Elsevier

Building and Environment

Volume 188, 15 January 2021, 107424
Building and Environment

Investigation of outdoor thermal comfort prediction models in South China: A case study in Guangzhou

https://doi.org/10.1016/j.buildenv.2020.107424Get rights and content

Highlights

  • All factors on thermal sensation in different seasons are reported.

  • Air temperature has the most significant effect on thermal sensation.

  • Effects of metabolic rate lower than 2.0 met on thermal sensation can be ignored.

  • Each season predicted model is obtained using multiple linear regression.

Abstract

A suitable outdoor thermal environment can encourage people to partake in outdoor activities, which, in turn, reduces building energy consumption. This can be achieved by accurately predicting the outdoor thermal environment. Most existing prediction models of the outdoor thermal environment focus on the relationship between environmental parameters and human perception, while ignoring the effects of personal factors (e.g., clothing level and metabolic rate). This study explores the relationships between the microclimate environment, personal factors, and human perception of the thermal environment during each season. A field survey was conducted between July 2016 and June 2017, across all four seasons. Thermal environment parameters, including air temperature, relative humidity, wind speed, and globe temperature were recorded and analyzed together with questionnaire survey responses. The results indicated that air temperature has the most significant effect on thermal sensation. In colder or warmer conditions, the mean thermal sensation vote increases with the increase in clothing insulation. Notably, when people kept low metabolic rate activities, including seated and quiet, standing, and walking at 3.2 km/h, the effect of metabolic rate on thermal sensation are negligible. Considering the effect of seasonal differences on the thermal environment parameters, prediction models for each season were obtained using multiple linear regression (the R-squared are 0.560(annual), 0.255 (spring), 0.207 (summer), 0.176 (autumn), 0.145 (winter)). Except for wind speed, all other factors were found to have a positive effect on the prediction models, especially air temperature and mean radiation temperature.

Introduction

Over the past decade, due to the rapid increase in urbanization, the outdoor thermal environment in urban areas has become an important area of research [1]. Recreation and leisure activities in the outdoor thermal environment, such as walking, jogging, and cycling, have led to increased exposure to urban weather. A comfortable outdoor thermal environment can increase the number of people partaking in outdoor activities, which in turn can effectively reduce the energy consumed by air conditioning and artificial lighting systems in buildings. Consequently, the creation of attractive outdoor spaces is an important part of urban planning and design [2,3].

It is well known that human thermal comfort has a great relationship with microclimate in outdoor spaces. Thus, for predicting thermal comfort, the environment parameters, including air temperature (Ta), Relative humidity (RH), wind speed (Va) and mean radiant temperature (MRT), need to be analyzed. In many previous investigations, the application of computational fluid dynamic (CFD) simulation to the urban environment has been developed to determine thermal comfort and air quality [[4], [5], [6], [7], [8]]. Toparlar et al. [7] reported a new temperature inlet profiles that can yield horizontal homogeneity for neutral and near-neutral ABL conditions when used in combination with the standard k-ε model, the Standard Gradient Diffusion Hypothesis (SGDH), and a temperature wall function. Antoniou et al. [8] used CFD to simulate the urban microclimate with validation using high-resolution field measurements, which was benefit for obtaining the high accuracy of individual target variables for evaluating the outdoor thermal environment. Some other software, including ENVI-met [9], RayMan [10,11], OTC3D [12] were also developed for microclimate design. Zolch et al. [13] studied the Hypothetical green projects for public squares, using ENVI-met. The result indicated the number of trees and their location can offer a greater shaded area and reduce heat storage. Additionally, many filed surveys and experiments with measurement were conducted to evaluate the outdoor spaces [14].

For more accurately predicting outdoor thermal comfort, numerous thermal comfort indices have been developed to evaluate human thermal comfort by considering the relationship between the microclimatic conditions and thermal sensation [15]. Based on the assessment method, these thermal indices can be divided into two types: steady-state assessment methods and non-steady-state assessment methods. Considering steady-state assessment methods, one of the most extensively used indices is the predicted mean vote index (PMV) [16]. Originally developed as an indoor thermal comfort index, it has subsequently been applied to outdoor thermal environment evaluations in field survey investigations [17,18]. Another notable index—physiologically equivalent temperature (PET)—is based on the Munich energy-balance model for individuals (MEMI), which has been widely used in different climate zones [17,18]. Other commonly used indices include the index of thermal stress (ITS) [19], OUT-SET* [20], and COMFA outdoor thermal comfort model [21]. Non-steady-state assessment methods have been used to develop thermal comfort indices to evaluate dynamic conditions. The universal thermal climate index (UTCI) is a recently developed outdoor thermal index for outdoor thermal environment assessment [22,23] that utilizes a complex heat budget-based approach [24]. Considering the effects of various climatic conditions, it has been validated in field surveys across China, in cities such as Hong Kong [25], Guangzhou [26,27], Tianjin [28], and Xi'an [29]. The UTCI has been used in other countries as well [30,31]. Recently, some investigations [[32], [33], [34], [35]] used skin temperatures to predict outdoor thermal comfort and get a high prediction accuracy. However, it is difficult to know the subjects' skin temperature in advance. Also, some new indices were also reported. Based on the questionnaire survey, Cheung and Jim [36] proposed a novel assessment of outdoor thermal comfort: 1-h acceptable temperature range. To evaluating thermal sensations in a mist spraying environment, Oh et al. [37] developed a new index through correlation analysis of the MTSV and heat storage rate that can properly reflect the thermal sensation.

Based on the above analysis, thermal comfort indices were developed to analyze thermal comfort considering the combination of the micro-climate and thermal sensation [38]. However, investigations to improve the adaptation of existing thermal comfort indices have resulted in significantly different outcomes. The primary reason is that people in different regions have different thermal adaptation, which causes the ranges of the neutral thermal index [39] and thermal comfort indices [40] to vary across regions.

Although many thermal comfort indices have developed rapidly in the past decade, there are still some issues about people's outdoor thermal sensitivity needed to be determined. In some previous experiential investigations [[41], [42], [43], [44]], the local human thermal sensitivity has been analyzed in the same regions. However, the results were different. in Hong Kong, Li et al. [41] found the most sensitive parameter is Tmrt in summer, while Chan and Chau [42] found it is Ta. In addition, Tmrt as one of the most sensitive parameters for outdoor thermal comfort has been proved [41,43]. But Peng et al. [44] found Tmrt has little effect on human thermal sensation. The primary reason may be seasonal variations. Thus, analysis of the sensitivity, seasonal variations need to be considered.

Moreover, to evaluate the local thermal environment, the multivariate linear regression method has been used by Salata et al. [45], Ruiz and Correa [46], Givoni et al. [47], Nikolopoulou et al. [48], Cheng et al. [49], Zhao et al. [50], and Liu et al. [51] to determine the relationship between the effect parameters and thermal sensation, and predict thermal comfort. However, most linear adaptive models only consider thermal environment parameters such as air temperature (Ta), wind speed (V), relative humidity (RH), and mean radiant temperature (Tmrt), while ignoring personal factors such as metabolic rate and clothing insulation. However, It's known that metabolic rate and clothing insulation are the key parameters affecting human thermal comfort [17,52] Therefore, to better evaluate the outdoor thermal environment, an adaptive model that combines the effects of thermal parameters and personal factors must be developed.

This study proposes an adaptive model to quantify the correlations among the microclimate, subjective thermal perception, and personal factors. Field measurements and subjective surveys are used to evaluate the outdoor thermal environment in South China. Based on the data, the sensitivities of various microclimate and personal factors on thermal sensation are analyzed. The proposed simplified model can be used to assess the outdoor thermal environment for outdoor space design and planning.

Section snippets

Methods

In this investigation, the field survey with concurrent micrometeorological measurements was conducted in Guangzhou in South China. It lasted nearly a year, covering four seasons, including spring, summer, autumn, and winter. A total of 4675 valid survey responses were collected. The detailed information of the field survey, including the climatic characteristics of the study area (Guangzhou), questionnaire, measurement parameters, and the analysis method will be described.

Results

The results are presented in two parts. The first part is in Section 3.1. It first describes the parameter variations, including Ta, RH, Va, Tmrt, clothing resistance, and metabolic rate. Their effects on thermal comfort were significant. The second part analyzes the relationships between thermal sensation and parameters, including Ta, RH, Va, Tmrt, clothing resistance, and metabolic rate. The sensitivity analysis is also described. The sensitivity analysis is also described. The questionnaire

Model establishment and discussion

The effects of air temperature, relative humidity, wind speed, mean radiation temperature, clothing thermal resistance, and metabolic rate on human thermal comfort were compared and analyzed using multiple linear regression. Based on the survey data and adopting the "input" (all factors input to the model) and “step-by-step” methods of multiple linear regression, models for the entire year and individual seasons were established (Table 7, Table 8, Table 9, Table 10, Table 11). The multiple

Conclusion

Based on a field survey conducted in Guangzhou, the understanding of the relationship between subjective thermal sensation and the outdoor thermal environment was enriched. A total of 4675 subjects were asked to indicate their thermal sensation while the microclimate of the area was measured. The survey was performed in all four seasons. The main findings are summarized in this section.

Comparing the effects of various parameters of the thermal environment on thermal sensation, the air

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Project No. 51978180) and the Opening Funds of State Key Laboratory of Building Safety and Built Environment and National Engineering Research Center of Building Technology (Project No. BSBE2018-03), and Fundamental Research Funds for the Central Universities (Project No. No.2020CDJQY-A009). The authors thank all students who participated in the surveys.

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