Tranquillity as a Determinant of Health: A Synthesis of Exposure Surrogates and Operationalization for Environmental Epidemiology

Abstract

Objective:

Tranquillity has historically been regarded as a subjective experience of peace and harmony with nature, yet it is increasingly recognized as a measurable environmental characteristic with potential implications for population health and urban planning. This review synthesizes common tranquillity concepts, their environmental surrogates, and links to health outcomes.

Methods:

This narrative, conceptual review synthesizes literature identified via iterative PubMed searches (up to May 2025) focused on two domains: environmental exposure surrogates used in epidemiological studies (e.g., green space, natural sound), and indices and modeling approaches developed to spatially map tranquillity. Literature selection was based on conceptual relevance rather than exhaustive coverage.

Results:

Tranquillity is a complex construct characterized by the presence of natural elements and low noise exposure. Evidence consistently links green space to reduced stress, improved mental wellbeing, and lower cardiovascular risk, while natural sounds such as birdsong and flowing water have been shown to promote psychological restoration and mitigate noise-related annoyance. The health relevance of tranquillity, however, is not only shaped by individual environmental inputs but also by their interactions: studies suggest that congruent visual and auditory natural elements elicit stronger restorative effects than either input alone.

Conclusion:

Despite these insights, existing surrogates and health impact assessments often focus on single exposures, overlooking multisensory dynamics, qualitative aspects such as biodiversity and accessibility, and population-level disparities. Advancing this field requires more integrated and context-sensitive approaches, including longitudinal and multisensory studies, to better quantify the health-promoting potential of tranquillity and inform equitable urban design and policy.

KEY MESSAGES

• (1)Multisensory health determinant: Beyond a subjective feeling, tranquillity is a multisensory concept that integrates principally visual and auditory elements, serving as a vital environmental determinant of population health.
• (2)Synergistic benefits: Health outcomes are maximized when green spaces or blue spaces and natural soundscapes are congruent, eliciting stronger restorative effects and stress recovery than single-sensory exposures.
• (3)Policy and design: Integrated, context-sensitive urban planning that prioritizes high-quality, accessible, tranquil areas has the potential to mitigate urban stressors and promote equitable health resources.

INTRODUCTION

Urbanization and its associated environmental stressors characterize the modern era, posing threats to population health. With over half of the global population now living in urban areas, and projections indicating further growth,[1] exposure to environmental pollutants such as air pollution and transportation noise has become a major public health concern.[2,3] At the same time, there is growing recognition that positive environmental features can act as health-promoting resources.

Among these, tranquillity—a state of calmness and mental restoration rooted in ancient philosophies of wellbeing—has gained attention as a potentially important determinant of health. Tranquil environments are often characterized by natural features such as forests, mountains, water bodies, and natural soundscapes.[4,5] A key theoretical framework for understanding the restorative quality of such environments is the Attention Restoration Theory (ART), proposed by Kaplan and Kaplan.[6,7] ART suggests that natural settings engage involuntary attention in a soft, effortless way, allowing cognitive resources to recover while reducing attention fatigue and mental exhaustion associated with sensory overload in urban environments.[6,7]

Growing evidence suggests that exposure to natural environments is associated with a range of health benefits at the population level. Indicators such as residential greenness or proximity to green spaces have been consistently linked to improved health outcomes, including better self-reported health, lower incidence of hypertension and diabetes, and reduced risks of all-cause and cardiovascular mortality across diverse epidemiological and experimental studies.[8,9,10,11,12,13,14] These associations are possibly driven by multiple interrelated pathways:[15,16,17] for example, through stress hormone reduction via hypothalamic–pituitary–adrenal axis modulation,[18,19] improved immune function,[20,21] and enhanced mood and cognitive functioning via emotional regulation and attention restoration.[22,23] Additionally, exposure to nature may encourage physical activity and social interaction,[24,25] while mitigating environmental stressors such as ambient air pollution, noise, and excess heat through the buffering and cooling effects of vegetation.[26,27]

Despite the growing evidence, integrating tranquillity into public health and epidemiological research remains a challenge as it is inherently a complex and multifaceted concept encompassing both subjective perceptual experiences and the objective environmental conditions that support restoration and wellbeing. To address this from an environmental epidemiological perspective, we operationally define "environmental tranquillity" as an objective property of the outdoor environment characterized by a predominance of natural sensory inputs and a minimum of anthropogenic disturbance. By treating tranquillity as a property of place rather than an internal psychological state, it becomes a testable exposure construct that can be operationalized in a consistent and spatially explicit manner.

Currently, several spatial indices and modeling approaches have been developed to map tranquillity, drawing on proxies such as naturalness, soundscape quality, and human perception. However, their application in epidemiological research remains limited. Bridging this gap requires clarifying how tranquillity has been operationalized across planning and health research, and identifying surrogate measures that can be meaningfully applied in epidemiological studies.

The overall goal of this paper was to provide concepts of tranquillity that overcome single-exposure surrogate approaches used in noise and soundscape health research. We therefore focused on the operationalization of exposure surrogates as a determinant of health for environmental epidemiology. Specifically, we aimed to review key outdoor environmental surrogates that approximate tranquillity applicable to epidemiological studies; summarize evidence linking these surrogates to physical and mental health outcomes; and identify available indices and models used to delineate tranquil areas and assess their relevance for health research and urban policy.

METHODS

We adopted a narrative, conceptual review approach to synthesize how tranquillity has been defined, measured, and applied in environmental planning and health research. We focused on two domains: (1) environmental surrogates (e.g., green space, blue space, natural sound) that have been used in epidemiological studies as proxies for tranquillity, and (2) indices and modeling approaches developed to spatially assess or map tranquillity.

As the primary aim of this review was to present key concepts rather than provide an exhaustive overview of the literature on this topic, relevant literature was identified through iterative searches in PubMed, combining terms such as tranquillity, tranquil areas, quiet spaces, soundscape, natural sound, green space, blue space, and health risk assessment. The original search covered publications up to December 2023 and was updated in May 2025 to include recent studies. Review articles were prioritized to identify an established evidence base. Primary empirical studies were subsequently identified through backward and forward citation tracking (snowballing) of key reviews, and by screening for more recent publications not yet covered by existing reviews. Screening and selection were conducted by XJ in consultation with coauthors.

RESULTS

Overview

Early studies by Herzog and colleagues demonstrated that natural scenes are perceived as more tranquil than urban scenes.[4,5] Subsequent research revealed that acoustic features alone could not fully explain the perception of tranquillity. Instead, tranquillity is recognized as a multisensory construct, shaped by the interaction of visual and auditory inputs.[28,29,30,31] Beyond these sensory aspects, individual and contextual factors, such as prior experiences, expectations, social setting, and accessibility, also influence how tranquillity is perceived. [32,33]

Together, these characteristics form the basis for integrated assessments of tranquillity. Several indices and modeling approaches have been developed to quantify and spatially represent tranquillity, combining environmental, perceptual, and contextual information. [28,34,35,36,37,38,39]

Figure 1 synthesizes these evidence-based components into an integrated operational model of the tranquillity–health pathway. The model illustrates the flow from objective environmental surrogates (i.e., exposures) through sensory integration and psychological restoration to physical and mental health outcomes.

Figure 1.

Operational pathway of environmental tranquillity: from objective environmental surrogates to health outcomes.

Acoustic Surrogates

Tranquil areas are broadly characterized by minimal environmental noise and, ideally, the presence of natural sounds. Environmental noise refers to unwanted or harmful outdoor sounds from sources such as transportation, wind turbines, and community activities.[40] These sources dominate acoustic environments in most populated areas and are the primary contributors to noise-related health impacts.[2]

In contrast, natural sounds are non-anthropogenic acoustic stimuli from biological sources (e.g., birdsong, insect sounds) or geophysical processes (e.g., flowing water, wind in vegetation). These sounds are fundamental to the perception of tranquillity.[30,41] Beyond masking environmental noise, natural sounds engage neurophysiological pathways that promote stress recovery and cognitive restoration,[42] making them ecological and health-supportive features of the environment.

Unwanted Sounds

Chronic environmental noise exposure, primarily from transportation sources such as road traffic, railway, and aircraft, is a well-documented public health threat.[2,43] Beyond direct auditory harm such as hearing loss and tinnitus,[44] epidemiological studies have associated long-term transportation noise exposure with increased risks of annoyance,[45] sleep disturbances,[46,47] cardiovascular diseases,[47,48,49] metabolic disorders,[47,50] adverse birth outcomes,[51] mental health conditions,[52,53] and cognitive impairments in children.[52] Road traffic noise is the most common transport source: a recent meta-analysis[43] reported that each 10 dB increase in road traffic noise (L_den, 24-h equivalent) was associated with a 3% increase in cardiovascular disease incidence (relative risk [RR] = 1.03, 95% confidence interval [CI]: 1.01, 1.05), a 6% increase in diabetes incidence (RR = 1.06, 95% CI: 1.04, 1.08), and a 5% increased risk of all-cause mortality (RR = 1.05, 95% CI: 1.02, 1.07).

Beyond transportation, other sources, including wind turbines, industrial operations, and community activities (e.g., construction, ventilation, and neighborhood activities) also contribute to the total environmental noise load. Systematic reviews and meta-analyses indicate that wind turbine noise is consistently linked to increased annoyance and, to a lesser extent, self-reported sleep disturbance,[54,55,56] while community noise contributes significantly to urban stress.[57,58] Longitudinal evidence for health for these sources, however, remains less abundant than for transportation.

As these anthropogenic noise sources dominate modern urban environments, the presence of quiet and tranquil areas serves as an important environmental buffer that allows for physiological recovery from chronic noise stress. Access to a residential quiet façade has been shown to help mitigate these risks. For example, a study in Malmö, Sweden,[59] involving 2612 participants found that having at least one window facing a quiet environment, such as a yard, green space, or water, significantly reduced the risk of noise annoyance (odds ratio [OR] = 0.47, 95% CI: 0.38, 0.59) and concentration problems (OR = 0.76, 95% CI: 0.61, 0.95). Additionally, having a bedroom window facing a quiet area was associated with improved sleep quality (OR = 0.80, 95% CI: 0.64, 1.00). To characterize the availability of such quiet environments at a population scale, researchers and planners primarily rely on large-scale noise modeling. As transportation noise is the most widespread source of noise pollution, its modeled spatial distribution serves as the primary surrogate for identifying potential tranquil areas in environmental health studies. These areas are typically mapped as A-weighted sound levels (e.g., L_den, L_night) derived from deterministic propagation models such as CNOSSOS-EU.[60,61] The utility of these models for characterizing tranquillity, however, is constrained by inherent technical challenges. Specifically, many strategic noise maps rely on spatial averaging across grids (e.g., 10 m in build-up areas), which frequently fails to capture highly localized quiet zones or the microscale shielding effects of urban geometry.[62,63] Furthermore, uncertainties in model inputs, such as traffic volume, vehicle speed, and building reflection coefficients, can introduce discrepancies between modeled and actual sound levels.[62,63,64] Paired with the fact that most strategic noise maps do not map below 55 dB L_den, these limitations suggest that while noise models are robust for identifying broad areas of lower acoustic energy, they lack the precision required to detect the small-scale, high-quality environments that constitute true tranquil spaces, especially in urban settings.

Consensus on specific sound-level thresholds to define a tranquil area is currently lacking. Existing guidelines, however, offer reference levels associated with increased risks of adverse health outcomes. The World Health Organization (WHO) 2018 Environmental Noise Guidelines recommend a maximum of 53 dB L_den for road traffic noise, primarily based on high annoyance, with corresponding limits of 54 dB L_den for railway noise and 45 dB L_den for aircraft noise.[2] The Environmental Noise Directive designates 55 dB L_den from transport sources as the threshold for significant health risks to prioritize noise-action planning.[65] As implied above, however, these noise-centric thresholds are likely inadequate for characterizing tranquillity. Specifically, they fail to account for the health burden in populations exposed to “moderate” noise levels (35–55 dB L_den), where subclinical cardiovascular damage and sleep fragmentation can still occur. Emerging evidence suggests that adverse effects, including stroke, diabetes, and cardiovascular mortality, may arise at levels as low as 35–40 dB L_den,[66,67,68,69,70] and pooled annoyance functions from various studies indicate that approximately 5%–10% of the population report being highly annoyed within this lower exposure range.[71]

Natural Sounds

Recent systematic reviews and meta-analyses have examined the health benefits of natural sound exposure.[72,73] Erfanian et al.[72] qualitatively reviewed studies on the psychophysiological effects of soundscapes. Based on findings from five experimental studies (at least four involved natural sounds), they reported that natural sounds are generally linked to faster stress recovery and positive physiological and psychological responses, though results across studies are not entirely consistent. Fan and Baharum[73] found that natural sounds significantly lowered heart rate (effect size = 1.68, P = 0.006), blood pressure (effect size = 0.82), and respiratory rate (effect size = 0.69, P = 0.032) based on meta-analyses of five to seven studies, with sample sizes ranging from 8 to 130 participants. No significant effects were observed for mean arterial pressure, perceived stress, or oxygen saturation.

The studies included in these reviews were mainly experimental and conducted in laboratory or field settings where the participants listened to the recordings in controlled environments while psychological responses were measured. These studies provide evidence for the mechanisms underlying the restorative effects of natural sounds. For example, Alvarsson et al.[74] found that exposure to a mixture of fountain and bird sounds led to faster recovery of the sympathetic nervous system after psychological stress, as measured by skin conductance level and heart rate variability in 40 university students.

Field evidence complements these findings in real-world settings. Ferraro et al.[75] conducted a field experiment with 665 hikers, using hidden speakers to augment birdsong (phantom chorus) on two trails. Perceived restoration was assessed via questionnaires. On one trail, the phantom chorus increased perceived avian biodiversity (direct effect = 0.22, P < 0.05), which in turn enhanced perceived restoration (effect = 0.20, P < 0.05), resulting in a significant indirect effect (effect = 0.073, P = 0.003). On the second trail, the phantom chorus had a small direct effect on perceived restoration (effect = 0.13, P = 0.028), but no mediating effect of biodiversity perception was observed. This study highlighted how natural sounds can promote restoration through both direct perceptual benefits and indirect pathways, such as enhancing biodiversity perception. Furthermore, evidence also suggests that soundscape complexity influences perceived pleasantness and restoration, with natural soundscapes composed of multiple bird species typically rated as more pleasant and biodiverse than those with single-species sounds.[75,76,77]

Despite these benefits, natural sounds have rarely been incorporated into large-scale epidemiological studies or health risk assessments (HRAs) due to the lack of a standardized exposure metric comparable to L_den for environmental noise. Advances in eco-acoustics offer a direct characterization of the acoustic environment through passive acoustic monitoring using networks of autonomous recorders.[78] Large audio datasets are typically summarized using acoustic indices such as the Acoustic Complexity Index (ACI),[79] Acoustic Evenness Index (AEI) and Acoustic Diversity Index,[80] Bioacoustic Index,[81] Acoustic Entropy (H),[82] or the Normalized Difference Soundscape Index (NDSI).[83] These indices were developed for biodiversity and ecosystem monitoring, and they quantify soundscape structure rather than assessing human health-relevant outcomes.[84,85,86] For instance, ACI acts as a proxy for biological vocal activity, while NDSI and AEI effectively quantify the dominance of anthropogenic noise. Meta-analyses and comparative studies have shown that some indices (e.g., ACI, H, NDSI) show only moderate correlations with biological diversity and perform inconsistently across habitats, especially in complex or urban soundscapes.[87,88,89,90,91] Furthermore, citizen science repositories such as Xeno-canto allow for the reconstruction of potential soundscapes by combining bird monitoring data with archived recordings,[92] offering a scalable alternative when field recordings are unavailable.

From an epidemiological perspective, using these acoustic indices as exposure surrogates presents key challenges regarding temporal variability and construct validity. Natural sounds exhibit strong diurnal and seasonal dynamics, making it nontrivial to define a stable “long-term average” to represent population exposure analogous to annual L_den. In addition, acoustic indices are sensitive to device characteristics, frequency filtering choices, weather (wind, rain), and other nontarget sounds. As a consequence, differences in parameters or recording conditions can change both the magnitude and sometimes the direction of between-site contrasts.[85,93,94,95] These factors can introduce exposure misclassification and biased exposure–response relationships if not explicitly addressed. Therefore, while current eco-acoustic methods can be used to successfully characterize the local acoustic environment, they do not yet provide a validated surrogate for identifying potential tranquil areas on a population scale.

Visual Surrogates

Green and blue spaces, including natural vegetation (e.g., trees, grasslands), water bodies (e.g., rivers, lakes), and built environments (e.g., urban parks, nature reserves), are critical visual surrogates of tranquillity.[37] The relationship between these spaces and the psychological construct of tranquillity is well-established in environmental psychology, rooted in two dominant restorative frameworks: ART[6] and Stress Reduction Theory (SRT).[96] According to ART, natural environments rich in vegetation or water features provide the visual cues that facilitate recovery from directed-attention fatigue through qualities such as "soft fascination" and "being away." Similarly, SRT emphasizes the role of these natural settings in reducing physiological stress and promoting positive affect. This theoretical link between natural visual stimuli and restorative processes is supported by a robust body of epidemiological evidence, which quantifies the health outcomes associated with consistent exposure to these tranquil surrogates.

Green Space

Commonly used metrics to quantify green space exposure include satellite-derived indices such as the Normalized Difference Vegetation Index (NDVI) and land-use mapping. Studies also consider accessibility or visibility metrics, such as distance to the nearest park or the proportion of tree canopy visible from participants’ homes. These indicators have been widely applied in epidemiological studies, providing evidence of associations with a variety of health outcomes.

Extensive evidence supports the health benefits of green space exposure. Systematic reviews and meta-analyses have consistently highlighted inverse relationships between greenness and outcomes such as type II diabetes, cardiovascular diseases, mental health conditions, adverse birth outcomes, cancer, and all-cause mortality.[97,98,99,100] For example, pooled analyses across millions of participants have quantified significant risk reductions for cardiovascular mortality (OR = 0.84, 95% CI: 0.76, 0.93) and all-cause mortality (OR = 0.69, 95% CI: 0.55, 0.87) when comparing the highest versus the lowest green space exposure.[99] Furthermore, increased green space exposure has been linked to improved birth outcomes[99] and improved mental wellbeing in both children and adults.[101,102]

Metrics such as NDVI and land-use maps have been applied in HRAs to estimate the burden of disease attributable to insufficient green space. Guidelines and initiatives have been proposed to improve green space access for residents.[103,104] The WHO recommends universal residential access to green space, defined as at least 0.5 ha within 300 m (or a 5-min walk) from home.[105,106] Epidemiological estimates based on this guideline suggest that meeting this target could prevent 42,968 annual deaths (2.3% of natural-cause mortality) across 978 European cities and 49 greater metropolitan areas.[107]

Building on the proximity-focused guidance from the WHO, the “3-30-300 rule” introduces a multidimensional framework to optimize urban green space visibility, availability, and accessibility.[108] The rule recommends that every resident should: (1) see at least three trees from home, (2) live in neighborhoods with at least 30% tree canopy cover, and (3) have access to a high-quality public green space within 300 meters (5-min walk). Recent applications of this framework have revealed that adherence remains low. For example, only 4.7% of sampled residents in Barcelona, Spain, and 3.6% in Xiamen, China, met all three criteria.[109,110] Despite these low rates, meeting the full criteria was found to be associated with significant health benefits, including lower odds of seeking professional mental health support and better self-reported physical and social health.

While the associations between green space and health are robust, the underlying mechanisms likely operate through multiple, intertwined pathways.[111] Green environments can buffer exposure to environmental stressors such as noise,[112,113] heat,[114,115,116] and air pollution,[117,118] while also encouraging physical activity and social interaction, which can foster a healthier lifestyle and stronger social cohesion.[17,119,120] Crucially, greenery offers restorative experiences that can reduce physiological stress and enhance mental wellbeing,[114] representing pathways that are central to the construct of tranquillity.

However, the effectiveness of green space as a surrogate for tranquillity is ultimately determined by the sensory congruence between the visual environment and other sensory inputs. For example, evidence indicates that vegetation can reduce noise-related annoyance, an early marker of health risk.[121,122,123,124] In Plovdiv, Bulgaria, Dzhambov et al.[125] found that higher exposure to green space was associated with lower noise annoyance, particularly when accompanied by high perceived greenery and lower daytime noise levels. Likewise, based on studies conducted in Switzerland and Germany, residential greenness was associated with lower annoyance from road and railway noise.[122,126,127]

On the other hand, the presence of greenery as an important contributor to a tranquil environment does not guarantee a restorative experience. An unexpected increase in aircraft noise annoyance was observed in greener areas in Switzerland,[126,127] and the authors speculated that this could be due to incongruence between the visual and acoustic landscape. In other words, the visual perception of a tranquil green environment can conflict with the auditory experience of intrusive aircraft noise, thereby increasing annoyance. Chronic annoyance itself has been associated with elevated stress hormones, inflammation, and increased cardiovascular risk,[128] highlighting the importance of tranquil environments for health.

At the population level, a recent health impact assessment (HIA) for Europe showed the potential real-world significance of this multisensory buffering effect. The HIA estimated the potential benefits of increasing green space in reducing noise annoyance related to road traffic and railway noise.[129] This analysis focused on the 38% of adults living in major agglomerations who did not meet the WHO green space guidelines.[105,106] Increasing green space among this population to the WHO-recommended level was estimated to reduce the burden of road traffic noise annoyance by 86 fewer highly annoyed individuals per 100,000 adult inhabitants, corresponding to the prevention of 1149 disability-adjusted life years (DALYs). For railway noise, the estimated reduction was nine fewer highly annoyed individuals per 100,000 adults, preventing 112 DALYs. In a second scenario, the HIA projected that a universal 10% increase in green space coverage across all agglomerations would result in even greater reductions in noise annoyance and its associated health burden.

Blue Space

Blue space, including natural and manmade aquatic environments, contributes to the perception of tranquillity and may yield health benefits through both visual and experiential pathways. In epidemiological studies, exposure has been assessed using GIS-based land-use/cover maps to quantify water bodies, distance to the nearest waterbody, visibility indices from homes, or questionnaires on time spent in blue space. Exposure contexts vary, including residential, school, or recreational locations, and types considered include inland freshwater bodies and coastal areas.

Evidence of the health benefits of blue space is supported by several systematic reviews and meta-analyses.[101,130,131,132,133] A meta-analysis of over two million individuals reported small but statistically significant protective effects across multiple domains, linking closer residential proximity to blue spaces to lower obesity levels, reduced all-cause mortality, and better self-reported general and mental health.[132] Evidence for other outcomes, such as adverse birth outcomes and specific anxiety symptoms, however, remains mixed or insufficient for meta-analysis. More recently, a systematic review examined the benefits of blue space through four main pathways: physical activity, restoration, social interaction, and environmental factors.[131] The findings suggested that most existing evidence supported benefits for physical activity and restoration, while the evidence for environmental buffering was less quantified, and the role of blue space in fostering social interaction remains unclear.

As with green space, blue space is central to the restorative potential of the environment. Within the framework of restoration theories (ART and SRT), the movement of water, reflections, and changing light provides a distinct form of “soft fascination” that facilitates attentional rest and psychological calm. Experimental and review work supports this, with studies demonstrating that water exposure reliably boosts perceived restorativeness and preference compared with equivalent scenes without water.[134,135,136] Notably, built environments with water have been rated as positively as natural green scenes, underscoring the strong tranquil signal from water bodies.[137]

Despite this potential, the integration of blue space into population-level health research and HRAs remains significantly limited. Compared to the robust and standardized metrics available for green space (e.g., NDVI), blue space indicators are rarely included in large-scale epidemiological models. Furthermore, evidence regarding how aquatic environments may modify the adverse health effects of environmental stressors, such as noise exposure, remains scarce.

Other Surrogates

In addition to the environmental determinants, various laboratory and field studies highlight the role of individual and contextual factors in shaping tranquillity experiences. Examples include personal disposition, prior experiences, expectations, and social context, which influence how people perceive and evaluate environments.

Personal dispositions such as noise sensitivity and environmental attitudes modify affective responses to environments, influencing annoyance, pleasantness, and restorative outcomes.[138,139,140,141] In a randomized controlled trial with 60 university-aged participants in South Korea experiencing mild depression, stress, and anxiety, Jo et al.[139] used immersive audio-visual simulations and found that individuals with higher noise sensitivity experienced significantly stronger negative emotions in urban settings but not in natural ones. Similarly, in a laboratory study with 30 adults, Gao et al.[138] reported that people with high noise sensitivity tended to overestimate negative soundscape dimensions, whereas those with high esthetic sensitivity tended to overestimate positive ones.

Psychological states, such as stress, can also affect how tranquillity is perceived. In a field study of 53 residents in Vitoria-Gasteiz, Spain,[142] negative emotional states and stress before visiting urban parks made it harder to achieve emotional restoration, although visits still generally reduced negative emotions and stress. Qiu and Zhang[143] conducted a field study in a National Wetland Park in China, with 562 visitors surveyed before and 341 visitors after the onset of the COVID-19 pandemic. Results showed that visitors reporting higher stress levels after the pandemic onset experienced stronger restorative effects.

Prior experiences and expectations help shape how people interpret their environment. Familiarity with a park and longer visits increase tolerance to potentially annoying sounds and are associated with more positive preferences. For example, Fang et al.[144] surveyed 2034 visitors in urban forest parks in Xi’an, China, and found that familiarity with the park and previous positive experiences enhanced tolerance to sounds that others might find annoying and improved overall soundscape appraisal. Expectation–experience mismatches can reduce perceived tranquillity even in visually or acoustically suitable settings. Filipan et al.[32] found that visitors whose expectations for tranquillity (e.g., silence or natural sounds) were not met reported lower perceived tranquillity, regardless of the park’s objective sound or visual quality.

Social context further alters appraisals beyond physical stimuli. The simple presence of other people can shift perceptions from tranquil to dynamic, regardless of their activities.[33] The distance of others also matters: VR experiments have suggested minimal impact on restorative outcomes beyond 3.8 m, with optimal restoration observed at distances greater than 20 m.[145] The behavior and activities of others influence perceived soundscapes. Engaging in quiet, stationary activities can enhance restorative potential, while frequent visitors experience benefits regardless of activity type.[146] Moreover, research in urban forest parks has shown that when people visit with companions or participate in activities such as parent–child play, fitness, or socializing, they noticed artificial sounds (e.g., surrounding speech, children playing, trains, and automobiles) more often and perceived them as louder.[144]

Although these factors are integral to the experiential dimension of tranquillity and have been incorporated into some modeling,[37,38,39] they have not yet been widely applied in epidemiological research.

In addition, accessibility to sensory inputs further shapes tranquillity experiences, as people are more likely to encounter and benefit from tranquillity in environments that are visible or easily reachable from their residence. Accessibility is commonly assessed using spatial proxies such as buffers around residences, distance measures, or visibility metrics, although these indicators may not fully capture individuals’ actual opportunities or constraints to reach tranquil areas. Such measures have already been applied in previous epidemiological studies, as discussed in the Acoustic surrogates and Visual surrogates sections.

Indices and Modeling Approaches for Tranquillity

Several indices and modeling approaches have been developed to quantify or spatially model tranquillity. 12 and summarize the key approaches and their characteristics. These methods fall into two broad categories, as described in Purves and Wartmann.[147]

Table 1.

Summary of environment-based (modeled) tranquillity indices

Index or modeling approach	Purpose	Methodology	Key characteristics	Pros and cons for mapping/identifying tranquillity	Epidemiological relevance
Tranquillity Rating Prediction Tool (TRAPT)[28,148]	Predicts perceived tranquillity of a location and supports planning and design decisions	Developed from laboratory experiments using 30 s binaural audio–video clips of 34 real environments ranging from open moorland to crowded city centers	Links sound level, visual naturalness/context, and specific modifiers to a 0–10 TR scale via a quantitative model. Uses a linear regression formula (TR = 10.55 + 0.041 NCF − 0.146 LAeq + MF) and provides category thresholds	Pros: Simple quantitative model; calibrated for urban/rural settings; accounts for cultural differences; validated in the United Kingdom and Hong Kong Cons: Short-clip lab basis, limited cultural validation, coarse treatment of some modifiers, mainly audio–visual	Provides a quantifiable tranquillity score that can be mapped for location-based epidemiological studies, but still requires local validation and calibration to ensure accurate exposure assessment across different cultural and environmental contexts
Quietness Suitability Index (QSI)[34]	Assesses potential quiet areas outside urban areas in Europe	GIS-based model combining multiple distance-based suitability scores (0–1) to noise sources (transport, airports, industry, agglomerations) with land-cover naturalness (Corine Land Cover reclassified to 0–1 scale). Used 55dB Lden contour maps (or literature-based buffers when not available) to define distance thresholds, which are scaled into a continuous 0–1 suitability map and combined multiplicatively with land-cover naturalness to produce the final index	Integrates multiple noise sources; applies distance-based suitability using 55dB Lden thresholds; uses multiplicative combination with land-cover naturalness; provides a continuous 0–1 index (100m raster)	Pros: Harmonized EU-wide method; explicit thresholds Cons: Rural focus; suitability-based (not direct exposure or perception)	Useful for identifying potential quiet areas, but limited exposure contrast and population relevance for epidemiological studies
Tranquillity map for England (CPRE)[35]	Identifies potential tranquil areas in England	21 positive and 21 negative tranquillity factors derived from a national survey (1,347 participants); factors combined using multi-criteria GIS modeling	Spatially explicit map integrating positive and negative tranquillity factors based on public perceptions	For tranquillity maps for England and the Swiss central plateau: Pros: Human perception-informed; policy-relevant Cons: Context-specific weighting; static representation	For tranquillity maps for England and the Swiss central plateau: Suitable for identifying potential tranquil environments and assigning spatial exposure metrics in population-based health studies, with limitations related to perception transferability and exposure misclassification
Tranquillity map for the Swiss central plateau[36]	Identifies potential tranquil areas for the Swiss central plateau	Adapted the CPRE approach using Swiss datasets	Highlights potential restorative environments in densely populated regions in Switzerland

Note: Abbreviations: TR = tranquillity rating; NCF = % of natural and contextual features (historic/religious buildings, landmarks, landscape-consistent built elements) visible, excluding sky; L_Aeq = or L_day in practical urban daytime applications of anthropogenic noise; MF = moderating factors including litter (negative) and water sounds (positive or negative depending on quality); GIS = Geographic Information Systems; CPRE = Council for the Protection of Rural England.

Table 2.

Summary of experience-based (perceived) tranquillity approaches.

Index or modeling approach	Purpose	Methodology	Key characteristics	Pros and cons for mapping/identifying tranquillity	Epidemiological relevance
Social media analysis for tranquillity exploration[37]	Identifies locations and landscape features associated with experienced tranquillity in Scotland	Flickr photos (Scotland): Used georeferenced Flickr photographs to identify locations tagged with tranquillity-related terms; combined photo metadata (coordinates, tags) with automated and manual analysis of image content to detect environmental features associated with tranquillity	National-scale, social-media-based exploration of experienced tranquillity; reveals tranquil hotspots in accessible landscapes and shows no clear overlap between modeled “potential quiet areas” and where people actually report tranquillity	Pros: Cost-efficient; captures experienced tranquillity; hybrid automated and manual approach enhances validity Cons: Platform-specific, nonrepresentative user base; only where Flickr photos are taken; spatial biases toward accessible, scenic areas; incomplete coverage of remote or less-photographed places (including many urban areas)	For all three approaches: Captures perceived tranquillity; scalable and spatially explicit; can inform area-level exposure (e.g., hotspot presence or predicted probability surfaces); not sufficient as a stand-alone, individual-level exposure metric
Field interview and social media analysis for tranquillity mapping[39]	Explores how and where visitors experience tranquillity in Loch Lomond and the Trossachs National Park in Scotland	Interviews + Flickr photos (a Scottish national park): Used semi-structured field interviews with free-listing in the national park to elicit tranquillity-related terms and experiences, combined with analysis of georeferenced Flickr photos tagged for tranquillity to map user-perceived tranquil areas	A hybrid approach combining direct on-site perception with crowd-sourced social media, treats tranquillity as multi-sensory and context-dependent; it shows that Flickr photo patterns broadly correspond to where tranquillity is experienced at the landscape scale	Pros: Cost-efficient; scalable; anchored in visitor language; captures experienced tranquillity; good correspondence with field ratings Cons: Social media and sampling biases; grid and keyword choices influence results; context- and situation-dependent; Flickr mapping misses finer-grained nuances and has spatial data gaps
NLP and landscape metrics for tranquillity mapping[38]	Identifies how people describe and experience tranquillity across different environmental settings and which landscape factors influence these experiences; demonstrates scalable mapping of perceived tranquillity	NLP + landscape metrics (text-based): Analyzed 15,350 georeferenced landscape descriptions from Geograph. The United Kingdom containing six validated tranquillity-related keywords; logistic regression linked landscape predictors (land cover, freshwater/saltwater, naturalness, land-cover diversity, built-up area, elevation, terrain roughness, population, latitude/longitude) to the presence/absence of tranquillity descriptions; NLP extracted nouns modified by tranquillity adjectives per land cover class to analyze semantic patterns and similarities between classes	Hybrid spatial–textual approach; integrates NLP of user-generated text with GIS landscape variables; models experienced (not potential) tranquillity across Great Britain; produces land-cover-specific logistic models plus semantic similarity analyses rather than a single uniform tranquillity surface	Pros: Cost-efficient, scalable, large spatial coverage; captures experienced tranquillity; links landscape metrics to tranquillity; reveals setting-specific predictors and semantics useful for nuanced landscape planning and policy Cons: Contributor and sampling bias; keyword-based selection misses some experiences and still includes ambiguities; coarse (250m–1km) predictors poorly capture small urban “oases”, giving weak urban model fit; cross-sectional and correlational; context-specific to Great Britain and to the contributor community from Geograph, UK

Note: Abbreviations: NLP = Natural Language Processing; GIS = Geographic Information System.

The first category estimates the environmental potential for tranquillity based on measurable environmental characteristics or surrogates. Examples include the Tranquillity Rating Prediction Tool (TRAPT), which applies regression models to combine noise levels with natural features,[28] and the Quietness Suitability Index (QSI) proposed by the European Environmental Agency (EEA), which maps quiet areas in rural areas across Europe based on noise and land cover data.[34] Other GIS-based multi-criteria models, such as the tranquillity map developed for England[35] and the Swiss central plateau tranquillity map,[36] also generate spatially continuous tranquillity assessments.

The second category captures experienced tranquillity through user-generated data, reflecting where people perceive tranquillity in situ. Wartmann et al.[37,38,39] demonstrated approaches using geographically referenced photographs, textual descriptions, and interviews. For example, tranquillity in Scotland has been mapped using Flickr photograph tags,[37] combined interview and Flickr data in a Scottish national park,[39] and natural language processing of UK-wide texts to model predictors of tranquillity.[38] Despite the availability of these indices and modeling approaches, the health benefits of tranquillity remain underexplored in epidemiological research, with most studies focusing on landscape and environmental planning.

DISCUSSION

Originally rooted in philosophical reflections on human–nature harmony, tranquillity has increasingly been recognized as a potential measurable environmental determinant of population health and a relevant consideration in urban planning.

A range of indices and models have been developed to map or model multisensory tranquillity concepts, including place-based regression models, GIS-based overlays, and analyses of user-generated content. These approaches effectively identify potentially tranquil areas, but their application has mostly been limited to landscape planning or environmental management, with relatively few studies addressing public health outcomes. By distinguishing potential environmental surrogates with established epidemiological evidence, we clarify how different measurement approaches may be aligned with population health research.

Multisensory Nature of Tranquillity and Current Surrogate Limitations

Tranquillity is inherently a multisensory construct. While reducing excessive noise is fundamental, the presence of natural sounds, such as birdsong or flowing water, can further enhance perceived calm, especially when they dominate over traffic or other anthropogenic noise.[141] Audio–visual synergy plays a distinct restorative role: environments that combine natural visuals with congruent natural sounds elicit stronger restoration, improved mood, and physiological relaxation than either stimulus alone.[42,149,150] While strongest in natural settings, this synergistic effect can also enhance the experience of urban or artificial environments when both natural visuals and sounds are present.[150,151,152,153]

However, the integration of this multisensory experience into population-level health research remains methodologically challenging, particularly when relying on residential addresses or coarse environmental metrics. Current epidemiological studies largely rely on a single surrogate, such as A-weighted decibels for noise or NDVI for green space, which fail to capture this essential sensory interaction. For acoustic surrogates, reliance on transportation noise models is common, but noise maps often lack the resolution to identify the high-quality, low-noise environments that constitute tranquil spaces in dense urban settings. In addition, large-scale data for other anthropogenic noise sources and natural sounds are scarce. The few attempts to link natural sounds to health typically rely on short-term experimental studies with small sample sizes; consequently, their ability to reflect long-term exposure or individual perception is limited, making extrapolation across space and time difficult. In addition, inconsistent definitions of “natural sounds” across studies contribute to variability in findings. While several bioacoustic indices are feasible for characterizing local acoustic environments, these surrogates require rigorous calibration against real-world data. Such calibration is essential to ensure their validity, both in identifying tranquil spaces and in assigning accurate exposure levels to individuals for epidemiological modeling.

Parallel challenges exist for visual surrogates. Metrics such as NDVI or land-use classifications are widely used but often overlook qualitative aspects such as biodiversity, esthetic quality, or landscape configuration. Accessibility provides a crucial link between environmental and individual determinants of tranquillity. Yet, estimating exposure solely from residential addresses can misrepresent actual opportunities to experience tranquil areas, particularly when such areas are located away from people’s homes. Moreover, although positive effects have been reported for green space and blue space, findings remain heterogeneous across studies due to differences in settings, populations, and exposure assessment methods, complicating cross-study comparisons and synthesis in HRAs. Most existing HRAs focus on single environmental exposures, potentially overlooking the combined benefits of visual and auditory inputs.

Finally, while individual and contextual determinants are integral to the experiential dimension of tranquillity and have been incorporated into some conceptual and modeling frameworks, their inclusion in epidemiological or spatial analyses remains limited. Most population-level studies to date have focused on quantifiable environmental surrogates due to data availability and modeling feasibility.

Forward-Looking Research Directions

Tranquillity is increasingly recognized as a multisensory construct with links to wellbeing, yet health-focused research remains limited compared to broader work on restoration, soundscapes, and exposure to nature. To reinforce the core innovation of a multisensory tranquillity concept, future studies should move beyond static, single-sensory surrogates (e.g., A-weighted noise levels or greenness metrics) and prioritize three areas. First, there is a need to develop multisensory indicators that are validated by physiological markers (e.g., heart-rate variability or cortisol change) to bridge the gap between experimental evidence and population-level epidemiology. Second, research should utilize high-resolution spatial and temporal data to track multisensory exposure in real-world settings. This would allow longitudinal studies to test whether long-term experience of high-quality tranquil environments offers stronger protection against chronic disease than exposure to quiet or green areas defined only by single physical indicators. Finally, integrating these multisensory constructs into HRAs and planning tools would complement traditional metrics like decibels or vegetation cover, allowing urban planners to transition from noise mitigation toward proactive health promotion.

Scope and Limitations of Current Review

This work has several limitations. First, it is a narrative review rather than a systematic review, with literature selection based on conceptual and methodological relevance rather than exhaustive coverage. Consequently, some relevant studies may not have been captured. Second, we focused on outdoor surrogates characterizing tranquillity, as these are currently the most widely used metrics for large-scale GIS modeling in environmental epidemiology. While individuals spend most of their time indoors, outdoor environmental exposure data remain the primary tool for urban planning; however, future research should investigate how indoor acoustic and visual conditions, and the shielding effects of building envelopes, modify the actual “dose” of tranquillity experienced at the individual level. There are also other potentially important contributors to perceived tranquillity that fell outside the scope of this review. For example, wilderness and open landscapes, which offer experiences of vastness, solitude, and escape, have been identified as contributors to tranquillity based on public consultations in the United Kingdom.[35] In addition, olfactory stimuli, such as the smells of vegetation, water, or soil, have been shown to enhance the restorative potential of natural environments.[154]

Furthermore, other environmental stressors, including air pollution, extreme heat, and cold, are established determinants of health acting through both direct physiological mechanisms and indirect perceptual pathways.[155,156,157,158] While these factors have been shown to disrupt feelings of comfort or tranquillity, they remain underrepresented in most definitions or measurements within the tranquillity framework, and were thus excluded here.[159,160,161]

Finally, personal perception and individual differences such as prior experiences, cultural background, or emotional state play a central role in how tranquillity is perceived and experienced. Yet, these subjective factors remain difficult to capture in epidemiological assessments and are insufficiently addressed in current research.

CONCLUSION

Tranquillity is a multidimensional concept encompassing environmental, sensory, and perceptual dimensions, and it represents a promising determinant of population health. Indices such as TRAPT, QSI, and participatory mapping techniques have advanced the operationalization of this concept. Nonetheless, challenges remain in terms of scalability, integration of subjective experiences, and application in epidemiological research. Expanding the evidence base through longitudinal, multisensory, and culturally responsive studies will be essential to fully harness the health-promoting potential of tranquillity and to inform equitable urban design and policy.

Availability of Data and Materials

No primary data were generated or analyzed in this study. All information is derived from published literature. Further details are available from the corresponding author upon reasonable request.

Author Contributions

Xing Jiang: conceptualization, methodology, formal analysis, investigation, data curation, writing − original draft, writing − review and editing. Nicole Engelmann: writing − review and editing. Danielle Vienneau: conceptualization, methodology, validation, writing − review and editing, supervision. Martin Röösli: conceptualization, methodology, validation, resources, writing − review and editing, supervision, project administration, funding acquisition. All authors read and approved the final version.

Ethics Approval and Consent to Participate

Not applicable.

Conflicts of Interest

The authors declare no competing interests.

Funding Statement

This work was supported by the Swiss National Science Foundation (Grant No. 324730B_201272). This work was also supported by the ETC Human Health and the Environment (ETC HE), which is a European consortium with 10 institutions contracted by the European Environment Agency (EEA) under a framework partnership agreement for the period 2022–2026.