Empirical study of landscape types, landscape elements and landscape components of the urban park promoting physiological and psychological restoration

Journal Pre-proof Empirical study of landscape types, landscape elements and landscape components of the urban park promoting physiological and psychological restoration Li Deng (Conceptualization) (Methodology) (Software) (Investigation) (Data curation) (Writing - original draft) (Writing review and editing), Xi Li (Writing - review and editing) (Supervision) (Funding acquisition), Hao Luo (Investigation) (Writing - review and editing), Er-Kang Fu (Writing - review and editing), Jun Ma (Writing review and editing), Ling-Xia Sun (Writing - review and editing), Zhuo Huang (Writing - review and editing), Shi-Zhen Cai (Writing review and editing), Yin Jia (Writing - review and editing)

PII:

S1618-8667(19)30242-0

DOI:

https://doi.org/10.1016/j.ufug.2019.126488

Reference:

UFUG 126488

To appear in:

Urban Forestry & Urban Greening

Received Date:

2 April 2019

Revised Date:

9 October 2019

Accepted Date:

21 October 2019

Please cite this article as: Deng L, Li X, Luo H, Fu E-Kang, Ma J, Sun L-Xia, Huang Z, Cai S-Zhen, Jia Y, Empirical study of landscape types, landscape elements and landscape components of the urban park promoting physiological and psychological restoration, Urban Forestry and amp; Urban Greening (2019), doi: https://doi.org/10.1016/j.ufug.2019.126488

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Empirical study of landscape types, landscape elements and landscape components of the urban park promoting physiological and psychological restoration

Li Denga, Xi Lia,∗ [email protected], Hao Luoa, Er-Kang Fua, Jun Maa, Ling-Xia Suna, Zhuo Huanga, Shi-Zhen Caia, Yin Jiaa a

College of Landscape Architecture, Sichuan Agricultural University, Chengdu, China

∗Corresponding

author: Xi Li (Tel: +028-86290876)

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Contact detail: No.211 Huimin Road, Wenjiang District, Chengdu City, Sichuan, P.R. China The word count of the manuscript: 8187, and there are 10 figures and 4 tables.

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Highlights:

Topography landscape type with natural forest appearance and more water features appears to

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provide the optimal restorative environment.

Landscape elements of water, topography and plants are significantly positive for human perceived restorativeness.

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Culture-related and art-related landscape components with high aesthetic values are important restorative attributes.

The single-item landscape composition can be applied to evaluate the perceived

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restorativeness.

Restorative effects of the area can be reinforced by suitable selection and configuration of

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landscape types, landscape elements and landscape components in future landscape design.

Abstract: Urban parks have been found to benefit human health and well-being. Many studies have addressed the relationship between spatial characteristics and health restoration, but little research has systematically focused on specific landscape components. In this study, the effects of three landscape types, six landscape elements and various landscape components of a traditional urban park on psychophysiological activities were investigated using physiological (blood pressure, blood glucose and electroencephalography) and psychological indicators (the abbreviated Profile of Mood States and the Landscape Perceived Restorativeness Scale). The results indicated that: (1) Different landscape types led to different physiological responses and mood states; (2) The topography landscape with natural mountain forest appearance had the most restorative effect; (3) Landscape elements of water, topography and plants had significant positive effects on human perceived restorativeness; (4) Bamboo forest, poetry walls and decorative openwork windows, were ranked as the top three landscape components in terms of perceived

restorativeness. These findings suggest that single-item landscape composition can be applied to evaluate perceived restorativeness, and the restorative potential of the area can be reinforced by suitable selection and configuration of landscape types, landscape elements and landscape components in future landscape design. An active intervention approach to the targeted improvement of restorative efficiency in existing urban parks can provide a feasible solution for satisfying the health recovery needs of growing populations. Key words: urban park, restorative environment, landscape types, landscape elements, landscape components.

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1. Introduction Significant global health challenges are being confronted in the 21st century, including increases in physical inactivity, non-communicable diseases (Beaglehole et al., 2012), harm from various adverse physiological and psychological symptoms, burnout syndrome, and neurological and immunological diseases (Danielsson et al., 2012). These factors, combined with population growth, rapid urbanization, and climate change, have prompted repeated calls to rethink approaches to prevention (Das, 2012; Watts et al., 2015). With the world’s population estimated to reach 10 billion by 2050 and 75% of this population living in cities, city planning is now recognized as part of a comprehensive solution to tackling adverse health outcomes (UNFPA, 2011). Healthy urban planning often refers to research on the positive outcomes of nature and human health relations, and there is a growing political interest in promoting natural environments for public health as part of creating sustainable cities (European Commission, 2014; World Health Organization, 2016). Many previous studies have examined the health and wellbeing outcomes associated with nature exposure, including reduced all-cause mortality and mortality from cardiovascular diseases (Donovan et al., 2013), improved healing times (Ulrich, 1984), reduced respiratory illness and allergies (Hanski et al., 2012), improved self-reported well-being and reduced the risk of poor mental health (Dallimer et al., 2012), and improved cognitive ability (Berman et al., 2008).

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Recent studies have further confirmed that urban nature has the potential to provide an inexpensive intervention for addressing a range of human health issues (Keniger et al., 2013, Hough, 2014). However, not all natural environments are equally restorative (Herzog et al., 2003). Previous studies have shown that people are able to relax and feel refreshed in parks and gardens (Kimbell et al., 2009). Several experts have increased public awareness of the differences between landscape types (Liu, 2016), landscape elements (White et al., 2010) and spatial characteristics (Peschardt and Stigsdotter, 2013) on health promotion. Some of them have deeply explored the effects of landscape characteristics (Wang et al., 2019) and fine-grained categories of designed urban planting (Hoyle et al., 2017) on aesthetic preference and perceived restorativeness. Detailed information about specific components of the physical environment that support restoration has also been reported, and it is found that restorativeness was consistently associated with a predominance of nature-based landscape components (Wang et al., 2016). However, focusing on urban green spaces as a significant aspect of urban nature is still a relatively new and growing field within research on human settlements, the built environment, and public health. The world is experiencing progressive urbanization. The incompatibility between the expectations of urban landscape users and the current status of cities has led to various negative

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effects, such as badly maintained public green spaces and abandoned landscape resources (Daniel, 2001). Several studies on the restorative potential of landscape elements, landscape scenes and landscape characteristics have provided evidence for the benefits of improving visual quality and restoration in urban areas (White et al., 2010; Nordh et al., 2011; Peschardt and Stigsdotter, 2013; Wang, 2016; Stigsdotter et al., 2017a; Wang et al., 2019), in which water features, diverse vegetation, open lawns, and bright flowers were confirmed to be important restorative attributes, while urban roadways and paved plazas were negative factors. These findings indicated that the study of landscape composition are important for health restoration. However, the systematical research on the restorative effects of these aspects in urban parks has not been fully investigated yet. Little physiological evidence has been collected regarding brain activity in response to specific landscape types and their role in supporting health recovery processes, as previous electroencephalography (EEG) studies mainly focused on a coarse comparison of nature and urban environments (Aspinall et al., 2013; Chen, 2016). Therefore, there is a growing need to improve our understanding of the effects of landscape types, landscape elements and landscape components that may support health processes or outcomes using scientific and evidence-based research into the therapeutic effects of brain activity and the elicitation of positive mood change in order to identify and develop these potentially restorative environments to increase their health-promoting effects.

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In this study, we first explored the differences in the restorative effects of exposure to three different landscape type environments (e.g., lake, lawn, topography) in an urban forest park, examining the relationship between human health and these three landscape types. Second, we examined the differences of landscape with different landscape elements or landscape components (e.g., landscape with different types of plants, landscape with different types of garden constructions) on human perceived restorativeness in the study sites, investigating whether any apparent differences in exposure to the area with different landscape composition had an impact on restorative experiences. To answer these research questions, we collected data both from physical activities and from subjective perceived experience using various health-related indicators. This study aimed to identify the sources of emotional influence (e.g., specific landscape types, landscape elements and landscape components) that may promote population health, and examined the importance of selection and configuration of landscape elements and landscape components in future planning and design of restorative environments. By doing so, we provide sustainable solutions for the development of optimal health environments through the targeted optimization and enhancement of the visual landscape. Enhancing the restorative efficiency of existing urban parks is an important goal for informing feasible approaches for satisfying the health recovery needs of growing urban populations. 2. Materials and methods 2.1. Participants Sixty volunteers (30 males and 30 females; mean age, 20.8 ± 1.02 years) were selected to participate in this study. Posters were placed around campus to recruit participants with the following characteristics: (1) individuals aged between 19 and 24 years old, (2) individuals without physical or mental illness, and (3) individuals not taking any drugs. Participants were instructed to avoid smoking, alcohol consumption, and vigorous physical activity throughout the

study period. The study was performed with the approval of the local Ethics Committee of the College of Landscape Architecture, Sichuan Agricultural University, China.

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2.2. Study sites The study was conducted in Huanhuaxi Park, which is the only five-star open park in Chengdu, China, and has the largest area and the highest number of users of any park in the region. To figure out the optimal restorative potential of landscape composition with different landscape types, three different landscape type environments (water, lawn, and topography), named Canglang Lake, the Lawn Area and Wanshu Mountain, respectively, were selected as study sites based on an indoor study that confirmed mountain forest had the most relaxing effect, followed by the lawn and the lake (Liu, 2016). Each study site has its own distinct spatial characteristics and major focal views. In addition, this park contained a low level of external impact on the experiment (i.e., no bikes or cars in visible sight) and was convenient (typically a 20-minute car trip) to reach. Detailed information about the three study sites is shown in Table 1. 2.3. Experimental Design The study lasted for 2 weeks, involving three park visits in autumn (temperature and

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humidity were 22 ± 3.0°C, 72.5± 4.5%; 25 ± 2.0°C, 76.5± 3.2%; 24 ± 2.8°C, 81.5± 3.5% for

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the three visits, respectively), and the experimental time intervals helped to eliminate legacy effects. Sixty participants were randomly divided into three groups (A, B and C) consisting of 20 individuals each. Groups switched study sites on the second and third visits to eliminate order effects, while the same experimental time (9:00 am–11:30 am) was maintained to eliminate the influence of diurnal changes in physiological rhythms (Fig. 1). Before conducting the experiments, detailed information about the instructions and study rules were provided to each participant, and written informed consent was obtained.

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The groups met at a university facility and were then driven to the park. Participants were instructed to not talk or consume their own food or drinks during the experiment. As shown in Fig. 2, upon arrival at the park, participants remained in the waiting area of the relevant study site where they rested in a seated position for 15 minutes, completing questionnaires (the abbreviated Profile of Mood States [POMS]) and physiological measurements (blood pressure and blood glucose). Participants were then given an 8-minute guided walk along the trail, where they were informed about the landscape type, landscape elements and landscape components of the study site, while other participants waited in order according to their assigned serial number. The average waiting time for each participant was approximately 8 minutes. It should be noted that the experiments at three study sites were conducted simultaneously. After arriving at the viewpoints, participants had a 3-minute break with their eyes closed to ensure that they had a similar level of physical fatigue for different study sites before EEG measurements. Electrodes and sensors were then connected to the participants by researchers. Because Ulrich (2002) reported that viewing natural settings can produce significant restoration within than 5 minutes, a 5-minute sit-down viewing period was used in this study, while baseline EEG was continually recording. Participants then completed physiological (blood pressure and blood glucose) and psychological measurements (POMS and Landscape Perceived Restorativeness Scale [LPRS]). The experimental protocol was

consistent throughout all study sites. Photographs showing the participants’ view and the guided tour routes of the three study sites are presented in Fig. 3 and Fig. 4, respectively.

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2.4. Measurements A total of 180 data samples regarding the differences in the effects of visual stimulation for three landscape types, six landscape elements and various landscape components in Huanhuaxi park were collected from physiological changes and from subjective perceived experience using a sphygmomanometer, a blood glucose meter, a portable EEG device and psychological scales. EEG power spectrum (theta, alpha, beta) data was collected to measure neural activity in the brain, since the increase of theta waves values is considered to indicate a state of deep relaxation, in which inspiration and creativity often occur (Schacter, 1977); alpha waves are associated with alertness, calmness, learning, and mental coordination (Kim et al., 2013); and increased beta wave activity is typically associated with an alert condition, while decreased beta wave activity is associated with a state of drowsiness (Lee et al., 2014). The frequency ranges of theta, alpha and beta used in the current study were 4–7 Hz, 8–13 Hz, 14–30 Hz.

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2.4.1. Physiological measurements Blood pressure (systolic [mmHg], diastolic [mmHg], pulse rate [bpm]) was measured three times in the left arm using a sphygmomanometer (Omron, HEM-7011, China), which is considered to provide an indicator of the body’s state of arousal or relaxation (Kulkarni, 1998). Blood glucose was measured with a blood glucose meter (Sinocare, GA-3, Sinocare Inc, China), reflecting emotional relaxation. EEG results were recorded using a NeuroSky MindWave-EEG headset (Beijing Oriental Creation Technology Co., Ltd., China). The headset records brainwaves from the Fp1 position (frontal lobe) above the eye (Robbins and Stonehill, 2014), and consists of four essential components: (1) a headband; (2) an ear-clip; (3) a sensor arm containing the EEG electrode; and (4) a Bluetooth device. The sensor tip measures electrical signals in the brain from the forehead, and detects ambient noise generated by human muscles, electrical sockets, computers, light bulbs, and other electrical devices, and the device measures the raw signal, power spectrum, mediation level and attention level. The raw EEG data are detected at a rate of 512 Hz, while others are obtained every second. E-SenseTM is used by NeuroSky to measure the individual’s current mental state in a digital parameter mode. The NeuroSky ThinkGearTM technology first amplifies the raw EEG data and filters out the interference caused by electrical noise and muscle tissue movement (Vourvopoulos and Liarokapis, 2014). The device has small microchips that preprocess data and transfer electrical signals directly to the computer via Bluetooth. The computer then calculates the eSense parameter value by applying the e-SenseTM algorithm to the above preprocess data (Cusoft, 2011). In this study, the raw EEG data were collected at 1-minute intervals at three study sites, and 5-minute averages were compared under three conditions. According to the EEG e-SenseTM, brainwave signals of meditation and attention levels are scaled from 1 to 100 (0 to 20: very low, 20 to 40: slightly low, 40 to 60: natural state, 60 to 80: slightly high, and 80 to 100: very high) (Sezer et al., 2017). 2.4.2. Psychological measurements The abbreviated POMS questionnaire is a self-report measure containing 40 adjectives rated on a four-point scale, ranging from 1 “not at all” to 4 “very much”, which allows an assessment of

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fluctuating affective mood states (McNair and Lorr, 1964) (see Appendix A for a full version of the questionnaire). In this scale, the mood states are measured using six factors: Tension-Anxiety (T-A), Depression (D), Anger-Hostility (A-H), Vigor (V), Fatigue (F), and Confusion (C). In addition, participants rated their perceived restorativeness of the landscape in three study sites using the LPRS scale designed by the research team on a scale from 0 to 9, where 9 represented the highest positive rating (perceived as the optimum) and 0 represented the lowest rating (perceived as not restorative at all) (see Appendix B for a full version of the questionnaire). LPRS is a questionnaire in which participants rate the overall environment first, and then landscape elements and landscape components. Before filling out LPRS scale, the relationship among landscape types, landscape elements and landscape components were informed to participants, as space is the three-dimensional organization of the landscape elements, meaning that different elements make up different landscape type environments; landscape components generally refer to specific expressions of landscape elements, and one single landscape element may contain various landscape components. In this study, they were three landscape types (e.g., lake, lawn, topography), six landscape elements (e.g., plants, water, topography…), and various landscape components (e.g., bamboo forest, pavilion, flagstone pavement…).

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2.5. Statistical analyses The EEG power spectrum data and mean meditation and attention scores were derived from the recorded signals by the e-SenseTM algorithm. The paired t-test and a repeated-measures analysis of variance (ANOVA) were used to analyze the mean values of the physiological data, followed by the Bonferroni test as a post hoc test if the between-subject effects were significant. The Wilcoxon signed-rank test and Kruskal-Wallis test were used to analyze the mean values of the psychological data. The statistical analysis was performed by SPSS 19.0 (SPSS Inc., Chicago, IL, USA) and a P-value < 0.05 was considered to indicate statistical significance.

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3 Results 3.1 Physiological results As shown in Fig. 5, diastolic blood pressure (76.8 ± 6.6 before and 72.1 ± 7.9 after; P < 0.01) and pulse rate (84.9 ± 11.4 and 78.5 ± 10.8; P < 0.01) were significantly decreased after park visits at Canglang Lake, and no significant changes were observed in systolic blood pressure and blood glucose. All physiological indicators exhibited a significant decline at the Lawn Area (systolic blood pressure [113.5 ± 9.5 and 108.7 ± 8.9; P < 0.01]; diastolic blood pressure [78.1 ± 6.14 and 73.9 ± 6.6; P < 0.01]; pulse rate [81.9 ± 8.5 and 72.5 ± 8.6; P < 0.01]; blood glucose [5.4 ± 0.7 and 5.1 ± 0.72; P < 0.05]), while most indicators were markedly decreased at Wanshu Mountain (systolic blood pressure [110.9 ± 9.5 and 107.7 ± 9.8; P < 0.01]; diastolic blood pressure [77.5 ± 6.9 and 74.2 ±7.6; P < 0.01]; pulse rate [80.7 ± 11.0 and 72.8 ± 10.4; P < 0.01]). No significant changes were found in blood glucose in general. As shown in Fig. 6, slight fluctuations were observed in EEG results during sit-down viewing. During the 1-minute analysis, the theta brainwave means values were always highest at Wanshu Mountain (Fig. 6a). A multifactor ANOVA comparing the theta mean values among the groups with regard to time changes revealed a significant difference (F = 7.40, d.f. = 2, P = 0.00), and no significant main effect for time was observed within the groups (F = 0.69, d.f. = 4, P =

0.60). The overall mean theta values in three study sites showed a significant difference (Canglang Lake [95951.7 ± 5770.3], Lawn Area [108996.4 ± 5708.1] and Wanshu Mountain [23789.8 ± 13363.28]; P < 0.05, Fig. 6b). As shown in Fig. 7, slight fluctuations were observed in EEG results at Canglang Lake and the Lawn Area, while a stable increase was found at Wanshu Mountain. During the 1-minute analysis, most alpha brainwave mean values increased after park views, and the highest values were always observed at Wanshu Mountain (Fig. 7a). A multifactor ANOVA revealed an obvious between-group effect of alpha mean values (F = 8.33, d.f. = 2, P = 0.00), while no significant main effect of time was observed (F = 0.22, d.f. = 4, P = 0.93). The overall mean alpha values showed a significant difference in three study sites (Canglang Lake [23497.3 ± 1749.1], Lawn Area [26468.9 ± 1064.3] and Wanshu Mountain [31219.4 ± 2651.2]; P < 0.05, Fig. 7b).

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As shown in Fig. 8, significant upward trends were observed in EEG results under three conditions. During the 1-minute analysis, most beta brainwave means values were increased, and values at Wanshu Mountain were much higher (Fig. 8a). A multifactor ANOVA comparing the beta mean values with regard to time changes revealed a significant difference in the betweengroup effect (F = 8.41, d.f. = 2, P = 0.00). However, no significant main effect for time was observed within the groups (F = 0.61, d.f. = 4, P = 0.65). Significant differences in the overall mean beta values were observed in three study sites (Canglang Lake [23,585.7 ± 3254.5], Lawn Area [23,585.7 ± 3254.5] and Wanshu Mountain [29,249.7 ± 4094.3]; P < 0.05, Fig. 8b).

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A significant increase in mean relaxation level was revealed after sit-down viewing under all conditions. As shown in Fig. 9, participants’ meditation and attention mean scores were highest at Wanshu Mountain, and significant between-group differences in mean attention scores were observed (relaxation score: Canglang Lake [57.6 ± 11.0], Lawn Area [56.0 ± 11.5], Wanshu Mountain [58.5 ± 9.3]; attention score: Canglang Lake [52.5 ± 11.25], Lawn Area [52.5 ± 10.7], Wanshu Mountain [58.2 ±10.9]; P < 0.01). Overall, participants were happier, more relaxed and focused at Wanshu Mountain compared with the other two sites.

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3.2 Psychological results In the POMS questionnaire, significant positive mood changes were observed under all conditions (Fig. 10). All of the negative subscales scores declined at the Lawn Area, where there was a significant decrease in the variables “tension-anxiety” (P < 0.01), “depression” (P < 0.05), “anger-hostility” (P < 0.05), “fatigue” (P < 0.01), “confusion” (P < 0.01) and total mood disturbance (P < 0.01). The POMS variables “tension-anxiety” (P< 0.01), “depression” (P < 0.01), “anger-hostility” (P < 0.01), “fatigue” (P < 0.01), “confusion” (P < 0.01) and total mood disturbance (P < 0.05) were significantly reduced at Wanshu Mountain. Fewer decreases were found at Canglang Lake, and only two POMS variables exhibited significant reductions (“fatigue” [P < 0.01] and total mood disturbance [P < 0.01]). Vigor scores, a positive subscale, were significantly improved at Canglang Lake (P < 0.05) and Wanshu Mountain (P < 0.05). Based on the LPRS questionnaire analysis, results of the ratings for different landscape types are presented in Table 2. The results revealed that Wanshu Mountain, a topography landscape type,

was rated as the most restorative environment, followed by the Lawn Area with free growing lawns and Canglang Lake with a spacious lake. The results of a Kruskal-Wallis test showed a significant difference in the ranking of the study sites (H = 41.70, d.f. = 2, P < 0.001).

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The average restorativeness scores of six landscape elements are shown in Table 3, revealing that water, topography and plants were closely associated with perceived restorativeness. Water was rated as the most restorative landscape element at Canglang Lake, while landscape constructions were rated as the least restorative (Table 3[a]). Plants were the most restorative element at the Lawn Area, while landscape constructions were the least restorative (Table 3[b]). Topography was the most restorative element at Wanshu Mountain, while roads and pavements were the least restorative (Table 3[c]). In the overall analysis of six landscape elements (Table 3[d]), water had the highest restoration evaluation, followed by topography and plants, while the elements rated as less restorative were garden facilities, followed by roads and pavements and finally landscape constructions, which were rated as the least restorative landscape element. The results of the Kruskal-Wallis test revealed a significant difference in the ranking of the landscape elements (Canglang Lake [H = 144.65, d.f. = 4, P < 0.001]; Lawn Area [H = 116.78, d.f. = 3, P < 0.001]; Wanshu Mountain [H = 52.71, d.f. = 4, P < 0.001]; overall elements [H = 260.17, d.f. = 5, P < 0.001]).

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The average restorativeness scores of different landscape components are shown in Table 4. In the overall analysis of the landscape components (Table 4[d]), the decorative openwork window, poetry wall and corridors were ranked highest for human perceived restorativeness, in addition to the generally accepted nature-related components (lake, bamboo forest, lawn…). Bamboo forest was the most restorative landscape component in the “plants” element, while the open lake and gentle slope were also rated as the most restorative components in the “water feature” element and the “topography” element, respectively. The corridor, rest platform and pavilion components received higher scores in the “landscape constructions” element, and the wooden walkway and decorated pavement got higher scores in the “roads and pavements” element. The decorative openwork window, poetry wall and landscape stone were rated as the top three restorative landscape components in the “garden facilities” element. Furthermore, the results revealed that the restorativeness score of the same landscape component varied between different study sites. For example, the forest in a distant view was rated as the best restorative landscape component at Canglang Lake, while it was the third at the Lawn Area and Wanshu Mountain (Table 4[a–c]). The results indicated that individuals may have different perceptions of the same landscape component in different landscape type environments. The results of the Kruskal-Wallis test revealed a significant difference in the ranking of specific landscape components (plants [H = 91.24, d.f. = 5, P < 0.001]; topography [H = 9.23, d.f. = 1, P = 0.002]; landscape constructions [H =195.15, d.f. = 6, P < 0.001]; roads and pavements [H = 14.98, d.f. = 3, P = 0.002]; garden facilities [H = 119.81, d.f. = 6, P < 0.001]). 4. Discussion 4.1. Landscape types, physiological relaxation, and psychological relaxation This current study investigated the restorative effects of exposure to three different landscape environments. Comparison of the results obtained from participants visiting different areas of

Huanhuaxi park indicated that participants’ blood pressure, brain activity and mood states were significantly different between three study sites. These findings are in accord with previous studies reporting that different landscape types had different impacts on cardiovascular relaxation, attentional restoration, psychological health and perceived stress recovery of populations (Park et al., 2011; Song et al., 2013; Lee et al., 2014; Takayama et al., 2014).

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The results from physiological and psychological measures indicated that Wanshu Mountain, a topography landscape type, was associated with more restorative experiences compared with two other study sites (a Lake Area and a Lawn Area), in accord with the findings of a indoor research on various landscape types promoting health among older people (Liu, 2016). These findings may be partially explained by a previous study on appropriate topography changes providing conditions for an optimal balance between openness and enclosure, and creating a sense of privacy and encirclement that is positive for meditating alone (Sonntag-Öström et al., 2015). It supports the prospect-refuge theory that emphasizes openness, providing an overview and a feeling of safety (Crawford and Appleton, 1988), and corresponds with the finding that people tend to experience tranquility and reduced stress when they are alone or in a small group in a natural environment (Hammitt and Brown, 2009). Landscape architects Grahn & Stigsdotter (2017b) have categorized the sensory experiences in the environment and identified 8 different perceived sensory dimensions (PSDs) which can be used to describe the features of different landscape environments from pocket city parks to larger regional green areas. The PSD prospect, where “it is possible to have a prospect, vistas over the surroundings” (Grahn and Stigsdotter, 2010), was considered to be the main reason why the Lawn Area was the second-most preferred environment. Participants did not consider the Lawn Area to be an ideal place for restoration, and they associated it with being active due to the large well-kept lawn. Canglang Lake was recognized as a restorative environment mainly due to its spacious lake, as aquatic areas are frequently included as aspects of people’s favorite places (Korpela et al., 2010). Most participants revealed that their poor perceived restorativeness here in Canglang Lake was closely associated with a lack of shady areas for people to gather and rest. This may be because spatial characteristics of privacy and mystery are important restorative attributes (Kaplan and Kaplan, 1989). As indicated by the findings described above, this current results are in accord with previous reports that different landscape scenes are associated with different levels of restorativeness (Wang et al., 2016), and further confirm that certain spatial qualities and landscapes composition are crucial for restoring attentional capacity and reducing mental fatigue (Stigsdotter et al., 2017b).

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4.2. Landscape elements and the perceived restorativeness In this study, it is confirmed that water was most predictive for restoration (Nordh et al., 2011), followed by topography and plants. Water and plants are two important landscape elements because they provide a comfortable environment by improving the micro-climate (Kendal et al., 2012), and the significant contributions of these two elements to the restorative experience have been reported in numerous previous studies (White, 2010; Wong and Domroes, 2016; Wang, 2019). Recently, topography has received substantial attention in landscape design. Appropriate topography design can create novel and varied spatial perception and experience, and can guide the sight of visitors through ups and downs in vertical space. However, most landscape constructions, garden facilities, roads and pavements were found to have no significant impacts on

psychological restoration, providing partial support for previous findings regarding the negative effects of human structures and artificial elements on visual quality and preferences (Arriaza et al., 2004). Generally, these findings suggest that better restorativeness is associated with nature-based landscape elements rather than man-made landscape elements.

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4.3. Specific landscape components and the perceived restorativeness In numerous studies, positive correlations have been identified between nature-related landscape components (e.g., bamboo forests, open lawn, lake) and restorativeness scores (Stigsdotter et al., 2017b; Hassan et al., 2018; Wang et al., 2019). This finding was confirmed by the results of this current study, as mentioned above. Traditional Chinese gardens are typically created in accord with the dialectic philosophy of the “unity of man with nature” (Chen and Wu, 2009), in which natural landscape features such as water, plants and animals can be united harmoniously with man-made components such as a pavilion or gallery. The significant positive effects of culture-related components (e.g., corridors, poetry walls, pavilions) and art-related components (e.g., decorative openwork windows, landscape statues) as a means of stimulating reflection on perceived restorativeness supports the findings that cultural heritage is often perceived as have a high level of restorative attributes (Packer and Bond, 2010). This phenomenon may be explained by previous research on visitor motivations (Packer and Ballantyne, 2002) suggesting that tourists are more likely to look for a learning and discovery experience, and culture relics and art venues are an important source of discovering new things and exercising the imagination. The components of the built environment, such as surface pavements, encirclement materials and activity facilities were expected to have a negative effect on the visual quality of the landscape (Acar et al., 2006), and the present finding in some respects supported this notion, as most hardscape components (e.g., kiosk, planting bed, retaining wall and lamp) were found to be less restorative. Therefore, focusing on the restorative benefits provided by positive landscape components may enhance and extend their contribution to human health and well-being in general.

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In addition, the current study suggested that the perceived restorativeness of the same landscape component varied with different environments, indicating that even with the same landscape elements and landscape components, spaces can be organized in different ways, with different results (Parsons, 1995).

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4.4. Implications for future research, planners and stakeholders Given that humans are living organisms, the environments we live in must have healthy attributes and ecological functions (Wang, 2019). Importantly, humans are natural beings. As one of the elements participating in the landscape, people connect with the environment through physiological and psychological responses (Bratman et al., 2012). The discipline of psychophysics specializes in the relationship between psychological variables and physical variables, and mainly focuses on the ways in which strong stimuli can cause feelings, and the magnitude of change in physical stimuli that can be perceived. In this study, we applied psychophysical measures to explore the effects of different landscape stimulations on physiological and psychological changes in human restorative experiences from macro (landscape types), meso (landscape elements) and micro (landscape components) perspectives. This study indicates that certain sources of emotional influence may promote population health, and the restorativeness of a single landscape element

and landscape component does not fully represent an entire space. Thus, we propose that the selection and configuration of landscape elements and landscape components in landscape design can strongly influence the human perceived restorativeness.

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Unlike non-human animals, which are typically forced to adapt to environmental changes, humans have the ability to radically change their environments (Wang, 2019). Urban environment research can provide opportunities for improving sub-optimal health status, chronic diseases, and mental and spiritual problems of human populations, and establishing an awareness of the importance of active intervention of planners and stakeholders for public health promotion. This finding supports more targeted landscape design and suggests that the restorative effects of a place can be reinforced by the optimizations and enhancements of environmental constructions and landscape components in urban planning and design. Such considerations are crucial to the balance between human beings, environments, and healthy urban research. Numerous studies have confirmed that water features provide the most restorative landscape (White et al., 2010; Sonntag-Öström et al., 2015), while Canglang Lake, which contains a spacious lake, was rated as the least restorative environment in this study. This maybe because the setting in Canglang Lake is a prospectdominant landscape which only provides an open surface, while the settings of Wanshu Mountain or the Lawn Area provide an optimal prospect-refuge landscape. Besides, the water pollution and garbage in the lake were found to negatively affect users' visiting experiences according to the questionnaire survey. Therefore, a high-quality restorative landscape should not only emphasize the integration between prospect-dominant landscape and refuge-dominate landscape in producing the framework of a landscape which most visitors find attractive or restorative, but also focus on the positive impacts of the management and maintenance of landscapes and facilities on improving visual quality and restorative potential.

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4.5. Limitations of the study The recent trend of people in rural China moving to urban areas emphasizes the importance of understanding restorative preferences and the nature-related stress recovery potential of different groups. However, in the present study, only relatively young participants were recruited. Future studies with larger mixed-age samples and participants with a broad range of educational backgrounds are required to confirm the validity of the findings in a more general context. In addition, using an urban forest park as the study site may limit the generalizability of the current findings, because such an environment is relatively unique. It may be useful for future research to include systematically-varied types of urban green spaces to confirm the reliability of the current method, and to determine whether it can be applied to more common environments. Furthermore, the identification of certain design criteria for specific landscape components (e.g., the optimal proportion of water surface area, topography changes, pedestrian walkway width, and number of rest facilities) requires further research. Finally, we only studied the restorative effects of walking and viewing experiences. The restorative outcomes gained from other recreation experiences, such as physical activities and social interactions, should be investigated in future studies. 5. Conclusion As a vital component of urban green space systems, urban parks provide valuable opportunities to improve human physical health and psychological well-being. The findings of this study confirmed that different landscape types, landscape elements and landscape components in urban parks potentially have different restorative effects. However, the level of perceived

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restorativeness of the same landscape components varied between different environments, suggesting that single-item landscape composition could be applied to evaluate perceived restorativeness. The topography landscape with natural mountain forest appearance and water features is likely to provide the optimal restorative experience, while culture-related and artrelated landscape components with high aesthetic values are important restorative attributes. This finding indicates that, when designing urban parks, the selection and configuration of landscape types, landscape elements and landscape components of the area should be considered before designs, emphasizing the importance of the spatial atmosphere, landscape sequences and organization of travel routes for restorative experiences. These findings provide an incentive to take human health restoration demands into account when planning urban development. Overall, this study may be useful for informing initiatives to improve the life quality of populations within cities by utilizing the positive effects of landscape features on physiological and psychological health, particularly in developing countries like China. CRediT author statement

Li Deng: Conceptualization, Methodology, Software, Investigation, Data curation, Writing-

Original draft preparation, Writing-Reviewing and Editing. Xi Li: Writing-Reviewing and Editing,

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Supervision, Funding Acquisition. Hao Luo: Investigation, Reviewing and Editing. Er-Kang Fu,

Conflicts of Interest The authors declare no conflict of interest.

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Jun Ma, Ling-Xia Sun, Zhuo Huang, Shi-Zhen Cai, Yin Jia: Writing-Reviewing and Editing.

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Acknowledgments The authors are grateful to the sixty participants in this study. This research was supported by the National Natural Science Foundation of China (No: 31870703).

Appendix A. The shortened Japanese version of the Profile of Mood States (POMS) 2.Gender：

3.Age：

4.Research date：

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1. Name：

5. Below is a list of words that describe feelings people have. Please CIRCLE THE NUMBER THAT BEST DESCRIBES HOW YOU FEEL RIGHT NOW. 1. 2. 3. 4. 5.

Tense Angry Worn Out Unhappy Proud

Not At All 0 0 0 0 0

A Little 1 1 1 1 1

Moderately 2 2 2 2 2

Quite a lot 3 3 3 3 3

Extremely 4 4 4 4 4

1 1 1 1 1 1 1 1 1

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32. Bewildered 0 33. Furious 0 34. Full of Pep 0 35. Worthless 0 36. Forgetful 0 37. Vigorous 0 38. Uncertain about things 0 39. Bushed 0 40. Embarrassed 0 THANK YOU FOR YOUR COOPERATION

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3

4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4

3 3 3 3 3 3 3 3 3

4 4 4 4 4 4 4 4 4

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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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Lively Confused Sad Active On-edge Grouchy Ashamed Energetic Hopeless Uneasy Restless Unable to concentrate Fatigued Competent Annoyed Discouraged Resentful Nervous Miserable Confident Bitter Exhausted Anxious Helpless Weary Satisfied

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6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31.

2 2 2 2 2 2 2 2 2

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PLEASE BE SURE YOU HAVE ANSWERED EVERY ITEM

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Appendix B. The Landscape Perceived Restorativeness Scale(LPRS) 1. Name： 2.Gender： 3.Age： 4.Research date： 5. Please complete the following questionnaire which is in regard to providing the restorative experience on a scale from 0 to 9, where 9 represented the highest positive rating and 0 the lowest rating. For each landscape component of each landscape element, Please CIRCLE THE NUMBER THAT BEST DESCRIBES HOW YOU FEEL RIGHT NOW.

6. The descriptions of the relationship among landscape types, landscape elements and landscape components.

Restorativeness

Landscape

Restorativeness

Landscape

of the overall site

elements

of the element

component

exist or not

aquatic plants

0123456789

0123456789

bushes & flowers

0123456789

open lawns

0123456789

arbors forest

0123456789

structures 0123456789

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Garden

facilities

ladder

0123456789

gentle slope

0123456789 0123456789 0123456789

corridor

0123456789

kiosk

0123456789

bridge

0123456789

wooden walkway

0123456789

flagstone pavement

0123456789

decorated pavement

0123456789

pebble pavement

0123456789

landscape stone

0123456789

poetry wall

0123456789

0123456789

0123456789

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pavements

0123456789

pavilion

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Roads &

0123456789

rest platform

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Landscape

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Topography

lake

0123456789

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0123456789

0123456789

forest in a distant view

bamboo forest

Water feature

of the component

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Plants

Restorativeness

0123456789

decorative openwork windows poems-engraved stela

0123456789 0123456789

landscape seat

0123456789

garden lamp

0123456789

sculpture

0123456789

planting bed

0123456789

retaining wall

0123456789

THANK YOU FOR YOUR COOPERATION PLEASE BE SURE YOU HAVE ANSWERED EVERY ITEM

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Reference Acar, C., Kurdoglu, B.C., Kurdoglu, O., Acar, H., 2006. Public preferences for visual quality and management in the Kackar Mountains National Park (Turkey). International Journal of Sustainable Development & World Ecology 13, 499-512. Arriaza, M., Cañas-Ortega, J.F., Cañas-Madueño, J.A., Ruiz-Aviles, P., 2004. Assessing the visual quality of rural landscapes. Landscape and Urban Planning 69, 115-125.

ro of

Aspinall, P., Mavros, P., Coyne, R., & Roe, J., 2013. The urban brain: analysing outdoor physical activity with mobile EEG. British Journal of Sports Medicine, 0, 1-6.

Beaglehole, R., Bonita, R., Horton, R., 2012. Measuring progress on NCDs: one goal and

-p

five targets. The Lancet 380, 1283-1285.

re

Berman, M.G., Jonides, J., Kaplan, S., 2008. The Cognitive Benefits of Interacting with Nature. Psychological Science 19, 1207-1212.

lP

Bratman, G.N., Hamilton, J.P., Daily, G.C., 2012. The impacts of nature experience on

na

human cognitive function and mental health. Annals of the New York Academy of Sciences 1249, 118-136.

ur

Chen, X.Q., Wu, J.G., 2009. Sustainable landscape architecture: implications of the Chinese philosophy of ‘unity of man with nature’ and beyond. Landscape Ecology 24,

Jo

1015–1026.

Chen, Z., He, Y., & Yu, Y., 2016. Enhanced functional connectivity properties of human brains during in-situ nature experience. PeerJ, 4, e2210. Cui, L., Shi, J., 2012. Urbanization and its environmental effects in Shanghai, China. Urban Climate 2, 1–15.

Cusoft, 2011. MINDXP Training Analysis System User Manual (Professional version). Beijing: Beijing Cusoft Co., Ltd. Dallimer, M., Irvine, K.N., Skinner, A.M.J., Davies, Z.G., Rouquette, J.R., Maltby, L.L., Warren, P.H., Armsworth, P.R., Gaston, K.J., 2012. Biodiversity and the Feel-Good Factor: Understanding Associations between Self-Reported Human Well-being and Species Richness. BioScience 62, 47-55.

21st century. Landscape and Urban Planning 54, 267-281.

ro of

Daniel, T.C., 2001. Whither scenic beauty? Visual landscape quality assessment in the

Danielsson, M., Heimerson, I., Lundberg, U., Perski, A., Stefansson, C.G., Akerstedt, T.,

-p

2012. Psychosocial stress and health problems: Health in Sweden: The National Public

re

Health Report 2012. Chapter 6. Scandinavian Journal of Public Health 40, 121-134. Das, P., 2012. Rethinking our approach to physical activity. The Lancet 380, 189-190.

lP

Donovan, G.H., Butry, D.T., Michael, Y.L., Prestemon, J.P., Liebhold, A.M., Gatziolis, D., Mao, M.Y., 2013. The relationship between trees and human health: evidence from the

na

spread of the emerald ash borer. American Journal of Preventive Medicine 44, 139-145.

ur

European Commission., 2014. General Union Environment Action Programme to 2020. Living well, within the limits of our planet. Luxembourg: Publications Office of the

Jo

European Union.

Grahn, P., Stigsdotter, U.K., 2010. The relation between perceived sensory dimensions of urban green space and stress restoration. Landscape and Urban Planning 94, 264275. Hammitt, W.E., Brown, G.F., 2009. Functions of privacy in wilderness environments.

Leisure Sciences 6, 151-166. Hanski, I., von Hertzen, L., Fyhrquist, N., Koskinen, K., Torppa, K., Laatikainen, T., Karisola, P., Auvinen, P., Paulin, L., Makela, M.J., Vartiainen, E., Kosunen, T.U., Alenius, H., Haahtela, T., 2012. Environmental biodiversity, human microbiota, and allergy are interrelated. Proceedings of the National Academy of Sciences 109, 8334-8339. Hassan, A., Jiang, T., Guo, L., Jiang, M., Liu, A., Jiang, Z., Liu, Z., Chen, Q.B., 2018.

ro of

Effects of Walking in Bamboo Forest and City Environments on Brainwave Activity in

Young Adults. Evidence-Based Complementary and Alternative Medicine 2018, 9653857. Herzog, T.R., Maguire, C., P., Nebel, M.B., 2003. Assessing the restorative components

-p

of environments. Journal of Environmental Psychology, 159-170.

re

Hough RL. 2014. Biodiversity and human health: Evidence for causality? Biodiversity and Conservation 23: 267–288.

lP

Hoyle, H., Hitchmough, J., Jorgensen, A., 2017. All about the ‘wow factor’? The relationships between aesthetics, restorative effect and perceived biodiversity in

na

designed urban planting. Landscape and Urban Planning, 109–123.

ur

Kaplan, R., Kaplan, S., 1989. The Experience of Nature: A Psychological Perspective. Cambridge University Press, Cambridge, UK.

Jo

Kendal, D., Williams, K.J.H., Williams, N.S.G., 2012. Plant traits link people’s plant preferences to the composition of their gardens. Landscape and Urban Planning 105, 34– 42.

Keniger LE, Gaston KJ, Irvine KN, Fuller RA. 2013. What are the benefits of interacting with nature? International Journal of Environmental Research and Public Health 10, 913–

935. Kim, S.C., Lee, M.H., Jang, C., KWon, J.W., ParK, J.W., 2013. The Effect of Alpha Rhythm Sleep on EEG Activity and Individuals’ Attention. Journal of Physical Therapy Science 25, 1515–1518. Kimbell, A.R., Schuhmann, A., Brown, H., 2009. More Kids in the Woods: Reconnecting Americans with Nature. Journal of Forestry 107, 373-377.

ro of

Korpela, M.K., Ylen, M., Tyrvainen, L., Silvennoinen, H., 2010. Favorite green, waterside

and urban environments, restorative experiences and perceived health in Finland. Health Promotion International 25, 200-209.

-p

Lee, B.G., Lee, B.L., Chung, W.Y., 2014. Mobile healthcare for automatic driving sleep-

re

onset detection using wavelet-based EEG and respiration signals. Sensors (Basel) 14, 17915-17936.

lP

Liu, B.-X., 2016. Study on the Effects of Different Landscapes on Elderly People's BodyMind Health. Landscape Architecture, 113-120.

na

http://www.cnki.net/kcms/doi/10.14085/j.fjyl.2016.07.0113.08.html

ur

McNair, D.M., Lorr, M., 1964. An analysis of mood in neurotics. Journal of Abnormal and Social Psychology 69 (6), 620-627.

Jo

Nordh, H., Alalouch, C., Hartig, T., 2011. Assessing restorative components of small urban parks using conjoint methodology. Urban Forestry & Urban Greening 10, 95-103. Packer, J., Ballantyne, R., 2002. Motivational factors and the visitor experience: A comparison of three sites. Curator the Museum Journal 45, 183-198. Packer, J., Bond, N., 2010. Museums as Restorative Environments. Curator the Museum

Journal, 421-436. Park, B.-J., Furuya, K., Kasetani, T., Takayama, N., Kagawa, T., Miyazaki, Y., 2011. Relationship between psychological responses and physical environments in forest settings. Landscape and Urban Planning 102, 24-32. Parsons, R., 1995. Conflict between ecological sustainability and environmental aesthetics: Conundrum, canärd or curiosity. Landscape and Urban Planning 32, 227-244.

ro of

Peschardt, K.K., Stigsdotter, U.K., 2013. Associations between park characteristics and perceived restorativeness of small public urban green spaces. Landscape and Urban Planning 112, 26-39.

-p

Robbins, R., Stonehill, M., 2014. PUBLISHED PROJECT REPORT PPR726 Investigating

re

the NeuroSky MindWave™ EEG Headset. Transport Research Foundation, 1-28. Schacter, D.L., 1977. EEG theta waves and psychological phenomena: A review and

lP

analysis. Biological Psychology 5, 47-82.

Sezer, A., İnel, Y., Seçkin, A.Ç., Uluçınar, U., 2017. The Relationship between Attention

na

Levels and Class Participation of First-Year Students in Classroom Teaching

ur

Departments. International Journal of Instruction, 55-68. https://scihub.tw/10.12973/iji.2017.1024a

Jo

Song, C., Ikei, H., Lee, J., Park, B.-J., Kagawa, T., Miyazaki, Y., 2013. Individual differences in the physiological effects of forest therapy based on Type A and Type B behavior patterns. Journal of Physiological Anthropology 32, 1-7. Sonntag-Öström, E., Stenlund, T., Nordin, M., Lundell, Y., Ahlgren, C., Fjellman-Wiklund, A., et al., 2015. “Nature’s effect on my mind” Patients’ qualitative experiences of a forest-

based rehabilitation programme. Urban Forestry & Urban Greening, 14, 607–614. Stigsdotter, U.K., Corazon, S.S., Sidenius, U., Kristiansen, J., Grahn, P., 2017a. It is not all bad for the grey city - A crossover study on physiological and psychological restoration in a forest and an urban environment. Health Place 46, 145-154. Stigsdotter, U.K., Corazon, S.S., Sidenius, U., Refshauge, A.D., Grahn, P., 2017b. Forest design for mental health promotion—Using perceived sensory dimensions to elicit

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restorative responses. Landscape and Urban Planning 160, 1-15.

Takayama, N., Korpela, K., Lee, J., Morikawa, T., Tsunetsugu, Y., Park, B.J., Li, Q., Tyrvainen, L., Miyazaki, Y., Kagawa, T., 2014. Emotional, restorative and vitalizing

-p

effects of forest and urban environments at four sites in Japan. International Journal of

re

Environmental Research and Public 11, 7207-7230.

Ulrich, R.S., 1984. View through a window may influence recovery from surgery. Science

lP

224, 224-225.

Ulrich, R.S., 2002. Health benefits of gardens in hospitals. Paper for conference: Plants

na

for People. International Exhibition Floriade.

ur

UNFPA, 2011. State of world population 2010. United Nations Population Fund, New York. https://www.unfpa.org/publications/state-world-population-2010

Jo

Vourvopoulos, A., Liarokapis, F., 2014. Evaluation of commercial brain–computer interfaces in real and virtual world environment: A pilot study. Computers & Electrical Engineering 40, 714-729. W. Crawford, Donald., Appleton, Jay., 1988. The Experience of Landscape. The Journal of Aesthetics and Art Criticism. 34. 367. 10.2307/430033.

Wang, R.-H., Zhao, J.-W., Meitner, M.J., Hu, Y., Xu, X.-L., 2019. Characteristics of urban green spaces in relation to aesthetic preference and stress recovery. Urban Forestry & Urban Greening. Wang, X.-R., 2019. Landscape Notes - Nature, Culture, Design. SDX Joint Publishing Company, Shanghai, China. ISBN 978-7-108-06414-1 Wang, X.-X., Rodiek, S., Wu, C.-Z., Chen, Y., Li, Y.-X., 2016. Stress recovery and

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restorative effects of viewing different urban park scenes in Shanghai, China. Urban Forestry & Urban Greening 15, 112-122.

Watts, N., Adger, W.N., Agnolucci, P., 2015. Health and climate change: policy

-p

responses to protect public health. The Lancet Commissions 386, 1861-1914.

re

White, M., Smith, A., Humphryes, K., Pahl, S., Snelling, D., Depledge, M., 2010. Blue space: The importance of water for preference, affect, and restorativeness ratings of

lP

natural and built scenes. Journal of Environmental Psychology 30, 482-493. Wong, K.-K., Domroes, M., 2016. The Visual Quality of Urban Park Scenes of Kowloon

na

Park, Hong Kong: Likeability, Affective Appraisal, and Cross-Cultural Perspectives.

ur

Environment and Planning B: Planning and Design 32, 617-632. World Health Organization, 2016. http://www.who.int/mediacentre/factsheets/fs220/en/.

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Ye, J.-H., Chen, X.-H., Li, Y., 2007. Analysis on the transportation characteristics changes during rapid urban expansion of Shanghai. Urban Planning Forum (6), 103–108.

Fig. 1. Experimental diagram.

Fig. 2. Photographs of experiments: (A) blood pressure measurement before experiment; (B) psychological measurement (POMS) before experiment; (C-D) an 8-minute walking; (E-F) a 3-minute close-eye break and EEG

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wearing; (G-H) a 5-minute viewing; (I) blood pressure measurement after experiment; (J) psychological

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measurements (POMS and LPRS) after experiment.

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Fig. 3. Photographs of three study sites from participants’ eye view: (A) Canglang Lake; (B) The Lawn Area; (C)

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Wanshu Mountain.

Fig. 4. Tour routes and sit-in viewpoints in three study sites.

Fig. 5. Comparison of blood pressure values and blood glucose after park visits in three study sites. N = 60; mean ±

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SD; *P < 0.05; **P < 0.01; verified by paired t-test.

Fig. 6. One-minute averages and the overall mean theta wave (power units) values in three study sites. (a) Change in each 1-min theta wave (power units) value; (b) overall mean theta wave (power units) values. N = 60; mean ± SD;

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between-group effect (F = 7.40, d.f. = 2, P = 0.00).

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*P < 0.05; Multifactor ANOVA showed a non-significant time effect (F =0.69, d.f. = 4, P = 0.60) and a significant

Fig.7. One-minute averages and the overall mean alpha wave (power units) values in three study sites. (a) Change in each 1-min alpha wave (power units) value; (b) overall mean alpha wave (power units) values. N = 60; mean ±

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SD; *P < 0.05; Multifactor ANOVA showed a non-significant time effect (F = 0.22, d.f. = 4, P = 0.93) and a significant between-group effect (F = 8.33, d.f. = 2, P = 0.00).

Fig. 8. One-minute averages and the overall mean beta wave (power units) values in three study sites. (a) Change in each 1-min beta wave (power units) value; (b) overall mean beta wave (power units) values. N = 60; mean ± SD; *P < 0.05; Multifactor ANOVA showed a non-significant time effect (F =0.61, d.f. = 4, P = 0.65) and a significant between-group effect (F = 8.41, d.f. = 2, P = 0.00).

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Fig. 9. Comparisons of participants’ mediation and attention mean scores during park views in three study sites. N = 60; mean ± SD; ∗ P < 0.05; verified by paired t-test.

Fig. 10. Comparison of the Profile of Mood States (POMS) subscale scores before and after visiting experiences in three study sites. T-A: tension-anxiety; D: depression-dejection; A-H: anger-hostility; F: fatigue; C: confusion; V:

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vigor. N= 60; mean ± SD; ∗P < 0.05; ∗∗P < 0.01; verified by Wilcoxon signed-rank test.

Table 1 The descriptions of three study sites. Study sites

Canglang Lake

Lawn Area

Wanshu Mountain

(Landscape types)

(Water)

(Lawn)

(Topography)

Illustrations

S= 65,000 ㎡

S= 3.5,000 ㎡

S= 60,000 ㎡

A sparse lawn area mainly with

A forest area composed of a man-

small islands and rest platforms,

tall trees and open lawns,

made mountain, natural jungle,

surrounded by many trees, bushes

creating the artistic conception

cultural constructions and facilities

and flowers, creating the artistic

of serene, prospect and well

(e.g., pavilions, corridors, poetry

conception of serene, beautiful,

maintained. Many benches and

wall…). Sunny and shady areas

clear. Here are almost sunny areas.

shady areas where people can

make one feel safe and undisturbed.

No traffic noise.

rest. Minor traffic noise.

No traffic noise.

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A lake area with shoal, stream,

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Descriptions

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Images

Table 2 Ranking of three study sites according to restorative experience. Study sites

Canglang Lake

The Lawn Area

Wanshu Mountain

(Landscape types)

(Water)

（Lawn）

（Topography）

Mean

4.72

4.95

5.29

SD

1.88

1.90

1.70

Mean rank

1287.60

1374.32

1523.15

Ranking

3

2

1

Note: The mean value, standard deviation and mean rank were calculated for each study site (N=60). A rank has

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been awarded to each site based on the succession in the mean ranks.

Table 3 Ranking of six landscape elements for each study site according to restorative experience. a. Site 1 Canglang Lake Landscape

Plants

Water feature

Topography

elements

Landscape

Roads &

Garden

constructions

pavements

facilities

Mean

5.41

7.21

None

4.10

4.50

4.58

SD

1.71

1.36

None

1.88

1.60

1.83

Mean rank

520.20

756.03

None

334.14

368.05

373.91

Ranking

2

1

None

5

4

3

Water feature

Topography

Landscape

Roads

constructions

pavements

facilities

b. Site 2 The Lawn Area Landscape

Plants

elements

&

Garden

5.60

None

None

3.45

4.70

4.76

SD

1.78

None

None

1.74

1.60

1.87

Mean rank

500.58

None

None

237.20

409.40

426.88

Ranking

1

None

None

4

3

2

Water feature

Topography

Landscape

c. Site 3 Wanshu Mountain Landscape

Plants

elements

4.70

5.36

1.67

1.74

437.34

590.99

5.70

5.13

None

1.45

1.46

Mean rank

626.41

None

671.64

533.15

Ranking

2

None

1

4

d. Overall

Plants

Water feature

Topography

Landscape

SD

1.79

1.36

1.45

Mean rank

1643.79

2378.08

1757.51

Ranking

3

1

2

Roads

3

&

Garden

constructions

pavements

facilities

4.42

4.67

4.98

1.81

1.62

1.84

1141.10

1218.86

1401.26

6

5

4

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5.70

5

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None

1.89

7.21

Garden

facilities

5.56

5.52

&

pavements

SD

Mean

Roads

constructions

Mean

elements

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Mean

Note: The mean value, standard deviation and mean rank were calculated for each landscape element (N=60). A rank

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has been awarded to each element based on the succession in the mean ranks.

Table 4 Ranking of specific landscape components for each study site according to restorative experience. a. Site 1 Canglang Lake Landscape components

Mean

SD

Mean rank

Ranking

Plants

aquatic plants

5.56

1.68

125.21

2

forest in a distant view

6.21

1.44

159.48

1

bushes & flowers

5.14

1.85

111.56

3

arbors forest

4.72

1.51

85.76

4

Water feature

open lake

7.21

1.36

200.35

1

Topography

None

None

None

None

None

Landscape constructions

rest platform

5.49

1.56

129.84

1

Roads & pavements

wooden walkway

5.12

1.90

146.41

1

flagstone pavement

4.70

1.59

119.55

2

decorated pavement

4.56

1.34

113.16

3

pebble pavement

4.44

1.48

102.88

4

landscape stone

5.44

1.54

garden lamp

3.63

1.47

landscape seat

5.21

1.93

planting bed

3.86

1.70

retaining wall

2.95

1.46

Landscape elements

Landscape components

Mean

SD

Plants

forest in a distant view

5.35

bushes & flowers

4.60

open lawns

5.77

Garden facilities

1

54.60

4

102.51

2

85.06

3

50.60

5

Mean rank

Ranking

1.81

110.72

3

1.73

78.72

4

1.49

124.22

2

re

arbors forest

114.39

-p

b. Site 2 The Lawn Area

ro of

Landscape elements

6.70

1.40

168.35

1

None

None

None

None

None

None

None

None

2.44

1.38

39.83

2

None

Topography

None

Landscape constructions

kiosk

Roads & pavements

flagstone pavement

4.53

1.53

84.06

3

decorated pavement

4.77

1.70

98.92

1

pebble pavement

4.53

1.59

88.53

2

landscape stone

5.16

1.56

150.80

1

poems-engraved stela

4.81

1.83

128.98

2

garden lamp

3.77

1.93

84.23

5

landscape seat

4.56

1.92

118.00

3

planting bed

4.47

1.47

101.17

4

na

Garden facilities

lP

Water feature

ur

c. Site 3 Wanshu Mountain

Landscape components

Mean

SD

Mean rank

Ranking

Plants

forest in a distant view

5.28

1.69

108.79

3

bushes & flowers

4.51

1.61

76.98

4

bamboo forest

7.02

1.64

177.12

1

arbors forest

5.42

1.71

119.12

2

Water feature

None

None

None

None

None

Topography

ladder

5.32

1.60

51.38

2

gentle slope

6.08

1.18

69.63

1

pavilion

5.16

1.46

131.10

2

corridor

5.95

1.52

155.76

1

bridge

4.86

1.27

104.83

3

flagstone pavement

4.56

1.64

83.70

3

decorated pavement

4.84

1.77

97.72

1

pebble pavement

4.70

1.61

90.08

2

landscape stone

5.26

1.67

178.81

4

Jo

Landscape elements

Landscape constructions

Roads & pavements

Garden facilities

poetry wall

5.60

1.81

200.03

3

decorative openwork windows

6.00

1.59

228.96

1

poems-engraved stela

5.81

1.59

205.78

2

landscape seat

4.56

1.86

126.24

6

sculpture

4.95

1.51

143.19

5

planting bed

4.32

1.20

110.31

7

d. Overall landscape components Landscape components

Mean

SD

Mean rank

Ranking

Plants

aquatic plants

5.56

1.68

360.96

5

forest in a distant view

5.61

1.70

379.06

3

bushes & flowers

4.75

1.74

262.06

6

open lawns

5.77

1.49

387.30

2

bamboo forest

7.02

1.64

545.53

1

arbors forest

5.61

1.74

369.62

4

lake

7.21

1.36

30.50

1

ladder

5.32

1.60

51.38

2

gentle slope

6.08

1.18

69.63

1

rest platform

5.49

1.56

373.19

2

pavilion

5.16

1.46

348.46

3

corridor

5.95

1.52

396.36

1

kiosk

2.44

1.38

106.83

5

bridge

4.86

1.27

305.79

4

wooden walkway

5.12

1.90

372.19

1

flagstone pavement

4.59

decorated pavement

4.72

pebble pavement

4.56

landscape stone

5.29

Landscape constructions

Roads & pavements

Garden facilities

poetry wall decorative openwork windows poems-engraved stela garden lamp sculpture planting bed

retaining wall

1.58

287.47

3

1.61

311.43

2

1.55

278.71

4

1.59

440.55

3

5.60

1.81

482.98

2

6.00

1.59

533.69

1

5.31

1.78

427.44

4

4.78

1.92

359.19

6

3.70

1.71

220.83

8

4.95

1.51

364.08

5

4.30

1.49

255.01

7

2.95

1.46

138.83

9

lP

landscape seat

-p

Topography

re

Water feature

ro of

Landscape elements

na

Note: The mea value, standard deviation and mean rank were calculated for each landscape component (N=60). A

Jo

ur

rank has been awarded to each component based on the succession in the mean ranks.