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17 pages, 7584 KiB  
Article
Seasonal Rise in the Contents of Microcystin-LR and Odorous Substances Due to Cyanobacterial Blooms in a Drinking Water Reservoir Supplying Xinyang City, China
by Wei Zhao, Yang Liu, Hua Li, Junguo Ma and Xiaoyu Li
Toxins 2024, 16(10), 448; https://doi.org/10.3390/toxins16100448 (registering DOI) - 17 Oct 2024
Abstract
Cyanobacterial blooms have become a serious water pollution problem in many parts of the world, and the monitoring and study of the impacts of biotoxins on human health are of vital importance. In this study, the contents of microcystin-LR, 2-methylisoborneol, and geosmin were [...] Read more.
Cyanobacterial blooms have become a serious water pollution problem in many parts of the world, and the monitoring and study of the impacts of biotoxins on human health are of vital importance. In this study, the contents of microcystin-LR, 2-methylisoborneol, and geosmin were measured in water and sediment samples from Nanwan Reservoir, China, by means of bimonthly sampling between February and December 2023. The physicochemical and hydrochemical factors and phytoplankton dynamics in the reservoir were also investigated. The results showed that the overall mean concentration of microcystin-LR (0.729 μg/L) in summer approached the guiding standard (1 μg/L) set by the WHO for drinking water. Furthermore, the content of 2-methylisoborneol (143.5 ng/L) was 14 times higher than the national standard (10 ng/L). The results of laboratory cultures showed that lower light levels and medium temperatures were suitable for the growth of Microcystis and Planktothricoides but higher temperatures promoted the synthesis and release of microcystin-LR and 2-methylisoborneol. In addition, the results of co-cultures showed that the growth of Planktothricoides was inhibited by Microcystis. Our results suggest that cyanobacterial bloom and the presence of the metabolites 2-methylisoborneol and microcystin-LR can decrease the drinking water quality of Nanwan Reservoir. Full article
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Figure 1

Figure 1
<p>Seasonal concentrations of MC-LR and odorous substances in water and/or sediment from Nanwan Reservoir and the results of a correlation analysis relating odorous substances and toxin with environmental factors. Sampling and contents determination of MC-LR and odorous substances were described in the <a href="#sec5-toxins-16-00448" class="html-sec">Section 5</a> Materials and Methods. The correlation analysis was conducted by using the Corrplot Package of R Language. (<b>A</b>) One-year content of MC-LR in the water of Nanwan Reservoir. (<b>B</b>) Contents of 2-MIB and geosmin in the water of Nanwan Reservoir. (<b>C</b>) Contents of 2-MIB and geosmin in the sediment of Nanwan Reservoir. (<b>D</b>) Correlation analysis on the odor substances and toxins with the environmental factors. Blue font indicates positive correlation, red font indicates negative correlation. The number indicates a correlation, and a negative number indicates a negative correlation. Blank space means irrelevant. MC-LR: microcystin-LR; 2-MIB: 2-methylisoborneol; GSM: geosmin.</p>
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<p>Seasonal alterations in the algal population and cyanobacteria in the water of Nanwan Reservoir and the results of a correlation analysis relating cyanobacteria with environmental factors and 2-MIB contents. Algal identification and biomass assay are described in <a href="#sec5dot5-toxins-16-00448" class="html-sec">Section 5.5</a>. The correlation analysis was conducted by using the Mantel Analysis of R Language. (<b>A</b>) Seasonal percentage of algal and cyanobacterial composition at various sampling sites. (<b>B</b>) Seasonal biomass of algae and cyanobacteria at various sampling sites. (<b>C</b>) Correlation analysis on the cyanobacterial density with the environmental factors. Blue font indicates positive correlation, red font indicates negative correlation. The size of the box indicates the correlation level. (<b>D</b>) Correlation analysis on the cyanobacterial density with 2-MIB content. 2-MIB: 2-methylisoborneol.</p>
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<p>Morphology and ultrastructure of <span class="html-italic">Planktothricoides raciborskii</span> isolated from Nanwan Reservoir. (<b>A</b>) Morphology of <span class="html-italic">P. raciborskii</span> under microscopy. (<b>a</b>) Colony filaments; (<b>b</b>,<b>c</b>) Mono-filaments; (<b>d</b>) Dead cells. (<b>B</b>) Ultrastructure of <span class="html-italic">P. raciborskii</span> by transmission electron microscopy. Cw: cell wall.</p>
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<p>Growth of <span class="html-italic">Microcystis aeruginosa</span> and <span class="html-italic">Planktothricoides raciborskii</span> and morphological alterations in <span class="html-italic">P. raciborskii</span> under co-culture conditions. Biomass assay on <span class="html-italic">M. aeruginosa</span> and <span class="html-italic">P. raciborskii</span> and morphological measurement on filamentous <span class="html-italic">P. raciborskii</span> are described in <a href="#sec5dot7-toxins-16-00448" class="html-sec">Section 5.7</a>. (<b>A</b>) Growth curves of <span class="html-italic">M. aeruginosa</span> and <span class="html-italic">P. raciborskii</span>. (<b>a</b>) Growth curves of <span class="html-italic">M. aeruginosa</span> co-cultured with <span class="html-italic">P. raciborskii</span>; (<b>b</b>) Growth curves of <span class="html-italic">P. raciborskii</span> co-cultured with <span class="html-italic">M. aeruginosa</span>. (<b>B</b>) Morphology alteration of <span class="html-italic">P. raciborskii</span> after co-culture with <span class="html-italic">M. aeruginosa</span>. (<b>a</b>) Cell width; (<b>b</b>) Cell length; (<b>c</b>) Filament length. M: <span class="html-italic">M. aeruginosa</span>; P: <span class="html-italic">P. raciborskii</span>. (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Chlorophyll a contents and photosynthetic fluorescence parameters of <span class="html-italic">Microcystis aeruginosa</span> and <span class="html-italic">Planktothricoides raciborskii</span> under filtrate culture conditions. The filtrate culture was designed to culture <span class="html-italic">P. raciborskii</span>, with the filtrate from the <span class="html-italic">M. aeruginosa</span> culture solution obtained by centrifugation to remove <span class="html-italic">M. aeruginosa</span> cells or to culture <span class="html-italic">M. aeruginosa</span> with the filtrate from the <span class="html-italic">P. raciborskii</span> culture solution. Determination of the Chl a content and the photosynthetic fluorescence parameters of the cyanobacteria is described in <a href="#sec5dot7-toxins-16-00448" class="html-sec">Section 5.7</a>. (<b>A</b>) Chl a content of <span class="html-italic">Microcystis aeruginosa</span> (<b>a</b>) and <span class="html-italic">Planktothricoides raciborskii</span> (<b>b</b>); (<b>B</b>) <span class="html-italic">F</span><sub>v</sub>/<span class="html-italic">F</span><sub>m</sub> of <span class="html-italic">Microcystis aeruginosa</span> (<b>a</b>) and <span class="html-italic">Planktothricoides raciborskii</span> (<b>b</b>). (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01). <span class="html-italic">Fv</span>: Variable Fluorescence; <span class="html-italic">F</span><sub>m</sub>: Maxima Fluorescence. M: <span class="html-italic">M. aeruginosa</span>; P: <span class="html-italic">P. raciborskii</span>; M-PF: <span class="html-italic">M. aeruginosa</span> with filtrate from <span class="html-italic">P. raciborskii</span>; P-MF: <span class="html-italic">P. raciborskii</span> with the filtrate from <span class="html-italic">M. aeruginosa</span>.</p>
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<p>Effects of temperature and light on the growth of <span class="html-italic">Microcystis aeruginosa</span> and <span class="html-italic">Planktothricoides raciborskii</span> under laboratory conditions. Cyanobacteria culture and biomass assay was described in the <a href="#sec5dot8-toxins-16-00448" class="html-sec">Section 5.8</a>. (<b>A</b>) Effect of temperature (<b>a</b>) and light (<b>b</b>) on the growth of <span class="html-italic">M. aeruginosa</span>; (<b>B</b>) Effect of temperature (<b>a</b>) and light (<b>b</b>) on the growth of <span class="html-italic">P. raciborskii</span>.</p>
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<p>Effects of temperature and light on the production of cyanotoxin and odorous substances by <span class="html-italic">Microcystis aeruginosa</span> and <span class="html-italic">Planktothricoides raciborskii</span>, respectively. The total MC-LR and extracellular MC-LR from <span class="html-italic">M. aeruginosa</span> as well as the contents of total 2-MIB and extracellular 2-MIB from filamentous <span class="html-italic">P. raciborskii</span> were measured as described in <a href="#sec5dot3-toxins-16-00448" class="html-sec">Section 5.3</a> and <a href="#sec5dot4-toxins-16-00448" class="html-sec">Section 5.4</a>. (<b>A</b>) Effects of temperature on the production of the total MC-LR (<b>a</b>) and extracellular MC-LR (<b>b</b>) from <span class="html-italic">M. aeruginosa</span>; (<b>B</b>) Effects of light on the production of the total MC-LR (<b>a</b>) and extracellular MC-LR (<b>b</b>) from <span class="html-italic">M. aeruginosa</span>; (<b>C</b>) Effects of temperature on the production of the total 2-MIB (<b>a</b>) and extracellular 2-MIB (<b>b</b>) from <span class="html-italic">P. raciborskii</span>; (<b>D</b>) Effects of light on the production of the total 2-MIB (<b>a</b>) and extracellular 2-MIB (<b>b</b>) from <span class="html-italic">P. raciborskii</span>. MC-LR: microcystin-LR; 2-MIB: 2-methylisoborneol.</p>
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<p>Map of the sampling sites in Nanwan Reservoir.</p>
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19 pages, 1837 KiB  
Article
Nutrient Use Efficiency and Cucumber Productivity as a Function of the Nitrogen Fertilization Rate and the Wood Fiber Content in Growing Media
by Rita Čepulienė, Lina Marija Butkevičienė and Vaida Steponavičienė
Plants 2024, 13(20), 2911; https://doi.org/10.3390/plants13202911 (registering DOI) - 17 Oct 2024
Abstract
A peat substrate is made from peat from drained peatlands, which is a limited resource. A realistic estimate is that 50% of the world’s wetlands have been lost. Peat is used in horticulture, especially for the cultivation of vegetables in greenhouses. The consequences [...] Read more.
A peat substrate is made from peat from drained peatlands, which is a limited resource. A realistic estimate is that 50% of the world’s wetlands have been lost. Peat is used in horticulture, especially for the cultivation of vegetables in greenhouses. The consequences of peatland exploitation are an increase in the greenhouse effect and a decrease in carbon stocks. Wood fiber can be used as an alternative to peat. The chemical properties of growing media interact and change continuously due to the small volume of growing media, which is limited by the growing container. This study aims to gain new knowledge on the impact of nutrient changes in the microbial degradation of carbon compounds in wood fiber and mixtures with a peat substrate on the content and uptake of nutrients required by plants. The cucumber (Cucumis sativus L.) variety ‘Dirigent H’ developed in the Netherlands was cultivated in growing media of a peat substrate and wood fiber: (1) peat substrate (PS); (2) wood fiber (WF); (3) wood fiber and peat substrate 50/50 v/v (WF/PS 50/50); (4) wood fiber and peat substrate 25/75 v/v (WF/PS 25/75). The rates of fertilization were the following: (1) conventional fertilization (CF); (2) 13 g N per plant (N13); (3) 23 g N per plant (N23); (4) 30 g N per plant (N30). The experiment was carried out with three replications. As the amount of wood fiber increased, the humidity and pH of the growing media increased. The fertilization of the cucumbers with different quantities of nitrogen influenced the nutrient uptake. The plants grown in the 50/50 and 25/75 growing media had the best Cu uptake when fertilized with N23. When the plants grown in the wood fiber media and the 50/50 media were fertilized with N13, N23, and N30, the Mn content in the growing media at the end of the growing season was significantly lower than the Mn content in the media with conventional fertilization. Thus, nitrogen improved the uptake of Mn by the plants grown not only in the wood fiber, but also in the combinations with a peat substrate. Growing plants in wood fiber and fertilizing them with N13 can result in the optimum uptake of micronutrients. The number and biomass of cucumber fruits per plant were influenced by the amount of wood fiber in the growing media and the application of nitrogen fertilizer. The highest number of fruits and biomass of fruits per plant obtained were significantly higher when the cucumbers were grown in WF/PS 50/50 growing media with additional N13 fertilization. Full article
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Figure 1
<p>Experiment after transplanting cucumbers into growing containers and experimental design.</p>
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<p>Nitrogen fertilization during cucumber growing season (days after transplanting). N13, N23, and N30—additional nitrogen fertilization.</p>
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<p>PCA analysis shows the relationship between the physicochemical properties of the growing media with wood fiber and the productivity parameters for the cucumbers grown under different nitrogen fertilization rates. Note: the density of the growing media (D), the electrical conductivity of the growing media (EC), the moisture of the growing media (M), the aboveground dry biomass content of the plants (ADM), the aboveground fresh biomass content of the plants (AFM), the number of cucumbers per plant (CUN), the cucumber biomass per plant (CUM), conventional fertilization (CF), the fertilization rates (N<sub>13</sub>, N<sub>23</sub> and N<sub>30</sub>), and active variables (*).</p>
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<p>PCA analysis shows the relationship between the micronutrients on the growing media with wood fiber and the productivity parameters for the cucumbers under different nitrogen fertilization rates. Note: the density of the growing media (D), the electrical conductivity of the growing media (EC), the moisture of the growing media (M), the aboveground dry biomass content of the plants (ADM), the aboveground fresh biomass content of the plants (AFM), the number of cucumber fruits per plant (CUN), the number of cucumber fruits per plant (CUM), conventional fertilization (CF), the fertilization rates (N<sub>13</sub>, N<sub>23</sub> and N<sub>30</sub>), and active variables (*).</p>
Full article ">Figure 4 Cont.
<p>PCA analysis shows the relationship between the micronutrients on the growing media with wood fiber and the productivity parameters for the cucumbers under different nitrogen fertilization rates. Note: the density of the growing media (D), the electrical conductivity of the growing media (EC), the moisture of the growing media (M), the aboveground dry biomass content of the plants (ADM), the aboveground fresh biomass content of the plants (AFM), the number of cucumber fruits per plant (CUN), the number of cucumber fruits per plant (CUM), conventional fertilization (CF), the fertilization rates (N<sub>13</sub>, N<sub>23</sub> and N<sub>30</sub>), and active variables (*).</p>
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<p>PCA analysis shows the relationship between the N<sub>total</sub>, N<sub>min</sub>, and C:N in the growing media with wood fiber on the productivity parameters for the cucumbers with different nitrogen fertilization rates. The density of the growing media (D), the electrical conductivity of the growing media (EC), the moisture of the growing media (M), the aboveground dry biomass content of the plants (ADM), the aboveground fresh biomass content of the plants (AFM), the number of cucumber fruits per plant (CUN), the number of cucumber fruits per plant (CUM), conventional fertilization (CF), the fertilization rates (N<sub>13</sub>, N<sub>23</sub> and N<sub>30</sub>), and active variables (*).</p>
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21 pages, 8416 KiB  
Article
Exploring Seasonal Changes in Coastal Water Quality: Multivariate Analysis in Odisha and West Bengal Coast of India
by Pravat Ranjan Dixit, Muhammad Saeed Akhtar, Rakesh Ranjan Thakur, Partha Chattopadhyay, Biswabandita Kar, Dillip Kumar Bera, Sasmita Chand and Muhammad Kashif Shahid
Water 2024, 16(20), 2961; https://doi.org/10.3390/w16202961 (registering DOI) - 17 Oct 2024
Abstract
Marine pollution poses significant risks to both human and marine health. This investigation explores the limnological status of the Odisha and West Bengal coasts during the annual cruise program, focusing on the influence of riverine inputs on coastal marine waters. To assess this [...] Read more.
Marine pollution poses significant risks to both human and marine health. This investigation explores the limnological status of the Odisha and West Bengal coasts during the annual cruise program, focusing on the influence of riverine inputs on coastal marine waters. To assess this impact, physicochemical parameters such as pH, salinity, total suspended solids (TSS), dissolved oxygen (DO), biochemical oxygen demand (BOD), and dissolved nutrients (NO2-N, NO3-N, NH4-N, PO4-P, SiO4-Si, total-N, and total-P) were analyzed from samples collected along 11 transects. Multivariate statistics and principal component analysis (PCA) were applied to the datasets, revealing four key factors that account for over 70.09% of the total variance in water quality parameters, specifically 25.01% for PC1, 21.94% for PC2, 13.13% for PC3, and 9.99% for PC4. The results indicate that the increase in nutrient and suspended solid concentrations in coastal waters primarily arises from weathering and riverine transport from natural sources, with nitrate sources linked to the decomposition of organic materials. Coastal Odisha was found to be rich in phosphorus-based nutrients, particularly from industrial effluents in Paradip and the Mahanadi, while ammonia levels were attributed to municipal waste in Puri. In contrast, the West Bengal coast exhibited higher levels of nitrogenous nutrients alongside elevated pH and DO values. These findings provide a comprehensive understanding of the seasonal dynamics and anthropogenic influences on coastal water quality in Odisha and West Bengal, highlighting the need for targeted conservation and management efforts. Full article
(This article belongs to the Section Oceans and Coastal Zones)
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Figure 1
<p>Sampling location of 11 different transects along Odisha and West Bengal Coasts.</p>
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<p>Contours showing variation in salinity in PSU unit.</p>
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<p>Contours showing variation in dissolved oxygen in mg/L unit by SURFER Analysis method.</p>
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<p>Contours showing variation in BOD in mg/L by SURFER Analysis.</p>
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<p>Contours showing variation in dissolved nitrite (µmol/L) by SURFER Analysis method.</p>
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<p>Contours showing variation in nitrate in µmol/L unit by SURFER Analysis method.</p>
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<p>Contours showing variation in ammonia in µmol/L unit by SURFER Analysis method.</p>
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<p>Contours showing variation in total nitrogenin µmol/L unit by SURFER Analysis method.</p>
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<p>Contours showing variation in Inorganic phosphatein µmol/L unit by SURFER Analysis method.</p>
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<p>Contours showing variation in total phosphorous in µmol/L unit by SURFER Analysis method.</p>
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<p>Contours showing variation in silicate in µmol/L unit by SURFER Analysis method.</p>
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<p>Contours showing variation in Chlorophyll-a in mg/L unit by SURFER Analysis method.</p>
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<p>Scree plot for components with its eigenvalue.</p>
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<p>Linear regression analysis: (<b>a</b>) Suspended solids and Chl-a; (<b>b</b>) BOD and Salinity; (<b>c</b>) Suspended solids and Salinity; (<b>d</b>) Nitrite and Salinity; (<b>e</b>) Chl-a and Salinity; (<b>f</b>) Nitrate and Salinity; (<b>g</b>) DO and Salinity; (<b>h</b>) Ammonia and Salinity; (<b>i</b>) Inorganic Phosphate and Salinity; (<b>j</b>) Silicate and Salinity.</p>
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17 pages, 2864 KiB  
Article
Organic Mulching Versus Soil Conventional Practices in Vineyards: A Comprehensive Study on Plant Physiology, Agronomic, and Grape Quality Effects
by Andreu Mairata, David Labarga, Miguel Puelles, Luis Rivacoba, Javier Portu and Alicia Pou
Agronomy 2024, 14(10), 2404; https://doi.org/10.3390/agronomy14102404 (registering DOI) - 17 Oct 2024
Abstract
Research into alternative vineyard practices is essential to maintain long-term viticulture sustainability. Organic mulching on the vine row improves vine cultivation properties, such as increasing soil water retention and nutrient availability. This study overviewed the effects of three organic mulches (spent mushroom compost [...] Read more.
Research into alternative vineyard practices is essential to maintain long-term viticulture sustainability. Organic mulching on the vine row improves vine cultivation properties, such as increasing soil water retention and nutrient availability. This study overviewed the effects of three organic mulches (spent mushroom compost (SMC), straw (STR), and grapevine pruning debris (GPD)) and two conventional soil practices (herbicide application (HERB) and tillage (TILL)) on grapevine physiology, agronomy, and grape quality parameters over three years. SMC mulch enhanced soil moisture and nutrient concentration. However, its mineral composition increased soil electrical conductivity (0.78 dS m⁻1) and induced grapevine water stress due to osmotic effects without significantly affecting yield plant development. Only minor differences in leaf physiological parameters were observed during the growing season. However, straw (STR) mulch reduced water stress and increased photosynthetic capacity, resulting in higher pruning weights. Organic mulches, particularly SMC and STR, increased grape pH, potassium, malic acid, and tartaric acid levels, while reducing yeast assimilable nitrogen. The effect of organic mulching on grapevine development depends mainly on soil and mulch properties, soil water availability, and environmental conditions. This research highlights the importance of previous soil and organic mulch analysis to detect vineyard requirements and select the most appropriate soil management treatment. Full article
(This article belongs to the Section Horticultural and Floricultural Crops)
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Figure 1

Figure 1
<p>Summary of field climatic conditions. (<b>A</b>) Monthly accumulated precipitation (mm) in 2020, 2021, and 2022 and average monthly precipitation in 2005–2019. (<b>B</b>) Annual accumulated precipitation (P, mm), reference evapotranspiration (ET0, mm), and growing degree days (GDDs, °C day<sup>−1</sup>).</p>
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<p>Average soil volumetric water content (VWC, %) and standard deviations of the soil under SMC (<span class="html-fig-inline" id="agronomy-14-02404-i001"><img alt="Agronomy 14 02404 i001" src="/agronomy/agronomy-14-02404/article_deploy/html/images/agronomy-14-02404-i001.png"/></span>), STR (<span class="html-fig-inline" id="agronomy-14-02404-i002"><img alt="Agronomy 14 02404 i002" src="/agronomy/agronomy-14-02404/article_deploy/html/images/agronomy-14-02404-i002.png"/></span>), GPD (<span class="html-fig-inline" id="agronomy-14-02404-i003"><img alt="Agronomy 14 02404 i003" src="/agronomy/agronomy-14-02404/article_deploy/html/images/agronomy-14-02404-i003.png"/></span>), HERB (<span class="html-fig-inline" id="agronomy-14-02404-i004"><img alt="Agronomy 14 02404 i004" src="/agronomy/agronomy-14-02404/article_deploy/html/images/agronomy-14-02404-i004.png"/></span>), and TILL (<span class="html-fig-inline" id="agronomy-14-02404-i005"><img alt="Agronomy 14 02404 i005" src="/agronomy/agronomy-14-02404/article_deploy/html/images/agronomy-14-02404-i005.png"/></span>) through the day of year (DOY) of the vine vegetative cycle in 2021 (<b>A1</b>–<b>A3</b>) and 2022 (<b>B1</b>–<b>B3</b>) at three soil depths: 5 (1), 15 (2), and 25 (3) cm. Precipitation events (mm) are represented by blue columns (<span class="html-fig-inline" id="agronomy-14-02404-i006"><img alt="Agronomy 14 02404 i006" src="/agronomy/agronomy-14-02404/article_deploy/html/images/agronomy-14-02404-i006.png"/></span>). Black lines indicate the phenology stages of flowering (F), fruit set (S), veraison (V), and grape maturity (M) and the day when leaf gas exchange parameters and water potentials were recorded. The treatment soil water content was monitored with three field devices.</p>
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<p>The mean and standard deviation of grape carbon isotope discrimination (δ<sup>13</sup>C) of the soil management treatments studied. Statistical differences indicated by letters were accepted when <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Must and grape physical-chemical parameters of the five soil management treatments from 2020 to 2022: (<b>A</b>) total soluble solids (Brix), (<b>B</b>) pH, (<b>C</b>) total acidity (TA), (<b>D</b>) tartaric acid, (<b>E</b>) malic acid, (<b>F</b>) potassium, (<b>G</b>) yeast assimilable nitrogen (YAN), (<b>H</b>) berry weight, (<b>I</b>) anthocyanins, and (<b>J</b>) total polyphenol index at 280 nm (TPI). Significant differences between soil management treatments are represented by letters when <span class="html-italic">p</span> &lt; 0.05 (n.s. = non-significant differences).</p>
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18 pages, 2059 KiB  
Article
Myrciaria jaboticaba Fruit Peel: Bioactive Composition as Determined by Distinct Harvest Seasons and In Vitro Anti-Cancer Activity
by Roberto de Paula do Nascimento, Julia Soto Rizzato, Gabriele Polezi, Hatim Boughanem, Non Gwenllian Williams, Renata Galhardo Borguini, Manuela Cristina Pessanha de Araujo Santiago, Mario Roberto Marostica Junior and Lee Parry
Plants 2024, 13(20), 2907; https://doi.org/10.3390/plants13202907 (registering DOI) - 17 Oct 2024
Abstract
Jaboticaba (Myrciaria jaboticaba) is a recognizable and unique crop from Brazil. The fruit’s byproducts are currently being studied, given their bioactive composition and promising anti-cancer potential. It is not evident, however, if different harvesting seasons can modify the chemical profile and [...] Read more.
Jaboticaba (Myrciaria jaboticaba) is a recognizable and unique crop from Brazil. The fruit’s byproducts are currently being studied, given their bioactive composition and promising anti-cancer potential. It is not evident, however, if different harvesting seasons can modify the chemical profile and antioxidant capacity of jaboticaba fruit fractions. Furthermore, as there is limited data for jaboticaba’s anti-proliferative effects, additional assessments are required to improve the robustness of these findings. Therefore, this study aimed to determine the composition of the peel of jaboticaba collected in two periods (May—off-season, sample 1—and August–October—peak season, sample 2) and test the peel’s richest anthocyanin sample against colorectal cancer (CRC) cell lines. To accomplish this, proximate, spectrophotometric, and chromatographic analyses were performed in two freeze-dried samples; and anti-proliferative and/or colony-forming assays were carried out in Caco-2, HT29, and HT29-MTX cells. As a result, sample 2 showed the highest levels of polyphenols overall, including flavonoids and anthocyanins. This sample displayed significative higher contents of cyanidin-3-O-glucoside (48%) and delphinidin-3-O-glucoside (105%), in addition to a superior antioxidant capacity (23% higher). Sample 1 showed higher amounts of total protein, gallic acid (20% higher), and specific carotenoids. An aqueous extract from sample 2 was tested against CRC, showing anti-proliferative effects for Caco-2 cells at 1 and 2 mg/mL concentrations, with IC50 values of 1.2–1.3 mg/mL. Additionally, the extract was able to inhibit cell colony formation when tested at both low and high concentrations. In conclusion, jaboticaba collected in the main season stands out regarding its polyphenol composition and holds potential against cancer cell growth. Full article
(This article belongs to the Special Issue Toxicity and Anticancer Activities of Natural Products from Plants)
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<p><b>Proximate and spectrophotometric composition differences between two samples of jaboticaba peel powder.</b> Except for moisture, all results are in dry weight of jaboticaba peel powder. Sample 1: jaboticaba collected in May; sample 2: jaboticaba collected in August–October. Data are represented by mean ± standard deviation (SD). Student’s <span class="html-italic">t</span>-test (Welch’s correction, two-tailed); the asterisk symbol (*) indicates statistical difference (<span class="html-italic">p</span> &lt; 0.05) between samples 1 and 2. (<b>A</b>) Proximate composition. (<b>B</b>) Spectrophotometric composition. (<b>C</b>) Antioxidant capacity. Abbreviations: FRAP—ferric-reducing antioxidant power; MA—monomeric anthocyanins; ORAC—oxygen radical absorbance capacity; TE—Trolox equivalent; TF—total flavonoids; TPC—total phenolic content.</p>
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<p><b>Anthocyanin chromatograms of jaboticaba peel samples and standards.</b> The analyses were carried out in a high-performance liquid chromatography coupled with a diode array detector. (<b>A</b>) Jaboticaba peel, sample 1 (May). (<b>B</b>) Jaboticaba peel, sample 2 (August–October). (<b>C</b>) Delphinidin-3-<span class="html-italic">O</span>-glucoside standard. (<b>D</b>) Cyanidin-3-<span class="html-italic">O</span>-glucoside standard.</p>
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<p><b>Chromatographic composition differences between two samples of jaboticaba peel powder.</b> The analyses were carried out in a high-performance liquid chromatography coupled with a diode array detector. All results are in dry weight of jaboticaba peel powder. Sample 1: jaboticaba collected in May; sample 2: jaboticaba collected in August–October. Data are represented by mean ± standard deviation (SD). Student’s <span class="html-italic">t</span>-test (Welch’s correction, two-tailed); the asterisk symbol (*) indicates statistical difference (<span class="html-italic">p</span> &lt; 0.05) between samples 1 and 2. (<b>A</b>) The composition of jaboticaba peel’s main polyphenols. The values for ellagic acid represent the sum of free and hydrolyzed fractions. The values for gallic acid represent the hydrolyzed fraction. (<b>B</b>) Other flavonoids and phenolic acids composition. The values for catechin, quercetin, rutin, and protocatechuic acid represent the sum of free and hydrolyzed fractions. The values for epicatechin, 4-hydroxybenzoic acid, ferulic acid, and <span class="html-italic">p</span>-coumaric acid represent the hydrolyzed fraction. The values for syringic acid represent the free fraction. (<b>C</b>) Carotenoids’ composition. Abbreviations: C3G—cyanidin-3-<span class="html-italic">O</span>-glucoside; D3G—delphinidin-3-<span class="html-italic">O</span>-glucoside; C—catechin; CA—<span class="html-italic">p</span>-coumaric acid; EA—ellagic acid; FA—ferulic acid; GA—gallic acid; HA—4-hydroxybenzoic acid; PA—protocatechuic acid; Q—quercetin; R—rutin; SA—syringic acid.</p>
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<p><b>The anti-proliferative activity of jaboticaba peel extract (August–October sample) on colorectal cancer cell lines.</b> Seeding: 10,000 cells per well. Extract concentrations: 1/control (0), 2 (0.025), 3 (0.05), 4 (0.1), 5 (0.5), 6 (1), and 7 (2 mg/mL). Cell death/survival was measured by the MTT assay. Data are represented by mean ± standard deviation (SD). One-way ANOVA followed by Tukey; the asterisk symbol (*) indicates statistical difference (<span class="html-italic">p</span> &lt; 0.05) (the jaboticaba treatments were compared to the control concentration). (<b>A</b>) Survival of Caco-2 cells after exposure for 24 or 48 h of jaboticaba peel extract. (<b>B</b>) The survival of HT29 and HT29-MTX cells after exposure for 24 h of jaboticaba peel extract.</p>
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<p><b>The anti-colony-forming activity of jaboticaba peel extract (August–October sample) on Caco-2 cells.</b> The crystal violet protocol was applied to observe cell colony formation. (<b>A</b>) First assay. Seeding: 500 cells per well. Extract concentrations: 0, 0.5, 1, and 2 mg/mL (6-well plate). (<b>B</b>) Second assay. Seeding: 1000 cells per well. Extract concentrations: 0, 0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 1, 1.5, and 2 mg/mL.</p>
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19 pages, 7476 KiB  
Article
Cyclic and Multi-Year Characterization of Surface Ozone at the WMO/GAW Coastal Station of Lamezia Terme (Calabria, Southern Italy): Implications for Local Environment, Cultural Heritage, and Human Health
by Francesco D’Amico, Daniel Gullì, Teresa Lo Feudo, Ivano Ammoscato, Elenio Avolio, Mariafrancesca De Pino, Paolo Cristofanelli, Maurizio Busetto, Luana Malacaria, Domenico Parise, Salvatore Sinopoli, Giorgia De Benedetto and Claudia Roberta Calidonna
Environments 2024, 11(10), 227; https://doi.org/10.3390/environments11100227 (registering DOI) - 17 Oct 2024
Abstract
Unlike stratospheric ozone (O3), which is beneficial for Earth due to its capacity to screen the surface from solar ultraviolet radiation, tropospheric ozone poses a number of health and environmental issues. It has multiple effects that drive anthropogenic climate change, ranging [...] Read more.
Unlike stratospheric ozone (O3), which is beneficial for Earth due to its capacity to screen the surface from solar ultraviolet radiation, tropospheric ozone poses a number of health and environmental issues. It has multiple effects that drive anthropogenic climate change, ranging from pure radiative forcing to a reduction of carbon sequestration potential in plants. In the central Mediterranean, which itself represents a hotspot for climate studies, multi-year data on surface ozone were analyzed at the Lamezia Terme (LMT) WMO/GAW coastal observation site, located in Calabria, Southern Italy. The site is characterized by a local wind circulation pattern that results in a clear differentiation between Western-seaside winds, which are normally depleted in pollutants and GHGs, and Northeastern-continental winds, which are enriched in these compounds. This study is the first detailed attempt at evaluating ozone concentrations at LMT and their correlations with meteorological parameters, providing new insights into the source of locally observed tropospheric ozone mole fractions. This research shows that surface ozone daily and seasonal patterns at LMT are “reversed” compared to the patterns observed by comparable studies applied to other parameters and compounds, thus confirming the general complexity of anthropogenic emissions into the atmosphere and their numerous effects on atmospheric chemistry. These observations could contribute to the monitoring and verification of new regulations and policies on environmental protection, cultural heritage preservation, and the mitigation of human health hazards in Calabria. Full article
(This article belongs to the Special Issue Advances in Urban Air Pollution: 2nd Edition)
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<p>(<b>A</b>) Location of Lamezia Terme’s observation site (LMT) in the Mediterranean basin. (<b>B</b>) DEM (Digital Elevation Model) shows the location of LMT in central Calabria and the key orographic features of the Catanzaro isthmus that play a major role in local wind circulation. Additional maps and details showing the observation site itself and local emission sources are available in D’Amico et al. (2024a, 2024b, 2024c) [<a href="#B87-environments-11-00227" class="html-bibr">87</a>,<a href="#B88-environments-11-00227" class="html-bibr">88</a>,<a href="#B89-environments-11-00227" class="html-bibr">89</a>].</p>
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<p>(<b>A</b>) Location of Lamezia Terme’s observation site (LMT) in the Mediterranean basin. (<b>B</b>) DEM (Digital Elevation Model) shows the location of LMT in central Calabria and the key orographic features of the Catanzaro isthmus that play a major role in local wind circulation. Additional maps and details showing the observation site itself and local emission sources are available in D’Amico et al. (2024a, 2024b, 2024c) [<a href="#B87-environments-11-00227" class="html-bibr">87</a>,<a href="#B88-environments-11-00227" class="html-bibr">88</a>,<a href="#B89-environments-11-00227" class="html-bibr">89</a>].</p>
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<p>Wind rose of frequency counts and wind speed thresholds, based on hourly data gathered at LMT between 2015 and 2023. Each bar has an angle of 8 degrees. Calm refers to instances of 0 m/s, that have never occurred (0%) during the observation period.</p>
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<p>Main characteristics of daily patterns as observed at the LMT observation site between 2015 and 2023. All data refer to hourly aggregations. (<b>A</b>) Ozone mole fractions are grouped on a yearly basis (2022 and 2023 are excluded due to their lower coverage rate, as shown in <a href="#environments-11-00227-t001" class="html-table">Table 1</a>). (<b>B</b>) Average hourly concentrations of ozone, differentiated by season. (<b>C</b>) Seasonal changes in temperatures.</p>
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<p>Smoothed seasonal percentile rose plots showing hourly variations in ozone concentration thresholds by wind direction. Shaded areas refer to percentiles, while the radius refers to observed mole fractions in ppb.</p>
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<p>Correlation between wind speeds and ozone mole fractions, divided by sector. (<b>A</b>) Western-seaside (240–300° N); (<b>B</b>) Northeastern-continental (0–90° N); (<b>C</b>) total data.</p>
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<p>Evaluation of the OWE (Ozone Weekend Effect) based on hourly ozone data gathered at LMT, differentiated by weekdays. The dotted horizontal line represents average concentrations. (<b>A</b>) Western-seaside (240–300° N); (<b>B</b>) Northeastern-continental (0–90° N); (<b>C</b>) total data.</p>
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<p>(<b>A</b>) Multi-year variability of surface ozone mole fractions at LMT. The years 2022 and 2023 are not shown due to their lower coverage rate. (<b>B</b>) yearly cycle with monthly averages differentiated by wind corridor. (<b>C</b>) differentiated monthly averages referring to the entire observation period (2015–2023).</p>
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<p>(<b>A</b>) Multi-year variability of surface ozone mole fractions at LMT. The years 2022 and 2023 are not shown due to their lower coverage rate. (<b>B</b>) yearly cycle with monthly averages differentiated by wind corridor. (<b>C</b>) differentiated monthly averages referring to the entire observation period (2015–2023).</p>
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13 pages, 921 KiB  
Article
Seasonal Variations in Multiple Sclerosis Relapses in Oman: A Single Tertiary Centre Experience
by Rashid Al-Shibli, Abdullah Al-Asmi, M. Mazharul Islam, Fatema Al Sabahi, Amira Al-Aamri, Mehwish Butt, Meetham Al-Lawati, Lubna Al-Hashmi and Jihad Al-Yahmadi
Int. J. Environ. Res. Public Health 2024, 21(10), 1371; https://doi.org/10.3390/ijerph21101371 (registering DOI) - 17 Oct 2024
Abstract
(1) Background and Aims: The seasonal factors influencing multiple sclerosis (MS) relapses remain elusive. This study aims to investigate the seasonal variation of MS relapses in Oman and compare it globally. (2) Subject and Methods: This retrospective study was conducted on N = [...] Read more.
(1) Background and Aims: The seasonal factors influencing multiple sclerosis (MS) relapses remain elusive. This study aims to investigate the seasonal variation of MS relapses in Oman and compare it globally. (2) Subject and Methods: This retrospective study was conducted on N = 183 Omani MS patients treated at Sultan Qaboos University Hospital, a tertiary hospital in Muscat, Oman, over sixteen-year period (2007–2022). Demographic and clinical data of all MS patients were juxtaposed with the monthly weather data during this period, using descriptive and inferential statistical techniques. (3) Results: Among the N = 183 MS patients studied, 508 relapses were recorded during the study period. The average number of relapses per patient was 2.8 (range: 1–15). There were significant seasonal variations in MS relapse rate, with the highest prevalence in the winter months of January and February. However, no correlation was found between MS relapses and other climatic parameters (humidity, temperature, and rainfall). (4) Conclusion: The seasonal patterns of MS relapses in Oman differ from other parts of the world, which the local clinicians should take into account while diagnosing and making management decisions. The potential impact of climate change on the anomalous changes in the seasonality of MS relapses warrants further investigation. Full article
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<p>Distribution of multiple sclerosis patients by number of relapses per patient during 2007–2022. (N = 183).</p>
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<p>(<b>a</b>) Bar chart of monthly variations in total MS relapses among Omani patients, 2007–2022. (<b>b</b>) Radar chart showing the proportion of total monthly MS relapses versus expected relapses, 2007–2022.</p>
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<p>Bar chart showing the frequency of multiple sclerosis relapses per month combined with three-line graphs showing monthly average humidity, temperature, and rainfall in Oman during 2007–2022.</p>
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18 pages, 4283 KiB  
Article
Global Warming and Fish Diversity Changes in the Po River (Northern Italy)
by Anna Gavioli, Giuseppe Castaldelli, Stefania Trasforini, Cesare Puzzi, Maria Pia Gervasio, Tommaso Granata, Daniela Colombo and Elisa Soana
Environments 2024, 11(10), 226; https://doi.org/10.3390/environments11100226 (registering DOI) - 17 Oct 2024
Abstract
In the context of climate change, the current rise in temperature, changes in precipitation, and extreme weather events are exceptional and impact biodiversity. Using the Mann–Kendall trend test, change-point analysis, and linear mixed models, we investigated the long-term trends (1978–2022) of water temperature [...] Read more.
In the context of climate change, the current rise in temperature, changes in precipitation, and extreme weather events are exceptional and impact biodiversity. Using the Mann–Kendall trend test, change-point analysis, and linear mixed models, we investigated the long-term trends (1978–2022) of water temperature and flow in the Po River, Italy’s largest river, and examined changes in the fish community over the same period. Our findings indicate that the daily water temperature of the Po River increased by ~4 °C from 1978 to 2022, with a significant rise starting in 2005. The river’s daily discharge showed higher variability and decreased from 2003 onwards. The number of days per year with water temperatures above the summer average increased steadily by 1 day per year, resulting in over 40 additional days with above-average temperatures in the last four decades. The number of summer days above the seasonal average water temperature was the most influential factor affecting fish diversity. Total fish species richness and native species richness significantly decreased between 1978 and 2022 with the increasing number of days above the summer average water temperature, while non-native species increased. Our results demonstrate that the Po River is experiencing significant impacts from global warming, affecting freshwater communities. Full article
(This article belongs to the Special Issue Environmental Risk Assessment of Aquatic Ecosystem)
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Graphical abstract
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<p>Daily water temperature (<b>a</b>,<b>b</b>) and flows (<b>c</b>,<b>d</b>) data of the Po River from 1978 to 2022, collected in its middle section. In (<b>b</b>,<b>d</b>), statistically significant trends are also shown by blue dashed lines, with the point of change marked by a red vertical bar.</p>
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<p>Trends in annual water temperature and water flow from 1978 to 2022 in the Po River are registered in its middle section. Blue-dashed lines show statistically significant trends: (<b>a</b>) annual mean water temperature, (<b>b</b>) annual maximum and (<b>c</b>) annual minimum water temperature, (<b>d</b>) number of winter days and (<b>e</b>) number of summer days above the seasonal temperature mean, (<b>f</b>) annual mean and minimum discharge, and (<b>g</b>) summer mean discharge. Statistical significance (<span class="html-italic">p</span>-value) and R<sup>2</sup> of the models are also shown.</p>
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<p>Traffic-light plot representing the fish species sampled in the middle reaches of Po River: green represents the species presence and orange represents the species absence in the sampling year. The scientific name, and native, and non-native statuses are also given.</p>
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<p>Summary of estimates for the best correlating models: (<b>a</b>) the total fish richness (model M4), (<b>b</b>) the native fish richness (model N4), and (<b>c</b>) the non-native fish richness (model E4). Gray indicates a negative effect, and black indicates a positive effect. The year of sampling was included as a random effect. (Summer_days = Days above the summer season mean). Significance is also shown: *** <span class="html-italic">p</span>-values &lt; 0.001, * <span class="html-italic">p</span>-values &lt; 0.05.</p>
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16 pages, 2439 KiB  
Article
Enhancing Mango Productivity with Wood Vinegar, Humic Acid, and Seaweed Extract Applications as an Environmentally Friendly Strategy
by Mahmoud Abdel-Sattar, Laila Y. Mostafa and Hail Z. Rihan
Sustainability 2024, 16(20), 8986; https://doi.org/10.3390/su16208986 (registering DOI) - 17 Oct 2024
Abstract
Although chemical fertilization has gained a lot of attention due to its ability to increase the yield of fruit trees, it has been known to cause numerous environmental problems such as soil deterioration, alleviating beneficial microorganisms, and reducing fruit quality and safety. Hence, [...] Read more.
Although chemical fertilization has gained a lot of attention due to its ability to increase the yield of fruit trees, it has been known to cause numerous environmental problems such as soil deterioration, alleviating beneficial microorganisms, and reducing fruit quality and safety. Hence, today, we aim to reduce these problems by using eco-friendly and sustainable biostimulants to promote nutritional status, yield, and quality. The effect of wood vinegar (WV) on mango production has yet to be investigated. Therefore, a field trial was conducted during the 2023 and 2024 seasons to evaluate the regulatory effect of individual and combined application of wood vinegar (WV), seaweed extract (SW), and humic acid (HA) on the performance of mango (Mangifera indica L.) cv. Ewais. The results revealed that all treatments had a pronounced effect and significantly improved the total chlorophyll content (107.7 and 106.6%), leaf N (2.02 and 2.23%), P (0.38 and 0.4), and K (1.07 and 1.13%), as well as enhancing the quality of mango fruits by increasing fruit length (11.68 and 12.38 cm), fruit width (7.8 and 8.59 cm), total sugars (40 and 37.3%), and TSS (21.9 and 20.8%) while reducing the total acidity (64.3 and 69.0%) in the 2023 and 2024 seasons, respectively, compared with the control. Based on this study, the treatment of 2 L/ha seaweed + 2 L/ha humic acid + 2 L/ha wood vinegar combined had the greatest effect on enhancing Ewais mango fruit yield by up-regulating leaf mineral acquisition, antioxidant response, and sugar accumulation. This study supports the application of HA and SW in combination with WV to improve mango fruit yield and quality. Full article
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<p>The impact of humic acid (HA), seaweed extract (SW), and wood vinegar (WV) applied to leaf chlorophyll (chlorophyll a (<b>A</b>), chlorophyll b (<b>B</b>), total chlorophyll (<b>C</b>), and total carbohydrate (<b>D</b>) content of Ewais mango trees assessed for the 2023 and 2024 seasons. Data are presented as means ± SE.</p>
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<p>Effect of applying humic acid (HA), seaweed extract (SW), and wood vinegar (WV) to soil on the yield of Ewais mango trees in the 2023 and 2024 seasons. Data are presented as means ± SE.</p>
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<p>Effect of applying humic acid (HA), seaweed extract (SW), and wood vinegar (WV) to soil on TSS (<b>A</b>), acidity (<b>B</b>), TSS/acidity ratio (<b>C</b>), and vitamin C (<b>D</b>) of Ewais mango trees in the 2023 and 2024 seasons.</p>
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<p>Effect of applying humic acid (HA), seaweed extract (SW), and wood vinegar (WV) to soil on total sugar (<b>A</b>), reducing sugar (<b>B</b>), non-reducing sugar (<b>C</b>), and total carotenoids (<b>D</b>) of Ewais mango trees in the 2023 and 2024 seasons.</p>
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29 pages, 9650 KiB  
Article
Seasonal Variations in the Rainfall Kinetic Energy Estimation and the Dual-Polarization Radar Quantitative Precipitation Estimation Under Different Rainfall Types in the Tianshan Mountains, China
by Yong Zeng, Lianmei Yang, Zepeng Tong, Yufei Jiang, Abuduwaili Abulikemu, Xinyu Lu and Xiaomeng Li
Remote Sens. 2024, 16(20), 3859; https://doi.org/10.3390/rs16203859 (registering DOI) - 17 Oct 2024
Abstract
Raindrop size distribution (DSD) has an essential effect on rainfall kinetic energy estimation (RKEE) and dual-polarization radar quantitative precipitation estimation (QPE); DSD is a key factor for establishing a dual-polarization radar QPE scheme and RKEE scheme, particularly in mountainous areas. To improve the [...] Read more.
Raindrop size distribution (DSD) has an essential effect on rainfall kinetic energy estimation (RKEE) and dual-polarization radar quantitative precipitation estimation (QPE); DSD is a key factor for establishing a dual-polarization radar QPE scheme and RKEE scheme, particularly in mountainous areas. To improve the understanding of seasonal DSD-based RKEE, dual-polarization radar QPE, and the impact of rainfall types and classification methods, we investigated RKEE schemes and dual-polarimetric radar QPE algorithms across seasons and rainfall types based on two classic classification methods (BR09 and BR03) and DSD data from a disdrometer in the Tianshan Mountains during 2020–2022. Two RKEE schemes were established: the rainfall kinetic energy flux–rain rate (KEtimeR) and the rainfall kinetic energy content–mass-weighted mean diameter (KEmmDm). Both showed seasonal variation, whether it was stratiform rainfall or convective rainfall, under BR03 and BR09. Both schemes had excellent performance, especially the KEmmDm relationship across seasons and rainfall types. In addition, four QPE schemes for dual-polarimetric radar—R(Kdp), R(Zh), R(Kdp,Zdr), and R(Zh,Zdr)—were established, and exhibited characteristics that varied with season and rainfall type. Overall, the performance of the single-parameter algorithms was inferior to that of the double-parameter algorithms, and the performance of the R(Zh) algorithm was inferior to that of the R(Kdp) algorithm. The results of this study show that it is necessary to consider different rainfall types and seasons, as well as classification methods of rainfall types, when applying RKEE and dual-polarization radar QPE. In this process, choosing a suitable estimator—KEtime(R), KEmm(Dm), R(Kdp), R(Zh), R(Kdp,Zdr), or R(Zh,Zdr)—is key to improving the accuracy of estimating the rainfall KE and R. Full article
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<p>(<b>a</b>) Topography (m) and location of the Tianshan Mountains, and (<b>b</b>) locations of Zhaosu (red dot) and Xinyuan (black dot; Zeng et al. [<a href="#B55-remotesensing-16-03859" class="html-bibr">55</a>]).</p>
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<p>(<b>a</b>) Topography (m) and location of the Tianshan Mountains, and (<b>b</b>) locations of Zhaosu (red dot) and Xinyuan (black dot; Zeng et al. [<a href="#B55-remotesensing-16-03859" class="html-bibr">55</a>]).</p>
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<p>Seasonal variations in the distributions of (<b>a</b>) <span class="html-italic">KE<sub>time</sub></span> and (<b>b</b>) <span class="html-italic">KE<sub>mm</sub></span> at Zhaosu.</p>
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<p>Scatterplots of <span class="html-italic">KE<sub>time</sub></span> vs. <span class="html-italic">R</span> for the entire data and the fitted <span class="html-italic">KE<sub>time</sub></span>–<span class="html-italic">R</span> relationship across seasons at Zhaosu. Dashed lines represent the <span class="html-italic">KE<sub>time</sub></span>–<span class="html-italic">R</span> relationship reported by Zeng et al. [<a href="#B55-remotesensing-16-03859" class="html-bibr">55</a>], Seela et al. [<a href="#B83-remotesensing-16-03859" class="html-bibr">83</a>], and Wu et al. [<a href="#B36-remotesensing-16-03859" class="html-bibr">36</a>].</p>
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<p>Scatterplots of <span class="html-italic">KE<sub>mm</sub></span> vs. <span class="html-italic">D<sub>m</sub></span> for the entire data and the seasonal variation in fitted <span class="html-italic">KE<sub>mm</sub></span>–<span class="html-italic">D<sub>m</sub></span> at Zhaosu. Dashed lines represent the <span class="html-italic">KE<sub>mm</sub></span>–<span class="html-italic">D<sub>m</sub></span> relationship reported by Zeng et al. [<a href="#B55-remotesensing-16-03859" class="html-bibr">55</a>] and Seela et al. [<a href="#B83-remotesensing-16-03859" class="html-bibr">83</a>].</p>
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<p>Scatterplot of estimated <span class="html-italic">KE<sub>time</sub></span> from RKEE schemes versus <span class="html-italic">KE<sub>time</sub></span> calculated from DSD for (<b>a</b>) the entire data, (<b>b</b>) spring, (<b>c</b>) summer, and (<b>d</b>) fall at Zhaosu. Scatterplot of estimated <span class="html-italic">KE<sub>mm</sub></span> from RKEE schemes versus the <span class="html-italic">KE<sub>mm</sub></span> calculated from DSD for (<b>e</b>) the entire data, (<b>f</b>) spring, (<b>g</b>) summer, and (<b>h</b>) fall at Zhaosu in Tianshan Mountains. Black dashed lines represent the 1:1 relationship.</p>
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<p>Violin plots of seasonal variations in <span class="html-italic">KE<sub>time</sub></span> under (<b>a</b>) BR09_S, (<b>c</b>) BR09_C, (<b>e</b>) BR03_S, and (<b>g</b>) BR03_C, and violin plots of seasonal variations in <span class="html-italic">KE<sub>mm</sub></span> under (<b>b</b>) BR09_S, (<b>d</b>) BR09_C, (<b>f</b>) BR03_S, and (<b>h</b>) BR03_C at Zhaosu.</p>
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<p>Scatterplots of <span class="html-italic">KE<sub>time</sub></span> vs. <span class="html-italic">R</span> for the entire data and the seasonal variation of the fitted <span class="html-italic">KE<sub>time</sub></span>–<span class="html-italic">R</span> relationship at Zhaosu under (<b>a</b>) BR09_S, (<b>b</b>) BR09_C, (<b>c</b>) BR03_S, and (<b>d</b>) BR03_C.</p>
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<p>Scatterplot of estimated <span class="html-italic">KE<sub>time</sub></span> from RKEE schemes versus <span class="html-italic">KE<sub>time</sub></span> calculated from DSD for (<b>a</b>) the entire data, (<b>e</b>) spring, (<b>i</b>) summer, and (<b>m</b>) fall under BR09_S; those for (<b>b</b>) the entire data, (<b>f</b>) spring, (<b>j</b>) summer, and (<b>n</b>) fall under BR09_C; those for (<b>c</b>) the entire data, (<b>g</b>) spring, (<b>k</b>) summer, and (<b>o</b>) fall under BR03_S; and those for (<b>d</b>) the entire data, (<b>h</b>) spring, and (<b>l</b>) summer under BR03_C at Zhaosu. Black dashed lines represent the 1:1 relationship.</p>
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<p>Scatterplots of <span class="html-italic">KE<sub>mm</sub></span> vs. <span class="html-italic">D<sub>m</sub></span> for the entire data and the fitted <span class="html-italic">KE<sub>mm</sub></span>–<span class="html-italic">D<sub>m</sub></span> relationship across seasons at Zhaosu under (<b>a</b>) BR09_S, (<b>b</b>) BR09_C, (<b>c</b>) BR03_S, and (<b>d</b>) BR03_C.</p>
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<p>Scatterplot of estimated <span class="html-italic">KE<sub>mm</sub></span> from RKEE schemes versus <span class="html-italic">KE<sub>mm</sub></span> calculated from DSD for (<b>a</b>) the entire data, (<b>e</b>) spring, (<b>i</b>) summer, and (<b>m</b>) fall under BR09_S; for (<b>b</b>) the entire data, (<b>f</b>) spring, (<b>j</b>) summer, and (<b>n</b>) fall under BR09_C; for (<b>c</b>) the entire data, (<b>g</b>) spring, (<b>k</b>) summer, and (<b>o</b>) fall under BR03_S; and for (<b>d</b>) the entire data, (<b>h</b>) spring, and (<b>l</b>) summer under BR03_C at Zhaosu. Black dashed lines represent the 1:1 relationship.</p>
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<p>Seasonal variations in the distributions of (<b>a</b>) <span class="html-italic">Z<sub>h</sub></span>, (<b>b</b>) <span class="html-italic">Z<sub>dr</sub></span>, and (<b>c</b>) <span class="html-italic">K<sub>dp</sub></span> at Zhaosu.</p>
Full article ">Figure 12
<p>Scatterplot of estimated <span class="html-italic">R</span> based on the <span class="html-italic">R</span>(<span class="html-italic">Z<sub>h</sub></span>) algorithm for (<b>a</b>) the entire data, (<b>e</b>) spring, (<b>i</b>) summer, and (<b>m</b>) fall; estimated <span class="html-italic">R</span> based on the <span class="html-italic">R</span>(<span class="html-italic">K<sub>dp</sub></span>) algorithm for (<b>b</b>) the entire data, (<b>f</b>) spring, (<b>j</b>) summer, and (<b>n</b>) fall; estimated <span class="html-italic">R</span> based on the <span class="html-italic">R</span>(<span class="html-italic">Z<sub>h</sub></span><sub>,</sub><span class="html-italic">Z<sub>dr</sub></span>) algorithm for (<b>c</b>) the entire data, (<b>g</b>) spring, (<b>k</b>) summer, and (<b>o</b>) fall; and estimated <span class="html-italic">R</span> based on the <span class="html-italic">R</span>(<span class="html-italic">K<sub>dp</sub></span><sub>,</sub><span class="html-italic">Z<sub>dr</sub></span>) algorithm for (<b>d</b>) the entire data, (<b>h</b>) spring, (<b>l</b>) summer, and (<b>p</b>) fall versus calculated <span class="html-italic">R</span> according to Equation (4) at Zhaosu. Black dashed lines represent the 1:1 relationship.</p>
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<p>Seasonal variations in the distributions of <span class="html-italic">Z<sub>h</sub></span> under (<b>a</b>) BR09_S, (<b>d</b>) BR09_C, (<b>g</b>) BR03_S, and (<b>j</b>) BR03_C; those of <span class="html-italic">Z<sub>dr</sub></span> under (<b>b</b>) BR09_S, (<b>e</b>) BR09_C, (<b>h</b>) BR03_S, and (<b>k</b>) BR03_C; and those of <span class="html-italic">K<sub>dp</sub></span> under (<b>c</b>) BR09_S, (<b>f</b>) BR09_C, (<b>i</b>) BR03_S, and (<b>l</b>) BR03_C at Zhaosu.</p>
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<p>Scatterplot of estimated <span class="html-italic">R</span> based on the <span class="html-italic">R</span>(<span class="html-italic">Z<sub>h</sub></span>) algorithm during BR09_S for (<b>a</b>) the entire data, (<b>e</b>) spring, (<b>i</b>) summer, and (<b>m</b>) fall; that based on the <span class="html-italic">R</span>(<span class="html-italic">K<sub>dp</sub></span>) algorithm during BR09_S for (<b>b</b>) the entire data, (<b>f</b>) spring, (<b>j</b>) summer, and (<b>n</b>) fall; that based on the <span class="html-italic">R</span>(<span class="html-italic">Z<sub>h</sub></span><sub>,</sub><span class="html-italic">Z<sub>dr</sub></span>) algorithm during BR09_S for (<b>c</b>) the entire data, (<b>g</b>) spring, (<b>k</b>) summer, and (<b>o</b>) fall; and that based on the <span class="html-italic">R</span>(<span class="html-italic">K<sub>dp</sub></span><sub>,</sub><span class="html-italic">Z<sub>dr</sub></span>) algorithm during BR09_S for (<b>d</b>) the entire data, (<b>h</b>) spring, (<b>l</b>) summer, and (<b>p</b>) fall versus calculated <span class="html-italic">R</span> according to Equation (4) at Zhaosu. Black dashed lines represent the 1:1 relationship.</p>
Full article ">Figure 15
<p>Scatterplot of estimated <span class="html-italic">R</span> based on the <span class="html-italic">R</span>(<span class="html-italic">Z<sub>h</sub></span>) algorithm during BR09_C for (<b>a</b>) the entire data, (<b>e</b>) spring, (<b>i</b>) summer, and (<b>m</b>) fall; that based on the <span class="html-italic">R</span>(<span class="html-italic">K<sub>dp</sub></span>) algorithm during BR09_C for (<b>b</b>) the entire data, (<b>f</b>) spring, (<b>j</b>) summer, and (<b>n</b>) fall; that based on the <span class="html-italic">R</span>(<span class="html-italic">Z<sub>h</sub></span><sub>,</sub><span class="html-italic">Z<sub>dr</sub></span>) algorithm during BR09_C for (<b>c</b>) the entire data, (<b>g</b>) spring, (<b>k</b>) summer, and (<b>o</b>) fall; and that based on the <span class="html-italic">R</span>(<span class="html-italic">K<sub>dp</sub></span><sub>,</sub><span class="html-italic">Z<sub>dr</sub></span>) algorithm during BR09_C for (<b>d</b>) the entire data, (<b>h</b>) spring, (<b>l</b>) summer, and (<b>p</b>) fall versus calculated <span class="html-italic">R</span> according to Equation (4) at Zhaosu. Black dashed lines represent the 1:1 relationship.</p>
Full article ">Figure 16
<p>Scatterplot of estimated <span class="html-italic">R</span> based on the <span class="html-italic">R</span>(<span class="html-italic">Z<sub>h</sub></span>) algorithm during BR03_S for (<b>a</b>) the entire data, (<b>e</b>) spring, (<b>i</b>) summer, and (<b>m</b>) fall; that based on the <span class="html-italic">R</span>(<span class="html-italic">K<sub>dp</sub></span>) algorithm during BR03_S for (<b>b</b>) the entire data, (<b>f</b>) spring, (<b>j</b>) summer, and (<b>n</b>) fall; that based on the <span class="html-italic">R</span>(<span class="html-italic">Z<sub>h</sub></span><sub>,</sub><span class="html-italic">Z<sub>dr</sub></span>) algorithm during BR03_S for (<b>c</b>) the entire data, (<b>g</b>) spring, (<b>k</b>) summer, and (<b>o</b>) fall; and that based on the <span class="html-italic">R</span>(<span class="html-italic">K<sub>dp</sub></span><sub>,</sub><span class="html-italic">Z<sub>dr</sub></span>) algorithm during BR03_S for (<b>d</b>) the entire data, (<b>h</b>) spring, (<b>l</b>) summer, and (<b>p</b>) fall versus calculated <span class="html-italic">R</span> according to Equation (4) at Zhaosu. Black dashed lines represent the 1:1 relationship.</p>
Full article ">Figure 17
<p>Scatterplot of estimated <span class="html-italic">R</span> based on the <span class="html-italic">R</span>(<span class="html-italic">Z<sub>h</sub></span>) algorithm during BR03_C for (<b>a</b>) the entire data, (<b>e</b>) spring, and (<b>i</b>) summer; that based on the <span class="html-italic">R</span>(<span class="html-italic">K<sub>dp</sub></span>) algorithm during BR03_C for (<b>b</b>) the entire data, (<b>f</b>) spring, and (<b>j</b>) summer; that based on the <span class="html-italic">R</span>(<span class="html-italic">Z<sub>h</sub></span><sub>,</sub><span class="html-italic">Z<sub>dr</sub></span>) algorithm during BR03_C for (<b>c</b>) the entire data, (<b>g</b>) spring, and (<b>k</b>) summer; and that based on the <span class="html-italic">R</span>(<span class="html-italic">K<sub>dp</sub></span><sub>,</sub><span class="html-italic">Z<sub>dr</sub></span>) algorithm during BR03_C for (<b>d</b>) the entire data, (<b>h</b>) spring, and (<b>l</b>) summer versus calculated <span class="html-italic">R</span> according to Equation (4) at Zhaosu. Black dashed lines represent the 1:1 relationship.</p>
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9 pages, 250 KiB  
Commentary
What COVID-19 Vaccination Strategy Should Be Implemented and Which Vaccines Should Be Used in the Post-Pandemic Era?
by Pedro Plans-Rubió
Vaccines 2024, 12(10), 1180; https://doi.org/10.3390/vaccines12101180 - 17 Oct 2024
Abstract
COVID-19 vaccines have reduced the negative health and economic impact of the COVID-19 pandemic by preventing severe disease, hospitalizations and deaths. In the new socio-economic normality, the COVID-19 vaccination strategy can be universal or high-risk and seasonal or not seasonal, and different vaccines [...] Read more.
COVID-19 vaccines have reduced the negative health and economic impact of the COVID-19 pandemic by preventing severe disease, hospitalizations and deaths. In the new socio-economic normality, the COVID-19 vaccination strategy can be universal or high-risk and seasonal or not seasonal, and different vaccines can be used. The universal vaccination strategy can achieve greater health and herd immunity effects and is associated with greater costs than the high-risk vaccination strategy. In each country, the optimal COVID-19 vaccination strategy must be decided by considering the advantages and disadvantages and assessing the costs, health effects and cost-effectiveness of the universal and high-risk vaccination strategies. The universal vaccination strategy should be implemented when the objective of the vaccination program is to achieve the greatest health benefits from COVID-19 vaccination and when its incremental cost-effectiveness ratio is lower than EUR 30,000−50,000 per QALY or LYG. The use of adapted vaccines targeting currently circulating variants of SARS-CoV-2 is necessary to avoid the immune escape of emerging variants. Full article
(This article belongs to the Section Vaccine Efficacy and Safety)
22 pages, 4837 KiB  
Article
A Machine Learning Approach to Forecasting Hydropower Generation
by Sarah Di Grande, Mariaelena Berlotti, Salvatore Cavalieri and Roberto Gueli
Energies 2024, 17(20), 5163; https://doi.org/10.3390/en17205163 - 17 Oct 2024
Viewed by 99
Abstract
In light of challenges like climate change, pollution, and depletion of fossil fuel reserves, governments and businesses prioritize renewable energy sources such as solar, wind, and hydroelectric power. Renewable energy forecasting models play a crucial role for energy market operators and prosumers, aiding [...] Read more.
In light of challenges like climate change, pollution, and depletion of fossil fuel reserves, governments and businesses prioritize renewable energy sources such as solar, wind, and hydroelectric power. Renewable energy forecasting models play a crucial role for energy market operators and prosumers, aiding in planning, decision-making, optimization of energy sales, and evaluation of investments. This study aimed to develop machine learning models for hydropower forecasting in plants integrated into Water Distribution Systems, where energy is generated from water flow used for municipal water supply. The study involved developing and comparing monthly and two-week forecasting models, utilizing both one-step-ahead and two-step-ahead forecasting methodologies, along with different missing data imputation techniques. The tested algorithms—Seasonal Autoregressive Integrated Moving Average, Random Forest, Temporal Convolutional Network, and Neural Basis Expansion Analysis for Time Series—produced varying levels of performance. The Random Forest model proved to be the most effective for monthly forecasting, while the Temporal Convolutional Network delivered the best results for two-week forecasting. Across all scenarios, the seasonal–trend decomposition using the LOESS technique emerged as the most successful for missing data imputation. The accurate predictions obtained demonstrate the effectiveness of using these models for energy planning and decision-making. Full article
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Figure 1
<p>Comparison between time series with outliers (in gray) and time series after outliers detection through single boxplot (in blue).</p>
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<p>Comparison between time series with outliers (in gray) and time series after outliers detection through multiple boxplots (in green).</p>
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<p>Comparison between the two time series aggregated monthly after reconstruction with the two different methods. The gray dashed line in the plot defines the starting point of the non-reconstructed data, which is used as the test set.</p>
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<p>Comparison between the two time series aggregated every two weeks after reconstruction with the two different methods. The gray dashed line in the plot defines the starting point of the non-reconstructed data, which is used as the test set.</p>
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<p>Comparison between actual and forecasted values for the four monthly models using datasets with STL reconstruction.</p>
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<p>SMAPE results of the four best-performing models for monthly hydropower forecasts.</p>
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<p>Comparison between actual and forecasted values from the four two-week models using datasets with STL reconstruction.</p>
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<p>SMAPE results of the four best-performing models for two-week hydropower forecasts.</p>
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8 pages, 863 KiB  
Brief Report
An Analysis of the Use of Topical Ocular Anti-Infectives in Galicia (Spain) between 2020 and 2023
by Severo Vázquez-Prieto, Antonio Vaamonde and Esperanza Paniagua
Diseases 2024, 12(10), 256; https://doi.org/10.3390/diseases12100256 - 17 Oct 2024
Viewed by 129
Abstract
Eye infections are a global health and economic problem that affect people of both sexes at any age. Topical application of anti-infectives is widely used in the treatment of these types of infections. However, little is known about the current status and trends [...] Read more.
Eye infections are a global health and economic problem that affect people of both sexes at any age. Topical application of anti-infectives is widely used in the treatment of these types of infections. However, little is known about the current status and trends of the use of topical ocular anti-infectives in Spain. In the present work, we evaluated the use of this type of drug in the Spanish autonomous community of Galicia and described the variability in its consumption between Galician provinces between 2020 and 2023. In addition, the possible existence of a deviation in consumption at a seasonal level was evaluated, as well as possible changes during the study period. A descriptive, cross-sectional and retrospective study of the use of drugs belonging to the subgroups S01A (anti-infectives) and S01C (anti-inflammatory agents and anti-infectives in combination) of the Anatomic Therapeutic Chemical Classification was carried out. This work demonstrated that the most used topical ocular anti-infective in Galicia was tobramycin and that the use of these types of drugs in our region varied according to the provinces. This study also revealed that the consumption of these medications has remained stable during the period 2020–2023, with no significant seasonal differences observed. Full article
(This article belongs to the Section Infectious Disease)
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<p>Number of packages of topical ocular anti-infectives dispensed in each province of Galicia (Spain) between 2020 and 2023.</p>
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<p>Number of packages of topical ocular anti-infectives dispensed per 10,000 inhabitants in each province of Galicia (Spain) between 2020 and 2023.</p>
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<p>Average number of packages of topical ocular anti-infectives per 10,000 inhabitants and year dispensed in Galicia (Spain) between 2020 and 2023.</p>
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13 pages, 1033 KiB  
Article
Adaptive Seedling Strategies in Seasonally Dry Tropical Forests: A Comparative Study of Six Tree Species
by Carlos Ivan Espinosa, Elvia Esparza and Andrea Jara-Guerrero
Plants 2024, 13(20), 2900; https://doi.org/10.3390/plants13202900 - 17 Oct 2024
Viewed by 166
Abstract
This study examines seed germination strategies and seedling establishment in six tree species typical of seasonally dry tropical forests. We focused on how interspecific and intraspecific differences in seed size and germination speed influence biomass allocation and seedling growth. Using generalized linear models, [...] Read more.
This study examines seed germination strategies and seedling establishment in six tree species typical of seasonally dry tropical forests. We focused on how interspecific and intraspecific differences in seed size and germination speed influence biomass allocation and seedling growth. Using generalized linear models, we analyzed the effects of these traits on root/shoot ratios and growth rates. Our findings reveal two main strategies: slow germination, high root/shoot ratio, and low growth rate in Erythrina velutina Willd and Terminalia valverdeae A.H. Gentry, associated with enhanced drought tolerance. In contrast, Cynophalla mollis (Kunth) J. Presl and Coccoloba ruiziana Lindau exhibited rapid germination, lower root/shoot ratios, and low to moderate growth rates, favoring competition during early establishment. Centrolobium ochroxylum Rose ex Rudd partially aligned with this second strategy due to its fast growth. Vachellia macracantha (Humb. & Bonpl. ex Willd.) Seigler & Ebinger presented a unique case, displaying slow germination and a broad range in both root/shoot ratios and growth rates. At the intraspecific level, significant variation in biomass allocation and growth rate was observed, influenced by germination speed and seed weight. We discuss the adaptive significance of seed traits in SDTFs and their role in seedling establishment under varying environmental conditions, providing insights for strategies for conservation and restoration in these ecosystems. Full article
(This article belongs to the Section Plant Ecology)
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<p>Curve of the accumulated proportion of seed germination along the time. The lines represent the proportion of seeds that germinated over time for each species. Upper curves represent higher germination proportion, and more pronounced slopes represent higher germination velocity.</p>
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<p>Generalized linear models of seed weight (<b>a</b>), germination speed (<b>b</b>), and competition strategies defined by biomass allocation (<b>c</b>), and growth rate (<b>d</b>). The letters show differences between species according to post hoc analysis.</p>
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<p>Variation in seed weight (g) of the six studied species. The size of the seed image illustrates the difference in seed weight between species, while the circles depict each species’ minimum, average, and maximum seed weight in proportion.</p>
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12 pages, 1029 KiB  
Article
Variation in the Biomass of Phragmites australis Across Community Types in the Aquatic Habitats of the Middle Volga Valley
by Vladimir Papchenkov and Hana Čížková
Diversity 2024, 16(10), 644; https://doi.org/10.3390/d16100644 - 17 Oct 2024
Viewed by 239
Abstract
Species composition and biomass are key indicators of vegetation performance. While Phragmites australis is extensively studied worldwide, data on its communities and biomass in natural habitats are limited in the European part of the Russian Federation. This study examines P. australis-dominated communities [...] Read more.
Species composition and biomass are key indicators of vegetation performance. While Phragmites australis is extensively studied worldwide, data on its communities and biomass in natural habitats are limited in the European part of the Russian Federation. This study examines P. australis-dominated communities and their biomass in wetlands along the Middle Volga River. P. australis was either the dominant or co-dominant species in seven community types. Their seasonal maximum aboveground biomass correlated with plant projective cover, being highest in Schoenoplecteto lacustris-Phragmitetum australis (mean 1.7 kg m−2), with nearly 100% cover, and lowest (0.5 kg m−2) in Spirodelo-Phragmitetum australis, with 50% cover. Compared with communities dominated by Glyceria maxima, Schoenoplectus lacustris, and Typha latifolia, those of P. australis had the highest seasonal maximum aboveground biomass in running waters (mean 1.32 kg m−2) but the lowest in standing waters of the Kuibyshev Reservoir (mean 0.70 kg m−2), likely reflecting nutrient availability. A similar pattern was observed for the dominant species alone. The mean belowground biomass of P. australis was 1.9 kg m−2, with a belowground/aboveground ratio of 1.5. Similar values were found for S. lacustris and T. latifolia. The community types and biomass values align with those found in other European regions with warm temperate climates. Full article
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<p>Map of the Volga River catchment.</p>
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<p>Seasonal maximum aboveground biomass of tall helophytes in running and standing water habitats in the Middle Volga Valley in different classes of projective cover (I: 0–30%, II: 30–60%, III: 60–90%, IV: 90–100%). <span class="html-italic">R</span><sup>2</sup> denote determination coefficients for exponential and linear fits for species cover in running and standing water habitats, respectively. The colors of fits correspond to the colors for species given in the legend.</p>
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