1. Introduction

The urban heat island (UHI) effect is the phenomenon in which urban areas are warmer than the nearby suburbs [1,2]. Random urban sprawl and the construction of high-density buildings obstruct airflow, resulting in low wind speeds within the city, markedly exacerbating the UHI effect [3]. In many cities, the urban heat island intensity (UHII) reaches about 5 °C [4,5,6]. UHI effect has become one of the important problems for urban environment and human health. Therefore, assessing the impact of UHI effect on outdoor thermal comfort (OTC) and the measures to mitigate UHI effect has become one of the most popular and difficult issues in current urban thermal environment research [7,8,9].

Urbanization has led to the intensification of the UHI effect, which has been the focus of numerous studies in recent years [10,11,12,13]. The UHI effect is particularly significant in dense urban areas [14]. Studies have shown that UHI is exacerbated by factors such as the increase in anthropogenic heat emissions, changes in soil cover, urban layout, building geometry and orientation, and climatic and geographic conditions [15,16,17,18]. Low-speed winds have been shown to be associated with higher UHII [19], and coastal cities with low altitude and humid air are generally more affected by UHI than inland cities with hot, dry air [20,21]. In recent studies, researchers have used on-site measurements, remote sensing techniques, and wearable sensors to assess the UHI effect and its impact on the urban environment. For instance, Mohan et al. [10] found that the UHI effect was noticeable in both the canopy layer (CUHI) and the surface (SUHI) in New Delhi, India. Van Hove et al. [12] studied the UHI of Rotterdam and found that the UHII varied widely within the city, depending on the specific location. Pioppi et al. [13] used wearable sensors to accurately assess the effects of environmental parameters on UHII in a park and found a large spatial and temporal variability in these parameters within the park. Sharmin et al. [22] studied the effects of building geometry and orientation on UHII in Dhaka, Bangladesh, and found that east–west streets in residential areas had higher UHII than north–south streets, and that buildings with the same height, spacing, and plot size could make local microclimates more severe. Overall, understanding the UHI effect and its impact on urban environments is crucial for developing sustainable cities that provide optimal thermal comfort for their inhabitants.

The OTC is heavily impacted by the UHI effect. Thermal comfort is defined as “that condition of mind which expresses satisfaction with the thermal environment” [23]. Due to the greater instability of temperature and humidity changes, the difficulty in controlling radiation heat and wind speed in the outdoor environment, and the larger variation in human metabolic rate, the complexity of studying OTC is much higher than that of indoor environment. High outdoor temperatures have been associated with heat-related health conditions such as heat rash, heat cramps, heat stroke, and even death [24]. A study of climate in 50 U.S. cities between 1989 and 2000 revealed a 5.7% increase in mortality during heat waves [25]. Similar findings were reported in other cities such as Hong Kong, Bangkok, and Delhi, where mortality increases range from 4.1% to 5.8% per 1 °C over a temperature threshold of approximately 29 °C [26]. In addition, urban overheating can worsen air pollution and increase cooling energy consumption [27,28,29]. Therefore, mitigating the UHI effect and improving OTC have become critical priorities for urban planning and sustainability.

Mitigating the UHI effect is one of the important measures to improve OTC. Currently, researchers have proposed many approaches to mitigate the heat island effect, such as the application of cool materials, the construction of greening facilities, and the addition of shading devices [17,30,31]. However, it is more important to forecast the thermal consequences in the early stages of the design process [32]. Chen et al. [33] used the SOLWEIG (solar long-wave environmental irradiance geometry) model to study the mean radiant temperature (MRT) in different environments in Shanghai. The results showed that MRT was largely influenced by building density and height, street orientation, and vegetation, with the highest MRT near open spaces and sun-exposed walls. He et al. [34] found that in an open low-rise gridiron precinct, the precinct ventilation performance (PVP), precinct outdoor thermal environment (POTE), and precinct outdoor thermal comfort (POTC) significantly varied with the combination of external meteorological conditions and precinct morphological characteristics, while the street orientation had an insignificant influence on PVP, POTE, and POTC. The PVP exhibited significant potential for UHII reduction and POTC improvement. The PVP driven by the sea breeze could further increase relative humidity for UHII reduction and POTC improvement. Mehrotra et al. [35] also showed that an increase in the proportion of compact high-rise buildings leads to an increase in UHII, which in turn leads to a decrease in physiological equivalent temperature (PET) and universal thermal climate index (UTCI). Increasing the ventilation and emission capabilities of urban underground parking lots can reduce the maximum air temperature by 1.5 °C and lower the average radiative temperature from 55–65 °C to 20–27 °C, resulting in an improvement in OTC [36]. There is also a better greening effect when the spatial pattern of green space is more aggregated, less fragmented, and more complex. The outdoor heat stress is relatively low for green spaces with simple shapes in low-density building areas and green spaces with complex shapes in high-density building areas [36]. In the case of the same greening area, counted wood greening is more effective than other forms of greening [37]. Salman et al. [38] constructed 18 scenarios from three UHI mitigation strategies (vegetation, cool materials, and urban morphology) and used the numerical simulation software ENVI-met to evaluate the effects of different scenarios on outdoor human comfort in Baghdad, Iraq. Sanagar et al. [39] performed microclimate simulations and analyses in ENVI-met software and calculated UHII using ArcMap. They ultimately demonstrated that zoning for UHII can be used as an alternative to zoning for urban form based on OTC, especially in large cities where urban form data collection may be difficult due to limited time and resources [40,41].

This paper aims to systematically summarize the research progress and problems in OTC evaluation and mitigation of UHI effect and to better understand the mechanism of UHI effect on OTC. Specifically, this paper firstly introduces the common indexes and data collection methods for OTC evaluation, including questionnaires, measurements and simulations, and formula calculations; secondly, this paper reviews the measures and methods for mitigating the UHI effect; finally, this paper discusses the problems of existing research and future research directions. This paper can provide reference for relevant researchers and policy makers and promote further development and in-depth exploration of OTC evaluation and UHI effect mitigation research.

2. Methodology

This section describes the methodology used to find, classify, and review relevant information for this review article. To obtain information on the impact of the UHI effect on OTC, five major databases (Science Direct, Scopus, Google Scholar, PubMed, and Web of Science) were used to search for the keywords “urban heat island”, “outdoor thermal comfort”, “outdoor thermal comfort indices”, “questionnaire”, “simulation”, and “migration”. A detailed analysis was then performed on manuscripts that met the following criteria:

Only English-language peer-reviewed scientific journal articles and international conference proceedings were considered; other official publications such as book chapters, reports, and theses/dissertations were not included.

Published in the last fifteen years (from January 2008 to December 2022).

A maximum of two papers by the same author.

Contain at least three of the above keywords.

A systematic review was conducted under the guidance of the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). It is a guide for standardizing systematic evaluation and meta-analysis reports, which aims to improve the transparency and quality of evaluation and reduce bias in the results, help researchers standardize the evaluation process, provide reporting and literature screening steps, and improve the reliability and reproducibility of evaluation results. The detailed process is shown in Figure 1 and consists of four main steps: identification, screening, eligibility, and inclusion of papers in the study.

After selection and evaluation, this review included 100 papers. Then, we summarized the year, author, research region, research method, tools, results, and limitations for each paper. Figure 2 depicts the growth trend of the related studies from 2008 to 2022. Analysis of the 100 selected manuscripts by year revealed that the number of relevant studies began to rise sharply in 2014, with 66 of the studies conducted in the last five years, from 2018 to 2022. It can be inferred that there has been an increase in awareness and interest in UHI and OTC in the past five years.

Figure 3 illustrates the global distribution of relevant studies, with study sites categorized by continent. It can be observed that 58% of the study sites were in Asia, especially in China and Iran, with 23 and 10, respectively, followed by Europe with 29 studies, mainly in Italy and Germany. Among all continents, Africa had the fewest number of research papers on the UHI effect, probably due to its lower level of urbanization and less significant UHI effect, resulting in less attention on this topic. It should be noted that some papers did not mention the study location. Therefore, they are not included in Figure 3. In addition to the 100 application-oriented papers mentioned above, some other review-oriented papers are also referenced in this paper.

3. Results

3.1. Outdoor Thermal Comfort Indices

Previous OTC studies have utilized various indexes, but the lack of a consensus on the appropriate index has raised questions about the validity of these studies. It is important to note that the choice of index can significantly impact the assessment of OTC and the resulting design recommendations. Therefore, this subsection critically examine the indexes used in previous studies. By doing so, we aim to provide a more comprehensive understanding of OTC and inform future research and design practices. Nearly 30 different kinds of thermal comfort indexes have been used to assess and predict people’s comfort [42]. The indexes include the physiological equivalent temperature (PET), universal thermal climate index (UTCI) and standard effective temperature (SET*). These three indexes were specifically designed for outdoor conditions and are widely used in OTC studies. Some other commonly used indexes are the predicted mean vote (PMV), apparent temperature (AT), discomfort index (DI), perceived temperature (PT) and wet-bulb globe temperature (WBGT). The application frequencies of the thermal comfort indexes are summarized in Figure 4. The indexes that were used only once in the reviewed papers were excluded. In addition, attention needs to be paid to PMV, which was originally developed for indoor environments [43,44]. According to ISO 15265 [45], PMV can be used as an indicator to determine the transition from heat/cold discomfort to heat/cold stress. This concept has been confirmed by supporting evidence [46]. Although, in recent years, more and more studies have shown that the PMV index can be used as a valid thermal comfort evaluation index in outdoor environments [38,47,48]. For example, researchers can calculate the thermal balance state of the human body based on outdoor meteorological data, human metabolic rate, clothing and activity level, and derive the corresponding PMV index based on the calculation results to evaluate the thermal comfort of the human body outdoors. However, even with the corresponding adjustments, there are still limitations and uncertainties in using the PMV index to evaluate outdoor thermal comfort. Therefore, PMV was not considered in this paper [49].

As shown in Figure 4, PET, UTCI, and WBGT were used the most widely in outdoor thermal comfort studies, and they were employed 43 times, 14 times, and 12 times, respectively, in the 100 selected manuscripts.

PET is defined as the air temperature without wind speed and solar radiation (indoor) at which the heat balance of the human body is maintained with the same core and skin temperature as under the conditions to quantify in the outdoor thermal environment [50]. This index is based on the Munich energy-balance model for individuals (MEMI), which models the human thermal comfort conditions in a physiological manner that defines the balanced equation of the human body as that given in Equation (1) [51]:

S = M ± W ± R ± C ± K ? E ? RES

where S is heat storage; M is metabolism; W is external work; R is heat exchange by radiation; C is heat exchange by convection; K is heat exchange by conduction; E is heat loss by evaporation; and RES is heat exchange by respiration (from latent heat and sensible heat). The assessment scale for PET is displayed in Table 1.

The universal thermal climate index (UTCI) can be defined as the air temperature (Ta) of the reference conditions (the activity level corresponds to a walk of 4 km/h; the environment is defined by calm air with a wind speed of 0.5 m/s and a distance of 10 m from the ground, corresponding to a personal level of about 0.3 m/s, without additional thermal radiation and at 50% relative humidity, but with a vapor pressure not exceeding 20 hPa) [53]. This index is based on a multi-node dynamic thermo-physiological UTCI-Fiala model that defines thermal effects on the whole human body [54]. The official equation for calculating the UTCI is extremely complex [55]. Therefore, an empirical equation is provided [47]:

Jianlin Ren

Read more:

?  Read More  Environmental Tech, Clean Tech, Smart City, Tech, Green Tech, Eco-Tech, Climate Tech, Bio-Tech