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Water, Volume 14, Issue 15 (August-1 2022) – 170 articles

Cover Story (view full-size image): This is an image of the Lis River, located in the centre of Mainland Portugal, taken in the agricultural irrigated area found at the downstream section of the Lis Valley. The green “carpet” is formed by duckweeds (Lemna minor), an aquatic weed that seriously infests this water course, which is the irrigation water source in this area. Duckweeds are widely found in freshwaters all over the world, but whereas this macrophyte provides multiple ecosystems’ functions and services, its excessive proliferation can have negative environmental impacts (including ecological and socio-economic impacts). This work explores the use of remote sensing tools for mapping the dynamics of duckweeds in open watercourses, which could contribute to identifying suitable monitoring programs and integrated management practices. View this paper
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15 pages, 5522 KiB  
Article
Spatiotemporal Variations in the Water Quality of Qionghai Lake, Yunnan–Guizhou Plateau, China
by Jiao Ran, Rong Xiang, Jie Li, Keyan Xiao and Binghui Zheng
Water 2022, 14(15), 2451; https://doi.org/10.3390/w14152451 - 8 Aug 2022
Cited by 7 | Viewed by 2687
Abstract
Although Qionghai Lake is one of the 11 large and medium-sized lakes (lake area > 25 km2) in the Yunnan–Guizhou Plateau (YGP), there has been little research on its water quality, especially over the long term. Herein, meteorological, hydrologic, trophic, and [...] Read more.
Although Qionghai Lake is one of the 11 large and medium-sized lakes (lake area > 25 km2) in the Yunnan–Guizhou Plateau (YGP), there has been little research on its water quality, especially over the long term. Herein, meteorological, hydrologic, trophic, and biochemical indices were investigated over the 2011–2020 period to explore the spatiotemporal variations in water quality in Qionghai Lake. The results showed that the CCME-WQI value for Qionghai Lake ranked between marginal and fair during 2011–2020, that the water quality of Qionghai Lake before 2017 was worse than after 2017, and that the water quality of the western part of Qionghai Lake was worse than that of the eastern part. Total nitrogen and total phosphorus were 0.39–0.51 and 0.019–0.027 mg/L during 2011–2020, respectively, and were the main pollution factors in Qionghai Lake. In addition, Qionghai Lake was at the mesotrophic level, but the chlorophyll and trophic state levels (TLI) increased year by year, and the levels in the western area were higher than in the eastern area. Increased anthropogenic activities (industrialization, urbanization, agricultural intensification, etc.) were the main reasons for the poor water quality of Qionghai Lake before 2017, while, after 2017, effective government environmental restoration and management measures improved the water quality. Moreover, the difference in land-use types within the watershed was the main reason for the spatial heterogeneity of water quality in Qionghai Lake. Potassium permanganate index (CODMn) and ammonia nitrogen content index (NH3-N) were not very high, but both showed seasonal variations. Water transparency (SD) in Qionghai Lake was reduced by sediment input and increased algal biomass, while dissolved oxygen (DO) decreased due to thermal stratification. This study is expected to provide a theoretical reference for understanding changes in the water quality and water environmental protection of Qionghai Lake and the YGP. Full article
(This article belongs to the Special Issue Water Quality Monitoring, Analysis and Restoration of Lakes)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Sampling sites for Qionghai Lake.</p> Full article ">Figure 2
<p>(<b>a</b>) Variations in water quality parameters for Qionghai Lake from 2011 to 2020. The Class II values for TP and TN were marked as red and blue lines, respectively. (<b>b</b>) Mann–Kendall test (MK test) results for water quality parameters of Qionghai Lake.</p> Full article ">Figure 3
<p>(<b>a</b>) Gross domestic product (GDP), gross agricultural product (GAP), gross industrial product (GIP), population (Pop), and urbanization rate (UR) of Xichang City from 2011 to 2020. (<b>b</b>) The results of linear fitting of TP and TN with GDP, population (Pop), and urbanization rate (UR), respectively. (<b>c</b>) Land-use patterns in Qionghai Basin in 2010 and 2019. (<b>d</b>) The sown area of crops and vegetables in the Qionghai Basin in 2016 and 2018. (<b>e</b>) The year-end quantity of hogs in Qionghai Basin in 2016, 2018, and 2020. (<b>f</b>) The new area of lakeside wetland in Qionghai Lake from 2010–2015. (<b>g</b>) The sewer pipe length in Qionghai Basin on the northern, northeastern, western, and southern lakeshores.</p> Full article ">Figure 4
<p>(<b>a</b>) Monthly average water temperature (WT), air temperature (AT), and precipitation (Pcp) in Qionghai Lake. Annual variation trends for (<b>b</b>) WT, (<b>c</b>) Pcp, (<b>d</b>) AT, and (<b>e</b>) the difference between WT and AT.</p> Full article ">Figure 5
<p>Spatial variations in (<b>a</b>) WT, (<b>b</b>) pH, (<b>c</b>) SD, (<b>d</b>) DO, (<b>e</b>) COD<sub>Mn</sub>, (<b>f</b>) NH<sub>3</sub>-N, (<b>g</b>) TN, and (<b>h</b>) TP over the 11 sampling sites of Qionghai Lake. (<b>i</b>) Dendrogram based on agglomerative hierarchical clustering of the 11 sampling sites for Qionghai Lake.</p> Full article ">Figure 6
<p>(<b>a</b>) Temporal variations in <span class="html-italic">TLI</span> and Chl-a in Qionghai Lake from 2011 to 2020. (<b>b</b>) Mann–Kendall (MK) test results for <span class="html-italic">TLI</span> and Chl-a in Qionghai Lake. (<b>c</b>) Spatial variations in <span class="html-italic">TLI</span> and Chl-a in Qionghai Lake in 2011, 2017, and 2020, respectively.</p> Full article ">Figure 7
<p>(<b>a</b>) <span class="html-italic">CCME-WQI</span> results with corresponding water quality status for Qionghai Lake during 2011–2020. (<b>b</b>) Spatial interpolation analysis based on the <span class="html-italic">CCME-WQI</span> values for 11 sampling sites in Qionghai Lake.</p> Full article ">
18 pages, 2147 KiB  
Article
Arsenite to Arsenate Oxidation and Water Disinfection via Solar Heterogeneous Photocatalysis: A Kinetic and Statistical Approach
by Felipe de J. Silerio-Vázquez, Cynthia M. Núñez-Núñez, José B. Proal-Nájera and María T. Alarcón-Herrera
Water 2022, 14(15), 2450; https://doi.org/10.3390/w14152450 - 8 Aug 2022
Cited by 2 | Viewed by 2268
Abstract
Arsenic (As) poses a threat to human health. In 2014, more than 200 million people faced arsenic exposure through drinking water, as estimated by the World Health Organization. Additionally, it is estimated that drinking water with proper microbiological quality is unavailable for more [...] Read more.
Arsenic (As) poses a threat to human health. In 2014, more than 200 million people faced arsenic exposure through drinking water, as estimated by the World Health Organization. Additionally, it is estimated that drinking water with proper microbiological quality is unavailable for more than 1 billion people. The present work analyzed a solar heterogeneous photocatalytic (HP) process for arsenite (AsIII) oxidation and coliform disinfection from a real groundwater matrix employing two reactors, a flat plate reactor (FPR) and a compound parabolic collector (CPC), with and without added hydrogen peroxide (H2O2). The pseudo first-order reaction model fitted well to the As oxidation data. The treatments FPR–HP + H2O2 and CPC–HP + H2O2 yielded the best oxidation rates, which were over 90%. These treatments also exhibited the highest reaction rate constants, 6.7 × 10−3 min−1 and 6.8 × 10−3 min−1, respectively. The arsenic removal rates via chemical precipitation reached 98.6% and 98.7% for these treatments. Additionally, no coliforms were detected at the end of the process. The collector area per order (ACO) for HP treatments was on average 75% more efficient than photooxidation (PO) treatments. The effects of the process independent variables, H2O2 addition, and light irradiation were statistically significant for the AsIII oxidation reaction rate (p < 0.05). Full article
(This article belongs to the Topic Advanced Oxidation Process: Applications and Prospects)
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Figure 1

Figure 1
<p>CPC reactor scheme.</p> Full article ">Figure 2
<p>FPR reactor scheme.</p> Full article ">Figure 3
<p>Process flow diagram.</p> Full article ">Figure 4
<p>As<sup>III</sup> photooxidation (PO) experiments carried out both in CPC and FPR, with and without MWTE spike. Experiments performed in the dark as control experiments (DC) are included as well.</p> Full article ">Figure 5
<p>As<sup>III</sup> photooxidation (PO) experiments carried out both in CPC and FPR, H<sub>2</sub>O<sub>2</sub> added, and with and without MWTE spike. Experiments performed in the dark as control experiments (DC) are included as well.</p> Full article ">Figure 6
<p>As<sup>III</sup> heterogeneous photocatalytic (HP) oxidation experiments carried out both in CPC and FPR, and with and without MWTE spike. Experiments performed in the dark as control experiments (DC) are included as well.</p> Full article ">Figure 7
<p>As<sup>III</sup> heterogeneous photocatalytic (HP) oxidation experiments carried out both in CPC and FPR, H<sub>2</sub>O<sub>2</sub> added, and with and without MWTE spike. Experiments performed in the dark as control experiments (DC) are included as well.</p> Full article ">
14 pages, 2498 KiB  
Article
A Coevolution Model of the Coupled Society—Water Resources—Environment Systems: An Application in a Case Study in the Yangtze River Economic Belt, China
by Haoyuan Liu, Xiang Zhang, Shiyong Tao, Xi Xiao, Keyi Wu and Jun Xia
Water 2022, 14(15), 2449; https://doi.org/10.3390/w14152449 - 8 Aug 2022
Cited by 4 | Viewed by 2012
Abstract
Interactions among society, water resources, and environment systems have become increasingly prominent with the progressively far-reaching impact of human activities. Therefore, this paper aims to construct a co-evolution model to establish the mutual feedback relationship among society, water resources, and environment from the [...] Read more.
Interactions among society, water resources, and environment systems have become increasingly prominent with the progressively far-reaching impact of human activities. Therefore, this paper aims to construct a co-evolution model to establish the mutual feedback relationship among society, water resources, and environment from the perspective of socio-hydrology. Firstly, social factors such as environmental sensitivity, environmental protection awareness, and technological level are introduced to this model to describe the coevolutionary trajectory of society, water resources and environment subsystems. Then, this model is implemented in 11 provincial administrative regions in the Yangtze River Economic Belt, and the degree of coordination of their coupling is evaluated. Results show that the water-use efficiency of each provincial administrative region in the Yangtze River Economic Belt gradually increases during the forecast period. The coupling-coordinated degree of each provincial administrative region of the Yangtze River Economic Belt has greatly improved during the 14th Five-Year Plan period, reflecting that policy support has played a significant role in the coordinated development of the Yangtze River Economic Belt. The dynamic fluctuation process of environmental sensitivity effectively depicts the co-evolution process of the coupling system, which provides a reference for the subsequent exploration and cognition of the human-water coevolutionary mechanism. Full article
(This article belongs to the Topic Hydrological Modeling and Engineering: Managing Risk and Uncertainties)
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Figure 1
<p>Location of the Yangtze River Economic Belt.</p> Full article ">Figure 2
<p>The mutual feedback relationship within the coevolution model. The variables of the society subsystem, water resources subsystem, environment subsystem, response module and policy factor are shown in purple, blue, orange, green and cyan, respectively.</p> Full article ">Figure 3
<p>Box diagram of relative error.</p> Full article ">Figure 4
<p>Simulation results of co-evolution of water resources-society-environment in 11 provincial administrative regions of the YREB. Note: In order to reflect the change in the proportion of water use during the forecast period, the simulated values of various water consumption results after 2020 are represented with corresponding colors with lower saturation.</p> Full article ">Figure 5
<p>Sensitivity analysis results.</p> Full article ">Figure 6
<p>Water use indicators of 11 provincial administrative regions of the YREB. (<b>a</b>) Water consumption per 10,000 yuan of GDP (m<sup>3</sup>). (<b>b</b>) Water consumption per 10,000 yuan of industrial added value (m<sup>3</sup>).</p> Full article ">Figure 7
<p>The trend of CCD of 11 provincial administrative regions of the YREB.</p> Full article ">
15 pages, 4297 KiB  
Article
An Integrated Bayesian and Machine Learning Approach Application to Identification of Groundwater Contamination Source Parameters
by Yongkai An, Yanxiang Zhang and Xueman Yan
Water 2022, 14(15), 2447; https://doi.org/10.3390/w14152447 - 7 Aug 2022
Cited by 8 | Viewed by 2518
Abstract
The identification of groundwater contamination source parameters is an important prerequisite for the control and risk assessment of groundwater contamination. This study developed an innovative approach for the optimal design of observation well locations and the high-precision identification of groundwater contamination source parameters. [...] Read more.
The identification of groundwater contamination source parameters is an important prerequisite for the control and risk assessment of groundwater contamination. This study developed an innovative approach for the optimal design of observation well locations and the high-precision identification of groundwater contamination source parameters. The approach involves Bayesian theory and integrates Markov Chain Monte Carlo, Bayesian design, information entropy, machine learning, and surrogate modeling. The optimal observation well locations are determined by information entropy, which is adopted to mine valuable information about unknown groundwater contamination source parameters from measurements of contaminant concentration according to Bayesian design. After determining the optimal observation well locations, the identification of groundwater contamination source parameters is implemented through a Bayesian-based Differential Evolution Adaptive Metropolis with Discrete Sampling–Markov Chain Monte Carlo approach. However, the processes of both determination and identification are time-consuming because the original simulation model (that is, the contaminant transport model) needs to be invoked multiple times. To overcome this challenge, a machine learning approach, that is, Multi-layer Perceptron, is used to build a surrogate model for the original simulation model, which can greatly accelerate the determination and identification processes. Finally, two hypothetical numerical case studies involving homogeneous and heterogeneous cases are used to verify the performance of the proposed approach. The results show that the optimal design of observation well locations and high-precision identification of groundwater contamination source parameters can be implemented accurately and effectively by using the proposed approach. In summary, this study highlights that the integrated Bayesian and machine learning approach provides a promising solution for high-precision identification of groundwater contamination source parameters. Full article
(This article belongs to the Special Issue Groundwater Quality and Human Health Risk)
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Figure 1

Figure 1
<p>Flow diagram of research process.</p> Full article ">Figure 2
<p>MLP approach structure diagram.</p> Full article ">Figure 3
<p>Discretization graph of groundwater flow field for Case 1 (<b>upper</b>) and Case 2 (<b>lower</b>).</p> Full article ">Figure 4
<p>Outputs and relative error of simulation model and surrogate model for Case 1 (the x-axis and y-axis represent the length and width of the flow field, respectively).</p> Full article ">Figure 5
<p>Output and relative error of simulation model and surrogate model for Case 2 (the x-axis and y-axis represent the length and width of the flow field, respectively).</p> Full article ">Figure 6
<p>The OWLs of the optimal design and 3 random designs for Case 1 (<b>upper</b>) and Case 2 (<b>lower</b>).</p> Full article ">Figure 7
<p>Trace plots of the MCMC simulation for Case 1.</p> Full article ">Figure 8
<p>Trace plots of the MCMC simulation for Case 2.</p> Full article ">Figure 9
<p>Comparison results of the posterior probability distributions for the optimal design and 3 other random designs for Case 1 (<b>Upper</b>) and Case 2 (<b>Lower</b>).</p> Full article ">
15 pages, 10379 KiB  
Article
Efficient Removal of Organic Matter from Biotreated Coking Wastewater by Coagulation Combined with Sludge-Based Activated Carbon Adsorption
by Yu Xia, Weijia Li, Xuwen He, Dannuo Liu, Yichen Sun, Jie Chang and Jing Liu
Water 2022, 14(15), 2446; https://doi.org/10.3390/w14152446 - 7 Aug 2022
Cited by 7 | Viewed by 2363
Abstract
Coagulation–adsorption can be effective in the removal of the organic matters remaining in biotreated coking wastewater (BTCW), and cheap and efficient adsorbents benefit the widespread application of this technology. In this study, a sludge-based activated carbon (SAC) was prepared using zinc chloride to [...] Read more.
Coagulation–adsorption can be effective in the removal of the organic matters remaining in biotreated coking wastewater (BTCW), and cheap and efficient adsorbents benefit the widespread application of this technology. In this study, a sludge-based activated carbon (SAC) was prepared using zinc chloride to activate sludge pyrolysis carbon for the treatment of BTCW with coagulation as the pretreatment process. According to Brunauer-Emmett-Teller (BET) and the scanning electron microscope (SEM) analysis, the SAC exhibited a specific surface area of 710.175 m2/g and well-developed pore structure. The removal characteristics of the organic matter in BTCW were systematically studied. The results show that 76.79% of the COD in the BTCW was removed by coagulation combined with SAC adsorption, and the effluent COD was below the discharge limit (80 mg/L) (GB16171-2012), with the optimal dosages of polyaluminum chloride and SAC being 150 mg/L and 4 g/L, respectively. Compared with a commercial powdered activated carbon (PAC) (48.26%), the SAC achieved a similar COD removal efficiency (47.74%) at a higher adsorption speed. The removal efficiencies of the hydrophobic components (77.27%) and fluorescent components by SAC adsorption were higher than those by PAC adsorption. The SAC also had an excellent removal effect on complex organic compounds and colored substances in the BTCW, as revealed by UV-vis spectra analyses. Full article
(This article belongs to the Section Wastewater Treatment and Reuse)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Variation in removal efficiency with polyaluminum chloride dosage.</p> Full article ">Figure 2
<p>(<b>a</b>) Variable of removal efficiency of SAC dosage, (<b>b</b>) Variable of removal efficiency of adsorption time.</p> Full article ">Figure 3
<p>Non-linear Freundlich adsorption isotherm.</p> Full article ">Figure 4
<p>(<b>a</b>) Proportion of DOM components in the raw BTCW; (<b>b</b>,<b>c</b>) proportion of DOM components after coagulation and PAC treatment; (<b>d</b>) removal efficiency of DOM components by coagulation combined and SAC treatment.</p> Full article ">Figure 5
<p>(<b>a</b>–<b>d</b>) EEM spectra of (<b>a</b>) raw wastewater, (<b>b</b>) coagulation effluent, (<b>c</b>) SAC adsorption effluent, and (<b>d</b>) PAC adsorption effluent.</p> Full article ">Figure 6
<p>Integral standard volume and proportion of each fluorescence area of 3D-EEM. Q1 corresponds to tyrosine-like organic compounds; Q2 corresponds to tryptophan-like organic compounds; Q3 corresponds to fulvic acid-like organic compounds; and Q4 and Q5 corresponded to soluble microbial by-products and humic acid-like organic compounds, respectively.</p> Full article ">Figure 7
<p>UV-vis absorbance spectra of BTCW and effluents of coagulation and SAC and PAC treatments.</p> Full article ">Figure 8
<p>Adsorption–desorption performance of SAC.</p> Full article ">
15 pages, 3642 KiB  
Article
“Sea Anemone”-like CeFe Oxides for High-Efficient Phosphate Removal
by Xiaoying Tan, Pingping Dong, Hongping Min, Jinxue Luo, Wenhai Huang, Xiaodong Wang, Qingqing Li and Qile Fang
Water 2022, 14(15), 2445; https://doi.org/10.3390/w14152445 - 7 Aug 2022
Cited by 1 | Viewed by 2092
Abstract
The excessive release of phosphorus is a prime culprit for eutrophication and algal bloom in the aquatic environment, and there is always an urgent need to develop effective methods to deal with phosphorus pollution. Ce-based oxide is a type of compelling adsorbent for [...] Read more.
The excessive release of phosphorus is a prime culprit for eutrophication and algal bloom in the aquatic environment, and there is always an urgent need to develop effective methods to deal with phosphorus pollution. Ce-based oxide is a type of compelling adsorbent for phosphate removal, and a self-templating strategy is used to construct high-performance Ce-based oxides for phosphate adsorption in this study. A “sea anemone”-like CeFe cyanometallate (CM) with a 3D microstructure is fabricated to provide a precursor for synthesizing CeFe-based oxides (CeFe-CM-T) by high-temperature pyrolysis. The as-prepared CeFe-CM-T maintains the “sea anemone” morphology well and has abundant micropores/mesopores, which render its superior phosphate adsorption capacity 1~2 orders of magnitude higher than that of the commercial CeO2 and Fe3O4 materials. Moreover, CeFe-CM-T shows high selectivity for phosphate removal when it co-exists with other anions and natural organic matter and exhibits excellent recycling performance. It demonstrates that both Ce3+ and Ce4+ are reserved in the oxides, where Ce3+ serves as the main active site for phosphate capture, which forms stable Ce-PO4 compounds via a ligand-exchange mechanism. Thus, the self-templating strategy using CM as a precursor is a potential method for synthesizing porous Ce-based oxides for phosphate removal. Full article
(This article belongs to the Special Issue Carbon Neutrality and Wastewater Treatment)
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Figure 1

Figure 1
<p>SEM images of (<b>a</b>–<b>c</b>) CeFe-CM, (<b>d</b>,<b>e</b>) CeFe-CM-T, (<b>f</b>) CeO<sub>2</sub>, and (<b>g</b>) Fe<sub>3</sub>O<sub>4</sub>. Element mapping of (<b>h</b>) CeFe-CM and (<b>i</b>) CeFe-CM-T.</p> Full article ">Figure 2
<p>XRD patterns of (<b>a</b>) CeFe-CM and (<b>c</b>) CeFe-CM-T, CeO<sub>2</sub>, Fe<sub>3</sub>O<sub>4</sub>; (<b>b</b>) TG and DTG curves of CeFe-CM; (<b>d</b>) FTIR spectra of CeFe-CM, CeFe-CM-T, CeO<sub>2</sub>, and Fe<sub>3</sub>O<sub>4</sub>.</p> Full article ">Figure 3
<p>(<b>a</b>) XPS spectra of CeFe-CM and CeFe-CM-T; high-resolution XPS spectra of Ce 3d and Fe 2p of CeFe-CM (<b>b</b>,<b>c</b>) and CeFe-CM-T (<b>d</b>,<b>e</b>).</p> Full article ">Figure 4
<p>(<b>a</b>) N<sub>2</sub> adsorption–desorption curves and (<b>b</b>) pore distribution curves of CeFe-CM and CeFe-CM-T.</p> Full article ">Figure 5
<p>Adsorption isotherm curves of phosphate onto CeFe-CM-T, CeO<sub>2</sub>, and Fe<sub>3</sub>O<sub>4</sub> at (<b>a</b>) 25 °C and (<b>b</b>) different temperatures (25 °C, 35 °C and 45 °C); (<b>c</b>,<b>d</b>) adsorption kinetics of phosphate onto CeFe-CM-T, CeO<sub>2</sub>, and Fe<sub>3</sub>O<sub>4</sub>.</p> Full article ">Figure 6
<p>(<b>a</b>) Distribution of phosphate species at different solution pH; (<b>b</b>) Effect of solution pH on phosphate adsorption onto CeFe-CM-T, CeO<sub>2</sub>, and Fe<sub>3</sub>O<sub>4</sub>; (<b>c</b>) change in solution pH before and after phosphate adsorption; (<b>d</b>) zeta potentials of CeFe-CM-T, CeO<sub>2</sub>, and Fe<sub>3</sub>O<sub>4</sub> at different equilibrium pHs.</p> Full article ">Figure 7
<p>The recycle performance of CeFe-CM-T for phosphate adsorption, and the inset is the display of magnetic separation.</p> Full article ">Figure 8
<p>Effect of (<b>a</b>) co-existing anions and (<b>b</b>) natural organic matters on the phosphate adsorption onto CeFe-CM-T.</p> Full article ">Figure 9
<p>(<b>a</b>) XRD patterns and (<b>b</b>) FTIR spectra of CeFe-CM-T before and after phosphate adsorption; high-resolution XPS spectra of (<b>c</b>) P 2p of CeFe-CM-T-P, (<b>d</b>) Ce 3d, and (<b>e</b>,<b>f</b>) O1s before and after phosphate adsorption.</p> Full article ">
13 pages, 2589 KiB  
Article
Analysis on the Return Period of “7.20” Rainstorm in the Xiaohua Section of the Yellow River in 2021
by Shuangyan Jin, Shaomeng Guo and Wenbo Huo
Water 2022, 14(15), 2444; https://doi.org/10.3390/w14152444 - 7 Aug 2022
Cited by 6 | Viewed by 2088
Abstract
The “7.20” rainstorm and flood disaster in 2021 occurred in Zhengzhou and adjacent areas of Henan province. According to the Maximum Rainfall Data of Different Periods and the “7.20” rainstorm data of the section from Xiaolangdi to Huayuankou of the Yellow River in [...] Read more.
The “7.20” rainstorm and flood disaster in 2021 occurred in Zhengzhou and adjacent areas of Henan province. According to the Maximum Rainfall Data of Different Periods and the “7.20” rainstorm data of the section from Xiaolangdi to Huayuankou of the Yellow River in 2021, i.e., thirteen kinds of automatic monitoring rainfall data in minutes and six kinds of manual monitoring rainfall data in hours, the rainfall frequency curves of two representative periods of ten reference stations are established using Pearson-III distribution. The return periods of “7.20” rainstorms with maximum 24 h greater than 300 mm and maximum 6 h greater than 100 mm are calculated. The results show that the influence of “7.20” rainstorms on the rainfall return period is obvious. For the ten reference stations, all the maximum 24 h rainfall of “7.20” rainstorms ranked in the first of the series. When establishing the frequency curve, if this value is considered, the largest return period occurs at Sishui station, with a return period of 486 years. Otherwise, the return period of Sishui, Mangling, Minggao, and Xicun stations will exceed 10,000 years. Among ten reference stations, the largest proportion of the maximum 6 h rainfall between “7.20” rainstorms and historical series is Minggao station. Taking Minggao station as an example, the return period is about 200 years when considering the “7.20” value to establish the frequency curve, otherwise it is about 3000 years. The results can provide technical support for the analysis of the rainstorm return period and the flood control operation in the lower Yellow River. Full article
(This article belongs to the Special Issue Yellow River Basin Management under Pressure: Present State, Restoration and Protection)
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Figure 1
<p>The location of the Xiaohua section of the Yellow River.</p> Full article ">Figure 2
<p>The runoff composition diagram of “7.21” flood at Huayuankou station.</p> Full article ">Figure 3
<p>Location of rainfall stations.</p> Full article ">Figure 4
<p>The maximum 24 h rainfall of ten reference stations between historical and “7.20” rainstorms.</p> Full article ">Figure 5
<p>The maximum 6 h rainfall of ten reference stations between historical and “7.20” rainstorms.</p> Full article ">Figure 6
<p>The isogram of rainfall and the location of reference stations in “7.20” rainstorms in the Xiaohua section.</p> Full article ">Figure 7
<p>The frequency curve of the maximum 24 h rainfall of discontinuous 40 years in 1978~2020 at Wulongmiao station.</p> Full article ">Figure 8
<p>The frequency curve of the maximum 24 h rainfall of discontinuous 41 years in 1978~2021 at Wulongmiao station.</p> Full article ">Figure 9
<p>The frequency curve of the maximum 6 h rainfall of discontinuous 41 years in 1978~2021 at Jiulongjiao station.</p> Full article ">Figure 10
<p>The frequency curve of the maximum 6 h rainfall of discontinuous 48 years in 1964~2021 at Minggao station.</p> Full article ">
15 pages, 3099 KiB  
Article
Network Subsystems for Robust Design Optimization of Water Distribution Systems
by Assefa Hayelom and Avi Ostfeld
Water 2022, 14(15), 2443; https://doi.org/10.3390/w14152443 - 7 Aug 2022
Cited by 1 | Viewed by 2021
Abstract
The optimal design of WDS has been extensively researched for centuries, but most of these studies have employed deterministic optimization models, which are premised on the assumption that the parameters of the design are perfectly known. Given the inherently uncertain nature of many [...] Read more.
The optimal design of WDS has been extensively researched for centuries, but most of these studies have employed deterministic optimization models, which are premised on the assumption that the parameters of the design are perfectly known. Given the inherently uncertain nature of many of the WDS design parameters, the results derived from such models may be infeasible or suboptimal when they are implemented in reality due to parameter values that differ from those assumed in the model. Consequently, it is necessary to introduce some uncertainty in the design parameters and find more robust solutions. Robust counterpart optimization is one of the methods used to deal with optimization under uncertainty. In this method, a deterministic data set is derived from an uncertain problem, and a solution is computed such that it remains viable for any data realization within the uncertainty bound. This study adopts the newly emerging robust optimization technique to account for the uncertainty associated with nodal demand in designing water distribution systems using the subsystem-based two-stage approach. Two uncertainty data models with ellipsoidal uncertainty set in consumer demand are examined. The first case, referred to as the uncorrelated problem, considers the assumption that demand uncertainty only affects the mass balance constraint, while the second case, referred to as the correlated case, assumes uncertainty in demand and also propagates to the energy balance constraint. Full article
(This article belongs to the Section Urban Water Management)
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<p>Network schematic of FOWM distribution system (Ormsbee and Kessler 1990).</p> Full article ">Figure 2
<p>Cost vs. minimum pressure demand for FOWM network.</p> Full article ">Figure 3
<p>Schematic of pair of backups: (<b>a</b>) The first subsystem generated following decreasing order; (<b>b</b>) The second subsystem generated following increasing order.</p> Full article ">Figure 4
<p>Results of uncorrelated optimization for FOWM network: (<b>a</b>) cost vs. protection factor Ω; (<b>b</b>) head constraint violation probability.</p> Full article ">Figure 5
<p>Results of correlated optimization for FOWM network.</p> Full article ">Figure 6
<p>Network schematic of Fossolo distribution system.</p> Full article ">Figure 7
<p>Cost vs. minimum pressure-demand tradeoff curve for Fossolo network.</p> Full article ">Figure 8
<p>Schematic of pair of optimal backup pairs for Fossolo network.</p> Full article ">Figure 9
<p>Results of uncorrelated optimization for Fossolo network: (<b>a</b>) cost vs. protection factor Ω; (<b>b</b>) head constraint violation probability.</p> Full article ">Figure 10
<p>Results of correlated optimization for Fossolo network: (<b>a</b>) cost vs. protection factor Ω; (<b>b</b>) head constraint violation probability.</p> Full article ">
19 pages, 2187 KiB  
Article
Major, Trace and Rare Earth Element Distribution in Water, Suspended Particulate Matter and Stream Sediments of the Ob River Mouth
by Andrei Soromotin, Dmitriy Moskovchenko, Vitaliy Khoroshavin, Nikolay Prikhodko, Alexander Puzanov, Vladimir Kirillov, Mikhail Koveshnikov, Eugenia Krylova, Aleksander Krasnenko and Aleksander Pechkin
Water 2022, 14(15), 2442; https://doi.org/10.3390/w14152442 - 6 Aug 2022
Cited by 14 | Viewed by 3364
Abstract
Ongoing climatic changes are influencing the volume and composition of the river waters that enter the Arctic Basin. This hydrochemical study was conducted within the mouth of the Ob River, which is one of the world’s largest rivers, providing 15% of the Arctic [...] Read more.
Ongoing climatic changes are influencing the volume and composition of the river waters that enter the Arctic Basin. This hydrochemical study was conducted within the mouth of the Ob River, which is one of the world’s largest rivers, providing 15% of the Arctic Ocean’s total intake. Concentrations of suspended and dissolved elements were determined using ICP–MS and ICP–AES. As compared to the world average values, the Ob river water had higher concentrations of dissolved P, As, Cu, Zn, Pb and Sb, i.e., the elements that form soluble organo-mineral complexes. The composition of suspended matter was characterized by low concentrations of most trace elements (Cd, Cr, Co, Cu, Mo, Al, Ni, Pb, V) due to their low contents in peat soils within the river drainage basin. Concentrations of dissolved forms were many times lower than concentrations of suspended forms in Al, Fe, Mn, Zn, Cr, Co, Ti, Sc, and all rare earth elements. Total concentrations of Ni, Cu, Bi, Pb, W in the river water increased by 2.5 to 4.2 times during the summer. The effects of climate change, which can cause an increase in the discharge of solid particles from thawing permafrost, are likely to lead to an increase in the discharge of certain elements into the Ob River estuary. Full article
(This article belongs to the Special Issue Water Resources under Growing Anthropogenic Loads)
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<p>Overview scheme of the location of the study area in the lower reaches of the Ob River.</p> Full article ">Figure 2
<p>Distribution of pH and TDS (total dissolved solids) at different depths: (<b>a</b>) pH value at different depths; (<b>b</b>) TDS value at different depths. Whiskers represents the standard error.</p> Full article ">Figure 3
<p>Total flux (t·day<sup>−1</sup>), mean value of Ca, Mg and trace elements with Ob waters: 1—summer–fall low-water period 2020 (our data); 2—ice period 2021 (our data); 3—average for Ob River (data from [<a href="#B22-water-14-02442" class="html-bibr">22</a>] was used).</p> Full article ">Figure 4
<p>Content of the soluble form of elements in Ob waters: (logarithmic values): 1—the year 2020 autumnal low-water period (our data); 2—Ob River average (data from [<a href="#B22-water-14-02442" class="html-bibr">22</a>] were used). Error bars represent 1 SD.</p> Full article ">Figure 5
<p>Content of insoluble form of elements in river waters (logarithmic values): 1—Ob River, summer–autumn low water 2020 (our data); 2—Arctic rivers of Eurasia average (data from [<a href="#B22-water-14-02442" class="html-bibr">22</a>] were used). Error bars represent 1 SD.</p> Full article ">Figure 6
<p>Ratio of concentration of elements in the sediment of Ob (our data) to the average concentration of elements in the sediment of the rivers of the Earth (for calculations, we used the data from [<a href="#B58-water-14-02442" class="html-bibr">58</a>]).</p> Full article ">
20 pages, 2347 KiB  
Article
Hydro-Geochemistry and Groundwater Quality Assessment of Ouargla Basin, South of Algeria
by Zina Mansouri, Youcef Leghrieb, Saber Kouadri, Nadhir Al-Ansari, Hadee Mohammed Najm, Nuha S. Mashaan, Moutaz Mustafa A. Eldirderi and Khaled Mohamed Khedher
Water 2022, 14(15), 2441; https://doi.org/10.3390/w14152441 - 6 Aug 2022
Cited by 9 | Viewed by 3690
Abstract
This study aims to evaluate the hydro-chemical characteristics of Ouargla, Algeria basin groundwaters harvested from the Mio Pliocene aquifer. The study covered 70 samples; the physical parameters, potential of hydrogen (pH), and electrical conductivity EC μS.cm−1 were determined in situ, using a [...] Read more.
This study aims to evaluate the hydro-chemical characteristics of Ouargla, Algeria basin groundwaters harvested from the Mio Pliocene aquifer. The study covered 70 samples; the physical parameters, potential of hydrogen (pH), and electrical conductivity EC μS.cm−1 were determined in situ, using a multiparameter; the laboratory analysis included dry residuals DR (mg/L), calcium Ca2+ (mg/L), magnesium Mg2+ (mg/L), sodium Na+ (mg/L), potassium K+ (mg/L), bicarbonates HCO3− (mg/L), sulfates SO42− (mg/L), and chloride Cl− (mg/L). The piper diagram shows that the Ouargla basin ground waters divided into two facies, sodic chlorinated in 93% and sodic sulphated in 7% of samples. The United States Salinity Laboratory Staff (USSL) diagram was used to detect the suitability of groundwater in irrigation where the results show that the groundwater was classed into two classes, poor water (C4 S4) and bad water (C4 S4). Furthermore, indices such as the Kelly index (KI), sodium adsorption ratio (SAR), sodium solubility percentage (Na%), and magnesium hazards (MH) confirm the negative effect of groundwater on soil permeability in 96%, 80%, 89%, and 53% of samples. The permeability index (PI) shows that the analyzed samples were considered as doubtful (71%) and safe (29%), otherwise there is no risk related to residual sodium carbonate (RSC). The geo-spatial distribution of deferent indices shows that all the study area has poor groundwater for irrigation, except the south-west part, where the groundwaters of this sub-area do not form a problem related to RSC. Full article
(This article belongs to the Special Issue Integrated Water Resources Management (IWRM), a Holistic Approach to Sustainable Water Management)
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<p>Geographical location of (<b>a</b>) Algeria, (<b>b</b>) Ouargla city and (<b>c</b>) Ouargla basin.</p> Full article ">Figure 2
<p>Cross-section of the hydrogeological layers in the study area.</p> Full article ">Figure 3
<p>Statistical presentation of groundwater ions.</p> Full article ">Figure 4
<p>Groundwater samples plotted on the Piper diagram.</p> Full article ">Figure 5
<p>Variation in saturation indices of reservoir rocks.</p> Full article ">Figure 6
<p>USSL diagram for EC and sodium percentage of groundwater suitability in irrigation.</p> Full article ">Figure 7
<p>Correlation circles on the F1–F2 factors plane.</p> Full article ">Figure 8
<p>Geo-spatial distribution of suitability for irrigation indices.</p> Full article ">
23 pages, 5009 KiB  
Article
Prediction of Groundwater Arsenic Hazard Employing Geostatistical Modelling for the Ganga Basin, India
by Sana Dhamija and Himanshu Joshi
Water 2022, 14(15), 2440; https://doi.org/10.3390/w14152440 - 6 Aug 2022
Cited by 8 | Viewed by 3188
Abstract
Elevated arsenic concentrations in groundwater in the Ganga–Brahmaputra–Meghna (GBM) river basin of India has created an alarming situation. Considering that India is one of the largest consumers of groundwater for a variety of uses such as drinking, irrigation, and industry, it is imperative [...] Read more.
Elevated arsenic concentrations in groundwater in the Ganga–Brahmaputra–Meghna (GBM) river basin of India has created an alarming situation. Considering that India is one of the largest consumers of groundwater for a variety of uses such as drinking, irrigation, and industry, it is imperative to determine arsenic occurrence and hazard for sustainable groundwater management. The current study focused on the evaluation of arsenic occurrence and groundwater arsenic hazard for the Ganga basin employing Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) models. Furthermore, arsenic hazard maps were prepared using a Kriging interpolation method and with overlay analysis in the GIS platform based on the available secondary datasets. Both models generated satisfactory results with minimum differences. The highest hazard likelihood has been displayed around and along the Ganges River. Most of the Uttar Pradesh and Bihar; and parts of Rajasthan, Chhattisgarh, Jharkhand, Madhya Pradesh, and eastern and western regions of West Bengal show a high arsenic hazard. More discrete results were rendered by the AHP model. Validation of arsenic hazard maps was performed through evaluating the Area Under Receiver Operating Characteristics metric (AUROC), where AUC values for both models ranged from 0.7 to 0.8. Furthermore, the final output was also validated against the primary arsenic data generated through field sampling for the districts of two states, viz Bihar (2019) and Uttar Pradesh (2021). Both models showed good accuracy in the spatial prediction of arsenic hazard. Full article
(This article belongs to the Special Issue Arsenic, Fluoride and Emerging Contaminants: Groundwater Quality and Water Security in the Indian Sub-Continent)
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<p>Study area—Ganga Basin, India [<a href="#B34-water-14-02440" class="html-bibr">34</a>].</p> Full article ">Figure 2
<p>Schematic flowchart of the methodology of arsenic occurrence, assessment, and mapping.</p> Full article ">Figure 3
<p>Thematic layers and arsenic occurrence percentages w.r.t.: (<b>a</b>) DTW; (<b>b</b>) slope; (<b>c</b>) geomorphology; (<b>d</b>) types of aquifers; (<b>e</b>) soil types; (<b>f</b>) LULC; (<b>g</b>) rainfall; (<b>h</b>) groundwater abstraction; (<b>i</b>) dissolved silica; (<b>j</b>) bicarbonate; (<b>k</b>) iron; (<b>l</b>) EC; (<b>m</b>) hardness; (<b>n</b>) sulphate; and (<b>o</b>) testing arsenic datasets of the study area.</p> Full article ">Figure 3 Cont.
<p>Thematic layers and arsenic occurrence percentages w.r.t.: (<b>a</b>) DTW; (<b>b</b>) slope; (<b>c</b>) geomorphology; (<b>d</b>) types of aquifers; (<b>e</b>) soil types; (<b>f</b>) LULC; (<b>g</b>) rainfall; (<b>h</b>) groundwater abstraction; (<b>i</b>) dissolved silica; (<b>j</b>) bicarbonate; (<b>k</b>) iron; (<b>l</b>) EC; (<b>m</b>) hardness; (<b>n</b>) sulphate; and (<b>o</b>) testing arsenic datasets of the study area.</p> Full article ">Figure 3 Cont.
<p>Thematic layers and arsenic occurrence percentages w.r.t.: (<b>a</b>) DTW; (<b>b</b>) slope; (<b>c</b>) geomorphology; (<b>d</b>) types of aquifers; (<b>e</b>) soil types; (<b>f</b>) LULC; (<b>g</b>) rainfall; (<b>h</b>) groundwater abstraction; (<b>i</b>) dissolved silica; (<b>j</b>) bicarbonate; (<b>k</b>) iron; (<b>l</b>) EC; (<b>m</b>) hardness; (<b>n</b>) sulphate; and (<b>o</b>) testing arsenic datasets of the study area.</p> Full article ">Figure 3 Cont.
<p>Thematic layers and arsenic occurrence percentages w.r.t.: (<b>a</b>) DTW; (<b>b</b>) slope; (<b>c</b>) geomorphology; (<b>d</b>) types of aquifers; (<b>e</b>) soil types; (<b>f</b>) LULC; (<b>g</b>) rainfall; (<b>h</b>) groundwater abstraction; (<b>i</b>) dissolved silica; (<b>j</b>) bicarbonate; (<b>k</b>) iron; (<b>l</b>) EC; (<b>m</b>) hardness; (<b>n</b>) sulphate; and (<b>o</b>) testing arsenic datasets of the study area.</p> Full article ">Figure 4
<p>Groundwater arsenic hazard map for the Ganga basin using (<b>a</b>) AHP, (<b>b</b>) FR.</p> Full article ">Figure 5
<p>Bar graph of groundwater arsenic prediction by geostatistical methods.</p> Full article ">Figure 6
<p>(<b>a</b>) AUROC validation curve with training datasets for the AHP and FR methods; (<b>b</b>) AUROC validation curve with testing datasets for the AHP and FR methods.</p> Full article ">Figure 7
<p>Validation through actual field data for: (<b>a</b>) AHP (Bihar); (<b>b</b>) FR (Bihar); (<b>c</b>) AHP (UP); (<b>d</b>) FR (UP). Each part represents four sections: (i) predicted arsenic hazard with the data points in the selected state boundaries; (ii) predicted arsenic hazard and the data points with the district boundaries for the selected districts; (iii) the percentage of arsenic occurrence; and (iv) a scatterplot of the predicted hazard index values and actual arsenic concentrations.</p> Full article ">
24 pages, 1958 KiB  
Review
Research Progress on Integrated Treatment Technologies of Rural Domestic Sewage: A Review
by Peizhen Chen, Wenjie Zhao, Dongkai Chen, Zhiping Huang, Chunxue Zhang and Xiangqun Zheng
Water 2022, 14(15), 2439; https://doi.org/10.3390/w14152439 - 6 Aug 2022
Cited by 28 | Viewed by 8947
Abstract
The improvement of rural living standards in developing countries and the continuous upgrading of the rural industrial economy have prompted the diversification of rural areas and residential forms. Thus, an integrated rural sewage treatment process has gradually become the mainstream technology for rural [...] Read more.
The improvement of rural living standards in developing countries and the continuous upgrading of the rural industrial economy have prompted the diversification of rural areas and residential forms. Thus, an integrated rural sewage treatment process has gradually become the mainstream technology for rural sewage treatment. Numerous studies have reported the effects of ecological wastewater treatment. Meanwhile, the relevant process technologies, evaluations, and operating models of the integrated rural sewage treatment process have yet to be thoroughly summarized. This review aims to fill these gaps. First, the applicability of artificial wetland, soil infiltration, stabilization pond, and integrated rural sewage treatment process technology in rural sewage treatment are outlined and compared. Second, the process flow, technical characteristics, and economic indicators of typical integrated sewage treatment processes (i.e., Anoxic/Oxic (A/O) process, Membrane Bio-Reactor (MBR) process, biological contact oxidation process, Sequencing Batch Reactor Activated Sludge (SBR) process) are introduced. The engineering application effects of the integrated rural sewage treatment process in different countries are also described. Third, the practical and effective evaluation methods of the integrated rural sewage treatment process are introduced. Bearing in mind the current operation and maintenance management modes of the integrated rural sewage treatment process in developed and developing countries, combined with the national conditions of developing countries, the prospect section provides development proposals for further optimization and improvement of the integrated rural sewage treatment process in developing countries. Full article
(This article belongs to the Section Wastewater Treatment and Reuse)
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<p>A/O process flow (<b>a</b>) and schematic diagram (<b>b</b>).</p> Full article ">Figure 2
<p>MBR process flow (<b>a</b>) and schematic diagram (<b>b</b>).</p> Full article ">Figure 3
<p>Biological contact oxidation process flow (<b>a</b>) and schematic diagram of the schematic diagram tank (<b>b</b>).</p> Full article ">Figure 4
<p>SBR process flow (<b>a</b>) and schematic diagram (<b>b</b>).</p> Full article ">Figure 5
<p>Multifactor weighted evaluation flow.</p> Full article ">Figure 6
<p>Structure of artificial neural network.</p> Full article ">
15 pages, 1678 KiB  
Article
Pollutant Removal and Energy Recovery from Swine Wastewater Using Anaerobic Membrane Bioreactor: A Comparative Study with Up-Flow Anaerobic Sludge Blanket
by Yunhui Pu, Jialing Tang, Ting Zeng, Yisong Hu, Jixiang Yang, Xiaochang Wang, Jin Huang and Abdelfatah Abomohra
Water 2022, 14(15), 2438; https://doi.org/10.3390/w14152438 - 6 Aug 2022
Cited by 12 | Viewed by 2559
Abstract
Due to its high content of organics and nutrients, swine wastewater has become one of the main environment pollution sources. Exploring high-efficient technologies for swine wastewater treatment is urgent and becoming a hot topic in the recent years. The present study introduces anaerobic [...] Read more.
Due to its high content of organics and nutrients, swine wastewater has become one of the main environment pollution sources. Exploring high-efficient technologies for swine wastewater treatment is urgent and becoming a hot topic in the recent years. The present study introduces anaerobic membrane bioreactor (AnMBR) for efficient treatment of swine wastewater, compared with up-flow anaerobic sludge blanket (UASB) as a traditional system. Pollutant removal performance, methanogenic properties, and microbial community structures were investigated in both reactors. Results showed that by intercepting particulate organics, AnMBR achieved stable and much higher chemical oxygen demand (COD) removal rate (approximately 90%) than UASB (around 60%). Due to higher methanogenic activity of anaerobic sludge, methane yield of AnMBR (0.23 L/g-COD) was higher than that of UASB. Microbial community structure analysis showed enrichment of functional bacteria that can remove refractory organic matter in the AnMBR, which promoted the organics conversion processes. In addition, obvious accumulation of acetotrophic and hydrotrophic methanogens in AnMBR system was recorded, which could broaden the organic matter degradation pathways and the methanogenesis processes, ensuring a higher methane yield. Through energy balance analysis, results concluded that the net energy recovery efficiency of AnMBR was higher than that of UASB system, indicating that applying AnMBR for livestock wastewater treatment could not only efficiently remove pollutants, but also significantly enhance the energy recovery. Full article
(This article belongs to the Special Issue Advances of Anaerobic Technologies on Wastewater Treatment)
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<p>Schematic diagram of the AnMBR (<b>left</b>) and UASB (<b>right</b>) used in the present study.</p> Full article ">Figure 2
<p>Variations of COD removal in AnMBR and UASB. (<b>a</b>) COD content; (<b>b</b>) COD components along the reactor. PCOD and SCOD represent particulate and soluble COD, respectively.</p> Full article ">Figure 3
<p>Variations in the content (<b>a</b>) and composition (<b>b</b>) of VFAs in the two studied reactors.</p> Full article ">Figure 4
<p>Variations of protein (<b>a</b>) and carbohydrate (<b>b</b>) in the studied AnMBR and UASB.</p> Full article ">Figure 5
<p>Methane production in AnMBR and UASB.</p> Full article ">Figure 6
<p>The rarefaction curves of the bacteria and archaea in the studied AnMBR and UASB.</p> Full article ">Figure 7
<p>Bacterial communities in AnMBR and UASB. (<b>a</b>) phylum level, (<b>b</b>) genus level.</p> Full article ">Figure 8
<p>Order (<b>a</b>) and genus (<b>b</b>) level of archaea in AnMBR and UASB.</p> Full article ">
13 pages, 3518 KiB  
Article
Unsteady Friction Modeling Technique for Lagrangian Approaches in Transient Simulations
by Mohamad Zeidan and Avi Ostfeld
Water 2022, 14(15), 2437; https://doi.org/10.3390/w14152437 - 6 Aug 2022
Cited by 2 | Viewed by 1969
Abstract
This study investigates the simulation of celerity attenuation and head damping in transient flows using a Lagrangian approach rather than an Eulerian approach. Typically, the Lagrangian approach requires orders of magnitude fewer calculations, resulting in the rapid solution of very large systems. Additionally, [...] Read more.
This study investigates the simulation of celerity attenuation and head damping in transient flows using a Lagrangian approach rather than an Eulerian approach. Typically, the Lagrangian approach requires orders of magnitude fewer calculations, resulting in the rapid solution of very large systems. Additionally, it is based on a simple physical model. As the method is continuous in both time and space, it is less sensitive to the structure of the network and the length of the simulation process. Most recent studies, however, have focused on the development and improvement of computational routines for modeling in an Eulerian environment. This results in the development of adequate models that are suitable for Eulerian models but not applicable in Lagrangian-based models. As a result of this fixation, a bias was created towards using Eulerian approaches in transient simulations. It also diverts resources from further development of Lagrangian models. Consequently, it is necessary to develop a friction model that is more accurate and compatible with Lagrangian methods without compromising their performance. To the authors’ knowledge, such a model is yet to be published in the literature. This study presents a new friction modeling technique that compensates for both the local and convective acceleration terms for the Lagrangian transient modeling approach without compromising the computational time. Full article
(This article belongs to the Section Urban Water Management)
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<p>The layout of the first case study describes a simple pipeline system with a junction and a valve downstream.</p> Full article ">Figure 2
<p>Comparison between the WCM and the U-MOC models at node 2, for the first case study.</p> Full article ">Figure 3
<p>The transient response at node 2 with different WCM tunings, in comparison to the unsteady MOC model in red, for the first case study. The subfigures depict in black the WCM transient evolution from quasi-state friction (<b>a</b>) to unsteady friction (<b>d</b>), in an ascending order.</p> Full article ">Figure 4
<p>A comparison of the transient behavior at node 2 for the refined U-WCM (black) and the U-MOC (red) for the first case study—A variation. The quasi-model (Q-WCM) is illustrated in gray in the background.</p> Full article ">Figure 5
<p>A comparison of the transient behavior at node 2 for the refined U-WCM (black) and the U-MOC (red) for the first case study—B variation. The quasi-model (Q-WCM) is illustrated in gray in the background.</p> Full article ">Figure 6
<p>The layout of the second case study, depicting a looped network system with a single source and a consumer at the downstream.</p> Full article ">Figure 7
<p>Transient response at node 6 in the second case study. U-MOC, U-WCM, and WCM in red, black, and gray, respectively.</p> Full article ">Figure 8
<p>Transient response at node 2 in the second case study. U-MOC, U-WCM, and WCM in red, black, and gray, respectively.</p> Full article ">Figure 9
<p>Transient response at node 3 in the second case study. U-MOC, U-WCM, and WCM in red, black, and gray, respectively.</p> Full article ">
40 pages, 11600 KiB  
Review
Nanomaterial-Based Sensors for the Detection of Glyphosate
by Karem Zúñiga, Georgette Rebollar, Mayra Avelar, José Campos-Terán and Eduardo Torres
Water 2022, 14(15), 2436; https://doi.org/10.3390/w14152436 - 6 Aug 2022
Cited by 15 | Viewed by 4781
Abstract
Due to its chemical properties, glyphosate [N-(phosphonomethyl)glycine] is one of the most commonly used agricultural herbicides globally. Due to risks associated with human exposure to glyphosate and its potential harmfulness, the need to develop specific, accurate, online, and sensitive methods is imperative. In [...] Read more.
Due to its chemical properties, glyphosate [N-(phosphonomethyl)glycine] is one of the most commonly used agricultural herbicides globally. Due to risks associated with human exposure to glyphosate and its potential harmfulness, the need to develop specific, accurate, online, and sensitive methods is imperative. In accordance with this, the present review is focused on recent advances in developing nanomaterial-based sensors for glyphosate detection. Reported data from the literature concerning glyphosate detection in the different matrices using analytical methods (mostly chromatographic techniques) are presented; however, they are expensive and time-consuming. In this sense, nanosensors’ potential applications are explained to establish their advantages over traditional glyphosate detection methods. Zero-dimensional (0D), one-dimensional (1D), two-dimensional (2D), and three- dimensional (3D) materials are reviewed, from biomolecules to metallic compounds. Bionanomaterials have generated research interest due to their selectivity with respect to using enzymes, DNA, or antibodies. On the other hand, Quantum Dots also are becoming relevant for their vast surface area and good limit of detection values (in the range of pM). This review presents all the characteristics and potential applications of different nanomaterials for sensor development, bearing in mind the necessity of a glyphosate detection method with high sensitivity, selectivity, and portability. Full article
(This article belongs to the Section New Sensors, New Technologies and Machine Learning in Water Sciences)
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<p>Chemical structure of (<b>a</b>) glyphosate (GLYP); (<b>b</b>) aminomethylphosphonic acid (AMPA).</p> Full article ">Figure 2
<p>The schematic representation of sensor parts includes recognition elements, transducers, and detectors.</p> Full article ">Figure 3
<p>(<b>a</b>) Schematic illustration of nanomaterials classification. Adapted with permission from [<a href="#B81-water-14-02436" class="html-bibr">81</a>] Copyright 2020 Elsevier (<b>b</b>) and their optical properties. Adapted with permission from [<a href="#B93-water-14-02436" class="html-bibr">93</a>] Copyright 2021 Elsevier. (<b>c</b>) Typical morphologies of solid and mesoporous/hollow inorganic nanoparticles with 0D, 1D, and 2D shapes and other 3D complex structures. Adapted with permission from [<a href="#B94-water-14-02436" class="html-bibr">94</a>]. Copyright 2016 Royal Society of Chemistry.</p> Full article ">Figure 4
<p>Fabrication steps of an imprinted sensor for GLYP detection: (<b>a</b>) electropolymerization in the template presence, template extraction, and equilibration with the unknown GLYP solution for its detection. Au bullets present AuNPs, before and after electropolymerization of polythioaniline, entrapping GLYP molecules as templates. At the bottom of the figure, the hydrogen bonds between aniline moieties and the O and N atoms of GLYP molecules in the MIP-MOF film are presented, and empty cavities after the extraction of GLYP molecules. (<b>b</b>) TEM observation of p-amino thiophenol-functionalized gold nanoparticles. Reprinted with permission from [<a href="#B100-water-14-02436" class="html-bibr">100</a>]. Copyright 2015 Taylor &amp; Francis.</p> Full article ">Figure 5
<p>(<b>a</b>) Schematic representation of a GLYP sensor design strategy using <sup>10Os</sup>CO-Au NPs as SERS probe. (<b>b</b>) TEM images of Au nanoparticles, <sup>10Os</sup>CO-Au NPs and aggregated <sup>10Os</sup>CO-Au NPs and (<b>c</b>) SERS spectra of <sup>10Os</sup>CO-Au NPs scanned at 633 nm (top) and 532 nm (bottom). Reprinted/adapted with permission from [<a href="#B105-water-14-02436" class="html-bibr">105</a>]. Copyright 2017 Elsevier.</p> Full article ">Figure 6
<p>Scheme illustration of the fabrication of the Cu-TCPP/AuNPs/CP electrode and the electrochemical sensor for GLYP. Reprinted/adapted with permission from [<a href="#B115-water-14-02436" class="html-bibr">115</a>]. Copyright 2022 Elsevier.</p> Full article ">Figure 7
<p>(<b>a</b>) Scheme of dual-signal sensing strategy based on ratiometric fluorescence and colorimetry for Cu<sup>2+</sup> and glyphosate determination. (<b>b</b>) Fluorescence spectra of SiNPs, SiNPs + Cu<sup>2+</sup>, SiNPs + OPD, SiNPs + OPD + Cu<sup>2+</sup>, and SiNPs + OPD + Cu<sup>2+</sup> +glyphosate. (<b>c</b>) UV–vis absorption spectra of SiNPs, SiNPs + Cu<sup>2+</sup>, SiNPs + Cu<sup>2+</sup> + OPD, SiNPs + Cu<sup>2+</sup> + OPD + glyphosate, Cu<sup>2+</sup>, Cu<sup>2+</sup> + OPD, OPD, and SiNPs + OPD. Reprinted/adapted with permission from [<a href="#B116-water-14-02436" class="html-bibr">116</a>]. Copyright 2022 Springer.</p> Full article ">Figure 8
<p>(<b>a</b>) Schematic representation of UCNP-H<sub>2</sub>O<sub>2</sub> -TMB-Cu (II) hybrid system for GLYP detection. (<b>b</b>) Fluorescence spectra of UCNPs (black line), UCNP-Cu(II) (blue line), UCNP-H<sub>2</sub>O<sub>2</sub>-TMB (red line), UCNP-H<sub>2</sub>O<sub>2</sub>-TMB-Cu(II) (purple line), UCNP-H<sub>2</sub>O<sub>2</sub>-TMB-Cu(II)-GLYP (green line). Reprinted with permission from [<a href="#B114-water-14-02436" class="html-bibr">114</a>]. Copyright 2019 Springer.</p> Full article ">Figure 9
<p>Schematic representation of the detection strategy by aptasensors, immunosensors, and nanozyme-based sensors and their assembled procedures used to develop pesticide sensors. (<b>a</b>) The scheme shows the aptamer-based sensing system’s operation for GLYP employing G-quadruplex-selective iridium (III) complex in TRES mode. Adapted with permission from [<a href="#B122-water-14-02436" class="html-bibr">122</a>]. Copyright 2020 Elsevier. (<b>b</b>) Schematic representation of detection principle using carbon dots labeled antibody and antigen magnetic beads for GLYP detection. Adapted with permission from [<a href="#B127-water-14-02436" class="html-bibr">127</a>]. Copyright 2016 ACS Publications. (<b>c</b>) Schematic representation of the label-free and enzyme-free fluorescent isocarbophos (ICP) aptasensor using multiwalled carbon nanotubes (MWCNTs) and G-quadruplex as the signal transducers. Adapted with permission from [<a href="#B128-water-14-02436" class="html-bibr">128</a>]. Copyright 2018 Elsevier. (<b>d</b>) Scheme of the electrochemical immunoassay coupled with disposable screen-printed carbon electrode. Steps to develop paramagnetic beads modified with anti-GLYP antibodies and horseradish peroxidase (HRP) (<b>a</b>–<b>c</b>). Adapted with permission from [<a href="#B129-water-14-02436" class="html-bibr">129</a>]. Copyright 2018 MPDI. (<b>e</b>) Schematic illustration of the sensing process of GLYP-based an Au–Pt nanozyme. Adapted with permission from [<a href="#B126-water-14-02436" class="html-bibr">126</a>]. Copyright 2021 Royal Society Chemistry.</p> Full article ">Figure 10
<p>Strategy detection for GLYP using carbon dots coupled to glyphosate antibodies (IgG-CDs and Fe<sub>3</sub>O<sub>4</sub>-GLYP. Reprinted with permission from [<a href="#B127-water-14-02436" class="html-bibr">127</a>]. Copyright 2016 ACS Publications.</p> Full article ">Figure 11
<p>Electrochemical immunoassay coupled with disposable screen-printed carbon electrode: (<b>a</b>) Paramagnetic beads modified with anti-glyphosate antibodies were incubated with samples; (<b>b</b>) a solution containing horseradish peroxidase (HRP) conjugate was added; (<b>c</b>) after several washing steps, the particles were magnetically blocked, and the substrate was added. The enzymatic product was measured by chronoamperometry [<a href="#B129-water-14-02436" class="html-bibr">129</a>]. Free use. Copyright 2018 MDPI.</p> Full article ">Figure 12
<p>Schematic illustration of the AuNP-BB-iPCR. (<b>a</b>) Synthesis procedure of the AuNP probe. (<b>b</b>) Amplification curves of real-time PCR for the standard curve of Ct value against concentrations of signal DNA (from 1 fM to 0.01 μM). Water was used instead of signal DNA for negative control. (<b>c</b>) The standard curve of Ct values against logarithm concentrations of signal DNA. Reprinted with permission from [<a href="#B130-water-14-02436" class="html-bibr">130</a>]. Copyright 2021 Elsevier.</p> Full article ">Figure 13
<p>Detection of GLYP by Co<sub>3</sub>O<sub>4</sub> nanoplates on a polyester membrane. (<b>a</b>) Representation of the inhibitory effect of GLYP on the peroxidase activity of nanoplates; (<b>b</b>) Fluorescence spectra in solution of the TMB reaction product in the presence and absence of GLYP; (<b>c</b>) Colorimetric signal changes of colorimetric nanozyme sheets without (sample 1) and with GLYP (sample 2); sample 3 and 4 are control assays without nanozyme. Reprinted with permission from [<a href="#B147-water-14-02436" class="html-bibr">147</a>]. Copyright 2021 ACS Publications.</p> Full article ">Figure 14
<p>(<b>a</b>) Schematic illustration of the sensing process of glyphosate, (<b>b</b>) SERS spectra and reference plot of SERS intensities of a Raman shift at 1605 cm<sup>−1</sup> vs. GLYP concentrations, and linear plot of SERS intensities of a Raman shift at 1605 cm<sup>−1</sup> vs. logarithm of GLYP concentrations. Reprinted with permission from [<a href="#B126-water-14-02436" class="html-bibr">126</a>]. Copyright 2021 Royal Society Chemistry.</p> Full article ">Figure 15
<p>(<b>a</b>) Schematic representation of the preparation of SPE /Chi/ CNOs/TYR electrodes to develop a GLYP sensor. (<b>b</b>) Calibration plots for SPE/Chi/CNO/TYR and SPE/Chi/TYR at different glyphosate concentrations. Reprinted with permission from [<a href="#B150-water-14-02436" class="html-bibr">150</a>]. Copyright 2019 Springer.</p> Full article ">Figure 16
<p>(<b>a</b>) Schematic Representation of Interaction of QD with Glyphosate, (<b>b</b>) Emission spectra (λex = 340 nm) of ZnO QD (5 × 10<sup>–5</sup> M) in the presence of different GLYP concentrations. Reprinted with permission from [<a href="#B155-water-14-02436" class="html-bibr">155</a>]. Copyright 2018 ACS Publications.</p> Full article ">Figure 17
<p>(<b>a</b>) The sensing mechanism of CdTe-CQD sensor towards GLYP, (<b>b</b>) Fluorescence decreasing of CdTe due to increasing concentration of CQD solution (<b>c</b>) Fluorescence response of CdTe CQD solution, (<b>d</b>) Linear range. Reprinted with permission from [<a href="#B157-water-14-02436" class="html-bibr">157</a>]. Copyright 2020 Elsevier.</p> Full article ">Figure 18
<p>(<b>a</b>) Schematic illustration of fluorescence detection based on the inhibition of CuOox/MWCNTs catalytic activity by GLYP. (<b>b</b>) Fluorescence spectra of resorufin as a reaction product in the absence and presence at different concentrations of GLYP. (<b>c</b>) Fluorescence intensity of resorufin as reaction product at increasing concentration of GLYP. The inset figure presents the linear range. Reprinted with permission from [<a href="#B112-water-14-02436" class="html-bibr">112</a>]. Copyright 2016 Elsevier.</p> Full article ">Figure 19
<p>(<b>a</b>) Schematic illustration of the extraction and in-situ electrochemical detection of GLYP (<b>b</b>) Differential pulse voltammetry for GLYP, 0.1 M PBS (pH 7), (<b>c</b>) Linear dependence of peak currents on GLYP concentrations as a calibration curve. Reprinted with permission from [<a href="#B160-water-14-02436" class="html-bibr">160</a>]. Copyright 2018 Elsevier.</p> Full article ">Figure 20
<p>(<b>a</b>) The schematic representation of the cd-PVA sensor system. (<b>b</b>) The visual colorimetric response of the cd-PVA sensor strips immediately after the addition of 1000 µg/mL of the pure glyphosate derivative (99.5%) a and b: cd-PVA sensor strips after the addition of the glyphosate sample (volume: 30 μL). Values are graphically presented as mean values ± SD (n = 2). Control reactions included: c: no glyphosate, d: no carbon disulfide, e: a no test sensor strip/blank. (<b>c</b>) The calibration curve for glyphosate (0.1–500µg/mL). The sample volume was 30 µL and the appropriate controls were used in this study. Values are presented as mean values ±SD (n= 3). Adapted with permission from [<a href="#B165-water-14-02436" class="html-bibr">165</a>]. Copyright 2015 Elsevier.</p> Full article ">Figure 21
<p>Aptamer regulated-MaBd catalysis of GC-HAuCl<sub>4</sub> nanogold reaction to detect GLYP with two scattering techniques. Reprinted with permission from [<a href="#B185-water-14-02436" class="html-bibr">185</a>]. Copyright 2021 Elsevier.</p> Full article ">Figure 22
<p>An electrochemically synthesized nanoporous copper microsensor for highly sensitive and selective determination of GLYP. Reprinted with permission from [<a href="#B111-water-14-02436" class="html-bibr">111</a>]. Copyright 2020 Wiley Online Library.</p> Full article ">Figure 23
<p>Nanocomposite development for an electrochemical sensor. (<b>a</b>) CuOx@mC composite formation, (<b>b</b>) Analytical principle of the electrochemical sensor based on the CuOx@mC composite, (<b>c</b>) A calibration curves between ΔI and the GLYP concentrations for CuOx@ mC GCE. Reprinted with permission from [<a href="#B198-water-14-02436" class="html-bibr">198</a>]. Copyright 2020 Elsevier.</p> Full article ">Figure 24
<p>(<b>a</b>) Analytical principle of the electrochemical sensor based on the hierarchically porous Cu-BTC MOF material. (<b>b</b>) Differential pulse stripping voltammograms of Cu-BTC/ITO at different GLYP concentrations from a to g (0, 1.0 × 10<sup>−6</sup>, 5.0 × 10<sup>−6</sup>, 1.0 × 10<sup>−5</sup>, 5.0 × 10<sup>−5</sup>, 1.0 × 10<sup>−4</sup>, 5.0 × 10<sup>−4</sup>. (<b>c</b>) from h to l (1.0 × 10<sup>−3</sup>, 1.0 × 10<sup>−2</sup>, 1.0 × 10<sup>−1</sup>, 1.0, 0.1 µM). Reprinted with permission from [<a href="#B73-water-14-02436" class="html-bibr">73</a>]. Copyright 2019 Elsevier.</p> Full article ">Figure 25
<p>(<b>a</b>) The fluorescence spectra of Tb-MOF after different concentrations of GLYP; (<b>b</b>) Linear correlation of Tb-MOF and different concentration of GLYP; (<b>c</b>) The change of color for the Tb-MOF before and after adding GLYP. Reprinted with permission from [<a href="#B203-water-14-02436" class="html-bibr">203</a>]. Copyright 2021 Elsevier.</p> Full article ">Figure 26
<p>Schematic representation of double-template imprinted polymer-modified GNPs-PGE fabrication and the suggested binding mechanism for simultaneous analysis of GLYP and GLU in their respective MIP cavities. Reprinted with permission from [<a href="#B206-water-14-02436" class="html-bibr">206</a>]. Copyright 2014 Elsevier.</p> Full article ">Figure 27
<p>(<b>a</b>) Schematic illustration of the synthesis of QD-encapsulated GLYP imprinted mesoporous organosilica (MIMO-zQ), (<b>b</b>) Photoluminescence decrease as a function of the GLYP concentration for both MIMO-zQ preparations, graphene QDs (left) and InP/ZnS QD (right). Reprinted with permission from [<a href="#B209-water-14-02436" class="html-bibr">209</a>]. Copyright 2019 Nature.</p> Full article ">Figure 28
<p>(<b>a</b>) Scheme of mechanism of GLYP colorimetric detection based on Mn–ZnS QD-MIP as a competitive inhibitor on the reaction between ABTS and H<sub>2</sub>O<sub>2</sub>. (<b>b</b>) Absorbance spectra of ABTS, a: after the addition of Mn–ZnS QD-MIP, b: ABTS-H<sub>2</sub>O<sub>2</sub>–Mn–ZnS QD-MIP, c: after addition of GLYP. Reprinted with permission from [<a href="#B210-water-14-02436" class="html-bibr">210</a>]. Copyright 2021 Elsevier.</p> Full article ">
26 pages, 14310 KiB  
Article
Mapping Prospective Areas of Water Resources and Monitoring Land Use/Land Cover Changes in an Arid Region Using Remote Sensing and GIS Techniques
by Tong Sun, Wuqun Cheng, Mohamed Abdelkareem and Nasir Al-Arifi
Water 2022, 14(15), 2435; https://doi.org/10.3390/w14152435 - 6 Aug 2022
Cited by 15 | Viewed by 4966
Abstract
Groundwater is a vital water resource for economic, agricultural, and domestic purposes in arid regions. To reduce water scarcity in arid regions, recently, remote sensing and GIS techniques have been successfully applied to predict areas with prospective water resources. Thus, this study attempted [...] Read more.
Groundwater is a vital water resource for economic, agricultural, and domestic purposes in arid regions. To reduce water scarcity in arid regions, recently, remote sensing and GIS techniques have been successfully applied to predict areas with prospective water resources. Thus, this study attempted to spatially reveal groundwater potential zones (GWPZs) and to conduct change detection on the desert fringes of Wadi Asyuti, a defunct tributary of Egypt’s Nile basin in eastern Sahara. Eleven influential groundwater factors generated from remote sensing imagery, and geological, hydrological, and climatic conditions were combined after giving a weight to each factor through a GIS-based Analytical Hierarchy Process (AHP) coupled with the weighted overlay technique (WOT). The results revealed six distinctive zones with scores ranging from very low (10.59%) to excellent (3.03%). Thirty-three productive groundwater wells, Interferometry Synthetic Aperture Radar (InSAR) coherence change detection (CCD), a land use map derived from Sentinel-2, and the delineated flooding zone derived from Landsat-8 data were used to validate the delineated zones. The GWPZs indicated that 48% of the collected wells can be classified as consistent to excellent. The Normalized Difference Vegetation Index (NDVI) and image classification were applied to the multi-temporal Landsat series and Sentinel-2 along with the InSAR CCD data derived from Sentinel-1 images to reveal dramatic changes in land use/land cover (LU/LC) in terms of agricultural and other anthropogenic activities in the structurally downstream area, which is the most promising area for future developments. Overall, the integration of radar and multispectral data through the GIS technique has the ability to provide valuable information about water resources in arid regions. Thus, the tested model is a promising technique, and such information is extremely significant for the guidance of planners and decision makers in the area of sustainable development. Full article
(This article belongs to the Section New Sensors, New Technologies and Machine Learning in Water Sciences)
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Figure 1
<p>(<b>a</b>) Location of Wadi Asyuti in the northern part of the Nile basin; (<b>b</b>) SRTM DEM overlaid by Wadi Asyuti; (<b>c</b>) Geological map of the W. Asyuti basin (Conoco, 1987) [<a href="#B58-water-14-02435" class="html-bibr">58</a>].</p> Full article ">Figure 2
<p>(<b>a</b>) Landsat mosaic overlaid with the W.Asyuti watershed; (<b>b</b>) 3D oblique view of Wadi Asyuti; (<b>b</b>) Rainfall storm on 8 to 9 March 2014 overlaid by the floodway extracted from Landsat data collected on 15 March 2014.</p> Full article ">Figure 3
<p>Data used and methods performed in the present study.</p> Full article ">Figure 4
<p>(<b>a</b>) Lineaments controlling the studied basin; (<b>b</b>) Lineament density map; (<b>c</b>) ALOS/PALSAR map; (<b>d</b>) ALOS/PALSAR classified map.</p> Full article ">Figure 5
<p>Topographical factors of the W. Asyuti basin (<b>a</b>) elevation classes; (<b>b</b>) slope degree classes; (<b>c</b>) Terrain Roughness Index (TRI); (<b>d</b>) Curvature classes.</p> Full article ">Figure 6
<p>Hydrological factors: (<b>a</b>) stream-network order; (<b>b</b>) drainage density; (<b>c</b>) distance to river; (<b>d</b>) TWI classes.</p> Full article ">Figure 7
<p>Precipitation data (mm/day) covering Wadi Asyuti: (<b>a</b>) Kriging distribution of precipitation following the storm on 29 December 2010, (<b>b</b>) distribution of precipitation on 8 to 9 March 2014; (<b>c</b>) precipitation on 8 to 9 March 2015, (<b>d</b>) average precipitation (years 1998–2015) in Wadi Asyuti.</p> Full article ">Figure 8
<p>GWPZs (<b>a</b>) GWPZs overlaid by well data in the downstream area; (<b>b</b>) Landsat data in the downstream area overlaid by wells that are presented in blue points; (<b>c</b>) InSAR CCD data for the downstream area; (<b>d</b>) Landsat image display water accumulation in blue color behind a dam; (<b>e</b>) InSAR CCD same as area in (<b>d</b>). (<b>f</b>) extracted floodway marked in light blue derived from the NDVI of the OLI image from 2014; (<b>g</b>) InSAR CCD data for February 2015 to February 2017; (<b>h</b>) subset of Landsat image as indicated subfigure (<b>a</b>); (<b>i</b>) ALOS/PALSAR display alluvial deposits in dark tone; (<b>j</b>) DEM, and elevation profile A–B; (<b>k</b>) elevation profile reveal the topographic characteristics of the wadi.</p> Full article ">Figure 9
<p>A column chart showing the GWPZ grades related to covered areas and the number of wells.</p> Full article ">Figure 10
<p>Dams along Wadi Asyuti: (<b>a</b>) W. Habib dam, (<b>b</b>) Wadi Hubara dam.</p> Full article ">Figure 11
<p>(<b>a</b>,<b>b</b>) Sentinel-2 data from the downstream area; (<b>c</b>) difference map between two scenes of Sentinel-2 14 March 2022 and 20 March 2016; (<b>d</b>) vegetated area in 2016; (<b>e</b>) vegetated area in 2022; (<b>f</b>) differences in vegetation between 2016 and 2022.</p> Full article ">Figure 12
<p>(<b>a</b>) Landsat-5 band composite data from bands 7, 4, and 2 from 29 September 1987; (<b>b</b>) Landsat-OLI band composite 7, 5, and 3 band composite data from 26 September 2021; (<b>c</b>) Difference image between 1987 and 2021; (<b>d</b>) InSAR CCD data from February 2015–February 2017; (<b>e</b>) subset of GeoEye-1 as marked by the dashed black polygon in figure (<b>c</b>); (<b>f</b>) a subset of Landsat overlain by wells in blue points.</p> Full article ">Figure 13
<p>Downstream area of Wadi Asyuti: (<b>a</b>) AlOS/PALSAR covering the downstream area; the dark tone reveals the structurally controlled area that is overlaid by productive groundwater wells in blue; (<b>b</b>) DEM data are overlaid by topographic profiles; the pink arrows refer to the downstream direction of the wadis from NW to SE; (<b>c</b>–<b>g</b>) Topographical profiles A–B to I,J; (<b>h</b>) subset of OLI data showing the dam site and the signature of the water in cyan; (<b>i</b>) the InSAR CCD data for February 2015–February 2017 reveal the changes in yellow, representing the flood zone; (<b>j</b>) PC1 of the 2022 Sentinel-2 data displays the dam site and wadis.</p> Full article ">
17 pages, 4452 KiB  
Article
Flash Flood Susceptibility Mapping in Sinai, Egypt Using Hydromorphic Data, Principal Component Analysis and Logistic Regression
by Mustafa El-Rawy, Wael M. Elsadek and Florimond De Smedt
Water 2022, 14(15), 2434; https://doi.org/10.3390/w14152434 - 6 Aug 2022
Cited by 19 | Viewed by 3024
Abstract
Flash floods in the Sinai often cause significant damage to infrastructure and even loss of life. In this study, the susceptibility to flash flooding is determined using hydro-morphometric characteristics of the catchments. Basins and their hydro-morphometric features are derived from a digital elevation [...] Read more.
Flash floods in the Sinai often cause significant damage to infrastructure and even loss of life. In this study, the susceptibility to flash flooding is determined using hydro-morphometric characteristics of the catchments. Basins and their hydro-morphometric features are derived from a digital elevation model from NASA Earthdata. Principal component analysis is used to identify principal components with a clear physical meaning that explains most of the variation in the data. The probability of flash flooding is estimated by logistic regression using the principal components as predictors and by fitting the model to flash flood observations. The model prediction results are cross validated. The logistic model is used to classify Sinai basins into four classes: low, moderate, high and very high susceptibility to flash flooding. The map indicating the susceptibility to flash flooding in Sinai shows that the large basins in the mountain ranges of the southern Sinai have a very high susceptibility for flash flooding, several basins in the southwest Sinai have a high or moderate susceptibility to flash flooding, some sub-basins of wadi El-Arish in the center have a high susceptibility to flash flooding, while smaller to medium-sized basins in flatter areas in the center and north usually have a moderate or low susceptibility to flash flooding. These results are consistent with observations of flash floods that occurred in different regions of the Sinai and with the findings or predictions of other studies. Full article
(This article belongs to the Special Issue Flash Floods: Forecasting, Monitoring and Mitigation Strategies)
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<p>Location of the study area.</p> Full article ">Figure 2
<p>Topography of the Sinai derived from a digital elevation model from NASA Earthdata.</p> Full article ">Figure 3
<p>Sub-basins with ID number and drainage channels with stream order, excluding first order, derived from the digital elevation model.</p> Full article ">Figure 4
<p>Predicted and observed flash flood probability against logit(<span class="html-italic">p</span>): dots are observed flash flood probabilities, the solid black line represents the probability predicted by the logistic model, the red dotted line corresponds to the mean of the model predictions, logit(<span class="html-italic">p</span>) = −4.74, the blue dotted line to the mean of the observations, logit(<span class="html-italic">p</span>) = −2.44, and the black dotted line with logit (<span class="html-italic">p =</span> 0.5) = 0.</p> Full article ">Figure 5
<p>Susceptibility to flash flooding in the Sinai predicted with the probabilistic model (Equation (2)) using the principal components of the hydro-morphometric parameters as predictors.</p> Full article ">
17 pages, 8225 KiB  
Article
Developing a Novel Alum Sludge-Based Floating Treatment Wetland for Natural Water Restoration
by Xinlong He, Xiaohong Zhao, Wenshan Zhang, Baiming Ren and Yaqian Zhao
Water 2022, 14(15), 2433; https://doi.org/10.3390/w14152433 - 5 Aug 2022
Cited by 7 | Viewed by 2471
Abstract
Novel alum sludge-based floating treatment wetland (FTW) was developed to enhance the purification performances of natural water bodies, i.e., rivers, lakes, and ponds. Polyurethane was applied to foam the lightweight alum sludge based-substrate (PU-AL) of FTW through the response surface method. Three FTWs [...] Read more.
Novel alum sludge-based floating treatment wetland (FTW) was developed to enhance the purification performances of natural water bodies, i.e., rivers, lakes, and ponds. Polyurethane was applied to foam the lightweight alum sludge based-substrate (PU-AL) of FTW through the response surface method. Three FTWs configurations were created for a half-year lab-scale operation, and the PU-AL FTW presents the greatest purification performance in the removal rate of chemical oxygen demand (COD) of 62.58 ± 6.65%, total nitrogen (TN) of 53.31 ± 4.65%, and total phosphorus (TP) of 45.39 ± 4.69%. PU-AL substrate could enhance the nutrient removal performance of existing FTW by providing a proper media for microbial and plants’ growth. This study provides a good solution and showcase not only from a natural water restoration point of view but also from the waterworks sludge management view for a better understanding of FTWs and good applications in engineering practice. Full article
(This article belongs to the Section Water Quality and Contamination)
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Graphical abstract
Full article ">Figure 1
<p>Synthesis of novel alum sludge-based FTWs substrate. (<b>a</b>) alum sludge mixed with organic foaming materials; (<b>b</b>) stirring and forming; (<b>c</b>) granulation; (<b>d</b>) natural drying.</p> Full article ">Figure 2
<p>FTIR of PU-AL and alum sludge.</p> Full article ">Figure 3
<p>SEM of PU-AL and alum sludge. (<b>a</b>) PU-AL for 100×; (<b>b</b>) PU-AL for 500×; (<b>c</b>) Alum sludge for 100×; (<b>d</b>) Alum sludge for 500×.</p> Full article ">Figure 4
<p>Configuration of the experimental system.</p> Full article ">Figure 5
<p>Pollutants removal performance in three FTWs. (<b>a</b>) COD removal performance; (<b>b</b>) TN removal performance; (<b>c</b>) TP removal performance.</p> Full article ">Figure 6
<p>Overall removal performance.</p> Full article ">Figure 7
<p>Comparison of plants’ growth states in three FTWs.</p> Full article ">Figure 8
<p>Venn diagram based on the OTUs of the substrates.</p> Full article ">Figure 9
<p>Microbial relative abundance on substrates at the phylum level.</p> Full article ">Figure 10
<p>Taxonomic classification of community structure at the class level.</p> Full article ">
18 pages, 3110 KiB  
Article
Evaluation of TIGGE Precipitation Forecast and Its Applicability in Streamflow Predictions over a Mountain River Basin, China
by Yiheng Xiang, Tao Peng, Qi Gao, Tieyuan Shen and Haixia Qi
Water 2022, 14(15), 2432; https://doi.org/10.3390/w14152432 - 5 Aug 2022
Cited by 6 | Viewed by 2011
Abstract
The number of numerical weather prediction (NWP) models is on the rise, and they are commonly used for ensemble precipitation forecast (EPF) and ensemble streamflow prediction (ESP). This study evaluated the reliabilities of two well-behaved NWP centers in the Observing System Research and [...] Read more.
The number of numerical weather prediction (NWP) models is on the rise, and they are commonly used for ensemble precipitation forecast (EPF) and ensemble streamflow prediction (ESP). This study evaluated the reliabilities of two well-behaved NWP centers in the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP), in EPF and ESP over a mountain river basin in China. This evaluation was carried out based on both deterministic and probabilistic metrics at a daily temporal scale. The effectiveness of two postprocessing methods, the Generator-based Postprocessing (GPP) method, and the Bayesian Model Averaging (BMA) method were also investigated for EPF and ESP. Results showed that: (1) The ECMWF shows better performances than NCEP in both EPF and ESP in terms of evaluation indexes and representation of the hydrograph. (2) The GPP method performs better than BMA in improving both EPF and ESP performances, and the improvements are more significant for the NCEP with worse raw performances. (3) Both ECMWF and NCEP have good potential for both EPF and ESP. By using the GPP method, there are desirable EPF performances for both ECMWF and NCEP at all 7 lead days, as well as highly skillful ECMWF ESP for 1~5 lead days and average moderate skillful NCEP ESP for all 7 lead days. The results of this study can provide a reference for the applications of TIGGE over mountain river basins. Full article
(This article belongs to the Special Issue Characterizing, Monitoring and Prediction of Hydrometeorological Extremes under Climate Change)
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<p>The study area.</p> Full article ">Figure 2
<p>Flowchart of the XAJ model.</p> Full article ">Figure 3
<p>Comparison of daily observed precipitation and raw EPFs from ECMWF (<b>a1</b>,<b>a2</b>) and NCEP (<b>b1</b>,<b>b2</b>) at 1 lead day during 2017. (<b>a1</b>,<b>b1</b>) are the time series of forecast and observed precipitation; (<b>a2</b>,<b>b2</b>) are the scatter plots of ensemble mean forecast and observed precipitation.</p> Full article ">Figure 4
<p>Line chart of evaluation indexes for ensemble precipitation forecasts by raw and postprocessed ECMWF (<b>a1</b>,<b>a2</b>) and NCEP (<b>b1</b>,<b>b2</b>).</p> Full article ">Figure 5
<p>Monthly variation of evaluation indexes for ensemble precipitation forecasts by raw and postprocessed ECMWF (<b>a1</b>,<b>a2</b>) and NCEP (<b>b1</b>,<b>b2</b>).</p> Full article ">Figure 6
<p>Relative operating characteristic (ROC) curve at 1, 3, 5, and 7lead days for ECMWF (solid line) and NCEP (dash line) for events of precipitation greater than 50 mm (the FPR is the hit rate of no-event “0”, and the TPR is the hit rate of event “1”). In the calculation of ROC, the daily data of 2017 were used.</p> Full article ">Figure 7
<p>Hydrographs simulated by XAJ model in calibration and validation periods, compared with daily observations.</p> Full article ">Figure 8
<p>Scatter plots of ensemble mean forecast and observed streamflow during the 2017 period. (<b>a1</b>–<b>a12</b>) represent ECMWF and (<b>b1</b>–<b>b12</b>) represent NCEP. Columns 1–4 represent 1, 2, 3, and 4 lead days, respectively.</p> Full article ">Figure 9
<p>Comparison of time series of precipitation obtained from basin-averaged raw ECMWF (blue color) and NCEP (pink color) at 1, 2, 3, and 4 lead days, with observed precipitation. The shaded area represents the 5th–95th percentile values obtained from 1000 postprocessed ensembles.</p> Full article ">Figure 10
<p>Hydrographs of ensemble streamflow simulated by XAJ model using raw and postprocessed EPFs from ECMWF as inputs at 1, 2, 3, and 4 lead days, compared with observed streamflow. The shaded area represents the 5th–95th percentile values driving by 1000 postprocessed ensembles.</p> Full article ">Figure 11
<p>Hydrographs of ensemble streamflow simulated by XAJ model using raw and postprocessed EPFs from NCEP as inputs at 1, 2, 3, and 4 lead days, compared with observed streamflow. The shaded area represents the 5th–95th percentile values driving by 1000 postprocessed ensembles.</p> Full article ">
15 pages, 3500 KiB  
Article
What Is the Suitable Sampling Frequency for Water Quality Monitoring in Full-Scale Constructed Wetlands Treating Tail Water?
by Siyuan Song, Sheng Sheng, Jianqiang Xu and Dehua Zhao
Water 2022, 14(15), 2431; https://doi.org/10.3390/w14152431 - 5 Aug 2022
Cited by 2 | Viewed by 2228
Abstract
Three years of hourly COD and NH4+-N measurements for two full-scale integrated constructed wetlands (CWs) treating secondary effluents from sewage treatment plants (STPs) were used to quantify the proper sampling frequency (SF). The modified coefficient of variation (CVm) [...] Read more.
Three years of hourly COD and NH4+-N measurements for two full-scale integrated constructed wetlands (CWs) treating secondary effluents from sewage treatment plants (STPs) were used to quantify the proper sampling frequency (SF). The modified coefficient of variation (CVm) and average variation rate (VRa) were calculated to monitor the dynamics and annual average performance, respectively. It was found that (1) under CVm 5%, VRa 5%, and VRm 5%, the sampling intervals (SI) of COD can be set as 1.19 h, 526.5 h, and 110.1 h, respectively, and the SI of NH4+-N should be 4.51 h, 66.3 h, and 26.8 h, respectively; (2) under CVm 10%, VRa 10%, and VRm 10%, the monitoring intervals of COD can be set as 11.92 h, 1401.7 h, and 233.5 h, respectively, and the monitoring intervals of NH4+-N should be 30.73 h, 139.3 h, and 50.5 h, respectively. Therefore, to meet the need of monitoring the dynamic changes in data, hourly and 4 h SIs were recommended for COD and NH4+-N evaluation, respectively, when it is necessary to consider the operation and maintenance costs at the same time, 11 h and 30 h SIs were proper for COD and NH4+-N evaluation, respectively. The methods proposed in this study could provide reference to improve the management and evaluation level of full-scale CWs. Full article
(This article belongs to the Special Issue Nutrient Biogeochemical Cycles in Eutrophic Inland Waters and Eutrophication Control)
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<p>Geographic location and layout of (<b>a</b>) CW1 and (<b>b</b>) CW2 (AP, aerated pond; FP, facultative pond; FWS, free water surface flow constructed wetland; EP, ecological pond; SSF, subsurface flow constructed wetland).</p> Full article ">Figure 2
<p>The temporal dynamics of the measured COD (<b>A</b>) and NH<sub>4</sub><sup>+</sup>-N (<b>B</b>) from 10 November 2015 to 28 November 2018 (n = 26,038). A logarithmic scale was used for the y-axis to improve the discrimination between samplings with values concentrated over relatively narrow regions (relatively low regions). The symbol “+” represents a data.</p> Full article ">Figure 2 Cont.
<p>The temporal dynamics of the measured COD (<b>A</b>) and NH<sub>4</sub><sup>+</sup>-N (<b>B</b>) from 10 November 2015 to 28 November 2018 (n = 26,038). A logarithmic scale was used for the y-axis to improve the discrimination between samplings with values concentrated over relatively narrow regions (relatively low regions). The symbol “+” represents a data.</p> Full article ">Figure 3
<p>The relationships between the sampling interval (<span class="html-italic">SI</span>) and the variation between the two sampling times in the modified coefficient of variation (CVm) for monitoring the temporal dynamics of COD (<b>A</b>) and NH<sub>4</sub><sup>+</sup>-N (<b>B</b>) (n = 26,038). R<sup>2</sup> values represent the coefficient of determination of the exponential or power model between the <span class="html-italic">SI</span> and CVm for monitoring the temporal dynamics of COD and NH<sub>4</sub><sup>+</sup>-N.</p> Full article ">Figure 4
<p>The relationships between the sampling intervals (<span class="html-italic">SI</span>s) and the average variation rates (VR<sub>a</sub>) for the evaluation of the three-year average performance of COD (<b>A</b>) and NH<sub>4</sub><sup>+</sup>-N (<b>B</b>) between 10 November 2015 and 28 November 2018. R<sup>2</sup> values represent the coefficient of determination of the power model between the <span class="html-italic">SI</span>s and VR<sub>a</sub> for the evaluation of the three-year average performance of COD and NH<sub>4</sub><sup>+</sup>-N.</p> Full article ">Figure 5
<p>The relationships between the SF and the maximum variation rate (VR<sub>m</sub>) in COD (<b>A</b>) and NH<sub>4</sub><sup>+</sup>-N (<b>B</b>) using all the data from 2016 to 2018 with the hourly SF data.</p> Full article ">
23 pages, 4927 KiB  
Article
Comparison of Urbanization, Climate Change, and Drainage Design Impacts on Urban Flashfloods in an Arid Region: Case Study, New Cairo, Egypt
by Bassma Taher Hassan, Mohamad Yassine and Doaa Amin
Water 2022, 14(15), 2430; https://doi.org/10.3390/w14152430 - 5 Aug 2022
Cited by 22 | Viewed by 5776
Abstract
Urban flooding is considered one of the hazardous disasters in metropolitan areas, especially for those located in arid regions. Due to the associated risks of climate change in increasing the frequency of extreme rainfall events, climate-induced migration to urban areas leads to the [...] Read more.
Urban flooding is considered one of the hazardous disasters in metropolitan areas, especially for those located in arid regions. Due to the associated risks of climate change in increasing the frequency of extreme rainfall events, climate-induced migration to urban areas leads to the intensification of urban settlements in arid regions as well as an increase in urban expansion towards arid land outskirts. This not only stresses the available infrastructure but also produces substantial social instability due to unplanned urban growth. Therefore, this study sheds light on the main factors that are increasing the flood risk, through examining the consequences of rapid urban growth and the performance of drainage networks on urban flood volumes and comparing it with the effects induced by climate change on the surface runoff. The effect of urbanization is assessed through land use maps showing the historical urbanization conditions for the past 30 years, while considering the role of urban planning and its effect on exacerbating surface runoff. Six climate projection scenarios adopted from three Global Climate Models under two Representative Concentration Pathways (4.5 and 8.5) during the period (2006–2020) were compared to ground observed rainfall data to identify which climate scenario we are likely following and then evaluate its effects on the current rainfall trends up to the year 2050. The significance of the drainage design in the mitigation or increase of surface runoff is evaluated through capacity-load balance during regular and extreme storms. It is found that using impervious surfaces coupled with poor planning causing the blockage of natural flood plains led to an increase in the total runoff of about 180%, which is three times more than the effect induced by climate change for the same analysis period. Climate change decreased the intensities of 2- and 5-year rainfall events by 6% while increasing the intensities of extreme events corresponds to 100-year by 17%. Finally, the urban drainage had a distinguished role in increasing surface runoff, as 70% of the network performed poorly during the smallest rainfall event of 2-year return period. The study emphasizes the urgency to re-evaluate the existing and future urban drainage design approach: although urban development and climate change have inevitable effects on the increase in urban flood volumes, it could be alleviated through improved infrastructures. Full article
(This article belongs to the Special Issue Urban Floods in a Changing Climate)
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<p>Location and topography of the study area.</p> Full article ">Figure 2
<p>Characteristics of rainfall data from Cairo Airport Station. The average cumulative monthly rainfall data is represented by the histogram, while the horizontal lines represent the maximum and minimum mean monthly data. The yellow oval, on the secondary axis, represents the maximum daily rainfall that occurred each month from the year 1976 to 2020.</p> Full article ">Figure 3
<p>New Cairo’s 17 neighborhoods included in the study showing their locations and areas (km<sup>2</sup>).</p> Full article ">Figure 4
<p>Land use percentages of 17 neighborhoods in New Cairo for four main categories (impervious areas, gardens, unurbanized areas and industrial areas) [<a href="#B66-water-14-02430" class="html-bibr">66</a>].</p> Full article ">Figure 5
<p>The results of historical land use changes for the years (<b>a</b>) 1990; (<b>b</b>) 1995; (<b>c</b>) 2000; (<b>d</b>) 2005; (<b>e</b>) 2010; (<b>f</b>) 2015; (<b>g</b>) 2020. The land use includes rural presented in beige, green areas in green, urban areas in red, and urban under construction in yellow.</p> Full article ">Figure 6
<p>Previous watershed passed through New Cairo before urbanization. Currently the natural streamlines are totally blocked due to the land use changes.</p> Full article ">Figure 7
<p>Land use historical changes between 1990 to 2020; (<b>a</b>) shows the total area in km<sup>2</sup> and percent change; (<b>b</b>) shows land use percentages which can significantly affect runoff values.</p> Full article ">Figure 8
<p>The change in runoff corresponding to the increase of urbanized area from 1990 to 2020 for the whole New Cairo Watershed.</p> Full article ">Figure 9
<p>(<b>a</b>) Total rainfall every year (mm/year) for the period of 1976 to 2020; (<b>b</b>) maximum rainfall event in each year (mm/day) for the period of 1976 to 2020; (<b>c</b>) total annual projected precipitation during the period 2006 to 2050 from the CNRM model for RCP4.5; the dashed inclined lines show the trend of the rainfall (whether increasing or decreasing).</p> Full article ">Figure 9 Cont.
<p>(<b>a</b>) Total rainfall every year (mm/year) for the period of 1976 to 2020; (<b>b</b>) maximum rainfall event in each year (mm/day) for the period of 1976 to 2020; (<b>c</b>) total annual projected precipitation during the period 2006 to 2050 from the CNRM model for RCP4.5; the dashed inclined lines show the trend of the rainfall (whether increasing or decreasing).</p> Full article ">Figure 10
<p>Total number of rainfall events occurred for each return period for two time periods (1976 to 1999 and 2000 to 2020).</p> Full article ">Figure 11
<p>(<b>a</b>) Pumping stations schematic plan for New Cairo’s districts showing their discharge destinations; (<b>b</b>) elevation map of New Cairo neighborhoods.</p> Full article ">Figure 12
<p>(<b>a</b>) Percentage used of the drainage network at different scenarios; (<b>b</b>) percentage used of the pump stations with different return periods. Shown for all 17 neighborhoods. * These areas had planned upgrade and the extra capacity was added in the calculations.</p> Full article ">Figure 13
<p>(<b>a</b>) Inundation areas simulated during March 2020 storm; (<b>b</b>) inundation areas recorded by local authorities during rainstorms.</p> Full article ">
18 pages, 5421 KiB  
Article
Spatiotemporal Evaluation of Blue and Green Water in Xinjiang River Basin Based on SWAT Model
by Xudong Zhang, Cong Jiang, Junzhe Huang, Zhenyu Ni, Jizhou Sun, Zuzhong Li and Tianfu Wen
Water 2022, 14(15), 2429; https://doi.org/10.3390/w14152429 - 5 Aug 2022
Cited by 3 | Viewed by 2315
Abstract
Poyang Lake is the largest freshwater lake in China. As an important tributary of Poyang Lake, Xinjiang River has an important influence on the water ecology and water resources of the Poyang Lake basin. Based on the hydrological simulation of the SWAT (Soil [...] Read more.
Poyang Lake is the largest freshwater lake in China. As an important tributary of Poyang Lake, Xinjiang River has an important influence on the water ecology and water resources of the Poyang Lake basin. Based on the hydrological simulation of the SWAT (Soil and Water Assessment Tool) model, the spatiotemporal distribution and evaluation of the blue and green water during the period (1982–2016) in the basin were explored by the Mann–Kendall test, precipitation anomaly percentage, and scenario simulation. It is found that the SWAT model presents a satisfactory performance in runoff simulation of the basin. The multi-year average blue water in the Xinjiang River basin is 1138 mm, and the green water is 829 mm, with a green water coefficient of 0.42. The amount of blue water in wet years is about 1.5 times that in normal years and 2.4 times that in dry years. Compared with the green water, the blue water of the basin is more sensitive to the variations in precipitation. In spatial distribution, the blue and green water in the middle of the basin is obviously more than those in other parts of the basin. During the study period, the blue water in the basin shows a slight decreasing trend, and the green water shows a significant decreasing trend. It is also found that climatic factors have a greater influence on the trend of blue and green water than land use, and the decrease in precipitation is the dominant cause for the trend of blue and green water. Full article
(This article belongs to the Section Water Quality and Contamination)
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<p>Distribution of Xinjiang River basin location, meteorological stations, and hydrological stations.</p> Full article ">Figure 2
<p>The research framework of spatiotemporal evaluation of blue and green water.</p> Full article ">Figure 3
<p>Evaluation of the simulation effect of the SWAT model in the calibration and validation periods.</p> Full article ">Figure 4
<p>Interannual variation in precipitation (PCP), blue water (BW), green water (GW), green water flow (GWF), and green water storage (GWS) in the Xinjiang River basin.</p> Full article ">Figure 5
<p>Spatial distribution characteristics of annual average precipitation (PCP), blue water (BW), green water (GW), green water flow (GWF), green water storage (GWS), and green water coefficient (GWC) in the Xinjiang River basin.</p> Full article ">Figure 6
<p>Discriminant results of typical years using the precipitation anomaly percentage.</p> Full article ">Figure 7
<p>The amount of precipitation (PCP), blue water (BW), green water (GW), green water flow (GWF), and green water coefficient (GWC) for typical years.</p> Full article ">Figure 8
<p>Spatial distribution characteristics of precipitation (PCP), blue water (BW), green water (GW), and green water coefficient (GWC) in typical years.</p> Full article ">Figure 9
<p>MK test results of precipitation (PCP) and the average temperature (TEMP) in the Xinjiang River basin.</p> Full article ">Figure 10
<p>MK test results of blue water (BW), green water (GW), green water flow (GWF), and green water coefficient (GWC).</p> Full article ">Figure 11
<p>Precipitation (PCP), blue water (BW), green water (GW), green water flow (GWF), green water storage (GWS), and green water coefficient (GWC) under different scenarios.</p> Full article ">
13 pages, 1742 KiB  
Article
Dew Evaporation Amount and Its Influencing Factors in an Urban Ecosystem in Northeastern China
by Yingying Xu, Chenzhuo Jia and Hongzhao Liu
Water 2022, 14(15), 2428; https://doi.org/10.3390/w14152428 - 5 Aug 2022
Cited by 4 | Viewed by 2863
Abstract
Dew is an important water input and promotes plant growth. Dew condenses on plant leaves at night, and a portion of this dew returns to the atmosphere through evaporation. The amount of dew that evaporates is not equal to the amount of condensation; [...] Read more.
Dew is an important water input and promotes plant growth. Dew condenses on plant leaves at night, and a portion of this dew returns to the atmosphere through evaporation. The amount of dew that evaporates is not equal to the amount of condensation; however, the dew evaporation process has not received enough attention. By monitoring the dew condensation and evaporation processes associated with four typical shrubs (Syringa, Hemiptelea, Buxus, and Cornus) in northeast China, we found that dew condensation started approximately 30 min after sunset, finished approximately 30 min before sunrise, and then turned to the evaporation phase. Dew had completely depleted approximately 4 h after sunrise. The dew evaporation period was negatively correlated with the wind speed (p < 0.01) and positively correlated with temperature, solar radiation, and relative humidity (RH) (p < 0.01). The average evaporation periods of Syringa, Buxus, Cornus, and Hemiptelea were 282 ± 21 min, 255 ± 26 min, 242 ± 22 min, and 229 ± 17 min, respectively. The daily evaporation amounts in May and September reached the minimum and maximum values, respectively, and the evaporation intensity of dew was positively correlated with RH (p < 0.01). There was no significant difference in the daily evaporation amounts of Syringa, Hemiptelea, Buxus, or Cornus (p > 0.05), and the annual evaporation amounts of these four plants were 17.05 mm/y, 16.38 mm/y, 21.94 mm/y, and 16.15 mm/y, respectively. The microstructure of leaves affected both the rate and amount of evaporation. Dew evaporated faster on hydrophilic leaves, and leaves with high trichome and stomatal densities had lower proportions of the dew evaporation amount to the condensation amount. The proportions of the dew evaporation amount to the condensation amount derived for Syringa, Hemiptelea, Buxus, and Cornus were 60.38%, 46.07%, 57.24%, and 52.81%, respectively. This study supplements our understanding of dew evaporation amounts, providing information that was missing in the near-surface hydrological cycle and aiding in the assessment of the ecological significance of dew to plants. Full article
(This article belongs to the Section Ecohydrology)
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<p>Meteorological factors in the study area and evaporation periods of different plants.</p> Full article ">Figure 2
<p><span class="html-italic">LAI</span>s, daily dew evaporation intensities, and amount changes derived for different plants in the experimental period.</p> Full article ">Figure 3
<p>DCAs and microstructures of different species derived using SEM: (<b>a</b>): DCA of the <span class="html-italic">Syringa</span> adaxial surface; (<b>b</b>): adaxial leaf surface of <span class="html-italic">Syringa</span> (1600×); (<b>c</b>): DCA of the <span class="html-italic">Syringa</span> abaxial surface; (<b>d</b>): abaxial leaf surface of <span class="html-italic">Syringa</span> (1000×); (<b>e</b>): one stomata of the abaxial leaf surface of <span class="html-italic">Syringa</span> (5000×); (<b>f</b>): DCA of the <span class="html-italic">Hemiptelea</span> adaxial surface; (<b>g</b>): adaxial leaf surface of <span class="html-italic">Hemiptelea</span> (1600×); (<b>h</b>): the base of one of a dense mat of glochid trichomes of <span class="html-italic">Hemiptelea</span> (8000×); (<b>i</b>): DCA of the <span class="html-italic">Hemiptelea</span> abaxial surface; (<b>j</b>): abaxial leaf surface of <span class="html-italic">Hemiptelea</span> (700×); (<b>k</b>): DCA of the <span class="html-italic">Cornus</span> adaxial surface; (<b>l</b>): adaxial leaf surface of <span class="html-italic">Cornus</span> (1200×); (<b>m</b>): one glochid trichome of the adaxial leaf surface of <span class="html-italic">Cornus</span> (4000×); (<b>n</b>): DCA of the <span class="html-italic">Cornus</span> adaxial surface; (<b>o</b>): abaxial leaf surface of <span class="html-italic">Cornus</span> (5000×); (<b>p</b>): DCA of the <span class="html-italic">Buxus</span> adaxial surface; (<b>q</b>): adaxial leaf surface of <span class="html-italic">Buxus</span> (500×); (<b>r</b>): DCA of the <span class="html-italic">Buxus</span> abaxial surface; (<b>s</b>): abaxial leaf surface of <span class="html-italic">Buxus</span> (2000×); (<b>t</b>): one stomata of the abaxial leaf surface of <span class="html-italic">Buxus</span> (12,000×).</p> Full article ">
16 pages, 6107 KiB  
Article
Conceptual and Methodological Foundations for the Articulation of Geospatial Data on Water Resources in South America’s Cross-Border Hydrographic Basins
by Valdir Adilson Steinke, Gabriella Emilly Pessoa, Romero Gomes Pereira da Silva and Carlos Hiroo Saito
Water 2022, 14(15), 2427; https://doi.org/10.3390/w14152427 - 5 Aug 2022
Viewed by 2279
Abstract
When observing transboundary waters through the lens of Integrated Water Resources Management (IWRM), it is essential to emphasize the principle of watershed unicity, which must overcome geopolitical interests while being supported by technical criteria. This paper used a multilevel and multiscale approach for [...] Read more.
When observing transboundary waters through the lens of Integrated Water Resources Management (IWRM), it is essential to emphasize the principle of watershed unicity, which must overcome geopolitical interests while being supported by technical criteria. This paper used a multilevel and multiscale approach for integrated management of transboundary water resources in two South American transboundary river basins: the Javari River Basin (between Brazil and Peru) and the Quaraí River Basin (between Brazil and Uruguay). The Food and Agriculture Organization (FAO) provided spatialized data for South American watersheds. Our research focused on several broad issues concerning regional transboundary water management: (a) Because only Peru and Brazil use the Otto–Pfafstetter method, a uniform method within a regional agreement about methodological differences for delimiting hydrographic basins in each country is required; (b) It is necessary to organize accurate databases to avoid problems of mismatched borders by overlapping national databases or mismatches due to scale problems; (c) It is also necessary to establish a coordinating body capable of working with each country’s representatives. In this case, building integrated and collaborative cartographic database becomes critical; (d) Because river meanders can change, historical data of a river’s morphology is required. In this sense, this research provides guidelines to make water management in transboundary rivers feasible in South America. Full article
(This article belongs to the Section Water Resources Management, Policy and Governance)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Multilevel analysis proposal. (Source: authors).</p> Full article ">Figure 2
<p>Location of the Quaraí/Cuareim River Basin.</p> Full article ">Figure 3
<p>Location of the Javari/Yavarí River Basin.</p> Full article ">Figure 4
<p>The Hydrographic Units file (in blue) in Peru contains the files related to Water Resources Management at the local level (in red) and the regional levels (in yellow).</p> Full article ">Figure 5
<p>The basins and sub-basins in Brazil elaborated by DNAEE are portrayed on the right. On the left are the Ottobacias’ derivation of water resources management bases.</p> Full article ">Figure 6
<p>Brazilian hydrographic basins (Ottobacias) in cross-border view.</p> Full article ">Figure 7
<p>Peruvian hydrographic basins. The country’s borders are depicted in red. Its transboundary basins are depicted in green.</p> Full article ">Figure 8
<p>Uruguayan hydrographic basins in five levels of detail.</p> Full article ">Figure 9
<p>Brazilian Ottobacias (levels 5, 4, and 3) and case study basins.</p> Full article ">Figure 10
<p>Peru’s transboundary basins. In blue, lines that delimit the Javari River basin.</p> Full article ">Figure 11
<p>Alphanumeric data referring to Ottobacias (Brazil—level 2).</p> Full article ">Figure 12
<p>Reminiscent signs of the ancient meanders of Javari River.</p> Full article ">Figure 13
<p>Basins and sub-basins available at Aquamaps (FAO).</p> Full article ">Figure 14
<p>Alphanumeric data for basins and sub-basins available at Aquamaps (FAO).</p> Full article ">Figure 15
<p>Selection (in yellow) of Uruguay River sub-basins.</p> Full article ">
19 pages, 3051 KiB  
Article
The Setpoint Curve as a Tool for the Energy and Cost Optimization of Pumping Systems in Water Networks
by Christian F. León-Celi, Pedro L. Iglesias-Rey, Francisco Javier Martínez-Solano and Daniel Mora-Melia
Water 2022, 14(15), 2426; https://doi.org/10.3390/w14152426 - 5 Aug 2022
Cited by 2 | Viewed by 2004
Abstract
In water distribution networks, the adjustment of the driving curves of pumping systems to the setpoint curves allows for determining the minimum energy cost that can be achieved in terms of pumping. This paper presents the methodology for calculating the optimal setpoint curves [...] Read more.
In water distribution networks, the adjustment of the driving curves of pumping systems to the setpoint curves allows for determining the minimum energy cost that can be achieved in terms of pumping. This paper presents the methodology for calculating the optimal setpoint curves in water networks with multiple pumping systems, pressure dependent and independent consumption, with and without storage capacity. In addition, the energy and cost implications of the setpoint curve are analyzed. Three objective functions have been formulated depending on the case study, one of minimum energy and two of costs that depend on whether or not the presence of storage tanks is considered. For the optimization process, two algorithms have been used, Hooke and Jeeves and differential evolution. There are two study networks: TF and Richmond. The results show savings of close to 10% in the case of the Richmond network. Full article
(This article belongs to the Section Urban Water Management)
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<p>General flow chart of the cost minimization process.</p> Full article ">Figure 2
<p>The TF distribution network.</p> Full article ">Figure 3
<p>Demand pattern and electricity rates.</p> Full article ">Figure 4
<p>Setpoint curve for pumping station N16.</p> Full article ">Figure 5
<p>Setpoint curve for pumping station N17.</p> Full article ">Figure 6
<p>Setpoint curve for pumping station N18.</p> Full article ">Figure 7
<p>Flow distribution in the case of energy optimization.</p> Full article ">Figure 8
<p>Flow distribution in the case of cost optimization.</p> Full article ">Figure 9
<p>Richmond distribution network.</p> Full article ">Figure 10
<p>Setpoint curves (SC) and characteristic curves of the pumps (CB) for the pumping stations of the Richmond network.</p> Full article ">Figure 11
<p>Readjusted setpoint curves for pumping stations on the Richmond network.</p> Full article ">Figure 12
<p>Evolution of tank levels in the Richmond network.</p> Full article ">
23 pages, 7070 KiB  
Article
Sanitary Sewerage Master Plan for the Sustainable Use of Wastewater on a University Campus
by Bethy Merchán-Sanmartín, Paul Carrión-Mero, Sebastián Suárez-Zamora, Maribel Aguilar-Aguilar, Omar Cruz-Cabrera, Katherine Hidalgo-Calva and Fernando Morante-Carballo
Water 2022, 14(15), 2425; https://doi.org/10.3390/w14152425 - 5 Aug 2022
Cited by 5 | Viewed by 3919
Abstract
Wastewater collection, transport, and treatment systems are essential to ensure human and environmental well-being. The Escuela Superior Politécnica del Litoral (ESPOL), has been implementing various sanitary sewerage systems; however, population growth has given rise to discussion on the installed capacity versus the necessary [...] Read more.
Wastewater collection, transport, and treatment systems are essential to ensure human and environmental well-being. The Escuela Superior Politécnica del Litoral (ESPOL), has been implementing various sanitary sewerage systems; however, population growth has given rise to discussion on the installed capacity versus the necessary capacity for the future population in the sustainable management of water resources. Therefore, this study aimed to develop a sanitary sewerage master plan by analysing the existing situation and applying technical criteria for the sustainable use of wastewater on a university campus. The methodology consisted of (i) evaluation and diagnosis of the area studied through data collection and processing, (ii) design of the sanitary sewerage system considering area-expansion zones, and (iii) SWOT analysis of a proposal to enhance wastewater transport and treatment systems. The proposal contemplates designing a sanitary sewer system that will manage the collection, transport, and treatment of wastewater over 15 years for 5667 inhabitants located in three expansion zones with occupation periods of 5, 10, and 15 years. The sewerage system comprises a pipe network 1.19 km long and 200 mm in diameter, transporting 12.37 L/s of wastewater generated to two treatment systems that guarantee efficient depuration and subsequent reuse. This design was complemented by a SWOT analysis of the existing sanitation system developed by experts in the area, which included optimising existing treatment systems and reusing wastewater for irrigation of green areas as tertiary treatment within the circular economy. The methodology used in the study allows us to offer a tool for efficiently managing wastewater on a university campus, guaranteeing human well-being, and promoting the circular economy of water. Full article
(This article belongs to the Special Issue Water Management: New Paradigms for Water Treatment and Reuse)
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<p>General scheme of the applied method.</p> Full article ">Figure 2
<p>Study area.</p> Full article ">Figure 3
<p>Methodological scheme for the design of sewerage in the expansion zones.</p> Full article ">Figure 4
<p>Topographic map of the ESPOL property.</p> Full article ">Figure 5
<p>Zoning of ESPOL property.</p> Full article ">Figure 6
<p>Map of protection zones of the ESPOL property.</p> Full article ">Figure 7
<p>ESPOL historical population registry [<xref ref-type="bibr" rid="B53-water-14-02425">53</xref>].</p> Full article ">Figure 8
<p>Existing sewerage system and final wastewater disposal.</p> Full article ">Figure 9
<p>Existing on-campus wastewater treatment systems. (<bold>a</bold>) Membrane biological reactor wastewater treatment plant (WWTP-MBR), (<bold>b</bold>) engineering area stabilisation pond, (<bold>c</bold>–<bold>e</bold>) dissolved air flotation system (WWTP-DAF), (<bold>f</bold>) activated sludge wastewater treatment plant, (<bold>g</bold>) technology area stabilisation pond.</p> Full article ">Figure 10
<p>Population projection curve.</p> Full article ">Figure 11
<p>Location of expansion areas restricted by protected zones.</p> Full article ">Figure 12
<p>Sewerage networks for expansion zones 1, 2, and 3.</p> Full article ">
11 pages, 1077 KiB  
Article
Drinking Bottled and Tap Water for Healthier Living in Volcanic Areas: Are All Waters the Same?
by Diana Linhares, Diogo Gaspar, Patrícia Garcia and Armindo Rodrigues
Water 2022, 14(15), 2424; https://doi.org/10.3390/w14152424 - 5 Aug 2022
Viewed by 4372
Abstract
In most volcanic areas, the population considers the use of bottled waters as a healthier and safer option. This study aimed to (i) assess the fluoride concentrations in tap and bottled water consumed on São Miguel Island, (ii) confirm the accuracy of the [...] Read more.
In most volcanic areas, the population considers the use of bottled waters as a healthier and safer option. This study aimed to (i) assess the fluoride concentrations in tap and bottled water consumed on São Miguel Island, (ii) confirm the accuracy of the labeling of fluoride levels on bottled water, and (iii) assess the fluoride daily intake and risk exposure and discuss the possible health effects in adults and children. Fluoride concentrations were measured in tap water (49 samples) and bottled water (23 samples) with a fluoride ion-selective electrode. The fluoride concentration was above the recommended limit in tap water from Sete Cidades (1.71 mg/L), in bottled waters nº 5 and 7 from category C (2.05 ± 0.04 mg/L and 2.36 ± 0.14 mg/L, respectively), and in bottled water nº 5 from category D (1.92 ± 0.03 mg/L). Fluoride daily intake in children reached a maximum value in gasified water nº 7 (0.059 mg F/day/kg). The risk assessment evidenced that all the brands with over 1.2 mgF/L might be a concern for potential non-cancer health effects, especially in adults. The most recognized brands of gasified and gasified flavored waters represent a higher risk of exceeding fluoride daily intake when compared to tap and mineral bottled waters. Full article
(This article belongs to the Section Water and One Health)
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<p>Daily intake assessment in a child and an adult considering the water sample’s fluoride mean concentration was higher than 1.2 mg/L.</p> Full article ">Figure 2
<p>Representation of the exposure assessment (mg/kg/day) and the risk assessment, in lines, for both adults and children. The red line marks the <italic>NHQ</italic> value of 1 (unit); values above this baseline value denote the possible occurrence of non-cancer effects.</p> Full article ">
14 pages, 3090 KiB  
Article
Prediction and Interpretation of Water Quality Recovery after a Disturbance in a Water Treatment System Using Artificial Intelligence
by Jungsu Park, Juahn Ahn, Junhyun Kim, Younghan Yoon and Jaehyeoung Park
Water 2022, 14(15), 2423; https://doi.org/10.3390/w14152423 - 5 Aug 2022
Cited by 5 | Viewed by 2325
Abstract
In this study, an ensemble machine learning model was developed to predict the recovery rate of water quality in a water treatment plant after a disturbance. XGBoost, one of the most popular ensemble machine learning models, was used as the main framework of [...] Read more.
In this study, an ensemble machine learning model was developed to predict the recovery rate of water quality in a water treatment plant after a disturbance. XGBoost, one of the most popular ensemble machine learning models, was used as the main framework of the model. Water quality and operational data observed in a pilot plant were used to train and test the model. Disturbance was determined when the observed turbidity was higher than the given turbidity criteria. Therefore, the recovery rate of water quality at a time t was defined during the falling limb of the turbidity recovery period. It was considered as a relative ratio of the differences between the peak and observed turbidities at time t to the difference between the peak turbidity and turbidity criteria. The root mean square error–observation standard deviation ratio of the XGBoost model improved from 0.730 to 0.373 by pretreatment, removing the observation for the rising limb of the disturbance from the training data. Moreover, Shapley value analysis, a novel explainable artificial intelligence method, was used to provide a reasonable interpretation of the model’s performance. Full article
(This article belongs to the Section Water Quality and Contamination)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Schematic of a pilot-scale water treatment plant.</p> Full article ">Figure 2
<p>Schematic of the recovery rate in a water treatment process.</p> Full article ">Figure 3
<p>Turbidity observation for training and testing.</p> Full article ">Figure 4
<p>Schematic of the GBDT data processing.</p> Full article ">Figure 5
<p>Comparison of the model predictions with the observations.</p> Full article ">Figure 6
<p>Model prediction of the two disturbance events between 3 March 2021 and 5 March 2021.</p> Full article ">Figure 7
<p>SHAP summary plot.</p> Full article ">Figure 8
<p>SHAP force plot. (<bold>a</bold>) Datum observed at 23:00 on 4 March 2021. (<bold>b</bold>) Datum observed at 00:00 on 5 March 2021.</p> Full article ">Figure 9
<p>Target plot of R in the training data. (<bold>a</bold>) TB_R2 and TB_R3. (<bold>b</bold>) L_R1 and TB_R3.</p> Full article ">Figure 10
<p>SHAP dependence plot of the input variables.</p> Full article ">
12 pages, 1878 KiB  
Article
Effects of Polyester Microfibers on the Growth and Toxicity Production of Bloom-Forming Cyanobacterium Microcystis aeruginosa
by Yufan Lu, Ruohan Huang, Jialin Wang, Liqing Wang and Wei Zhang
Water 2022, 14(15), 2422; https://doi.org/10.3390/w14152422 - 4 Aug 2022
Cited by 7 | Viewed by 2401
Abstract
The global pollution of microplastics (MPs) has attracted wide attention, and many studies have been conducted on the effects of MP qualities or types and particle sizes on aquatic organisms. However, few studies on the impact of polyethylene terephthalate microplastic (mPET) with different [...] Read more.
The global pollution of microplastics (MPs) has attracted wide attention, and many studies have been conducted on the effects of MP qualities or types and particle sizes on aquatic organisms. However, few studies on the impact of polyethylene terephthalate microplastic (mPET) with different colors on phytoplankton in aquatic ecosystems have been carried out. In this study, mPET of three common colors (green, black, and white) in different concentrations (0, 10, 50, 100, and 200 mg/L) were selected to explore effects on a bloom-forming cyanobacterium Microcystis aeruginosa. The growth, photosynthesis, the number and size of colony, and MC-LR production of M. aeruginosa were studied within a 25-days exposure experiment. The results showed that colors of mPET had significant effects on the growth and photosynthesis of this species but the concentration of mPET had no significant effect. The low concentration of green mPET group promoted algal growth, photosynthesis, and the M. aeruginosa exposed to it was easier to agglomerate into colonies. Moreover, both mPET colors and concentrations have a significant impact on the microcystin production of M. aeruginosa. The low concentration of the green mPET group significantly inhibited the production throughout the experiment, while the white and black mPET significantly increased the concentration of extracellular microcystin (MC-LR). Our results provided new insights into the effects of MPs with different colors and concentrations on the growth and physiology of cyanobacteria and provide basic data for the ecological risk assessment and pollution prevention of MPs. Full article
(This article belongs to the Section Biodiversity and Functionality of Aquatic Ecosystems)
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<p>The cell density dynamics of <italic>M. aeruginosa</italic> treated with different color and concentration mPET conditions ((<bold>a</bold>): 10 mg/L; (<bold>b</bold>):50 mg/L; (<bold>c</bold>): 100mg/L; (<bold>d</bold>): 200 mg/L). All the values are means ± S.D. Identical letters denote no significant difference (<italic>p</italic> &lt; 0.05).</p> Full article ">Figure 2
<p>The chlorophyll <italic>a</italic> content dynamics of <italic>M. aeruginosa</italic> after being exposed to different color and concentration mPET ((<bold>a</bold>): 10 mg/L; (<bold>b</bold>):50 mg/L; (<bold>c</bold>): 100mg/L; (<bold>d</bold>): 200 mg/L). All the values are means ± S.D. Identical letters denote no significant difference (<italic>p</italic> &lt; 0.05).</p> Full article ">Figure 3
<p>The colony formation of <italic>M. aeruginosa</italic> exposure to mPET at different times ((<bold>a</bold>): 13d; (<bold>b</bold>): 19d; (<bold>c</bold>): 25d). G–10 refers to green–10 mg/L mPET, B–10 refers to black–10 mg/L mPET, W–10 refers to white–10 mg/L mPET and the rest may be deduced by analogy.</p> Full article ">Figure 4
<p>Effect of mPET on the MC−LR production of <italic>M. aeruginosa</italic>. G−10 refers to green−10 mg/L mPET, B−10 refers to black−10 mg/L mPET, W−10 refers to white−10 mg/L mPET and the rest may be deduced by analogy.</p> Full article ">Figure A1
<p>IR of <italic>M. aeruginosa</italic> under PET microplastic at different concentrations ((<bold>a</bold>), 10 mg/L; (<bold>b</bold>), 50 mg/L; (<bold>c</bold>), 100 mg/L; (<bold>d</bold>), 200 mg/L) with time.</p> Full article ">
10 pages, 430 KiB  
Article
Order Out of Chaos in Soil–Water Retention Curves
by Lucas Parreira de Faria Borges, André Luís Brasil Cavalcante and Luan Carlos de Sena Monteiro Ozelim
Water 2022, 14(15), 2421; https://doi.org/10.3390/w14152421 - 4 Aug 2022
Cited by 1 | Viewed by 1804
Abstract
Water flow in porous media is one of many phenomena in nature that can demonstrate both simple and complex behaviors. A soil–water retention curve (SWRC) is needed to characterize this flow properly. This curve relates the soil water content and the matric potential [...] Read more.
Water flow in porous media is one of many phenomena in nature that can demonstrate both simple and complex behaviors. A soil–water retention curve (SWRC) is needed to characterize this flow properly. This curve relates the soil water content and the matric potential (or porepressure), being fundamental for simulating unsaturated soil behaviors. This article proposes a new model based on simple assumptions regarding the saturated and unsaturated branches of soil–water retention curves. Despite its simplicity, the modeling capability of the proposed SWRC is shown for two types of soil. This new SWRC is obtained as a logistic function after solving an ordinary differential equation (ODE). This ODE can also be solved numerically using the Finite Difference Method (FDM), which indicates that the discrete version of the SWRC can be represented as the logistic map for specific parameters. On the other hand, this discrete representation is known to encompass chaotic and fractal behaviors. This link is used to investigate the stability and convergence of the FDM scheme, indicating that for values pre-bifurcation, both the FDM and the analytical solution of the ODE represent the new SWRC. This way, the present paper is the first step to better understating how a chaotic framework could be related to SWRCs and geotechnics in general. Full article
(This article belongs to the Special Issue Unsaturated Zone: Advances in Experimental and Theoretical Investigations)
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<p>Qualitative description of the volumetric water content curve.</p> Full article ">Figure 2
<p>Qualitative description of the normalized water content.</p> Full article ">Figure 3
<p>Analytical fitting for the fine Sand G.E. # 13 from Brooks and Corey [<xref ref-type="bibr" rid="B21-water-14-02421">21</xref>].</p> Full article ">Figure 4
<p>Analytical fitting for the silty material from Aubertin et al. [<xref ref-type="bibr" rid="B22-water-14-02421">22</xref>].</p> Full article ">Figure 5
<p>Logistic map of the SWRC discrete equation.</p> Full article ">Figure 6
<p>Magnification of the logistic map of the SWRC.</p> Full article ">
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