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Search Results (3,488)

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39 pages, 2349 KiB  
Article
Redefining Urbanism in Perspective of Climate Change: Floating Cities Concept
by Krystyna Januszkiewicz, Jakub Gołębiewski, Bartosz Czarnecki and Adam Turecki
Arts 2024, 13(6), 183; https://doi.org/10.3390/arts13060183 (registering DOI) - 14 Dec 2024
Viewed by 130
Abstract
This article analyzes the concept of floating cities in the context of increasing threats resulting from climate change. It explores the potential of a floating city concept to provide sustainable and livable conditions on a large scale in response to the growing climate [...] Read more.
This article analyzes the concept of floating cities in the context of increasing threats resulting from climate change. It explores the potential of a floating city concept to provide sustainable and livable conditions on a large scale in response to the growing climate crisis. Specifically, this article considers whether climate change is prompting a redefinition of urbanism and examines how the floating city concept can be useful from this perspective. The analysis draws on ideas related to megastructures, particularly those based on platforms. A pioneer in this field was Kiyonori Kikutake, who in 1958–1963 presented three concepts of floating cities under the name Marine City. His designs were centered around modularity and mobility. Today, Kikutake’s vision is experiencing a resurgence as climate change forces architects and urban planners to rethink traditional cities. Contemporary architects such as Vincent Callebaut and Bjarke Ingels are now gaining attention for their innovative designs of floating cities, which are being closely examined by experts and policymakers. The first part of this article provides a comparative analysis of Marine City with contemporary examples of megastructures, such as the Lilypad and Oceanix projects, illustrating how the concept of floating cities have evolved over the centuries. The question is, which solutions developed by Japanese Metabolists remain relevant and how has modern technology enriched and advanced the concept of living on water? The second part of the article analyzes the potential of floating cities to redefine urbanism in response to the growing threat of climate change. This analysis primarily focuses on the possible interactions between floating cities and the environment. The results show that the challenges posed by climate change are redefining the urban planning paradigms formed in the first half of the 20th century. The floating city concept shows some potential as a viable response to these challenges. Full article
(This article belongs to the Section Applied Arts)
20 pages, 15937 KiB  
Article
Numerical Simulation of Airflow and Pollutant Dispersion Around High-Rise Buildings with Different Rotation Angles
by Xiaohui Huang, Peng Wang, Lihua Song, Yufeng Bai, Lijie Zhang and Lizhen Gao
Processes 2024, 12(12), 2828; https://doi.org/10.3390/pr12122828 - 10 Dec 2024
Viewed by 321
Abstract
The increase in urban building density will have a significant impact on pedestrian wind environments, especially in high-density urban building environments. Architectural designers should consider the impact of the urban microclimate through reasonable architectural designs and layouts, effectively improve the pedestrian wind environment, [...] Read more.
The increase in urban building density will have a significant impact on pedestrian wind environments, especially in high-density urban building environments. Architectural designers should consider the impact of the urban microclimate through reasonable architectural designs and layouts, effectively improve the pedestrian wind environment, and enhance the comfort of urban dwellers and the sustainable development of cities. Therefore, on the basis of the Reynolds number average Navier–Stokes (RANS) method, a standard k-ε turbulence model was adopted to simulate the effects of high-rise buildings with different rotation angles on the flow and dispersion of pollutants. The results showed that the rotation angle has an obvious influence on the flow structure, turbulent kinetic energy, and near-ground concentration, and the effect is more significant with the increase in building height. When the building is rotated by a certain angle (10°, 20°, and 30°), the whole flow is deflected and no longer symmetrical. When the rotation angles are 20° and 30°, it is found that two large vortices are formed in the wake region of the entire building array, as if the building array can be regarded as a whole. Because the pollution source is located in the recirculation zone or the reverse-flow zone, the high-concentration area is mainly concentrated upwind of the source. As the building is rotated counterclockwise (10°, 20°, and 30°), the pollutant plume is also deflected counterclockwise, presenting an asymmetry. Full article
(This article belongs to the Section Environmental and Green Processes)
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Figure 1
<p>A residential area in Taiyuan City, Shanxi Province, China. (<b>a</b>) Top view; (<b>b</b>) 3-D view.</p>
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<p>Wind tunnel model (<b>a</b>) and measuring points (<b>b</b>).</p>
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<p>Grid independence verification.</p>
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<p>Comparison of normalized velocity at measurement points and correlation of experimental (EXP) and calculation (CFD) results. (<b>a</b>) Wind direction of 0°; (<b>b</b>) Wind direction of 45°.</p>
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<p>Contours of normalized velocity distribution around center building. (<b>a</b>) Wind direction of 0°; (<b>b</b>) Wind direction of 45°.</p>
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<p>Contours of normalized velocity distribution around center building. (<b>a</b>) Wind direction of 0°; (<b>b</b>) Wind direction of 45°.</p>
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<p>Building configurations (top view). (<b>a</b>) <span class="html-italic">α</span> = 0°; (<b>b</b>) <span class="html-italic">α</span> = 10°; (<b>c</b>) <span class="html-italic">α</span> = 20°; (<b>d</b>) <span class="html-italic">α</span> = 30°; (<b>e</b>) <span class="html-italic">α</span> = 45°; (<b>f</b>) Top view of the building rotation.</p>
Full article ">Figure 6 Cont.
<p>Building configurations (top view). (<b>a</b>) <span class="html-italic">α</span> = 0°; (<b>b</b>) <span class="html-italic">α</span> = 10°; (<b>c</b>) <span class="html-italic">α</span> = 20°; (<b>d</b>) <span class="html-italic">α</span> = 30°; (<b>e</b>) <span class="html-italic">α</span> = 45°; (<b>f</b>) Top view of the building rotation.</p>
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<p>Streamline and velocity ratio distribution at near-ground level (<span class="html-italic">z</span>/<span class="html-italic">H</span> = 0.1). (<b>a</b>) CASE [0, 1 <span class="html-italic">H</span>]; (<b>b</b>) CASE [0, 2 <span class="html-italic">H</span>]; (<b>c</b>) CASE [10, 2 <span class="html-italic">H</span>]; (<b>d</b>) CASE [20, 2 <span class="html-italic">H</span>]; (<b>e</b>) CASE [30, 3 <span class="html-italic">H</span>]; (<b>f</b>) CASE [45, 2 <span class="html-italic">H</span>]. (The red arrow indicates the direction of air flow that needs attention; The red squares are the vortices that need attention).</p>
Full article ">Figure 7 Cont.
<p>Streamline and velocity ratio distribution at near-ground level (<span class="html-italic">z</span>/<span class="html-italic">H</span> = 0.1). (<b>a</b>) CASE [0, 1 <span class="html-italic">H</span>]; (<b>b</b>) CASE [0, 2 <span class="html-italic">H</span>]; (<b>c</b>) CASE [10, 2 <span class="html-italic">H</span>]; (<b>d</b>) CASE [20, 2 <span class="html-italic">H</span>]; (<b>e</b>) CASE [30, 3 <span class="html-italic">H</span>]; (<b>f</b>) CASE [45, 2 <span class="html-italic">H</span>]. (The red arrow indicates the direction of air flow that needs attention; The red squares are the vortices that need attention).</p>
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<p>Velocity ratio of the lines (<span class="html-italic">x</span> = [−6 <span class="html-italic">H</span>, 8 <span class="html-italic">H</span>], <span class="html-italic">y</span> = ±1 H, <span class="html-italic">z</span> = 0.1 <span class="html-italic">H</span>). (<b>a</b>) CASE [<span class="html-italic">α</span>, 1 <span class="html-italic">H</span>]; (<b>b</b>) CASE [<span class="html-italic">α</span>, 2 <span class="html-italic">H</span>]; (<b>c</b>) CASE [<span class="html-italic">α</span>, 3 <span class="html-italic">H</span>].</p>
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<p>Statistical table of velocity ratio.</p>
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<p>Distribution of normalized turbulence kinetic energy <math display="inline"><semantics> <mrow> <mrow> <mrow> <mi mathvariant="normal">k</mi> </mrow> <mo>/</mo> <mrow> <msubsup> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0.1</mn> <mi>H</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msubsup> </mrow> </mrow> </mrow> </semantics></math> at near-ground level (<span class="html-italic">z</span>/<span class="html-italic">H</span> = 0.1). (<b>a</b>) CASE [0, 1 <span class="html-italic">H</span>]; (<b>b</b>) CASE [0, 3 <span class="html-italic">H</span>]; (<b>c</b>) CASE [30, 1 <span class="html-italic">H</span>]; (<b>d</b>) CASE [30, 3 <span class="html-italic">H</span>]; (<b>e</b>) CASE [45, 1 <span class="html-italic">H</span>]; (<b>f</b>) CASE [45, 3 <span class="html-italic">H</span>].</p>
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<p><math display="inline"><semantics> <mrow> <mrow> <mrow> <mi mathvariant="normal">k</mi> </mrow> <mo>/</mo> <mrow> <msubsup> <mrow> <mi>U</mi> </mrow> <mrow> <mn>0.1</mn> <mi>H</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msubsup> </mrow> </mrow> </mrow> </semantics></math> of the lines (<span class="html-italic">x</span> = [−6 <span class="html-italic">H</span>, 8 <span class="html-italic">H</span>], <span class="html-italic">y</span> = ±1 <span class="html-italic">H</span>, <span class="html-italic">z</span> = 0.1 <span class="html-italic">H</span>). (<b>a</b>) CASE [<span class="html-italic">α</span>, 1 <span class="html-italic">H</span>]; (<b>b</b>) CASE [<span class="html-italic">α</span>, 2 <span class="html-italic">H</span>]; (<b>c</b>) CASE [<span class="html-italic">α</span>, 3 <span class="html-italic">H</span>].</p>
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<p>Distribution of normalized concentration <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> <mo>/</mo> <mi>C</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> at near-ground level (<span class="html-italic">z</span>/<span class="html-italic">H</span> = 0.1). (<b>a</b>) CASE [0, 1 <span class="html-italic">H</span>]; (<b>b</b>) CASE [0, 3 <span class="html-italic">H</span>]; (<b>c</b>) CASE [20, 1 <span class="html-italic">H</span>]; (<b>d</b>) CASE [20, 3 <span class="html-italic">H</span>]; (<b>e</b>) CASE [45, 1 <span class="html-italic">H</span>]; (<b>f</b>) CASE [45, 3 <span class="html-italic">H</span>].</p>
Full article ">Figure 12 Cont.
<p>Distribution of normalized concentration <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> <mo>/</mo> <mi>C</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> at near-ground level (<span class="html-italic">z</span>/<span class="html-italic">H</span> = 0.1). (<b>a</b>) CASE [0, 1 <span class="html-italic">H</span>]; (<b>b</b>) CASE [0, 3 <span class="html-italic">H</span>]; (<b>c</b>) CASE [20, 1 <span class="html-italic">H</span>]; (<b>d</b>) CASE [20, 3 <span class="html-italic">H</span>]; (<b>e</b>) CASE [45, 1 <span class="html-italic">H</span>]; (<b>f</b>) CASE [45, 3 <span class="html-italic">H</span>].</p>
Full article ">
18 pages, 2671 KiB  
Article
Spatial Pattern and Influencing Factors of Tourist Attractions in Coastal Cities: A Case Study of Qingdao
by Yue Xu, Xuliang Zhang, Kuncheng Zhang, Jing Yu and Jia Liu
ISPRS Int. J. Geo-Inf. 2024, 13(12), 444; https://doi.org/10.3390/ijgi13120444 - 9 Dec 2024
Viewed by 472
Abstract
The spatial distribution of tourist attractions plays a critical role in the development of coastal cities. Qingdao, with its coastal geography, rich cultural heritage, and rapid urbanization, serves as a representative case. This study integrates POI and multi-source data, employing methods such as [...] Read more.
The spatial distribution of tourist attractions plays a critical role in the development of coastal cities. Qingdao, with its coastal geography, rich cultural heritage, and rapid urbanization, serves as a representative case. This study integrates POI and multi-source data, employing methods such as the average nearest neighbor index, kernel density estimation, standard deviational ellipse, and Geodetector to analyze the spatial characteristics and influencing factors of Qingdao’s tourist attractions. Additionally, path dependence theory is innovatively applied to elucidate the mechanisms of the city’s development trajectory. Both natural and social factors influence this distribution, where the resource environment forms the foundational basis, the economic development provides impetus, and the urban development orientation exerts a regulatory effect. The findings are broadly applicable to other coastal tourist cities and offer strategic insights for sustainable development in such contexts. Full article
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<p>Geographic location and distribution of districts and counties in Qingdao.</p>
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<p>Flow diagram of research ideas.</p>
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<p>Spatial distribution of tourist attractions in Qingdao.</p>
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<p>Distribution of kernel density estimation of Qingdao tourist attractions.</p>
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<p>Standard deviation ellipse of 6 types of tourist attractions in Qingdao.</p>
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<p>The formation mechanism of the spatial pattern of tourist attractions.</p>
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11 pages, 589 KiB  
Article
Levels of Anxiety, Depression, Self-Esteem, and Guilt in Women with High-Risk Pregnancies
by Sevim Tuncer Can, Sevler Yildiz, Raziye Torun, Ibrahim Omeroglu and Hakan Golbasi
J. Clin. Med. 2024, 13(23), 7455; https://doi.org/10.3390/jcm13237455 - 7 Dec 2024
Viewed by 397
Abstract
Objectives: Pregnancy is an inherently delicate process characterized by physiological and psychological changes, even in the absence of any health complications. This study compares the levels of anxiety, depression, self-esteem, and guilt in women diagnosed with high-risk pregnancies to those in a control [...] Read more.
Objectives: Pregnancy is an inherently delicate process characterized by physiological and psychological changes, even in the absence of any health complications. This study compares the levels of anxiety, depression, self-esteem, and guilt in women diagnosed with high-risk pregnancies to those in a control group consisting of women with healthy pregnancies. Methods: A total of 172 women participated in the study, 108 of whom had high-risk pregnancies, and 64 had healthy pregnancies. All participants were administered a semi-structured Sociodemographic Data Form, Beck Depression Inventory (BDI), Beck Anxiety Scale (BAI), Rosenberg Self-Esteem Scale (RSES), and Guilt Inventory (GI). The findings were statistically analyzed and compared. Results: Women with high-risk pregnancies had significantly higher scores on the BAI (p = 0.002), BDI (p = 0.035), and GI (p = 0.001) compared to the control group. In the logistic regression analysis for calculating the risk of high-risk pregnancy, the multivariate analysis revealed that living in rural areas posed 3.5 times higher risk for high-risk pregnancy compared to urban living (OR = 3.500, 95% CI = 1.484–8.254). Additionally, for every one-point increase in the GI score, the risk of high-risk pregnancy increased by 1.064 times (OR = 1.064, 95% CI = 1.017–1.114). In the patient group, significant positive correlations were found between the BAI score and BDI, RSES, and GI scores, while a significant negative correlation was observed between BAI and parity. There were also significant positive correlations between the BDI and RSES as well as the GI scores. Additionally, a positive significant correlation was found between the RSES and GI scores. Conclusions: Our findings may help in identifying the psychological states of women with high-risk pregnancies and Full article
(This article belongs to the Section Obstetrics & Gynecology)
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<p>The ROC curve of BAI, BDI, and Guilt Inventory for risky pregnancies.</p>
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33 pages, 45544 KiB  
Article
A Study of Historic Urban Landscape Change Management Based on Layered Interpretation: A Case Study of Dongxi Ancient Town
by Xiaotian Ma and Junqiao Sun
Land 2024, 13(12), 2116; https://doi.org/10.3390/land13122116 - 6 Dec 2024
Viewed by 588
Abstract
In the face of external shocks from urbanization and the inherent needs of economic development, it is essential for urban and rural heritage to adapt timely to achieve sustainability in development. Employing Historic Urban Landscape (HUL) methodologies for change management holds significant implications [...] Read more.
In the face of external shocks from urbanization and the inherent needs of economic development, it is essential for urban and rural heritage to adapt timely to achieve sustainability in development. Employing Historic Urban Landscape (HUL) methodologies for change management holds significant implications for the sustainable preservation and utilization of heritage. This study used Dongxi Ancient Town as a case study, characterized by a distinct evolutionary trajectory and diverse layers of accumulation throughout its historical progression, making it an exemplary instance for change analysis. This paper analyzed the processes and outcomes of historic urban landscape changes through a layered historical approach. Combining historical data translation methods with ArcGIS spatial analysis, we documented and mapped the cultural and natural characteristics of Dongxi Ancient Town. The layered process of the town’s historical landscape was categorized into four stages: the primary formative period from the Western Han to the Ming dynasties, the rapid development during the Qing dynasty, the prosperous period of the Republic of China, and the transitional expansion period following the establishment of the People’s Republic of China. The study analyzed the morphological changes and values of the historical landscape throughout these periods. Based on the analysis results, we suggest three transformation management strategies for historical landscapes oriented towards economic development: (1) converting cultural heritage into cultural assets, (2) implementing moderate and controlled quantitative changes, and (3) enhancing operational feasibility through collaborative efforts among multiple stakeholders. These strategies aim to establish a sustainable model that balances heritage conservation with economic growth. Full article
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Figure 1
<p>Location of the research area. (Source: Self-drawn by the author.).</p>
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<p>Change management approach based on the layered interpretation of urban historical landscape (Source: Self-drawn by the author).</p>
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<p>Dongxi Ancient Town historical landscape stratification stage division. (Source: Self-drawn by the author).</p>
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<p>The historical landscape of Dongxi Ancient Town in its primary formative phase. (Source: Self-drawn by the author).</p>
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<p>The historical landscape of Dongxi Ancient Town in the rapid development phase. (Source: Self-drawn by the author).</p>
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<p>The historical landscape of Dongxi Ancient Town in the prosperous phase. (Source: Self-drawn by the author).</p>
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<p>The historical landscape of Dongxi Ancient Town in the transformation and expansion phase. (Source: Self-drawn by the author).</p>
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<p>Stratified section of historical landscape of Dongxi Ancient Town. (Source: Self-drawn by the author).</p>
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<p>Distribution map of historical value richness of Dongxi Ancient Town in different periods: (<b>a</b>) the primary formative phase; (<b>b</b>) the rapid development phase; (<b>c</b>) the prosperous phase; (<b>d</b>) the transformative expansion phase. (Source: ArcGIS Screenshot).</p>
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<p>Analysis of the overall spatial distribution density of micro-landmark nodes in Dongxi Ancient Town. (Source: ArcGIS Screenshot).</p>
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<p>(<b>a</b>) Space autocorrelation statement; (<b>b</b>) High/low clustering analysis. (Source: ArcGIS Screenshot).</p>
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<p>The value characteristics of the macro-regional pattern of historical landscape in Dongxi Ancient Town. (Source: Self-drawn by the author).</p>
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<p>The value characteristics of the cluster system in the historical landscape of Dongxi Ancient Town. (Source: Self-drawn by the author).</p>
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<p>The value characteristics of micro-landmark nodes in the historical landscape of Dongxi Ancient Town. (Source: Self-drawn by the author).</p>
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33 pages, 59097 KiB  
Article
Street Canyon Vegetation—Impact on the Dispersion of Air Pollutant Emissions from Road Traffic
by Paulina Bździuch, Marek Bogacki and Robert Oleniacz
Sustainability 2024, 16(23), 10700; https://doi.org/10.3390/su162310700 - 6 Dec 2024
Viewed by 439
Abstract
Roadside vegetation helps to retain air pollutants emitted by road traffic. On the other hand, its presence makes it difficult to ventilate street canyons. The paper examines the influence of vegetation on the dispersion of air pollution generated by road traffic, using the [...] Read more.
Roadside vegetation helps to retain air pollutants emitted by road traffic. On the other hand, its presence makes it difficult to ventilate street canyons. The paper examines the influence of vegetation on the dispersion of air pollution generated by road traffic, using the example of two street canyons—both-sided and one-sided street canyons. The study was conducted taking into account the actual emission conditions occurring on the analyzed road sections estimated using the HBEFA methodology. Subsequently, a three-dimensional pollution dispersion model named MISKAM was employed to simulate the air pollutant dispersion conditions in the analyzed street canyons. The modelling results were compared with the measurement data from air quality monitoring stations located in these canyons. The obtained results indicated that the presence of vegetation can significantly impact on the air dispersion of traffic-related exhaust and non-exhaust emissions. The impact of vegetation is more pronounced in the case of a street canyon with dense, high-rise development on both sides than in the case of a street canyon with such development on only one side. The results for the both-sided street canyon demonstrate that the discrepancy between the scenario devoid of vegetation and the scenario with vegetation was approximately 5 µg/m3 (10%) for PM10 and approximately 54 µg/m3 (45%) for NOx, with the former scenario showing lower values than the latter. Nevertheless, the scenario with the vegetation exhibited a lesser discrepancy with the air quality measurements. Vegetation functions as a natural barrier, reducing wind speed in the street canyon, which in turn limits the spread of pollutants in the air, leading to pollutant accumulation near the building walls that form the canyon. Consequently, atmospheric dispersion modelling must consider the presence of vegetation to accurately evaluate the effects of road traffic emissions on air quality in urban areas, particularly in street canyons. The results of this study may hold importance for urban planning and decision-making regarding environmental management in cities aimed at improving air quality and public health. Full article
(This article belongs to the Special Issue Air Quality Characterisation and Modelling—2nd Edition)
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Figure 1
<p>Research area—the city of Krakow (Poland) with the location of air quality monitoring and meteorological stations, and computational areas marked.</p>
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<p>Detailed locations of street canyons and computational areas for modelling the dispersion of traffic-related pollutants.</p>
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<p>Visualization of shapefile input data for the calculation area at the traffic air quality monitoring station (AQMS) at the Krasińskiego Avenue street canyon.</p>
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<p>Visualization of shapefile input data for the calculation area at the traffic air quality monitoring station (AQMS) at the Dietla street canyon.</p>
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<p>Mapping in WinMISKAM of the computational area in the Krasińskiego Avenue street canyon: (<b>a</b>) visualization of meshing for the study area; (<b>b</b>) fragment of the mesh for the AQMS location area (red star—MpKrakAlKras). Legend: black objects—vegetation outline; pink objects—roads, emission source; other coloured objects—visualization of buildings.</p>
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<p>Mapping in WinMISKAM of the computational area in the Dietla street canyon: (<b>a</b>) visualization of meshing for the study area; (<b>b</b>) fragment of the mesh for the AQMS location area (red star—MpKrakDietla). Legend: black objects—vegetation outline; pink objects—roads, emission source; other coloured objects—visualization of buildings.</p>
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<p>Simulation results of PM<sub>10</sub> dispersion in the street canyon of Krasińskiego Avenue for variant K1. Top left: cross-sectional projection of the PM<sub>10</sub> dispersion simulation at the AQMS site.</p>
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<p>Simulation results of PM<sub>10</sub> dispersion in the street canyon of Krasińskiego Avenue for variant K2. Top left: cross-sectional projection of the PM<sub>10</sub> dispersion simulation at the AQMS site.</p>
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<p>Simulation results of NOx dispersion in the street canyon of Krasińskiego Avenue for variant K1. Top left: cross-sectional projection of the NOx dispersion simulation at the AQMS site.</p>
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<p>Simulation results of NOx dispersion in the street canyon of Krasińskiego Avenue for variant K2. Top left: cross-sectional projection of the NOx dispersion simulation at the AQMS site.</p>
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<p>Simulation results of PM<sub>10</sub> dispersion in the Dietla street canyon, with AQMS marked for variant D1. Bottom right: cross-sectional projection of the PM<sub>10</sub> dispersion simulation at the AQMS site.</p>
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<p>Simulation results of PM<sub>10</sub> dispersion in the Dietla street canyon for variant D2. Bottom right: cross-sectional projection of the PM<sub>10</sub> dispersion simulation at the AQMS site.</p>
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<p>Simulation results of NOx dispersion in the Dietla street canyon for variant D1. Bottom right: cross-sectional projection of the NOx dispersion simulation at the AQMS site.</p>
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<p>Simulation results of NOx dispersion in the Dietla street canyon for variant D2. Bottom right: cross-sectional projection of the NOx dispersion simulation at the AQMS site.</p>
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<p>The optimum mesh distribution in the street canyon for the post-horizontal projection in the MISKAM model. The yellow element indicates the horizontal position of the grid point most often considered when analyzing the results of pollutant dispersion modeling in street canyons.</p>
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<p>The optimum mesh distribution in the street canyon for the vertical projection in the MISKAM model. The red element indicates the vertical position of the grid point most often considered when analyzing the results of pollutant dispersion modeling in street canyons.</p>
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<p>Krasińskiego Avenue street canyon study area (own study based on Google Earth map).</p>
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<p>Dietla street canyon study area (own study based on Google Earth map).</p>
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<p>Comparison of simulation results of PM<sub>10</sub> dispersion in the Krasińskiego Avenue street canyon for the horizontal section: (<b>a</b>) variant K1 (without vegetation); (<b>b</b>) variant K2 (with vegetation). Modelling results at height: 1.2–1.8 m.</p>
Full article ">Figure A6
<p>Comparison of simulation results of NOx dispersion in the Krasińskiego Avenue street canyon for the horizontal section: (<b>a</b>) variant K1 (without vegetation); (<b>b</b>) variant K2 (with vegetation). Modelling results at height: 1.2–1.8 m.</p>
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<p>Comparison of simulation results of PM<sub>10</sub> dispersion in the Dietla street canyon for the horizontal section: (<b>a</b>) variant D1 (without vegetation); (<b>b</b>) variant D2 (with vegetation). Modelling results at height: 1.2–1.8 m.</p>
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<p>Comparison of simulation results of NOx dispersion in the Dietla street canyon for the horizontal section: (<b>a</b>) variant D1 (without vegetation); (<b>b</b>) variant D2 (with vegetation). Modelling results at height: 1.2–1.8 m.</p>
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<p>Comparison of simulation results of PM<sub>10</sub> dispersion in the Krasińskiego Avenue street canyon for a vertical section: (<b>a</b>) variant K1 (without vegetation); (<b>b</b>) variant K2 (with vegetation). Modelling results for the cross-section passing through the MpKrakAlKras AQMS site.</p>
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<p>Comparison of simulation results of NOx dispersion in the Krasińskiego Avenue street canyon for a vertical section: (<b>a</b>) variant K1 (without vegetation); (<b>b</b>) variant K2 (with vegetation). Modelling results for the cross-section passing through the MpKrakAlKras AQMS site.</p>
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<p>Comparison of simulation results of NOx dispersion in the Krasińskiego Avenue street canyon for a vertical section: (<b>a</b>) variant K1 (without vegetation); (<b>b</b>) variant K2 (with vegetation). Modelling results for the cross-section passing through the MpKrakAlKras AQMS site.</p>
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<p>Comparison of simulation results of PM<sub>10</sub> dispersion in the Dietla street canyon for a vertical section: (<b>a</b>) variant D1 (without vegetation); (<b>b</b>) variant D2 (with vegetation). Modelling results for the cross-section passing through the MpKrakDietla AQMS site.</p>
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<p>Comparison of simulation results of NOx dispersion in the Dietla street canyon for a vertical section: (<b>a</b>) variant D1 (without vegetation); (<b>b</b>) variant D2 (with vegetation). Modelling results for the cross-section passing through the MpKrakDietla AQMS site.</p>
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<p>Comparison of modelling results of average annual air pollutant concentrations in the cross-section of the Krasińskiego Avenue street canyon passing through the MpKrakAlKras AQMS site for variants K1 (without vegetation) and K2 (with vegetation): (<b>a</b>) PM<sub>10</sub>; (<b>b</b>) NOx. Data for simulation at height: 1.2–1.8 m.</p>
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<p>Comparison of modelling results of average annual air pollutant concentrations in the cross-section of the Krasińskiego Avenue street canyon passing through the MpKrakAlKras AQMS site for variants K1 (without vegetation) and K2 (with vegetation): (<b>a</b>) PM<sub>10</sub>; (<b>b</b>) NOx. Data for simulation at height: 1.2–1.8 m.</p>
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<p>Comparison of modelling results of average annual air pollutant concentrations in the cross-section of the Dietla street canyon passing through the MpKrakDietla AQMS site for variants D1 (without vegetation) and D2 (with vegetation): (<b>a</b>) PM<sub>10</sub>; (<b>b</b>) NOx. Data for simulation at height: 1.2–1.8 m.</p>
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<p>Box-plot graph illustrating the variability of percentage differences between the average annual concentrations of the analyzed pollutants in individual variants. Simulation results at a height of 1.2–1.8 m for the cross-section of a given street canyon passing through the AQMS with a step of 2 m: (<b>a</b>) Krasińskiego Avenue street canyon; (<b>b</b>) Dietla street canyon.</p>
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<p>Comparison of simulation results of NOx dispersion in the Krasińskiego Avenue street canyon for the axial longitudinal vertical section: (<b>a</b>) variant K1 (without vegetation); (<b>b</b>) variant K2 (with vegetation).</p>
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<p>Comparison of simulation results of NOx dispersion in the Krasińskiego Avenue street canyon for the axial longitudinal vertical section: (<b>a</b>) variant K1 (without vegetation); (<b>b</b>) variant K2 (with vegetation).</p>
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19 pages, 2195 KiB  
Article
The Impact of Urban Transportation Development on Daily Travel Carbon Emissions in China: Moderating Effects Based on Urban Form
by Wanwan Yang, Yingzi Chen, Yuchan Gao and Yaqi Hu
Land 2024, 13(12), 2107; https://doi.org/10.3390/land13122107 - 5 Dec 2024
Viewed by 498
Abstract
Carbon emissions from transportation account for an increasing proportion of total carbon emissions, and daily travel carbon emissions are an essential part of carbon emissions from transportation. Urban form influences the transportation network layout, so the degree of influence of urban transportation development [...] Read more.
Carbon emissions from transportation account for an increasing proportion of total carbon emissions, and daily travel carbon emissions are an essential part of carbon emissions from transportation. Urban form influences the transportation network layout, so the degree of influence of urban transportation development on daily travel carbon emissions varies according to urban form. This paper uses panel data from 254 prefecture-level cities in China from 2006 to 2019 to explore the impact of urban transportation development on daily travel carbon emissions based on the moderating effect of urban form. The results show that urban transportation development plays a pivotal role in significantly reducing daily travel carbon emissions. The urban form further amplifies the impact of carbon emission reductions. Specifically, polycentric urban structures enable residents to meet their daily travel needs through short-distance trips, thereby alleviating traffic congestion. The impact of urban transportation development on daily travel carbon emission intensity exhibits heterogeneity. In both low-carbon pilot cities and large cities, urban transportation development markedly decreases daily travel carbon emission intensity. Additionally, it is observed that cities with lower economic development levels exhibit more pronounced effects in carbon emission reduction compared to their more economically developed counterparts. This paper provides empirical support for rational planning of urban transportation systems and low-carbon development of daily travel. Full article
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<p>Research Framework.</p>
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<p>Spatial Distribution of Daily Travel Carbon Emission Intensity and Urban Transportation Development Levels.</p>
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<p>Sensitivity Analysis. Predicted intensity of carbon emission intensity from urban transportation development at sensitivity at baseline boundaries of 1×, 2×, and 3× on a biased R2 scale. The left panel shows the sensitivity contour plots for point estimates; the right panel shows the sensitivity contour plots for t values. Note: The dots in the figure indicate the strength of the prediction of carbon emission intensity by urban transportation development at sensitivities of 1×, 2×, and 3× at the baseline limits on the biased R2 scale. The left panel shows contour plots of sensitivity for point estimates; the right panel shows contour plots of sensitivity for t values.</p>
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33 pages, 4548 KiB  
Article
Current Cadastral Trends—A Literature Review of the Last Decade
by Burak Uşak, Volkan Çağdaş and Abdullah Kara
Land 2024, 13(12), 2100; https://doi.org/10.3390/land13122100 - 5 Dec 2024
Viewed by 468
Abstract
Today, population growth, high urbanization rates, and global agenda issues have led to the intensive use of land and air and water spaces, and cadastral systems that manage the people–land relationship have evolved into a multi-purpose form that supports various land-based activities. This [...] Read more.
Today, population growth, high urbanization rates, and global agenda issues have led to the intensive use of land and air and water spaces, and cadastral systems that manage the people–land relationship have evolved into a multi-purpose form that supports various land-based activities. This situation has necessitated the modernization of traditional land administration and cadastral systems to manage the people–land relationship effectively. This study conducts a literature review on current cadastral trends emerging from the perspective of modern land administration systems (LASs). A total of 367 studies published in the Web of Science (WoS) database in the last decade on 3D cadastre, technical infrastructure cadastre, maritime cadastre, public law restriction (PLR) cadastre, fit-for-purpose land management, and disaster-sensitive cadastral trends are analyzed. The study aims to analyze the interest of the land administration community in current cadastral trends and present the results. The analysis results show that the most researched trend is 3D cadastre, and the least researched trends are PLRs cadastre and disaster-responsive cadastre. LADM stands out as a widely used framework across the studies. Full article
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<p>The eight-step methodology used for this systematic review.</p>
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<p>The distribution of 3D cadastre publications by country.</p>
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<p>The distribution of marine and coastal cadastre publications by country.</p>
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<p>The distribution of FFPLA publications by country.</p>
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<p>The distribution of technical infrastructure cadastre publications by country.</p>
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<p>The distribution of PLR cadastre publications by country.</p>
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<p>The distribution of disaster-responsive cadastre publications by country.</p>
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<p>Distribution of total papers related to cadastral trends in the analyzed literature.</p>
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<p>Publications per year.</p>
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<p>The country distribution of the publications.</p>
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22 pages, 8940 KiB  
Article
Wind-Driven Dynamics Around Building Clusters: Impact of Convex and Concave Curvilinear Morphologies and Central Angles
by Wei Gan, Han Guo, Hongliang Zhang, Fuyun Zhao, Jinyu Li, Shuqi Peng and Yi He
Atmosphere 2024, 15(12), 1454; https://doi.org/10.3390/atmos15121454 - 5 Dec 2024
Viewed by 326
Abstract
Curvilinear building forms are increasingly common in modern architecture, yet their impact on wind dynamics remains understudied. This research examines the wind flow behavior around high-rise residential building rows configured with convex and concave curvilinear shapes, focusing on the influence of varying central [...] Read more.
Curvilinear building forms are increasingly common in modern architecture, yet their impact on wind dynamics remains understudied. This research examines the wind flow behavior around high-rise residential building rows configured with convex and concave curvilinear shapes, focusing on the influence of varying central angles. Using parametric models generated in Rhino’s Grasshopper, followed by experimentally validated CFD simulations, we analyze the aerodynamic effects of these configurations. The findings indicate contrasting impacts: convex curvilinear rows amplify wind flow as their central angles increase, while concave rows hinder airflow under similar conditions. These variations are linked to changes in the windward projective areas. This study enhances the understanding of wind behavior in urban settings and provides key insights for optimizing natural ventilation and designing sustainable curvilinear building clusters. Full article
(This article belongs to the Section Meteorology)
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<p>(<b>Top</b>): Satellite images of buildings with curvilinear designs, including residential, educational and office buildings in Wuhan. (<b>Bottom</b>): The location of Wuhan City (Google map).</p>
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<p>(<b>Top</b>): Curvilinear-configuration building rows of Group 1 (<b>left</b>) and Group 2 (<b>right</b>). (<b>Bottom</b>): Framework of wind-environment study of curvilinear-configuration building.</p>
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<p>(<b>Top</b>): Parametric modelling of curvilinear-configuration building row. (<b>Bottom</b>): Description of computational domain (H = 36 m is the building height).</p>
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<p>Experimental instruments and layout. (<b>a</b>) The laser. (<b>b</b>) The control panel of wind tunnel. (<b>c</b>) The building model in the wind tunnel. (<b>d</b>) The experimental layout.</p>
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<p>Comparison of vortices on the horizontal plane. (<b>Top</b>): experimental pictures. (<b>Bottom</b>): airflow streamlines and vorticity magnitudes.</p>
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<p>Comparison of vortices on the vertical plane. (<b>Top</b>): experimental pictures. (<b>Bottom</b>): airflow streamlines and vorticity magnitudes.</p>
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<p>(<b>Top</b>): The geometry of the cylindrical roof. (<b>Bottom</b>): The measurement points.</p>
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<p>Comparisons of V and TKE of three grids sets (Grid-1-Coarse grid, Grid-2-Medium grid, Grid-3-Fine grid).</p>
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<p>Comparison of pressure coefficient (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi mathvariant="normal">p</mi> </mrow> </msub> </mrow> </semantics></math>) of experiment and simulation.</p>
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<p>Comparisons of wind-velocity magnitudes on horizontal and vertical planes of the curvilinear-configuration building rows of Group 1.</p>
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<p>The influenced areas with different wind-velocity ranges of Group 1.</p>
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<p>Comparisons of the air-pressure magnitudes and the wind-flow streamlines of the curvilinear-configuration building rows of Group 1.</p>
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<p>Comparisons of wind-velocity magnitudes on horizontal and vertical planes of the curvilinear-configuration building rows of Group 2.</p>
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<p>The influenced areas with different wind-velocity ranges of Group 2.</p>
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<p>Comparisons of the air-pressure magnitudes and the wind-flow streamlines of the curvilinear-configuration building rows of Group 2.</p>
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<p>Wind environments influenced by the curvilinear-configuration building rows with the convex and concave surfaces on the windward side.</p>
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22 pages, 3268 KiB  
Article
Unveiling Sustainable Co-Creation Patterns in Entrepreneurial Ecosystems of Shanghai’s High-Density Urban Communities
by Chenhan Jiang, Rui Huang, Shengyu Huang and Tao Shen
Sustainability 2024, 16(23), 10642; https://doi.org/10.3390/su162310642 - 4 Dec 2024
Viewed by 491
Abstract
Communities in China’s high-density cities, like Shanghai, are evolving from traditional residential roles into vibrant centers of entrepreneurial innovation. This research delves into the development of community-supported entrepreneurial ecosystems (CSEEs) in the city, with a specific focus on the sustainable co-creation mechanisms facilitated [...] Read more.
Communities in China’s high-density cities, like Shanghai, are evolving from traditional residential roles into vibrant centers of entrepreneurial innovation. This research delves into the development of community-supported entrepreneurial ecosystems (CSEEs) in the city, with a specific focus on the sustainable co-creation mechanisms facilitated by stakeholders, explored through a comparative study framework. By utilizing Kelly’s Repertory Grid Technique, 14 essential elements of co-creation are identified, which form the framework for classifying the cases into three distinct types. This study employs in-depth interviews and content analysis to analyze and contrast how these co-creation patterns are applied across cases representing each type. The results show that key factors, such as resource origins, the interaction between CSEEs and embedded communities, and participant selection strategies, significantly shape the variations in value co-creation mechanisms, processes, and outcomes. Recognizing the variety of co-creation models is crucial for enhancing both the vitality and efficiency of Shanghai’s CSEEs. Furthermore, this study offers valuable insights into managing co-creation efforts and predicting risks in similar contexts, contributing to the sustainable regeneration of urban areas through community-driven entrepreneurship and innovation. Full article
(This article belongs to the Section Sustainable Products and Services)
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<p>Research framework and methods.</p>
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<p>The building rules of RGT constructs.</p>
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<p>(<b>a</b>) The results of the cluster analysis; (<b>b</b>) results of principal component analysis.</p>
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<p>The implementation patterns of the co-creation mechanism from C1.</p>
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<p>The implementation patterns of the co-creation mechanism from C2.</p>
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<p>The implementation patterns of the co-creation mechanism from C5.</p>
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25 pages, 14687 KiB  
Article
Spatio-Temporal Evolution, Internal Diversity, and Driving Factors of Economy of Guanzhong Plain Urban Agglomeration in Northwestern China Based on Nighttime Light Data
by Limeng Liu, Wenheng Wu, Xiaoying Bai and Wanying Shang
Land 2024, 13(12), 2093; https://doi.org/10.3390/land13122093 - 4 Dec 2024
Viewed by 355
Abstract
Urban agglomeration (UA) is a highly developed spatial form of urban complex, which is one of the important carriers of regional economic cooperation, international industrial division of labor, and flow of capital and information elements. In China, urban agglomerations (UAs) have become the [...] Read more.
Urban agglomeration (UA) is a highly developed spatial form of urban complex, which is one of the important carriers of regional economic cooperation, international industrial division of labor, and flow of capital and information elements. In China, urban agglomerations (UAs) have become the spatial subject of the national new-type urbanization strategy since the early 21st century and have made irreplaceable contributions to China’s urbanization and economic development. The Guanzhong Plain urban agglomeration (GPUA) is an important economic growth pole in northwest China and a key node in China’s open-door pattern. Exploring the spatial and temporal characteristics and driving factors of its economic development will be an important revelation for the promotion of high-quality economic development of the GPUA. This paper characterizes the level of economic development of GPUA with a long series of nighttime light data between 2002 and 2022. The standard deviation ellipse, spatial autocorrelation analysis, the economic difference index, and grey correlation analysis are used to analyze the characteristics of spatio-temporal evolution, internal diversity, and driving factors of economic development of the GPUA. The results show that the economic development level of the GPUA continued to increase from 2002 to 2022. The spatial distribution of the GPUA economy is “northeast-southwest” axial distribution, and the center of gravity of economic development gradually moves westward. The differences in the level of economic development within the GPUA show a typical core–periphery structure, but the degree of difference tends to weaken over time. The internal expansion force and economic promotion force were the dominant factors for the economic development of the GPUA in the early years. However, with the passage of time, scientific and technological support and government support have gradually become the main influencing factors for the economic development of the GPUA nowadays. Full article
(This article belongs to the Section Land Socio-Economic and Political Issues)
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<p>The study area. (<b>a</b>) Locations in Gansu, Shaanxi, and Shanxi provinces. (<b>b</b>) Location of the GPUA. (<b>c</b>) Municipal and county zoning in the GPUA. Source: China National Basic Geographic Information Database and author’s revisions.</p>
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<p>Distribution of nighttime lights in the GPUA in 2002 (<b>a</b>), 2007 (<b>b</b>), 2012 (<b>c</b>), 2017 (<b>d</b>), and 2022 (<b>e</b>). Source: the NTL data and authors’ calculation.</p>
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<p>Total nighttime lights of the GPUA from 2002 to 2022. Source: authors’ calculation.</p>
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<p>Standard deviation ellipse of the GPUA in each representative year. Source: authors’ calculation.</p>
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<p>Nighttime light cluster diagram of counties of the GPUA in 2002 (<b>a</b>), 2007 (<b>b</b>), 2012 (<b>c</b>), 2017 (<b>d</b>), and 2022 (<b>e</b>). Source: authors’ calculation.</p>
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<p>(<b>a</b>) Division of high-, medium-, and low-level zones in the GPUA. (<b>b</b>) The change in economic difference index among zones. Source: authors’ calculation.</p>
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<p>The proportion of low-level counties in each economic difference index range in 2002, 2012, and 2022. Source: authors’ calculation.</p>
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<p>Spatial evolution of low-level counties in each economic difference index range in 2002 (<b>a</b>), 2012 (<b>b</b>), and 2022 (<b>c</b>). Source: authors’ calculation.</p>
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<p>Elevation of the GPUA. Source: created by the authors using DEM data.</p>
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<p>Schematic diagram of opening-up pattern of the GPUA. Source: China Standard Map Service website and author’s revisions.</p>
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20 pages, 8697 KiB  
Article
Matching Trees to Streets by Street Type: A Case Study of Street Tree Suitability and Services in a Highly Urbanized City
by Heejung Nam, Seunghyun Hong, Dohyuk Im, Ayun Maeng, Sunmi Je, Wanmo Kang and Hanna Chang
Land 2024, 13(12), 2079; https://doi.org/10.3390/land13122079 - 3 Dec 2024
Viewed by 377
Abstract
Street trees are a representative form of urban green space that play an important role in mitigating the environmental impact of urbanization. Planting the right tree in the right place in urban streetscapes can improve tree health and ecosystem services. Here, we propose [...] Read more.
Street trees are a representative form of urban green space that play an important role in mitigating the environmental impact of urbanization. Planting the right tree in the right place in urban streetscapes can improve tree health and ecosystem services. Here, we propose a novel approach to selecting appropriate street trees using street type classifications. In the highly urbanized area of Uijeongbu City, South Korea, 221.9 km of streets with 19,717 street trees were classified into 12 types based on road width, aspect ratio, land use, and the presence of power lines. Appropriate tree species were selected for each street type, taking into account tree traits and functions as well as street environments. Then, we analyzed the structure and ecosystem-regulating services of street trees by type, also comparing the services of appropriate and non-appropriate trees. As a result, all 12 street types were identified, but their distribution was uneven. Tree dimension was the key factor in determining appropriate species, and, for the second most common street type, characterized by narrow roads, low aspect ratios, and power lines, only four appropriate species were identified, indicating an urgent need for more options. Additionally, the most dominant species accounted for over 20%, averaging 44% across the 12 street types, further highlighting the necessity of introducing more diverse tree species. Overall, appropriate street trees generally provided higher service efficiency compared to non-appropriate trees across four ecosystem regulating services. These findings emphasize the need for policies and guidelines that promote street tree diversity and enhance the ecological benefits of street trees. This study provides a foundation for developing sustainable street tree management strategies that contribute to healthier and more resilient urban streetscapes. Full article
(This article belongs to the Special Issue Urban Ecosystem Services: 5th Edition)
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<p>Land cover and street tree distribution map of Uijeongbu City, Republic of Korea.</p>
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<p>Classification and systematization of street types by their physical features and functions.</p>
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<p>Examples of street view images used for data validation. (<b>a</b>) NAVER Panorama (07. 2023) and (<b>b</b>) KAKAO Road View (12. 2023).</p>
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<p>A total of 221.9 km of tree-planted roads classified into 12 street types.</p>
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<p>Proportions of suitable, recommended, and non-appropriate trees by street type.</p>
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<p>Relationship between the proportion of appropriate street trees by street type and the ecosystem service they provide for (<b>a</b>) carbon storage, (<b>b</b>) carbon sequestration, (<b>c</b>) avoided runoff, and (<b>d</b>) air pollution removal. The dashed line represents the 1:1 relationship.</p>
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<p>Examples of street trees planted in constrained spaces and improperly managed in Uijeongbu City, Republic of Korea: (<b>a</b>) <span class="html-italic">Metasequoia glyptostroboides</span> in a street type B environment, (<b>b</b>) <span class="html-italic">Metasequoia glyptostroboides</span> in a type D environment, and (<b>c</b>) <span class="html-italic">Chionanthus retusus</span> in a type M environment (the red square indicates a removed tree).</p>
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14 pages, 5107 KiB  
Article
Land-Use and Land-Cover Changes and Urban Expansion in Central Vietnam: A Case Study in Hue City
by Nguyen Hoang Khanh Linh, Tung Gia Pham, Ty Huu Pham, Chau Thi Minh Tran, Tan Quang Nguyen, Nam Thang Ha and Nguyen Bich Ngoc
Urban Sci. 2024, 8(4), 242; https://doi.org/10.3390/urbansci8040242 - 3 Dec 2024
Viewed by 682
Abstract
During the past two decades, Hue city has undergone significant changes in its economic development, leading to a rapid transformation of its land-use and land-cover (LULC) patterns. This study used remote sensing data and Geographic Information Systems (GIS) to analyze changes in the [...] Read more.
During the past two decades, Hue city has undergone significant changes in its economic development, leading to a rapid transformation of its land-use and land-cover (LULC) patterns. This study used remote sensing data and Geographic Information Systems (GIS) to analyze changes in the land-use and land-cover in Hue city, providing essential insights for the city’s future development. This research examines indicators such as area and land-cover changes, urban development trends, and the morphology of urban areas during the period from 2000 to 2020, with assessments conducted at ten-year intervals. The results showed that built-up and forest land have increased, while agricultural and unused land have decreased over time. By 2020, the urban area had expanded by more than 60% in the north and northeast directions. Hue city developed through infilling and edge expansion of existing urban areas, while some regions primarily expanded into outlying sections in the east and south by constructing high-end residential areas on former paddy rice fields. These findings yield valuable policy implications that extend beyond the case study of Hue city, offering insights for other cities to pursue inclusive and prosperous futures. Full article
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<p>The location of Hue city (red color) in Thua Thien Hue province and administrative map of Hue in 2020.</p>
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<p>The classification of urban form expansions.</p>
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<p>The LULC in 2000 (<b>a</b>), 2010 (<b>b</b>), and 2020 (<b>c</b>), and LULC change (<b>d</b>) of Hue city.</p>
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<p>The LULC by direction in 2000 (<b>a</b>), 2010 (<b>b</b>), and 2020, (<b>c</b>) and the percentage of BL class (<b>d</b>) of Hue city.</p>
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<p>The urban form expansion of Hue city in the period 2000–2010 (<b>a</b>) and 2010–2020 (<b>b</b>).</p>
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25 pages, 5127 KiB  
Article
Exploring the Impact of Spatial Arrangements on BREEAM Outstanding Projects in London, UK
by Anosh Nadeem Butt and Carolina Rigoni
Urban Sci. 2024, 8(4), 239; https://doi.org/10.3390/urbansci8040239 - 2 Dec 2024
Viewed by 651
Abstract
The spatial configuration of urban areas impacts environmental sustainability, social equity, and economic and social resilience. This study examines the intricate relationship between spatial arrangements and the planning and design of BREEAM Outstanding projects in London, UK. It analyses the relationship between urban [...] Read more.
The spatial configuration of urban areas impacts environmental sustainability, social equity, and economic and social resilience. This study examines the intricate relationship between spatial arrangements and the planning and design of BREEAM Outstanding projects in London, UK. It analyses the relationship between urban morphology and the effectiveness of sustainable building practices and contributes to the broader objectives of urban sustainability. This research focuses on London, UK—a city renowned for its complex urban fabric and architectural heterogeneity—using a multi-case study approach to dissect the elements that facilitate the development of BREEAM Outstanding projects. This study analyses key spatial characteristics such as land use diversity, subway network analysis, and street network analysis using betweenness centrality of edges and node degrees. These factors are considered due to their impact on energy performance, carbon emissions, and social sustainability metrics. Furthermore, this research explores how urban design strategies, such as enhanced walkability and mixed-use development, reinforce the success of BREEAM-certified Outstanding-rated projects. The findings of this investigation reveal a correlation between urban environments and the development of BREEAM Outstanding-rated projects in London. By aligning the spatial organisation of urban form with BREEAM principles, urban planners, policymakers, and architects can facilitate the creation of cities that are environmentally sustainable, socially inclusive, and economically prosperous. The research offers substantive insights and actionable recommendations for future urban development, advocating for a comprehensive and interdisciplinary approach to sustainable city planning and design. The spatial arrangement of urban form impacts the planning and design of BREEAM Outstanding projects. Findings from current and future research will be used to investigate the connections between spatial arrangement and various categories in BREEAM and how they can influence future sustainable urban environments to set a benchmark for sustainability for contributing to a more equitable urban future. Full article
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<p>Clusters (C1–C3) and micro-clusters (C4–C16) of BREEAM Outstanding-rated projects in London.</p>
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<p>Conceptual map of data sources, tools, and research results.</p>
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<p>Environmental features and BREEAM Outstanding-rated projects in London—the numbers denote the BREEAM Outstanding-rated certifications for projects in London.</p>
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<p>Subway networks and BREEAM Outstanding-rated projects in London—the numbers denote the BREEAM Outstanding-rated certifications for projects in London.</p>
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<p>Analysing street networks using betweenness centrality of Study Area A—the numbers denote the BREEAM Outstanding-rated certifications for projects in clusters and micro-clusters in London.</p>
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<p>Analysing street networks using betweenness centrality of Study Area B—the numbers denote the BREEAM Outstanding-rated certifications for projects and micro-clusters in London.</p>
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<p>Analysing street networks using node degrees of Study Area A—the numbers denote the BREEAM Outstanding-rated certifications for projects in clusters and micro-clusters in London.</p>
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<p>Analysing street meshedness using nodes of Study Area A—the numbers denote the BREEAM Outstanding-rated certifications for projects in clusters and micro-clusters in London.</p>
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<p>Analysing street networks using node degree of Study Area B—the numbers denote the BREEAM Outstanding-rated certifications for projects in clusters and micro-clusters in London.</p>
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<p>Analysing street meshedness using nodes of Study Area B—the numbers denote the BREEAM Outstanding-rated certifications for projects in clusters and micro-clusters in London.</p>
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<p>Relationship between clusters and micro-clusters, land use diversity types, and number of subway stations across clusters.</p>
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14 pages, 1311 KiB  
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Quantification of the Total and Extractable Content of Micro- and Trace Elements in Linden Blossom and Infusions—The Impact of Urban Pollution on Health Risk for Samples from Plovdiv, Bulgaria
by Evelina Varbanova, Deyana Georgieva and Violeta Stefanova
Environments 2024, 11(12), 274; https://doi.org/10.3390/environments11120274 - 2 Dec 2024
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Abstract
Linden (Tilia) is one of the most frequently utilized plants for the preparation of infusions because of its salutary effects, including the reduction in inflammatory processes and pain, alleviation of stress, and lowering of blood pressure. As Linden is a common [...] Read more.
Linden (Tilia) is one of the most frequently utilized plants for the preparation of infusions because of its salutary effects, including the reduction in inflammatory processes and pain, alleviation of stress, and lowering of blood pressure. As Linden is a common species in Bulgarian cities, it is frequently used for homemade infusions. The regular consumption of these tea beverages may contribute to the attainment of the recommended daily allowances of certain minerals, but it may also result in the accumulation of toxic elements within the human body. The present study compares the concentrations of essential and toxic elements in linden blossom collected from disparate locations in Plovdiv, Bulgaria, with those of samples gathered in ecologically pristine regions and commercially available products labeled “bio”. Both total element content and extractable forms in infusions were quantified by ICP-MS. The health risk due to consuming infusions was assessed by comparing the water, tea, drinks, and EFSA regulations. The applied cluster analysis divided the samples from the urban area into three groups related to traffic pollution. In spite of the short blooming period, the concentrations of Al, Fe, Pb, V, Cr, Co, Ni, and Cd in the samples from the most polluted areas are increased by a factor of two compared to those from the clean zones. Full article
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Figure 1
<p>Map of Plovdiv, Bulgaria, with sample collection sites.</p>
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<p>Content of Fe, Al, Sr, Mn, Ba, Zn, Cu, Pb, Cr, Ni, V, As, Co, and Cd presented as a logarithm of concentrations (mg kg<sup>−1</sup>) in dry samples.</p>
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