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Article

Effects of the Natural Environment on the Subjective and Psychological Well-Being of Older People in the Community in China

by
Kangcheng Gong
,
Chendi Wang
* and
Jun Yin
Department of Construction Management and Real Estate, School of Economics and Management, Nanjing Tech University, No. 30 Puzhu South Road, Nanjing 211816, China
*
Author to whom correspondence should be addressed.
Buildings 2024, 14(9), 2854; https://doi.org/10.3390/buildings14092854
Submission received: 21 July 2024 / Revised: 6 September 2024 / Accepted: 6 September 2024 / Published: 10 September 2024
(This article belongs to the Special Issue Metrology for Living Environment and Comfort)
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Abstract

:
As people age, their activity radius gradually narrows, leading to increased interaction with the community environment. The natural environment (NE) within the community may influence the subjective and psychological well-being (SPWB) of older people (i.e., air quality, noise, green space, and blue space). To enhance the NE and promote the SPWB of older people, this study explored the effect of the community NE on the SPWB of older people. A questionnaire survey involving 180 older people from three communities was collected, alongside observations and measurements of the NE within the community. Finally, a NE-SPWB model was established, indicating that smell (air), ambient noise, green space areas, water landscapes, and smell (water) all positively influenced the SPWB of older people. Based on these findings, recommendations were proposed to enhance community environmental conditions, such as improving water landscapes and green spaces, to further enhance the well-being of older people.

1. Introduction

The aging population not only shifts the demographic distribution of society but also presents new challenges for public policy and individual well-being. Well-being is a multidimensional psychological experience influenced by various factors, such as health status, social relationships, income, and environmental factors [1,2]. For older people, subjective and psychological well-being (SPWB) has been considered an essential criterion for measuring successful aging [3,4]. Older people prefer to age in place in China, meaning most choose to live in their own homes and communities rather than in care homes or nursing institutions [5]. The communities provide environmental and social support for the residents [6]. However, older people’s activity ranges become narrower due to physical declination, and they spend most of their time in the communities engaging in social activities, physical exercise, leisure activities, and so on [7,8]. As the most frequently exposed environment, a supportive, comfortable, and healthy natural environment (NE) in the community contributes to maintaining their SPWB.
Because of the physical, mental, and cognitive health benefits of the NE, it may have a therapeutic effect on older people and subsequently improve their SPWB [9,10]. A satisfactory NE, such as fresh air, pleasant scenery, and green space, may help increase outdoor activities, alleviate stress, and uplift the spirits of older people [7,11]. People are instinctively drawn to nature, and interacting with NEs can help alleviate declines in physical and cognitive functions in older adults while improving their mental health and overall well-being [12,13,14]. However, due to the acceleration of urbanization and the degradation and fragmentation of natural environments [15], the NE in communities may not be as conducive to promoting well-being [16]. Inappropriate NEs may lead to reduced access to green and blue spaces and increased air pollution or noise [17]. Although previous studies have shown a positive relationship between general community environmental conditions and the well-being of older people [18], it is still unclear how the NE in the communities influences the SPWB of older people. This paper aims to investigate the effects of NEs on the SPWB of older people and offer practical recommendations to improve the NE in communities and enhance the SPWB of older people.

2. Literature Review

2.1. Natural Environment

The NE encompasses the acoustic environment, green space, blue space, and air quality [19,20,21]. Due to physical decline and reduced mobility [22], older people’s daily travel ranges and frequency tend to decrease [23]. The community has become the main living space and environmental setting for older people. In addition, they also display a fondness for NEs [24]. A NE is increasingly recognized as a potential buffer against poor mental health [25,26,27]. The air quality indicates the pollution levels in the air and whether it is healthy for human inhalation [28]. In China’s newly revised air quality standards of 2012, the Chinese Air Quality Standards 2012 broadly encompass six pollutants (SO2, CO, NO2, O3, PM10, and PM2.5) within the monitoring scope, aligning with international standards. In some countries, particularly in Asia, dense industrial activity, large populations, and increased vehicle usage have heightened the impact of air quality on health [29,30]. Long-term exposure to higher levels of PM2.5 and NO2 pollutants increases the risk of respiratory diseases in older people, inevitably leading to a decline in their living quality [31]. Older people in the community who are exposed to unpleasant odors, such as traffic fumes and industrial emissions, may develop anxiety and depressive emotions, leading to decreased satisfaction with the environmental air quality [32]. The average concentration of PM10 in winter is typically higher than in summer [33]. Because of the distinct time–activity patterns, older people tend to experience higher concentrations of PM10 particles in the community compared to other age groups [34].
Noise is an auditory stimulus caused by unwanted sounds, and older people are more sensitive and less able to adapt to it as they age [35]. This ongoing discomfort and stress diminish their positive assessment of life, leading to a decreased living quality [36]. Ambient noise, such as traffic and industrial noise, has been demonstrated to disrupt residents’ sleep and cause annoyance [37]. Older people in high-noise environments may face greater cognitive burdens, such as limitations on their participation in learning and hobbies, which can hinder their pursuit of personal growth [38]. During periods of high noise, older people may be forced to change their daily activities, limiting their autonomy [39]. Not all types of noise have a negative impact on people, and natural sounds often have a soothing effect [40].
Green space typically refers to the open, natural areas within a community or city designed for relaxation and recreation, including green space areas and various plant types [41]. Green space is crucial in supporting residents’ well-being by providing shade, protection, aesthetic value, and cultural significance [42,43]. It has been found that the amount and accessibility of residential green space may affect adults’ mental health, life satisfaction, and cognitive ability [44,45]. When older people in the community are exposed to various plants, their visual and sensory experiences are enriched, contributing to enhanced well-being [46]. Living in high-rise apartment buildings is often associated with poor mental health outcomes [47]. Community residents can also benefit from green and blue spaces, especially in affluent areas [48]. Compared to frequent interactions with green space, living in areas with less green space may increase feelings of social isolation and depression among older people [49].
Blue space typically refers to all visible surface water, including lakes and rivers [50]. Blue spaces offer attractive and accessible areas for older people to exercise and stroll, aiding in their pursuit of the purpose of life [50,51]. As the primary gathering place for community residents, older people may experience increased well-being in such a collective and cohesive atmosphere [52]. Among older people, those who frequently visit blue spaces may experience better psychological well-being [53,54]. Furthermore, a blue space is recognized as a tranquil backdrop in community housing, contrasting with the architectural features of urban environments. This contrast can help alleviate mental stress and promote spiritual relaxation, enhancing overall well-being [55]. Blue spaces also promote physical activities such as walking and fishing among older people, enhancing their autonomy [50].

2.2. Subjective and Psychological Well-Being

The concept of well-being can be divided into various aspects, including subjective and mental well-being and social well-being in public life [56,57]. This study focused on the SPWB. Subjective well-being (SWB) includes living quality, living satisfaction, and self-value [58,59]. It emphasizes individual psychological experiences. It typically refers to the overall perception and evaluation of one’s living conditions [60,61]. Psychological well-being (PWB) includes personal growth, purpose in life, and autonomy [62,63]. It includes self-realization and efforts to understand its significance and pathways [64]. At the same time, PWB needs to fulfill the needs of autonomy, competence, and relationships [65]. Therefore, this study will explore the effects of the NE on older people’s subjective (living quality, life satisfaction, and self-value) and psychological (personal growth, purpose in life, and autonomy) well-being.
Population aging presents challenges, and an age-friendly living environment is needed to enhance the living quality of older people [66]. The SWB is a core indicator of an individual’s internal state, encompassing satisfaction and well-being across various dimensions [67]. Older people often depend on recognizing their self-value and achieving their purpose in life to shape their SWB [68]. After retirement, they often seek to continue receiving affirmation from others [69]. Additionally, many older people in communities enjoy cultivating numerous green plants, creating a unique landscape that reflects their deep connection with the NE and psychological desire to continue fulfilling their personal and social values [70].
Substantial evidence has suggested that spending time in nature can improve PWB [71,72]. For instance, some researchers have found that the landscape visible from community windows is believed to cause stress and anxiety [73]. Removing the landscape may alleviate community residents’ psychological discomfort [74]. Aging generally leads to declining physical abilities and production efficiency among older people [75]. Most older people retire after reaching a certain age, often experiencing a psychological gap between their former work roles and current social needs [76]. One of the manifestations of older people’s pursuits of life and spiritual growth is their strong desire for self-improvement [77]. Through greening activities such as planting green plants, they demonstrate their close association with the NE and enhance their capacity to achieve purpose in life [78].

2.3. Conceptual Model

The neighborhood effect theory supports that the local environmental features can influence people’s thoughts and behaviors [79,80]. Based on the neighborhood effect theory, this study hypothesizes that NEs in the communities may have an impact on older people’s SPWB [81]. A conceptual model was developed accordingly to investigate the relationship between the NE in the communities (i.e., air quality, noise, green space, and blue space) and the SPWB of older people (i.e., living quality, life satisfaction, self-value, personal growth, purpose in life, and autonomy) (see Figure 1).

3. Research Methods

3.1. Study Design

By assessing the NE of the community as a whole, this study aimed to understand how these factors significantly influence the SPWB of older people. A mixed-methods research design combining environmental measurements (i.e., air quality, noise, green space, and blue space) and questionnaire surveys with older residents was used to comprehensively assess how the community environment impacted the well-being of older people [82,83,84]. This study selected three communities in Nanjing as the research cases, primarily due to the general range of community types available. To ensure the completeness of data collection and consistency in research subjects, the following classification criteria were applied for community selection: (1) different types of communities; (2) near a blue space or not; and (3) different plot ratios [85].
The first community was newly built in recent years, with well-developed infrastructure and sufficient public services. The second one is an old community that has been renovated, generally built two or three decades ago and recently renovated, with relatively good infrastructure and some public services. The third old community has not been renovated and was built two or three decades ago, with aging buildings and deficient infrastructure. The basic information for the three communities is shown in Table 1 [86,87,88]. Community A was a renovated old community with 1518 households and a green space rate of 25%; Community B was a newly built community with 3528 households and a green space rate of 30%; Community C was an unrenovated community with about 1000 households and still had a high green space rate of 40%. Participants were randomly selected to ensure representativeness, encompassing a variety of genders, ages, health conditions, and socioeconomic backgrounds. By combining the environmental measurements and questionnaire survey results, this study aimed to further verify each NE factor’s unique contribution to the SPWB of older people.

3.2. Environmental Measurements and Observations

In order to determine the existing situations of the NE in the communities, the air quality and noise levels were measured, and green and blue spaces were observed [89,90,91]. The air quality was monitored from 8 a.m. to 8 p.m. to ensure coverage of the different times of the day and capture potential variations [92]. Given that air quality can be affected by weather, traffic, and industrial activities, continuous monitoring over a week indicated a clearer picture of the trends or patterns [93]. Real-time monitoring data were collected from the National City Air Quality Real-time Release platform, using the monitoring stations nearest to the three communities for data acquisition. The monitoring indices included the AQI (air quality index), PM2.5, PM10, sulfur dioxide, nitrogen dioxide, and carbon monoxide, and the average levels of those pollutants on each day for 7 days were calculated and are shown in bar charts [94,95]. For the noise pollution environmental assessment, this study used a sound level meter (AR844) to measure the noise levels in the community every two hours throughout the day. This study recorded any fluctuations or extreme situations and calculated the average A-weighted noise level to assess the overall community noise levels [96]. Furthermore, the locations and conditions of green and blue spaces were depicted through on-site observations and photography.

3.3. Questionnaire Survey

The targets for the questionnaire survey were residents aged 60 years or older in selected communities. The survey gathered (1) background information; (2) participants’ levels of satisfaction with the community’s NE on a scale from 1 to 5, where 1 means extremely dissatisfied, and 5 means extremely satisfied; (3) self-reported levels of the SWPB on a scale from 1 to 5, where 1 means strongly disagreed, and 5 means strongly agreed (see the supplementary material). The SWB items were developed by Diener [58], and the PWB items were developed by Ryff [62]. In total, 180 valid questionnaires were obtained from three communities. The questionnaire survey was conducted through a face-to-face interview. In the way of “I ask, and you answer”, the trained volunteers asked the questions on the survey and marked the answers given by the older participants [97]. From September to November 2023, 226 questionnaires were distributed across three communities. Finally, 180 validated questionnaires were successfully retrieved, resulting in an overall response rate of 79.64% (i.e., 81.33% in Community A, 78.87% in Community B, and 78.75% in Community C). To reveal the relationships between the NE and SPWB of older persons, the data collected from the questionnaire survey were analyzed using Pearson’s correlation analysis and multiple regression analysis [18,98]. An integrated NE-SPWB model was developed on the basis of the correlation and regression analyses [99].

4. Results

4.1. Environmental Results

4.1.1. Air Quality

Continuous air quality data were collected for one week from the respective monitoring stations of the three communities, including the AQI, PM2.5, PM10, sulfur dioxide, nitrogen dioxide, carbon monoxide, and ozone. The time series charts depicting changes in the air quality are shown in Figure 2. According to the Chinese environmental air quality standards [100], the air quality index (AQI) ranges from 0 to 50 for Grade I (Excellent), 51 to 100 for Grade II (Good), and 101 to 150 for Grade III (Moderate pollution). Throughout the observation period, almost no AQI readings exceeded 50. Nearly all the indicators remained excellent during the observation period, indicating good air quality conditions. Notably, the PM2.5 levels in the three communities occasionally approached or exceeded 35 µg/m3, and the PM10 levels occasionally surpassed 50 µg/m3.

4.1.2. Noise

The noise levels in the three communities were measured in the primary living spaces of older people, which primarily included parks and squares. Each community had at least three testing points distributed as evenly as possible. Using an AR844 sound level meter, sound pressure level data were recorded at each point for a minimum of five minutes. Each point should be measured at least 5 times daily, with all noise measurements conducted between 10:00 a.m. and 8:00 a.m. on October and November 2023. The measurement indicators included the Lmax (maximum sound level), Lmin (minimum sound level), and the average sound pressure level. During the measurements, the sound level meter’s microphone was positioned at least 1.2 m above the ground and 1.5 m from the nearest reflecting surface. To minimize the influence of weather conditions on the experimental data, the measurements were conducted on clear days with minimal or no wind. The results indicated that among the nine measurement points across the three communities, the noise levels ranged from 45 to 56 dBA (see Table 2), with nearly all points registering noise levels below 55 dBA. These levels met the international standards for noise in urban quiet areas. However, at locations such as A 1 and B 1, the maximum noise levels were relatively high, primarily due to traffic noise, such as vehicles and a small number of birds and other animal sounds.

4.1.3. Green Space

The distribution and picture of the green space in the three communities are shown as follows (see Figure 3). It was evident that green spaces were predominantly situated along the edges of roads. Compared with Community C, the green areas in Community B and Community C were smaller, at nearly 30 percent. However, they still offered ample green resources, allowing residents to connect with nature. During the field survey, we assessed the distribution of green space in each community and mapped them accordingly. The main green space in these three communities was predominantly semi-open green space. Additionally, the green spaces were mainly arranged in strips or blocks, with a higher percentage of shrubs.

4.1.4. Blue Space

The three communities selected for the survey exhibited varying blue space conditions. According to the 2023 Nanjing Ecological Environment Status Report, Nanjing’s overall water environment quality was extremely high [101]. All 42 surface water monitoring sections met or exceeded the Class III water quality standards, with no water quality being worse than Class V. Community A was adjacent to the broad river, with an excellent source condition and favorable blue space smell (see Figure 3). According to the local community, the river’s water quality was poor a few years ago but has gradually improved through governance efforts to its current state [101]. The other two communities (B and C) did not have access to a natural water body like a broad river.

4.2. Survey Results

4.2.1. Pearson’s Correlation Analysis

Pearson’s correlation analysis was conducted to explore the relationship between SPWB and the NE (see Table 3). The correlation between each factor was statistically significant as “*” (p < 0.05) or “**” (p < 0.01). Pearson’s correlation analysis revealed that (1) living quality and life satisfaction were significantly positively correlated with all of the natural factors except green space areas and types; (2) purpose in life was significantly positively correlated with all of the natural factors; (3) personal growth was significantly positively correlated with all of the natural factors except green space types; (4) except for green spaces and types, autonomy was significantly positively correlated with all of the natural factors; (5) self-value was only positively correlated with airborne particles, community noise, and water landscape, and negatively correlated with green space areas. Overall, the green space type was less important for the SPWB of older persons than other variables belonging to the NE.

4.2.2. Multiple Regression Analysis

Significant natural environmental factors influencing SPWB were analyzed using multiple regression analysis (see Table 4). As the most common metric for evaluating the explanatory power of a regression model, the value of R2 was used in this study to indicate the proportion of the variance that the dependent variable (i.e., SPWB) was explained by the independent variables (i.e., NE) [102]. In model 1, the results showed that the living quality is positively influenced by the ambient noise and water landscape around the community, explaining 17.2% of the variance. In model 2, which explained 18.5% of the variance, both ambient noise and water landscapes were found to positively predict life satisfaction. In model 4, green space areas and smell (water) were found to be positively related to a purpose in life, explaining 25.7% of the variance. In model 6, smell (air) and water landscapes were found to positively impact personal growth, explaining 26.5% of the variance.

4.3. Model Establishment

A comprehensive model integrating Pearson’s correlational analysis and multiple regression analysis was developed to uncover the complex relationships between the SPWB of older people and the NE of the community. To reduce biases in the analysis methods and improve the model’s validity, only relationships confirmed by the correlation and regression analyses were shown in the final NE-SPWB model (see Figure 4). The integrated NE-SPWB model indicated that the various aspects of the NE had significant effects on the SPWB of older people, including that (1) the living quality was significantly affected by ambient noise and water landscapes around the community, with regression coefficients of 0.159 and 0.126, respectively; (2) life satisfaction was affected by ambient noise and water landscapes around the community, with regression coefficients of 0.189 and 0.095, respectively; (3) purpose in life was significantly associated with smell (water) and green space areas, with regression coefficients of 0.226 and 0.149, respectively; (3) personal growth was significantly influenced by water landscapes and smell (air), with regression coefficients of 0.132 and 0.268, respectively.

5. Discussion

Some intolerable smells of air will also seriously affect the SPWB of older people, such as using excessive disinfectants, insecticides, or burning unclean fuel. It may lead to issues like depression and anxiety [103]. No severe air pollution events have been recorded in the three communities’ air quality data collection. Furthermore, occasional spikes in the PM2.5 index are also noteworthy (see Figure 2). High levels of PM2.5 can negatively impact the well-being of older people, such as depression [104]. Poor air quality can lead to various environmental hazards, health issues, and economic losses and even threaten mental and physical health and well-being [105,106,107,108,109]. Rapid urbanization and industrialization are frequently associated with an increased risk of exposure to air pollution [110]. As a result, people may prefer to stay indoors rather than enjoy outdoor activities [111]. It further confirms that air pollution significantly reduces well-being and life satisfaction [112].
According to the NE-SPWB model, the satisfaction of ambient noise positively affected older people’s living quality and life satisfaction. The relatively low environmental noise levels recorded in three communities (see Table 2) help to reduce the stress and anxiety caused by noise for older people, thereby enhancing their well-being. It is consistent with previous studies from other researchers that a quiet living environment has a restorative effect on people’s psychological health [113], while the high level of noise in the community (e.g., communities near the airport) causes residents’ mental illness and decreases their life satisfaction [114]. In a previous study, natural sounds were found to positively impact older people’s well-being [115]. Compared to young people, older adults with poor physical health are more susceptible to the effects of noise [116]. This might be why the ambient noises were identified as a key factor influencing their SWB.
The comprehensive model of NE-SWPB suggested that a green space area in the community has a positive impact on the purpose of life for older people. The research shows that daily activities in green spaces align older adults’ exercise habits with good green space accessibility, benefiting their well-being [117]. This could also mean that older people can achieve their goals, such as exercising and socializing. Additionally, the roadside greening observed in the community may also promote the PWB of older people (see Figure 4). Roadside greening can enhance the beauty of community streets [118]. Studies have also found that features such as sidewalks, benches, and plant species diversity in urban green spaces can affect their use and activities and positively affect people’s mental health [119,120]. As a positive sensory and symbolic resource, green spaces provide users with valuable visual and auditory information, which can encourage more frequent visits and use of the space, creating a pleasant environment [121,122].
In the final model, water landscapes have been proven crucial in enhancing older people’s living quality and life satisfaction. Due to physical limitations, it may be difficult for older people to walk long distances. Walking along a nearby riverbank can reduce stress hormone levels, contributing to increased well-being [50]. Therefore, blue space enhanced the community’s attractiveness, promoting social interaction and personal growth. These natural elements not only enhanced the beauty of the community setting but also offered a respite for the body and mind, affirming the positive effects of hydro landscapes and green spaces on living quality and satisfaction. Some also believe blue space views from home may be particularly important for elderly individuals with limited mobility [123]. Additionally, blue spaces may offer benefits through the following mechanisms: reducing injuries by lowering heat-related mortality, enhancing resilience by alleviating stress, and promoting capacity building by providing opportunities for health-promoting physical activities [51,124,125].

6. Recommendations

6.1. Practical Recommendations

Aside from using monitoring stations to gather data, community or local management departments can employ more advanced technological methods for more precise air quality monitoring. By focusing on monitoring data on the air quality within a community, accurate real-time information can be provided to older people in the community. Additionally, encouraging outdoor activities when the air quality improves can enhance their SPWB. Trees and shrubs (e.g., plane trees or privet) should be added in the communities to absorb pollutants and particulate matter, especially in areas near roads [126]. Moreover, regulations should be applied to encourage factories to adopt cleaner technologies and reduce emissions from industrial sources. The residents could be educated and propagandized about the importance of air quality, leading to their environmental protection behaviors.
Compared to traffic noise, natural sounds can evoke more positive emotions [127]. Therefore, in community sound control, adding plants can increase the presence of birds and insects, and certain plants play a role in reducing noise pollution. Additionally, sound barriers can be set up, especially near bustling traffic areas. Communities or regions can advocate for residents to use quieter forms of transportation, such as electric cars, and enforce nighttime noise regulations to ensure a peaceful environment for older people. Zoning regulations can be implemented to separate noisy commercial or industrial activities from residential areas [128,129]. The quiet zones should be arranged in residential neighborhoods or parks where noise levels are strictly controlled during certain hours.
The green space has been found to strengthen older people’s purpose in life. It encourages older people to take walks outdoors, thereby increasing their SPWB within the community [130]. However, the land resources of a community are ultimately limited. The community can increase the variety of plants grown to provide visual diversity for older people in the community. Additionally, locating green space near the homes of older people reduces their travel distance. It is also suggested that vertical gardens, such as green walls or roofs, could be added to the communities to provide more green space for older people. Policies can be made to advocate and create shared green spaces in schools or other institutions, which can be used by older people in the community during off-hours or weekends [131].
This study highlighted the significance of blue space on older people’s SPWB. However, most communities lack blue space (see Figure 3). Hence, it is necessary to increase and improve blue spaces in the community. Older people prefer aesthetically appealing and diversified landscapes, particularly those easily accessible and well-maintained [132]. Communities can enlarge hydrological features like lakes, waterfalls, and fountains to improve the water quality and aesthetic appeal. They can also develop barrier-free walkways along bodies of water to improve accessibility for older adults, allowing them to enjoy blue spaces, thus enhancing their SPWB. Blue spaces should also be well-managed and cleaned to maintain good water quality. Water gardens with aquatic plants and small bodies of water are advised to be designed for the community [53].

6.2. Research Limitations and Future Study

This study proposes a comprehensive model of NE-SPWB in the community, exploring how community NEs affect the SPWB of older people. However, any research method inherently carries biases that can influence the final results [133]. This study incorporates several mitigating measures to reduce the impact of these biases. Firstly, a model was developed using a questionnaire survey, with the data collected through face-to-face interviews with older people to ensure the accuracy of the answers and improve the data quality. Secondly, two statistical methods, including correlation analysis and multiple regression analysis, confirmed the relationships in the final NE-SPWB model. Thirdly, this study utilized a combination of environmental measurement and questionnaire surveys to fully understand the existing situations in the communities by using objective and subjective data. In future research, the community noise level can be measured over extended periods. Surveys can reveal different types of blue space features (such as fountains, ponds, swimming pools, etc.) to understand their impact on the SPWB of older people. By long-term observation of the changes in community green space areas and layouts, fluctuations in the emotions and well-being of older people can be summarized. Differences in air quality conditions, emphasized during different seasons (like the contrasting environments of winter and summer), can be studied to understand their impact on the SPWB of older people.

7. Conclusions

Older people are more sensitive to various aspects of the community’s NE, such as noise, air quality, and green and blue spaces, influencing their overall well-being. Therefore, it is crucial to investigate the impact of the NE on the SPWB of older people. This study investigated the impact of the NE on the SPWB of older people in the community. Based on the NE-SPWB, it indicates that factors such as the smell (air), ambient noise, green space area, smell (water), and water landscape have a significant impact on the SPWB of older people. Therefore, practical suggestions for enhancing the NE in a community are as follows. For air quality, it is suggested that accurate real-time information be provided by narrowing the monitoring range and encouraging a community’s older population to go out when the air quality is good. For noise in the community, it is recommended to increase greenery and bird habitats, set up sound barriers, especially in areas with high traffic volumes, and enforce nighttime noise restrictions. For green space, it is recommended to increase the variety of plants grown in the community and to locate green spaces near older people’s homes. This study can assist designers and property managers of residential communities in prioritizing the SPWB of older people and aid them in effectively managing and improving the community’s NEs. For blue space, it is advised to enlarge hydrological features such as lakes and fountains and create accessible pedestrian paths along water bodies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/buildings14092854/s1.

Author Contributions

Conceptualization, K.G., C.W. and J.Y.; methodology, K.G. and C.W.; software, K.G. and C.W.; validation, C.W. and J.Y.; formal analysis, K.G. and J.Y.; investigation, K.G.; resources, C.W.; data curation, K.G. and J.Y.; writing—original draft preparation, K.G.; writing—review and editing, C.W.; visualization, K.G. and C.W.; supervision, C.W.; project administration, C.W.; funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript.

Funding

The work described in this paper was supported by the National Natural Science Foundation of China (Grant No. 72401129) and a General Project of the Social Science Foundation in Jiangsu Higher Education Institutions, China (Grant No. 2023SJYB0208).

Data Availability Statement

The air quality data used in this paper are published, open-source data and are available at https://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/dqhjbh/dqhjzlbz/ (accessed on 10 April 2024) and http://sthjj.nanjing.gov.cn/njshjbhj/202401/P020240130409758858417.pdf (accessed on 12 April 2024). The authors collected the other questionnaire and environmental data used in this paper. The data presented in this study are available upon request from the corresponding author (the data are not publicly available due to privacy).

Acknowledgments

We sincerely thank all those who contributed to this paper.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Zhang, Y.; Sun, L. The health status, social support, and subjective well-being of older individuals: Evidence from the Chinese General Social Survey. Front. Public Health 2024, 12, 1312841. [Google Scholar] [CrossRef] [PubMed]
  2. Wang, X.; Shang, X.; Xu, L. Subjective well-being poverty of the elderly population in China. Soc. Policy Admin. 2011, 45, 714–731. [Google Scholar] [CrossRef]
  3. Momtaz, Y.A.; Ibrahim, R.; Hamid, T.A.; Yahaya, N. Sociodemographic predictors of elderly’s psychological well-being in Malaysia. Aging Ment. Health 2011, 15, 437–445. [Google Scholar] [CrossRef] [PubMed]
  4. Tovel, H.; Carmel, S. Maintaining successful aging: The role of coping patterns and resources. J. Happiness Stud. 2014, 15, 255–270. [Google Scholar] [CrossRef]
  5. Li, Y.; Zhang, J.; Luo, H.; Pei, X.; Wu, T.; Jing, J. The role of contextual factors in shaping urban older adults’ intention of institutional care in China: A mixed-methods study. Int. J. Environ Res. Public Health 2023, 20, 4731. [Google Scholar] [CrossRef] [PubMed]
  6. Srivarathan, A.; Lund, R.; Christensen, U.; Kristiansen, M. Social relations, community engagement and potentials: A qualitative study exploring resident engagement in a community-based health promotion intervention in a deprived social housing area. Int. J. Environ Res. Public Health 2020, 17, 2341. [Google Scholar] [CrossRef] [PubMed]
  7. Ren, Z.; Wang, S.; He, M.; Shi, H.; Zhao, H.; Cui, L.; Zhao, J.; Li, W.; Wei, Y.; Zhang, W.; et al. The effects of living arrangements and leisure activities on depressive symptoms of Chinese older adults: Evidence from panel data analysis. J. Affect. Disord. 2024, 349, 226–233. [Google Scholar] [CrossRef]
  8. Ward Thompson, C.; Elizalde, A.; Cummins, S.; Leyland, A.H.; Botha, W.; Briggs, A.; Tilley, S.; Silveirinha de Oliveira, E.; Roe, J.; Aspinall, P.; et al. Enhancing health through access to nature: How effective are interventions in woodlands in deprived urban communities? A quasi-experimental study in Scotland, UK. Sustainability 2019, 11, 3317. [Google Scholar]
  9. Elsadek, M.; Shao, Y.; Liu, B. Benefits of indirect contact with nature on the physiopsychological well-being of elderly people. Herd Health Env. Res. 2021, 14, 227–241. [Google Scholar] [CrossRef]
  10. Li, X.; Liu, H. The influence of subjective and objective characteristics of urban human settlements on residents’ life satisfaction in China. Land 2021, 10, 1400. [Google Scholar] [CrossRef]
  11. Liu, B.P.; Huxley, R.R.; Schikowski, T.; Hu, K.J.; Zhao, Q.; Jia, C.X. Exposure to residential green and blue space and the natural environment is associated with a lower incidence of psychiatric disorders in middle-aged and older adults: Findings from the UK Biobank. BMC Med. 2024, 22, 15. [Google Scholar] [CrossRef] [PubMed]
  12. Qiu, L.; Chen, Q.; Gao, T. The effects of urban natural environments on preference and self-reported psychological restoration of the elderly. Int. J. Environ Res. Public Health 2021, 18, 509. [Google Scholar] [CrossRef] [PubMed]
  13. De Keijzer, C.; Tonne, C.; Sabia, S.; Basagaña, X.; Valentín, A.; Singh-Manoux, A.; María Antó, J.; Alonso, J.; Nieuwenhuijsen, M.J.; Sunyer, J.; et al. Green and blue spaces and physical functioning in older adults: Longitudinal analyses of the Whitehall II study. Environ. Int. 2019, 122, 346–356. [Google Scholar] [CrossRef] [PubMed]
  14. Finlay, J.; Franke, T.; McKay, H.; Sims-Gould, J. Therapeutic landscapes and wellbeing in later life: Impacts of blue and green spaces for older adults. Health Place 2015, 34, 97–106. [Google Scholar] [CrossRef]
  15. Shi, C.; Zhu, X.; Wu, H.; Li, Z. Assessment of urban ecological resilience and its influencing factors: A case study of the Beijing-Tianjin-Hebei urban agglomeration of China. Land 2022, 11, 921. [Google Scholar] [CrossRef]
  16. Andersson, E.; Barthel, S.; Borgström, S.; Colding, J.; Elmqvist, T.; Folke, C.; Gren, Å. Reconnecting cities to the biosphere: Stewardship of green infrastructure and urban ecosystem services. Ambio 2014, 43, 445–453. [Google Scholar] [CrossRef] [PubMed]
  17. Wang, R.; Su, H.; Xu, T.; Jiang, W.; Liu, H.; Wang, W.; Chen, C.; Ma, X.; Chen, Y.; Wang, W. The association between urbanization and depression in the elderly: A network analysis from the complexity science perspective. J. Affect. Disord. 2024, 356, 72–79. [Google Scholar] [CrossRef]
  18. Zhang, Z.; Zhang, J. Perceived residential environment of neighborhood and subjective well-being among the elderly in China: A mediating role of sense of community. J. Environ. Psychol. 2017, 51, 82–94. [Google Scholar] [CrossRef]
  19. MacKerron, G.; Mourato, S. Happiness is greater in natural environments. Global Environ. Chang. 2013, 23, 992–1000. [Google Scholar] [CrossRef]
  20. Reed, S.E.; Boggs, J.L.; Mann, J.P. A GIS tool for modeling anthropogenic noise propagation in natural ecosystems. Environ. Modell. Softw. 2012, 37, 1–5. [Google Scholar] [CrossRef]
  21. Trammell, J.P.; Aguilar, S.C. Natural is not always better: The varied effects of a natural environment and exercise on affect and cognition. Front. Psychol. 2021, 11, 575245. [Google Scholar] [CrossRef]
  22. Carlson, J.A.; Sallis, J.F.; Conway, T.L.; Saelens, B.E.; Frank, L.D.; Kerr, J.; Cain, K.L.; King, A.C. Interactions between psychosocial and built environment factors in explaining older adults’ physical activity. Prev. Med. 2012, 54, 68–73. [Google Scholar] [CrossRef]
  23. Alexander, N.B.; Goldberg, A. Gait disorders: Search for multiple causes. Clev. Clin. J. Med. 2005, 72, 586. [Google Scholar] [CrossRef]
  24. Seresinhe, C.I.; Preis, T.; MacKerron, G.; Moat, H.S. Happiness is greater in more scenic locations. Sci. Rep. 2019, 9, 4498. [Google Scholar] [CrossRef]
  25. Yao, W.; Zhang, X.; Gong, Q. The effect of exposure to the natural environment on stress reduction: A meta-analysis. Urban Urban Gree. 2021, 57, 126932. [Google Scholar] [CrossRef]
  26. Gascon, M.; Triguero-Mas, M.; Martínez, D.; Dadvand, P.; Forns, J.; Plasència, A.; Nieuwenhuijsen, M.J. Mental health benefits of long-term exposure to residential green and blue spaces: A systematic review. Int. J. Environ Res. Public Health 2015, 12, 4354–4379. [Google Scholar] [CrossRef]
  27. Houlden, V.; Weich, S.; Porto de Albuquerque, J.; Jarvis, S.; Rees, K. The relationship between greenspace and the mental wellbeing of adults: A systematic review. PLoS ONE 2018, 13, e0203000. [Google Scholar] [CrossRef]
  28. Qiu, G.; Song, R.; He, S. The aggravation of urban air quality deterioration due to urbanization, transportation and economic development–Panel models with marginal effect analyses across China. Sci. Total Environ. 2019, 651, 1114–1125. [Google Scholar] [CrossRef]
  29. Hopke, P.K.; Cohen, D.D.; Begum, B.A.; Biswas, S.K.; Ni, B.; Pandit, G.G.; Santoso, M.; Chung, Y.; Davy, P.; Markwitz, A.; et al. Urban air quality in the Asian region. Sci. Total Environ. 2008, 404, 103–112. [Google Scholar] [CrossRef]
  30. HEI International Oversight Committee. Health Effects of Outdoor Air Pollution in Developing Countries of Asia: A Literature Review; The Health Effects Institute: Boston, MA, USA, 2004. [Google Scholar]
  31. Brook, R.D.; Rajagopalan, S.; Pope III, C.A.; Brook, J.R.; Bhatnagar, A.; Diez-Roux, A.V.; Holguin, F.; Hong, Y.; Luepker, R.V.; Mittleman, M.A.; et al. Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American Heart Association. Circulation 2010, 121, 2331–2378. [Google Scholar] [CrossRef]
  32. Clougherty, J.E.; Levy, J.I.; Kubzansky, L.D.; Ryan, P.B.; Suglia, S.F.; Canner, M.J.; Wright, R.J. Synergistic effects of traffic-related air pollution and exposure to violence on urban asthma etiology. Environ. Health Persp. 2007, 115, 1140–1146. [Google Scholar] [CrossRef] [PubMed]
  33. Katragkou, E.; Kazadzis, S.; Amiridis, V.; Papaioannou, V.; Karathanasis, S.; Melas, D. PM10 regional transport pathways in Thessaloniki, Greece. Atmos. Environ. 2009, 43, 1079–1085. [Google Scholar] [CrossRef]
  34. Zhou, J.; Han, B.; Bai, Z.; You, Y.; Zhang, J.; Niu, C.; Liu, Y.; Zhang, N.; He, F.; Ding, X.; et al. Particle exposure assessment for community elderly (PEACE) in Tianjin, China: Mass concentration relationships. Atmos. Environ. 2012, 49, 77–84. [Google Scholar] [CrossRef]
  35. Zeng, J.; Peng, J.; Zhao, Y. Comparison of speech intelligibility of elderly aged 60–69 years and young adults in the noisy and reverberant environment. Appl. Acoust. 2020, 159, 107096. [Google Scholar] [CrossRef]
  36. Smith, M.G.; Cordoza, M.; Basner, M. Environmental noise and effects on sleep: An update to the WHO systematic review and meta-analysis. Environ. Health Persp. 2022, 130, 076001. [Google Scholar] [CrossRef] [PubMed]
  37. Shah, B.U.D.; Raj, R.; Kaur, P.; Karim, A.; Bansari, R.B.; Mehmoodi, A.; Malik, J. Association of transportation noise with cardiovascular diseases. Clin. Cardiol. 2024, 47, e24275. [Google Scholar] [CrossRef]
  38. Clark, C.; Paunovic, K. WHO environmental noise guidelines for the European region: A systematic review on environmental noise and cognition. Int. J. Environ Res. Public Health 2018, 15, 285. [Google Scholar] [CrossRef]
  39. Münzel, T.; Sørensen, M.; Gori, T.; Schmidt, F.P.; Rao, X.; Brook, J.; Chen, L.C.; Brook, R.D.; Rajagopalan, S. Environmental stressors and cardio-metabolic disease: Part I–epidemiologic evidence supporting a role for noise and air pollution and effects of mitigation strategies. Eur. Heart J. 2017, 38, 550–556. [Google Scholar] [CrossRef]
  40. Francis, C.D.; Newman, P.; Taff, B.D.; White, C.; Monz, C.A.; Levenhagen, M.; Petrelli, A.R.; Abbott, L.C.; Newton, J.; Burson, S.; et al. Acoustic environments matter: Synergistic benefits to humans and ecological communities. J. Environ. Manag. 2017, 203, 245–254. [Google Scholar] [CrossRef]
  41. Liu, Y.; Kwan, M.P.; Wong, M.S.; Yu, C. Current methods for evaluating people’s exposure to green space: A scoping review. Soc. Sci. Med. 2023, 338, 116303. [Google Scholar] [CrossRef]
  42. Basri, S.; Leksono, A.S.; Yanuwiadi, B. Profile and function of green open space vegetation in Malang. Spec. Divers. 2020, 2, 3–05. [Google Scholar] [CrossRef]
  43. Zhang, J.; Li, D.; Ning, S.; Furuya, K. Sustainable urban green blue space (UGBS) and public participation: Integrating multisensory landscape perception from online reviews. Land 2023, 12, 1360. [Google Scholar] [CrossRef]
  44. Chen, K.; Zhang, T.; Liu, F.; Zhang, Y.; Song, Y. How does urban green space impact residents’ mental health: A literature review of mediators. Int. J. Environ Res. Public Health 2021, 18, 11746. [Google Scholar] [CrossRef] [PubMed]
  45. Astell-Burt, T.; Feng, X. Association of urban green space with mental health and general health among adults in Australia. JAMA Netw. Open 2019, 2, e198209. [Google Scholar] [CrossRef]
  46. Mitchell, R.; Popham, F. Effect of exposure to natural environment on health inequalities: An observational population study. Lancet 2008, 372, 1655–1660. [Google Scholar] [CrossRef]
  47. Barros, P.; Fat, L.N.; Garcia, L.M.; Slovic, A.D.; Thomopoulos, N.; de Sa, T.H.; Morais, P.; Mindell, J.S. Social consequences and mental health outcomes of living in high-rise residential buildings and the influence of planning, urban design and architectural decisions: A systematic review. Cities 2019, 93, 263–272. [Google Scholar] [CrossRef]
  48. Larcombe, E.; Logan, P.; Horwitz, A. High-rise apartments and urban mental health—Historical and contemporary views. Challenges 2019, 10, 34. [Google Scholar] [CrossRef]
  49. Maas, J.; Verheij, R.A.; de Vries, S.; Spreeuwenberg, P.; Schellevis, F.G.; Groenewegen, P.P. Morbidity is related to a green living environment. J. Epidemiol. Commun. Health 2009, 63, 967–973. [Google Scholar] [CrossRef] [PubMed]
  50. Chen, Y.; Yuan, Y. The neighborhood effect of exposure to blue space on elderly individuals’ mental health: A case study in Guangzhou, China. Health Place 2020, 63, 102348. [Google Scholar] [CrossRef]
  51. Elliott, L.R.; White, M.P.; Taylor, A.H.; Herbert, S. Energy expenditure on recreational visits to different natural environments. Soc. Sci. Med. 2015, 139, 53–60. [Google Scholar] [CrossRef]
  52. De Bell, S.; Graham, H.; Jarvis, S.; White, P. The importance of nature in mediating social and psychological benefits associated with visits to freshwater blue space. Landsc. Urban Plan. 2017, 167, 118–127. [Google Scholar] [CrossRef]
  53. Luo, S.; Xie, J.; Furuya, K. Assessing the preference and restorative potential of urban park blue space. Land 2021, 10, 1233. [Google Scholar] [CrossRef]
  54. Luo, S.; Xie, J.; Wang, H.; Wang, Q.; Chen, J.; Yang, Z.; Furuya, K. Natural dose of blue restoration: A field experiment on mental restoration of urban blue spaces. Land 2023, 12, 1834. [Google Scholar] [CrossRef]
  55. Nutsford, D.; Pearson, A.L.; Kingham, S.; Reitsma, F. Residential exposure to visible blue space (but not green space) associated with lower psychological distress in a capital city. Health Place 2016, 39, 70–78. [Google Scholar] [CrossRef]
  56. Ryff, C.D.; Singer, B.H.; Dienberg Love, G. Positive health: Connecting well–being with biology. Philos. Trans. R. Soc. London Series B Bio. Sci. 2004, 359, 1383–1394. [Google Scholar] [CrossRef]
  57. Keyes, C.L.; Shmotkin, D.; Ryff, C.D. Optimizing well-being: The empirical encounter of two traditions. J. Pers. Soc. Psychol. 2002, 82, 1007. [Google Scholar] [CrossRef]
  58. Diener, E. Subjective well-being. Psychol. Bull. 1984, 95, 542. [Google Scholar] [CrossRef] [PubMed]
  59. Ma, L.; Zhang, X.; Ding, X.; Wang, G. Bike sharing and users’ subjective well-being: An empirical study in China. Transport. Res. A Pol. 2018, 118, 14–24. [Google Scholar] [CrossRef]
  60. Chan, M. Mobile phones and the good life: Examining the relationships among mobile use, social capital and subjective well-being. New Media Soc. 2015, 17, 96–113. [Google Scholar] [CrossRef]
  61. Veenhoven, R. The overall satisfaction with life: Subjective approaches (1). In Global Handbook of Quality of Life: Exploration of Well-Being of Nations and Continents; Glatzer, W., Camfield, L., Møller, V., Rojas, M., Eds.; Springer: Berlin/Heidelberg, Germany, 2015; pp. 207–238. [Google Scholar]
  62. Ryff, C.D.; Keyes, C.L.M. The structure of psychological well-being revisited. J. Pers. Soc. Psychol. 1995, 69, 719. [Google Scholar] [CrossRef]
  63. Keyes, C.L. Promoting and protecting mental health as flourishing: A complementary strategy for improving national mental health. Am. Psychol. 2007, 62, 95. [Google Scholar] [CrossRef]
  64. Ryan, R.M.; Deci, E.L. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 2000, 55, 68. [Google Scholar] [CrossRef]
  65. RM, R. On happiness and human potential: A review of research on hedonic and eudaimonic well-being. Annu. Rev. Psychol. 2001, 52, 167–196. [Google Scholar]
  66. Leung, M.Y.; Famakin, I.O.; Wang, C. Developing an integrated indoor built environment–quality of life model for the elderly in public and subsidized housing. Eng. Constr. Archit. Ma. 2019, 26, 1498–1517. [Google Scholar] [CrossRef]
  67. Lyubomirsky, S.; Sheldon, K.M.; Schkade, D. Pursuing happiness: The architecture of sustainable change. Rev. Gen. Psychol. 2005, 9, 111–131. [Google Scholar] [CrossRef]
  68. Diener, E.; Oishi, S.; Tay, L. Advances in subjective well-being research. Nat. Hum. Behav. 2018, 2, 253–260. [Google Scholar] [CrossRef]
  69. Pinquart, M. Correlates of subjective health in older adults: A meta-analysis. Psychol. Aging 2001, 16, 414. [Google Scholar] [CrossRef]
  70. Kuo, F.E.; Sullivan, W.C. Aggression and violence in the inner city: Effects of environment via mental fatigue. Environ. Behav. 2001, 33, 543–571. [Google Scholar] [CrossRef]
  71. Pretty, J. How nature contributes to mental and physical health. Spirit. Health Inter. 2004, 5, 68–78. [Google Scholar] [CrossRef]
  72. Bowler, D.E.; Buyung-Ali, L.M.; Knight, T.M.; Pullin, A.S. A systematic review of evidence for the added benefits to health of exposure to natural environments. BMC Public Health 2010, 10, 456. [Google Scholar] [CrossRef]
  73. Kaplan, R. The Experience of Nature: A Psychological Perspective; Cambridge University Press: Cambridge, UK, 1989. [Google Scholar]
  74. Chang, C.Y.; Chen, P.K. Human response to window views and indoor plants in the workplace. HortScience 2005, 40, 1354–1359. [Google Scholar] [CrossRef]
  75. Piñero, L.H. Autonomy and the elderly, not always a perfect pair. Medwave 2014, 14, e6027. [Google Scholar]
  76. Chen, L.; Zhang, Z. Community participation and subjective wellbeing: Mediating roles of basic psychological needs among Chinese retirees. Front. Psychol. 2021, 12, 743897. [Google Scholar] [CrossRef] [PubMed]
  77. Martineau, A.; Plard, M. Successful aging: Analysis of the components of a gerontological paradigm. Geriatr. Psychol. Neur. 2018, 16, 67–77. [Google Scholar] [CrossRef] [PubMed]
  78. Chan, H.S.; Chu, H.Y.; Chen, M.F. Effect of horticultural activities on quality of life, perceived stress, and working memory of community-dwelling older adults. Geriatr. Nurs. 2022, 48, 303–314. [Google Scholar] [CrossRef]
  79. Sampson, R.J.; Morenoff, J.D.; Gannon-Rowley, T. Assessing “neighborhood effects”: Social processes and new directions in research. Annu. Rev. Sociol. 2002, 28, 443–478. [Google Scholar] [CrossRef]
  80. Gong, R.; Xia, D.; Hu, Z.; Hu, Y. The impact of neighborhood mental health on the mental health of older adults. BMC Public Health 2023, 23, 1352. [Google Scholar] [CrossRef]
  81. Sampson, R.J. Great American City: Chicago and the Enduring Neighborhood Effect; University Chicago Press: Chicago, IL, USA, 2012. [Google Scholar]
  82. Lauwers, L.; Trabelsi, S.; Pelgrims, I.; Bastiaens, H.; De Clercq, E.; Guilbert, A.; Guyot, M.; Leone, M.; Nawrot, T.; Nieuwenhuyse, A.V.; et al. Urban environment and mental health: The NAMED project, protocol for a mixed-method study. BMJ Open 2020, 10, e031963. [Google Scholar] [CrossRef]
  83. McCartan, C.; Davidson, G.; Bradley, L.; Greer, K.; Knifton, L.; Mulholland, A.; Webb, P.; White, C. ‘Lifts your spirits, lifts your mind’: A co-produced mixed-methods exploration of the benefits of green and blue spaces for mental wellbeing. Health Expect. 2023, 26, 1679–1691. [Google Scholar] [CrossRef]
  84. Chen, Y.; Yuan, Y.; Zhou, Y. Exploring the association between neighborhood blue space and self-rated health among elderly adults: Evidence from guangzhou, China. Int. J. Environ Res. Public Health 2022, 19, 16342. [Google Scholar] [CrossRef]
  85. Völker, S.; Heiler, A.; Pollmann, T.; Claßen, T.; Hornberg, C.; Kistemann, T. Do perceived walking distance to and use of urban blue spaces affect self-reported physical and mental health? Urban For. Urban Green. 2018, 29, 1–9. [Google Scholar] [CrossRef]
  86. Anjuke. Available online: https://nanjing.anjuke.com/community/view/189329 (accessed on 25 November 2023).
  87. Anjuke. Available online: https://nanjing.anjuke.com/community/view/1029360 (accessed on 25 November 2023).
  88. Anjuke. Available online: https://nanjing.anjuke.com/community/view/174010 (accessed on 25 November 2023).
  89. Völker, S.; Kistemann, T. Developing the urban blue: Comparative health responses to blue and green urban open spaces in Germany. Health Place 2015, 35, 196–205. [Google Scholar] [CrossRef] [PubMed]
  90. Sinda, H.J.; Aude, L.; Benoit, G.; Alexandre, A.; Arnaud, C.; Bertrand, C.; Noémie, G.; Julia, H.; Claudia, L.R.; Delphine, C.; et al. Cross-analysis for the assessment of urban environmental quality: An interdisciplinary and participative approach. Environ. Plan. B Environ. Plan B Urban 2022, 49, 1024–1047. [Google Scholar] [CrossRef]
  91. Moradi, B.; Akbari, R.; Taghavi, S.R.; Fardad, F.; Esmailzadeh, A.; Ahmadi, M.Z.; Attarroshan, S.; Nickravesh, F.; Arsanjani, J.J.; Amirkhani, M.; et al. A scenario-based spatial multi-criteria decision-making system for urban environment quality assessment: Case study of tehran. Land 2023, 12, 1659. [Google Scholar] [CrossRef]
  92. Miskell, G.; Salmond, J.A.; Williams, D.E. Use of a handheld low-cost sensor to explore the effect of urban design features on local-scale spatial and temporal air quality variability. Sci. Total Environ. 2018, 619, 480–490. [Google Scholar] [CrossRef]
  93. Robinson, J.A.; Novak, R.; Kanduč, T.; Maggos, T.; Pardali, D.; Stamatelopoulou, A.; Saraga, D.; Vienneau, D.; Flückiger, B.; Mikeš, O.; et al. User-centred design of a final results report for participants in multi-sensor personal air pollution exposure monitoring campaigns. Int. J. Environ Res. Public Health 2021, 18, 12544. [Google Scholar] [CrossRef] [PubMed]
  94. Yu, H.; Russell, A.; Mulholland, J.; Odman, T.; Hu, Y.; Chang, H.H.; Kumar, N. Cross-comparison and evaluation of air pollution field estimation methods. Atmos. Environ. 2018, 179, 49–60. [Google Scholar] [CrossRef]
  95. Wu, W.L.; Shan, C.Y.; Liu, J.; Zhao, J.L.; Long, J.Y. Analysis of factors influencing air quality in different periods during COVID-19: A case study of Tangshan, China. Int. J. Environ Res. Public Health 2023, 20, 4199. [Google Scholar] [CrossRef]
  96. World Health Organization. Environmental Noise Guidelines for the European Region; World Health Organization: Geneva, Switzerland, 2018. [Google Scholar]
  97. Schwingel, A.; Niti, M.M.; Tang, C.; Ng, T.P. Continued work employment and volunteerism and mental well-being of older adults: Singapore longitudinal ageing studies. Age Ageing 2009, 38, 531–537. [Google Scholar] [CrossRef]
  98. Meléndez, J.C.; Satorres, E.; Cujiño, M.A.; Reyes, M.F. Big Five and psychological and subjective well-being in Colombian older adults. Arch. Gerontol. Geriat. 2019, 82, 88–93. [Google Scholar] [CrossRef]
  99. Nisbet, E.K.; Zelenski, J.M.; Murphy, S.A. Happiness is in our nature: Exploring nature relatedness as a contributor to subjective well-being. J. Happiness Stud. 2011, 12, 303–322. [Google Scholar] [CrossRef]
  100. Chinese Research Academy of Environmental Sciences & Chinese Environment Monitoring Station. Ambient Air Quality Standards in China. 2012. Available online: https://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/dqhjbh/dqhjzlbz/201203/W020120410330232398521.pdf (accessed on 20 September 2023).
  101. Nanjing Ecological Environment Status Report. Nanjing Municipal Ecological Environment Bureau. 2023. Available online: http://sthjj.nanjing.gov.cn/njshjbhj/202401/P020240130409758858417.pdf (accessed on 21 September 2023).
  102. Proctor, C.; Maltby, J.; Linley, P.A. Strengths use as a predictor of well-being and health-related quality of life. J. Happiness Stud. 2011, 12, 153–169. [Google Scholar] [CrossRef]
  103. Du, J.; Cui, Y.; Yang, L.; Duan, Y.; Qi, Q.; Liu, H. Associations of indoor ventilation frequency with depression and anxiety in Chinese older adults. Indoor Air 2024, 2024, 9943687. [Google Scholar] [CrossRef]
  104. Jo, K.H.; Ryu, S.Y.; Han, M.A.; Choi, S.W.; Shin, M.H.; Park, J. Cross-sectional associations between Particulate Matter (PM 2.5) and depression (PHQ-9) in the elderly. J. Health Inform. Stat. 2021, 46, 163–170. [Google Scholar] [CrossRef]
  105. Guo, W.; Chen, L.; Fan, Y.; Liu, M.; Jiang, F. Effect of ambient air quality on subjective well-being among Chinese working adults. J. Clean Prod. 2021, 296, 126509. [Google Scholar] [CrossRef]
  106. Chaudhuri, S.; Chowdhury, A.R. Air quality index assessment prelude to mitigate environmental hazards. Nat Hazards 2018, 91, 1–17. [Google Scholar] [CrossRef]
  107. Yin, P.; Brauer, M.; Cohen, A.J.; Wang, H.; Li, J.; Burnett, R.T.; Stanaway, J.D.; Causey, K.; Larson, S.; Godwin, W.; et al. The effect of air pollution on deaths, disease burden, and life expectancy across China and its provinces, 1990–2017: An analysis for the Global Burden of Disease Study 2017. Lancet Planet Health 2020, 4, e386–e398. [Google Scholar] [CrossRef]
  108. Liu, X.; Tu, R.; Qiao, D.; Niu, M.; Li, R.; Mao, Z.; Huo, W.; Chen, G.; Xiang, H.; Guo, Y.; et al. Association between long-term exposure to ambient air pollution and obesity in a Chinese rural population: The Henan Rural Cohort Study. Environ. Pollut. 2020, 260, 114077. [Google Scholar] [CrossRef]
  109. Clark, W.A.; Yi, D.; Huang, Y. Subjective well-being in China’s changing society. Proc. Natl. Acad. Sci. USA 2019, 116, 16799–16804. [Google Scholar] [CrossRef]
  110. Wang, R.; Yang, B.; Yao, Y.; Bloom, M.S.; Feng, Z.; Yuan, Y.; Zhang, J.; Liu, P.; Wu, W.; Lu, Y.; et al. Residential greenness, air pollution and psychological well-being among urban residents in Guangzhou, China. Sci. Total Environ. 2020, 711, 134843. [Google Scholar] [CrossRef]
  111. Von Lindern, E.; Hartig, T.; Lercher, P. Traffic-related exposures, constrained restoration, and health in the residential context. Health Place 2016, 39, 92–100. [Google Scholar] [CrossRef] [PubMed]
  112. Abed Al Ahad, M.; Demšar, U.; Sullivan, F.; Kulu, H. Air pollution and individuals’ mental well-being in the adult population in United Kingdom: A spatial-temporal longitudinal study and the moderating effect of ethnicity. PLoS ONE 2022, 17, e0264394. [Google Scholar]
  113. Payne, S.R.; Bruce, N. Exploring the relationship between urban quiet areas and perceived restorative benefits. Int. J. Environ Res. Public Health 2019, 16, 1611. [Google Scholar] [CrossRef]
  114. Lawton, R.N.; Fujiwara, D. Living with aircraft noise: Airport proximity, aviation noise and subjective wellbeing in England. Transp. Res. Part D Transp. Environ. 2016, 42, 104–118. [Google Scholar] [CrossRef]
  115. Alvarsson, J.J.; Wiens, S.; Nilsson, M.E. Stress recovery during exposure to nature sound and environmental noise. Int. J. Environ Res. Public Health 2010, 7, 1036–1046. [Google Scholar] [CrossRef]
  116. Lan, L.; Sun, Y.; Wyon, D.P.; Wargocki, P. Pilot study of the effects of ventilation and ventilation noise on sleep quality in the young and elderly. Indoor Air. 2021, 31, 2226–2238. [Google Scholar] [CrossRef] [PubMed]
  117. Gao, S.; Bosman, C.; Dupre, K. Understanding the well-being of older Chinese immigrants in relation to green spaces: A gold coast study (Australia). Front. Psychol. 2020, 11, 551213. [Google Scholar] [CrossRef]
  118. Ligtermoet, E.; Ramalho, C.E.; Foellmer, J.; Pauli, N. Greening urban road verges highlights diverse views of multiple stakeholders on ecosystem service provision, challenges and preferred form. Urban For. Urban Green. 2022, 74, 127625. [Google Scholar] [CrossRef]
  119. Voigt, A.; Kabisch, N.; Wurster, D.; Haase, D.; Breuste, J. Structural diversity: A multi-dimensional approach to assess recreational services in urban parks. Ambio 2014, 43, 480–491. [Google Scholar] [CrossRef]
  120. Fuller, R.A.; Irvine, K.N.; Devine-Wright, P.; Warren, P.H.; Gaston, K.J. Psychological benefits of greenspace increase with biodiversity. Biol. Lett. 2007, 3, 390–394. [Google Scholar] [CrossRef]
  121. Benton, J.S.; Anderson, J.; Cotterill, S.; Dennis, M.; Lindley, S.J.; French, D.P. Evaluating the impact of improvements in urban green space on older adults’ physical activity and wellbeing: Protocol for a natural experimental study. BMC Public Health 2018, 18, 923. [Google Scholar] [CrossRef] [PubMed]
  122. Anderson, J.; Ruggeri, K.; Steemers, K.; Huppert, F. Lively social space, well-being activity, and urban design: Findings from a low-cost community-led public space intervention. Environ. Behav. 2017, 49, 685–716. [Google Scholar] [CrossRef]
  123. White, M.P.; Elliott, L.R.; Gascon, M.; Roberts, B.; Fleming, L.E. Blue space, health and well-being: A narrative overview and synthesis of potential benefits. Environ. Res. 2020, 191, 110169. [Google Scholar] [CrossRef] [PubMed]
  124. Burkart, K.; Meier, F.; Schneider, A.; Breitner, S.; Canário, P.; Alcoforado, M.J.; Scherer, D.; Endlicher, W. Modification of heat-related mortality in an elderly urban population by vegetation (urban green) and proximity to water (urban blue): Evidence from Lisbon, Portugal. Environ. Health Persp. 2016, 124, 927–934. [Google Scholar] [CrossRef]
  125. White, M.P.; Pahl, S.; Ashbullby, K.; Herbert, S.; Depledge, M.H. Feelings of restoration from recent nature visits. J. Environ. Psychol. 2013, 35, 40–51. [Google Scholar] [CrossRef]
  126. Eisenman, T.S.; Churkina, G.; Jariwala, S.P.; Kumar, P.; Lovasi, G.S.; Pataki, D.E.; Weinberger, K.R.; Whitlow, T.H. Urban trees, air quality, and asthma: An interdisciplinary review. Landsc. Urban Plan. 2019, 187, 47–59. [Google Scholar] [CrossRef]
  127. Shu, S.; Meng, L.; Piao, X.; Geng, X.; Tang, J. Effects of audio–visual interaction on physio-psychological recovery of older adults in residential public space. Forests 2024, 15, 266. [Google Scholar] [CrossRef]
  128. Morillas, J.M.B.; Gozalo, G.R.; González, D.M.; Moraga, P.A.; Vílchez-Gómez, R. Noise pollution and urban planning. Curr. Pollut. Rep. 2018, 4, 208–219. [Google Scholar] [CrossRef]
  129. Masum, M.H.; Pal, S.K.; Akhie, A.A.; Ruva, I.J.; Akter, N.; Nath, S. Spatiotemporal monitoring and assessment of noise pollution in an urban setting. Environ. Chall. 2021, 5, 100218. [Google Scholar] [CrossRef]
  130. Zandieh, R.; Martinez, J.; Flacke, J. Older adults’ outdoor walking and inequalities in neighbourhood green spaces characteristics. Int. J. Environ Res. Public Health 2019, 16, 4379. [Google Scholar] [CrossRef]
  131. Fumagalli, N.; Fermani, E.; Senes, G.; Boffi, M.; Pola, L.; Inghilleri, P. Sustainable co-design with older people: The case of a public restorative garden in Milan (Italy). Sustainability 2020, 12, 3166. [Google Scholar] [CrossRef]
  132. Andreucci, M.B.; Russo, A.; Olszewska-Guizzo, A. Designing urban green blue infrastructure for mental health and elderly well-being. Sustainability 2019, 11, 6425. [Google Scholar] [CrossRef]
  133. Maul, A.; Irribarra, D.T.; Wilson, M. On the philosophical foundations of psychological measurement. Measurement 2016, 79, 311–320. [Google Scholar] [CrossRef]
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Figure 1. A conceptual NE–SPWB model for older people.
Figure 1. A conceptual NE–SPWB model for older people.
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Figure 2. Air quality in communities A, B, and C.
Figure 2. Air quality in communities A, B, and C.
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Figure 3. Green and blue spaces in communities A, B, and C. Note: ①–②: locations of blue spaces in community A; ③–⑥: locations of green spaces in community A; ⑦–⑩: locations of green spaces in community B; ⑪–⑭: locations of green spaces in community C.
Figure 3. Green and blue spaces in communities A, B, and C. Note: ①–②: locations of blue spaces in community A; ③–⑥: locations of green spaces in community A; ⑦–⑩: locations of green spaces in community B; ⑪–⑭: locations of green spaces in community C.
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Figure 4. Integrated NE-SPWB model for older people. Note: ** = significant at the 0.01 level; * = significant at the 0.05 level.
Figure 4. Integrated NE-SPWB model for older people. Note: ** = significant at the 0.01 level; * = significant at the 0.05 level.
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Table 1. Basic information of the three communities.
Table 1. Basic information of the three communities.
CommunityTypeResidential Buildings (Stories)HouseholdGreening RatePlot Ratio
ARenovated Old38151825%3
BNewly Built14352830%2.5
CUnrenovated Old15100040%2
Table 2. Community noise levels.
Table 2. Community noise levels.
CommunityLocationMean (dB)Max (dB)Min (dB)Std.
AA155.691.147.56.6
AA247.563.143.82.5
AA353.165.448.72.8
BB153.172.249.13.5
BB253.061.149.32.7
BB353.563.649.23.2
CC152.570.647.43.5
CC250.868.645.93.8
CC349.163.144.53.4
Note: Std. = standard deviation.
Table 3. Correlation analysis of NE and SPWB of older people.
Table 3. Correlation analysis of NE and SPWB of older people.
ItemsSWB1SWB2SWB3PWB1PWB2PWB3
SWB1—Living Quality1
SWB2—Life Satisfaction0.454 **1
SWB3—Self-Value0.323 **0.179 *1
PWB1—Purpose in Life0.167 *0.1340.1381
PWB2—Autonomy0.356 **0.171 *0.339 **0.148 *1
PWB3—Personal Growth0.1330.160 *0.190 *0.319 **0.228 **1
NE1—Smell (air)0.274 **0.269 **0.1090.172 *0.319 **0.477 **
NE2—Airborne Particles0.286 **0.333 **0.229 **0.149 *0.403 **0.421 **
NE3—Community Noise0.357 **0.268 **0.257 **0.156 *0.402 **0.381 **
NE4—Ambient Noise0.362 **0.398 **0.0970.166 *0.389 **0.243 **
NE5-Green Space Area−0.0590.106−0.149 *0.402 **0.0390.215 **
NE6—Green Space Types0.0120.096−0.1120.248 **0.0470.128
NE7—Transparency0.199 **0.244 **0.0310.355 **0.148 *0.411 **
NE8—Smell (water)0.151 *0.236 **−0.0480.476 **0.151 *0.330 **
NE9—Water Landscape0.350 **0.334 **0.175 *0.382 **0.274 **0.377 **
Note: ** = correlation significant at the 0.01 level (2-tailed); * = correlation significant at the 0.05 level (2-tailed).
Table 4. Multiple regression model for NE and SPWB of older people.
Table 4. Multiple regression model for NE and SPWB of older people.
ModelsBS.E.Sig.VIFRR2ANOVA
FSig.
1Living Quality← IBE Components
Constant2.4280.1590.000 0.4140.17218.3290.000 **
NE4—Ambient Noise0.1590.0490.0011.3
NE8—Water Landscape0.1260.0430.0041.3
2Life Satisfaction← IBE Components
Constant2.5450.1460.000 0.4300.18520.0850.000 **
NE4—Ambient Noise0.1800.0450.0001.3
NE8—Water Landscape0.0950.0390.0171.3
3Self-Value← IBE Components
Constant2.7510.1790.000 0.2730.0744.7130.003 **
NE3—Community Noise0.1100.0590.0651.819
4Purpose in Life← IBE Components
Constant2.0270.1850.000 0.5070.25730.6190.000 **
NE5—Green Space Area0.1490.0560.0081.406
NE7—Smell (water)0.2260.0480.0001.406
5Autonomy← IBE Components
Constant2.6750.1560.000 0.3920.15416.1080.000 **
NE4—Ambient Noise0.2520.0480.0001.319
6Personal Growth← IBE Components
Constant2.2150.1690.000 0.5140.26531.8570.000 **
NE1—Smell (air)0.2680.0490.0001.221
NE8—Water Landscape0.1320.0440.0031.221
Note: B = beta; R = correlation coefficient; R2 = coefficient of determination; F = F-test statistics; S.E. = standard error; Sig. = significance; VIF = variance inflation factor; ** = significant at the 0.01 level.
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Gong, K.; Wang, C.; Yin, J. Effects of the Natural Environment on the Subjective and Psychological Well-Being of Older People in the Community in China. Buildings 2024, 14, 2854. https://doi.org/10.3390/buildings14092854

AMA Style

Gong K, Wang C, Yin J. Effects of the Natural Environment on the Subjective and Psychological Well-Being of Older People in the Community in China. Buildings. 2024; 14(9):2854. https://doi.org/10.3390/buildings14092854

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Gong, Kangcheng, Chendi Wang, and Jun Yin. 2024. "Effects of the Natural Environment on the Subjective and Psychological Well-Being of Older People in the Community in China" Buildings 14, no. 9: 2854. https://doi.org/10.3390/buildings14092854

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