Nothing Special   »   [go: up one dir, main page]

You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (32,664)

Search Parameters:
Keywords = vegetation

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
14 pages, 4235 KiB  
Article
Recycled Low Density Polyethylene Reinforced with Deverra tortuosa Vegetable Fibers
by Tahani Zorgui, Hibal Ahmad, Mehrez Romdhane and Denis Rodrigue
J. Compos. Sci. 2024, 8(10), 394; https://doi.org/10.3390/jcs8100394 (registering DOI) - 1 Oct 2024
Abstract
In this work, natural fibers extracted from the medicinal aromatic plant Deverra tortuosa, with different sizes (S1 = 2 mm and S2 = 500 μm), were incorporated into recycled low density polyethylene (rLDPE) to produce sustainable biocomposites. Compounding was performed with different [...] Read more.
In this work, natural fibers extracted from the medicinal aromatic plant Deverra tortuosa, with different sizes (S1 = 2 mm and S2 = 500 μm), were incorporated into recycled low density polyethylene (rLDPE) to produce sustainable biocomposites. Compounding was performed with different fiber concentrations (0 to 30% wt.) via twin-screw extrusion followed by injection molding. Based on the samples obtained, a comprehensive series of characterization was conducted, encompassing morphological and mechanical (flexural, tensile, hardness, and impact) properties. Additionally, thermal properties were assessed via differential scanning calorimetry (DSC), while Fourier transform infrared spectroscopy (FTIR) was used to elucidate potential chemical interactions and changes with processing. Across the range of conditions investigated, substantial improvements were observed in the rLDPE properties, in particular for the tensile modulus (23% for S1 and 104% for S2), flexural modulus (47% for S1 and 61% for S2), and flexural strength (31% for S1 and 65% for S2). Nevertheless, the tensile strength decreased (15% for S1 and 46% for S2) due to poor fiber–matrix interfacial adhesion. These preliminary results can be used for further development in sustainable packaging materials. Full article
(This article belongs to the Special Issue Polymer Composites and Fibers, 3rd Edition)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Typical images of the plant <span class="html-italic">D. tortuosa</span>.</p>
Full article ">Figure 2
<p>SEM micrographs of selected samples: (<b>a</b>) rLDPE, (<b>b</b>) rLDPE with 10% of S1 (2 mm) at 500×, (<b>c</b>) rLDPE with 10% of S2 (500 μm) at 500×, (<b>d</b>) rLDPE with 20% of S1 (2 mm) at 800×, (<b>e</b>) rLDPE with 20% of S2 (500 μm) at 800×, (<b>f</b>) rLDPE with 30% of S1 (2 mm) at 300×, and (<b>g</b>) rLDPE with 30% of S2 (500 μm) at 300×.</p>
Full article ">Figure 3
<p>FTIR spectra of the neat rLDPE, rLDPE with 20% wt. of S1 (2 mm) and S2 (500 μm) for different spectral range: (<b>a</b>) 2800–3000 cm<sup>−1</sup> and (<b>b</b>) 600–2400 cm<sup>−1</sup>.</p>
Full article ">Figure 4
<p>DSC thermograms of biocomposites with different fiber contents (10%, 20%, and 30% wt.) and sizes (S1 = 2 mm and S2 = 500 μm): (<b>a</b>) heating curve and (<b>b</b>) cooling curve.</p>
Full article ">Figure 5
<p>Hardness as a function of fiber contents (10%, 20%, and 30% wt.) and sizes (S1 = 2 mm and S2 = 500 μm): (<b>a</b>) Shore A and (<b>b</b>) Shore D.</p>
Full article ">Figure 6
<p>Tensile properties as a function of fiber contents (10%, 20%, and 30% wt.) and sizes (S1 = 2 mm and S2 = 500 μm): (<b>a</b>) modulus, (<b>b</b>) strength, (<b>c</b>) elongation at break, and (<b>d</b>) typical stress–strain curves.</p>
Full article ">Figure 7
<p>Flexural properties as a function of fiber contents (10%, 20%, and 30% wt.) and sizes (S1 = 2 mm and S2 = 500 μm): (<b>a</b>) modulus and (<b>b</b>) strength.</p>
Full article ">Figure 8
<p>Charpy impact strength as a function of fiber contents (10%, 20%, and 30% wt.) and sizes (S1 = 2 mm and S2 = 500 μm).</p>
Full article ">
21 pages, 16266 KiB  
Article
Urban Perception Evaluation and Street Refinement Governance Supported by Street View Visual Elements Analysis
by Fengliang Tang, Peng Zeng, Lei Wang, Longhao Zhang and Weixing Xu
Remote Sens. 2024, 16(19), 3661; https://doi.org/10.3390/rs16193661 (registering DOI) - 1 Oct 2024
Viewed by 110
Abstract
As street imagery and big data techniques evolve, opportunities for refined urban governance emerge. This study delves into effective methods for urban perception evaluation and street refinement governance by using street view data and deep learning. Employing DeepLabV3+ and VGGNet models, we analyzed [...] Read more.
As street imagery and big data techniques evolve, opportunities for refined urban governance emerge. This study delves into effective methods for urban perception evaluation and street refinement governance by using street view data and deep learning. Employing DeepLabV3+ and VGGNet models, we analyzed street view images from Nanshan District, Shenzhen, identifying critical factors that shape residents’ spatial perceptions, such as urban greenery, road quality, and infrastructure. The findings indicate that robust vegetation, well-maintained roads, and well-designed buildings significantly enhance positive perceptions, whereas detractors like fences reduce quality. Furthermore, Moran’s I statistical analysis and multi-scale geographically weighted regression (MGWR) models highlight spatial heterogeneity and the clustering of perceptions, underscoring the need for location-specific planning. The study also points out that complex street networks in accessible areas enhance living convenience and environmental satisfaction. This research shows that integrating street view data with deep learning provides valuable tools for urban planners and policymakers, aiding in the development of more precise and effective urban governance strategies to foster more livable, resilient, and responsive urban environments. Full article
(This article belongs to the Special Issue Data-Driven City and Society—a Remote Sensing Perspective)
Show Figures

Figure 1

Figure 1
<p>The research flowchart.</p>
Full article ">Figure 2
<p>Study area location: (<b>a</b>) China; (<b>b</b>) Guangdong Province; (<b>c</b>) Shenzhen City; (<b>d</b>) Nanshan District.</p>
Full article ">Figure 3
<p>The methodology roadmap.</p>
Full article ">Figure 4
<p>The process of VggNet neural network model.</p>
Full article ">Figure 5
<p>An example of TrueSkill algorithm computation.</p>
Full article ">Figure 6
<p>The spatial distribution of six types of perceptional scores in Nanshan District.</p>
Full article ">Figure 7
<p>An overall perception performance with scores (<b>left</b>) and their clustering (<b>right</b>).</p>
Full article ">Figure 8
<p>The spatial autocorrelation clustering.</p>
Full article ">Figure 9
<p>The spatial distribution of the urban perception’s influencing factors.</p>
Full article ">Figure 10
<p>The spatial distribution of the street accessibility.</p>
Full article ">Figure 11
<p>The spatial distribution of the perception performance with a high accessibility.</p>
Full article ">
15 pages, 16201 KiB  
Article
Remote-Sensed Determination of Spatiotemporal Properties of Drought and Assessment of Influencing Factors in Ordos, China
by Sinan Wang, Quancheng Zhou, Yingjie Wu, Wei Li and Mingyang Li
Agronomy 2024, 14(10), 2265; https://doi.org/10.3390/agronomy14102265 (registering DOI) - 1 Oct 2024
Viewed by 128
Abstract
Ordos drought impacts are complex; the Geodetector model is able to explore the interaction between impact factors. Based on the drought severity index (DSI), this study explored the spatio-temporal dynamics and changing trends of drought, and analyzed the driving factors of DSI spatial [...] Read more.
Ordos drought impacts are complex; the Geodetector model is able to explore the interaction between impact factors. Based on the drought severity index (DSI), this study explored the spatio-temporal dynamics and changing trends of drought, and analyzed the driving factors of DSI spatial differentiation by using the Geodetector model. The results show that: the evapotranspiration (ET) and normalized difference vegetation index (NDVI) in Ordos showed a significant increasing trend (p < 0.05). The increasing rates were ET (4.291 mm yr−1) and NDVI (0.004 yr−1). In addition, the interannual variation of the DSI also showed a significant increase, with a trend change rate of 0.089. The spatial pattern of ET and the NDVI was low in the southwest and high in the northeast, and the spatial pattern of potential evapotranspiration (PET) was high in the southwest and low in the northeast, while the distribution of the DSI was dry in the west and wet in the east. The spatial differentiation of the DSI was mainly affected by five factors: air temperature, precipitation, land use type, soil type, and the digital elevation model (DEM), with q exceeding 0.15, which were the main driving factors of drought in the Loess Plateau. Under the interaction of multiple factors, the four combinations of temperature and the DEM, precipitation and the DEM, sunshine duration and the DEM, and relative humidity and the DEM jointly drive drought, in which precipitation (0.156) ∩ DEM (0.248) has the strongest influence on drought occurrence, and q reaches 0.389. This study directly informs specific drought management strategies or ecological conservation efforts in the region. Full article
(This article belongs to the Section Grassland and Pasture Science)
Show Figures

Figure 1

Figure 1
<p>Geographic location of study area: (<b>a</b>) digital elevation model, (<b>b</b>) land use type.</p>
Full article ">Figure 2
<p>Inter-year changes in evapotranspiration (ET) (<b>a</b>), potential evapotranspiration (PET) (<b>b</b>), normalized difference vegetation index (NDVI) (<b>c</b>) and drought severity index (DSI) (<b>d</b>) in Ordos from 2001 to 2020.</p>
Full article ">Figure 3
<p>Spatial distributions in mean evapotranspiration (ET) (<b>a</b>), potential evapotranspiration (PET) (<b>b</b>), normalized difference vegetation index (NDVI) (<b>c</b>) and drought severity index (DSI) (<b>d</b>) in Ordos from 2001 to 2020.</p>
Full article ">Figure 4
<p>Spatial change characteristics of evapotranspiration (ET) (<b>a</b>), potential evapotranspiration (PET) (<b>b</b>), normalized difference vegetation index (NDVI) (<b>c</b>) and drought severity index (DSI) (<b>d</b>) in Ordos from 2001 to 2020.</p>
Full article ">Figure 5
<p>Significant spatial changes in evapotranspiration (ET) (<b>a</b>), potential evapotranspiration (PET) (<b>b</b>), normalized difference vegetation index (NDVI) (<b>c</b>) and drought severity index (DSI) (<b>d</b>) in Ordos from 2001 to 2020.</p>
Full article ">Figure 6
<p>Area variations in drought degree classification.</p>
Full article ">Figure 7
<p>Explanatory power of each driving factor to DSI. Note: q value is power of determinant (PD).</p>
Full article ">Figure 8
<p>Explanatory powers of factors and their interactions on drought severity index (DSI) in Ordos from 2001 to 2020.</p>
Full article ">Figure 9
<p>Spatial distribution of correlations between climate factors (<b>a</b>), precipitation; (<b>b</b>), temperature and drought severity index (DSI) as well as their significant ((<b>c</b>), precipitation; (<b>d</b>), temperature) in Ordos from 2001 to 2020.</p>
Full article ">
17 pages, 2974 KiB  
Article
TreeSeg—A Toolbox for Fully Automated Tree Crown Segmentation Based on High-Resolution Multispectral UAV Data
by Sönke Speckenwirth, Melanie Brandmeier and Sebastian Paczkowski
Remote Sens. 2024, 16(19), 3660; https://doi.org/10.3390/rs16193660 (registering DOI) - 1 Oct 2024
Viewed by 143
Abstract
Single-tree segmentation on multispectral UAV images shows significant potential for effective forest management such as automating forest inventories or detecting damage and diseases when using an additional classifier. We propose an automated workflow for segmentation on high-resolution data and provide our trained models [...] Read more.
Single-tree segmentation on multispectral UAV images shows significant potential for effective forest management such as automating forest inventories or detecting damage and diseases when using an additional classifier. We propose an automated workflow for segmentation on high-resolution data and provide our trained models in a Toolbox for ArcGIS Pro on our GitHub repository for other researchers. The database used for this study consists of multispectral UAV data (RGB, NIR and red edge bands) of a forest area in Germany consisting of a mix of tree species consisting of five deciduous trees and three conifer tree species in the matured closed canopy stage at approximately 90 years. Information of NIR and Red Edge bands are evaluated for tree segmentation using different vegetation indices (VIs) in comparison to only using RGB information. We trained Faster R-CNN, Mask R-CNN, TensorMask and SAM in several experiments and evaluated model performance on different data combinations. All models with the exception of SAM show good performance on our test data with the Faster R-CNN model trained on the red and green bands and the Normalized Difference Red Edge Index (NDRE) achieving best results with an F1-Score of 83.5% and an Intersection over Union of 65.3% on highly detailed labels. All models are provided in our TreeSeg toolbox and allow the user to apply the pre-trained models on new data. Full article
(This article belongs to the Section Forest Remote Sensing)
Show Figures

Figure 1

Figure 1
<p>Flowchart showing the overall workflow of our study.</p>
Full article ">Figure 2
<p>Study areas in Germany: (<b>a</b>) The sampling locations near Freiburg im Breisgau; (<b>b</b>) Location of the study area is in Germany; (<b>c</b>) Example of some trees with segmentation masks.</p>
Full article ">
14 pages, 1660 KiB  
Article
Use of Vegetable Waste for New Ecological Methods in Wool Fibre Treatments
by Simona Gavrilaș, Mihaela Dochia, Andreea-Raluca Sărsan, Bianca-Denisa Chereji and Florentina-Daniela Munteanu
Clean Technol. 2024, 6(4), 1326-1339; https://doi.org/10.3390/cleantechnol6040063 (registering DOI) - 1 Oct 2024
Viewed by 146
Abstract
In this current research, various amino acids (lysine, betaine, and cysteine) and peptides (oxidised or reduced glutathione) were considered as potential environmentally friendly alternatives to wool bleaching. A greener methodology was also applied to dyeing. Different agro-wastes (red cabbage, peppercorns, and red and [...] Read more.
In this current research, various amino acids (lysine, betaine, and cysteine) and peptides (oxidised or reduced glutathione) were considered as potential environmentally friendly alternatives to wool bleaching. A greener methodology was also applied to dyeing. Different agro-wastes (red cabbage, peppercorns, and red and yellow onion peels) served as raw pigment materials. The process’s efficiency was characterised by the whiteness degree, colour strength, and fastness to accelerated washing and perspiration. A higher whiteness index value was observed in the cysteine-based formulations. The onion peel exhibited significant tinctorial properties due to the presence of some natural mordants. All the proposed treatments were designed with a primary focus on environmental sustainability. These treatments offer a sustainable and environmentally friendly alternative to traditional bleaching and dyeing methods for wool. They reduce costs and energy consumption while creating added value by valorising waste. Full article
(This article belongs to the Special Issue Recovery of Bioactive Compounds from Waste and By-Products)
Show Figures

Figure 1

Figure 1
<p>The natural dyeing procedure applied to wool fibres.</p>
Full article ">Figure 2
<p>Berger whiteness index after different bleaching treatments: w—water; cbp—cysteine and betaine + pepsin; cbHCl—cysteine and betaine + HCl; cl—cysteine and lysine; l—lysine; bp—betaine and pepsin; bHCl—betaine and HCl; c—cysteine.</p>
Full article ">Figure 3
<p>Berger whiteness index for different quantities of treated wool fibres under magnetic stirring: w—water; cbp—cysteine and betaine + pepsin; cbHCl—cysteine and betaine + HCl; cl—cysteine and lysine; l—lysine; bp—betaine and pepsin; bHCl—betaine and HCl; c—cysteine.</p>
Full article ">Figure 4
<p>Berger whiteness index of wool fibres treated with different peptidic and aminoacidic solutions: w—water; GoxbpHCl—oxidised glutathione, betaine + HCl and betaine + pepsin; GredbpHCl—reduced glutathione, betaine + HCl and betaine + pepsin; cbp—cysteine and betaine + pepsin; cbHCl—cysteine and betaine + HCl; cl—cysteine and lysine; Goxl—oxidised glutathione and lysine; Gredl—reduced glutathione and lysine; l-lysine; Goxbp—oxidised glutathione and betaine + pepsin; GoxHCl—oxidised glutathione and betaine + HCl; Gredbp-reduced glutathione and betaine + pepsin; GredHCl—reduced glutathione and betaine + HCl; bp-betaine + pepsin; bHCl-betaine + HCl; Gox—oxidised glutathione; Gred—reduced glutathione; <b>c</b>—cysteine.</p>
Full article ">Figure 5
<p>Perspiration colourfastness values were evaluated using the grey scale for colour differences: pm—fibres dyed with pepper and mordant; om—fibres dyed with onion and mordant; cm—fibres dyed with red cabbage and mordant; p—fibres dyed with pepper without mordant; o—fibres dyed with onion without mordant; c—fibres dyed with red cabbage without mordant.</p>
Full article ">Figure 6
<p>Accelerated laundering colourfastness values were evaluated using the grey scale for colour differences: pm—fibres dyed with pepper and mordant; om—fibres dyed with onion and mordant; cm—fibres dyed with red cabbage and mordant; p—fibres dyed with pepper without mordant; o—fibres dyed with onion without mordant; c—fibres dyed with red cabbage without mordant.</p>
Full article ">Figure 7
<p>Accelerated laundering colourfastness values were evaluated using the staining grade for colour differences evaluation: pm—fibres dyed with pepper and mordant; om—fibres dyed with onion and mordant; cm—fibres dyed with red cabbage and mordant; p—fibres dyed with pepper without mordant; o—fibres dyed with onion without mordant; c—fibres dyed with red cabbage without mordant.</p>
Full article ">Figure 8
<p>Colour strength was determined for all dyeing treatments used: pml—long wool fibres dyed with pepper and mordant; pl—long wool fibres dyed with pepper without mordant; pms—cut wool fibres dyed with pepper and mordant; ps—cut wool fibres dyed with pepper without mordant; oml—long wool fibres dyed with onion and mordant; ol—long wool fibres dyed with onion without mordant; oms—cut wool fibres dyed with onion and mordant; os—cut wool fibres dyed with onion without mordant; cml—long wool fibres dyed with red cabbage and mordant; cl—long wool fibres dyed with red cabbage without mordant; cms—cut wool fibres dyed with red cabbage and mordant; cs—cut wool fibres dyed with red cabbage without mordant.</p>
Full article ">
15 pages, 1605 KiB  
Article
A Tomato Recognition and Rapid Sorting System Based on Improved YOLOv10
by Weirui Liu, Su Wang, Xingjun Gao and Hui Yang
Machines 2024, 12(10), 689; https://doi.org/10.3390/machines12100689 (registering DOI) - 30 Sep 2024
Viewed by 151
Abstract
In order to address the issue of time-consuming, labor-intensive traditional industrial tomato sorting, this paper proposes a high-precision tomato recognition strategy and fast automatic grasping system. Firstly, the Swin Transformer module is integrated into YOLOv10 to reduce the resolution of each layer by [...] Read more.
In order to address the issue of time-consuming, labor-intensive traditional industrial tomato sorting, this paper proposes a high-precision tomato recognition strategy and fast automatic grasping system. Firstly, the Swin Transformer module is integrated into YOLOv10 to reduce the resolution of each layer by half and double the number of channels, improving recognition accuracy. Then, the Simple Attention Module (SimAM) and the Efficient Multi-Scale Attention (EMA) attention mechanisms are added to achieve complete integration of features, and the Bi-level Routing Attention (BiFormer) is introduced for dynamic sparse attention and resource allocation. Finally, a lightweight detection head is added to YOLOv10 to improve the accuracy of tiny target detection. To complement the recognition system, a single-vertex and multi-crease (SVMC) origami soft gripper is employed for rapid adaptive grasping of identified objects through bistable deformation. This innovative system enables quick and accurate tomato grasping post-identification, showcasing significant potential for application in fruit and vegetable sorting operations. Full article
(This article belongs to the Section Machine Design and Theory)
16 pages, 562 KiB  
Article
Omitting the Application of Nitrogen or Potassium Reduced the Growth of Young Chestnut (Castanea sativa) Trees, While a Lack of Boron Decreased Fruit Yield
by Margarida Arrobas, Soraia Raimundo, Carlos Manuel Correia and Manuel Ângelo Rodrigues
Soil Syst. 2024, 8(4), 104; https://doi.org/10.3390/soilsystems8040104 - 30 Sep 2024
Viewed by 241
Abstract
The chestnut tree (Castanea sativa Mill.) is gaining importance in the mountainous regions of southern Europe due to the high value of its fruits. It is essential to establish effective cultivation protocols, considering that this species is still relatively understudied. In this [...] Read more.
The chestnut tree (Castanea sativa Mill.) is gaining importance in the mountainous regions of southern Europe due to the high value of its fruits. It is essential to establish effective cultivation protocols, considering that this species is still relatively understudied. In this study, we present the outcomes of the initial establishment of a chestnut orchard conducted through a nutrient omission trial for four years. The treatments included a fertilization plan with nitrogen, phosphorus, potassium, and boron (NPKB), the control, and four other treatments corresponding to the omission of each nutrient (-NPKB, N-PKB, NP-KB, NPK-B). The -NPKB and NP-KB treatments showed significantly lower trunk circumferences and canopy volumes compared to the other treatments. The NPK-B treatment resulted in the lowest fruit production, with a total accumulated yield (2020–2022) of 0.56 kg tree–1, a value significantly lower than that of NPKB (1.12 kg tree–1) and N-PKB (1.19 kg tree–1). The assessment of nutrient concentrations in the leaves revealed plants with deficient levels of B and K in treatments that did not receive these nutrients. Conversely, N levels in the leaves in the -NPKB treatment fell within the sufficiency range (20 to 28 g kg–1). This suggests that the sufficiency range should be adjusted to higher values, given the treatment’s effect on tree growth. It was also observed that the -NPKB treatment led to lower soil organic matter compared to the other treatments, likely due to reduced herbaceous vegetation development under the canopy, leading to decreased organic substrate deposition in the soil. The main findings of this study are that N and K were crucial elements for the optimal growth of chestnut trees, while B played a significant role in fruit production. Full article
16 pages, 2873 KiB  
Article
Olive Growing Farming System and Damage by Cicadas
by Ramón González-Ruiz, Valentina Cuevas-López, María Sainz-Pérez, Juan F. Cuesta Cocera and Antonio García-Fuentes
World 2024, 5(4), 832-847; https://doi.org/10.3390/world5040043 - 30 Sep 2024
Viewed by 153
Abstract
Although cicadas have traditionally been considered pests of little or no importance, in recent decades, an increase in damages is being recorded in olive groves of southern Spain. New agricultural practices that affect soil management are behind it. During 2024, intensive sampling has [...] Read more.
Although cicadas have traditionally been considered pests of little or no importance, in recent decades, an increase in damages is being recorded in olive groves of southern Spain. New agricultural practices that affect soil management are behind it. During 2024, intensive sampling has been carried out in an organic grove with herbaceous cover (VC2), and in a second one with mixed vegetation cover (VC1, in which the crushed remains of the annual pruning are added). In both ecological groves, inventories of the vegetation have been carried out, as well as intensive sampling in the olive canopy, with the densities of oviposition injuries being recorded and compared with respect to conventional management (CONV). The objectives of this study are to compare the three managements based on the density of oviposition injuries, to determine the priority areas for cicadas’ oviposition within the trees; and to develop a sampling method to assess damage over large areas. The results show significant increases in the density of injuries in organic groves, with maximum values recorded in the olive grove with mixed cover. Oviposition injuries show an altitudinal gradient distribution, with maximum values in the lower zone of the trees. The factors involved are discussed. Full article
Show Figures

Figure 1

Figure 1
<p>The location of the study area in the province of Jaén (Southern Spain). An olive grove with conventional farming (CONV) and an olive grove with ecological farming (ECO), including the two following large extensions: an olive grove with single herbaceous cover (VC2), and an olive grove with mixed cover (VC1). Three blocks in each olive grove are delimited by dotted lines (a, b, c). Source: Own elaboration, using the Google Earth Pro geographic information system.</p>
Full article ">Figure 2
<p>Frequency histograms of the length of the oviposition injuries (<b>right series</b>) and the diameters of the terminal branches affected (<b>left series</b>) in the olive trees of blocks (a, b, and c) of the VC1 olive grove.</p>
Full article ">Figure 3
<p>Frequency distribution (%) of the position of injuries on the terminal branches, indicating the values on the upper, lower, and lateral sides. Data correspond to the average values to the olive trees in the three blocks of the VC1 olive grove. The arrows point in the acropetal direction (towards the apex of the branch).</p>
Full article ">Figure 4
<p>Mean values (and standard error, SE) of the number of injuries/30 cm segment of subterminal (<b>top</b>) and terminal branches (<b>bottom</b>) in the olive trees from the three samplings carried out in each growing farming system (mixed cover (VC1, dark gray), simple herbaceous cover (VC2, medium gray) and conventional farming system (CONV, light gray). The mean values per tree and the standard error are indicated inside the diagrams. Statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) are indicated by different letters (a, b, c).</p>
Full article ">Figure 5
<p>Mean values (and standard error, SE) of the number of injuries/30 cm segment of subterminal (<b>top</b>) and terminal branches (<b>bottom</b>) in the high (light gray), medium (intermediate gray) and low (dark gray) zones in the olive trees of each growing farming system (mixed cover (VC1), simple herbaceous cover (VC2) and conventional farming system (CONV). The mean values per tree branch are indicated inside the diagrams. Statistically significant differences (<span class="html-italic">p</span> &lt; 0.05) are indicated by different letters (a, b, c).</p>
Full article ">Figure 6
<p>Simple linear regression between the number (arctangent transformation) of oviposition injuries in terminal branches (olive VC1; lower, middle, and upper-per zones) and the sampling size.</p>
Full article ">Figure 7
<p>Variation of the % Error in the estimate of the mean values of injuries/30 cm segment of terminal twig branch, with increasing sampling size, in the three altitudinal zones of the trees (olive grove VC1).</p>
Full article ">
23 pages, 7647 KiB  
Article
The Impact of Green Infrastructure on the Quality of Stormwater and Environmental Risk
by Izabela Godyń, Agnieszka Grela, Krzysztof Muszyński and Justyna Pamuła
Sustainability 2024, 16(19), 8530; https://doi.org/10.3390/su16198530 - 30 Sep 2024
Viewed by 221
Abstract
Increasing urbanization and the associated sealing of areas and the use of storm sewer systems for drainage not only increase the risk of flooding but also reduce water quality in streams into which stormwater is discharged. Green infrastructure (GI) measures are applied with [...] Read more.
Increasing urbanization and the associated sealing of areas and the use of storm sewer systems for drainage not only increase the risk of flooding but also reduce water quality in streams into which stormwater is discharged. Green infrastructure (GI) measures are applied with the aim of managing this stormwater sustainably and reducing the associated risks. To this end, a quantitative–qualitative approach was developed to simulate GI—namely, rain gardens, bioretention cells, and vegetative bioswales—at the urban catchment scale. The findings highlight the potential of applying GI measures to managing stormwater more effectively in urban environments and mitigating its negative pollution-related impacts. For the housing estate analyzed, a simulated implementation of GI resulted in a reduction in pollution, measured as total nitrogen (N; 9–52%), nitrate-N (5–30%), total phosphorus (11–59%), chemical oxygen demand (8–46%), total suspended solids (13–73%), copper (12–64%), zinc (Zn; 16–87%), polycyclic aromatic hydrocarbons (16–91%), and the hydrocarbon oil index (HOI; 15–85%). Reducing the concentrations of pollutants minimizes the risk to human health determined via the HOI from a low-risk level to zero risk and reduces the ecological risk in terms of Zn pollution from a significant risk to a low risk of adverse effects. The modeling conducted clearly shows that the GI solutions implemented facilitated a quantitative reduction and a qualitative improvement in stormwater, which is crucial from an environmental perspective and ensures a sustainable approach to stormwater management. Lowering the levels of stormwater pollution through the implementation of GI will consequently lower the environmental burden of pollutants in urban areas. Full article
16 pages, 2575 KiB  
Article
Strategic Use of Vegetable Oil for Mass Production of 5-Hydroxyvalerate-Containing Polyhydroxyalkanoate from δ-Valerolactone by Engineered Cupriavidus necator
by Suk-Jin Oh, Yuni Shin, Jinok Oh, Suwon Kim, Yeda Lee, Suhye Choi, Gaeun Lim, Jeong-Chan Joo, Jong-Min Jeon, Jeong-Jun Yoon, Shashi Kant Bhatia, Jungoh Ahn, Hee-Taek Kim and Yung-Hun Yang
Polymers 2024, 16(19), 2773; https://doi.org/10.3390/polym16192773 - 30 Sep 2024
Viewed by 133
Abstract
Although efforts have been undertaken to produce polyhydroxyalkanoates (PHA) with various monomers, the low yield of PHAs because of complex metabolic pathways and inhibitory substrates remains a major hurdle in their analyses and applications. Therefore, we investigated the feasibility of mass production of [...] Read more.
Although efforts have been undertaken to produce polyhydroxyalkanoates (PHA) with various monomers, the low yield of PHAs because of complex metabolic pathways and inhibitory substrates remains a major hurdle in their analyses and applications. Therefore, we investigated the feasibility of mass production of PHAs containing 5-hydroxyvalerate (5HV) using δ-valerolactone (DVL) without any pretreatment along with the addition of plant oil to achieve enough biomass. We identified that PhaCBP-M-CPF4, a PHA synthase, was capable of incorporating 5HV monomers and that C. necator PHB−4 harboring phaCBP-M-CPF4 synthesized poly(3HB-co-3HHx-co-5HV) in the presence of bean oil and DVL. In fed-batch fermentation, the supply of bean oil resulted in the synthesis of 49 g/L of poly(3HB-co-3.7 mol% 3HHx-co-5.3 mol%5HV) from 66 g/L of biomass. Thermophysical studies showed that 3HHx was effective in increasing the elongation, whereas 5HV was effective in decreasing the melting point. The contact angles of poly(3HB-co-3HHx-co-5HV) and poly(3HB-co-3HHx) were 109 and 98°, respectively. In addition, the analysis of microbial degradation confirmed that poly(3HB-co-3HHx-co-5HV) degraded more slowly (82% over 7 days) compared to poly(3HB-co-3HHx) (100% over 5 days). Overall, the oil-based fermentation strategy helped produce more PHA, and the mass production of novel PHAs could provide more opportunities to study polymer properties. Full article
(This article belongs to the Special Issue Development and Application of Bio-Based Polymers)
11 pages, 1921 KiB  
Article
After the Megafires: Effects of Fire Severity on Reptile Species Richness and Occupancy in South-Eastern Australia
by Maddison L. Archer, Mike Letnic, Brad R. Murray and Jonathan K. Webb
Fire 2024, 7(10), 349; https://doi.org/10.3390/fire7100349 - 30 Sep 2024
Viewed by 273
Abstract
The Australian megafires of 2019–2020 were considered catastrophic for flora and fauna, yet little is known about their impacts on reptiles. We investigated the impacts of the 2019–2020 megafires on reptiles in Morton National Park, New South Wales, in eastern Australia. To understand [...] Read more.
The Australian megafires of 2019–2020 were considered catastrophic for flora and fauna, yet little is known about their impacts on reptiles. We investigated the impacts of the 2019–2020 megafires on reptiles in Morton National Park, New South Wales, in eastern Australia. To understand how fire severity affects reptile species richness and occupancy, we surveyed 28 replicate plots across unburnt areas and areas affected by high and low fire severity. We estimated reptile species richness and occupancy by performing systematic searches for reptiles during five sampling occasions in 2023, three years after the megafires. We measured vegetation structure and quantified the thermal environment in shelter sites used by reptiles. Vegetation structure varied significantly between burn severity groups. High-severity plots had the least canopy cover and the thinnest leaf litter depth but had a taller understorey with more stems. The thermal quality within reptile retreat sites did not differ between fire severity classes. Despite strong differences in post-fire vegetation structure, there was no evidence that fire severity affected reptile species richness or occupancy of the delicate skink, Lampropholis delicata. These results highlight the complexity of reptile responses to fires and contribute to increasing our understanding of the impacts of megafires on reptile communities. Full article
Show Figures

Figure 1

Figure 1
<p>Location of Morton National Park, NSW in south-eastern Australia (<b>A</b>), showing the extent of the 2019–2020 fires (<b>B</b>), and locations of fire monitoring plots with respect to fire severity (<b>C</b>). The grey shading indicates burnt area, while the dots indicate locations of monitoring plots.</p>
Full article ">Figure 2
<p>Vegetation structure at fire monitoring plots at high and low-severity burnt sites, and unburnt sites. Figure shows leaf litter depth (<b>A</b>), percent vegetation cover (<b>B</b>), number of stems (<b>C</b>), understorey height (<b>D</b>), canopy cover above understorey (<b>E</b>), and number of trees per plot (<b>F</b>). Box and whisker plots display the lower and upper interquartile values and the median, while whiskers show the minimum and maximum values (excluding outliers). Circles and stars indicate outliers.</p>
Full article ">
17 pages, 21293 KiB  
Article
Apigenin Ameliorates H2O2-Induced Oxidative Damage in Melanocytes through Nuclear Factor-E2-Related Factor 2 (Nrf2) and Phosphatidylinositol 3-Kinase (PI3K)/Protein Kinase B (Akt)/Mammalian Target of Rapamycin (mTOR) Pathways and Reducing the Generation of Reactive Oxygen Species (ROS) in Zebrafish
by Qing-Qing Tang, Zu-Ding Wang, Xiao-Hong An, Xin-Yuan Zhou, Rong-Zhan Zhang, Xiao Zhan, Wei Zhang and Jia Zhou
Pharmaceuticals 2024, 17(10), 1302; https://doi.org/10.3390/ph17101302 - 30 Sep 2024
Viewed by 198
Abstract
Background: Apigenin is one of the natural flavonoids found mainly in natural plants, as well as some fruits and vegetables, with celery in particular being the most abundant. Apigenin has antioxidant, anti-tumor, anti-inflammatory, and anticancer effects. In this research, we attempted to further [...] Read more.
Background: Apigenin is one of the natural flavonoids found mainly in natural plants, as well as some fruits and vegetables, with celery in particular being the most abundant. Apigenin has antioxidant, anti-tumor, anti-inflammatory, and anticancer effects. In this research, we attempted to further investigate the effects of apigenin on the mechanism of repairing oxidative cell damage. The present study hopes to provide a potential candidate for abnormal skin pigmentation disorders. Methods: We used 0.4 mM H2O2 to treat B16F10 cells for 12 h to establish a model of oxidative stress in melanocytes, and then we gave apigenin (0.1~5 μM) to B16F10 cells for 48 h, and detected the expression levels of melanin synthesis-related proteins, dendritic regulation-related proteins, antioxidant signaling pathway- and Nrf2 signaling pathway-related proteins, autophagy, and autophagy-regulated pathways by immunoblotting using Western blotting. The expression levels of PI3K/Akt/mTOR proteins were measured by β-galactosidase staining and Western blotting for cellular decay, JC-1 staining for mitochondrial membrane potential, and Western blotting for mitochondrial fusion- and mitochondrial autophagy-related proteins. Results: Apigenin exerts antioxidant effects by activating the Nrf2 pathway, and apigenin up-regulates the expression of melanin synthesis-related proteins Tyr, TRP1, TRP2, and gp100, which are reduced in melanocytes under oxidative stress. By inhibiting the expression of senescence-related proteins p53 and p21, and delaying cellular senescence, we detected the mitochondrial membrane potential using JC-1, and found that apigenin improved the reduction in mitochondrial membrane potential in melanocytes under oxidative stress, and maintained the normal function of mitochondria. In addition, we further detected the key regulatory proteins of mitochondrial fusion and division, MFF, p-DRP1 (S637), and p-DRP1 (S616), and found that apigenin inhibited the down-regulation of fusion-associated protein, p-DRP1 (S637), and the up-regulation of division-associated proteins, MFF and p-DRP1 (S616), due to oxidative stress in melanocytes, and promoted the mitochondrial fusion and ameliorated the imbalance between mitochondrial division and fusion. We further detected the expression of fusion-related proteins OPA1 and Mitofusion-1, and found that apigenin restored the expression of the above fusion proteins under oxidative stress, which further indicated that apigenin promoted mitochondrial fusion, improved the imbalance between mitochondrial division and fusion, and delayed the loss of mitochondrial membrane potential. Apigenin promotes the expression of melanocyte autophagy-related proteins and the key mitochondrial autophagy proteins BNIP3L/Nix under oxidative stress, and activates the PINK1/Parkin signaling pathway by up-regulating the expression of autophagy-related proteins, as well as the expression of PINK1 and Parkin proteins, to promote melanocyte autophagy and mitochondrial autophagy. Conclusions: Apigenin exerts anti-melanocyte premature aging and detachment effects by promoting melanin synthesis, autophagy, and mitochondrial autophagy in melanocytes, and inhibiting oxidative cell damage and senescence. Full article
(This article belongs to the Section Pharmacology)
Show Figures

Figure 1

Figure 1
<p>Apigenin repressed oxidative stress-induced ROS production. (<b>A</b>) Effects of apigenin (10 µM) on ROS level of zebrafish. Zebrafish were treated with egg water containing apigenin (10 µM) for 24 h, then treated with H<sub>2</sub>O<sub>2</sub> (0.5 mM) for 4 h. DCFH-DA (10 µM) was added to zebrafish for 30 min. Images were captured by fluorescence microscope. (<b>B</b>) Treated with various concentrations (0.1~1 µM) of H<sub>2</sub>O<sub>2</sub> for 12 h; relative cell viability was determined by MTT assay (n = 7). (<b>C</b>,<b>D</b>) B16F10 cells were pretreated with apigenin (0.1, 1, and 5 µM) for 48 h, then treated with H<sub>2</sub>O<sub>2</sub> (0.4 mM) for 12 h. Intracellular ROS levels were indicated by DCFH-DA fluorescence probe. Intracellular ROS changes in B16F10 cells were observed by 200× fluorescence microscope, Bar = 200 μm. Data are expressed as mean ± SEM (n = 3). * <span class="html-italic">p</span> &lt; 0.05, and *** <span class="html-italic">p</span> &lt; 0.001 vs. control.</p>
Full article ">Figure 2
<p>Apigenin activated Nrf2 pathway. (<b>A</b>,<b>B</b>) Measurement of SOD activity and MDA contents in B16F10 cells. Data are presented as the mean ± SEM (n = 3). (<b>C</b>–<b>G</b>) Effects of apigenin (0.1, 1, and 5 µM) on Nrf2 pathway in oxidative stress B16F10 cells. Treated with apigenin (0.1, 1, and 5 µM) for 48 h and then treated with H<sub>2</sub>O<sub>2</sub> (0.4 mM) for 12 h in B16F10 cells, and WB was then applied to detect the protein levels of Keap1, Nrf2, HO-1, and NQO1. Data are expressed as the mean ± SEM (n = 3). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 vs. control. <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 and <sup>###</sup> <span class="html-italic">p</span> &lt; 0.001, vs. model.</p>
Full article ">Figure 3
<p>Apigenin inhibits cellular senescence induced by oxidative stress in B16F10 cells. (<b>A</b>) Effect of apigenin on senescence-associated β-galactosidase of B16F10 cells. Cells were seeded on 6-well culture plates and treated with various concentrations of apigenin (0.1, 1, and 5 µM) for 48 h, then treated with H<sub>2</sub>O<sub>2</sub> (0.4 mM) for 12 h. Senescence-associated β-galactosidase was stained blue. Intracellular color changes in B16F10 cells were observed by microscope at 400× magnification. (<b>B</b>–<b>D</b>) Apigenin inhibits the expression of p53 and p21 in B16F10 cells. Treated with apigenin (0.1, 1, and 5 µM) for 48 h and then treated with H<sub>2</sub>O<sub>2</sub> (0.4 mM) for 12 h in B16F10 cells, and WB was then applied to detect the protein levels of p53 and p21. Data are expressed as the mean ± SEM (n = 3). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 vs. control.</p>
Full article ">Figure 4
<p>Apigenin ameliorates oxidative stress-induced impairment of melanin synthesis and dendritic atrophy. (<b>A</b>–<b>E</b>) B16F10 cells were treated with apigenin (0.1, 1, and 5 µM) for 48 h and then treated with H<sub>2</sub>O<sub>2</sub> (0.4 mM) for 12 h, and WB was then applied to detect the protein levels of TYR, TRP1, TRP2, and gp100. (<b>F</b>) Treated with apigenin (0.1, 1, and 5 µM) for 48 h and then treated with H<sub>2</sub>O<sub>2</sub> (0.4 mM) for 12 h in B16F10 cells, stained with FITC–Phalloidin fluorescence probe to visualize the cytoskeleton. (<b>G</b>–<b>J</b>) B16F10 cells were treated with apigenin (0.1, 1, and 5 µM) for 48 h and then treated with H<sub>2</sub>O<sub>2</sub> (0.4 mM) for 12 h, and WB was then applied to detect the protein levels of Rac-1, Cdc42, and E-Cadherin. Data are expressed as the mean ± SEM (n = 3). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 vs. control.</p>
Full article ">Figure 5
<p>Apigenin ameliorates oxidative stress-induced mitochondrial damage and cellular autophagy inhibition. (<b>A</b>) Effect of apigenin on mitochondrial membrane potential in oxidant model of B16F10 cells. B16F10 cells were treated with apigenin (0.1, 1, and 5 µM) for 48 h, then cultured in H<sub>2</sub>O<sub>2</sub> (0.4 mM) for 12 h. Mitochondrial membrane potential was stained with JC-1. Intracellular color changes in oxidant model of B16F10 cells were observed by fluorescence microscope at 400× magnification. (<b>B</b>–<b>N</b>) B16F10 cells were treated with apigenin (0.1, 1, and 5 µM) for 48 h and then treated with H<sub>2</sub>O<sub>2</sub> (0.4 mM) for 12 h, and WB was then applied to detect protein levels of MFF, p-DRP1(S616), p-DRP1(S637), DRP1, Mitofusion1, OPA1, Beclin-1, Atg5, Atg8, Atg12, LC3-Ⅰ, LC3-II, and p62. Data are expressed as mean ± SEM (n = 3). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 vs. control.</p>
Full article ">Figure 6
<p>The effects of apigenin on the expression of BNIP3L/Nix, PINK1, and Parkin and the activity of the PI3K/Akt/mTOR signaling pathway in melanocytes. (<b>A</b>–<b>D</b>) The B16F10 cells were treated with apigenin (5 µM) for 48 h and then treated with H<sub>2</sub>O<sub>2</sub> (0.4 mM) for 12 h, and WB was then applied to detect the protein levels of BNIP3L/Nix, PINK1, and Parkin. (<b>E</b>–<b>K</b>) The B16F10 cells were pretreated with LY294002 (10 µM) or RAPA (0.1 µM) for 2 h and incubated with apigenin (5 μM) for 24 h, then cells were treated with H<sub>2</sub>O<sub>2</sub> (0.4 mM) for 12 h. The protein levels of PI3K, p-Akt, Akt, p-mTOR, mTOR, and p62 were determined by Western blot. Data are expressed as the mean ± SEM (n = 3). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 vs. control. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 and <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, (<b>B</b>): 5+LY294002 vs. LY294002; (<b>C</b>): 5+RAPA vs. RAPA.</p>
Full article ">
31 pages, 41260 KiB  
Article
Remote Sensing Evaluation and Monitoring of Spatial and Temporal Changes in Ecological Environmental Quality in Coal Mining-Intensive Cities
by Qiqi Huo, Xiaoqian Cheng, Weibing Du, Hao Zhang and Ruimei Han
Appl. Sci. 2024, 14(19), 8814; https://doi.org/10.3390/app14198814 - 30 Sep 2024
Viewed by 281
Abstract
In coal-dependent urban economies, the dichotomy between resource exploitation and ecological conservation presents a pronounced challenge. Traditional remote sensing ecological assessments often overlook the interplay between mining activities and urban environmental dynamics. To address this gap, researchers developed an innovative Resource-Based City Ecological [...] Read more.
In coal-dependent urban economies, the dichotomy between resource exploitation and ecological conservation presents a pronounced challenge. Traditional remote sensing ecological assessments often overlook the interplay between mining activities and urban environmental dynamics. To address this gap, researchers developed an innovative Resource-Based City Ecological Index (RCEI), anchored in a Pressure–State–Response (PSR) framework and synthesized from six discrete ecological indicators. Utilizing geodetic remote sensing data, the RCEI facilitated a comprehensive spatiotemporal analysis of Jincheng City’s ecological quality from 1990 to 2022. The findings corroborated the RCEI’s efficacy in providing a nuanced portrayal of the ecological state within mining regions. (1) Jincheng City’s ecological quality predominantly sustained a mudhopper-tier status, exhibiting an overarching trend of amelioration throughout the study period. (2) Disparities in ecological landscape quality were pronounced at the county level, with Moran’s Index exceeding 0.9, signifying a clustered ecological quality pattern. High–high (H–H) zones were prevalent in areas of elevated altitude and dense vegetation, whereas low–low (L–L) zones were prevalent in urban and mining sectors. (3) Further, a buffer zone analysis of two coal mines, differing in their mining chronology, geographical positioning, and operational status, elucidated the ecological impact exerted over a 32-year trajectory. These insights furnish a robust scientific and technical foundation for resource-centric cities to fortify ecological safeguarding and to advance sustainable development stratagems. Full article
(This article belongs to the Section Ecology Science and Engineering)
Show Figures

Figure 1

Figure 1
<p>Geographical location of Jincheng City.</p>
Full article ">Figure 2
<p>The overall workflow of this study.</p>
Full article ">Figure 3
<p>Trends in the RCEI and RSEI eigenvalues and contributions. (<b>a</b>) Comparison of trends in RCEI and RSEI eigenvalues; (<b>b</b>) Comparison of changes in RCEI and RSEI contributions.</p>
Full article ">Figure 4
<p>Results of the principal component analysis of indicators from 1990 to 2022.</p>
Full article ">Figure 5
<p>A boxplot of the RCEI in Jincheng City from 1990 to 2022.</p>
Full article ">Figure 6
<p>Characteristics of the spatial distribution of the ecological environmental quality level in Jincheng City in 1990–2022.</p>
Full article ">Figure 7
<p>Annual trends of the average RCEI values in county-level regions of Jincheng City.</p>
Full article ">Figure 8
<p>Changing trend in the RCEI for the horizontal area in Jincheng City.</p>
Full article ">Figure 9
<p>Images of the RCEI change monitoring in Jincheng.</p>
Full article ">Figure 10
<p>Land use mapping of Jincheng City in 1990–2022. (<b>a</b>) City land use classification. (<b>b</b>) Land use changes.</p>
Full article ">Figure 11
<p>Positions of different RCEI sample loci in an OLI image, as well as in an RCEI image and OLI image (RGB) for different RCEI levels. (<b>a</b>) Location of samples with different RCEI levels in the RCEI image, (<b>b</b>) Location of samples with different RCEI levels in OLI images, (<b>c</b>) Images of national parks with excellent RCEI levels, (<b>d</b>) OLI images of the Sihe mine with poor RCEI levels, (<b>e</b>) OLI images of the YiCheng mine with poor RCEI levels, (<b>f</b>) OLI images of the Gu mine located in urban areas with poor RCEI levels, (<b>g</b>) Images of national parks with good RCEI levels located on the urban fringe, (<b>h</b>) OLI images of national wetlands with excellent RCEI levels.</p>
Full article ">Figure 12
<p>3D scatterplot of the WET, NDVI, EVI, NDBSI, LST, ICDI and RCEI at sampling points: (<b>a</b>) 3D spatial relationship between the RCEI, NDVI, and EVI; (<b>b</b>) 3D spatial relations between the RCEI, NDBSI, and WET; (<b>c</b>) 3D spatial relations between the RCEI, ICDI, and LST.</p>
Full article ">Figure 13
<p>Correlation analysis between the RCEI and six ecological indicators from 1990 to 2022.</p>
Full article ">Figure 14
<p>Scatterplot of the RCEI Moran’s I in Jincheng from 1990 to 2022.</p>
Full article ">Figure 15
<p>LISA clustering of the RCEI in Jincheng City from 1990 to 2022.</p>
Full article ">Figure 16
<p>Areas producing a million tons of coal and their mean RCEI values with distance. (<b>a</b>) Location of Jincheng City’s Sihe Mine Area and Fenghuangshan Mine Area; (<b>b</b>,<b>c</b>) Raster plots showing the mean RCEI values of the Sihe Mine Area and Fenghuangshan Mine Area with distance; (<b>d</b>,<b>e</b>) Line plots depicting the mean RCEI values of the Fenghuangshan Mine Area and Sihe Mine Area with buffer zones from 1990 to 2022.</p>
Full article ">
11 pages, 1459 KiB  
Article
Salt Tolerance of Phragmites australis and Effect of Combing It with Topsoil Filters on Biofiltration of CaCl2 Contaminated Soil
by Jin-Hee Ju
Sustainability 2024, 16(19), 8522; https://doi.org/10.3390/su16198522 - 30 Sep 2024
Viewed by 282
Abstract
De-icing salt used for safe winter driving can have negative impacts on local water quality, vegetation, and soils. This study aimed to evaluate the salt tolerance of reeds (Phragmites australis) against calcium chloride (CaCl2) and the biofiltration effect of [...] Read more.
De-icing salt used for safe winter driving can have negative impacts on local water quality, vegetation, and soils. This study aimed to evaluate the salt tolerance of reeds (Phragmites australis) against calcium chloride (CaCl2) and the biofiltration effect of combining it with topsoil biofilters for desalination in roadside ditches. Two experiments were conducted in a controlled environmental greenhouse over a period of 150 days. For the first experiment, the salt tolerance of P. australis was examined after treating reeds with five different concentrations of de-icing salt: 0, 1, 2, 5, and 10 g·L−1. In a second experiment, the effect of combining two topsoil filters (expanded clay and activated carbon), each planted with and without reeds, was investigated under a high CaCl2 concentration of 10 g·L−1. As the CaCl2 concentration increased, the electrical conductivity (EC) of soil leachate and the level of salt exchangeable cations (K+, Ca2+, Na+, and Mg2+) significantly increased whereas the acidity (pH) significantly decreased (all p ≤ 0.05). No statistical difference was observed in leaf length or width, while plant height, number of leaves, and both fresh and dry weights were significantly increased with increasing CaCl2 concentrations (p ≤ 0.05). Treatments using topsoil filters, particularly those with activated carbon and reeds, showed the greatest reduction in leachate EC and total exchange cations values. These results suggest that combining P. australis with topsoil filters can assist biofiltration effectively, demonstrating its applicability even in roadside soils subject to extreme levels of de-icing salts. Full article
Show Figures

Figure 1

Figure 1
<p>Average electric conductivity (EC) and acidity (pH) in the leachate of substrate-grown <span class="html-italic">P. australis</span> treated with increasing concentrations of CaCl<sub>2</sub> for 150 days in a greenhouse. Different letters indicate significant differences among treatments at <span class="html-italic">p</span> ≤ 0.05 by Duncan’s multiple range test. The vertical bar indicates the standard error (±SE) of the mean (<span class="html-italic">n</span> = 30). Cont.: treatment with 0 g∙L<sup>−1</sup> CaCl<sub>2</sub> solution, C1, C2, C5, and C10: 1, 2, 5, and 10 g∙L<sup>−1</sup> CaCl<sub>2</sub> solution, respectively.</p>
Full article ">Figure 2
<p>Average levels of exchangeable cations (K<sup>+</sup>, Ca<sup>2+</sup>, Na<sup>+</sup>, and Mg<sup>2+</sup>) in leachates of substrate-grown <span class="html-italic">P. australis</span> treated with increasing concentrations of CaCl<sub>2</sub> for 150 days in a greenhouse. The vertical bar indicates the standard error (± SE) of the mean (<span class="html-italic">n</span> = 30). Different letters indicate significant differences among treatments at <span class="html-italic">p</span> ≤ 0.05 by Duncan’s multiple range test. Cont.: treatment with 0 g∙L<sup>−1</sup> CaCl<sub>2</sub> solution, C1, C2, C5, and C10: 1, 2, 5, and 10 g∙L<sup>−1</sup> CaCl<sub>2</sub> solution, respectively.</p>
Full article ">Figure 3
<p>Average fresh weight and dry weight of <span class="html-italic">P. autralis</span> treated with increasing concentrations of CaCl<sub>2</sub> for 150 days in a greenhouse. Different letters indicate significant differences among treatments at <span class="html-italic">p</span> ≤ 0.05 by Duncan’s multiple range test. The vertical bar indicates the standard error (±SE) of the mean (<span class="html-italic">n</span> = 9). S.F.W.: shoot fresh weight; R.F.W.: root fresh weight; S.D.W.: shoot dry weight; R.D.W.; root dry weight. Cont.: treatment with 0 g∙L<sup>−1</sup> CaCl<sub>2</sub> solution, C1, C2, C5, and C10: 1, 2, 5, and 10 g∙L<sup>−1</sup> CaCl<sub>2</sub> solution, respectively.</p>
Full article ">Figure 4
<p>Average EC and pH in leachates of substrate-grown <span class="html-italic">P. australis</span> added with topsoil filters (expanded clay and activated carbon) and treated with CaCl<sub>2</sub> at a concentration treatment of 10 g∙L<sup>−1</sup> for 150 days in a greenhouse. Different letters indicate significant differences between treatments at <span class="html-italic">p</span> ≤ 0.05 by Duncan’s multiple range test. The vertical bar indicates the standard error (±SE) of the mean (<span class="html-italic">n</span> = 36). Cont.: treatment with 10 g∙L<sup>−1</sup> CaCl<sub>2</sub> solution; H: expanded clay; AC: activated carbon; P: <span class="html-italic">P. australis</span> planted alone; H + P: expanded clay + <span class="html-italic">P. australis</span> planted; AC + P: activated carbon + <span class="html-italic">P. australis</span> planted.</p>
Full article ">Figure 5
<p>Average levels of exchangeable cations (K<sup>+</sup>, Ca<sup>2+</sup>, Na<sup>+</sup>, and Mg<sup>2+</sup>) in the leachate of substrate-grown <span class="html-italic">P. australis</span> with topsoil filters (expanded clay and activated carbon) and treated with a CaCl<sub>2</sub> at a concentration of 10 g∙L<sup>−1</sup> for 150 days in a greenhouse. Different letters indicate significant differences among treatments at <span class="html-italic">p</span> ≤ 0.05 by Duncan’s multiple range test. The vertical bar indicates the standard error (±SE) of the mean (<span class="html-italic">n</span> = 36). Cont.: treatment with 10 g∙L<sup>−1</sup> CaCl<sub>2</sub> solution; H: expanded clay; AC: activated carbon; P: <span class="html-italic">P. australis</span> planted alone; H + P: expanded clay + <span class="html-italic">P. australis</span> planted; AC + P: activated carbon + <span class="html-italic">P. australis</span> planted.</p>
Full article ">Figure 6
<p>Average fresh weight and dry weight of <span class="html-italic">P. australis</span> added with topsoil filters (expanded clay and activated carbon) and CaCl<sub>2</sub> at a concentration treatment of 10 g∙L<sup>−1</sup> for 150 days in a greenhouse. Different letters indicate significant differences among treatments at <span class="html-italic">p</span> ≤ 0.05 by Duncan’s multiple range test. The vertical bar indicates the standard error (±SE) of the mean (<span class="html-italic">n</span> = 9). S.F.W.: shoot fresh weight; R.F.W.: root fresh weight; S.D.W.: shoot dry weight; R.D.W.: root dry weight; P: <span class="html-italic">P. australis</span> planted alone; H + P: expanded clay + <span class="html-italic">P. australis</span> planted; AC + P: activated carbon + <span class="html-italic">P. australis</span> planted.</p>
Full article ">
22 pages, 11417 KiB  
Article
The Synergistic Evolution Characteristics and Influencing Factors of Tourism Economy and Urban Green Development Efficiency in the Yellow River Basin
by Weimin Gong, Chengxin Wang, Dan Men, Ming Zhang and Aixia Xu
Sustainability 2024, 16(19), 8519; https://doi.org/10.3390/su16198519 - 30 Sep 2024
Viewed by 246
Abstract
In the context of the “ecological priority and green development” strategy, examining the co-evolution between the tourism economy and the efficiency of urban green development can offer both theoretical insights and quantitative foundations to support ecological preservation and high-quality development in China’s Yellow River [...] Read more.
In the context of the “ecological priority and green development” strategy, examining the co-evolution between the tourism economy and the efficiency of urban green development can offer both theoretical insights and quantitative foundations to support ecological preservation and high-quality development in China’s Yellow River Basin. This research utilized approaches such as the Haken model and the geographically and temporally weighted regression model to investigate the spatiotemporal patterns, synergistic characteristics, and driving factors of the tourism economy and urban green development efficiency within the Yellow River Basin. The findings reveal the following: (1) Regional disparities in the tourism economy are progressively widening, whereas the efficiency of green development tends to decline. Furthermore, both the tourism economy and urban green development efficiency display “high-high clustering” and “low-low clustering” spatially. (2) The synergistic evolution of the two systems displays spatial characteristics of transitioning from polarization to trickle-down effects. (3) Natural factors such as topography and vegetation coverage, as well as human economic factors like industrial structure and the degree of openness, positively promote the synergy. However, elements such as temperature, precipitation, economic development level, and openness to innovation have a certain inhibitory effect on the synergistic evolution. Full article
Show Figures

Figure 1

Figure 1
<p>Location map of the Yellow River Basin.</p>
Full article ">Figure 2
<p>Evolution trend of tourism economic development level and urban green development efficiency in the Yellow River Basin.</p>
Full article ">Figure 3
<p>Kernel density distribution of tourism economic development and urban green development efficiency in the Yellow River Basin. (<b>a</b>) Tourism economy; (<b>b</b>) urban green development efficiency.</p>
Full article ">Figure 4
<p>Spatial distribution of tourism economy in the Yellow River Basin from 2002 to 2018.</p>
Full article ">Figure 5
<p>Spatial distribution of urban green development efficiency in the Yellow River Basin from 2002 to 2018.</p>
Full article ">Figure 6
<p>Temporal characteristics of the synergistic evolution of tourism economy and urban green development efficiency from 2002 to 2018. (<b>a</b>) Evolution trend. (<b>b</b>) Kernel density estimation.</p>
Full article ">Figure 7
<p>Synergistic evolution classification of tourism economy and urban green development efficiency from 2002 to 2018.</p>
Full article ">Figure 8
<p>Spatial distribution of factors influencing the synergistic evolution of tourism economy and urban green development in the Yellow River Basin.</p>
Full article ">
Back to TopTop