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27 pages, 4128 KiB  
Review
Outdoor Thermal Comfort Research and Its Implications for Landscape Architecture: A Systematic Review
by Tingfeng Liu, Yaolong Wang, Longhao Zhang, Ninghan Xu and Fengliang Tang
Sustainability 2025, 17(5), 2330; https://doi.org/10.3390/su17052330 - 6 Mar 2025
Abstract
Amid global warming and urbanization, outdoor thermal comfort has become a critical consideration in landscape architecture. This study integrates a systematic review and bibliometric analysis of 1417 empirical studies (1980–2024) sourced from Web of Science, aiming to clarify the current state of research, [...] Read more.
Amid global warming and urbanization, outdoor thermal comfort has become a critical consideration in landscape architecture. This study integrates a systematic review and bibliometric analysis of 1417 empirical studies (1980–2024) sourced from Web of Science, aiming to clarify the current state of research, identify core themes, and propose future directions. This study examines key evaluation models, the influence of spatial morphology, and their practical applications using keyword co-occurrence, citation networks, and thematic analyses. Findings show a significant rise in research over the past decade, particularly in tropical and subtropical regions. Core themes include thermal comfort indices (PMV, PET, and UTCI), microclimate regulation, and important spatial indicators (height-to-width ratio, sky view factor, and greening). The field is increasingly shifting towards simulation tools (such as ENVI-met and CFD) rather than traditional field measurements, with artificial intelligence emerging as a tool for predictive and regulatory purposes, though its application remains limited. However, much of the research focuses on small-scale morphological optimization and lacks a systematic framework for spatial representation. Future research should prioritize developing a comprehensive evaluation system adaptable to diverse landscapes, investigating the interplay between spatial form and thermal comfort, and advancing sustainable, low-carbon design strategies. The insights from this study provide a solid foundation for improving outdoor thermal comfort and guiding sustainable urban development through landscape architecture. Full article
(This article belongs to the Section Social Ecology and Sustainability)
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<p>PRISMA flow diagram: literature screening and selection process.</p>
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<p>Annual trends in the number of publications on outdoor thermal comfort (2003–2024).</p>
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<p>Temporal distribution of keywords in outdoor thermal comfort research.</p>
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<p>Keyword co-occurrence network: research themes and focus areas. The axes represent keywords (on the horizontal axis) and their frequency of co-occurrence (on the vertical axis). This visualization highlights how certain keywords, such as thermal comfort and vegetation, are closely related, signifying the interconnectedness of these concepts within the literature.</p>
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<p>Thematic structure and keyword relationships in outdoor thermal comfort research. The color-coded map represents thematic clusters in the research, such as thermal comfort, microclimate, and design strategies. The varying colors of the clusters indicate different thematic groups, with overlapping themes reflecting an interdisciplinary approach to the topic, involving both environmental and design aspects.</p>
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<p>Tree map of keyword distribution in outdoor thermal comfort research. The size of each box represents the relative frequency of each keyword in recent publications. Larger boxes indicate that a term has been more frequently used, highlighting key topics and research trends in the field of outdoor thermal comfort. Keywords like PET, microclimate, and urban heat islands are central to current research.</p>
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<p>Evolution of research methods and application of simulation techniques. This figure tracks the usage of simulation methods like CFD, ENVI-met, and PET over time. The bars represent the number of studies using each method within specified time frames.</p>
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<p>Thematic evolution of keywords in outdoor thermal comfort research (2013–2024).</p>
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31 pages, 15855 KiB  
Article
Assessing the Impact of Urban Area Size on Thermal Comfort in Compact Urban Fabrics Considering the Saharan City of Ghardaïa, Algeria
by Roufaida Benbrahim, Leila Sriti, Soumaya Besbas, Francesco Nocera and Andrea Longhitano
Sustainability 2025, 17(5), 2213; https://doi.org/10.3390/su17052213 - 4 Mar 2025
Viewed by 183
Abstract
Improving microclimate conditions is a pivotal aspect of urban design, particularly in hot, arid climates, where it directly influences outdoor comfort, mitigates the urban heat island (UHI) effect, and reduces the indoor cooling energy demand. The objective of this study is to quantitatively [...] Read more.
Improving microclimate conditions is a pivotal aspect of urban design, particularly in hot, arid climates, where it directly influences outdoor comfort, mitigates the urban heat island (UHI) effect, and reduces the indoor cooling energy demand. The objective of this study is to quantitatively assess the impacts of neighborhoods’ urban size when combined with compact streets’ geometry regarding the outdoor thermal comfort generated in a typical vernacular settlement of the Saharan region of Algeria. The Ksar of Al-Atteuf in the city of Ghardaïa is taken as a case study. The related interior thermal conditions of buildings assumed to be potentially affected by the urban morphology are also examined. To study the effectiveness of the two urban morphology parameters (i.e., urban size and compactness) on outdoor and indoor thermal conditions, a mixed methods approach was adopted, integrating in situ climatic measurements and dynamic simulations. Indoor temperatures were examined in a traditional house located in the core of the Ksar. Year-round operative temperature (OT) simulations were achieved using the Ladybug tool within Grasshopper, and they were complemented by the Universal Thermal Climate Index (UTCI) values calculated during peak hot and cold weeks. Furthermore, a parametric analysis was conducted, focusing on the thermal performance of the compact urban fabric by varying progressively the neighborhood sizes from 20 m, 40 m, and 60 m. The results indicate stable indoor thermal conditions across the monitored residential building, which suggests that the architectural envelope is closely affected by its immediate surroundings. On the other hand, the UTCI analysis revealed significant differences in outdoor thermal comfort since the larger urban area provides better mitigation of heat stress in summer and cold stress in winter, the improved outdoor thermal conditions generated at the neighborhood level, being proportional to the size of the urban area. The findings underscore the value of compact urban fabrics in creating climate-responsive built environments and provide further insights into sustainable urban planning and energy-efficient design practices in hot, arid regions. Full article
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<p>Map of Algeria indicating the primary climatic zones as per the Köppen–Geiger climate classification and the situation of the city of Ghardaïa in the BWh climatic zone.</p>
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<p>The M’Zab Valley and its five ksour [<a href="#B33-sustainability-17-02213" class="html-bibr">33</a>].</p>
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<p>View of Ksar El-Atteuf dominated by the quadrangular minaret of its mosque.</p>
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<p>Location of the case study dwelling in Ksar El-Atteuf.</p>
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<p>Architectural details of the Case study dwelling. (<b>a</b>) Ground floor plan, (<b>b</b>) first-floor plan, (<b>c</b>) section, (<b>d</b>) view on the Ammas Enteddar (courtyard), (<b>e</b>) view on the Innayen (kitchen), and (<b>f</b>) Chebek (top opening) and Ikomar (gallerie).</p>
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<p>Construction detail showing the roofing system layers, the load-bearing wall, and the local materials used in the case study dwelling.</p>
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<p>(<b>1</b>) The thermo-hygrometer “Testo 480” device; (<b>2</b>) surface temperature sensor; (<b>3</b>) air temperature and relative humidity sensor.</p>
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<p>Location of the measurement point.</p>
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<p>Flowchart of the simulation process for predicting OT (<b>top</b>) and UTCI (<b>bottom</b>).</p>
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<p>UTCI stress category scale [<a href="#B52-sustainability-17-02213" class="html-bibr">52</a>].</p>
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<p>Simulation workflow for thermal indoor conditions using Grasshopper, Ladybug, and Honeybee tools.</p>
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<p>Grasshopper models of the three neighborhood zones.</p>
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<p>Grasshopper model of the case study dwelling assumed to be a standing-alone building.</p>
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<p>Correlation between simulated and measured operative temperatures during summer.</p>
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<p>Correlation between simulated and measured operative temperatures during winter.</p>
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<p>Distribution of the Relative Error (RE) for the 9 to 11 of August 2021.</p>
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<p>Distribution of the Relative Error (RE) for 10, 11, and 12 of January 2022.</p>
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<p>Urban area size variations. (<b>A</b>) The base-case building is self-standing; the neighborhood size is 20 m. (<b>B</b>) The base-case building is at the center of an urban area surrounding it by 20 m. (<b>C</b>) The urban area size of the neighborhood has been extended to 40 m. (<b>D</b>) The urban area size has been extended to 60 m.</p>
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<p>Comparative results of operative temperatures (OT) simulations for 20 m, 40 m, and 60 m urban size variations on 26 AUG at 18:00.</p>
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<p>UTCI simulation results for extremely hot week (20–26 July).</p>
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<p>UTCI simulation results for extreme cold week (6–12 January).</p>
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<p>UTCI values according to urban sizes’ variations during Summer.</p>
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<p>UTCI values according to urban sizes’ variations during winter.</p>
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29 pages, 20747 KiB  
Article
Research on Plant Landscape Design of Urban Industrial Site Green Space Based on Green Infrastructure Concept
by Jiahui Ai and Myun Kim
Plants 2025, 14(5), 747; https://doi.org/10.3390/plants14050747 - 1 Mar 2025
Viewed by 258
Abstract
With the acceleration of the global urbanization process, more and more industrial plants are being abandoned, which puts great pressure on urban ecology and land resource management. These abandoned industrial spaces not only lead to persistent pollution problems, but also exacerbate the urban [...] Read more.
With the acceleration of the global urbanization process, more and more industrial plants are being abandoned, which puts great pressure on urban ecology and land resource management. These abandoned industrial spaces not only lead to persistent pollution problems, but also exacerbate the urban heat island effect, leading to a worsening microclimate. To address these issues, the concept of green infrastructure (GI) has emerged as a sustainable ecological restoration strategy, and it is an important tool for urban renewal and industrial land transformation. In this study, the landscape environment of the industrial site of Henrichshutte in Germany and the surrounding industrial plant was taken as an example, and ecological restoration and plant landscape design were carried out using the GI concept. Two climate simulation tools, ENVI-met and WindPerfect DX, were comprehensively adopted to simulate the environment of the site in detail. Based on an analysis of the potential temperature, PMV, wind speed, and UTCI data of the site, it was demonstrated that the plant landscape improved the microclimate of the industrial plant. The results show that the reasonable allocation of plants can effectively reduce surface temperature and building temperature, increase air humidity, alleviate the local heat island effect, and enhance the thermal comfort of the human body. The simulation results highlight the practical application value of the GI concept in improving the ecological benefit, social function, and landscape aesthetics of industrial land. This study provides a new idea for the ecological restoration and environmental optimization of urban industrial land through the combination of green infrastructure and plant landscape design, and emphasizes the important role of green infrastructure in alleviating the urban heat island effect and promoting the sustainable development of urban landscape spaces. Full article
(This article belongs to the Section Horticultural Science and Ornamental Plants)
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<p>Plant landscape analysis of Nordstern Park.</p>
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<p>Plant landscape analysis of Westerpark.</p>
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<p>Plant landscape analysis of Millennium Park.</p>
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<p>Plant landscape analysis of Buttes–Chaumont Park.</p>
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<p>Average high and low temperatures in August in Hattingen.</p>
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<p>Wind speed on August 1 in Hattingen.</p>
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<p>Wind direction on August 1 in Hattingen.</p>
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<p>Location and current status of Henrichshutte.</p>
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<p>Potential temperature simulation results. Time: 0:00–10:00. (The white shape represents the building).</p>
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<p>Potential temperature simulation results. Time: 12:00–22:00. (The white shape represents the building).</p>
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<p>PMV indicator data results. Time: 0:00–10:00. (The white shape represents the building).</p>
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<p>PMV indicator data results. Time: 12:00–22:00. (The white shape represents the building).</p>
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<p>Wind speed distribution. (The white shape represents the building).</p>
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<p>UTCI. (The white shape represents the building).</p>
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<p>Plant landscape design plan.</p>
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<p>Plant species analysis.</p>
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<p>Plant hierarchy analysis.</p>
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<p>Before-and-after comparison of plant landscape design.</p>
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<p>Potential temperature simulation results. Time: 00:00–10:00 (After plant landscape design).</p>
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<p>Potential temperature simulation results. Time: 12:00–22:00 (After plant landscape design).</p>
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<p>PMV indicator data results. Time: 00:00–10:00 (After plant landscape design).</p>
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<p>PMV indicator data results. Time: 12:00–22:00 (After plant landscape design).</p>
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<p>Wind speed distribution (After plant landscape design).</p>
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<p>UTCI (After plant landscape design).</p>
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26 pages, 26207 KiB  
Article
Micro-Renovation Method of Old Residential Areas Based on Parametric Energy Simulation: An Aging Community in Middle China as an Example
by Ziyun Ye, Shulin Ouyang, Xiong Gao and Yaming Ren
Buildings 2025, 15(5), 758; https://doi.org/10.3390/buildings15050758 - 25 Feb 2025
Viewed by 292
Abstract
In the context of urbanization, the renewal of old communities constitutes a crucial aspect for enhancing the living environment. Currently, the social benefits and utilization efficiency achieved through the renewal of such neighborhoods fall short of meeting the demands of society, urban management, [...] Read more.
In the context of urbanization, the renewal of old communities constitutes a crucial aspect for enhancing the living environment. Currently, the social benefits and utilization efficiency achieved through the renewal of such neighborhoods fall short of meeting the demands of society, urban management, and residents. Existing research on old community renewal predominantly centers on environmental beautification and infrastructure augmentation or relies on diverse data for interpreting and analyzing the current state of these spaces while overlooking in-depth investigations into the design methodologies for public spaces within old communities. This paper presents a comprehensive strategy, termed “Demands–Design–Verify (DDV)”. This paper selected an old community in Changsha City (China) as a case study and used the following: (1) a questionnaire investigation and observation (Demands); (2) parameterized simulation data and UTCI energy data put forward for the optimization strategy of aging community public space (Design); (3) space syntax, physical environmental simulation data, and a one-way analysis of variance to validate the effectiveness of the design (Verify). The research results indicate that the public space renewal outcomes of old-aged communities under the guidance of this method have positive effects in aspects such as solar radiation, thermal comfort, spatial accessibility, and pedestrian flow. The method exhibits universality and ease of implementation for public space renewal in old communities, thus providing scientific methodological guidance for urban residential renewal. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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<p>Parametric energy simulation application research status. (<b>a</b>): Keyword co-occurrence network (<b>b</b>): Keyword occurrence frequency and connection strength ranking.</p>
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<p>Design method flow chart.</p>
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<p>Project floor plan.</p>
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<p>Results of the questionnaire survey of community residents. (<b>a</b>): The frequency of daily activities (<b>b</b>): The missing space in the community (<b>c</b>): The importance ranking of leisure space.</p>
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<p>Live map of representative nodes in the venue.</p>
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<p>Design flow chart.</p>
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<p>Original data (<b>a</b>) General diagram of winter photothermal environment, (<b>b</b>) node diagram of winter photothermal environment, (<b>c</b>) general diagram of summer photothermal environment, and (<b>d</b>) node diagram of summer photothermal environment.</p>
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<p>Original Data (<b>a</b>) Winter thermal comfort map, (<b>b</b>) winter node thermal comfort map, (<b>c</b>) summer thermal comfort map, (<b>d</b>) summer node thermal comfort map, and (<b>e</b>) visualization data of thermal comfort value for each node throughout the year.</p>
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<p>(<b>a</b>) Dry bulb temperature, (<b>b</b>) dew point temperature, (<b>c</b>) wind speed, (<b>d</b>) wind direction, (<b>e</b>) relative humidity, (<b>f</b>) atmospheric station pressure, and (<b>g</b>) ground temperature data lake visualization.</p>
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<p>Master plan.</p>
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<p>(<b>a</b>) Axis strength and (<b>b</b>) field of view analysis.</p>
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<p>Agents flow analysis of site (<b>a</b>) at the original and (<b>b</b>) after design.</p>
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<p>After design Data (<b>a</b>) General diagram of winter photothermal environment, (<b>b</b>) node diagram of winter photothermal environment, (<b>c</b>) general diagram of summer photothermal environment, and (<b>d</b>) node diagram of summer photothermal environment.</p>
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<p>(<b>a</b>) Winter thermal comfort map, (<b>b</b>) winter node thermal comfort map, (<b>c</b>) summer thermal comfort map, (<b>d</b>) summer node thermal comfort map.</p>
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22 pages, 9224 KiB  
Article
Street Geometry Factors Influencing Outdoor Pedestrian Thermal Comfort in a Historic District
by Bin Lai, Jian-Ming Fu, Cheng-Kai Guo, Dan-Yin Zhang and Zhi-Gang Wu
Buildings 2025, 15(4), 613; https://doi.org/10.3390/buildings15040613 - 17 Feb 2025
Viewed by 163
Abstract
As China’s urbanization progresses, the urban heat island (UHI) effect has become more pronounced, impacting the health of residents and the activity intentions of visitors within historic urban areas. This study focuses on the historic district of the Three Lanes and Seven Alleys [...] Read more.
As China’s urbanization progresses, the urban heat island (UHI) effect has become more pronounced, impacting the health of residents and the activity intentions of visitors within historic urban areas. This study focuses on the historic district of the Three Lanes and Seven Alleys Tourist Area (SFQX) in Fuzhou, where simulations were conducted on four representative streets across various times during a typical summer meteorological day. Typological methods were employed to simplify neighborhood modeling, and Phoenics software was utilized to simulate the neighborhood’s wind environment and the outdoor pedestrian thermal comfort index. Aspect ratio (AR), sky view factor (SVF), air velocity (Va), and universal thermal climate index (UTCI) values at specific locations were collected for statistical analysis. The findings reveal that: (1) the N–S orientation exhibits more significant correlations between Va, the UTCI, and street geometry compared to the E–W orientation; (2) the relationship between SVF and the UTCI fluctuates with time; (3) areas with higher AR values, such as medium and deep canyons, offer better thermal comfort for outdoor pedestrians; and (4) at 8:00, the UTCI and wind speed show minimal correlations with street geometry and direction, being predominantly influenced by objective climatic factors. These insights are expected to significantly inform the geometric design and planning of streets in Fuzhou’s historic districts, aiming to create more comfortable outdoor environments for inhabitants and visitors alike. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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<p>Research methods.</p>
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<p>Selection of streets, locations, and building types in “SFQX”. (<b>a</b>) Street selection and location setup. (<b>b</b>) Types of buildings in “SFQX”.</p>
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<p>Selection of streets, locations, and building types in “SFQX”. (<b>a</b>) Street selection and location setup. (<b>b</b>) Types of buildings in “SFQX”.</p>
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<p>Simulated and field-measured SVF values.</p>
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<p>Street canyon classification diagram.</p>
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<p>Geometric characteristics of north–south streets.</p>
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<p>Geometric characteristics of east–west streets.</p>
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<p>Street wind speed maps for different time periods.</p>
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<p>Street wind speed line chart.</p>
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<p>The UTCI graphs for different time periods.</p>
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<p>Street UTCI line chart.</p>
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<p>Full indicator calendar chart.</p>
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<p>Scatter plot of all indicators. (<b>a</b>) N–S Orientation. (<b>b</b>) E–W Orientation.</p>
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28 pages, 12150 KiB  
Article
Cooling Heritage Scenarios: Transforming Historic Squares for Thermal Comfort
by Pegah Rezaie, Victoria Patricia Lopez-Cabeza, Javier Sola-Caraballo and Carmen Galan-Marin
Buildings 2025, 15(4), 564; https://doi.org/10.3390/buildings15040564 - 12 Feb 2025
Viewed by 421
Abstract
Urban squares in historic neighborhoods are vital public spaces, often the only nearby option available for an aging population. However, these spaces face increasing thermal discomfort exacerbated by urban heat island (UHI) effects. This research focuses on improving thermal comfort for two case [...] Read more.
Urban squares in historic neighborhoods are vital public spaces, often the only nearby option available for an aging population. However, these spaces face increasing thermal discomfort exacerbated by urban heat island (UHI) effects. This research focuses on improving thermal comfort for two case studies located in Seville’s high-density and historically rich Casco Antiguo neighborhood. Although their significance and social value make them central meeting points for locals and visitors, these squares face major challenges regarding thermal comfort, mainly due to a lack of greenery or adequate shading. This study examines the conditions by conducting in-person monitoring and simulations, identifying factors contributing to discomfort. On the basis of this, the research proposes mitigation strategies to address these issues. These solutions include the installation of green walls, the addition of canopies, and the application of specific surface materials to improve the conditions of these squares. Canopies provided the most significant cooling, reducing universal thermal climate index (UTCI) values by up to 6.5 °C. Green walls delivered localized cooling, lowering the mean radiant temperature (MRT) by up to 5 °C. The results reveal how these approaches can bring about changes in thermal comfort in a way that benefits historic city environments. Full article
(This article belongs to the Special Issue Climate-Responsive Architectural and Urban Design)
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<p>Methodology flowchart.</p>
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<p>Casco Antiguo neighborhood and San Julian Square (SJS) and Cristo de Burgos Square (CBS) locations.</p>
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<p>General view of SJS.</p>
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<p>Central perspective of CBS.</p>
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<p>Selected measurement points in SJS and CBS.</p>
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<p>Selected analysis points in SJS and CBS.</p>
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<p>Mitigation strategies proposal for San Julian Square.</p>
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<p>Mitigation strategies proposal for Cristo de Burgos Square.</p>
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<p>The monitoring results of the case studies: (<b>a</b>) the SJS air temperature and relative humidity were measured, and the MRT and UTCI were calculated in 9 locations. (<b>b</b>) The CBS air temperature and relative humidity were measured, and the MRT and UTCI were calculated in 10 locations.</p>
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<p>The UTCI results of SJS-CSCS in selected hours.</p>
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<p>Simulation hourly results at the selected points in SJS-CSCS.</p>
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<p>The absolute difference in climatic variables of the mitigation scenario at four selected hours.</p>
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<p>The UTCI results of CBS in the selected hours.</p>
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<p>Simulation of hourly MRT and UTCI results at the selected points in CBS-CS.</p>
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<p>The absolute difference in climatic variables of the CBS mitigation scenario at four selected hours.</p>
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<p>The absolute difference in climatic variables of the CBS mitigation scenario at four selected hours.</p>
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15 pages, 25108 KiB  
Article
The Impact of the Small Urban Green Space on the Urban Thermal Environment: The Belgrade Case Study (Serbia)
by Snežana Kecman, Nadežda Stojanović, Milena Vukmirović, Nevena Vasiljević, Ivana Bjedov and Dragana Vujović
Forests 2025, 16(2), 321; https://doi.org/10.3390/f16020321 - 12 Feb 2025
Viewed by 482
Abstract
Small green spaces are the most common type of greenery in cities, but very little is known about their impact on thermal comfort. It has been established that larger green spaces (large city parks, urban forests, etc.) have a significant effect on the [...] Read more.
Small green spaces are the most common type of greenery in cities, but very little is known about their impact on thermal comfort. It has been established that larger green spaces (large city parks, urban forests, etc.) have a significant effect on the formation of thermal comfort in cities. Conversely, it has been shown that this effect is highly variable in smaller green spaces (particularly those <3 ha). This study investigated the impact of smaller green spaces (<3 ha) of various categories (parks, squares, and street tree lines) on the thermal comfort of urban open spaces. In total, 18 green spaces in Belgrade were selected, where specialised meteorological measurements were conducted during summer and winter, and the PET index and UTCI were calculated using the RayMan Pro (Version 3.1 Beta) software. Research has shown that green spaces ranging from 0.9 to 3 ha have an average difference of 4.04 °C in the PET index and 3.27 °C in the UTCI. For areas between 0.3 and 0.9 ha, the differences are 2.32 °C for PET and 2.05 °C for UTCI, while for spaces <0.3 ha, the differences are 2.19 °C for PET and 2.12 °C for UTCI. In all cases, the values of the PET index and UTCI were higher in green spaces compared to areas without greenery, with differences ranging from 2.19 to 4.04 °C for PET and 2.05–3.27 °C for UTCI. It was determined that green spaces <3 ha increased the PET index by an average of 2.75 °C and the UTCI by 2.41 °C. The results of this study showed that despite their size, small green areas can significantly improve thermal comfort. This study highlights the importance of these green spaces and provides a basis for the planning of new or renovated existing urban green spaces to mitigate the effects of climate change in cities. Full article
(This article belongs to the Section Urban Forestry)
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<p>Location of the study area (map modified from the General Regulation Plan of the Green Areas System of Belgrade [<a href="#B36-forests-16-00321" class="html-bibr">36</a>]).</p>
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<p>Graphical review of the differences in the PET thermal comfort index for the analysed areas of green spaces.</p>
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<p>Graphical review of the differences in the UTCI for the analysed areas of green spaces.</p>
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28 pages, 14496 KiB  
Article
Intelligent Optimization Pathway and Impact Mechanism of Age-Friendly Neighborhood Spatial Environment Driven by NSGA-II and XGBoost
by Lu Zhang, Zizhuo Qi, Xin Yang and Ling Jiang
Appl. Sci. 2025, 15(3), 1449; https://doi.org/10.3390/app15031449 - 31 Jan 2025
Viewed by 419
Abstract
A comfortable outdoor environment, like its indoor counterpart, can significantly enhance the quality of life and improve the physical and mental health of elderly populations. Urban spatial morphology is one of the key factors influencing outdoor environmental performance. To explore the interactions between [...] Read more.
A comfortable outdoor environment, like its indoor counterpart, can significantly enhance the quality of life and improve the physical and mental health of elderly populations. Urban spatial morphology is one of the key factors influencing outdoor environmental performance. To explore the interactions between urban spatial morphology and the outdoor environment for the elderly, this study utilized parametric tools to establish a performance-driven workflow based on a “morphology generation–performance evaluation–morphology optimization” framework. Using survey data from 340 elderly neighborhoods in Beijing, a parametric urban morphology generation model was constructed. The following three optimization objectives were set: maximizing the winter pedestrian Universal Thermal Climate Index (UTCI), minimizing the summer pedestrian UTCI, and maximizing sunlight hours. Multi-objective optimization was conducted using a genetic algorithm, generating a “morphology–performance” dataset. Subsequently, the XGBoost (eXtreme Gradient Boosting) and SHAP (Shapley Additive Explanations) explainable machine learning algorithms were applied to uncover the nonlinear relationships among variables. The results indicate that optimizing spatial morphology significantly enhances environmental performance. For the summer elderly UTCI, the contributing morphological indicators include the Shape Coefficient (SC), Standard Deviation of Building Area (SA), and Deviation of Building Volume (SV), while the inhibitory indicators include the average building height (AH), Average Building Volume (AV), Mean Building Area (MA), and floor–area ratio (FAR). For the winter elderly UTCI, the contributing indicators include the AH, Volume–Area Ratio (VAR), and FAR, while the inhibitory indicators include the SC and porosity (PO). The morphological indicators contributing to sunlight hours are not clearly identified in the model, but the inhibitory indicators for sunlight hours include the AH, MA, and FAR. This study identifies the morphological indicators influencing environmental performance and provides early-stage design strategies for age-friendly neighborhood layouts, reducing the cost of later-stage environmental performance optimization. Full article
(This article belongs to the Section Applied Physics General)
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<p>Workflow of this study. (In the figure, Y/N indicates whether the generated result meets the constraints. If it is Y (yes), the generated result will proceed to the next step along the solid line. If it is N (no), the generated result will return to the previous step along the dotted line and enter a loop).</p>
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<p>Study area and distribution of survey points.</p>
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<p>Building type simplification with specific modeling parameters. (The figure shows the information of nine different types of buildings, including axonometric drawings (on the left), floor plans (on the upper right), and elevation drawings (on the lower right)).</p>
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<p>Neighborhood generation process.</p>
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<p>Visualization of morphological indicators: definitions and calculation methods.</p>
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<p>UTCI calculation flowchart. The arrows in the figure represent the simulation and calculation process of UTCI values using basic climate data.</p>
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<p>Standard deviations of objectives and average values of Pareto solution sets during the optimization process. (<b>a</b>) Standard deviation of objectives in the optimization process for UTCI-S. (<b>b</b>) Standard deviation of objectives in the optimization process for UTCI-W. (<b>c</b>) Standard deviation of objectives in the optimization process for SH. (<b>d</b>) Average values of Pareto solution sets for UTCI-S. (<b>e</b>) Average values of Pareto solution sets for UTCI-W. (<b>f</b>) Average values of Pareto solution sets for SH.</p>
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<p>Boxplots of data distribution for feasible solutions and Pareto solutions. (<b>a</b>) Data distribution for feasible solutions and Pareto solutions of UTCI-S. (<b>b</b>) Data distribution for feasible solutions and Pareto solutions of UTCI-W. (<b>c</b>) Data distribution for feasible solutions and Pareto solutions of SHs. The points in the figure represent the discrete data points in the three - group data, and the lines are the markers of the median.</p>
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<p>Three-dimensional (3D) bar charts of spatial morphology optimization results. (<b>a</b>) Data distribution of feasible solutions in the design space. (<b>b</b>) Data distribution of Pareto solutions optimized by the algorithm.</p>
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<p>Interpretability analysis of the UTCI-S model using explainable machine learning. (<b>a</b>) SHAP value bar plot. (<b>b</b>) SHAP summary plot. The black line in the figure is a reference line for a SHAP value of 0. The features corresponding to the points on the left of the line have negative SHAP values, while the features corresponding to the points on the right of the line have positive SHAP values.</p>
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<p>Interpretability analysis of the UTCI-W model using explainable machine learning. (<b>a</b>) SHAP value bar plot. (<b>b</b>) SHAP summary plot.</p>
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<p>Interpretability analysis of the SH model using explainable machine learning. (<b>a</b>) SHAP value bar plot. (<b>b</b>) SHAP summary plot.</p>
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18 pages, 4741 KiB  
Article
Construction of Evaluation Index System for Suitability of Sunshine and Thermal Environment of Public Activity Space for the Elderly with Young Children
by Minmin Yang and Yu Liu
Buildings 2025, 15(1), 13; https://doi.org/10.3390/buildings15010013 - 24 Dec 2024
Viewed by 595
Abstract
With the aging of China’s population and the liberalization of the birth policy, the country’s social population structure has changed, and the public activity spaces of residential areas are expected to meet the new needs created by such change. According to the existing [...] Read more.
With the aging of China’s population and the liberalization of the birth policy, the country’s social population structure has changed, and the public activity spaces of residential areas are expected to meet the new needs created by such change. According to the existing literature, the most common population group in the public activity spaces in China’s current residential areas consists of the elderly with young children. However, how to evaluate the suitability of these spaces from the perspective of sunshine and thermal environments remains unclear. To fill this research gap, this article takes Xi’an as a case to explore the construction of an evaluation index system for sunshine and thermal environment suitability in the public activity spaces of residential areas for the elderly with young children. Firstly, based on field research and literature review, four initial indices were established, including sunshine demand hours, ultraviolet intensity, the universal thermal climate index (UTCI), and the wet bulb globe temperature index (WBGT). Secondly, on the basis of field research and expert interviews, these indices were revised and finalized, mainly with regard to the sunshine and thermal environment suitability for the elderly with young children in the urban residential area, so as to make the indices more in line with the actual research problems. Thirdly, a scoring method for each of the finalized indexes was proposed, resulting in a comprehensive evaluation index system. Finally, the suitability and operability of the constructed index system were validated through practical application in a sample residential area. The results show that, based on some known indices, the evaluation index system of the sunshine and thermal environment suitability of public activity space for the elderly with young children can be established. Its application is potentially helpful in making the public activity space environment in existing residential areas in China more adaptable to the needs of the elderly with young children. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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<p>Typical meteorological annual mean temperature in Xi’an City, China (Source network).</p>
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<p>Typical meteorological annual average humidity in Xi’an, China (Source network).</p>
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<p>Typical meteorological annual average rainfall in Xi’an, China (Source network).</p>
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<p>Typical meteorological annual average rainfall days in Xi’an, China (Source network).</p>
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<p>Typical meteorological average annual sunshine duration/average sunshine duration (Source network).</p>
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<p>Typical annual mean UV index (Source network).</p>
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<p>Schematic diagram of residential area demarcation (source: author’s own drawing).</p>
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21 pages, 4819 KiB  
Article
Analyzing the Cooling Effects of Water Facilities in Urban Park: The Case of Sangju Namsan Park, South Korea
by Young-Shin Lim, Hyunmin Daniel Zoh, Tae Hyoung Kim and Tae Kyung Kwon
Atmosphere 2024, 15(12), 1456; https://doi.org/10.3390/atmos15121456 - 5 Dec 2024
Viewed by 772
Abstract
This study evaluates the cooling effects of small-scale water features and fog systems in Sangju Namsan Park, South Korea, focusing on their impact on thermal comfort. While previous studies have demonstrated the potential of urban parks in reducing temperatures, studies on small-scale interventions [...] Read more.
This study evaluates the cooling effects of small-scale water features and fog systems in Sangju Namsan Park, South Korea, focusing on their impact on thermal comfort. While previous studies have demonstrated the potential of urban parks in reducing temperatures, studies on small-scale interventions that examine their effects on thermal comfort and analyze microclimate data collected in specific areas are limited. This study collected and analyzed microclimate data using the Universal Thermal Climate Index (UTCI) and physiological equivalent temperature (PET) to assess the effectiveness of a small water path and a cooling fog system. The results indicate that surface temperature reductions reached up to 1.1 °C, with the pergola area showing the most significant cooling effect, lowering PET values to an average of 36.2 °C. In contrast, the small water path recorded the highest PET values, peaking at nearly 50.2 °C, likely due to radiant heat from the surrounding surfaces. While these interventions provided localized cooling, their overall effect on urban temperature reduction remained modest. This study suggests that small-scale water features are effective in enhancing thermal comfort in neighborhood parks but must be integrated into broader urban cooling strategies to maximize their impact. Full article
(This article belongs to the Section Biometeorology and Bioclimatology)
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<p>Locations for measuring devices and aerial view.</p>
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<p>(<b>a</b>) View of smart park from north; (<b>b</b>) view of smart park from south side; (<b>c</b>) park central zone ④; (<b>d</b>) water path ③; and (<b>e</b>) pergola ⑤(with cooling fog). AWS Location: ③④⑤.</p>
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<p>(<b>a</b>) Park central zone; (<b>b</b>) small water path; (<b>c</b>) pergola; and (<b>d</b>) overlook of small water path.</p>
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<p>(<b>a</b>) Comparison of hourly air temperature measurements; and (<b>b</b>) comparison of hourly humidity measurements.</p>
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<p>Comparison of hourly UTCI values during 26–27 August.</p>
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<p>Comparison of hourly air temperature during 26–27 August.</p>
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<p>Comparison of hourly PET values during 26–27 August.</p>
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<p>(<b>a</b>) Sangju daily park usage (9 August–7 September); (<b>b</b>) Sangju hourly park usage (9 August–7 September).</p>
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<p>Correlation between the number of observed people and measured climate data: (<b>left</b>) temperature; (<b>middle</b>) humidity; and (<b>right</b>) solar and UVI.</p>
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29 pages, 16013 KiB  
Article
Multi-Objective Optimization of Outdoor Thermal Comfort and Sunlight Hours in Elderly Residential Areas: A Case Study of Beijing, China
by Hainan Yan, Lu Zhang, Xinyang Ding, Zhaoye Zhang, Zizhuo Qi, Ling Jiang and Deqing Bu
Buildings 2024, 14(12), 3770; https://doi.org/10.3390/buildings14123770 - 26 Nov 2024
Cited by 1 | Viewed by 887
Abstract
To optimize the outdoor thermal comfort and sunlight hours of elderly residential areas in cold regions of China, we collected data on streets and building forms from 121 elderly residential sites in Beijing. Utilizing parametric modeling tools to generate ideal residential models, a [...] Read more.
To optimize the outdoor thermal comfort and sunlight hours of elderly residential areas in cold regions of China, we collected data on streets and building forms from 121 elderly residential sites in Beijing. Utilizing parametric modeling tools to generate ideal residential models, a multi-objective optimization algorithm was applied to identify 144 Pareto solutions. The optimal solutions were analyzed using K-means clustering and Pearson correlation analysis to examine how block form affects outdoor environmental performance. The universal thermal climate index (UTCI) in summer showed significant positive correlations (r > 0.72) with the distance between buildings (DB), building density (BD), shape coefficient (SC), and coefficient of variation for building height (CVH), and significant negative correlations (r < ?0.82) with average building height (AH), floor area ratio (FAR), volume area ratio (VAR), mean building area (MA), average building volume (AV), and open space ratio (OSR). Winter UTCI was significantly positively correlated with AH, FAR, VAR, MA, and AV (r > 0.83) and significantly negatively correlated with DB, porosity (PO), SC, and CVH (r < ?0.88). Sunlight hours were significantly positively correlated with DB, PO, OSR, and CVH (r > 0.84) and significantly negatively correlated with AH, BD, FAR, SC, VAR, MA, and AV (r > 0.88). Courtyard and point-building configurations performed the best across all optimization objectives. (The value of r, Pearson’s correlation coefficient, ranges from ?1 to +1. r = +1: Perfect positive correlation, r = ?1: Perfect negative correlation, r = 0: No linear correlation). Full article
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
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<p>MOO framework used in this study to design block forms.</p>
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<p>Location of the study area and investigated residential blocks for the elderly.</p>
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<p>Nine simplified building types extracted from senior residential areas in Beijing.</p>
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<p>Abstraction and calculation of block feature parameters in this study.</p>
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<p>Illustration of block form scenario generation.</p>
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<p>(<b>a</b>) UTCI calculation process overview and (<b>b</b>) the boundaries of the virtual wind tunnel.</p>
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<p>Flowchart of the NSGA-II algorithm.</p>
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<p>(<b>a</b>–<b>c</b>)Trends of change in different objectives in the optimization process: (<b>a</b>) UTCI-S, (<b>b</b>) UTCI-W, and (<b>c</b>) SH. (<b>d</b>–<b>f</b>) Visualization of the objective values of the Pareto solution set: (<b>d</b>) UTCI-S, (<b>e</b>) UTCI-W, and (<b>f</b>) SH.</p>
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<p>Performance comparative analysis of Pareto solutions: (<b>a</b>) UTCI-S, (<b>b</b>) UTCI-W, and (<b>c</b>) SH.</p>
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<p>Trends for different building types. (<b>a</b>) The distribution frequency of the building types in each land unit for the feasible solutions. (<b>b</b>) Further insight into the preferences of the optimization algorithm and building layout characteristics of the Pareto optimal solutions.</p>
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<p>Heatmap of Pearson correlation coefficients between block form feature parameters and environmental performance parameters.</p>
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<p>Elbow method for determining the optimal number of clusters.</p>
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<p>Clustering distributions of Pareto solutions. (<b>a</b>) Three-dimensional view of the clustering, with black lines connecting the centroids of each cluster. The green, yellow, and red colors represent the three clusters, showing the distribution of each solution set in three-dimensional space. (<b>b</b>) Distribution of SH and UTCI-W values in the three clusters, (<b>c</b>) Distribution of UTCI-S and SH values in the three clusters, (<b>d</b>) Distribution of UTCI-S and UTCI-W values in the three clusters.</p>
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<p>Box plots of UTCI-S, UTCI-W, and SH indices for the three clusters. (<b>a</b>) Performance and distribution of the three clusters in the UTCI-S index; (<b>b</b>) performance and distribution of the three clusters in the UTCI-W index; (<b>c</b>) performance and distribution of the three clusters in the SH index.</p>
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<p>Feasible urban design solutions for three clusters based on the Pareto optimal solutions, including block form features for each design.</p>
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16 pages, 4194 KiB  
Article
The Influence of the Spatial Morphology of Township Streets on Summer Microclimate and Thermal Comfort
by Wanqi Zhao, Qingtao Hu and Anhong Bao
Buildings 2024, 14(11), 3616; https://doi.org/10.3390/buildings14113616 - 14 Nov 2024
Viewed by 630
Abstract
Slow progress has been made on the study of thermal comfort studies in rural streets. The street construction lacks a corresponding theoretical basis, and the difference between city streets and township streets leads to the situation that the increased focus on improving the [...] Read more.
Slow progress has been made on the study of thermal comfort studies in rural streets. The street construction lacks a corresponding theoretical basis, and the difference between city streets and township streets leads to the situation that the increased focus on improving the thermal comfort of city streets has not been effectively transferred to township construction. Therefore, this paper takes Huilongba Village as the research object, researching the mechanisms by which the spatial pattern of township streets influences the microclimate. This paper defines the spatial morphology of township streets by three indexes: the street aspect ratio, building density, and staggered arrangement of buildings. Additionally, it analyzes the microclimate influences of spatial morphology changes on township streets, verifies the validity of the ENVI-met model through field measurements, and designs a three-factor orthogonal experiment. With the help of software simulation, allowing for an investigation of the effects of indicators and their interactions on pedestrian thermal comfort, the optimal street spatial pattern construction scheme is proposed. The results show that the greater the density of street buildings, the more obvious the cooling effect and the better the comfort; in the staggered arrangement of buildings, the higher the high point of the building is to the south, the lower the overall temperature of the street and the better the cooling effect; and the larger the aspect ratio of the street, the better the cooling effect. Through orthogonal test and ANOVA, we can obtain the relationship between the contribution of each index to air temperature and the Universal Thermal Climate Index (UTCI) as street aspect ratio > building density > staggered building arrangement, and the overall thermal comfort of the street is the best when the aspect ratio of the street building is 1.5, the density of the building is 100%, and the south side of the building is higher. This study can provide a basis for rural street construction and thermal comfort retrofitting. Full article
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
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<p>Examples of townships and streets in Beibei District, Chongqing.</p>
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<p>ENVI met modeling rendering and monitoring points.</p>
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<p>Figures (<b>a</b>,<b>b</b>) show, respectively, the comparison between the simulated and measured values of temperature and humidity.</p>
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<p>Figures (<b>a</b>,<b>b</b>) show the temperature difference between streets with different building densities compared to D2 streets, and the temperature difference between streets with different staggered rows, respectively. Note: ∆T<sub>D1</sub> and ∆T<sub>D3</sub> are the temperature differences between the D1 and D3 scenarios and the D2 scenario, respectively; ∆T<sub>S1</sub>, ∆T<sub>S2</sub>, and ∆T<sub>S3</sub> are the temperature differences between the S1, S2, and S3 scenarios and the D2 scenario, respectively.</p>
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<p>Figures (<b>a</b>–<b>c</b>) show the UTCI clouds at 13:00 for three different building density scenarios, D3, D2, and D1, with building densities of 75%, 87%, and 100%, respectively, and Figure (<b>d</b>) shows the daytime UTCI curves for the three different building density scenarios.</p>
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<p>Figures (<b>a</b>–<b>c</b>) show the UTCI clouds at 13:00 when the high point of the complex is located at the north, south, and center, respectively; and Figure (<b>d</b>) shows the daytime UTCI curves when the high point of the complex is located at the north, south, and center, respectively.</p>
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<p>Figure (<b>a</b>) represents the temperature difference between streets with different street aspect ratios compared to D2 streets, and Figure (<b>b</b>) represents the average value of each water of the factor. Note: ∆T<sub>A1</sub>, ∆T<sub>A2</sub>, and ∆T<sub>A3</sub> are the temperature difference between A1, A2, and A3 and D2, respectively.</p>
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<p>Figures (<b>a</b>–<b>c</b>) show the UTCI clouds at 13:00 for three different aspect ratios, A1, A2, and A3, with aspect ratios of 0.5, 1, and 1.5, respectively, and Figure (<b>d</b>) shows the daytime UTCI curves for the three different aspect ratios.</p>
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31 pages, 14508 KiB  
Article
Optimizing Outdoor Thermal Comfort for Educational Buildings: Case Study in the City of Riyadh
by Jamil Binabid, Abdulrahman Alymani and Ammar Alammar
Buildings 2024, 14(11), 3568; https://doi.org/10.3390/buildings14113568 - 9 Nov 2024
Viewed by 1135
Abstract
In hot, arid climates, educational buildings often face the challenge of limited outdoor space usage. This research, through comprehensive simulation, aims to propose practical solutions to enhance outdoor thermal comfort, particularly during school break times and student dismissal periods, thereby fostering more comfortable [...] Read more.
In hot, arid climates, educational buildings often face the challenge of limited outdoor space usage. This research, through comprehensive simulation, aims to propose practical solutions to enhance outdoor thermal comfort, particularly during school break times and student dismissal periods, thereby fostering more comfortable and functional outdoor school environments. That will happen through achieving the main objective of the study, which is evaluating the suggested passive strategies. Riyadh was selected as the case study, and four representative schools were analyzed through simulation and optimization processes to identify key areas for improvement. The research leveraged simulation tools such as Ladybug and Grasshopper in Rhino, highlighting the practicality and impact of this approach. Simulations were performed to assess the existing outdoor thermal conditions using the universal thermal climate index (UTCI) and to pinpoint regions with elevated thermal discomfort. Passive design interventions, such as shading devices and vegetation, were explored and optimized using the Galapagos in Grasshopper. This methodology supports the originality of this research in its integration of simulation tools, such as Ladybug and Grasshopper, with optimization techniques using the Galapagos plugin, specifically applied to the unique site-specific context of educational outdoor environments in a hot, dry climate in Riyadh. Additionally, insights for urban planners and architects demonstrate the possibility of integrating passive design principles to improve the usability and sustainability of outdoor spaces. The findings indicated that fewer apertures in shade devices combined with greater tree canopies might double the effectivity in lowering UTCI values, thereby enhancing thermal comfort, especially during peak summer months. Full article
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
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<p>Computational workflow.</p>
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<p>All case studies and corresponding details.</p>
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<p>Base model Case Study A.</p>
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<p>Base model Case Study B.</p>
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<p>Base model Case Study C.</p>
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<p>Base model Case Study D.</p>
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<p>Hourly current conditions highlighting months with highest thermal stress.</p>
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<p>Variation of strategies by month for Case Study A.</p>
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<p>UTCI effectiveness.</p>
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<p>Variation of strategies by month for Case Study B.</p>
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<p>UTCI effectiveness.</p>
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<p>Variation of strategies by month for Case Study C.</p>
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<p>UTCI effectiveness.</p>
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<p>Variation of strategies by month for Case Study D.</p>
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<p>UTCI effectiveness.</p>
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<p>Case Study A base model and optimal solutions from May to October.</p>
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<p>Case Study B base model and optimal solutions from July to October.</p>
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<p>Case Study C base model and optimal solutions from July to October.</p>
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<p>Case Study D base model and optimal solutions from July to October.</p>
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25 pages, 15616 KiB  
Article
Thermal Stress in Outdoor Spaces During Mediterranean Heatwaves: A PET and UTCI Analysis of Different Demographics
by Tousi Evgenia, Athina Mela and Areti Tseliou
Urban Sci. 2024, 8(4), 193; https://doi.org/10.3390/urbansci8040193 - 31 Oct 2024
Cited by 1 | Viewed by 884
Abstract
Urban public space comfort is essential for improving quality of life, particularly as climate change affects outdoor thermal environments. This study utilizes ENVI-met, a 3D microclimate simulation tool, to assess thermal comfort concerning demographic factors such as age and gender. The findings indicate [...] Read more.
Urban public space comfort is essential for improving quality of life, particularly as climate change affects outdoor thermal environments. This study utilizes ENVI-met, a 3D microclimate simulation tool, to assess thermal comfort concerning demographic factors such as age and gender. The findings indicate significant disparities in thermal stress vulnerability among demographic groups. On the hottest day of July 2023, at 10 a.m., children’s PET values were approximately 2 °C higher than those of other groups. By 3 p.m., females experienced slightly higher upper-range thermal stress than males. Elderly individuals aged 80 exhibited a broad range of PET values, from 38.14 °C to 62.39 °C, with prevailing values above 56.9 °C, indicating greater vulnerability to extreme heat. Children aged 8 showed PET values ranging from 40.20 °C to 59.34 °C, with prevailing estimates between 54.2 °C and 55.7 °C. Minimum PET values for children were significantly higher than those for adults, suggesting a greater baseline level of thermal stress. Despite cooling effects in the evening, children remained exposed to more pronounced stress than elderly individuals, males, and females. The UTCI values recorded indicate a period of extreme heat stress for all demographic groups assessed. While individuals aged 35 may encounter considerable discomfort, the severity of the impact is notably more pronounced for both older adults and children. This study underscores the need for tailored management strategies and advocates for expanding ENVI-met’s capabilities to enhance urban resilience and well-being amid rising temperatures. Full article
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<p>Methodology scheme.</p>
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<p>(<b>a</b>) Spatial layout of the area of study, 2D and 3D models; (<b>b</b>) 2D and 3D ENVI-met Spaces V5.6.1 model.</p>
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<p>ENVI-met simulation of the area of study. Potential air temperature at 10 a.m.</p>
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<p>ENVI-met simulation of the area of study. Potential air temperature at 3 p.m.</p>
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<p>ENVI-met simulation of the area of study. Potential air temperature at 8 p.m.</p>
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<p>ENVI-met simulation of the area of study. Mean radiant temperature at 10 a.m.</p>
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<p>ENVI-met simulation of the area of study, mean radiant temperature at 3 p.m.</p>
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<p>ENVI-met simulation of the area of study. Mean radiant temperature at 8 p.m.</p>
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<p>ENVI-met simulation of the area of study. PET 10 a.m., male 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.</p>
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<p>ENVI-met simulation of the area of study. PET 10 a.m., female 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.</p>
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<p>ENVI-met simulation of the area of study. PET 10 a.m., elderly male 80 years old, summer clothing, 0.5 clo, pref.speed 0.90 m/s.</p>
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<p>ENVI-met simulation of the area of study. PET 10 a.m., male child 8 years old, summer clothing, 0.5 clo, pref.speed 1.10 m/s.</p>
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<p>ENVI-met simulation of the area of study. PET 3 p.m., male 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.</p>
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<p>ENVI-met simulation of the area of study. PET 3 p.m., female 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.</p>
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<p>ENVI-met simulation of the area of study. PET 3 p.m., elderly male 80 years old, summer clothing, 0.5 clo, pref.speed 0.90 m/s.</p>
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<p>ENVI-met simulation of the area of study. PET 3 p.m., male child 8 years old, summer clothing, 0.5 clo, pref.speed 1.10 m/s.</p>
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<p>ENVI-met simulation of the area of study. PET 8 p.m., male 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.</p>
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<p>ENVI-met simulation of the area of study. PET 8 p.m., female 35 years old, summer clothing, 0.5 clo, pref.speed 1.34 m/s.</p>
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<p>ENVI-met simulation of the area of study. PET 8 p.m., elderly male 80 years old, summer clothing, 0.5 clo, pref.speed 0.90 m/s.</p>
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<p>ENVI-met simulation of the area of study. PET 8 p.m., male child 8 years old, summer clothing, 0.5 clo, pref.speed 1.10 m/s.</p>
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<p>ENVI-met simulation of the area of study. UTCI 10 a.m., male 35 years old, female 35 years old, elderly male 80 years old, and male child 8 years old.</p>
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<p>ENVI-met simulation of the area of study. UTCI 3 p.m., male 35 years old, female 35 years old, elderly male 80 years old, and male child 8 years old.</p>
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<p>ENVI-met simulation of the area of study. UTCI 8 p.m., male 35 years old, female 35 years old, elderly male 80 years old, and male child 8 years old.</p>
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22 pages, 5931 KiB  
Article
Thermal Comfort Simulation-Based Forest Management Scenarios for Forest Healing
by Doyun Song, Sujin Park, Yeonhee Lee and Geonwoo Kim
Forests 2024, 15(11), 1859; https://doi.org/10.3390/f15111859 - 23 Oct 2024
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Abstract
Background and objectives: Forest environments provide various healing benefits for humans and have been widely studied. Nevertheless, the field of forest management for forest healing remains relatively understudied. The purpose of this study is to utilize thermal environmental simulation to derive forest management [...] Read more.
Background and objectives: Forest environments provide various healing benefits for humans and have been widely studied. Nevertheless, the field of forest management for forest healing remains relatively understudied. The purpose of this study is to utilize thermal environmental simulation to derive forest management scenarios that are optimized for forest healing. Methods: This study focused on the Seogwipo Experimental Forest on Jeju Island, Korea. Three-dimensional forest models were generated based on field surveys. Thermal environment simulations were conducted using Grasshopper with the Ladybug and Honeybee plug-ins, and the thermal comfort levels of six forest management scenarios were evaluated using the Universal Thermal Climate Index (UTCI). Results: The simulation results showed that, among all the scenarios, only scenario (c), “10% thinning in the buffer zone”, led to an improvement in thermal comfort. Additionally, the study identified discrepancies in thermal comfort between different forest management scenarios. Conclusions: In the management of forests for healing forestry purposes, the distinction of forest zones by use and the application of different forest management scenarios have thermal comfort implications. Thus, the methodology could be employed in forest management for forest healing purposes. Full article
(This article belongs to the Special Issue Advances and Future Prospects in Science-Based Forest Therapy)
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<p>Study site (Jeju Island, South Korea).</p>
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<p>Study site (Seogwipo Experimental Forest, 33°18′28″ N, 126°32′50″ E).</p>
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<p>Tree modeling algorithm. (<b>a</b>) coniferous and (<b>b</b>) broadleaf tree modeling algorithms.</p>
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<p>3D forest mode. (<b>a</b>) pre-thinning (815/ha) and (<b>b</b>) post-thinning forest models (601/ha).</p>
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<p>UTCI simulation model.</p>
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<p>3D forest models. (<b>a</b>) top-open forest model 1, (<b>b</b>) top-open forest model 2, (<b>c</b>) side-open forest 1, (<b>d</b>) side-open forest 2, (<b>e</b>) full-open forest 1, and (<b>f</b>) full-open forest 2.</p>
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<p>Results of thermal environment simulation pre- and post-thinning. (<b>a</b>) pre-thinning forest models (815/ha) (<b>b</b>) post-thinning forest models (601/ha).</p>
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<p>Results of thermal environment simulation analysis under different forest management scenarios: (<b>a</b>) top-open forest model 1, (<b>b</b>) top-open forest model 2, (<b>c</b>) side-open forest 1, (<b>d</b>) side-open forest 2, (<b>e</b>) full-open forest 1, (<b>f</b>) full-open forest 2.</p>
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<p>Results of thermal environment simulation analysis under different forest management scenarios: (<b>a</b>) top-open forest model 1, (<b>b</b>) top-open forest model 2, (<b>c</b>) side-open forest 1, (<b>d</b>) side-open forest 2, (<b>e</b>) full-open forest 1, (<b>f</b>) full-open forest 2.</p>
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<p>Comparing comfortable activity times for forest management scenarios. (<b>a</b>) Comparing comfortable activity time for all scenarios and (<b>b</b>) Seasonal percentage change in comfortable activity time between scenario C and the control group.</p>
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