<p>Multi-parameter optimization of the most sensitive yield-related parameters. (<b>a</b>) Yield surfaces based on G5 and G2 pair for four calibration fields in 2019 and 2020. (<b>b</b>) Yield surfaces based on ILUE and GRG pair for four calibration fields in 2019 and 2020.</p>
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<p>Measured and simulated daily total soil water (TSW) of soil depth of 0.0–0.3 m, 0.3–0.6 m, and 0.0–1.0 m (SD1, SD2, and SD3, respectively) for the calibration fields in 2019 and 2020 growing seasons. DAP = days after planting.</p>
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<p>Measured and simulated daily total soil water (TSW) in the top SD1 layer (0.0–0.3 m soil depth) for the validation fields in 2019 and 2020. DAP = days after planting.</p>
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<p>Measured and simulated daily total soil water (TSW) in the SD2 layer (0.3–0.6 m soil depth) for the validation fields in 2019 and 2020. DAP = days after planting.</p>
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<p>Measured and simulated daily total soil water (TSW) in the top 0.0–1.0 m soil depth (SD3) for the validation fields in 2019 and 2020. DAP = days after planting.</p>
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<p>Field-measured and model-simulated grain yield for the pooled data using GY-PC3.</p>
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18 pages, 6327 KiB  
Article
Evaluating the Effectiveness of Rainwater Storage Tanks Based on Different Enabling Rules
by Yongwei Gong, Ge Meng, Kun Tian and Zhuolun Li
Water 2024, 16(5), 787; https://doi.org/10.3390/w16050787 - 6 Mar 2024
Viewed by 1146
Abstract
A proposed method for analyzing the effectiveness of rainwater storage tanks (RWSTs) based on various enabling rule scenarios has been proposed to address the issue of incomplete strategies and measures for controlling excessive rainwater runoff. Three enabling rules for RWSTs have been proposed, [...] Read more.
A proposed method for analyzing the effectiveness of rainwater storage tanks (RWSTs) based on various enabling rule scenarios has been proposed to address the issue of incomplete strategies and measures for controlling excessive rainwater runoff. Three enabling rules for RWSTs have been proposed, as follows: enabling rule I, which involves activation upon rainfall; enabling rule II, which requires the rainfall intensity to reach a predetermined threshold; and enabling rule III, which necessitates the cumulative rainfall to reach a set threshold. In order to assess the effectiveness of these enabling rules when reducing the total volume of rainwater outflow (TVRO), peak flow rate (PFR), and peak flow velocity (PFV), a comparative analysis was conducted to determine which enabling rule yielded the most optimal control effect. The findings indicate that the enabling rule I is responsible for determining the optimal unit catchment’s rainfall capture volume (UCRCV), which is measured at 300 m3·ha−1. Additionally, the control effect of the TVRO of the RWSTs remains largely unaffected by the peak proportion coefficient. Enabling rule II establishes the optimal activation threshold at a rainfall intensity of 1 mm·min−1; under this enabling rule, RWSTs demonstrate the most effective control over PFR and PFV. Enabling rule III enables the determination of the optimal activation threshold, which is set at a cumulative rainfall of 20 mm; under this enabling rule, the implementation of the RWST technique yields the most effective control over the TVRO. Consequently, the optimal rainwater runoff reduction plan for the study area has been successfully determined, providing valuable guidance for the implementation of scientific and reasonable optimal runoff management. Full article
(This article belongs to the Special Issue Urban Flooding Control and Sponge City Construction)
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<p>Location of the study area.</p>
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<p>Multi model coupling: (<b>a</b>) surface runoff model, (<b>b</b>) network convergence model, (<b>c</b>) ground surface flowing model of 2D, and (<b>d</b>) river confluence model.</p>
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<p>Location of RWSTs.</p>
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<p>Rainfall pattern in the study area.</p>
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<p>Calibration and validation results of InfoWorks ICM: (<b>a</b>) calibration result (6 August 2016), (<b>b</b>) calibration result (26 July 2017), (<b>c</b>) verification result (6 August 2017), and (<b>d</b>) verification result (18 August 2017).</p>
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<p>Control effect of TVRO from RWSTs under different enabling rule scenarios. (<b>a</b>,<b>c</b>,<b>e</b>) TVRO at different return periods with enabling rules I, II and III, respectively (<span class="html-italic">P</span> = 1, 3, 5, 10, 20, 50a, <span class="html-italic">r</span> = 0.3). (<b>b</b>,<b>d</b>,<b>f</b>) TVRO at different rain peak coefficients with enabling rules I, II and III, respectively (<span class="html-italic">P</span> = 20a, <span class="html-italic">r</span> = 0.3, 0.5, 0.7).</p>
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<p>PFR control effect of RWSTs under different enabling rule scenarios. (<b>a</b>,<b>c</b>,<b>e</b>) PFR at different return periods with enabling rules Ⅰ, Ⅱ and III, respectively (<span class="html-italic">P</span> = 1, 3, 5, 10, 20, 50a, <span class="html-italic">r</span> = 0.3). (<b>b</b>,<b>d</b>,<b>f</b>) PFR at different rain peak coefficients with enabling rules Ⅰ, Ⅱ and III, respectively (<span class="html-italic">P</span> = 20a, <span class="html-italic">r</span> = 0.3, 0.5, 0.7).</p>
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<p>PFR control effect of RWSTs under different enabling rule scenarios. (<b>a</b>,<b>c</b>,<b>e</b>) PFR at different return periods with enabling rules Ⅰ, Ⅱ and III, respectively (<span class="html-italic">P</span> = 1, 3, 5, 10, 20, 50a, <span class="html-italic">r</span> = 0.3). (<b>b</b>,<b>d</b>,<b>f</b>) PFR at different rain peak coefficients with enabling rules Ⅰ, Ⅱ and III, respectively (<span class="html-italic">P</span> = 20a, <span class="html-italic">r</span> = 0.3, 0.5, 0.7).</p>
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<p>Effect of RWST flow control in different enabling rule scenarios. (<b>a</b>,<b>c</b>,<b>e</b>) PFV at different return periods with enabling rules Ⅰ, Ⅱ and III, respectively (<span class="html-italic">P</span> = 1, 3, 5, 10, 20, 50a, <span class="html-italic">r</span> = 0.3). (<b>b</b>,<b>d</b>,<b>f</b>) PFV at different rain peak coefficient with enabling rules Ⅰ, Ⅱ and III, respectively (<span class="html-italic">P</span> = 20a, <span class="html-italic">r</span> = 0.3, 0.5, 0.7).</p>
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<p>Effect of RWST flow control in different enabling rule scenarios. (<b>a</b>,<b>c</b>,<b>e</b>) PFV at different return periods with enabling rules Ⅰ, Ⅱ and III, respectively (<span class="html-italic">P</span> = 1, 3, 5, 10, 20, 50a, <span class="html-italic">r</span> = 0.3). (<b>b</b>,<b>d</b>,<b>f</b>) PFV at different rain peak coefficient with enabling rules Ⅰ, Ⅱ and III, respectively (<span class="html-italic">P</span> = 20a, <span class="html-italic">r</span> = 0.3, 0.5, 0.7).</p>
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<p>Comparison of TVRO under different enabling rules of RWSTs (<span class="html-italic">P</span> = 1, 3, 5, 10, 20, 50a, <span class="html-italic">r</span> = 0.3). (<b>a</b>) Effect of TVRO control and (<b>b</b>) reduction rate of total external emissions.</p>
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<p>Comparison of PFR under different enabling rules of RWSTs (<span class="html-italic">P</span> = 1, 3, 5, 10, 20, 50a, <span class="html-italic">r</span> = 0.3). (<b>a</b>) PFR control effect and (<b>b</b>)PFR reduction rate.</p>
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<p>Comparison of PFV under different enabling rules of RWSTs (<span class="html-italic">P</span> = 1, 3, 5, 10, 20, 50a, <span class="html-italic">r</span> = 0.3). (<b>a</b>) PFV control effect and (<b>b</b>) PFV reduction rate.</p>
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<p>Performance of enabling rules of different RWSTs: (<b>a</b>,<b>b</b>) represent the control effects of TVRO, PFR, and PFV on 1 August 2016 and 14 August 2016, respectively.</p>
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15 pages, 20041 KiB  
Article
Rainfall Runoff and Nitrogen Loss Characteristics on the Miyun Reservoir Slope
by Na Wang, Lei Wang, Liang Jin, Jiajun Wu, Min Pang, Dan Wei, Yan Li, Junqiang Wang, Ting Xu, Zhixin Yang and Jianzhi Xie
Water 2024, 16(5), 786; https://doi.org/10.3390/w16050786 - 6 Mar 2024
Cited by 1 | Viewed by 1106
Abstract
Rainfall intensity and slope gradient are the main drivers of slope surface runoff and nitrogen loss. To explore the distribution of rainfall runoff and nitrogen loss on the Miyun Reservoir slopes, we used artificial indoor simulated rainfall experiments to determine the distribution characteristics [...] Read more.
Rainfall intensity and slope gradient are the main drivers of slope surface runoff and nitrogen loss. To explore the distribution of rainfall runoff and nitrogen loss on the Miyun Reservoir slopes, we used artificial indoor simulated rainfall experiments to determine the distribution characteristics and nitrogen migration paths of surface and subsurface runoff under different rainfall intensities and slope gradients. The initial runoff generation time of subsurface runoff lagged that of surface runoff, and the lag time under different rainfall intensity and slope conditions ranges from 3.97 to 12.62 min. Surface runoff rate increased with increasing rainfall intensity and slope gradient; compared with a rainfall intensity of 40 mm/h, at a slope of 15°, average surface runoff rate at 60 and 80 mm/h increased by 2.38 and 3.60 times, respectively. Meanwhile, the subsurface runoff rate trended upwards with increasing rainfall intensity, in the order 5 > 15 > 10°. It initially increased and then decreased with increasing slope gradient, in the order 5 > 10 > 15°. Total nitrogen (TN) loss concentration of surface runoff shows a decrease followed by a stabilization trend; the concentration of TN loss decreases with decreasing rainfall intensity, and the stabilization time becomes earlier and is most obvious in 5° slope conditions. TN loss concentration in subsurface runoff decreased with increasing rainfall intensity, i.e., 40 > 60 > 80 mm/h. The surface runoff rainfall coefficient was mainly affected by rainfall intensity, a correlation between αs and slope gradients S was not obvious, and the fitting effect was poor. The subsurface runoff rainfall coefficient was mainly affected by slope gradient, the R2 of all rainfall intensities was <0.60, and the fitting effect was poor. The main runoff loss pathway from the Miyun Reservoir slopes was surface runoff, which was more than 62.57%. At the same time, nitrogen loss was subsurface runoff, more than 51.14%. The proportion of surface runoff to total runoff increases with the increase of rainfall intensity and slope, with a minimum of 62.57%, and the proportion of nitrogen loss from subsurface runoff also decreases with increasing rainfall intensity but does not change with slope gradient. The order of different runoff modulus types was mixed runoff (surface and subsurface runoff occur simultaneously) > surface runoff > subsurface runoff. The surface and mixed runoff modulus increased significantly with increasing rain intensity under different rain intensities and slope gradients. Overall, rainfall intensity significantly affected slope surface runoff, and slope gradient significantly affected nitrogen loss. Full article
(This article belongs to the Special Issue Effects of Hydrology on Soil Erosion and Soil Conservation)
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<p>Schematic diagram and physical diagram of experimental structure.</p>
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<p>Location of the study area.</p>
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<p>Runoff rate under different slope gradient and rainfall intensity.</p>
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<p>Surface runoff and subsurface runoff TN loss under different slope gradient and rainfall intensity.</p>
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<p>Runoff coefficient under different slope gradients and rainfall intensity.</p>
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<p>Variation trend of Surface runoff, subsurface runoff, and mixed runoff modulus with rainfall intensity under different slopes.</p>
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<p>Correlation between rainfall intensity, slope, slope runoff, and nitrogen loss.</p>
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19 pages, 5206 KiB  
Article
Many-Objective Hierarchical Pre-Release Flood Operation Rule Considering Forecast Uncertainty
by Yongqi Liu, Guibing Hou, Baohua Wang, Yang Xu, Rui Tian, Tao Wang and Hui Qin
Water 2024, 16(5), 785; https://doi.org/10.3390/w16050785 - 6 Mar 2024
Cited by 1 | Viewed by 1080
Abstract
Flood control operation of cascade reservoirs is an important technology to reduce flood disasters and increase economic benefits. Flood forecast information can help reservoir managers make better use of flood resources and reduce flood risks. In this paper, a hierarchical pre-release flood operation [...] Read more.
Flood control operation of cascade reservoirs is an important technology to reduce flood disasters and increase economic benefits. Flood forecast information can help reservoir managers make better use of flood resources and reduce flood risks. In this paper, a hierarchical pre-release flood operation rule considering the flood forecast and its uncertainty information is proposed for real-time flood control. A many-objective optimization model considering the cascade reservoir’s power generation objective, flood control objective, and navigation objective is established. Then, a region search evolutionary algorithm is applied to optimize the many-objective optimization model in a real-world case study upstream of the Yangtze River basin. The optimization experimental results show that the region search evolutionary algorithm can balance convergence and diversity well, and the HV value is 40% higher than the MOEA/D algorithm. The simulation flood control results of cascade reservoirs upstream of the Yangtze River demonstrate that the optimized flood control rule can increase the average multi-year power generation of cascade reservoirs by a maximum of 27.72 × 108 kWh under the condition of flood control safety. The rules proposed in this paper utilize flood resources by identifying runoff forecast information, and pre-release to the flood limit level 145 m before the big flood occurs, so as to ensure the safety downstream and the dam’s own flood control and provide reliable decision support for reservoir managers. Full article
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<p>A flowchart of the many-objective hierarchical pre-release flood operation rule considering forecast uncertainty.</p>
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<p>The process of the two-stage reservoir dispatch model.</p>
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<p>The impact of forecast uncertainty on reservoir decision.</p>
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<p>The representation of the HPFOR.</p>
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<p>Location of Xiluodu, Xiangjiaba, and Three Gorges in the Yangtze River basin.</p>
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<p>Radar figures of the final solution set obtained by MOEA/D, NSGAIII, <span class="html-italic">θ</span>-DEA, and RSEA (F1: power generation objective, F2: reservoir safety objective, F3: flood control station security objective, F4: upstream cascade reservoirs flood control objective, F5: navigation objective). (<b>a</b>) MOEA/D, (<b>b</b>) NSGA-III, (<b>c</b>) <span class="html-italic">θ</span>-DEA, and (<b>d</b>) RSEA.</p>
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<p>Release processes and water level processes of Three Gorges in 1981: (<b>a</b>) scheme 1, (<b>b</b>) scheme 25, (<b>c</b>) scheme 57, and (<b>d</b>) scheme 85.</p>
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<p>Release processes and water level processes of Three Gorges in 1998: (<b>a</b>) scheme 1, (<b>b</b>) scheme 25, (<b>c</b>) scheme 57, and (<b>d</b>) scheme 85.</p>
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<p>Water level processes of Three Gorges in 1981 and 1998: (<b>a</b>) 1981 and (<b>b</b>) 1998.</p>
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15 pages, 4135 KiB  
Article
Nitrite Degradation by a Novel Marine Bacterial Strain Pseudomonas aeruginosa DM6: Characterization and Metabolic Pathway Analysis
by Zhe Chen, Wenying Yu, Yingjian Zhan, Zheng Chen, Tengda Han, Weiwei Song and Yueyue Zhou
Water 2024, 16(5), 784; https://doi.org/10.3390/w16050784 - 6 Mar 2024
Cited by 1 | Viewed by 1696
Abstract
High concentrations of nitrite in marine aquaculture wastewater not only pose a threat to the survival and immune systems of aquatic organisms but also contribute to eutrophication, thereby impacting the balance of coastal ecosystems. Compared to traditional physical and chemical methods, utilizing microorganism-mediated [...] Read more.
High concentrations of nitrite in marine aquaculture wastewater not only pose a threat to the survival and immune systems of aquatic organisms but also contribute to eutrophication, thereby impacting the balance of coastal ecosystems. Compared to traditional physical and chemical methods, utilizing microorganism-mediated biological denitrification is a cost-effective and efficient solution. However, the osmotic pressure changes and salt-induced enzyme precipitation in high-salinity seawater aquaculture environments may inhibit the growth and metabolism of freshwater bacterial strains, making it more suitable to select salt-tolerant marine microorganisms for treating nitrite in marine aquaculture wastewater. In this study, a salt-tolerant nitrite-degrading bacterium, designated as DM6, was isolated from the seawater (salinity of 25–30‰) of Portunus trituberculatus cultivation. The molecular identification of strain DM6 was conducted using 16S rRNA gene sequencing technology. The impacts of various environmental factors on the nitrite degradation performance of strain DM6 were investigated through single-factor and orthogonal experiments, with the selected conditions considered to be the key factors affecting the denitrification efficiency of microorganisms in actual wastewater treatment. PCR amplification of key genes involved in the nitrite metabolism pathway of strain DM6 was conducted, including denitrification pathway-related genes narG, narH, narI, nirS, and norB, as well as assimilation pathway-related genes nasC, nasD, nasE, glnA, gltB, gltD, gdhB, and gdhA. The findings indicated that strain DM6 is classified as Pseudomonas aeruginosa and exhibits efficient nitrite degradation even under a salinity of 35‰. The optimal nitrite degradation efficiency of DM6 was observed when using sodium citrate as the carbon source, a C/N ratio of 20, a salinity of 13‰, pH 8.0, and a temperature of 35 °C. Under these conditions, DM6 could completely degrade an initial nitrite concentration of 156.33 ± 1.17 mg/L within 36 h. Additionally, the successful amplification of key genes involved in the nitrite denitrification and assimilation pathways suggests that strain DM6 may possess both denitrification and assimilation pathways for nitrite degradation simultaneously. Compared to freshwater strains, strain DM6 demonstrates higher salt tolerance and exhibits strong nitrite degradation capability even at high concentrations. However, it may be more suitable for application in the treatment of wastewater from marine aquaculture systems during summer, high-temperature, or moderately alkaline conditions. Full article
(This article belongs to the Section Oceans and Coastal Zones)
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<p>Phylogenetic tree of strain DM6.</p>
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<p>Effects of different carbon sources on the growth and nitrite degradation ability of strain DM6. (<b>a</b>) Growth curve of strain DM6. (<b>b</b>) Nitrite concentration decrease curve.</p>
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<p>Effects of different C/N ratios on the growth and nitrite degradation ability of strain DM6. (<b>a</b>) Growth curve of strain DM6. (<b>b</b>) Nitrite concentration decrease curve.</p>
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<p>Effects of different initial pH on the growth and nitrite degradation ability of strain DM6. (<b>a</b>) Growth curve of strain DM6. (<b>b</b>) Nitrite concentration decrease curve.</p>
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<p>Effects of different salinity on the growth and nitrite degradation ability of strain DM6. (<b>a</b>) Growth curve of strain DM6. (<b>b</b>) Nitrite concentration decrease curve.</p>
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<p>Effects of different temperatures on the growth and nitrite degradation ability of strain DM6. (<b>a</b>) Growth curve of strain DM6. (<b>b</b>) Nitrite concentration decrease curve.</p>
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<p>Effects of different nitrite concentrations on the growth and nitrite degradation ability of strain DM6. (<b>a</b>) Growth curve of strain DM6. (<b>b</b>) Nitrite concentration decrease curve.</p>
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<p>(<b>a</b>) Gel electrophoresis image of denitrification functional genes. (<b>b</b>) Refined predicted nitrate metabolism pathway of strain DM6.</p>
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19 pages, 3744 KiB  
Article
Simulating Aquifer for Nitrate Ion Migration Processes in Soil
by Oanamari Daniela Orbuleţ, Cristina Modrogan and Cristina-Ileana Covaliu-Mierla
Water 2024, 16(5), 783; https://doi.org/10.3390/w16050783 - 6 Mar 2024
Viewed by 1064
Abstract
The objective of this study was to explore the removal of nitrate ions from groundwater by employing dynamic permeable reactive barriers (PRBs) with A400-nZVI. This research aimed to determine the parameters of the barrier and evaluate its overall capacity to retain nitrate ions [...] Read more.
The objective of this study was to explore the removal of nitrate ions from groundwater by employing dynamic permeable reactive barriers (PRBs) with A400-nZVI. This research aimed to determine the parameters of the barrier and evaluate its overall capacity to retain nitrate ions during percolation with a potassium nitrate solution. The process involves obtaining zerovalent iron (nZVI) nanoparticles, which were synthesized and incorporated onto an anionic resin support material (A400) through the reduction reaction of ferrous ions with sodium borohydride (NaBH4). This is achieved by preparing a ferrous sulfate solution, contacting it with the ion exchange resin at various solid–liquid mass ratios and gradually adding sodium borohydride under continuous stirring in an oxygen-free environment to create the A400-nZVI barrier. The results of the study, focusing on the development of permeable reactive barriers composed of nano zero-valent iron and ion exchangers, highlight the significant potential of water treatment processes when appropriately sized. The research specifically assesses the effectiveness of NO3 removal by using the A400-nZVI permeable reactive barrier, conducting laboratory tests that simulate a naturally stratified aquifer with high nitrate contamination. Full article
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<p>Experimental scheme for the synthesis of iron nanoparticles.</p>
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<p>Scheme of the experimental column for the dynamic study on the remediation of nitrate-contaminated groundwater, using a permeable reactive barrier: (A) feed vessel with synthetic water with nitrate content, (B) peristaltic pump, (C) column of soil with PRB–A400-nZVI, and (D) manometers, used to indicate the pressure difference, 1–3 valves.</p>
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<p>SEM images for (<b>a</b>) original resin A400, (<b>b</b>) A400-nZVI before reaction N, (<b>c</b>) A400-nZVI after reaction N, (<b>d</b>) EDAX original resin A400, (<b>e</b>) EDAX for A400-nZVI before reaction N, and (<b>f</b>) EDAX A400-nZVI after reaction N.</p>
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<p>SEM images for (<b>a</b>) original resin A400, (<b>b</b>) A400-nZVI before reaction N, (<b>c</b>) A400-nZVI after reaction N, (<b>d</b>) EDAX original resin A400, (<b>e</b>) EDAX for A400-nZVI before reaction N, and (<b>f</b>) EDAX A400-nZVI after reaction N.</p>
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<p>The FTIR spectra of sample (<b>a</b>) A400, (<b>b</b>) A400-nZVI, and (<b>c</b>) A400-nZVI after reaction with <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">N</mi> <mi mathvariant="normal">O</mi> </mrow> <mrow> <mn>3</mn> </mrow> <mrow> <mo>−</mo> </mrow> </msubsup> </mrow> </semantics></math>.</p>
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<p>XRD spectra of polymeric materials: (<b>a</b>) A400-nZVI and (<b>b</b>,<b>c</b>) A400-nZVI and after the reaction with nitrate ions.</p>
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<p>Variation in nitrate concentration in samples after redox reaction associated with solution recirculation at three flow rates.</p>
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<p>Logistic curves compared to the experimental points for the three samples.</p>
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<p>Logistic curves compared to the experimental points for the three samples.</p>
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<p>Concentration profile for nitrate, nitrite, and ammonia for the nitrate ion for sample P1.</p>
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<p>Scheme of the nitrate reduction mechanism with nZVI.</p>
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16 pages, 2149 KiB  
Article
Effects of a Fishing Ban on the Ecosystem Stability and Water Quality of a Plateau Lake: A Case Study of Caohai Lake, China
by Tangwu Yang, Dianpeng Li, Qing Xu, Yijia Zhu, Zhengjie Zhu, Xin Leng, Dehua Zhao and Shuqing An
Water 2024, 16(5), 782; https://doi.org/10.3390/w16050782 - 6 Mar 2024
Viewed by 1447
Abstract
Long-term fishing bans have spurred extensive debate regarding their impacts on ecosystem structures, functions, and water qualities. However, data on the effects of specific changes induced by fishing bans on ecosystem structures, functions, and water qualities in lakes are still lacking. Therefore, the [...] Read more.
Long-term fishing bans have spurred extensive debate regarding their impacts on ecosystem structures, functions, and water qualities. However, data on the effects of specific changes induced by fishing bans on ecosystem structures, functions, and water qualities in lakes are still lacking. Therefore, the present study addresses this knowledge gap by employing an Ecopath model to assess alterations in an ecosystem’s structure and function before (2011) and after (2021) the implementation of the fishing ban in Caohai Lake and its association with changes in water quality. (1) We observed a substantial reduction in the area covered by submerged aquatic vegetation after the ban, amounting to a 65% decrease in coverage compared with that before the ban, and a 60% reduction in the total ecosystem’s biomass. (2) Following the ban, the number of fish species increased from 7 to 14, and this was accompanied by a rise in the fish biomass from 14.16 t·km−2 to 25.81 t·km−2; a 4.5-fold increase in the total system consumption was observed, signifying accelerated energy and material flows within the ecosystem. (3) The fishing ban exhibited no significant impact on the total nitrogen concentration; however, it significantly reduced the water’s transparency and increased the total phosphorus, ammonia nitrogen, chemical oxygen demand, and chlorophyll contents (p < 0.05). This shift in nutrient dynamics fostered a transformation from a macrophyte-dominant lake to an alga-dominant lake. The fish abundance and diversity increase in closed-type macrophytic lakes, thereby accelerating energy and material flows within food webs. These findings present novel insights into the effective policy management of fishing bans within the Yangtze River Basin, thus enhancing our understanding of sustainable lake ecosystem management. Full article
(This article belongs to the Special Issue Water Pollution Control and Ecological Restoration)
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<p>Location of Caohai Lake in China.</p>
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<p>Energy flow efficiencies among the trophic levels in the Caohai Lake ecosystem before (<b>a</b>) and after (<b>b</b>) the implementation of the fishing ban. Trophic levels are represented by grades II, III, IV, and V.</p>
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<p>Mixed nutritional effects in the Caohai Lake ecosystem before (<b>a</b>) and after (<b>b</b>) the implementation of the fishing ban.</p>
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<p>Changes in water transparency (<b>a</b>), total phosphorus (TP) level (<b>b</b>), total nitrogen (TN) level (<b>c</b>), ammonia nitrogen (NH<sub>3</sub>-N) level (<b>d</b>), chemical oxygen demand (COD) level (<b>e</b>), and chlorophyll-a (Chl.a) level (<b>f</b>) in Caohai Lake before (PFB) and after (ABF) the implementation of the fishing ban. * (<span class="html-italic">p</span> &lt; 0.05), ** (<span class="html-italic">p</span> &lt; 0.01), and *** (<span class="html-italic">p</span> &lt; 0.001) indicate significant differences in water qualities before and after the fishing ban.</p>
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15 pages, 1295 KiB  
Article
Continuous Heterogeneous Fenton for Swine Wastewater Treatment: Converting an Industry Waste into a Wastewater Treatment Material
by João Lincho, João Gomes, Rui C. Martins and Eva Domingues
Water 2024, 16(5), 781; https://doi.org/10.3390/w16050781 - 6 Mar 2024
Viewed by 1386
Abstract
Swine wastewater (SW) was treated using industrial wastes as raw materials in a pre-treatment process (coagulation or adsorption), followed by a continuous heterogeneous Fenton reaction. Before the treatment conducted as a continuous operation, two different batch optimization strategies were evaluated, in which the [...] Read more.
Swine wastewater (SW) was treated using industrial wastes as raw materials in a pre-treatment process (coagulation or adsorption), followed by a continuous heterogeneous Fenton reaction. Before the treatment conducted as a continuous operation, two different batch optimization strategies were evaluated, in which the effects of H2O2 concentration and pH were studied. The results show that using excessive H2O2 results in the same behavior, regardless of whether the pH is 3 or 7.5, while at low H2O2 concentrations, the acidic pH improves the chemical oxygen demand (COD) removal due to a higher solubility of iron. The partial addition of H2O2 after 60 min of the reaction proved to be unbeneficial. Considering other perspectives, a continuous Fenton process using iron filings (IF) as the iron source ([H2O2] = 50 mg/L) was applied after the SW pre-treatment, consisting of adsorption with red mud (RM) or coagulation with poly-diallyldimethylammonium chloride (PDADMAC). The RM adsorption presented higher COD removal and lower toxicity than the PDADMAC coagulation, revealing to be a suitable material for this purpose, but for both pre-treatments, the application of a subsequent continuous Fenton process revealed to be essential to achieve the COD discharge limits imposed by the Portuguese law. In addition, high amounts of dissolved iron were present in the samples (55–58 mg/L) after the Fenton process. However, after the overall treatment, the samples showed no harmful characteristics for Lepidium sativum, being classified as “non-toxic”, contrary to the initial wastewater. Full article
(This article belongs to the Section Wastewater Treatment and Reuse)
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<p>Evaluation of pH effect in Fenton reaction (coagulated effluent, [IF] = 15 g/L, t = 60 min, Fe:H<sub>2</sub>O<sub>2</sub> molar ratio and COD/Fe:H<sub>2</sub>O<sub>2</sub> ratio: 183 and 4.6 ([H<sub>2</sub>O<sub>2</sub>] = 50 mg/L) and 18 and 47.2 ([H<sub>2</sub>O<sub>2</sub>] = 500 mg/L)). * Experiments previously reported in [<a href="#B35-water-16-00781" class="html-bibr">35</a>].</p>
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<p>Evaluation of time in batch Fenton reaction ([IF] = 15 g/L, [H<sub>2</sub>O<sub>2</sub>] = 50 mg/L, pH = 3, coagulated effluent).</p>
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<p>(<b>a</b>) Relative COD removal during Fenton process after coagulation or adsorption pre-treatment and (<b>b</b>) global COD removal with the combined processes (Fenton reaction experimental conditions: pH = 3, τ = 49 ± 2 min, mIF = 250 g, mGSF = 250 g. X axis: dimensionless).</p>
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16 pages, 4403 KiB  
Article
Geospatial Analysis of Transmissivity and Uncertainty in a Semi-Arid Karst Region
by Thiago dos Santos Gonçalves, Harald Klammler and Luíz Rogério Bastos Leal
Water 2024, 16(5), 780; https://doi.org/10.3390/w16050780 - 6 Mar 2024
Cited by 1 | Viewed by 1168
Abstract
Aquifer properties, such as hydraulic transmissivity T and its spatial variability, are fundamental for sustainable groundwater exploitation in arid regions. Especially in karst aquifers, spatial variability can be considerable, and the application of geostatistical methods allows for spatial interpolation and mapping based on [...] Read more.
Aquifer properties, such as hydraulic transmissivity T and its spatial variability, are fundamental for sustainable groundwater exploitation in arid regions. Especially in karst aquifers, spatial variability can be considerable, and the application of geostatistical methods allows for spatial interpolation and mapping based on observations combined with the quantification of uncertainties. Moreover, direct measurements of T are typically scarce, while those of specific capacity Sc are more frequent. In this study, we establish the linear regression relationship between the logarithms of T and Sc measured in 51 wells in a semi-arid karst region in Northeastern Brazil. This relationship is used to estimate empirical values logTemp based on measurements of logSc at 269 wells. LogTemp values are found to be normally distributed with an isotropic variogram of a significant nugget effect (attributed to local-scale karst features) and approximately 10 km range (attributed to larger-scale gradual changes in karst feature density). Ordinary kriging cross-validation indicates an optimum number of 25 neighboring wells for interpolation, which is used in a conditional sequential Gaussian simulation (SGSIM) to generate 500 realizations of logTemp with respective maps of standard deviations and probabilities of (not) exceeding threshold values. High-transmissivity areas mostly coincide with karstified river valleys, while low-transmissivity areas occur toward the edges where aquifer thickness decreases. The resulting transmissivity maps are relevant for optimizing regional water management strategies, which includes stochastic approaches where transmissivity realizations can be used to parameterize multiple runs of numerical groundwater models. Full article
(This article belongs to the Section Hydrogeology)
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<p>(<b>a</b>) Location, schematic geological map of the central region of Chapada Diamantina and detailed stratigraphy for the Salitre Formation. (<b>b</b>) Detailed hydrogeological conditions for the Salitre Karst Aquifer (SKA). Modified from [<a href="#B15-water-16-00780" class="html-bibr">15</a>,<a href="#B28-water-16-00780" class="html-bibr">28</a>].</p>
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<p>Location of the Salitre aquifer pumping wells used in this study. (<b>a</b>) Well data from the CERB database used for the calculation of transmissivity <span class="html-italic">T</span> by the recovery method [<a href="#B40-water-16-00780" class="html-bibr">40</a>] and of specific capacity <span class="html-italic">S<sub>c</sub></span>. (<b>b</b>) Specific capacity well data available on all databases.</p>
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<p>Scatter plots between <span class="html-italic">T</span> and <span class="html-italic">S<sub>c</sub></span> (green circles) with linear regression lines (red) and confidence intervals (green shaded). (<b>a</b>) Raw data displaying skewed distributions and (<b>b</b>) decimal logarithms with more symmetrical distributions.</p>
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<p>Theoretical semivariogram (solid line) overlying the experimental omnidirectional semivariogram (dots with size indicating the number <span class="html-italic">n</span> of data pairs used) of log<span class="html-italic">T<sub>emp</sub></span> in m<sup>2</sup>/d as a function of lag distance <span class="html-italic">h</span> in m.</p>
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<p>Variation in RMSE from cross-validated log<span class="html-italic">T<sub>emp</sub></span> values in m<sup>2</sup>/d as a function of the number <span class="html-italic">m</span> of nearest neighbors used in ordinary kriging (vertical black line indicating optimum value chosen of <span class="html-italic">m</span> = 25).</p>
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<p>Spatial representation of (<b>a</b>) ordinary kriging estimates of log<span class="html-italic">T<sub>emp</sub></span> and (<b>b</b>) respective ordinary kriging standard deviations SD.</p>
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<p>Decrease of the mean variance increment (MVI) with a growing number of realizations <span class="html-italic">r</span> used for the stochastic simulation.</p>
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<p>Spatial representation of results from 500 stochastic realizations of log<span class="html-italic">T<sub>emp</sub></span> (with <span class="html-italic">T<sub>emp</sub></span> in m<sup>2</sup>/d). (<b>a</b>) Means of log<span class="html-italic">T<sub>emp</sub></span> at each location, (<b>b</b>) standard deviations SD, (<b>c</b>,<b>d</b>) probability of log<span class="html-italic">T<sub>emp</sub></span> values lower than 2 (i.e., <span class="html-italic">T<sub>emp</sub></span> &lt; 100 m<sup>2</sup>/d) and higher than 2.5 (i.e., <span class="html-italic">T<sub>emp</sub></span> &gt; 316 m<sup>2</sup>/d), respectively.</p>
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18 pages, 9004 KiB  
Article
Numerical Analysis of Water–Sediment Flow Fields within the Intake Structure of Pumping Station under Different Hydraulic Conditions
by Cundong Xu, Junjiao Tian, Guoxia Wang, Haidong Lian, Rongrong Wang and Xiaomeng Hu
Water 2024, 16(5), 779; https://doi.org/10.3390/w16050779 - 5 Mar 2024
Cited by 1 | Viewed by 1274
Abstract
The vortices, backflow, and siltation caused by sediment-laden flow are detrimental to the safe and efficient operation of pumping stations. To explore the effects of water–sediment two-phase flow on the velocity field, vorticity field, and sediment distribution within intake structures, field tests and [...] Read more.
The vortices, backflow, and siltation caused by sediment-laden flow are detrimental to the safe and efficient operation of pumping stations. To explore the effects of water–sediment two-phase flow on the velocity field, vorticity field, and sediment distribution within intake structures, field tests and numerical simulations were conducted in this study with consideration for the sediment concentration, flow rate, and start-up combination. We applied a non-contact laser scanner and ultrasonic Doppler velocimetry to obtain the field data and reverse modeling of the three-dimensional model of the intake structure under siltation. A multiphase flow model based on the Euler–Euler approach combined with the k-ε turbulence model was adopted for numerical simulation under 10 working conditions, and the reliability was verified with field data. The results indicate that sediment promotes the evolution of coaxial vortices into larger-scale spiral vortices along the water depth, and the process of sediment deposition is controlled by the range, intensity, and flow velocity of the backflow zone. Furthermore, the maximum volume fraction of the near-bottom sediment increased by 202.01% compared to the initial state. The increase in flow rate exacerbates the turbulence of the flow field. Although the increase in sediment concentration benefits the flow diffusion, it further promotes sediment deposition. This study provides a new idea for modeling complex surfaces and considers different operating conditions. It can serve as a scientific reference for the structural optimization and anti-siltation design of similar water-conservancy projects. Full article
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<p>Site investigation of the actual intake structure. (<b>a</b>) Sedimentation morphology, (<b>b</b>) Adverse flow patterns.</p>
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<p>Schematic of structure and characteristic sections.</p>
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<p>Field and instrument for flow field measurement.</p>
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<p>Leica P30 ground non-contact laser scanner. (<b>a</b>) The schematic of field operation, (<b>b</b>) Composition and principle of the instrument.</p>
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<p>The original point cloud model.</p>
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<p>Complete computational domain model.</p>
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<p>Schematic of the boundary conditions and grid division.</p>
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<p>Comparison of flow velocity values at test points on <span class="html-italic">l<sub>X</sub></span><sub>5-<span class="html-italic">Z</span>1</sub>.</p>
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<p>Cloud and vector plots of flow velocity distribution at section Z3 (Case1, Case3).</p>
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<p>Cloud and vector plots of flow velocity distribution in section <span class="html-italic">Z</span>3 (Case2, Case9, Case10).</p>
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<p>Distribution diagram of <span class="html-italic">X</span> axis average flow velocity in section <span class="html-italic">X</span>3.</p>
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<p>Distribution of sediment volume fraction near the bottom (Case2, Case3, Case9, Case10).</p>
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<p>Cloud and vector plots of flow velocity distribution in section <span class="html-italic">Z</span>3 (Case4~Case8).</p>
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<p>Distribution of sediment volume fraction near the bottom (Case4~Case8).</p>
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19 pages, 6656 KiB  
Article
Development and Application of a New Exponential Model for Hydraulic Conductivity with Depth of Rock Mass
by Zhi Dou, Xin Huang, Weifeng Wan, Feng Zeng and Chaoqi Wang
Water 2024, 16(5), 778; https://doi.org/10.3390/w16050778 - 5 Mar 2024
Cited by 1 | Viewed by 1360
Abstract
Hydraulic conductivity generally decreases with depth in the Earth’s crust. The hydraulic conductivity–depth relationship has been assessed through mathematical models, enabling predictions of hydraulic conductivity in depths beyond the reach of direct measurements. However, it is observed that beyond a certain depth, hydraulic [...] Read more.
Hydraulic conductivity generally decreases with depth in the Earth’s crust. The hydraulic conductivity–depth relationship has been assessed through mathematical models, enabling predictions of hydraulic conductivity in depths beyond the reach of direct measurements. However, it is observed that beyond a certain depth, hydraulic conductivity tends to stabilize; this phenomenon cannot be effectively characterized by the previous models. Thus, these models may make inaccurate predictions at deeper depths. In this work, we introduce an innovative exponential model to effectively assess the conductivity–depth relationship, particularly addressing the stabilization at greater depths. This model, in comparison with an earlier power-like model, has been applied to a globally sourced dataset encompassing a range of lithologies and geological structures. Results reveal that the proposed exponential model outperforms the power-like model in correctly representing the stabilized conductivity, and it well captures the fast stabilization effect of multiple datasets. Further, the proposed model has been utilized to analyze three distinct groups of datasets, revealing how lithology, geological stabilization, and faults impact the conductivity–depth relationship. The hydraulic conductivity decays to the residual hydraulic conductivity in the order (fast to slow): metamorphic rocks, sandstones, igneous rock, mudstones. The mean hydraulic conductivity in stable regions is roughly an order of magnitude lower than unstable regions. The faults showcase a dual role in both promoting and inhibiting hydraulic conductivity. The new exponential model has been successfully applied to a dataset from a specific engineering site to make predictions, demonstrating its practical usage. In the future, this model may serve as a potential tool for groundwater management, geothermal energy collection, pollutant transport, and other engineering projects. Full article
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<p>Sensitivity analysis of two models on log <span class="html-italic">K</span><sub>s</sub>:l (<b>a</b>) the proposed exponential model and (<b>b</b>) the power-like model.</p>
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<p>Topography and geomorphology of the project area.</p>
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<p>Display of specific datasets used for validation of sensitive analysis.</p>
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<p>Fitting effect diagrams: (<b>a</b>) fitting effect diagram of Dataset 1, (<b>b</b>) fitting effect diagram of Dataset 2.</p>
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<p>Distribution of hydraulic conductivities with depth in a depth range of 0–1600 m, (<b>a</b>) igneous rocks, (<b>b</b>) metamorphic rocks, (<b>c</b>) sedimentary rocks (sandstone).</p>
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<p>Distribution of hydraulic conductivities with depth in a depth range of 0–7000 m, (<b>a</b>) sandstone, and (<b>b</b>) mudstone.</p>
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<p>Distribution of decay rates with depth for igneous rocks, metamorphic rocks, sandstones, and mudstones.</p>
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<p>Relationship of parameters in the exponential model, (<b>a</b>) the Log <span class="html-italic">K</span><sub>s</sub>–Log <span class="html-italic">K</span><sub>r</sub> relationship, (<b>b</b>) the (Log <span class="html-italic">K</span><sub>s</sub>–Log <span class="html-italic">K</span><sub>r</sub>)–<span class="html-italic">α</span> relationship.</p>
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<p>Influence of geological stability on the distribution of hydraulic conductivities, (<b>a</b>) unstable geological region, (<b>b</b>) stable geological region.</p>
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<p>Influence of faults on the distribution of hydraulic conductivities, (<b>a</b>) faulted rock, which means faults cross rock, (<b>b</b>) non-faulted rock, which means no fault crosses rock.</p>
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<p>Variation of hydraulic conductivity with depth in the engineer project, (<b>a</b>) the general hydraulic conductivity distribution, (<b>b</b>) the low hydraulic conductivity distribution.</p>
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<p>Analysis of the exponential model prediction accuracy in the engineering project.</p>
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<p>Influence of faults on the distribution of hydraulic conductivities.</p>
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15 pages, 3602 KiB  
Article
Assessing Objective Functions in Streamflow Prediction Model Training Based on the Naïve Method
by Yongen Lin, Dagang Wang, Tao Jiang and Aiqing Kang
Water 2024, 16(5), 777; https://doi.org/10.3390/w16050777 - 5 Mar 2024
Cited by 2 | Viewed by 1396
Abstract
Reliable streamflow forecasting is a determining factor for water resource planning and flood control. To better understand the strengths and weaknesses of newly proposed methods in streamflow forecasting and facilitate comparisons of different research results, we test a simple, universal, and efficient benchmark [...] Read more.
Reliable streamflow forecasting is a determining factor for water resource planning and flood control. To better understand the strengths and weaknesses of newly proposed methods in streamflow forecasting and facilitate comparisons of different research results, we test a simple, universal, and efficient benchmark method, namely, the naïve method, for short-term streamflow prediction. Using the naïve method, we assess the streamflow forecasting performance of the long short-term memory models trained with different objective functions, including mean squared error (MSE), root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), and mean absolute error (MAE). The experiments over 273 watersheds show that the naïve method attains good forecasting performance (NSE > 0.5) in 88%, 65%, and 52% of watersheds at lead times of 1 day, 2 days, and 3 days, respectively. Through benchmarking by the naïve method, we find that the LSTM models trained with squared-error-based objective functions, i.e., MSE, RMSE, NSE, and KGE, perform poorly in low flow forecasting. This is because they are more influenced by training samples with high flows than by those with low flows during the model training process. For comprehensive short-term streamflow modeling without special demand orientation, we recommend the application of MAE instead of a squared-error-based metric as the objective function. In addition, it is also feasible to perform logarithmic transformation on the streamflow data. This work underscores the critical importance of appropriately selecting the objective functions for model training/calibration, shedding light on how to effectively evaluate the performance of streamflow forecast models. Full article
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<p>The spatial distribution of 273 hydrological stations used in this study.</p>
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<p>The daily streamflow forecasting performance of the naïve method under different lead times for the (<b>a</b>) entire dataset, (<b>b</b>) training set, and (<b>c</b>) testing sets, respectively.</p>
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<p>Performance differences in terms of metrics MAPE, NSE, KGE, α, and β between the LSTM models trained with different objective functions and the naïve method with 1-day forecasting lead time in the testing set. The <span class="html-italic">y</span>-axis represents the evaluation metric values of LSTM minus the evaluation metric values of the naïve method. For the <span class="html-italic">x</span>-axis tick labels, “Q” stands for streamflow. The accompanying subscript number denotes the range of streamflow percentile in ascending order. To facilitate better comparison between different boxes, the <span class="html-italic">y</span>-axis range of the figures is limited, resulting in some boxes not being fully visible.</p>
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<p>The comparisons between the prediction evaluations of the LSTM<sub>MSE</sub> and that of the naïve method at different forecasting lead times for the (<b>a</b>) training set and (<b>b</b>) testing set, respectively.</p>
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<p>The relationship between the amplitude index (AI) of streamflow series and the forecasting performance of the naïve method. The numbers in the top-right corners represent the Pearson correlation coefficients.</p>
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<p>(<b>a</b>) The influence of different training samples on the model training process of an LSTM model with MSE as its objective function. The red dots denote the samples that contributed 50% of the objective function, while the green dots denote the remaining samples. (<b>b</b>) The proportion of the contributions of the training samples with a streamflow within Q<sub>0–0.5</sub> bin on the training process of an LSTM model when applying MSE as the objective function.</p>
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<p>(<b>a</b>) The influence of different training samples on the model training process of an LSTM model with MAE as its objective function. The red dots denote the samples that contributed 50% of the objective function, while the green dots denote the remaining samples. (<b>b</b>) The proportion of the contributions of the training samples with a streamflow within Q<sub>0–0.5</sub> bin on the training process of an LSTM model when applying MAE as the objective function.</p>
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12 pages, 5872 KiB  
Article
Numerical Study of Pore Water Pressure in Frozen Soils during Moisture Migration
by Bicheng Zhou, Anatoly V. Brouchkov and Jiabo Hu
Water 2024, 16(5), 776; https://doi.org/10.3390/w16050776 - 5 Mar 2024
Cited by 1 | Viewed by 1448
Abstract
Frost heaving in soils is a primary cause of engineering failures in cold regions. Although extensive experimental and numerical research has focused on the deformation caused by frost heaving, there is a notable lack of numerical investigations into the critical underlying factor: pore [...] Read more.
Frost heaving in soils is a primary cause of engineering failures in cold regions. Although extensive experimental and numerical research has focused on the deformation caused by frost heaving, there is a notable lack of numerical investigations into the critical underlying factor: pore water pressure. This study aimed to experimentally determine changes in soil water content over time at various depths during unidirectional freezing and to model this process using a coupled hydrothermal approach. The agreement between experimental water content outcomes and numerical predictions validates the numerical method’s applicability. Furthermore, by applying the Gibbs free energy equation, we derived a novel equation for calculating the pore water pressure in saturated frozen soil. Utilizing this equation, we developed a numerical model to simulate pore water pressure and water movement in frozen soil, accounting for scenarios with and without ice lens formation and quantifying unfrozen water migration from unfrozen to frozen zones over time. Our findings reveal that pore water pressure decreases as freezing depth increases, reaching near zero at the freezing front. Notably, the presence of an ice lens significantly amplifies pore water pressure—approximately tenfold—compared to scenarios without an ice lens, aligning with existing experimental data. The model also indicates that the cold-end temperature sets the maximum pore water pressure value in freezing soil, with superior performance to Konrad’s model at lower temperatures in the absence of ice lenses. Additionally, as freezing progresses, the rate of water flow from the unfrozen region to the freezing fringe exhibits a fluctuating decline. This study successfully establishes a numerical model for pore water pressure and water flow in frozen soil, confirms its validity through experimental comparison, and introduces an improved formula for pore water pressure calculation, offering a more accurate reflection of the real-world phenomena than previous formulations. Full article
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<p>Microscopic schematic of soil (<b>a</b>) particle–ice–water and (<b>b</b>) particle–lens–water at the freezing fringe.</p>
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<p>(<b>a</b>)The schematic of a freezing soil column and (<b>b</b>) the diagram of the physical principle for calculating water flow.</p>
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<p>Experimental and model depth variation in frost front with time.</p>
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<p>Experimental and model water content variation with time at different heights. The freezing times are (<b>a</b>) 36 h, (<b>b</b>) 72 h, (<b>c</b>) 96 h, and (<b>d</b>) 120 h, respectively.</p>
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<p>Pore water pressure variation with time at different depths (temperature of cold end <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </semantics></math> °C). (<b>a</b>) Frozen soil with ice lens and (<b>b</b>) frozen soil without ice lens.</p>
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<p>Pore water pressure variation with time at different depths (temperature of cold end <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </semantics></math> °C). (<b>left</b>) Frozen soil with ice lens and (<b>right</b>) frozen soil without ice lens.</p>
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<p>Comparison of the results of this paper’s model with Konrad’s model (cold-end temperature <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </semantics></math> °C and freezing time 20 h).</p>
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<p>Variation in water flow (<b>a</b>) per unit of time and (<b>b</b>) total water flow with time.</p>
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23 pages, 7012 KiB  
Article
Sensor Fish Deployments at the Xayaburi Hydropower Plant: Measurements and Simulations
by Pedro Romero-Gomez, Thanasak Poomchaivej, Rajesh Razdan, Wayne Robinson, Rudolf Peyreder, Michael Raeder and Lee J. Baumgartner
Water 2024, 16(5), 775; https://doi.org/10.3390/w16050775 - 5 Mar 2024
Viewed by 1292
Abstract
Fish protection is a priority in regions with ongoing and planned development of hydropower production, like the Mekong River system. The evaluation of the effects of turbine passage on the survival of migratory fish is a primary task for informing hydropower plant operators [...] Read more.
Fish protection is a priority in regions with ongoing and planned development of hydropower production, like the Mekong River system. The evaluation of the effects of turbine passage on the survival of migratory fish is a primary task for informing hydropower plant operators and authorities about the environmental performance of plant operations. The present work characterizes low pressures and collision rates through the Kaplan-type runners of the Xayaburi hydropower station. Both an experimental method based on the deployment of Sensor Fish and a numerical strategy based on flow and passage simulations were implemented on the analysis of two release elevations at one operating point. Nadir pressures and pressure drops through the runner were very sensitive to release elevation, but collision rates on the runner were not. The latter showed a frequency of occurrence of 8.2–9.3%. Measured magnitudes validated the corresponding simulation outcomes in regard to the averaged magnitudes as well as to the variability. Central to this study is that simulations were conducted based on current industry practices for designing turbines. Therefore, the reported agreement helps turbine engineers gain certainty about the prediction power of flow and trajectory simulations for fish passage assessments. This can accelerate the development of environmentally enhanced technology with minimum impact on natural resources. Full article
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<p>A representation of the steps towards sustainable turbine design.</p>
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<p>A schematic representation of the SF passage at the moment that it approaches the leading edge of the runner blade.</p>
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<p>Absolute pressure variation during passage through the Kaplan turbine of Xayaburi HPP and SF release locations.</p>
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<p>Map showing location of the Xayaburi hydropower plant on the Mekong River in the Xayaburi Province of Lao PDR (<b>left</b>), as well as a perspective view of the tested Kaplan-type runner (not to scale) (<b>right</b>).</p>
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<p>(<b>a</b>) A CAD model of the Sensor Fish and (<b>b</b>) a photo of the Sensor Fish, taken from Deng et al. [<a href="#B14-water-16-00775" class="html-bibr">14</a>]. (<b>c</b>) A surrogate sensor fish was also fabricated and released during the preparation phase for deployments (see <a href="#sec3dot3-water-16-00775" class="html-sec">Section 3.3</a>).</p>
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<p>Assembly of access point to bring sensors into the intake flow stream.</p>
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<p>(<b>a</b>) The 3D geometric model of the Xayaburi Kaplan turbine and (<b>b</b>) the mesh on the solid runner walls.</p>
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<p>Location of Group A shows the points at which streamlines pass a crossing plane; Group B shows the physical locations of nadir pressures; and Group C shows the pressure at the distributor entrance, with which the pressure drop (<math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>P</mi> </mrow> </semantics></math>) could be calculated.</p>
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<p>Example of a Sensor Fish passage. The upper plot shows time series of absolute pressure (<math display="inline"><semantics> <msup> <mi>P</mi> <mo>∗</mo> </msup> </semantics></math> in Equation (<a href="#FD4-water-16-00775" class="html-disp-formula">4</a>)) and acceleration, whereas the bottom plot shows time series of absolute pressure and rotations. A close-up of the pressure time series during distributor and runner passage is shown in <a href="#water-16-00775-f010" class="html-fig">Figure 10</a>.</p>
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<p>Close-up of the pressure time series with a focus on distributor and runner passage. The inset shows corresponding computer-generated time series of pressure (<math display="inline"><semantics> <msup> <mi>P</mi> <mo>∗</mo> </msup> </semantics></math> in Equation (<a href="#FD4-water-16-00775" class="html-disp-formula">4</a>)) with CFD and streamlines, which assist in marking the instants where SF entered the distributor (T1), the runner (T2), and the draft tube (T3).</p>
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<p>Distributions of estimated release elevations based on pressure measurement at T0 for all releases.</p>
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<p>Cumulative distributions of both simulated (with CFD) and measured (with SF) nadir pressures are shown for both release elevations. Pressures were normalized according to Equation (<a href="#FD4-water-16-00775" class="html-disp-formula">4</a>).</p>
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<p>Nadir pressure points from top releases are shown on the left colored by pressure value (dimensionless), whereas the radial locations of nadir points from both releases are on the right.</p>
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<p>Cumulative distributions of both simulated (with CFD) and measured (with SF) <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>P</mi> </mrow> </semantics></math> (*, dimensionless form) are shown for both release elevations.</p>
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<p>On the left, outcomes of collision frequency from Sensor Fish (vertical dashed lines) and collision probability (distributions) from simulation-based estimates for both release elevations. On the right, all crossing points above the runner were estimated via simulations for both release elevations.</p>
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20 pages, 7930 KiB  
Article
An Improved One-Line Evolution Formulation for the Dynamic Shoreline Planforms of Embayed Beaches
by Hung-Cheng Tao, Tai-Wen Hsu and Chia-Ming Fan
Water 2024, 16(5), 774; https://doi.org/10.3390/w16050774 - 5 Mar 2024
Viewed by 1049
Abstract
In this paper, an improved one-line evolution formulation is proposed and derived for the dynamic shoreline planforms of embayed beaches. Although embayed sandy beaches can perform several functions, serving as leisure spots and areas of coastal protection, shoreline advances and retreats occur continuously [...] Read more.
In this paper, an improved one-line evolution formulation is proposed and derived for the dynamic shoreline planforms of embayed beaches. Although embayed sandy beaches can perform several functions, serving as leisure spots and areas of coastal protection, shoreline advances and retreats occur continuously as a result of many natural forces, such as winds, waves, currents, tides, etc. The one-line evolution formulation for dynamic shoreline planforms based on the polar coordinate can be adopted to simulate high-planform-curvature shorelines and achieve better stability and simplicity in comparison with other description coordinates. While the polar coordinate and rectangular control volume are adopted to derive the one-line evolution formulation for dynamic shoreline planforms, the difference between the radial direction of the polar coordinate and the normal direction of the shoreline segment may result in inaccurate predictions of shoreline movements. In this study, a correction coefficient, which can adjust the influence of these two misaligned directions, is derived and included in the one-line evolution formulation, which is based on the polar coordinate. Thus, by considering the correction coefficient, an improved one-line evolution formulation for dynamic shoreline planforms of crenulate-shaped bays is proposed in this paper. Some numerical examples are provided to verify the merits of the proposed improved one-line evolution formulation. Moreover, the proposed numerical approach is applied to simulate the dynamic movements of the shoreline in Taitung—the southeastern part of Taiwan—and the effectiveness of the proposed formulation in solving realistic engineering applications is evidently verified. Full article
(This article belongs to the Special Issue Advanced Research in Civil, Hydraulic, and Ocean Engineering)
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<p>A schematic diagram of the shoreline planform of crenulate-shaped bay.</p>
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<p>The control volume between two sequential time steps.</p>
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<p>The control volume defined in this study and its dimension.</p>
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<p>A schematic diagram of the different angles adopted in this study.</p>
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<p>A schematic of the numerical example for verification of consistency and stability.</p>
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<p>Numerical results in different specific time frames when using the proposed improved one-line model (<math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <mo>=</mo> <mn>223</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>∆</mo> <mi mathvariant="normal">t</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>).</p>
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<p>Numerical results and comparisons with Ref. [<a href="#B14-water-16-00774" class="html-bibr">14</a>] when using different ∆t for (<b>a</b>) m = 109, (<b>b</b>) m = 179 and (<b>c</b>) m = 223.</p>
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<p>Numerical results and comparisons with Ref. [<a href="#B14-water-16-00774" class="html-bibr">14</a>] when using different ∆t for (<b>a</b>) m = 109, (<b>b</b>) m = 179 and (<b>c</b>) m = 223.</p>
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<p>Numerical results and comparison of Example 2 [<a href="#B14-water-16-00774" class="html-bibr">14</a>,<a href="#B25-water-16-00774" class="html-bibr">25</a>].</p>
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<p>Changes in the shoreline near JinZum Island in the past years (2012~2021, TWD 97).</p>
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<p>The spatial position of the nine shoreline positions over the past years.</p>
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<p>Numerical solution from EEMSE for the wave field in nearshore area.</p>
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<p>Geometric definitions of the DEP in JinZum Island.</p>
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<p>Numerical results of DEP, GenCade, Tao et al. (2022) [<a href="#B14-water-16-00774" class="html-bibr">14</a>] and the present formulation for the shoreline planform in August 2013.</p>
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<p>Comparisons of numerical solutions along the shoreline portion between the control point and the JinZum Island [<a href="#B14-water-16-00774" class="html-bibr">14</a>].</p>
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<p>Numerical comparisons for the shoreline planform in the southern coastal area of the control point [<a href="#B14-water-16-00774" class="html-bibr">14</a>].</p>
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17 pages, 3138 KiB  
Article
Water Quality of Roof-Harvested Drinking Water Tanks in a Rural Area near a Gold and Copper Mine: Potential Health Risk from a Layer of Metal-Enriched Water and Sediment
by Ian A. Wright, Anna Christie and Amy-Marie Gilpin
Water 2024, 16(5), 773; https://doi.org/10.3390/w16050773 - 5 Mar 2024
Cited by 2 | Viewed by 2942
Abstract
This study investigated the drinking water quality of house water tanks that harvested roof runoff in a rural area surrounding a large copper and gold mine in Central Western New South Wales (NSW). Water was sampled from (1) the tops of water tanks, [...] Read more.
This study investigated the drinking water quality of house water tanks that harvested roof runoff in a rural area surrounding a large copper and gold mine in Central Western New South Wales (NSW). Water was sampled from (1) the tops of water tanks, (2) the bottoms of water tanks, and (3) kitchen taps. Water samples collected from the bottoms of tanks were turbid with suspended sediment. Concentrations of metals (lead, nickel, arsenic and manganese) from bottom-of-tank water samples often exceeded Australian drinking water guidelines. Overall, 37.2% of samples from bottoms of tanks exceeded arsenic guidelines (<10 µg L−1). The mean concentration of lead in water from bottoms of tanks was 695 µg L−1, with 18.6% of these samples exceeding lead guidelines (<10 µg L−1) by >100 times. Our results highlight the risk of contaminated water and sediment at the bottoms of tanks. Further investigation of private household drinking water tanks is recommended for properties in other rural areas, including areas with and without nearby mining activity. We describe a layer of contaminated water and sediment at the bottoms of water tanks, near the water outlet, which is a potential contamination pathway and substantial health risk for house water supplies. Full article
(This article belongs to the Section Water Quality and Contamination)
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Graphical abstract

Graphical abstract
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<p>Map of study area in Central Tablelands area of NSW showing the location of 42 properties (numbered) that were sampled in the study, using red- or green-coloured pin icons, coloured according to lead concentration in bottom-of-tank samples. The two largest urban centres (Bathurst and Orange) along with smaller settlements are identified. The Cadia Valley Operation gold and copper mine is circled in red. Most properties were located between Cadia and Millthorpe.</p>
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<p>Simplified layout of home water harvesting and storage systems in Cadia region. Collection of water samples from water tanks and house taps is shown as ‘X’. Water within water tanks were sampled from the ‘Top of Tank’, collected just below the upper surface of water in tank and from the ‘Bottom of Tank’, from the water/tank sediment layer at the bottom of the tank, often near the tank water supply outlet to the house. A water sample was also collected in the house (generally from the Kitchen Tap) from water supplied by the tank.</p>
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<p>Retrieving a ‘bottom-of-water tank sample from a Cadia district home water tank, using the PVC Biobailer, fastened by plastic tape to an aluminium sampling pole to reach the bottom of the tank. It contains a sediment-enriched water sample that was collected from the bottom of the tank (Photo: Fleur Connick).</p>
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<p>Mean metal (plus/minus standard error of mean) concentration, by sample location, for six metals/metalloids with health drinking water guidelines. (<b>a</b>) Cadmium, (<b>b</b>) Copper, (<b>c</b>) Nickel, (<b>d</b>) Manganese, (<b>e</b>) Arsenic, (<b>f</b>) Lead. The bar is red if the mean value exceeds the health guideline, and green if below the guideline. The dotted line indicates the maximum drinking water health guideline value for that metal/metalloid; see <a href="#water-16-00773-t002" class="html-table">Table 2</a> for details [<a href="#B4-water-16-00773" class="html-bibr">4</a>].</p>
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<p>Mean metal (plus/minus standard error of mean) concentration, by sample location, for six metals/metalloids with health drinking water guidelines. (<b>a</b>) Cadmium, (<b>b</b>) Copper, (<b>c</b>) Nickel, (<b>d</b>) Manganese, (<b>e</b>) Arsenic, (<b>f</b>) Lead. The bar is red if the mean value exceeds the health guideline, and green if below the guideline. The dotted line indicates the maximum drinking water health guideline value for that metal/metalloid; see <a href="#water-16-00773-t002" class="html-table">Table 2</a> for details [<a href="#B4-water-16-00773" class="html-bibr">4</a>].</p>
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<p>Mean metal (plus/minus standard error of mean) concentration, by sample location, for six metals/metalloids with health drinking water guidelines. (<b>a</b>) Cadmium, (<b>b</b>) Copper, (<b>c</b>) Nickel, (<b>d</b>) Manganese, (<b>e</b>) Arsenic, (<b>f</b>) Lead. The bar is red if the mean value exceeds the health guideline, and green if below the guideline. The dotted line indicates the maximum drinking water health guideline value for that metal/metalloid; see <a href="#water-16-00773-t002" class="html-table">Table 2</a> for details [<a href="#B4-water-16-00773" class="html-bibr">4</a>].</p>
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13 pages, 3213 KiB  
Article
Water Level Fluctuation Rather than Eutrophication Induced the Extinction of Submerged Plants in Guizhou’s Caohai Lake: Implications for Lake Management
by Fusheng Chao, Xin Jiang, Xin Wang, Bin Lu, Jiahui Liu and Pinhua Xia
Water 2024, 16(5), 772; https://doi.org/10.3390/w16050772 - 5 Mar 2024
Cited by 1 | Viewed by 1689
Abstract
The intensifying global decline in submerged aquatic lake plants is commonly attributed to lake eutrophication, while other drivers such as water levels are seldom considered. This study focused on the sudden extinction of the submerged plants in Caohai Lake, Guizhou, and employed long-term [...] Read more.
The intensifying global decline in submerged aquatic lake plants is commonly attributed to lake eutrophication, while other drivers such as water levels are seldom considered. This study focused on the sudden extinction of the submerged plants in Caohai Lake, Guizhou, and employed long-term data and a whole-lake water level manipulation experiment to explore the impacts of nutrients and water level changes on the decline in submerged plants. The results indicated that over the past 40 years, the total nitrogen and ammonia nitrogen in the water did not change significantly, while the total phosphorus showed a significant decreasing trend. In recent years, however, the water level rose. The biomass of submerged plants continuously increased until a sudden large-scale extinction occurred in 2021; chlorophyll a also significantly increased. It is speculated that the large-scale extinction of the submerged plants was caused by water level fluctuations rather than eutrophication. After the restoration of the natural hydrological regime of low water levels in winter and spring and high levels in summer and autumn, the submerged plants gradually recovered, with the biomass increasing to 922.6 g/m2 in 2023. The structural equation modeling indicated that the water depth and bottom light availability were the main drivers for the changes in the submerged plants. However, in lake protection and management, more attention is often paid to controlling nutrients, while other influencing factors are neglected. These findings confirm the importance of water levels in the decline in and restoration of submerged plants in shallow lakes, suggesting a focus on water level management in lake protection and aquatic vegetation restoration. Full article
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<p>Sampling sites in Caohai Lake. (<b>a</b>): For the years 2021 and 2022; and (<b>b</b>): for the year 2023.</p>
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<p>Changes in total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH<sub>3</sub>-N) in Caohai Lake (mean ± SE). The different letters (a, b, c) represent significant differences in the means based on the Kruskal–Wallis test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>a</b>): Annual and seasonal variations in water level; and (<b>b</b>): changes in bottom light availability (water depth (WD), Secchi depth (SD), and SD/WD).</p>
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<p>Changes in chlorophyll a concentration in Caohai Lake over recent years (2010–2023) (mean ± SE). The different letters represent significant differences in the means based on the Kruskal–Wallis test (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Piecewise structural equation model (SEM) exploring the relationships between WD, SD, SD/WD, Chl a, NH<sub>3</sub>-N, TP, and plant coverage. (<b>a</b>) The relationship between the variables during the first year (2022) of restoration in the entire lake. CHISQ = 11.388, <span class="html-italic">p</span> = 0.077, GFI = 0.995, CFI = 0.969, and SRMR = 0.067; (<b>b</b>) The relationship between the variables during the second year (2023) of restoration in the entire lake. CHISQ = 12.442, <span class="html-italic">p</span> = 0.087, GFI = 0.998, CFI = 0.976, and SRMR = 0.058. Solid and dashed arrows indicate significant (<span class="html-italic">p</span> &lt; 0.05) and nonsignificant (<span class="html-italic">p</span> &gt; 0.05) relationships, respectively. The red and blue lines represent positive and negative pathways, respectively. The arrow thickness is proportional to the strength of the relationship. The number above each arrow is the normalized path coefficient.</p>
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13 pages, 3221 KiB  
Article
Holocene Paleoclimate Records in Equatorial West Africa: Insights Based on the Characterization of Glycerol Dialkyl Glycerol Tetraethers
by Peining Yang, Shengyi Mao, Yiyun Cao, Li Liu, Mengyue Zhai, Zhongyan Qiu and Lihua Liu
Water 2024, 16(5), 771; https://doi.org/10.3390/w16050771 - 5 Mar 2024
Viewed by 1178
Abstract
One gravity core retrieved from the Niger Delta was used to explore the origin of deposited organic matter (OM) and the paleo-climatic and environmental conditions over the Holocene in equatorial West Africa. The geochemical properties of sediments including glycerol dialkyl glycerol tetraethers (GDGTs) [...] Read more.
One gravity core retrieved from the Niger Delta was used to explore the origin of deposited organic matter (OM) and the paleo-climatic and environmental conditions over the Holocene in equatorial West Africa. The geochemical properties of sediments including glycerol dialkyl glycerol tetraethers (GDGTs) and elemental (%OC, %N, C/N) and isotopic (δ13Corg, δ15N) signatures were determined. The determination constrained the age of the column and revealed that the sediment OM was mainly derived from a marine source. The isoprenoid (iso)GDGTs were the dominant GDGTs, with a small amount of branched (br)GDGTs, which led to a low-branched and isoprenoid tetraether index (BIT, 0.02–0.21) and represented a low terrestrial input. Most isoGDGTs and OH-GDGTs were produced in situ by Marine Group I (MG-I) Thaumarchaeota, while the brGDGTs were mainly transported from land. A two-endmember model quantified the contribution of terrestrial OM, as 0.9–19.9% by BIT and 1.1–32.6% by δ13C. Accordingly, the millennium-scale sea surface temperatures (SSTs) were reconstructed based on the cyclopentane ring distribution (TEX86H) and the ring index of OH-GDGTs (RI-OH). The top core SSTs were lower than the modern mean annual SST due to the growth season and habitat depth of Thaumarchaeota. The reconstructed SSTs clearly revealed the four stages of paleoclimate change, in particular, the drought episode of 8.2 kyr and the following humid period. The above research has enhanced our understanding of the paleoclimate change in river outflow during the Holocene at the millennium scale. Full article
(This article belongs to the Section Water Quality and Contamination)
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<p>Geological background of the Niger Delta. (<b>Left</b>): Locations of the discussed record and modern SST in the Gulf of Guinea; blue arrows indicate the Guinea Current (GC). The surface location of the Intertropical Convergence Zone (ICTZ) in northern winter (black dotted line) and northern summer (solid red line). Two green lines represent the main rivers that converge into the Gulf of Guinea (Niger River and Sanaga River). Plot modified from Ocean Data view. (<b>Right</b>): Niger River System and major cities; the blue dot represents the capital (modified from The Republic of Nigeria, West Africa).</p>
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<p>(<b>a</b>) Age–depth model and (<b>b</b>) sedimentation rate of core GC10 in the Niger fan.</p>
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<p>The profile of δ<sup>15</sup>N, TN (total nitrogen), δ<sup>13</sup>C, TOC (total organic carbon), and C/N (TOC/TN). The blue bar marks the 8.2 kyr BP, and the brown bar marks the end of the humid period. The blue dashed line divides the five main stages of bulk organic parameters.</p>
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<p>The contribution of (<b>a</b>) specific isoGDGTs (isoprenoid glycerol dialkyl glycerol tetraethers) and (<b>b</b>) brGDGTs (branched glycerol dialkyl glycerol tetraethers) and (<b>c</b>) the profiles of each type of GDGT in core GC10.</p>
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<p>SST (sea surface temperatures) reconstructed by various indices. The blue area indicates the surface temperature, and the brown bar marks the SST trends of the ~8.2 kry BP.</p>
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<p>(<b>a</b>) Profile of BIT (branched and isoprenoid tetraether index) and DC (degree of cyclization of brGDGTs) of the core GC10. (<b>b</b>) Percentage of soil organic carbon (%OC <sub>terr</sub>) or terrestrial organic carbon (%OC <sub>soil</sub>) based on a binary mixing model of δ<sup>13</sup>C and BIT.</p>
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<p>Distribution of GDGT-0/Cren, GDGT-2/Cren (GDGT-0–2, GDGTs with 0–2 cyclopentane rings; Cren, <span class="html-italic">Crenarchaeol</span>), %GDGT-2, and MI (Methane index).</p>
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22 pages, 2325 KiB  
Review
Survival and Development Strategies of Cyanobacteria through Akinete Formation and Germination in the Life Cycle
by Hye-In Ho, Chae-Hong Park, Kyeong-Eun Yoo, Nan-Young Kim and Soon-Jin Hwang
Water 2024, 16(5), 770; https://doi.org/10.3390/w16050770 - 4 Mar 2024
Cited by 1 | Viewed by 1607
Abstract
Eutrophic freshwater ecosystems are vulnerable to toxin-producing cyanobacteria growth or harmful algal blooms. Cyanobacteria belonging to the Nostocales order form akinetes that are similar to the seeds of vascular plants, which are resting cells surrounded by a thick membrane. They overwinter in sediment [...] Read more.
Eutrophic freshwater ecosystems are vulnerable to toxin-producing cyanobacteria growth or harmful algal blooms. Cyanobacteria belonging to the Nostocales order form akinetes that are similar to the seeds of vascular plants, which are resting cells surrounded by a thick membrane. They overwinter in sediment and germinate when conditions become favorable, eventually developing into vegetative cells and causing blooms. This review covers the cyanobacterial akinete of the Nostocales order and summarizes the environmental triggers and cellular responses involved in akinete germination and formation based on data from the literature. It also emphasizes the intimate and dynamic relationship that exists between the germination and formation of akinete in the annual life cycle of cyanobacteria. After comparing many published data, it is found that the tolerance ranges for factors affecting both akinete germination and formation do not differ significantly and are broadly consistent with the tolerance ranges for vegetative cell growth. However, the optimal range varies with different species and strains of cyanobacteria. The life cycle of cyanobacteria, as a result of akinete germination and formation, has a seasonal periodicity and spatial connectivity between the water column and the sediment. However, during the summer growing season, intimate coupling between akinete formation and germination can occur in the water column, and this can contribute to high population densities being maintained in the water column. During this time, shallow sediment could also provide suitable conditions for akinete germination, thereby contributing to the establishment of water column populations. The information summarized in this review is expected to help improve our shared understanding of the life cycle of the Nostocales cyanobacteria while also providing insights into the monitoring and management of harmful algal blooms. Full article
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<p>Graphic summary of temperature-dependent akinete germination and formation of cyanobacterial species from the Nostocales order. Numbers appearing on the right column are those in accordance with the list in the references. GR: akinete germination rate.</p>
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<p>Graphic summary of light-dependent akinete germination and formation of some cyanobacterial species from the Nostocales order. Numbers appearing on the right column are those in accordance with the list in the references. GR: akinete germination rate. FR: akinete formation rate.</p>
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<p>Graphic summary of nutrient-dependent akinete germination and formation of some cyanobacterial species from the Nostocales order. Numbers appearing on the right column are those in accordance with the list in the references. GR: akinete germination rate.</p>
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<p>Graphic summary of pH-dependent akinete germination of some cyanobacterial species from the Nostocales order. Numbers appearing on the right column are those in accordance with the list in the references. GR: akinete germination rate.</p>
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<p>Conceptual model of the proposed life cycle of <span class="html-italic">Dolichospurmum circinale</span> (Nostocales order) highlighting the role of akinetes in the summer season (coupling of formation and germination).</p>
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28 pages, 9615 KiB  
Article
Landscape-Scale Mining and Water Management in a Hyper-Arid Catchment: The Cuajone Mine, Moquegua, Southern Peru
by Morag Hunter, D. H. Nimalika Perera, Eustace P. G. Barnes, Hugo V. Lepage, Elias Escobedo-Pacheco, Noorhayati Idros, David Arvidsson-Shukur, Peter J. Newton, Luis de los Santos Valladares, Patrick A. Byrne and Crispin H. W. Barnes
Water 2024, 16(5), 769; https://doi.org/10.3390/w16050769 - 4 Mar 2024
Cited by 1 | Viewed by 2387
Abstract
The expansion of copper mining on the hyper-arid pacific slope of Southern Peru has precipitated growing concern for scarce water resources in the region. Located in the headwaters of the Torata river, in the department of Moquegua, the Cuajone mine, owned by Southern [...] Read more.
The expansion of copper mining on the hyper-arid pacific slope of Southern Peru has precipitated growing concern for scarce water resources in the region. Located in the headwaters of the Torata river, in the department of Moquegua, the Cuajone mine, owned by Southern Copper, provides a unique opportunity in a little-studied region to examine the relative impact of the landscape-scale mining on water resources in the region. Principal component and cluster analyses of the water chemistry data from 16 sites, collected over three seasons during 2017 and 2018, show distinct statistical groupings indicating that, above the settlement of Torata, water geochemistry is a function of chemical weathering processes acting upon underlying geological units, and confirming that the Cuajone mine does not significantly affect water quality in the Torata river. Impact mitigation strategies that firstly divert channel flow around the mine and secondly divert mine waste to the Toquepala river and tailings dam at Quebrada Honda remove the direct effects on the water quality in the Torata river for the foreseeable future. In the study area, our results further suggest that water quality has been more significantly impacted by urban effluents and agricultural runoff than the Cuajone mine. The increase in total dissolved solids in the waters of the lower catchment reflects the cumulative addition of dissolved ions through chemical weathering of the underlying geological units, supplemented by rapid recharge of surface waters contaminated by residues associated with agricultural and urban runoff through the porous alluvial aquifer. Concentrations in some of the major ions exceeded internationally recommended maxima for agricultural use, especially in the coastal region. Occasionally, arsenic and manganese contamination also reached unsafe levels for domestic consumption. In the lower catchment, below the Cuajone mine, data and multivariate analyses point to urban effluents and agricultural runoff rather than weathering of exposed rock units, natural or otherwise, as the main cause of contamination. Full article
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<p>Map of selected study site locations in the foothill and headwaters (site 0A, 40 km downstream from site 1 near Ilo, is not shown). Inset shows underlying geological units adapted from Decou et al. [<a href="#B39-water-16-00769" class="html-bibr">39</a>]. Geological lithologies coded by color: pink—Coastal Batholith (intrusive 145–155 Ma); buff—Moquegua Group (sedimentary 50–54 Ma); green—Cretaceous volcanics and Eocene intrusives; grey—Miocene to recent pyroclastic deposits. Study area shown in dashed box.</p>
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<p>Spatial and temporal variation in cations (<math display="inline"><semantics> <msup> <mrow> <mi mathvariant="normal">Na</mi> </mrow> <mo>+</mo> </msup> </semantics></math>, <math display="inline"><semantics> <msup> <mrow> <mi mathvariant="normal">Ca</mi> </mrow> <mrow> <mn>2</mn> <mo>+</mo> </mrow> </msup> </semantics></math>, <math display="inline"><semantics> <msup> <mrow> <mi mathvariant="normal">Mg</mi> </mrow> <mrow> <mn>2</mn> <mo>+</mo> </mrow> </msup> </semantics></math>, <math display="inline"><semantics> <msup> <mrow> <mi mathvariant="normal">K</mi> </mrow> <mo>+</mo> </msup> </semantics></math>), anions (<math display="inline"><semantics> <msubsup> <mrow> <mi mathvariant="normal">SO</mi> </mrow> <mn>4</mn> <mrow> <mn>2</mn> <mo>−</mo> </mrow> </msubsup> </semantics></math>, <math display="inline"><semantics> <msup> <mrow> <mi mathvariant="normal">Cl</mi> </mrow> <mo>−</mo> </msup> </semantics></math>, <math display="inline"><semantics> <msup> <mrow> <mi mathvariant="normal">F</mi> </mrow> <mo>−</mo> </msup> </semantics></math>, <math display="inline"><semantics> <msubsup> <mrow> <mi mathvariant="normal">NO</mi> </mrow> <mn>3</mn> <mo>−</mo> </msubsup> </semantics></math>) and total trace metals (Cd, Cu, Co, Cr, Li, Pb, Mo, Ni, Se, U, V, and Zn) in water samples from Moquegua river sites plotted against distance upstream from site 1 in km. (<b>a</b>) January 2017 (17-R), (<b>b</b>) July 2017 (17-D) and (<b>c</b>) January 2018 (18-R). Site numbers are given in the labels.</p>
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<p>(<b>a</b>) Na/Cl equivalent molar ratio and (<b>b</b>) SO<sub>4</sub>/Cl equivalent molar ratio for Moquegua river system.</p>
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<p>Al, Fe, and Mn concentrations and pH, showing elevated wet season metal concentrations in the Torata river below the Cuajone mine. Distance is measured upstream from site 1. Red shaded regions show the concentration levels above the safe limits for aquatic life. The Cuajone mine is located between site 5B and site 16 (vertical shaded region). Error bars represent standard deviation of measurement fluctuation. Labels represent site numbers.</p>
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<p>(<b>a</b>) Gibbs plot for the Moquegua river showing changes in water chemistry along the river and (<b>b</b>) compared with other major rivers, Hauang He river, China and Amazon river, rio Grande, AB, USA, and Amazon river, South America. Data from [<a href="#B70-water-16-00769" class="html-bibr">70</a>,<a href="#B71-water-16-00769" class="html-bibr">71</a>].</p>
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<p>Arsenic concentrations in samples from Torata and Moquegua river site. Error bars show the standard deviations of measurement. Red-shaded regions show the concentration levels above the safe limits for potable use. The Cuajone mine is located between site 5B and site 16 (vertical shaded region).</p>
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<p>(<b>a</b>) Dendrogram showing the Euclidean dissimilarity between the 33 measured water parameters taking 17 Torata–Moquegua sample sites and seasons 17-R, 17-D, and 18-R into account. (<b>b</b>) Dendrogram showing the largest dissimilarity between the 17 Torata–Moquegua sample sites in different seasons (identified by the code [SITE]/[YEAR]–[SEASON]), taking 33 measured water parameters into account. (<b>c</b>) Beck map showing the spatial distribution of site clusters. The color map is the same as in (<b>b</b>). The color of the outer ring of each circle identifies the season using the key on the right.</p>
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<p>PCA biplots using 33 water parameters measured at 17 Torata–Moquegua sample sites in the three seasons 17-R, 17-D and 18-R. The colors of the parameter vectors are taken from <a href="#water-16-00769-f007" class="html-fig">Figure 7</a>a. The colors for site points are taken from <a href="#water-16-00769-f007" class="html-fig">Figure 7</a>b,c. (<b>a</b>) PC1 vs. PC2, (<b>b</b>) PC1 vs. PC3. Dashed lines at ±0.2 indicate the qualitative boundary used to indicate where components become significant at a site.</p>
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<p>Combined data sources from field work, ANA data and INGEMMET groundwater reports [<a href="#B5-water-16-00769" class="html-bibr">5</a>,<a href="#B88-water-16-00769" class="html-bibr">88</a>,<a href="#B89-water-16-00769" class="html-bibr">89</a>,<a href="#B90-water-16-00769" class="html-bibr">90</a>]. Concentration of major ions (<b>a</b>) calcium, (<b>b</b>) sodium, and (<b>c</b>) sulphate in the Torata river system.</p>
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<p>Combined data sources from field work, ANA data and INGEMMET groundwater report [<a href="#B5-water-16-00769" class="html-bibr">5</a>,<a href="#B88-water-16-00769" class="html-bibr">88</a>,<a href="#B89-water-16-00769" class="html-bibr">89</a>,<a href="#B90-water-16-00769" class="html-bibr">90</a>]. Concentration of trace elements in the Torata river system. The red dashed line in panel (<b>a</b>) indicates the safe limit for arsenic. The safety limits for cadmium and copper concentrations do not appear in panels (<b>b</b>,<b>c</b>), as they are above every data point. The green dotted lines in panel (<b>a</b>) delimit the area of intensive agricultural land ranging from above Torata to below Moquegua. The red circle in panel (<b>a</b>) identifies data taken at Site O. The blue box in panel (<b>c</b>) outlines data that were taken after a flash flood event.</p>
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<p>Black polygon: area of direct mining and mine related infrastructural impact. Red polygons: upper red polygon = Southern Copper Cuajone mine, middle red polygon = Anglo American Quellaveco mine, lower red polygon = Southern Copper Toquepala mine. Yellow lines: mine waste channels. Blue polygons: Cortaderas 2 and Quebrada Honda tailings dams. Purple line: railway line to Ilo smelter. Pink polygon: Ilo smelter. White lines: Pasto Grande project irrigation canals (tunnels, concrete-lined channels, and existing river channels). Green polygon: AH Pampa Sitana irrigation project.</p>
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17 pages, 3030 KiB  
Article
Source Apportionment and Health Risk Assessment of Groundwater Potentially Toxic Elements (PTEs) Pollution Characteristics in an Accident Site in Zhangqiu, China
by Min Wang, Xiaoyu Song, Yu Han, Guantao Ding, Ruilin Zhang, Shanming Wei, Shuai Gao and Yuxiang Liu
Water 2024, 16(5), 768; https://doi.org/10.3390/w16050768 - 4 Mar 2024
Cited by 1 | Viewed by 1162
Abstract
In order to understand the pollution degree and source of potentially toxic elements (PTEs) in groundwater around the accident site and evaluate their harm to human health, 22 groundwater samples were collected around the accident well, and the contents of As, Cd, Cr, [...] Read more.
In order to understand the pollution degree and source of potentially toxic elements (PTEs) in groundwater around the accident site and evaluate their harm to human health, 22 groundwater samples were collected around the accident well, and the contents of As, Cd, Cr, Cu, Hg, Ni, Pb, Zn, CH2Cl2 and C2H4Cl2 were determined. On the basis of water quality evaluation, the source apportionment method combining qualitative and quantitative analysis was used to determine the main sources of PTEs in the region, and the health risk assessment model was used to evaluate the health risk of PTEs to the human body. The results show that pH, TDS, Th and COD all exceed the standard to varying degrees, among which TH is the index with the largest number exceeding the standard. The quality of the groundwater environment in the study area is at a very poor level, and the F value is between 7.25 and 8.49. The exposure results model showed that there was no non-carcinogenic risk of PTEs in the study area, and the health risk of oral intake in the exposed population was greater than that of skin contact. Compared with adults, children were more vulnerable to the health risk stress of PTEs in groundwater. The total carcinogenic risk is higher than the total non-carcinogenic risk. As, Cd and Cr are the primary factors causing carcinogenic health risks in this area. Principal component analysis (PCA) was used to analyze the sources of PTEs in groundwater, and three principal components were extracted. It was preliminarily determined that PTE pollution was mainly related to agricultural sources, anthropogenic industrial sources and industrial sedimentation sources. The results of positive definite factor matrix analysis (PMF) were basically similar to those of PCA, but PMF further clarified the contribution rate of three pollution sources, among which agricultural sources contributed the most to the accumulation of PTEs. Full article
(This article belongs to the Topic Human Impact on Groundwater Environment)
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<p>The schematic diagram of sampling points in the study area.</p>
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<p>Distribution map of PTE content in groundwater.</p>
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<p>Correlation analysis matrix.</p>
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<p>Analytical results of PMF model.</p>
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<p>The proportion of PTE factors in groundwater.</p>
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<p>Contribution rate distribution diagram of sources.</p>
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13 pages, 1865 KiB  
Article
Adsorption Removal Characteristics of Hazardous Metalloids (Antimony and Arsenic) According to Their Ionic Properties
by Seung-Hun Lee, Jinwook Chung and Yong-Woo Lee
Water 2024, 16(5), 767; https://doi.org/10.3390/w16050767 - 4 Mar 2024
Viewed by 1235
Abstract
Antimony and arsenic, which have a high carcinogenicity, should be removed depending on their ionic charge in water. Therefore, we attempted to confirm the adsorption characteristics of antimony and arsenic considering ionic charge to improve removal efficiency. We used palm-based activated carbon (PAC), [...] Read more.
Antimony and arsenic, which have a high carcinogenicity, should be removed depending on their ionic charge in water. Therefore, we attempted to confirm the adsorption characteristics of antimony and arsenic considering ionic charge to improve removal efficiency. We used palm-based activated carbon (PAC), coal-based activated carbon (CAC), modified activated carbon (MAC), styrene-divinylbenzene copolymer (SP825), activated alumina (AA), and zeolite as adsorbents for antimony and arsenic. Negatively charged adsorbents (CAC, PAC, MAC, and zeolite) with similar zeta potentials showed better removal efficiency as the surface area increased. However, SP825, which is almost neutral, and AA, which is positively charged, exhibited a high removal efficiency (100%) for arsenic and Sb(V), which are anions, regardless of surface area. However, due to the price, coal-based activated carbon or palm-based activated carbon is considered more advantageous than using AA or SP825. Last, during the arsenic adsorption process, As(III) was oxidized to As(V) due to Fe(II) contained in activated carbon. The addition of activated carbon can improve oxidation efficiencies of As(III) before coagulation and precipitation, in which As(V) is easier to remove than As(III). Full article
(This article belongs to the Section Water Quality and Contamination)
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<p>Calibration curve ((<b>a</b>) Sb(III), (<b>b</b>) Total Sb, (<b>c</b>) As(III), (<b>d</b>) Total As).</p>
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<p>Removal efficiency of Sb by contact time ((<b>a</b>) Total Sb, (<b>b</b>) Sb(III), (<b>c</b>) Sb(V)).</p>
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<p>Removal efficiency of As by contact time ((<b>a</b>) Total As, (<b>b</b>) As(III), (<b>c</b>) As(V)).</p>
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<p>Change in iron ions in As solution ((<b>a</b>) As(III), (<b>b</b>) As(V)).</p>
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22 pages, 15310 KiB  
Article
The Applicability of the Drought Index and Analysis of Spatiotemporal Evolution Mechanisms of Drought in the Poyang Lake Basin
by Zihan Gui, Heshuai Qi, Faliang Gui, Baoxian Zheng, Shiwu Wang and Hua Bai
Water 2024, 16(5), 766; https://doi.org/10.3390/w16050766 - 4 Mar 2024
Viewed by 1349
Abstract
Poyang Lake, the largest freshwater lake in China, is an important regional water resource and a landmark ecosystem. In recent years, it has experienced a period of prolonged drought. Using appropriate drought indices to describe the drought characteristics of the Poyang Lake Basin [...] Read more.
Poyang Lake, the largest freshwater lake in China, is an important regional water resource and a landmark ecosystem. In recent years, it has experienced a period of prolonged drought. Using appropriate drought indices to describe the drought characteristics of the Poyang Lake Basin (PLB) is of great practical significance in the face of severe drought situations. This article explores the applicability of four drought indices (including the precipitation anomaly index (PJP), standardized precipitation index (SPI), China Z-index (CPZI), and standardized precipitation evapotranspiration index (SPEI)) based on historical facts. A systematic study was conducted on the spatiotemporal evolution patterns of meteorological drought in the PLB based on the optimal drought index. The results show that SPI is more suitable for the description of drought characteristics in the PLB. Meteorological droughts occur frequently in the summer and autumn in the PLB, with the frequency of mild drought being 17.29% and 16.88%, respectively. The impact range of severe drought or worse reached 22.19% and 28.33% of the entire basin, respectively. The probability of drought occurrence in the PLB shows an increasing trend in spring, while in most areas, it shows a decreasing trend in other seasons, with only a slight increase in the upper reaches of the Ganjiang River (UGR). One of the important factors influencing drought in the PLB is atmospheric circulation. The abnormal variation of the Western Pacific Subtropical High was one of the key factors contributing to the severe drought in the PLB in 2022. This study is based on a long-term series of meteorological data and selects the drought index for the PLB. It describes the spatiotemporal distribution characteristics and evolution patterns of drought and investigates the developmental path and influencing factors of drought in typical years. This study provides a reliable scientific basis for similar watershed water resource management. Full article
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<p>Water resource zoning map of the Poyang Lake Basin.</p>
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<p>Comparison of drought index in each sub-basin in 2003: (<b>a</b>) UGR; (<b>b</b>) MGR; (<b>c</b>) LGR; (<b>d</b>) FR; (<b>e</b>) XJR; (<b>f</b>) RR; (<b>g</b>) XR; (<b>h</b>) PYL.</p>
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<p>Comparison of drought index in each sub-basin in 2019: (<b>a</b>) UGR; (<b>b</b>) MGR; (<b>c</b>) LGR; (<b>d</b>) FR; (<b>e</b>) XJR; (<b>f</b>) RR; (<b>g</b>) XR; (<b>h</b>) PYL.</p>
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<p>Radar map of drought occurrence probability at different time scales in each sub-watershed.</p>
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<p>Annual frequency of drought in each sub-basin of Poyang Lake.</p>
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<p>Spatial distribution of drought frequency above severe severity at different time scales in Poyang Lake Basin: (<b>a</b>) spring; (<b>b</b>) summer; (<b>c</b>) autumn; (<b>d</b>) winter; (<b>e</b>) annual scale; (<b>f</b>) monthly scale.</p>
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<p>Assessment results of regional meteorological drought in Poyang Lake at different time scales.</p>
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<p>Drought trends at different time scales in Poyang Lake Basin: (<b>a</b>) spring; (<b>b</b>) summer; (<b>c</b>) autumn; (<b>d</b>) winter; (<b>e</b>) annual scale; (<b>f</b>) monthly scale.</p>
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<p>Drought trends at different time scales in Poyang Lake Basin: (<b>a</b>) spring; (<b>b</b>) summer; (<b>c</b>) autumn; (<b>d</b>) winter; (<b>e</b>) annual scale; (<b>f</b>) monthly scale.</p>
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<p>Temporal and spatial distribution characteristics of meteorological drought in Poyang Lake Basin in 2022: (<b>a</b>) January; (<b>b</b>) February; (<b>c</b>) March; (<b>d</b>) April; (<b>e</b>) May; (<b>f</b>) June; (<b>g</b>) July; (<b>h</b>) August; (<b>i</b>) September; (<b>j</b>) October; (<b>k</b>) November; (<b>l</b>) December.</p>
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<p>Temporal and spatial distribution characteristics of meteorological drought in Poyang Lake Basin in 2022: (<b>a</b>) January; (<b>b</b>) February; (<b>c</b>) March; (<b>d</b>) April; (<b>e</b>) May; (<b>f</b>) June; (<b>g</b>) July; (<b>h</b>) August; (<b>i</b>) September; (<b>j</b>) October; (<b>k</b>) November; (<b>l</b>) December.</p>
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<p>Development path of drought in a typical year (2022).</p>
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<p>Correlation of drought center location, SPI, and WPSH index in typical years (The asterisk of the correlation coefficient indicates that the correlation is more significant. “*” represents <span class="html-italic">p</span> &lt; 0.05, and “***” represent <span class="html-italic">p</span> &lt; 0.001.).</p>
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15 pages, 4289 KiB  
Article
Flood Forecasting Method and Application Based on Informer Model
by Yiyuan Xu, Jianhui Zhao, Biao Wan, Jinhua Cai and Jun Wan
Water 2024, 16(5), 765; https://doi.org/10.3390/w16050765 - 4 Mar 2024
Cited by 2 | Viewed by 2098
Abstract
Flood forecasting helps anticipate floods and evacuate people, but due to the access of a large number of data acquisition devices, the explosive growth of multidimensional data and the increasingly demanding prediction accuracy, classical parameter models, and traditional machine learning algorithms are unable [...] Read more.
Flood forecasting helps anticipate floods and evacuate people, but due to the access of a large number of data acquisition devices, the explosive growth of multidimensional data and the increasingly demanding prediction accuracy, classical parameter models, and traditional machine learning algorithms are unable to meet the high efficiency and high precision requirements of prediction tasks. In recent years, deep learning algorithms represented by convolutional neural networks, recurrent neural networks and Informer models have achieved fruitful results in time series prediction tasks. The Informer model is used to predict the flood flow of the reservoir. At the same time, the prediction results are compared with the prediction results of the traditional method and the LSTM model, and how to apply the Informer model in the field of flood prediction to improve the accuracy of flood prediction is studied. The data of 28 floods in the Wan’an Reservoir control basin from May 2014 to June 2020 were used, with areal rainfall in five subzones and outflow from two reservoirs as inputs and flood processes with different sequence lengths as outputs. The results show that the Informer model has good accuracy and applicability in flood forecasting. In the flood forecasting with a sequence length of 4, 5 and 6, Informer has higher prediction accuracy, and the prediction accuracy is better than other models under the same sequence length, but the prediction accuracy will decline to a certain extent with the increase in sequence length. The Informer model stably predicts the flood peak better, and its average flood peak difference and average maximum flood peak difference are the smallest. As the length of the sequence increases, the number of fields with a maximum flood peak difference less than 15% increases, and the maximum flood peak difference decreases. Therefore, the Informer model can be used as one of the better flood forecasting methods, and it provides a new forecasting method and scientific decision-making basis for reservoir flood control. Full article
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Figure 1

Figure 1
<p>The flow chart of the proposed approach for forecasting.</p>
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<p>Conceptual diagram of Informer.</p>
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<p>Map of the Wan’an Reservoir basin.</p>
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<p>No. 1 Flood rainfall flow chart (Informer).</p>
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<p>No. 2 Flood rainfall flow chart (Informer).</p>
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<p>Prediction results indicator bar chart (prediction set). (<b>a</b>) NSE bar chart, (<b>b</b>) R<sup>2</sup> bar chart, (<b>c</b>) RMSE bar chart, (<b>d</b>) RE bar chart.</p>
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<p>Prediction results indicator bar chart. (<b>a</b>) NSE bar chart, (<b>b</b>) R<sup>2</sup> bar chart, (<b>c</b>) RMSE bar chart, (<b>d</b>) RE bar chart.</p>
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<p>Prediction results indicator bar chart. (<b>a</b>) NSE bar chart, (<b>b</b>) R<sup>2</sup> bar chart, (<b>c</b>) RMSE bar chart, (<b>d</b>) RE bar chart.</p>
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<p>Histogram of hydrographic indicators bar chart. (<b>a</b>) flood peak difference values less than 15%, (<b>b</b>) total flood difference less than 15%, (<b>c</b>) NSE more than 0.8, (<b>d</b>) max flood peak gap.</p>
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19 pages, 5537 KiB  
Article
Time Series Analysis of Water Quality Factors Enhancing Harmful Algal Blooms (HABs): A Study Integrating In-Situ and Satellite Data, Vaal Dam, South Africa
by Altayeb A. Obaid, Elhadi M. Adam, K. Adem Ali and Tamiru A. Abiye
Water 2024, 16(5), 764; https://doi.org/10.3390/w16050764 - 3 Mar 2024
Viewed by 1944
Abstract
The Vaal Dam catchment, which is the source of potable water for Gauteng province, is characterized by diverse human activities, and the dam encounters significant nutrient input from multiple sources within its catchment. As a result, there has been a rise in Harmful [...] Read more.
The Vaal Dam catchment, which is the source of potable water for Gauteng province, is characterized by diverse human activities, and the dam encounters significant nutrient input from multiple sources within its catchment. As a result, there has been a rise in Harmful Algal Blooms (HABs) within the reservoir of the dam. In this study, we employed time series analysis on nutrient data to explore the relationship between HABs, using chlorophyll-a (Chl−a) as a proxy, and nutrient levels. Additionally, Chl−a data extracted from Landsat-8 satellite images was utilized to visualize the spatial distribution of HABs in the reservoir. Our findings revealed that HAB productivity in the Vaal Dam is influenced by the levels of total phosphorus (TP) and organic nitrogen (KJEL_N), which exhibited a positive correlation with chlorophyll-a (Chl−a) concentration. Long-term analysis of Chl−a in-situ data (1986–2022) collected at a specific point within the reservoir showed an average concentration of 11.25 μg/L. However, on certain stochastic dates, Chl−a concentration spiked to very high values, reaching a maximum of 452.8 μg/L, coinciding with elevated records of TP and KJEL_N concentrations on those dates, indicating their effect on productivity levels. The decadal time series and trend analysis demonstrated an increasing trend in Chl−a productivity over the studied period, rising from 4.75 μg/L in the first decade (1990–2000) to 10.51 μg/L in the second decade (2000–2010), and reaching 16.7 μg/L in the last decade (2010–2020). The rising averages of the decadal values were associated with increasing decadal averages of its driving factors, TP from 0.1043 to 0.1096 to 0.1119 mg/L for the three decades, respectively, and KJEL_N from 0.80 mg/L in the first decade to 1.14 mg/L in the last decade. Satellite data analysis during the last decade revealed that the spatial dynamics of HABs are influenced by the dam’s geometry and the levels of discharge from its two feeding rivers, with higher concentrations observed in meandering areas of the reservoir and within zones of restricted water circulation. Full article
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<p>The location map of the Vaal Dam.</p>
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<p>Frequency distribution plots of targeted water quality parameters in the Vaal Dam: (<b>a</b>) Temperature; (<b>b</b>) Dissolved oxygen; (<b>c</b>) Chlorophyll-a; (<b>d</b>) Total phosphorus; (<b>e</b>) Organic nitrogen; (<b>f</b>) Ammonia; and (<b>g</b>) Nitrate and Nitrite. The extreme values falling outside whiskers of the boxplots represented by black dots.</p>
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<p>Frequency distribution plots of targeted water quality parameters in the Vaal Dam: (<b>a</b>) Temperature; (<b>b</b>) Dissolved oxygen; (<b>c</b>) Chlorophyll-a; (<b>d</b>) Total phosphorus; (<b>e</b>) Organic nitrogen; (<b>f</b>) Ammonia; and (<b>g</b>) Nitrate and Nitrite. The extreme values falling outside whiskers of the boxplots represented by black dots.</p>
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<p>Time series of the targeted water quality parameters in the Vaal Dam.</p>
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<p>Decadal time series of the targeted water quality parameters in the Vaal Dam (<b>a</b>) first decade 1990–2000, (<b>b</b>) second decade 2000–2010, and (<b>c</b>) third decade 2010–2020.</p>
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<p>Decadal time series of the targeted water quality parameters in the Vaal Dam (<b>a</b>) first decade 1990–2000, (<b>b</b>) second decade 2000–2010, and (<b>c</b>) third decade 2010–2020.</p>
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<p>Decadal trends of Chl−a, TP, DO, NO<sub>3</sub>NO<sub>2</sub>_N, NH<sub>4</sub>_N, KJEL_N, and Temperature in the Vaal Dam. (<b>a</b>–<b>c</b>) showing the decadal trends for the first, second, and third decades respectively.</p>
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<p>Decadal trends of Chl−a, TP, DO, NO<sub>3</sub>NO<sub>2</sub>_N, NH<sub>4</sub>_N, KJEL_N, and Temperature in the Vaal Dam. (<b>a</b>–<b>c</b>) showing the decadal trends for the first, second, and third decades respectively.</p>
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<p>Scatter plots between Chl−a and various water quality parameters; (<b>a</b>) Chl−a vs. TP; (<b>b</b>) Chl−a vs. KJEL_N; (<b>c</b>) Chl−a vs. temperature; (<b>d</b>) Chl−a vs. DO; (<b>e</b>) Chl−a vs. NO<sub>3</sub>NO<sub>2</sub>; and (<b>f</b>) Chl−a vs. NH<sub>4</sub>. The trend lines (red) showing the relationship between the two variables composing the scatter plot.</p>
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<p>Productivity in the Vaal Dam between 2013 and 2023 from Landsat.</p>
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<p>Productivity in the Vaal Dam between 2013 and 2023 from Landsat.</p>
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