A Review on the Impacts of Urban Heat Islands on Outdoor Thermal Comfort

Abstract

The worsening urban heat island (UHI) effect poses a great challenge to the thermal comfort of people outdoors. However, there has not been a summary of the mechanisms by which UHI affects outdoor thermal comfort (OTC). This paper reviews the commonly used OTC evaluation indexes, data collection methods, and mitigation measures and discusses the relationship between UHI and OTC. The review is limited to peer-reviewed journal publications found in five databases: Science Direct, Scopus, Google Scholar, PubMed, and Web of Science. The review results indicate that physiological equivalent temperature (PET), universal thermal climate index (UTCI), and wet bulb globe temperature (WBGT) are the most widely used indexes in outdoor thermal comfort studies. The data collection methods mainly include questionnaire surveys, measurement, simulation, and formula calculation. There are four main approaches to mitigating the UHI effect in order to improve the comfort of people outdoors: vegetation strategies, water strategies, urban planning strategies, and material strategies. Future research can focus on developing OTC research methods and indexes and combine thermal comfort with visual comfort, auditory comfort, etc. to better evaluate the overall comfort.

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

(1)

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. Assessment scale for PET [52].

Thermal Sensation	PET Range (°C) (in Subtropical Climate)	PET Range (°C) (in Temperate Climate)
Very hot	<42	<41
Hot	38–42	35–41
Warm	34–38	29–35
Slightly warm	30–34	23–29
Neutral	26–30	18–23
Slightly cool	22–26	13–18
Cool	18–22	8–13
Cold	14–18	4–8
Very cold	<14	<4

.

The universal thermal climate index (UTCI) can be defined as the air temperature (T_a) 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]:

$$\mathrm{U}\mathrm{T}\mathrm{C}\mathrm{I}=3.21+0.872T_a+0.2459T_{mrt}-2.5078V-0.0176RH$$

(2)

where T_a is air temperature (°C); T_mrt is mean radiant temperature (°C); the details are shown in Equation (3); V is wind speed (m/s); and RH is the relative humidity (%). It should be noted that the parameters in the equation need to be measured at the same time and position. The equation is based on statistical analysis of research and experimental data on thermal physiology, heat radiation, and heat transfer. It is a simplified approximation equation designed to estimate human thermal comfort by using environmental factors such T_a, T_mrt, V, and RH. This equation is designed to provide a simple, fast, and cost-effective method to estimate the UTCI for thermal comfort assessment under general environmental conditions.

$$T_{mrt}={\left[{\left(T_g+273\right)}^4+\frac{1.1\times {10}^8\times V^{0.6}}{\epsilon D^{0.4}}\left(T_g-T_a\right)\right]}^{\frac{1}{4}}-273$$

(3)

where D is the globe diameter (0.15 m for standard globe); and ε is the globe emissivity (0.95 for black globe). T_g is the globe temperature (°C). It should be noted that T_g must be measured using a diameter 150 mm with a black surface and uniform radiation absorption characteristics of a hollow sphere [56,57].

The UTCI assessment scale is given in

Table 2. Assessment scale for UTCI [54].

Thermal Sensation	UTCI Range (°C) (in Subtropical Climate)
Extreme heat stress	>46
Very strong heat stress	38–46
Strong heat stress	32–38
Moderate heat stress	26–32
No thermal stress	9–26
Slight cold stress	0–9
Moderate cold stress	−13–0
Strong cold stress	−27 to −13
Very strong cold stress	−40 to −27
Extreme cold stress	<−40

.

In addition, the WBGT is widely used globally for assessing heat stress levels. WBGT is a meteorological index that integrates ambient humidity, air temperature, wind speed, and radiation to assess the effects of heat stress on humans. The basic equation for WBGT is Equation (4) [58]:

$$\mathrm{W}\mathrm{B}\mathrm{G}\mathrm{T}=0.7T_{nw}+0.2T_g+0.1T_d$$

(4)

where $T_{nw}$ is the natural wet bulb temperature; $T_g$ is the black bulb temperature; and $T_d$ is the dry bulb temperature. It should be noted that the measurement of T_g must follow the requirements in ISO 7243 and ACGIH, as mentioned in Equation (3); otherwise, serious measurement errors will occur and affect the heat stress assessment [59].

The assessment scale for WBGT is displayed in

Table 3. Assessment scale for WBGT [56,59].

Class of Metabolic Rate	Reference Metabolic Rate Value (W)	WBGT Reference Limit Value (°C)
		Acclimatized	Not Acclimatized
Resting	115	33	32
Low metabolic rate	190	30	29
Moderate metabolic rate	300	28	26
High metabolic rate	415	26	23
Very high metabolic rate	520	25	20

.

3.2. Data Collection Methods for Outdoor Thermal Comfort Survey

3.2.1. Questionnaire Survey

Subjective questionnaires are the most commonly used method to determine personnel comfort. In the selected studies, the questionnaires consisted mainly of relevant personal information (gender, age, height, weight, activity level, type of clothing, etc.), thermal sensation, and thermal preference. The ASHRAE 7-point sensation scale and the McIntyre scale were used to record the thermal sensations and preference data, respectively [60]. Such questionnaires can be prepared using ISO 10551 and ASHRAE Standard 55-2021 [61].

Table 4. Summary of studies based on questionnaires.

Year	Location	TSV Scale	Number of Questionnaires	Index	Reference
2022	Xanthi, Greece	Other 5-point	216	ASV	[11]
2022	Rome, Italy	ASHRAE 7-point	-	MOCI	[64]
2021	Harbin, China	Other 11-point	1632	PET	[62]
2021	Malaysia	ASHRAE 7-point	329	PET	[63]
2021	Chengdu, China	ASHRAE 7-point	419	PET, UTCI	[54]
2021	Taiwan, China	ASHRAE 7-point	-	PET	[65]
2020	Madrid, Spain	5-point Likert scale	145	PET	[66]
2019	Bangladesh	ASHRAE 7-point	1286	PET	[67]
2018	Xian, China	ASHRAE 7-point	37	PET, UTCI	[68]
2018	Guangzhou, China	ASHRAE 7-point	644	WBGT, PET, SET, UTCI	[47]
2017	Hong Kong, China	ASHRAE 7-point	-	PET, UTCI, UCB	[69]
2015	Argentina	Other 5-point	622	IZA	[70]
2013	Singapore	ASHRAE 7-point	7	TSV	[71]
2012	Guangzhou, China	ASHRAE 7-point	114	SET	[72]
2011	Curitiba, Brazil	ASHRAE 7-point	1654	PET	[73]

lists some of the studies that employed the questionnaire approach. For example, Yin et al. [62] surveyed 1632 participants for basic information, outdoor thermal sensation, thermal comfort, thermal acceptability, and preference of meteorological parameters in Harbin, China. The basic information included gender, age, activity level, and clothing resistance. The 11-point thermal sensation vote (TSV) scale (−5 unbearably cold/−4 very cold/−3 cold/−2 cool/−1 slightly cool/0 neutral/1 slightly warm/2 warm/3 hot/4 very hot/5 unbearably hot) was used in the survey. For the outdoor thermal comfort vote (TCV), a three-point system was adopted, with −1 for discomfort, 0 for neutral, and 1 for comfort. Aghamohammadi et al. [63] conducted a cross-sectional study by adopting random clustered sampling on a tropical university campus. Their questionnaire survey included 392 interviewers and covered a socio-demographic profile, thermal sensation vote, and physical health.

3.2.2. Measurement and Simulation

The measurement and simulation method focuses on the use of meteorological stations of different sizes in the selected study area and measures the basic parameters of OTC and the UHI effect, which usually include meteorological parameters such as air temperature, relative humidity, and wind speed [74,75,76]. Table 5 summarizes some of the studies based on measurement and simulation (out of a total of 46 studies applying this method). The measured meteorological parameters in these studies were then used to simulate thermal comfort parameters such as PET, UTCI, and outdoor climate micro-environments. The most widely used numerical simulation software programs were ENVI-met and RayMan, which were used 29 times and 20 times, respectively.

RayMan was designed to quantify the thermal conditions in different climates and regions by inputting various meteorological parameters (temperature, humidity, wind speed, black-bulb temperature, solar radiation, etc.) and calculating various thermal comfort indices. ENVI-met is a three-dimensional non-hydrostatic model based on computational fluid dynamic (CFD) solving of the Navier–Stokes equations using finite difference numerical methods. Solid surface-vegetation–air interactions in urban environments are simulated with a spatial resolution of 0.5–10 m and a temporal resolution of 1–10 s. The model calculation includes shortwave and longwave radiation fluxes with respect to shading; transpiration, evaporation, and sensible heat flux from the vegetation into the air including full simulation of all plant physical parameters (e.g., photosynthesis rate); surface and wall temperature for each grid point and wall; water and heat exchange inside the soil system; and calculation of biometeorological parameters such as MRT.

The ENVI-met model needs to be calibrated and validated. The accuracy of a model’s result is heavily dependent on the quality of the input data and the initial or boundary conditions, namely the weather data and geometric data for the study area. In the selected studies, most of these data were collected with field measurements. For example, Niu et al. [30] selected a residential area in Tempe, USA, and measured the air temperature to verify the accuracy of the ENVI-met numerical simulation. PET values were estimated with the ENVI-met BioMet package. For the human parameter setting in BioMet, the author used a 35-year-old male with a weight of 75 kg, height of 1.75 m, static clothing insulation index (clo) of 0.2 (T-shirt and walking shorts), and metabolic rate of 93 W/m² (standing or light activity) based on the ISO standard. Sun et al. [77] used measured meteorological parameters and basic human information (gender, age, clothing, and activity level) to calculate PET in ENVI-met and RayMan and validated the accuracy of the model by comparing the two methods.

Table 5. Summary of studies based on simulation.

Year	Location	Software	Index	Reference
2022	United Arab Emirates	ENVI-met	PMV	[78]
2022	Israel	Rhino	PET, UTCI, ITS	[79]
2022	Osaka, Japan	CFD	Ta, WBGT, SET	[80]
2022	Harbin, China	ENVI-met	PET	[77]
2021	Bilbao, Spain	ENVI-met	PET, UTCI	[81]
2021	Tehran, Iran	CFD	PET	[82]
2021	Panama	ENVI-met	PET	[83]
2020	Iran	RayMan	PET	[84]
2020	Iran	ENVI-met	PMV	[48]
2020	Tehran, Iran	Grasshopper	UTCI	[53]
2020	Sydney, Australia	RayMan	PET	[5]
2020	Ghent, Belgium	RayMan	PET	[85]
2019	Xian, China	ENVI-met	PET, UTCI	[86]
2019	Zurich, Switzerland	BioKlima	UTCI	[87]
2018	Tempe, USA	ENVI-met	PET	[88]
2018	Rome, Italy	ENVI-met	MOCI	[89]
2018	Serres, Greece	ENVI-met	PMV	[49]
2018	Singapore	ENVI-met	PET	[90]
2017	Iran	ENVI-met	PET	[91]
2017	Toronto, Canada	RayMan	PET	[92]
2016	Shenzhen, China	ENVI-met	SET*	[93]
2016	Beijing, China	ENVI-met	UTCI	[94]
2016	Putrajaya, Malaysia	ENVI-met	MRT, PET	[50]
2015	Hong Kong, China	RayMan	PET	[30]
2015	Bahrain	PHOENICS	PMV	[95]

Table 5. Summary of studies based on simulation.

Year	Location	Software	Index	Reference
2022	United Arab Emirates	ENVI-met	PMV	[78]
2022	Israel	Rhino	PET, UTCI, ITS	[79]
2022	Osaka, Japan	CFD	Ta, WBGT, SET	[80]
2022	Harbin, China	ENVI-met	PET	[77]
2021	Bilbao, Spain	ENVI-met	PET, UTCI	[81]
2021	Tehran, Iran	CFD	PET	[82]
2021	Panama	ENVI-met	PET	[83]
2020	Iran	RayMan	PET	[84]
2020	Iran	ENVI-met	PMV	[48]
2020	Tehran, Iran	Grasshopper	UTCI	[53]
2020	Sydney, Australia	RayMan	PET	[5]
2020	Ghent, Belgium	RayMan	PET	[85]
2019	Xian, China	ENVI-met	PET, UTCI	[86]
2019	Zurich, Switzerland	BioKlima	UTCI	[87]
2018	Tempe, USA	ENVI-met	PET	[88]
2018	Rome, Italy	ENVI-met	MOCI	[89]
2018	Serres, Greece	ENVI-met	PMV	[49]
2018	Singapore	ENVI-met	PET	[90]
2017	Iran	ENVI-met	PET	[91]
2017	Toronto, Canada	RayMan	PET	[92]
2016	Shenzhen, China	ENVI-met	SET*	[93]
2016	Beijing, China	ENVI-met	UTCI	[94]
2016	Putrajaya, Malaysia	ENVI-met	MRT, PET	[50]
2015	Hong Kong, China	RayMan	PET	[30]
2015	Bahrain	PHOENICS	PMV	[95]

3.2.3. Formula Calculation

In addition to the two widely used methods described above, the formula calculation method has been adopted by some scholars. A total of eight studies took this approach. Thermal comfort parameters are calculated by measuring certain parameters in the field and bringing them into an empirical formula. The specific thermal comfort assessment parameters and calculation formulas used are shown in

Table 6. Summary of papers using formula calculation to study outdoor thermal comfort.

Year	Location	Index	Formula	Reference
2021	Shanghai, China	Ta, WBGT	$\mathrm{WBGT}=0.567{\mathrm{T}}_{\mathrm{a}}+0.393\mathrm{e}+3.94$ (Ta is air temperature (°C), e is actual vapor pressure (hPa))	[96]
2021	Tehran, Iran	UTFVI	$\mathrm{UTFVI}=\frac{\mathrm{LST}-{\mathrm{LST}}_{\mathrm{mean}}}{{\mathrm{LST}}_{\mathrm{mean}}}$ (LST is land surface temperature (°C))	[97]
2021	Noida, India	UTFVI	$\mathrm{UTFVI}=\frac{\mathrm{LST}-{\mathrm{LST}}_{\mathrm{mean}}}{{\mathrm{LST}}_{\mathrm{mean}}}$	[98]
2020	Delhi, India	RI	$\mathrm{RI}=1.53{\mathrm{T}}_{\mathrm{d}}-0.32{\mathrm{T}}_{\mathrm{w}}-1.38\mathrm{v}+44.65$ (Td is dry temperature (°C), Tw is wet bulb temperature (°C), v is wind speed (m/s))	[10]
2020	Iran	THI, ETI, RSI	$\mathrm{THI}=0.8\mathrm{T}+0.002·\mathrm{T}·\mathrm{RH}$ $\mathrm{ETI}=\mathrm{T}-0.4(\mathrm{T}-10\left)\right(1-0.002\mathrm{RH})$ $\mathrm{RSI}=\frac{10.7+0.74({\mathrm{T}}_{\mathrm{a}}-5)}{44-0.0075·\mathrm{RH}·\mathrm{V}}$ (Ta is air temperature (°C), RH is relative humidity (%), V is saturation vapor pressure (hPa))	[76]
2017	Italy	MOCI	$\mathrm{MOCI}=-4.068-0.272·\mathrm{WS}+0.005·\mathrm{RH}+0.083·\mathrm{MRT}+0.058·{\mathrm{T}}_{\mathrm{a}}+0.264·{\mathrm{I}}_{\mathrm{CL}}$ (WS is wind speed (m/s), RH is relative humidity (%), MRT is mean radiant temperature (°C), Ta is air temperature (°C), ICL is clothing thermal resistance)	[64]
2015	Singapore	TSV	$\mathrm{TSV}=0.398{\mathrm{T}}_{\mathrm{a}}+0.023\mathrm{RH}-0.329\mathrm{V}+0.038{\mathrm{T}}_{\mathrm{mrt}}-14.061$ (Ta is air temperature (°C), RH is relative humidity (%), Tmrt is mean radiant temperature (°C))	[99]
2014	Greece	CP	$\mathrm{CP}=(0.421+0.087\mathrm{v}\left)\right(36.5-\mathrm{T})$ (v is wind speed (m/s), T is mean ambient temperature (°C))	[100]

.

3.3. Mitigating UHI to Improve OTC

An increase in the UHI effect will worsen the thermal comfort of people outdoors and pose a health risk. In this subsection, selected manuscripts are summarized. There are four main approaches to mitigating the UHI effect in order to improve the OTC: vegetation strategies (e.g., parks, vegetation, and green roofs), water strategies (e.g., lakes and fountains), urban planning strategies (e.g., building height, geometry, street H/W and SVF (sky view factor)), and material strategies (e.g., high albedo materials, cold materials) [28].

3.3.1. Vegetation Strategies

The most common cause of the increased UHI effect and the marked increase in outdoor human discomfort over the last 30 years is the lack of vegetation cover [4,97]. Covering roofs with vegetation and creating green urban areas can provide multiple benefits such as increased biodiversity, reduced stormwater runoff, reduced air pollution, improved indoor and outdoor urban thermal comfort (UHI mitigation), energy savings, and noise attenuation [38,67]. According to Aram et al. [66], in parks and green spaces, the combined effect of transpiration of plants and shaded areas created by tree canopies leads to a significant decrease in temperature, which can reduce air temperature by 2.4–2.8 °C and PET by about 3.9 °C, with a significant increase in thermal comfort. More specifically, the cooling effect of the studied park resulted in an average thermal comfort level that was 1.3–3.8% higher within 600 m of the neighborhood than in the central UHI area. Several other studies have confirmed that green spaces are cooler than spaces without any greenery [48,101]. Cao et al. [102] proposed a park vegetation and shape index (PVSI) that can be used to predict the intensity of cool islands in parks. It can help urban planners and park designers to better understand the cooling effects of parks and thus design cooler and more comfortable parks. However, the mechanisms by which parks impact the UHI effect and the extent to which parks affect UHII and OTC are not fully understood. Although it is known that the larger the park area is, the better the effect is, the exact relationship between the two is not clear.

Furthermore, Kim et al. [103] noted that the pattern of the green landscape also has an important effect on thermal comfort, with the effect being more pronounced when the green spaces are more clustered and simpler in shape. Green roofs are useful in mitigating the UHI effect but have essentially no effect on outdoor pedestrian comfort in densely built-up urban areas [104]. Radhi et al. [95] found that the presence of green space would also reduce cooling energy consumption in summer.

However, in cold climates, planting trees and vegetation can also negatively affect the urban microclimate and the comfort of outdoor occupants, and dense shade trees can instead increase heating costs [105]. In the construction of parks and planting of grass and trees, the climate and architectural specifics should be taken into account.

3.3.2. Water Strategies

City planners have long considered bodies of water as vital components of any strategy to relieve urban heat stress. The cooling effect of a water body occurs mainly in the daytime. Under the action of solar radiation, the water body evaporates and absorbs heat, converting sensible heat into latent heat and producing water vapor [95]. This significantly reduces the ambient temperature and improves the comfort of people outdoors. A fountain system works on the same principle and can reduce the ambient temperature by an average of 2–3 °C [65].

The cooling effect of water bodies is mainly influenced by their size, distribution, and distance from each other. The simulation results of Theeuwes et al. [106] show that the cooling effect of relatively large-area water bodies is stronger in their surrounding and downwind regions, but the size of their effect depends mainly on the wind speed. Moreover, several smaller bodies of water do not have as strong a cooling effect as a single body of water with the same total area. The geometry of the water body is also significant, with square or circular shapes having a stronger cooling effect than irregular shapes in the Beijing area [107].

However, it is not recommended that oversized water bodies be used, or that this mitigation measure be considered in isolation in urban planning. Evapotranspiration leads to an increase in ambient relative humidity and the evaporation rate of sweat will decrease to inhibit body thermoregulation, which can lead to more heat capture and re-radiation. The 60% cooling effect of a body of water may be offset by an increase in relative humidity [106]. The effect on the improvement of OTC is not as significant.

3.3.3. Urban Planning Strategies

The layout of buildings in a city can influence the formation of the UHI effect. An important reason for the formation of the UHI effect is the density of buildings in urban areas, as the buildings store some of the heat during the day and then slowly release it to the outdoor environment after sunset [19,38,108]. Building height, orientation, geometry, street H/W, and sky view factor (SVF) all affect the absorption of solar radiation by buildings during the daytime, thus contributing to the UHI effect and OTC [50,78,90].

Kristl and Krainer [109] evaluate the effects of building height and orientation using the iso-shadow method and find that for low-rise buildings (H = 6 m), a north–south orientation is best. For mid-rise (H = 12 m) and high-rise (H = 36 m) buildings, the orientation has virtually no impact. Sun et al. [77] show that PET and SVF are highly correlated, and that the difference in their PET can reach a maximum of 7.6 °C. Shallow and medium canyons (0.5 < H/W < 1.5) are more favorable in severe cold climates, while deeper canyons (H/W > 1.5) are not recommended due to the lack of solar absorption. However, in tropical rainforest areas, such as Singapore, higher building heights combined with low-density shapes can significantly reduce solar radiation and thus improve OTC [71]. Meanwhile, in coastal areas, sea breeze-driven sheet ventilation performance can significantly increase relative humidity and thus significantly improve OTC. Street orientation is no longer a key factor in UHI and OTC [5]. Lin et al. [52] showed that, in Taiwan, high SVF (almost no shade) causes discomfort in the summer, while low SVF (more shade) causes discomfort in the winter; however, due to Taiwan’s hot summer and mild winter climate characteristics, people are less tolerant of the cold, and thus, outdoor spaces should not incorporate too many shaded areas. Meanwhile, in severe cold regions where people are less heat-tolerant, the conclusion may be the opposite, and more shaded areas may be desired [62,77].

Urban ventilation performance can also be greatly influenced by the urban layout. Dense high-rise buildings and narrow streets increase the surface roughness and thus generate low-speed winds, which are not conducive to the diffusion of heat and pollutants [17,110]. This leads to increased UHII and discomfort for people outdoors.

3.3.4. Material Strategies

Common urban materials include concrete, asphalt, and glass [111]. These materials are used in large areas in parking lots, sidewalks, and built-up areas, which are also more likely to become heat island centers [80]. An important reason for this is the albedo of these materials. Albedo is the magnitude of the portion of the total incident solar radiation that is reflected by an object and not absorbed and has a value in the range of 0–1 [84]. Asphalt pavement, now commonly used, is a typical low-albedo material that absorbs a large amount of heat in response to solar radiation, causing the surface temperature to rise. Pavements using cool materials with high albedo are different. This kind of pavements indicates to “pavement surfaces which are more reflective for solar radiation, improve water evaporation, or have been otherwise changed to stay cooler than traditional pavements”, which can minimize the temperature and heat absorption of the pavement surface [92]. Some studies have shown that cool-color coatings can reduce surface temperatures by 12% [112]. Applying cool materials to roofing or building surfaces produces the same effect. Moreover, statistical analysis has found that when low-albedo materials such as concrete and asphalt are exposed to solar radiation, the surface temperature and UHII of the surrounding area rise, and the PET index increases [84,112,113]. Thus, urban surface materials directly exposed to solar radiation increase the diffuse emission, reflection, and surface temperature, which affects OTC. The use of materials with high albedo and high emissivity, i.e., cool materials, can significantly reduce urban surface temperatures [50].

4. Discussion

4.1. The Link between the UHI and OTC

Human thermal comfort is influenced by both physiological and psychological factors [114,115]. The heat exchange between the human body and the environment affects the physiological state of a person, and the physiological state affects the psychological activity, creating the thermal sensation and ultimately influencing the thermal comfort [116]. Six basic factors are generally considered to have an impact on human comfort, and these factors can be divided into two categories. The first category is that of environmental factors, including air temperature, relative air humidity, air flow rate, and mean radiation temperature, while the second category is that of human factors, including clothing thermal resistance and metabolic rate [117]. The impact of the UHI effect on OTC is mainly reflected in environmental factors. The intensification of the UHI effect has led to increasing outdoor ambient temperature, frequent heat waves, more frequent occurrence of heat stroke, and decreasing OTC.

The effect of air flow rate is greater. Reasonable wind-sensitive design is a feasible approach to UHI mitigation and OTC improvement, especially in coastal areas. The precinct ventilation performance driven by the sea breeze could significantly mitigate the UHI effect. Meanwhile, the precinct ventilation performance driven by the sea breeze could significantly increase relative humidity, which could further significantly improve outdoor thermal comfort [5]. The air flow rate affects the convective and evaporative heat transfer to the human body, thus influencing thermal comfort. The effect of air relative humidity on UHII is smaller, and some studies have shown that changes in relative humidity have little effect on human thermal comfort when the temperature is in the comfortable range (16–25 °C).

A quantitative analysis was conducted to better understand the relationship between UHI effect and OTC, and its main conclusions are shown in

Table 7. Quantitative analysis of the link between UHI effect and OTC.

Quantitative Relationships	Reference
UHII increased from 4 °C to 5–6 °C; the period of extreme discomfort increased from an average of 10 h to 13 h per day	[10]
Modifying the properties of the underlayment of city underground parking can lead to a temperature reduction of 1.5 °C and lower the temperature of MRT from 55–65 °C to 20–27 °C	[36]
Shadows formed by trees and buildings can reduce PMV by 2–4	[38]
PET values for high-rise residential buildings and boulevard streets are 4 °C lower than those for low-rise buildings and 1 °C lower than those for nearby suburbs	[50]
Air temperature down by 2.4 °C, PET index can drop by 10.4 °C	[90]
Using bright marble can reduce the PET value by about 10 °C compared to black granite	[92]

.

4.2. Limitation of OTC Evaluation Index and the Data Collection Methods

The commonly used OTC evaluation index has some limitations. In general, the following points are included:

(1) Lack of individual differences: evaluation indexes are usually calculated based on the population average, without taking into account the differences between different individuals, such as age, gender, health status, etc.

(2) No consideration of human activity level: the evaluation index does not take into account people’s activity level and metabolic rate in the outdoor environment, which is important for assessing people’s thermal comfort at different activity levels.

(3) Lack of comprehensive consideration of other environmental factors: outdoor thermal comfort evaluation indexes usually only consider factors such as temperature, relative humidity, and wind speed, while ignoring the influence of other environmental factors, such as solar radiation, precipitation, and atmospheric pollution. Moreover, only short-term outdoor environmental parameters are usually considered, which cannot reflect the impact of changes in environmental parameters on human comfort, such as changes in meteorological factors and the intensity of sunshine at different time periods.

(4) Different adaptability to different regions and cultures: commonly used indexes are established according to the climatic characteristics of certain regions and are not applicable to other regions. People in different regions and different cultures have different requirements and adaptability to thermal comfort, so the evaluation indexes need to be revised and adapted to the actual local situation.

These problems are also presented in these most commonly used evaluation indexes: PET, UTCI, and WBGT, as statistically presented in Section 3.1. In addition, the measurement of these OTC indexes presents certain difficulties and challenges. First, special equipment is required. The measurement of these indexes requires the use of special instruments, such as thermometers, hygrometers, anemometers, etc. The accuracy and stability of these devices may affect the accuracy of the measurement results. Second, the measurement environment is complex. The measurement environment may be affected by a variety of factors, such as solar radiation, wind speed, humidity, and altitude, and changes in these factors may have an impact on the measurement results. Third, data processing is difficult. These indexes require complex calculations and processing to produce the final results, and these processing processes may affect the accuracy of the measurement results.

To investigate OTC, questionnaire survey, measurement and simulation, and formula calculation all have certain limitations: questionnaire survey can collect respondents’ feelings and opinions, but there are limitations such as subjectivity and individual differences. Measurement and simulation can provide environmental parameter data and thermal comfort index, but there are spatial and temporal limitations due to factors such as the location of monitoring instruments and the accuracy of collected data. The formula calculation method is simple and easy to use, but it can only calculate the thermal comfort index for a specific situation and cannot consider all influencing factors.

4.3. Future Works

Research on the UHI effect and OTC has made some progress over the past 10 years. In the review we have also identified a number of problems in the current research and directions worthy of future research. First, a clear and universal evaluation method and evaluation indexes for OTC are lacking. Second, the interaction mechanism between thermal comfort and other senses (e.g., visual and auditory) has not been thoroughly studied. These two aspects will be discussed separately below [118].

The criteria and methods for assessing OTC mainly follow the common criteria for indoor conditions. However, in outdoor conditions, unlike indoors, human thermal comfort is subject to a combination of factors. The criteria for evaluating indoor thermal comfort may not be as accurate in outdoor conditions. Moreover, the evaluation of OTC varies greatly from one climate zone to another. The number of studies on the impact of the UHI on OTC has increased dramatically. Therefore, it is crucial to address the measurement issues associated with the assessment of thermal comfort and heat stress indices. Regrettably, the current literature often neglects these metrological considerations when measuring the input variables. This oversight has led to the widespread use of approximate equations that are only valid under specific conditions, resulting in a lack of comparability among different studies. To ensure accurate and reliable assessment of outdoor environments, it is imperative to establish international standards and guidelines that outline the best practices for measuring and assessing these parameters. A solid foundation for such standards can be found in ISO Standard 7726 [119], which provides comprehensive guidance on the measurement and interpretation of environmental parameters related to human thermal comfort. The adoption of these standards will contribute to a more robust and unified approach to studying thermal comfort and heat stress, enabling better comparisons and advancing our understanding of these critical factors. Ruiz et al. [69] developed an urban thermal comfort index for arid regions of Argentina, an index that would be more accurate in the study area than common parameters such as PET. The OTC assessment methods summarized in Section 3.3 are also essentially the same as those for the indoor environment, mainly questionnaires and numerical simulations. However, the outdoor environment is variable and difficult to control. The development of standard OTC study guidelines with standardized assessment procedures can improve the reliability of the data and provide a higher confidence level for thermal comfort conditions in various environments.

In addition, research on outdoor comfort has focused on “thermal comfort”, ignoring the effects of other sensory stimuli on overall human comfort, such as the effects of light pollution on visual comfort and noise on auditory comfort. For wide urban street valleys, which absorb a large amount of solar radiation during the day, the use of light-colored materials on the building façade will improve people’s thermal comfort outdoors, but the light-colored materials will produce glare and reduce visual comfort. Outdoor comfort should take a variety of factors into account.

5. Conclusions

One hundred manuscripts were selected for this review of commonly used OTC evaluation indexes, data collection methods, and mitigation measures, and the relationship between UHI and OTC was examined. The main conclusions are as follows:

(1) The most commonly used indexes in OTC studies were PET, UTCI, and WBGT. Of these, PET was used the most frequently (43 times), followed by UTCI (14 times), and WBGT (12 times).

(2) Data collection methods varied widely across the reviewed manuscripts, but questionnaire surveys, measurement, simulation, and formula calculation were the most commonly used. Among them, thermal sensation was most often measured using the ASHRAE 7-point sensation scale in questionnaire surveys, while ENVI-met and RayMan software were the most frequently used in numerical simulations.

(3) To mitigate the UHI effect and improve OTC, the reviewed manuscripts proposed several strategies, including vegetation strategies, water strategies, urban planning strategies, and material strategies.

(4) The UHI effect has significant impacts on OTC, particularly through environmental factors. As the UHI effect intensifies, the outdoor ambient temperature increases; heat waves become more frequent; heat stroke becomes more common; and OTC decreases. Additionally, the air flow rate plays a critical role in convective and evaporative heat transfer, which in turn affects thermal comfort.

(5) Measurement issues related to the evaluation of OTC and heat stress indexes were found to be frequently overlooked in the reviewed manuscripts. It is crucial to address this issue by establishing international standards and guidelines that promote better practices for accurate measurement and assessment of the outdoor environment.