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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 464
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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Figure 1
<p>Typical meteorological annual mean temperature in Xi’an City, China (Source network).</p> Full article ">Figure 2
<p>Typical meteorological annual average humidity in Xi’an, China (Source network).</p> Full article ">Figure 3
<p>Typical meteorological annual average rainfall in Xi’an, China (Source network).</p> Full article ">Figure 4
<p>Typical meteorological annual average rainfall days in Xi’an, China (Source network).</p> Full article ">Figure 5
<p>Typical meteorological average annual sunshine duration/average sunshine duration (Source network).</p> Full article ">Figure 6
<p>Typical annual mean UV index (Source network).</p> Full article ">Figure 7
<p>Schematic diagram of residential area demarcation (source: author’s own drawing).</p> Full article ">
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 627
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)
►▼ Show Figures

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<p>Locations for measuring devices and aerial view.</p> Full article ">Figure 2
<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> Full article ">Figure 3
<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> Full article ">Figure 4
<p>(<b>a</b>) Comparison of hourly air temperature measurements; and (<b>b</b>) comparison of hourly humidity measurements.</p> Full article ">Figure 5
<p>Comparison of hourly UTCI values during 26–27 August.</p> Full article ">Figure 6
<p>Comparison of hourly air temperature during 26–27 August.</p> Full article ">Figure 7
<p>Comparison of hourly PET values during 26–27 August.</p> Full article ">Figure 8
<p>(<b>a</b>) Sangju daily park usage (9 August–7 September); (<b>b</b>) Sangju hourly park usage (9 August–7 September).</p> Full article ">Figure 9
<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> Full article ">
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 713
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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Figure 1
<p>MOO framework used in this study to design block forms.</p> Full article ">Figure 2
<p>Location of the study area and investigated residential blocks for the elderly.</p> Full article ">Figure 3
<p>Nine simplified building types extracted from senior residential areas in Beijing.</p> Full article ">Figure 4
<p>Abstraction and calculation of block feature parameters in this study.</p> Full article ">Figure 5
<p>Illustration of block form scenario generation.</p> Full article ">Figure 6
<p>(<b>a</b>) UTCI calculation process overview and (<b>b</b>) the boundaries of the virtual wind tunnel.</p> Full article ">Figure 7
<p>Flowchart of the NSGA-II algorithm.</p> Full article ">Figure 8
<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> Full article ">Figure 9
<p>Performance comparative analysis of Pareto solutions: (<b>a</b>) UTCI-S, (<b>b</b>) UTCI-W, and (<b>c</b>) SH.</p> Full article ">Figure 10
<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> Full article ">Figure 11
<p>Heatmap of Pearson correlation coefficients between block form feature parameters and environmental performance parameters.</p> Full article ">Figure 12
<p>Elbow method for determining the optimal number of clusters.</p> Full article ">Figure 13
<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> Full article ">Figure 14
<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> Full article ">Figure 15
<p>Feasible urban design solutions for three clusters based on the Pareto optimal solutions, including block form features for each design.</p> Full article ">
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 536
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)
►▼ Show Figures

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Figure 1
<p>Examples of townships and streets in Beibei District, Chongqing.</p> Full article ">Figure 2
<p>ENVI met modeling rendering and monitoring points.</p> Full article ">Figure 3
<p>Figures (<b>a</b>,<b>b</b>) show, respectively, the comparison between the simulated and measured values of temperature and humidity.</p> Full article ">Figure 4
<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> Full article ">Figure 5
<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> Full article ">Figure 6
<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> Full article ">Figure 7
<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> Full article ">Figure 8
<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> Full article ">
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 947
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)
►▼ Show Figures

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Figure 1
<p>Computational workflow.</p> Full article ">Figure 2
<p>All case studies and corresponding details.</p> Full article ">Figure 3
<p>Base model Case Study A.</p> Full article ">Figure 4
<p>Base model Case Study B.</p> Full article ">Figure 5
<p>Base model Case Study C.</p> Full article ">Figure 6
<p>Base model Case Study D.</p> Full article ">Figure 7
<p>Hourly current conditions highlighting months with highest thermal stress.</p> Full article ">Figure 8
<p>Variation of strategies by month for Case Study A.</p> Full article ">Figure 9
<p>UTCI effectiveness.</p> Full article ">Figure 10
<p>Variation of strategies by month for Case Study B.</p> Full article ">Figure 11
<p>UTCI effectiveness.</p> Full article ">Figure 12
<p>Variation of strategies by month for Case Study C.</p> Full article ">Figure 13
<p>UTCI effectiveness.</p> Full article ">Figure 14
<p>Variation of strategies by month for Case Study D.</p> Full article ">Figure 15
<p>UTCI effectiveness.</p> Full article ">Figure 16
<p>Case Study A base model and optimal solutions from May to October.</p> Full article ">Figure 17
<p>Case Study B base model and optimal solutions from July to October.</p> Full article ">Figure 18
<p>Case Study C base model and optimal solutions from July to October.</p> Full article ">Figure 19
<p>Case Study D base model and optimal solutions from July to October.</p> Full article ">
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
Viewed by 735
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
(This article belongs to the Special Issue Sustainable Approaches on Urban Space in Mediterranean Cities: From XS Design to XL Planning)
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<p>Methodology scheme.</p> Full article ">Figure 2
<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> Full article ">Figure 3
<p>ENVI-met simulation of the area of study. Potential air temperature at 10 a.m.</p> Full article ">Figure 4
<p>ENVI-met simulation of the area of study. Potential air temperature at 3 p.m.</p> Full article ">Figure 5
<p>ENVI-met simulation of the area of study. Potential air temperature at 8 p.m.</p> Full article ">Figure 6
<p>ENVI-met simulation of the area of study. Mean radiant temperature at 10 a.m.</p> Full article ">Figure 7
<p>ENVI-met simulation of the area of study, mean radiant temperature at 3 p.m.</p> Full article ">Figure 8
<p>ENVI-met simulation of the area of study. Mean radiant temperature at 8 p.m.</p> Full article ">Figure 9
<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> Full article ">Figure 10
<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> Full article ">Figure 11
<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> Full article ">Figure 12
<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> Full article ">Figure 13
<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> Full article ">Figure 14
<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> Full article ">Figure 15
<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> Full article ">Figure 16
<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> Full article ">Figure 17
<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> Full article ">Figure 18
<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> Full article ">Figure 19
<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> Full article ">Figure 20
<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> Full article ">Figure 21
<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> Full article ">Figure 22
<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> Full article ">Figure 23
<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> Full article ">
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
Viewed by 654
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> Full article ">Figure 2
<p>Study site (Seogwipo Experimental Forest, 33°18′28″ N, 126°32′50″ E).</p> Full article ">Figure 3
<p>Tree modeling algorithm. (<b>a</b>) coniferous and (<b>b</b>) broadleaf tree modeling algorithms.</p> Full article ">Figure 4
<p>3D forest mode. (<b>a</b>) pre-thinning (815/ha) and (<b>b</b>) post-thinning forest models (601/ha).</p> Full article ">Figure 5
<p>UTCI simulation model.</p> Full article ">Figure 6
<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> Full article ">Figure 7
<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> Full article ">Figure 8
<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> Full article ">Figure 8 Cont.
<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> Full article ">Figure 9
<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> Full article ">
36 pages, 50364 KiB  
Article
MITIGATING THE URBAN HEAT ISLAND EFFECT: The Thermal Performance of Shade-Tree Planting in Downtown Los Angeles
by Yuzhou Zhu and Karen M. Kensek
Sustainability 2024, 16(20), 8768; https://doi.org/10.3390/su16208768 - 11 Oct 2024
Cited by 1 | Viewed by 2050
Abstract
The intensifying urban heat island (UHI) effect presents a growing challenge for urban environments, yet there is a lack of comprehensive strategies that account for how multiple factors influence tree-cooling effectiveness throughout the year. While most studies focus on the effects of individual [...] Read more.
The intensifying urban heat island (UHI) effect presents a growing challenge for urban environments, yet there is a lack of comprehensive strategies that account for how multiple factors influence tree-cooling effectiveness throughout the year. While most studies focus on the effects of individual factors, such as tree shading or transpiration, over specific time periods, fewer studies address the combined impact of various factors—such as seasonal variations, building shading, transpiration rates, tree placement, and spacing—on tree cooling across different seasons. This study fills this gap by investigating the thermal environment in downtown Los Angeles through ENVI-met simulations. A novel tree-planting strategy was developed to enhance cooling performance by adjusting tree positions based on these key factors. The results show that the new strategy reduces Universal Thermal Climate Index (UTCI) temperatures by 2.2 °C on the hottest day, 0.97 °C on the coldest day, and 1.52 °C annually. The study also evaluates the negative cooling effects in colder months, demonstrating that, in cities with climates similar to Los Angeles, the benefits of tree cooling in hot weather outweigh the drawbacks during winter. These findings provide a new method for optimizing tree placement in urban planning, contributing to more effective UHI mitigation strategies. Full article
(This article belongs to the Special Issue A Systems Approach to Urban Greenspace System and Climate Change)
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<p>The range of research area. The red dashed box formed by ABCD represents the study area for this research.</p> Full article ">Figure 2
<p>Daily temperature data of downtown Los Angeles in 2022.</p> Full article ">Figure 3
<p>With or without tree in date comparison diagram. The green area indicates the locations of trees in the simulation.</p> Full article ">Figure 4
<p>Tree under the shade of building’s simulation diagram. The green area indicates the locations of trees in the simulation. The gray area represents the locations of buildings in the simulation.</p> Full article ">Figure 5
<p>Tree’s eight location’s simulation diagram. The green area indicates the locations of trees in the simulation. The gray area represents the locations of buildings in the simulation.</p> Full article ">Figure 6
<p>Trees’ canopies separated by 15-feet diagram. The green area indicates the locations of trees in the simulation. The gray area represents the locations of buildings in the simulation.</p> Full article ">Figure 7
<p>Trees touching canopies diagram. The green area indicates the locations of trees in the simulation. The gray area represents the locations of buildings in the simulation.</p> Full article ">Figure 8
<p>Overlapping trees’ canopies diagram. The green area indicates the locations of trees in the simulation. The gray area represents the locations of buildings in the simulation.</p> Full article ">Figure 9
<p>No tree and one tree modeling in ENVI-met.</p> Full article ">Figure 10
<p>Hottest day UTCI diagram comparison by date factor at 10 a.m., 12 p.m., and 2 p.m.</p> Full article ">Figure 11
<p>Coldest day UTCI diagram comparison by date factor at 10 a.m., 12 p.m., and 2 p.m.</p> Full article ">Figure 12
<p>Hottest and coldest days reduced UTCI comparison (center of the tree).</p> Full article ">Figure 13
<p>Hottest and coldest days reduced UTCI comparison (shadow area of the tree).</p> Full article ">Figure 14
<p>Each month reduced UTCI comparison.</p> Full article ">Figure 15
<p>Building shade without a tree and with a tree.</p> Full article ">Figure 16
<p>Hottest day UTCI diagram comparison by building shade factor at 10 a.m., 12 p.m., and 2 p.m.</p> Full article ">Figure 17
<p>Cooling ability reduction comparison.</p> Full article ">Figure 18
<p>Building shade without a tree and with a tree for transpiration analysis.</p> Full article ">Figure 19
<p>Hottest day UTCI diagram comparison by transpiration factor at 10 a.m.</p> Full article ">Figure 20
<p>Coldest day UTCI diagram comparison by transpiration factor at 10 a.m.</p> Full article ">Figure 21
<p>Transpiration cooling effect on UTCI comparison.</p> Full article ">Figure 22
<p>Trees in different eight locations surrounding a building diagram.</p> Full article ">Figure 23
<p>Hottest day sunrise, noon, and sunset time UTCI comparison (8 a.m., 12 p.m., and 5 p.m.).</p> Full article ">Figure 24
<p>Hottest day building shadow analysis. The green area indicates the locations of trees in the simulation. The dark gray area represents the locations of buildings in the simulation. The shaded area represents the extent of building shadow coverage. The darker the shade, the longer the duration of the building’s shadow coverage.</p> Full article ">Figure 25
<p>Coldest day building shadow analysis. The green area indicates the locations of trees in the simulation. The dark gray area represents the locations of buildings in the simulation. The shaded area represents the extent of building shadow coverage. The darker the shade, the longer the duration of the building’s shadow coverage.</p> Full article ">Figure 26
<p>Whole-year building shadow analysis. The green area indicates the locations of trees in the simulation. The dark gray area represents the locations of buildings in the simulation. The shaded area represents the extent of building shadow coverage. The darker the shade, the longer the duration of the building’s shadow coverage.</p> Full article ">Figure 27
<p>Trees’ best cooling location ranking.</p> Full article ">Figure 28
<p>Three types of trees’ spacing UTCI comparison at 12 pm (hottest day).</p> Full article ">Figure 29
<p>New trees’ layout 3D map.</p> Full article ">Figure 30
<p>Full-site new trees’ 3D model in ENVI-met.</p> Full article ">Figure 31
<p>Full-site no trees’ UTCI diagram.</p> Full article ">Figure 32
<p>Full-site existing trees’ UTCI diagram.</p> Full article ">Figure 33
<p>Full-site new trees’ UTCI diagram.</p> Full article ">Figure 34
<p>Full-site pedestrian area UTCI comparisons at 12 p.m.</p> Full article ">Figure 35
<p>Reduced UTCI for each month (new scheme).</p> Full article ">Figure A1
<p>Research area 3D model in ENVI-met.</p> Full article ">Figure A2
<p>Assigned materials for building façade and road surface in ENVI-met.</p> Full article ">Figure A3
<p>Climate file setting in ENVI-met (hottest day).</p> Full article ">Figure A4
<p>Climate file setting in ENVI-met (coldest day).</p> Full article ">Figure A5
<p>Daily climate data of each month in downtown Los Angeles.</p> Full article ">Figure A6
<p>February average climate data calculation.</p> Full article ">Figure A7
<p>February air-temperature average data calculation (part).</p> Full article ">Figure A8
<p>February average climate data file in ENVI-met.</p> Full article ">Figure A9
<p>Preliminary simulation UTCI diagram.</p> Full article ">Figure A10
<p>Sidewalk area pixel-count image.</p> Full article ">Figure A11
<p>Coldest day UTCI diagram comparison by building shade factor at 10 a.m., 12 p.m., and 2 p.m.</p> Full article ">Figure A12
<p>Coldest day sunrise, noon, and sunset time UTCI comparison (8 a.m., 12 p.m., and 5 p.m.).</p> Full article ">Figure A13
<p>New tree scheme layout 2D map.</p> Full article ">Figure A14
<p>Full-site no tree 3D model in ENVI-met.</p> Full article ">Figure A15
<p>Full-site existing trees’ 3D model in ENVI-met.</p> Full article ">Figure A16
<p>Full-site no trees’ coldest day at 12 p.m.</p> Full article ">Figure A17
<p>Full-site existing trees coldest day at 12 p.m.</p> Full article ">Figure A18
<p>Full-site new trees’ coldest day at 12 p.m.</p> Full article ">Figure A19
<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (January).</p> Full article ">Figure A20
<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (February).</p> Full article ">Figure A21
<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (March).</p> Full article ">Figure A22
<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (April).</p> Full article ">Figure A23
<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (May).</p> Full article ">Figure A24
<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (June).</p> Full article ">Figure A25
<p>Existing trees’ and new trees’ UTCI diagram 12 p.m. (July).</p> Full article ">Figure A26
<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (August).</p> Full article ">Figure A27
<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (September).</p> Full article ">Figure A28
<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (October).</p> Full article ">Figure A29
<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (November).</p> Full article ">Figure A30
<p>Existing trees’ and new trees’ UTCI diagram at 12 p.m. (December).</p> Full article ">
27 pages, 88524 KiB  
Article
Cold Coastal City Neighborhood Morphology Design Method Based on Multi-Objective Optimization Simulation Analysis
by Sheng Xu, Peisheng Zhu, Fei Guo, Duoduo Yan, Shiyu Miao, Hongchi Zhang, Jing Dong and Xianchao Fan
Buildings 2024, 14(10), 3176; https://doi.org/10.3390/buildings14103176 - 5 Oct 2024
Cited by 1 | Viewed by 1333
Abstract
In the context of global warming and the frequent occurrence of extreme weather, coastal cities are more susceptible to the heat island effect and localized microclimate problems due to the significant influence of the oceanic climate. This study proposes a computer-driven simulation optimization [...] Read more.
In the context of global warming and the frequent occurrence of extreme weather, coastal cities are more susceptible to the heat island effect and localized microclimate problems due to the significant influence of the oceanic climate. This study proposes a computer-driven simulation optimization method based on a multi-objective optimization algorithm, combined with tools such as Grasshopper, Ladybug, Honeybee and Wallacei, to provide scientific optimization decision intervals for morphology control and evaluation factors at the initial stage of coastal city block design. The effectiveness of this optimization strategy is verified through empirical research on typical coastal neighborhoods in Dalian. The results show that the strategy derived from the multi-objective optimization-based evaluation significantly improves the wind environment and thermal comfort of Dalian neighborhoods in winter and summer: the optimization reduced the average wind speed inside the block by 0.47 m/s and increased the UTCI by 0.48 °C in winter, and it increased the wind speed to 1.5 m/s and decreased the UTCI by 0.59 °C in summer. This study shows that the use of simulation assessment and multi-objective optimization technology to adjust the block form of coastal cities can effectively improve the seasonal wind and heat environment and provide a scientific basis for the design and renewal of coastal cities. Full article
(This article belongs to the Topic Advances in Low-Carbon, Climate-Resilient, and Sustainable Built Environment)
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<p>Research flowchart.</p> Full article ">Figure 2
<p>Three-dimensional schematic of the boundary condition setting and variable control for the ideal model.</p> Full article ">Figure 3
<p>Top view of a typical neighborhood building plan layout.</p> Full article ">Figure 4
<p>Ideal model for the experimental control group.</p> Full article ">Figure 5
<p>Data logging and export module for multi-objective optimization processes.</p> Full article ">Figure 6
<p>Map of Dalian City location and study area LCZ, and aerial photographs of the area.</p> Full article ">Figure 7
<p>On-site measured points and instrument models.</p> Full article ">Figure 8
<p>Parametric modeling and building numbering.</p> Full article ">Figure 9
<p>Results of the UTCI vs. the average wind speed simulations for different neighborhood types.</p> Full article ">Figure 10
<p>Boxplots of the distribution of the mean site wind speed and UTCI for each neighborhood type.</p> Full article ">Figure 11
<p>Calculated wind speed and UTCI in the control group with different building orientations.</p> Full article ">Figure 12
<p>Calculation of the wind speed and UTCI in the control group of different building floors.</p> Full article ">Figure 13
<p>Mean trendline for 50 iterations of 4 optimization objectives.</p> Full article ">Figure 14
<p>Multi-objective optimization solution set distribution and average optimal solution shape.</p> Full article ">Figure 15
<p>Multi-objective optimization solution set distribution and cluster analysis.</p> Full article ">Figure 16
<p>Neighborhood morphology of the non-dominated solution sets for the 5 clusters.</p> Full article ">Figure 17
<p>Calculated results of the wind–heat environmental assessment for the winter and summer seasons (before optimization).</p> Full article ">Figure 18
<p>Schematic diagram of the optimized design measures and local thermal comfort after the retrofit.</p> Full article ">Figure 18 Cont.
<p>Schematic diagram of the optimized design measures and local thermal comfort after the retrofit.</p> Full article ">
17 pages, 5854 KiB  
Article
The Impact of Street Trees on Temperature Reduction in a Nature-Based Climate Adaptation Program in George Town, Malaysia
by Sofia Castelo, Victor Moura Bussolotti, Izabela Pellegrini, Filipa Ferreira, Nor Atiah Ismail, Francesca Poggi and Miguel Amado
Climate 2024, 12(10), 154; https://doi.org/10.3390/cli12100154 - 2 Oct 2024
Viewed by 1805
Abstract
Nature-based solutions have been promoted as an effective strategy to address climate impacts, including urban temperature reduction. In this paper, we analyze the impacts of the introduction of street trees on temperature (Universal Thermal Climate Index, UTCI) for three different dates, 2000, 2023, [...] Read more.
Nature-based solutions have been promoted as an effective strategy to address climate impacts, including urban temperature reduction. In this paper, we analyze the impacts of the introduction of street trees on temperature (Universal Thermal Climate Index, UTCI) for three different dates, 2000, 2023, and 2050. A 3D model was developed in Rhinoceros software for a part of George Town, on Penang Island. Four different sections of streets were simulated after integration of the model with the Grasshopper plug-in, where a parametric system was built for temperature measurements based on simulations in the Ladybug and Honeybee plug-ins. The tree species used were selected from a pool of tree species commonly planted in urban settings in Malaysia that have low and medium sensitivity to climate impacts. The results show a maximum reduction of 7 °C between 2000 and 2050, achieved on a street with an NW–SE orientation that was planted with three rows of trees. The minimum UTCI reduction achieved was 3 °C, between 2023 and 2050, in a street with NW–SE orientation that was planted with one tree row. The two streets with a SW–NE orientation showed a 5 °C temperature reduction between 2023 and 2050. Both streets have only one row of trees but different species and sizes, with the bigger trees reducing the temperature in a slightly larger area. The results show the importance of introducing and safeguarding street trees to reduce urban temperatures in the country, potentially keeping temperatures below life-threatening levels, thereby safeguarding urban health, while also reducing costs of energy consumption. Solar orientation, the number of tree rows, and their distribution impact the outcomes. The findings provide useful guidance for climate-conscious urban planning practices in Malaysia. Full article
(This article belongs to the Section Climate Adaptation and Mitigation)
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<p>Map of Penang, George Town and Bayan Lepas mukims. Copyright by Think City. Source: Adaptation Fund, 2021 [<a href="#B40-climate-12-00154" class="html-bibr">40</a>].</p> Full article ">Figure 2
<p>View over George Town, Penang Island. Copyright by Think City. Source: Castelo et al., 2023 [<a href="#B39-climate-12-00154" class="html-bibr">39</a>].</p> Full article ">Figure 3
<p>Land surface temperature of George Town, 2019. Source: Castelo et al., 2024 [<a href="#B41-climate-12-00154" class="html-bibr">41</a>].</p> Full article ">Figure 4
<p>Location of street sections modeled, S01–S04.</p> Full article ">Figure 5
<p>S01, Gat Lebuh China, 2023. Copyright: Melissa Sivaraj, Think City.</p> Full article ">Figure 6
<p>Tree-lined trees in Gat Lebuh China, George Town, Penang, 2000, 2023, and 2050. Ce: <span class="html-italic">Conocarpus erectus</span>; Pi: <span class="html-italic">Pterocarpus indicus</span>; Ti: <span class="html-italic">Talipariti tiliaceum</span>.</p> Full article ">Figure 7
<p>Street tree plan for S02 (Gat Lebuh Gereja). Ls: <span class="html-italic">Lagerstroemia speciosa</span>.</p> Full article ">Figure 8
<p>Street tree plans for S03 and S04 (Lebuh Penang and Lebuh King). Pl: <span class="html-italic">Pteleocarpa lamponga</span>; Tt: <span class="html-italic">Talipariti tiliaceum</span>.</p> Full article ">Figure 9
<p>Digital model developed for part of George Town, Penang, in Rhinoceros 3D 7 software. The year 2050, with all trees illustrated as being mature—including Gat Lebuh China (S01), Gat Lebuh Gereja (S02), Lebuh Penang (S03), and Lebuh King (S04).</p> Full article ">Figure 10
<p>General workflow of the experiment.</p> Full article ">Figure 11
<p>Simulated UTCI for S01, 2000 (<b>a</b>), 2023 (<b>b</b>), and 2050 (<b>c</b>).</p> Full article ">Figure 12
<p>Simulated UTCI for S02, 2023 (<b>a</b>) and 2050 (<b>b</b>).</p> Full article ">Figure 13
<p>Simulated UTCI for S03, 2023 (<b>a</b>) and 2050 (<b>b</b>).</p> Full article ">Figure 14
<p>Simulated UTCI for S04, 2023 (<b>a</b>) and 2050 (<b>b</b>).</p> Full article ">
16 pages, 4906 KiB  
Article
A Study on Outdoor Thermal Comfort of College Students in the Outdoor Corridors of Teaching Buildings in Hot and Humid Regions
by Qiuwan Zhang, Yuxi Li and Chang Lin
Buildings 2024, 14(9), 2756; https://doi.org/10.3390/buildings14092756 - 2 Sep 2024
Viewed by 960
Abstract
It is important to create a favorable environment for various student activities and interactions by improving the thermal comfort of semi-outdoor spaces in teaching buildings. However, there has been limited research focusing on the thermal comfort levels of college students in these areas, [...] Read more.
It is important to create a favorable environment for various student activities and interactions by improving the thermal comfort of semi-outdoor spaces in teaching buildings. However, there has been limited research focusing on the thermal comfort levels of college students in these areas, such as corridors (access ways connecting different buildings outdoors). This study aims to assess the thermal comfort levels of college students in the corridors of teaching buildings in hot and humid regions. Based on field measurements and questionnaire surveys, the study evaluated the thermal comfort levels of male and female college students. The findings indicate the following: (1) air temperature and air velocity are the primary thermal environmental parameters affecting college students in corridor spaces, regardless of gender; (2) physiological equivalent temperature (PET) and Universal Thermal Climate Index (UTCI) were used as indices to evaluate the thermal environment of outdoor corridor spaces. Males and females perceive the outdoor environment as hot when PET (UTCI) values reach 33.5 (34.5) °C and 33.3 (33.5) °C, respectively. When the PET (UTCI) values reach 39.0 °C (37.5 °C) for males and 37.7 °C (38.3 °C) for females, individuals in corridor spaces will face extreme heat stress; (3) females find it more challenging than males to tolerate hot outdoor environments. The unacceptable temperatures for males and females are 31.1 °C and 31.8 °C, respectively; and (4) in hot outdoor environments, females are more susceptible than males to experiencing fatigue and negative emotions. The results of this study provide valuable insights for the future design and renovation of teaching buildings on university campuses. Full article
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
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<p>Testing site.</p> Full article ">Figure 2
<p>Outdoor thermal parameters data: (<b>a</b>) Ta; (<b>b</b>) Tmrt; (<b>c</b>) RH; (<b>d</b>) Va.</p> Full article ">Figure 3
<p>Thermal parameter preference votes.</p> Full article ">Figure 4
<p>Physical fatigue evaluation votes: 1, heavy head; 2, body feels lazy; 3, body stiffness; 4, yawning/nodding off; 5, feeling sleepy/wanting to lie down; 6, none.</p> Full article ">Figure 5
<p>Psychological evaluation votes: 1, distracted/unable to concentrate; 2, no desire to talk; 3, anger/easily irritable; 4, anxiety; 5, none.</p> Full article ">Figure 6
<p>(<b>a</b>) Air temperature &lt; 32.0 °C, thermal sensation votes; (<b>b</b>) air temperature &gt; 32.0 °C, thermal sensation votes; (<b>c</b>) air temperature &lt; 32.0 °C, wind sensation votes; (<b>d</b>) air temperature &gt; 32.0 °C, wind sensation votes.</p> Full article ">Figure 6 Cont.
<p>(<b>a</b>) Air temperature &lt; 32.0 °C, thermal sensation votes; (<b>b</b>) air temperature &gt; 32.0 °C, thermal sensation votes; (<b>c</b>) air temperature &lt; 32.0 °C, wind sensation votes; (<b>d</b>) air temperature &gt; 32.0 °C, wind sensation votes.</p> Full article ">Figure 7
<p>Thermal adaptation behavior votes: 1, drinking hot beverages; 2, drinking cold beverages; 3, seeking shade; 4, using an umbrella; 5, using a portable fan; 6, wearing sun-protective clothing.</p> Full article ">Figure 8
<p>Relationship between MTSV and thermal parameters: (<b>a</b>) Ta; (<b>b</b>) Tmrt; (<b>c</b>) RH; (<b>d</b>) Va.</p> Full article ">Figure 9
<p>(<b>a</b>) Relationship between MWSV and Va; (<b>b</b>) relationship between unacceptable percentage and Va.</p> Full article ">Figure 10
<p>(<b>a</b>) The heart rate data of subjects; (<b>b</b>) the metabolic rate data of subjects.</p> Full article ">Figure 11
<p>(<b>a</b>) Relationship between PET and MTSV; (<b>b</b>) relationship between UTCI and MTSV.</p> Full article ">Figure 12
<p>(<b>a</b>) Relationship between PET and unacceptable percentage; (<b>b</b>) relationship between UTCI and unacceptable percentage.</p> Full article ">
37 pages, 9060 KiB  
Article
Exploring Thermal Discomfort during Mediterranean Heatwaves through Softscape and Hardscape ENVI-Met Simulation Scenarios
by Evgenia Tousi, Areti Tseliou, Athina Mela, Maria Sinou, Zoe Kanetaki and Sébastien Jacques
Sustainability 2024, 16(14), 6240; https://doi.org/10.3390/su16146240 - 22 Jul 2024
Cited by 1 | Viewed by 1242
Abstract
The study examines the effectiveness of various design strategies in alleviating the impacts of heatwaves in the Mediterranean region, focusing on a densely populated post-refugee urban area in Greece. By analyzing five different design scenarios, the study aims to identify the most efficient [...] Read more.
The study examines the effectiveness of various design strategies in alleviating the impacts of heatwaves in the Mediterranean region, focusing on a densely populated post-refugee urban area in Greece. By analyzing five different design scenarios, the study aims to identify the most efficient approach to mitigate thermal stress outdoors. The five design scenarios include changes in albedo values and coatings and alterations in the number and type of trees. The methodology includes a literature review, field work and microclimate simulations with the use of ENVI-met 5.6.1. The study evaluates ENVI-met data through potential air temperature, PET and UTCI analysis. The experimental results indicate that the most effective strategy is associated with urban greening. In particular, increasing tree cover considerably reduces air temperature, PET and UTCI values by 4 to 10 degrees Celsius. This finding highlights the potential of urban greening to enhance thermal comfort and combat heatwave effects. The research findings may be useful to landscape architects and urban designers, in light of a more climate-responsive urban design in the Mediterranean region. Future research may also assess the combined impact of multiple mitigation strategies on a larger scale, informing evidence-based policies for heatwave resilience. Full article
(This article belongs to the Special Issue Technology and Engineering Education in the Digital Era: Towards Sustainable Development Goals)
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<p>Map and aerial view (google maps) of the selected case study, in the refugee enclave of the Kapodistrian Municipality of Nikaia; source of background AutoCAD map: Laboratory of Spatial Planning and GIS, School of Architecture NTUA, 2013.</p> Full article ">Figure 2
<p>Methodology scheme.</p> Full article ">Figure 3
<p>Scenario 1 (baseline) 2D and 3D ENVI-met models.</p> Full article ">Figure 4
<p>Scenario 2, ENVI-met model, with coating materials.</p> Full article ">Figure 5
<p>Scenario 3, 2D and 3D ENVI-met models.</p> Full article ">Figure 6
<p>Scenario 4, 2D and 3D ENVI-met models.</p> Full article ">Figure 7
<p>Scenario 5, 2D and 3D ENVI-met models.</p> Full article ">Figure 8
<p>(<b>a</b>) Scenario 1, Simulation using ENVI-met, 23 July 2023, 10:00 a.m., potential air temperature. (<b>b</b>) Scenario 1, Simulation using ENVI-met, 23 July 2023, 10:00 a.m., PET. (<b>c</b>) Scenario 1, Simulation using ENVI-met, 23 July 2023, 10:00 a.m., UTCI.</p> Full article ">Figure 8 Cont.
<p>(<b>a</b>) Scenario 1, Simulation using ENVI-met, 23 July 2023, 10:00 a.m., potential air temperature. (<b>b</b>) Scenario 1, Simulation using ENVI-met, 23 July 2023, 10:00 a.m., PET. (<b>c</b>) Scenario 1, Simulation using ENVI-met, 23 July 2023, 10:00 a.m., UTCI.</p> Full article ">Figure 9
<p>(<b>a</b>) Scenario 1, Simulation using ENVI-met, 23 July 2023, potential air temperature, 15:00 p.m. (<b>b</b>) Scenario 1, Simulation using ENVI-met, 23 July 2023, PET, 15:00 p.m. (<b>c</b>) Scenario 1, Simulation using ENVI-met, 23 July 2023, UTCI, 15:00 p.m.</p> Full article ">Figure 10
<p>(<b>a</b>) Scenario 1, Simulation using ENVI-met, 23 July 2023, potential air temperature, 20:00 p.m. (<b>b</b>) Scenario 1, Simulation using ENVI-met, 23 July 2023 PET, 20:00 p.m. (<b>c</b>) Scenario 1, Simulation using ENVI-met, 23 July 2023, UTCI, 20:00 p.m.</p> Full article ">Figure 10 Cont.
<p>(<b>a</b>) Scenario 1, Simulation using ENVI-met, 23 July 2023, potential air temperature, 20:00 p.m. (<b>b</b>) Scenario 1, Simulation using ENVI-met, 23 July 2023 PET, 20:00 p.m. (<b>c</b>) Scenario 1, Simulation using ENVI-met, 23 July 2023, UTCI, 20:00 p.m.</p> Full article ">Figure 11
<p>(<b>a</b>) Scenario 2, Simulation using ENVI-met, 23 July 2023, 10:00 a.m., potential air temperature. (<b>b</b>) Scenario 2, Simulation using ENVI-met, 23 July 2023, 10:00 a.m., PET. (<b>c</b>) Scenario 2, Simulation using ENVI-met, 23 July 2023, 10:00 a.m., UTCI.</p> Full article ">Figure 12
<p>(<b>a</b>) Scenario 2, Simulation using ENVI-met, 23 July 2023, 15:00 p.m., potential air temperature. (<b>b</b>) Scenario 2, Simulation using ENVI-met, 23 July 2023, 15:00 p.m., PET. (<b>c</b>) Scenario 2, Simulation using ENVI-met, 23 July 2023, 15:00 p.m., UTCI.</p> Full article ">Figure 13
<p>(<b>a</b>) Scenario 2, Simulation using ENVI-met, 23 July 2023, 20:00 p.m., potential air temperature. (<b>b</b>) Scenario 2, Simulation using ENVI-met, 23 July 2023, 20:00 p.m., PET. (<b>c</b>) Scenario 2, Simulation using ENVI-met, 23 July 2023, 20:00 p.m., UTCI.</p> Full article ">Figure 13 Cont.
<p>(<b>a</b>) Scenario 2, Simulation using ENVI-met, 23 July 2023, 20:00 p.m., potential air temperature. (<b>b</b>) Scenario 2, Simulation using ENVI-met, 23 July 2023, 20:00 p.m., PET. (<b>c</b>) Scenario 2, Simulation using ENVI-met, 23 July 2023, 20:00 p.m., UTCI.</p> Full article ">Figure 14
<p>(<b>a</b>) Scenario 3, Simulation using ENVI-met, 23 July 2023, 10:00 a.m., potential air temperature. (<b>b</b>) Scenario 3, Simulation using ENVI-met, 23 July 2023, 10:00 a.m., PET. (<b>c</b>) Scenario 3, Simulation using ENVI-met, 23 July 2023, 10:00 a.m., UTCI.</p> Full article ">Figure 15
<p>(<b>a</b>) Scenario 3, Simulation using ENVI-met, 23 July 2023, 15:00 p.m., potential air temperature. (<b>b</b>) Scenario 3, Simulation using ENVI-met, 23 July 2023, 15:00 p.m., PET. (<b>c</b>) Scenario 3, Simulation using ENVI-23 July 2023, 15:00 p.m., UTCI.</p> Full article ">Figure 16
<p>(<b>a</b>) Scenario 3, Simulation using ENVI-met, 23 July 2023, 20:00 p.m., potential air temperature. (<b>b</b>) Scenario 3, Simulation using ENVI-met, 23 July 2023, 20:00 p.m., PET. (<b>c</b>) Scenario 3, Simulation using ENVI-met, 23 July 2023, 20:00 p.m., UTCI.</p> Full article ">Figure 17
<p>(<b>a</b>) Scenario 4, Simulation using ENVI-met, 23 July 2023, 10:00 a.m., potential air temperature. (<b>b</b>) Scenario 4, Simulation using ENVI-met, 23 July 2023, 10:00 a.m., PET. (<b>c</b>) Scenario 4, Simulation using ENVI-met, 23 July 2023, 10:00 a.m., UTCI.</p> Full article ">Figure 17 Cont.
<p>(<b>a</b>) Scenario 4, Simulation using ENVI-met, 23 July 2023, 10:00 a.m., potential air temperature. (<b>b</b>) Scenario 4, Simulation using ENVI-met, 23 July 2023, 10:00 a.m., PET. (<b>c</b>) Scenario 4, Simulation using ENVI-met, 23 July 2023, 10:00 a.m., UTCI.</p> Full article ">Figure 18
<p>(<b>a</b>)Scenario 4, Simulation using ENVI-met, 23 July 2023, 15:00 p.m., potential air temperature. (<b>b</b>) Scenario 4, Simulation using ENVI-met, 23 July 2023, 15:00 p.m., PET. (<b>c</b>) Scenario 4, Simulation using ENVI-met, 23 July 2023, 15:00 p.m., UTCI.</p> Full article ">Figure 18 Cont.
<p>(<b>a</b>)Scenario 4, Simulation using ENVI-met, 23 July 2023, 15:00 p.m., potential air temperature. (<b>b</b>) Scenario 4, Simulation using ENVI-met, 23 July 2023, 15:00 p.m., PET. (<b>c</b>) Scenario 4, Simulation using ENVI-met, 23 July 2023, 15:00 p.m., UTCI.</p> Full article ">Figure 19
<p>(<b>a</b>) Scenario 4, Simulation using ENVI-met, 23 July 2023, 20:00 p.m., potential air temperature. (<b>b</b>) Scenario 4, Simulation using ENVI-met, 23 July 2023, 20:00 p.m., PET. (<b>c</b>) Scenario 4, Simulation using ENVI-met, 23 July 2023, 20:00 p.m., UTCI.</p> Full article ">Figure 20
<p>(<b>a</b>) Scenario 5, Simulation using ENVI-met, 23 July 2023, 10:00 p.m., potential air temperature. (<b>b</b>) Scenario 5, Simulation using ENVI-met, 23 July 2023, 10:00 p.m., PET. (<b>c</b>) Scenario 5, Simulation using ENVI-met, 23 July 2023, 10:00 p.m., UTCI.</p> Full article ">Figure 21
<p>(<b>a</b>) Scenario 5, Simulation using ENVI-met, 23 July 2023, 15:00 p.m., potential air temperature. (<b>b</b>) Scenario 5, Simulation using ENVI-met, 23 July 2023, 15:00 p.m., PET. (<b>c</b>) Scenario 5, Simulation using ENVI-met, 23 July 2023, 15:00 p.m., UTCI.</p> Full article ">Figure 22
<p>(<b>a</b>) Simulation using ENVI-met, 23 July 2023, 20:00 p.m., potential air temperature. (<b>b</b>) Simulation using ENVI-met, 23 July 2023, 20:00 p.m., PET. (<b>c</b>) Simulation using ENVI-met, 23 July 2023, 20:00 p.m., UTCI.</p> Full article ">
22 pages, 47765 KiB  
Article
Pix2Pix-Assisted Beijing Hutong Renovation Optimization Method: An Application to the UTCI and Thermal and Ventilation Performance
by Rui Wu, Ming Huang, Zhenqing Yang, Lili Zhang, Lei Wang, Wei Huang and Yongqiang Zhu
Buildings 2024, 14(7), 1957; https://doi.org/10.3390/buildings14071957 - 27 Jun 2024
Viewed by 1082
Abstract
In response to the issues of low outdoor thermal comfort and poor ventilation environment in Beijing Hutong, this paper proposes a rapid intelligent optimization method combining Pix2Pix (Image-to-Image Translation with Conditional Adversarial Networks) with a genetic algorithm. Firstly, the architectural types of the [...] Read more.
In response to the issues of low outdoor thermal comfort and poor ventilation environment in Beijing Hutong, this paper proposes a rapid intelligent optimization method combining Pix2Pix (Image-to-Image Translation with Conditional Adversarial Networks) with a genetic algorithm. Firstly, the architectural types of the research objects are highly refined and summarized into four traditional building types. Then, they are placed in the site with open spaces in a certain proportion, and a multi-objective optimization model for the UTCI (Universal Thermal Climate Index) and building area is constructed using a genetic algorithm, generating and iteratively optimizing the spatial layout of the building population. Finally, Pix2Pix is used to learn and train a large number of Hutong combination samples, rapidly generating the UTCI and ventilation results, which serve as the optimization objectives to obtain the optimal solution set for Hutong spatial forms. Compared with traditional empirical design methods, this method allows for a rapid and efficient traversal of vast solution spaces, intelligently generating Hutong renovation schemes that balance cultural heritage and healthy comfort. The research results demonstrate that this method can quickly find (26.4 times faster than traditional performance simulation methods) that the reasonable proportions of Siheyuan, Sanheyuan, Erheyuan, new buildings, and empty spaces in the Da Yuan Hutong in Beijing should be controlled at 11.8%, 16.9%, 23.8%, 33.8%, and 13.7%, respectively. Meanwhile, the building density should be maintained between 0.5 and 0.58, and the floor area ratio should be kept between 0.96 and 1.14. This significantly improves outdoor comfort, enhances the living environment of the Hutong, and promotes sustainable urban development. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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<p>Workflow of genetic algorithms.</p> Full article ">Figure 2
<p>Visually representing the processes of UTCI generation: illustration of the Pix2Pix architecture.</p> Full article ">Figure 3
<p>Generator workflow diagram (Through four down-sampling operations, convolutional feature extraction is performed on the input images. Then, by employing four up-sampling operations, the images are restored to their original dimensions, and pixel-wise classification is carried out).</p> Full article ">Figure 4
<p>Research workflow diagram.</p> Full article ">Figure 5
<p>Research framework diagram.</p> Full article ">Figure 6
<p>The PMV (predicted mean vote) situation for the Beijing standard meteorological year based on the ASHRAE 55 comfort standard: monthly air temperature.</p> Full article ">Figure 7
<p>Enthalpy–humidity chart for the standard meteorological year in Beijing based on the ASHRAE 55 comfort standard.</p> Full article ">Figure 8
<p>Diagram of isomorphic relationship between quadrangle courtyard and the forbidden city: (<b>a</b>) Beijing Imperial city overall layout plan and (<b>b</b>) the plan of Beijing da yuan quadrangle courtyard.</p> Full article ">Figure 9
<p>Satellite map of Da Yuan Hutong in Beijing with highly refined top-view and perspective-view diagrams of four building types and leisure squares.</p> Full article ">Figure 10
<p>Optimization process based on UTCI and building area. Site plan: (<b>a</b>) the 12th generation, 6th individual and (<b>b</b>) the 14th generation, 8th individual.</p> Full article ">Figure 11
<p>The optimization process based on UTCI and building area. Aerial view: (<b>a</b>) the 12th individual of the 14th generation and (<b>b</b>) the 12th individual of the 16th generation.</p> Full article ">Figure 12
<p>The Pareto frontiers dominated by genetic optimization based on UTCI and building area—plan view.</p> Full article ">Figure 13
<p>The Pareto frontiers dominated by genetic optimization based on UTCI and building area—aerial view.</p> Full article ">Figure 14
<p>The Pareto frontiers dominated by genetic optimization based on UTCI and building area—local magnification map.</p> Full article ">Figure 15
<p>The pairing style of training data for UTCI simulation with Pix2Pix.</p> Full article ">Figure 16
<p>The processing of training data for UTCI simulation with Pix2Pix.</p> Full article ">Figure 17
<p>Implementing UTCI prediction simulation using Pix2Pix technology in Grasshopper.</p> Full article ">Figure 18
<p>Performing UTCI prediction simulation using Pix2Pix technology for (<b>a</b>) the initial round of the first batch and (<b>b</b>) the 15,500th batch of the 200th round.</p> Full article ">Figure 19
<p>Original site wind speed simulation results.</p> Full article ">Figure 20
<p>Optimized wind speed results using Pix2Pix technology.</p> Full article ">Figure 21
<p>Scatter plot of building density, plot ratio, and UTCI.</p> Full article ">Figure 22
<p>Comparison of G loss, D loss, Adv loss, and Pixel loss before and after incorporating the self-attention mechanism in the Pix2Pix algorithm.</p> Full article ">
22 pages, 13447 KiB  
Article
Understanding Outdoor Cold Stress and Thermal Perception of the Elderly in Severely Cold Climates: A Case Study in Harbin
by Xiaoyun He, Long Shao, Yuexing Tang and Liangbo Hao
Land 2024, 13(6), 864; https://doi.org/10.3390/land13060864 - 15 Jun 2024
Viewed by 1584
Abstract
This study collected data through microclimate monitoring, surface temperature measurements, and questionnaire surveys, and used indicators, such as the universal thermal climate index (UTCI), surface temperature (Ts), and wind chill temperature (tWC), to determine the thermal comfort [...] Read more.
This study collected data through microclimate monitoring, surface temperature measurements, and questionnaire surveys, and used indicators, such as the universal thermal climate index (UTCI), surface temperature (Ts), and wind chill temperature (tWC), to determine the thermal comfort threshold of the elderly in severely cold climates and evaluate their cold stress. The results indicated that (1) the neutral UTCI (NUTCI) for elderly individuals in winter was 13.3 °C, and the NUTCI range was from 1.4 to 25.2 °C; (2) the intensity of elderly individuals’ physical activity affected the magnitude of risk of whole-body cooling, with duration-limited exposures corresponding to 0.5, 3.3, and over 8 h for light, moderate, and vigorous activity levels, respectively; (3) the tWC in all four spaces was below −10 °C, potentially inducing discomfort or even frostbite in the elderly; (4) for a 10 s touch, the maximum Ts (−17.2 °C) of stone was lower than the numbness threshold (−15.0 °C), while that (−15.1 °C) of steel materials remained below the frostbite threshold (−13 °C), posing risks for the elderly during physical activity. This study’s results will provide valuable insights and theoretical references for the landscape design of urban park activity spaces for elderly individuals in cold climate regions. Full article
(This article belongs to the Topic Climate Change and Environmental Sustainability, 3rd Edition)
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<p>Monthly mean/maximum/minimum air temperature (<span class="html-italic">T<sub>a</sub></span>) and mean relative humidity (<span class="html-italic">RH</span>) in Harbin from 1991 to 2021.</p> Full article ">Figure 2
<p>Site location and measurement spaces: (<b>a</b>) site location; (<b>b</b>) photos of open spaces; (<b>c</b>) winter fisheye photos.</p> Full article ">Figure 3
<p>Meteorological measurements.</p> Full article ">Figure 4
<p>An infrared thermal image taken in area FA (17 January 2022).</p> Full article ">Figure 5
<p>Simplified garment checklist (clo).</p> Full article ">Figure 6
<p>Meteorological parameter preference votes.</p> Full article ">Figure 7
<p>Pearson correlation statistics of thermal sensation votes and meteorological parameters.</p> Full article ">Figure 8
<p>Pearson correlation statistics of thermal sensation votes and personal parameters.</p> Full article ">Figure 9
<p>Thermal perception vote distribution for different intensities of physical activity.</p> Full article ">Figure 10
<p>Correlation between UTCI and MTSV: (<b>a</b>) total, (<b>b</b>) women and men.</p> Full article ">Figure 11
<p>Daytime variation of wind chill temperature in four open spaces.</p> Full article ">Figure 12
<p>Different materials’ <span class="html-italic">T<sub>s</sub></span> (°C) and cold risk in the sun or shadow.</p> Full article ">Figure 13
<p>Preferred thermal adaptation behavior: (<b>a</b>) first choice, (<b>b</b>) last choice.</p> Full article ">
26 pages, 4842 KiB  
Article
A Systematic Assessment of Greening Interventions for Developing Best Practices for Urban Heat Mitigation—The Case of Huế, Vietnam
by Sebastian Scheuer, Luca Sumfleth, Long Dac Hoang Nguyen, Ylan Vo, Thi Binh Minh Hoang and Jessica Jache
Urban Sci. 2024, 8(2), 67; https://doi.org/10.3390/urbansci8020067 - 13 Jun 2024
Viewed by 1093
Abstract
The health of urban populations is increasingly at risk due to the amplification and chronification of urban heat stress by climate change. This is particularly true for urban environments in humid tropical climates, including many cities in Southeast Asia. It is also in [...] Read more.
The health of urban populations is increasingly at risk due to the amplification and chronification of urban heat stress by climate change. This is particularly true for urban environments in humid tropical climates, including many cities in Southeast Asia. It is also in these locations where increasing climatic risks may be exacerbated by urban growth, underscoring the need to develop effective mitigation strategies for strengthening urban resilience and supporting climate change adaptation. Conservation and widespread implementation of green infrastructure (GI) are regarded as one means to counter heat as a public health threat. However, for lower-income countries across Southeast Asia, such as Vietnam, knowledge gaps remain with respect to the effectiveness of greening interventions for heat mitigation. To address this gap, in the context of urban expansion in the humid tropical city of Huế, Vietnam, diurnal cooling potential and regulation of outdoor thermal comfort (OTC) within a wide, shallow street canyon were systematically assessed for selected elements of GI along a quantitative and qualitative dimension using ENVI-met. Tree-based interventions were found to be most effective, potentially decreasing UTCI by −1.9 K at the domain level. Although lower in magnitude, green verges and green facades were also found to contribute to OTC, with green verges decreasing UTCI by up to −1.7 K and green facades by up to −1.4 K locally. Potential synergistic cooling impacts were identified through a combination of GI elements. However, no scenario was found to decrease heat stress to zero or moderate levels. Substantially reducing heat stress may thus require further measures and a closer consideration of local morphological characteristics. Full article
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<p>Location of Huế city in Central Vietnam, location of An Cuu City within the city of Huế, and extent of the case study area in the southeastern part of An Cuu City. Squares indicate the location of Huế city in Vietnam, and of the case study area within Huế city.</p> Full article ">Figure 2
<p>Suggested greening interventions along a qualitative and quantitative dimension. Columns represent the qualitative dimension through choice of GI element, i.e., street trees (ST; <b>left</b>), green verges (GV; <b>middle</b>), and vertical greenery systems (VGS; <b>right</b>). Rows represent the quantitative dimension, i.e., for street trees and green verges, the tree density or green verge area is increased from top to bottom, denoted as ST.1 to ST.3, and GV.1 to GV.2, respectively. Thereby, a so-called triangle of interventions is formed (bottom middle). This intervention triangle is subsequently amended with scenarios C.1 and C.2, which suggest simultaneous greening interventions for a potential maximization of cooling benefits. Scenario C.1 is obtained by combining ST.3 and GV.2, and Scenario C.2 is obtained by combining C.1 and VGS.1. The baseline scenario, including six chosen observer locations, is depicted in the bottom-right corner. See <a href="#urbansci-08-00067-t001" class="html-table">Table 1</a> for more details on each scenario.</p> Full article ">Figure 3
<p>Simulated impacts averaged at the domain level, at a height of 1.4 m above ground level: (<b>a</b>) mean and range of air temperature (T<sub>a</sub>, °C) under baseline; (<b>b</b>) mean and range of relative humidity (RH, %) under baseline; (<b>c</b>) mean and range of mean radiant temperature (T<sub>mrt</sub>, °C) under baseline; (<b>d</b>) mean and range of UTCI (°C) under baseline. Plotted thresholds indicate strong (UTCI &gt; 32 °C), very strong (UTCI &gt; 38 °C), and extreme heat stress (UTCI &gt; 46 °C) [<a href="#B71-urbansci-08-00067" class="html-bibr">71</a>]; (<b>e</b>) cooling potential over time of day, in terms of averaged differences in modeled T<sub>a</sub> to baseline (K), per scenario; (<b>f</b>) averaged differences in modeled RH to baseline (%), per scenario; (<b>g</b>) averaged differences in modelled T<sub>mrt</sub> to baseline (K), per scenario; (<b>h</b>) averaged differences in modeled UTCI to baseline (K), per scenario.</p> Full article ">Figure 4
<p>Simulation results at the pedestrian level for 6 a.m.: (<b>a</b>) local cooling potential, i.e., modeled difference in air temperature (K) compared to baseline; (<b>b</b>) changes in relative humidity (%) compared to baseline; (<b>c</b>) difference in T<sub>mrt</sub> (K) compared to baseline; (<b>d</b>) local regulation of OTC, i.e., difference in UTCI (K) compared to baseline; (<b>e</b>) classified local regulation of OTC, where Adverse: ΔUTCI &gt; +0.25; None: −0.25 &lt; ΔUTCI ≤ +0.25; Negligible: −0.5 &lt; ΔUTCI ≤ −0.25; Low: −1.0 &lt; ΔUTCI ≤ −0.5; Moderate: −2.0 &lt; ΔUTCI ≤ −1.0; High: −2.0 &gt; ΔUTCI; (<b>f</b>) heat stress, with classes derived through a classification of modeled UTCI (°C), where None: UTCI ≤ 26; Moderate: 26 &lt; UTCI ≤ 32; Strong: 32 &lt; UTCI ≤ 38; Very strong: 38 &lt; UTCI ≤ 46; Extreme: UTCI &gt; 46 [<a href="#B71-urbansci-08-00067" class="html-bibr">71</a>]. Prevailing wind speed and wind direction are shown as a Quiver plot. The significance of differences is based on a one-sided Wilcoxon signed-rank test (H<sub>A</sub>: scenario &lt; baseline) and is denoted as follows: ***, highly significant (<span class="html-italic">p</span> &lt; 0.001); non-significant otherwise (<span class="html-italic">p</span> &gt; 0.05).</p> Full article ">Figure 5
<p>Simulation results at the pedestrian level for 12 p.m.: (<b>a</b>) local cooling potential (K); (<b>b</b>) changes in relative humidity (%); (<b>c</b>) difference in T<sub>mrt</sub> (K); (<b>d</b>) local regulation of OTC; (<b>e</b>) classified local regulation of OTC; (<b>f</b>) heat stress. Prevailing wind speed and wind direction are shown as a Quiver plot. Please refer to <a href="#urbansci-08-00067-f004" class="html-fig">Figure 4</a> for a description of the classes and significances. ***, highly significant (<span class="html-italic">p</span> &lt; 0.001); non-significant otherwise (<span class="html-italic">p</span> &gt; 0.05).</p> Full article ">Figure 6
<p>Simulation results at the pedestrian level for 3 p.m.: (<b>a</b>) local cooling potential (K); (<b>b</b>) changes in relative humidity (%); (<b>c</b>) difference in T<sub>mrt</sub> (K); (<b>d</b>) local regulation of OTC; (<b>e</b>) classified local regulation of OTC; (<b>f</b>) heat stress. Prevailing wind speed and wind direction are shown as a Quiver plot. Please refer to <a href="#urbansci-08-00067-f004" class="html-fig">Figure 4</a> for a description of the classes and significances. ***, highly significant (<span class="html-italic">p</span> &lt; 0.001); non-significant otherwise (<span class="html-italic">p</span> &gt; 0.05).</p> Full article ">Figure 7
<p>Simulation results at the pedestrian level for 6 p.m.: (<b>a</b>) local cooling potential (K); (<b>b</b>) changes in relative humidity (%); (<b>c</b>) difference in T<sub>mrt</sub> (K); (<b>d</b>) local regulation of OTC; (<b>e</b>) classified local regulation of OTC; (<b>f</b>) heat stress. Prevailing wind speed and wind direction are shown as a Quiver plot. Please refer to <a href="#urbansci-08-00067-f004" class="html-fig">Figure 4</a> for a description of the classes and significances. ***, highly significant (<span class="html-italic">p</span> &lt; 0.001); non-significant otherwise (<span class="html-italic">p</span> &gt; 0.05); **, very significant (<span class="html-italic">p</span> &lt; 0.01).</p> Full article ">Figure 8
<p>Simulated differences to baseline at chosen observer locations over time of day: PW (parking lot, western side), PE (parking lot, eastern side), CW (courtyard, western side), CE (courtyard, eastern side), SW (sidewalk, western side), and SE (sidewalk, eastern side). (<b>a</b>) Difference in air temperature (K); (<b>b</b>) difference in relative humidity (%); (<b>c</b>) difference in T<sub>mrt</sub> (K); (<b>d</b>) difference in UTCI (K). Shaded areas refer to classified impact on UTCI, from adverse to high.</p> Full article ">
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