# Research on Night Light Comfort of Pedestrian Space in Urban Park

Authors: Jun Zhang; Wenhan Dai
Journal: Computational and Mathematical Methods in Medicine
 (2021)

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## Abstract

The outdoor light environment significantly affects aspects of public psychological and physiological health. This study conducted experiments to quantify the effects of the light environment on visitor light comfort in urban park pedestrian space. Nine sets of lighting conditions with different average horizontal illuminance (2 lx, 6 lx, 10 lx) and colour temperatures (5600 K, 4300 K, 3000 K) were established virtual reality scenarios. Subjective light comfort was evaluated, and electroencephalogram (EEG) was measured on 18 subjects to comprehensively study the effects of different light environments on human light comfort. The results of the comprehensive evaluation showed that colour temperature had a very significant impact on subjective light comfort, with warm light being generally more favourable than cool light in enhancing human subjective light comfort. The results of the EEG analysis show that the average horizontal illuminance is an important factor in the level of physiological fatigue, and that physiological fatigue can be maintained in a superior state at an appropriate level of illuminance. Based on the results of both subjective and objective factors, a comprehensive analysis was carried out to propose a range of average horizontal illuminance (4.08 lx, 6.99 lx) and a range of colour temperature (3126 K, 4498 K) for the comprehensive light comfort zone in urban park pedestrian space.


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## Body

## 1. Introduction

As one of the most important public spaces in a healthy urban environment, urban parks are essential places for modern city dwellers to engage in various social activities [1, 2], relax, and relieve stress [2, 3]. As the backbone of the urban park and the medium for connecting attractions, the pedestrian space is an objective spatial sequence for visitors to engage in extended leisure activities. The outdoor artificial light at night (ALAN) dramatically influences the quality of experience of visitors [4].The outdoor light environment is an outdoor space formed by light irradiation, and its function should meet the physical, psychological, physiological, aesthetic, social, and other requirements. Light can affect human health [5], which can be both positive and negative [6]. Inappropriate ALAN can affect human emotions [7, 8], perception, evaluation, behavior [9, 10], and feeling of safety [11] and can even have a significant impact on physical health [5, 6, 12–14]. If humans are exposed to an uncomfortable light environment for a long time, such as insufficient brightness, uneven lighting, uncomfortable glare, and intense strobe light, it will not only cause serious harm to their physical health but also affect their psychological health [12, 15, 16].It has become a multidisciplinary trend to study the impact of the built environment on health to improve the quality of life and the quality of life of residents [17]. As an essential component of the built environment, the research and application of light environment extend from visual effects to the broader “healing effects of light,” such as emotional regulation and rhythm repair. Therefore, it is of practical importance for light comfort research to transform light environment design from basic functional requirements to enhance subjective and objective factors such as human comfort, psychological, and physiological responses.This paper investigates the subjective and physiological evaluation of subjects by simulating different combinations of average horizontal illuminance (Ehave) and correlated colour temperature (CCT) for a specific space, the urban park pedestrian space. The relationship between the influence of the light environment on people is analysed from both subjective and objective aspects to arrive at a range of light comfort that takes into account a variety of factors. The aim is to provide a quantitative reference for the design of light environments in urban park pedestrian spaces and to create urban outdoor environments that are more conducive to human leisure activities at night.


## 2. Experimental Methodology

### 2.1. Experimental Environment

The experiment was completed in the laboratory, and to ensure that the subjects were not disturbed by other environmental factors, the controlled room temperature was 26-28°C,windspeed≤0.1m/s, and noiselevel≤40dB. The lighting was switched off in the room during the experiment. There were no other additional disturbing facilities except for the experimental equipment and computer screens and other equipment that needed to be switched on.


### 2.2. Experimental Conditions

The two factors of illuminance and colour temperature in the light environment were selected as the object of the study. Concerning the common values of illuminance and colour temperature for parks, squares, pavements, and public activity areas in the Chinese Code for lighting design of urban nightscape (JGJ/T 163-2008), a reasonable range of illuminance and colour temperature was selected for the scenario simulation, and then the experiment was carried out. Through preexperimental screening of conditions, combined with psychological perception and physiological data, the experimental conditions were determined to be three levels of average horizontal illuminance, with three colour temperatures: warm, neutral, and cool, and nine outdoor light conditions were established for research. The specific experimental condition numbers and combinations of average horizontal illuminance and colour temperature parameters are shown in Table1.Table 1
Experimental parameter conditions.
Condition numberAverage horizontal illuminance (lx)Colour temperature (K)A25600B6C10D24300E6F10G23000H6I10


### 2.3. Building Virtual Reality Scenarios

To provide a more realistic simulation of the outdoor light environment that visitors are exposed to in a nighttime setting, virtual reality (VR) technology was used to establish the experimental scenario. In this article, 300 VR data have been selected and compared with real field measurement data.VR integrates traditional image technology, interactive technology, and immersive displays. Its advantages lie in the high immersion and presence created by multidimensional interactive functions and immersive displays and its superiority in shaping space and conveying visual information [18].Studies by Murdoch et al. [19] describe the pathways for building virtual reality scenes, including modelling, light simulation, tone mapping, and display, and recommend the appropriate software and equipment.In recent years, several scholars have researched in the field of human factors light environments using VR instead of reality light environments [20–25].The virtual reality scene in this experiment first uses SketchUp to create a 3D model, then uses Dialux Evo 8.2 software to set the lighting parameters, then creates a panoramic view of the VR scene through PTGui Pro software on the output scene effects, uploads the scene to 720YUN platform, and then experiences WEB VR through SteamVR, the virtual reality scene for each experimental condition is shown in Figure1. The process of building the virtual reality scenes is shown in Figure 2.Figure 1
Virtual reality scenarios for each experimental condition.
(a)(b)(c)(d)(e)(f)(g)(h)(i)Figure 2
The process of building a virtual reality scene.


### 2.4. Experimental Contents

#### 2.4.1. Subjective Evaluation

The subjective questionnaire for the evaluation of light comfort was designed using the semantic differential method. Eight indicators were selected to evaluate the light environment in the experiment: brightness, sense of security, pleasure, colour richness, excitement, intimacy, comfort, and satisfaction. Each subject was required to score the eight evaluation indicators in each condition, and the evaluation scores were measured using a 7-point Likert-type scale [26]. The self-report Likert scale is an essential part of modern psychology [27]. In recent years, the self-report Likert scale has been one of the most common methods of measuring psychological data [28, 29]. It provides a very convenient way to measure unobservable structures.


#### 2.4.2. Physiological Evaluation

There are various methods of assessing fatigue, divided into two main approaches: subjective and objective. Subjective methods are mainly in the form of questionnaires or scales [30–34]. However, they rely on self-report, and therefore, the results are highly susceptible to bias. Among the objective methods, monitoring the subject’s physiological signals is considered the most reliable way of assessing fatigue [35].The electroencephalogram (EEG) band power and the EEG waveform vary significantly in people in different states. They are closely related to complex psychological conditions in people, such as burnout [36], stress [37–39], mental fatigue [40, 41], and emotional exhaustion [42, 43]. Due to its low invasiveness [44] and the clear relationship between the power spectral characteristics of the different frequency bands and fatigue levels [45], the measurement of EEG is one of the most objective and reliable methods for assessing fatigue.EEG is a measurement of potentials that reflect the electrical activity of the human brain and contains a complex combination of waves with multiple frequency components. Depending on their frequency range, they can be classified as: 0.5-4 Hz (delta, δ), 4-8 Hz (theta, θ), 8-13 Hz (alpha, α), 13-30 Hz (beta, β), and >30 Hz (gamma, γ) [46].The characteristic rhythms in EEG signals are closely linked to brain activity and have shown that EEG energy parameters vary with the degree of fatigue [47, 48]. Studies by Borghini et al. [49] found that neuropsychological indicators such as the EEG changed accordingly during normal driving, high mental load, and mental fatigue states in humans, that theta, delta, and alpha waves altered during the transition from high mental load to mental fatigue states, and that the accuracy of detecting these mental fatigue states was around 90%. Studies by Szirmai et al. [50] found corresponding changes in the alpha and beta waves of subjects during simulated mental fatigue.Due to individual differences between subjects, the absolute energy of the EEG rhythm is difficult to effectively compare the fatigue status of different subjects. In contrast, the relative energy of the various energy bands can be used as a more effective feature [51]. There have been many studies that have used the ratio of the EEG rhythm energy: α+θ/β, α/β, α+θ/α+β, θ/β, etc. as indicators of human fatigue [52–54], and have indicated that their parameters are all statistically significant for fatigue state analysis [55]. De Waard and Brookhuis [52] found that the relative energy parameter α+θ/β decreased with a decrease in alertness level. It could be used as a fatigue factor to measure fatigue levels in humans effectively.In recent years, some scholars have used a combination of VR and EEG to study the relationship between human cognition and built environment characters [56, 57]. And its feasibility has been proven. In this experiment, the EEG of the subjects was measured, and the energy ratio α+θ/β of each subject’s EEG waves was compared in different light environments in the virtual reality scenario, which was used to determine whether there was a difference in their fatigue state for physiological evaluation.


### 2.5. Subjects

To obtain objective and valid experimental data, the experiment used a method to determine the sample size based on statistical power and effect size (ES) [58]. The G∗Power 3.1.9.7 software was used to calculate the required planned sample size in the study, with a preset moderate effect size of f=0.25 [59] and a preset statistical test power of 1−β=0.8 and significance level of α=0.05 as the criteria for calculating the planned sample size. The results indicated that at least 15 subjects were required.Considering the spare capacity of the experiment, a total of 18 volunteers, aged between 23 and 38 years old, including nine males and nine females, were recruited to participate in the experiment. All subjects were in good health, had normal stereo sensation and perception, normal visual acuity (optically corrected visual acuity of not less than 5.0 and visual correction of fewer than 300 degrees), no colour blindness or colour weakness, no cardiovascular disease or neurological disease, emotionally stable, normal mental state, good physical condition, no subjective discomfort, and none of them had participated in similar experiments. All subjects gave written consent about their participation in the study. The basic information is shown in Table2.Table 2
Basic information about the subjects.
ItemsStatisticalMaleFemaleOverallAge (year)Max383138Min232323M±SD26.78±4.8925±2.6025.89±3.91Height (cm)Max182170182Min170160160M±SD176.67±3.57165.67±4.74171.17±6.97Weight (kg)Max908090Min604848M±SD73.22±10.7055.61±9.8464.42±13.47Vision correctionYes6410No358To ensure the accuracy of the experimental data and to reduce additional disturbances caused by changes in the subjects’ physical state, the subjects were asked to ensure a regular resting time and sleep quality during the experiment and to maintain sufficient sleep. Any medication known to affect visual and electroencephalographic signals is prohibited for one day before the test. No alcohol for 12 hours, no smoking for 8 hours, as well as no coffee, tea, functional drinks, and other beverages that can be stimulating to the heart, brain, and nerves, and no strenuous exercise for 3 hours to avoid affecting the autonomic activity in a state of excessive excitement or fatigue [60].To avoid the effect of cumulative fatigue from the top-ranked experiments on the results of the bottom-ranked experiments, the experimental order was arranged in Latin Square Sequence, and the subjects were divided into nine groups of two subjects each, as shown in Table3.Table 3
Experimental order.
Experimental orderGroupIGroupIIGroup IIIGroup IVGroup VGroup VIGroup VIIGroup VIIIGroup IX1ABCDEFGHI2BCDEFGHIA3CDEFGHIAB4DEFGHIABC5EFGHIABCD6FGHIABCDE7GHIABCDEF8HIABCDEFG9IABCDEFGH


### 2.6. Experimental Equipment

#### 2.6.1. Virtual Reality Equipment

The experiment uses the HTC VIVE head-mounted VR device, one of the more effective and widely used VR devices today, to create a movable space in the room to allow the user to move around within a specific range, thus, allowing the user to have a good immersion experience, as shown in Figure3.Figure 3
HTC VIVE.


#### 2.6.2. Wearable EEG Device

The traditional EEG acquisition monitoring system, although the acquisition signal accuracy is high, generally has the characteristics of strong operational expertise, cumbersome to use, expensive, and large size. Subjects usually need to apply a large amount of conductive paste and wear special electrode caps, which is not conducive to continuous real-time monitoring and daily use. If the VR device is worn at the same time, it will make the subject’s head equipment cumbersome and interfere with each other.Today’s wearable EEG devices are noninvasive, easy to wear, small in size, simple to operate, and can be transmitted to computers and other devices using Bluetooth or WiFi, enabling real-time acquisition of EEG signals. The quality and feasibility of the data have been proven [61, 62] and have been used in many areas of research [37, 43, 63–65]. In addition, they can be applied to implicitly control advanced context-aware applications, such as lighting systems or virtual reality environments [66]. They can respond to the mental state of the subject and can provide insight into which factors can help the subject to relax or focus.This experiment uses the Genius II wearable EEG device, which uses the TGAM modular EEG sensor developed by Neurosky (NeuroSky Inc., San Jose, CA, USA), a single-channel EEG acquisition device that uses advanced dry electrode technology to detect and acquire weak electrical signals in the brain effectively.The TGAM chip is a highly integrated single-chip EEG sensor that enables the integration of signal acquisition, filtering, amplification, A/D conversion, and calculation. And its sampling frequency is 512 Hz. It can automatically filter out all kinds of noise interference from the environment and has low power consumption. It is more convenient to use than conventional wet electric sensors [67]. This module can process and output power values for eight EEG bands: delta, theta, high-alpha, low-alpha, high-beta, low-beta, middle-gamma, and low-gamma. The output of the EEG signal is done using the system that comes with the device, which is shown in Figure 4.Figure 4
Wearable EEG.


### 2.7. Experimental Procedure

The subjects enter the laboratory and are first given a preexperiment briefing by the researcher, who explains the steps and matters needing attention. Then, the subjects take a rest to ensure they are relaxed. Subjects are allowed to move within a specific range during the experiment and may engage in simple communication activities but are not permitted to communicate about the experiment and are required to cooperate with the fitting and fixation of the equipment. Once everything is ready, the experiment begins.The experiment required subjects to experience scenes with different experimental conditions by first sitting still for 5 minutes for visual adaptation. Wait for their visual adaptation to the light environment in the current experimental condition before starting the experiment for that condition. Subjects were asked to experience each experimental condition scenario for 10 minutes and have their EEG data recorded simultaneously. After the experience, subjects were asked to fill in a subjective evaluation questionnaire based on the experience and then take a five-minute eye rest by sitting quietly with their eyes closed to reduce the effect on the results of the experiment caused by the cumulative fatigue of the subjects resulting from the continuous experiment. Each experimental condition scenario took 20 minutes, i.e., each subject took about 3 hours to complete the experiment. In each scene in the experiment process, observe the condition of the experimental subject for 10 minutes to fully ensure the required effect of the experiment. The experimental procedure is shown in Figure5.Figure 5
Experimental procedure.To avoid the effects of jet lag and circadian rhythms, the experiment was conducted during the summer evenings from 19 : 00 to 22 : 00. This time is a common time for the public to be active at night in urban parks, thus, ensuring comparability of the experimental data for all subjects, at the same time, scenes can be repeated during the experiment.


## 2.1. Experimental Environment

The experiment was completed in the laboratory, and to ensure that the subjects were not disturbed by other environmental factors, the controlled room temperature was 26-28°C,windspeed≤0.1m/s, and noiselevel≤40dB. The lighting was switched off in the room during the experiment. There were no other additional disturbing facilities except for the experimental equipment and computer screens and other equipment that needed to be switched on.


## 2.2. Experimental Conditions

The two factors of illuminance and colour temperature in the light environment were selected as the object of the study. Concerning the common values of illuminance and colour temperature for parks, squares, pavements, and public activity areas in the Chinese Code for lighting design of urban nightscape (JGJ/T 163-2008), a reasonable range of illuminance and colour temperature was selected for the scenario simulation, and then the experiment was carried out. Through preexperimental screening of conditions, combined with psychological perception and physiological data, the experimental conditions were determined to be three levels of average horizontal illuminance, with three colour temperatures: warm, neutral, and cool, and nine outdoor light conditions were established for research. The specific experimental condition numbers and combinations of average horizontal illuminance and colour temperature parameters are shown in Table1.Table 1
Experimental parameter conditions.
Condition numberAverage horizontal illuminance (lx)Colour temperature (K)A25600B6C10D24300E6F10G23000H6I10


## 2.3. Building Virtual Reality Scenarios

To provide a more realistic simulation of the outdoor light environment that visitors are exposed to in a nighttime setting, virtual reality (VR) technology was used to establish the experimental scenario. In this article, 300 VR data have been selected and compared with real field measurement data.VR integrates traditional image technology, interactive technology, and immersive displays. Its advantages lie in the high immersion and presence created by multidimensional interactive functions and immersive displays and its superiority in shaping space and conveying visual information [18].Studies by Murdoch et al. [19] describe the pathways for building virtual reality scenes, including modelling, light simulation, tone mapping, and display, and recommend the appropriate software and equipment.In recent years, several scholars have researched in the field of human factors light environments using VR instead of reality light environments [20–25].The virtual reality scene in this experiment first uses SketchUp to create a 3D model, then uses Dialux Evo 8.2 software to set the lighting parameters, then creates a panoramic view of the VR scene through PTGui Pro software on the output scene effects, uploads the scene to 720YUN platform, and then experiences WEB VR through SteamVR, the virtual reality scene for each experimental condition is shown in Figure1. The process of building the virtual reality scenes is shown in Figure 2.Figure 1
Virtual reality scenarios for each experimental condition.
(a)(b)(c)(d)(e)(f)(g)(h)(i)Figure 2
The process of building a virtual reality scene.


## 2.4. Experimental Contents

### 2.4.1. Subjective Evaluation

The subjective questionnaire for the evaluation of light comfort was designed using the semantic differential method. Eight indicators were selected to evaluate the light environment in the experiment: brightness, sense of security, pleasure, colour richness, excitement, intimacy, comfort, and satisfaction. Each subject was required to score the eight evaluation indicators in each condition, and the evaluation scores were measured using a 7-point Likert-type scale [26]. The self-report Likert scale is an essential part of modern psychology [27]. In recent years, the self-report Likert scale has been one of the most common methods of measuring psychological data [28, 29]. It provides a very convenient way to measure unobservable structures.


### 2.4.2. Physiological Evaluation

There are various methods of assessing fatigue, divided into two main approaches: subjective and objective. Subjective methods are mainly in the form of questionnaires or scales [30–34]. However, they rely on self-report, and therefore, the results are highly susceptible to bias. Among the objective methods, monitoring the subject’s physiological signals is considered the most reliable way of assessing fatigue [35].The electroencephalogram (EEG) band power and the EEG waveform vary significantly in people in different states. They are closely related to complex psychological conditions in people, such as burnout [36], stress [37–39], mental fatigue [40, 41], and emotional exhaustion [42, 43]. Due to its low invasiveness [44] and the clear relationship between the power spectral characteristics of the different frequency bands and fatigue levels [45], the measurement of EEG is one of the most objective and reliable methods for assessing fatigue.EEG is a measurement of potentials that reflect the electrical activity of the human brain and contains a complex combination of waves with multiple frequency components. Depending on their frequency range, they can be classified as: 0.5-4 Hz (delta, δ), 4-8 Hz (theta, θ), 8-13 Hz (alpha, α), 13-30 Hz (beta, β), and >30 Hz (gamma, γ) [46].The characteristic rhythms in EEG signals are closely linked to brain activity and have shown that EEG energy parameters vary with the degree of fatigue [47, 48]. Studies by Borghini et al. [49] found that neuropsychological indicators such as the EEG changed accordingly during normal driving, high mental load, and mental fatigue states in humans, that theta, delta, and alpha waves altered during the transition from high mental load to mental fatigue states, and that the accuracy of detecting these mental fatigue states was around 90%. Studies by Szirmai et al. [50] found corresponding changes in the alpha and beta waves of subjects during simulated mental fatigue.Due to individual differences between subjects, the absolute energy of the EEG rhythm is difficult to effectively compare the fatigue status of different subjects. In contrast, the relative energy of the various energy bands can be used as a more effective feature [51]. There have been many studies that have used the ratio of the EEG rhythm energy: α+θ/β, α/β, α+θ/α+β, θ/β, etc. as indicators of human fatigue [52–54], and have indicated that their parameters are all statistically significant for fatigue state analysis [55]. De Waard and Brookhuis [52] found that the relative energy parameter α+θ/β decreased with a decrease in alertness level. It could be used as a fatigue factor to measure fatigue levels in humans effectively.In recent years, some scholars have used a combination of VR and EEG to study the relationship between human cognition and built environment characters [56, 57]. And its feasibility has been proven. In this experiment, the EEG of the subjects was measured, and the energy ratio α+θ/β of each subject’s EEG waves was compared in different light environments in the virtual reality scenario, which was used to determine whether there was a difference in their fatigue state for physiological evaluation.


## 2.4.1. Subjective Evaluation

The subjective questionnaire for the evaluation of light comfort was designed using the semantic differential method. Eight indicators were selected to evaluate the light environment in the experiment: brightness, sense of security, pleasure, colour richness, excitement, intimacy, comfort, and satisfaction. Each subject was required to score the eight evaluation indicators in each condition, and the evaluation scores were measured using a 7-point Likert-type scale [26]. The self-report Likert scale is an essential part of modern psychology [27]. In recent years, the self-report Likert scale has been one of the most common methods of measuring psychological data [28, 29]. It provides a very convenient way to measure unobservable structures.


## 2.4.2. Physiological Evaluation

There are various methods of assessing fatigue, divided into two main approaches: subjective and objective. Subjective methods are mainly in the form of questionnaires or scales [30–34]. However, they rely on self-report, and therefore, the results are highly susceptible to bias. Among the objective methods, monitoring the subject’s physiological signals is considered the most reliable way of assessing fatigue [35].The electroencephalogram (EEG) band power and the EEG waveform vary significantly in people in different states. They are closely related to complex psychological conditions in people, such as burnout [36], stress [37–39], mental fatigue [40, 41], and emotional exhaustion [42, 43]. Due to its low invasiveness [44] and the clear relationship between the power spectral characteristics of the different frequency bands and fatigue levels [45], the measurement of EEG is one of the most objective and reliable methods for assessing fatigue.EEG is a measurement of potentials that reflect the electrical activity of the human brain and contains a complex combination of waves with multiple frequency components. Depending on their frequency range, they can be classified as: 0.5-4 Hz (delta, δ), 4-8 Hz (theta, θ), 8-13 Hz (alpha, α), 13-30 Hz (beta, β), and >30 Hz (gamma, γ) [46].The characteristic rhythms in EEG signals are closely linked to brain activity and have shown that EEG energy parameters vary with the degree of fatigue [47, 48]. Studies by Borghini et al. [49] found that neuropsychological indicators such as the EEG changed accordingly during normal driving, high mental load, and mental fatigue states in humans, that theta, delta, and alpha waves altered during the transition from high mental load to mental fatigue states, and that the accuracy of detecting these mental fatigue states was around 90%. Studies by Szirmai et al. [50] found corresponding changes in the alpha and beta waves of subjects during simulated mental fatigue.Due to individual differences between subjects, the absolute energy of the EEG rhythm is difficult to effectively compare the fatigue status of different subjects. In contrast, the relative energy of the various energy bands can be used as a more effective feature [51]. There have been many studies that have used the ratio of the EEG rhythm energy: α+θ/β, α/β, α+θ/α+β, θ/β, etc. as indicators of human fatigue [52–54], and have indicated that their parameters are all statistically significant for fatigue state analysis [55]. De Waard and Brookhuis [52] found that the relative energy parameter α+θ/β decreased with a decrease in alertness level. It could be used as a fatigue factor to measure fatigue levels in humans effectively.In recent years, some scholars have used a combination of VR and EEG to study the relationship between human cognition and built environment characters [56, 57]. And its feasibility has been proven. In this experiment, the EEG of the subjects was measured, and the energy ratio α+θ/β of each subject’s EEG waves was compared in different light environments in the virtual reality scenario, which was used to determine whether there was a difference in their fatigue state for physiological evaluation.


## 2.5. Subjects

To obtain objective and valid experimental data, the experiment used a method to determine the sample size based on statistical power and effect size (ES) [58]. The G∗Power 3.1.9.7 software was used to calculate the required planned sample size in the study, with a preset moderate effect size of f=0.25 [59] and a preset statistical test power of 1−β=0.8 and significance level of α=0.05 as the criteria for calculating the planned sample size. The results indicated that at least 15 subjects were required.Considering the spare capacity of the experiment, a total of 18 volunteers, aged between 23 and 38 years old, including nine males and nine females, were recruited to participate in the experiment. All subjects were in good health, had normal stereo sensation and perception, normal visual acuity (optically corrected visual acuity of not less than 5.0 and visual correction of fewer than 300 degrees), no colour blindness or colour weakness, no cardiovascular disease or neurological disease, emotionally stable, normal mental state, good physical condition, no subjective discomfort, and none of them had participated in similar experiments. All subjects gave written consent about their participation in the study. The basic information is shown in Table2.Table 2
Basic information about the subjects.
ItemsStatisticalMaleFemaleOverallAge (year)Max383138Min232323M±SD26.78±4.8925±2.6025.89±3.91Height (cm)Max182170182Min170160160M±SD176.67±3.57165.67±4.74171.17±6.97Weight (kg)Max908090Min604848M±SD73.22±10.7055.61±9.8464.42±13.47Vision correctionYes6410No358To ensure the accuracy of the experimental data and to reduce additional disturbances caused by changes in the subjects’ physical state, the subjects were asked to ensure a regular resting time and sleep quality during the experiment and to maintain sufficient sleep. Any medication known to affect visual and electroencephalographic signals is prohibited for one day before the test. No alcohol for 12 hours, no smoking for 8 hours, as well as no coffee, tea, functional drinks, and other beverages that can be stimulating to the heart, brain, and nerves, and no strenuous exercise for 3 hours to avoid affecting the autonomic activity in a state of excessive excitement or fatigue [60].To avoid the effect of cumulative fatigue from the top-ranked experiments on the results of the bottom-ranked experiments, the experimental order was arranged in Latin Square Sequence, and the subjects were divided into nine groups of two subjects each, as shown in Table3.Table 3
Experimental order.
Experimental orderGroupIGroupIIGroup IIIGroup IVGroup VGroup VIGroup VIIGroup VIIIGroup IX1ABCDEFGHI2BCDEFGHIA3CDEFGHIAB4DEFGHIABC5EFGHIABCD6FGHIABCDE7GHIABCDEF8HIABCDEFG9IABCDEFGH


## 2.6. Experimental Equipment

### 2.6.1. Virtual Reality Equipment

The experiment uses the HTC VIVE head-mounted VR device, one of the more effective and widely used VR devices today, to create a movable space in the room to allow the user to move around within a specific range, thus, allowing the user to have a good immersion experience, as shown in Figure3.Figure 3
HTC VIVE.


### 2.6.2. Wearable EEG Device

The traditional EEG acquisition monitoring system, although the acquisition signal accuracy is high, generally has the characteristics of strong operational expertise, cumbersome to use, expensive, and large size. Subjects usually need to apply a large amount of conductive paste and wear special electrode caps, which is not conducive to continuous real-time monitoring and daily use. If the VR device is worn at the same time, it will make the subject’s head equipment cumbersome and interfere with each other.Today’s wearable EEG devices are noninvasive, easy to wear, small in size, simple to operate, and can be transmitted to computers and other devices using Bluetooth or WiFi, enabling real-time acquisition of EEG signals. The quality and feasibility of the data have been proven [61, 62] and have been used in many areas of research [37, 43, 63–65]. In addition, they can be applied to implicitly control advanced context-aware applications, such as lighting systems or virtual reality environments [66]. They can respond to the mental state of the subject and can provide insight into which factors can help the subject to relax or focus.This experiment uses the Genius II wearable EEG device, which uses the TGAM modular EEG sensor developed by Neurosky (NeuroSky Inc., San Jose, CA, USA), a single-channel EEG acquisition device that uses advanced dry electrode technology to detect and acquire weak electrical signals in the brain effectively.The TGAM chip is a highly integrated single-chip EEG sensor that enables the integration of signal acquisition, filtering, amplification, A/D conversion, and calculation. And its sampling frequency is 512 Hz. It can automatically filter out all kinds of noise interference from the environment and has low power consumption. It is more convenient to use than conventional wet electric sensors [67]. This module can process and output power values for eight EEG bands: delta, theta, high-alpha, low-alpha, high-beta, low-beta, middle-gamma, and low-gamma. The output of the EEG signal is done using the system that comes with the device, which is shown in Figure 4.Figure 4
Wearable EEG.


## 2.6.1. Virtual Reality Equipment

The experiment uses the HTC VIVE head-mounted VR device, one of the more effective and widely used VR devices today, to create a movable space in the room to allow the user to move around within a specific range, thus, allowing the user to have a good immersion experience, as shown in Figure3.Figure 3
HTC VIVE.


## 2.6.2. Wearable EEG Device

The traditional EEG acquisition monitoring system, although the acquisition signal accuracy is high, generally has the characteristics of strong operational expertise, cumbersome to use, expensive, and large size. Subjects usually need to apply a large amount of conductive paste and wear special electrode caps, which is not conducive to continuous real-time monitoring and daily use. If the VR device is worn at the same time, it will make the subject’s head equipment cumbersome and interfere with each other.Today’s wearable EEG devices are noninvasive, easy to wear, small in size, simple to operate, and can be transmitted to computers and other devices using Bluetooth or WiFi, enabling real-time acquisition of EEG signals. The quality and feasibility of the data have been proven [61, 62] and have been used in many areas of research [37, 43, 63–65]. In addition, they can be applied to implicitly control advanced context-aware applications, such as lighting systems or virtual reality environments [66]. They can respond to the mental state of the subject and can provide insight into which factors can help the subject to relax or focus.This experiment uses the Genius II wearable EEG device, which uses the TGAM modular EEG sensor developed by Neurosky (NeuroSky Inc., San Jose, CA, USA), a single-channel EEG acquisition device that uses advanced dry electrode technology to detect and acquire weak electrical signals in the brain effectively.The TGAM chip is a highly integrated single-chip EEG sensor that enables the integration of signal acquisition, filtering, amplification, A/D conversion, and calculation. And its sampling frequency is 512 Hz. It can automatically filter out all kinds of noise interference from the environment and has low power consumption. It is more convenient to use than conventional wet electric sensors [67]. This module can process and output power values for eight EEG bands: delta, theta, high-alpha, low-alpha, high-beta, low-beta, middle-gamma, and low-gamma. The output of the EEG signal is done using the system that comes with the device, which is shown in Figure 4.Figure 4
Wearable EEG.


## 2.7. Experimental Procedure

The subjects enter the laboratory and are first given a preexperiment briefing by the researcher, who explains the steps and matters needing attention. Then, the subjects take a rest to ensure they are relaxed. Subjects are allowed to move within a specific range during the experiment and may engage in simple communication activities but are not permitted to communicate about the experiment and are required to cooperate with the fitting and fixation of the equipment. Once everything is ready, the experiment begins.The experiment required subjects to experience scenes with different experimental conditions by first sitting still for 5 minutes for visual adaptation. Wait for their visual adaptation to the light environment in the current experimental condition before starting the experiment for that condition. Subjects were asked to experience each experimental condition scenario for 10 minutes and have their EEG data recorded simultaneously. After the experience, subjects were asked to fill in a subjective evaluation questionnaire based on the experience and then take a five-minute eye rest by sitting quietly with their eyes closed to reduce the effect on the results of the experiment caused by the cumulative fatigue of the subjects resulting from the continuous experiment. Each experimental condition scenario took 20 minutes, i.e., each subject took about 3 hours to complete the experiment. In each scene in the experiment process, observe the condition of the experimental subject for 10 minutes to fully ensure the required effect of the experiment. The experimental procedure is shown in Figure5.Figure 5
Experimental procedure.To avoid the effects of jet lag and circadian rhythms, the experiment was conducted during the summer evenings from 19 : 00 to 22 : 00. This time is a common time for the public to be active at night in urban parks, thus, ensuring comparability of the experimental data for all subjects, at the same time, scenes can be repeated during the experiment.


## 3. Results

### 3.1. Subjective Evaluation Results

The questionnaire scale reliability test was conducted by the software SPSS26, and the Cronbach’s Alpha was 0.935. The questionnaire had a good consistency and passed the test. Next, a Pearson test was carried out to analyse the relationship between average horizontal illuminance and colour temperature and each of the evaluation indicators, and the results are shown in Table4. The analysis showed that colour temperature had a highly significant negative correlation (p<0.01) with all indicators except brightness. In contrast, average horizontal illuminance only had a highly significant positive correlation (p<0.01) with brightness and a significant positive correlation (p<0.05) with excitement.Table 4
Correlation of average horizontal illuminance and colour temperature with various indicators.
Evaluation indicatorAverage horizontal illuminanceColour temperatureBrightness0.337∗∗-0.059Sense of security0.081-0.329∗∗Pleasure0.133-0.296∗∗Colour richness0.083-0.339∗∗Excitement0.175∗-0.233∗∗Intimacy0.078-0.398∗∗Comfort0.109-0.310∗∗Satisfaction-0.005-0.287∗∗∗p<0.05, ∗∗p<0.01.Significance analyses of average horizontal illuminance and colour temperature were carried out for each of the eight subjective evaluation indicators, and the results are shown in Tables5–12. Among the eight subjective evaluation indicators, colour temperature was a highly significant factor (p<0.001) for all indicators except brightness. However, average horizontal illuminance was only a significant factor (p<0.001) for brightness. The interaction between average horizontal illuminance and colour temperature was not significant for all indicators.Table 5
Significance analysis results (brightness).
SourceType III sum of squaresdfMean squareFSig.Partial eta squaredEhave42.926221.4639.980<0.0010.115CCT3.11121.5560.7230.4870.009Ehave×CCT1.85240.4630.2150.9300.006Table 6
Significance analysis results (sense of security).
SourceType III sum of squaresdfMean squareFSig.Partial eta squaredEhave2.81521.4070.6920.5020.009CCT45.370222.68511.146<0.0010.127Ehave×CCT2.70440.6760.3320.8560.009Table 7
Significance analysis results (pleasure).
SourceType III sum of squaresdfMean squareFSig.Partial eta squaredEhave4.67922.3401.6080.2040.021CCT22.309211.1547.6640.0010.091Ehave×CCT4.09941.0250.7040.5900.018Table 8
Significance analysis results (colour richness).
SourceType III sum of squaresdfMean squareFSig.Partial eta squaredEhave2.97521.4880.9100.4050.012CCT43.790221.89513.397<0.0010.149Ehave×CCT2.17340.5430.3320.8560.009Table 9
Significance analysis results (excitement).
SourceType III sum of squaresdfMean squareFSig.Partial eta squaredEhave8.64224.3212.8080.0630.035CCT26.679213.3408.668<0.0010.102Ehave×CCT2.24740.5620.3650.8330.009Table 10
Significance analysis results (intimacy).
SourceType III sum of squaresdfMean squareFSig.Partial eta squaredEhave3.37021.6850.8290.4380.011CCT78.259239.13019.261<0.0010.201Ehave×CCT0.48140.1200.0590.9930.002Table 11
Significance analysis results (comfort).
SourceType III sum of squaresdfMean squareFSig.Partial eta squaredEhave4.30922.1541.2700.2840.016CCT48.383224.19114.266<0.0010.157Ehave×CCT1.91440.4780.2820.8890.007Table 12
Significance analysis results (satisfaction).
SourceType III sum of squaresdfMean squareFSig.Partial eta squaredEhave0.16020.0800.0460.9550.001CCT42.457221.22812.059<0.0010.136Ehave×CCT2.98840.7470.4240.7910.011


### 3.2. Comprehensive Evaluation Results

The mean values of the subjective evaluations of all subjects under each experimental condition were obtained, and the results are shown in Table13.Table 13
Subjective assessment score statistics.
Evaluation indicatorABCDEFGHIBrightness3.834.505.174.114.945.444.284.505.39Sense of security3.613.564.224.724.894.834.944.945.11Pleasure3.834.004.063.944.724.724.834.725.06Colour richness3.223.003.564.114.444.444.394.224.56Excitement3.283.394.064.174.614.784.224.224.50Intimacy3.223.393.504.614.944.834.615.065.00Comfort3.673.944.285.005.445.174.835.065.17Satisfaction3.893.944.175.225.394.895.004.945.00To analyse the importance of the eight indicators, the entropy weight method (EWM) was applied to analyse the weight of each indicator and calculate the total comprehensive evaluation score.The EWM is an objective weighting method, and the principle of which is to determine the weights in the indicators based on the amount of information reflected in the degree of variation of each indicator value [68]. The size of the entropy value represents the disordered degree of the system in information theory [69]. The lower the entropy value of the indicator, the higher its entropy weight. Conversely, the higher the entropy value of the indicator, the lower its entropy weight [70]. The calculation is as follows.In the first step, the data is normalized according to Equation (1). Where i denotes the evaluation object i=1,2,⋯,n and j denotes the indicator (j=1,2,⋯,m); xij is the original value of the jth indicator for the ith evaluation object; pij is the normalized value. The ratio of each evaluation object under each indicator is obtained, i.e., the weight of the ith evaluation object regarding the jth indicator value. The normalized matrix is then obtained, as shown in Equation (2).
(1)pij=xij∑i=1nxij,(2)P=pijn×m.In the second step, the information entropy value of each indicator is calculated according to Equation (3) where the coefficient k is related to the number of evaluation objects n, as shown in Equation (4).
(3)ej=−k∑i=1npijlnpij,(4)k=1lnn>0,ej≥0.In the third step, the coefficient of variation of each indicator is calculated according to Equation (5).
(5)dj=1−ej.In the fourth step, the weights of each indicator are calculated according to Equation (6).
(6)wj=dj∑j=1mdj.In the final step, the overall score of each evaluation object is calculated according to Equation (7).
(7)si=∑j=1mwjpij.According to the above method, the weights of each evaluation indicator were calculated, as shown in Figure6, and the weights of each evaluation indicator were relatively average. The comprehensive evaluation score for each experimental condition is then calculated by weighting. Finally, use the resulting discrete data to generate a best-fitting surface, and the relationship between the comprehensive evaluation score and the average horizontal illuminance and colour temperature is shown in Figure 7.Figure 6
The weighting of eight evaluation indicators.Figure 7
The relationship of comprehensive evaluation score andEhave and CCT.


### 3.3. Physiological Evaluation Results

The EEG is a spontaneous physiological electrical signal, and one of the characteristics of which is the considerable variation between subjects. It is therefore essential to eliminate the effects of differences between subjects and to process all experimental results relatively so that the data from all subjects can be analysed together.In this experiment, the 10-minute EEG data recorded from all subjects under each experimental condition were statistically analysed to calculate the fatigue factorα+θ/β parameter, and the results were normalized to 0-1 using the maximum difference normalization method to obtain the relative values of the fatigue factor α+θ/β parameter. After processing the data, a larger value means the body is less fatigued, with an optimum value of 1. A smaller value means that the body is more fatigued, with the worst value of 0. Finally, the arithmetic mean of the fatigue factor parameters of the subjects under each experimental condition was calculated to investigate further the relationship between the level of physiological fatigue and the average horizontal illuminance and colour temperature. The maximum difference normalization of the data was calculated according to Equation (8), and the following analysis is based on the normalized values.
(8)fx=max−xmax−min.As some of the data were nonnormal distribution and parametric analysis could not be used, the Scheirer-Ray-Hare test [71, 72] was used. The Scheirer-Ray-Hare test is an extension of the Kruskal-Wallis H test [71–74]. All significance analyses were based on 95% confidence intervals, and the results are shown in Table 14.Table 14
The results of Scheirer-Ray-Hare test ANOVA (physiological fatigue).
SourceSum SqdfHSig.Ehave26716.95426.0870.048CCT1847.12020.4210.810Ehave×CCT711.03740.0810.999The analysis showed a significant difference in the physiological fatigue of the subjects at different average horizontal illuminance (H=6.087, p=0.048) and no effect of different colour temperatures on the physiological fatigue of the subjects (H=0.421, p=0.810). The interaction between average horizontal illuminance and colour temperature was not significant for physiological fatigue (H=0.027, p=0.987).The resulting discrete data was generated a best-fitting surface, and the relationship between the physiological fatigue level and the average horizontal illuminance and colour temperature is shown in Figure8.Figure 8
The relationship of physiological fatigue andEhave and CCT.


### 3.4. Integrated Light Comfort Zone

A comprehensive analysis of the subjects’ comprehensive evaluation scores and physiological fatigue levels was carried out to investigate the range of lighting comfort zone in urban park pedestrian space. The interval where the score (z-coordinate) of the two fitting surfaces obtained was not less than 85% of the highest score was delineated as the light comfort zone [60]. Proposal refers to the plan and planning before the action, and the drafting of the proposed plan, which is applicable to the preparation process of lighting, is common in the experiment. The corresponding average horizontal illuminance (Y-coordinate) and colour temperature (X-coordinate) values are calculated by the scores (z-coordinate) in the fitting surface, which are the light parameters of the light comfort zone, as shown in Table 15.Table 15
The corresponding average horizontal illuminance and colour temperature range for light comfort zones.
Light comfort indicatorsAverage horizontal illuminance (lx)Colour temperature (K)Comprehensive evaluation score2(3000, 4921.93184)6(3125.63906, 4569.00906)10(3000, 4280.06694)Physiological fatigue levels(3.31117, 6.97196)3000(4.07726, 7.19484)4300(3.94185, 7.17052)5600The data in Table15 can completely create a boundary and then apply the interpolation line drawing method when lx=6 and K=3000. At this time, the uncertainty of the data points can be seen.When the values of the average horizontal illuminance and colour temperature are both in the light comfort range of comprehensive evaluation and physical fatigue, the subjective and physiological of the human body can obtain a more comfortable experience. The parameter range corresponding to the green area in Figure9 is the light comfort zone for the combined analysis of both subjective and objective aspects.Figure 9
Integrated light comfort zone.The recommended range of values is shown in red in Figure10, with an average horizontal illuminance range of (4.08 lx, 6.99 lx) and a colour temperature range of (3126 K, 4498 K). When the average horizontal illuminance and colour temperature parameters of the nighttime light environment in urban park pedestrian space are within the range mentioned above, the lighting effect can meet the subjective comfort of the human body and also ensure that the human body is not at a high level of physiological fatigue.Figure 10
Recommended range of values for the integrated light comfort zone.


## 3.1. Subjective Evaluation Results

The questionnaire scale reliability test was conducted by the software SPSS26, and the Cronbach’s Alpha was 0.935. The questionnaire had a good consistency and passed the test. Next, a Pearson test was carried out to analyse the relationship between average horizontal illuminance and colour temperature and each of the evaluation indicators, and the results are shown in Table4. The analysis showed that colour temperature had a highly significant negative correlation (p<0.01) with all indicators except brightness. In contrast, average horizontal illuminance only had a highly significant positive correlation (p<0.01) with brightness and a significant positive correlation (p<0.05) with excitement.Table 4
Correlation of average horizontal illuminance and colour temperature with various indicators.
Evaluation indicatorAverage horizontal illuminanceColour temperatureBrightness0.337∗∗-0.059Sense of security0.081-0.329∗∗Pleasure0.133-0.296∗∗Colour richness0.083-0.339∗∗Excitement0.175∗-0.233∗∗Intimacy0.078-0.398∗∗Comfort0.109-0.310∗∗Satisfaction-0.005-0.287∗∗∗p<0.05, ∗∗p<0.01.Significance analyses of average horizontal illuminance and colour temperature were carried out for each of the eight subjective evaluation indicators, and the results are shown in Tables5–12. Among the eight subjective evaluation indicators, colour temperature was a highly significant factor (p<0.001) for all indicators except brightness. However, average horizontal illuminance was only a significant factor (p<0.001) for brightness. The interaction between average horizontal illuminance and colour temperature was not significant for all indicators.Table 5
Significance analysis results (brightness).
SourceType III sum of squaresdfMean squareFSig.Partial eta squaredEhave42.926221.4639.980<0.0010.115CCT3.11121.5560.7230.4870.009Ehave×CCT1.85240.4630.2150.9300.006Table 6
Significance analysis results (sense of security).
SourceType III sum of squaresdfMean squareFSig.Partial eta squaredEhave2.81521.4070.6920.5020.009CCT45.370222.68511.146<0.0010.127Ehave×CCT2.70440.6760.3320.8560.009Table 7
Significance analysis results (pleasure).
SourceType III sum of squaresdfMean squareFSig.Partial eta squaredEhave4.67922.3401.6080.2040.021CCT22.309211.1547.6640.0010.091Ehave×CCT4.09941.0250.7040.5900.018Table 8
Significance analysis results (colour richness).
SourceType III sum of squaresdfMean squareFSig.Partial eta squaredEhave2.97521.4880.9100.4050.012CCT43.790221.89513.397<0.0010.149Ehave×CCT2.17340.5430.3320.8560.009Table 9
Significance analysis results (excitement).
SourceType III sum of squaresdfMean squareFSig.Partial eta squaredEhave8.64224.3212.8080.0630.035CCT26.679213.3408.668<0.0010.102Ehave×CCT2.24740.5620.3650.8330.009Table 10
Significance analysis results (intimacy).
SourceType III sum of squaresdfMean squareFSig.Partial eta squaredEhave3.37021.6850.8290.4380.011CCT78.259239.13019.261<0.0010.201Ehave×CCT0.48140.1200.0590.9930.002Table 11
Significance analysis results (comfort).
SourceType III sum of squaresdfMean squareFSig.Partial eta squaredEhave4.30922.1541.2700.2840.016CCT48.383224.19114.266<0.0010.157Ehave×CCT1.91440.4780.2820.8890.007Table 12
Significance analysis results (satisfaction).
SourceType III sum of squaresdfMean squareFSig.Partial eta squaredEhave0.16020.0800.0460.9550.001CCT42.457221.22812.059<0.0010.136Ehave×CCT2.98840.7470.4240.7910.011


## 3.2. Comprehensive Evaluation Results

The mean values of the subjective evaluations of all subjects under each experimental condition were obtained, and the results are shown in Table13.Table 13
Subjective assessment score statistics.
Evaluation indicatorABCDEFGHIBrightness3.834.505.174.114.945.444.284.505.39Sense of security3.613.564.224.724.894.834.944.945.11Pleasure3.834.004.063.944.724.724.834.725.06Colour richness3.223.003.564.114.444.444.394.224.56Excitement3.283.394.064.174.614.784.224.224.50Intimacy3.223.393.504.614.944.834.615.065.00Comfort3.673.944.285.005.445.174.835.065.17Satisfaction3.893.944.175.225.394.895.004.945.00To analyse the importance of the eight indicators, the entropy weight method (EWM) was applied to analyse the weight of each indicator and calculate the total comprehensive evaluation score.The EWM is an objective weighting method, and the principle of which is to determine the weights in the indicators based on the amount of information reflected in the degree of variation of each indicator value [68]. The size of the entropy value represents the disordered degree of the system in information theory [69]. The lower the entropy value of the indicator, the higher its entropy weight. Conversely, the higher the entropy value of the indicator, the lower its entropy weight [70]. The calculation is as follows.In the first step, the data is normalized according to Equation (1). Where i denotes the evaluation object i=1,2,⋯,n and j denotes the indicator (j=1,2,⋯,m); xij is the original value of the jth indicator for the ith evaluation object; pij is the normalized value. The ratio of each evaluation object under each indicator is obtained, i.e., the weight of the ith evaluation object regarding the jth indicator value. The normalized matrix is then obtained, as shown in Equation (2).
(1)pij=xij∑i=1nxij,(2)P=pijn×m.In the second step, the information entropy value of each indicator is calculated according to Equation (3) where the coefficient k is related to the number of evaluation objects n, as shown in Equation (4).
(3)ej=−k∑i=1npijlnpij,(4)k=1lnn>0,ej≥0.In the third step, the coefficient of variation of each indicator is calculated according to Equation (5).
(5)dj=1−ej.In the fourth step, the weights of each indicator are calculated according to Equation (6).
(6)wj=dj∑j=1mdj.In the final step, the overall score of each evaluation object is calculated according to Equation (7).
(7)si=∑j=1mwjpij.According to the above method, the weights of each evaluation indicator were calculated, as shown in Figure6, and the weights of each evaluation indicator were relatively average. The comprehensive evaluation score for each experimental condition is then calculated by weighting. Finally, use the resulting discrete data to generate a best-fitting surface, and the relationship between the comprehensive evaluation score and the average horizontal illuminance and colour temperature is shown in Figure 7.Figure 6
The weighting of eight evaluation indicators.Figure 7
The relationship of comprehensive evaluation score andEhave and CCT.


## 3.3. Physiological Evaluation Results

The EEG is a spontaneous physiological electrical signal, and one of the characteristics of which is the considerable variation between subjects. It is therefore essential to eliminate the effects of differences between subjects and to process all experimental results relatively so that the data from all subjects can be analysed together.In this experiment, the 10-minute EEG data recorded from all subjects under each experimental condition were statistically analysed to calculate the fatigue factorα+θ/β parameter, and the results were normalized to 0-1 using the maximum difference normalization method to obtain the relative values of the fatigue factor α+θ/β parameter. After processing the data, a larger value means the body is less fatigued, with an optimum value of 1. A smaller value means that the body is more fatigued, with the worst value of 0. Finally, the arithmetic mean of the fatigue factor parameters of the subjects under each experimental condition was calculated to investigate further the relationship between the level of physiological fatigue and the average horizontal illuminance and colour temperature. The maximum difference normalization of the data was calculated according to Equation (8), and the following analysis is based on the normalized values.
(8)fx=max−xmax−min.As some of the data were nonnormal distribution and parametric analysis could not be used, the Scheirer-Ray-Hare test [71, 72] was used. The Scheirer-Ray-Hare test is an extension of the Kruskal-Wallis H test [71–74]. All significance analyses were based on 95% confidence intervals, and the results are shown in Table 14.Table 14
The results of Scheirer-Ray-Hare test ANOVA (physiological fatigue).
SourceSum SqdfHSig.Ehave26716.95426.0870.048CCT1847.12020.4210.810Ehave×CCT711.03740.0810.999The analysis showed a significant difference in the physiological fatigue of the subjects at different average horizontal illuminance (H=6.087, p=0.048) and no effect of different colour temperatures on the physiological fatigue of the subjects (H=0.421, p=0.810). The interaction between average horizontal illuminance and colour temperature was not significant for physiological fatigue (H=0.027, p=0.987).The resulting discrete data was generated a best-fitting surface, and the relationship between the physiological fatigue level and the average horizontal illuminance and colour temperature is shown in Figure8.Figure 8
The relationship of physiological fatigue andEhave and CCT.


## 3.4. Integrated Light Comfort Zone

A comprehensive analysis of the subjects’ comprehensive evaluation scores and physiological fatigue levels was carried out to investigate the range of lighting comfort zone in urban park pedestrian space. The interval where the score (z-coordinate) of the two fitting surfaces obtained was not less than 85% of the highest score was delineated as the light comfort zone [60]. Proposal refers to the plan and planning before the action, and the drafting of the proposed plan, which is applicable to the preparation process of lighting, is common in the experiment. The corresponding average horizontal illuminance (Y-coordinate) and colour temperature (X-coordinate) values are calculated by the scores (z-coordinate) in the fitting surface, which are the light parameters of the light comfort zone, as shown in Table 15.Table 15
The corresponding average horizontal illuminance and colour temperature range for light comfort zones.
Light comfort indicatorsAverage horizontal illuminance (lx)Colour temperature (K)Comprehensive evaluation score2(3000, 4921.93184)6(3125.63906, 4569.00906)10(3000, 4280.06694)Physiological fatigue levels(3.31117, 6.97196)3000(4.07726, 7.19484)4300(3.94185, 7.17052)5600The data in Table15 can completely create a boundary and then apply the interpolation line drawing method when lx=6 and K=3000. At this time, the uncertainty of the data points can be seen.When the values of the average horizontal illuminance and colour temperature are both in the light comfort range of comprehensive evaluation and physical fatigue, the subjective and physiological of the human body can obtain a more comfortable experience. The parameter range corresponding to the green area in Figure9 is the light comfort zone for the combined analysis of both subjective and objective aspects.Figure 9
Integrated light comfort zone.The recommended range of values is shown in red in Figure10, with an average horizontal illuminance range of (4.08 lx, 6.99 lx) and a colour temperature range of (3126 K, 4498 K). When the average horizontal illuminance and colour temperature parameters of the nighttime light environment in urban park pedestrian space are within the range mentioned above, the lighting effect can meet the subjective comfort of the human body and also ensure that the human body is not at a high level of physiological fatigue.Figure 10
Recommended range of values for the integrated light comfort zone.


## 4. Discussion

This study is aimed at examining the effects of outdoor light environments consisting of different combinations of average horizontal illuminance and colour temperature on the subjective and physiological evaluation of the human body in urban park pedestrian space.As shown from Figure7, when the colour temperature changes from 3000 K to 4300 K, the subjects’ comprehensive subjective rating decreases under the average horizontal illuminance of 2 lx and 10 lx but increases under the average horizontal illuminance of 6 lx. When the colour temperature changed from 4300 K to 5600 K, the subjects’ comprehensive subjective ratings under each level of average horizontal illuminance decreased significantly. Overall, the best comprehensive evaluation score for the subjects is the I experimental condition.Therefore, within the range of colour temperature values for the experimental conditions, warm colour temperature is beneficial for enhancing human light comfort at average horizontal illuminance of 2 lx and 10 lx. In comparison, the neutral colour temperature is beneficial for enhancing human light comfort at average horizontal illuminance levels of 6 lx. However, at each level of average horizontal illuminance, the change in colour temperature from neutral to warm colour has a more minor impact on the comprehensive evaluation results than the change from cold to a neutral colour.Figure8 shows that average horizontal illuminance is a crucial factor in the level of physiological fatigue. At each colour temperature level taken in the experiment, the subjects’ physiological fatigue levels were significantly better under experimental conditions of 6 lx average horizontal illuminance than at low and high illuminance levels. Subjects’ physiological fatigue levels were optimal in the E experimental condition. When lighting is performed vertically, the subject’s physiological fatigue level is obviously better than that of cylindrical lighting, which is more suitable for the human body’s light comfort.When the average horizontal illuminance was increased from 2 lx to 6 lx, the relative values of the subjects’ fatigue factors increased, indicating that the increase in illuminance was beneficial in relieving physiological fatigue in humans. However, when the average horizontal illuminance was increased from 6 lx to 10 lx, the relative values of the fatigue factor of the subjects decreased significantly, indicating that excessive average horizontal illuminance can exacerbate the physiological fatigue of human body. Therefore, within the experimental range of values, medium illuminance conditions are favourable for enhancing human light comfort.


## 5. Conclusion

The effects of different outdoor light environments on people are investigated in terms of subjective comprehensive evaluation and objective physiological fatigue through various combinations of average horizontal illuminance and colour temperature.The change in colour temperature had a significant effect on the comprehensive subjective evaluation of the subjects, with the subjects’ comprehensive evaluation score being better under warm light conditions than under cold light. However, under medium illuminance conditions, the comprehensive evaluation score of neutral light is better than that of warm light. It shows that setting the colour temperature of urban park pedestrian space at neutral to warm colours results in a better subjective light comfort.The change in average horizontal illuminance had a significant effect on the physiological fatigue of the subjects, with the physiological fatigue level of the subjects in the medium illuminance condition being significantly better than in the low and high illuminance conditions. It shows that the use of appropriate illuminance conditions effectively keeps visitor fatigue levels in urban parks at a relatively better level.Based on a comprehensive analysis of both subjective and objective aspects, the colour temperature range of the light comfort zone in urban park pedestrian space is (3126 K, 4498 K), and the average horizontal illuminance range is (4.08 lx, 6.99 lx). When the lighting parameters fall within this range, it helps to give visitors a better feeling of light comfort, which can provide a valuable reference for the design and optimisation of outdoor light environments.



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*Source: 3130747-2021-12-21.xml*