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Article Outline

Abstract

Keywords

Nomenclature

1. Introduction

2. Methodology: systematic review and parametric thinking

3. Thematic reviews

4. Discussion

5. Conclusion

Appendix A

Appendix B

Funding

Author contributions

Declaration of competing interest

References


Figures and tables

Figures (10)

Tables (1)

Review | 9 November 2025
Volume 12 Issue 2 pp. 468-490 • doi: 10.15627/jd.2025.28

Human Interaction with Urban Morphology under the Influence of Urban Heat Island: A Systematic Review

Fataneh Shoghi,1 Seyed Morteza Hosseini,2,* Shahin Heidari,1 Julian Wang,3 Mohammadjavad Mahdavinejad,4 Shady Attia5

Author affiliations

1 Department of Architecture and Energy, Faculty of Architecture and Urbanism, University of Tehran, Tehran, Iran
2 Department of Architecture, Design and Media Technology. Aalborg University, Copenhagen, Denmark
3 Department of Architectural Engineering, Penn State University, United States of America
4 College of Engineering and Architecture, University of Nizwa, Oman
5 Sustainable Building Design Lab, Dept. UEE, Faculty of Applied Science, University of Liege, Liege, Belgium

*Corresponding author.
shoghifattane@gmail.com (F. Shoghi)
smho@create.aau.dk (S. M. Hosseini)
shheidari@ut.ac.ir (S. Heidari)
jqw5965@psu.edu (J. Wang)
mahdavinejad@unizwa.edu.om (M. Mahdavinejad)
shady.attia@uliege.be (S. Attia)

History: Received 3 September 2025 | Revised 14 October 2025 | Accepted 18 October 2025 | Published online 9 November 2025

2383-8701/© 2025 The Author(s). Published by solarlits.com. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 License.

Citation: Fataneh Shoghi, Seyed Morteza Hosseini, Shahin Heidari, Julian Wang, Mohammadjavad Mahdavinejad, Shady Attia, Human Interaction with Urban Morphology under the Influence of Urban Heat Island: A Systematic Review, Journal of Daylighting, 12:2 (2025) 468-490. doi: 10.15627/jd.2025.28

Figures and tables

Abstract

Outdoor urban spaces are essential to residents’ well-being, yet their thermal comfort is increasingly compromised by urbanization and climate change. Although urban morphology has been widely studied, its effects on human thermal comfort within mi-croclimates remain inadequately understood. This study addresses this gap by exam-ining the interactions between urban morphology, microclimate, and pedestrian ther-mal comfort. We employed a systematic literature review guided by the PRISMA framework, alongside parametric thinking using General Morphological Analysis (GMA) to systematically explore how variations in urban form parameters influence microclimatic conditions and pedestrian thermal comfort. The study’s objectives were threefold: (1) to systematically analyze the existing literature, identify key trends, and uncover knowledge gaps; (2) to explore the psychological, physical, and social factors influencing thermal perception; and (3) to assess how urban morphological features affect microclimate and pedestrian thermal comfort. To address these challenges, we developed a novel framework, Design Tools, which quantitatively links urban mor-phology parameters, outdoor thermal indices, and pedestrian comfort. By prioritizing outdoor thermal comfort in urban design, this approach offers valuable insights to en-hance climate-responsive design strategies and improve pedestrian well-being amid the growing challenges of urban heat islands.

Keywords

general morphological analysis, outdoor thermal comfort, parametric thinking, urban morphology

Nomenclature

AT Apparent Temperature
DI Discomfort Index
ESI Environmental Stress Index
ET Effective Temperature
H Humidex
HI Heat Index
RSI Relative Strain Index
WBGT Wet-Bulb Globe Temperature Index
WCI Wind Chill Index
WCT Wind Chill Temperature
COMFA COMfort Formula
ETU Universal Effective Temperature
HL Heat Load Index
HTCI Outdoor Human Thermal Comfort Index
ITS Index of Thermal Stress
PHS Predicted Heat Strain
mPET Modified Physiological Equivalent Temp
OUT_SET* Standard Effective Temperature (Outdoor)
PMV Predicted Mean Vote
PET Physiological Equivalent Temperature
PT Perceived Temperature
SET* Standard Effective Temperature
STI Subjective Temperature Index
UTCI Universal Thermal Climate Index
IREQ Required Clothing Insulation
TSV Thermal Sensation Vote
ASV Actual Sensation Vote
TSI Tropical Summer Index
PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses
GMA General Morphological Analysis
SLR Systematic Literature Review
ASHRAE American Society of Heating, Refrigerating and Air-Conditioning Engineers
ISO International Organization for Standardization
SDGs United Nations' Sustainable Development Goals
UM Urban Morphology
UF Urban Form
UHI Urban Heat Island
UG Urban Green
UGS Urban Green Spaces
LAI Leaf Area Index
UGI Urban Green Infrastructure
GI Green Infrastructure
UWB Urban Water Body
GHG Greenhouse Gas
CO2 Carbon Dioxide
LST Land Surface Temperature
2D Two-dimensional
3D Three-dimensional
FAR Floor Area Ratio
SVF Sky View Factor
H/W Height-to-Width Ratio
WWR Window-to-Wall Ratio
SRI Solar Reflectance Index
NW Northwest
NE Northeast
SE Southeast
SW Southwest
V Air Velocity
WD Wind Direction
WS Wind Speed
W Wind
Ta Air Temperature
RH Relative Humidity
Met Metabolic Rate
Iclo Clothing Insulation
PTC Pedestrian Thermal Comfort
OT Outdoor Thermal
EN European Standards

1. Introduction

The global urban population is projected to grow from 56% in 2020 to 68% by 2050, while climate models indicate a potential rise of 1.5°C in global temperatures over the next two to three decades, accompanied by elevated risks of severe climate impacts [1,2]. Increasing energy demands across buildings, transportation, and industry are the primary drivers of this trend. Urban areas, as centers of human activity, currently consume approximately 66% of the world’s primary energy and contribute over 71% of energy-related greenhouse gas emissions [3-6]. Continued urbanization and economic growth are expected to increase energy consumption by 70% and carbon emissions by 50% by 2050 relative to 2013 levels [2]. Rapid urban expansion necessitates high-density development, profoundly shaping urban morphology, influencing energy dynamics, and intensifying the urban heat island (UHI) effect [7,8]. These challenges underscore the critical need for integrated mitigation strategies and adaptive measures, including nature-based solutions, to support sustainable and climate-resilient urban futures [3,9]. Urban morphology, particularly the spatial organization of buildings and open spaces, strongly influences outdoor thermal comfort, affecting pedestrian well-being and satisfaction [10,11]. Thermal comfort, as defined by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), refers to the state of mental satisfaction with the thermal environment. Ensuring outdoor thermal comfort has become essential for public health amid accelerating urbanization and increasing extreme heat events [12,13]. Although traditional research has primarily addressed indoor thermal comfort, understanding outdoor conditions requires a paradigm shift embracing the complex interactions among environmental variables, urban form, and human perception [14]. Assessing outdoor conditions requires consideration of complex interactions among environmental variables, urban form, and human perception [15,16]. Urban morphology encompasses both the physical characteristics of buildings and their spatial arrangement within urban areas [17,18]. Features such as urban canyons, building form and orientation, construction materials, vegetation, and water bodies significantly impact the urban microclimate [19-21]. Microclimatic conditions including air and surface temperature, humidity, wind speed, and wind direction are shaped by building attributes such as height, façade design, orientation, and incorporation of green materials [22-25]. Given the substantial effects of microclimates on outdoor thermal comfort, integration of human factors such as activity level, age, gender, clothing, cultural practices, and social norms is essential for accurate evaluation [26-29]. Recognizing the dynamic interplay between environmental and human factors supports the development of sustainable urban environments and enhances adaptive capacity in response to climate change [18,30].

This research investigates the influence of urban morphology on microclimates and pedestrian thermal comfort. It evaluates a range of thermal comfort indicators applicable to outdoor environments, assessing implications for human health and alignment with urban design objectives. By applying established benchmarks and thermal comfort thresholds, adaptive strategies can be developed to mitigate UHI effects and respond to local environmental conditions. The study aligns with the United Nations Sustainable Development Goals, particularly Target 13 on Climate Action [31], emphasizing the role of urban resilience in addressing increasing urban heat challenges [32]. Cities are conceptualized as dynamic systems, where environmental and human factors are intricately interconnected, facilitating a comprehensive understanding of complex urban interactions. The research addresses two primary questions:

  1. How does urban morphology influence pedestrian thermal comfort in urban environments?
  2. How can a parametric design tool be developed to integrate urban morphology and optimize pedestrian thermal comfort?

The study is structured into methodology, thematic literature review, presentation of the design tool, and key findings, providing insights to inform adaptive strategies for mitigating the effects of urban heat islands.

2. Methodology: systematic review and parametric thinking

A structured five-stage research framework was applied (Fig. 1): (1) Research Initiation, (2) Systematic Literature Review, (3) Parameter Identification, (4) General Morphological Analysis (GMA), and (5) Evaluation and Design Tool Development. This framework systematically explores the relationship between urban morphology and outdoor thermal comfort, emphasizing the role of the urban heat island (UHI) and integrating environmental, morphological, and human factors for climate-responsive urban design.

Figure 1

Five-stage framework: (1) Research Initiation, (2) Systematic Review (PRISMA), (3) Pa-rameter Identification, (4) Morphological Analysis (GMA + CCA), and (5) Design Tool Develop-ment—linking urban morphology, thermal comfort, and human-responsive design.

Fig. 1. Five-stage framework: (1) Research Initiation, (2) Systematic Review (PRISMA), (3) Pa-rameter Identification, (4) Morphological Analysis (GMA + CCA), and (5) Design Tool Develop-ment—linking urban morphology, thermal comfort, and human-responsive design.

2.1. Systematic literature review

A systematic literature review was conducted following the PRISMA protocol (Fig. 2), to ensure transparent and rigorous selection of relevant studies. Peer-reviewed publications from 2015 to 2025 were searched in Scopus and Google Scholar, covering research on urban morphology, outdoor thermal comfort (OTC), and microclimate. The period was selected to capture recent developments and trends in the field. The initial search yielded 1,901 records. After removing 250 duplicates, 1,651 records were screened based on titles, abstracts, and keywords. Inclusion criteria encompassed relevance to urban morphology, outdoor thermal comfort indices, microclimate, UHI, and human factors. Exclusion criteria included indoor thermal comfort studies, energy simulations, non-English publications, and non-urban scale studies. Following screening, 1,080 records remained, and 571 full-text articles were assessed for eligibility. After applying exclusion criteria at the full-text stage, 330 studies were considered eligible. Ultimately, 186 studies were included in the synthesis, representing the most relevant and high-quality research for this study.

Figure 2

PRISMA flow diagram illustrating the systematic literature review process, including identification, screening, eligibility, and inclusion of studies.

Fig. 2. PRISMA flow diagram illustrating the systematic literature review process, including identification, screening, eligibility, and inclusion of studies.

2.2. Parametric framework for urban morphology and outdoor thermal comfort using generalized morphological analysis (GMA)

To examine human interaction with urban morphology under UHI conditions, a structured three-stage parameter specification process was conducted. The process aimed to extract and organize key variables influencing human interaction across environmental, spatial, and behavioral dimensions.

In the first stage, comprehensive parametric identification was conducted through an extensive literature review. Key variables were identified across multiple scales of the built environment, including urban density and form, canyon geometry and orientation, surface materials (e.g., greenery, water features), building characteristics, thermal comfort indices, and pedestrian-level behavioral factors In the second stage, Generalized Morphological Analysis (GMA) was applied to explore potential interactions among variables and develop new system configurations [33]. Constraint Cross-Analysis (CCA) was integrated to ensure internal consistency within the GMA framework. Incompatible parameter combinations were systematically eliminated through pairwise comparisons, refining the morphological matrix into a coherent and viable design space (Table 1). In the final stage, insights from the GMA-CCA process informed the development of a climate-responsive design tool. This tool provides evidence-based morphological configurations to enhance outdoor thermal comfort and support positive human interaction in urban spaces affected by UHI. Minor design adjustments were shown to significantly influence environmental performance and pedestrian experience. The framework contributes to human-centered, climate-adaptive urban design, offering a practical foundation for shaping thermally comfortable and resilient public spaces under urban heat and microclimatic variability.

Table 1

Parameter analysis of urban morphology and human comfort using the Generalized Morphological Analysis (GMA) method. The table illustrates the relationships between urban morphological parameters, thermal comfort indices, and human factors. Checkmarks indicate significant interactions, highlighting the multidimensional effects of urban form on outdoor thermal comfort under UHI conditions.

Table 1. Parameter analysis of urban morphology and human comfort using the Generalized Morphological Analysis (GMA) method. The table illustrates the relationships between urban morphological parameters, thermal comfort indices, and human factors. Checkmarks indicate significant interactions, highlighting the multidimensional effects of urban form on outdoor thermal comfort under UHI conditions.

2.3. Bibliometric analysis

A bibliometric analysis was conducted using data extracted from Scopus and Google Scholar and visualized with VOSviewer to identify key research trends at the intersection of urban morphology, outdoor thermal comfort, and the UHI phenomenon. Keyword co-occurrence analysis, based on titles, abstracts, and author keywords, revealed four primary thematic clusters (Fig. 3). The first cluster (red) centers on thermal and morphological terms such as urban heat island and urban form. The second cluster (blue) highlights spatial and structural aspects, including urban morphology, urban canyon, and land use. The third cluster (green) reflects the increasing role of nature-based solutions, represented by terms such as green infrastructure, vegetation, and urban water. The fourth cluster (yellow) emphasizes human-related dimensions, including thermal perception, human behavior, and psychological factors. Overall, the analysis demonstrates a multidisciplinary trend toward integrating urban form, climate responsiveness, and human experience. Computational modeling tools such as ENVI-met, CFD, and GIS are increasingly applied to assess urban microclimates. Nevertheless, significant gaps remain in addressing thermal perception and behavioral aspects within these models, underscoring the need for their stronger integration into urban morphological and energy resilience frameworks.

Figure 3

Keyword co-occurrence network showing four key research clusters in urban morphology, thermal comfort, and urban heat island studies.

Fig. 3. Keyword co-occurrence network showing four key research clusters in urban morphology, thermal comfort, and urban heat island studies.

2.4. Global and temporal research trends

A global overview of the geographical distribution of studies on the urban morphology and UHI phenomenon is shown Fig. 4(a). Red circles indicate locations where research has focused on the intersection of urban morphology and UHI. The size of each circle corresponds to the number of publications, with larger circles representing higher research output. The figure clearly shows that the most studies are concentrated in developed countries. In contrast, many rapidly urbanizing developing nations despite being highly vulnerable to climate-related challenges have produced fewer than 15 relevant publications. Temporal trends in research output between 2015 and 2025 are illustrated Fig. 4(b), showing a notable increase in publications from 2018 to 2022, reflecting growing interest in the relationships among urban morphology, outdoor thermal comfort, and UHI. However, research growth remains uneven across regions. The top publishing journals in this field are highlighted Fig. 4(c). The majority of relevant studies appear in Building and Environment, Sustainable Cities and Society, and Energy and Buildings, identifying these outlets as leading contributors to interdisciplinary research on urban form and climate responsiveness. Overall, the analysis underscores both the geographic concentration of studies and the rising attention to human–environment interactions within UHI and thermal comfort research.

Figure 4

(a) Global distribution of studies; circle size shows publication count. (b) Publication trends from 2015 to 2025. (c) Leading journals publishing research on urban morphology, outdoor thermal comfort, and UHI.

Fig. 4. (a) Global distribution of studies; circle size shows publication count. (b) Publication trends from 2015 to 2025. (c) Leading journals publishing research on urban morphology, outdoor thermal comfort, and UHI.

3. Thematic reviews

3.1. Outdoor thermal comfort

Outdoor thermal comfort is a multidisciplinary field integrating environmental, physiological, and psychological factors to assess human thermal perception in outdoor environments Developed from indoor comfort research, it now examines interactions between meteorological variables and human responses [34]. Over the past century, around 165 thermal comfort indices have been introduced, from empirical models such as WBGT and WCI to advanced methods including PMV, PET, and multi-node thermoregulation models [15,35-37]. These developments reflect a shift toward integrative, non-steady-state assessments suitable for outdoor settings. Previous studies from 2015–2025 are summarized in Table A1.

3.1.2. Categorizing outdoor thermal comfort indices

OTC indices are commonly classified into three groups: linear, mechanistic, and empirical (Table B1), each with distinct theoretical and methodological bases. Linear indices consider only environmental variables, such as air temperature, humidity, and wind speed. While simple, they often oversimplify comfort by excluding physiological and psychological responses [38,39]. Mechanistic indices, including the Universal Thermal Climate Index (UTCI) and Physiologically Equivalent Temperature (PET), use thermophysiological models to simulate heat exchange between the human body and environment, accounting for metabolic rate, clothing insulation, and radiant heat [40-42]. Despite scientific robustness, computational demands may limit real-time application in fieldwork and urban design [43]. Empirical indices, derived from field surveys, relate subjective thermal sensation to microclimatic measurements, providing local insights but often constrained by cultural, regional, and acclimatization biases [18,44-46]. Each category has limitations: linear models neglect human adaptability, mechanistic models lack socio-cultural sensitivity, and empirical models face scalability challenges [47,48]. Future directions suggest hybrid models combining mechanistic precision with empirical contextualization, supported by cross-climate validation and advances in biometric sensing and machine learning [49-52].

3.1.3 Frequency of outdoor thermal comfort indicators

The most frequently used OTC indices are PET, UTCI, PMV, and SET (Fig. 5 .(Other commonly applied indices include Apparent Temperature (AT), Discomfort Index (DI), Perceived Temperature (PT), and Wet Bulb Globe Temperature (WBGT). Indices cited only once were excluded from the analysis.

Figure 5

Frequency of outdoor thermal comfort indicators.

Fig. 5. Frequency of outdoor thermal comfort indicators.

OTC is often assessed using PET and UTCI, valued for applicability across diverse climates. PET, derived from the Munich Energy Balance Model for Individuals (MEMI), integrates environmental and physiological variables and often outperforms SET, PMV, and UTCI in dynamic outdoor conditions [15,16,30,53]. UTCI, based on the multi-node UTCI-Fiala model, offers high accuracy but is limited by reliance on European–Russian datasets and fixed clothing assumptions [54]. PMV, developed for stable indoor settings, shows lower accuracy outdoors due to insensitivity to solar radiation and variable wind [16,55]. Its adaptation, OUT-SET, improves radiation modeling but may overestimate physiological responses despite strong correlations with thermal sensation votes [30,56]. WBGT, widely applied for heat stress assessment, demonstrates reduced accuracy in humid climates and often produces results comparable to UTCI [35].

3.1.4 Research methodologies in OTC studies

OTC studies employ empirical methods, based on field measurements, and numerical approaches using simulation models. Choice depends on objectives, resources, and data availability. Increasingly, integrated approaches combine real-world accuracy with analytical depth, as illustrated in (Fig. 6), enhancing the robustness and comprehensiveness of OTC assessments.

Figure 6

Research methodologies in OTC studies.

Fig. 6. Research methodologies in OTC studies.

3.1.4.1 Numerical and simulation-based approaches

Numerical methods in OTC research employ tools such as computational fluid dynamics (CFD), ENVI-met, RayMan, and Rhino, along with parametric platforms like Ladybug and Dragonfly, integrated with EnergyPlus and OpenFOAM [57-61]. Geographic Information Systems (GIS) and sensing technologies further enable localized, real-time assessments [62-65]. Advanced predictive tools, including machine learning algorithms such as support vector machines, random forests, and neural networks and agent-based modeling are increasingly applied to forecast thermal comfort in complex outdoor environments. These methods bridge computational rigor with empirical validation, supporting robust urban microclimate analysis and sustainable urban design [66-68].

3.1.4.2 Empirical methodologies

Empirical methodologies in OTC research integrate conventional and advanced techniques to evaluate both environmental conditions and human thermal responses. Environmental data are obtained using weather stations, data loggers, and microclimate sensors, measuring parameters such as air temperature, humidity, wind speed, and solar radiation [17]. Urban morphology is analyzed through fisheye lens photography, video documentation, and geospatial tools, with hemispherical imagery frequently employed to calculate the SVF and solar exposure patterns [69]. Subjective data are collected via spot measurements and thermal satisfaction surveys based on ASHRAE Standard 55, complemented by structured interviews and observational approaches such as activity logs and posture tracking. For instance, Peng (2019) applied path analysis to examine how age, BMI, health status, and outdoor activity frequency influence thermal perception, emphasizing individual variability [70]. Qualitative methods, including photographic comparisons, enrich OTC assessments by linking visual preferences to perceived comfort [71]. Since the late 20th century, technological progress has advanced Objective Physical Environment Measurement (OPEM), replacing manual techniques with automated systems such as Data Acquisition Systems (DAS) and GIS for high-resolution spatial analysis [72,73]. Recent innovations, including Building Information Modeling (BIM), the Internet of Things (IoT), and Virtual Reality (VR), enable real-time data integration and immersive visualization. For example, Shahin Moghadam et al. (2021) developed an IoT-based BIM platform employing edge computing to estimate MRT in alignment with PMV and PPD indices [74]. Non-contact technologies, such as smartphone thermal cameras and video-based motion detection, also support mobile, behavior-sensitive OTC monitoring, though challenges remain regarding outdoor measurement accuracy [64,75].

3.2. The impact of urban morphology and microclimate interactions on outdoor human thermal comfort

Urban morphology (UM) the spatial arrangement of buildings and open spaces significantly affects local microclimates and outdoor thermal comfort (Fig. 2). Early research focused on two-dimensional aspects like green cover and impervious surfaces related to land surface temperature (LST), while recent studies highlight three-dimensional factors such as building height, density, and volume [23]. This section reviews macro- and micro-scale literature to inform thermally responsive urban design.

3.2.1 Interaction between urban density, form

Global urbanization is projected to reach 68–70% by 2050, driving denser city structures typically measured by Building Coverage Ratio (BCR) and Floor Area Ratio (FAR) [23]. While density improves infrastructure efficiency and reduces per capita energy use, it also intensifies microclimatic extremes. Urban canyons and impermeable surfaces amplify the UHI effect, elevating nighttime temperatures by 2–5°C and diminishing OTC [9,28,76]. Tall building clusters, as observed in cities such as Beijing and Toronto, reduce daytime solar exposure but trap heat after sunset; in contrast, low-density sprawl facilitates ventilation yet increases daytime heat stress [23,77]. Field investigations and CFD simulations reveal density-related temperature increases of up to 1°C and 2.5°C, respectively [78]. Vertical configurations aggravate surface temperature gradients, while horizontal sprawl restricts airflow. A large-scale study in southern China identified urban density as the dominant predictor of thermal discomfort, surpassing vegetation cover

and surface reflectivity [79].

Urban form also plays a decisive role in shaping thermal conditions. Low SVF designs, such as mid-rise buildings with narrow streets, enhance summer shading but obstruct winter sunlight, whereas high SVF grids promote solar gain yet disrupt airflow, intensifying heat stress [78]. Seasonal trade-offs persist: compact forms reduce summer PET but limit winter warmth, while open forms yield opposite outcomes [80,81]. Beyond physical form, human adaptation reflects both environmental and sociocultural contexts. High density and steep thermal gradients elevate psychological stress, requiring hybrid solutions such as shaded courtyards and comfort-oriented urban design [82]. Cultural background and behavioral adaptability also shape thermal perception. Populations in warmer climates, such as Greece, often report lower heat sensitivity, whereas residents of colder regions, such as Harbin, display higher TSV under similar PET levels [83,84]. Temporary populations during heatwaves exhibit elevated TSV, reflecting differences in thermal expectations [85]. Evidence from Shanghai and Cairo further demonstrates how migration and gender influence thermal behavior, underscoring the need for culturally responsive design. Achieving urban thermal resilience requires the integration of ecological infrastructure, density-sensitive planning, and sociocultural understanding [86,87].

3.2.2 Interaction between urban canyons, orientation

The geometry and orientation of urban canyons strongly shape microclimates and OTC by regulating solar access, ventilation, and UHI intensity. Core parameters H/W, SVF, and L/W control solar penetration and heat retention within canyon spaces [10,88]. Among these, the interaction between H/W and orientation is particularly critical. East–west canyons often record higher PET due to prolonged solar exposure, while diagonal orientations (e.g., NW–SE, NE–SW) provide more balanced conditions through improved shading and solar modulation [89,90]. In hot climates, high H/W ratios (> 2.0) lower SVF and PET, enhancing summer comfort, as reported in Agadir and Fez [81]. However, excessive shading combined with limited ventilation may reduce comfort in colder or humid climates. Cultural and climatic contexts further influence perception; for example, residents in Phoenix and Marrakech display distinct responses to heat stress [91].

3.2.3 Interactions between urban materials and urban heat islands

UHI intensity is influenced by land use, vegetation cover, and material properties, such as albedo, emissivity, and thermal conductivity, which affect OTC and urban energy demand at multiple scales [32,92]. Surface albedo and the SRI are critical for UHI mitigation. High-SRI, light-colored materials reduce surface temperatures, improve pedestrian comfort, and lower building energy consumption [93]. Their effectiveness depends on urban form; wide street canyons typically show air temperature reductions, whereas narrow canyons may experience increased Tmrt from reflected radiation [94]. Combining reflective surfaces with shading elements, such as street trees, optimizes air and radiant temperatures, enhancing UTCI [95]. Thermal emissivity mitigates UHI by allowing surfaces to radiate stored heat [96]. Cooling strategies prioritize surface reflectivity, urban geometry, and materials with appropriate thermal properties. Pavement color and texture influence surface temperatures, with light, smooth surfaces lowering ST by up to 5°C [97,98]. Thermal conductivity and heat capacity govern heat transfer; high heat capacity suits ventilated canyons, while low conductivity benefits dense urban areas [20].

3.2.4 Interactions between urban green and outdoor thermal comfort

Urban green infrastructure including forests, street trees, gardens, wetlands, green roofs, and green walls significantly mitigates UHI effects and enhances OTC [99]. Vegetation improves microclimates by providing shade, promoting evapotranspiration, and facilitating airflow. In the U.S., shade trees reduce surface temperatures by approximately 3.1 °C, while dense tree planting in Riyadh lowers PET by up to 16.9 °C, outperforming green walls [100-103]. Seasonal and contextual factors strongly influence effectiveness. In Changchun, summer LST decreased by 1.27 °C, with vegetation density (NDVI) affecting thermal conditions more than patch size or fragmentation [104]. In Chengdu, tree cover enhanced summer comfort, whereas open lawns were preferred in winter [22]. In Xi’an, winter thermal sensation was driven primarily by solar radiation, followed by air temperature and humidity, with residents favoring sunlit areas, emphasizing the need for seasonally adaptive, climate-sensitive urban design [105].

Time of day strongly influences the cooling effect of urban vegetation. In Cairo, increasing canopy cover to 35–50% reduced PET by over 5 °C during peak afternoon hours, while efficient irrigation decreased water use by 85% [101]. Tree placement near tall buildings, as observed in Prague, reduced cooling by 1 °C [103]. Mixed tree species, including Acer, Ulmus, and Pinus in Tabriz, lowered PMV to 2.39 [106]. In Xi’an, Ginkgo biloba significantly reduced UTCI, and aesthetic characteristics affected perceived warmth by up to 2 °C [107]. Seasonal performance varies with species composition; mixed deciduous-evergreen canopies were most effective in tropical and temperate zones, whereas evergreens performed best in arid climates [104,108-110]. At the building scale, green roofs reduce energy loads but provide limited pedestrian benefits, whereas green walls lower Tmrt by up to 5 °C and reduce pollutants, enhancing street-level comfort [105]. Thermal comfort is both physical and perceptual: in Nuevo Bosque Park, Colombia, 90.91% of visitors reported comfort under tree cover despite a THI of 55 °C, while 69.69% experienced discomfort in open area [111]. Physiological studies confirm brief exposure to greenery lowers heart rate, increases parasympathetic activity, and enhances emotional well-being [112].

3.2.5 Interactions between urban water bodies and urban heat island mitigation

Urban water bodies (UWBs) including lakes, rivers, ponds, and wetlands significantly mitigate UHI effects and improve thermal comfort through thermal inertia and evaporative cooling (Fig. 7). By absorbing solar radiation and releasing moisture, UWBs can lower nearby air temperatures by up to 3 °C during peak heat [113,114]. Cooling effects vary by climate: tropical regions experience year-round moderation but limited evaporation due to high humidity; Mediterranean climates benefit most during hot summers; arid regions experience both daytime and nighttime cooling due to low humidity. Diurnal cooling ranges from 3–5 °C, with minor nighttime warming of 1–2 °C, while seasonal cooling peaks in late spring and summer, dropping by 0.5–2 °C in winter [114,115]. Cooling efficiency depends on size, depth, shape, and location. Larger, deeper water bodies retain cooling longer, and alignment with prevailing winds enhances airflow. Urban density and surface roughness limit UWB influence to 100–50 [116], with optimal relief occurring 10–20 m from edges. While UWBs have limited effect on mean radiant temperature, combining them with vegetation and permeable surfaces increases effectiveness [113]. Modern mist systems reduce temperatures by 2–3 °C, improving PET during heatwaves [115-117]. Traditional designs, such as Iranian gardens, also enhance comfort. Urban blue spaces are increasingly recognized for their thermal and health benefits [81,118].

Figure 7

Influence of urban morphology on environmental factors and urban heat island effect.

Fig. 7. Influence of urban morphology on environmental factors and urban heat island effect.

3.2.6 Building-scale morphology and façade strategies for outdoor thermal comfort

Building height, spacing, and orientation significantly influence OTC by shaping radiative and convective exchanges at street level. ENVI-met simulations in Toronto indicate that clustering high-rise towers enhances airflow and reduces air temperature, lowering MRT and mitigating UHI effects in cooler climates [119]. In Tabriz, characterized by cold winters and hot summers, combined ENVI-met and RayMan analyses show that a specific H/W ratio and street orientation optimally balance solar access and shading, improving PET throughout the year [120]. Façade geometry including canopies, podiums, and permeable floors can be designed to improve wind conditions at street level [121]. Canopies reduce wind intensity around pedestrian areas, podiums lower wind speeds near buildings, and mid-tower permeable floors contribute to airflow control. Poorly positioned ground-level openings, however, may increase local wind exposure. Strategic façade design is critical for enhancing pedestrian comfort and creating favorable microclimates [122-127]. Large-eddy simulations demonstrate that windward-facing balconies may block canyon airflow, reduce wind speeds, increase pollutant concentrations, and raise sidewalk exposure, impairing convective cooling and worsening localized UHI [24]. ENVI-met simulations in Mediterranean climates show that green façades and roofs reduce air temperature and UTCI, particularly in courtyard designs [25]. Reflective glass and metal façades increase heat absorption and re-radiation in tropical areas, exacerbating pedestrian thermal stress [128,129]. Advanced materials, such as phase change materials, cool coatings, and glass-ceramics, further reduce UHI, highlighting the importance of climate-responsive façades [130,131]. External shading devices including overhangs, louvers, and canopies effectively reduce solar gain and lower MRT. Passive shading in Southern China decreases PET and UTCI during summer [132]) Fig. 8(.

Figure 8

Microclimate strategies for enhancing outdoor thermal comfort.

Fig. 8. Microclimate strategies for enhancing outdoor thermal comfort.

3.3 Human–environment interactions and outdoor thermal comfort

This section introduces a holistic framework for OTC using GMA. It considers OTC as a multidimensional outcome influenced by direct factors physical, physiological, and psychological and indirect factors such as behavioral, personal, social, cultural, and alliesthesia contexts (Fig. 9). The framework guides climate-sensitive urban design to enhance comfort in diverse outdoor environments.

Figure 9

Human–environment interactions and outdoor thermal comfort.

Fig. 9. Human–environment interactions and outdoor thermal comfort.

3.3.1 Direct influences

3.3.1.1 Physical factors

OTC results from the complex interaction of environmental factors air temperature, solar radiation, wind, and humidity that influence the body’s heat exchange through conduction, convection, radiation, and evaporation. Air temperature is the most influential factor. A year-long study in Harbin, China, confirmed temperature as the primary driver of thermal perception, shaped by seasonal behavior and clothing adaptations [39]. Cognitive and behavioral factors also affect thermal sensation; Liu et al. reported discrepancies between expected and actual thermal experiences [70]. In Guilin’s hot-summer, cold-winter climate, children exhibited heightened thermal sensitivity, with a neutral temperature near 15 °C and a comfort range of 5–26 °C, emphasizing the importance of passive strategies such as shading, high-albedo materials, and cross-ventilation [133]. Solar radiation and wind substantially modify thermal comfort: sun exposure improves comfort in cool conditions but increases MRT in hot climates, while wind enhances convective cooling, particularly in warm, breezy environments [134]. Humidity significantly affects comfort in hot-humid regions, such as Singapore [135].

3.3.1.2 Physiological factors

Physiological responses including sweating, vasodilation, and cardiovascular regulation are essential for thermal balance outdoors. Key indicators such as skin temperature, core temperature, sweat rate, and heart rate variability measure thermal strain. Skin temperature, around 32.7 °C in neutral conditions, is highly sensitive to environmental changes. Solar radiation, wind, and clothing influence regional skin temperature variations, sometimes exceeding core temperature effects [136,137]. Transient heat transfer models estimate mean skin temperature, but local deviations can reach 15 K in extreme cold [52]. Machine learning methods, including Support Vector Machines, accurately predict thermal states from localized skin temperature and thermal load [138]. Urban morphology, via SVF, affects physiological responses. Sweat rate varies by sex, fitness, and humidity, with high humidity reducing evaporative cooling and increasing thermal strain [127,139].

3.3.1.3 Psychological factor

Psychological factors critically influence outdoor thermal comfort, encompassing subjective aspects such as prior experience, individual expectations, and perceived control, beyond physiological responses. ASHRAE (2017) defines thermal comfort as a “condition of mind expressing satisfaction with the thermal environment,” emphasizing mental and emotional dimensions amid variable outdoor conditions affected by solar radiation, wind, and humidity [12]. Adaptation and experience shape thermal neutrality and preference. In Nepal, residents tolerated higher temperatures than recent migrants, reflecting behavioral and physiological acclimatization to subtropical climates [140]. European studies indicated that neutral PET increased with annual mean temperature, demonstrating climate-responsive adaptation [141]. A large survey in Szeged, Hungary, revealed notable seasonal variations in neutral PET, highlighting the importance of regional and seasonal factors in thermal comfort models [142]. Expectations influence perception; indoor conditions, time of day, and environmental context affect comfort independently of outdoor climate [143]. Anticipated shade, wind, or leisure settings enhance comfort, as shown in Hong Kong [144] and the Caribbean [145]. Additionally, greenery, shading, and perceived control significantly improve well-being and emotional adaptation [146,174].

3.3.2 Indirect influences

3.3.2.1 Behavioral factors: clothing and activities

Behavioral responses including clothing choices and activity patterns significantly influence outdoor thermal comfort, reflecting complex interactions between environmental conditions and socio-cultural norms. In urban environments, user attendance strongly correlates with microclimatic factors such as air temperature, solar radiation, wind, and humidity. A study in Taichung City, Taiwan, reported peak attendance at moderate PET ranges, with over 75% of users preferring shaded areas, engaging primarily in passive activities, and prolonging their stay, emphasizing psychological aversion to direct sunlight and the critical role of shade and tree canopy in warm-climate urban design [38,148]. Clothing insulation regulates heat exchange and strongly affects thermal perception. A survey of 563 tourists in Porto showed that both clothing and activity levels significantly influenced comfort, with clothing insulation highly correlated with air temperature. Seasonal and demographic variations were observed: women wore lighter clothing in summer, while older adults preferred higher insulation in winter. In culturally conservative settings, such as Tehran, clothing flexibility is constrained. Observed behaviors including sun avoidance, activity-driven thermal tolerance, gender-based solar sensitivity, and adaptive strategies in hot-arid climates underscore the complex and adaptive nature of thermal comfort responses [39,149-152].

3.3.2.2 Personal factors

Gender significantly affects outdoor thermal perception across climates. Women often report thermal neutrality at higher PET values and demonstrate greater sensitivity to environmental fluctuations than men [151]. In Harbin, females preferred warmer conditions and recorded lower Mean Thermal Sensation Votes at equivalent UTCI levels [153]. In Al Ain, men tolerated higher heat, while women sought shade and reported greater discomfort, emphasizing the need for gender-responsive design [152]. Generally, females exhibit lower tolerance to cold and wind, whereas males prefer stronger airflow in hot, stagnant settings, though differences diminish with increased air velocity. ASHRAE-55 has been criticized for overestimating airflow’s cooling effect, especially in mixed-gender contexts [154]. Simulations indicate females experience higher PET and greater thermal sensitivity, reinforcing the importance of gender-informed urban planning [155]. In Xi’an, girls showed greater heat resistance during light activity, whereas boys tolerated intense exertion more effectively [156]. Cross-climate studies further reveal females’ higher susceptibility to cold and reduced thermal neutrality, with younger individuals reporting greater discomfort than older adults [157]. Age also influences thermal comfort through physiological and behavioral mechanisms. In Prague, middle-aged adults reported the highest comfort during hot summers, whereas younger and older groups felt less comfortable; the elderly often experienced discomfort even in cooler conditions due to reduced metabolism and adaptability [158]. Older adults in northeastern China displayed narrower, lower UTCI neutrality ranges, indicating cold sensitivity but greater heat tolerance [159]. However, a year-long study in Kitakyushu found no significant age–comfort relationship [29,160]. Body characteristics such as skin color, body weight, and composition affect thermal comfort. In Mexico, brown-skinned women exhibited higher BMI and obesity rates than white-skinned women, a gender-specific association not observed in men [161]. Among young male students, fitness and body fat percentage influenced comfort under neutral–cool conditions [162]. Integrating BMI, exercise habits, tissue thickness, and skin temperature shows that greater fat or muscle mass shifts thermal preference toward cooler environments, with muscle mass enhancing cold tolerance [163].

3.3.2.3 Social & cultural factors

Social characteristics significantly influence OTC by shaping perception and adaptive behaviors. In cold-climate regions of China, occupational roles and daily routines markedly affected thermal perception, demonstrating the role of social identity in mediating environmental experience [164]. In Lebanon, social behaviors, cultural norms, and spatial usage patterns strongly impacted perceived comfort, emphasizing the importance of urban design that reflects local social dynamics [165]. In Mexico, higher obesity rates among brown-skinned women compared to white-skinned women were linked to structural inequalities, such as limited education, discrimination, and reduced access to neighborhood services, which also affect comfort and well-being [161]. Cultural background further shapes OTC by influencing thermal preferences, adaptive behaviors, clothing choices, and environmental attitudes. Traditional dress codes in Marrakech and Phoenix altered clothing insulation and behavioral adaptation [91], while tourists’ cultural origins in Porto affected both comfort perception and attire selection [150]. Collectively, these findings highlight the need for socially and culturally sensitive urban climate strategies that integrate occupation, daily routines, local practices, and cultural norms to enhance outdoor thermal comfort inclusively and effectively.

3.3.2.4 Site

Site specific factors critically shape OTC by altering local microclimates through material selection and urban form. In Hangzhou, China, high-albedo surfaces in children’s play areas increased thermal discomfort during peak sunlight, highlighting the need for adequate shading [50]. In hot climates, pavement albedo affected air temperature and UTCI more than building façades [166]. In Harbin, open campus spaces with diverse landscape elements promoted winter outdoor activity, showing how thoughtful spatial design can reduce cold stress [167]. Conversely, simulations at Tehran’s Mehr-Abad Airport indicated that tree cover improved summer comfort by lowering PET but also blocked beneficial solar gain in winter, reducing comfort [21]. These findings highlight the importance of seasonally adaptive, site-specific design balancing shading, material choice, and vegetation to optimize OTC year-round.

3.3.2.5 Alliesthesia

Recent research on alliesthesia has clarified how dynamic thermal environments shape OTC through interconnected temporal, seasonal, microclimatic, and neurophysiological processes. Field research in Sydney identified four categories of thermal experience strong (Hot/Cold) and moderate (Warm/Cool) and demonstrated that thermal pleasure increases as conditions approach neutrality and decreases when they deviate from it. Seasonal adaptation was evident through preferences for cooler conditions in summer and warmer conditions in winter, illustrating temporal alliesthesia [168]. Experimental studies using the humidity-inclusive Adaptive Thermal Comfort model (ATCRH) emphasized the interaction between humidity and airflow. Low humidity produced comfort across all airspeeds, whereas high humidity required higher airflow to maintain satisfaction. Perceptual differences were also influenced by culture: British participants described humid heat as sauna-like, while Indian participants perceived it as heavy and oppressive [169]. Seasonal alliesthesia also revealed that individuals preferred slightly warm conditions in cool seasons and slightly cool ones in warm seasons, reinforcing the need for seasonally adaptive outdoor design [170]. Seasonal alliesthesia indicated preferences for slightly warm conditions during cold periods and slightly cool conditions during warm ones, reinforcing the importance of seasonally adaptive design [155]. Field-based thermal walks in Phoenix revealed that microclimatic features such as shading and lower sky view factor enhanced thermal pleasure. The PET, which integrates temperature, humidity, radiation, and wind speed, effectively captured variations in perceived comfort [171]. Neurophysiological investigations confirmed the biological foundation of alliesthesia, as thermoreceptor-based models combined with machine-learning algorithms accurately identified pleasant and unpleasant states. These findings emphasize that outdoor spaces should be designed to promote sensory delight rather than mere thermal neutrality [172].

4. Discussion

This research aimed to unpack the complex relationship between urban morphology and OTC, with a particular focus on addressing the challenges posed by UHI effects in dense cityscapes. A systematic literature review, guided by the PRISMA framework, synthesized a wide array of empirical studies, modeling approaches, and theoretical insights from across climate-sensitive urban design literature. Analytical methods, including GMA and CCA were employed to translate this body of knowledge into a structured design logic. These methods facilitated the identification of key parameters, interactions, and constraints that shape OTC outcomes in complex urban environments.

4.1 Development of the design tool

The principal outcome of this study is the Design Tool. This five-layered framework helps urban designers and planners assess and optimize the thermal impacts of morphological and material decisions. It integrates urban form, environmental modifiers, thermal comfort indices, and human adaptive responses, functioning as both an analytical tool and an early-stage decision-support system. As illustrated in (Fig. 10), its hierarchical structure and feedback loops connect design, climate, and user experience. Adaptable to diverse climatic, cultural, and demographic contexts, the tool supports evidence-based, climate-responsive design by demonstrating how spatial choices shape environmental conditions and ultimately influence human thermal comfort.

Figure 10

Development of the design tool.

Fig. 10. Development of the design tool.

4.1.1 Urban morphological parameters

The first layer comprises the fundamental spatial and material determinants of the urban microclimate, including urban form, density, canyon geometry (e.g., H/W, SVF), surface material properties (e.g., albedo, thermal mass), and green–blue infrastructure. These parameters collectively influence solar exposure, wind patterns, air temperature, humidity, and radiative exchange—ultimately shaping the pedestrian thermal experience. For example, narrow, high-density urban canyons with low SVF provide shade in hot climates but may restrict ventilation. in contrast, more open forms enhance airflow and solar access, which is beneficial in colder regions. Street orientation also plays a crucial role: east–west-oriented streets tend to accumulate heat in the afternoon, while diagonal layouts distribute solar gains more evenly throughout the day. Surface material characteristics further impact thermal conditions; high-albedo materials can reduce heat absorption but may increase MRT if not adequately shaded. Vegetation offers evaporative cooling and psychological benefits, while water features can help mitigate heat but may also increase humidity, especially in already humid climates. Therefore, these strategies must be contextually adapted and aligned with user behavior and prevailing environmental conditions to ensure their effectiveness.

4.1.2 Environmental modifiers and microclimatic strategies

The second layer focuses on refining microclimatic conditions through strategic material and landscape interventions that modulate four key environmental variables: air temperature, wind speed, humidity, and solar radiation. Air temperature is influenced by factors such as thermal mass, shading, and surface albedo; wind dynamics are shaped by building configuration, orientation, and vegetation density; humidity is moderated by vegetation, water features, and permeable surfaces; and solar radiation is governed by orientation, canyon geometry, and SVF. High-albedo surfaces reflect shortwave radiation and reduce surface heating, though they may elevate MRT if not sufficiently shaded. Vegetation contributes to microclimatic regulation through evapotranspirative cooling and also offers psychological benefits. Similarly, water features can provide localized cooling but may increase humidity levels, particularly in already humid environments. The design rationale emphasizes climate- and site-specific strategies that are responsive to temporal dynamics, including diurnal and seasonal variations as well as patterns of human activity. Accounting for these temporal factors is essential to ensure that microclimatic interventions align with actual exposure scenarios and user behavior.

4.1.3 Thermal comfort indices

The third layer functions as an interpretive bridge between environmental conditions and human thermal perception by incorporating a range of thermal comfort indices. Mechanistic indices—such as PET, UTCI, and PMV—simulate thermo-physiological responses under standardized assumptions, providing reliable benchmarks for evaluating thermal performance. However, these models may not fully capture the variability of microclimatic conditions or the dynamic nature of human responses. Empirical and hybrid indices address these limitations by integrating subjective comfort feedback, behavioral adaptations, and cultural expectations. Such approaches enhance contextual relevance and inclusivity by accounting for local climatic conditions and population-specific sensitivities. Incorporating demographic variables and aligning with established standards, such as ASHRAE 55 and ISO 7730, further improves the precision of comfort assessments. Recent advances in machine learning and real-time environmental sensing enable dynamic comfort modeling by linking sensor data with user feedback, offering predictive and adaptive insights for responsive urban design. By combining quantitative and qualitative assessments, this layer ensures that thermal comfort evaluations are both scientifically grounded and human-centered.

4.1.4 Human factors and behavioral adaptation

The fourth and fifth layers of the design tool underscore the critical role of human variability and adaptive behavior in shaping outdoor thermal comfort, moving beyond the simplified standardized occupant model assumptions commonly embedded in conventional frameworks. These layers incorporate a wide spectrum of factors: physiological characteristics such as age, gender, metabolic rate, and body composition influence thermal tolerance and sensitivity, with vulnerable populations such as the elderly and women often exhibiting heightened susceptibility to heat and cold extremes. Behavioral adaptations including clothing insulation, activity levels, shade-seeking behavior, and timing of outdoor exposure significantly affect thermal perception and comfort outcomes. Socio-cultural dimensions, such as regional dress codes, lifestyle patterns, and cultural expectations, further mediate adaptive responses, as illustrated by diverse practices ranging from Mediterranean siestas to attire norms in desert cities. Psychological factors, including alliesthesia and thermal history, reveal that thermal comfort is not static but is dynamically shaped by prior thermal experiences and emotional states. By integrating demographic profiling, real-time environmental sensing, and behavioral mapping, the tool enables context-sensitive and inclusive design strategies. This comprehensive, human-centered framework is essential for promoting equitable thermal comfort and enhancing urban resilience in the face of escalating heat stress and increasing climatic, cultural, and demographic diversity.

5. Conclusion

Urban morphology plays a pivotal role in shaping pedestrian thermal comfort in urban environments by influencing microclimatic conditions such as solar exposure, ventilation, humidity, and radiant heat exchange. Key morphological parameters including urban density, canyon geometry, street orientation, surface materials, vegetation, and water features determine how pedestrians experience outdoor conditions. These features interact dynamically with environmental modifiers, human adaptive behaviors, and the localized effects of the urban heat island, producing thermal comfort outcomes that are highly context-dependent. Physiological characteristics, behavioral adaptations, and socio-cultural or psychological factors further influence individual perception of comfort. Consequently, pedestrian outdoor thermal comfort emerges as a multidimensional, context-sensitive phenomenon arising from the complex interplay between urban morphology, human interaction with urban morphology, and the influence of the urban heat island. Addressing this complexity, the study developed a five-layered parametric design tool that integrates urban morphology, microclimatic strategies, thermal comfort indices, and human adaptive behavior into a single, unified framework. Analytical methods, including General Morphological Analysis and Constraint Cross-Analysis, translate theoretical insights into practical design logic, allowing systematic exploration of urban form variations. The tool enables simulation of alternative configurations, prediction of microclimatic impacts, and evaluation of pedestrian comfort outcomes across diverse climatic and cultural contexts. By bridging urban morphology with human-centered considerations, the parametric design tool provides a decision-support system for climate-responsive, evidence-based, and inclusive urban design strategies.

This integrated approach demonstrates that optimizing pedestrian thermal comfort requires both an understanding of morphological influences and the ability to translate these insights into actionable urban design interventions. The relationship between urban form, human behavior, and environmental conditions underscores the value of parametric tools for creating adaptive, resilient, and socially inclusive urban environments that respond effectively to the challenges posed by the urban heat island.

5.1 Limitations

Several limitations should be acknowledged when interpreting the findings. The systematic review relied on selected peer-reviewed sources published in specific languages, potentially excluding regional studies or non-English research addressing local climatic and cultural conditions. The proposed Design Tools framework, though comprehensive, simplifies the multiscale and context-dependent interactions among urban morphology, microclimatic parameters, thermal indices, and human adaptive responses. Existing comfort models such as PET, PMV, and UTCI assume steady-state conditions, which may not capture transient or subjective outdoor thermal perception. Microclimatic variations from vegetation, water bodies, and materials are dynamic and may not be fully represented. Empirical validation and predictive capacity under future climatic or socio-technological changes remain uncertain, highlighting the need for longitudinal, cross-cultural, and multi-scale studies.

5.2 Future research directions

Future research should prioritize empirical testing and refinement of the parametric design tool in diverse climatic, urban, and socio-cultural contexts. Longitudinal field studies incorporating real-time microclimatic measurements and participatory pedestrian feedback will enhance predictive capacity. Expanding data sources to underrepresented regions and integrating non-traditional knowledge can further illuminate urban morphology–comfort interactions. Advances in computational modeling, GIS-based visualization, mobile platforms, and machine learning offer opportunities to improve precision, scalability, and interactivity, ultimately supporting the design of inclusive, equitable, and thermally comfortable urban environments.

APPENDIX A

Summarizes key previous studies conducted between 2015 and 2025, highlighting major findings and methodologies related to outdoor thermal comfort (Table A1).

Table A1

Previous studies from 2015–2025.

Table A1. Previous studies from 2015–2025.

APPENDIX B

Classifies outdoor thermal comfort indices into three categories: Linear (environmental variables), Mechanistic (physiological and environmental factors), and Empirical (subjective or objective assessments (Table B1).

Table B1

Categorizing outdoor thermal comfort indices.

Table B1. Categorizing outdoor thermal comfort indices.

Funding

This research received no external funding.

Contributions

F. Shoghi: Conceptualization, Methodology, Data curation, Writing- Original draft preparation, Visualization, Investigation, Validation, Writing- Reviewing and Editing; S. M. Hosseini: Conceptualization, Methodology, Investigation, Validation, Writing- Reviewing and Editing, Supervision; S. Heidari: Methodology, Writing- Reviewing and Editing, Supervision; J. Wangc: Writing- Reviewing and Editing; M. Mahdavinejad : Writing- Reviewing and Editing; S. Attia: Writing- Reviewing and Editing.

Declaration of competing interest

The authors declare no conflict of interest.

References

  1. C. Winston, D. Richard, G. Bruce, H. Marjolijn, P. Mark, and S. William, IPCC Sixth Assessment Report (AR6): Climate Change 2022 - Impacts, Adaptation and Vulnerability: Factsheet Human Settlements, Intergovernmental Panel on Climate Change, (2022).
  2. W. He, L. Zhang, and C. Yuan, Future air temperature projection in high-density tropical cities based on global climate change and urbanization - a study in Singapore, Urban Climate, 42 (2022) 101115. https://doi.org/10.1016/j.uclim.2022.101115
  3. D. Mauree, E. Naboni, S. Coccolo, A. T. D. Perera, V. M. Nik, and J.-L. Scartezzini, A review of assessment methods for the urban environment and its energy sustainability to guarantee climate adaptation of future cities, Renewable and Sustainable Energy Reviews, 112 (2019) 733-746. https://doi.org/10.1016/j.rser.2019.06.005
  4. I. Nakach, O. Mouhat, R. Shamass, and F. El Mennaouy, Review of strategies for sustainable energy in Morocco, Polityka Energetyczna, 26 (2023). https://doi.org/10.33223/epj/163373
  5. A. Attia, S. Petersen, E. Hoxha, E. Gobbo, A. Bertinit, M. Dasse, et al. Framework to Model Building Carbon Emissions, Report, Sustainable Building Design Lab, Liege University, Liege, Belgium, 2024.
  6. E. Hoxha, S. M. Hosseini, B. Soust-Verdaguer, J. De Boer, Environmental Impacts of Light Sources in Buildings: Analysis of Environmental Product Declarations (EPDs) in European Union, Buildings, 15 (2025) 1279. https://doi.org/10.3390/buildings15081279
  7. P. He, et al., The impact of neighborhood layout heterogeneity on carbon emissions in high-density urban areas: A case study of new development areas in Hong Kong, Energy and Buildings, 287 (2023) 113002. https://doi.org/10.1016/j.enbuild.2023.113002
  8. K. Y. Cheng, K. Lau, Y. T. Shek, Z. Liu, and E. Ng, Evaluation on the performance of tree view factor in a high-density subtropical city: A case study in Hong Kong, Building and Environment, 239 (2023) 110431. https://doi.org/10.1016/j.buildenv.2023.110431
  9. G. Evola, et al., UHI effects and strategies to improve outdoor thermal comfort in dense and old neighbourhoods, Energy Procedia, 134 (2017) 692-701. https://doi.org/10.1016/j.egypro.2017.09.589
  10. J.-Y. Deng and N. H. Wong, Impact of urban canyon geometries on outdoor thermal comfort in central business districts, Sustainable Cities and Society, 53 (2020) 101966. https://doi.org/10.1016/j.scs.2019.101966
  11. X. Deng, et al., Influence of built environment on outdoor thermal comfort: A comparative study of new and old urban blocks in Guangzhou, Building and Environment, 234 (2023) 110133. https://doi.org/10.1016/j.buildenv.2023.110133
  12. V. F. Ličina, et al., Development of the ASHRAE global thermal comfort database II, Building and Environment, 142 (2018) 502-512.
  13. X. Liu, ASTM and ASHRAE standards for the assessment of indoor air quality, in: Handbook of Indoor Air Quality, Springer, (2022) 1511-1545. https://doi.org/10.1007/978-981-16-7680-2_50
  14. S. Masoud, Z. Zamani, S. M. Hosseini, M. Mahdavinejad, J. Wang, Enhancing Classroom Lighting Quality in Tehran Through the Integration of a Dynamic Light Shelf and Solar Panels, Buildings, 15 (2025) 2215. https://doi.org/10.3390/buildings15132215
  15. I. Golasi, F. Salata, E. de Lieto Vollaro, and M. Coppi, Complying with the demand of standardization in outdoor thermal comfort: a first approach to the Global Outdoor Comfort Index (GOCI), Building and Environment, 130 (2018) 104-119. https://doi.org/10.1016/j.buildenv.2017.12.021
  16. F. A. Majidi and S. Heidari, Analysis of adaptability signs in the thermal comfort of open spaces (Case study: Isfahan residential neighborhoods), Journal of Fine Arts: Architecture & Urban Planning, 24 (2019) 17-28.
  17. G.-S. Song and M.-A. Jeong, Morphology of pedestrian roads and thermal responses during summer in the urban area of Bucheon city, Korea, International Journal of Biometeorology, 60 (2016) 999-1014. https://doi.org/10.1007/s00484-015-1092-9
  18. C. Vasilikou and M. Nikolopoulou, Outdoor thermal comfort for pedestrians in movement: thermal walks in complex urban morphology, International Journal of Biometeorology, 64 (2020) 277-291. https://doi.org/10.1007/s00484-019-01782-2
  19. L. P. Muniz-Gäal, C. C. Pezzuto, M. F. H. de Carvalho, and L. T. M. Mota, Urban geometry and the microclimate of street canyons in tropical climate, Building and Environment, 169 (2020) 106547. https://doi.org/10.1016/j.buildenv.2019.106547
  20. G. Lobaccaro, J. A. Acero, G. Sanchez Martinez, A. Padro, T. Laburu, and G. Fernandez, Effects of orientations, aspect ratios, pavement materials and vegetation elements on thermal stress inside typical urban canyons, International Journal of Environmental Research and Public Health, 16 (2019) 3574. https://doi.org/10.3390/ijerph16193574
  21. Z. Azimi, S. S. Kashfi, A. Semiari, and A. Shafaat, Outdoor thermal comfort in open transitional spaces with limited greenery in hot summer/cold winter climates, Discover Environment, 2 (2024) 31. https://doi.org/10.1007/s44274-024-00062-0
  22. J. Xiao and T. Yuizono, Climate-adaptive landscape design: Microclimate and thermal comfort regulation of station square in the Hokuriku Region, Japan, Building and Environment, 212 (2022) 108813. https://doi.org/10.1016/j.buildenv.2022.108813
  23. J. Zhang, Z. Li, Y. Wei, and D. Hu, The impact of the building morphology on microclimate and thermal comfort-a case study in Beijing, Building and Environment, 223 (2022) 109469. https://doi.org/10.1016/j.buildenv.2022.109469
  24. N. Ishak, W. Hien, H. Jenatabadi, N. Mustafa, and E. Zawawi, Effect of reflective building façade on pedestrian visual comfort, in: IOP Conference Series: Earth and Environmental Science, 385 (2019) 012059. https://doi.org/10.1088/1755-1315/385/1/012059
  25. X. Zheng, H. Montazeri, and B. Blocken, Impact of building façade geometrical details on pollutant dispersion in street canyons, Building and Environment, 212 (2022) 108746. https://doi.org/10.1016/j.buildenv.2021.108746
  26. L. B. Yeo, G. H. T. Ling, M. L. Tan, and P. C. Leng, Interrelationships between Land Use Land Cover (LULC) and Human Thermal Comfort (HTC): A Comparative Analysis of Different Spatial Settings, Sustainability, 13 (2021) 382. https://doi.org/10.3390/su13010382
  27. K. Sadeghi, et al., Resolving shortwave and longwave irradiation distributions across the human body in outdoor built environments, Building and Environment, 277 (2025) 112934. https://doi.org/10.1016/j.buildenv.2025.112934
  28. D. Khamchiangta and S. Dhakal, Physical and non-physical factors driving urban heat island: Case of Bangkok Metropolitan Administration, Thailand, Journal of Environmental Management, 248 (2019) 109285. https://doi.org/10.1016/j.jenvman.2019.109285
  29. D. Hartabela, B. Dewancker, and C. Vidyana, Relationship of Age, Gender, and Body Proportion to Outdoor Thermal Comfort in Open Space, Case Study: Green Park, Kitakyushu, Japan, in: IOP Conference Series: Earth and Environmental Science, 1058 (2022) 012002. https://doi.org/10.1088/1755-1315/1058/1/012002
  30. O. Potchter, P. Cohen, T.-P. Lin, and A. Matzarakis, Outdoor human thermal perception in various climates: A comprehensive review of approaches, methods and quantification, Science of the Total Environment, 631 (2018) 390-406. https://doi.org/10.1016/j.scitotenv.2018.02.276
  31. W. L. Filho, T. Wall, A. L. Salvia, M. A. P. Dinis, and M. Mifsud, The central role of climate action in achieving the United Nations' Sustainable Development Goals, Scientific Reports, 13 (2023) 20582. https://doi.org/10.1038/s41598-023-47746-w
  32. A. M. M. Irfeey, H.-W. Chau, M. M. F. Sumaiya, C. Y. Wai, N. Muttil, and E. Jamei, Sustainable mitigation strategies for urban heat island effects in urban areas, Sustainability, 15 (2023) 10767. https://doi.org/10.3390/su151410767
  33. A. Álvarez and T. Ritchey, Applications of general morphological analysis: from engineering design to policy analysis, Acta Morphological Generalis, (2015).
  34. Z. Liu, J. Li, and T. Xi, A Review of Thermal Comfort Evaluation and Improvement in Urban Outdoor Spaces, Buildings, 13 (2023) 3050. https://doi.org/10.3390/buildings13123050
  35. G. Kalogeropoulos, A. Dimoudi, P. Toumboulidis, and S. Zoras, Urban heat island and thermal comfort assessment in a medium-sized Mediterranean city, Atmosphere, 13 (2022) 1102. https://doi.org/10.3390/atmos13071102
  36. T. Xi, M. Wang, E. Cao, J. Li, Y. Wang, and S. U. Sa'ad, Preliminary research on outdoor thermal comfort evaluation in severe cold regions by machine learning, Buildings, 14 (2024) 284. https://doi.org/10.3390/buildings14010284
  37. H. V. Lankford and L. R. Fox, The wind-chill index, Wilderness & Environmental Medicine, 32 (2021) 392-399. https://doi.org/10.1016/j.wem.2021.04.005
  38. L. Zhang, et al., Outdoor thermal comfort of urban park-a case study, Sustainability, 12 (2020) 1961. https://doi.org/10.3390/su12051961
  39. X. Chen, P. Xue, L. Liu, L. Gao, and J. Liu, Outdoor thermal comfort and adaptation in severe cold area: A longitudinal survey in Harbin, China, Building and Environment, 143 (2018) 548-560. https://doi.org/10.1016/j.buildenv.2018.07.041
  40. K. Boussaidi, D. Djaghrouri, M. Benabbas, and H. Altan, Assessment of outdoor thermal comfort in urban public space, during the hottest period in Annaba City, Algeria, Sustainability, 15 (2023) 11763. https://doi.org/10.3390/su151511763
  41. T. Zhang, B. Hong, X. Su, Y. Li, and L. Song, Effects of tree seasonal characteristics on thermal-visual perception and thermal comfort, Building and Environment, 212 (2022) 108793. https://doi.org/10.1016/j.buildenv.2022.108793
  42. Y.-C. Chen and A. Matzarakis, Modified physiologically equivalent temperature-Basics and applications for western European climate, Theoretical and Applied Climatology, 132 (2018) 1275-1289. https://doi.org/10.1007/s00704-017-2158-x
  43. S. Coccolo, J. Kämpf, J.-L. Scartezzini, and D. Pearlmutter, Outdoor human comfort and thermal stress: A comprehensive review on models and standards, Urban Climate, 18 (2016) 33-57. https://doi.org/10.1016/j.uclim.2016.08.004
  44. M. H. Elnabawi and N. Hamza, Outdoor thermal comfort: coupling microclimatic parameters with subjective thermal assessment to design urban performative spaces, Buildings, 10 (2020) 238. https://doi.org/10.3390/buildings10120238
  45. M. Zhen, X. Liu, X. Liu, and G. Bian, Adaptive thermal comfort analysis based on interaction between outdoor thermal environment and air pollution: Case study in Xi'an, China, (2024). https://doi.org/10.21203/rs.3.rs-3503300/v1
  46. H. Saaroni, D. Pearlmutter, and T. Hatuka, Human-biometeorological conditions and thermal perception in a Mediterranean coastal park, International Journal of Biometeorology, 59 (2015) 1347-1362. https://doi.org/10.1007/s00484-014-0944-z
  47. M. N. Mistry, A high spatiotemporal resolution global gridded dataset of historical human discomfort indices, Atmosphere, 11 (2020) 835. https://doi.org/10.3390/atmos11080835
  48. A. A. Williams, J. G. Allen, P. J. Catalano, and J. D. Spengler, The role of individual and small-area social and environmental factors on heat vulnerability to mortality within and outside of the home in Boston, MA, Climate, 8 (2020) 29. https://doi.org/10.3390/cli8020029
  49. J. Stephan and S. Merhebi, Contextual non-physiological factors affecting outdoor thermal comfort perception in alleyways of El Mina Lebanon, International Planning Studies, (2025) 1-24. https://doi.org/10.1080/13563475.2025.2460717
  50. Y. Wang, et al., Research on the outdoor thermal comfort of children in Hangzhou and Its influence on the underlying surface reflectance, International Journal of Biometeorology, 68 (2024) 1649-1662. https://doi.org/10.1007/s00484-024-02692-8
  51. F. Briegel, J. Wehrle, D. Schindler, and A. Christen, High-resolution multi-scaling of outdoor human thermal comfort and its intra-urban variability based on machine learning, Geoscientific Model Development, 17 (2024) 1667-1688. https://doi.org/10.5194/gmd-17-1667-2024
  52. K. Liu, T. Nie, W. Liu, Y. Liu, and D. Lai, A machine learning approach to predict outdoor thermal comfort using local skin temperatures, Sustainable Cities and Society, 59 (2020) 102216. https://doi.org/10.1016/j.scs.2020.102216
  53. O. Aboubakri, N. Khanjani, Y. Jahani, and B. Bakhtiari, Thermal comfort and mortality in a dry region of Iran, Kerman; a 12-year time series analysis, Theoretical and Applied Climatology, 139 (2020) 403-413. https://doi.org/10.1007/s00704-019-02977-8
  54. X. Chen, L. Gao, P. Xue, J. Du, and J. Liu, Investigation of outdoor thermal sensation and comfort evaluation methods in severe cold area, Science of the Total Environment, 749 (2020) 141520. https://doi.org/10.1016/j.scitotenv.2020.141520
  55. H. Tang, Y. Gao, S. Tan, Y. Guo, and W. Gao, Field investigation on adaptive thermal comfort in rural dwellings: A case study in Linyi (China) during summer, Buildings, 14 (2024) 1429. https://doi.org/10.3390/buildings14051429
  56. Y. Zhang, X. Zhang, J. Han, and X. Liu, Study on the outdoor thermal comfort of college students under different activity intensities in a high-altitude climate zone, Frontiers in Public Health, 12 (2024) 1365470. https://doi.org/10.3389/fpubh.2024.1365470
  57. Y. I. Ibrahim, T. Kershaw, and P. Shepherd, A methodology for modelling microclimate: A ladybug-tools and ENVI-met verification study, in: Proceedings of the 35th PLEA Conference - Sustainable Architecture and Urban Design: Planning Post Carbon Cities, (2020).
  58. H. Huo, et al., Simulation of the urban space thermal environment based on computational fluid dynamics: A comprehensive review, Sensors, 21 (2021) 6898. https://doi.org/10.3390/s21206898
  59. Y. Xiong and H. Chen, Impacts of uneven surface heating of an ideal street canyon on airflows and indoor ventilation: Numerical study using OpenFOAM coupled with EnergyPlus, Building Simulation, 15 (2022) 265-280. https://doi.org/10.1007/s12273-021-0788-5
  60. G. Evola, V. Costanzo, C. Magrì, G. Margani, L. Marletta, and E. Naboni, A novel comprehensive workflow for modelling outdoor thermal comfort and energy demand in urban canyons: Results and critical issues, Energy and Buildings, 216 (2020) 109946. https://doi.org/10.1016/j.enbuild.2020.109946
  61. T. M. Kamel, A new comprehensive workflow for modelling outdoor thermal comfort in Egypt, Solar Energy, 225 (2021) 162-172. https://doi.org/10.1016/j.solener.2021.07.029
  62. H. Xu, H. Lu, and S. Liu, Online street view-based approach for sky view factor estimation: A case study of Nanjing, China, Applied Sciences, 14 (2024) 2133. https://doi.org/10.3390/app14052133
  63. A. Middel, J. Lukasczyk, R. Maciejewski, M. Demuzere, and M. Roth, Sky view factor footprints for urban climate modeling, Urban Climate, 25 (2018) 120-134. https://doi.org/10.1016/j.uclim.2018.05.004
  64. D. Li, C. C. Menassa, and V. R. Kamat, Non-intrusive interpretation of human thermal comfort through analysis of facial infrared thermography, Energy and Buildings, 176 (2018) 246-261. https://doi.org/10.1016/j.enbuild.2018.07.025
  65. R. Ashrafi, M. Azarbayjani, and H. Tabkhi, Machine learning-based automated thermal comfort prediction: Integration of low-cost thermal and visual cameras for higher accuracy, arXiv preprint, arXiv:2204.08463 (2022).
  66. L. Yu, S. Qin, M. Zhang, C. Shen, T. Jiang, and X. Guan, Deep reinforcement learning for smart building energy management: A survey, arXiv preprint, arXiv:2008.05074 (2020).
  67. E. Kocaman, M. K. Erdem, and G. Calis, Machine learning thermal comfort prediction models based on occupant demographic characteristics, Journal of Thermal Biology, 123 (2024) 103884. https://doi.org/10.1016/j.jtherbio.2024.103884
  68. M. Fayyaz, A. A. Farhan, and A. R. Javed, Thermal comfort model for HVAC buildings using machine learning, Arabian Journal for Science and Engineering, (2022) 1-16.
  69. L. Sima, Y. Liu, X. Shang, Q. Yuan, and Y. Zhang, A Review of the Application of Hemispherical Photography in Urban Outdoor Thermal Comfort Studies, Buildings, 15 (2025). https://doi.org/10.3390/buildings15010123
  70. Y. Peng, T. Feng, and H. Timmermans, A path analysis of outdoor comfort in urban public spaces, Building and Environment, 148 (2019) 459-467. https://doi.org/10.1016/j.buildenv.2018.11.023
  71. J. Cortesão, F. Brandão Alves, and K. Raaphorst, Photographic comparison: A method for qualitative outdoor thermal perception surveys, International Journal of Biometeorology, 64 (2020) 173-185. https://doi.org/10.1007/s00484-018-1575-6
  72. I. M. Oroud, Integration of GIS and remote sensing to derive spatially continuous thermal comfort and degree days across the populated areas in Jordan, International Journal of Biometeorology, 66 (2022) 2273-2285. https://doi.org/10.1007/s00484-022-02355-6
  73. P. Sidiqui, et al., Urban Heat Island vulnerability mapping using advanced GIS data and tools, Journal of Earth System Science, 131 (2022) 266. https://doi.org/10.1007/s12040-022-02005-w
  74. M. Shahinmoghadam, W. Natephra, and A. Motamedi, BIM-and IoT-based virtual reality tool for real-time thermal comfort assessment in building enclosures, Building and Environment, 199 (2021) 107905. https://doi.org/10.1016/j.buildenv.2021.107905
  75. M. El Alaoui, M. Rougui, A. Lamrani, and O. Mouhat, Building energy prediction using artificial neural networks and analysis of covariance in the six thermal zones of Morocco, Materials Today: Proceedings, (2023). https://doi.org/10.1016/j.matpr.2023.06.228
  76. E. Bahadori, F. Rezaei, B.-J. He, M. Heiranipour, and S. Attia, Evaluating urban heat island mitigation strategies through coupled UHI and building energy modeling, Building and Environment, (2025) 113111. https://doi.org/10.1016/j.buildenv.2025.113111
  77. Y. Wang, U. Berardi, and H. Akbari, Comparing the effects of urban heat island mitigation strategies for Toronto, Canada, Energy and Buildings, 114 (2016) 2-19. https://doi.org/10.1016/j.enbuild.2015.06.046
  78. J. Allegrini, V. Dorer, and J. Carmeliet, Influence of morphologies on the microclimate in urban neighbourhoods, Journal of Wind Engineering and Industrial Aerodynamics, 144 (2015) 108-117. https://doi.org/10.1016/j.jweia.2015.03.024
  79. X. Zheng, L. Chen, and J. Yang, Simulation framework for early design guidance of urban streets to improve outdoor thermal comfort and building energy efficiency in summer, Building and Environment, 228 (2023) 109815. https://doi.org/10.1016/j.buildenv.2022.109815
  80. M. Hadianpour, M. Mahdavinejad, M. Bemanian, and F. Nasrollahi, Seasonal differences of subjective thermal sensation and neutral temperature in an outdoor shaded space in Tehran, Iran, Sustainable Cities and Society, 39 (2018) 751-764. https://doi.org/10.1016/j.scs.2018.03.003
  81. E. S. Darbani, M. Rafieian, D. M. Parapari, and J.-M. Guldmann, Urban design strategies for summer and winter outdoor thermal comfort in arid regions: The case of historical, contemporary and modern urban areas in Mashhad, Iran, Sustainable Cities and Society, 89 (2023) 104339. https://doi.org/10.1016/j.scs.2022.104339
  82. A. Chokhachian, D. Santucci, and T. Auer, A Human-Centered Approach to Enhance Urban Resilience, Implications and Application to Improve Outdoor Comfort in Dense Urban Spaces, Buildings, 7 (2017) 113. https://doi.org/10.3390/buildings7040113
  83. M. Lu, T. Hou, J. Fu, and Y. Wei, The Effects of Microclimate Parameters on Outdoor Thermal Sensation in Severe Cold Cities," Sustainability, 11 (2019) 1572. https://doi.org/10.3390/su11061572
  84. A. Tseliou, I. X. Tsiros, and M. Nikolopoulou, Seasonal differences in thermal sensation in the outdoor urban environment of Mediterranean climates-the example of Athens, Greece, International Journal of Biometeorology, 61 (2017) 1191-1208. https://doi.org/10.1007/s00484-016-1298-5
  85. C. K. C. Lam, S. Cui, J. Liu, X. Kong, C. Ou, and J. Hang, Influence of acclimatization and short-term thermal history on outdoor thermal comfort in subtropical South China, Energy and Buildings, 231 (2021) 110541. https://doi.org/10.1016/j.enbuild.2020.110541
  86. M. H. Elnabawi, N. Hamza, and S. Dudek, Thermal perception of outdoor urban spaces in the hot arid region of Cairo, Egypt, Sustainable Cities and Society, 22 (2016) 136-145. https://doi.org/10.1016/j.scs.2016.02.005
  87. L. Chen, Y. Wen, L. Zhang, and W.-N. Xiang, Studies of thermal comfort and space use in an urban park square in cool and cold seasons in Shanghai, Building and Environment, 94 (2015) 644-653. https://doi.org/10.1016/j.buildenv.2015.10.020
  88. L. Yola and H. C. Siong, Impact of Urban Canyon Direction on Solar Radiation and Airflow in Hot and Humid Regions, Asian Journal of Behavioural Studies, 3 (2016) 88-97. https://doi.org/10.21834/ajbes.v3i13.146
  89. A. Chatzidimitriou and S. Yannas, Street canyon design and improvement potential for urban open spaces; the influence of canyon aspect ratio and orientation on microclimate and outdoor comfort, Sustainable Cities and Society, 33 (2017) 85-101. https://doi.org/10.1016/j.scs.2017.05.019
  90. M. A. Adly, H. Mahmoud, and O. M. Galal, The impact of densification and orientation manipulation on outdoor thermal comfort at social housing in arid regions: a sensitivity analysis, HBRC Journal, 19 (2023) 523-541. https://doi.org/10.1080/16874048.2023.2287776
  91. F. Aljawabra and M. Nikolopoulou, Thermal comfort in urban spaces: a cross-cultural study in the hot arid climate, International Journal of Biometeorology, 62 (2018) 1901-1909. https://doi.org/10.1007/s00484-018-1592-5
  92. S. Patel, M. Indraganti, and R. N. Jawarneh, A comprehensive systematic review: Impact of Land Use/Land Cover (LULC) on Land Surface Temperatures (LST) and outdoor thermal comfort, Building and Environment, 249 (2024) 111130. https://doi.org/10.1016/j.buildenv.2023.111130
  93. D. Lai, W. Liu, T. Gan, K. Liu, and Q. Chen, A review of mitigating strategies to improve the thermal environment and thermal comfort in urban outdoor spaces, Science of the Total Environment, 661 (2019) 337-353. https://doi.org/10.1016/j.scitotenv.2019.01.062
  94. Y. Liu, et al., Impacts of high-albedo urban surfaces on outdoor thermal environment across morphological contexts: A case of Tianjin, China, Sustainable Cities and Society, 100 (2024) 105038. https://doi.org/10.1016/j.scs.2023.105038
  95. M. V. Adrian, Cooling cities: innovative water-based cooling systems in the era of urban heat, (2024).
  96. B. Ziaeemehr, Z. Jandaghian, H. Ge, M. Lacasse, and T. Moore, Increasing solar reflectivity of building envelope materials to mitigate urban heat islands: State-of-the-art review, Buildings, 13 (2023) 2868. https://doi.org/10.3390/buildings13112868
  97. D. Senevirathne, V. Jayasooriya, S. Dassanayake, and S. Muthukumaran, "Effects of pavement texture and colour on Urban Heat Islands: An experimental study in tropical climate," Urban Climate, 40 (2021) 101024. https://doi.org/10.1016/j.uclim.2021.101024
  98. M. Taleghani, Outdoor thermal comfort by different heat mitigation strategies-A review, Renewable and Sustainable Energy Reviews, 81: (2018) 2011-2018. https://doi.org/10.1016/j.rser.2017.06.010
  99. A. Dehghan Lotfabad, S. M. Hosseini, P. Dabove, M. Heiranipour, and F. Sommese, Impacts of Vertical Greenery on Outdoor Thermal Comfort and Carbon Emission Reduction at the Urban Scale in Turin, Italy, Buildings, 15 (2025) 450. https://doi.org/10.3390/buildings15030450
  100. W. Krull, et al., Towards an EU research and innovation policy agenda for nature-based solutions & re-naturing cities. Final report of the Horizon 2020 expert group on nature-based solutions and re-naturing cities, (2015).
  101. C. Wang, Z. H. Wang, and J. Yang, Cooling effect of urban trees on the built environment of contiguous United States, Earth's Future, 6 (2018) 1066-1081. https://doi.org/10.1029/2018EF000891
  102. J. Binabid, Greenery Impact on Physiological Equivalent Temperature for Pedestrians in Residential Zones in Hot and Arid Climates, Journal Architecture & Planning, 37 (2025). https://doi.org/10.33948/JAP-KSU-37-1-3
  103. V. Harris, D. Kendal, A. K. Hahs, and C. G. Threlfall, Green space context and vegetation complexity shape people's preferences for urban public parks and residential gardens, Landscape Research, 43 (2018) 150-162. https://doi.org/10.1080/01426397.2017.1302571
  104. H. Taher, H. Elsharkawy, and H. F. Rashed, Urban Green Systems for Improving Pedestrian Thermal Comfort and Walkability in Future Climate Scenarios in London, Buildings, 14:3 (2024) 651. https://doi.org/10.3390/buildings14030651
  105. R. Laurel, The impact of green walls on air pollution and thermal comfort at pedestrian level in street canyon, Journal Name, (2023).
  106. H. Li, Y. Zhao, C. Wang, D. Ürge-Vorsatz, J. Carmeliet, and R. Bardhan, Cooling efficacy of trees across cities is determined by background climate, urban morphology, and tree trait, Communications Earth & Environment, 5 (2024) 1-14. https://doi.org/10.1038/s43247-024-01908-4
  107. T. Silva, M. Matias, C. Girotti, J. Vasconcelos, and A. Lopes, Heat stress mitigation by exploring UTCI hotspots and enhancing thermal comfort through street trees, Theoretical and Applied Climatology, 156 (2025) 162. https://doi.org/10.1007/s00704-025-05400-7
  108. M. M. Baruti, M. W. Yahia, and E. Johansson, Spatial and temporal variations of microclimate and outdoor thermal comfort in informal settlements of warm humid Dar es Salaam, Tanzania, Heliyon, 10 (2024). https://doi.org/10.1016/j.heliyon.2023.e23160
  109. Z. Janků, et al., Towards climate-responsible tree positioning: Detailed effects of trees on heat exposure in complex urban environments, Urban Forestry & Urban Greening, 101 (2024) 128500. https://doi.org/10.1016/j.ufug.2024.128500
  110. A. Y. Abdelmejeed and D. Gruehn, Optimizing an efficient urban tree strategy to improve microclimate conditions while considering water scarcity: a case study of Cairo, Discover Sustainability, 5 (2024) 66. https://doi.org/10.1007/s43621-024-00247-w
  111. J. Rosso-Alvarez, J. Jiménez-Caldera, G. Campo-Daza, R. Hernández-Sabié, and A. Caballero-Calvo, Integrating Objective and Subjective Thermal Comfort Assessments in Urban Park Design: A Case Study of Monteria, Colombia, Urban Science, 9 (2025) 139. https://doi.org/10.3390/urbansci9050139
  112. C. Song, H. Ikei, M. Igarashi, M. Takagaki, and Y. Miyazaki, Physiological and psychological effects of a walk in urban parks in fall, International Journal of Environmental Research and Public Health, 12 (2015) 14216-14228. https://doi.org/10.3390/ijerph121114216
  113. Y. Lin, Z. Wang, C. Y. Jim, J. Li, J. Deng, and J. Liu, Water as an urban heat sink: Blue infrastructure alleviates urban heat island effect in mega-city agglomeration, Journal of Cleaner Production, 262 (2020) 121411. https://doi.org/10.1016/j.jclepro.2020.121411
  114. N. Gupta, A. Mathew, and S. Khandelwal, Analysis of cooling effect of water bodies on land surface temperature in nearby region: A case study of Ahmedabad and Chandigarh cities in India, The Egyptian Journal of Remote Sensing and Space Science, 22 (2019) 81-93. https://doi.org/10.1016/j.ejrs.2018.03.007
  115. A. N. Moyer and T. W. Hawkins, River effects on the heat island of a small urban area, Urban Climate, 21 (2017) 262-277. https://doi.org/10.1016/j.uclim.2017.07.004
  116. L. Cheng, D. Guan, L. Zhou, Z. Zhao, and J. Zhou, Urban cooling island effect of main river on a landscape scale in Chongqing, China, Sustainable Cities and Society, 47 (2019) 101501. https://doi.org/10.1016/j.scs.2019.101501
  117. K.-M. Wai, L. Xiao, and T. Z. Tan, Improvement of the Outdoor Thermal Comfort by Water Spraying in a High-Density Urban Environment under the Influence of a Future (2050) Climate, Sustainability, 13 (2021) 7811. https://doi.org/10.3390/su13147811
  118. M. Ganjimorad, J. D. Fernandez, and M. Heiranipour, Impact of wind in urban planning: A comparative study of cooling and natural ventilation systems in traditional Iranian architecture across three climatic zones, Architecture Papers of the Faculty of Architecture and Design STU, 29 (2024) 15-29. https://doi.org/10.2478/alfa-2024-0020
  119. U. Berardi and Y. Wang, The effect of a denser city over the urban microclimate: The case of Toronto, Sustainability, 8 (2016) 822. https://doi.org/10.3390/su8080822
  120. M. Karimimoshaver and M. S. Shahrak, The effect of height and orientation of buildings on thermal comfort, Sustainable Cities and Society, 79 (2022) 103720. https://doi.org/10.1016/j.scs.2022.103720
  121. S. Y. Bahri, M. A. Forment, A. S. Riera, M. Heiranipour, and S. N. Hosseini, Kinetic facades as a solution for educational buildings: A multi-objective optimization simulation-based study, Energy Reports, 13 (2025) 3915-3928. https://doi.org/10.1016/j.egyr.2025.03.021
  122. S. M. Hosseini, M. Showkatbakhsh, M. Mahdavinejad, M. Najafi, An integrated generative-analytic framework for the performance-driven design of kinetic façades, Smart and Sustainable Built Environment, (2025) 1-28. https://doi.org/10.1108/SASBE-07-2025-0388
  123. F. Farmani, S. M. Hosseini, M. K. Assadi, S. Hassanzadeh, Computational Evaluation of a Biomimetic Kinetic Façade Inspired by the Venus Flytrap for Daylight and Glare Performance, Buildings, 15 (2025) 1853. https://doi.org/10.3390/buildings15111853
  124. T. van Druenen, T. Van Hooff, H. Montazeri, and B. Blocken, CFD evaluation of building geometry modifications to reduce pedestrian-level wind speed, Building and Environment, 163 (2019) 106293. https://doi.org/10.1016/j.buildenv.2019.106293
  125. M. Heiranipour, M. Juaristi, S. Avesani, and F. Favoino, Towards early-stage facade design for heat resilient buildings: impact of weather file generation for office buildings in temperate climates, Building and Environment, (2025) 113459. https://doi.org/10.1016/j.buildenv.2025.113459
  126. M. Heiranipour, M. Juaristi, S. Avesani, F. Favoino, V. Serra, Contrasting Building Performance and Thermal Resiliency: A Simulation-Based Quantitative Evaluation Framework for Evaluating the Impact of Building Envelope Technologies, in: Proceedings of the International Association of Building Physics, Springer, (2024), pp. 432-437. https://doi.org/10.1007/978-981-97-8313-7_60
  127. A. Goharian, M. Forment, A. Bahri, Designing Adaptability Strategy to a Novel Kinetic Adaptive Façade (NKAF); Toward a Pioneering Method in Dynamic-objects Daylight Simulation (Post-Processing), Journal of Daylighting, 12 (2025) 69-90. https://doi.org/10.15627/jd.2025.5
  128. A. Tseliou, E. Melas, A. Mela, I. Tsiros, E. Zervas, The Effect of Green Roofs and Green Façades in the Pedestrian Thermal Comfort of a Mediterranean Urban Residential Area, Atmosphere, 14 (2023) 1512. https://doi.org/10.3390/atmos14101512
  129. M. Heirani Pour, R. Fayaz, M. Mahdavinia, Optimization of Window Dimensions Regarding Light and Heat Parameters in Residential Buildings of Cold Climate; Case Study: Ilam City, Armanshahr Architecture & Urban Development, 14 (2021) 91-101.
  130. J. Ahluwalia, L. Khurana, A. M. Ganapathy, Building Envelopes and the Urban Heat Island Effect: A Review of Mitigation Strategies through Advanced Façade and Roofing Materials, (2025). https://doi.org/10.46610/JoIDRP.2024.v010i01.003
  131. H. Oukmi, B. Chegari, O. Mouhat, M. Rougui, M. E. Ganaoui, M. Cherkaoui, Improving the efficiency of the trombe wall by integrating multi-fold glazing and sustainable materials: Ifrane, Morocco as a case study, Journal of Building Engineering, 89 (2024) 109310. https://doi.org/10.1016/j.jobe.2024.109310
  132. C. K. C. Lam, J. Weng, K. Liu, J. Hang, The effects of shading devices on outdoor thermal and visual comfort in Southern China during summer, Building and Environment, 228 (2023) 109743. https://doi.org/10.1016/j.buildenv.2022.109743
  133. H. Luyao, L. Ling, L. Xinkai, D. Junfeng, Outdoor thermal comfort benchmarks and optimization design for children in open parks of hot summer and cold winter region, Scientific Reports, 15 (2025) 16702. https://doi.org/10.1038/s41598-025-95979-8
  134. J. Niu, X. Li, Y. Wang, H. Zhang, A new method to assess spatial variations of outdoor thermal comfort: Onsite monitoring results and implications for precinct planning, Building and Environment, 91 (2015) 263-270. https://doi.org/10.1016/j.buildenv.2015.02.017
  135. J. A. Acero, L. A. Ruefenacht, E. J. Koh, Y. S. Tan, L. K. Norford, Measuring and comparing thermal comfort in outdoor and semi-outdoor spaces in tropical Singapore, Urban Climate, 42 (2022) 101122. https://doi.org/10.1016/j.uclim.2022.101122
  136. D. Lai, X. Zhou, Q. Chen, Modelling dynamic thermal sensation of human subjects in outdoor environments, Energy and Buildings, 149 (2017) 16-25. https://doi.org/10.1016/j.enbuild.2017.05.028
  137. D. Lai, X. Zhou, Q. Chen, Measurements and predictions of the skin temperature of human subjects on outdoor environment, Energy and Buildings, 151 (2017) 476-486. https://doi.org/10.1016/j.enbuild.2017.07.009
  138. N. Aghamohammadi, C. S. Fong, M. H. M. Idrus, L. Ramakreshnan, U. Haque, Outdoor thermal comfort and somatic symptoms among students in a tropical city, Sustainable Cities and Society, 72 (2021) 103015. https://doi.org/10.1016/j.scs.2021.103015
  139. Z. Niu, T. Goto, Effects of individual characteristics and local body functions on sweating response: A review, International Journal of Biometeorology, 68 (2024) 2185-2204. https://doi.org/10.1007/s00484-024-02758-7
  140. B. Gautam, H. B. Rijal, H. Imagawa, G. Kayo, M. Shukuya, Investigation on adaptive thermal comfort considering the thermal history of local and migrant peoples living in sub-tropical climate of Nepal, Building and Environment, 185 (2020) 107237. https://doi.org/10.1016/j.buildenv.2020.107237
  141. K. Pantavou, S. Lykoudis, M. Nikolopoulou, I. X. Tsiros, Thermal sensation and climate: a comparison of UTCI and PET thresholds in different climates, International Journal of Biometeorology, 62 (2018) 1695-1708. https://doi.org/10.1007/s00484-018-1569-4
  142. N. Kántor, A. Kovács, Á. Takács, Seasonal differences in the subjective assessment of outdoor thermal conditions and the impact of analysis techniques on the obtained results, International Journal of Biometeorology, 60 (2016) 1615-1635. https://doi.org/10.1007/s00484-016-1151-x
  143. M. Schweiker, R. Rissetto, A. Wagner, Thermal expectation: Influencing factors and its effect on thermal perception, Energy and Buildings, 210 (2020) 109729. https://doi.org/10.1016/j.enbuild.2019.109729
  144. J. Li, J. Niu, C. M. Mak, T. Huang, Y. Xie, Assessment of outdoor thermal comfort in Hong Kong based on the individual desirability and acceptability of sun and wind conditions, Building and Environment, 145 (2018) 50-61. https://doi.org/10.1016/j.buildenv.2018.08.059
  145. M. Rutty, D. Scott, Bioclimatic comfort and the thermal perceptions and preferences of beach tourists, International Journal of Biometeorology, 59 (2015) 37-45. https://doi.org/10.1007/s00484-014-0820-x
  146. W. Yang, Y. Lin, C.-Q. Li, Effects of landscape design on urban microclimate and thermal comfort in tropical climate, Advances in Meteorology, 2018 (2018) 2809649. https://doi.org/10.1155/2018/2809649
  147. C. K. C. Lam, J. Hang, D. Zhang, Q. Wang, M. Ren, C. Huang, Effects of short-term physiological and psychological adaptation on summer thermal comfort of outdoor exercising people in China, Building and Environment, 198 (2021) 107877. https://doi.org/10.1016/j.buildenv.2021.107877
  148. K.-T. Huang, T.-P. Lin, H.-C. Lien, Investigating thermal comfort and user behaviors in outdoor spaces: A seasonal and spatial perspective, Advances in Meteorology, 2015 (2015) 423508. https://doi.org/10.1155/2015/423508
  149. S. Amindeldar, S. Heidari, M. Khalili, The effect of personal and microclimatic variables on outdoor thermal comfort: A field study in Tehran in cold season, Sustainable Cities and Society, 32 (2017) 153-159. https://doi.org/10.1016/j.scs.2017.03.024
  150. H. S. Lopes, P. C. Remoaldo, V. Ribeiro, J. Martín-Vide, I. Ribeiro, Clothing and Outdoor Thermal Comfort (OTC) in tourist environments: a case study from Porto (Portugal), International Journal of Biometeorology, 68 (2024) 2333-2355. https://doi.org/10.1007/s00484-024-02753-y
  151. L. Chen, N. Kántor, M. Nikolopoulou, Meta-analysis of outdoor thermal comfort surveys in different European cities using the RUROS database: The role of background climate and gender, Energy and Buildings, 256 (2022) 111757. https://doi.org/10.1016/j.enbuild.2021.111757
  152. M. H. Elnabawi, K. A. Tabet Aoul, A. Alhumaidi, B. Osman, R. Alshehhi, S. AlMahri, A behavioural analysis of outdoor thermal comfort in a hot, arid climate: a culture and gender equality perspective, Architectural Science Review, 68 (2025) 41-55. https://doi.org/10.1080/00038628.2024.2337041
  153. H. Jin, S. Liu, J. Kang, Gender differences in thermal comfort on pedestrian streets in cold and transitional seasons in severe cold regions in China, Building and Environment, 168 (2020) 106488. https://doi.org/10.1016/j.buildenv.2019.106488
  154. H. Du, et al., Gender differences in thermal comfort under coupled environmental factors, Energy and Buildings, 295 (2023) 113345. https://doi.org/10.1016/j.enbuild.2023.113345
  155. S. Chowdhury, S. Chowdhury, M. F. H. Rezve, Using simulated human comfort matrix to measure urban campus gender-based thermal performances, Frontiers in Built Environment, 10 (2024) 1449351. https://doi.org/10.3389/fbuil.2024.1449351
  156. Y. Li, Y. Li, G. Shang, Z. Peng, X. Zhang, B. Hong, How gender difference affects children's outdoor thermal physio-psychological responses? A comparative field study, Building and Environment, 272 (2025) 112638. https://doi.org/10.1016/j.buildenv.2025.112638
  157. C. Mountzouris, G. Protopsaltis, J. Gialelis, The Impact of Gender and Age on Thermal Comfort, Procedia Computer Science, 257 (2025) 314-320. https://doi.org/10.1016/j.procs.2025.03.042
  158. V. Kirschner, A. Urban, L. Chlapcová, V. Řezáčová, Thermal comfort perception among park users in Prague, Central Europe on hot summer days A comparison of thermal indices, PloS One, 20 (2025) e0299377. https://doi.org/10.1371/journal.pone.0299377
  159. B. Wang, H. Zhao, B. Han, X. Jiang, An Investigation of Outdoor Thermal Comfort Assessment for Elderly Individuals in a Field Study in Northeastern China, Buildings, 13 (2023) 2458. https://doi.org/10.3390/buildings13102458
  160. M. T. Baquero Larriva, E. Higueras García, Influence of Microclimate on Older Peoples' Outdoor Thermal Comfort and Health during Autumn in Two European Cities, Designs, 7 (2023) 27. https://doi.org/10.3390/designs7010027
  161. L. Ortiz-Hernandez, I. P. Miranda-Quezada, Differences in Body Weight According to Skin Color and Sex in Mexican Adults, Journal of Racial and Ethnic Health Disparities, 11 (2024) 3773-3781. https://doi.org/10.1007/s40615-023-01829-6
  162. M. Zhang, X. Li, et al., Impact of body fat and fitness on human thermal responses under transient neutral-cool indoor conditions, Building and Environment, 234 (2023) 110206. https://doi.org/10.1016/j.buildenv.2023.110206
  163. L. Wang, Y. Li, et al., Effects of body muscle and fat on differences in thermal preference, Building and Environment, 243 (2023) 110643. https://doi.org/10.1016/j.buildenv.2023.110643
  164. Y. Tian, B. Hong, et al., Factors influencing resident and tourist outdoor thermal comfort: a comparative study in China's cold region, Science of the Total Environment, 808 (2022) 152079. https://doi.org/10.1016/j.scitotenv.2021.152079
  165. J. Stephan, S. Merhebi, Contextual non-physiological factors affecting outdoor thermal comfort perception in alleyways of El Mina, Lebanon, International Planning Studies, 30 (2025) 213-236. https://doi.org/10.1080/13563475.2025.2460717
  166. O. Ahmadizadeh, R. Vakilinezhad, Evaluating the effect of urban surfaces albedo on the microclimate and outdoor thermal comfort in hot climate, International Journal of Environmental Studies, 82 (2025) 1074-1098. https://doi.org/10.1080/00207233.2024.2444834
  167. Y. Yan, H. Jin, Study on the Influence of Microclimate Comfort on the Winter Vigor of Campus Open Space in Severe Cold Areas-Taking the Campus of Harbin Institute of Technology as an Example, in: Proceedings of the International Conference on Resources and Environmental Research, Harbin, China, 20-22 June (2023), pp. 169-185. https://doi.org/10.1007/978-3-031-56359-1_13
  168. S. Liu, N. Nazarian, et al., Dynamic thermal pleasure in outdoor environments-temporal alliesthesia, Science of The Total Environment, 771 (2021) 144910. https://doi.org/10.1016/j.scitotenv.2020.144910
  169. H. Sadagopan, J. L. Kitchley, S. Natarajan, Humidity or air-speed? A climate chamber investigation into adaptive thermal comfort potential, Building Services Engineering Research & Technology, 46 (2025) 339-359. https://doi.org/10.1177/01436244241296756
  170. M. Schweiker, K. Schakib-Ekbatan, X. Fuchs, S. Becker, A seasonal approach to alliesthesia. Is there a conflict with thermal adaptation?, Energy and Buildings, 212 (2020) 109745. https://doi.org/10.1016/j.enbuild.2019.109745
  171. Y. Dzyuban, et al., Evidence of alliesthesia during a neighborhood thermal walk in a hot and dry city, Science of the Total Environment, 834 (2022) 155294. https://doi.org/10.1016/j.scitotenv.2022.155294
  172. T. Parkinson, et al., Predicting thermal pleasure experienced in dynamic environments from simulated cutaneous thermoreceptor activity, Indoor Air, 31 (2021) 2266-2280. https://doi.org/10.1111/ina.12859
  173. S. Zare, et al., Investigating the levels of thermal stress in Kerman city in 2016 using thermal indices: WBGT, ESI, HI, HSI, and SWreq, Journal of Kerman University of Medical Sciences, 25 (2018) 339-354.
  174. B. Mohammadi, S. Karimi, The relationship between thermal sensation and the rate of hospital admissions for cardiovascular disease in Kermanshah, Iran, Theoretical and Applied Climatology, 134 (2018) 1101-1114. https://doi.org/10.1007/s00704-017-2332-1
  175. H. Ahmadi, F. Ahmadi, Mapping thermal comfort in Iran based on geostatistical methods and bioclimatic indices, Arabian Journal of Geosciences, 10 (2017) 342. https://doi.org/10.1007/s12517-017-3129-3
  176. T. Fritze, The effect of heat and cold waves on the mortality of persons with dementia in Germany, Sustainability, 12 (2020) 3664. https://doi.org/10.3390/su12093664
  177. J. Lin, S. Chen, J. Yang, Z. Li, Research on summer outdoor thermal comfort based on COMFA model in an urban park of Fuzhou, China, Theoretical and Applied Climatology, 155 (2024) 2311-2322. https://doi.org/10.1007/s00704-023-04782-w
  178. M. Chàfer, A. L. Pisello, C. Piselli, L. F. Cabeza, Greenery system for cooling down outdoor spaces: Results of an experimental study, Sustainability, 13 (2020) 5888. https://doi.org/10.3390/su12155888
  179. F. Acs, A. Zsákai, E. Kristóf, A. I. Szabó, H. Breuer, Human thermal climate of the Carpathian Basin, International Journal of Climatology, 41 (2021) E1846-E1859. https://doi.org/10.1002/joc.6816
  180. C. Du, B. Li, Y. Li, M. Xu, R. Yao, Modification of the Predicted Heat Strain (PHS) model in predicting human thermal responses for Chinese workers in hot environments, Building and Environment, 165 (2019) 106349. https://doi.org/10.1016/j.buildenv.2019.106349
  181. C. L. Kienbacher, M. Hutter, A. Raggam, S. Rinner, G. Oroszi, Extreme weather conditions as a gender-specific risk factor for acute myocardial infarction, The American Journal of Emergency Medicine, 43: (2021) 50-53. https://doi.org/10.1016/j.ajem.2021.01.045
  182. P. K. Cheung, C. Y. Jim, Subjective outdoor thermal comfort and urban green space usage in humid-subtropical Hong Kong, Energy and Buildings, 173 (2018) 150-162. https://doi.org/10.1016/j.enbuild.2018.05.029
  183. R. A. Angelova, The effect of clothing insulation on the thermophysiological comfort of workers in artificial cold environment/Efectul izolatiei termice a îmbracamintei asupra confortului termofiziologic al lucratorilor din mediul rece artificial, Industria Textila, 67 (2016) 302.
  184. A. Hassani, B. Jancewicz, M. Wrotek, F. Chwałczyk, N. Castell, Understanding thermal comfort expectations in older adults: The role of long-term thermal history, Building and Environment, 263 (2024) 111900. https://doi.org/10.1016/j.buildenv.2024.111900
  185. C. S. Fong, S. Manavvi, R. S. Priya, L. Ramakreshnan, N. M. Sulaiman, N. Aghamohammadi, Traits of Adaptive Outdoor Thermal Comfort in a Tropical Urban Microclimate, Atmosphere, 14 (2023) 852. https://doi.org/10.3390/atmos14050852
  186. H. Gu, Q. Hu, D. Zhu, J. Diao, Y. Liu, M. Fang, Research on outdoor thermal comfort of children's activity space in high-density urban residential areas of Chongqing in summer, Atmosphere, 13 (2022) 2016. https://doi.org/10.3390/atmos13122016

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