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Water, Volume 14, Issue 23 (December-1 2022) – 191 articles

Cover Story (view full-size image): The Yellow River Delta is one of the largest deltaic systems on Earth. The harsh hydrological environment causes soil salinization which hinders agriculture development in this coastal area of East China. Nitrogen fertilizer, particularly urea, has been used to improve salt-affected soil fertility for thirty years in China. Excess fertilization application may lead to secondary salinization, water contamination, and nutritional imbalance. Reasonable control of fertilizer application can not only increase crop yield but also reduce environmental pollution. In our study, the nitrogen content of wheat shoots and roots was mainly affected by N addition usage, and the 270 kg/ha N addition treatment was the optimal usage. Rational nitrogen fertilizer application is useful for saving resources and protecting the coastal ecological environment. View this paper
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15 pages, 1911 KiB  
Article
The Role of Habitat Protection in Maintaining the Diversity of Aquatic Fauna in Rural and Industrial Areas
by Anna Cieplok, Mariola Krodkiewska, Izabella Franiel, Rafał Starzak, Martina Sowa and Aneta Spyra
Water 2022, 14(23), 3983; https://doi.org/10.3390/w14233983 - 6 Dec 2022
Cited by 1 | Viewed by 2175
Abstract
In Natural Landscape Complexes and Ecological Sites, local environmental protection is used to cover previous industrial activities, fragments of the cultural landscape, and habitats of both vertebrates and invertebrates. In water bodies within the different types of habitat protection, aquatic invertebrate fauna was [...] Read more.
In Natural Landscape Complexes and Ecological Sites, local environmental protection is used to cover previous industrial activities, fragments of the cultural landscape, and habitats of both vertebrates and invertebrates. In water bodies within the different types of habitat protection, aquatic invertebrate fauna was studied to investigate whether it is a general rule that different forms of protection ensure the diversity of aquatic invertebrates in rural and industrial areas. The research revealed differences between invertebrate assemblages within complexes and between reservoirs. Compared with unprotected reservoirs located in the same area, in the majority of the studied water bodies, either no alien species were found or their relative abundance in assemblages was very low. Significant differences in the density, the number of taxa, the diversity, and the percentage of alien species were observed between different geographical locations. The location of water bodies within the protected area plays an important role in maintaining benthos diversity in industrial areas. These findings are useful for comparison with those of future research to document possible improvements or ongoing ecological regression in the quality of aquatic ecosystems in industrial areas. This study can help guide revisions of protected habitat networks for adequate protection of freshwater biodiversity in industrial areas. Full article
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<p>Location of the studied water bodies in the five protected complexes in Poland; 1–13 water bodies studied.</p>
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<p>Similarities in benthos communities in water bodies from different habitat protections; the “Kocia Góra” Natural Landscape Complex and the “Stawy Pluderskie” Ecological Site (no. 1 to no. 5), the “Szopienice-Borki” Natural Landscape Complex (no. 6 and 7), the “Paprocany” Ecological Site (no. 8–12), the “Pogoria II” Ecological Site (no. 13); a—spring, b—summer, c—autumn.</p>
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<p>The mean density of benthic fauna (<b>A</b>), the mean number of macroinvertebrate taxa (<b>B</b>), mean value of the Shannon–Wiener index (<b>C</b>), and the mean proportion of alien species in benthic fauna (<b>D</b>) in water bodies from different geographical locations.</p>
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18 pages, 5678 KiB  
Article
Analytical Method for Groundwater Seepage through and Beneath a Fully Penetrating Cut-off Wall Considering Effects of Wall Permeability and Thickness
by Jinling Mei, Hong Cao, Guanyong Luo and Hong Pan
Water 2022, 14(23), 3982; https://doi.org/10.3390/w14233982 - 6 Dec 2022
Cited by 3 | Viewed by 3548
Abstract
A fully penetrating cut-off wall is a vertical seepage barrier that fully penetrates an aquifer and is embedded in an underlying aquitard to a certain depth. Groundwater seepage with this type of wall occurs through three paths: leakage through the body of the [...] Read more.
A fully penetrating cut-off wall is a vertical seepage barrier that fully penetrates an aquifer and is embedded in an underlying aquitard to a certain depth. Groundwater seepage with this type of wall occurs through three paths: leakage through the body of the wall in the aquifer, leakage through the body of the wall embedded in the aquitard, and seepage under the wall. Seepage through the first path can be simply treated as one-dimensional flow. However, due to the mutual influence of seepage through the latter two paths, the seepage problem is complicated and still needs to be studied. An analytical method is proposed to solve this problem in this study. Mathematic expressions for flow rate and head value are obtained by superposition of drawdowns of two exact models, namely, the model with only leakage through the wall body and the model with only seepage under the wall, respectively. Exact solutions are quoted or derived for the exact models, but they involve Legendre’s elliptic integrals of the first and third kinds. To facilitate an engineering application, approximate models of the exact models are introduced and their solutions are applied to the analytical formulas. The accuracy and applicability of the proposed method are verified compared with the numerical method. The proposed method provides a simple but effective method for quickly estimating the quantity of seepage in the aquitard (including leakage through the wall body and seepage under the wall) when simultaneously considering the effects of wall permeability and thickness. Full article
(This article belongs to the Special Issue Flow and Transport Processes in Groundwater Systems)
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Figure 1
<p>Seepage problems with two types of cut-off walls: (<b>a</b>) partially penetrating cut-off wall; (<b>b</b>) fully penetrating cut-off wall; and (<b>c</b>) seepage model in the aquitard.</p>
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<p>Two exact models: (<b>a</b>) model with only seepage under the wall; (<b>b</b>) model with only leakage through the wall body; and (<b>c</b>) equivalent model of (<b>b</b>).</p>
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<p>(<b>a</b>,<b>b</b>) are the approximate models of the exact models in <a href="#water-14-03982-f002" class="html-fig">Figure 2</a>a and <a href="#water-14-03982-f002" class="html-fig">Figure 2</a>c, respectively, when <span class="html-italic">w</span>/<span class="html-italic">d</span> and <span class="html-italic">w’</span>/<span class="html-italic">s</span> are sufficiently large.</p>
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<p>SCT mapping from the <span class="html-italic">z</span>-plane and <span class="html-italic">ω</span>-plane to the <span class="html-italic">t</span>-plane for the model in <a href="#water-14-03982-f003" class="html-fig">Figure 3</a>b.</p>
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<p>Consistency in the flow nets between the approximate and exact models when the ratios <span class="html-italic">w</span>/<span class="html-italic">d</span> and <span class="html-italic">w’</span>/<span class="html-italic">s</span> are sufficiently large: (<b>a</b>,<b>b</b>) show the calculated flow nets for the approximate models, respectively; (<b>c</b>,<b>d</b>) show the calculated flow nets for the exact models, respectively.</p>
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<p>Calculation results of the four normalized average drawdowns: (<b>a</b>) <span class="html-italic">R</span><sub>BC(<span class="html-italic">q</span><sub>2</sub>)</sub>, (<b>b</b>) <span class="html-italic">R</span><sub>CD(<span class="html-italic">q</span><sub>2</sub>)</sub>, (<b>c</b>) <span class="html-italic">R</span><sub>BC(<span class="html-italic">q</span><sub>1</sub>)</sub>, and (<b>d</b>) <span class="html-italic">R</span><sub>CD(<span class="html-italic">q</span><sub>1</sub>)</sub>.</p>
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<p>Calculation results of the normalized flow rate when (<b>a</b>) <span class="html-italic">s</span>/<span class="html-italic">T</span> = 0.1, <span class="html-italic">k’</span>/<span class="html-italic">k</span> = 0.1 and (<b>b</b>) <span class="html-italic">s</span>/<span class="html-italic">T</span> = 0.75, <span class="html-italic">k’</span>/<span class="html-italic">k</span> = 0.9.</p>
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<p>Linear fitting for (<b>a</b>) <span class="html-italic">R</span><sub>BC(<span class="html-italic">q</span><sub>2</sub>)</sub> and (<b>b</b>) <span class="html-italic">R</span><sub>CD(<span class="html-italic">q</span><sub>1</sub>)</sub>.</p>
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<p>Flow calculation results of representative examples for (<b>a</b>) <span class="html-italic">s</span>/<span class="html-italic">T</span> = 0.1, (<b>b</b>) <span class="html-italic">s</span>/<span class="html-italic">T</span> = 0.25, (<b>c</b>) <span class="html-italic">s</span>/<span class="html-italic">T</span> = 0.5, and (<b>d</b>) <span class="html-italic">s</span>/<span class="html-italic">T</span> = 0.75.</p>
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<p>Comparison of flow calculation results between TPM and FEM when <span class="html-italic">k’</span>/<span class="html-italic">k</span> = 0.9: (<b>a</b>) flow calculation results and (<b>b</b>) relative error.</p>
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<p>Flow calculation results of examples for (<b>a</b>) <span class="html-italic">s</span>/<span class="html-italic">T</span> = 0 (or <span class="html-italic">k’</span>/<span class="html-italic">k</span> = 1) and (<b>b</b>) <span class="html-italic">s</span>/<span class="html-italic">T</span> = 1.</p>
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<p>Comparisons of flow calculation results among Wang, TPM, and FEM for (<b>a</b>) <span class="html-italic">s</span>/<span class="html-italic">T</span> = 0.25, (<b>b</b>) <span class="html-italic">s</span>/<span class="html-italic">T</span> = 0. 5, and (<b>c</b>) <span class="html-italic">s</span>/<span class="html-italic">T</span> = 0.75.</p>
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<p>Comparisons of flow calculation results among Yakimov, TPM, and FEM for (<b>a</b>) <span class="html-italic">w</span>/<span class="html-italic">T</span> = 0.01, (<b>b</b>) <span class="html-italic">w</span>/<span class="html-italic">T</span> = 0.1, (<b>c</b>) <span class="html-italic">w</span>/<span class="html-italic">T</span> = 0.5, and (<b>d</b>) <span class="html-italic">w</span>/<span class="html-italic">T</span> = 1.</p>
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21 pages, 3766 KiB  
Article
Assessment and Spatiotemporal Variability of Heavy Metals Pollution in Water and Sediments of a Coastal Landscape at the Nile Delta
by Ahmed Abdelaal, Ahmed I. Abdelkader, Fahad Alshehri, Asmaa Elatiar and Sattam A. Almadani
Water 2022, 14(23), 3981; https://doi.org/10.3390/w14233981 - 6 Dec 2022
Cited by 9 | Viewed by 2203
Abstract
This study assessed the spatiotemporal variability and pollution grades of heavy metals in water and sediments of Bahr El-Baqar drain, Eastern Nile Delta, Egypt, by integration of geochemical analysis, metal pollution indices, correlation, and multivariate statistical analyses. Twenty samples of water and sediments [...] Read more.
This study assessed the spatiotemporal variability and pollution grades of heavy metals in water and sediments of Bahr El-Baqar drain, Eastern Nile Delta, Egypt, by integration of geochemical analysis, metal pollution indices, correlation, and multivariate statistical analyses. Twenty samples of water and sediments were collected during 2018 and analyzed for heavy metal concentrations using ICP-OES. Heavy metal contents in the water samples followed the order: Fe > Zn > Al > Pb > Mn > Cu > Ni. The drain sediments were highly contaminated with heavy metals that followed the order: Fe > Al > Mn > V > Zn > Cu > Cr > Ba > Ni > Pb > As. Spatiotemporally, most metals in the drain sediments showed a decreasing trend from upstream (south) to downstream sites (north). Results of principal component analysis (PCA) supported those from the Pearson correlation between investigated heavy metals. In water, Mn, Ni, Pb, Zn, Cu, and Fe showed highly significant correlations. In sediments, Ba, Ni, Zn, Fe, Al, Mn, and V showed strong positive correlations indicating that these metals were derived from similar anthropogenic sources. The calculated metal pollution indices: enrichment factor (EF), contamination factor (CF), pollution load index (PLI), degree of contamination (DC), and index of geo-accumulation (Igeo) indicated high loadings of heavy metals in the drain sediments. EFs revealed low, moderate to significant enrichment, whereas CFs showed low, moderate, and considerable contamination. PLI indicated low, baseline, and progressive contamination, while DC indicated low, moderate, and considerable degree of contamination. Igeo of all investigated metals (except for As; class 1) indicated extremely contaminated sediments (class 7). Full article
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<p>Location map for the study area and sampling sites (S1−5) draped over a high−resolution Sentinel−2A satellite image (<b>a</b>) and Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) digital elevation model (DEM) (<b>b</b>) displaying the elevation buffering zone of Bahr El-Baqar drain, Eastern Nile Delta, Egypt. Belbeis and Qalubiya secondary drains meet in the south at Zagazig, Sharqia Governorate, to form Bahr El-Baqar drain, which flows north to Lake Manzala, south of Port Said city.</p>
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<p>Spatiotemporal distribution plots of heavy metal concentrations (mg/L) in water sampling sites of Bahr El-Baqar drain during summer and winter of 2018: Pb (<b>a</b>), Cu (<b>b</b>), Mn (<b>c</b>), Ni (<b>d</b>), Zn (<b>e</b>), Al (<b>f</b>), and Fe (<b>g</b>).</p>
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<p>Spatiotemporal distribution plots of total contents of heavy metals (mg/kg) in Bahr El-Baqar drain sediments during summer and winter of 2018: Ba (<b>a</b>), As (<b>b</b>), Pb (<b>c</b>), Cu (<b>d</b>), Mn (<b>e</b>), Ni (<b>f</b>), Zn (<b>g</b>), Cr (<b>h</b>), V (<b>i</b>), Al (<b>j</b>) and Fe (<b>k</b>) collected in summer and winter of 2018.</p>
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<p>Bivariate plots of significant relationships between heavy metals in water and sediments of Bahr El-Baqar drain: Cu versus Fe (<b>a</b>), Zn versus Ni (<b>b</b>), and Zn versus Al (<b>c</b>) in water (mg/L); Ni versus Al (<b>d</b>), Fe versus Al (<b>e</b>), and Ni versus Fe (<b>f</b>) in sediments (mg/kg); Pb in sediments (mg/kg) versus Ni in water (mg/L) (<b>g</b>), and Zn in sediments (mg/kg) versus Cu in water (mg/L) (<b>h</b>), and Mn in sediments (mg/kg) versus Fe in water (mg/L) (<b>i</b>).</p>
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<p>PCA analysis of the studied heavy metals in water (<b>a</b>) and sediments (<b>b</b>).</p>
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<p>The average values of enrichment factor (EF; (<b>a</b>)), contamination factor (CF; (<b>b</b>)), geo−accumulation index (I<sub>geo</sub>; (<b>c</b>)), pollution load index (PLI), and degree of contamination (DC; (<b>d</b>)) of the eleven studied heavy metals in sediments of Bahr El−Baqar drain.</p>
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20 pages, 3453 KiB  
Review
Bioremediation Treatment of Polyaromatic Hydrocarbons for Environmental Sustainability
by Marjan Salari, Vahid Rahmanian, Seyyed Alireza Hashemi, Wei-Hung Chiang, Chin Wei Lai, Seyyed Mojtaba Mousavi and Ahmad Gholami
Water 2022, 14(23), 3980; https://doi.org/10.3390/w14233980 - 6 Dec 2022
Cited by 15 | Viewed by 4162
Abstract
Polycyclic aromatic hydrocarbons (PAHs) distributed in air and soil are harmful because of their carcinogenicity, mutagenicity, and teratogenicity. Biodegradation is an environmentally friendly and economical approach to control these types of contaminants and has become an essential method for remediating environments contaminated with [...] Read more.
Polycyclic aromatic hydrocarbons (PAHs) distributed in air and soil are harmful because of their carcinogenicity, mutagenicity, and teratogenicity. Biodegradation is an environmentally friendly and economical approach to control these types of contaminants and has become an essential method for remediating environments contaminated with petroleum hydrocarbons. The bacteria are isolated and identified using a mineral nutrient medium containing PAHs as the sole source of carbon and energy and biochemical differential tests. Thus, this study focuses on some bacteria and fungi that degrade oil and hydrocarbons. This study provides a comprehensive, up-to-date, and efficient overview of petroleum hydrocarbon contaminant bioremediation considering hydrocarbon modification by microorganisms, emphasizing the new knowledge gained in recent years. The study shows that petroleum hydrocarbon contaminants are acceptably biodegradable by some microorganisms, and their removal by this method is cost-effective. Moreover, microbial biodegradation of petroleum hydrocarbon contaminants utilizes the enzymatic catalytic activities of microorganisms and increases the degradation of pollutants several times compared to conventional methods. Biological treatment is carried out in two ways: microbial stimulation and microbial propagation. In the first method, the growth of indigenous microorganisms in the area increases, and the pollution is eliminated. In the second method, on the other hand, there are no effective microorganisms in the area, so these microorganisms are added to the environment. Full article
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<p>The Food Supply Contaminated With PAHs From many Different Sources (License Number: 5411951000689) [<a href="#B29-water-14-03980" class="html-bibr">29</a>].</p>
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<p>PAH contamination in a diverse environment (License Number: 5411951000689) [<a href="#B29-water-14-03980" class="html-bibr">29</a>].</p>
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<p>Various variables, both abiotic and biotic, influence the PAH ability to degrade in soil [<a href="#B58-water-14-03980" class="html-bibr">58</a>].</p>
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<p>The future path for a better knowledge of microbial PAHs is outlined by a summary of several molecular approaches [<a href="#B58-water-14-03980" class="html-bibr">58</a>].</p>
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<p>Internal (<b>A</b>), external (<b>B</b>), and thermoneutral (<b>C</b>) are the three options for heat input (C). For the heat input necessary for CH<sub>4</sub> breakdown, there are three options. Exhaust gases, heat transfer medium, and hydrogen-rich gas (NG HRG) are all used. Methane breakdown reactor, reactor heating, and catalyst particle heating are the three components (License Number: 5411970124733) [<a href="#B88-water-14-03980" class="html-bibr">88</a>].</p>
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<p>H<sub>2</sub> and carbon are created by catalytic cracking of natural gas using both endothermic reactions (<b>A</b>) and exothermic reactions (<b>B</b>) (License Number: 5411971299711) [<a href="#B19-water-14-03980" class="html-bibr">19</a>].</p>
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<p>Three central PAH breakdown mechanisms by bacteria and fungus [<a href="#B96-water-14-03980" class="html-bibr">96</a>].</p>
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<p>Overview of oily sludge treatment methods (License Number: 5435450889910) [<a href="#B105-water-14-03980" class="html-bibr">105</a>].</p>
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13 pages, 2016 KiB  
Article
Watershed-Scale Shallow Groundwater Anthropogenic Nitrate Source, Loading, and Contamination Assessment in a Typical Wheat Production Region: Case Study in Yiluo River Watershed, Middle of China
by Xihua Wang, Shunqing Jia, Zejun Liu and Boyang Mao
Water 2022, 14(23), 3979; https://doi.org/10.3390/w14233979 - 6 Dec 2022
Cited by 7 | Viewed by 1808
Abstract
Nitrate pollution in groundwater has become a global concern for agriculture and regional ecology. However, tracing the spatiotemporal groundwater nitrate pollution sources, calculating the total nitrogen loading, and assessing contamination at the watershed scale have not been well documented. In this study, 20 [...] Read more.
Nitrate pollution in groundwater has become a global concern for agriculture and regional ecology. However, tracing the spatiotemporal groundwater nitrate pollution sources, calculating the total nitrogen loading, and assessing contamination at the watershed scale have not been well documented. In this study, 20 groundwater samplings from 2020 to 2021 (in dry and wet seasons) on the Yiluo River watershed in middle China were collected. Tracing groundwater nitrate pollution sources, calculating total nitrogen loading, and assessing contamination using dual isotopes (18ONO3 and 15NNO3), conservation of mass, and the nitrate pollution index (NPI), respectively. The results indicated that there were three nitrate sources in groundwater: (1) manure and sewage waste input (MSWI), (2) sediment nitrogen input (SNI), and (3) agriculture chemical fertilizer input (ACFI) in the Yiluo River watershed. ACFI and SNI were the main groundwater nitrogen pollution sources. The average nitrogen loading percentages of ACFI, SNI, and MSWI in the whole watershed were 94.7%, 4.34%, and 0.96%, respectively. The total nitrogen loading in the Yiluo River watershed was 7,256,835.99 kg/year, 4,084,870.09 kg/year in downstream areas, 2,121,938.93 kg/year in midstream areas, and 1,050,026.95 kg/year in upstream areas. Sixty percent of groundwater in the Yiluo River watershed has been polluted by nitrate. Nitrate pollution in midstream areas is more severe. Nitrite pollution was more serious in the wet season than in the dry season. The results of this study can provide useful information for watershed-scale groundwater nitrogen pollution control and treatment. Full article
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<p>Location of the study area and groundwater sampling sites for the Yiluo River watershed in middle China.</p>
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<p>Keeling plot of 1/NO<sub>3</sub><sup>−</sup> and δ<sup>15</sup>N<sub>NO<sub>3</sub></sub>(‰) and the relationship of δ<sup>15</sup>N<sub>Nitrate</sub>(‰) and δ<sup>18</sup>O<sub>Ntrate</sub>(‰) [<a href="#B4-water-14-03979" class="html-bibr">4</a>] in groundwater of the Yiluo River watershed during the dry season. MSWI represents manure and sewage waste input. SNI represents sediment nitrogen input. ACFI represents agricultural chemical fertilizer input.</p>
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<p>Keeling plot of 1/NO<sub>3</sub><sup>−</sup> and δ<sup>15</sup>N<sub>NO<sub>3</sub></sub>(‰) and the relationship of δ<sup>15</sup>N<sub>Nitrate</sub>(‰) and δ<sup>18</sup>O<sub>Nitrate</sub>(‰) [<a href="#B4-water-14-03979" class="html-bibr">4</a>] in groundwater of the Yiluo River watershed during the wet season. MSWI represents manure and sewage waste input. SNI represents sediment nitrogen input. ACFI represents agricultural chemical fertilizer input.</p>
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<p>The total nitrogen loading percentage of groundwater in the Yiluo River watershed.</p>
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<p>The nitrate pollution assessment of groundwater in the Yiluo River watershed. (<b>a</b>) Dry season and (<b>b</b>) wet season.</p>
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14 pages, 2685 KiB  
Article
A Monthly Hydropower Scheduling Model of Cascaded Reservoirs with the Zoutendijk Method
by Binbin Zhou, Suzhen Feng, Zifan Xu, Yan Jiang, Youxiang Wang, Kai Chen and Jinwen Wang
Water 2022, 14(23), 3978; https://doi.org/10.3390/w14233978 - 6 Dec 2022
Cited by 3 | Viewed by 1562
Abstract
A monthly hydropower scheduling determines the monthly flows, storage, and power generation of each reservoir/hydropower plant over a planning horizon to maximize the total revenue or minimize the total operational cost. The problem is typically a complex and nonlinear optimization that involves equality [...] Read more.
A monthly hydropower scheduling determines the monthly flows, storage, and power generation of each reservoir/hydropower plant over a planning horizon to maximize the total revenue or minimize the total operational cost. The problem is typically a complex and nonlinear optimization that involves equality and inequality constraints including the water balance, hydraulic coupling between cascaded hydropower plants, bounds on the reservoir storage, etc. This work applied the Zoutendijk algorithm for the first time to a medium/long-term hydropower scheduling of cascaded reservoirs, where the generating discharge capacity is handled with an iterative procedure, while the other head-related nonlinear constraints are represented with exponential functions fitting to discrete points. The procedure starts at an initial feasible solution, from which it finds a feasible improving direction, along which a better feasible solution is sought with a one-dimensional search. The results demonstrate that the Zoutendijk algorithm, when applied to six cascaded hydropower reservoirs on the Lancang River, worked very well in maximizing the hydropower production while ensuring the highest firm power output to be secured. Full article
(This article belongs to the Section Hydraulics and Hydrodynamics)
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<p>High-level flowchart of the solution procedure.</p>
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<p>Hydraulic layout of the six reservoirs on the Lancang River.</p>
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<p>The results of the fitting curves of the Nuozhadu.</p>
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<p>Converging process of the optimization.</p>
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<p>Output of the cascade hydropower plants.</p>
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<p>Monthly schedules over a year.</p>
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15 pages, 11438 KiB  
Article
Effective Removal of Biogenic Substances Using Natural Treatment Systems for Wastewater for Safer Water Reuse
by Wojciech Halicki and Michał Halicki
Water 2022, 14(23), 3977; https://doi.org/10.3390/w14233977 - 6 Dec 2022
Cited by 4 | Viewed by 1859
Abstract
Natural Treatment Systems for Wastewater (NTSW) show great potential for economic, socially acceptable and environmentally friendly wastewater treatment, along with the renewal of water for its safe reuse. This article presents the reduction in nitrogen and phosphorus compounds in domestic wastewater, which was [...] Read more.
Natural Treatment Systems for Wastewater (NTSW) show great potential for economic, socially acceptable and environmentally friendly wastewater treatment, along with the renewal of water for its safe reuse. This article presents the reduction in nitrogen and phosphorus compounds in domestic wastewater, which was achieved in a 2.5-year operation of the newly developed NTSW. The presented installation was developed by the Institute of Applied Ecology in Skórzyn (Poland) and implemented as a pilot plant serving the institute building with three permanent residents and up to five employees. The installation consisted of two parts, responsible for: wastewater treatment (septic tank and compost beds) and water renewal (denitrification beds, phosphorus beds and activated carbon beds). The mean values of nitrogen and phosphorus compound concentrations obtained in the renewed water for the entire research period were: 0.8, 49.4, 12 and 3.1 mg/L for ammonium nitrogen (NH4), nitrates (NO3), total nitrogen and phosphates (PO4), respectively. Thus, average reductions of 99.6%, 90.9% and 94.4% were obtained for NH4, total nitrogen and PO4, respectively. Treatment of domestic sewage to such a level, similar to drinking water, enables versatile, safe water reuse, which in the situation of increasingly limited water resources will constitute increasing ecological and economic value. Full article
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<p>Scheme of the NTSW pilot plant (adapted from Halicki and Halicki [<a href="#B12-water-14-03977" class="html-bibr">12</a>]). The three parallel wastewater treatment and water renewal processes are conducted in different daily hydraulic loads, namely 70, 100 and 130 L/m<sup>2</sup> for the A, B and C letters, respectively.</p>
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<p>The NTSW pilot plant (adapted from Halicki and Halicki [<a href="#B12-water-14-03977" class="html-bibr">12</a>]). The three parallel wastewater treatment and water renewal processes are conducted in different daily hydraulic loads, namely 70, 100 and 130 L/m<sup>2</sup> for the A, B and C letters, respectively. (<b>a</b>) Compost beds. (<b>b</b>) Water renewal beds: denitrification beds (1), phosphorus beds (2) and activated carbon beds (3). (<b>c</b>) Setting of the NTSW pilot plant.</p>
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<p>Concentration of biogenic substances in the treated sewage (sample 1) and in the renewed water (sample 2). The letters A, B and C refer to three different hydraulic loads: 70, 100 and 130 L/m<sup>2</sup>, respectively. (<b>a</b>) Ammonium (NH<sub>4</sub>), (<b>b</b>) nitrate (NO<sub>3</sub>), (<b>c</b>) nitrite (NO<sub>2</sub>), (<b>d</b>) total nitrogen, (<b>e</b>) phosphate (PO<sub>4</sub>) and (<b>f</b>) total phosphorus. The drinking water quality recommendation is taken from the European Council Directive [<a href="#B32-water-14-03977" class="html-bibr">32</a>].</p>
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23 pages, 5819 KiB  
Article
Plain Stilling Basin Performance below 30° and 50° Inclined Smooth and Stepped Chutes
by Ivan Stojnic, Michael Pfister, Jorge Matos and Anton J. Schleiss
Water 2022, 14(23), 3976; https://doi.org/10.3390/w14233976 - 6 Dec 2022
Cited by 3 | Viewed by 3380
Abstract
Energy dissipators, such as stilling basins, are usually required at the toe of stepped chutes to achieve adequate and safe operation of the spillway. Stepped chute hydraulics has been extensively studied in last several decades, however, only limited knowledge is available on the [...] Read more.
Energy dissipators, such as stilling basins, are usually required at the toe of stepped chutes to achieve adequate and safe operation of the spillway. Stepped chute hydraulics has been extensively studied in last several decades, however, only limited knowledge is available on the stilling basin performance below stepped chutes. In particular, the effect of the chute slope remains unknown, despite being a central design issue. Therefore, an experimental campaign was performed using a 30° or 50° inclined smooth or stepped chute with an adjacent conventional plain stilling basin. The experimental results indicated that, within the stilling basin, the surface characteristics and the roller as well as hydraulic jump lengths are practically independent of the chute slope. This further strengthens the previous findings that stepped chutes require 17% longer dimensionless jump lengths and consequently stilling basin lengths. The experimental results also confirmed that stepped chutes generated increased extreme and fluctuating bottom pressure characteristics at the stilling basin entrance area. With increasing chute slope, the latter were found to significantly magnify. However, such increased magnitudes were not expected to provoke cavitation damage as stepped chute inflows induced bottom aeration at the basin entrance, irrespective of the chute slope. Full article
(This article belongs to the Special Issue Advances in Spillway Hydraulics: From Theory to Practice)
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Figure 1

Figure 1
<p>Definition sketch with instrumentation, notations and nomenclature. FOP—Fiber optical probe; PPT—Pitot-Prandtl tube; US—Ultrasonic displacement meter; APS—Automatic positioning system.</p>
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<p>Photo of the spillway model with <span class="html-italic">Q =</span> 0.14 m<sup>3</sup>/s, the 50° smooth chute in the background and the plain stilling basin at the front. Flow direction from left to right.</p>
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<p>(<b>a</b>) Air concentration profiles at the inflow section for the 50° smooth chute (Runs 16–24, <a href="#water-14-03976-t001" class="html-table">Table 1</a>) and comparison with (—) advective diffusion model [<a href="#B17-water-14-03976" class="html-bibr">17</a>] with <span class="html-italic">C</span><sub>1 =</sub> 0.16, 0.29 and 0.36, and (<b>b</b>) Dimensionless velocity <span class="html-italic">V</span>/<span class="html-italic">V</span><sub>90</sub> profiles at the inflow section for the 50° smooth chute (Runs 16–24, <a href="#water-14-03976-t001" class="html-table">Table 1</a>) and comparison with (—) Equation (2).</p>
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<p>(<b>a</b>) Air concentration profiles at the inflow section for 50° stepped chute tests (Runs 25–30, <a href="#water-14-03976-t001" class="html-table">Table 1</a>), comparison with (―) advective diffusion model [<a href="#B17-water-14-03976" class="html-bibr">17</a>] with <span class="html-italic">C</span><sub>1</sub> = 0.55 and 0.46, and (<b>b</b>) Dimensionless velocity <span class="html-italic">V</span>/<span class="html-italic">V</span><sub>90</sub> profiles at the inflow section for 50° stepped chute test runs (Runs 25–30, <a href="#water-14-03976-t001" class="html-table">Table 1</a>), comparison with (—) Equation (2).</p>
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<p>Comparison between measured mean air concentrations <span class="html-italic">C</span><sub>1</sub> at 50° stepped chute inflow section (Runs 25–30, <a href="#water-14-03976-t001" class="html-table">Table 1</a>) and quasi-uniform values <span class="html-italic">C<sub>u</sub></span> of [<a href="#B18-water-14-03976" class="html-bibr">18</a>]. Note: T&amp;O is Takahashi and Ohtsu [<a href="#B18-water-14-03976" class="html-bibr">18</a>].</p>
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<p>(<b>a</b>) Streamwise mean flow depths <span class="html-italic">η</span> along the stilling basin, and (<b>b</b>) Sequent depth ratio <span class="html-italic">h</span><sub>2</sub>/<span class="html-italic">h</span><sub>1</sub> as a function of the approach Froude number F<sub>1</sub>; (—) Equation (3); [Runs 1–9: 30° smooth chute, Runs 10–15: 30° stepped chute, Runs 16–24: 50° smooth chute, Runs 25–30: 50° stepped chute].</p>
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<p>Dimensionless flow depths <span class="html-italic">Z</span> along the jump roller for 30° and 50°: (<b>a</b>) smooth and (<b>b</b>) stepped chute inflows; (—) Equation (4); [Runs 1–9: 30° smooth chute, Runs 10–15: 30° stepped chute, Runs 16–24: 50° smooth chute, Runs 25–30: 50° stepped chute].</p>
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<p>Roller lengths obtained from ultrasonic displacement meter measurement <span class="html-italic">L<sub>R,η</sub></span> and visual observation <span class="html-italic">L<sub>R,D</sub></span> plotted against F<sub>1</sub> as: (<b>a</b>) <span class="html-italic">L<sub>R</sub></span>/<span class="html-italic">h</span><sub>2</sub> and (<b>b</b>) <span class="html-italic">L<sub>R</sub></span>/<span class="html-italic">h</span><sub>1</sub>; (− −) Equation (5); [Runs 1–9: 30° smooth chute, Runs 10–15: 30° stepped chute, Runs 16–24: 50° smooth chute, Runs 25–30: 50° stepped chute].</p>
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<p>(<b>a</b>) Streamwise flow depth fluctuations <span class="html-italic">η</span>’ along the stilling basin (<a href="#water-14-03976-t001" class="html-table">Table 1</a>) and (<b>b</b>) Dimensionless jump lengths <span class="html-italic">L<sub>J,η’</sub></span> obtained from flow depth fluctuations <span class="html-italic">η</span>’ as a function of the inflow Froude number F<sub>1</sub>, compared to jump length prediction of [<a href="#B20-water-14-03976" class="html-bibr">20</a>]; [Runs 1–9: 30° smooth chute, Runs 10–15: 30° stepped chute, Runs 16–24: 50° smooth chute, Runs 25–30: 50° stepped chute].</p>
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<p>Streamwise development of the flow depth fluctuation coefficient <span class="html-italic">C<sub>H</sub></span>’ versus the normalized streamwise coordinate <span class="html-italic">x</span>/<span class="html-italic">L<sub>J,η’</sub></span> for 30° and 50° smooth and stepped chute tests; (− −) Equation (6), (—) Equation (7); [Runs 1–9: 30° smooth chute, Runs 10–15: 30° stepped chute, Runs 16–24: 50° smooth chute, Runs 25–30: 50° stepped chute].</p>
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<p>Streamwise pressure distribution (Run 22, smooth chutes, <a href="#water-14-03976-t001" class="html-table">Table 1</a>) of: (<b>a</b>) extreme maximum <span class="html-italic">p<sub>max</sub></span>, 99.9% probability <span class="html-italic">p</span><sub>99.9</sub>, mean <span class="html-italic">p<sub>m</sub></span>, 0.1% probability <span class="html-italic">p</span><sub>0.1</sub>, and extreme minimum pressure <span class="html-italic">p<sub>min</sub></span>, and (<b>b</b>) fluctuating pressure <span class="html-italic">p’</span>, skewness <span class="html-italic">S</span> and excess kurtosis <span class="html-italic">K</span>.</p>
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<p>Dimensionless jump lengths <span class="html-italic">L<sub>J</sub></span>/<span class="html-italic">h</span><sub>2</sub> downstream of 30° and 50° smooth and stepped chutes from pressure (<span class="html-italic">L<sub>J,p</sub></span><sub>′</sub> and <span class="html-italic">L<sub>J,SK</sub></span>) and flow depth (<span class="html-italic">L<sub>J,η′</sub></span>/<span class="html-italic">h</span><sub>2</sub>) measurements, plotted against the inflow Froude number F<sub>1</sub>; (− −) [<a href="#B20-water-14-03976" class="html-bibr">20</a>]; [Runs 1–9: 30° smooth chute, Runs 10–15: 30° stepped chute, Runs 16–24: 50° smooth chute, Runs 25–30: 50° stepped chute].</p>
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<p>Streamwise distribution of: (<b>a</b>) mean pressure coefficient <span class="html-italic">P<sub>m</sub></span>, (<b>b</b>) pressure fluctuation coefficient <span class="html-italic">C<sub>P</sub></span>’, (<b>c</b>) maximum pressure coefficient <span class="html-italic">C<sub>P</sub><sup>max</sup></span>, (<b>d</b>) 99th percentile coefficient <span class="html-italic">C<sub>P</sub></span><sup>99.9</sup>, (<b>e</b>) minimum pressure coefficient <span class="html-italic">C<sub>P</sub><sup>min</sup></span>, (<b>f</b>) 0.1th percentile coefficient <span class="html-italic">C<sub>P</sub></span><sup>0.1</sup>, (<b>g</b>) skewness <span class="html-italic">S</span>, and (<b>h</b>) excess kurtosis <span class="html-italic">K</span>; (—) Equations (10) and (11); (− −) Equations (6)–(9) and (12); [Runs 16–24: 50° smooth chute; Runs 25–30: 50° stepped chute].</p>
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<p>Streamwise distribution of: (<b>a</b>) mean pressure coefficient <span class="html-italic">P<sub>m</sub></span>, (<b>b</b>) pressure fluctuation coefficient <span class="html-italic">C<sub>P</sub></span>’, (<b>c</b>) maximum pressure coefficient <span class="html-italic">C<sub>P</sub><sup>max</sup></span>, (<b>d</b>) 99th percentile coefficient <span class="html-italic">C<sub>P</sub></span><sup>99.9</sup>, (<b>e</b>) minimum pressure coefficient <span class="html-italic">C<sub>P</sub><sup>min</sup></span>, (<b>f</b>) 0.1th percentile coefficient <span class="html-italic">C<sub>P</sub></span><sup>0.1</sup>, (<b>g</b>) skewness <span class="html-italic">S</span>, and (<b>h</b>) excess kurtosis <span class="html-italic">K</span>; (—) Equations (10) and (11); (− −) Equations (6)–(9) and (12); [Runs 16–24: 50° smooth chute; Runs 25–30: 50° stepped chute].</p>
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<p>Streamwise distribution of mean pressure coefficients <span class="html-italic">P<sub>m</sub></span> downstream of 30° and 50°: (<b>a</b>) smooth chutes, and (<b>b</b>) stepped chutes; (—) Equation (10); (− −) Equations (8) and (9); [Runs 1–9: 30° smooth chute, Runs 10–15: 30° stepped chute, Runs 16–24: 50° smooth chute, Runs 25–30: 50° stepped chute].</p>
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<p>Mean pressure coefficients <span class="html-italic">C<sub>P</sub><sup>def</sup></span> against inflow Froude number F<sub>1</sub>; [Runs 1–9: 30° smooth chute, Runs 10–15: 30° stepped chute, Runs 16–24: 50° smooth chute, Runs 25–30: 50° stepped chute].</p>
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<p>Streamwise development of pressure fluctuation coefficient <span class="html-italic">C<sub>P</sub>’</span> for 30° and 50°: (<b>a</b>) smooth chute, and (<b>b</b>) stepped chute; (—) Equation (11); (− −) Equations (12) and (13); [Runs 1–9: 30° smooth chute, Runs 10–15: 30° stepped chute, Runs 16–24: 50° smooth chute, Runs 25–30: 50° stepped chute].</p>
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<p>Pressure fluctuation coefficients <span class="html-italic">C<sub>P</sub>’</span> at the flow deflection point and the jump toe against inflow Froude number F<sub>1</sub>; [Runs 1–9: 30° smooth chute, Runs 10–15: 30° stepped chute, Runs 16–24: 50° smooth chute, Runs 25–30: 50° stepped chute].</p>
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<p>Streamwise development of bottom pressure fluctuations <span class="html-italic">p′</span> for Run 24 (50°, smooth chute) and Run 30 (50°, stepped chute chute).</p>
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<p>Streamwise development of extreme pressure coefficients: (<b>a</b>) <span class="html-italic">C<sub>P</sub><sup>max</sup></span> for 30° and 50° smooth chutes, (<b>b</b>) <span class="html-italic">C<sub>P</sub><sup>max</sup></span> for 30° and 50° stepped chutes, (<b>c</b>) <span class="html-italic">C<sub>P</sub><sup>min</sup></span> for 30° and 50° smooth chutes, and (<b>d</b>) <span class="html-italic">C<sub>P</sub><sup>min</sup></span> for 30° and 50° stepped chutes; (—) Equation (11); (− −) Equations (12) and (13); [Runs 1–9: 30° smooth chute, Runs 10–15: 30° stepped chute, Runs 16–24: 50° smooth chute, Runs 25–30: 50° stepped chute].</p>
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<p>Extreme pressure coefficients: (<b>a</b>) <span class="html-italic">C<sub>P</sub><sup>max</sup></span> and (<b>b</b>) <span class="html-italic">C<sub>P</sub><sup>min</sup></span> at the flow deflection point and jump toe; [Runs 1–9: 30° smooth chute, Runs 10–15: 30° stepped chute, Runs 16–24: 50° smooth chute, Runs 25–30: 50° stepped chute].</p>
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<p>Streamwise development of bottom air concentration <span class="html-italic">C<sub>b</sub></span>; (—) Equation (13); (− −) Equations (14) and (15); [Runs 1–9: 30° smooth chute, Runs 10–15: 30° stepped chute, Runs 16–24: 50° smooth chute, Runs 25–30: 50° stepped chute].</p>
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27 pages, 1133 KiB  
Review
Bioponics as a Promising Approach to Sustainable Agriculture: A Review of the Main Methods for Producing Organic Nutrient Solution for Hydroponics
by Iris Szekely and M. Haïssam Jijakli
Water 2022, 14(23), 3975; https://doi.org/10.3390/w14233975 - 6 Dec 2022
Cited by 17 | Viewed by 11477
Abstract
Hydroponics is a soilless cultivation technique in which plants are grown in a nutrient solution typically made from mineral fertilizers. This alternative to soil farming can be advantageous in terms of nutrient and water use efficiency, plant pest management, and space use. However, [...] Read more.
Hydroponics is a soilless cultivation technique in which plants are grown in a nutrient solution typically made from mineral fertilizers. This alternative to soil farming can be advantageous in terms of nutrient and water use efficiency, plant pest management, and space use. However, developing methods to produce nutrient solutions based on local organic materials is crucial to include hydroponics within a perspective of sustainability. They would also allow hydroponics to be developed in any context, even in remote areas or regions that do not have access to commercial fertilizers. This emerging organic form of hydroponics, which can be qualified as “bioponics”, typically recycles organic waste into a nutrient-rich solution that can be used for plant growth. Many methods have been developed and tested in the past three decades, leading to greatly heterogenous results in terms of plant yield and quality. This review describes the main organic materials used to produce nutrient solutions and characterizes and categorizes the different types of methods. Four main categories emerged: a “tea”-type method, an aerobic microbial degradation method, an anaerobic digestion method, and a combined anaerobic-aerobic degradation method. The advantages and drawbacks of each technique are discussed, as well as potential lines of improvement. This aims at better understanding the links between agronomic results and the main biochemical processes involved during the production, as well as discussing the most suitable method for certain plants and/or contexts. Full article
(This article belongs to the Special Issue New Advances in Hydroponics and Aquaponics for Urban Agriculture)
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Figure 1
<p>Summary diagram of tea, aerobic microbial degradation, anaerobic digestion and combined anaerobic–aerobic degradation methods for the production of organic nutrient solution.</p>
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<p>Schematic of organic materials aerobic degradation integrated and/or external to the bioponic system.</p>
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11 pages, 1626 KiB  
Article
Exploration and Optimisation of High-Salt Wastewater Defluorination Process
by Dianhua Chen, Minyan Zhao, Xinyuan Tao, Jing Ma, Ankang Liu and Mingxiu Wang
Water 2022, 14(23), 3974; https://doi.org/10.3390/w14233974 - 6 Dec 2022
Cited by 2 | Viewed by 2069
Abstract
The typical lime precipitation method is used to treat high-concentration fluorine-containing wastewater. In this way, the fluorine in the wastewater can be removed in the form of CaF2. Thus, this method has a good fluoride removal effect. In this study, calcium [...] Read more.
The typical lime precipitation method is used to treat high-concentration fluorine-containing wastewater. In this way, the fluorine in the wastewater can be removed in the form of CaF2. Thus, this method has a good fluoride removal effect. In this study, calcium hydroxide was used to adjust the pH and achieve a significant fluoride removal effect at the same time. The removal rate of fluoride ion decreases gradually with the increase in the concentration of sulphate in the raw water. When the synergistic defluorination cannot meet the requirements of water production, adding a step of aluminium salt flocculation and precipitation can further reduce the fluoride ion concentration. According to the feasibility of the actual project, this study improves the lime coagulation precipitation defluorination process on this basis, and the combined process is synchronised. In the process optimisation, barium chloride is added to remove the influence of sulphate radicals in the water, and then, the pH is adjusted to 5–6. The fluoride ion concentration in high-salt wastewater can be reduced from 446.6 mg/L to 35.4 mg/L by defluorination after pre-treatment whose removal rate was 92.1%. The combined process synchronously removes fluorine and purifies the water quality to a certain extent. Indicators such as COD, total phosphorus, ammonia nitrogen, and chloride ions in wastewater are reduced, and the removal rate is increased by 35.5% under the same conditions. This scheme improves the wastewater treatment effect without increasing the existing treatment equipment. Thus, it achieves a better defluorination effect and reduces the dosage of chemicals as much as possible, which is conducive to lowering the discharge of sludge after treatment. Full article
(This article belongs to the Special Issue Water-Sludge-Nexus)
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<p>Effect of sulphate radicals on fluoride removal.</p>
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<p>Effects of different reaction times on (<b>a</b>) phosphorus removal and (<b>b</b>) fluorine and chlorine removal efficiency.</p>
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<p>Effect of pH on the defluorination.</p>
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<p>Processing effects of different schemes.</p>
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14 pages, 2491 KiB  
Article
Optimization of Water Network Topology and Pipe Sizing to Aid Water Utilities in Deciding on a Design Philosophy: A Real Case Study in Belgium
by Ina Vertommen, Djordje Mitrović, Karel van Laarhoven, Pieter Piens and Maarten Torbeyns
Water 2022, 14(23), 3973; https://doi.org/10.3390/w14233973 - 6 Dec 2022
Cited by 5 | Viewed by 3631
Abstract
Numerical optimization is gradually finding its way into drinking water practice. For successful introduction of optimization into the sector, it is important that researchers and utility experts work together on the problem formulation with the water utility experts. Water utilities heed the solutions [...] Read more.
Numerical optimization is gradually finding its way into drinking water practice. For successful introduction of optimization into the sector, it is important that researchers and utility experts work together on the problem formulation with the water utility experts. Water utilities heed the solutions provided by optimization techniques only when the underlying approach and performance criteria match their specific goals. In this contribution, we demonstrate the application of numerical optimization on a real-life problem. The Belgian utility De Watergroep is looking to not only reinforce its distribution networks but to also structurally modify the network’s topology to enhance the quality of water delivered in the future. To help the utility explore the possibilities of these far-reaching changes in the most flexible way possible, an optimization problem was formulated to optimize topology and pipe sizing simultaneously for the distribution network of a Belgian city. The objective of the problem is to minimize the volume of the looped network and thereby work towards a situation where most of the customers are fed by branched extremities of the network. This objective is constrained by pressure and fire flow requirements and thresholds on the number of customers on the branched sections. The requirements for continuity of supply under failure scenarios are guaranteed by these constraints, as verified in the final solution. The results of the optimization process show that it is possible to design a network which is 18.5% cheaper than the currently existing network. Moreover, it turns out the—previously completely meshed—topology can be restructured so that 67% of the network length is turned into branched clusters, with a meshed superstructure of 33% of the length remaining. Full article
(This article belongs to the Special Issue Optimization Studies for Water Distribution Systems)
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<p>Illustration of the structured design of a WDN according to Dutch best practices, i.e., considering primary (blue), secondary (orange) and tertiary pipes (grey) [<a href="#B26-water-14-03973" class="html-bibr">26</a>].</p>
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<p>Illustration of the EPANET-model of the drinking water distribution network of City X. Connections to the utility’s transport network (modeled as reservoirs in the hydraulic model) are indicated by a magenta circle.</p>
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<p>Convergence curve of the optimization of the final design, with the number of generations on the <span class="html-italic">x</span>-axis (log-scale) and the performance on the <span class="html-italic">y</span>-axis.</p>
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<p>Layout of the secondary (magenta) and tertiary (grey) pipes in the current design (<b>a</b>) and the optimized design (<b>b</b>). Compared to the current design, the optimized design shows a clearer secondary structure and a greater number of branched tertiary pipes.</p>
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<p>Sections with more than 50 connections in the optimized network design. Sections that do not meet the requirement even in the current design are marked with a color with a corresponding number of connections.</p>
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<p>Network model of City X: Each color identifies a different double-fed cluster.</p>
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15 pages, 1699 KiB  
Review
Sustainable Use of Nano-Assisted Remediation for Mitigation of Heavy Metals and Mine Spills
by Neetu Sharma, Gurpreet Singh, Monika Sharma, Saglara Mandzhieva, Tatiana Minkina and Vishnu D. Rajput
Water 2022, 14(23), 3972; https://doi.org/10.3390/w14233972 - 6 Dec 2022
Cited by 6 | Viewed by 3598
Abstract
Increasing globalization in the last two decades has transformed the environment; hence, the demand for sustainable remediation approaches has also recorded an increasing trend. The varied sources of soil pollution include the application of chemical fertilizers and pesticides, industrial discharge, and transformed products [...] Read more.
Increasing globalization in the last two decades has transformed the environment; hence, the demand for sustainable remediation approaches has also recorded an increasing trend. The varied sources of soil pollution include the application of chemical fertilizers and pesticides, industrial discharge, and transformed products of these accumulated chemical residues. These processes may hamper the composition and soil ecosystem. Different types of methodologies ranging from physical, chemical, and biological approaches have been exploited to tackle of this challenge. The last decade has observed a significant application of nanotechnology for the treatment and removal of contaminants. Nanomaterial (NMs) research has contributed to a new dimension for the remediation of polluted soils. The use of engineered NMs has not only carried out the remediation of contaminated sites but also has proven useful in combatting the release of soil pollutants. They have paved the way for eco-friendly approaches for the detection of pollutants along with the restoration of polluted sites to their nascent stages, which will also help in increasing soil fertility. Nano-enabled remediation mechanisms require extensive field and target-specific research to deliver the required output. This review focused on recent trends, emphasized the areas for further improvement, and intended to understand the requirement of an interdisciplinary approach to utilize nanotechnology for multitasking remediation approaches comprising different contaminants. Full article
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<p>Sources and remediation strategies of water and soil contaminated with heavy metals.</p>
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<p>Remediation strategy for acid mine drainage (AMD).</p>
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20 pages, 16080 KiB  
Article
Compatibility between Continental Shelf Deposits and Sediments of Adjacent Beaches along Western Sardinia (Mediterranean Sea)
by Giovanni De Falco, Simone Simeone, Alessandro Conforti, Walter Brambilla and Emanuela Molinaroli
Water 2022, 14(23), 3971; https://doi.org/10.3390/w14233971 - 6 Dec 2022
Viewed by 2016
Abstract
The compatibility of sediments in terms of grain size, composition and colour among beaches and strategic sediment deposits (SSD) along Western Sardinia (Western Mediterranean Sea) were assessed to explore management strategy in the protection and adaptation to counteract the beach erosion and the [...] Read more.
The compatibility of sediments in terms of grain size, composition and colour among beaches and strategic sediment deposits (SSD) along Western Sardinia (Western Mediterranean Sea) were assessed to explore management strategy in the protection and adaptation to counteract the beach erosion and the effect of sea level rise along sandy shores. Twelve beaches, mainly conditioned by geological control, due to the presence of extensive rocky outcrops in the sea, enclosed in seven sedimentary cells (defined by the continuity of sediment transport pathways and by identification of boundaries where there are discontinuities), were characterised in terms of sediment composition and grain size. One hundred ninety-three beach sediments and one hundred sediments from SSDs were collected and analysed for sediment grain size, carbonate content and sediment colour. The beach sediments are composed by gravel to fine sands (D50: from 81 µm to 4986 µm) with siliciclastic and biogenic carbonate sediments mixed in different proportions (0–100% in CaCO3). The SSDs sediments are gravels to medium-fine sand (D50: from 96 µm to 1769 µm) composed by biogenic carbonate sands mixed with siliciclastic grains (0–100% in CaCO3). To be able to evaluate the compatibility between the beaches and SSDs, a multivariate statistical procedure was applied to grain size dataset. Our results show that 8 beaches have strategic deposits of compatible grain size and composition, whereas only 2 beaches have compatible strategic deposits of both grain size and colour. This may be related to the different sediment sources and depositional processes of sediment along the coastal cells and the continental shelf. Full article
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<p>Map of the study area with localities and geomorphic features. Location of continental shelf with samples in the submerged sand deposits and beach areas (numbers from 1 to 12) analysed in this study are also shown.</p>
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<p>Coastal littoral cells as defined in the present study. (<b>A</b>–<b>G</b>): cells or primary compartments. 1–12: beach areas or sub-cells. Blue arrows: longshore sediment transport direction.</p>
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<p>Mean grain size distribution groups (textural facies) in the beaches of the study area derived from EntropyMax.</p>
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<p>Discriminant score scatterplot for two discriminant functions: classification of 4 groups of sediment samples obtained by EM analysis (see <a href="#water-14-03971-f002" class="html-fig">Figure 2</a>) and the unknown SSDs. Discriminant variables are: 4000 μm, 2000 μm, 500 μm, 250 μm, 125 μm, and 63 μm; (<b>a</b>) discriminant score scatterplot for two discriminant functions for siliciclastic beach sediments; (<b>b</b>) discriminant score scatterplot for two discriminant functions for siliciclastic SSD sediments; (<b>c</b>) discriminant score scatterplot for two discriminant functions for mixed beach sediments; (<b>d</b>) discriminant score scatterplot for two discriminant functions for mixed SSD sediments (<b>e</b>) discriminant score scatterplot for two discriminant functions for biogenic carbonate beach sediments; (<b>f</b>) discriminant score scatterplot for two discriminant functions for biogenic carbonate SSD sediments.</p>
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<p>Discriminant score scatterplot for two discriminant functions: classification of 4 groups of sediment samples obtained by EM analysis (see <a href="#water-14-03971-f002" class="html-fig">Figure 2</a>) and the unknown SSDs. Discriminant variables are: 4000 μm, 2000 μm, 500 μm, 250 μm, 125 μm, and 63 μm; (<b>a</b>) discriminant score scatterplot for two discriminant functions for siliciclastic beach sediments; (<b>b</b>) discriminant score scatterplot for two discriminant functions for siliciclastic SSD sediments; (<b>c</b>) discriminant score scatterplot for two discriminant functions for mixed beach sediments; (<b>d</b>) discriminant score scatterplot for two discriminant functions for mixed SSD sediments (<b>e</b>) discriminant score scatterplot for two discriminant functions for biogenic carbonate beach sediments; (<b>f</b>) discriminant score scatterplot for two discriminant functions for biogenic carbonate SSD sediments.</p>
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<p>Spatial distribution of both beaches and SSDs sediment samples defined by Discriminant analysis and carbonates content. G1, G2, G3, and G4: Groups identified by Discriminant analysis; 0–20% carbonate: siliciclastic sediments; 20–60% carbonate: mixed sediments; 60–100% carbonate: biogenic carbonate sediments. Square (in bold): to assess the availability of SSDs to a specific group (see <a href="#sec4-water-14-03971" class="html-sec">Section 4</a> for description). (<b>1-A</b>) spatial distribution of siliciclastic beach sediments; (<b>2-A</b>) spatial distribution of mixed beach sediments; (<b>3-A</b>) spatial distribution of carbonate beach sediments; (<b>1-B</b>) spatial distribution of siliciclastic SSD sediments; (<b>2-B</b>) spatial distribution of mixed SSD sediments; (<b>3-B</b>) spatial distribution of carbonate SSD sediments.</p>
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19 pages, 3741 KiB  
Article
Hydrometeorological Forecast of a Typical Watershed in an Arid Area Using Ensemble Kalman Filter
by Ganchang He, Yaning Chen, Gonghuan Fang and Zhi Li
Water 2022, 14(23), 3970; https://doi.org/10.3390/w14233970 - 6 Dec 2022
Cited by 1 | Viewed by 1956
Abstract
The stationarity test and systematic prediction of hydrometeorological parameters are becoming increasingly important in water resources management. Based on the Ensemble Kalman Filter (EnKF) and wavelet analysis, this study selects precipitation, evaporation, temperature, and runoff as model variables, builds a model, tests and [...] Read more.
The stationarity test and systematic prediction of hydrometeorological parameters are becoming increasingly important in water resources management. Based on the Ensemble Kalman Filter (EnKF) and wavelet analysis, this study selects precipitation, evaporation, temperature, and runoff as model variables, builds a model, tests and analyzes the stationarity of the hydrometeorological parameters of the Manas River, and forecasts the selected parameters for two years. The results of the study show that during the 2000–2020 study period, precipitation in the Manas River Basin on the northern slope of the Tianshan Mountains shows a significant downward trend from 2016 to 2020, with an annual average decline rate of 23.30 mm/a over five years. The proportion of runoff during the flood season also increases, with the statistical probability of an extremely low value of runoff increasing by 37.62% on average. After using wavelet decomposition to provide input to EnKF, the NSE of the model for the prediction of precipitation, evaporation, temperature, and runoff reached 0.86, 0.89, 0.96, and 0.9 respectively. At the same time, the K-S value increases from 0.28 to 0.40, which means that the wavelet analysis technique has great potential as a preprocessing of the Ensemble Kalman filter. Full article
(This article belongs to the Special Issue Hydrological Modelling and Hydrometeorological Extreme Prediction)
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<p>Sites in the study area, with DEM distribution. The map is from Chinese Standard Map (<a href="http://bzdt.ch.mnr.gov.cn/" target="_blank">http://bzdt.ch.mnr.gov.cn/</a>, GS (2019)1822 (accessed on 22 November 2022))</p>
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<p>Wavelet power diagram of meteorological parameters from 2000 to 2020.</p>
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<p>Periodic wavelet decomposition based on multi-year moving average. The red boxes represent significant downtrends, which were observed by stationarity test.</p>
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<p>Monthly data forecast for four hydrological parameters using EnKF.</p>
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<p>Predictions and confidence intervals at two levels (0.8 and 0.95) with EnKF.</p>
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<p>Relationship between precipitation and altitude. In the figure, the blue line is the regression line which represents the optimal parameters, the gray area is enclosed by the upper and lower confidence bounds under the 0.95 confidence level, and the red points represent the meteorological station.</p>
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13 pages, 3710 KiB  
Article
Seasonal and Spatial Variations of Dissolved Organic Matter Biodegradation along the Aquatic Continuum in the Southern Taiga Bog Complex, Western Siberia
by Tatiana V. Raudina, Sergei V. Smirnov, Inna V. Lushchaeva, Georgyi I. Istigechev, Sergey P. Kulizhskiy, Evgeniya A. Golovatskaya, Liudmila S. Shirokova and Oleg S. Pokrovsky
Water 2022, 14(23), 3969; https://doi.org/10.3390/w14233969 - 6 Dec 2022
Cited by 1 | Viewed by 2039
Abstract
The inland aquatic ecosystems play a significant role in the global carbon cycle, owing to the metabolism of terrestrially derived organic matter as it moves through fluvial networks along the water continuum. During this transport, dissolved organic matter (DOM) is microbial processed and [...] Read more.
The inland aquatic ecosystems play a significant role in the global carbon cycle, owing to the metabolism of terrestrially derived organic matter as it moves through fluvial networks along the water continuum. During this transport, dissolved organic matter (DOM) is microbial processed and released into the atmosphere, but the degree and intensity of this processing vary greatly both spatially and temporally. The Western Siberian Lowlands is of particular interest for a quantitative assessment of DOM biodegradation potential because the global areal-scale effects of DOM biodegradation in abundant surface organic-rich waters might be the highest in this region. To this end, we collected water samples along a typical aquatic continuum of the Bakchar Bog (the north-eastern part of the Great Vasyugan Mire) and, following standardized protocol, conducted an experimental study aimed at characterizing the seasonal and spatial variability of dissolved organic carbon (DOC) biodegradability. The biodegradable DOC fraction (BDOC) over the exposure incubation period ranged from 2% to 25%. The natural aquatic continuum “mire–forest–stream–river” demonstrated the systematic evolution of biodegradable DOC among the sites and across the seasons. The highest biodegradation rates were measured during spring flood in May and decreased along the continuum. The maximum possible CO2 production from DOM yielded the maximum possible flux in the range of 0.1 and 0.2 g C-CO2 m−2 day−1 d, which is an order of magnitude lower than the actual net CO2 emissions from the inland waters of the WSL. This study suggests that although the biodegradation of the humic waters of the WSL can sizably modify the concentration and nature of the DOM along the aquatic continuum, it plays only a subordinary role in overall C emissions from the lakes and rivers of the region. Full article
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<p>Map of the study site in the Western Siberian Lowland with study site (red asterisk) and sampling points along the aquatic continuum as shown by yellow asterisks: Of—open sedge-sphagnum fen; Tr—tall ryam (pine–shrub–sphagnum phytocenosis with high pine trees); F—waterlogged pine-birch forest; KR—Klyuch River (small mire stream); BR—Bakchar River.</p>
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<p>The aquatic continuum of the study site represented by Klyuch River (<b>A</b>), waterlogged mixed forest (<b>B</b>), tall ryam (pine−shrub−sphagnum phytocenosis with high pine trees) (<b>C</b>); low ryam (pine−shrub−sphagnum phytocenosis with low pine trees) (<b>D</b>); open sedge−sphagnum fen (<b>E</b>). The numbers on the diagram represent the following: 1—sedge or sedge−wood mainly highly decomposed peat (sapric); 2—wood−grass and wood−sphagnum medium decomposed peat (hemic); 3—sphagnum (dominated by <span class="html-italic">S. magellanicum</span>, and <span class="html-italic">S. fuscum</span>), grass−sphagnum, pine−cotton and pine shrub mostly undecomposed and medium decomposed peat (fibric and hemic); 4—sphagnum and sedge−sphagnum undecomposed peat (fibric); 5—mire stream (Klyuch River); 6—water level (20.05.2015/18); 7—water level (20.08.2015/18); 8—water flow in May (surface streamflow is absent during summer-autumn drought period).</p>
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<p>Some initial water parameters in different mire micro-landscapes and rivers by season.</p>
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<p>DOC concentration (mg L<sup>−1</sup>, (<b>A1</b>–<b>A5</b>)) and BDOC (%, (<b>B1</b>–<b>B5</b>)) along the aquatic continuum by season during incubation time. The error bars are ±1 SD of the triplicates unless within the symbol size.</p>
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<p>SUVA<sub>254</sub> (L mg C<sup>−1</sup>m<sup>−1</sup>) along the aquatic continuum by season during incubation time. The error bars are ±1 SD of the triplicates unless within the symbol size.</p>
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30 pages, 2407 KiB  
Review
Aquatic Microplastic Pollution Control Strategies: Sustainable Degradation Techniques, Resource Recovery, and Recommendations for Bangladesh
by Abir Mahmud, Mustafa Md Wasif, Hridoy Roy, Fareen Mehnaz, Tasnim Ahmed, Md. Nahid Pervez, Vincenzo Naddeo and Md. Shahinoor Islam
Water 2022, 14(23), 3968; https://doi.org/10.3390/w14233968 - 6 Dec 2022
Cited by 12 | Viewed by 10567
Abstract
Microplastics’ dangers and the absence of effective regulation technologies have risen to prominence as a worldwide issue in recent years. South Asian countries, such as Bangladesh, are among the most threatened nations to face the drastic consequence of releasing microplastics into the aquatic [...] Read more.
Microplastics’ dangers and the absence of effective regulation technologies have risen to prominence as a worldwide issue in recent years. South Asian countries, such as Bangladesh, are among the most threatened nations to face the drastic consequence of releasing microplastics into the aquatic environment. The research on managing and degrading microplastics is ongoing, however, sustainable techniques have not yet been found. To create a green and efficient microplastic management plan, we have compiled all the information on the existing removal and degradation techniques for microplastics and provided an overview of all the noteworthy methods that can be implemented in Bangladesh. In the portrayed biotic and abiotic techniques, coagulation and photocatalysis were found to be most efficient in removing microplastics (as high as 99%) in different studies. The concept of microplastic is new to the researchers of Bangladesh, therefore, the characteristics, occurrence, fate, and threats are briefly discussed in this paper. Sampling, extraction, and identification methods of microplastic in freshwater and sediment samples are also thoroughly specified. The sources of microplastic pollution in Bangladesh and possible strategies that can be implemented to minimize additional microplastic discharge into aquatic environments are discussed. Although Bangladesh was the very first country to ban polythene, the failure of the implementation of rules and regulations and a lack of management strategy made Bangladesh the 10th worst country in managing plastic waste. This work is a wake-up call for other researchers to conduct an in-depth investigation to improve microplastic degrading technologies and develop a sustainable strategy to end microplastic pollution in Bangladesh. Full article
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<p>Contribution of various products in releasing microplastic.</p>
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<p>Usage of single-use plastic in different sectors of Bangladesh.</p>
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<p>Lifecycle of microplastic: generation to food chain.</p>
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<p>Steps in biotic degradation of microplastics.</p>
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<p>A diagram of the currently available advanced oxidation processes for the removal of microplastics.</p>
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<p>Mechanism for microplastic degradation using photocatalysis method [<a href="#B87-water-14-03968" class="html-bibr">87</a>].</p>
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<p>Coagulation, flocculation, and settling of microplastic.</p>
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<p>Setup of electrocoagulation.</p>
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<p>Various stages of waste management [<a href="#B134-water-14-03968" class="html-bibr">134</a>].</p>
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25 pages, 10278 KiB  
Article
Ensemble Evaluation and Member Selection of Regional Climate Models for Impact Models Assessment
by Amin Minaei, Sara Todeschini, Robert Sitzenfrei and Enrico Creaco
Water 2022, 14(23), 3967; https://doi.org/10.3390/w14233967 - 5 Dec 2022
Cited by 3 | Viewed by 2144
Abstract
Climate change increasingly is affecting every aspect of human life on the earth. Many regional climate models (RCMs) have so far been developed to carefully assess this important phenomenon on specific regions. In this study, ten RCMs captured from the European Coordinated Downscaling [...] Read more.
Climate change increasingly is affecting every aspect of human life on the earth. Many regional climate models (RCMs) have so far been developed to carefully assess this important phenomenon on specific regions. In this study, ten RCMs captured from the European Coordinated Downscaling Experiment (EURO CORDEX) platform are evaluated on the river Chiese catchment located in the northeast of Italy. The models’ ensembles are assessed in terms of the uncertainty and error calculated through different statistical and error indices. The uncertainties are investigated in terms of signal (increase, decrease, or neutral changes in the variables) and value uncertainties. Together with the spatial analysis of the data over the catchment, the weighted averaged values are used for the models’ evaluations and data projections. Using weighted catchment variables, climate change impacts are assessed on 10 different hydro-climatological variables showing the changes in the temperature, precipitation, rainfall events’ features, and the hydrological variables of the Chiese catchment between historical (1991–2000) and future (2071–2080) decades under RCP (Representative Concentration Path for increasing greenhouse gas emissions) scenario 4.5. The results show that, even though the multi-model ensemble mean (MMEM) could cover the outputs’ uncertainty of the models, it increases the error of the outputs. On the other hand, the RCM with the least error could cause high signal and value uncertainties for the results. Hence, different multi-model subsets of ensembles (MMEM-s) of 10 RCMs are obtained through a proposed algorithm for different impact models’ calculations and projections, making tradeoffs between two important shortcomings of model outputs, which are error and uncertainty. The single model (SM) and multi-model (MM) outputs imply that catchment warming is obvious in all cases and, therefore, evapotranspiration will be intensified in the future where there are about 1.28% and 6% value uncertainties for monthly temperature increase and the decadal relative balance of evapotranspiration, respectively. While rainfall events feature higher intensity and shorter duration in the SM, there are no significant differences for the mentioned features in the MM, showing high signal uncertainties in this regard. The unchanged catchment rainfall events’ depth can be observed in two SM and MM approaches, implying good signal certainty for the depth feature trend; there is still high uncertainty about the depth values. As a result of climate change, the percolation component change is negligible, with low signal and value uncertainties, while decadal evapotranspiration and discharge uncertainties show the same signal and value. While extreme events and their anomalous outcomes direct the uncertainties in rainfall events’ features’ values towards zero, they remain critical for yearly maximum catchment discharge in 2071–2080 as the highest value uncertainty is observed for this variable. Full article
(This article belongs to the Section Hydrology)
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<p>The river Chiese catchment, located at the northeast of Italy.</p>
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<p>The grid points of the E-OBS (pink circles) in the Chiese domain. Blue diamonds and red stars refer to the real gauge rainfall stations and CMs grid points. E-OBS grid points within the catchment are called nodes.</p>
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<p>The Thiessen polygons of the fictitious stations of E-OBS and CMs’ data, left and right.</p>
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<p>Algorithm for balancing ensemble members between error and uncertainty for an impact model assessment.</p>
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<p>The time axis of three independent rainfall events.</p>
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<p>TOPKAPI user interface windows for inserting the inputs.</p>
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<p>Ensemble model for the under-study climate models’ performances in the reconstruction of monthly cumulative precipitation data of Chiese catchment (for finding the exact error values, please refer to <a href="#app1-water-14-03967" class="html-app">Table S1</a>).</p>
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<p>Under study climate models’ outputs deviation from grand ensemble mean (MMEM) for the reconstruction of monthly cumulative precipitation of the Chiese catchment (for the exact values of uncertainty, please refer to <a href="#app1-water-14-03967" class="html-app">Table S2</a>).</p>
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<p>Ensemble model for the under-study climate models’ performances in the reconstruction of monthly average temperature data of Chiese catchment (for finding the exact error values, please refer to <a href="#app1-water-14-03967" class="html-app">Table S3</a>).</p>
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<p>Under study climate models’ output deviation from grand ensemble mean (MMEM) for the reconstruction of the monthly average temperature of Chiese catchment (for the exact values of uncertainty, please refer to <a href="#app1-water-14-03967" class="html-app">Table S4</a>).</p>
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<p>Ensemble model for analyzing spatial variability of 12 models, 10 RCMs, E-OBS, and MMEM models for reconstructing of historical, 1971–2000, seasonal cumulative precipitation values. The red and pink nodes in the figures refer to the fictitious stations of climate and E-OBS models.</p>
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<p>Ensemble model for analyzing spatial variability of 12 models, 10 RCMs, E-OBS, and MMEM models for reconstructing of historical, 1971–2000, seasonal cumulative precipitation values. The red and pink nodes in the figures refer to the fictitious stations of climate and E-OBS models.</p>
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<p>Ensemble model for analyzing spatial variability of 12 models, 10 RCMs, E-OBS, and MMEM models for reconstruction of historical, 1971–2000, seasonal average temperature values. The red and pink nodes in the figures refer to the fictitious stations of climate and E-OBS models.</p>
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<p>Temperature and precipitation changes in the Chiese catchment under Scenario 4.5 through the single-model (SM) and multi-model (MM) approaches.</p>
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<p>The rainfall event characteristics of Chiese catchment, duration, depth, and intensity change under Scenario 4.5 obtained by single and multi-model approaches.</p>
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<p>The rainfall event characteristics of Chiese catchment, duration, depth, and intensity change under Scenario 4.5 obtained by single and multi-model approaches.</p>
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<p>The yearly maximum discharge CFD for Chiese catchment over the historical and future periods obtained by single-model and multi-model approaches.</p>
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34 pages, 36674 KiB  
Article
Structural Failure of the Cohesive Core of Rockfill Dams: An Experimental Research Using Sand-Bentonite Mixtures
by Ricardo Monteiro-Alves, Rafael Moran, Miguel Á. Toledo and Javier Peraita
Water 2022, 14(23), 3966; https://doi.org/10.3390/w14233966 - 5 Dec 2022
Cited by 2 | Viewed by 1975
Abstract
This article presents experimental research focusing on the structural failure of the central core of a rockfill dam using sand-bentonite mixtures. It comprised an extensive geotechnical characterization of soil materials and mixtures, including compaction and strength tests, as well as the construction of [...] Read more.
This article presents experimental research focusing on the structural failure of the central core of a rockfill dam using sand-bentonite mixtures. It comprised an extensive geotechnical characterization of soil materials and mixtures, including compaction and strength tests, as well as the construction of 1 m high and 1.5 m wide physical models. The displacements of the cohesive cores were recorded using a tailored measuring system, based on a laser pointer and a mirror, designed to amplify the real displacements. The cohesive cores were extremely sensitive to small oscillations and behaved as rigid bodies, similar to concrete slabs with three fixed sides and another free. The shape and dimensions of the breach formed on the cohesive cores had roughly the same shape and dimensions as the unprotected area. This experimental research has the potential to be used as validation tool for several models available in the literature to predict the failure of embankment dams. Full article
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<p>Scheme of the location of a cohesive central core inside the UPM flume: (<b>a</b>) Side view of the left wall; (<b>b</b>) Top view of the flume.</p>
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<p>Images of the mixing process: (<b>a</b>) Sprinkling the sand-bentonite mixture with water while the concrete mixer is working; (<b>b</b>) Final moisture homogenization using a hoe.</p>
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<p>Images of the construction of the cohesive central core: (<b>a</b>) Placement of tape on the ‘vertical’ joints between the cohesive core and the metallic L profiles to avoid seepage; (<b>b</b>) Cut excess material from the core downstream face; (<b>c</b>) Formwork to compact the cohesive material; (<b>d</b>) Removal of the two upper panels of the formwork after compaction is finished; (<b>e</b>) Cut excess material from the core crest; (<b>f</b>) Protection of the downstream face and crest of the core with industrial vaseline (B-2 from Tecmasol); (<b>g</b>) Placement of downstream protection to simulate the downstream shoulder support; (<b>h</b>) Protection of the upstream face of the core with industrial vaseline (B-2 from Tecmasol); (<b>i</b>) Placement of tape on the horizontal joint between the cohesive core and the floor and placement of a metallic profile to avoid lifting and buoyancy of the tape.</p>
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<p>Images of the aluminum protection system for the simulation of the support of the core on the downstream shoulder: (<b>a</b>) Smaller unprotection width; (<b>b</b>) Larger unprotection width; (<b>c</b>) Detail of the ‘1/3’ height metallic supports on the left wall of the flume; (<b>d</b>) Detail of the clamps used to support the upper section; (<b>e</b>) Detail of the metallic supports on the right wall of the flume; (<b>f</b>) Detail of the metallic supports on the left wall of the flume.</p>
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<p>Images of the displacement measuring system: (<b>a</b>) View from the upstream side; (<b>b</b>) View from the downstream side; (<b>c</b>) View along the cohesive core crest.</p>
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<p>Top view scheme of the measuring system designed to control the displacements of the cohesive core. The dimensions of the elements of this system are not scaled to their true dimensions.</p>
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<p>Summary of the Standard Proctor Tests: (<b>a</b>) Soil mixtures with a bentonite content of 18%; (<b>b</b>) Soil mixtures with a bentonite content of 31%. The subscript ‘r’ stands for ‘repeated’.</p>
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<p>(<b>a</b>) Strength measurements obtained with the Geotester Pocket Penetrometer distinguished by the size of the plunger. The logarithmic regression curve was fitted to all tests except CB18-P8, and the quadratic regression curve to the tests CB18-P4, CB18-P9 and CB18-P10; (<b>b</b>) Measurements made with the Pocket Shear Vane Tester distinguished by the vane size. The quadratic regression curve is fitted using all data points except CB18-P5 as it was obtained with the smaller vane instead of the standard; In both charts, the big solid dots represent the average strength value, the small white dots represent each of the measurements, and the horizontal lines represent the extension of one standard deviation from the mean.</p>
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<p>Strength measurements obtained during the construction of cohesive cores with the Geotester Pocket Penetrometer (<span class="html-italic">c</span><sub>p</sub>) and the Humboldt H−4212MH Pocket Shear Vane Tester (<span class="html-italic">s</span><sub>u</sub>). Cohesive cores used for (<b>a</b>) PRELIM1 and PRELIM2, (<b>b</b>) MAIN1, (<b>c</b>) MAIN2; (<b>d</b>) MAIN3 and MAIN3+ (<a href="#water-14-03966-t004" class="html-table">Table 4</a>). The strengths <span class="html-italic">s</span><sub>u,goal</sub> and <span class="html-italic">c</span><sub>p,goal</sub> are estimates for a CB18 mixture with an optimal moisture content of 20.0% and a relative maximum dry density <span class="html-italic">ρ</span><sub>d,max</sub> = 1692 kN·m<sup>−3</sup> (<a href="#water-14-03966-f008" class="html-fig">Figure 8</a>). On the other hand, <span class="html-italic">s</span><sub>u,mean</sub> and <span class="html-italic">c</span><sub>p,mean</sub> are the average strengths using all measurements.</p>
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<p>Reservoir level elevation and the cohesive core displacements. (<b>a</b>) Test MAIN1 including the reservoir filling period; (<b>b</b>) Detail of the test MAIN1; (<b>c</b>) Test MAIN2 including the reservoir filling period; (<b>d</b>) Detail of the test MAIN2; (<b>e</b>) Tests MAIN3 and MAIN3+ including the reservoir filling period; (<b>f</b>) Detail of the test MAIN3. The times 10 s, 100 s, 1000 s, and 2000 represent the time in seconds elapsed from the moment a given protection slot was removed.</p>
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<p>Sequential images of the failures of the Main Laboratory Experiments. (<b>a</b>) Test MAIN1; (<b>b</b>) Test MAIN2; (<b>c</b>) Test MAIN3; (<b>d</b>) Extra test MAIN3+ from the downstream side; (<b>e</b>) Extra test MAIN3+ from the upstream side.</p>
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<p>Relative maximum dry density and optimal moisture content (white dot) for mixtures with 18% bentonite content.</p>
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<p>Phase I Simple Compression samples compared to the standard Proctor samples. (<b>a</b>) Moisture content of the soil mixtures (ωmix) prepared for the compaction of the Simple Compression Phase I samples versus the target moisture content (ωProctor); (<b>b</b>) Apparent density of the Simple Compression Phase I samples (ρsample) versus the target density (ρProctor); values refer to the number of the sample.</p>
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<p>Relation between the compacted soil sample’s density and the unconfined shear strength. Values refer to the number of the sample.</p>
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<p>Direct Shear samples compared to the standard Proctor samples. (<b>a</b>) Moisture content of the Direct Shear samples (<span class="html-italic">ω</span><sub>sample</sub>) versus the target moisture content (<span class="html-italic">ω</span><sub>Proctor</sub>); (<b>b</b>) Apparent density of the Direct Shear samples (<span class="html-italic">ρ</span><sub>sample</sub>) versus the target density (<span class="html-italic">ρ</span><sub>Proctor</sub>).</p>
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<p>Results of the Simple Compression tests. (<b>a</b>–<b>f</b>) Phase I; (<b>g</b>–<b>h</b>) Phase II.</p>
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14 pages, 4329 KiB  
Article
Smart Water Solutions for the Operation and Management of a Water Supply System in Aracatuba, Brazil
by Kyudae Shim, Eduardo Berrettini and Yong-Gyun Park
Water 2022, 14(23), 3965; https://doi.org/10.3390/w14233965 - 5 Dec 2022
Cited by 3 | Viewed by 3332
Abstract
Because of population growth, rapid urbanization, and climate change, many water supply utilities globally struggle to provide water that is safe to drink. A particular problem is the aging of the water supply facilities, which is exacerbated by their inefficient operation and maintenance [...] Read more.
Because of population growth, rapid urbanization, and climate change, many water supply utilities globally struggle to provide water that is safe to drink. A particular problem is the aging of the water supply facilities, which is exacerbated by their inefficient operation and maintenance (O&M). For this reason, many water utilities have recently been actively adopting intelligent and integrated water supply O&M solutions that utilize information and communication technology, the Internet of Things, big data, and artificial intelligence to solve water supply system problems. In this study, smart water solutions (GSWaterS) were implemented to enhance the efficiency of the water supply system in the city of Aracatuba, Brazil. They were used to monitor and analyze the operating conditions of the water supply system in real time, thus allowing for the effective management of water supply assets. GSWaterS also supports the design and optimization of district metered areas, the reduction and management of water losses, real-time water network analysis, and big data analysis using artificial intelligence. Economic analysis revealed that GSWaterS produces various direct and indirect benefits for the water supply system. Full article
(This article belongs to the Section Urban Water Management)
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<p>Water supply system in Aracatuba, Brazil.</p>
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<p>Functional diagram of the network system in GSWaterS.</p>
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<p>System configuration of the water network system in Aracatuba, Brazil.</p>
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<p>Customized graphical user interface for GSWaterS.</p>
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<p>Deep learning algorithm and RNN model.</p>
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<p>DMA design and information for the Jussara area, Aracatuba.</p>
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<p>Water loss rates (Aracatuba vs. Jussara).</p>
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28 pages, 4876 KiB  
Article
Degradation of Carbamazepine by HF-Free-Synthesized MIL-101(Cr)@Anatase TiO2 Composite under UV-A Irradiation: Degradation Mechanism, Wastewater Matrix Effect, and Degradation Pathway
by J. W. Goh, Y. Xiong, W. Wu, Z. Huang, S. L. Ong and J. Y. Hu
Water 2022, 14(23), 3964; https://doi.org/10.3390/w14233964 - 5 Dec 2022
Cited by 2 | Viewed by 2450
Abstract
TiO2 has been hampered by drawbacks such as rapid photoelectron and hole recombination and a wide energy band gap of 3.2 eV. In this study, MIL-101(Cr)@TiO2 was synthesised without any mineraliser (HF) as part of material modification approach to overcome those [...] Read more.
TiO2 has been hampered by drawbacks such as rapid photoelectron and hole recombination and a wide energy band gap of 3.2 eV. In this study, MIL-101(Cr)@TiO2 was synthesised without any mineraliser (HF) as part of material modification approach to overcome those pitfalls. The composite was well characterized by XRD, FT-IR, TEM, XPS, BET, TGA, and Raman spectroscopy. Under optimal synthesis conditions, the 9.17% MIL-101(Cr)@TiO2 composite exhibited 99.9% CBZ degradation after 60 min under UV-A irradiation. This can be attributed to the delayed recombination of photo-generated h+ and e and a reduced band gap energy of 2.9 eV. A Type II heterojunction structure was proposed for the composite using the Mulligan function of electronegativity with the calculated Ecb and Evb. Besides, trapping experiments and ESR spectroscopy confirmed O2•− as the main ROS for CBZ degradation. The effects of the operating parameters such as pH, UV intensity, composite dosage, and initial pollutant concentration were also evaluated. The scavenging effects of inorganic and organic constituents of pharmaceutical wastewater on the process were also evaluated, with HCO3, CO32−, and THF having more significant inhibition on the overall CBZ degradation. The degradation pathways of CBZ were also proposed based on detected intermediates with the aid of LC/MS/MS. The composite illustrated reusability and stability without considerable loss in the degradation performance after repeated runs. This work builds on the development of more effective photocatalysts and provides a glimpse into applications for similar MOF heterojunction photocatalysts. Full article
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<p>(<b>A</b>) Transmission electron microscopy (TEM); (<b>B</b>) high-resolution transmission electron microscopy (HRTEM); (<b>C</b>) transmission electron microscopy energy-dispersive spectroscopy (TEM EDS) point detection, EDS mapping; and the (<b>D</b>) Ti, (<b>E</b>) Cr, and (<b>F</b>) O of the MIL-101(Cr)@TiO<sub>2</sub> composite.</p>
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<p>FTIR spectra of (<b>A</b>) H<sub>2</sub>BDC and MIL−101(Cr), and (<b>B</b>) MIL−101(Cr)@TiO<sub>2</sub>, MIL−101(Cr), and TiO<sub>2;</sub> XRD spectra of (<b>C</b>) synthesised and simulated MIL−101(Cr), and (<b>D</b>) synthesised MIL−101(Cr)@TiO<sub>2</sub> and respective simulated constituents; Raman spectroscopy of (<b>E</b>) MIL−101(Cr), and (<b>F</b>) MIL−101(Cr)@TiO<sub>2</sub> and TiO<sub>2</sub>.</p>
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<p>(<b>A</b>) Adsorption kinetics models (Pseudo 1st and 2nd) for varied percentages of TiO<sub>2</sub>: MIL-101(Cr) composite; (<b>B</b>) N<sub>2</sub> adsorption–desorption isotherms; (<b>C</b>) BET cumulative specific surface area of MIL-101(Cr) and 9.17% MIL-101(Cr)@TiO<sub>2</sub> composite; (<b>D</b>) ln(C<sub>o</sub>/C<sub>CBZ</sub>) against time; and (<b>E</b>) k<sub>Obs</sub>, min<sup>−1</sup> for varied MIL-101(Cr): TiO<sub>2</sub> ratio composites.</p>
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<p>(<b>A</b>) ln(C<sub>o</sub>/C<sub>CBZ</sub>) against time at varied temperatures; (<b>B</b>) k<sub>Obs</sub>, min<sup>−1</sup> due to varied calcination temperatures. TGA curves of (<b>C</b>) MIL−101(Cr) and (<b>D</b>) anatase TiO<sub>2</sub> under inert conditions (N<sub>2</sub>).</p>
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<p>CBZ photocatalytic degradation and adsorption of different samples (2 g/L dosage, 12 mg/L of CBZ, and UV-A irradiation at 35–38 mW/cm<sup>2</sup> with a reaction time of 60 min).</p>
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<p>(<b>A</b>) CBZ removal efficiency and rate constants of CBZ degradation (min<sup>−1</sup>) for various different samples; (<b>B</b>) TOC mineralization (%) of CBZ using UV−A/MIL−101(Cr)@TiO<sub>2</sub>; (<b>C</b>) Tauc plot of MIL−101(Cr), MIL−101(Cr)@TiO<sub>2</sub>, and TiO<sub>2</sub>; and (<b>D</b>) fluorescence emission spectrum of MIL−101(Cr)@TiO<sub>2</sub> and TiO<sub>2</sub>.</p>
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<p>Proposed degradation mechanism using the UV−A/MIL−101(Cr)@TiO<sub>2</sub> system.</p>
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<p>(<b>A</b>) C<sub>CBZ</sub>/C<sub>CBZo</sub> against time; (<b>B</b>) removal efficiency of CBZ (%) at 50 min; (<b>C</b>) ln(C<sub>CBZo</sub>/C<sub>CBZ</sub>) against time; (<b>D</b>) degradation rate constants, min<sup>−1</sup> due to the effects of different scavengers (MIL−101(Cr)@TiO<sub>2</sub> composite = 2 g/L, CBZ = 10 ppm, UV−A intensity = 36.6 mW/cm<sup>2</sup>); and ESR spectra of (<b>E</b>) DMPO−OH against dark state and (<b>F</b>) DMPO−OOH against dark state (DMPO = 100 mM, MIL−101(Cr)@TiO<sub>2</sub> = 2 g/L).</p>
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<p>The k<sub>Obs</sub>, min<sup>−1</sup> due to (<b>A</b>) varied pH conditions, (<b>B</b>) varied UV intensities, (<b>C</b>) varied dosages of composite, and (<b>D</b>) varied CBZ concentrations.</p>
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<p>(<b>A</b>) ln (C<sub>o</sub>/C<sub>ave</sub>) against time and (<b>B</b>) k<sub>obs</sub>, min<sup>−1</sup> for varied [HCO<sub>3</sub><sup>−</sup>] conditions; (<b>C</b>) ln (C<sub>o</sub>/C<sub>ave</sub>) against time and (<b>D</b>) k<sub>obs</sub>, min<sup>−1</sup> for varied [CO<sub>3</sub><sup>2−</sup>] conditions; (<b>E</b>) ln (C<sub>o</sub>/C<sub>ave</sub>) against time and (<b>F</b>) k<sub>obs</sub>, min<sup>−1</sup> for varied [Cl<sup>−</sup>] conditions; and (<b>G</b>) ln (C<sub>o</sub>/C<sub>ave</sub>) against time and (<b>H</b>) k<sub>obs</sub>, min<sup>−1</sup> for varied [SO<sub>4</sub><sup>2−</sup>] conditions.</p>
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<p>k<sub>obs</sub>, min<sup>−1</sup> for (<b>A</b>) varied concentrations of DMF, (<b>B</b>) varied concentrations of THF, (<b>C</b>) different dilution factors of PWS 1, and (<b>D</b>) different dilution factors of PWS 2.</p>
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<p>(<b>A</b>) Recycle experiments of MIL−101(Cr)@TiO<sub>2</sub> composite for CBZ degradation. (<b>B</b>) FTIR spectrum of MIL−101(Cr)@TiO<sub>2</sub>; black: Virgin, red: after 5th cycle.</p>
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<p>Proposed degradation pathways of CBZ using MIL-101(Cr)@TiO<sub>2</sub> under UV-A irradiation.</p>
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45 pages, 49213 KiB  
Article
Assessment of Implementing Land Use/Land Cover LULC 2020-ESRI Global Maps in 2D Flood Modeling Application
by Mohamed Soliman, Mohamed M. Morsy and Hany G. Radwan
Water 2022, 14(23), 3963; https://doi.org/10.3390/w14233963 - 5 Dec 2022
Cited by 3 | Viewed by 4404
Abstract
Floods are one of the most dangerous water-related risks. Numerous sources of uncertainty affect flood modeling. High-resolution land-cover maps along with appropriate Manning’s roughness values are the most significant parameters for building an accurate 2D flood model. Two land-cover datasets are available: the [...] Read more.
Floods are one of the most dangerous water-related risks. Numerous sources of uncertainty affect flood modeling. High-resolution land-cover maps along with appropriate Manning’s roughness values are the most significant parameters for building an accurate 2D flood model. Two land-cover datasets are available: the National Land Cover Database (NLCD 2019) and the Land Use/Land Cover for Environmental Systems Research Institute (LULC 2020-ESRI). The NLCD 2019 dataset has national coverage but includes references to Manning’s roughness values for each class obtained from earlier studies, in contrast to the LULC 2020-ESRI dataset, which has global coverage but without an identified reference to Manning’s roughness values yet. The main objectives of this study are to assess the accuracy of using the LULC 2020-ESRI dataset compared with the NLCD 2019 dataset and propose a standard reference to Manning’s roughness values for the classes in the LULC 2020-ESRI dataset. To achieve the research objectives, a confusion matrix using 548,117 test points in the conterminous United States was prepared to assess the accuracy by quantifying the cross-correspondence between the two datasets. Then statistical analyses were applied to the global maps to detect the appropriate Manning’s roughness values associated with the LULC 2020-ESRI map. Compared to the NLCD 2019 dataset, the proposed Manning’s roughness values for the LULC 2020-ESRI dataset were calibrated and validated using 2D flood modeling software (HEC-RAS V6.2) on nine randomly chosen catchments in the conterminous United States. This research’s main results show that the LULC 2020-ESRI dataset achieves an overall accuracy of 72% compared to the NLCD 2019 dataset. The findings demonstrate that, when determining the appropriate Manning’s roughness values for the LULC 2020-ESRI dataset, the weighted average technique performs better than the average method. The calibration and validation results of the proposed Manning’s roughness values show that the overall Root Mean Square Error (RMSE) in depth was 2.7 cm, and the Mean Absolute Error (MAE) in depth was 5.32 cm. The accuracy of the computed peak flow value using LULC 2020-ESRI was with an average error of 5.22% (2.0% min. to 8.8% max.) compared to the computed peak flow values using the NLCD 2019 dataset. Finally, a reference to Manning’s roughness values for the LULC 2020-ESRI dataset was developed to help use the globally available land-use/land-cover dataset to build 2D flood models with an acceptable accuracy worldwide. Full article
(This article belongs to the Special Issue The Impact of Climate Change and Land Use on Water Resources)
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<p>NLCD 2019 Classifications [<a href="#B31-water-14-03963" class="html-bibr">31</a>].</p>
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<p>LULC 2020-ESRI classifications for conterminous United States only [<a href="#B33-water-14-03963" class="html-bibr">33</a>].</p>
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<p>Research methodology for part 1: data preparation, accuracy assessment, and roughness analysis.</p>
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<p>Research methodology for part 2: calibrating and validating the developed roughness maps using flood modeling.</p>
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<p>Frequency analysis for the extracted samples from the NLCD 2019 dataset.</p>
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<p>Frequency analysis for the extracted samples from the LULC 2020-ESRI dataset.</p>
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<p>Frequency analysis for the biased classes from the LULC 2020-ESRI map. (<b>A</b>) Tree, (<b>B</b>) Built area, (<b>C</b>) Grass.</p>
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<p>Frequency analysis for the biased classes from the LULC 2020-ESRI map. (<b>A</b>) Tree, (<b>B</b>) Built area, (<b>C</b>) Grass.</p>
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<p>LULC 2020-ESRI base Manning’s roughness values using both average and weighted average methods.</p>
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<p>The selected nine catchments’ locations on a satellite-based image.</p>
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<p>DEM and land-use maps for Catchment CA-01 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>DEM and land-use maps for Catchment CA-01 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>HEC-RAS calibration outputs: flow hydrographs at catchment outlet and maximum depths over the catchment area for CA-01.</p>
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<p>Peak-flow error using LULC 2020-ESRI along with (<b><span class="html-italic">n</span></b><span class="html-italic"><sub>avg</sub></span>) and (<b><span class="html-italic">n</span></b><span class="html-italic"><sub>w.avg</sub></span>) values compared to the base calibrated results.</p>
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<p>Peak-flow error using LULC 2020-ESRI along with (<b><span class="html-italic">n</span></b><span class="html-italic"><sub>avg</sub></span>) and (<b><span class="html-italic">n</span></b><span class="html-italic"><sub>w.avg</sub></span>) values compared to the base validation results.</p>
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<p>Time to peak flow in hours using LULC 2020-ESRI with (<b><span class="html-italic">n</span></b><span class="html-italic"><sub>avg</sub></span>) and (<b><span class="html-italic">n</span></b><span class="html-italic"><sub>w.avg</sub></span>), compared to base map calibration results.</p>
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<p>Time to peak flow in hours using LULC 2020-ESRI with (<b><span class="html-italic">n</span></b><span class="html-italic"><sub>avg</sub></span>) and (<b><span class="html-italic">n</span></b><span class="html-italic"><sub>w.avg</sub></span>), compared to base map validation results.</p>
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<p>DEM and land-use maps for Catchment CA-01 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>DEM and land-use maps for Catchment CA-01 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>DEM and land-use maps for Catchment CA-02 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>DEM and land-use maps for Catchment CA-02 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>DEM and land-use maps for Catchment CA-03 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>DEM and land-use maps for Catchment CA-03 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>DEM and land-use maps for Catchment CA-04 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>DEM and land-use maps for Catchment CA-04 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>DEM and land-use maps for Catchment CA-05 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>DEM and land-use maps for Catchment CA-05 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>DEM and land-use maps for Catchment CA-06 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>DEM and land-use maps for Catchment CA-06 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>DEM and land-use maps for Catchment CA-07 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>DEM and land-use maps for Catchment CA-07 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>DEM and land-use maps for Catchment CA-08 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>DEM and land-use maps for Catchment CA-08 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>DEM and land-use maps for Catchment CA-09 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>DEM and land-use maps for Catchment CA-09 (<b>A</b>) DEM, (<b>B</b>) NLCD 2019 land-use map, (<b>C</b>) LULC 2020-ESRI land-use map.</p>
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<p>HEC-RAS 2D calibration results, maximum depth, hydrograph at outlet (<b>A</b>) Catchment CA-01, (<b>B</b>) Catchment CA-02, (<b>C</b>) Catchment CA-03.</p>
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<p>HEC-RAS 2D calibration results, maximum depth, hydrograph at outlet (<b>A</b>) Catchment CA-01, (<b>B</b>) Catchment CA-02, (<b>C</b>) Catchment CA-03.</p>
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<p>HEC-RAS 2D calibration results, maximum depth, hydrograph at outlet (<b>A</b>) Catchment CA-04, (<b>B</b>) Catchment CA-05, (<b>C</b>) Catchment CA-06.</p>
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<p>HEC-RAS 2D calibration results, maximum depth, hydrograph at outlet (<b>A</b>) Catchment CA-04, (<b>B</b>) Catchment CA-05, (<b>C</b>) Catchment CA-06.</p>
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<p>HEC-RAS 2D calibration results, maximum depth, hydrograph at outlet (<b>A</b>) Catchment CA-07, (<b>B</b>) Catchment CA-08, (<b>C</b>) Catchment CA-09.</p>
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<p>HEC-RAS 2D calibration results, maximum depth, hydrograph at outlet (<b>A</b>) Catchment CA-07, (<b>B</b>) Catchment CA-08, (<b>C</b>) Catchment CA-09.</p>
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18 pages, 5696 KiB  
Article
Modeling the Effectiveness of Sustainable Agricultural Practices in Reducing Sediments and Nutrient Export from a River Basin
by José Pedro Ramião, Cláudia Carvalho-Santos, Rute Pinto and Cláudia Pascoal
Water 2022, 14(23), 3962; https://doi.org/10.3390/w14233962 - 5 Dec 2022
Cited by 6 | Viewed by 3607
Abstract
Water pollution from unsustainable agricultural practices is a global problem that undermines human health and economic development. Sustainable agricultural practices have been considered to maintain global food production without compromising water quality and ecosystem health. However, the effectiveness of sustainable agricultural practices in [...] Read more.
Water pollution from unsustainable agricultural practices is a global problem that undermines human health and economic development. Sustainable agricultural practices have been considered to maintain global food production without compromising water quality and ecosystem health. However, the effectiveness of sustainable agricultural practices in reducing sediments and nutrient export and the combination of practices that will best achieve water quality objectives is still under-explored. In this study, we assess the effectiveness of sustainable agricultural practices in reducing sediments and nutrients export to rivers and determine the combination of practices that would allow the highest reductions of sediments and nutrients, using the Soil and Water Assessment Tool (SWAT) in a Portuguese river basin highly affected by agricultural pollution. SWAT was calibrated and validated for river discharge, sediments, phosphorous, and nitrate loads at the outlet of the basin, with a good agreement between simulated and observed values. The effects of filter strips, fertilizer incorporation, and conservation tillage were analyzed considering both individual and combined effects. Our study shows that sustainable agricultural practices can substantially reduce sediments and nutrients export from a river basin, with the highest average combined depletion of sediments, phosphorus, and nitrate export (25%) achieved when fertilizer incorporation, conservation tillage, and filter strips were implemented simultaneously. Additional studies exploring the effect of sustainable agricultural practices across a range of climate and watershed characteristics, as well as their capacity to deal with challenges related to climate change, will further improve our understanding of the effectiveness of sustainable agricultural practices. Full article
(This article belongs to the Special Issue Water Quality Management of Inland Waters)
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<p>Location of the study area in the Cávado River Basin, land cover [<a href="#B21-water-14-03962" class="html-bibr">21</a>], calibration sites, and dams (SNIRH).</p>
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<p>Selected scenarios to examine single and combined effects of sustainable agricultural practices on sediments, nitrate, and phosphorous export, considering 2 fertilizer application methods, 3 tillage operations, and the implementation of filter strips. The first scenario refers to the current agricultural practices in the basin, the scenarios in gray refer to the single effects of SAPs, while the other scenarios refer to the combined effects of SAPs.</p>
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<p>Monthly observed and simulated data at the basin outlet for river discharge, sediment, total phosphorous, and nitrate for calibration (1995–1997) and validation (1998–2000) of the SWAT model; 95PPU refers to the 95% prediction uncertainty of the model.</p>
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<p>Percentage of change in monthly average sediment export under different sustainable agricultural practices. Percentage of change regarding the baseline scenario, with a broadcast application of fertilizer, conventional tillage, and current riparian cover (i.e., no filter strips).</p>
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<p>Percentage of change in monthly average phosphorous export under different sustainable agricultural practices. Percentage of change regarding the baseline scenario, with broadcast application of fertilizer, conventional tillage, and current riparian cover (i.e., no filter strips).</p>
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<p>Percentage of change in monthly average nitrate export under different sustainable agricultural practices. Percentage of change regarding the baseline scenario, with broadcast application of fertilizer, conventional tillage, and current riparian cover (i.e., no filter strips).</p>
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13 pages, 2504 KiB  
Article
Numerical Investigation on EOR in Porous Media by Cyclic Water Injection with Vibration Frequency
by Hongen Yang, Junming Lao, Delin Tong and Hongqing Song
Water 2022, 14(23), 3961; https://doi.org/10.3390/w14233961 - 5 Dec 2022
Cited by 1 | Viewed by 1741
Abstract
Water injection with an oscillatory pressure boundary is a promising technology, which can achieve a more economical and environment-friendly EOR (enhanced oil recovery). However, due to the unclear critical injection frequency, its oil production performance has been unstable and is far from reaching [...] Read more.
Water injection with an oscillatory pressure boundary is a promising technology, which can achieve a more economical and environment-friendly EOR (enhanced oil recovery). However, due to the unclear critical injection frequency, its oil production performance has been unstable and is far from reaching the optimal level. Here, a numerical model is established for oil recovery by the water injection with the oscillatory boundary condition to find out the critical frequency for the optimal EOR. The correlations between the water injection frequency and the EOR level at diverse oil–water surface tensions and oil viscosities are integrated into the model. Our numerical model reveals that an optimal EOR of roughly 10% is achieved at the critical water injection frequency compared with water injection without an oscillatory boundary. The EOR mechanism is revealed showing that upon water injection with the optimum frequency, the formation of the preferential pathways is inhibited and the pressure transmits to the wall sides to displace the oil. Moreover, it is indicated that the required critical frequency increases with higher surface tension and larger oil viscosity. In addition, the difference between the residual oil saturation at the optimal frequency increases with the increase in surface tension compared with water injection without an oscillatory boundary. Last but not least, it is elucidated that at a constant injection frequency, a higher EOR is achieved when the water–oil surface tension is lower but the oil viscosity is larger. Our work promises economic, eco-friendly and controllable enhanced oil recovery. Full article
(This article belongs to the Topic Energy-Water Nexus)
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<p>The schematics of the simulation for the cyclic water injection stimulated two-phase displacements in porous media. The computational domain and initial fluid distributions.</p>
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<p>The schematic of droplet displacement by an elastic wave in a pore-scale channel.</p>
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<p>The threshold pressure difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>P</mi> <mrow> <mi>A</mi> <mo>,</mo> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> required for the elastic wave to successfully squeeze droplets under different frequencies: comparisons between the simulation results and theoretical predictions by Deng et al. [<a href="#B41-water-14-03961" class="html-bibr">41</a>].</p>
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<p>The effect of wave frequency on EOR. (<b>a</b>) The relationship between the frequency and the final remaining oil saturation. (<b>b</b>) The final remaining oil saturation of three typical frequencies.</p>
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<p>Comparison of residual oil saturation affected by various interfacial tensions <math display="inline"><semantics> <mi>γ</mi> </semantics></math> in different frequencies.</p>
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<p>Schematic diagram of a single trapped oil droplet in the pore, where the yellow area represents trapped oil droplet, the blue area is water, and the black area is grain.</p>
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<p>Comparison of residual oil saturation affected by various viscosities of oil <math display="inline"><semantics> <mrow> <msub> <mi>μ</mi> <mi>o</mi> </msub> </mrow> </semantics></math> in different frequencies.</p>
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19 pages, 3976 KiB  
Article
Optimization of Irrigation Scheduling for Improved Irrigation Water Management in Bilate Watershed, Rift Valley, Ethiopia
by Kedrala Wabela, Ali Hammani, Taky Abdelilah, Sirak Tekleab and Moha El-Ayachi
Water 2022, 14(23), 3960; https://doi.org/10.3390/w14233960 - 5 Dec 2022
Cited by 4 | Viewed by 2692
Abstract
The availability of water for agricultural production is under threat from climate change and rising demands from various sectors. In this paper, a simulation-optimization model for optimizing the irrigation schedule in the Bilate watershed was developed, to save irrigation water and maximize the [...] Read more.
The availability of water for agricultural production is under threat from climate change and rising demands from various sectors. In this paper, a simulation-optimization model for optimizing the irrigation schedule in the Bilate watershed was developed, to save irrigation water and maximize the yield of deficit irrigation. The model integrated the Soil and Water Assessment Tool (SWAT) and an irrigation-scheduling optimization model. The SWAT model was used to simulate crop yield and evapotranspiration. The Jensen crop-water-production function was applied to solve potato and wheat irrigation-scheduling-optimization problems. Results showed that the model can be applied to manage the complicated simulation-optimization irrigation-scheduling problems for potato and wheat. The optimization result indicated that optimizing irrigation-scheduling based on moisture-stress-sensitivity levels can save up to 25.6% of irrigation water in the study area, with insignificant yield-reduction. Furthermore, optimizing deficit-irrigation-scheduling based on moisture-stress-sensitivity levels can maximize the yield of potato and wheat by up to 25% and 34%, respectively. The model developed in this study can provide technical support for effective irrigation-scheduling to save irrigation water and maximize yield production. Full article
(This article belongs to the Section Water, Agriculture and Aquaculture)
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<p>Location map of the study area.</p>
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<p>(<b>a</b>) Land use. (<b>b</b>) Dominant soil-group.</p>
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<p>Diagrammatic representation of the study.</p>
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<p>Monthly observed- and simulated-stream-flow for calibration and validation period.</p>
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<p>Mean monthly ET<sub>o</sub> and rainfall in the irrigation season.</p>
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<p>Coupling degree between P<sub>e</sub> and ET<sub>c</sub> in the irrigation season: (<b>a</b>) potato, (<b>b</b>) wheat.</p>
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<p>Cumulative-sensitivity-index curve.</p>
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<p>Optimal relative ETa for potato under different levels of seasonal-irrigation water.</p>
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<p>Optimal relative ETa for potato under different levels of seasonal-irrigation water.</p>
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<p>Optimal relative ETa for wheat under different levels of seasonal-irrigation water.</p>
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<p>Optimal relative ETa for wheat under different levels of seasonal-irrigation water.</p>
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<p>Optimal relative yield before and after optimization: (<b>a</b>) potato and (<b>b</b>) wheat.</p>
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15 pages, 4944 KiB  
Article
Nonstationary Annual Maximum Flood Frequency Analysis Using a Conceptual Hydrologic Model with Time-Varying Parameters
by Ling Zeng, Hongwei Bi, Yu Li, Xiulin Liu, Shuai Li and Jinfeng Chen
Water 2022, 14(23), 3959; https://doi.org/10.3390/w14233959 - 5 Dec 2022
Cited by 5 | Viewed by 2069
Abstract
Recent evidence of the impact of watershed underlying conditions on hydrological processes have made the assumption of stationarity widely questioned. In this study, the temporal variations of frequency distributions of the annual maximum flood were investigated by continuous hydrological simulation considering nonstationarity for [...] Read more.
Recent evidence of the impact of watershed underlying conditions on hydrological processes have made the assumption of stationarity widely questioned. In this study, the temporal variations of frequency distributions of the annual maximum flood were investigated by continuous hydrological simulation considering nonstationarity for Weihe River Basin (WRB) in northwestern China. To this end, two nonstationary versions of the GR4J model were introduced, where the production storage capacity parameter was regarded as a function of time and watershed conditions (e.g., reservoir storage and soil-water conservation land area), respectively. Then the models were used to generate long-term runoff series to derive flood frequency distributions, with synthetic rainfall series generated by a stochastic rainfall model as input. The results show a better performance of the nonstationary GR4J model in runoff simulation than the stationary version, especially for the annual maximum flow series, with the corresponding NSE metric increasing from 0.721 to 0.808. The application of the nonstationary flood frequency analysis indicates the presence of significant nonstationarity in the flood quantiles and magnitudes, where the flood quantiles for an annual exceedance probability of 0.01 range from 4187 m3/s to 8335 m3/s for the past decades. This study can serve as a reference for flood risk management in WRB and possibly for other basins undergoing drastic changes caused by intense human activities. Full article
(This article belongs to the Special Issue Impacts of Climate Change on Water Resources and Water Risks)
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<p>Location of Weihe River Basin (WRB) in China (<b>a</b>) and stations with hydrological and meteorological measurements (<b>b</b>).</p>
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<p>Annual and the 11-year moving average value of 6 covariates related to human activities. (<b>a</b>–<b>f</b>) represent population, gross domestic product, cultivated land area, irrigated land area, reservoir storage and soil-water conservation land area, respectively.</p>
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<p>Comparison between the observed and simulated flow-duration curves for the whole period (<b>a</b>) and autumn season (<b>b</b>) from model zero, one and two.</p>
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<p>Comparison of the observed and simulated annual maximum flow. (<b>a</b>–<b>c</b>) represent model zero, one and two, respectively.</p>
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<p>Annual variation of the parameter <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mn>1</mn> </msub> </mrow> </semantics></math> in model one (<b>a</b>) and model two (<b>b</b>).</p>
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<p>Comparison of empirical probability distributions of observed and model-generated annual maximum rainfall series for Tianshui station.</p>
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<p>Variation of the estimated annual maximum floods for an exceedance probability of 0.01 based on model zero and one.</p>
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<p>Variation of the estimated annual maximum floods for an exceedance probability of 0.01 based on model zero and two.</p>
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21 pages, 5926 KiB  
Article
Evaluation and Optimization of Hydrological Connectivity Based on Graph Theory: A Case Study in Dongliao River Basin, China
by Naixu Tian, Yue Zhang, Jianwei Li, Walian Du, Xingpeng Liu, Haibo Jiang and Hongfeng Bian
Water 2022, 14(23), 3958; https://doi.org/10.3390/w14233958 - 5 Dec 2022
Cited by 3 | Viewed by 1937
Abstract
Hydrological connectivity affects the material cycling and energy transfer of ecosystems and is an important indicator for assessing the function of aquatic ecosystems. Therefore, clarification of hydrologic connectivity and its optimization methods is essential for basin water resources management and other problems; however, [...] Read more.
Hydrological connectivity affects the material cycling and energy transfer of ecosystems and is an important indicator for assessing the function of aquatic ecosystems. Therefore, clarification of hydrologic connectivity and its optimization methods is essential for basin water resources management and other problems; however, most of the current research is focused on intermittently flooded areas, especially in terms of optimization, and on hydrological regulation within mature water structures, while research on hydrological connectivity in dry, low rainfall plain areas remains scarce. Based on the graph and binary water cycle theories, this study assessed and hierarchically optimized the structural hydrological connectivity of the Dongliao River Basin (DRB), integrating artificial and natural connectivity, and explored the hydrological connectivity optimization method in the arid plain region at the basin scale to increase connectivity pathways. The spatial analysis and evaluation of hydrological connectivity was also carried out based on the results of the hierarchical optimization, and provided three scenarios for the construction of hydrological connectivity projects in the basin. The hierarchical optimization yielded a total of 230 new water connectivity paths, and the overall hydrological connectivity increased from 5.07 to 7.64. Our results suggest a large spatial correlation in hydrological flow obstruction in the DRB. The center of gravity of circulation obstruction shifted to the south after optimization for different levels of connectivity. With the increase in the optimization level of hydrological connectivity, the national Moran index rose and then fell. The magnitude of the increase in hydrological connectivity effects varied at different optimization levels, and there were sudden points’ increase points. From an application point of view, Scenario 1 is necessary and the most cost effective is Scenario 2, which provides a scientific basis for guiding the construction of future ecological projects in the DRB. Full article
(This article belongs to the Section Hydrology)
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<p>Maps of the Dongliao River Basin (DRB) for the year 2020.</p>
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<p>Research framework. MCR, minimum cumulative resistance.</p>
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<p>Structure of hydrological connectivity in the Dongliao River Basin (DRB). River, the original water system is generalized as a river network link; link-edge, an edge node in the river network node, connected to only one link; link-center, a non-edge node in the river network node, connected to multiple links in the whole network.</p>
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<p>Introduction to the three connectivity indices. (<b>a</b>) with larger <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">β</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mi mathvariant="sans-serif">γ</mi> </semantics></math> and IIC; (<b>b</b>) with smaller <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">β</mi> <mo>,</mo> <mrow> <mo> </mo> <mi mathvariant="sans-serif">γ</mi> </mrow> </mrow> </semantics></math> and IIC.</p>
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<p>Existing water systems in the Dongliao River Basin (DRB).</p>
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<p>Slope and independent presence of water bodies in the Dongliao River Basin (DRB). Independent presence of water bodies occurs in areas where the slope is relatively small in relation to the surrounding area, a demonstration of the 7 objectives of 1 level of optimization.</p>
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<p>Spatial distribution of impediments and superimposed resistance cost for hydrological connectivity in the Dongliao River Basin (DRB).</p>
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<p>Optimization results for hydrological connectivity (water flow obstruction and additional pathways) at all levels in the Dongliao River Basin (DRB).</p>
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<p>Changes in hydrological connectivity of the Dongliao River Basin (DRB).</p>
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<p>Center-of-gravity migration in the Dongliao River Basin (DRB) for each resistance area at different optimization levels.</p>
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<p>Global Moran index and scatter plots of water flow obstruction for optimization results from level 1 to level 5 for the Dongliao River Basin (DRB).</p>
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<p>Results of the Local indicators of spatial association (LISA) clustered clustering of water flow obstruction for different levels of optimization the Dongliao River Basin (DRB).</p>
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22 pages, 3391 KiB  
Article
Comprehensive Evaluation Model for Urban Water Security: A Case Study in Dongguan, China
by Jianye Cao, Zhicheng Yan, Jinquan Wan, Yan Wang, Gang Ye, Yingping Long and Quanmo Xie
Water 2022, 14(23), 3957; https://doi.org/10.3390/w14233957 - 5 Dec 2022
Cited by 2 | Viewed by 2356
Abstract
Water security plays a critical role in the development and stability of a region. Constructing an objective and reasonable evaluation indicator system is beneficial to quantitatively evaluating the regional water security status and improving water resource management. In this paper, an urban water [...] Read more.
Water security plays a critical role in the development and stability of a region. Constructing an objective and reasonable evaluation indicator system is beneficial to quantitatively evaluating the regional water security status and improving water resource management. In this paper, an urban water security indicator system was established based on the Driving–Pressure–State–Impact–Response (DPSIR) framework with Dongguan City as a case study. By introducing the projection pursuit (PP) algorithm, a DPSIR–PP model was developed to quantitatively evaluate urban water security. The evaluation results show that Dongguan City’s water security index had an overall upward trend during the 13th Five-Year Plan period, with the evaluation grade rising from IV to III. The indicators with the top five weights are: river water quality condition, ecological index, the leakage rate of water supply network, the value added by industry, and the Dongjiang water resources development and utilization rate. The evaluation results are essentially in line with the reality of Dongguan City. On this basis, the internal links of water security and future trends were further analyzed. Through the evaluation results and policy analysis, it is shown that the water security-related measures implemented during the 13th Five-Year Plan period have been effective. Overall, the methodology proposed in this study is beneficial for gaining an in-depth understanding of urban water security impact factors and provides some theoretical basis and reference for future water resources management. Full article
(This article belongs to the Section Water Resources Management, Policy and Governance)
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<p>The Water System Map of Dongguan City.</p>
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<p>The structure of the DPSIR model.</p>
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<p>The projection weights of each indicator.</p>
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<p>The weight proportion of each subsystem.</p>
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<p>The WSI evaluation results and predicted trends.</p>
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<p>(<b>a</b>–<b>e</b>) Trends in normalized indicators for each subsystem.</p>
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<p>(<b>a</b>–<b>e</b>) Trends in the projection eigenvalues of each subsystem and the proportion of indicators; (<b>f</b>) trends of change in each subsystem.</p>
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<p>(<b>a</b>) The decoupling state of water security pressures and socio-economic drivers; (<b>b</b>) The decoupling state of water and ecological status and response measures; I~VIII are eight categories of decoupling states, see <a href="#app1-water-14-03957" class="html-app">Table S5</a>.</p>
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25 pages, 8392 KiB  
Article
Groundwater Modeling with Process-Based and Data-Driven Approaches in the Context of Climate Change
by Matia Menichini, Linda Franceschi, Brunella Raco, Giulio Masetti, Andrea Scozzari and Marco Doveri
Water 2022, 14(23), 3956; https://doi.org/10.3390/w14233956 - 5 Dec 2022
Cited by 3 | Viewed by 3510
Abstract
In the context of climate change, the correct management of groundwater, which is strategic for meeting water needs, becomes essential. Groundwater modeling is particularly crucial for the sustainable and efficient management of groundwater. This manuscript provides different types of modeling according to data [...] Read more.
In the context of climate change, the correct management of groundwater, which is strategic for meeting water needs, becomes essential. Groundwater modeling is particularly crucial for the sustainable and efficient management of groundwater. This manuscript provides different types of modeling according to data availability and features of three porous aquifer systems in Italy (Empoli, Magra, and Brenta systems). The models calibrated on robust time series enabled the performing of forecast simulations capable of representing the quantitative and qualitative response to expected climate regimes. For the Empoli aquifer, the process-based models highlighted the system’s ability to mitigate the effects of dry climate conditions thanks to its storage capability. The data-driven models concerning the Brenta foothill aquifer pointed out the high sensitivity of the system to climate extremes, thus suggesting the need for specific water management actions. The integrated data-driven/process-based approach developed for the Magra Valley aquifer remarked that the water quantity and quality effects are tied to certain boundary conditions over dry climate periods. This work shows that, for groundwater modeling, the choice of the suitable approach is mandatory, and it mainly depends on the specific aquifer features that result in different ways to be sensitive to climate. This manuscript also provides a novel outcome involving the integrated approach wherein it is a very efficient tool for forecasting modeling when boundary conditions, which significantly affect the behavior of such systems, are subjected to evolve under expected climate scenarios. Full article
(This article belongs to the Special Issue Groundwater Hydrological Model Simulation)
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<p>Location of the three case studies.</p>
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<p>Block diagram describing the approach for developing a flow and transport model dedicated to specific parameters based on machine learning.</p>
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<p>Geometrical reconstruction of the horizons constituting the Empoli aquifer system.</p>
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<p>Spatial discretization in raw and column (cells of 50 × 50 m) and in 4 layers, and hydraulic properties (K in m/day) assigned.</p>
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<p>Boundary conditions and calibration target.</p>
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<p>Calibration and validation of the model: (<b>a</b>) graph of observed vs. simulated level (in m a.s.l.) and (<b>b</b>) calibration statistics of the steady-state model; (<b>c</b>) piezometric level (in m a.s.l.) over time (day) measured (in red) and simulated (in blue) in the Isola target continuous monitoring point and (<b>d</b>) piezometric level (in m a.s.l.) over time (day) measured (in red) and simulated (in blue) in the 6A target point of the Empoli profile (only one experimental datum is available for the 6A piezometer over the validation period).</p>
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<p>Water balance for (<b>a</b>) steady-state and (<b>b</b>) transient-state models in the dry and rainy seasons, and (<b>c</b>) section with flow line of the Empoli aquifer (for the section trace see <a href="#water-14-03956-f005" class="html-fig">Figure 5</a>; CH: constant head, Riv: river, Rch: recharge).</p>
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<p>Simulated evolutions of storage inflow into the domain and piezometric level at observation wells A, B, and Giardino, 6A (wells location is in <a href="#water-14-03956-f005" class="html-fig">Figure 5</a>).</p>
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<p>(<b>a</b>) Middle-high plain of the Brenta River and (<b>b</b>) schematic section of the aquifer system (modified after [<a href="#B48-water-14-03956" class="html-bibr">48</a>]).</p>
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<p>(<b>a</b>) Error values between the predicted and observed data along the regression line and (<b>b</b>) distribution of residuals.</p>
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<p>Calibration and validation of the piezometric level model.</p>
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<p>Forecasting simulation of 6 months using fictitious rainfall and hydrometric level data.</p>
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<p>Multidisciplinary data elaboration to develop the conceptual model [<a href="#B58-water-14-03956" class="html-bibr">58</a>,<a href="#B59-water-14-03956" class="html-bibr">59</a>]: (<b>a</b>) 3D reconstruction by mean borehole information (where A is available boreholes used, B is 3-D solid of Magra Aquifer and C is an example of 3-D stratigraphic cross section); (<b>b</b>) Piezometric map (m) a.s.l. for the “2004 May–June” period; (<b>c</b>) Cl + SO<sub>4</sub> vs. HCO<sub>3</sub> diagram (A–D letters indicate the main recharge components of the groundwater system in <a href="#water-14-03956-f014" class="html-fig">Figure 14</a> and M1–3 indicate mixing processes); (<b>d</b>) SO<sub>4</sub> concentrations in the Magra River (station MAS-017) and in a drinking water well (P030-1r) located very close to the river.</p>
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<p>Simplified sketch map showing the main recharge components of the groundwater system [<a href="#B53-water-14-03956" class="html-bibr">53</a>]: A—feeding from River Magra and its alluvial fan; B—feeding from River Vara and its alluvial fan; C—groundwater transfer from western hills and secondary input from minor creeks; D—groundwater transfer from northern hills; E and F—groundwater recharge from secondary creeks flowing in the western sectors.</p>
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<p>Calibration of flow and transport model: (<b>a</b>) Observed and simulated piezometric head [<a href="#B60-water-14-03956" class="html-bibr">60</a>] (<b>b</b>) SO<sub>4</sub> and Cl observed and simulated value at their respective time (2004–2011) [<a href="#B60-water-14-03956" class="html-bibr">60</a>].</p>
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<p>Observed SO<sub>4</sub> concentration (in grey) and calculated (in red) by the model for observation points of the Magra River.</p>
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<p>Rainfall and Magra hydrometric level for the calibration period (2004–2011) and forecasting simulation period (2012–2042).</p>
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<p>Map and time series of SO<sub>4</sub> concentration in the calibration and prediction periods.</p>
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<p>Time series of piezometric value (<b>a</b>) and SO<sub>4</sub> concentration (<b>b</b>) for Fornola Well 3 in the calibration and prediction periods.</p>
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9 pages, 1900 KiB  
Article
Effects of Hypoxia and Hypomagnetic Field on Morphometric and Life-History Traits in Freshwater Cladoceran Daphnia magna
by Viacheslav V. Krylov, Anastasia A. Sizova and Daniil A. Sizov
Water 2022, 14(23), 3955; https://doi.org/10.3390/w14233955 - 5 Dec 2022
Cited by 2 | Viewed by 1945
Abstract
The intensity of climatic changes and human activities is increasing every year. The general consequence of these processes for freshwater ecosystems can be a dissolved oxygen decrease. There is also a possibility of a reduction in geomagnetic field intensity due to a reversal [...] Read more.
The intensity of climatic changes and human activities is increasing every year. The general consequence of these processes for freshwater ecosystems can be a dissolved oxygen decrease. There is also a possibility of a reduction in geomagnetic field intensity due to a reversal of the Earth’s magnetic poles. It is assumed that the magnetic poles’ reversal may proceed relatively quickly and coincide with global climatic changes. To evaluate the influence of these processes on aquatic organisms, we studied the effects of different dissolved oxygen levels (2 mg/L, 5 mg/L, and 8 mg/L) under the geomagnetic field (51.7 ± 0.2 μT) and hypomagnetic field (0 ± 0.2 μT) on the model freshwater crustacean Daphnia magna Straus. It was found that reduced oxygen levels and the hypomagnetic field led to a decrease in the sizes of parental females, a reduction in the number of produced offspring, and an increase in the period between broods. The newborns from the first brood in the hypomagnetic field were larger than that from the geomagnetic field. The dissolved oxygen level and magnetic environment affected the age of the first brood release and caudal spine length. The results imply that the probable coincidence of the geomagnetic pole reversal and the decrease in the dissolved oxygen level due to global climatic and geophysical processes will have a more negative impact on freshwater crustaceans than the occurrence of these processes at different times. Full article
(This article belongs to the Section Water and Climate Change)
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<p>Age of the first brood release (<b>A</b>) and newborns’ body length (<b>B</b>) in studied groups of daphnids. Values are means ± standard deviation. Different letters indicate significant differences among groups (<span class="html-italic">p</span> &lt; 0.05) after Kruskal–Wallis test and Dunn’s post-hoc test. For all variables with the same letters, the difference between the groups is not statistically significant. Significantly different groups have different letters.</p>
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<p>Dynamics of changing mean brood size in studied groups of daphnids. Bars denote standard error.</p>
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<p>Average brood size in the first five broods (<b>A</b>) and the period between broods (<b>B</b>) in studied groups of daphnids. Values are means ± standard deviation. Different letters indicate significant differences among groups (<span class="html-italic">p</span> &lt; 0.05) (<span class="html-italic">p</span> &lt; 0.05) after Tukey’s post-hoc multiple comparison tests (<b>A</b>) or Kruskal–Wallis test and Dunn’s post-hoc test (<b>B</b>). For all variables with the same letters, the difference between the groups is not statistically significant. Significantly different groups have different letters.</p>
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<p>The body length (<b>A</b>), carapace height (<b>B</b>), and caudal spine length (<b>C</b>) in parental females. Values are means ± standard deviation. Different letters indicate significant differences among groups (<span class="html-italic">p</span> &lt; 0.05) after Tukey’s post-hoc multiple comparison tests. For all variables with the same letters, the difference between the groups is not statistically significant. Significantly different groups have different letters.</p>
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14 pages, 1483 KiB  
Article
Sediment Source Fingerprinting and Its Control Strategies of the Lakes in Jiuzhaigou World Natural Heritage Site
by Xiaoxue Shen, Ruili Li, Jie Du, Xianchenghao Jiang and Guoyu Qiu
Water 2022, 14(23), 3954; https://doi.org/10.3390/w14233954 - 5 Dec 2022
Cited by 1 | Viewed by 1917
Abstract
Reliable quantitative information regarding sediment sources is essential for target mitigation, particularly in settings with a large number of loose provenances caused by earth disasters. The lakes in the Jiuzhaigou World Natural Heritage Site (WNHS) are facing serious environmental problems of silting and [...] Read more.
Reliable quantitative information regarding sediment sources is essential for target mitigation, particularly in settings with a large number of loose provenances caused by earth disasters. The lakes in the Jiuzhaigou World Natural Heritage Site (WNHS) are facing serious environmental problems of silting and swamping, which threaten the sustainability of the area, especially after the earthquake on 8 August 2017 (the “8.8 earthquake”). Therefore, a field investigation was conducted after the “8.8 earthquake” (June 2020), and the Arrow Bamboo and Rhino Lakes, which were affected by the earthquakes to different degrees, were selected as the research objects. Based on the data of 27 environmental indicators from 31 surface sediment and soil samples in and around the lakes, the spatial distribution characteristics of the lake sediment sources were quantified using composite fingerprint recognition technology. Furthermore, a high protection standard of a WHNS and a process treatment scheme for reducing the siltation of the Jiuzhaigou lakes were proposed. The results showed that the contribution ratio of loose matter sources entering the lake on the road-side of the Arrow Bamboo and Rhino Lakes (16.5% and 21.8%, respectively) was lower than that on the forest-side (83.5% and 78.2%, respectively), indicating that physical barriers such as roads can effectively reduce the sediment input, while the lake forest side contributes a large number of loose matter sources, which has not attracted attention in the past and requires protection. High protection standards for the Jiuzhaigou WHNS are suggested. Accordingly, the entire control scheme of Jiuzhaigou lake sediment reduction including “monitoring–control–interception–buffer–cleaning” is provided. Source erosion monitoring is the first step in blocking the sediment source. Vegetation restoration and surface coverage should be conducted in areas where water and soil losses have occurred. Necessary engineering measures should be implemented to intercept loose material sources at points where geological disasters occur frequently. A buffer zone should be established between the lake and the mountain to intercept the sediment. Sediment caused by geological disasters with low interference must also be cleaned from the lake. The level of nutrients in the lake must be controlled by the regular cleaning of plant debris from the lake and lakeside. Full article
(This article belongs to the Section Water Erosion and Sediment Transport)
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<p>Study area (left and middle) and sampling sites (right). (<b>a</b>) Sampling sites at Rhino Lake; (<b>b</b>) Sampling sites at Arrow Bamboo Lake. The blue points in (<b>a</b>,<b>b</b>) are the target sediment samples, the green points are the forest side samples, and yellow points are the roadside samples.</p>
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<p>Digital orthophoto maps of (<b>a</b>) Arrow Bamboo Lake and (<b>b</b>) Rhino Lake (2020). (<b>c</b>,<b>e</b>) Field photos of the forest-side and road-side of Arrow Bamboo Lake, respectively; and (<b>d</b>,<b>f</b>) field photos of the forest-side and road-side of Rhino Lake, respectively.</p>
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<p>Contribution ratio of different sediment sources at (<b>a</b>) Arrow Bamboo Lake and (<b>b</b>) Rhino Lake.</p>
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