Field study of pedestrians' comfort temperatures under outdoor and semi-outdoor conditions in Malaysian university campuses

Research on the outdoor thermal comfort of children in Hangzhou and Its influence on the underlying surface reflectance

The outdoor thermal comfort (OTC) of children is more specific than that of adults, and the complex influence of outdoor activity spaces on children's thermal comfort warrants further investigation. To investigate the outdoor thermal comfort baseline (OTCB) of children in Hangzhou and explore the thermal impact of outdoor surfaces on children, a survey was conducted in six typical outdoor activity spaces in Hangzhou, China, during spring and summer utilizing physical measurements, questionnaire surveys, and the universal thermal climate index (UTCI). This study analyzed the differences in thermal perception among children in Hangzhou in different seasons, their OTCB, and the impact of surface reflectance (Rs) on children's OTC. The results indicated the following: 1) In spring, children in Hangzhou generally felt comfortable, but their discomfort with heat noticeably increased in summer. 2) The neutral UTCIs (NUTCIs) for Hangzhou children were 11.6 °C (spring) and 27.7 °C (summer), and the NUTCI ranges (NUTCIRs) were 9.7-17.5 °C (spring) and 25.7-30.0 °C (summer); additionally, the thermal acceptability ranges (TARs) were 13.2-25.2 °C (spring) and 11.8-34.8 °C (summer). 3) A high Rs made children feel more uncomfortable with heat, which was primarily due to the space's total shortwave and longwave radiation, which peaked between 14:00 and 15:00. 4) Based on the research findings, corresponding bioclimatic design strategies were proposed. Recommendations include using high Rs underlays with shading, composite underlays, or the future adoption of thermochromic coatings. Keeping permeable underlays moist is essential for activating their cooling mechanisms. Fundamental safety measures are imperative. This study provides valuable data for urban planners and landscape designers to create public spaces suitable for children's outdoor activities, contributing to a harmonious and unified living environment.

Enhancing thermal comfort prediction in high-speed trains through machine learning and physiological signals integration

Heating, Ventilation, and Air Conditioning (HVAC) systems in high-speed trains (HST) are responsible for consuming approximately 70% of non-operational energy sources, yet they frequently fail to ensure provide adequate thermal comfort for the majority of passengers. Recent advancements in portable wearable sensors have opened up new possibilities for real-time detection of occupant thermal comfort status and timely feedback to the HVAC system. However, since occupant thermal comfort is subjective and cannot be directly measured, it is generally inferred from thermal environment parameters or physiological signals of occupants within the HST compartment. This paper presents a field test conducted to assess the thermal comfort of occupants within HST compartments. Leveraging physiological signals, including skin temperature, galvanic skin reaction, heart rate, and ambient temperature, we propose a Predicted Thermal Comfort (PTC) model for HST cabin occupants and establish an intelligent regulation model for the HVAC system. Nine input factors, comprising physiological signals, individual physiological characteristics, compartment seating, and ambient temperature, were formulated for the PTS model. In order to obtain an efficient and accurate PTC prediction model for HST cabin occupants, we compared the accuracy of different subsets of features trained by Machine Learning (ML) models of Random Forest, Decision Tree, Vector Machine and K-neighbourhood. We divided all the predicted feature values into four subsets, and did hyperparameter optimisation for each ML model. The HST compartment occupant PTC prediction model trained by Random Forest model obtained 90.4% Accuracy (F1 macro = 0.889). Subsequent sensitivity analyses of the best predictive models were then performed using SHapley Additive explanation (SHAP) and data-based sensitivity analysis (DSA) methods. The development of a more accurate and operationally efficient thermal comfort prediction model for HST occupants allows for precise and detailed feedback to the HVAC system. Consequently, the HVAC system can make the most appropriate and effective air supply adjustments, leading to improved satisfaction rates for HST occupant thermal comfort and the avoidance of energy wastage caused by inaccurate and untimely predictive feedback.

Investigating the impacts of shaded outdoor spaces on thermal adaptation and cognitive performance of university students in classroom environments

Shading strategies are effective means to reduce urban risk factors such as the Urban Heat Island (UHI) effect. The influence of shaded outdoor spaces on university students' thermal adaptability and cognitive performance is limited researched. The study aims at evaluating the effect of shaded outdoor spaces upon thermal comfort; and, linking such results upon university students' cognitive performance in a classroom environment with natural ventilation. A case study was conducted with students the ages of 19-22 at Bilkent University in Ankara, during the mid-season in October.The quantitative microclimatic conditions of the university campus's unshaded/shaded areas and indoor studios were obtained through Physiologically Equivalent Temperature (PET) index. The qualitative evaluation was undertaken by the adaptive model and thermal comfort survey. D2 test of attention was conducted to measure cognitive performance of students.This study revealed that the shade may increase thermal adaptation with the lowest mean PET of 18.7°C, while the highest mean PET of 33.2°C was obtained in sun-exposed space. Also, experiencing shaded outdoor space contributed to an improvement in concentration performance (CP) of students resulting in the mean CP score of 182.8, while those with sun-exposed outdoor space experience had the mean CP score of 167.6 within studios.

Outdoor environmental comfort evaluation for retail planning in a tropical business district using Integrated Environmental Modeller

This research proposes a simulation-based assessment of outdoor thermal and acoustic comfort for a planned business urban district in Singapore for retail planning using a customized OpenFOAM-centric multi-physics environmental simulation platform called the Integrated Environmental Modeller (IEM). IEM was employed to simulate the coupled impacts of solar radiation on wind and air temperature and wind and air temperature effects on traffic noise propagation in the district on the equinox and solstice day of the hottest period. Using IEM simulation results, we computed the thermal and acoustic comfort acceptability indicators derived from local field studies' results. The spatial distribution of environmental comfort acceptability indicators in the worst-case scenario can be used to distinguish the zones exposed to thermal or noise influence. The noise-affected zones are near the main roads and overlap a part of the thermal-affected area. The thermal-affected area is almost everywhere in the studied sites in the worst-case scenario. Having outdoor retail spaces with both poor thermal and acoustic comfort is not recommended if the thermal and acoustic comfort cannot be improved simultaneously. For the high-level retail planning, a simplified parametric analysis considering solar irradiance blockage and wind speed enhancements, is provided. Considering the worst-case scenario, ≥50% thermal acceptability can be achieved by blocking 54%-68% solar irradiance among the pedestrian thoroughfares and the retail spaces. Coupled together, blocking the solar irradiance and enhancing the wind speed can further improve thermal comfort locally. These results can guide the retail mix (e.g., al fresco restaurants, pop-up kiosks etc.) near high footfall areas and provide reference for future plans combining landscape and infrastructure, (e.g., trees with shelter walkaways, green walls with outdoor ventilation fans etc.) taking into account the environmental acceptability of people working in or visiting the tropical urban district.

A systematic review advocating a framework and benchmarks for assessing outdoor human thermal perception

Since the early 2000's, much attention has been paid to human thermal assessment in urban outdoor environments in different climatic zones. Some previous studies have argued that an absence of an agreed protocol for outdoor human bio-meteorological research causes complexity in comparing the studies' results for several reasons: An abundance of human thermal indices, a variety of interpretations of bio-meteorological terms, an array of procedures for data collection and a lack of agreed methods in determining thermal comfort ranges and index modifications. This study aims to review strategies and methods for human bio-meteorological research and to examine their suitability for thermal perception assessment. From 2001 to 2021, 254 case studies assessed human thermal perception by investigating in-situ thermal conditions versus subjective thermal perception, relying on protocols such as ASHRAE Standard 55 and EN ISO 10551 that were originally developed for indoor environments. Fifty-four cases determined different ranges for thermal comfort. Although 43 studies tried to modify indices to various climatic zones, only 13 studies modified the nine PET physiological stress categories and 4 studies modified the ten UTCI stress categories). Thus, comparisons between the studies' results become complicated. Our review points to three main reasons for the complexity: first, the 7-point TSV scales, does not always fit the scales of the applied thermal index; second, measurement procedures do not always represent the local climate conditions; third, certain methods for modifying thermal index scale thresholds are not capable of modifying the entire index scale. On the basis of our findings, we suggest a framework for bio-meteorological research, with attention to measurement procedure, appropriate questionnaire design, careful data control and suitable methods to enable modification of thermal indices. This study recommends applying systematic and objective statistical methods like linear regression and discriminant analysis in order to successfully modify the entire index scale.

Study on the Influence of Globe Thermometer Method on the Accuracy of Calculating Outdoor Mean Radiant Temperature and Thermal Comfort

With global warming and the rapid development of urbanization, the outdoor thermal environment is deteriorating. More and more research focuses on the outdoor thermal environment and thermal comfort. The globe thermometer method is widely used in more than half of the outdoor thermal environment research studies, but there is a large error compared with the six-direction method. In order to explore the accuracy of the results of the globe thermometer method and its impact on the subsequent thermal comfort indicators, this study carried out a year-round comparative experiment under multiple working conditions outdoors in cold areas to explore the impact of meteorological parameters such as shortwave radiation, wind speed, and wind direction on the results of the globe thermometer method. The results show that the continuous increase of shortwave radiation reduces the accuracy of the black bulb thermometer to less than 60%, and the instantaneous change of wind speed will make the deviation of the mean radiation temperature obtained by the globe thermometer method exceed 5 °C. The influence of the mean radiation temperature obtained by the globe thermometer method on the thermal comfort index is mainly reflected in the working condition of a high temperature and strong radiation in summer. Taking the six-direction method as the standard, this study gives the scope of application of the globe thermometer method; and taking the human body calculation model of PET as an example, a universal optimization method for detailed division of radiation heat transfer calculation is proposed, so that it can get more accurate and rigorous conclusions in the evaluation of outdoor complex radiation environment.

Revealing Microclimate around Buildings with Long-Term Monitoring through the Neural Network Algorithms

The profile of urban microclimates is important in many engineering fields, such as occupant’s thermal comfort and health, and other building engineering. To predict the profile of urban microclimate, this study applies the artificial neural network and long short-term memory network predictive models, and an urban microclimate dataset was obtained with a long-term monitoring from year 2017 to 2019 with 5-min resolution including temperature, relative humidity, and solar radiation. Two predictive models were applied, and the first (Model 1) is to apply the predictive techniques to predict the urban microclimate in the real-time sequence, and then extract the characteristics of urban microclimate, while the second (Model 2) is to directly extract the characteristics of the microclimate, and then predict the characteristics of the microclimate. Backpropagation artificial neural network (BP-ANN) and long-short term memory (LSTM) techniques were applied in both models. The results show Model 1 with as the time-series prediction can reach the best (99.92%) of correlation coefficient and 98% of the mean average percentage error (MAPE), for temperature, while 99.66% and 98.18% for relative humidity, respectively, while accuracies in Model 2 decreased to 79% and 88.6% of MAPE for temperature and relative humidity, respectively. The prediction of solar radiation using ANN and LSTM are 51.1% and 57.8% of the correlation coefficient, respectively.