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Outdoor microclimatic conditions strongly affect the thermal comfort of pedestrians. A transversal field survey was conducted in Guangzhou, together with micrometeorological measurements. The outdoor physiological equivalent temperature (PET) varied from 3 to 59 °C. Regression lines were obtained to establish correlations of the mean thermal sensation vote (MTSV) with the PET bins with a width of 1 °C. Furthermore, the thermal comfort range of PET, neutral PET (NPET), and preferred PET was analyzed. The results indicated that, for the young people, thermal comfort range of PET spanned from 19.2 to 24.6 °C. The NPET and preferred PET significantly differed in different seasons. The NPET was higher in the summer than that in the winter and transitional seasons. However, the preferred PET of the summer was lower than that of the winter. The PET limits of different thermal stress categories were also confirmed, which differed from those in other cities. Thus, the impacts of adaptation on thermal comfort range were significant for people in outdoor environment.

Children differ from adults in their physiology and cognitive ability. Thus, they are extremely vulnerable to classroom thermal comfort. However, very few reviews on the thermal comfort of primary school students are available. Further, children-focused surveys have not reviewed the state-of-the-art in thermal comfort prediction using machine learning (AI/ML). Consequently, there is a need for discussion on children-specific challenges in AI/ML-based prediction. This article bridges these research gaps. It presents a comprehensive review of thermal comfort studies in primary school classrooms since 1962. It considers both conventional (non-ML) studies and the recent AI/ML studies performed for children, classrooms, and primary students. It also underscores the importance of AI/ML prediction by analyzing adaptive opportunities for children/students in classrooms. Thereafter, a review of AI/ML-based prediction studies is presented. Through an AI/ML case-study, it demonstrates that model performance for children and adults differs markedly. Performance of classification models trained on ASHRAE-II database and a recent primary students’ dataset shows a 29% difference in thermal sensation and 86% difference in thermal preference, between adults and children. It then highlights three major children-specific AI/ML challenges, viz., “illogical votes”, “multiple comfort metrics”, and “extreme class imbalance”. Finally, it offers several technical solutions and discusses open problems.

The thermal environment outdoors affects human comfort and health. Mental and physical performance is reduced at high levels of air temperature being a problem especially in tropical climates. This paper deals with human comfort in the warm-humid city of Guayaquil, Ecuador. The main aim was to examine the influence of urban micrometeorological conditions on people’s subjective thermal perception and to compare it with two thermal comfort indices: the physiologically equivalent temperature (PET) and the standard effective temperature (SET*). The outdoor thermal comfort was assessed through micrometeorological measurements of air temperature, humidity, mean radiant temperature and wind speed together with a questionnaire survey consisting of 544 interviews conducted in five public places of the city during both the dry and rainy seasons. The neutral and preferred values as well as the upper comfort limits of PET and SET* were determined. For both indices, the neutral values and upper thermal comfort limits were lower during the rainy season, whereas the preferred values were higher during the rainy season. Regardless of season, the neutral values of PET and SET* are above the theoretical neutral value of each index. The results show that local people accept thermal conditions which are above acceptable comfort limits in temperate climates and that the subjective thermal perception varies within a wide range. It is clear, however, that the majority of the people in Guayaquil experience the outdoor thermal environment during daytime as too warm, and therefore, it is important to promote an urban design which creates shade and ventilation.

Outdoor thermal comfort is an important indicator to create a quality and livable environment. This study examines a relationship between micro-meteorological and personal variables of outdoor thermal comfort conditions in an urban park. The data collection of outdoor thermal comfort is carried out using two methods in combination: micro-meteorological measurement and questionnaire survey. This finding shows that most of the respondents were comfortable with the thermal, wind, and humidity condition. The acceptability and satisfaction level of thermal comfort were positive. The most significant micro-meteorological variable for the physiologically equivalent temperature (PET) value is mean radiant temperature (Tmrt). As the Tmrt value is influenced by how much shading is produced from the presence of vegetation or buildings around the measurement location, this finding shows that the shadow was very important to the thermal comfort conditions in the Green Park Kitakyushu. The most influential micro-meteorological variable for the three different personal variables (TSV, WFSV, and HSV) is air temperature. The strongest relationship among the four variables is between TSV and PET. The findings will be the basis for the city authorities in preparing regional development plans, especially those related to the planning of city parks or tourist attractions.

There is still a lack of research on the evaluation of summer thermal comfort for the elderly. This study conducted a questionnaire survey and indoor thermal environment measurement in Nanning, China, with a hot‐humid and less‐windy climate. Six hundred elderly subjects were selected to answer the thermal sensation questionnaire in the naturally ventilated residential buildings during the summer. The analysis results of the collected data show that the neutral temperature for the local elderly is 28.0°C, and the range of operative temperature for 80% thermal acceptability is 26.4°C–29.5°C. An adaptive thermal comfort model was developed using the measured data, and its predicted thermal neutral temperature is 28.1°C, nearly equal to the measured value (28.0°C). The thermal neutral temperatures for the local male and female elderly are 27.7°C and 28.2°C, respectively, which reveals that there are gender differences in the thermal comfort. It was also found that the outdoor air temperature has a significant impact on the thermal neutral temperature, and the developed adaptive thermal comfort model can more accurately predict the indoor thermal comfort of the local elderly. The findings in this study can provide theoretical support and supporting data for the indoor thermal environment design of new old‐age facilities and aging‐adapted renovation of existing buildings in hot and humid climates.

The regulation of indoor thermal comfort is a critical aspect of smart building design, significantly influencing energy efficiency and occupant well-being. Traditional comfort models, such as Fanger’s equation and adaptive approaches, often fall short in capturing individual occupant preferences and the dynamic nature of indoor environmental conditions. To overcome these limitations, we introduce a Digital Twin-driven framework integrated with an advanced attention-based Long Short-Term Memory (LSTM) model specifically tailored for personalised thermal comfort prediction and intelligent HVAC control. The attention mechanism effectively focuses on critical temporal features, enhancing both predictive performance and interpretability. Next, the Digital Twin enables the real-time simulation of indoor environments and occupant responses, facilitating proactive comfort management. We utilise a subset of the ASHRAE Global Thermal Comfort Database II, and extensive pre-processing, including median-based data imputation and feature normalisation, is conducted. The proposed model categorises Thermal Sensation Votes (TSVs) recorded on a 7-point ASHRAE scale into three classes: Uncomfortably Cold (UC) for TSV -1, Neutral (N) for TSV = 0, and Uncomfortably Warm (UW) for TSV +1. The model achieves a test accuracy of 83.8%, surpassing previous state-of-the-art methods. Furthermore, Explainable AI (XAI) techniques, such as SHAP and LIME, are integrated to enhance transparency and interpretability, complemented by scenario-based energy efficiency analyses to evaluate energy-comfort trade-offs. This comprehensive approach provides a robust, interpretable, and energy-efficient solution for occupant-centric HVAC management in smart building systems.

Both global climate change and urbanization trends will demand adaptation measures in cities. Large agglomerations and impacts on landscape and natural environments due to city growth will require guided densification schemes in urban areas, particularly in developing countries. Human biometeorological indices such as the Universal Thermal Climate Index (UTCI) could guide this process, as they provide a clear account of expected effects on thermal sensation from a given change in outdoor settings. However, an earlier step should optimally include an adequacy test of suggested comfort and thermal stress ranges with calibration procedures based on surveys with the target population. This paper compares obtained thermal comfort ranges for three different locations in Brazil: Belo Horizonte, 20° S, Aw climate type; Curitiba, 25.5° S, Cfb subtropical climate, both locations in elevation (above 900 m a.s.l.); and Pelotas, at sea level, latitude 32° S, with a Cfa climate type. In each city, a set of outdoor comfort field campaigns has been carried out according to similar procedures, covering a wide range of climatic conditions over different seasons of the year. Obtained results indicate a variation of neutral temperatures up to 3 °C (UTCI units) as a possible latitude and local climate effect between the southern locations relative to the northernmost location. Low UTCI values were found in the two subtropical locations for the lower threshold of the thermal comfort band as compared with the original threshold. A possible explanation for that is a longer exposure to cold conditions as buildings are seldom provided with heating systems.

To better understand the influence of urban green infrastructure (UGI) on outdoor human thermal comfort, a survey and physical measurements were performed at the campus of the University of Groningen, The Netherlands, in spring and summer 2015. Three hundred eighty-nine respondents were interviewed in five different green spaces. We aimed to analyze people’s thermal comfort perception and preference in outdoor urban green spaces, and to specify the combined effects between the thermal environmental and personal factors. The results imply that non-physical environmental and subjective factors (e.g., natural view, quiet environment, and emotional background) were more important in perceiving comfort than the actual thermal conditions. By applying a linear regression and probit analysis, the comfort temperature was found to be 22.2 °C and the preferred temperature was at a surprisingly high 35.7 °C. This can be explained by the observation that most respondents, who live in temperate regions, have a natural tendency to describe their preferred state as “warmer” even when feeling “warm” already. Using the Kruskal-Wallis H test, the four significant factors influencing thermal comfort were people’s exposure time in green spaces, previous thermal environment and activity, and their thermal history. However, the effect of thermal history needs further investigation due to the unequal sample sizes of respondents from different climate regions. By providing evidence for the role of the objective and subjective factors on human thermal comfort, the relationship between UGI, microclimate, and thermal comfort can assist urban planning to make better use of green spaces for microclimate regulation.

The urban heat island impacts the thermal comfort of pedestrians in cities. In this paper, the effects of four heat mitigation strategies on micrometeorology and the thermal comfort of pedestrians were simulated for a neighborhood in eastern Los Angeles County. The strategies investigated include solar reflective 'cool roofs', vegetative 'green roofs', solar reflective 'cool pavements', and increased street-level trees. A series of micrometeorological simulations for an extreme heat day were carried out assuming widespread adoption of each mitigation strategy. Comparing each simulation to the control simulation assuming current land cover for the neighborhood showed that additional street-trees and cool pavements reduced 1.5 m air temperature, while cool and green roofs mostly provided cooling at heights above pedestrian level. However, cool pavements increased reflected sunlight from the ground to pedestrians at a set of unshaded receptor locations. This reflected radiation intensified the mean radiant temperature and consequently increased physiological equivalent temperature (PET) by 2.2 °C during the day, reducing the thermal comfort of pedestrians. At another set of receptor locations that were on average 5 m from roadways and underneath preexisting tree cover, cool pavements caused significant reductions in surface air temperatures and small changes in mean radiant temperature during the day, leading to decreases in PET of 1.1 °C, and consequent improvements in thermal comfort. For improving thermal comfort of pedestrians during the afternoon in unshaded locations, adding street trees was found to be the most effective strategy. However, afternoon thermal comfort improvements in already shaded locations adjacent to streets were most significant for cool pavements. Green and cool roofs showed the lowest impact on the thermal comfort of pedestrians since they modify the energy balance at roof level, above the height of pedestrians.

Urban drainage infrastructure is generally designed to rapidly export stormwater away from the urban environment to minimize flood risk created by extensive impervious surface cover. This deficit is resolved by importing high-quality potable water for irrigation. However, cities and towns at times face water restrictions in response to drought and water scarcity. This can exacerbate heating and drying, and promote the development of unfavourable urban climates. The combination of excessive heating driven by urban development, low water availability and future climate change impacts could compromise human health and amenity for urban dwellers. This paper draws on existing literature to demonstrate the potential of Water Sensitive Urban Design (WSUD) to help improve outdoor human thermal comfort in urban areas and support Climate Sensitive Urban Design (CSUD) objectives within the Australian context. WSUD provides a mechanism for retaining water in the urban landscape through stormwater harvesting and reuse while also reducing urban temperatures through enhanced evapotranspiration and surface cooling. Research suggests that WSUD features are broadly capable of lowering temperatures and improving human thermal comfort, and when integrated with vegetation (especially trees) have potential to meet CSUD objectives. However, the degree of benefit (the intensity of cooling and improvements to human thermal comfort) depends on a multitude of factors including local environmental conditions, the design and placement of the systems, and the nature of the surrounding urban landscape. We suggest that WSUD can provide a source of water across Australian urban environments for landscape irrigation and soil moisture replenishment to maximize the urban climatic benefits of existing vegetation and green spaces. WSUD should be implemented strategically into the urban landscape, targeting areas of high heat exposure, with many distributed WSUD features at regular intervals to promote infiltration and evapotranspiration, and maintain tree health.