A Meta-Analysis of the Impact of Urban Blue Spaces on Residents' Mental Health

Highlights

	● Conducts a meta-analysis of the impact of urban blue spaces on residents' mental health
	● Clarifies the measuring indicators and research models of the impact of urban blue spaces on residents' mental health
	● Quantifies the effect of urban blue spaces on residents' mental health in dimensions of proximity, availability, and visibility
	● The effect of urban blue spaces on negative psychology is controversial, especially their effect of depression and other psychological disorders, and the findings among existing studies vary significantly

Abstract

The research on the impact of urban blue spaces on residents' mental health has attracted great attention from scholars internationally, and quantitative studies of the effects dominate the current academia. This study, on the basis of reviewing the theories of urban blue spaces and residents' mental health, conducted a meta-analysis of 47 key studies by systematically selecting and examining the literature from Web of Science, CNKI, and other databases. This paper analyzed the measuring indicators and research models among the literature and standardized the effect size of the research findings. The meta-analysis results include that: 1) the measurements of the characteristics of urban blue spaces are mainly conducted in space-based and individual-based dimensions; 2) residents' mental health is mainly measured from aspects of general mental health, positive psychology, and negative psychology; 3) the proximity of blue space has a significant positive effect in improving residents' general mental health and positive psychology; 4) the availability of blue space is significantly positively correlated with general mental health and positive psychology; 5) although there are studies confirming that factors such as blue space visibility, frequency of visit, and exposure types have an impact on mental health, the relevant studies are still limited; and 6) research on the effect of blue spaces on negative psychology is controversial, especially on mental disorders such as depression, and the findings among existing studies vary significantly. The results of this meta-analysis can provide guidelines for future research and the construction of healthy cities.

Graphical abstract

Keywords

Urban Blue Spaces / Mental Health / Meta Analysis / Environmental Exposure / Effect Size

1 Introduction

Rapid urbanization, while significantly boosting economic development, has also caused a number of environmental and social issues, posing threats to people's physical and mental health^[1]. Statistics indicate that the depression risk rate among Chinese residents reaches as high as 10.6%^[2]. Improving public mental health has become one of the United Nations' Sustainable Development Goals^[3][4], highlighting mental health as a global concern. Urban blue space refers to all surface water in urban environments^[5], including natural water bodies like coasts and lakes, as well as artificial water bodies like fountains and ponds^[6]. As a crucial part of the urban natural environment, approximately half of the global population lives within 3 km from blue spaces^[7]. These blue spaces not only mitigate the urban heat island effect and promote social activities, but also help alleviate psychological stress, thereby improving mental health^[8][9]. Therefore, exploring the influence of urban blue spaces on mental health is of great significance for future urban planning and design.

Scholars have increasingly focused on the relationship between urban blue spaces and mental health, though research results vary. Some studies suggest that residents living near urban blue spaces tend to have better mental health^[10], more effective stress recovery ^[11], and lower rates of antidepressant use^[12]. Residents who have regular visits to urban blue spaces report higher subjective well-being^[13], while those with more blue space around their residences^[14] or more views of blue space^[15] report more positive mental health. Compared with urban green spaces, some research indicates that blue spaces have a more pronounced health-promoting effect ^[16] because they can offer diverse water activities that provide psychological benefits^[17]. However, other studies have found no significant correlations between urban blue spaces and mental health indicators due to the variety of geographic environment, research design, and individual factors^[18]. Additionally, the effects of urban blue spaces on different types of mental health issues vary^[19]; for example, they can reduce anxiety through stress relief pathways but have less effect on depression and other mental disorders^[17][20][21]. Given these research disparities, a systematic review of relevant studies is necessary to outline existing theoretical frameworks and discuss research shortcomings and controversies.

Although some reviews have examined the effects of urban blue spaces on mental health, most are qualitative studies and offer limited guidance for future research directions. In contrast, meta-analysis adopts systematic and rigorous literature retrieval and screening criteria, and selects analytical models based on literature heterogeneity to provide more reliable and accurate conclusions ^[22]. Given the variations in research design and sample size in environmental and health studies, meta-analysis has been increasingly adopted to mitigate individual bias on the overall results^[6][23][24]. For instance, Niamh Smith et al. conducted a meta-analysis of 25 studies, revealing the positive effects of blue spaces on residents' obesity rates and mental health^[6]; however, this study did not differentiate types of mental health, and the literature for the meta-analysis was the works published before August 2019. Thus, an updated meta-analysis is needed to more comprehensively and precisely elucidate the effects of urban blue spaces on residents' mental health.

As a response, building on the existing theories of urban blue spaces and mental health, this research screened and analyzed empirical studies published between 2000 and 2022 sourced from databases of WoS (Web of Science), Medline, CNKI (China National Knowledge Infrastructure), and Wanfang Data. The study aims to reveal: 1) the characteristics of urban blue spaces that affect residents' mental health, the types of mental health, and the corresponding measurement methods; and 2) the effect size of each characteristic of urban blue spaces on mental health. This study will provide scientific evidence for urban planning and design practices for improving residents' mental health.

2 Theoretical Foundation and Research Framework

Research on urban blue spaces and mental health, as an emerging branch in the field of environment and health, integrates theory and knowledge systems from disciplines such as health geography, landscape ecology, and environmental psychology. Despite the varied interests of different disciplines, they collectively provide theoretical support for understanding the influencing mechanisms of urban blue spaces on residents' mental health^[8].

Specifically, research in health geography is centered on the Social-ecological Theory, regarding urban blue spaces as crucial components of the built environment that can influence residents' mental health directly or indirectly^[25][26]. Such studies often operate on a macro scale (e.g., national, regional, city), using large-scale standardized survey data (with analytical unit of community), and explore the heterogeneity of urban blue spaces' effects on mental health across different community types.

Landscape ecology research, supported by the Ecosystem Services Theory, examines the relationship between urban blue spaces and residents' mental health from a supply-demand perspective, proposing evaluation models for the health benefits of natural environments^[27]. This theory considers urban blue spaces as an essential component of urban ecosystems, where blue spaces are the supplier of ecosystem services and residents are the demander. The effective matching of supplier and demander occurs through environmental exposure and contact ^[28], where residents engage with urban blue spaces via multi-sensory perception (e.g., visual, auditory) to promote mental health. Such studies typically focus on meso- and micro-scales (e.g., community, single or several sites of blue spaces), emphasizing the differential impacts of various types of urban blue spaces on mental health.

In environmental psychology, the Attention Restoration Theory and the Biophilia Hypothesis are the most widely discussed foundational theories. The former, proposed by American environmental psychologists Stephen and Rachel Kaplan, posits that natural environmental elements (including blue spaces) have eight restorative qualities—such as being away, extent, and mystery—that aid in the recovery of directed attention^[29][30]. And the latter, emphasizing humans' innate affinity for nature, holds that nature plays a significant role in healing and improving human mental and physical health ^[31]. Similar to that in landscape ecology, environmental psychology research also focuses on meso- and micro-scales and utilizes experimental methods to explore the differential impacts of urban blue space characteristics and exposure types on residents' mental health.

Despite the disparity of research emphases across disciplines, all of them stress the significant role of urban blue spaces in strengthening residents' mental well -being, by measuring spatial dimensions (e.g., proximity, availability, visibility) and individual dimensions (e.g., visiting characteristics, exposure types). The influence of urban blue spaces on mental health can be summarized into three pathways^[8][32]. 1) Regulatory pathway: urban blue spaces regulate microclimates (e.g., reducing temperature, increasing humidity), improving residents' psychological and emotional states^[33]; additionally, sounds from water bodies like streams and fountains can improve the soundscape, enhancing psychological comfort^[34]. 2) Promoting pathway: urban blue spaces can provide places for physical and recreational activities that promote mental health ^{[17][35]~[37]}, and also foster social interactions, enhancing a sense of belonging and neighborhood cohesion, which benefits mental health^[38]. 3) Restorative pathway: as a vital component of natural environments, urban blue spaces can relieve psychological stress and restore attention, thereby promoting mental health^[8] (Fig.1).

Fig.1 Theoretical framework of the impact of urban blue spaces on residents' mental health.

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3 Data and Methods

3.1 Data Sources

Sourcing from databases of CNKI and Wanfang Data for Chinese literature, and WoS and Medline for English literature, this meta-analysis constructed three thematic keyword inventories, "blue space, " "resident, " and "mental health" in both languages (Tab.1). Literature searches were conducted using these combined keywords, covering the works published from January 1, 2000, to December 31, 2022, among relevant fields such as "Urban Studies, " "Geography, " and "Environmental Sciences." Additionally, a further search was performed among the "highly cited papers" from WoS. In total, 5, 765 journal articles were collected (Fig.2).

Tab.1 Literature retrieval subject terms used in this study

Category	Language	Subject terms
Blue space	Chinese	蓝色空间; 江; 河; 湖; 海; 滨水; 海岸; 河岸; 湿地
	English	Blue space; river; lake; sea; ocean; waterfront; coastal;
Resident	Chinese	居民; 人; 公众; 老人; 儿童; 青少年; 学生
	English	Resident; people; public; old; children; teenager; student
Mental health	Chinese	心理健康; 抑郁; 焦虑; 幸福; 情绪; 压力; 强迫症
	English	Mental health; depression; anxiety; well-being; emotion; stress; obsessive-compulsive

Fig.2 Literature searching and screening procedure.

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Subsequently, a two-step screening process was employed to select the valid samples. The initial screening, using the Rayyan (a tool for systematic literature review) examined titles, abstracts, and keywords to remove duplicates and irrelevant studies, and obtained 481 articles (432 in English and 49 in Chinese). The second round screening was to select empirical studies through full-text reading: 1) selecting empirical studies that employed cross- section analysis, longitudinal analysis, randomized crossover trial or group experiment, and excluding qualitative studies; 2) selecting natural and constructed blue spaces in urban areas, and excluding studies which utilized blue spaces as an intermediary factor or mixed with green spaces; 3) selecting research that focused on urban regions, and excluding those studied microelements and microorganisms; 4) eliminating the literature without available full text. This process yielded 47 eligible articles^{[10]~[15][17][18][20][21][32]~[36][38]~[69]}, 45 in English and 2 in Chinese (Tab.2).

Tab.2 List of selected literature for meta-analysis in the research

Literature No.	Published year	Country/region of case study	Population	Indicator(s) of characteristics of urban blue space	Indicator(s) of types of residents' mental health	Research model
39	2010	Finland	Adults	Favorite blue space type; visit frequency; visit duration	Restorative experience	Cross-section analysis
18	2013	England	Adults	Euclidean distance to blue space from residence	Self-reported mental health; life satisfaction	Longitudinal analysis
40	2013	Canada (students)	Children	Presence of blue space within a 5-km buffer from school	Subjective well-being	Cross-section analysis
41	2013	England	Adults	Visit duration; distance travelled	Self-reported emotional restoration	Cross-section analysis
42	2014	Spain	Children (students)	Visits per year	ADHD/inattention	Cross-section analysis
43	2014	USA	Adults (veterans)	Weekly surfing for 5 consecutive weeks	Post Traumatic Stress Disorder (PTSD) and depression	Controlled experiment
36	2015	Spain	Adults	Presence of blue space within a 300-m buffer from residence	Self-reported mental health; depression and/or anxiety; visits to mental health specialists; intake of medication	Cross-section analysis
44	2015	Scotland	Adults (employees of science park workplaces)	Window view of blue space from usual work-station	Subjective well-being	Cross-section analysis
45	2016	The Netherlands	Adults	Percentage of blue space within a 1-km buffer from residence	Mental disorder; self-reported mental health	Cross-section analysis
46	2016	New Zealand	Adults	Blue space visibility of each cell in a residential grid	Stress	Cross-section analysis
13	2017	England	Adults	Neighborhood exposure; visit frequency; whether visiting blue space yesterday	Subjective well-being	Cross-section analysis
33	2017	Spain	Adults (individuals with psychological distress)	Physical activities and social interactions in blue spaces	Self-reported mental health, and physiological measures (blood pressure, heart rate, salivary cortisol, etc.)	Randomized crossover trial
17	2018	Spain	Adults (Alzheimer and families)	Presence of blue space within 100- m, 300-m, and 550-m buffers from residence	Depression; anxiety	Cross-section analysis
21	2018	The Netherlands	All-aged (psychiatric patients)	Percentage of blue space within a 300-m buffer	Duration of hospital admission	Cross-section analysis
47	2018	USA	Children	Euclidean distance to blue space; presence of blue space within 250- and 1, 250-m buffers from residence	Odds of high depression	Cross-section analysis
48	2018	Ireland	Older adults	Euclidean distance to blue space; share of visible blue space from residence	Depression	Cross-section analysis
49	2018	Bulgaria	Younger adults (students)	Euclidean distance to blue space; presence of blue space within a 300-m buffer	Self-reported mental health	Cross-section analysis
50	2018	Bulgaria	Younger adults (students)	Presence of blue space within 100-m, 300-m, and 550-m buffers from residence	Self-reported mental health	Longitudinal analysis
51	2018	Germany	Adults	Visit frequency; perceived walking distance to blue space	Self-reported mental health	Cross-section analysis
52	2018	Switzerland	Adults	Field trip	Physiological parameters; stress; attention restoration	Controlled experiment
10	2019	England	Adults	Euclidean distance to blue space	Self-reported mental health; anxiety and depression	Cross-section analysis
32	2019	Hong Kong, China	Older adults	Blue space quality (rating on safety, presence of wildlife, whether generally be free from litter, and have good facilities); types of exposures; visit frequency; walking distance to blue space; activity intensity; visit duration	Subjective well-being	Cross-section analysis
35	2019	England	Adults	Euclidean distance to blue space; freshwater presence	Self-reported mental health	Cross-section analysis
53	2019	New Zealand	Children (students)	Euclidean distance to blue space; presence of inland blue space in the neighborhood	Subjective well-being; depression	Cross-section analysis
54	2019	Chinese mainland	Older adults	Ratio of the number of blue space pixels per street view image; Normalized Difference Water Index (NDWI)	Geriatric depression	Cross-section analysis
55	2019	Singapore	Children (students)	Whether visit blue spaces	Momentary happiness	Cross-section analysis
56	2019	UK	Adults	Taking part in twice a week wetland nature-based health intervention for six weeks	Subjective well-being; stress; anxiety	Controlled experiment
11	2020	Spain	Adults	Walking distance to blue space from residence; visit frequency; visit duration	Perceived restorativeness	Cross-section analysis
15	2020	Chinese mainland	Adults	Ratio of the number of blue space pixels per street view image; blue space within a 1, 500-m buffer from residence	Self-reported mental health	Cross-section analysis
57	2020	Chinese mainland	Older adults	Euclidean distance to blue space; NDWI	Self-reported mental health	Cross-section analysis
58	2020	Chinese mainland	Older adults	Euclidean distance to blue space; ratio of blue space, per capita water area, and patch separation index of blue space within a 1-km buffer	Self-reported mental health	Cross-section analysis
59	2020	Spain	Adults	Walking in blue space (on 4 days each week, 20 min per day, for 3 weeks)	Subjective well-being; self-reported mental health; mood disturbance; physiological parameters	Randomized crossover trial
12	2021	Scotland	Older adults	Euclidean distance to blue space; percentage of blue space within 800-m and 1, 600-m buffers from residence	Antidepressant medication prevalence	Cross-section analysis
14	2021	Chinese mainland	Adults	Percentage of blue space within a 500-m buffer from residence and a 200-m buffer around travel route	Subjective well-being	Cross-section analysis
20	2021	The Netherlands	Adults	Proportion of blue space within 50-m and 100-m buffers from residence	Depression	Cross-section analysis
34	2021	USA	Younger adults (students)	Walking in blue spaces (compared with walking in the urban environment)	Restorative experience	Randomized crossover trial
38	2021	UK	Adults	Visit frequency	Subjective well-being; life satisfaction	Cross-section analysis
60	2021	Chinese mainland	All-aged	Euclidian distance to blue space; blue space area within 300-m, 500-m, and 1, 000-m buffers from residence	Depression; subjective well-being	Cross-section analysis
61	2021	18 countries/regions including the UK and the USA	Adults	Percentage of blue space within a 1, 000-m buffer from residence; visit frequency in the last 4 weeks; nature connectedness	Subjective well-being; mental distress; depression/anxiety medication use	Cross-section analysis
62	2021	Denmark	Adults (blood donors)	Percentage of blue space within 500 -m, 1, 000-m, and 3, 000-m buffers from residence	Subjective well-being	Longitudinal analysis
63	2021	Belgium	Adults	Whether live in coastal cities; visit frequency	Subjective well-being	Cross-section analysis
64	2021	Chinese mainland	Children	Environmental characteristics of blue space based on the adolescents' activity paths in the park	Restorative perception	Cross-section analysis
65	2022	18 countries/regions including the UK and USA	Adults	Childhood exposure to blue spaces (availability, parents/guardians' attitude to blue space visits, and visit frequency); visit frequency in adulthood	Subjective well-being	Cross-section analysis
66	2022	USA	Adults	Visit frequency; distance to blue space from residence; visit duration	Stress; subjective well-being; life satisfaction	Cross-section analysis
67	2022	England; Welsh	Adults	Whether can see blue space; whether visit blue space in the past 24 hours	Self-reported mental health	Cross-section analysis
68	2022	UK	Adults	Perceived proximity to blue space from residence; visit frequency; contact time	Subjective well-being	Cross-section analysis
69	2022	Belgium	Younger adults (students)	Rating for blue space pictures	Restorative perception	Cross-section analysis

NOTEDue to the disparity of the classification of research population' among different countries/regions and studies, the statistics in this study were conducted according to the sample characteristics in included literature as 1) children: 0 ~ 18 years old; 2) younger adults: 18 ~ 35 years old; 3) adults: 18 years old and above; 4) older adults: mainly 50 years old and above; and 5) all-aged: covering multiple age groups.

3.2 Research Methods

Meta-analysis typically includes both qualitative summarization and quantitative synthesis ^[6]. Qualitative summarization provides an overview of the included studies, encompassing research design, measurement indicators, sample characteristics, etc. Quantitative synthesis is conducted by combining effect sizes, such as standardized mean difference (SMD) and correlation coefficients, to represent the strength of the correlation between two variables. Among environmental health meta-analyses, SMD is often adopted as the indicator of effect size^[23][24]. In this paper, health effect size refers to the variables' impact on health, and the quantitative synthesis comprises two steps: 1) using Psychometrica, an effect size conversion calculator, the effect sizes of some included studies were converted to SMD; 2) using RevMan 5.4 software, the sample sizes and converted SMD values were aggregated—due to the heterogeneity of psychological health dimensions among study subjects, indicated by a high I² (I-squared, a statistical measure for heterogeneity in meta-analysis), a random effect model was used^[23][70]. Given the disparity of research design and the feasibility of effect size conversion, not all included studies were suitable for quantitative synthesis. Therefore, only the studies meeting the requirements underwent effect size aggregation, while the others were performed qualitative summarization.

4 Results Analysis

Statistical results (Fig.3) indicate that the number of publications has shown a fluctuating upward trend since 2010, with a peak period from 2018 to 2022, during which 74.5% of the total literature was published. Totally, 29 articles took adults as research subject, followed by 6 studies focusing on children and older adults each, 4 on younger adults, and 2 on all -aged groups. Geographically, 29 studies were conducted in Europe, 9 in Asia, 5 in North America, 2 in Oceania, and 2 were cross-continental.

Fig.3 Statistics of the included literature for meta-analysis (3-1. published year; 3-2. research subjects; 3-3. geographical distribution).

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4.1 Measurement and Models of Urban Blue Space Characteristics and Residents' Mental Health

4.1.1 Measurement of Urban Blue Space Characteristics

Among the included literature, the measurement of the characteristics of urban blue spaces was conducted from the space-based (i.e., residential or workplace) dimension or individual- based (i.e., respondent) dimension. The number of space-based and individual-based is 21 for each, while 5 studies combining the both.

(1) Measurement from space-based dimension

Space-based measurements typically use macro data (e.g., land use and transportation data) to determine residents' exposure levels to urban blue spaces. Such studies generally assume residents remain stationary throughout the day, and use the blue spaces within the buffers around their residence or workplace (often in a radius of 0.3 ~ 1.5 km) to represent the exposure level, indicated by proximity, availability, and visibility. Proximity refers to whether live in coastal areas^[63], the Euclidean distance from the population centroid of the analysis unit to the urban blue space^{[10][12][13][18][35][47][48][49][53][57][58][60]}; availability is determined by the presence of urban blue spaces within the given buffers^{[17][36][40][47][49][50]}, percentage of blue space in area^{[12][14][15][20][21][45][58][60]~[62]} or per capita blue space area^[58]; and visibility is usually measured by the visible rate of urban blue spaces from specific locations based on land use and elevation data^[46][48]. With advancements in artificial intelligence and big data, some researchers have started using street view images and deep learning techniques to measure the proportion of urban blue spaces within certain visible ranges^[15][54].

However, statically measuring residents' exposure to urban blue spaces sees limitations. The Uncertain Geographic Context Problem (UGCoP)^[71][72] indicates that results would vary depending on the different divisions of geographic units (e.g., community, census tract, postal code precinct)^[73]. Additionally, the Neighborhood Effect Averaging Problem (NEAP)^[74] highlights that static measurement methods cannot capture variations of individual visit characteristics, economic status, and exposure types, leading to discrepancies between measured and actual exposure levels.

(2) Measurement from individual-based dimension

Individual-based measurements often use survey data to assess residents' active visit to urban blue spaces, and can accurately evaluate exposure levels by examining individual behavioral characteristics and exposure types to urban blue spaces. The former focuses on the influence of individual behavioral characteristics including whether urban blue space is visible^[44], whether residents visit^{[13][41][55][67]}, frequency/number of visits^{[11][13][32][38][39][42][51][61][63][65][66][68]}, travel distance^{[11][32][51][66][68]}, duration of visit^{[11][32][39][66][68]}, and residents' activity types taken place within blue spaces^{[33][34][43][52][56][59][64]} on mental health. The latter explores the different impacts of different exposure types including indirect exposure (visible from home), incidental exposure (visible during commute), intentional exposure (purposeful visit)^[32], and virtual exposure (viewing photos of blue space^[69]) on mental health.

(3) Measurement combined space- and individual-based dimensions

The studies considering both space- and individual-based dimensions tracked urban residents' spatio-temporal behaviors through activity logs, calculate blue space coverage around homes and travel routes^[14][20], or survey residents' addresses and urban blue space visitation data to comprehensively assess individual exposure levels to urban blue spaces^[13][61][63].

4.1.2 Measurement of Residents' Mental Health

In the selected literature, the measurement of residents' mental health mainly focuses on three aspects: general mental health, positive psychology, and negative psychology (Tab.3). General mental health is often assessed using overall questionnaires to evaluate individuals' psychological status. Positive psychology involves self-reported well-being (including subjective happiness and life satisfaction), perceived restorativeness, and positive emotions; negative psychology includes negative emotions (e.g., stress, frustration), and mental disorders (e.g., generalized anxiety disorder, depression). Additionally, a few studies examine mental health with medical statistics (e.g., duration of hospital admission, incidence of depression disorder) and physiological indicators (e.g., blood pressure, heart rate).

Tab.3 Overview of methods for measuring the level of mental health

Mental health		Measurement method	Literature
General mental health		The General Health Questionnaire (GHQ-12)	Refs.[10] [15] [18] [33] [35] [36] [50]
		Mental Health Inventory (MHI-5, a sub-scale 36-Item Short Form Survey)	Refs. [45] [57]~[60] [66]
		12-Item Short Form Health Survey (SF-12)	Refs. [51] [62]
		Spanish short version of the Profile of Mood States (POMS)	Refs. [33] [59]
		10 questions about current emotions using a 5-point Likert scale	Ref. [67]
Positive psychology	Self-reported well-being	The World Health Organization's Five Wellbeing Indexes (WHO-5)	Refs.[14] [32] [38] [53] [59] [61] [65] [68]
		The Short Warwick Edinburgh Mental Well-being Scale (SWEMWBS)/Warwick-Edinburgh Mental Well-being Scale (WEMWBS)	Refs. [44] [56] [63]
		Four subjective well-being questions (life satisfaction) developed by the UK's Office of National Statistics	Refs. [13] [59] [60]
		A single question assessing overall life satisfaction	Refs. [18] [66]
		Cantril Ladder	Ref. [40]
	Perceived Restorativeness	Perceived Restorativeness Scale (PRS)	Refs. [11] [64] [69]
		Restoration Outcome Scales (ROS)	Refs. [11] [39]
		Short-version revised restoration scale (SRRS)	Ref. [34]
		Emotional restoration survey overview	Ref. [41]
	Positive emotions	Positive and Negative Affect Schedule PANAS (positive)	Ref. [56]
	Others	Reporting "happy moments" at any point in time by pressing one of the sensor buttons	Ref. [55]
Negative psychology	Negative emotions	Perceived Stress Scale (PSS)	Refs. [56] [66]
		Kessler Psychological Distress Scale (K10)	Ref. [46]
		Positive and Negative Affect Schedule PANAS (negative)	Ref. [56]
	Mental disorder	The anxious/depression dimension of the EuroQOL five dimensions questionnaire (EQ-5D)	Ref. [10]
		Patient Health Questionnaire (PHQ-9)	Ref. [20]
		ADHD/DSM-IV questionnaires	Ref. [42]
		The Major Depression Inventory (MDI)	Ref. [43]
		The Post Traumatic Stress Disorder Checklist Military Version (PCL-M)	Ref. [43]
		McKnight Risk Factor Survey (MRFS)	Ref. [47]
		The Center for Epidemiologic Studies Depression Scale (CES-D)	Ref. [48]
		Short form of the Reynolds Adolescent Depression Scale (RADS-SF)	Ref. [53]
		The shortened Geriatric Depression Scale (GDS-15)	Ref. [54]
		Generalized Anxiety Disorder Scale (GAD-7)	Ref. [56]
	Others	Self-reported visits to mental health specialists; history of anxiety or depression; history of medication; frequency of medication intake	Refs. [12] [17] [36] [61]
		Observation of different physiological effects on blood pressure or heart rates	Refs. [52] [59]
		The duration of hospital admission for patients of mental disorders	Ref. [21]

4.1.3 Research Models

The research models in the selected literature mainly include observational studies and experimental studies. Observational studies generally use cross-section or longitudinal analyses to explore the effect of urban blue space characteristics on residents' mental health through models such as multiple linear regression^[11], Poisso regression^[48], structural equation modelling^[49], and mediation effect model^[57], and analyze their spatial heterogeneity. Cross-section analysis is easier to obtain data and more used to explore correlations, while longitudinal analysis is often used for in-depth investigation of influencing mechanisms, though more challenging in data collection.

Experimental studies typically employ randomized crossover trials^[33][34][59] or controlled experiments^[43][52][56] that require respondents to be exposed to urban blue spaces in virtual (e.g., viewing photos^[69]) or real experiences (e.g., surfing^[43], walking^[34]) to observe the mental health effects obtained. These studies can better control experimental conditions such as exposure duration and mode, resulting in a more accurate assessment of short-term effects of blue space on mental health. However, due to the difficulty of recruiting respondents, the results of such studies may be biased by the limited sample size.

4.2 Effect of Urban Blue Space Characteristics on Residents' Mental Health

4.2.1 Effect of Urban Blue Space Characteristics on Residents' Mental Health Through Space-based Measurement

In this meta-analysis, space-based studies measured the correlations between urban blue space characteristics and residents' mental health from aspects of proximity, availability, and visibility.

(1) Proximity

Meta-analysis results (Tab.4) indicate that the proximity of urban blue spaces is significantly positively correlated with residents' self-reported general mental health (SMD = 0.33, p = 0.0001) and positive psychology (SMD = 0.15, p = 0.006). Specifically, residents living near urban blue spaces (e.g., those close to coasts or rivers) generally have better self-reported mental health^[57], which is particularly significant among low-income groups^[10] and strengthens with longer residency^[18]. Studies also found that outdoor activities (e.g., walking in coastal area) and perceived restorative quality of the living environment^[49] are the affecting pathways of urban blue spaces on mental health. For example, a study in Belgium found that coastal residents experienced higher happiness during pandemic lockdowns than inland residents^[63].

Tab.4 Effect size aggregation results of the proximity to urban blue spaces on residents' mental health (n = 11)

		SMD (95%CI)	Heterogeneity test				Statistical test		Literature
			I2	Chi2	Df		z	p
General mental health		0.33[0.16, 0.50]	81%	10.44	2		3.82	0.0001	Refs. [10] [18] [35]
Positive psychology	Subjective happiness	0.15[–0.07, 0.36]	98%	141.34	3		1.35	0.18	Refs.[32] [40] [53] [63]
	Life satisfaction	0.19[0.13, 0.26]	0%	0.73	1		5.79	< 0.00001	Refs. [13] [18]
	Total	0.15[–0.01, 0.30]	97%	41.06	4		2.73	0.006	—
Negative psychology	Depression	–0.31[–0.75, 0.4]	91%	32.89	3		1.00	0.17	Refs.[10] [17] [48] [60]
	Anxiety	–0.44[–0.91, 0.03]	—	—	—		1.83	0.07	Ref. [17]
	Total	–0.33[–0.72, 0.06]	89%	37.91	4		1.67	0.10	—

However, studies on the effect of proximity to urban blue spaces on negative emotions show inconsistent results. Meta-analysis results suggest that the proximity to urban blue spaces does not significantly influence negative emotions (SMD = –0.33, p = 0.10), especially for children^[40][53]. This may be due to the complex and diverse factors influencing negative emotions, where blue space exposure, though helpful in alleviating anxiety and stress, may play a limited role, especially in treating mental disorders like depression. Additionally, different age groups may have varying attitudes and usage patterns of urban blue spaces, with adults possibly requiring more emotional solace and psychological recovery from natural landscapes, enhancing the positive effects of urban blue spaces on their mental health^[40]. Lastly, geographical variety can also impact exposure effects. For example, a study in the UK proved the positive effects of proximity to the coast on residents' mental health^[18], while in relatively small nations with islands^[53] or sparsely populated areas like Canada^[40], the effect of proximity is not significant.

(2) Availability

The availability of urban blue spaces is significantly positively correlated with residents' general mental health (SMD = 0.16, p < 0.0001) and positive psychology (SMD = 0.91, p < 0.0001) (Tab.5). In other words, residents living in areas with blue spaces nearby or more blue spaces have better self-assessed mental health and stronger happiness. Studies suggest that the availability of urban blue spaces may have a greater effect in enhancing residents' mental health through improved environmental perception and physical activities^[49][58], compared with the effect of green spaces^[45]. Additionally, studies have found that childhood residential environments would affect their mental health in adulthood. A study in Denmark found that a higher proportion of blue spaces within 1 km of childhood residences was associated with greater calmness in adulthood, though the effect on mental health was not statistically significant, the regression coefficient was positive^[62]. Consistent outcome has been validated with studies in China^[14][60], England^[10], Bulgaria^[50], etc. Besides, some studies examine the impact of urban blue spaces around residential areas and travel paths by considering residents' spatio-temporal behaviors. For example, a study in China found that urban blue spaces around daily travel paths (≤ 200 m) had a more significant impact on individual mental health^[14], while a study in the Netherlands did not find significant difference in health effects with urban blue spaces around residential areas and travel paths^[20]. Overall, the availability of urban blue spaces is confirmed to be beneficial for mental health, but the specific impact may vary due to geographical and individual behavioral variety.

Tab.5 Effect size aggregation results of the availability of urban blue spaces on residents' mental health (n = 13)

		SMD (95%CI)	Heterogeneity test				Statistical test		Literature
			I2	Chi2	Df		z	p
General mental health		0.16[0.08, 0.24]	100%	1, 106.70	5		4.04	< 0.0001	Refs.[10] [18] [35] [45] [49] [62]
Positive psychology	Subjective happiness	1.02[0.26, 1.79]	72%	7.15	2		2.62	0.009	Refs. [53] [60] [61]
	Life satisfaction	0.87[0.48, 1.26]	—	—	—		4.38	< 0.0001	Ref. [60]
	Total	0.91[0.47, 1.35]	58%	7.21	3		4.06	< 0.0001	—
Negative psychology	Depression	–0.33[–0.72, 0.06]	99%	422.21	6		2.52	0.01	Refs.[10] [12] [20] [21] [36] [60] [61]

Furthermore, the meta-analysis results suggest that the effect of the accessibility of urban blue spaces on psychological disorders such as depression is not significant (SMD = –0.33, p = 0.01), and the research findings vary. Although some studies have found positive effects of urban blue spaces in specific cases—for example, a study in Scotland found that elderly community members with high freshwater coverage had lower frequencies of antidepressant intake^[12]; a study in China revealed that the residents living within a 300-m buffer to blue spaces reported significantly less depression^[60]—most studies did not find significant effects. Examples included the studies about children in USA^[47] and New Zealand^[53], adults in the Netherlands^[20] and Spain^[36], and among cohort of Alzheimer and families in Spain^[17]. These findings suggest that while urban blue spaces may benefit certain groups, their effects on preventing or alleviating mental disorders may be limited, requiring further research to explore the influencing mechanisms and conditions.

(3) Visibility

Due to data availability constraints, only four studies explored the association between the visibility of urban blue spaces and residents' mental health. Although the meta-analysis did not cover these studies for effect size conversion reasons, they all found that the visibility of urban blue spaces not only helps residents' self-reported mental health but also has significant positive effects on negative psychology. Daniel Nutsford is one of the earliest scholars focusing on the health effects of the visibility of urban blue space. His study in New Zealand found that residents with ocean views had lower psychological stress even when controlling other factors such as proximity to urban blue spaces^[46]. Similar conclusions were validated in a study in Ireland, where residents with high visibility of coastal views from their homes had a lower risk of depression, and visibility had a more significant effect than proximity^[48]. Recently, Chinese scholars have also analyzed the effects of blue space visibility on residents' mental health based on street view images in Beijing^[54] and Guangzhou^[15], finding that a higher proportion of blue space area in the images can reduce the risk of depression in the elderly and improve mental health.

4.2.2 Impact of Urban Blue Space Characteristics on Residents' Mental Health Through Individual-based Measurement

In the selected literature, 25 individual-based studies explored the association between urban blue space characteristics and residents' mental health by examining individual behavioral characteristics (e.g., visit frequency, types of blue spaces visited, visiting distance) and exposure types. Due to the diverse measurement methods of these studies, meta-analysis is not feasible; instead, this paper qualitatively summarized these studies in both aspects.

(1) Individual behavioral characteristics

Scholars have found that individuals who frequently visit urban blue spaces (at least once a week) generally have higher subjective well-being^[13][32] and lower mental distress^[61] and inattention^[42]. Visiting urban blue spaces more frequently in childhood also positively influences mental health in adulthood^[65]. Different types of urban blue spaces lead to varied mental health effects. For instance, a study in England found that frequent visits (more than twice a month) to rivers, canals, and seas are positively correlated with better mental health, while frequent visits to lakes show no significant correlation^[68]. This may be due to the different spatial experiences and physical activities provided by various types of urban blue spaces. In addition to visit frequency, studies indicate that whether residents visit urban blue spaces^[55], the visibility of blue spaces, the likelihood of visiting blue spaces during the pandemic^[63], and the characteristics of blue spaces along recreational routes^[64] are important factors affecting mental health. In addition, although beaches, harbor areas, and other waterside areas can significantly promote restorative benefits^[39][41], blue spaces' associations with depressive symptoms and stress are debatable^[20][66], especially for the areas with a low amount of blue spaces where green spaces may overwhelm blue spaces in alleviating negative psychology^[51].

Experimental studies, compared with observational studies such as cross-sectional analysis, often evaluate the benefits of physical activities (e.g., walking^[59], surfing^[43]) in urban blue spaces on mental health. These studies compare changes in physiological indicators (e.g., pulse, blood pressure) before and after activities, revealing that physical activities in urban blue spaces can promote restorative experience^[34] and perception^[64], and psychological improvement^[33][52]. For example, a study in Spain found that members of the experimental group who walked in urban blue spaces reported significantly enhanced well-being and mood after the activity^[59]. This positive effect is not evident in the general population but also in individuals with severe psychological disorders. For instance, PTSD patients who participated in surfing sessions showed significant symptom relief^[43]. Similar findings have been validated in studies conducted in Spain^[33], USA^[34], and UK^[56]. Experimental studies also found that different types of urban blue spaces have varying effects on residents' restoration. For example, the restorative capability of beaches is about 30% higher than that of harbors^[69].

(2) Exposure types

Only a few studies have evaluated the impact of exposure types to urban blue spaces on residents' mental health. For instance, a study in Hong Kong, China found that intentional exposure to blue spaces was positively correlated with higher well-being, while indirect exposure (viewing from home) was positively correlated with better self-perceived physical health but had no significant association with mental health^[32]. A study in Guangzhou, China discovered that incidental exposure to blue spaces from travel had more significant mental health benefits than indirect exposure^[14]. Additionally, short-term intentional exposure to urban blue spaces positively impacts mental health. Studies in UK indicated that residents who recently and intentionally visited blue spaces like rivers and canals reported higher life satisfaction and better mental health^[38][67]. Another study in Spain compared the effects of intentional visits to urban parks and beaches on psychological restoration, finding that blue space visitors reported higher relaxation and attention restoration^[11]. These findings suggest that different exposure types to urban blue spaces have varying impacts on residents' mental health.

5 Conclusions and Discussion

This paper, as a meta-analysis, focuses on the effect of urban blue space characteristics on residents' mental health, systematically reviewing the measurement indicators, research models, and mental health effects among relevant literature, with main findings as follows. First, urban blue space characteristics can be measured from space-based dimension (i.e., proximity, availability, visibility) and individual-based dimension (e.g., individual visit characteristics, exposure types). Second, the impact of urban blue space characteristics on mental health is mainly measured by general mental health, positive psychology (self-reported well-being, perceived restorativeness, and positive emotions), and negative psychology (negative emotions and mental disorder). Third, the effect of urban blue space characteristics on residents' mental health varies significantly across studies, particularly concerning psychological disorders such as depression and stress disorders. Specifically, the proximity to urban blue spaces positively affects general mental health and positive psychology, while the accessibility of blue spaces is significantly positively correlated with general mental health and positive psychology; although factors like visibility, visit frequency, and exposure types influence mental health, related research is still limited, necessitating further exploration in the future.

Existing research on the health benefits of natural environments often focuses on green spaces. This study's findings reveal that blue spaces are equally valuable for residents' mental health. By quantifying the effects of urban blue space characteristics on general mental health and positive psychology, this meta-analysis supports the view that blue spaces can positively influence mental health. In terms of the effects of green spaces on mental health, researchers found that exposure to green spaces effectively alleviates negative psychology such as anxiety and depression^[23]. This study's results show that the proximity to urban blue spaces has a similar effect size to green spaces in alleviating anxiety, while the impact of proximity and accessibility on depression and anxiety remains controversial. Blue spaces are important components in most cities—the median distance of urban residents to freshwater bodies is only 3.1 km^[7]—thus, analyzing the health effects of urban blue spaces and leveraging their health-promoting capabilities are significant for the construction of healthy cities.

Although existing research has yielded substantial results, future studies should focus on the following aspects.

1) Increasing empirical research in developing countries and inland cities. The health benefits of urban blue spaces vary significantly in different geographical contexts. Most current studies are conducted in developed countries in Europe and America, while fewer studies in developing countries—limited empirical research in China also focuses on coastal cities like Guangzhou and Hong Kong, with insufficient attention to inland cities. In the future, empirical studies should be conducted in more areas, especially in developing countries and inland cities, to enrich the evidences on the mental health benefits of urban blue spaces.

2) Exploring refined measurement of urban blue space and mental health indicators. Current measurements of the characteristics of urban blue spaces are mostly based on large-scale land use data or remote sensing data, ignoring the impact of numerous small-scale urban blue spaces on residents' mental health. Ponds and lakes, for example, are the most commonly found water bodies on the earth, which are crucial for freshwater biodiversity and playing a significant role in ecosystem services^[75]. Moreover, more experimental research should be performed with physiological mental health data (e.g., heart rate, blood pressure) to refine the methods and systems of measurement indicators.

3) Expanding research on the effect of urban blue space characteristics on mental health. First, current studies mainly focus on a single type of blue space (e.g., oceans or inland blue spaces), and future studies need to explore the differences of varied inland blue space types (e.g., rivers, lakes, ponds) in health effects. Second, most studies examine the impact of quantitative indicators of blue spaces (e.g., proximity, availability) on mental health, and future research should also explore the impact of qualitative factors of blue spaces on mental health. Third, evaluations on urban blue space characteristics are often conducted from either space-based or individual-based dimensions, and future studies should combine travel data to comprehensively analyze the differences of mental health effects of blue space characteristics.

4) Deepening the exploration of influencing mechanisms. Current research rarely explores the influencing mechanisms of urban blue spaces on residents' mental health, especially the unique mechanisms of blue spaces own. Future research should put more efforts in investigating the effects of factors such as sound environment, physical activity, and social interaction.

References

[1] Leng, H. , Yan, T. , & Yan, Q. (2022) Research progress on mental health effect of blue-green space and its enlightenments. Urban Planning International, 37 ( 2), 34– 43.

[2] Fu, X., Zhang, K., Chen, X., & Chen, Z. (2023). Report on National Mental Health Development in China (2021–2022). Social Sciences Academic Press (China).

[3] United Nations. (2015). Transforming our world: The 2030 Agenda for Sustainable Development.

[4] Mills, C. (2018) From 'invisible problem' to global priority: The inclusion of mental health in the Sustainable Development Goals. Development and Change, 49 ( 3), 843– 866.

[5] Volker, S. , & Kistemann, T. (2011) The impact of blue space on human health and well-being—Salutogenetic health effects of inland surface waters: A review. International Journal of Hygiene and Environmental Health, 214 ( 6), 449– 460.

[6] Smith, N. , Georgiou, M. , King, A. C. , Tieges, Z. , Webb, S. , & Chastin, S. (2021) Urban blue spaces and human health: A systematic review and meta-analysis of quantitative studies. Cities, ( 119), 103413.

[7] Kummu, M. , de Moel, H. , Ward, P. J. , & Varis, O. (2011) How close do we live to water? A global analysis of population distance to freshwater bodies. PloS ONE, 6 ( 6), e20578.

[8] White, M. P. , Elliott, L. R. , Gascon, M. , Roberts, B. , & Fleming, L. E. (2020) Blue space, health and well-being: A narrative overview and synthesis of potential benefits. Environmental Research, ( 191), 110169.

[9] Zhang, J. , Yu, Z. , & Zhao, B. (2020) Impact mechanism of urban green spaces in promoting public health: Theoretical framework and inspiration for practical experiences. Landscape Architecture Frontiers, 8 ( 4), 104– 113.

[10] Garrett, J. K. , Clitherow, T. J. , White, M. P. , Wheeler, B. W. , & Fleming, L. E. (2019) Coastal proximity and mental health among urban adults in England: The moderating effect of household income. Health & Place, ( 59), 102200.

[11] Subiza-Perez, M. , Vozmediano, L. , & San Juan, C. (2020) Green and blue settings as providers of mental health ecosystem services: Comparing urban beaches and parks and building a predictive model of psychological restoration. Landscape and Urban Planning, ( 204), 103926.

[12] McDougall, C. W. , Hanley, N. , Quilliam, R. S. , Bartie, P. J. , Robertson, T. , Griffiths, M. , & Oliver, D. M. (2021) Neighbourhood blue space and mental health: A nationwide ecological study of antidepressant medication prescribed to older adults. Landscape and Urban Planning, ( 214), 104132.

[13] White, M. P. , Pahl, S. , Wheeler, B. W. , Depledge, M. H. , & Fleming, L. E. (2017) Natural environments and subjective wellbeing: Different types of exposure are associated with different aspects of wellbeing. Health & Place, ( 45), 77– 84.

[14] Zhang, L. , Zhou, S. , Kwan, M.-P. , & Shen, M. (2021) Assessing individual environmental exposure derived from the spatiotemporal behavior context and its impacts on mental health. Health & Place, ( 71), 102655.

[15] Liu, Y. , Wang, R. , Lu, Y. , Li, Z. , Chen, H. , Cao, M. , ... & Song, Y. (2020) Natural outdoor environment, neighbourhood social cohesion and mental health: Using multilevel structural equation modelling, streetscape and remote-sensing metrics. Urban Forestry & Urban Greening, ( 48), 126576.

[16] White, M. , Smith, A. , Humphryes, K. , Pahl, S. , Snelling, D. , & Depledge, M. (2010) Blue space: The importance of water for preference, affect, and restorativeness ratings of natural and built scenes. Journal of Environmental Psychology, 30 ( 4), 482– 493.

[17] Gascon, M. , Sanchez-Benavides, G. , Dadvand, P. , Martinez, D. , Gramunt, N. , Gotsens, X. , ... & Nieuwenhuijsen, M. (2018) Long-term exposure to residential green and blue spaces and anxiety and depression in adults: A cross-sectional study. Environmental Research, ( 162), 231– 239.

[18] White, M. P. , Alcock, I. , Wheeler, B. W. , & Depledge, M. H. (2013) Coastal proximity, health and well-being: Results from a longitudinal panel survey. Health & Place, ( 23), 97– 103.

[19] Hermanski, A. , McClelland, J. , Pearce-Walker, J. , Ruiz, J. , & Verhougstraete, M. (2022) The effects of blue spaces on mental health and associated biomarkers. International Journal of Mental Health, 51 ( 3), 203– 217.

[20] Roberts, H. , & Helbich, M. (2021) Multiple environmental exposures along daily mobility paths and depressive symptoms: A smartphone-based tracking study. Environment International, ( 156), 106635.

[21] Boers, S. , Hagoort, K. , Scheepers, F. , & Helbich, M. (2018) Does residential green and blue space promote recovery in psychotic disorders? A cross-sectional study in the Province of Utrecht, The Netherlands. International Journal of Environmental Research and Public Health, 15 ( 10), 2195.

[22] Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2013). Introduction to Meta-Analysis (G. Li, M. Wu, & X. Yu, Trans.). Science Press.

[23] Yang, C. , Tan, S. , Gao, Y. , Dong, M. , & Chen, L. (2023) A study on the effects of urban green space on residents' health based on meta-analysis. City Planning Review, 47 ( 6), 1– 21.

[24] Chen, Z. , Zhai, X. , Ye, S. , Zhang, Y. , & Yu, Y. (2016) A meta-analysis of restorative nature landscapes and mental health benefits on urban residents and its planning implication. Urban Planning International, 31 ( 4), 16– 26.

[25] Sarkar, C. , & Webster, C. (2017) Urban environments and human health: Current trends and future directions. Current Opinion in Environmental Sustainability, ( 25), 33– 44.

[26] Xia, S. , Zhang, T. , Xu, S. , & Liu, Q. (2020) Evaluation on neighborhood health performance: A post-occupancy evaluation approach based on socioecological models. Urban Development Studies, 27 ( 2), 24– 30.

[27] Bratman, G. N. , Anderson, C. B. , Berman, M. G. , Cochran, B. , de Vries, S. , Flanders, J. , ... & Daily, G. C. (2019) Nature and mental health: An ecosystem service perspective. Science Advances, 5 ( 7), eaax0903.

[28] Yang, C. , Tan, S. , & Dong, M. (2021) Urban green space health influence based on ESs: Service function, connotation and mechanism. Chinese Landscape Architecture, 37 ( 3), 32– 37.

[29] Kaplan, S. (1995) The restorative benefits of nature: Toward an integrative framework. Journal of Environmental Psychology, 15 ( 3), 169– 182.

[30] Kaplan, S., & Talbot, J. F. (1983). Psychological Benefits of a Wilderness Experience. In: Behavior and the Natural Environment. Plenum Press.

[31] Kellert, S. R., & Wilson, E. O. (Eds.). (1993). The Biophilia Hypothesis. Island Press.

[32] Garrett, J. K. , White, M. P. , Huang, J. , Ng, S. , Hui, Z. , Leung, C. , ... & Wong, M. C. S. (2019) Urban blue space and health and wellbeing in Hong Kong: Results from a survey of older adults. Health & Place, ( 55), 100– 110.

[33] Triguero-Mas, M. , Gidlow, C. J. , Martinez, D. , de Bont, J. , Carrasco-Turigas, G. , Martinez-Iniguez, T. , ... & Nieuwenhuijsen, M. J. (2017) The effect of randomised exposure to different types of natural outdoor environments compared to exposure to an urban environment on people with indications of psychological distress in Catalonia. PloS ONE, 12 ( 3), e0172200.

[34] Nicolosi, V. , Wilson, J. , Yoshino, A. , & Viren, P. (2021) The restorative potential of coastal walks and implications of sound. Journal of Leisure Research, 52 ( 1), 41– 61.

[35] Pasanen, T. P. , White, M. P. , Wheeler, B. W. , Garrett, J. K. , & Elliott, L. R. (2019) Neighbourhood blue space, health and wellbeing: The mediating role of different types of physical activity. Environment International, ( 131), 105016.

[36] Triguero-Mas, M. , Dadvand, P. , Cirach, M. , Martinez, D. , Medina, A. , Mompart, A. , ... & Nieuwenhuijsen, M. J. (2015) Natural outdoor environments and mental and physical health: Relationships and mechanisms. Environment International, ( 77), 35– 41.

[37] Guan, P. , Xu, X. , Xu, N. , & Wang, W. (2020) Analyses of the impact of built environment factors of small public green spaces on public health—A case study on the old city center of Nanjing, Jiangsu Province. Landscape Architecture Frontiers, 8 ( 5), 76– 92.

[38] Van Den Bogerd, N. , Elliott, L. R. , White, M. P. , Mishra, H. S. , Bell, S. , Porter, M. , ... & Fleming, L. E. (2021) Urban blue space renovation and local resident and visitor well-being: A case study from Plymouth, UK. Landscape and Urban Planning, ( 215), 104232.

[39] Korpela, K. M. , Ylen, M. , Tyrvainen, L. , & Silvennoinen, H. (2010) Favorite green, waterside and urban environments, restorative experiences and perceived health in Finland. Health Promotion International, 25 ( 2), 200– 209.

[40] Huynh, Q. , Craig, W. , Janssen, I. , & Pickett, W. (2013) Exposure to public natural space as a protective factor for emotional well-being among young people in Canada. BMC Public Health, ( 13), 407.

[41] White, M. P. , Pahl, S. , Ashbullby, K. , Herbert, S. , & Depledge, M. H. (2013) Feelings of restoration from recent nature visits. Journal of Environmental Psychology, ( 35), 40– 51.

[42] Amoly, E. , Dadvand, P. , Forns, J. , Lopez-Vicente, M. , Basagana, X. , Julvez, J. , ... & Sunyer, J. (2014) Green and blue spaces and behavioral development in Barcelona schoolchildren: The BREATHE Project. Environmental Health Perspectives, 122 ( 12), 1351– 1358.

[43] Rogers, C. M. , Mallinson, T. , & Peppers, D. (2014) High-intensity sports for posttraumatic stress disorder and depression: Feasibility study of ocean therapy with veterans of operation enduring freedom and operation Iraqi freedom. The American Journal of Occupational Therapy, 68 ( 4), 395– 404.

[44] Gilchrist, K. , Brown, C. , & Montarzino, A. (2015) Workplace settings and wellbeing: Greenspace use and views contribute to employee wellbeing at peri-urban business sites. Landscape and Urban Planning, ( 138), 32– 40.

[45] de Vries, S. , ten Have, M. , van Dorsselaer, S. , van Wezep, M. , Hermans, T. , & de Graaf, R. (2016) Local availability of green and blue space and prevalence of common mental disorders in the Netherlands. BJPsych Open, 2 ( 6), 366– 372.

[46] Nutsford, D. , Pearson, A. L. , Kingham, S. , & Reitsma, F. (2016) Residential exposure to visible blue space (but not green space) associated with lower psychological distress in a capital city. Health & Place, ( 39), 70– 78.

[47] Bezold, C. P. , Banay, R. , Coull, B. A. , Hart, J. E. , James, P. , Kubzansky, L. D. , ... & Laden, F. (2018) The association between natural environments and depressive symptoms in adolescents living in the United States. Journal of Adolescent Health, 62 ( 4), 488– 495.

[48] Dempsey, S. , Devine, M. T. , Gillespie, T. , Lyons, S. , & Nolan, A. (2018) Coastal blue space and depression in older adults. Health & Place, ( 54), 110– 117.

[49] Dzhambov, A. M. , Markevych, I. , Hartig, T. , Tilov, B. , Arabadzhiev, Z. , Stoyanov, D. , ... & Dimitrova, D. D. (2018) Multiple pathways link urban green- and bluespace to mental health in young adults. Environmental Research, ( 166), 223– 233.

[50] Dzhambov, A. M. (2018) Residential green and blue space associated with better mental health: A pilot follow-up study in university students. Archives of Industrial Hygiene and Toxicology, 69 ( 4), 340– 349.

[51] Völker , S, . , Heiler, A. , Pollmann, T. , Claßen , T, . , Hornberg, C. , & Kistemann, T. (2018) Do perceived walking distance to and use of urban blue spaces affect self-reported physical and mental health?. Urban Forestry & Urban Greening, 29 , 1– 9.

[52] Arnberger, A. , Eder, R. , Allex, B. , Ebenberger, M. , Hutter, H.-P. , Wallner, P. , ... & Frank, T. (2018) Health-related effects of short stays at mountain meadows, a river and an urban site—Results from a field experiment. International Journal of Environmental Research and Public Health, 15 ( 12), 2647.

[53] Mavoa, S. , Lucassen, M. , Denny, S. , Utter, J. , Clark, T. , & Smith, M. (2019) Natural neighbourhood environments and the emotional health of urban New Zealand adolescents. Landscape and Urban Planning, ( 191), 103638.

[54] Helbich, M. , Yao, Y. , Liu, Y. , Zhang, J. , Liu, P. , & Wang, R. (2019) Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China. Environment International, ( 126), 107– 117.

[55] Benita, F. , Bansal, G. , & Tuncer, B. (2019) Public spaces and happiness: Evidence from a large-scale field experiment. Health & Place, ( 56), 9– 18.

[56] Maund, P. R. , Irvine, K. N. , Reeves, J. , Strong, E. , Cromie, R. , Dallimer, M. , & Davies, Z. G. (2019) Wetlands for wellbeing: Piloting a nature-based health intervention for the management of anxiety and depression. International Journal of Environmental Research and Public Health, 16 ( 22), 4413.

[57] Chen, Y. , Yuan, Y , Zhou, Y. , & Liu, Y. (2020) The neighborhood effect of exposure to green and blue space on the elderly's health: A case study of Guangzhou, China. Scientia Geographica Sinica, 40 ( 10), 1679– 1687.

[58] Chen, Y. , & Yuan, Y. (2020) The neighborhood effect of exposure to blue space on elderly individuals' mental health: A case study in Guangzhou, China. Health & Place, ( 63), 102348.

[59] Vert, C. , Gascon, M. , Ranzani, O. , Marquez, S. , Triguero-Mas, M. , Carrasco-Turigas, G. , ... & Nieuwenhuijsen, M. (2020) Physical and mental health effects of repeated short walks in a blue space environment: A randomised crossover study. Environmental Research, ( 188), 109812.

[60] Liu, H. , Ren, H. , Remme, R. P. , Nong, H. , & Sui, C. (2021) The effect of urban nature exposure on mental health—A case study of Guangzhou. Journal of Cleaner Production, ( 304), 127100.

[61] White, M. P. , Elliott, L. R. , Grellier, J. , Economou, T. , Bell, S. , Bratman, G. N. , ... & Fleming, L. E. (2021) Associations between green/blue spaces and mental health across 18 countries. Scientific Reports, ( 11), 8903.

[62] Engemann, K. , Svenning, J.-C. , Arge, L. , Brandt, J. , Bruun, M. T. , Didriksen, M. , ... & Pedersen, C. B. (2021) A life course approach to understanding associations between natural environments and mental well-being for the Danish blood donor cohort. Health & Place, ( 72), 102678.

[63] Severin, M. I. , Vandegehuchte, M. B. , Hooyberg, A. , Buysse, A. , Raes, F. , & Everaert, G. (2021) Influence of the Belgian Coast on well-being during the COVID-19 pandemic. Psychologica Belgica, 61 ( 1), 284– 295.

[64] Zhou, S. , Huang, C. , & Zhang, L. (2021) Impacts of urban park environment on individual restorative perception and design implications: A case study of adolescent activity environment perception. Landscape Architecture, 28 ( 5), 16– 22.

[65] Vitale, V. , Martin, L. , White, M. P. , Elliott, L. R. , Wyles, K. J. , Browning, M. H. E. M. , ... & Fleming, L. E. (2022) Mechanisms underlying childhood exposure to blue spaces and adult subjective well-being: An 18-country analysis. Journal of Environmental Psychology, ( 84), 101876.

[66] Poulsen, M. N. , Nordberg, C. M. , Fiedler, A. , DeWalle, J. , Mercer, D. , & Schwartz, B. S. (2022) Factors associated with visiting freshwater blue space: The role of restoration and relations with mental health and well-being. Landscape and Urban Planning, ( 217), 104282.

[67] Bergou, N. , Hammoud, R. , Smythe, M. , Gibbons, J. , Davidson, N. , Tognin, S. , ... & Mechelli, A. (2022) The mental health benefits of visiting canals and rivers: An ecological momentary assessment study. PloS ONE, 17 ( 8), e0271306.

[68] McDougall, C. W. , Hanley, N. , Quilliam, R. S. , & Oliver, D. M. (2022) Blue space exposure, health and well-being: Does freshwater type matter?. Landscape and Urban Planning, ( 224), 104446.

[69] Hooyberg, A. , Michels, N. , Allaert, J. , Vandegehuchte, M. B. , Everaert, G. , De Henauw, S. , & Roose, H. (2022) 'Blue' coasts: Unravelling the perceived restorativeness of coastal environments and the influence of their components. Landscape and Urban Planning, ( 228), 104551.

[70] Wang, H. , Xia, Y. , Sun, D. , Zhang, L. , & Wei, H. (2021) Meta-analysis of the influencing factors of Chinese farmers' subjective well-being. Chinese Journal of Agricultural Resources and Regional Planning, 42 ( 6), 203– 214.

[71] Kwan, M.-P. (2012) The uncertain geographic context problem. Annals of the Association of American Geographers, 102 ( 5), 958– 968.

[72] Kwan, M.-P. (2018) The limits of the neighborhood effect: Contextual uncertainties in geographic, environmental health, and social science research. Annals of the American Association of Geographers, 108 ( 6), 1482– 1490.

[73] Perchoux, C. , Chaix, B. , Cummins, S. , & Kestens, Y. (2013) Conceptualization and measurement of environmental exposure in epidemiology: Accounting for activity space related to daily mobility. Health & Place, ( 21), 86– 93.

[74] Kwan, M.-P. (2018b) The neighborhood effect averaging problem (NEAP): An elusive confounder of the neighborhood effect. International Journal of Environmental Research and Public Health, 15 ( 9), 1841.

[75] Biggs, J. , von Fumetti, S. , & Kelly-Quinn, M. (2017) The importance of small waterbodies for biodiversity and ecosystem services: Implications for policy makers. Hydrobiologia, ( 793), 3– 39.

Acknowledgements

·Project of "Old Residential Communities in Large Cities of Central China: Changes, Effects and Policy Implications—A Case Study of Wuhan, " National Natural Science Foundation of China (No. 42171203) ·Project of "Examining the Immigrants' Spatial-Temporal Behavioral Segregation Using Multi-Source Data: Implications for Social Integration, " Young Scientist Fund of the National Natural Science Foundation of China (No. 42301255)

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