虚拟现实技术在绿色基础设施健康效益评估中的应用

Human Health Assessments of Green Infrastructure Designs Using Reality Virtual

著：（美）马修·布朗宁 （泰）彭瑟刚·萨布卡特派桑 姜珊 （美）安贾利·约瑟夫 译：翁羽西 袁帅Authors: (USA) Matthew Browning, (THA) Pongsakorn Suppakittpaisarn, JIANG Shan, (USA) Anjali Joseph Translators: WENG Yuxi, YUAN Shuai摘要：快速的城市化进程在不经意间将人类与自然环境割裂开来。因此所带来的日益严重的城市污染和人们进行户外活动的局限性，对人的身心健康和认知都产生了影响。城市规划师可以通过绿色基础设施营建来降低城市化带来的负面影响，如自然式公园、生态滤沟、植物墙和行道树等。然而，哪些绿色基础设施要素能否用于改善人们的健康福祉仍不十分明确。研究人员在绿色基础设施健康效益研究中引入虚拟现实技术（VR），为循证设计提供科学可靠的依据。VR技术仅需要较低的成本和专业技术。受试者佩戴仪器进入沉浸式虚拟世界，过程中同步采集心理和生理指标，用于预测不同自然场景对健康和认知功能的长期影响。通过对VR系统、内容创作、实验设计、健康指标测量和安全注意事项的方法论进行概述，使读者了解VR技术如何用于研究，并作为一种替代治疗的手段，用于改善绿色基础设施的健康福祉，为VR技术的研究应用提供理论与实践基础。关键词：风景园林；绿色空间；建成环境；仿真模拟；公共健康；环境心理学；沉浸式虚拟环境；实验研究

Abstract: Rapid urbanization inadvertently separates people from the natural landscapes in which we evolved. This disconnect can impact human health and cognitive functioning by exposing people to increased levels of pollution and limiting people's opportunities for physical activity. Built environment researchers may prevent the negative effects of urbanization through studying and providing empiricalbased recommendations for green infrastructure, such as nature parks, bioswales, green walls, and street trees. Determining which infrastructure elements improve health and wellbeing for their clients and future users is challenging. However, researchers can use virtual reality (VR) to compare the benefits of different infrastructure elements to inform design interventions. VR can require relatively little cost and technical expertise. Users are transported into immersive virtual worlds where their psychological and physiological responses can be collected to predict the long-term health and cognitive functioning impacts of each design option. In the current essay, we provide a methodological overview of VR systems, content creation, study design, health outcome measurement, and safety recommendations. Our goal is to provide the reader with an understanding of how VR may be employed as a research and therapeutic tool for improving health outcomes related to green infrastructure as well as to provide an elementary set of tools and knowledge to use VR in their research.

Keywords: landscape architecture; green space; built environments; simulations; public health; environmental psychology; immersive virtual environments; experimental research

2020/09 1人们熟悉的和不太熟悉的“新奇”的绿色基础设施示例Examples of familiar and less familiar (“novel”) GI elements

研究表明，基因对人类健康与寿命变化的影响约占30%[1]。而影响健康的主要因素包括行为习惯（体育锻炼、睡眠和饮食状况）、医疗保健和物理环境。因此，随着城市化进程加快，越来越多的人涌入城市，人们的健康问题也愈发凸显。虽然逐渐完善的城市医疗水平能够为城市居民提供更好的健康保障，但城市居民同样也面临着由空气污染、生活压力等给健康带来的负面影响[2]。庆幸的是，城市规划、风景园林、公共卫生政策等领域的研究结果能够为降低城市化带来的负面影响提供一定参考，促进城市绿色基础设施的生态服务功能是改善人类健康的重要途径。绿色基础设施是指“能够保护自然生态系统的功能与价值，并由此为人类提供相关利益的、互相关联的绿色空间网络”，在城市规划与设计的背景下，植物也是低影响开发的重要构成要素[3-4]。具体来说，绿色基础设施元素包括人们所熟悉的行道树、绿化隔离带、花园和公园等，还有让人感觉“新奇”的元素，如绿色屋顶、植物墙、雨水花园、生态滤沟和湿地等 [4]（图 1 ）。在这2种情况下，绿色基础设施为生态系统带来了诸多益处，如城市雨洪管理、调节极端温度、为本地和跨地区物种（如鸟类和授粉昆虫）提供栖息地等 [5-6]。

包括绿色基础设施在内的自然环境对人类健康的影响包括3个方面：降低城市化带来的弊端、恢复注意力、塑造环境的容纳能力[7]。植物可以通过吸附空气中的污染物、降

地从应激性压力中恢复过来，这些是通过测量焦虑、压力和回避性行为的调查问卷[38]，以及被试者唾液中更低的皮质醇指标等体现

[39]

出来的 。除此之外，通过VR营造的人造森林里，如果座椅区域附近空旷、视野良好，则比隐藏在密林中的座椅有更强的压力恢复效益 [40]。

影响绿色基础设施健康效益的其中一个因素是其被感知的安全性。超过45项的研究发现，多种类型的植物空间会引发人们的恐惧和焦虑感[41]，而恐惧感会妨碍人们在使用这些公共空间时获得健康方面的益处[42]，并且对人们的主观幸福感产生消极的影响[43]。低噪声的负面感知和隔离夜间的人造光来减容易让人产生恐惧感的空间因素包括：及膝少对人体的伤害 [8-10]。自然环境还可以帮助我至视平线高度范围内植物密度过高，杂乱的们恢复注意力，从人类进化角度看，自然界灌木丛和封闭的围合空间，例如道路两侧的中熟悉的事物能够让人们获得更多的安全感植被或广场四周[41]。植被的空间布局（可见边和依恋感 [11-15]。此外，绿色空间不仅为促进公界）和通透性（深度、高度、孔隙度）共同影众健身活动、社会交往提供机会，还有益于响感知的安全性和主观恢复性 [44]。改善睡眠，维持人体肠道、皮肤等部位的微景观偏好也会影响绿色基础设施的健康生物群落平衡 [16-18]。效益。与偏好度低的自然环境或室内环境相

大量的科学实验和观察性的研究证实绿比，当VR用户在观看他们喜欢的自然环境色基础设施有益于促进人类的健康和福祉。（如海滩、田园景观）时，悲伤、不安、易怒近期的文献总结回顾了绿色基础设施在某些和紧张等负面情绪显著降低[45]。数十项研究疾病中所发挥的健康效益，包括哮喘 [19-20]、心都表明，绿色空间的健康效益也同样受到景血管健康 [21]、老年痴呆 [22-23]，以及在生育健观偏好的影响 [33, 46]。

康 [24]、心理健康 [25-26]、死亡率 [27]、压力 [28] 等由于影响绿色基础设施健康效益的因素各个方面的益处。诸多，因此，针对绿色基础设施要素的循证

不同类别的绿色基础设施可能发挥不研究能够为具体的环境设计提供有价值的参同的健康效益[4]。绿色基础设施中为居民所考。然而，依然存在许多局限性。例如，在熟悉的景观要素可以发挥保护性的效益，这实验室中开展研究可能会对心理和生理指标

[4, 29-30]

一点已经有广泛的研究支撑 。然而另造成一定影响，天气条件、交通等因素也会有小部分的研究表明，诸如生态植物墙、绿使得实景实验可操作性降低，而且采用2D 图色屋顶、生物滞留系统，以及其他并不为片充当感知媒介可能会导致受试者对实景中人所熟知的绿色基础设施也能发挥一定的的感知敏感性下降 [47-48]。

[7, 27, 31-33]

健康效益 。在绿色基础设施的植物VR技术的新手段，提高了研究的生态效类型方面，有观测研究发现，相较于单纯的度，为绿色基础设施健康效益研究的开展提草本植被，人们住宅区附近的乔木覆盖程度供了可能。生态效度是指在实验条件下参与与人体的健康体质指数（ BMI）水平密切相者的行为和感知与真实现象相符的程度 [49-50]。关[34]，并且影响居民在医院外的死亡率 [35] 以VR技术可以作为人类健康治疗的一

[36-37]

及小学生的学习成绩 等。相关的VR 实验种替代手段在过去多项研究中得到证实。也支持以上的结论。相比草本植物景观，居Nukarinen 等发现，在利用VR技术模拟的民区附近的乔木和行道树景观可以使人更快360°立体森林视频中暴露10 min能够有效地1

降低交感神经系统活性（即“类似‘战斗或逃跑’的过度应激反应”），抑制负面情绪。与真实森林环境相比，虚拟环境更有益于主观情绪的恢复 [51]。Browning 等的研究表明，在真实的森林里停留6 min，对受试者在情绪调节与注意力恢复上的效果与观看360°森林视频相当，而这两者（真实与虚拟的森林环境）均比没有绿色基础设施的对照组有更显

[52]

著的健康效益 。Chirico 和 Gaggoli 的研究也证实，暴露在真实和虚拟的全景式山湖景观场景所减少的消极影响和引起敬畏感的程度相似 [53]。Yin 等利用VR技术观察4种不同的办公室环境（“亲生物设计”与“非亲生物设计”）对受试者压力和焦虑恢复的影响。结果发现，在“亲生物设计”的办公室环境中停留 5 min，与观看360°“亲生物环境”视频产生的健康效益（血压和心率）相似 [54]。

本研究概述了VR系统、内容创作、实验设计、健康指标测量和安全注意事项等多个重要问题，为研究人员开展相关实验提供了科学的方法建议和指导。

VR系统主要分为2类：一是沉浸式物理空间，如洞穴式自动虚拟环境（ CAVE），这类系统是房间大小的立方体空间，带有视频投影仪，可将移动的图像投影至参与者周围的半透明屏幕；二是头戴式显示设备（HMD）系统，涵盖了用于展示视听刺激的护目镜。下文将简要介绍这2类系统的历史和使用情况。

1.1 沉浸式物理空间

CAVE由伊利诺伊大学香槟分校的电子可视化实验室发明，并于1992 年首次向广大观众展示 [55]。CAVE 系统通常包括一个3.33 m3的立方体房间，光线较暗。房间内4~6 个侧面都配有投影幕。以一定角度旋转的反射镜放置在短焦投影仪和投影幕之间，可将高分辨率场景投射到投影幕上 [56]。

CAVE可有效操纵触觉信息，因而其沉浸感、真实感和体验感比其他形式的VR 更

[57]

强 。该系统可有效激发与环境设计存在关联的情绪，例如与恐高症相关的焦虑和恐22种主要类型VR系统的图示

Illustration of two main types of VR systems惧[58]，并能辅助寻路决策和导航 [59]。然而， CAVE系统需要昂贵的初始安装费用和高级的计算机技能。该系统还要求较大的实验室空间，其可移植性和灵活性都十分有限。或许恰恰因为空间需求大、成本高、要求技术专长[60]，以及对设备的设置和调试有很高的要求 [56]，CAVE 系统很少用于对绿色基础设施的健康益处的研究（存在2 个例外）[61-62]。

1.2 头戴式显示设备

基于HMD的 VR发轫于20 世纪 60 年代[63]，但对于研究者，此类设备直至近期才被引入到科研领域中 [64]。2012 年，Oculus Rift的问世标志着第二次HMD开发浪潮，推广了低价、舒适且高质量的设备。新设备、新软件百花齐放。HMD VR已有不少于50 种型号，每年都有新型号问世，旧型号也有大量更新。至少有2篇科学文献介绍了可用的HMD型号及其提供沉浸感的潜力（分辨率、帧速率和视野），对研究的效用（可移动性和成本）以及用户体验 [65-66]。由于 HMD高速发展，最新信息的来源可能是VR专业组织网站，如 Virtual Reality Society ①。截至本文撰写时，HMD可分为三大类：手机HMD、桌面HMD和一体机HMD（图 2）。

手机HMD是价格低廉、便携性好且易于2

使用的头戴设备，需要使用智能手机投影图像、播放声音。Google Cardboard 是一个较为知名的例子，而其他型号的设备有不同的沉浸体验 [67]。 Oculus Gear VR 和 Google Expedition套件对于研究者较为陌生，但它们质量高，并且价格仅相当于一体机和桌面机的一小部

[66]

分 。然而相较之下，手机HMD受限于手机运算能力，用户体验和控制能力都较差。并且播放高分辨率和高帧率影像需要高端手机，后者的价格或与其他类型HMD相当，甚至更高。手机HMD售价通常为 70~350 人民币（约 10~50 美元），不包含手机价格。

与手机HMD不同，一体机HMD内置了处理器、GPU、显示器、内存、电池和传感器，因此不需要连接其他设备。较知名的型号包括 Oculus Go 和 Oculus Quest。一体机HMD不受线缆束缚，某些型号更内置侦测物体的传感器，可防止用户意外触碰周边物体。一体机HMD的体验比手机HMD更真实，但体验的复杂性无法与桌面HMD相比。一体机不同型号的价格在1 400~3 500 人民币（约200~500 美元）之间。

桌面HMD是最高级机型，需要配合计算机使用，它配备遥控器、头部跟踪器和收集外部数据的传感器。这一类型包含Oculus

2020/09

Rift 和 HTC VIVE。桌面 HMD还使实验研究易于控制和操纵，提高生态效度，实现完全沉浸和高度真实感。桌面HMD的初始设置需要操作者具备较高的计算机技能，因为计算机软件不如其他类型HMD软件对初学者友好。桌面系统的价格最为昂贵，其HMD约为 7 000 元人民币（约1 000 美元），它还需要功能强大的台式计算机和专用显卡来提供流畅的渲染。

4 编辑 VR内容的工作流程：1）供VR研究使用的 360º 实景图像2）移除三脚架和提升光照，3）对绿色基础设施要素进行增、删、改

Workflow describing changes to 1) 360º imagery from real-world settings for use in VR research, including 2) tripod removal and lighting improvement and 3) additions, removals, and edits to green infrastructure elements 4

2020/09表 1 用于量化VR中绿色基础设施导致的情绪健康/认知能力变化的工具和方法

Tab. 1 Survey instruments and approaches to measuring self-reported changes in emotional health/cognitive performance resulting from GI in VR

其他身体信号所干扰，比如脑神经活动造成的认知负荷[98]。这些数据的分析难度大，而且采集分析脑电数据的成本更高。

心理量表是心理学研究及应用的重要工具，优点之一是研究人员易于操作。问卷测量通常在VR环境体验之前和之后进行，测试前和测试后的差异对比用于说明确定环境条件对某方面心理或行为变化的效应。常见的心理健康评价方法如表1所示。

评估注意力和短时工作记忆的实验设计通常先引入让被试者感到疲劳或有压力的任务，然后向被试者呈现一系列绿色场景后对认知任务的完成程度进行评估。这一研究假设基于环境心理学的“注意力恢复理论”，该理论认为长时间使用定向注意力会发生功能衰退，自然景观能够有效地将人的注意力从定向注意向非定向注意转移，从而缓解精神疲劳[99]，这是绿色基础设施健康效益的主要理论之一[13]。根据另一个心理进化理论“压力缓解理论”，压力引入环节也可以应用于其他研究内容，如被感知的压力 [100-101]。这也解释

[102]了为什么绿色基础设施有益于人类的健康 。

现有的技术还有很多，但是并未在绿色基础设施的健康效益研究中获得广泛的应用。值得注意的是，眼动追踪技术可用于揭示受试者观看不同绿色基础设施时的眼动特征并进行分类 [11]。HTC Vive Pro Eye VR眼镜搭载了眼球追踪系统，该设备通过多个指标（扫视和注视）记录360°场景或 3D环境中的眼球中央凹关注点。借助生物传感器可以同步获得生理、心理数据例如皮肤导电性等，多种技术的融合帮助我们更好地了解受试者的视觉注意力被哪些绿色基础设施特征吸引，何种环境要素影响人们的情绪感受。刺激（物理运动）或视觉刺激（观察到的运动）引起[116]。症状包括眼睛疲劳、头痛、面色苍白、出汗、口干、胃胀、定向障碍、脑内摇晃、共济失调、恶心、呕吐、唾液分泌增多、打嗝等 [116-117]。

眩晕的原因有很多，包括用户个体特征、VR环境中的运动、暴露时间和频率，以及设备规格（帧速率、视场和分辨率） [118]。有研究发现VR用户在虚拟空间中“身临其境”的感觉程度与眩晕症状呈负相关[119]。针对这些原因，解决方法包括使用高质量、舒适的HMD，减少三维环境中物体的视觉复杂性，例如增加三角形、顶点和纹理大小的数量，而不牺牲真实性来减少延迟和优化帧速率[76, 120]。

5.2 传染性疾病传播

VR设备通常佩戴在人们的头部，靠近眼睛、鼻子和嘴，这为细菌、病毒和真菌在人与人之间传播提供了机会。在VR实验中对仪器进行事先清洁是十分必要的，不遵守实验管理规范可能会违反伦理委员会的规定和要求，导致实验被终止，HMD也可能成为疾病传播的媒介。HMD中的海绵可以吸收体液，比如汗液。一次性口罩和可替换的海绵垫可以在不同的被试者之间进行替换，两者大约是相同的金额（10~20 美元/个，约 70~140 元 / 个）。

每次使用后，需要对HMD进行清洗消毒②。同时可以配合紫外线（UV）灯进行消毒杀菌。据报道，紫外线灯可以在60 s内杀死耳机表面 99.99% 的细菌、病毒和真菌③。

VR技术为研究人员探索绿色基础设施健康效益提供了一种令人兴奋、方便且相对廉价的方法。VR实验必须仔细考虑研究设计、健康测量指标、安全预防措施、伦理道德审查等。由于这项技术的快速发展，研究人员可以将笔者提出的建议与其他VR研究的实际情况相结合，以确保设备的可操作性和实验的可实施性。面对日益严峻的城市化问题，绿色基础设施建设在促进健康型城市方面应发挥关键作用，将健康和健康风险数据应用到绿色公共空间规划，有利于实现全球共同利益最大化。

致谢：感谢美国克莱姆森大学博士生鲁塔利·乔希和研究生尤尼则·赫拉曼提供的图像支持。注释：

① https://www.vrs.org.uk。

② HMDs的安全使用的更多信息，建议浏览以下网站： https://aixr.org/press/articles/covid-19-safety-for-virtualaugmented-reality-aixr-guidelines/、https://uploadvr.com/ sanitize-clean-vr-headsets-oculus/。

③ https://www.cleanboxtech.com。参考文献 (References)：

[1] SCHROEDER S A. We Can Do Better: Improving the Health of the American People[J]. Massachusetts Medical Society, 2007, 357(12): 1221-1228.

[2] MANZO L C. City Life and Well-Being in Environmental Psychology and Human Well-Being: Effects of Built and Natural Settings[M]. Cambridge, MA: Academic Press, 2018: 107-133.

[3] BENEDICT M A, MCMAHON E T. Green Infrastructure: Smart Conservation for the 21st Century[J]. Renewable Resources Journal, 2002, 20(3): 12-17.

[4] SUPPAKITTPAISARN P, LARSEN L, SULLIVAN W C. Preferences for Green Infrastructure and Green Stormwater Infrastructure in Urban Landscapes: Differences Between Designers and Laypeople[J]. Urban Forestry and Urban Greening, 2019, 43: 126378.

[5] GILL S E, HANDLEY J F, ENNOS A R, et al. Adapting Cities for Climate Change: The Role of the Green Infrastructure[J]. Built Environment, 2007, 33(1): 115-133. [6] 仇保兴.建设绿色基础设施，迈向生态文明时代：走有中国特色的健康城镇化之路[J]. 中国园林，2010，26（7）：1-9. [7] MARKEVYCH I, SCHOIERER J, HARTIG T, et al. Exploring Pathways Linking Greenspace to Health: Theoretical and Methodological Guidance[J]. Environmental Research, 2017, 158: 301-317.

[8] BALDAUF R. Roadside Vegetation Design Characteristics That Can Improve Local, Near-Road Air Quality[J]. Transportation Research Part D Transport and Environment, 2017, 52(11): 354-361.

[9] VAN RENTERGHEM T, FORSSÉN J, ATTENBOROUGH K, et al. Using Natural Means to Reduce Surface Transport Noise during Propagation Outdoors[J]. Applied Acoustics, 2015: 92, 86-101.

[10] LEVIN N, KYBA C C M, ZHANG Q, et al. Remote Sensing of Night Lights: A Review and an Outlook for the Future[J]. Remote Sensing of Environment, 2020, 237: 111443.

[11] YIN J, YUAN J, ARFAEI N, et al. Effects of Biophilic Indoor Environment on Stress and Anxiety Recovery: A Between-Subjects Experiment in Virtual Reality[J]. Environment International, 2020, 136: 105427.

[12] LEE K E, WILLIAMS K J H, SARGENT L D, et al. 40-Second Green Roof Views Sustain Attention: The Role of Micro-Breaks in Attention Restoration[J]. Journal of Environmental Psychology, 2015, 42: 182-189.

[13] KAPLAN S. The Restorative Benefits of Nature: Toward

2020/09 an Integrative Framework[J]. Journal of Environmental Psychology, 1995, 15(3): 169-182.

[14] ULRICH R S, SIMONS R F, LOSITO B D, et al. Stress Recovery During Exposure to Natural and Urban Environments[J]. Journal of Environmental Psychology, 1991, 11(3): 201-230.

[15] BROWNING M, OLVERA ALVAREZ H A. Editorial Commentary: Scanning for Threats and Natural Environments Giving Our Reptilian Brains a Break[J]. Trends in Cardiovascular Medicine, 2020, 30(4): 247-248.

[16] DADVAND P, BARTOLL X, BASAGAÑA X, et al. Green Spaces and General Health: Roles of Mental Health Status, Social Support, and Physical Activity[J]. Environment International, 2016, 91: 161-167.

[17] SHIN J C, PARAB K V, AN R, et al. Greenspace Exposure and Sleep: A Systematic Review[J]. Environmental Research, 2019, 182: 109081.

[18] PEARSON A L, RZOTKIEWICZ A, PECHAL J L, et al. Initial Evidence of the Relationships Between the Human Postmortem Microbiome and Neighborhood Blight and Greening Efforts[J]. Annals of the American Association of Geographers, 2019, 109(3): 958-978.

[19] LAMBERT K, BOWATTE G, THAM R, et al. Greenspace and Atopic Sensitization in Children and Adolescents: A Systematic Review[J]. International Journal of Environmental Research and Public Health, 2018, 15(11): 2539.

[20] EISENMAN T S, CHURKINA G, JARIWALA S P, et al. Urban Trees, Air Quality, and Asthma: An Interdisciplinary Review[J]. Landscape and Urban Planning, 2019, 187: 4759.

[21] YEAGER R A, SMITH T R, BHATNAGAR A. Green Environments and Cardiovascular Health[J]. Trends in Cardiovascular Medicine, 2019: 1-6.

[22] MMAKO N J, COURTNEY-PRATT H, MARSH P. Green Spaces, Dementia and a Meaningful Life in the Community: A Mixed Studies Review[J]. Health and Place, 2020, 63: 102344.

[23] LAKHANI A, NORWOOD M F, WATLING D P, et al. Using the Natural Environment to Address the Psychosocial Impact of Neurological Disability: A Systematic Review[J]. Health and Place, 2019, 55: 188-201.

[24] AKARACI S, FENG X, SUESSE T, et al. A Systematic Review and Meta-Analysis of Associations between Green and Blue Spaces and Birth Outcomes[J]. International Journal of Environmental Research and Public Health, 2020, 17(8): 2949.

[25] VANAKEN G-J, DANCKAERTS M. Impact of Green Space Exposure on Children’s and Adolescents’ Mental Health: A Systematic Review[J]. International Journal of Environmental Research and Public Health, 2018, 15(12): 2668.

[26] HOULDEN V, WEICH S, PORTO DE ALBUQUERQUE J, et al. The Relationship between Greenspace and the Mental Wellbeing of Adults: A Systematic Review[J]. Plos One, 2018, 13(9): e0203000.

[27] ROJAS-RUEDA D, NIEUWENHUIJSEN M J, GASCON M, et al. Green Spaces and Mortality: A Systematic Review and Meta-analysis of Cohort Studies[J]. The Lancet Planetary Health, 2019, 3(11): e469-e477.

[28] MYGIND L, KJELDSTED E, HARTMEYER R, et al. Effects of Public Green Space on Acute Psychophysiological Stress Response: A Systematic Review and Metaanalysis

of the Experimental and Quasi-experimental Evidence[J]. Environment and Behavior, 2019. DOI: 10.1177/0013916519873376.

[29] FRUMKIN H, BRATMAN G N, BRESLOW S J, et al. Nature Contact and Human Health: A Research Agenda[J]. Environmental Health Perspectives, 2017, 125(7): 1-18.

[30] JENNINGS V L, BROWNING M, RIGOLON A. Urban Green Spaces[M]. Cham: Springer, 2019.

[31] VAN DEN BOSCH M, SANG Å O. Urban Natural Environments as Nature-Based Solutions for Improved Public Health: A Systematic Review of Reviews[J]. Environmental Research, 2017, 158: 373-384.

[32] TWOHIG-BENNETT C, JONES A. The Health Benefits of the Great Outdoors: A Systematic Review and Metaanalysis of Greenspace Exposure and Health Outcomes[J]. Environmental Research, 2018, 166: 628-637.

[33] DZHAMBOV A M, BROWNING M, MARKEVYCH I, et al. Analytical Approaches to Testing Pathways Linking Greenspace to Health: A Scoping Review of the Empirical Literature[J]. Environmental Research, 2020, 186: 109613. [34] TSAI W-L, DAVIS A J S, JACKSON L E. Associations between Types of Greenery Along Neighborhood Roads and Weight Status in Different Climates[J]. Urban Forestry and Urban Greening, 2019: 1-44.

[35] WU J, RAPPAZZO K M, SIMPSON R J, et al. Exploring Links between Greenspace and Sudden Unexpected Death: A Spatial Analysis[J]. Environment International, 2018, 113: 114-121.

[36] HODSON C B, SANDER H A. Green Urban Landscapes and School-Level Academic Performance[J]. Landscape and Urban Planning, 2017, 160: 16-27.

[37] KUO F, BROWNING M, SACHDEVA S, et al. Might School Performance Grow on Trees? Examining the Link between “Greenness” and Academic Achievement in Urban, High-Poverty Schools[J]. Frontiers in Psychology, 2018, 9: 109-14.

[38] JIANG B, LI D, LARSEN L, et al. A Dose-Response Curve Describing the Relationship between Urban Tree Cover Density and Self-reported Stress Recovery[J]. Environment and Behavior, 2016, 48(4): 607-629.

[39] JIANG B, CHANG C-Y, SULLIVAN W C. A Dose of Nature: Tree Cover, Stress Reduction, and Gender Differences[J]. Landscape and Urban Planning, 2014, 132: 26-36.

[40] SHI Y, ZHANG B, CHIANG Y. The Influence of Forest Resting Environments on Stress Using Virtual Reality[J]. International Journal of Environmental Research and Public Health, 2019, 16(18): 3263.

[41] JANSSON M, FORS H, LINDGREN T, et al. Perceived Personal Safety in Relation to Urban Woodland Vegetation: A Review[J]. Urban Forestry and Urban Greening, 2013, 12(2): 127-133.

[42] BARAN P K, TABRIZIAN P, ZHAI Y, et al. An Exploratory Study of Perceived Safety in a Neighborhood Park Using Immersive Virtual Environments[J]. Urban Forestry and Urban Greening, 2018, 35: 72-81.

[43] ALFARO-BERACOECHEA L, PUENTE A, DA COSTA S, et al. Effects of Fear of Crime on Subjective Well-Being: A Meta-analytic Review[J]. The European Journal of Psychology Applied to Legal Context, 2018, 10(2): 89-96. [44] TABRIZIAN P, BARAN P K, SMITH W R, et al. Exploring Perceived Restoration Potential of Urban Green Enclosure

Through Immersive Virtual Environments[J]. Journal of Environmental Psychology, 2018, 55: 99-109.

[45] ANDERSON A P, MAYER M D, FELLOWS A M, et al. Relaxation with Immersive Natural Scenes Presented Using Virtual Reality[J]. Aerospace Medicine and Human Performance, 2017, 88(6): 520-526.

[46] BROWNING M, SAEIDI-RIZI F, MCANIRLIN O, et al. The Role of Methodological Choices in the Effects of Experimental Exposure to Simulated Natural Landscapes on Human Health and Cognitive Performance: A Systematic Review[J]. Environment and Behavior, 2020, 7(1): 1-43.

[47] CUMMINGS J J. How Immersive is Enough? A Metaanalysis of the Effect of Immersive Technology on User Presence[J]. Media Psychology, 2014, 19(2): 272-309.

[48] DE KORT Y A W, IJSSELSTEIJN W A. Reality Check: The Role of Realism in Stress Reduction Using Media Technology[J]. CyberPsychology and Behavior, 2006, 9(2): 230-233.

[49] LANGE E. The Limits of Realism: Perceptions of Virtual Landscapes[J]. Landscape and Urban Planning, 2001, 54(1-4): 163-182.

[50] DOLE L, JU W. Face and Ecological Validity in Simulations[C]//BREWSTER S, FITZPATRICK G. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). New York: ACM, 2019: 1-8.

[51] NUKARINEN T, ISTANCE H O, RANTALA J, et al. Physiological and Psychological Restoration in Matched Real and Virtual Natural Environments[C]//BERNHAUPT R, MUELLER F, VERWEIJ D, et al. Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. New York: ACM, 2020: 1-8.

[52] BROWNING M, MIMNAUGH K J, VAN RIPER C J, et al. Can Simulated Nature Support Mental Health? Comparing Short, Single-Doses of 360-Degree Nature Videos in Virtual Reality with the Outdoors[J]. Frontiers in Psychology, 2020, 10: 520.

[53] CHIRICO A, GAGGIOLI A. When Virtual Feels Real: Comparing Emotional Responses and Presence in Virtual and Natural Environments[J]. Cyberpsychology, Behavior and Social Networking, 2019, 22: 1-7.

[54] CALOGIURI G, LITLESKARE S, FAGERHEIM K A, et al. Experiencing Nature Through Immersive Virtual Environments: Environmental Perceptions, Physical Engagement, and Affective Responses during a Simulated Nature Walk[J]. Frontiers, 2018, 8: 8661.

[55] CRUZ-NEIRA C, SANDIN D J, DEFANTI T A, et al. The CAVE: Audio Visual Experience Automatic Virtual Environment[J]. Communications of the ACM, 1992, 35(6): 64-72.

[56] MUHANNA M A. Virtual Reality and the CAVE: Taxonomy, Interaction Challenges and Research Directions[J]. Journal of King Saud University: Computer and Information Sciences, 2015, 27(3): 344-361.

[57] HAN P H, CHEN Y S, LEE K C, et al. Haptic Around: Multiple Tactile Sensations for Immersive Environment and Interaction in Virtual Reality[C]//SPENCER S N. Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology. New York: ACM, 2018: 1-10.

[58] GROMER D, MADEIRA O, GAST P, et al. Height Simulation in a Virtual Reality CAVE System: Validity of Fear Responses and Effects of an Immersion Manipulation[J]. Frontiers in Human Neuroscience, 2018, 12: 372.

[59] EDELSTEIN E, GRAMANN K, SCHULZA J, et al. Neural Responses during Navigation in the Virtual Aided Design Laboratory: Brain Dynamics of Orientation in Architecturally Ambiguous Space[G]//HAQ S, HÖLSCHER C, TORGRUDE S. Movement and Orientation in Built Environments Evaluating Design Rationale and User Cognition. Bremen: 2008 SFB/TR 8 Spatial Cognition, 2008: 35-41.

[60] NILSSON D, KINATEDER M. Virtual Reality Experiments: The Future or a Dead End[C]//Human Behaviour in Fire 2015 Symposium Proceedings. Cambridge, UK: Downing College, 2015.

[61] ANNERSTEDT M, JÖNSSON P, WALLERGÅRD M, et al. Inducing Physiological Stress Recovery with Sounds of Nature in a Virtual Reality Forest: Results from a Pilot Study[J]. Physiology Behavior, 2013, 118: 240-250.

[62] CROSSAN C, SALMONI A. A Simulated Walk in Nature: Testing Predictions from the Attention Restoration Theory[J]. Environment and Behavior, 2019, 68: 001391651988277. DOI: 10.1177/0013916519882775.

[63] SUTHERLAND I E. The Ultimate Display[J]. Proceedings of the IFIPS Congress, 2001, 65(2): 506-508. [64] Virtual Reality Society. History of Virtual Reality[EB/OL]. (2017-01-20)[2020-02-26]. https://www.vrs.org.uk/virtualreality/history.html.

[65] MEHRFARD A, FOTOUHI J, TAYLOR G, et al. A Comparative Analysis of Virtual Reality Head-Mounted Display Systems[EB/OL]. (2019-12-05)[2020-05-29]. https://arxiv.org/pdf/1912.02913v1.pdf.

[66] ANTHES C, GARCIA-HERNANDEZ R J, WIEDEMANN M, et al. State of the Art of Virtual Reality Technology[C]// IEEE. 2016 IEEE Aerospace Conference. Piscataway: IEEE, 2016: 1-19.

[67] CHESSA M, MAIELLO G, BORSARI A, et al. The Perceptual Quality of the Oculus Rift for Immersive Virtual Reality[J]. Human-Computer Interaction, 2019, 34(1): 5182.

[68] HEDBLOM M, GUNNARSSON B, IRAVANI B, et al. Reduction of Physiological Stress by Urban Green Space in a Multisensory Virtual Experiment[J]. Scientific Reports, 2019, 9(1): 1-12.

[69] COLAVITA F B. Human Sensory Dominance[J]. Perception and Psychophysics, 1974, 16(2): 409-412.

[70] SCHREUDER E, VAN ERP J, TOET A, et al. Emotional Responses to Multisensory Environmental Stimuli[J]. 5th ed. SAGE Open, 2016, 6(1): 215824401663059-19.

[71] GANG S M, CHOI H W, KIM D R, et al. A Study on the Construction of the Unity 3D Engine Based on the WebGIS System for the Hydrological and Water Hazard Information Display[J]. Procedia Engineering, 2016, 154: 138-145.

[72] JIE J, YANG K, HAIHUI S. Study on the Virtual Natural Landscape Walkthrough by Using Unity 3D[C]//IEEE. 2011 IEEE International Symposium on VR Innovation. Singapore: IEEE, 2013: 1-4.

[73] JIANG S. Visual Engagement with Nature and Spatial Cognition: A Study of Hospital Circulation Spaces through Immersive Virtual Environment Technology[R/OL]. Houston: Academy of Architecture for Health Foundation, 2020. [2020-05-27]. https://aahfoundation.org/research/researchsummaries-2/.

[74] YU T, BEHM H, BILL R, et al. Audio-Visual Perception of New Wind Parks[J]. Landscape and Urban Planning, 2017, 165: 1-10. [75] LIN L, HOMMA R, IKI K. Preferences for a Lake Landscape: Effects of Building Height and Lake Width[J]. Environmental Impact Assessment Review, 2018, 70: 2233.

[76] JOSEPH A, BROWNING M, JIANG S. Using Immersive Virtual Environments (IVEs) to Conduct Environmental Design Research: A Primer and Decision Framework[J]. HERD: Health Environments Research and Design Journal, 2020, 10(6). DOI: 10.1177/1937586720924787.

[77] KANG M, RAGAN B G, PARK J-H. Issues in Outcomes Research: An Overview of Randomization Techniques For Clinical Trials[J]. Journal of Athletic Training, 2008, 43(2): 215-221.

[78] GREENWALD A. Within-Subjects Designs: To Use or Not to Use?[J]. Psychological Bulletin, 1976, 83(2): 314320.

[79] CHARNESS G, GNEEZY U, KUHN M A. Experimental Methods: Between-Subject and Within-Subject Design[J]. Journal of Economic Behavior and Organization, 2012, 81(1): 1-8.

[80] ROSENTHAL R, KOHN P, GREENFIELD P M, et al. Data Desirability, Experimenter Expectancy, and the Results of Psychological Research[J]. Journal of Personality and Social Psychology, 1966, 3(1): 20-27.

[81] SCHUTTE N S, BHULLAR N, STILINOVIĆ E J, et al. The Impact of Virtual Environments on Restorativeness and Affect[J/OL]. Ecopsychology, 2017, 9(1)[2020-05-29]. http:// doi.org/10.1089/eco.2016.0042.

[82] VALTCHANOV D, BARTON K R, ELLARD C. Restorative Effects of Virtual Nature Settings[J]. Cyberpsychology, Behavior and Social Networking, 2010, 13(5): 503-512.

[83] YU C-P, LEE H-Y, LUO X-Y. The Effect of Virtual Reality Forest and Urban Environments on Physiological and Psychological Responses[J]. Urban Forestry and Urban Greening, 2018, 35: 106-114.

[84] GAO T, ZHANG T, ZHU L, et al. Exploring Psychophysiological Restoration and Individual Preference in the Different Environments Based on Virtual Reality[J]. International Journal of Environmental Research and Public Health, 2019, 16(17): 3102-3114.

[85] WEIBEL R P, GRÜBEL J, ZHAO H, et al. Virtual Reality Experiments with Physiological Measures[J]. Journal of Visualized Experiments, 2018(138): 1-23.

[86] CURTIS B M, O’KEEFE J H. Autonomic Tone as a Cardiovascular Risk Factor: The Dangers of Chronic Fight or Flight[J]. Mayo Clinic Proceedings, 2002, 77(1): 45-54. [87] BAKER L M, TAYLOR W M. The Relationship under Stress Between Changes in Skin Temperature, Electrical Skin Resistance, and Pulse Rate[J]. Journal of Experimental Psychology, 1954, 48(5): 361-366.

[88] JUSTER R-P, MCEWEN B S, LUPIEN S J. Allostatic Load Biomarkers of Chronic Stress and Impact on Health and Cognition[J]. Neuroscience and Biobehavioral Reviews, 2010, 35(1): 2-16.

[89] FAIRCLOUGH S H, GILLEADE K. Advances in Physiological Computing[M]. London: Springer-Verlag London, 2014.

[90] HELMINEN E C, MORTON M L, WANG Q, et al. A Meta-analysis of Cortisol Reactivity to the Trier Social Stress Test in Virtual Environments[J]. Psychoneuroendocrinology, 2019, 110: 104437.

[91] ROE J, THOMPSON C, ASPINALL

P, et al. Green

Space and Stress: Evidence from Cortisol Measures in Deprived Urban Communities[J]. International Journal of Environmental Research and Public Health, 2013, 10(9): 4086-4103.

[92] ROSMOND R, HOLM G, BJÖRNTORP P. Food-Induced Cortisol Secretion in Relation to Anthropometric, Metabolic and Haemodynamic Variables in Men[J]. International Journal of Obesity, 2000, 24(4): 416-422.

[93] World Health Organization. Mental Health: Strengthening our Response[EB/OL]. (2018-01-05)[202005-20]. https://www.who.int/news-room/fact-sheets/detail/ mental-health-strengthening-our-response.

[94] SVORAY T, DORMAN M, SHAHAR G, et al. Demonstrating the Effect of Exposure to Nature on Happy Facial Expressions via Flickr Data: Advantages of Nonintrusive Social Network Data Analyses and Geoinformatics Methodologies[J]. Journal of Environmental Psychology, 2018, 58: 93-100.

[95] TURPIN G, SHINE P, LADER M. Ambulatory Electrodermal Monitoring: Effects of Ambient Temperature, General Activity, Electrolyte Media, and Length of Recording[J]. Psychophysiology, 1983, 20(2): 219-224.

[96] DAWSON M E, SCHELL A M, FILION D L. The Electrodermal System[M]//CACIOPPO J T, TASSINARY L G, BERNTSON G G. 4th ed. Handbook of Psychophysiology. Cambridge: Cambridge University Press, 2017: 217-243. [97] NORWOOD M F, LAKHANI A, MAUJEAN A, et al. Brain Activity, Underlying Mood and the Environment: A Systematic Review[J]. Journal of Environmental Psychology, 2019, 65: 101321.

[98] COULL J T. FMRI Studies of Temporal Attention: Allocating Attention within, or towards, Time[J]. Cognitive Brain Research, 2004, 21(2): 216-226.

[99] HARTIG T. Issues in Restorative Environments Research: Matters of Measurement[J]. Psicología Ambiental, 2011: 41-66.

[100] BOESCH M, SEFIDAN S, EHLERT U, et al. Mood and Autonomic Responses to Repeated Exposure to the Trier Social Stress Test for Groups (TSST-G)[J]. Psychoneuroendocrinology, 2014, 43: 41-51.

[101] HAWN S E, PAUL L, THOMAS S, et al. Stress Reactivity to an Electronic Version of the Trier Social Stress Test: A Pilot Study[J]. Frontiers Media SA, 2015, 6: 724. [102] ULRICH R S. Aesthetic and Affective Response to Natural Environment[M]//ALTMAN I, WOHLWILL J F. Behavior and the Natural Environment. Boston: Springer, Boston, MA, 1983: 85-125.

[103] SPIELBERGER C D, GORSUCH R L, LUSHENE R E. Manual for the State-Trait Anxiety Inventory[M]. Palo Alto: Consulting Psychologists Press, 1970: 1-76.

[104] WATSON D, CLARK L A, TELLEGEN A. Development and Validation of Brief Measures of Positive and Negative Affect: The PANAS Scales[J]. Journal of Personality and Social Psychology, 1988, 54(6): 1063-1070.

[105] MCNAIR D M, LORR M, DROPPELMAN L D. Manual for the Profile of Mood States (POMS)[M]. San Diego: Educational and Industrial Testing Service, 1971.

[106] ZUCKERMAN M. Development of a Situation-Specific Trait-State Test for the Prediction and Measurement of Affective Responses[J]. Journal of Consulting and Clinical Psychology, 1977, 45(4): 513-523.

[107] IZARD C E. Basic Emotions, Relations Among

2020/09

Emotions, and Emotion-Cognition Relations[J]. Psychological Review, 1992, 99(3): 561-565.

[108] BRADLEY M M, LANG P J. Measuring Emotion: The Self-Assessment Manikin and the Semanatic Differential[J]. Journal of Behavior Therapy and Experimental Psychiatry, 1994, 25(1): 49-59.

[109] FELNHOFER A, KOTHGASSNER O D, SCHMIDT M, et al. Is Virtual Reality Emotionally Arousing? Investigating Five Emotion Inducing Virtual Park Scenarios[J]. International Journal of Human-Computer Studies, 2015, 82: 48-56.

[110] KORPELA K M, YLÉN M, TYRVÄINEN L, et al. Determinants of Restorative Experiences in Everyday Favorite Places[J]. Health and Place, 2008, 14(4): 636-652. [111] HARTIG T, KORPELA K M, EVANS G W, et al. A Measure of Restorative Quality in Environments[J]. Scandinavian Housing and Planning Research, 1997, 14(4): 175-194.

[112] WATTS G, MIAH A, PHEASANT R J. Tranquillity and Soundscapes in Urban Green Spaces: Predicted and Actual Assessments from a Questionnaire Survey[J]. Environment and Planning B: Planning and Design, 2013, 40(1): 170-181.

[113] SUPPAKITTPAISARN P, JIANG B, SLAVENAS M, et al. Does Density of Green Infrastructure Predict Preference?[J]. Urban Forestry and Urban Greening, 2018, 40: 1-9.

[114] LEZAK M D, HOWIESON D B, BIGLER E D, et al. Neuropsychological Assessment[M]. New York: Oxford University Press, 2012.

[115] MANLY T, ROBERTSON I H. The Sustained Attention to Response Test (SART)[J]. Neurobiology of Attention, 2005: 337-338.

[116] LAVIOLA J J Jr. A Discussion of Cybersickness in Virtual Environments[J]. ACM SIGCHI Bulletin, 2000, 32(1): 47-56.

[117] DAVIS S, NESBITT K, NALIVAIKO E. A Systematic Review of Cybersickness[C]//BLACKMORE K, NESBITT K, SMITH S P. IE2014: Proceedings of the 2014 Conference on Interactive Entertainment. New York: ACM, 2014: 1-9.

[118] JONES M B, KENNEDY R S, STANNEY K M. Toward Systematic Control of Cybersickness[J]. Presence, 2004, 13(5): 589-600.

[119] WEECH S, KENNY S, BARNETT-COWAN M. Presence and Cybersickness in Virtual Reality Are Negatively Related: A Review.[J]. Frontiers in Psychology, 2019, 10: 158.

[120] BRUNS C R, CHAMBERLAIN B C. The Influence of Landmarks and Urban Form on Cognitive Maps Using Virtual Reality[J]. Landscape and Urban Planning, 2019, 189: 296-306.图表来源：

图 1由彭瑟刚·萨布卡特派桑提供；图2、3由姜珊提供；图4由鲁塔利·乔希和尤尼则·赫拉曼提供；表1由马修·布朗宁绘制。

（编辑 /王一兰）

Authors: (USA) Matthew Browning, (THA) Pongsakorn Suppakittpaisarn, JIANG Shan, (USA) Anjali Joseph Translators: WENG Yuxi, YUAN Shuai

0 Introduction

Only an estimated 30% of our health, wellbeing, and life span is determined by our genetics[1]. Other major drivers of health are behavior (physical activity, sleep, and eating patterns), access to health care, and the physical environment. Thus, when people increasingly move to urbanized areas, their health is likely to change. While cities can improve health by improving access to high quality healthcare, they can also degrade health by negatively impacting some health-promoting behaviors and by exposing residents to possibly stress-inducing and toxic environments[2].

Fortunately, researchers studying urban planning, design, public health policy, and other aspects of the built environment can play a role in reducing the potential negative impacts of cities on health. Incorporating green infrastructure (GI) into cities can provide array of beneficial ecosystem services that improve human health. GI refers to the “interconnected network of green space that conserves natural ecosystem values and functions and provides associated benefits to human populations”[3], but in the context of city planning and design, GI also includes the small but interconnected vegetative elements in outdoor designs[4]. These elements can be quite familiar (i.e., street trees, median plantings, gardens, and parks) or less unfamiliar or “novel” (i.e., green roofs, green walls, rain gardens, bioswales, and constructed wetlands) to city residents[4] (Fig. 1). In both cases, GI provides an array for ecosystem benefits, such as managing urban stormwater, mitigating extreme temperature, and providing habitats for local and transregional species such as birds and pollinating insects[5-6].

The health-related impacts of any natural setting, including GI, encompass three domains: reducing harm, restoring capacities, and building capacities[7]. Vegetative elements can reduce harm by filtering air pollutants, buffering against noise, and reducing artificial light at night[8-10]. Vegetative elements can also restore our attention and focus through exposure to elements in the landscape that humans have evolved in, and therefore feel safe in, or have otherwise become familiar and preferred[11-15]. Finally, vegetative elements build the capacity for physical activity, sleep, social interaction, and commensal bacteria in the gut, the skin and elsewhere[16-18].

A large number of experimental and observational literature supports the consensus that GI can benefit people. Recent reviews have synthesized the available research for specific health outcomes, including asthma/atopy[19-20], cardiovascular health[21], dementia[22-23], birth outcomes[24], mental health[25-26], mortality[27], and stress[28].

The ability of GI to activate these pathways toward health may depend on the type of GI[4]. A large body of evidence supports protective effects of GI elements with which many residents are familiar[4, 29, 30], while a much smaller mounting body of evidence is available for the protective effects of green walls, green roofs, bioswales, and other less-familiar elements[7, 27, 31-33]. Regarding types of vegetation, observational studies have found that tree canopy cover levels near people’s homes are more strongly associated with healthy body mass index (BMI) levels[34], protection from out-of-hospital deaths[35] and school test scores in elementary schools[36-37] than herbaceous or grass

cover. Additional support for these findings has been shown in experimental studies. Higher levels of residential street tree canopy cover in virtual environments have facilitated recovery from an acute stressor in measures of self-reported levels of anxiety, tension and avoidance scores[38] and salivary cortisol levels in men[39] more than lower levels of canopy cover. In addition, made-made elements in forested settings have been shown in VR to have differential effects on recovery from an acute stressor — seating areas with sufficient open space between the seats were more restorative than settings with more obscure seating areas[40].

Another mediator of the health benefits of GI are their perceived safety. Over 45 studies have observed that many types of vegetative space could invoke fear and threats to perceived personal safety[41], and fear can prohibit the health benefits of using these spaces[42] as well as negatively impact subjective well-being[43]. Factors that are more likely to induce fear include high levels of vegetative density between eye level and knee level, untidy clusters of shrubby vegetation, and complete enclosure (i.e., vegetation on both sides of a path, or all sides of a plaza/open space)[41]. Both the spatial arrangement of vegetation (the boundaries of visibility set by vegetative planting) and permeability of vegetation (depth, height, and porosity) have been shown in VR to influence perceived safety and perceived restorativeness[44].

Landscape preferences may also influence the protective effects of green infrastructure. Negative moods (i.e., distress, upset, irritable, and nervous) were lowered more strongly when VR users watched a natural setting that they preferred (a beach or pastural agricultural setting) compared with a natural setting that they preferred less or an indoor setting[45]. Similar mediating effects of landscape preferences on health benefits of green spaces have been shown in dozens of observational studies[33, 46].

Due to the many factors that influence the health benefits of GI, new empirical research with populations and GI elements of interest would best inform context-specific design interventions. Yet researching psychological and physiological indicators of health in field experiments or laboratory settings with two-dimensional (2D) imagery is inadequate; weather conditions and travel requirements make field experiments expensive and difficult to conduct, and participant reactions to non-immersive 2D imagery may not represent how they would respond in the real world[47-48].

Here enters virtual reality (VR) technology. VR provides the opportunity for researchers to test the health benefits of multiple environmental scenarios with high levels of ecological validity, which describes the extent to which participant’s behaviors and perceptions in controlled research settings mimic the real-world[49-50].

This use of VR has been validated in several studies that compared responses to physical GI elements and their virtual counterparts. Nukarinen et al. found that a 10-minute forest exposure decreased sympathetic nervous system activity (the “fight or flight response”) and negative emotions similarly as exploring a three-dimensional (3D) or 360-degree forest video[51]. Browning et al., showed that improvements in positive emotions and the perceived restorativeness of a 6-minute real forest exposure was similar to watching a 360-degree forest video when each setting (real and virtual) was compared to an indoor setting without GI[52]. Chirico & Gaggioli demonstrated that a 5-minute exposure to a panoramic mountain and lake view decreased negative affect and induced awe to a similar extent as a 360-degree video of the same scene[53]. Yin et al. compared environments with and without plants or views of GI (“biophilic” versus “non-biophilic” environments) and discovered that a 5-minute exposure to a biophilic indoor environment resulted in similar beneficial changes to blood pressure and heart rate as watching a 360-degree video of a biophilic environment[54].

This essay summarizes and makes recommendations for researchers interested in health promotion through the GI. We describe VR systems, content creation, study design, health outcome measurement, and safety considerations. Our objective is to support researchers in evaluating the impacts of GI elements on health outcomes.

1 VR Systems

There are two main types of VR systems. Immersive physical spaces, such as the Cave Automated Virtual Environment (CAVE), are room-sized cubical spaces with video projectors that direct moving imagery on translucent screens surrounding the participant. Head-mounted display (HMD) systems involve goggles worn on the head that display visual and acoustic stimuli. Brief reviews of the history and use of these two systems are provided below.

1.1 Immersive Physical Spaces

The CAVE was invented by a group of researchers at the University of Illinois at UrbanaChampaign in 1992[55]. Such systems generally include a 3.33 m3 cubic room with darkened lighting conditions. Four to six sides of the room are equipped with projection screens. Scenes display on the screens are reflected by mirrors positioned and rotated between high-resolution, short-throw projectors and the screens[56].

The CAVE allows effective manipulation of tactile/haptic cues, which enhances immersion, realism, and experienced presence over other forms of simulated environments[57]. These systems have been shown to be particularly effective at provoking certain emotions related to environmental design, such as anxiety and fear associated with acrophobia[58], and facilitating wayfinding decisions and navigation[59]. CAVE systems have been rarely used in studies of the health benefits of exposure to GI, likely because of the large space requirements, high cost and technical expertise[60] and extensive setup demands[56]. However, there are at least two exceptions[61-62].

1.2 Head-Mounted Displays

Head-mounted VR displays were proposed in the 1960s[63] but have been unavailable to most researchers until recently[64]. In 2012, the introduction of the Oculus Rift signaled a second wave of HMD development with devices that were inexpensive, comfortable, and of high-quality. A vast range of new devices and software have since been developed. At least two reviews in the scientific literature describe the available HMD models in respect to their immersive potential (i.e., resolution, frame rate, and field of view), research utility (i.e., mobility and cost), and user experience[65-66]. Given the rapid development of HMDs, the websites of established VR professional organizations, such as the Virtual Reality Society ①, may provide updated information. At the time of writing this article, there are three broad categories of HMDs: phone-based, tethered, and all-in-one (Fig. 2).

Phone-based HMDs are affordable, portable, and easy to use headsets that require a smartphone to project imagery and emit sound. The Google Cardboard is a well-known example, albeit unrepresentative of the immersiveness available in other models[67]. The Oculus Gear VR and Google Expedition kits may be less familiar to many researchers but are relatively high-quality devices that cost a fraction of the cost of other HMD systems (i.e., all-in-one and tethered)[66]. Due to the limited processing power of some smartphones, phone-based HMDs may deliver a lower-quality experience with reduced user controls compared to other types of HMDs. Also, premium smartphones are required to display high-resolution, high-frame rate imagery in these headsets, and the price of compatible smartphones can match or supersede the cost of other HMD options. For the headset without the accompanying phone, the price of a phone-based HMD generally ranges between 70 and 350 RMB（10 and 50 USD).

Unlike phone-based HMDs, all-in-one models include built-in processors, GPUs, displays, memory, batteries, and sensors, so no additional equipment is necessary. Notable examples of these devices are the Oculus Go and Oculus Quest. Such HMDs are wireless and sometimes have builtin sensors for detection of physical objects in a room to prevent the user from colliding into them. All-in-one HMDs can offer a more powerful VR experience than phone-based systems but may deliver a less sophisticated experience than tethered systems. All-in-one models generally range in price between 1 400 to 3 500 RMB (200 to 500 USD.)

The most sophisticated HMDs are tethered to external computers and come with remote controllers, a head tracker, and external data collection sensors. Examples include the Oculus Rift and HTC VIVE. Advantages of tethered HMDs include high experimental control and manipulation, high ecological validity, full immersion, and high realism. Tethered systems require a higher level of computer skills for the initial setup, because the computer software is often not as beginner friendly as software that accompanies other types of HMDs. Tethered systems are highest in price, around 7,000 RMB (1,000 USD) for the HMD, and require a powerful PC with dedicated a GPU to provide fluid rendering.

2 VR Content Creation

Virtual environments can be entirely computer generated or they can be based on real environments that are recorded in 360º videos and digitally edited. Although these approaches focus exclusively on visual inputs of virtual environments, sounds (i.e., water flowing, leaves rustling in the wind) may be essential to the health-promoting effects of GI[61]. Similarly, many aspects of the natural environments emit smells that can induce beneficial physiological changes[68]. Therefore, multisensory VR experiences provide rich opportunities for trendsetting researchers. Such experiences are not discussed further in the current essay, however. Our critical review of the literature and professional experience leads us to believe that researchers interested in the health benefits of exposure to GI but unfamiliar with VR will focus — at least initially — on the dominant human sense (vision)[69]. Also, how multiple sensory inputs relate to emotional responses is poorly understood[70]. Therefore, comparisons of different GI scenarios through vision may have greater ecological validity than multisensory comparisons until empirical evidence on the mutualistic effects of vision, sounds and scents are available[70].

2.1 Computer-Generated Environments

Different VR systems require different programs to create and display GI content. For models and animations that are purely generated by computers, the content is first generated in a game engine (i.e., Unity 3D, Autodesk 3ds Max, or Maya) or professional modeling program (i.e., SketchUp or Autodesk Revit). Game engines have built-in functions that can model, render, and generate VR environments in a single program. These engines are also particularly powerful platforms to create environments with user interaction, because they can simulate several scenarios and provide many navigation/interaction options. Early attempts at using game engines to create GI content in VR can be found in studies of environmental impact assessments. For example, Gang, Choi, Kim, and Choung[71] invented a tool kit using Unity 3D and WebGIS system to display hydrological and water hazard information in VR. The basic concepts and procedures of using a game engine (Unity 3D) to create VR content is described elsewhere[72].

The second approach to creating computergenerated environments is may be more accessible to researchers who are newer to VR. The workflow starts with a Building Information Modelling (BIM) process and converts these models to VR environments. The actual VR experience is delivered through a mobile app (i.e., InsiteVR or Kubity) or computer-based platform (i.e., eyecad VR or Enscape). The major difference between the phone- and computer-based platforms is that the former converts the entire VR model while the

latter delivers real-time renderings. An example of this workflow can be found in a recent study of spatial cognition and wayfinding assisted through hospital gardens[73]. Figure 3 illustrates the quality of real-time renderings of the hospital gardens as viewed by the VR user.

Researchers can blend the two workflows (game engines and BIM) to create and deliver GI content in VR as well. Yu, Behm, Bill, and Kang[74] used Autodesk 3ds Max and Unity 3D to study differences in visual and noise impacts between ambient wind park soundscapes. Similarly, Lin, Homma & Iki[75] generated 3D models with ArcGIS 10.0 and Adobe Photoshop CS6 to examine people’s visual preferences for different sizes of blue spaces.

2.2 Computer-Modified Real Environments

behavior and responses to questionnaires based on the perceived desires and expectations of the researcher. This type of bias is of particular concern for within-subject experiments during which participants experience multiple experimental conditions and are likely to identify the differences between each[79]. Researchers also have a tendency to adjust their behavior based on their own desires and expectations. Such a bias is called an expectancy effect, and it is reflected in experimenters expressing or withholding enthusiasm for one condition over another in their speech and body language[80].

These biases can be reduced by blinding the participant and/or the researcher to the condition. Blinding the participant involves withholding the condition(s)/treatment(s) that other participants are receiving. Blinding the researchers involves withholding the condition/treatment that each participant is assigned. Double blinding both the participant and researcher is most effective at reducing these biases but difficult or impossible in VR studies. One approach to double-blinding the condition is by using a within-subjects design and programming the HMD to present virtual environments in a randomized order that is different for each participant. The software must also record the order of presented environments for each participant.

4 Health Outcome Measurement

Numerous health outcomes may be expected to occur from exposure to GI in VR. For example, green walls and views of extensive forest cover in VR have been associated with stress recovery and anxiety reduction[11]. Eucalyptus trees, meadows, and streams have improved mood states better than an urban setting without GI[81]. Dense forests and meadows have also improved mood states or affective arousal compared with an indoor settings[52], abstract paintings[82], or urban centers without GI[83]. Urban parks and forests have improved mood and attentional states better than barren landscapes with buildings and trees only in the background[84]. The stress-reducing potential of street trees have been documented in multiple studies[38-39]. Across these articles, the findings have been measured with both objective measures (physiological responses) and subjective measures (self-reported responses). Both types of measures can be used to examine either physical health or mental health/cognitive performance outcomes.

4.1 Physical Health Outcome Measurement

Physical reactions to environmental stimuli are primarily measured with devices that measure physiological changes. These changes involve either cardiovascular responses, including blood pressure, blood volume pulse (BVP), heart rate, and heart rate variability (HRV)[85], or hormones (i.e., cortisol in the saliva or blood).

Both cardiovascular responses and hormones yield attractive and usable data that link to longterm physical health. Elevated heart rates for long periods of time increase the risks of blood clot, high blood pressure, stroke, and other cardiovascular diseases[86]. Also, these indicators serve as objective measurement of stress[87], and chronic stress can lead to several physical and mental health issues such as insomnia, reproductive problems, or cancer[88].

Physiological data give accurate results if measured corrected and are relatively easy to collect with increasingly low-cost wearables[85]. However, the resulting data can be difficult to analyze, particularly if the sensors do not record data throughout an experiment[89]. In regard to hormone biomarkers, data collection can also be intrusive and stressful to the participants and requires extended experimental timing. Salivary cortisol, for example, requires 20 minutes to travel from blood to the mouth and participants will be required to remain in the laboratory setting throughout this rest period[90]. Time of day, food, tobacco and caffeine use, physical activity, sleep, and gender may also affect fluctuations in cortisol levels and other biomarkers[91-92]. Accurate data collection of these measures requires particularly careful and rigorous protocols to control for myriad confounding effects.

4.2 Mental Health and Cognitive Function Outcome Measurement

Changes in mood and attention/working memory are commonly studied outcomes of GI exposure[32, 46]. These outcomes can be measured with physiological measures as well as standardized psychological survey batteries. Cognitive function is considered a form of mental health that includes the ability to process information, make decisions, and succeed in life[93].

Physiological measures include bodily responses to stress and negative moods. These include facial movements, such as frowning with forehead muscle tension[94], sweating through skin conductivity[95-96], and activity in the brain[97]. The advantage of these measures is that they are involuntary and not influenced by participants. They can measure moods with objectivity, but some are confounded by other bodily processes, such as cognitive load in the case of neural activity[98]. Like physiological measures of physical health, these data can be difficult to analyze and, in the case of brain activity, expensive to collect.

Many standardized survey approaches have been psychometrically validated and are readily available to researchers. Surveys are typically deployed both before and after exposure to each VR environment. Change scores are calculated to determine whether the environment influenced the outcome of interest. Common survey approaches related to emotional health are summarized in Tab. 1.

Attention and working memory can be measured with tasks that are performed after VR exposure but are commonly accompanied with a pre-exposure depletion exercise. These exercises control the antecedent condition from which the participant’s cognitive abilities can be measured[99] in accordance with Attention Restoration Theory, which is a common theoretical explanation as to why green infrastructure is beneficial to health[13].

Pre-condition tasks can also be applied for other outcomes, such as perceived stress[100-101], in accordance Stress Restoration Theory. This provides an alternative explanation of why GI benefits human health related to evolutionary psychology[102].

Additional technologies are available but underutilized in research on the health effects of GI. Notably, built-in eye-tracking can categorize GI features by participant response[11]. An example of a device with built-in eye-tracking is the tethered HTC Vive Pro Eye. This device records foveal attention throughout each 360-degree or 3D environment using multiple parameters (saccades and fixations). Data can be measured simultaneously with biosensor data (i.e., skin conductivity) to objectively determine to what extent different GI features demand participant's attention, are preferred by participants or activate emotional responses.

5 5.1 Safety Considerations

Conducting VR research generally poses little risk to subjects. However, there is always the possibility of cybersickness. Also, as a result of the recent COVID-19 pandemic, communicable disease transmission has emerged as a safety concern.

Cybersickness

Cybersickness describes a variety of possible symptoms similar to motion sickness that can be caused either by vestibular stimulation (physical movement) or visual stimulation (observed movement) in VR[116]. Symptoms may include eye strain, headache, pallor, sweating, dryness of the mouth, fullness of the stomach, disorientation, vertigo (disordered state characterized by surroundings appearing to swirl dizzily), ataxia (lack of coordination), nausea, vomiting, dizziness, salivation, and burping[116-117].

Numerous determinants of cybersickness have been long discussed in the scientific literature, such as user characteristics, movement in the VR environment, duration and frequency of exposure, and device specifications (i.e., frame rate, field-ofview, and resolution)[118]. Also, the extent to which the VR user feels like they are “being there” in the virtual space (presence) is negatively related to cybersickness symptoms[119]. Based on these determinants of cybersickness, proposed solutions include use of high-quality, comfortable HMDs and minimizing the number of visual complexity of objects in the three-dimensional (3D) environments (i.e., the number of triangles, vertices, and texture sizes) as much as possible without sacrificing realism to reduce latency and optimize frame rate[76, 120].

5.2 Communicable Disease Transmission

VR research often involves placing a device with porous materials near people’s eyes, nose and mouth. This operation can quickly spread bacteria, viruses, and fungi between participants. Appropriate cleaning and pre-screening of participants is a necessary process of VR research, and inadequate adherence to these safety protocols may cause human subject review boards to terminate a project, or worse, HMDs may serve as a vector of disease.

The foam inserts that sit inside the HMD soak up bodily fluids, such as sweat. Disposable face cover masks can be used and switched between users. Replacement foam inserts can be bought for approximately the same amount (10 to 20 USD per insert).

Hard surfaces of the HMD should also be cleaned after every use . For greatest convenience,

② boxes may be used with specially placed ultraviolet (UV) lights that are reported to kill 99.99% of bacteria, viruses, and fungi from every surface of the headset in 60 seconds ③.

6 Conclusion

Virtual reality provides an exciting, convenient, and relatively inexpensive method for researchers to test the human health benefits of green infrastructure prior to implementation. Continued use of VR research to study health promotion through urban planning and design, recreation, engineering, and other aspects of the built environment may help reduce the global burden of physical and health conditions as the globe becomes increasingly urbanized.

Notes:

① https://www.vrs.org.uk.

② For more information on the safe use of HMDs, we recommend visiting postings from The Academy of the International Extended Reality (https://aixr.org/press/ articles/covid-19-safety-for-virtual-augmented-realityaixr-guidelines/) and a summary of the Facebook᱐s recommendations of Oculus hardware (https://uploadvr. com/sanitize-clean-vr-headsets-oculus/).

③ https://www.cleanboxtech.com.

Acknowledgments:

We would like to thank Rutali Joshi, Ph.D. candidate and Uniza Rahman, graduate student in Architecture + Health at Clemson University for developing Figure 4 in the paper.

Sources of Figures and Table:

Fig. 1 © Pongsakorn Suppakittpaisarn; Fig. 2-3 © JIANG Shan; Fig. 4 © Rutali Joshi and Uniza Rahman; Tab. 1 © Matthew Browning. (Editor / WANG Yilan)