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30 pages, 9693 KiB  
Article
Characteristics of Summer Flash Drought and Its Effect on Maize Growth in Liaoning Province, China
by Ruipeng Ji, Wenying Yu, Baihui Guo, Rui Feng, Jinwen Wu, Dongming Liu and Changhua Xu
Agronomy 2024, 14(8), 1791; https://doi.org/10.3390/agronomy14081791 - 14 Aug 2024
Abstract
Flash droughts, characterized by their abrupt onset and rapid intensification, are predicted to increase in frequency and severity under global warming. Understanding the incidence and progression of a flash drought and its impact on maize growth is crucial for maize production to withstand [...] Read more.
Flash droughts, characterized by their abrupt onset and rapid intensification, are predicted to increase in frequency and severity under global warming. Understanding the incidence and progression of a flash drought and its impact on maize growth is crucial for maize production to withstand flash drought events. This study used the evaporative demand drought index (EDDI) method to evaluate the incidence of summer drought in Liaoning during the period 1961–2020. It examined the incidence and characteristics of summer flash droughts in Liaoning Province in the period of 1961–2020 and evaluated the factors responsible and the impact on maize during the critical development period. The ratio of the number of stations recording a disaster to total number of stations (IOC) curve, i.e., the ratio of the number of stations recording disasters and total stations, for summer flash droughts in Liaoning showed an upward trend during the period of 1961–2020, with large-scale, regional, and local flash droughts occurring in 8, 10, and 31 years, respectively. Summer flash droughts in Liaoning were mainly in the extreme drought category and ranged in frequency from 10% to 20% in most areas. Before the flash drought occurrence in three typical years (1989, 1997, and 2018), a precipitation deficit without large-scale high-temperature events was observed, and the cumulative water deficit caused the flash drought. Regional or large-scale high-temperature events were often accompanied by flash droughts, and the drought intensified rapidly, owing to the influence of heat waves and water deficits. Summer flash droughts caused a reduction in total primary productivity (GPP) of maize by more than 20% in most areas in the three typical years. The yield reduction rate in 1989, 1997, and 2018, was 27.6%, 26.4%, and 5%, respectively. The degree of decline in maize productivity and yield was associated with the onset and duration of the flash drought. The atmospheric conditions of summer flash droughts were characterized by high-pressure anomalies and atmospheric subsidence, which were unconducive for precipitation but conducive to flash drought occurrence. The continuous high-pressure anomaly promoted the maintenance of the flash drought. Full article
(This article belongs to the Section Crop Breeding and Genetics)
25 pages, 9842 KiB  
Article
Urbanization Effect on Regional Thermal Environment and Its Mechanisms in Arid Zone Cities: A Case Study of Urumqi
by Aerzuna Abulimiti, Yongqiang Liu, Jianping Tang, Ali Mamtimin, Junqiang Yao, Yong Zeng and Abuduwaili Abulikemu
Remote Sens. 2024, 16(16), 2939; https://doi.org/10.3390/rs16162939 - 10 Aug 2024
Viewed by 502
Abstract
Urumqi is located in the arid region of northwestern China, known for being one of the most delicate ecological environments and an area susceptible to climate change. The urbanization of Urumqi has progressed rapidly, yet there is a lack of research on the [...] Read more.
Urumqi is located in the arid region of northwestern China, known for being one of the most delicate ecological environments and an area susceptible to climate change. The urbanization of Urumqi has progressed rapidly, yet there is a lack of research on the urbanization effect (UE) in Urumqi in terms of the regional climate. This study investigates the UE of Urumqi (urban built-up area) on the regional thermal environment and its mechanisms for the first time, based on the WRF (Weather Research and Forecasting) model (combined with the Urban Canopy Model, UCM) simulation data of 10 consecutive years (2012–2021). The results show that the UE on surface temperature (Ts) and air temperature at 2 m (T2m) is strong (weak) during the night (daytime) in all seasons, and the UE on these is largest (smallest) in spring (winter). In addition, the maximum UE on both Ts and T2m is present over southern Urumqi in winter, whereas the maximum UE is identified over the northern Urumqi in other seasons. The maximum UE on Ts occurred in northwestern Urumqi at 18 LST (Local Standard Time, i.e., UTC+6) in autumn (reaching 5.2 °C), and the maximum UE on T2m occurred in northern Urumqi at 4 LST in summer (reaching 2.6 °C). Urbanization showed a weak cooling effect during daytime in summer and winter, reflecting the unique characteristics of the UE in arid regions, which are different from those in humid regions. The maximum cooling of Ts occurred in northern Urumqi at 11 LST in summer (reaching −0.4 °C), while that of T2m occurred at 10 LST in northern and northwestern Urumqi in winter (reaching −0.25 °C), and the cooling effect lasted for a longer period of time in summer than in winter. The UE of Urumqi causes the increase of Ts mainly through the influence of net short-wave radiation and geothermal flux and causes the increase of T2m through the influence of sensible heat flux and net long-wave radiation. The UE on the land surface energy balance in Urumqi can be used to explain the seasonal variation and spatial differences of the UEs on the regional thermal environment and the underlying mechanism. Full article
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Figure 1

Figure 1
<p>Geographical location and terrain altitude (shading, units: m) of the study area; the sky-blue thick solid line represents the boundary of the built-up area of Urumqi, and the black fine solid lines represent the administrative boundaries of Urumqi and its districts. The small red box in the small globe in the upper left corner shows the location of the study area from a broader perspective.</p>
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<p>Terrain altitude (shading, DEM, units: m) and locations of the 20 meteorological stations (indicated by red dots) selected in this study to verify the simulation results. The blue line represents the outline of the built-up area of Urumqi, and the thin black lines indicate administrative boundaries of the districts in Urumqi.</p>
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<p>(<b>a</b>) The geographic locations and terrain elevation (shading, DEM, units: m) of the WRF model domains, where d01 represents the outer domain, d02 represents the inner domain, and the thin black lines represent administrative boundaries of Xinjiang. (<b>b</b>) The location of the d01 domain on a map of China; the shading represents the terrain elevation (units: m).</p>
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<p>(<b>a</b>) Land use categories (shading) derived from CLCD dataset in the d02 domain of the Urban (control) experiment of numerical simulation. The land use category of “urban and build-up” is highlighted with red underline in the color bar information. (<b>b</b>) The same as (<b>a</b>) but for the enlarged area (main study area) centered around the built-up area of Urumqi. The small black dot in the central area (indicated by letter C) shows the location of the urban centroid of the built-up area of Urumqi. The black boxes indicate the proximity areas in different directions of the urban centroid of the built-up area of Urumqi, and the abbreviated letters NW, N, NE, W, C, E, S, and SE represent the corresponding northwestern, northern, northeastern, western, central, eastern, southern, and southeastern areas of Urumqi.</p>
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<p>(<b>a</b>) Land use categories (shading) derived from CLCD dataset in the d02 domain of the NoUrban (sensitivity) experiment of numerical simulation. (<b>b</b>) The same as (<b>a</b>) but for the enlarged area, which shows the same area of <a href="#remotesensing-16-02939-f004" class="html-fig">Figure 4</a>b. The black boxes and corresponding abbreviated letters (NW, N, NE, W, C, E, S, SE) represent the same locations of Urumqi, which are shown in <a href="#remotesensing-16-02939-f004" class="html-fig">Figure 4</a>b. All of the original built-up areas were replaced by grasslands in the NoUrban (sensitivity) experiment of the numerical simulation. The small black dot in the central area (indicated by letter C) shows the location of the urban centroid of the built-up area of Urumqi.</p>
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<p>Scatter plots and linear regression fitting lines (small blue circles and red lines), with regression equations, Pearson’s correlation coefficient (r), and root mean square error (RMSE) shown in the top of each panel, showing the correspondence of the WRF numerical simulation results (monthly average value) with corresponding observational data from 2012 to 2021. (<b>a</b>–<b>c</b>) indicate the Tmax, Tmean, and Tmin at 2 m in spring, respectively; (<b>d</b>–<b>f</b>) represent the Tmax, Tmean, and Tmin at 2 m in summer, respectively; (<b>g</b>–<b>i</b>) present the Tmax, Tmean, and Tmin at 2 m in autumn, respectively; (<b>j</b>–<b>l</b>) show the Tmax, Tmean, and Tmin at 2 m in winter, respectively.</p>
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<p>(<b>a</b>–<b>d</b>) Spatial distribution of the UE on the average surface temperature (Ts, unit: °C) in Urumqi in spring, summer, autumn, and winter, respectively. The black boxes indicate the proximity areas in different directions of the urban centroid of the built-up area of Urumqi, which are also shown in <a href="#remotesensing-16-02939-f004" class="html-fig">Figure 4</a>b and <a href="#remotesensing-16-02939-f005" class="html-fig">Figure 5</a>b; The blue line represents the outline of the built-up area of Urumqi, and the thin black lines indicate administrative boundaries of districts in Urumqi. The small black dot in the central area (indicated by letter C) shows the location of the urban centroid of the built-up area of Urumqi. (<b>e</b>–<b>h</b>) The average values of Ts over the eight proximity areas (only calculated values for built-up area) in different directions of the urban centroid of the built-up area of Urumqi in spring, summer, autumn, and winter, respectively. The abbreviated letters NW, N, NE, W, C, E, S, and SE in horizontal axis represent the corresponding eight areas shown in <a href="#remotesensing-16-02939-f004" class="html-fig">Figure 4</a>b and <a href="#remotesensing-16-02939-f005" class="html-fig">Figure 5</a>b, and ALL represents the average value of all built-up areas of Urumqi (i.e., the averaged value of all eight areas).</p>
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<p>(<b>a</b>–<b>d</b>) Diurnal variation characteristics of UE on the average surface temperature (Ts, unit: °C) in different areas in different directions of the urban centroid of the built-up area of Urumqi in spring, summer, autumn, and winter, respectively. The NW, N, NE, W, C, E, S, and SE represent the corresponding values of Ts calculated in eight areas (only calculated values for built-up area) shown in <a href="#remotesensing-16-02939-f004" class="html-fig">Figure 4</a>b and <a href="#remotesensing-16-02939-f005" class="html-fig">Figure 5</a>b, and ALL represents the average value of all built-up areas of Urumqi (i.e., the averaged value of all eight areas).</p>
Full article ">Figure 9
<p>(<b>a</b>–<b>d</b>) Diurnal variation characteristics of average surface temperature (Ts, unit: °C) over the built-up area of Urumqi in Urban experiment, NoUrban experiment, and the UE on Ts in spring, summer, autumn, and winter, respectively.</p>
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<p>(<b>a</b>–<b>d</b>) Spatial distribution of the UE on the average air temperature at 2 m (T2m, unit: °C) in Urumqi in spring, summer, autumn, and winter, respectively. The black boxes indicate the proximity areas in different directions of the urban centroid of the built-up area of Urumqi, which are also shown in <a href="#remotesensing-16-02939-f004" class="html-fig">Figure 4</a>b and <a href="#remotesensing-16-02939-f005" class="html-fig">Figure 5</a>b, and the small black dot in the central area (indicated by letter C) shows the location of the urban centroid of the built-up area of Urumqi. (<b>e</b>–<b>h</b>) The average values over of T2m in the eight proximity areas (only calculated values on built-up area) in different directions of the urban centroid of built-up area of Urumqi in spring, summer, autumn, and winter, respectively. The abbreviated letters NW, N, NE, W, C, E, S, SE in horizontal axis represent the corresponding eight areas shown in <a href="#remotesensing-16-02939-f004" class="html-fig">Figure 4</a>b and <a href="#remotesensing-16-02939-f005" class="html-fig">Figure 5</a>b, and ALL represents the average value of all built-up area of Urumqi (i.e., the averaged value of all eight areas).</p>
Full article ">Figure 11
<p>(<b>a</b>–<b>d</b>) Diurnal variation characteristics of the UE on average air temperature at 2 m (T2m, unit: °C) in different areas in different directions of the urban centroid of built-up area of Urumqi in spring, summer, autumn, and winter, respectively. The NW, N, NE, W, C, E, S, SE represent the corresponding values of T2m calculated in eight areas (only calculated values on built-up area) shown in <a href="#remotesensing-16-02939-f004" class="html-fig">Figure 4</a>b and <a href="#remotesensing-16-02939-f005" class="html-fig">Figure 5</a>b, and ALL represents the average value of all built-up area of Urumqi (i.e., the averaged value of all eight areas).</p>
Full article ">Figure 12
<p>(<b>a</b>–<b>d</b>) Diurnal variation characteristics of average air temperature at 2 m (T2m, unit: °C) over the built-up area of Urumqi in the Urban experiment, NoUrban experiment, and the UE of Urumqi on T2m in spring, summer, autumn, and winter, respectively.</p>
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<p>The UE on surface energy budget (SEB, unit: W m<sup>−2</sup>) in all built-up areas of Urumqi in four seasons. The SWn, LWn, SH, LH, GH, and SEB represent net shortwave radiation, net longwave radiation, sensible heat flux, latent heat flux, ground heat flux, and surface energy budget, respectively.</p>
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<p>The UE on surface energy budget (SEB, unit: W m<sup>−2</sup>) in the northern part of the built-up area of Urumqi in four seasons. The SWn, LWn, SH, LH, GH, and SEB represent net shortwave radiation, net longwave radiation, sensible heat flux, latent heat flux, ground heat flux, and surface energy budget, respectively.</p>
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<p>The UE on surface energy budget (SEB, unit: W m<sup>−2</sup>) in the southern part of the built-up area of Urumqi in four seasons. The SWn, LWn, SH, LH, GH, and SEB represent net shortwave radiation, net longwave radiation, sensible heat flux, latent heat flux, ground heat flux, and surface energy budget, respectively.</p>
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<p>Difference in the UE on surface energy budget (SEB, unit: W m<sup>−2</sup>) between the northern and southern parts of the built-up area of Urumqi in four seasons. The SWn, LWn, SH, LH, GH, and SEB represent net shortwave radiation, net longwave radiation, sensible heat flux, latent heat flux, ground heat flux, and surface energy budget, respectively.</p>
Full article ">
15 pages, 7815 KiB  
Article
Evaluation of Medium-Deep Geothermal Resources Based on Seismic Imaging Technology: A Case Study of the Midu Basin in Yunnan Province
by Jie Li, Xuebin Zhang, Chao Xu, Chuan Li, Hui Tan, Ziye Yu and Yunpeng Zhang
Energies 2024, 17(16), 3948; https://doi.org/10.3390/en17163948 - 9 Aug 2024
Viewed by 274
Abstract
The effective utilization of medium-high temperature geothermal energy is pivotal in reducing carbon emissions and plays a crucial role in developing clean energy technologies. The MiDu geothermal field, situated in the southeastern region of Dali Prefecture, Yunnan Province, lies within the Mediterranean–Himalayan high-temperature [...] Read more.
The effective utilization of medium-high temperature geothermal energy is pivotal in reducing carbon emissions and plays a crucial role in developing clean energy technologies. The MiDu geothermal field, situated in the southeastern region of Dali Prefecture, Yunnan Province, lies within the Mediterranean–Himalayan high-temperature geothermal belt and is characterized by abundant geothermal resources. However, due to its considerable depth, exploration poses significant risks, resulting in a total utilization rate of less than 0.5% of the total reserves. This study employs natural seismic data to perform a tomographic analysis of the geothermal system in the Midu basin. By examining the P-wave velocity (Vp) and the velocity ratio of P-waves and S-waves (Vp/Vs) at various depths, the findings reveal that the basin comprises two distinct structural layers: the thrust basement of the Mesozoic and Paleozoic eras and the strike–slip extensional sedimentary layer of the Cenozoic era. A low-velocity anomaly in the central basin corresponds to the loose Cenozoic sedimentary layer. In contrast, high-velocity anomalies at the basin edges correlate with boundary faults and the Mesozoic–Paleozoic strata. Below a depth of 4 km, the Red River Fault and MiDu Fault continue to dominate the basin’s structure, whereas the influence of the Malipo Fault diminishes. The MiDu Fault exhibits higher thermal conductivity than the Yinjie Fault. It interfaces with multiple carbonate and basalt formations characterized by well-developed pores and fractures, making it a crucial conduit for water and a control point for geothermal storage. Consequently, the existence of medium-high temperature (>90 °C) geothermal resources for power generation should be concentrated around the Midu fault on the western side of the basin, while the Yinjie fault area is more favorable for advancements in heating and wellness. Full article
(This article belongs to the Special Issue Advances in Geothermal and Solar Energy Development and Utilization)
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Figure 1

Figure 1
<p>(<b>A</b>) Map of China, with the blue arearepresenting Yunnan Province, the small black rectangle represents the study area; (<b>B</b>) map of the location of the study area and related feature (modified from [<a href="#B23-energies-17-03948" class="html-bibr">23</a>]).</p>
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<p>A Map showing the distribution of major faults in the Midu area (modified from [<a href="#B25-energies-17-03948" class="html-bibr">25</a>]).</p>
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<p>Locations of the seismic stations of Midu geothermal field. (Triangles indicate stations; red line indicates fault; velocity profile taken along the white dashed line in the next section).</p>
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<p>Monitoring period station locations and local earthquake distribution in the Midu geothermal field research area. (<b>a</b>) Triangles and stars represent stations and seismic events, respectively. The gray line indicates the travel time from sources to stations; (<b>b</b>) green dots represent initial S-wave arrivals, while red dots represent initial P-wave arrivals.</p>
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<p>This figure depicts the average Vp/Vs and the selection of the initial P−wave velocity model, where (<b>a</b>) shows the yellow line as the Vp/Vs fitting line, and (<b>b</b>) shows the green line as the initial P−wave velocity model by Chen et al., 2016 [<a href="#B33-energies-17-03948" class="html-bibr">33</a>] and the black line represents the initial velocity model used by Zhang et al., 2020 [<a href="#B34-energies-17-03948" class="html-bibr">34</a>] for the dense station tomographic imaging in Binchuan, which is also the initial velocity model used in this study.</p>
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<p>Trade-off curves for damping parameters of Vp (<b>a</b>) and Vp/Vs (<b>b</b>), and travel-time residuals before and after inversion for Vp (<b>c</b>) and Vp/Vs (<b>d</b>).</p>
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<p>Synthetic checkerboard test results for Vp and Vp/Vs at different depths. The theoretical model applies ±5% perturbations to Vp and Vp/Vs in adjacent grid cells. Blue contour lines enclose areas with a resolution greater than 0.1.</p>
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<p>The map views of Vp and Vp/Vs tomography at depths of 0 km, 3 km, and 6 km, obtained by inverting P-wave and S-wave data. The black lines represent faults.</p>
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<p>The vertical cross-section of Vp (<b>a</b>) and Vp/Vs (<b>b</b>) model. The line of section is the white dashed line in <a href="#energies-17-03948-f003" class="html-fig">Figure 3</a>.</p>
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<p>Geothermal genetic model of Midu basin, the red arrows in this figure represent the direction of heat flow. The main heat flow rises upwards along the lower segment of the Midu fault, while the secondary heat flow radiates from the granite body to the surrounding areas.</p>
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15 pages, 6966 KiB  
Article
Xylogenesis Responses to a Mediterranean Climate in Holm Oak (Quercus ilex L.)
by Iqra Liyaqat, Angela Balzano, Francesco Niccoli, Jerzy Piotr Kabala, Maks Merela and Giovanna Battipaglia
Forests 2024, 15(8), 1386; https://doi.org/10.3390/f15081386 - 8 Aug 2024
Viewed by 506
Abstract
Quercus ilex L., an evergreen oak species typical of the western and central Mediterranean basin, is facing decline and dieback episodes due to the increase in the severity and frequency of heat waves and drought events. Studying xylogenesis (the wood formation process) is [...] Read more.
Quercus ilex L., an evergreen oak species typical of the western and central Mediterranean basin, is facing decline and dieback episodes due to the increase in the severity and frequency of heat waves and drought events. Studying xylogenesis (the wood formation process) is crucial for understanding how trees respond with their secondary growth to environmental conditions and stress events. This study aimed to characterize the wood formation dynamics of Quercus ilex and their relationship with the meteorological conditions in an area experiencing prolonged drought periods. Cambial activity and xylem cell production were monitored during the 2019 and 2020 growing seasons in a Q. ilex forest located at the Vesuvius National Park (southern Italy). The results highlighted the significant roles of temperature and solar radiation in stimulating xylogenesis. Indeed, the correlation tests revealed that temperature and solar radiation positively influenced growth and cell development, while precipitation had an inhibitory effect on secondary wall formation. The earlier cell maturation in 2020 compared to 2019 underscored the impact of global warming trends. Overall, the trees studied demonstrated good health, growth and adaptability to local environmental fluctuations. This research provides novel insights into the intra-annual growth dynamics of this key Mediterranean species and its adaptation strategies to climatic variability, which will be crucial for forest management in the context of climate change. Full article
(This article belongs to the Section Forest Ecology and Management)
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Figure 1
<p>Study site located within Vesuvius National Park, Naples. Red triangle indicates the <span class="html-italic">Quercus ilex</span> stand.</p>
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<p>Weather conditions of the study site during the monitoring period of 2019 and 2020. In red, the maximum temperature; in black, the average temperature; in grey, the minimum temperature. The blue bars represent precipitation.</p>
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<p>Number of cambial cells and width of different developmental xylem zones in <span class="html-italic">Quercus ilex</span> trees in 2019 (<b>A</b>–<b>D</b>) and 2020 (<b>E</b>–<b>H</b>): cambial cells (CCs), enlarging post-cambial cells (PCs), cells developing secondary walls (SW) cells and mature (MT) cells with a lignified secondary wall. Mean values are shown for the days of the year (DOY) when the sampling was performed.</p>
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<p>Final tree ring width (TRW) containing fully mature cells formed for the years 2019 and 2020. Scale bar = 200 µm.</p>
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<p>Non-collapsed phloem (NCP) width in 2019 (<b>A</b>) and 2020 (<b>B</b>). Mean values are shown on the days of the year (DOY) when the sampling was performed.</p>
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<p>Correlations between xylogenesis and meteorological conditions. CC = cambial cells (number of cells), PC = post-cambial cells (width, μm), SW = secondary wall-forming cells (width, μm), MT = mature cells (width, μm). Meteorological variables: mean radiation (MJ/day), maximum temperature (°C), mean temperature (°C), minimum temperature (°C), and total precipitation (mm/day). Positive correlations are displayed in red, negative correlations in blue, non-significant ones in grey.</p>
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18 pages, 4001 KiB  
Article
Experimental Study on Heat Release Performance for Sorption Thermal Battery Based on Wave Analysis Method
by Meng Yu, Wei Liu, Yuchen Lin, Neng Gao, Xuejun Zhang and Long Jiang
Sustainability 2024, 16(15), 6654; https://doi.org/10.3390/su16156654 - 3 Aug 2024
Viewed by 421
Abstract
Recent developments in water-based open sorption thermal batteries (STBs) have drawn burgeoning attention due to their advantages of high energy storage density and flexible working modes for space heating. One of the main challenges is how to improve heat release performance, e.g., longer [...] Read more.
Recent developments in water-based open sorption thermal batteries (STBs) have drawn burgeoning attention due to their advantages of high energy storage density and flexible working modes for space heating. One of the main challenges is how to improve heat release performance, e.g., longer stable heat output and effective output temperature. This paper aims to explore the heat release performance of sorption thermal batteries based on wave analysis methods. Zeolite 13X is used for the experimental investigation in terms of the relative humidity of inlet gas, system air velocity, and the length of the reactor. The results demonstrate that the optimal stable temperature output time of the sorption thermal battery experimental rig is 80 min, and heat release per unit volume reaches 115.6 MJ for the most appropriate reactor length. Thus, the optimal heat release time of the STB under the condition of various relative humidity and air velocities is 152 min and 182 min, respectively, and the corresponding stable heat release could reach 161.1 MJ and 136.5 MJ, respectively. Therefore, the heat release performance of STBs could be adjusted by adopting the wave analysis method, which would facilitate the reactor design and system arrangement. Full article
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Figure 1

Figure 1
<p>The general concept of wave analysis method for the open STB: (<b>a</b>) molecular structure of sorbents.</p>
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<p>The whole STB system: (<b>a</b>) system schematic, (<b>b</b>) photo of the main components, and (<b>c</b>) sorption reactor design.</p>
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<p>Heat release performance of STB: (<b>a</b>) air condition, (<b>b</b>) output power, and (<b>c</b>) heat density.</p>
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<p>The heat release performance of STB vs. various lengths of the sorption reactor: (<b>a</b>) 0.05 m, (<b>b</b>) 0.1 m, (<b>c</b>) and 0.15 m. (<b>d</b>) Heat output.</p>
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<p>The heat release performance of STB vs. various RHs: (<b>a</b>) 50%, (<b>b</b>) 60%, (<b>c</b>) 70%, and (<b>d</b>) heat release of the STB under the conditions of different RHs.</p>
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<p>The heat release performance of STB vs. various air velocities: (<b>a</b>) 16 m<sup>−3</sup>·h<sup>−1</sup>; (<b>b</b>) 24 m<sup>−3</sup>·h<sup>−1</sup>; (<b>c</b>) 40 m<sup>−3</sup>·h<sup>−1</sup>; and (<b>d</b>) heat release of the STB under the conditions of different air velocities.</p>
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<p>Comparison of the stable heat release time: (<b>a</b>) time ratio and (<b>b</b>) time.</p>
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<p>The stable heat release per unit volume by (<b>a</b>) air velocity, (<b>b</b>) RH, and (<b>c</b>) length of the reactor.</p>
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20 pages, 2781 KiB  
Article
H3K27me3 and EZH Are Involved in the Control of the Heat-Stress-Elicited Morphological Changes in Diatoms
by Mhammad Zarif, Ellyn Rousselot, Bruno Jesus, Leïla Tirichine and Céline Duc
Int. J. Mol. Sci. 2024, 25(15), 8373; https://doi.org/10.3390/ijms25158373 - 31 Jul 2024
Viewed by 359
Abstract
Marine water temperatures are increasing due to anthropogenic climate change, constituting a major threat to marine ecosystems. Diatoms are major marine primary producers, and as such, they are subjected to marine heat waves and rising ocean temperatures. Additionally, under low tide, diatoms are [...] Read more.
Marine water temperatures are increasing due to anthropogenic climate change, constituting a major threat to marine ecosystems. Diatoms are major marine primary producers, and as such, they are subjected to marine heat waves and rising ocean temperatures. Additionally, under low tide, diatoms are regularly exposed to high temperatures. However, physiological and epigenetic responses to long-term exposure to heat stress remain largely unknown in the diatom Phaeodactylum tricornutum. In this study, we investigated changes in cell morphology, photosynthesis, and H3K27me3 abundance (an epigenetic mark consisting of the tri-methylation of lysine 27 on histone H3) after moderate and elevated heat stresses. Mutants impaired in PtEZH—the enzyme depositing H3K27me3—presented reduced growth and moderate changes in their PSII quantum capacities. We observed shape changes for the three morphotypes of P. tricornutum (fusiform, oval, and triradiate) in response to heat stress. These changes were found to be under the control of PtEZH. Additionally, both moderate and elevated heat stresses modulated the expression of genes encoding proteins involved in photosynthesis. Finally, heat stress elicited a reduction of genome-wide H3K27me3 levels in the various morphotypes. Hence, we provided direct evidence of epigenetic control of the H3K27me3 mark in the responses of Phaeodactylum tricornutum to heat stress. Full article
(This article belongs to the Section Biochemistry)
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<p>Growth and photosynthesis analysis of <span class="html-italic">Ptezh</span> mutants in FM and TM lines. (<b>a</b>,<b>b</b>) Growth rates are represented with error bars indicating standard deviations based on triplicate cultures in FM (<b>a</b>) and TM (<b>b</b>) lines. Maximum growth rates were determined from the growth curve obtained by cell counting performed every 2 days for 25 days on cultures grown at 19 °C (12/12 light–dark period; light intensity of 55 μmol photons.m<sup>−2</sup>.s<sup>−1</sup>). (<b>c</b>–<b>j</b>) Photosynthesis analysis performed when Fv/Fm was at its maximum for the <span class="html-italic">Ptezh</span> mutants in FM and TM lines grown at 19 °C (12/12 light–dark period; light intensity of 55 μmol photons.m<sup>−2</sup>.s<sup>−1</sup>). (<b>c</b>,<b>d</b>) α slope for rapid light curves in FM (<b>c</b>) and TM (<b>d</b>) lines. (<b>e</b>,<b>f</b>) rETRmax is the PSII maximum relative electron transfer rate in FM (<b>e</b>) and TM (<b>f</b>) lines. (<b>g</b>,<b>h</b>) NPQmax is the maximum non-photochemical quenching in FM (<b>g</b>) and TM (<b>h</b>) lines. (<b>i</b>,<b>j</b>) E50 is the light level at which 50% of the NPQ occurs in FM (<b>i</b>) and TM (<b>j</b>) lines. Student’s <span class="html-italic">t</span>-test: * <span class="html-italic">p</span> &lt; 0.05; ns, non-significant.</p>
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<p>Expression of genes encoding zeaxanthin epoxidase (ZEP), violaxanthin de-epoxidase (VDE), and violaxanthin de-epoxidase-like (VDL). (<b>a</b>,<b>b</b>) Relative transcript levels of genes encoding ZEP1, ZEP2, ZEP3, VDE, VDL1, and VDL2 enzymes in the FM (<b>a</b>) and TM (<b>b</b>) lines. The ZEP1, ZEP2, and ZEP3 enzymes putatively convert (i) zeaxanthin via the antheraxanthin intermediate to violaxanthin and (ii) diadinoxanthin in diatoxanthin. The VDE, VDL1, and VDL2 enzymes putatively catalyze the reverse reactions [<a href="#B10-ijms-25-08373" class="html-bibr">10</a>]. Levels are measured by qRT-PCR on three biological replicates consisting of 5-day-old cultures at 19 °C (12/12 light–dark period; light intensity of 55μmol photons.m<sup>−2</sup>.s<sup>−1</sup>) in (<b>a</b>,<b>b</b>). Student’s <span class="html-italic">t</span>-test: * <span class="html-italic">p</span> &lt; 0.05; ns, non-significant.</p>
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<p>Expression of genes encoding proteins of the photosynthesis apparatus. (<b>a</b>,<b>b</b>) Relative transcript levels of genes encoding proteins of PSII (antenna: PsbB and PsbC; reaction center: PsbA and PsbD; OEC: PsbO and PsbU; small transmembrane protein: PsbM) in the FM (<b>a</b>) and TM (<b>b</b>) lines. (<b>c</b>) Relative transcript levels of genes encoding proteins of the cytochrome b6-f complex (Rieske proteins: petC1 and petC2) and ferredoxin in the FM (upper panel) and TM (lower panel) lines. (<b>d</b>) Relative transcript levels of genes encoding proteins of PSI (trans-membrane subunits: PsaA, PsaB; stromal subunit: PsaC) in the FM (upper panel) and TM (lower panel) lines. Levels are displayed with a logarithmic scale (<b>a</b>–<b>c</b>) and measured by qRT-PCR on three biological replicates consisting of 5-day-old cultures grown at 19 °C (12/12 light–dark period; light intensity of 55 μmol photons.m<sup>−2</sup>.s<sup>−1</sup>) in (<b>a</b>–<b>d</b>). Student’s <span class="html-italic">t</span>-test: * <span class="html-italic">p</span> &lt; 0.05; ns, non-significant.</p>
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<p>The impact of prolonged-heat stress exposures in representative ecotypes of <span class="html-italic">P. tricornutum</span>. (<b>a</b>,<b>c</b>) Relative abundance of each morphotype in Pt1 8.6-FM, Pt3-OM, and Pt16-TM that are representative of the FM, OM, and TM morphotypes, respectively. The Pt9 tropical strain was used as a control. (<b>b</b>,<b>d</b>) Cell concentration measured for each morphotype (FM and OM) for Pt1 8.6-FM, Pt3-OM and Pt9 subjected to MHS (<b>b</b>) and for Pt1 8.6-FM, Pt3-OM, Pt16-TM, and Pt9 subjected to EHS (<b>d</b>). Relative abundance presented in panels <span class="html-italic">a</span> and <span class="html-italic">c</span> were inferred from these measures. A moderate heat stress (MHS) was applied for 5 days at 30 °C to 7-day-old cultures pre-acclimated at 19 °C (<b>a</b>,<b>b</b>). An elevated heat stress (EHS) was applied for 2 days at 37 °C to 5-day-old cultures pre-acclimated at 19 °C (<b>c</b>,<b>d</b>). Measures were carried out before transferring the cultures to heat-stress conditions (T0). They were then performed every day for 5 days in MHS and 2 days in EHS after transfer under heat stress.</p>
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<p>Kinetics of the morphological changes observed during exposure of 5-day-old cultures to 37 °C in representative ecotypes of <span class="html-italic">P. tricornutum</span>. Synchronized cells were transferred to 37 °C at T0, simultaneously with illumination to release cell cycle progression from G1. The relative abundance of each morphotype in Pt1 8.6-FM (<b>left</b>), Pt3-OM (<b>middle</b>), and Pt16-TM (<b>right</b>) was then assessed every 2 h for 14 h.</p>
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<p>Impact of prolonged-heat stress exposures on the morphology of <span class="html-italic">Ptezh</span> mutants. (<b>a</b>,<b>b</b>) Relative abundance of FM (<b>a</b>) and TM (<b>b</b>) lines subjected to a moderate heat stress (MHS). (<b>c</b>,<b>d</b>) Relative abundance of FM (<b>c</b>) and TM (<b>d</b>) lines subjected to an elevated heat stress (EHS). A moderate heat stress (MHS) was applied for 5 days at 30 °C on 7-day-old cultures pre-acclimated at 19 °C (<b>a</b>,<b>b</b>). An elevated heat stress (EHS) was applied for 2 days at 37 °C on 5-day-old cultures pre-acclimated at 19 °C (<b>c</b>,<b>d</b>). Measures were carried out before transferring cultures to heat stress conditions (T0). They were then performed every day for 5 days in MHS and 2 days in EHS after transfer under heat stress.</p>
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<p>Expression of genes encoding zeaxanthin epoxidase (ZEP), violaxanthin de-epoxidase (VDE), and violaxanthin de-epoxidase-like (VDL) in response to prolonged heat stresses. (<b>a</b>,<b>b</b>) Relative transcript level of genes encoding ZEP1, ZEP2, ZEP3, VDE, VDL1, and VDL2 enzymes in MHS (<b>a</b>) and EHS (<b>b</b>) lines. The ZEP1, ZEP2, and ZEP3 enzymes putatively convert (i) zeaxanthin <span class="html-italic">via</span> the antheraxanthin intermediate to violaxanthin and (ii) diadinoxanthin in diatoxanthin. The VDE, VDL1, and VDL2 enzymes putatively catalyze the reverse reactions [<a href="#B10-ijms-25-08373" class="html-bibr">10</a>]. Levels are displayed with a logarithmic scale and measured by qRT-PCR on three biological replicates consisting of 7-day-old cultures pre-acclimated at 19 °C transferred to 30 °C for 4 days (<b>a</b>) and 5-day-old cultures pre-acclimated at 19 °C transferred to 37 °C for 2 days (<b>b</b>). Student’s <span class="html-italic">t</span>-test; * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Expression of genes encoding proteins of the photosynthesis apparatus in response to MHS. (<b>a</b>) Relative transcript levels of genes encoding proteins of PSII (antenna: PsbB and PsbC; reaction center: PsbA and PsbD; OEC: PsbO and PsbU; small transmembrane protein: PsbM). (<b>b</b>) Relative transcript levels of genes encoding proteins of the cytochrome b6-f complex (Rieske proteins: petC1 and petC2) and ferredoxin. (<b>c</b>) Relative transcript levels of genes encoding proteins of PSI (trans-membrane subunits: PsaA, PsaB; stromal subunit: PsaC). (<b>d</b>) Relative transcript levels of genes encoding heat shock proteins (HSP, heat shock protein; HSF3, heat shock factor protein 3). Levels are displayed with a logarithmic scale and measured by qRT-PCR on three biological replicates consisting of 7-day-old cultures pre-acclimated at 19 °C transferred to 30 °C for 4 days. Student’s <span class="html-italic">t</span>-test; * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Expression of genes encoding proteins of the photosynthesis apparatus in response to EHS. (<b>a</b>) Relative transcript level of genes encoding proteins of PSII (antenna: PsbB and PsbC; reaction center: PsbA and PsbD; OEC: PsbO and PsbU; small transmembrane protein: PsbM). (<b>b</b>) Relative transcript level of genes encoding proteins of the cytochrome b6-f complex (Rieske proteins: petC1 and petC2) and ferredoxin. (<b>c</b>) Relative transcript level of genes encoding proteins of PSI (trans-membrane subunits: PsaA, PsaB; stromal subunit: PsaC). (<b>d</b>) Relative transcript level of genes encoding heat shock proteins (HSP, heat shock protein; HSF3, heat shock factor protein 3). Levels are displayed with a logarithmic scale and measured by qRT-PCR on three biological replicates consisting of 5-day-old cultures pre-acclimated at 19 °C transferred to 37 °C for 2 days. Student’s <span class="html-italic">t</span>-test; * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Genome-wide levels of the epigenetic mark H3K27me3 in response to MHS. The H3K27me3 levels were quantified by Western blotting in 7-day-old cultures pre-acclimated at 19 °C transferred to 30 °C for 4 days. H4 was used as a loading control. Presented blots are representative of several blots and biological replicates. Two biological replicates (referred to as 1 and 2) consisting of independent cultures collected at the same time are presented for each ecotype.</p>
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14 pages, 2101 KiB  
Article
Temperature Behavior in Headlights: A Comparative Analysis between Battery Electric Vehicles and Internal Combustion Engine Vehicles
by Tabea Schlürscheid, Tran Quoc Khanh, Alexander Buck and Stefan Weber
Appl. Sci. 2024, 14(15), 6654; https://doi.org/10.3390/app14156654 - 30 Jul 2024
Viewed by 445
Abstract
In the context of a global shift towards renewable energies and climate change mitigation, the market for electric vehicles has experienced a remarkable upswing, with battery electric vehicles (BEVs) leading this transformative wave. The appeal of BEVs lies in their ability to significantly [...] Read more.
In the context of a global shift towards renewable energies and climate change mitigation, the market for electric vehicles has experienced a remarkable upswing, with battery electric vehicles (BEVs) leading this transformative wave. The appeal of BEVs lies in their ability to significantly curtail CO2 emissions by supplanting the traditional internal combustion engine (ICE) with an electric motor. This pivotal change in vehicular technology extends its influence to various subsystems, including automotive lighting. Headlights are particularly sensitive to the thermal environment they operate in, which can profoundly affect their functionality and durability. The removal of an ICE in BEVs typically results in a reduction in heat exposure to headlight components, prompting a potential reevaluation of their design. This article presents a comprehensive analysis of temperature distributions within headlight units, comparing BEVs and ICE vehicles. The study encompasses a robust dataset of nearly 30,000 vehicles from around the globe, taking into account the impact of ambient temperature on headlight operation. The investigation delineates the distinct thermal behaviors of the two vehicle categories and offers strategic recommendations for conceptual modifications of headlights in BEVs. These adjustments are aimed at enhancing headlight efficacy, prolonging lifespan, and furthering the sustainability objectives of electric mobility. Full article
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<p>Histogram of the mileage in km for all vehicles for the BEV and ICE engine types.</p>
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<p>Number of BEV (light blue) and ICE (dark blue) vehicles analyzed in the different ambient temperature classes.</p>
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<p>Distribution of headlamp temperature histogram across all vehicles, all ambient temperature classes, and all light functions for BEVs (light blue) and ICEs (dark blue).</p>
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<p>Distribution of the headlamp temperature histogram across all vehicles and all lighting functions for ambient temperature class 1 of &lt;15 °C annual mean ambient temperature for BEVs (light blue) and ICEs (dark blue).</p>
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<p>Distribution of the headlamp temperature histogram across all vehicles and all lighting functions for ambient temperature class 2 of 15–20 °C annual mean ambient temperature for BEVs (light blue) and ICEs (dark blue).</p>
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<p>Distribution of the headlamp temperature histogram across all vehicles and all lighting functions for ambient temperature class 3 of 20–25 °C annual mean ambient temperature for BEVs (light blue) and ICEs (dark blue).</p>
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<p>Distribution of the headlamp temperature histogram across all vehicles and all lighting functions for ambient temperature class 4 of 25–30 °C annual mean ambient temperature for BEVs (light blue) and ICEs (dark blue).</p>
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<p>Distribution of the headlamp temperature histogram across all vehicles and all lighting functions for ambient temperature class 5 of &gt;30 °C annual mean ambient temperature for BEVs (light blue) and ICEs (dark blue).</p>
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<p>The calculated temperature center of gravity in the headlamps of the respective ambient temperature classes for ICEs and BEVs along with their linear fit for BEVs (light blue) and ICEs (dark blue).</p>
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23 pages, 9533 KiB  
Article
Experimental Investigation on the Damage Evolution of Thermally Treated Granodiorite Subjected to Rapid Cooling with Liquid Nitrogen
by Mohamed Elgharib Gomah, Enyuan Wang and Ahmed A. Omar
Sustainability 2024, 16(15), 6396; https://doi.org/10.3390/su16156396 - 26 Jul 2024
Viewed by 417
Abstract
In many thermal geotechnical applications, liquid nitrogen (LN2) utilization leads to damage and cracks in the host rock. This phenomenon and associated microcracking are a hot topic that must be thoroughly researched. A series of physical and mechanical experiments were conducted [...] Read more.
In many thermal geotechnical applications, liquid nitrogen (LN2) utilization leads to damage and cracks in the host rock. This phenomenon and associated microcracking are a hot topic that must be thoroughly researched. A series of physical and mechanical experiments were conducted on Egyptian granodiorite samples to investigate the effects of liquid nitrogen cooling on the preheated rock. Before quenching in LN2, the granodiorite was gradually heated to 600 °C for two hours. Microscopical evolution was linked to macroscopic properties like porosity, mass, volume, density, P-wave velocity, uniaxial compressive strength, and elastic modulus. According to the experiment results, the thermal damage, crack density, porosity, and density reduction ratio increased gradually to 300 °C before severely degrading beyond this temperature. The uniaxial compressive strength declined marginally to 200 °C, then increased to 300 °C before monotonically decreasing as the temperature rose. On the other hand, at 200 °C, the elastic modulus and P-wave velocity started to decline significantly. Thus, 200 and 300 °C were noted in this study as two mutation temperatures in the evolution of granodiorite mechanical and physical properties, after which all parameters deteriorated. Moreover, LN2 cooling causes more remarkable physical and mechanical modifications at the same target temperature than air cooling. Through a deeper comprehension of how rocks behave in high-temperature conditions, this research seeks to avoid and limit future geological risks while promoting sustainability and understanding the processes underlying rock failure. Full article
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<p>An Eastern Desert geological map of Egypt that includes the research area [<a href="#B53-sustainability-16-06396" class="html-bibr">53</a>].</p>
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<p>The XRD patterns of granodiorite samples at ambient temperature and following different treatments.</p>
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<p>The principal devices used in this study: (<b>a</b>) the WiseTherm electric furnace, (<b>b</b>) quenched samples of liquid nitrogen, (<b>c</b>) the SEM apparatus, (<b>d</b>) the optical microscope device, and (<b>e</b>) a uniaxial compression test unit.</p>
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<p>Porosity and water absorption responses of granodiorite samples following various thermal treatments and LN<sub>2</sub> cooling.</p>
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<p>Mass loss, volume increase, and density reduction rates of granodiorite with target temperatures.</p>
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<p>Change in P-wave velocity of granodiorite samples with temperature with LN<sub>2</sub> cooling treatments.</p>
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<p>SEM images of granodiorite thermally treated and quickly cooled via LN<sub>2</sub> at 200 °C (<b>a</b>), 300 °C (<b>b</b>), 400 °C (<b>c</b>), and 600 °C (<b>d</b>). (bc) is boundary microcracks, and (tc) is transgranular microcracks.</p>
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<p>Crack length and width evolution of granodiorite specimens subjected to various thermal treatments and LN<sub>2</sub> rapid cooling.</p>
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<p>Diagram of the microstructural changes in studied samples after heat treatment, followed by rapid cooling using LN<sub>2</sub>.</p>
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<p>The change in the uniaxial compressive strength (UCS) of granodiorite after heat treatment and LN<sub>2</sub> cooling.</p>
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<p>The elastic modulus (E) of granodiorite as a function of temperature and LN<sub>2</sub> cooling approach.</p>
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<p>Physical and mechanical property responses during heating and LN<sub>2</sub> cooling processes.</p>
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<p>Crack evolution in granodiorite specimens subjected to different thermal treatments (<b>a</b>) at 200 °C and (<b>b</b>) at 300 °C and rapid cooling with LN<sub>2</sub>, as seen using an optical microscope.</p>
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<p>Crack evolution in granodiorite specimens subjected to different thermal treatments (<b>a</b>) at 400 °C and (<b>b</b>) at 600 °C and rapid cooling with LN<sub>2</sub>, as seen using an optical microscope.</p>
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<p>Relation between P-wave velocity and porosity of thermally heated granodiorite, followed by the LN<sub>2</sub> cooling method.</p>
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<p>Thermal damage evolution of P-wave velocity and elastic modulus of thermally heated granodiorite, followed by the LN<sub>2</sub> cooling approach.</p>
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<p>Cooling impact analysis on the physical properties of thermally heated granodiorite at various temperatures is followed by air cooling (A-C) and liquid nitrogen (LN<sub>2</sub>).</p>
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<p>Cooling impact analysis on the mechanical properties of thermally heated granodiorite at various temperatures is followed by air cooling (A-C) and liquid nitrogen cooling (LN<sub>2</sub>).</p>
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42 pages, 12236 KiB  
Article
Conducting a Tailored and Localised Marine Heat Wave Risk Assessment for Vanuatu Fisheries
by Isabella Aitkenhead, Yuriy Kuleshov, Chayn Sun and Suelynn Choy
Climate 2024, 12(8), 108; https://doi.org/10.3390/cli12080108 - 25 Jul 2024
Viewed by 499
Abstract
In Vanuatu, communities are predicted to be at high risk of more frequent and severe Marine Heat Wave (MHW) impacts in the future, as a result of climate change. A critical sector at risk in Vanuatu is fisheries, which vitally support food security [...] Read more.
In Vanuatu, communities are predicted to be at high risk of more frequent and severe Marine Heat Wave (MHW) impacts in the future, as a result of climate change. A critical sector at risk in Vanuatu is fisheries, which vitally support food security and livelihoods. To sustain local communities, the MHW risk for Vanuatu fisheries must be extensively explored. In this study, an efficient MHW risk assessment methodology is demonstrated specifically for assessing MHW risk to Vanuatu fisheries. The fisheries specific MHW risk assessment was conducted on the local area council scale for two retrospective case study periods: 2015–2017 and 2020–2022. An integrated GIS-based approach was taken to calculating and mapping monthly hazard, vulnerability, exposure, and overall risk indices. Key areas and time periods of concern for MHW impacts are identified. Area councils in the Shefa province area are particularly concerning, displaying consistently high-risk levels throughout both case studies. Risk levels in 2022 were the most concerning, with most months displaying peak risk to MHW impacts. A sensitivity analysis is employed to validate the selection and weighting of the indicators used. However, it is recommended that a more comprehensive validation of the retrospective risk assessment results, using multiple ground-truth sources, be conducted in the future. Once results are sufficiently validated, management recommendations for fisheries resilience can be made. Full article
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<p>The key steps involved in the MHW risk assessment methodology. This figure is adapted from Wang et al. [<a href="#B23-climate-12-00108" class="html-bibr">23</a>].</p>
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<p>A series of maps for Vanuatu. Map (<b>A</b>) shows a Vanuatu administrative map with each of the six provinces displayed (Malampa, Penama, Sanma, Shefa, Tafea, and Torba) and includes a zoomed-out view of where Vanuatu is located in the Pacific Islands [<a href="#B24-climate-12-00108" class="html-bibr">24</a>]. Map (<b>B</b>) gives a spatial thermal history for Vanuatu in terms of Degree Heating Weeks (DHWs) [<a href="#B25-climate-12-00108" class="html-bibr">25</a>]. Map (<b>C</b>) displays the Exclusive Economic Zone (EEZ) boundaries for Vanuatu [<a href="#B26-climate-12-00108" class="html-bibr">26</a>].</p>
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<p>List and display of the 66 local area councils spread throughout Vanuatu. Basemap gathered from Esri [<a href="#B27-climate-12-00108" class="html-bibr">27</a>].</p>
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<p>Comparison of monthly hazard index maps for Vanuatu area councils in 2015.</p>
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<p>Comparison of monthly hazard index maps for Vanuatu area councils in 2016.</p>
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<p>Comparison of monthly hazard index maps for Vanuatu area councils in 2017.</p>
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<p>Comparison of yearly vulnerability index maps for Vanuatu area councils in 2015 (<b>A</b>), 2016 (<b>B</b>), and 2017 (<b>C</b>). Local area councils at high (severe or extreme) vulnerability levels are labelled.</p>
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<p>Comparison of yearly exposure index maps for Vanuatu area councils in 2015 (<b>A</b>), 2016 (<b>B</b>), and 2017 (<b>C</b>). Local area councils at high (severe or extreme) exposure levels are labelled.</p>
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<p>Comparison of monthly high-risk levels throughout each year of 2015–2017. The number of local area councils that expressed high risk (severe to extreme) is displayed for each year (2015 is represented by black bars, 2016 is represented by dark grey bars, and 2017 is represented by light grey bars) from January to December.</p>
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<p>Comparison of monthly hazard index maps for Vanuatu area councils in 2020.</p>
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<p>Comparison of monthly hazard index maps for Vanuatu area councils in 2021.</p>
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<p>Comparison of monthly hazard index maps for Vanuatu area councils in 2022.</p>
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<p>Comparison of yearly vulnerability index maps for Vanuatu area councils in 2020 (<b>A</b>), 2021 (<b>B</b>), and 2022 (<b>C</b>). Local area councils at high (severe or extreme) vulnerability levels are labelled.</p>
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<p>Comparison of yearly exposure index maps for Vanuatu area councils in 2020 (<b>A</b>), 2021 (<b>B</b>), and 2022 (<b>C</b>). Local area councils at high (severe or extreme) exposure levels are labelled.</p>
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<p>Comparison of monthly high-risk levels throughout each year of 2020–2022. The number of local area councils that expressed high risk (severe to extreme) is displayed for each year (2020 is represented by black bars, 2021 is represented by dark grey bars, and 2022 is represented by light grey bars) from January to December.</p>
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17 pages, 9303 KiB  
Article
Continuous Wave Mode Test of Conduction-Cooled Nb3Sn Radio Frequency Superconducting Cavities at Peking University
by Manqian Ren, Lin Lin, Jiankui Hao, Gai Wang, Ziyu Wang, Deyang Wang, Haoyu Shen, Shengwen Quan, Fang Wang, Liwen Feng, Fei Jiao, Feng Zhu, Kun Zhu, Xueqing Yan and Senlin Huang
Appl. Sci. 2024, 14(14), 6350; https://doi.org/10.3390/app14146350 - 21 Jul 2024
Viewed by 514
Abstract
A liquid helium-free cryostat for radio frequency (RF) test of the superconducting cavity is designed and constructed. Gifford-Mcmahon (G-M) cryocoolers are used to provide cooling capacity, and the heat leakage at 4 K is less than 0.02 W. Vertical and horizontal tests of [...] Read more.
A liquid helium-free cryostat for radio frequency (RF) test of the superconducting cavity is designed and constructed. Gifford-Mcmahon (G-M) cryocoolers are used to provide cooling capacity, and the heat leakage at 4 K is less than 0.02 W. Vertical and horizontal tests of two Nb3Sn cavities are carried out in the cryostat with different surface treatments outside the cavities. Both of the cavities achieve stable continuous wave (CW) operation. A novel treatment, which cold-sprayed a 3.5 mm thick Cu layer onto the outside of the cavity, enables the maintenance of an average temperature of 5.5 K in the cavity at a RF loss of 10 W, implying that the thermal stability and uniformity of the cavity has been significantly improved. Through the synergistic control of four metal film resistors, a cooling rate of 0.06 K/min near 18 K is realized, and the cavity temperature gradient is reduced to 0.17 K/m, which effectively improves the RF performance of the cavity. The maximum Eacc of the cavity reaches 3.42 MV/m, and the Q0 is 1.1 × 109. An electromagnetic–thermal coupling simulation model for the superconducting cavity is established and is in good agreement with the experimental results. The simulation results show that the cavity with a Cu-spraying treatment and the thermal links of 5N Al can satisfy the Eacc of 10 MV/m under conduction cooling. Full article
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<p>Cryostat model drawing and installation process.</p>
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<p>Temperature distribution at steady state for two thermal shields obtained by simulation: (<b>a</b>) outer shield and (<b>b</b>) inner shield.</p>
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<p>Magnetic field distribution on the center axis of the cryostat obtained by simulation; the horizontal axis is the distance from the top of the inner magnetic shield, and the superconducting cavity is located at a distance of about 100 to 520 mm from the top.</p>
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<p><math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Q</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>−</mo> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi>a</mi> <mi>c</mi> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math> curves of the two Nb<sub>3</sub>Sn cavities tested in 4.2 K LHe.</p>
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<p>(<b>a</b>) the NS04 cavity is placed on a G-10 base, and the ellipsoid is clamped to the beam tube with Cu hoops. (<b>b</b>) OFC hoops, straps and cavity actually installed.</p>
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<p>(<b>a</b>) Pictures taken during the deposition and machining processes: sealed cavity, (<b>b</b>) sprayed base layer, (<b>c</b>) sprayed 3.2 mm Cu layer and 3 flanges, (<b>d</b>) machining process.</p>
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<p>(<b>a</b>) Al plates and rings for thermal links, (<b>b</b>) indium foils were inserted in positions of the thermal links on the cavity, (<b>c</b>) cavity with thermal links installed, and (<b>d</b>) cavity placed in the cryostat for testing.</p>
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<p>Approximate mounting location of the thermometers (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>−</mo> <msub> <mrow> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mn>11</mn> </mrow> </msub> </mrow> </semantics></math>), magnetometers (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>−</mo> <msub> <mrow> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math>), and heaters (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">H</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>−</mo> <msub> <mrow> <mi mathvariant="normal">H</mi> </mrow> <mrow> <mn>4</mn> </mrow> </msub> </mrow> </semantics></math>).</p>
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<p>(<b>a</b>) Metallographic samples of niobium–copper bonding interface; (<b>b</b>) stress–displacement relationship with stroke between Nb and Cu during stretching process.</p>
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<p>Surface morphology of sprayed Cu on Nb samples with significant deformation of Cu particles.</p>
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<p>(<b>a</b>) Cu sample and PPMS sample stands, (<b>b</b>) installation for thermal conductivity measuring of Al plates, (<b>c</b>) resistance measurements and electrical conductivity calculations for Cu sample, (<b>d</b>) thermal conductivity of Cu samples and 5N Al plates used in experiments.</p>
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<p>The <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Q</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>−</mo> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi>a</mi> <mi>c</mi> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math> curves of the Nb<sub>3</sub>Sn cavity for RF testing under different conditions. The color of the data points shows the <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>a</mi> <mi>v</mi> <mi>g</mi> </mrow> </msub> </mrow> </semantics></math> of the cavity.</p>
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<p>Cavity surface temperature and ambient magnetic field variations under three cooling conditions: (<b>a</b>) direct cooling without controlling the cooling rate, (<b>b</b>) two cycles of cooling by switching the cryocoolers on and off between 30 K and 12 K, and (<b>c</b>) using the heaters to control the cooling rate. The location of the thermometer and heater is shown in <a href="#applsci-14-06350-f008" class="html-fig">Figure 8</a>.</p>
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<p><math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Q</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>−</mo> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi>a</mi> <mi>c</mi> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math> curves under the three cooling conditions. The color of the points indicates the average temperature of the cavity, and each point was at steady state during the measurement.</p>
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<p>Variations in cavity temperatures with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math> for CW tests. The dashed line indicates the cavity temperature versus <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math> for the 1.3 GHz TESLA single cell cavity at <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>r</mi> <mi>e</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> of 25 nΩ, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi>a</mi> <mi>c</mi> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math> of 10 MV/m. At a cavity temperature of 4.2 K, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Q</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> is about 1 × 10<sup>10</sup>.</p>
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<p><math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>r</mi> <mi>e</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> − <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>E</mi> </mrow> <mrow> <mi>a</mi> <mi>c</mi> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math> curves of RF tests on NS03 cavity.</p>
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<p>Contamination or defect in the vicinity of the cavity iris: before cold spraying (<b>a</b>), after alcohol sonication and HPR (<b>b</b>).</p>
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<p><span class="html-italic">Q</span><sub>0</sub> − <span class="html-italic">E<sub>acc</sub></span> curves of Nb<sub>3</sub>Sn cavity obtained by vertical testing in 4.2 K LHe and simulations of different conditions. The diamonds are test results in 4.2 K LHe. The circles are case 1: <span class="html-italic">R<sub>res</sub></span> is identical to that measured in LHe. The triangles are case 2: <span class="html-italic">R<sub>res</sub></span> is twice that measured in LHe. The color of the points indicates the average body temperature of the cavity.</p>
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<p>Temperature distribution obtained by simulation at 9.91 MV/m in case 2, (<b>a</b>) cavity and thermal links; (<b>b</b>) temperature distribution on the cavity only.</p>
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26 pages, 13277 KiB  
Review
Remote Sensing Technologies for Mapping Ecosystem Services: An Analytical Approach for Urban Green Infrastructure
by Martina Di Palma, Marina Rigillo and Mattia Federico Leone
Sustainability 2024, 16(14), 6220; https://doi.org/10.3390/su16146220 - 20 Jul 2024
Viewed by 706
Abstract
Urban Green Infrastructures (UGIs) have gained increasing relevance in the field of climate adaptive design because of their capacity to provide regulating ecosystem services apt to respond to the impacts of global warming with short-term strategies. The effectiveness of UGIs in reducing climate [...] Read more.
Urban Green Infrastructures (UGIs) have gained increasing relevance in the field of climate adaptive design because of their capacity to provide regulating ecosystem services apt to respond to the impacts of global warming with short-term strategies. The effectiveness of UGIs in reducing climate risks depends on both the state of natural resources and the understanding of urban ecosystem processes over time. The implementation of analytic methods to better understand urban ecosystem dynamics, as well as the local effective potential of ESs, is crucial for addressing climate impacts in cities. The advances in remote sensing methodologies for mapping and monitoring urban ecosystems represent a key opportunity to deepen the ecological features of existing urban green areas as a potential planning asset to respond to climate impacts. Indeed, remote sensing technologies implement a new data-driven planning approach that enables models and simulations of different project scenarios by supporting planning decisions and reducing the risk of failures. According to these assumptions, this paper discusses the results of a literature review aimed at providing the current state of the art in applying remote sensing technologies for mapping and monitoring ecosystem services, focusing on operational opportunities in urban environments. It examines how remote sensing can depict ESs and ensure data quality and reliability for UGI design. The emphasis is on the potential of ESs to mitigate and adapt to heat wave risks which will be more frequent in the next decade, particularly in cities, as highlighted by the IPCC Report 2023. Therefore, UGIs are strategic tools for addressing heat wave impacts, necessitating a shift from empirical approaches to analytical, data-driven planning methods. Full article
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Figure 1
<p>The spatial components of Green Infrastructure.</p>
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<p>Ecosystem services framework for heat waves in the multiscale approach.</p>
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<p>The three phases of the methodological scoping process.</p>
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<p>Results of the scoping review process across three phases.</p>
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<p>Temporal evolution of the results for each string by phase.</p>
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<p>Top keyword occurrences across the three phases.</p>
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<p>Heatmap of top keywords occurrences over time.</p>
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<p>Bubble chart of key indicators recorded in the third phase of analysis.</p>
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<p>Temporal overview across the three phases of investigation.</p>
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<p>Overview of remote sensing data types and purposes.</p>
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<p>Comparison and classification of key indicators recorded in the third phase of the analysis.</p>
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<p>Research string used in phase 1.</p>
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<p>Research string used in phase 2.</p>
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<p>Research string used in phase 3.</p>
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<p>Results of applying of remote sensing technologies for mapping and monitoring GI. The bibliographic references are listed in the first column and can also be found in the bibliography.</p>
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<p>Results of applying remote sensing technologies for mapping, modeling, and simulation of UGI at the urban scale. The bibliographic references are listed in the first column and can also be found in the bibliography.</p>
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<p>Results of the application of remote sensing technologies for climate adaptation project for the heat wave. The bibliographic references are listed in the first column and can also be found in the bibliography.</p>
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<p>Results of applying remote sensing technologies for climate adaptation projects to address heat waves. The bibliographic references are listed in the first column and can also be found in the bibliography.</p>
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27 pages, 12664 KiB  
Article
Genotype-Specific Activation of Autophagy during Heat Wave in Wheat
by Kathleen Hickey, Yunus Şahin, Glenn Turner, Taras Nazarov, Vadim Jitkov, Mike Pumphrey and Andrei Smertenko
Cells 2024, 13(14), 1226; https://doi.org/10.3390/cells13141226 - 20 Jul 2024
Viewed by 494
Abstract
Recycling of unnecessary or dysfunctional cellular structures through autophagy plays a critical role in cellular homeostasis and environmental resilience. Therefore, the autophagy trait may have been unintentionally selected in wheat breeding programs for higher yields in arid climates. This hypothesis was tested by [...] Read more.
Recycling of unnecessary or dysfunctional cellular structures through autophagy plays a critical role in cellular homeostasis and environmental resilience. Therefore, the autophagy trait may have been unintentionally selected in wheat breeding programs for higher yields in arid climates. This hypothesis was tested by measuring the response of three common autophagy markers, ATG7, ATG8, and NBR1, to a heat wave under reduced soil moisture content in 16 genetically diverse spring wheat landraces originating from different geographical locations. We observed in the greenhouse trials that ATG8 and NBR1 exhibited genotype-specific responses to a 1 h, 40 °C heat wave, while ATG7 did not show a consistent response. Three genotypes from Uruguay, Mozambique, and Afghanistan showed a pattern consistent with higher autophagic activity: decreased or stable abundance of both ATG8 and NBR1 proteins, coupled with increased transcription of ATG8 and NBR1. In contrast, three genotypes from Pakistan, Ethiopia, and Egypt exhibited elevated ATG8 protein levels alongside reduced or unaltered ATG8 transcript levels, indicating a potential suppression or no change in autophagic activity. Principal component analysis demonstrated a correlation between lower abundance of ATG8 and NBR1 proteins and higher yield in the field trials. We found that (i) the combination of heat and drought activated autophagy only in several genotypes, suggesting that despite being a resilience mechanism, autophagy is a heat-sensitive process; (ii) higher autophagic activity correlates positively with greater yield; (iii) the lack of autophagic activity in some high-yielding genotypes suggests contribution of alternative stress-resilient mechanisms; and (iv) enhanced autophagic activity in response to heat and drought was independently selected by wheat breeding programs in different geographic locations. Full article
(This article belongs to the Special Issue Role of Autophagy in Plant Cells)
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<p>Phylogeny and predicted structure of wheat ATG8. (<b>A</b>) Phylodendrogram of ATG8 proteins from <span class="html-italic">T. aestivum</span> (Ta; highlighted in red), <span class="html-italic">A. thaliana</span> (At), and <span class="html-italic">O. sativa</span> (Os). <span class="html-italic">S. cerevisiae</span> (Sc; highlighted in blue) ATG8 was used as the outgroup. (<b>B</b>) Structure of ATG8c-2A (uniport: Q7XY24) based on AlphaFold prediction. (<b>C</b>–<b>E</b>) Predicted structures of ATG8 homoeologs on chromosome 6 superimposed with ATG8c-2A (pink). (<b>C</b>) ATG8-6A (uniport: A0A3B6NXU7). (<b>D</b>) ATG8l-6B (uniport: A0A3B6PL31). (<b>E</b>) ATG8m-6D (uniport: A0A3B6QLC9).</p>
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<p>Characterization of ATG8 heterogeneity. (<b>A</b>) Western blot of total protein extract from <span class="html-italic">T.aestivum</span>, <span class="html-italic">A. thaliana</span>, <span class="html-italic">B. distachyon</span>, and <span class="html-italic">O. sativa</span> using anti-ATG8. (<b>B</b>) Western blot of proteins that were immunoprecipitated from total protein extract of Berkut probed with anti-ATG8. Red boxes indicate gel slices that were excised and sent for proteomics analysis. Asterisks (*) denote slices in which ATG8 was detected. (<b>C</b>,<b>D</b>) Delipidation assay of ATG8. Western blotting and corresponding densitometric plots of total protein extracts (<b>C</b>) or microsomal fractions (<b>D</b>) from leaves of var. Berkut before and after incubation with Phospholipase D with anti-ATG8. Arrows point peaks that disappear after the delipidation. Bars and numbers indicate the position and corresponding size of molecular weight markers.</p>
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<p>Response of ATG8 bands to heat and drought. (<b>A</b>–<b>F</b>) Representative images of Western blotting with anti-ATG8 and corresponding densitometric scans of total protein extracts from leaves of control and heat- and drought-stressed (H + D) Berkut plants (<b>A</b>), LDRC2 (<b>B</b>), LDRC9 (<b>C</b>), LDRC10 (<b>D</b>), LDRC16 (<b>E</b>), LDRC43 (<b>F</b>). Bars and numbers indicate the position and size of molecular weight markers. (<b>G</b>) Fold change of cumulative ATG8 bands intensity in extracts from heat and drought stress material relative to the control. <span class="html-italic">p</span>-values represent statistical differences for Student’s <span class="html-italic">t</span>-test at 95% confidence (<span class="html-italic">n</span> = 4).</p>
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<p>Impact of heat and drought stress on the abundance of ATG8. (<b>A</b>–<b>F</b>) Western blotting with anti-ATG8 of total protein extracts from leaves of control and heat- and drought-stressed (H + D) plants of Berkut (<b>A</b>), LDRC9 (<b>B</b>), LDRC 19 (<b>C</b>), LDRC 43 (<b>D</b>), LDRC 48 (<b>E</b>), LDRC 65 (<b>F</b>). Bars and numbers indicate the position and corresponding size of molecular weight markers. Amido black staining of the corresponding membrane showing Rubisco protein. (<b>G</b>) Fold change of ATG8 protein abundance in response to heat and drought stress relative to the control. <span class="html-italic">p</span>-values represent statistical differences between control and stress treatments for Student’s <span class="html-italic">t</span>-test at 95% confidence (<span class="html-italic">n</span> = 4, two different plants in two independent experiments). (<b>H</b>) Representative images showing immunostaining of ATG8 in control or heat and drought-treated leaves of LDRC48. Each image is a single 1 mm thick optical section. Scale bar, 10 mm. (<b>I</b>) Average fluorescence signal of individual ATG8 puncta in mesophyll cells of control or heat and drought-treated leaves. <span class="html-italic">p</span>-values represent statistical differences for Student’s <span class="html-italic">t</span>-test at 95% confidence (<span class="html-italic">n</span> = 8, 17, or 16 puncta for negative control, watered or stressed plants, at least 5 cells per each sample).</p>
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<p>Impact of heat and drought stress on different ATG8 bands. (<b>A</b>,<b>B</b>) A representative Western blot (<b>A</b>) and corresponding colloidal-silver stained membrane (<b>B</b>) of total protein extract from leaves of control and stressed LDRC16 with anti-ATG8. The intensity of bands corresponding to approximately 15 kD, 30 kD, and 50 kD denoted by the rectangles were used for quantification in panels (<b>C</b>–<b>E</b>). Bars and numbers indicate the position and corresponding size of molecular weight markers. Fold change of 15 kD (<b>C</b>), 30 kD (<b>D</b>), or 50 kD (<b>E</b>) ATG8 bands abundance in the heat- and drought-stressed plants relative to the control. <span class="html-italic">p</span>-values represent statistical differences for Student’s <span class="html-italic">t</span>-test at 95% confidence (<span class="html-italic">n</span> = 4, two different plants in two independent experiments).</p>
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<p>Impact of heat and drought stress on <span class="html-italic">ATG8</span> transcription. (<b>A</b>) Fold change of cumulative transcription level of <span class="html-italic">ATG8c-2A</span>, <span class="html-italic">ATG8f-2D</span>, <span class="html-italic">ATG8d-2B</span>, and <span class="html-italic">ATG8l-6B</span> in response to heat and drought stress relative to control. Student’s <span class="html-italic">t</span>-test at 95% confidence (<span class="html-italic">n</span> = 4, two different plants in two independent experiments). (<b>B</b>,<b>C</b>) Fold change of <span class="html-italic">ATG8b-2A</span> (<b>B</b>) or <span class="html-italic">ATG8i-5A</span> (<b>C</b>) transcription level in response to heat and drought stress relative to control. ADP-ribosylation factor 2 was used as a housekeeping gene for the normalization of the transcript level (Genebank: XM_044502292.1; [<a href="#B64-cells-13-01226" class="html-bibr">64</a>]) (<span class="html-italic">n</span> = 4, two different plants in two independent experiments). Student’s <span class="html-italic">t</span>-test at 95% confidence (<span class="html-italic">n</span> = 4, two different plants in two independent experiments).</p>
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<p>Impact of heat and drought on ATG7. (<b>A</b>–<b>C</b>) Western blotting with anti-ATG7 of total protein extracts from leaves of control and heat- and drought-stressed (H + D) Berkut (<b>A</b>), LDRC5 (<b>B</b>), LDRC43 (<b>C</b>). Bars and numbers indicate the position and size of molecular weight markers. Corresponding membrane stained with amido black shows Rubisco protein in each lane. Intensity of the Rubisco band was used for the normalization of the signal on the Western blotting. (<b>D</b>) Fold change of ATG7 protein abundance in response to heat and drought stress relative to the control. <span class="html-italic">p</span>-values represent statistical differences between control and stress treatments for Student’s <span class="html-italic">t</span>-test at 95% confidence (<span class="html-italic">n</span> = 4, two different plants in two independent experiments). (<b>E</b>) Fold change of <span class="html-italic">ATG7</span> transcript abundance in response to heat and drought stress relative to the control. ADP-ribosylation factor 2 (Genebank: XM_044502292.1; [<a href="#B64-cells-13-01226" class="html-bibr">64</a>]) was used as a housekeeping gene for the normalization of RT-qPCR values. <span class="html-italic">p</span>-values represent statistical differences between control and stress treatments for Student’s <span class="html-italic">t</span>-test at 95% confidence (<span class="html-italic">n</span> = 4, two different plants in two independent experiments).</p>
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<p>Impact of heat and drought stress on NBR1. (<b>A</b>–<b>G</b>) Western blotting with anti-NBR1 of total protein extracts from leaves of well-watered (control) or heat- and drought-stressed (H + D) genotypes. Bars and numbers indicate the position and corresponding size of molecular weight markers. Amido black staining of the corresponding Western blotting membrane shows Rubisco protein. (<b>H</b>) Fold change of NBR1 protein abundance in response to heat and drought stress relative to the control. <span class="html-italic">p</span>-values represent statistical differences between control and stress treatments for Student’s <span class="html-italic">t</span>-test at 95% confidence (<span class="html-italic">n</span> = 4, two different plants in two independent experiments). (<b>I</b>) Fold change of <span class="html-italic">NBR1</span> transcript abundance in response to heat and drought stress relative to the control. ADP-ribosylation factor 2 (Genebank: XM_044502292.1 [<a href="#B64-cells-13-01226" class="html-bibr">64</a>]) was used as a housekeeping gene for the normalization of RT-qPCR values. <span class="html-italic">p</span>-values represent statistical differences between control and stress treatments for Student’s <span class="html-italic">t</span>-test at 95% confidence (<span class="html-italic">n</span> = 4, two different plants in two independent experiments).</p>
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<p>Relationship between yield and autophagy parameters. (<b>A</b>) Principal component analysis of yield, ATG8 parameters, and ATG7 for all genotypes. (<b>B</b>) R<sup>2</sup> values for principal component analysis in (<b>A</b>). Blue font denotes a positive correlation and red denotes a negative correlation between the values.</p>
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<p>Relationship between yield, autophagy, and NBR1 parameters. (<b>A</b>) Principal component analysis of yield ATG8 parameters, ATG7, and NBR1 parameters for 7 genotypes. (<b>B</b>) R<sup>2</sup> values for the principal component analysis in (<b>A</b>). Blue color denotes a positive correlation and red denotes a negative correlation between the values.</p>
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16 pages, 2553 KiB  
Article
Mulching with Municipal Solid Waste (MSW) Compost Has Beneficial Side Effects on Vineyard Soil Compared to Mulching with Synthetic Films
by Ileana Blanco, Massimiliano Cardinale, Corrado Domanda, Gianluca Pappaccogli, Piergiorgio Romano, Gianni Zorzi and Laura Rustioni
Horticulturae 2024, 10(7), 769; https://doi.org/10.3390/horticulturae10070769 - 20 Jul 2024
Viewed by 503
Abstract
Municipal solid waste (MSW) compost represents a sustainable alternative to plastic film for mulching in viticulture. This study investigated the effects of MSW compost on vineyard soil properties, specifically focusing on side effects such as soil temperature and microbial decomposition activity, independently from [...] Read more.
Municipal solid waste (MSW) compost represents a sustainable alternative to plastic film for mulching in viticulture. This study investigated the effects of MSW compost on vineyard soil properties, specifically focusing on side effects such as soil temperature and microbial decomposition activity, independently from its role in weed control. The experiment was conducted in a vineyard located in the Mediterranean region (Southern Italy), with six different mulching treatments: black polyethylene (PE) film, black and white biodegradable film, three different amounts of MSW compost (8, 15, and 22 kg plant−1), and a control without mulching. Weed growth was monitored to determine the optimal compost application amount. The 15 kg plant−1 treatment was selected for further analyses, as it did not significantly impact weed growth compared to the control. Results indicated that MSW compost mulching maintained lower soil temperatures compared to other treatments (up to 5 °C in the warmest hours) and reduced the amplitude of the thermal wave up to 50% compared to the non-mulched soil and even more compared to black film mulched soil, particularly during the warmest periods. This suggests that MSW compost can mitigate heat stress on plant roots, potentially enhancing plant resilience and preserving crop production also in stressful growing conditions. Microbial decomposition activity, assessed using the tea bag index, was higher in the MSW compost treatment during spring compared to the control, indicating temperature as a key driver for organic matter decomposition, but this effect disappeared during summer. These findings highlight the potential of MSW compost to support sustainable viticulture by reducing reliance on synthetic mulching materials and promoting environmental sustainability through the recycling of organic municipal waste. Full article
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<p>Experimental design. Three randomized replicates per each treatment were created, one in each treated row (rows 2, 3, and 4). Each block contained six <span class="html-italic">Vitis vinifera</span> cv. “Primitivo” plants. Rows 1 and 5, and blocks A and H were considered as borders.</p>
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<p>Trend in weed growth, expressed as percentage of the soil covered by green canopies. The whisker indicates the standard error. Different lowercase letters indicate significantly different means between the treatments for each date (Duncan post hoc test, <span class="html-italic">p</span> &lt; 0.05). Different capital letters indicate significantly different means between the treatments with repeated measures for the entire duration of the surveys (Duncan post hoc test, <span class="html-italic">p</span> &lt; 0.05). n.s.= not significant.</p>
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<p>Mean values ± standard errors (whiskers) of the temperature of the soil treated with the black film (T<sub>Fb</sub>) and the compost layer (T<sub>C15</sub>), of the control soil (T<sub>Ctrl</sub>) and of the air (T<sub>air</sub>), during July–September 2023. Different lowercase letters indicate significant differences between the means for each month (Duncan post hoc test, <span class="html-italic">p</span> &lt; 0.05). Different capital letters indicate significant differences between the means for the entire duration of the surveys (Duncan post hoc test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Diurnal pattern of the soil and air temperature. The whisker indicates the standard error. Different lowercase letters indicate significant differences between the three selected treatments for each hour (hsd post hoc test, <span class="html-italic">p</span> &lt; 0.05). Different capital letters indicate significant differences between treatments with repeated measures for the entire monitored period (hsd post hoc test, <span class="html-italic">p</span> &lt; 0.05). The air temperature pattern is shown for reference only.</p>
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<p>Mean values ± standard errors (whiskers) of the temperature difference between the soil under the black film, the compost layer and the control treatments and the air temperature, in relation to T<sub>air</sub> (1 °C intervals). Different lowercase letters indicate significant differences between ΔT<sub>Fb</sub>, ΔT<sub>C15</sub> and ΔT<sub>Ctrl</sub> means for each T<sub>air</sub> value (Duncan post hoc test, <span class="html-italic">p</span> &lt; 0.05). Different capital letters indicate significant differences between ΔT<sub>Fb</sub>, ΔT<sub>C15</sub> and ΔT<sub>Ctrl</sub> means for the entire range of T<sub>air</sub> values (Duncan post hoc test, <span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Means ± standard errors (whiskers) of the tea bag index, measured in two periods: April–June (panel (<b>A</b>)) and July–September (panel (<b>B</b>)), for the treatments “Film-black”, “Compost” (15) and “Control”. Different lowercase letters indicate significantly different means between treatments for each type of tea (Duncan post hoc test, <span class="html-italic">p</span> &lt; 0.05). Different capital letters indicate significantly different means between treatments with repeated measures over the two periods for each type of tea separately (Duncan post hoc test, <span class="html-italic">p</span> &lt; 0.05).</p>
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11 pages, 7224 KiB  
Article
Connection between Winter East Asia Flow Patterns and Stratospheric Polar Vortex Anomalies
by Masakazu Taguchi
Atmosphere 2024, 15(7), 844; https://doi.org/10.3390/atmos15070844 - 17 Jul 2024
Viewed by 364
Abstract
Using a reanalysis dataset, this work investigates the possible connection of winter East Asia (EA) flow patterns to stratospheric polar vortex (SPV) anomalies. Cluster analysis is performed on the principal components of daily 500 hPa geopotential height fields to identify five distinct flow [...] Read more.
Using a reanalysis dataset, this work investigates the possible connection of winter East Asia (EA) flow patterns to stratospheric polar vortex (SPV) anomalies. Cluster analysis is performed on the principal components of daily 500 hPa geopotential height fields to identify five distinct flow patterns. SPV anomalies are considered in terms of the occurrence of major sudden stratospheric warmings (MSSWs). The results reveal that for the 15 days before the MSSWs, one of the five patterns occurs more frequently than usual, whereas another occurs less frequently. The former constructively interferes with the climatological EA trough in the troposphere and strengthens the planetary wave activity (heat flux) in the extratropical troposphere and stratosphere. It has a similar pattern in the 500 hPa height to the composite leading to the MSSWs, implying that such strengthening can contribute to the forcing of the MSSWs. The latter is in the opposite sense (destructive interference) and is disadvantageous before the MSSWs. Evidence of a stratospheric downward influence on the five flow patterns is relatively unclear. These results suggest a potential coupling between flow patterns or weather regimes in different regions through the SPV, as well as warrant further investigation of the downward influence on EA weather regimes. Full article
(This article belongs to the Section Meteorology)
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Figure 1

Figure 1
<p>Composite Z500a maps for each FPi during DJF; FP1–FP5 are shown in (<b>a</b>–<b>e</b>), respectively. Panels (<b>f</b>–<b>j</b>) are similar but plot a hemispheric-scale distribution. Green contours plot the DJF climatological Z500 waves 1–3 pattern, drawn at ±75 and ±150 m. Solid contours represent positive values, and dotted contours negative values. The EA region is marked by violet lines in (<b>f</b>–<b>j</b>).</p>
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<p>(<b>a</b>) Composite time series of U10a (m/s, solid line) and U100a (m/s, dotted line, multiplied by 5) with respect to the MSSW central date. (<b>b</b>) Same as (<b>a</b>), but for HF100a (K m/s). Values of statistical significance at the 95% confidence level according to the two-sided <span class="html-italic">t</span>-test are highlighted in black. The PRE and POST periods are denoted in the panels.</p>
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<p>Composite Z500a maps for each of the PRE and POST periods, shown in (<b>a</b>,<b>b</b>), respectively. Note the color bar is different between <a href="#atmosphere-15-00844-f001" class="html-fig">Figure 1</a> and <a href="#atmosphere-15-00844-f003" class="html-fig">Figure 3</a>. Green contours plot the DJF climatological Z500 waves 1–3 pattern as in <a href="#atmosphere-15-00844-f001" class="html-fig">Figure 1</a>. The EA region is marked by violet lines.</p>
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<p>Bar chart for POi for the PRE (dark shades) and POST (light shades) periods. Error bars indicate 95% intervals obtained using the bootstrap method. Circles mean that the POi value exceeds the 95% interval.</p>
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<p>Cosine similarity between each FPi pattern and the PRE or POST composite of Z500a. Unfilled markers represent PRE and filled markers POST. Several angles from 0° to 180° are also shown for reference.</p>
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<p>HFa (K m/s) averaged over the DJF days for the five flow patterns (<b>a</b>–<b>e</b>) as indicated. Interval for solid contours is 10 K m/s. Broken contours are drawn at ±1, ±2, and ±3 below 100 hPa, in addition to ±5 K m/s. Negative values are shaded.</p>
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<p>(Left column) Time–height sections of Za (averaged poleward of 65°N with area weighting, further weighted with the square root of pressure/1000) composited with respect to the MSSW central date. Panel (<b>a</b>) uses all MSSWs, whereas the rest use (<b>c</b>,<b>e</b>,<b>g</b>) subsets, as indicated. For example, panel (<b>c</b>) uses a subset of MSSWs for which the dominant flow pattern is FP1. The contour interval is 20 m. Color shades denote values of statistical significance at the 95% level according to the two-sided <span class="html-italic">t</span>-test. Panels in the right column (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) are similar, but for HFa (averaged between 45–75° N, further weighted with the square root of pressure/1000). Contour interval is 2.5 K m/s.</p>
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<p>Same as <a href="#atmosphere-15-00844-f004" class="html-fig">Figure 4</a>, but for the POST period only, when classifying all MSSWs according to the dominant flow pattern in the PRE period. The dominant pattern is denoted in each panel as FPi.</p>
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Review
Climate Change Impacts on Legume Physiology and Ecosystem Dynamics: A Multifaceted Perspective
by Kirtan Dave, Anand Kumar, Nischal Dave, Mukul Jain, Parmdeep Singh Dhanda, Alpa Yadav and Prashant Kaushik
Sustainability 2024, 16(14), 6026; https://doi.org/10.3390/su16146026 - 15 Jul 2024
Viewed by 663
Abstract
As valuable sources of plant-based protein, leguminous vegetables (grain legumes) are essential for global food security and contribute to body growth and development in humans as well as animals. Climate change is a major challenge for agriculture development that creates major problems for [...] Read more.
As valuable sources of plant-based protein, leguminous vegetables (grain legumes) are essential for global food security and contribute to body growth and development in humans as well as animals. Climate change is a major challenge for agriculture development that creates major problems for the growth and development of plants. However, legume productivity is threatened by climate change factors, including rising temperatures, shifting precipitation patterns, increased atmospheric carbon dioxide levels, intensified extreme events, and altered pest/pathogen activity. This review synthesizes approximately 136 studies to assess the climate effects on major legume crops. Under all the global emissions trajectories, the mean temperatures are projected to rise beyond the optimal legume growing thresholds by 2050, carrying yield reductions between 10 and 49% for beans, soybeans, cowpeas, and lentils without adaptation measures. The elevated carbon dioxide may transiently enhance the yields up to 18%, but the benefits dramatically decline above 550 ppm and cannot offset the other climate impacts. Altered rainfall along with recurrent drought and heat waves are also expected to decrease the legume crop yields, seed quality, and soil nitrogen levels worldwide. Furthermore, the proliferation of legume pests and fungal diseases poses significant risks, amplified by climate shifts in 84% of the reviewed studies. These multifaceted impacts threaten the productivity gains in leguminous vegetables essential to sustainably meeting the global protein demand. Realizing resilience will require the accelerated development of heat/drought-tolerant legume varieties, enhanced climate-informed agronomic practices, strong policy interventions, and social safety nets explicitly supporting legume producers, in addition to the policies/steps that governments are taking to address the challenges of the climate crisis. This review highlights the essential adaptations and mechanisms required for legume crops to thrive and fulfill their significant roles in global nutrition. It explores how these crops can be improved to better withstand the environmental stresses, enhance their nutritional profiles, and increase their yields. Additionally, the review discusses the importance of legumes in sustainable agriculture and food security, emphasizing their potential to address the future challenges in feeding the growing global population. By focusing on these critical aspects, the review aims to underscore the importance of legumes in ensuring a healthy and sustainable food supply. Full article
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<p>Schematic representation of the benefits of legumes for humans and soil.</p>
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<p>Effects of heat stress during the reproductive phase.</p>
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<p>Impacts of heavy precipitation and drought on agricultural insect pests.</p>
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