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14 pages, 2020 KiB  
Article
Thiobacter aerophilum sp. nov., a Thermophilic, Obligately Chemolithoautotrophic, Sulfur-Oxidizing Bacterium from a Hot Spring and Proposal of Thiobacteraceae fam. nov.
by Anna M. Dukat, Alexander G. Elcheninov, Alexandra A. Klyukina, Andrei A. Novikov and Evgenii N. Frolov
Microorganisms 2024, 12(11), 2252; https://doi.org/10.3390/microorganisms12112252 - 7 Nov 2024
Viewed by 378
Abstract
An aerobic, obligately chemolithoautotrophic, sulfur-oxidizing bacterium, strain AK1T, was isolated from a terrestrial hot spring of the Uzon Caldera, Kamchatka, Russia. The cells of the new isolate were Gram-negative motile rods with a single polar flagellum. Strain AK1T grew at [...] Read more.
An aerobic, obligately chemolithoautotrophic, sulfur-oxidizing bacterium, strain AK1T, was isolated from a terrestrial hot spring of the Uzon Caldera, Kamchatka, Russia. The cells of the new isolate were Gram-negative motile rods with a single polar flagellum. Strain AK1T grew at 37–55 °C (optimum 50 °C) with 0–1.0% NaCl (optimum 0%) and within the pH range 4.8–7.0 (optimum pH 5.2–5.5). The new isolate was able to grow by aerobic respiration with sulfide, sulfur, or thiosulfate as the electron donor and HCO3/CO2 as the carbon source. The major fatty acids were C16:0, C17:1 Δ, and C16:1 ω7c. The respiratory lipoquinone was ubiquinone UQ-8. The size of the genome and genomic DNA G+C content of the strain AK1T were 2.55 Mb and 64.0%, respectively. The closest 16S rRNA gene sequence of a validly published species belonged to Thiobacter subterraneus C55T (97.94% identity). According to the 16S rRNA gene sequence-based and conserved protein sequences-based phylogenetic analyses, strain AK1T represented a distinct lineage of the genus Thiobacter within a new family, Thiobacteraceae of the order Burkholderiales. As inferred from the morphology, physiology, chemotaxonomy, and phylogeny, strain AK1T ought to be recognized as a novel species for which we propose the name Thiobacter aerophilum sp. nov. The type strain is AK1T (=CGMCC 1.18099T = UQM 41819T). Full article
(This article belongs to the Special Issue Chemolithotrophic Microorganisms)
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<p>Cell morphology and ultrastructure of strain AK1<sup>T</sup>: (<b>a</b>) Electron micrograph showing overall cell morphology and localization of the single flagellum; bar, 400 nm. (<b>b</b>) Ultrathin section showing cell wall structure; bar, 400 nm.</p>
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<p>Time-courses of oxidation of thiosulfate (red), the production of sulfate (green), and concomitant bacterial growth (blue) of strain AK1<sup>T</sup>.</p>
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<p>Maximum likelihood phylogenetic tree based on comparison of 120 conserved proteins and showing the position of the strain AK1<sup>T</sup> (in bold) within the order <span class="html-italic">Burkholderiales</span>. Species are collapsed into family-level clusters (* some members from two different families formed a single cluster). The branch lengths correspond to the number of substitutions per site (see scale) according to the corrections associated with the LG + I + G4 model. The numbers at the nodes indicate the percentage of the corresponding support values. <span class="html-italic">Escherichia coli</span> K-12 was an outgroup.</p>
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<p>An overview of the metabolism of the strain AK1<sup>T</sup> reconstructed from its genome. Abbreviations: 2PGA, 2-phosphoglycerate; 3PGA, 3-phosphoglycerate; AH, aconitate hydratase; BPG, 1,3-bisphosphoglycerate; CS, citrate synthase; cyt, cytochrome; DHAP, dihydroxyacetone phosphate; E4P, erythrose-4-phosphate; F6P, fructose-6-phosphate; FBP, fructose-1,6-bisphosphate; FBPA, fructose-1,6-bisphosphate aldolase; FBPase, fructose-1,6-bisphosphatase; FCC, flavocytochrome <span class="html-italic">c</span> sulfide dehydrogenase; FH, fumarate hydratase; GAP, glyceraldehyde-3-phosphate; GAPHD, glyceraldehyde-3-phosphate dehydrogenase; OGD, 2-oxoglutarate decarboxylase; OGFOR, 2-oxoglutarate:ferredoxin oxidoreductase; IDH, isocitrate dehydrogenase; ME, malic enzyme; PEP, phosphoenolpyruvate; PFOR, pyruvate:ferredoxin oxidoreductase; PGK, phosphoglycerate kinase; PGM, phosphoglycerate mutase; PK, pyruvate kinase; PRK, phosphoribulokinase; R5P, ribose-5-phosphate; rDsr, reverse dissimilatory sulfite reductase complex; RubisCO, ribulose-1,5-bisphosphate carboxylase/oxygenase; Ru5P, ribulose-5-phosphate; RuBP, ribulose-1,5-bisphosphate; RPI, ribose-5-phosphate isomerase; RuPE, ribulose-phosphate 3-epimerase; SBP, sedoheptulose-1,7-bisphosphate; SBPA, sedoheptulose-1,7-bisphosphate aldolase; SBPase, sedoheptulose-1,7-bisphosphatase; SCS, succinyl-CoA synthetase; SDH, succinate dehydrogenase; SoeABC, sulfite:quinone oxidoreductase; SorAB, sulfite: cytochrome <span class="html-italic">c</span> oxidoreductase; Sox complex, sulfur-oxidizing complex; SQR, sulfide:quinone oxidoreductase; SSADH, succinyl-semialdehyde dehydrogenase; TCA cycle, tricarboxylic acid cycle; TK, transketolase; TPI, triosephosphat isomerase; Xu5P, xylulose-5-phosphate.</p>
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25 pages, 7381 KiB  
Article
Radiation Limits the Yield Potential of Main Crops Under Selected Agrivoltaic Designs—A Case Study of a New Shading Simulation Method
by Sabina Thaler, Karl Berger, Josef Eitzinger, Abdollahi Mahnaz, Vitore Shala-Mayrhofer, Shokufeh Zamini and Philipp Weihs
Agronomy 2024, 14(11), 2511; https://doi.org/10.3390/agronomy14112511 - 25 Oct 2024
Viewed by 710
Abstract
Agrivoltaics (APVs) represent a growing technology in Europe that enables the co-location of energy and food production in the same field. Photosynthesis requires photosynthetic active radiation, which is reduced by the shadows cast on crops by APV panels. The design of the module [...] Read more.
Agrivoltaics (APVs) represent a growing technology in Europe that enables the co-location of energy and food production in the same field. Photosynthesis requires photosynthetic active radiation, which is reduced by the shadows cast on crops by APV panels. The design of the module rows, material, and field orientation significantly influences the radiation distribution on the ground. In this context, we introduce an innovative approach for the effective simulation of the shading effects of various APV designs. We performed an extensive sensitivity analysis of the photovoltaic (PV) geometry influence on the ground-incident radiation and crop growth of selected cultivars. Simulations (2013–2021) for three representative arable crops in eastern Austria (winter wheat, spring barley, and maize) and seven different APV designs that only limited to the shading effect showed that maize and spring barley experienced the greatest annual above-ground biomass and grain yield reduction (up to 25%), with significant differences between the APV design and the weather conditions. While spring barley had similar decreases within the years, maize was characterized by high variability. Winter wheat had only up to a 10% reduction due to shading and a reduced photosynthetic performance. Cold/humid/cloudy weather during the growing season had more negative yield effects under APVs than dry/hot periods, particularly for summer crops such as maize. The lowest grain yield decline was achieved for all three crops in the APV design in which the modules were oriented to the east at a height of 5 m and mounted on trackers with an inclination of +/−50°. This scenario also resulted in the highest land equivalent ratios (LERs), with values above 1.06. The correct use of a tracker on APV fields is crucial for optimizing agricultural yields and electricity production. Full article
(This article belongs to the Section Agricultural Biosystem and Biological Engineering)
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<p>Mean temperature [°C] (red lines with black dots = monthly averages) and precipitation [mm] (blue bars) of the Hohe Warte weather station, Vienna, Austria, for the growing periods of spring barley (March–June), maize (April–October), and winter wheat (October–July) from 2013 to 2021.</p>
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<p>Overview of the calculation process for determining the influence of PV on incident solar radiation and the preparation of the meteorological input data for the DSSAT simulations.</p>
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<p>(<b>a</b>–<b>f</b>). The V01–V07 scenarios. PV: PV modules. The height above the ground and the distance between the rows are indicated. Please note that (<b>b</b>) describes scenarios V02 and V05, which only differ in the length of their rows (V02 11.98 m and V05 147.7 m, respectively). (<b>a</b>) In scenario V01, the PV modules are assumed to be in the vertical position with a lowest height above the ground of 0.8. (<b>b</b>) Scenarios V02 and V05 have different row lengths. The PV modules are at a 5 m height with 30-degree inclination, facing south. (<b>c</b>) Scenario V03 has PV modules with 12-degree inclination at a 5 m height and facing east. (<b>d</b>) Scenario V04 has a tracker mounted at a 1.3 m height with a minimum height between the PV modules and ground of 0.8 m. The PV modules are aligned north–south, so they are facing the eastern or western direction and their inclination changes within ±50 degrees. (<b>e</b>) The V06 scenario is similar to V05 but with a module inclination of 38 degrees. (<b>f</b>) V07 is similar to V04, but the PV modules are at a 5 m height. Blue lines show in which direction the PV module can move. Black lines show the PV modules.</p>
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<p>Example of an edited fisheye photograph showing rows of APV modules (scenario V02) with the observer at a 3.09 m distance from the northern APV row and a 5.96 m distance from the southern APV row. The image was processed using the HemiView software, version 2.1 which drew the sun orbit of the individual months and the actual day (purple line) into the image. Circular yellow lines are lines with the same zenith angle, straight yellow lines are lines with the same azimuth angle.</p>
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<p>Photograph of the PV system installed at Raasdorf. The picture on the right shows a fisheye photograph. The different symbols used in the equations are depicted. V is the point of vision. P is the part of the image for which the number of vertical pixels (Ny) is specified. Circular yellow lines are lines with same zenith angle and straight yellow lines are lines with same azimuth angle.</p>
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<p>Calculated hourly global radiation data for scenario V02 for the period 2–7 May 2020. The blue line displays the original global radiation data of the ARAD network for Vienna, and the red line depicts the incident solar radiation below the APV modules for scenario V02.</p>
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<p>Comparison of (<b>a</b>) edited fisheye photograph and (<b>b</b>) real fisheye photograph taken in Raasdorf. (<b>b</b>) The fisheye photograph was taken at a distance of 3 m west of the PV modules. The background fisheye photograph of the edited photograph was taken from a cloud-monitoring camera situated in BOKU, Vienna, on April 5th. However, the information in the background photograph was not used during the subsequent digitization with the HemiView software. A grid with the zenith and azimuth angles was placed on the images to ensure comparability.</p>
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<p>Comparison of simulated and measured radiation on the PV field with the vertical panels. The daily cycle of 27 April 2024 is shown. (<b>a</b>) Radiation at a distance of 1 m west of the panels. (<b>b</b>) Radiation at a distance of 5 m west of the panels. The measured global radiation above the PV system is also displayed to visualize attenuation.</p>
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<p>Comparison of simulated and measured daily sums of radiation from 17 April to 8 May 2024. (<b>a</b>) Radiation sums at a distance of 1 m west of the panels. (<b>b</b>) Radiation sums at a distance of 5 m west of the panels. The measured global radiation above the PV system is also displayed to visualize attenuation.</p>
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<p>Input and output data of the DSSAT crop growth model.</p>
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<p>Annual changes (2013–2021) in global radiation [MJ/m<sup>2</sup>] values of different variants (V00–V07). The vertical bars (whiskers) indicate the range of the different annual global radiation values from min. to max., while the black dots signify outliers that fall outside the expected range.</p>
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<p>Percentage changes (2013–2021) in global radiation [MJ/m<sup>2</sup>] of different APV design variants (V01–V07) compared to the open field (V00) for each crop (period from sowing to maturity/harvest). The vertical bars (whiskers) indicate the range of different annual global radiation values from min. to max., while the black dots signify outliers that fall outside the expected range.</p>
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<p>Simulated (<b>a</b>) above-ground biomass and (<b>b</b>) yield differences between APV design variants for spring barley, maize, and winter wheat compared to the open field in %. The vertical bars (whiskers) indicate the range of the above-ground biomass and yield differences from min. to max., while the black dots signify outliers which fall outside the expected range.</p>
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<p>Relative yield differences vs. relative global radiation differences in the different scenarios (V01–V07) compared to the open field.</p>
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<p>Relative difference in yields vs. relative difference in evapotranspiration of the different scenarios (V01–V07) compared to the open field.</p>
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<p>Annual simulated spring barley (<b>a</b>) above-ground biomass and (<b>b</b>) yield deviation relative to the open field.</p>
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<p>Simulated annual (<b>a</b>) above-ground biomass and (<b>b</b>) grain yield deviation of maize relative to the open field.</p>
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<p>Simulated annual (<b>a</b>) above-ground biomass and (<b>b</b>) yield deviation of winter wheat relative to the open field.</p>
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9 pages, 467 KiB  
Systematic Review
Chronic Immune Sensory Polyradiculopathy (CISP): A Systematic Review of the Literature
by Saurabh Singhal, Rahul Khanna, Anudeep Surendranath, Jayksh Chhabra, Vismay Thakkar and Rajesh Gupta
Neurol. Int. 2024, 16(6), 1214-1222; https://doi.org/10.3390/neurolint16060092 - 25 Oct 2024
Viewed by 517
Abstract
Chronic immune sensory polyradiculopathy (CISP) is a rare inflammatory immune disorder affecting the nervous system, primarily targeting the proximal sensory nerve roots. The condition was first described by Sinreich in 2004. We conducted a systematic review of CISP cases published on PubMed to [...] Read more.
Chronic immune sensory polyradiculopathy (CISP) is a rare inflammatory immune disorder affecting the nervous system, primarily targeting the proximal sensory nerve roots. The condition was first described by Sinreich in 2004. We conducted a systematic review of CISP cases published on PubMed to identify common clinical presentations, along with neurophysiological, radiological, cerebrospinal fluid (CSF), and other findings. Our review included a total of 22 patients from 8 articles. Many patients presented with gait difficulties and sensory ataxia and were found to have normal nerve conduction studies (NCS) and electromyography (EMG) but exhibited characteristic abnormalities in somatosensory evoked potentials (SSEP), elevated CSF protein levels, thickened nerve roots on contrast-enhanced lumbar spine MRIs, and histological changes on nerve root biopsies. Clinical improvement was observed following treatment with steroids and/or intravenous immunoglobulin (IVIG). The study concluded that while CISP is rare, it is an important clinical entity to consider, as accurate diagnosis and appropriate treatment can lead to significant improvements in neurological symptoms and disabilities. Full article
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<p>PRISMA table for the literature review.</p>
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19 pages, 22283 KiB  
Article
The Impact of Different Types of Trees on Annual Thermal Comfort in Hot Summer and Cold Winter Areas
by Xiao Chen, Zilong Li, Zhenyu Wang, Jiayu Li and Yihua Zhou
Forests 2024, 15(11), 1880; https://doi.org/10.3390/f15111880 - 25 Oct 2024
Viewed by 466
Abstract
Trees positively improve the annual thermal comfort of the built environment in tropical areas, where climate change is slight throughout the year. However, for areas with high changes in climate all year, the current studies have only explored the summer cooling performance of [...] Read more.
Trees positively improve the annual thermal comfort of the built environment in tropical areas, where climate change is slight throughout the year. However, for areas with high changes in climate all year, the current studies have only explored the summer cooling performance of trees without the impact of different types of trees on annual thermal comfort, especially in cold seasons. Therefore, to quantify the impacts and scientifically guide the optimization of green space layout in hot summer and cold winter areas, this study selected Changsha City as the study area and analyzed how the annual thermal comfort is affected by evergreen trees and deciduous trees, which are two common types of trees in hot summer and cold winter areas. The analytical results indicated that the difference in the effect of deciduous and evergreen trees on outdoor thermal comfort was insignificant in summer, where the difference in the monthly mean PET for the three summer months was slight, being 0.28 °C, 0.14 °C, and 0.29 °C, respectively. However, evergreen trees greatly exacerbated winter cold compared to deciduous trees, with a monthly mean PET decrease by nearly 1.0 °C and an hourly PET reduced by up to 3.57 °C. The difference is mainly attributed to the absorption and reflection of solar radiation by the tree canopy, as well as the cooling and humidifying effect of the tree leaf. In hot summer and cold winter areas, outdoor thermal comfort is still in the “comfortable” and “slightly warm” acceptable stage despite the warming effect of deciduous trees in the spring and autumn seasons. Planting evergreen trees is an inevitable thermal mitigation choice for tropical areas. However, for the areas with high annual climate change, such as hot summer and cold winter areas in China, a change in empirical tree planting patterns and selecting deciduous trees where appropriate will improve year-round outdoor thermal comfort. Full article
(This article belongs to the Section Urban Forestry)
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<p>Climate zones and location of the study area.</p>
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<p>Annual air temperature distribution.</p>
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<p>Annual relative humidity distribution.</p>
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<p>Annual wind speed distribution.</p>
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<p>Study site and building parameter model.</p>
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<p>Field measurement and simulation.</p>
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<p>Verification of the accuracy.</p>
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<p>Air temperature distribution of deciduous and evergreen trees.</p>
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<p>Hourly air temperature distribution during the four seasons.</p>
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<p>Monthly mean air temperature variations in deciduous and evergreen trees.</p>
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<p>Relative humidity distribution of deciduous and evergreen trees at 14:00.</p>
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<p>Hourly Ta distribution during the four seasons.</p>
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<p>Monthly mean relative humidity variations in deciduous and evergreen trees.</p>
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<p>Wind speed distribution of deciduous and evergreen trees at 14:00.</p>
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<p>Hourly Ws distribution during the four seasons.</p>
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<p>Monthly mean wind speed variations in deciduous and evergreen trees.</p>
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<p>Tmrt distribution of deciduous and evergreen trees at 14:00.</p>
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<p>Hourly Tmrt distribution during the four seasons.</p>
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<p>Monthly mean Tmrt variations in deciduous and evergreen trees.</p>
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<p>PET distribution of deciduous and evergreen trees.</p>
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<p>Hourly PET distribution during the four seasons.</p>
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<p>Monthly mean PET variations in deciduous and evergreen trees.</p>
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19 pages, 3558 KiB  
Article
Spatial–Temporal Variation and the Influencing Factors of NO2 Column Concentration in the Plateau Mountains of Southwest China
by Fei Dong, Zhongfa Zhou, Denghong Huang, Xiandan Du and Shuanglong Du
Atmosphere 2024, 15(11), 1263; https://doi.org/10.3390/atmos15111263 - 22 Oct 2024
Viewed by 487
Abstract
Given the complex terrain and economic development status of Guizhou Province, research on tropospheric NO2 column concentration using satellite remote sensing is still insufficient. Observing the spatial–temporal evolution characteristics of tropospheric NO2 column concentration can ensure the stable development of air [...] Read more.
Given the complex terrain and economic development status of Guizhou Province, research on tropospheric NO2 column concentration using satellite remote sensing is still insufficient. Observing the spatial–temporal evolution characteristics of tropospheric NO2 column concentration can ensure the stable development of air quality. Based on the Google Earth Engine (GEE) platform, NO2 column concentration data retrieved from Sentinel-5P TROPOMI were analyzed using spatial autocorrelation, hotspot analysis, and geographic detector methods (Geodetector). The results show that NO2 column concentration in Guizhou Province exhibits seasonal variation, characterized by higher levels in winter and lower levels in summer, with transitional values in spring and autumn. The annual average concentration was highest in 2021 at 3.47 × 10−5 mol/m2 and lowest in 2022 at 2.85 × 10−5 mol/m2. Spatially, NO2 column concentration displays a distribution pattern of “high in the west, low in the east; high in the north, low in the south”, with significant spatial clustering. The distribution of cold and hot spots aligns with areas of high and low values. NO2 column concentration is primarily influenced by socio-economic factors, with the interaction between any two factors enhancing the explanatory power of individual factors on NO2 column concentration. Full article
(This article belongs to the Special Issue Atmospheric Pollutants: Monitoring and Observation)
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<p>Elevation distribution map of Guizhou Province. (<b>a</b>) Global map distribution, (<b>b</b>) China elevation distribution map, (<b>c</b>) Guizhou elevation distribution map).</p>
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<p>Research method flowchart.</p>
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<p>Linear fitting between tropospheric NO<sub>2</sub> column concentration and ground measured values.</p>
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<p>Time variation chart of NO<sub>2</sub> column concentration in Guizhou Province from 2019 to 2022 (note: (<b>a</b>) represents the monthly average column concentration variation of NO<sub>2</sub>, (<b>b</b>) represents the seasonal and annual average column concentration variation trend of NO<sub>2</sub>).</p>
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<p>Annual mean spatial distribution of NO<sub>2</sub> column concentration in Guizhou Province from 2019 to 2022.(note: (<b>a</b>) Spatial and temporal distribution map of NO<sub>2</sub> column concentration in 2019, (<b>b</b>) Spatial and temporal distribution map of NO<sub>2</sub> column concentration in 2020, (<b>c</b>) Spatial and temporal distribution map of NO<sub>2</sub> column concentration in 2021, (<b>d</b>) Spatial and temporal distribution map of NO<sub>2</sub> column concentration in 2022).</p>
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<p>NO<sub>2</sub> column concentration Global Moran’s I index map.(note: (<b>a</b>) 2019 Global Moran’s I index map, (<b>b</b>) 2020 Global Moran’s I index map, (<b>c</b>) 2021 Global Moran’s I index map, (<b>d</b>) 2022 Global Moran’s I index map. The scatter points in the figure represent the observed values, which are the standard NO<sub>2</sub> column concentration values and their spatial lag values for each region. The purple line is a regression line that shows the linear relationship between the standard NO<sub>2</sub> column concentration and its spatial lag value).</p>
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<p>Local spatial aggregation distribution of NO<sub>2</sub> column concentration in Guizhou Province from 2019 to 2022. (note: In the (<b>a1</b>) figure, the distributions of NO<sub>2</sub> column concentration hotspots and cold spots are shown for (a) 2019, (b) 2020, (c) 2021, and (d) 2022. In the (<b>b1</b>) figure, the Local Moran’s index index of NO<sub>2</sub> column concentration is shown for (a) 2019, (b) 2020, (c) 2021, and (d) 2022).</p>
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<p>Interaction of factors influencing NO<sub>2</sub> column concentration. ((*) indicates that the interaction between two factors is non-linear enhancement, while the rest is linear enhancement).</p>
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21 pages, 8165 KiB  
Article
Field Investigation on the Thermal Environment and Comfort of People Exercising in a Fitness Center
by Haiying Wang, Yongwang Hao, Huxiang Lin, Rongfu Hou and Kefei Gong
Buildings 2024, 14(10), 3296; https://doi.org/10.3390/buildings14103296 - 18 Oct 2024
Viewed by 464
Abstract
A favorable thermal environment in fitness centers is important to attract more members and is beneficial to the health of exercising people. The purpose of this study was to research the actual thermal environment of a typical fitness center in different seasons and [...] Read more.
A favorable thermal environment in fitness centers is important to attract more members and is beneficial to the health of exercising people. The purpose of this study was to research the actual thermal environment of a typical fitness center in different seasons and the thermal requirement of exercising people. A field investigation covering winter, spring, and summer was conducted. The environmental parameters were measured. Subjective questionnaires involving individual information, clothing insulation, thermal sensation, etc., were collected. Participants’ heart rates were tested to estimate their metabolic rate (MR). A total of 740 valid questionnaires were collected. The results showed that a scissors gap existed between the predictive mean vote (PMV) and the thermal sensation vote (TSV) for the exercising people. For the higher MR group, there was a separation between the TSV and thermal preference vote, e.g., most participants would not prefer to cooler or warmer thermal environment when they felt hot or cold. The CO2 concentration changed greatly among seasons and the distribution in the fitness center was not uniform. With mechanical ventilation, the CO2 concentration in summer was the lowest. In other seasons it became much higher due to limited natural ventilation. However, subjective response to indoor air quality showed no significant difference among seasons. The participants felt more satisfied to the overall thermal environment in the transition season. The results can be referenced in the thermal environment management in fitness centers during seasonal changes. Full article
(This article belongs to the Special Issue Recently Advances in the Thermal Performance of Buildings)
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<p>Inside view and layout of fitness center (the red dots show the location of ten measuring points).</p>
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<p>Field pictures of measuring HR.</p>
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<p>Distribution of Age.</p>
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<p>Environmental parameters. (<b>a</b>) Indoor and outdoor air temperature, (<b>b</b>) indoor and outdoor RH, (<b>c</b>) the CO<sub>2</sub> concentration.</p>
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<p>Statistics of clothing insulation in each season.</p>
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<p>Percentage of people with different metabolic rate by season.</p>
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<p>Thermal sensation vote (the explanation of black symbol applies for all colors in the figure).</p>
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<p>Thermal preference.</p>
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<p>Statistics of thermal sensation and thermal preference, (<b>a</b>) winter, (<b>b</b>) transition season, and (<b>c</b>) summer.</p>
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<p>Thermal comfort vote.</p>
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<p>Overall thermal environment satisfaction.</p>
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<p>Statistics of the TCV (green part shows the data of low MR group, the red part shows the data of high MR group).</p>
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<p>Statistics of thermal satisfaction.</p>
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<p>Sweat feeling, (<b>a</b>) changes with operative temperature, (<b>b</b>) effect of MR and season.</p>
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<p>Air quality satisfaction, (<b>a</b>) changes with operative temperature, (<b>b</b>) effect of MR and season.</p>
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<p>Smell sensation, (<b>a</b>) changes with operative temperature, (<b>b</b>) effect of MR and season.</p>
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<p>Comparison of TSV and PMV.</p>
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<p>Comparison of TSV (the yellow part covers the temperature range of the gym in summer).</p>
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26 pages, 8947 KiB  
Article
Angle of Attack Characteristics of Full-Active and Semi-Active Flapping Foil Propulsors
by Lei Mei, Wenhui Yan, Junwei Zhou, Yongqi Tang and Weichao Shi
Water 2024, 16(20), 2957; https://doi.org/10.3390/w16202957 - 17 Oct 2024
Viewed by 503
Abstract
As a propulsor with a good application prospect, the flapping foil has been a hot research topic in the past decade. Although the research results of flapping foils have been very abundant, the performance-influencing mechanism of flapping foils is still not perfect, and [...] Read more.
As a propulsor with a good application prospect, the flapping foil has been a hot research topic in the past decade. Although the research results of flapping foils have been very abundant, the performance-influencing mechanism of flapping foils is still not perfect, and the research considering three-dimensional (3D) effects for engineering applications is still very limited. Based on the above considerations, a systematic and parametric analysis of a small aspect ratio flapping foil is conducted to correlate the influencing factors including angle of attack (AoA) characteristics and wake vortex on the propulsive efficiency. Three-dimensional numerical analyses of full-active and semi-active flapping foils are carried out in this paper, in which the former focuses on different heave amplitudes and pitch amplitudes, and the latter concentrates on different spring stiffnesses. The analysis covers the full range of advance coefficient, which starts around 0 and ends at a thrust drop of 0. Firstly, the influence of the maximum AoA (αmax) on the efficiency and thrust coefficient of these two kinds of flapping foils is analyzed. The results show that for the small aspect ratio flapping foil in this paper, regardless of the full-active or semi-active form, the peak efficiency as high as 75% for both generally appears around αmax = 0.2 rad, while the peak thrust coefficient of 0.5 occurs near αmax = 0.3 rad. Then, by analyzing the wake flow field, it is found that the lower efficiency of larger αmax working points is mainly due to the larger vortex dissipation loss, while the lower efficiency of smaller αmax working points is mainly due to the larger friction loss of the foil surface. Furthermore, the plumpness of different AoA curves is compared and analyzed. It was found that, unlike the results of full-active flapping foils, the shape of the AoA curve of semi-active flapping foils with different spring stiffnesses is similar, and the relationship with efficiency is not strictly corresponding. This study is expected to provide guidance on both academics and industries in relevant fields. Full article
(This article belongs to the Special Issue CFD in Fluid Machinery Design and Optimization)
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<p>Three-dimensional geometric shape of the flapping foil.</p>
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<p>Sketch of the flapping foil propulsion motion.</p>
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<p>Schematic illustration of a semi-active flapping foil with forced heave motion and attached torsion spring (The blue part is the torsion spring, and the red part is the rigid connection with the actuator).</p>
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<p>Schematic diagram of the computational domain and gradual mesh refinement.</p>
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<p>Comparisons of the propulsive efficiency <span class="html-italic">η</span> and the thrust coefficient <span class="html-italic">c<sub>T</sub></span> with previous experimental results for <span class="html-italic">α<sub>max</sub></span> = 20°.</p>
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<p>Comparison of vorticity patterns visualized in the foil wake (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>S</mi> <mi>t</mi> </mrow> <mrow> <mi>D</mi> </mrow> </msub> <mo>=</mo> <mn>0.08</mn> <mo>,</mo> <mtext> </mtext> <msub> <mrow> <mi>A</mi> </mrow> <mrow> <mi>D</mi> </mrow> </msub> <mo>=</mo> <mn>1.4</mn> </mrow> </semantics></math>). (Experimental results are from Figure 3c in Schnipper [<a href="#B29-water-16-02957" class="html-bibr">29</a>]).</p>
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<p>Experimental site and related equipment.</p>
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<p>Comparisons of the propulsive efficiency <span class="html-italic">η</span> with experimental results for <span class="html-italic">α<sub>max</sub></span> = 20°.</p>
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<p>Comparison of hydrodynamic force between simulation and experimental results. (<b>a</b>) <span class="html-italic">J</span> = 2.45, (<b>b</b>) <span class="html-italic">J</span> = 5.24.</p>
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<p>Propulsive efficiency <span class="html-italic">η</span> and thrust coefficient <span class="html-italic">K<sub>T</sub></span> of a full-active flapping foil as function of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math>, for different pitching angles. (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>η</mi> <mo>−</mo> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> </mrow> </msub> <mo>−</mo> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Propulsive efficiency <span class="html-italic">η</span> and thrust coefficient <span class="html-italic">K<sub>T</sub></span> of a full-active flapping foil as function of advance coefficient <span class="html-italic">J</span>, for different pitching angles. (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>η</mi> <mo>−</mo> <mi>J</mi> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> </mrow> </msub> <mo>−</mo> <mi>J</mi> </mrow> </semantics></math>.</p>
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<p>Propulsive efficiency <span class="html-italic">η</span> of a full-active flapping foil as function of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math>, for a series of heaving amplitudes. (<b>a</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.3 rad, (<b>b</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.5 rad.</p>
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<p>Thrust coefficient <span class="html-italic">K<sub>T</sub></span> of a full-active flapping foil as function of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math>, for a series of heaving amplitudes. (<b>a</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.3 rad, (<b>b</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.5 rad.</p>
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<p>Propulsive efficiency <span class="html-italic">η</span> of a semi-active flapping foil as function of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mtext> </mtext> </mrow> </semantics></math>and advance coefficient <span class="html-italic">J</span> for a series of spring stiffness ratios. (<b>a</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.3 rad, (<b>b</b>) <span class="html-italic">θ</span><sub>0</sub> = 0.5 rad.</p>
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<p>Thrust coefficient <span class="html-italic">K<sub>T</sub></span> of a semi-active flapping foil as function of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math><span class="html-italic">,</span> for a series of heaving amplitudes. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> </mrow> </msub> <mo>−</mo> <mi>J</mi> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>K</mi> </mrow> <mrow> <mi>T</mi> </mrow> </msub> <mo>−</mo> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Vortex structure and distribution of an active flapping foil under six working conditions (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>y</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>/</mo> <mi>c</mi> <mo>=</mo> <mn>2.5</mn> <mo>,</mo> <mtext> </mtext> <msub> <mrow> <mi>θ</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>0.5</mn> <mtext> </mtext> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">d</mi> </mrow> </semantics></math>).</p>
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<p>Velocity distributions and tip vortex structure of flow field at different pitching angles.</p>
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<p>Sketch of forces on a flapping foil.</p>
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<p>Velocity cloud diagrams and tip vortex structures of flow fields at different heave amplitudes.</p>
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<p>Flow field vortex structure of a semi-active flapping foil with different spring stiffnesses.</p>
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<p>Comparison of AoA time-history curves at different pitch amplitudes.</p>
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<p>AoA duration curves of a semi-active flapping foil with different spring stiffnesses. (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>0.18</mn> <mtext> </mtext> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">d</mi> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>0.5</mn> <mtext> </mtext> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">d</mi> </mrow> </semantics></math>.</p>
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<p>Pitch motion and AoA time-history curves of a semi-active flapping foil (<math display="inline"><semantics> <mrow> <msup> <mi>K</mi> <mo>′</mo> </msup> <mo>=</mo> <mn>0.8</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> <mo>=</mo> <mn>0.18</mn> <mo>,</mo> <mtext> </mtext> <mn>0.5</mn> <mtext> </mtext> <mi mathvariant="normal">r</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">d</mi> </mrow> </semantics></math>).</p>
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12 pages, 1426 KiB  
Article
Resilience of Pinus pinea L. Trees to Drought in Central Chile Based on Tree Radial Growth Methods
by Verónica Loewe-Muñoz, Rodrigo Del Río, Claudia Delard, Antonio M. Cachinero-Vivar, J. Julio Camarero, Rafael Navarro-Cerrillo and Mónica Balzarini
Forests 2024, 15(10), 1775; https://doi.org/10.3390/f15101775 - 9 Oct 2024
Viewed by 703
Abstract
The increasing occurrence of dry and hot summers generates chronic water deficits that negatively affect tree radial growth. This phenomenon has been widely studied in natural stands of native species but not in commercial plantations of exotic tree species. In central Chile, where [...] Read more.
The increasing occurrence of dry and hot summers generates chronic water deficits that negatively affect tree radial growth. This phenomenon has been widely studied in natural stands of native species but not in commercial plantations of exotic tree species. In central Chile, where the species is increasingly planted, the dynamics of stone pine (Pinus pinea L.) growth under drought have been little explored. We studied the impact of drought on four stone pine plantations growing in central Chile. We sampled and cross-dated a total of 112 trees from four sites, measured their tree-ring width (RWL) series, and obtained detrended series of ring width indices (RWIs). Then, we calculated three resilience indices during dry years (Rt, resistance; Rc, recovery; and Rs, resilience), and the correlations between the RWI series and seasonal climate variables. We found the lowest growth rate (1.94 mm) in the driest site (Peñuelas). Wet conditions in the previous winter and current spring favored growth. In the wettest site (Pastene), the growth rates were high (4.87 mm) and growth also increased in response to spring thermal amplitude. Overall, fast-growing trees were less resilient than slow-growing trees. Drought reduced stone pine stem growth and affected tree resilience to hydric deficit. At the stand level, growth rates and resistance were driven by winter and spring precipitation. Fast-growing trees were more resistant but showed less capacity to recover after a drought. In general, stone pine showed a high post-drought resilience due to a high recovery after drought events. The fact that we found high resilience in non-native habitats, opens new perspectives for stone pine cropping, revealing that it is possible to explore new areas to establish the species. We conclude that stone pine shows a good acclimation in non-native, seasonally dry environments. Full article
(This article belongs to the Special Issue Effects of Disturbances and Climate Change on Woody Plants)
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<p>Series of tree-ring width (RWL) difference in percentage from one year versus the previous one along years. Black bars correspond to negative pointer years, in which a growth deviation above 40% was observed in at least 75% of the trees in each of the three study plantations (Peñuelas, Cahuil, and Paredones) or in at least 50% of the trees in the wettest site (Pastene). RWL deviation was calculated as the ratio between RWL and the average RWL of the previous three years.</p>
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<p>Heatmap of Pearson’s correlation coefficients between series of ring width indices (RWIs) and seasonal climatic variables that were statistically correlated with RWIs in at least one site. Significant correlations (<span class="html-italic">p</span> &lt; 0.05) are shown with an asterisk. AT Wi: winter average temperature; Min T Wi: winter minimum temperature; Max T Sp: spring maximum temperature; TO Wi: winter thermal oscillation; TO Sp: spring thermal oscillation; RF Wi: winter rainfall; RF Sp: spring rainfall; HI Wi: winter hydric index; HI Sp: spring hydric index.</p>
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<p>Triplot from PLS regression showing how tree-ring width (RWL) and resilience components (Rs: resilience index, Rt: resistance index, Rc: recovery index) were related to climate variables (TO Wi: winter thermal oscillation; TO Sp: spring thermal oscillation; HI Wi: winter hydric index; HI Sp: spring hydric index; AT Wi: winter average temperature; Min T Wi: winter minimum temperature; Max T Sp: spring maximum temperature; RF Wi: winter rainfall; RF Sp: spring rainfall). Black dots show plantation sites.</p>
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23 pages, 7766 KiB  
Article
Hydrochemical Characteristics and Formation Mechanism of Geothermal Fluids in Zuogong County, Southeastern Tibet
by Sihang Han, Dawa Nan, Zhao Liu, Nima Gesang, Chengcuo Bianma, Haihua Zhao, Yadong Zheng and Peng Xiao
Water 2024, 16(19), 2852; https://doi.org/10.3390/w16192852 - 8 Oct 2024
Viewed by 571
Abstract
Zuogong County is located in the southeast of Tibet, which is rich in hot spring geothermal resources, but its development and utilization degree are low, and the genetic mechanism of the geothermal system is not clear. Hydrogeochemical characteristics of geothermal water are of [...] Read more.
Zuogong County is located in the southeast of Tibet, which is rich in hot spring geothermal resources, but its development and utilization degree are low, and the genetic mechanism of the geothermal system is not clear. Hydrogeochemical characteristics of geothermal water are of great significance in elucidating the genesis and evolution of geothermal systems, as well as the sustainable development and utilization of geothermal resources. The hydrogeochemical characteristics and genesis of the geothermal water in Zuogong County were investigated using hydrogeochemical analysis, a stable isotope (δD, δ18O) approach, and an inverse simulation model for water–rock reactions using the PHREEQC. The results indicated that the Zuogong geothermal system is a deep circulation heating type without a magmatic heat source. The chemical types present in the geothermal water from the Zuogong area are HCO3 and HCO3·SO4, and the main cations are Na+ and Ca2+. The groundwater is replenished by atmospheric precipitation and glacier meltwater. The salt content of geothermal water mainly comes from the interaction between water and surrounding rocks during the deep circulation process. The reservoir temperature of geothermal water in Zuogong is 120–176 °C before mixing with non-geothermal water and drops to 62–98 °C after mixing with 58 to 79% of non-geothermal water. According to the proposed conceptual model, geothermal water mainly produces water–rock interaction with aluminosilicate minerals in the deep formation, while in shallow areas it interacts mainly with sulfate minerals. These findings contribute to a better understanding of the geothermal system in Zuogong County, Tibet. Full article
(This article belongs to the Section Hydrogeology)
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<p>(<b>a</b>) Geological diagram of Zuogong County, Tibet. (<b>b</b>) Sampling location diagram of Zuogong County, Tibet.</p>
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<p>Piper diagram of thermal groundwater and non-thermal groundwater from Zuogong County, Tibet.</p>
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<p>The correlations of TDS vs. main anions and cations in thermal groundwater and non-thermal groundwater from Zuogong County, Tibet.</p>
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<p>The correlations of TDS vs. typical geothermal components in thermal groundwater and non-thermal groundwater from Zuogong County, Tibet.</p>
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<p>Cl–SO<sub>4</sub>–HCO<sub>3</sub> ternary diagram for the water samples in Zuogong County, Tibet.</p>
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<p>Box diagram of F, Li, B, and As components in geothermal water from Rehai, Yangbajing, and Zuogong.</p>
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<p>Box plots for measuring and estimating temperature. (Note: MEE: multicomponent mineral equilibrium; SEMM: silica-enthalpy mixing model).</p>
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<p>Saturation indices (SIs) vs. temperature to estimate reservoir temperature for four thermal wells and five thermal springs.</p>
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<p>Si-enthalpy of the thermal reservoir from the Zuogong, Tibet.</p>
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<p>The δD-δ<sup>18</sup>O relationship of groundwater in Zuogong County, Tibet.</p>
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<p>Na-K-Mg ternary diagram for the water samples in Zuogong County, Tibet.</p>
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<p>Conceptual model of geothermal genesis in Zuogong County, Tibet.</p>
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23 pages, 3848 KiB  
Article
Evaluation of Tidal Asymmetry and Its Effect on Tidal Energy Resources in the Great Island Region of the Gulf of California
by Anahí Bermúdez-Romero, Vanesa Magar, Manuel López-Mariscal and Jonas D. De Basabe
J. Mar. Sci. Eng. 2024, 12(10), 1740; https://doi.org/10.3390/jmse12101740 - 2 Oct 2024
Viewed by 625
Abstract
Hydrokinetic tidal energy is one of the few marine renewable energy resources with sufficiently mature technology for commercial exploitation. However, several parameters affect its exploitability, such as the minimum speed threshold, ambient turbulence levels, or tidal asymmetry, to name but a few. These [...] Read more.
Hydrokinetic tidal energy is one of the few marine renewable energy resources with sufficiently mature technology for commercial exploitation. However, several parameters affect its exploitability, such as the minimum speed threshold, ambient turbulence levels, or tidal asymmetry, to name but a few. These parameters are particularly important in regions with lower mean speeds than those in first-generation sites, such as the North Sea. The Gulf of California is one of those regions. In this paper, a Delft3D Flexible Mesh Suite (Delft3D FM) model in barotropic configuration is set up over the Gulf of California using a flexible mesh with resolution varying from O (500 m) in the deep regions to O (10 m) in the coastal regions. A simulation is run over the year of 2020, with a tidal forcing of 75 components. The model is validated at four tidal gauge locations and four Acoustic Doppler Current profiler (ADCP) locations. The speed, U, and tidal power density (TPD) indicators used for the validation were the annual means, the annual means for speeds above the 0.5 m s−1 threshold, the annual means of the spring tide maxima, and the annual maxima. The contour maps of the annual means, that is, the annual means for speeds above the 0.5 m s−1 threshold, allow us to identify tidal energy hot spots throughout the Gulf of California, particularly in the Great Island region (GIR). In this region, these hot spots have higher U and TPD values, in agreement with previous studies. The patterns of circulation around Tiburón Island and San Esteban Island on the East, and Ángel de la Guarda Island and San Lorenzo Island on the West, the four islands in the region with the highest tidal energy potential, are also discussed while recognizing that Tiburón Channel, between Tiburón Island and San Esteban Island, has proved to be the best siting location, based on the technical results obtained so far. The hot spots sites are further characterized by computing the tidal asymmetry in these small regions, showing the locations of the sites with smallest asymmetry, which would be the best for tidal energy exploitation. The hot spots around San Esteban Island are particularly important because they have the largest TPD in the GIR, with the model predicting a TPD on the order of 500–1000 W m−2. Here, complementary field measurements obtained with two ADCPs, close to San Esteban Island, one at 15 m depth, SEs (shallow region), and the other at 60 m depth, SEd (deep region), produced TPDs of 1200 W m−2 and 400 W m−2, respectively. The analysis of the vertical profiles and the tidal asymmetry over the vertical shows the importance of developing 3D models in future investigations. Full article
(This article belongs to the Special Issue Advances in Marine Computational Fluid Dynamics)
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<p>Study region with bathymetry provided by the product GEBCO2019, and locations of islands and ADCP moorings used for validation in the midriff region. Island Acronyms—AGI: Angel de la Guarda Island. SLI: San Lorenzo Island. SEI: San Esteban Island. TI: Tiburon Island. ADCP Mooring Acronyms—BC: Ballenas Channel. DE: Delfin Sill. SL: San Lorenzo. SE: San Esteban. The insert shows the boundaries of the 24 subdomains generated to run the model. A coordinate system showing the velocity component <span class="html-italic">u</span> perpendicular to the GoC and the velocity component <span class="html-italic">v</span> along the GoC, is included for reference.</p>
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<p>Central computing subdomain with part of SEI, showing the grid refinement near the shoreline.</p>
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<p>Annual mean speed (m s<sup>−1</sup>) predicted by the model. The thin black lines show the model subdomains used for computational purposes, as described in the grid generation and analysis <a href="#sec2dot2-jmse-12-01740" class="html-sec">Section 2.2</a>.</p>
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<p>Time series of observed (blue continuous line) and modeled (red dashed line) flow velocity (<b>left panel</b>) and tidal levels (<b>right panel</b>) for two spring–neap cycles.</p>
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<p>Maps for the two largest islands in the GIR, AGI, and TI. (<b>Upper panels</b>): annual mean of threshold speed, <math display="inline"><semantics> <msub> <mover> <mi>U</mi> <mo>¯</mo> </mover> <mrow> <mi>t</mi> <mi>h</mi> </mrow> </msub> </semantics></math> [m s<sup>−1</sup>]. (<b>Lower panels</b>): annual mean threshold Tidal Power Density, <math display="inline"><semantics> <msub> <mover> <mrow> <mi>T</mi> <mi>P</mi> <mi>D</mi> </mrow> <mo>¯</mo> </mover> <mrow> <mi>t</mi> <mi>h</mi> </mrow> </msub> </semantics></math> [W m<sup>−2</sup>].</p>
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<p>Maps for SEI and SLI. (<b>Upper panels</b>): annual mean threshold speed, <math display="inline"><semantics> <msub> <mover> <mi>U</mi> <mo>¯</mo> </mover> <mrow> <mi>t</mi> <mi>h</mi> </mrow> </msub> </semantics></math> [m s<sup>−1</sup>]. (<b>Lower panels</b>): annual mean threshold Tidal Power Density, <math display="inline"><semantics> <msub> <mover> <mrow> <mi>T</mi> <mi>P</mi> <mi>D</mi> </mrow> <mo>¯</mo> </mover> <mrow> <mi>t</mi> <mi>h</mi> </mrow> </msub> </semantics></math> [W m<sup>−2</sup>].</p>
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<p><math display="inline"><semantics> <mrow> <mi>T</mi> <mi>F</mi> <mi>A</mi> </mrow> </semantics></math> maps around AGI (<b>left panel</b>) and TI (<b>right panel</b>), with contour lines of the <math display="inline"><semantics> <msub> <mi>U</mi> <mrow> <mi>t</mi> <mi>h</mi> </mrow> </msub> </semantics></math>. The white arrows represent a schematic of the direction of the residual circulation around each of the islands.</p>
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<p>Maps of <math display="inline"><semantics> <mrow> <mi>T</mi> <mi>F</mi> <mi>A</mi> </mrow> </semantics></math> around SEI (<b>left panel</b>) and SLI (<b>right panel</b>) with contour lines of the <math display="inline"><semantics> <msub> <mi>U</mi> <mrow> <mi>t</mi> <mi>h</mi> </mrow> </msub> </semantics></math>. Schematics of general residual circulation around SEI and SLI are also shown.</p>
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<p>Current speed (<b>left panels</b>) and TPD (<b>right panels</b>) for S1 (flood dominant tide), S2 (ebb dominant tide), and S3 (symmetrical tide). The blue line indicates flood periods and red lines ebb periods.</p>
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<p>Speed profiles of a neap–spring tidal cycle for SEd (<b>upper panel</b>) and SEs (<b>low panel</b>). The white line shows <span class="html-italic">v</span>, the depth-averaged velocity component along the GoC.</p>
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<p>TPD profiles for a 15-day period at SEd (<b>upper panel</b>) and SEs (<b>low panel</b>).</p>
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<p>Vertical profile for SEd (<b>left panel</b>) and SEs (<b>right panel</b>). Vertical averages of speed for flood and ebb periods are shown on the top of the panels.</p>
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32 pages, 5708 KiB  
Review
Plastic-Degrading Enzymes from Marine Microorganisms and Their Potential Value in Recycling Technologies
by Robert Ruginescu and Cristina Purcarea
Mar. Drugs 2024, 22(10), 441; https://doi.org/10.3390/md22100441 - 26 Sep 2024
Viewed by 3196
Abstract
Since the 2005 discovery of the first enzyme capable of depolymerizing polyethylene terephthalate (PET), an aromatic polyester once thought to be enzymatically inert, extensive research has been undertaken to identify and engineer new biocatalysts for plastic degradation. This effort was directed toward developing [...] Read more.
Since the 2005 discovery of the first enzyme capable of depolymerizing polyethylene terephthalate (PET), an aromatic polyester once thought to be enzymatically inert, extensive research has been undertaken to identify and engineer new biocatalysts for plastic degradation. This effort was directed toward developing efficient enzymatic recycling technologies that could overcome the limitations of mechanical and chemical methods. These enzymes are versatile molecules obtained from microorganisms living in various environments, including soil, compost, surface seawater, and extreme habitats such as hot springs, hydrothermal vents, deep-sea regions, and Antarctic seawater. Among various plastics, PET and polylactic acid (PLA) have been the primary focus of enzymatic depolymerization research, greatly enhancing our knowledge of enzymes that degrade these specific polymers. They often display unique catalytic properties that reflect their particular ecological niches. This review explores recent advancements in marine-derived enzymes that can depolymerize synthetic plastic polymers, emphasizing their structural and functional features that influence the efficiency of these catalysts in biorecycling processes. Current status and future perspectives of enzymatic plastic depolymerization are also discussed, with a focus on the underexplored marine enzymatic resources. Full article
(This article belongs to the Special Issue Bioactive Molecules from Extreme Environments III)
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Graphical abstract

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<p>Plastic life cycle: from production using fossil or biomass resources to disposal or recycling through mechanical (blue line), chemical (yellow line), and biological (green line) methods. Recycling processes involving enzymes and/or microorganisms are indicated with a red asterisk (*).</p>
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<p>Schematic representation of the enzymatic hydrolysis of semicrystalline and highly amorphous PET. Hydrolases preferentially bind to the mobile amorphous regions, breaking them down into simpler molecules: TA, EG, MHET, and BHET.</p>
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<p>Unrooted phylogenetic tree of 15 PET hydrolases derived from marine microorganisms, along with LCC, <span class="html-italic">Is</span>PETase, and HiC. The tree illustrates three distinct enzyme clusters: type I (grey background), type IIa (yellow background), and type IIb (reddish background), as per the classification proposed by Joo et al. [<a href="#B74-marinedrugs-22-00441" class="html-bibr">74</a>]. Unclassified enzymes are shown on a white background. Symbols in the legend indicate the taxonomic origin of each PET hydrolase. Amino acid sequences, with accession numbers in parentheses, were obtained from GenBank or UniProtKB. Sequence alignment was performed using ClustalW version 2.0. The phylogenetic tree was constructed using MEGA X 10.0 software [<a href="#B75-marinedrugs-22-00441" class="html-bibr">75</a>] with the maximum likelihood method, employing the LG + F model and 100 bootstrap replications.</p>
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<p>Cartoon representations of the three-dimensional structures of PET6 and Mors1. Disulfide bonds are labeled DS1–3 in PET6 and DB1–3 in Mors1. Insets for Mors1 show close-ups of the three DBs, with black mesh representing the electron density of each DB. The catalytic residues Ser-Asp-His are shown in both representations. Reproduced with permission from ref [<a href="#B64-marinedrugs-22-00441" class="html-bibr">64</a>] (PET6) and ref [<a href="#B63-marinedrugs-22-00441" class="html-bibr">63</a>] (Mors1).</p>
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<p>Crystal structure of PE-H. (<b>A</b>) Cartoon representation of the three-dimensional structure. The extended loop region and the Cys residues forming disulfide bonds are highlighted in orange. Residues of the catalytic triad are shown as gray ball-and-stick models with labels. (<b>B</b>) Surface representation of the wild-type (WT) PE-H and the Y250S mutant variant. The electrostatic surface is color-coded: blue for positive charge and red for negative charge. The active site cleft is indicated by a dashed line. Adapted from ref [<a href="#B68-marinedrugs-22-00441" class="html-bibr">68</a>].</p>
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<p>Crystal structure of PET46. (<b>A</b>) Cartoon representation of the three-dimensional structure, with highlighted lid domain, loop 1, and loop 2. The four aromatic residues of the lid domain (1–4, bright green) and the residues involved in catalytic activity and substrate binding are shown as stick models. (<b>B</b>) Cartoon representation of PET46′s three-dimensional structure (coral orange) overlaid with the structure of the cinnamoyl esterase LJ0536 S106A mutant from <span class="html-italic">Lactobacillus johnsonii</span> (dark gray) in complex with ethylferulate (EF, cyan). Adapted from ref [<a href="#B73-marinedrugs-22-00441" class="html-bibr">73</a>].</p>
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<p>Crystal structure of RPA1511. (<b>A</b>) Cartoon representation of the three-dimensional structure, with the core domain shown in gray and the lid domain colored in cyan. (<b>B</b>) Protomer with bound PEG 3350 (dodecaethylene glycol, shown as sticks). (<b>C</b>) Surface representation of the protomer revealing the active site cleft with bound PEG 3350 (shown as green sticks). The electrostatic surface is color-coded: blue for positive charge, red for negative charge, and white for neutral. (<b>D</b>) Close-up view of the PEG 3350 molecule bound close to the catalytic triad (shown as sticks). Adapted with permission from ref [<a href="#B103-marinedrugs-22-00441" class="html-bibr">103</a>]. Copyright 2016 American Chemical Society.</p>
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16 pages, 541 KiB  
Review
Advances in Extremophile Research: Biotechnological Applications through Isolation and Identification Techniques
by Giovanni Gallo and Martina Aulitto
Life 2024, 14(9), 1205; https://doi.org/10.3390/life14091205 - 23 Sep 2024
Viewed by 2522
Abstract
Extremophiles, organisms thriving in extreme environments such as hot springs, deep-sea hydrothermal vents, and hypersaline ecosystems, have garnered significant attention due to their remarkable adaptability and biotechnological potential. This review presents recent advancements in isolating and characterizing extremophiles, highlighting their applications in enzyme [...] Read more.
Extremophiles, organisms thriving in extreme environments such as hot springs, deep-sea hydrothermal vents, and hypersaline ecosystems, have garnered significant attention due to their remarkable adaptability and biotechnological potential. This review presents recent advancements in isolating and characterizing extremophiles, highlighting their applications in enzyme production, bioplastics, environmental management, and space exploration. The unique biological mechanisms of extremophiles offer valuable insights into life’s resilience and potential uses in industry and astrobiology. Full article
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<p>Schematic overview of the main environments where extremophilic microorganisms are found.</p>
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23 pages, 17565 KiB  
Article
Mapping the Spatial and Seasonal Details of Heat Health Risks in Different Local Climate Zones: A Case Study of Shanghai, China
by Lilong Yang, Chaobin Yang, Weiqi Zhou, Xueye Chen, Chao Wang and Lifeng Liu
Remote Sens. 2024, 16(18), 3513; https://doi.org/10.3390/rs16183513 - 21 Sep 2024
Viewed by 684
Abstract
In the context of global climate change and rapid population growth, more people in cities are facing heat threats. Although health risk assessment is critical for reducing heat-related morbidity and mortality, previous studies have not accurately identified the spatial details of heat risk [...] Read more.
In the context of global climate change and rapid population growth, more people in cities are facing heat threats. Although health risk assessment is critical for reducing heat-related morbidity and mortality, previous studies have not accurately identified the spatial details of heat risk levels on a fine scale within a complete framework. Therefore, this study developed a systematic method to conduct a spatially explicit assessment of heat-related health risks using local climate zones (LCZs) in Shanghai, China. First, multisource data were used to map LCZs in Shanghai. Second, a modified temperature-humidity index, population density, and ecological parameters were employed to construct a heat hazard–exposure–vulnerability framework for heat risk assessment. Finally, the differences in heat-related health risks among LCZs were compared. The results indicate that in Shanghai (1) the LCZ concept could help estimate the heat health risk (HHR) at the fine block level, and the area proportion of LCZ5 (open mid-rise buildings) accounted for more than 50%; (2) detailed spatial patterns of heat risk levels were similar in spring, summer, and autumn, but different in winter due to seasonal variations in heat hazards; and (3) the built LCZs usually had higher heat risk levels than natural land cover LCZs, with LCZ2 (compact mid-rise), LCZ3 (compact low-rise), and LCZ5 facing the most serious heat risks. The high-rise LCZs might reduce the heat risk level in hot seasons owing to shading effects but add to this risk in winter. These findings contribute to our understanding of HHR assessment. Full article
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<p>Study area location.</p>
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<p>Study framework and workflow.</p>
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<p>LCZ spatial patterns in Shanghai.</p>
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<p>Spatial distributions of LST in Shanghai.</p>
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<p>Spatial patterns of thermal comfort in Shanghai.</p>
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<p>Area proportions of different MTHI levels among LCZ types in Shanghai.</p>
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<p>Spatial patterns of population density (<b>a</b>), ISA (<b>b</b>), exposure levels (<b>c</b>), and the area proportion of different exposure levels for the whole area and among LCZ types (<b>d</b>).</p>
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<p>Distribution of sensitive populations (<b>a</b>), proportion of vegetation and water (<b>b</b>), vulnerability levels (<b>c</b>), and the proportion of different heat vulnerabilities among LCZ types (<b>d</b>).</p>
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<p>Seasonal spatial patterns of heat hazard health risk for Shanghai in spring (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>), and winter (<b>d</b>).</p>
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<p>Area proportion of different HHR levels among seasons.</p>
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<p>Area proportion of different HHR levels for Shanghai city in (<b>a</b>) spring, (<b>b</b>) summer, (<b>c</b>) autumn, and (<b>d</b>) winter.</p>
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<p>The Landsat LST time series from 1 January 2020 to 31 December 2022; Winter: January, February, December; Spring: March, April, May; Summer: June, July, August, September; Fall: October, November.</p>
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12 pages, 2208 KiB  
Article
Coaggregation Occurs between a Piliated Unicellular Cyanobacterium, Thermosynechococcus, and a Filamentous Bacterium, Chloroflexus aggregans
by Megumi Kono and Shin Haruta
Microorganisms 2024, 12(9), 1904; https://doi.org/10.3390/microorganisms12091904 - 19 Sep 2024
Viewed by 729
Abstract
Cyanobacteria are widely distributed in natural environments including geothermal areas. A unicellular cyanobacterium, Thermosynechococcus, in a deeply branching lineage, develops thick microbial mats with other bacteria, such as filamentous anoxygenic photosynthetic bacteria in the genus Chloroflexus, in slightly alkaline hot-spring water [...] Read more.
Cyanobacteria are widely distributed in natural environments including geothermal areas. A unicellular cyanobacterium, Thermosynechococcus, in a deeply branching lineage, develops thick microbial mats with other bacteria, such as filamentous anoxygenic photosynthetic bacteria in the genus Chloroflexus, in slightly alkaline hot-spring water at ~55 °C. However, Thermosynechococcus strains do not form cell aggregates under axenic conditions, and the cells are dispersed well in the culture. In this study, Thermosynechococcus sp. NK55a and Chloroflexus aggregans NBF, isolated from Nakabusa Hot Springs (Nagano, Japan), were mixed in an inorganic medium and incubated at 50 °C under incandescent light. Small cell aggregates were detected after 4 h incubation, the size of cell aggregates increased, and densely packed cell aggregates (100–200 µm in diameter) developed. Scanning electron microscopy analysis of cell aggregates found that C. aggregans filaments were connected with Thermosynechococcus sp. cells via pili-like fibers. Co-cultivation of C. aggregans with a pili-less mutant of Thermosynechococcus sp. did not form tight cell aggregates. Cell aggregate formation was observed under illumination with 740 nm LED, which was utilized only by C. aggregans. These results suggested that Chloroflexus filaments gather together via gliding motility, and piliated cyanobacterial cells cross-link filamentous cells to form densely packed cell aggregates. Full article
(This article belongs to the Special Issue Phototrophic Bacteria 2.0)
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<p>Co-cultivation of <span class="html-italic">Thermosynechococcus</span> sp. NK55a and <span class="html-italic">C. aggregans</span> NBF under incandescent light. <span class="html-italic">Thermosynechococcus</span> sp. NK55a and <span class="html-italic">C. aggregans</span> NBF were co-inoculated into 5 mL of BG11 medium in a 25 mL glass vial and cultivated at 50 °C in incandescent light. (<b>a</b>) Photographs of the glass vial (top) and bright-field micrographs of the culture solution (bottom; bars, 100 μm) after 0 h, 4 h, 8 h, and 12 h incubation. (<b>b</b>) The cellular protein amount was determined for the filtrate and residue after size fractionation by filtration (pore size, 20 μm), and the percentage of the protein amount of residue on the filter in the total amount (filtrate and residue) was calculated as the aggregation index. Each bar indicates the average of three independent cultivations with three replicates. Error bars indicate standard deviations.</p>
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<p>Microscopic observation of cell aggregates in the co-culture. (<b>a-1</b>,<b>a-2</b>) Fluorescence micrographs of cell aggregates after 16 h incubation with acridine orange staining. Reddish and greenish colors were detected by 460 nm excitation and 395 to 440 nm excitation, respectively, with a long-pass emission filter. Bars, 20 µm. (<b>b-1</b>,<b>b-2</b>) SEM images of cell aggregates after 12 h incubation. Bars, 1 µm.</p>
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<p>Analyses of the Δ<span class="html-italic">pilB</span> mutant strain of <span class="html-italic">Thermosynechococcus</span> sp. NK55a. (<b>a</b>) TEM images of cells of the Δ<span class="html-italic">pilB</span> mutant strain of <span class="html-italic">Thermosynechococcus</span> sp. NK55a (Δ<span class="html-italic">pilB</span> mutant) and the wild-type strain (Wild type). Top, bars, 500 nm; bottom, close-up view of the area surrounded by a dotted square in the top images, bars, 100 nm. (<b>b</b>) The Δ<span class="html-italic">pilB</span> mutant strain and <span class="html-italic">C. aggregans</span> NBF were co-cultivated, and aggregation indexes just before cultivation and after 12 h cultivation were determined as carried out in <a href="#microorganisms-12-01904-f001" class="html-fig">Figure 1</a>. Each bar indicates the average of three independent cultivations with three replicates. Error bars indicate standard deviations. (<b>c</b>) Photographs of the glass vial (top) and bright-field micrographs of the culture solution (bottom: bars, 100 μm) after 12 h co-cultivation of the Δ<span class="html-italic">pilB</span> mutant strain and <span class="html-italic">C. aggregans</span> NBF.</p>
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<p>Co-cultivation of <span class="html-italic">Thermosynechococcus</span> sp. NK55a and <span class="html-italic">C. aggregans</span> NBF in the dark and LED arrays. <span class="html-italic">Thermosynechococcus</span> sp. NK55a and <span class="html-italic">C. aggregans</span> NBF were co-cultivated for 12 h as in <a href="#microorganisms-12-01904-f001" class="html-fig">Figure 1</a> but under different illumination conditions: dark, 630 nm LED array, 630 nm and 740 nm LED arrays, and 740 nm LED array. Top, photographs of the glass vials; bottom, bright-field micrographs of the culture solution. Bars, 100 μm.</p>
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<p>Schematic drawing of cell aggregate formation steps (from left to right) by <span class="html-italic">C. aggregans</span> (thin filaments) with <span class="html-italic">Thermosynechococcus</span> sp. (piliated rod-shaped cell). <span class="html-italic">Thermosynechococcus</span> cells attach to <span class="html-italic">C. aggregans</span> filaments via pili. The gliding motility of <span class="html-italic">C. aggregans</span> hauls in its filamentous cells. <span class="html-italic">C. aggregans</span> filaments are cross-linked by <span class="html-italic">Thermosynechococcus</span> cells to form firmly packed cell aggregates. Arrows indicate the direction of the gliding motility of <span class="html-italic">C. aggregans</span> filaments.</p>
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6 pages, 4329 KiB  
Proceeding Paper
Study of the Amylolytic Activity of Thermophilic Bacteria Isolated from an Algerian Hot Spring (Azzaba, Skikda)
by Sarra Bouaita, Zahra Sayad, Douaa Ziani, Rayane Bouguerba and Mohamed Amine Gomri
Biol. Life Sci. Forum 2024, 36(1), 8; https://doi.org/10.3390/blsf2024036008 - 6 Sep 2024
Viewed by 424
Abstract
Thermostable amylases are among the most widely used and desirable enzymes in the food industry. Indeed, they guarantee faster reactions at high temperatures, enhanced substrate solubility and reduced microbial contamination and cooling costs. The objective of this work is to study the amylase [...] Read more.
Thermostable amylases are among the most widely used and desirable enzymes in the food industry. Indeed, they guarantee faster reactions at high temperatures, enhanced substrate solubility and reduced microbial contamination and cooling costs. The objective of this work is to study the amylase activity of three strains of aerobic thermophilic bacteria isolated from the hot spring of Hammam Salhine, located in the wilaya of Skikda, Algeria. The three extracellular amylase-producing strains were subjected to the quantification of amylase activity. They presented medium to high activity, with significantly the best production for the AS1 strain with an activity of 10.62 ± 1.289 U (p > 0.05). Monitoring the kinetics of AS1 amylase activity reveals that the maximum enzymatic activity was reached after 52 h with a value of 53.665 ± 2.534 U. The maximum growth was reached after 54 h of fermentation at an OD of 0.865 ± 0.081 at 600 nm. The study of the effect of the variation in physicochemical parameters on the activity of AS1 amylase extract shows that the enzymatic activity was maximal at a temperature of 100 °C, a pH of 8.0 and in the absence of NaCl. The amylase extract of this strain showed significant thermostability at 100 °C. Full article
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<p>Observation of strains under an immersion photonic microscope (×1000) after Gram staining.</p>
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<p>Morphological and growth optima. (<b>A</b>) Temperature, (<b>B</b>) pH, (<b>C</b>) NaCl. The starred bars indicate significant difference (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Results of total amylolytic activity assay for the three strains. One unit of amylase activity is defined as the amount of enzyme that releases 1 µmol of glucose-equivalent reducing sugar per minute under the conditions of the assay. The starred bar indicates a significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Growth and production kinetics of extracellular amylases in the AS1 strain.</p>
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<p>Study of the effect of the variation in physico-chemical parameters on the activity of the amylase extract of strain AS1. (<b>A</b>) Effect of T°; (<b>B</b>) thermostability; (<b>C</b>) pH variation; (<b>D</b>) NaCl concentration.</p>
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