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Search Results (1,491)

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13 pages, 3229 KiB  
Article
Characterization of Silica Sand-Based Pervious Bricks and Their Performance under Stormwater Treatment
by Meijuan Chen, Weiying Li, Zhiqiang Dong and Dawei Zhang
Water 2024, 16(18), 2625; https://doi.org/10.3390/w16182625 - 16 Sep 2024
Viewed by 294
Abstract
The acceleration of urbanization has disrupted natural water cycles, resulting in increased impervious urban surfaces and non-point source pollution from stormwater runoff. Addressing urban stormwater recharge has become crucial. This study introduces a novel silica sand-based permeable filtration material, investigating its surface characteristics, [...] Read more.
The acceleration of urbanization has disrupted natural water cycles, resulting in increased impervious urban surfaces and non-point source pollution from stormwater runoff. Addressing urban stormwater recharge has become crucial. This study introduces a novel silica sand-based permeable filtration material, investigating its surface characteristics, pore structure, permeability, and pollutant interception capabilities. The results demonstrate that hydrophilic binder coating modification of the permeable surface sand aggregate, combined with hydrophilic inorganic additives, having a porous structure with an average pore size of less than 50 μm and a porosity between 15% and 35%, significantly enhances surface hydrophilicity, achieving a permeation rate of up to 6.8 mL/(min·cm²). Moreover, it shows exceptional filtration and anti-clogging properties, achieving over 98% suspended solids interception and strong resistance to fouling. Dynamic biofilm formation experiments using simulated rain and domestic wastewater explore biofilm morphology and function on silica sand filtration well surfaces. Mature biofilms sustain COD removal efficiency exceeding 70%, with levels consistently below 50 mg/L, NH4+ decreasing to 2 mg N/L, and total nitrogen maintained below 10 mg N/L. The system features anoxic, anoxic, and aerobic zones, fostering synergistic organic matter and nitrogen removal by diverse microorganisms, enhancing pollutant mitigation. Silica sand-based permeable filtration material effectively mitigates urban stormwater runoff pollutants—suspended solids, organic matter, and nitrogen—offering an innovative solution for sponge city development and rainwater resource management. Full article
(This article belongs to the Special Issue Urban Stormwater Harvesting, and Wastewater Treatment and Reuse)
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<p>(<b>A</b>) Original silica sand photo; (<b>B</b>) schematic diagram of sand grain coating modification; (<b>C</b>) photo of modified sand grains; (<b>D</b>) silica sand permeable and filter brick; (<b>E</b>) water purification filter wall structure made from silica sand permeable and filter bricks; (<b>F</b>) structure of silica sand filter well.</p>
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<p>(<b>A</b>) SEM image of the surface layer of the permeable and filterable brick; and (<b>B</b>) schematic diagram of the structure of the permeable and filterable brick.</p>
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<p>XPS spectra of the permeable surface of the water-permeable filter brick for Si2p (<b>A</b>) and C1s (<b>B</b>). The observed different colors refer to different elements or its chemical states for the easily distinguish and identify, as indicated by the arrow.</p>
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<p>(<b>A</b>) Schematic diagram of the water permeation mechanism of the permeable and filterable brick, (<b>B</b>) variation of water flux (<span class="html-italic">J</span>), porosity (<span class="html-italic">ε</span>), pore diameter (<span class="html-italic">r<sub>p</sub></span>), and membrane resistance (<span class="html-italic">R</span>) along the direction of water flow in the permeable and filterable brick.</p>
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<p>(<b>A</b>) The underwater oil contact angle of the water-permeable and filterable brick; (<b>B</b>) the oil-blocking effect of the water-permeable and filterable brick when wetted by water; (<b>C</b>) schematic diagram of the oil-blocking mechanism of the water-permeable and filterable brick when wetted by water.</p>
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<p>SEM observation of the biofilm on the surface of silicon sand filter bricks with different magnification. (<b>A</b>) ×5.00k; (<b>B</b>) ×20.0k; (<b>C</b>) ×5.00k; (<b>D</b>) ×30.0k.</p>
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<p>Schematic diagram of the biofilm in the silicon sand filter well: (<b>A</b>) Schematic diagram of denitrification mechanism, (<b>B</b>) Variation of the concentrations of COD, ammonia nitrogen, and nitrate nitrogen in each layer.</p>
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16 pages, 15653 KiB  
Article
Characteristics of Water Vapor Transport during the “7·20” Extraordinary Heavy Rain Process in Zhengzhou City Simulated by the HYSPLIT Model
by Xiuzhu Sha, Jianfang Ding, Ronghao Chu, Xinxin Ma, Xingyu Li, Yao Xiao, Bo Cheng, Fan Zhang, Can Song and Shanhai Wang
Water 2024, 16(18), 2607; https://doi.org/10.3390/w16182607 - 14 Sep 2024
Viewed by 210
Abstract
Water vapor transport is an important foundation and prerequisite for the occurrence of rainstorms. Consequently, the understanding of water vapor transport as well as the sources of water vapor during rainstorm processes should be considered as essential to study the formation mechanism of [...] Read more.
Water vapor transport is an important foundation and prerequisite for the occurrence of rainstorms. Consequently, the understanding of water vapor transport as well as the sources of water vapor during rainstorm processes should be considered as essential to study the formation mechanism of rainstorms. In this study, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model is adopted for backward tracking of water vapor transport trajectories and sources during the “7·20” extraordinary heavy rain process in Zhengzhou City of China that occurred on 20 July 2021. On this basis, the trajectory clustering method is applied to quantitatively analyze the contributions of water vapor sources, aiming to provide a basis for exploring the maintenance mechanism of this extreme rainstorm event. The spatio-temporal characteristics of this rainstorm event show that there are 4 consecutive days with the precipitation reaching or exceeding the rainstorm level across the whole Zhengzhou City, with the daily rainfall amounts at eight national meteorological stations all breaking their respective historical extreme values. The regional-averaged rainfall amount in Zhengzhou City is 527.4 mm, while the maximum accumulated rainfall amount reaches 985.2 mm at Xinmi station and the maximum hourly rainfall amount at Zhengzhou national meteorological station reaches 201.9 mm h−1. The water vapor sources for this rainfall process, ranked in descending order of contribution, are the Western Pacific, inland areas of Northwest China and South China, and South China Sea. The water vapor at lower levels is mainly transported from the Western Pacific and the South China Sea, while those from the inland areas of Northwest China and South China provide a supply of water vapor at upper levels to a certain extent. The water vapor at 950 hPa is mainly sourced from the Western Pacific and South China Sea, accounting for 56% and 44%, respectively. The water vapor at 850 hPa mainly derives from the Western Pacific and the inland areas of South China, contributing 58% and 34% of the total, respectively. The water vapor at 700 hPa mainly comes from the inland areas of Northwest China and South China Sea. Specifically, the water vapor from inland Northwest China contributes 44% of the total, acting as the primary source. The water vapor at 500 hPa is mainly transported from the inland areas of South China and Northwest China, with that from the inland South China (56%) being more prominent. The water vapor at all levels is mainly transported to the rainstorm region through the eastern and southern regions of China from the source areas. Additionally, there are some differences in the water vapor trajectories at a 6 h interval. Full article
(This article belongs to the Section Water and Climate Change)
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<p>Geographical location of the study area: (<b>a</b>) China, (<b>b</b>) Henan, and (<b>c</b>) Zhengzhou.</p>
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<p>Observed daily rainfall amounts in Henan Province on (<b>a</b>) 18, (<b>b</b>) 19, (<b>c</b>) 20, and (<b>d</b>) 21 July 2021.</p>
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<p>Variations in hourly rainfall intensities from 0000 Beijing Time (BJT) on 18 July to 2300 BJT on 22 July 2021 at (<b>a</b>) Gongyi, Xinzheng, Dengfeng, and Zhengzhou urban district station, and at (<b>b</b>) Songshan, Xinmi, Xinzheng and Zhongmu station. The red dot indicates the hourly maximum precipitation point at each station. <a href="#water-16-02607-f003" class="html-fig">Figure 3</a> is generated after processing precipitation data using Microsoft Excel 2019.</p>
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<p>Moving 24 h accumulated rainfall amounts averaged over the whole Zhengzhou City from 0000 BJT on 18 July to 2300 BJT on 22 July 2021.</p>
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<p>Fields of geopotential height and wind at (<b>a</b>) 500 hPa and (<b>b</b>) 850 hPa at 0800 BJT on 20 July 2021. D and G are low- and high-pressure systems, respectively.</p>
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<p>Daily accumulated vertical integrals of water vapor flux divergence on (<b>a</b>) 18, (<b>b</b>) 19, (<b>c</b>) 20, and (<b>d</b>) 21 July 2021. The result in <a href="#water-16-02607-f006" class="html-fig">Figure 6</a> was generated after the water vapor flux data were processed by Matlab R2016b software.</p>
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<p>Seven-day backward trajectories of water vapor for target region at (<b>a</b>,<b>e</b>,<b>i</b>) 0200 BJT, (<b>b</b>,<b>f</b>,<b>j</b>) 0800 BJT, (<b>c</b>,<b>g</b>,<b>k</b>) 1400 BJT and (<b>d</b>,<b>h</b>,<b>l</b>) 2000 BJT on (<b>a</b>–<b>d</b>) 19, (<b>e</b>–<b>h</b>) 20 and (<b>i</b>–<b>l</b>) 21 July 2021.</p>
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<p>Total spatial variances of clustered water vapor trajectories at different levels.</p>
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<p>Spatial distributions of water vapor transport channels at (<b>a</b>) 950 hPa, (<b>b</b>) 850 hPa, (<b>c</b>) 700 hPa, and (<b>d</b>) 500 hPa. The numbers at the end of each clustered trajectory line represent a specific cluster number, and the numbers in brackets indicate the proportion of the number of trajectories of this cluster to the total trajectories. The red, blue, green and lake blue solid lines represent the first, second, third and fourth clustered trajectories, respectively. The solid lines of red, blue, green and lake blue represent the 1–4 clustering tracks. The black star represents Zhengzhou city.</p>
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18 pages, 6810 KiB  
Article
Water Ecological Security Pattern Based on Hydrological Regulation Service: A Case Study of the Upper Hanjiang River
by Xinping Ma, Jing Li, Yuyang Yu and Xiaoting Xu
Sustainability 2024, 16(18), 7913; https://doi.org/10.3390/su16187913 - 10 Sep 2024
Viewed by 473
Abstract
Water ecological problems involve flood, drought, water pollution, destruction of water habitat and the tense relationship between humans and water. Water ecological problems are the main problems in the development of countries all over the world. In terms of ecological protection, China has [...] Read more.
Water ecological problems involve flood, drought, water pollution, destruction of water habitat and the tense relationship between humans and water. Water ecological problems are the main problems in the development of countries all over the world. In terms of ecological protection, China has put forward the ecological red line policy. Water ecology is an important component of the ecosystem, and the delineation of the water ecological red line is an important basis for ecological protection. Based on ecosystem services, this paper tries to determine the red line of the water ecology space and tries to solve various water problems comprehensively. Based on the ecosystem services accounting method, the SWAT (soil and water assessment tool) model was used to simulate the spatial–temporal dynamic quantities of water purification and rainwater infiltration services in the upper reaches of the Hanjiang River. The basin was divided into 106 sub-basins and 1790 HRUs (hydrological response units). Water quality data taken from 8 sites were used to verify the simulation results, and the verification results have high reliability. The grid scale spatialization of water quality and rainwater infiltration is realized based on HRU. The seasonal characteristics of hydrological regulation and control services were analyzed, the red line of hydrological regulation and control in the upper reaches of the Hanjiang River was defined, and the dynamic characteristics of water ecological red line were analyzed. According to the research results, the water ecological protection strategy of the basin is proposed. The prevention and control of water pollution should be emphasized in spring and summer, the prevention and control of rain flood infiltration in autumn and winter, and the normal monitoring and management should be adopted in the regulation and storage. The results of this study can provide scientific reference for water resources management and conservation policy making. Full article
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<p>Study area geographic location.</p>
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<p>SWAT model N, P simulation cycle diagram.</p>
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<p>Water quality station spatial distribution map.</p>
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<p>Relationship among infiltration, precipitation and surface runoff in SWAT Model.</p>
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<p>The process of obtaining the red line of watershed eco-hydrological regulation.</p>
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<p>Spatial distribution of total N/P in seasons.</p>
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<p>Pixel percentage of N/P content of various water quality on a monthly scale.</p>
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<p>Safety pattern of water purification Service.</p>
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<p>The spatial distribution of surface water sources in the upper reaches of the Hanjiang River.</p>
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<p>Interannual variation of water storage capacity of upper reaches of Hanjiang River.</p>
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<p>Spatial distribution and service security pattern of rain and flood infiltration in the upper reaches of Hanjiang River.</p>
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<p>Security pattern of hydro logical regulation in the upper reaches of Hanjiang River.</p>
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14 pages, 1319 KiB  
Article
Future Scenarios of Design Rainfall Due to Upcoming Climate Changes in NSW, Australia
by Iqbal Hossain, Shirley Gato-Trinidad, Monzur Imteaz and Scott Rayburg
Atmosphere 2024, 15(9), 1101; https://doi.org/10.3390/atmos15091101 - 10 Sep 2024
Viewed by 229
Abstract
The occurrence of rainfall is significantly affected by climate change around the world. While in some places this is likely to result in increases in rainfall, both winter and summer rainfall in most parts of New South Wales (NSW), Australia are projected to [...] Read more.
The occurrence of rainfall is significantly affected by climate change around the world. While in some places this is likely to result in increases in rainfall, both winter and summer rainfall in most parts of New South Wales (NSW), Australia are projected to decrease considerably due to climate change. This has the potential to impact on a range of hydraulic and hydrologic design considerations for water engineers, such as the design and construction of stormwater management systems. These systems are currently planned based on past extreme rain event data, and changes in extreme rainfall amounts due to climate change could lead to systems being seriously undersized (if extreme precipitation events become more common and/or higher in magnitude) or oversized (if extreme rainfall events become less frequent or decrease in magnitude). Both outcomes would have potentially serious consequences. Consequently, safe, efficient, and cost-effective urban drainage system design requires the consideration of impacts arising from climate change on the approximation of design rainfall. This study examines the impacts of climate change on the probability of occurrence of daily extreme rainfall in New South Wales (NSW), Australia. The analysis was performed for 29 selected meteorological stations located across NSW. Future design rainfall in this research was determined from the projected rainfall for different time periods (2020 to 2039, 2040 to 2059, 2060 to 2079, and 2080 to 2099). The results of this study show that design rainfall for the standard return periods was, in most cases, lower than that derived employing the design rainfall obtained from the Australian Bureau of Meteorology (BoM). While most of the analysed meteorological stations showed significantly different outcomes using the climate change scenario data, this varied considerably between stations and different time periods. This suggests that more work needs to be performed at the local scale to incorporate climate change predicted rainfall data into future stormwater system designs to ensure the best outcomes. Full article
(This article belongs to the Special Issue Statistical Approaches in Climatic Parameters Prediction)
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<p>Annual average rainfall for NSW over the 1991–2020 period.</p>
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<p>The relative position of the rainfall stations in NSW, Australia that were used to generate future scenarios of design rainfall due to upcoming climate change.</p>
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<p>Framework for identifying the influence of climate change effects on design rainfall [<a href="#B22-atmosphere-15-01101" class="html-bibr">22</a>]. The processes inside the red dotted box are performed by the Australian Bureau of Meteorology. The blue arrows show how the process progresses from one step to the next.</p>
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<p>Visual representation of return level estimation for different recurrence intervals for all the periods of extreme rainfall. Symbols ‘a’ represents 1900–2019, ‘b’ represents 2020–2039, ‘c’ represents 2040–2059, ‘d’ represents 2060–2079, and ‘e’ represents 2080–2099. The lines on the left of the figure represent the dendrogram which shows the structure of the cluster for rainfall stations.</p>
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<p>Design rainfall comparison between ARR and projected rainfall for different time periods for four selected meteorological stations. The symbols (<b>A</b>–<b>D</b>) are for different meteorological stations as shown.</p>
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39 pages, 3760 KiB  
Review
Quaternary Treatment of Urban Wastewater for Its Reuse
by Jakub Jurík, Barbora Jankovičová, Ronald Zakhar, Nikola Šoltýsová and Ján Derco
Processes 2024, 12(9), 1905; https://doi.org/10.3390/pr12091905 - 5 Sep 2024
Viewed by 810
Abstract
In today’s ongoing rapid urban expansion, deforestation and climate changes can be observed mainly as unbalanced rain occurrence during the year, long seasons without any rain at all and unordinary high temperatures. These adverse changes affect underground water levels and the availability of [...] Read more.
In today’s ongoing rapid urban expansion, deforestation and climate changes can be observed mainly as unbalanced rain occurrence during the year, long seasons without any rain at all and unordinary high temperatures. These adverse changes affect underground water levels and the availability of surface water. In addition, quite a significant proportion of drinking water is used mainly for non-drinking purposes. With several EU countries increasingly suffering from droughts, reusing quaternary treated urban wastewater can help address water scarcity. At the European level, Regulation 2020/741 of the European Parliament and of the Council of 25 May 2020 on minimum requirements for water reuse was adopted. This regulation foresees the use of recycled wastewater mainly for agricultural irrigation. This article provides an overview of various processes, such as filtration, coagulation, adsorption, ozonation, advanced oxidation processes and disinfection, for quaternary treatment of urban wastewater in order to remove micropollutants and achieve the requirements for wastewater reuse. According to the literature, the most effective method with acceptable financial costs is a combination of coagulation, membrane filtration (UF or NF) and UV disinfection. These processes are relatively well known and commercially available. This article also helps researchers to identify key themes and concepts, evaluate the strengths and weaknesses of previous studies and determine areas where further research is needed. Full article
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<p>Adsorption according to pore shape [<a href="#B103-processes-12-01905" class="html-bibr">103</a>].</p>
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<p>Difference between physisorption, chemisorption and mono/multilayer adsorption [<a href="#B104-processes-12-01905" class="html-bibr">104</a>].</p>
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<p>Adsorption mechanism of pollutants [<a href="#B112-processes-12-01905" class="html-bibr">112</a>].</p>
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<p>Some types of AOPs by in situ radical production.</p>
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<p>Principle of dielectric barrier discharge [<a href="#B171-processes-12-01905" class="html-bibr">171</a>].</p>
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<p>Schematic representation of the mechanism in anodic oxidation [<a href="#B189-processes-12-01905" class="html-bibr">189</a>].</p>
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<p>Schematic representation of the mechanism in the electro-Fenton technique [<a href="#B189-processes-12-01905" class="html-bibr">189</a>].</p>
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<p>Spectrum of light and its disinfection capacities [<a href="#B214-processes-12-01905" class="html-bibr">214</a>].</p>
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<p>In the two graphs, there is shown dependence of log reduction of microorganisms from time. In graph (<b>a</b>) we see microplastics affecting ozonation and in graph (<b>b</b>), UV/H<sub>2</sub>O<sub>2</sub> [<a href="#B204-processes-12-01905" class="html-bibr">204</a>].</p>
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18 pages, 3202 KiB  
Review
Vertical Green Wall Systems for Rainwater and Sewage Treatment
by Wen Wang, Xiaolin Zhou, Suqing Wu, Min Zhao, Zhan Jin, Ke Bei, Xiangyong Zheng and Chunzhen Fan
Sustainability 2024, 16(17), 7593; https://doi.org/10.3390/su16177593 - 2 Sep 2024
Viewed by 648
Abstract
Rainwater and sewage are important pollution sources for surface water bodies. Vertical greening systems (VGSs) are extensively employed for these wastewater treatments due to the green and sustainable characteristics, as well as their high-efficiency in pollutant (organic matter, nitrogen, and phosphorus) removal. At [...] Read more.
Rainwater and sewage are important pollution sources for surface water bodies. Vertical greening systems (VGSs) are extensively employed for these wastewater treatments due to the green and sustainable characteristics, as well as their high-efficiency in pollutant (organic matter, nitrogen, and phosphorus) removal. At present, more and more VGSs are designed with green buildings, serving city ecosystems. This study provides an overview of different kinds of VGSs for rain and sewage treatment, emphasizing their types, design, mechanisms, selection of plants, and growth substrate. Plants play a crucial role in pollutant removal, and different plants usually obtain different efficiencies of water treatment. Climbing plants and ornamental plants with fast growth rates are priority selections for VGSs, including Canna lilies, Jasmine, Grape vine, Boston ivy, Pittosporum tobira, Pelargonium australe, Mentha aquatica, and Lythrum salicaria. The substrate is the most critical part of the VGS, which plays an important role in regulating water flow, supporting plant growth, promoting biofilm growth, filtering pollutants, and adsorbing nutrients. The single substrate either has a blockage problem or has a short holding time. Therefore, a number of studies have mixed the substrates and integrated the advantages of the substrates to form a complementary effect, thereby improving the overall purification efficiency and stability. Novel substrates (sand, spent coffee grounds, date seeds, coffee grinds, reed-based, etc.) are usually mixed with coco coir, light-weight expanded clay, growstone, or perlite at a certain ratio to obtain optimum treatment performance. Moreover, plants in clay show more significant growth advantages and health statuses than in zeolite or soil. Operating parameters are also significant influences on the treatment performance. This review provides theoretical and technical support for designing sustainable, environmentally friendly, and cost-effective VGSs in treating rainwater and sewage. Full article
(This article belongs to the Section Pollution Prevention, Mitigation and Sustainability)
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<p>Publications on the application of VGSs in rainwater and sewage treatment in the last ten years.</p>
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<p>Depending on how they were built, several types of green walls are categorized. Reproduced with permission from ref [<a href="#B20-sustainability-16-07593" class="html-bibr">20</a>], Copyright 2015, Elsevier.</p>
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<p>Green facade and image: (<b>a</b>) direct green facade; (<b>b</b>) indirect greening facade.</p>
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<p>Living wall sketch and image: (<b>a</b>) continuous living wall; (<b>b</b>) modular living wall-type 1; (<b>c</b>) modular living wall-type 1.</p>
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<p>The removal mechanisms of plants, media, and microbes. Reproduced with permission from ref [<a href="#B37-sustainability-16-07593" class="html-bibr">37</a>], Copyright 2018, Elsevier.</p>
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20 pages, 20182 KiB  
Article
Use of Indices Applied to Remote Sensing for Establishing Winter–Spring Cropping Areas in the Republic of Kazakhstan
by Asset Arystanov, Natalya Karabkina, Janay Sagin, Marat Nurguzhin, Rebecca King and Roza Bekseitova
Sustainability 2024, 16(17), 7548; https://doi.org/10.3390/su16177548 - 31 Aug 2024
Viewed by 524
Abstract
Farmers in Kazakhstan face unreliable water resources. This includes water scarcity in the summer, high fluctuations in precipitation levels, and an increase in extreme weather events such as snow, rain, floods, and droughts. Wheat production is regulated and subsidized by the Kazakh government [...] Read more.
Farmers in Kazakhstan face unreliable water resources. This includes water scarcity in the summer, high fluctuations in precipitation levels, and an increase in extreme weather events such as snow, rain, floods, and droughts. Wheat production is regulated and subsidized by the Kazakh government to strengthen food security. The proper monitoring of crop production is vital to government agencies, as well as insurance and banking structures. These organizations offer subsidies through different levels support. Some farmers already use farmland soil monitoring combined with adaptive combinations of different crops. These include winter–spring plowing crop programs. Winter wheat crops are generally more adaptive and may survive summer droughts. Kazakhstan is a large country with large plots of farmland, which are complicated to monitor. Therefore, it would be reasonable to adapt more efficient technologies and methodologies, such as remote sensing. This research work presents a method for identifying winter wheat crops in the foothills of South Kazakhstan by employing multi-temporal Sentinel-2 data. Here, the researchers adapted and applied a Plowed Land Index, derived from the Brightness Index. The methodology encompasses satellite data processing, the computation of Plowed Land Index values for the swift recognition of plowed fields and the demarcation of winter wheat crop sowing regions, along with a comparative analysis of the acquired data with ground surveys. Full article
(This article belongs to the Special Issue Farmers’ Adaptation to Climate Change and Sustainable Development)
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<p>Boundary of the Tyulkubas district, Turkestan region.</p>
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<p>Sentinel-2 coverage scenes for the territory in the Tyulkubas district.</p>
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<p>Field survey routes and test farms in the Tyulkubas District of the Turkestan Region during the period of 2–7 June 2022.</p>
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<p>Visual differences between agricultural crops in the foothill zone of the Turkestan region during a route survey in April 2018.</p>
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<p>Photographic survey of surveyed fields in the foothill zone of the Turkestan region in April 2018: (<b>a</b>) winter wheat; (<b>b</b>) perennial alfalfa; (<b>c</b>) spring barley; (<b>d</b>) winter wheat (beginning of tillering); (<b>e</b>) safflower seedlings; (<b>f</b>) safflower cultivation and sowing.</p>
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<p>Graphical representation of pixel values of the Plowed Land Index for various types of objects: plowed fields 0–5, bare soil 5–10, fires 10–15, dense vegetation 15–20 and sparse vegetation 20–25.</p>
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<p>Comparison of results for identification of autumn plowing in the fields of the Tyulkubas district using the Plowed Land Index and NDVI data for 11 November 2021.</p>
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<p>Boundaries of plowed fields based on the Plowed Land Index: (<b>a</b>) for 2 November 2021; (<b>b</b>) for 1 May 2022.</p>
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<p>Methodology for recognizing the final mask of winter wheat crop sowing in South Kazakhstan.</p>
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<p>Visualization of changes in the field array of the Tyulkubas district on Sentinel-2 images: (<b>a</b>) field processing for the autumn period (September–November 2021); (<b>b</b>) field processing for the spring period (April–May 2022).</p>
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<p>Autumn plowing mask of fields in 2021 over the territory of the Tyulkubas district, Turkistan region, based on the Plowed Land Index, Sentinel-2 Images.</p>
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<p>Confirmation of winter wheat crop vegetation based on the NDVI index and ground-based data on the Sentinel-2 satellite image for 24 May 2022.</p>
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<p>Map of autumn and spring plowing fields in the Tyulkubas district of the Turkestan region based on the Plowed Land Index in 2021–2022.</p>
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<p>Satellite map depicting the final locations of winter grain crop sowing areas in the Tyulkubas district of the Turkestan region for the years 2021–2022.</p>
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<p>Plowing rates (%) in the Tyulkubas district of the Turkestan region in autumn 2021 based on satellite imagery.</p>
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<p>Fragment of satellite field with field verification of winter grain crops with ground truth data for “Kyzylzhar” farm in 2022.</p>
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<p>Fragment of satellite processed data output verification with the results of ground field surveys in the Tyulkubas district in 2022.</p>
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16 pages, 5226 KiB  
Article
Evaluating Uncertainties in an SM-Based Inversion Algorithm for Irrigation Estimation in a Subtropical Humid Climate
by Laura Almendra-Martín, Jasmeet Judge, Alejandro Monsivaís-Huertero and Pang-Wei Liu
Water 2024, 16(17), 2445; https://doi.org/10.3390/w16172445 - 29 Aug 2024
Viewed by 482
Abstract
Monitoring irrigation is crucial for sustainable water management in freshwater-limited regions. Even though soil moisture (SM)-based inversion algorithms have been widely used to estimate irrigation, scarcity of irrigation records has prevented a thorough understanding of their uncertainties, especially in humid regions. This study [...] Read more.
Monitoring irrigation is crucial for sustainable water management in freshwater-limited regions. Even though soil moisture (SM)-based inversion algorithms have been widely used to estimate irrigation, scarcity of irrigation records has prevented a thorough understanding of their uncertainties, especially in humid regions. This study assesses the suitability of the SM2RAIN algorithm for estimating irrigation at field scale using high-temporal-resolution data from four corn growing experiments conducted in north-central Florida. Daily irrigation estimates were compared with observations, revealing root mean squared differences of 1.26 to 3.84 mm/day and Nash–Sutcliffe Efficiencies of 0.33 to 0.89. The estimates were more sensitive to uncertainties in static inputs of porosity, saturation moisture and soil thickness than they were to noise in time series inputs. Defining the saturation moisture as porosity made the algorithm insensitive to both parameters, while increasing soil thickness from 40 to 200 mm improved detection accuracies by 34–46%. In addition, the impact of SM on the estimations was investigated based on satellite overpass times. The analysis showed that morning passes produced more accurate estimates for the study site, while evening passes doubled the uncertainty. This study enhances the understanding of the SM2RAIN algorithm for irrigation estimation in subtropical humid conditions, guiding future high-resolution applications. Full article
(This article belongs to the Section Hydrology)
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<p>Location of the Plant Science Research and Education Unit (PSREU) in Florida, US. Background source: the National Land Cover Database [<a href="#B37-water-16-02445" class="html-bibr">37</a>].</p>
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<p>Daily series of water inputs for each MicroWEX. Values for each day of the year (DOY) observed during the MicroWEXs and estimated with SM2RAIN irrigation water are shown in blue and orange bars, respectively. Rainfall rates are displayed in gray. Amount in mm of total precipitation (<math display="inline"><semantics> <mrow> <mi>P</mi> <mi>P</mi> <msub> <mi>T</mi> <mi>T</mi> </msub> </mrow> </semantics></math>), observed irrigation (<math display="inline"><semantics> <mrow> <mi>I</mi> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>M</mi> <mi>i</mi> <mi>c</mi> <mi>r</mi> <mi>o</mi> <mi>W</mi> <mi>E</mi> <mi>X</mi> </mrow> </msub> </mrow> </semantics></math>) and estimated irrigation (<math display="inline"><semantics> <mrow> <mi>I</mi> <mi>R</mi> <msub> <mi>R</mi> <mrow> <mi>S</mi> <mi>M</mi> <mn>2</mn> <mi>R</mi> <mi>A</mi> <mi>I</mi> <mi>N</mi> </mrow> </msub> </mrow> </semantics></math>) accumulated during each growing season are also displayed.</p>
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<p>Sensitivity to static soil parameters for MicroWEX-2 (top row) and MicroWEX-5 (bottom row): porosity, <span class="html-italic">n</span>, (<b>a</b>,<b>e</b>), saturated moisture content, <math display="inline"><semantics> <msub> <mi>θ</mi> <mi>s</mi> </msub> </semantics></math>, (<b>b</b>,<b>f</b>), equal <span class="html-italic">n</span> and <math display="inline"><semantics> <msub> <mi>θ</mi> <mi>s</mi> </msub> </semantics></math> (<b>c</b>,<b>g</b>) and soil thickness, <span class="html-italic">Z</span>, (<b>d</b>,<b>h</b>). Blue lines represent the difference in percentage (<math display="inline"><semantics> <msub> <mo>%</mo> <mi>D</mi> </msub> </semantics></math>) between the total estimated and observed irrigation water amount during the growing season, and the red lines represent the critical success index (CSI) for irrigation occurrence detection.</p>
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<p>Sensitivity to time series parameters for MicroWEX-2 (top row) and MicroWEX-5 (bottom row) for ET (<b>a</b>,<b>d</b>), SM (<b>b</b>,<b>e</b>), and drainage (<b>c</b>,<b>f</b>) noise. The difference in percentage (<math display="inline"><semantics> <msub> <mo>%</mo> <mi>D</mi> </msub> </semantics></math>) between the total estimated and observed irrigation water amount during the growing season is shown in blue, and the critical success index (CSI) for irrigation occurrence detection is shown in red. Solid lines indicate the median of the repetitions that introduce random noise, while the colored areas represent the range between the 75th and 25th percentiles.</p>
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<p>Contributions in percentage of <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>e</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>g</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> terms from Equation (1a) to the total estimated irrigation amount of water during the growing season for each MicroWEX.</p>
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<p>Metrics used for evaluating SM2RAIN capability to detect irrigation occurrences during the different MicroWEXs when utilizing daily SM observations, as well as utilizing solely at 6 am and 6 pm observations.</p>
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<p>RMSD between the estimated with SM2RAIN and observed irrigation water amounts when utilizing daily SM observations, as well as utilizing solely at 6 am and 6 pm observations during the different MicroWEXs.</p>
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25 pages, 3325 KiB  
Article
Effects of Paddy Rain-Flood Storage on Rice Growth Physiological Indices and Nitrogen Leaching under Organic Planting in Erhai Lake Basin
by Qingsheng Liu, Qiling Lu, Liudong Zhang, Shufang Wang, Aiqing Zou, Yong Su, Jun Sha, Ying Wang and Lihong Chen
Plants 2024, 13(17), 2381; https://doi.org/10.3390/plants13172381 - 26 Aug 2024
Viewed by 523
Abstract
In order to address the increasingly prominent issues of water resource protection and agricultural non-point source pollution in the Erhai Lake Basin, this study conducted a two-year field experiment in Gusheng Village, located in the Erhai Lake Basin. In 2022, two irrigation treatments [...] Read more.
In order to address the increasingly prominent issues of water resource protection and agricultural non-point source pollution in the Erhai Lake Basin, this study conducted a two-year field experiment in Gusheng Village, located in the Erhai Lake Basin. In 2022, two irrigation treatments were set up: conventional flooding irrigation (CK) and controlled irrigation (C), with three replicates for each treatment. In 2023, aiming to enhance the utilization rate of rainwater resources and reduce the direct discharge of dry-farming tailwater from upstream into Erhai Lake. The paddy field was used as an ecological storage basin, and the water storage depth of the paddy field was increased compared to the depth of 2022. Combined with the deep storage of rainwater, the dry-farming tailwater was recharged into the paddy field to reduce the drainage. In 2023, two water treatments, flooding irrigation with deep storage and controlled drainage (CKCD) and water-saving irrigation with deep storage and controlled drainage (CCD) were set up, and each treatment was set up with three replicates. The growth and physiological index of rice at various stages were observed. Nitrogen leaching of paddy field in surface water, soil water, and groundwater under different water treatments after tillering fertilizer were observed. The research results show that the combined application of organic and inorganic fertilizers under organic planting can provide more reasonable nutrient supply for rice, promote dry matter accumulation and other indices, and also reduce the concentration of NH4+-N in surface water. Compared with CK, the yield, 1000-grain weight, root-to-shoot ratio, and leaf area index of C are increased by 4.8%, 4.1%, 20.9%, and 9.7%, respectively. Compared with CKCD, the yield, 1000-grain weight, root-to-shoot ratio, and leaf area index of CCD are increased by 6.5%, 3.8%, 19.6%, and 21.9%, respectively. The yield in 2023 is 19% higher than that in 2022. Treatment C can increase the growth indicators and reduce the net photosynthetic rate to a certain extent, while CCD rain-flood storage can alleviate the inhibition of low irrigation lower limit on the net photosynthetic rate of rice. Both C and CCD can reduce nitrogen loss and irrigation amount in paddy fields. CCD can reduce the tailwater in the Gusheng area of the Erhai Lake Basin to Erhai Lake, and also can make full use of N, P, and other nutrients in the tailwater to promote the formation and development of rice. In conclusion, the paddy field rain-flood storage methodology in the Erhai Lake Basin can promote various growth and physiological indicators of rice, improve water resource utilization efficiency, reduce direct discharge of tailwater into Erhai Lake, and decrease the risk of agricultural non-point source pollution. Full article
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<p>Site location.</p>
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<p>Precipitation and average air temperature in rice season (Gusheng Village, China). Date is presented as month/day.</p>
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<p>Schematic diagram of deep storage and emission reduction in paddy fields (Gusheng Village, China).</p>
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<p>Maps of plant length and weight of dry matter on the ground during the reproductive period. TS, PIS, HFS, MRS, and RS represent tillering stage, panicle initiation stage, heading and flowering stage, milk-ripe stage, and ripening stage. Different letters in subfigures (<b>a</b>,<b>b</b>) indicate statistical significances at the <span class="html-italic">p</span> = 0.05 level within the same measurement date. “**” indicate that the significance test of 0.05 level has been passed, respectively.</p>
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<p>Root-to-shoot ratio and leaf area index at each growth stage. TS, PIS, HFS, MRS, and RS represent tillering stage, panicle initiation stage, heading and flowering stage, milk-ripe stage, and ripening stage. Different letters in subfigure indicate statistical significances at the <span class="html-italic">p</span> = 0.05 level within the same measurement date.</p>
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<p>The correlations between various growth indicators. Yield, DM, R/S, LAI, and NOGPS represent the rice yield, the weight of dry matter accumulation, the root-to-shoot ratio, the leaf area index, and the number of grains per spike. “*” indicates that the significance test of the 0.05 level has been passed.</p>
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<p>Diurnal variation in net photosynthetic rate at each growth stage (Gusheng Village, China). Subfigures (<b>a</b>–<b>c</b>) represent the net photosynthetic rate of rice during the tillering stage, panicle initiation stage, and milk-ripe stage in 2022; subfigures (<b>d</b>–<b>h</b>) represent the net photosynthetic rate of rice during the tillering stage, panicle initiation stage, heading and flowering stage, milk-ripe stage, and ripening stage in 2023.</p>
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<p>Diurnal variation in stomatal conductance at each growth stage (Gusheng Village, China). Subfigures (<b>a</b>–<b>c</b>) represent the stomatal conductance of rice during the tillering stage, panicle initiation stage, and milk-ripe stage in 2022; subfigures (<b>d</b>–<b>h</b>) represent the stomatal conductance of rice during the tillering stage, panicle initiation stage, heading and flowering stage, milk-ripe stage, and ripening stage in 2023.</p>
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<p>Diurnal variation in transpiration rate in each growth stage (Gusheng Village, China). Subfigures (<b>a</b>–<b>c</b>) represent the transpiration rate of rice during the tillering stage, panicle initiation stage, and milk-ripe stage in 2022; subfigures (<b>d</b>–<b>h</b>) represent the transpiration rate of rice during the tillering stage, panicle initiation stage, heading and flowering stage, milk-ripe stage, and ripening stage in 2023.</p>
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<p>Changes in surface water nitrogen concentration after fertilization (Gushengcun, China). (<b>a</b>): surface water NH<sub>4</sub><sup>+</sup>-N concentration in 2022, (<b>b</b>): surface water NH<sub>4</sub><sup>+</sup>-N concentration in 2023.</p>
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<p>Changes in soil water nitrogen concentration after fertilization (Gushengcun, China): (<b>a</b>): (0–20 cm, 2022), (<b>b</b>): (20–40 cm, 2022), (<b>c</b>): (0–20 cm, 2023), and (<b>d</b>): (20–40 cm, 2023).</p>
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<p>Changes in groundwater nitrogen concentration after fertilization (Gushengcun, China). (<b>a</b>): groundwater NH<sub>4</sub><sup>+</sup>-N concentration in 2022, (<b>b</b>): groundwater NH<sub>4</sub><sup>+</sup>-N concentration in 2023.</p>
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20 pages, 5118 KiB  
Article
Co-Occurrence of Cyanotoxins and Phycotoxins in One of the Largest Southeast Asian Brackish Waterbodies: A Preliminary Study at the Tam Giang—Cau Hai Lagoon (Vietnam)
by Devleena Sahoo, Ngoc Khanh Ni Tran, Thi Gia-Hang Nguyen, Thi Thu Hoai Ho, Thi Thuy Hang Phan, Duong Thu Huong Hoang, Ngo Huu Binh, Thi Thu Lien Nguyen, Luong Quang Doc, Noureddine Bouaïcha and Tri Nguyen-Quang
Limnol. Rev. 2024, 24(3), 335-353; https://doi.org/10.3390/limnolrev24030020 - 25 Aug 2024
Viewed by 491
Abstract
The Tam Giang-Cau Hai lagoon (TGCH) in Thua Thien Hue province (Vietnam) is a marsh/lagoon system and ranks among the largest waterbodies in Southeast Asia. It plays a significant role in terms of both socio-economic and environmental resources. However, anthropogenic stress, as well [...] Read more.
The Tam Giang-Cau Hai lagoon (TGCH) in Thua Thien Hue province (Vietnam) is a marsh/lagoon system and ranks among the largest waterbodies in Southeast Asia. It plays a significant role in terms of both socio-economic and environmental resources. However, anthropogenic stress, as well as the discharge of untreated domestic and industrial sewage with agricultural runoff from its three major tributaries, dramatically damages the water quality of the lagoon. Especially after heavy rain and flash floods, the continuous degradation of its water quality, followed by harmful algal and cyanobacterial bloom patterns (HABs), is more perceptible. In this study, several physicochemical factors, cyanotoxins (anatoxins (ATXs), saxitoxins (STXs), microcystins (MCs)), phycotoxins (STXs, okadaic acid (OA), and dinophysistoxins (DTXs)) were analyzed in water and shellfish samples from 13 stations in June 2023 from 13 stations, using enzyme-linked immunosorbent assay (ELISA) kits for the ATXs and STXs, and the serine/threonine phosphatase type 2A (PP2A) inhibition assay kit for the MCs, OA, and DTXs. The results showed for the first time the co-occurrence of freshwater cyanotoxins and marine phycotoxins in water and shellfish samples in this lagoon. Traces of ATXs and STXs were detected in the shellfish and the orders of magnitude were below the seafood safety action levels. However, toxins inhibiting the PP2A enzyme, such as MCs and nodularin (NODs), as well as OA and DTXs, were detected at higher concentrations (maximum: 130.4 μg equiv. MC-LR/kg shellfish meat wet weight), approaching the actionable level proposed for this class of toxin in shellfish (160 μg of OA equivalent per kg of edible bivalve mollusk meat). It is very important to note that due to the possible false positives produced by the ELISA test in complex matrices such as a crude shellfish extract, this preliminary and pilot research will be repeated with a more sophisticated method, such as liquid chromatography coupled with mass spectroscopy (LC-MS), in the upcoming research plan. Full article
(This article belongs to the Special Issue Hot Spots and Topics in Limnology)
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<p>Sampling locations with a zoomed view of Tam Giang lagoon, Vietnam.</p>
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<p>A local market in the fishery village at Tam Giang lagoon. Below: Seven different batches of shellfish samples collected from the lagoon at TC 13, including: (1) <span class="html-italic">Cyrenobatissa subsulcata</span>; (2) <span class="html-italic">Corbicula subsulcata</span>; (3) <span class="html-italic">Cristaria plicata</span>; (4) <span class="html-italic">Pila polita</span>; (5, 6) <span class="html-italic">Crasscostrea rivularis</span>; (7) <span class="html-italic">Perna viridis</span>.</p>
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<p>(<b>A</b>) Secchi depth from Stations TC1 to TC13; (<b>B</b>) trend of dissolved oxygen (DO), pH, and Salinity in TG-CH Lagoon.</p>
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<p>Relative abundances of phytoplankton from all phyla identified at 13 sites in the Tam Giang lagoon (Vietnam) during June 2023.</p>
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<p>Concentrations (µg/L) of paralytic shellfish toxins (PSTs) expressed as saxitoxin (STX) equivalent in water samples collected from the different sites (TC1 to TC13) in the Tam Giang lagoon, Vietnam. The regulatory guidance level for saxitoxin is 3 µg/L in drinking water and 30 µg/L in recreational water.</p>
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<p>Concentrations (µg/L) of anatoxins (ATXs) expressed as anatoxin-a (ATX-a) equivalent in water samples collected from the different sites (TC1 to TC13) in the Tam Giang lagoon, Vietnam. The regulatory guidance level for ATX-a is 30 µg/L in drinking water and 60 µg/L in recreational water.</p>
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<p>Concentrations (µg/L) of toxins inhibiting the PP2A, such as microcystins (MCs), nodularin (NODs), okadaic acid (OA), and dinophysistoxins (DTXs), expressed as microcystin-LR (MC-LR) in water samples collected from the different sites (TC1 to TC13) in the Tam Giang lagoon, Vietnam. The regulatory guidance level for MC-LR is 1 µg/L in drinking water (dotted red line) and 24 µg/L in recreational water.</p>
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<p>Levels (µg/kg) of paralytic shellfish toxins (PSTs) expressed as saxitoxin (STX) equivalent in shellfish samples collected from the Tam Giang lagoon, Vietnam. The regulatory guidance level is 800 µg STX equivalent/kg.</p>
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<p>Levels (µg/kg) of anatoxins (ATXs) expressed as anatoxin-a (ATX-a) equivalent in shellfish samples collected from the Tam Giang lagoon, Vietnam. No regulatory guidelines are mentioned for ATX in shellfish samples.</p>
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<p>Levels (µg/kg) of toxins inhibiting the PP2A, such as microcystins (MCs), nodularin (NODs), okadaic acid (OA), and dinophysistoxins (DTXs), expressed as microcystin-LR (MC-LR) equivalent in shellfish samples collected from the Tam Giang lagoon, Vietnam. The regulatory guidance level (dotted red line) for diarrheic toxins in shellfish is 160 μg of OA equivalent per kg of edible bivalve mollusk meat, by total amounts of OA, DTXs, and pectenotoxins.</p>
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23 pages, 9292 KiB  
Article
Potential Impacts of Future Climate Change on Super-Typhoons in the Western North Pacific: Cloud-Resolving Case Studies Using Pseudo-Global Warming Experiments
by Chung-Chieh Wang, Min-Ru Hsieh, Yi Ting Thean, Zhe-Wen Zheng, Shin-Yi Huang and Kazuhisa Tsuboki
Atmosphere 2024, 15(9), 1029; https://doi.org/10.3390/atmos15091029 - 25 Aug 2024
Viewed by 628
Abstract
Potential impacts of projected long-term climate change toward the end of the 21st century on rainfall and peak intensity of six super-typhoons in the western North Pacific (WNP) are assessed using a cloud-resolving model (CRM) and the pseudo-global warming (PGW) method, under two [...] Read more.
Potential impacts of projected long-term climate change toward the end of the 21st century on rainfall and peak intensity of six super-typhoons in the western North Pacific (WNP) are assessed using a cloud-resolving model (CRM) and the pseudo-global warming (PGW) method, under two representative concentration pathway (RCP) emission scenarios of RCP4.5 and RCP8.5. Linear long-term trends in June–October are calculated from 38 Coupled Model Intercomparison Project phase 5 (CMIP5) models from 1981–2000 to 2081–2100, with warmings of about 3 °C in sea surface temperature, 4 °C in air temperature in the lower troposphere, and increases of 20% in moisture in RCP8.5. The changes in RCP4.5 are about half the amounts. For each typhoon, three experiments are carried out: a control run (CTL) using analysis data as initial and boundary conditions (IC/BCs), and two future runs with the trend added to the IC/BCs, one for RCP4.5 and the other for RCP8.5, respectively. Their results are compared for potential impacts of climate change. In future scenarios, all six typhoons produce more rain rather consistently, by around 10% in RCP4.5 and 20% in RCP8.5 inside 200–250 km from the center, with increased variability toward larger radii. Such increases are tested to be highly significant and can be largely explained by the increased moisture and water vapor convergence in future scenarios. However, using this method, the results on peak intensity are mixed and inconsistent, with the majority of cases becoming somewhat weaker in future runs. It is believed that in the procedure to determine the best initial time for CTL, which yielded the strongest TC, often within a few hPa in minimum central sea-level pressure to the best track data, an advantage was introduced to the CTL unintentionally. Once the long-term trends were added in future runs, the environment of the storm was altered and became not as favorable for subsequent intensification. Thus, the PGW approach may have some bias in assessing the peak intensity of such super-typhoon cases, and caution should be practiced. Full article
(This article belongs to the Special Issue Multi-Scale Climate Simulations)
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<p>The averaged long-term trend (Δ values) of (<b>a</b>) height (gpm, blue contours, every 1 gpm) and horizontal wind (m s<sup>−1</sup>, vector and color, reference vector length and scale at bottom) at 1000 hPa and (<b>b</b>) SST (K) in the WNP, between Jun and Oct of 1981–2000 and 2081–2100 from 38 CMIP5 models for the RCP8.5 scenario. Initial positions of the six typhoons in CTL are marked (typhoon symbols). (<b>c</b>–<b>h</b>) Vertical profiles of areal-mean Δ values over the domain of 6°–16° N, 135°–155° E, i.e., dashed box in (<b>b</b>), for the RCP4.5 (blue) and RCP8.5 (scarlet) scenarios for the changes in (<b>c</b>) temperature (Δ<span class="html-italic">T</span>, K), (<b>d</b>) specific humidity (Δ<span class="html-italic">q<sub>v</sub></span>, g kg<sup>−1</sup>), (<b>e</b>) <span class="html-italic">u</span>- and (<b>f</b>) <span class="html-italic">v</span>-components of wind (Δ<span class="html-italic">u</span> and Δ<span class="html-italic">v</span>, m s<sup>−1</sup>), (<b>g</b>) saturation specific humidity (Δ<span class="html-italic">q<sub>s</sub></span>, g kg<sup>−1</sup>), and (<b>h</b>) deficit in specific humidity to saturation (Δ<span class="html-italic">q<sub>d</sub></span>, Δ<span class="html-italic">q<sub>d</sub></span> = Δ<span class="html-italic">q<sub>v</sub></span> − Δ<span class="html-italic">q<sub>s</sub></span>, g kg<sup>−1</sup>), respectively.</p>
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<p>Comparison between JTWC (red) and JMA (green) best tracks and the CReSS-simulated track in CTL (blue) for each of the six super-typhoons: (<b>a</b>) Megi (2010), (<b>b</b>) Haiyan (2013), (<b>c</b>) Vongfong (2014), (<b>d</b>) Soudelor (2015), (<b>e</b>) Meranti (2016), and (<b>f</b>) Yutu, respectively. Typhoon center locations are given every 6 h in UTC (circles) during the simulation period, with solid dots at 0000 UTC with the date labeled. The topography (m) is also plotted (scale at lower right).</p>
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<p>Similar to <a href="#atmosphere-15-01029-f002" class="html-fig">Figure 2</a>, but for comparison of TC intensity in minimum central (sea-level) pressure <span class="html-italic">p<sub>min</sub></span> (hPa, thick curves and left axis) and maximum wind speed <span class="html-italic">V<sub>max</sub></span> (m s<sup>−1</sup>, thin curves and right axis) between JTWC (red) and JMA (green) best tracks and the CTL simulation (blue) for four typhoons: (<b>a</b>) Megi (2010) in October, (<b>b</b>) Haiyan (2013) in November, (<b>c</b>) Soudelor (2015) in August, and (<b>d</b>) Meranti (2016) in September, respectively. Data points are 6 h apart, and the time is in UTC.</p>
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<p>(<b>a</b>) TRMM satellite brightness temperature observation (<span class="html-italic">T<sub>B</sub></span>, K) at 2242 UTC and (<b>b</b>) column-maximum mixing ratio of precipitation (g kg<sup>−1</sup>, rain + snow + graupel) in CTL at 2100 UTC, both on 17 Oct for STY Megi (2010). (<b>c</b>–<b>f</b>) As in (<b>a</b>,<b>b</b>), except (<b>c</b>) at 1101 UTC for TRMM <span class="html-italic">T<sub>B</sub></span> and (<b>d</b>) at 1200 UTC for model mixing ratio in CTL on 7 Nov for STY Haiyan (2013), and (<b>e</b>) at 1650 UTC for TRMM <span class="html-italic">T<sub>B</sub></span> and (<b>f</b>) at 1800 UTC for model mixing ratio in CTL on 13 Sep for STY Meranti (2016), respectively. The domain of the upper panels is approximately 15° × 15° and that of lower panels is 750 km × 750 km, with the model simulation time (h) also labeled inside. (Source of TRMM images: NRL).</p>
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<p>As in <a href="#atmosphere-15-01029-f002" class="html-fig">Figure 2</a>, except showing (<b>a</b>) model-simulated tracks of the six super-typhoons in CTL (color), and (<b>b</b>–<b>d</b>) for comparison between tracks in CTL (blue), R4.5 (green), and R8.5 (red) for STYs (<b>b</b>) Megi (2010), (<b>c</b>) Haiyan (2013), and (<b>d</b>) Meranti (2016), respectively. Typhoon locations are given every 3 h in UTC (circles), with solid dots at 0000 UTC (date labeled). The scale for topography (m) is at the bottom of panel (<b>a</b>).</p>
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<p>As in <a href="#atmosphere-15-01029-f003" class="html-fig">Figure 3</a>, but for comparison of intensity in <span class="html-italic">p<sub>min</sub></span> (hPa, thick curves) and <span class="html-italic">V<sub>max</sub></span> (m s<sup>−1</sup>, thin curves) between CTL (blue), R4.5 (green), and R8.5 (red) for the six typhoons: (<b>a</b>) Megi (2010) in October, (<b>b</b>) Haiyan (2013) in November, (<b>c</b>) Vongfong (2014) in October, (<b>d</b>) Soudelor (2015) in August, (<b>e</b>) Meranti (2016) in September, and (<b>f</b>) Yutu (2018) in October, respectively. Data points are 3 h apart.</p>
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<p>(<b>a</b>) Radial profile of azimuthally-averaged rainfall from 0 to 500 km and (<b>b</b>) time series of areal-mean rainfall (both in mm per 3 h) inside the radius of 350 km over the full simulation period for TY Megi (2010) in CTL (blue), R4.5 (green), and R8.5 (red), respectively. The observation from GPM IMERG is also plotted (black) in (<b>b</b>). (<b>c</b>,<b>d</b>) As in (<b>a</b>,<b>b</b>), except for TY Haiyan (2013) and inside 250 km. (<b>e</b>,<b>f</b>) As in (<b>a</b>,<b>b</b>), except for TY Meranti (2016) and inside 300 km.</p>
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<p>The mean radius–height profiles of (<b>a</b>) tangential wind (m s<sup>−1</sup>, isotachs every 4 m s<sup>−1</sup>) and radial wind and vertical velocity (<span class="html-italic">w</span>, m s<sup>−1</sup>, vectors, reference length at lower right of panel), with <span class="html-italic">w</span> colored (scale at bottom), and mixing ratio of (<b>b</b>) graupel and (<b>c</b>) rain (both in g kg<sup>−1</sup>, scale at bottom), respectively, from 0 to 400 km and averaged azimuthally and over the full simulation period for Haiyan in CTL. (<b>d</b>–<b>f</b>) As in (<b>a</b>–<b>c</b>), except for their differences of R85 minus CTL.</p>
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13 pages, 4156 KiB  
Article
Transformation of Biomass Power Plant Ash into Composite Fertilizers: A Perspective to Prepare a Rain-Controlled Ammonium Ion–Releasing Composite Fertilizer
by László Kótai, Márk Windisch and Kende Attila Béres
J. Compos. Sci. 2024, 8(9), 336; https://doi.org/10.3390/jcs8090336 - 24 Aug 2024
Viewed by 483
Abstract
We have developed a convenient route to transform biomass power plant ashes (BPPA) into porous sponge-like fertilizer composites. The absence of water prevents the chemical reaction and carbon dioxide formation when concentrated sulfuric acid is mixed with BPPA and CaCO3. Adding [...] Read more.
We have developed a convenient route to transform biomass power plant ashes (BPPA) into porous sponge-like fertilizer composites. The absence of water prevents the chemical reaction and carbon dioxide formation when concentrated sulfuric acid is mixed with BPPA and CaCO3. Adding water, however, initiates the protonation reaction of carbonate ion content and starts CO2 evolution. The key element of the method was that the BPPA and, optionally, CaCO3 and/or CaSO4·0.5H2O were mixed with concentrated sulfuric acid to make a paste-like consistency. No gas evolution occurred at this stage; however, with the subsequent and controlled addition of water, CO2 gas evolved and was released through the channels developed in the pastry-like material due to the internal gas pressure, but without foaming. Using a screw-containing tube reactor, the water can be introduced under pressure. Due to the pressure, the pores in the pastry-like material became smaller, and consequently, the mechanical strength of the granulated and solidified mixture became higher than that of the reaction products prepared under atmospheric pressure. The main reaction products were syngenite (K2Ca(SO4)2·H2O) and polyhalite (K2Ca2Mg(SO4)4·2H2O). These compounds are valuable fertilizer components in themselves, but the material’s porous nature helps absorb solutions of microelement fertilizers. Surprisingly, concentrated ammonium nitrate solutions transform the syngenite content of the porous fertilizer into ammonium calcium sulfate ((NH4)2Ca(SO4)2·2H2O, koktaite). Koktaite is slightly soluble in water, thus the amount of ammonium ion released on the dissolution of koktaite depends on the amount of available water. Accordingly, ammonium ion release for plants can be increased with rain or irrigation, and koktaite is undissolved and does not decompose in drought situations. The pores (holes) of this sponge-like fertilizer product can be filled with different solutions containing other fertilizer components (phosphates, zinc, etc.) to adjust the composition of the requested fertilizer compositions for particular soils and plant production. The method allows the preparation of ammonium nitrate composite fertilizers containing metallic microelements, and various solid sponge-like composite materials with adjusted amounts of slowly releasing fertilizer components like syngenite and koktaite. Full article
(This article belongs to the Special Issue From Waste to Advance Composite Materials)
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<p>XRD of the biomass power plant ash (BPPA) made by the combustion of willow.</p>
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<p>A screw-supplied tube reactor to neutralize biomass power plant ash with sulfuric acid. Annotations: 1—Motor; 2—Console; 3—Clutch; 4—Sealing; 5—Trough; 6—Lid; 7—Insert; 8—Bearing; 9—Axle.</p>
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<p>The XRD of NPG made from PBF and a concentrated ammonium nitrate solution.</p>
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<p>IR spectra of BPPA (a); sulfuric acid-treated/calcium carbonate neutralized (b) and ammonium nitrate solution absorbed (c) composites in the 900 and 1600 cm<sup>−1</sup> region.</p>
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<p>The morphology of a sulfuric acid-treated and CaCO<sub>3</sub>-neutralized BPPS sample.</p>
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<p>The morphology of the ammonium nitrate solution-treated sample prepared from BPPA with sulfuric acid/calcium carbonate treatment. EDS measurements were performed from the area that is annotated with orange square.</p>
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<p>The dependence of ammonium ion release from NPG fertilizer as a function of water amo unt (rain or irrigation).</p>
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23 pages, 663 KiB  
Review
Climate Change Impacts on and Response Strategies for Kiwifruit Production: A Comprehensive Review
by Priyanka Rajan, Premkumar Natraj, Misun Kim, Mockhee Lee, Yeon Jin Jang, Young Jae Lee and Seong Cheol Kim
Plants 2024, 13(17), 2354; https://doi.org/10.3390/plants13172354 - 23 Aug 2024
Viewed by 521
Abstract
Climate change, a pressing global concern, poses significant challenges to agricultural systems worldwide. Among the myriad impacts of climate change, the cultivation of kiwifruit trees (Actinidia spp.) faces multifaceted challenges. In this review, we delve into the intricate effects of climate change [...] Read more.
Climate change, a pressing global concern, poses significant challenges to agricultural systems worldwide. Among the myriad impacts of climate change, the cultivation of kiwifruit trees (Actinidia spp.) faces multifaceted challenges. In this review, we delve into the intricate effects of climate change on kiwifruit production, which span phenological shifts, distributional changes, physiological responses, and ecological interactions. Understanding these complexities is crucial for devising effective adaptation and mitigation strategies to safeguard kiwifruit production amidst climate variability. This review scrutinizes the influence of rising global temperatures, altered precipitation patterns, and a heightened frequency of extreme weather events on the regions where kiwifruits are cultivated. Additionally, it delves into the ramifications of changing climatic conditions on kiwifruit tree physiology, phenology, and susceptibility to pests and diseases. The economic and social repercussions of climate change on kiwifruit production, including yield losses, livelihood impacts, and market dynamics, are thoroughly examined. In response to these challenges, this review proposes tailored adaptation and mitigation strategies for kiwifruit cultivation. This includes breeding climate-resilient kiwifruit cultivars of the Actinidia species that could withstand drought and high temperatures. Additional measures would involve implementing sustainable farming practices like irrigation, mulching, rain shelters, and shade management, as well as conserving soil and water resources. Through an examination of the literature, this review showcases the existing innovative approaches for climate change adaptation in kiwifruit farming. It concludes with recommendations for future research directions aimed at promoting the sustainability and resilience of fruit production, particularly in the context of kiwifruit cultivation, amid a changing climate. Full article
(This article belongs to the Section Plant Response to Abiotic Stress and Climate Change)
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<p>Adaptation and mitigation strategies for kiwifruit cultivation in response to climate change.</p>
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19 pages, 6728 KiB  
Article
Temporary Road Marking Paint for Vehicle Perception Tests
by Nils Katzorke, Lisa-Marie Langwaldt and Lara Schunggart
Appl. Sci. 2024, 14(16), 7362; https://doi.org/10.3390/app14167362 - 21 Aug 2024
Viewed by 414
Abstract
In order to test camera- and LiDAR-based perception of road markings for automated driving, vehicle developers have started to utilize concepts for the agile alteration of road marking patterns on proving grounds. Road marking materials commonly used within this concept are different types [...] Read more.
In order to test camera- and LiDAR-based perception of road markings for automated driving, vehicle developers have started to utilize concepts for the agile alteration of road marking patterns on proving grounds. Road marking materials commonly used within this concept are different types of tape that can easily be applied and removed on asphalt and concrete. Due to the elasticity of tape, it cannot be used efficiently for small radii, symbols, lettering, and specific corner shapes (e.g., for parking slots). These road marking patterns are common in urban environments. With the growing capability of automated driving systems and more applications for urban environments, edgy road marking shapes gain importance for proving ground testing. This study examines the use of water-soluble road marking paint specifically designed for the use case of temporary applications on proving grounds for camera- and LiDAR-based perception testing. We found that white, water-soluble paint with 1.5% binder content and 2.25% coalescing agent content can provide realistic road markings for vehicle testing purposes. However, solubility affects the paint’s vulnerability to fading during rain. Hence, renewal activities over the course of longer test drives might be necessary. The paint could be removed using water pressure without significant residue or damaging of the asphalt. Full article
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<p>Example use cases for temporary road marking paints on proving grounds are shapes with round edges.</p>
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<p>Overview of the experiment’s process. The changes in visibility over time and the occurrence of phantom tracks after removing the paint were investigated.</p>
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<p>Experiment setup (sample 3). Road marking paints were applied with and without retroreflective beads.</p>
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<p>Retroreflectometer measurement. The measurement device creates diffuse lighting similar to daylight conditions to measure diffuse reflectivity and directional light similar to the headlights of vehicles to measure retroreflectivity.</p>
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<p>Diffuse reflectivity (daytime).</p>
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<p>Retroreflectivity (nighttime).</p>
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<p>Weather during experiment phase in 2023.</p>
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<p>Contrast for sample 4 without beads after 28 days.</p>
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<p>Contrast for sample 3 without beads after 28 days.</p>
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<p>Removal and residue of sample 3.</p>
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<p>Contrast between the spots on the road where sample 3 without beads was removed and the adjacent pavement.</p>
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<p>Research model for follow-up study.</p>
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27 pages, 8836 KiB  
Article
Improving Urban Stormwater Management Using the Hydrological Model of Water Infiltration by Rain Gardens Considering the Water Column
by Maryna Kravchenko, Grzegorz Wrzesiński, Katarzyna Pawluk, Marzena Lendo-Siwicka, Anna Markiewicz, Tetiana Tkachenko, Viktor Mileikovskyi, Olga Zhovkva, Sylwia Szymanek and Konrad Piechowicz
Water 2024, 16(16), 2339; https://doi.org/10.3390/w16162339 - 20 Aug 2024
Viewed by 706
Abstract
Implementing rain garden (RG) designs is widespread worldwide to reduce peak flow rates, promote stormwater infiltration, and treat pollutants. However, inadequate RG design degrades its hydrological behaviour, requiring the development and validation of an appropriate hydrological model for the design and analysis of [...] Read more.
Implementing rain garden (RG) designs is widespread worldwide to reduce peak flow rates, promote stormwater infiltration, and treat pollutants. However, inadequate RG design degrades its hydrological behaviour, requiring the development and validation of an appropriate hydrological model for the design and analysis of structures. This study aimed to improve a hydrological infiltration model based on Darcy’s law by taking into account the height of the water column (HWC) at the surface of the RG and the filtration coefficients of soil materials. The model was tested by simulating the hydrological characteristics of a rain garden based on a single rain event of critical intensity (36 mm/h). Using the validated model, design curves were obtained that predict the performance of the RG as a function of the main design parameters of the structure: water column height, ratio of catchment area to structure area, layer thickness, and soil filtration coefficient. The hydrological efficiency of the RG was evaluated in terms of the time of complete saturation, filling of the structure with water, and determining the change in HWC caused by changes in the parameters. The filtration coefficient and thickness of the upper and intermediate infiltration layers of the RG are the main parameters that affect the depth of saturation of the layers of the structure and the HWC on the surface. The model is not very sensitive to the model parameters related to the lower gravel layer. If the top layer’s thickness increases by 10 cm, it takes longer to fill the structure with water, and the HWC on the surface reaches 0.341 m. The rain garden’s performance improves when the filtration coefficient of the top layer is 7.0 cm/h. Complete saturation and filling of the structure with rainwater do not occur within 7200 s, and the water column reaches a height of 0.342 m at this filtration coefficient. However, the rain garden’s effectiveness decreases if the filtration coefficient of the upper and intermediate layers exceeds 15 cm/h and 25 cm/h, respectively, or if the catchment area to RG area ratio decreases to values below 15. The modelling results confirm that considering the HWC in RG hydrological models is essential for designing structures to minimise the risk of overflow during intense rainfall events. Full article
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<p>An example of the formation of a water column on the soil surface (Kyiv, author’s photo).</p>
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<p>Critical intensity rainfall event curve.</p>
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<p>Design scheme of hydrological processes in the thickness of the RG structure (adapted from [<a href="#B45-water-16-02339" class="html-bibr">45</a>]).</p>
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<p>Changes in RG performance over time: (<b>a</b>) without taking into account the HWC and the filtration coefficient; (<b>b</b>) with taking into account the HWC and the filtration coefficient. The solid lines represent HWC, and the dashed lines represent the saturation depth of the RG layers.</p>
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<p>Changes in RG performance and the HWC in time depending on the thickness of the upper soil layer <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>δ</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>, m. The solid lines—HWC, the dashed lines—saturation depth of the RG layers.</p>
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<p>Changes in RG performance and the HWC in time depending on the thickness of the infiltration layer <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>δ</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>, m. The solid lines—HWC, the dashed lines—saturation depth of the RG layers.</p>
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<p>Changes in RG performance and HWC over time, depending on the thickness of the gravel layer. The solid lines—HWC, the dashed lines—saturation depth of the RG layers.</p>
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<p>Changes in rain garden performance and the HWC in time depending on the filtration coefficient of the upper soil layer <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>k</mi> </mrow> <mrow> <mi>f</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>, cm/h. The solid lines—HWC, the dashed lines—saturation depth of the RG layers.</p>
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<p>Changes in RG performance and the HWC in time depending on the filtration coefficient of the infiltration layer <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>k</mi> </mrow> <mrow> <mi>f</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>, cm/h. The solid lines—HWC, the dashed lines—saturation depth of the RG layers.</p>
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<p>Changes in RG performance and the HWC in time depending on the drainage coefficient of the infiltration layer <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>k</mi> </mrow> <mrow> <mi>f</mi> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math>, cm/h. The solid line—HWC, the dashed line—saturation depth of the RG layers.</p>
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<p>Model software algorithm.</p>
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<p>Model software algorithm.</p>
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<p>Model software algorithm.</p>
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