Study on the outdoor thermal comfort of college students under different activity intensities in a high-altitude climate zone.

Introduction: Research on the outdoor thermal comfort (OTC) of a university campus is beneficial to the physical and mental health of college students. Methods: In this study, the OTC of students attending Tibet University in Lhasa, which experiences high-altitude cold climate conditions, under different activity intensities was studied using field measurements and a questionnaire survey. Results: With the increase in activity intensity, the comfort physiologically equivalent temperature (PET) value gradually increased in summer, while the comfortable PET value gradually decreased in winter. The most comfortable PET value is 17.6°C in summer and 11.5°C in winter. The neutral PET of Tibetan college students during outdoor activities in summer was 16.3°C, and the neutral PET of outdoor activities in winter was 12.1°C. Gender and ethnicity had different effects on thermal sensation under different activity intensities. Under vigorous-intensity activities, PET in winter and summer had the greatest influence on thermal sensation. The situation was different under moderate-intensity activity. PET had the greatest influence on thermal sensation in summer, and Tmrt had the greatest influence on thermal sensation in winter. Discussion: These findings provide a basis for an improved design of the outdoor environment under different outdoor activity intensities in high-altitude areas.

The effects of different landscape strategies on outdoor thermal comfort in village squares: a case study in Dayuwan village in Wuhan City.

Appropriate landscape configurations significantly mitigate rural thermal degradation. However, limited research exists on seasonal thermal comfort and the interconnections among landscape elements. Using ENVI-met software and field measurements, this study analyzed the microclimate of Dayuwan Village Square in Wuhan City. Fifteen design scenarios, including tree planting, building greening, albedo adjustment, and expanded tree coverage, were quantitatively evaluated to assess their impact on outdoor thermal comfort. Additionally, synergistic interactions between mitigation strategies were explored. The study found that increasing evergreen tree coverage by 50% has minimal impact on comfort during winter and spring. However, it significantly reduces temperatures in summer and autumn, resulting in average predicted mean vote (PMV) decreases of 0.315 and 0.643, respectively. Additionally, this strategy optimizes PMV values at 18:00 on extreme days, with a 0.102 decrease in summer and a 0.002 increase in winter. This research offers practical and sustainable guidance to designers for enhancing mitigation effects through optimal landscape configuration, providing a technical framework for rural environmental improvements.

How outdoor horticultural activities affect elderly adults' thermal, physiological and psychological responses: a field study.

We recruited 162 healthy elderly adults to determine the thermal, physiological, and psychological effects of horticultural activities (flower arranging, transplanting, and rubble masonry) in outdoor open spaces. We linked these to local climate conditions, physiology, and comfort through a questionnaire survey. The results showed that: (1) the neutral physiological equivalent temperature (NPET) before the horticultural activities were 22.18 â„ƒ for flower arranging, 23.67 â„ƒ for transplanting, and 20.78 â„ƒ for rubble masonry, while the NPET decreased to 18.53 â„ƒ, 20.73 â„ƒ and 18.04 â„ƒ (respectively) after activities. (2) The heart rate and blood oxygen saturation changed significantly (p < 0.05) only after rubble masonry. (3) The average positive affect (PA) scores increased after flower arranging by 4.83, transplanting by 3.30, and rubble masonry by 4.00. (4) After activities, the thermal sensation vote was mainly influenced by globe temperature (41.36%), air temperature (33.47%), and wind speed (25.17%). Thermal comfort vote could be promoted because of 37.35% of an increasing positive and 21.20% of decreasing negative emotion.

A study of acoustic-light-thermal effects on pedestrians' overall comfort in a Cfa-climate campus during the summer.

The environmental quality, in terms of acoustic, visual, and thermal environments, significantly affects people's comfort levels. Along these lines, in this work, their comprehensive impact on people's overall comfort was systematically explored. Pedestrians' outdoor neutral points on various environmental parameters were found by performing linear regressions. Similarly, people's thermal perceptions (indicated by neutral temperatures, NT) were found to vary for both acoustic and light environments. They would be increasingly heat sensitive (R2 increases) in a noisier environment while the NTs varied for either sound or light intensity levels. From our analysis, it was demonstrated that people's overall comforts were negatively correlated with these parameters in different degrees. This work provides valuable insights for future urban design and planning studies to create better outdoor environments.

Optimization of air quality and energy consumption in the cabin of electric vehicles using system simulation.

In electric vehicles, the Heating, Ventilation and Air-Conditioning (HVAC) function is often performed by a heat pump. Heating and cooling the cabin air drains energy directly from the vehicle's battery. In addition, these vehicles may operate in environments with high level of air pollution. In the cabin, passengers are confined to a small space where particles and harmful gases can accumulate. In addition, the ventilation system must also handle the air which does not enter the cabin through blower operation. This "infiltration" is a function of the vehicle speed and allows pollution to enter the cabin without being filtered or thermally treated. The objective of the study is to optimize the competing goals of the HVAC system: achieving the best air quality while maintaining good thermal comfort, at minimum energy costs. A system simulation tool is calibrated to represent the heating and cooling of an electric car. With this model, the influence of key factors is evaluated. Depending on ambient conditions and other parameters (number of occupants, vehicle speed, etc.), the blower flow rate and recirculation ratio can be adjusted to reach the objectives. The management of the proportion of fresh and recirculated air allows to regulate the humidity and carbon dioxide levels. Optimum controls are proposed as good trade-offs to reduce the power consumption, while maintaining a safe and comfortable environment for occupants. Compared to the full fresh air mode, the driving range gains are estimated in cold (-15 °C) and hot (30 °C) scenarios at 9 and 26 km respectively.

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.

Effects of thermal and acoustic environments on human comfort in urban and suburban campuses in the cold region of China.

In this study, we investigated the effects of thermal-acoustic environments on human comfort in a cold region by focusing on Xi'an in China as a representative city. Four typical open spaces were identified within two universities, with one located in an urban area and the other in a suburban area. Surveys were conducted using questionnaires and environmental parameters were measured to assess thermal-acoustic perception. The physiological equivalent temperature (PET) and noise sound pressure levels were employed to measure the thermal-acoustic environments in the four open spaces. The results showed that the neutral PET was 19.1 °C in Xi'an from March to May, and the neutral temperature range was 14.9-23.4 °C. The preferred temperature was 20.0 °C. Acoustic sensation votes were lower in suburban areas than urban areas. Respondents in urban areas exhibited significantly higher sensitivity to the thermal and acoustic environments compared with those in suburban areas. Sensitivity to the thermal environment decreased as the environmental noise levels decreased. The temperature perceived as most suitable increased as the sensitivity to the thermal environment decreased. Acoustic comfort evaluations were generally higher in colder and warmer environments. Warmer environments heightened the sensitivity to the acoustic environment and this effect gradually decreased as the sound pressure levels increased. Thus, based on empirical analyses, we compared the effects of outdoor thermal-acoustic parameters on comfort to provide experimental data support for further outdoor thermal comfort research.

Analysis of the summer thermal comfort indices in Istanbul.

Thermal indices and thermal comfort maps have great importance in developing health-minded climate action strategies and livable urban layouts. Especially in cities where vulnerability to heatwaves is high, it is necessary to detect the most appropriate indicators for the regional characteristics and action planning with respect to thermal comfort. The aim of the study is to examine thermal indices as indicators of regional climate characteristics by relating to meteorological parameters and spatial features. Atmospheric variables including air temperature, wind speed, cloud cover, and relative humidity data were obtained from 30 meteorological stations located in districts having different climatic features. Heat stress levels for apparent temperature (AT), heat index (HI), wet bulb globe temperature (WBGT), physiological equivalent temperature (PET), universal thermal climate index (UTCI), and perceived temperature (PT) indices were calculated and associated with meteorological parameters. Thermal comfort maps have been created with the daily mean and maximum values of all indices. As a result, the meteorological parameters with the strongest correlation with all thermal indices are air temperature (Ta) with r = 0.89 ± 0.01 and mean radiant temperature (Tmrt) with r = 0.75 ± 0.16. The differences in thermal stress levels over the city have been distinctively observed in the ATmax, PETmax, and PTmax maps, which are generated by the daily maximum values of the indices. Çatalca, where forests cover large areas compared to highly urbanized districts, has the lowest heat stress defined by all indices.

The effects of brightness and prominent colors on outdoor thermal perception in Chongqing, China.

Outdoor thermal comfort has become an important factor affecting human mental and physical health due to rapid urbanization. This study aimed to investigate the influence of brightness and prominent colors on thermal perception in hot summer and cold winter regions. Meteorological measurements were conducted accompanied by subjective thermal and visual questionnaires (n = 2020) during summer and winter. The physiological equivalent temperature (PET) was applied as thermal indices to evaluate the influence of visual conditions on thermal perception. The results showed that (1) the neutral PET is 20.2 °C with a range of 14.8 ~ 25.7 °C in Chongqing and neutral illumination range is 0 ~ 8663 lx. (2) Thermal sensitivity is most great in neutral brightness than bright and too bright groups. The influence of outdoor prominent colors in winter supports hue-heat hypothesis. However, in summer, result only supports the hypothesis under low thermal stress. Both cool and warm colors can reduce the thermal sensitivity of visitors compared to neutral colors (gray and white). (3) The interactions between colors and brightness are more obvious under low thermal stress levels. (4) Thermal perceptions of females are more greatly affected by brightness and prominent colors compared with males. These results could help landscape designers better understand the correlation between the thermal and visual environments and provide a reference for comprehensive designs of urban open spaces.

Application of DIY Electrodermal Activity Wristband in Detecting Stress and Affective Responses of Students.

This paper describes the analysis of electrodermal activity (EDA) in the context of students' scholastic activity. Taking a multidisciplinary, citizen science and maker-centric approach, low-cost, bespoken wearables, such as a mini weather station and biometric wristband, were built. To investigate both physical health as well as stress, the instruments were first validated against research grade devices. Following this, a research experiment was created and conducted in the context of students' scholastic activity. Data from this experiment were used to train machine learning models, which were then applied to interpret the relationships between the environment, health, and stress. It is hoped that analyses of EDA data will further strengthen the emerging model describing the intersections between local microclimate and physiological and neurological stress. The results suggest that temperature and air quality play an important role in students' physiological well-being, thus demonstrating the feasibility of understanding the extent of the effects of various microclimatic factors. This highlights the importance of thermal comfort and air ventilation in real-life applications to improve students' well-being. We envision our work making a significant impact by showcasing the effectiveness and feasibility of inexpensive, self-designed wearable devices for tracking microclimate and electrodermal activity (EDA). The affordability of these wearables holds promising implications for scalability and encourages crowd-sourced citizen science in the relatively unexplored domain of microclimate's influence on well-being. Embracing citizen science can then democratize learning and expedite rapid research advancements.

Personal Thermal Management by Radiative Cooling and Heating.

Maintaining thermal comfort within the human body is crucial for optimal health and overall well-being. By merely broadening the set-point of indoor temperatures, we could significantly slash energy usage in building heating, ventilation, and air-conditioning systems. In recent years, there has been a surge in advancements in personal thermal management (PTM), aiming to regulate heat and moisture transfer within our immediate surroundings, clothing, and skin. The advent of PTM is driven by the rapid development in nano/micro-materials and energy science and engineering. An emerging research area in PTM is personal radiative thermal management (PRTM), which demonstrates immense potential with its high radiative heat transfer efficiency and ease of regulation. However, it is less taken into account in traditional textiles, and there currently lies a gap in our knowledge and understanding of PRTM. In this review, we aim to present a thorough analysis of advanced textile materials and technologies for PRTM. Specifically, we will introduce and discuss the underlying radiation heat transfer mechanisms, fabrication methods of textiles, and various indoor/outdoor applications in light of their different regulation functionalities, including radiative cooling, radiative heating, and dual-mode thermoregulation. Furthermore, we will shine a light on the current hurdles, propose potential strategies, and delve into future technology trends for PRTM with an emphasis on functionalities and applications.

Improving the operational forecasts of outdoor Universal Thermal Climate Index with post-processing.

The Universal Thermal Climate Index (UTCI) is a thermal comfort index that describes how the human body experiences ambient conditions. It has units of temperature and considers physiological aspects of the human body. It takes into account the effect of air temperature, humidity, wind, radiation, and clothes. It is increasingly used in many countries as a measure of thermal comfort for outdoor conditions, and its value is calculated as part of the operational meteorological forecast. At the same time, forecasts of outdoor UTCI tend to have a relatively large error caused by the error of meteorological forecasts. In Slovenia, there is a relatively dense network of meteorological stations. Crucially, at these stations, global solar radiation measurements are performed continuously, which makes estimating the actual value of the UTCI more accurate compared to the situation where no radiation measurements are available. We used seven years of measurements in hourly resolution from 42 stations to first verify the operational UTCI forecast for the first forecast day and, secondly, to try to improve the forecast via post-processing. We used two machine-learning methods, linear regression, and neural networks. Both methods have successfully reduced the error in the operational UTCI forecasts. Both methods reduced the daily mean error from about 2.6 ∘ C to almost zero, while the daily mean absolute error decreased from 5 ∘ C to 3 ∘ C for the neural network and 3.5 ∘ C for linear regression. Both methods, especially the neural network, also substantially reduced the dependence of the error on the time of the day.

Privacy preserved and decentralized thermal comfort prediction model for smart buildings using federated learning.

Thermal comfort is a crucial element of smart buildings that assists in improving, analyzing, and realizing intelligent structures. Energy consumption forecasts for such smart buildings are crucial owing to the intricate decision-making processes surrounding resource efficiency. Machine learning (ML) techniques are employed to estimate energy consumption. ML algorithms, however, require a large amount of data to be adequate. There may be privacy violations due to collecting this data. To tackle this problem, this study proposes a federated deep learning (FDL) architecture developed around a deep neural network (DNN) paradigm. The study employs the ASHRAE RP-884 standard dataset for experimentation and analysis, which is available to the general public. The data is normalized using the min-max normalization approach, and the Synthetic Minority Over-sampling Technique (SMOTE) is used to enhance the minority class's interpretation. The DNN model is trained separately on the dataset after obtaining modifications from two clients. Each client assesses the data greatly to reduce the over-fitting impact. The test result demonstrates the efficiency of the proposed FDL by reaching 82.40% accuracy while securing the data.

Thermo-Hydric Study of Wood-Based Materials under Thermal Comfort Conditions.

This paper tackles the issue of moisture variation in wood-based materials, explicitly focusing on melamine-coated particleboard (hereafter referred to as melamine) and medium-density fiberboard (MDF) used in the third phase of wood industry transformation. The approach involves a comprehensive strategy for predicting moisture content variation, incorporating numerical simulation, experimental testing, and the application of artificial neural network (ANN) technology to enhance accuracy in furniture manufacturing. The developed ANN models are tailored to predict moisture content changes under specific thermal comfort conditions. Remarkably, these models demonstrate high precision, with an average error margin of only 1.40% for 8% moisture content (MC) and 2.85% for 12% MC in melamine, as well as 1.42% for 8% MC and 2.25% for 12% MC in MDF. These levels of precision surpass traditional models, emphasizing this study's novelty and practical relevance to the industrial context. The findings indicate that ANN models adapt to diverse environmental conditions, presenting a robust tool for optimizing moisture management in wood-based materials. This research contributes valuable insights for improving the reliability and efficiency of moisture content predictions in the wood industry.

Surviving indoor heat stress in United States: A comprehensive review exploring the impact of overheating on the thermal comfort, health, and social economic factors of occupants.

In the face of escalating global climate change and the increasing frequency of extreme heat events, the mitigation of building overheating has become an urgent priority. This comprehensive review converges insights from building science and public health domains to offer a thorough understanding of the multifaceted impacts of indoor overheating on occupants. The paper addresses a significant research gap by offering a holistic exploration of indoor overheating of residential buildings and its consequences, with a specific focus on the United States, an economically diverse nation that has been underrepresented in the literature. The review illuminates the effects of overheating on thermal comfort, health, and socio-economic aspects within the built environment. It emphasizes associated repercussions, including heightened cooling energy consumption, increased peak electricity demand, and elevated vulnerability, leading to exacerbated heat-related mortality and morbidity rates, especially among disadvantaged groups. The study concludes that vulnerabilities to these impacts are intricately tied to regional climatic conditions, highlighting the inadequacy of a one-size-fits-all approach. Tailored interventions for each climate zone are deemed necessary, considering the consistent occurrence of indoor temperatures surpassing outdoor levels, known as superheating, which poses distinct challenges. The research underscores the urgency of addressing indoor overheating as a critical facet of public health, acknowledging direct socioeconomic repercussions. It advocates for further research to inform comprehensive policies that safeguard public health across diverse indoor environments.

Climate projections of human thermal comfort for indoor workplaces.

Climate models predict meteorological variables for outdoor spaces. Nevertheless, most people work indoors and are affected by heat indoors. We present an approach to transfer climate projections from outdoors to climate projections of indoor air temperature (Ti) and thermal comfort based on a combination of indoor sensors, artificial neural networks (ANNs), and 22 regional climate projections. Human thermal comfort and Ti measured by indoor sensors at 90 different workplaces in the Upper Rhine Valley were used as training data for ANN models predicting indoor conditions as a function of outdoor weather. Workplace-specific climate projections were modeled for the time period 2070-2099 and compared to the historical period 1970-1999 using the same ANNs, but ERA5-Land reanalysis data as input. It is shown that heat stress indoors will increase in intensity, frequency, and duration at almost all investigated workplaces. The rate of increase depends on building and room properties, the workplace purpose, and the representative concentration pathway (RCP2.6, RCP4.5, or RCP8.5). The projected increase of the mean air temperature in the summer (JJA) outdoors, by + 1.6 to + 5.1 K for the different RCPs, is higher than the increase in Ti at all 90 workplaces, which experience on average an increase of + 0.8 to + 2.5 K. The overall frequency of heat stress is higher at most workplaces than outdoors for the historical and the future period. The projected hours of indoor heat stress will increase on average by + 379 h, + 654 h, and + 1209 h under RCP2.6, RCP4.5, and RCP8.5, respectively.

Investigation of indoor thermal comfort and energy demand in different locations along the sub-Himalayan belt - A simulation based study.

The sub-Himalayan region extends over 2500 km, extending over several countries. Though the effects of climate change is widely anticipated in the diverse but fragile ecosystem of the Himalayas, very less research has been conducted on the indoor environment of the buildings in these regions. In this study, a pre-validated model of 3-storey concrete residential building was used to study the indoor performance and thermal comfort in the face of climate change in the 8 (eight) different hill towns (hill stations) located from west to the east. Rise in ambient and indoor conditions were evident as a part of climate change with colder locations being affected the most. The thermal comfort assessment using both the climate chamber based PMV model and adaptive models revealed the decrease in cold related discomfort and increase in hot related discomfort. On an overall, the indoor conditions improved in these cold locations. The indoor and outdoor thermal condition and thermal comfort plummeted significantly with latitude and elevation. The heating demand in the future climate reduced by about 50-70 % in warmer locations, while the cooling demand increased by as much as 1000-2000 % in cold locations, respectively. Additionally, it was seen that the thermal environment and comfort both declined more rapidly with elevation in the locations lying in the western Himalayas as compared to those in the eastern Himalayas.

Classification of thermal environment control indicators according to the thermal sensitivity of office occupants.

The control that have the greatest influence on comfortable in the office occupants are the heating, ventilation, and air conditioning (HVAC) system operation and the thermal environment. However, comfortable HVAC operation is difficult in the office space characterized by a recommended standard thermal environment or a centralized HVAC system. To consider the occupant's thermal comfort to the greatest possible extent, must establish a method to quantify the variables related to the occupant's thermal comfort. This study aims to group occupants in Thermal sensation vote (TSV) clusters and perform sensitivity analysis (SA) on the relationship between thermal environmental factors in an office building and each cluster's TSV to establish the typology of the control indicators for each cluster. A total of 10 field experiments were conducted in the same office. This field study was carried out 2022. The indoor thermal environmental parameters, the subjective evaluation of the thermal comfort of the resident and the operation pattern of the heating system were monitored at the same time. A total of 4,200 datasets related to indoor thermal environmental parameters and a total of 1,680 datasets related to occupants' thermal comfort were collected and analyzed. The results of this study show that people have different levels of adaptability and sensitivity to a given thermal environment. This study founded distinguishable similarities in their thermal sensation traits and grouped similar TSV values into five clusters that responded differently to the same thermal environment. Each cluster showed different TSV and Thermal comfort vote (TCV) patterns, which allowed us to classify the groups that had sensitive responses to the thermal environment and those that did not. This study was determined different control indicators and guidelines for the divided groups according to thermal sensitivity.

Exploring the relationship between land use/land cover and apparent temperature in China (1996-2020): implications for urban planning.

In recent decades, rising air temperatures (AT) and apparent temperatures (AP) have posed growing health risks. In the context of China's rapid urbanization and global climate change, it is crucial to understand the impact of urban land use/land cover (LULC) changes on AP. This study investigates the spatial distribution and long-term variation patterns of AT and AP, using data from 834 meteorological stations across China from 1996 to 2020. It also explores the relationship between AT, AP, and LULC in the urban core areas of 30 major cities. Study reveals that AT and AP exhibit overall high spatial similarity, albeit with greater spatial variance in AP. Notably, regions with significant disparities between the two have been identified. Furthermore, it's observed that the spatial range of high AP change rates is wider than that of AT. Moreover, the study suggests a potential bivariate quadratic function relationship between ΔT (the difference between AT and AP) and Wa_ratio and Ar_ratio, indicating the presence of a Least Suitable Curve (LSC), [Formula: see text]. Urban LULC planning should carefully avoid intersecting with this curve. These findings can provide valuable insights for urban LULC planning, ultimately enhancing the thermal comfort of urban residents.

A systematic review of Personal Comfort Systems from a post-phenomenological view.

Personal Comfort Systems (PCS) are equipments that heat and/or cool occupants without affecting surrounding environments, ranging from commonly used devices to innovative technologies, and that tend to be controlled by people. These systems aim to address energy consumption and occupant satisfaction issues related to centralised air-conditioning. Although there are systematic studies on these systems, there is a lack of documentation regarding mediation characteristics between people and the built environment. This article presents a systematic review of PCS using a search of academic literature and patents, classifying PCS based on thermal categories and device typologies while introducing post-phenomenological mediation categories. The results show that most PCS fall into the thermal categories of 'Heating' and 'Cooling and ventilation'. The review also presents a view of the PCS territory based on mediation attributes and technological complexity. Finally, the PCS' characteristics are discussed based on the post-phenomenological concepts of Embodiment, Hermeneutic, and Background providing insights for future research opportunities and PCS development.

Practitioner summary: This study presents a systematic review of Personal Comfort Systems (PCS) found in patents and academic databases, integrating post-phenomenological concepts into PCS discussion. It visualises the PCS territory using a matrix based on mediation relations and technological complexity, providing insights into PCS development and research opportunities.