Skip to main content

Residential surrounding greenness is associated with improved lung function in adults: a cross-sectional study in eastern China



While benefits of greenness exposure to health have been reported, findings specific to lung function are inconsistent. The purpose of this study is to assess the correlations of greenness exposure with multiple lung function indicators based on chronic obstructive pulmonary disease (COPD) monitoring database from multiple cities of Anhui province in China.


We assessed the greenness using the annual average of normalized difference vegetation index (NDVI) with a distance of 1000-meter buffer around each local community or village. Three types of lung function indicators were considered, namely indicators of obstructive ventilatory dysfunction (FVC, FEV1, FEV1/FVC, and FEV1/FEV3); an indicator of large-airway dysfunction (PEF); indicators of small-airway dysfunction (FEF25%, FEF50%, FEF75%, MMEF, FEV3, FEV6, and FEV3/FVC). Linear mixed effects model was used to analyze associations of greenness exposure with lung function through adjusting age, sex, educational level, occupation, residence, smoking status, history of tuberculosis, family history of lung disease, indoor air pollution, occupational exposure, PM2.5, and body mass index.


A total of 2768 participants were recruited for the investigations. An interquartile range (IQR) increase in NDVI was associated with better FVC (153.33mL, 95%CI: 44.07mL, 262.59mL), FEV1 (109.09mL, 95%CI: 30.31mL, 187.88mL), FEV3 (138.04mL, 95%CI: 39.43mL, 236.65mL), FEV6 (145.42mL, 95%CI: 42.36mL, 248.47mL). However, there were no significant associations with PEF, FEF25%, FEF50%, FEF75%, MMEF, FEV1/FVC, FEV1/FEV6, FEV3/FVC. The stratified analysis displayed that an IQR increase in NDVI was related with improved lung function in less than 60 years, females, urban populations, nonsmokers, areas with medium concentrations of PM2.5 and individuals with BMI of less than 28 kg/m2. Sensitivity analyses based on another greenness indice (enhanced vegetation index, EVI) and annual maximum of NDVI remained consistent with the main analysis.


Our findings supported that exposure to greenness was strongly related with improved lung function.

Peer Review reports


Chronic respiratory diseases are a global public health issue accounting for 3.9 million deaths, affecting nearly 544.9 million people in 2017. It has become the third leading cause of death after tumors and cardiovascular disease in the worldwide scope [1]. Because chronic respiratory diseases affect the airways and other structures of the lung, a vitally essential sign of respiratory health is lung function, which used to evaluate and diagnose respiratory outcomes. For instance, obstructive abnormalities are detected using both the forced expiratory volume in the first second (FEV1) and the forced vital capacity (FVC). As a sensitive indicator for the detection of chronic obstructive pulmonary disease, FEV1 to FVC ratio is utilized [2]. Besides, chronic respiratory diseases cannot be cured, but any treatment that can help open air passages and perfect dyspnea can assist patients in controlling symptoms and improving the quality of life [3]. Finding environmental variables that may be changed and have an impact on lung function can benefit the public with preventative tips.

In recent years, greenness has been a research hotspot in the environmental epidemiology. Greenness has been generally associated with health benefits in humans, such as reduced all-cause mortality [4, 5], reduced incidence of adverse pregnancy outcomes [6, 7], and reduced risk of overweight and obesity [8, 9]. Therefore, one study suggested that greenness was considered to act on health through various ways, and these were divided into three fields – reducing harmful exposure, resilience, and building capacity [10]. In addition, it could provide better support for researches of greenness and health with the development of technologies such as remote sensing satellites [11, 12].

Previous studies have examined the positive correlations between greenness exposure and chronic respiratory diseases. Protective effects of more greenness on COPD were observed by a cross-sectional survey in the United Kingdom [13, 14] and Greece [15]. In addition, researches in the Netherlands [16] and Chinese northeast cities [17] reported favorable influence for greenness and morbidity of asthma. However, few studies have investigated the relationship between exposure to greenness and lung function, with the subsistent testimony miscellaneous. From birth to age 24 years, exposure to greenness was favorably correlated with lung function, according to a Britannic birth cohort research. Greenness in a 100-meter buffer was related with better FEV1 and FVC using repeated greenness and lung function data [18]. One study including 50,991 participants aged 20 years and above indicated that greenness was associated with better lung function and lower odds of COPD [19]. Contrary to popular belief, greenness was found to be a risk factor for reduced lung function in adults in the RHINESSA research conducted in Norway and Sweden [20].

Prior researches on lung function and greenness were mostly done in rich nations [18, 20,21,22], and only infrequently in developing nations like China [19, 23]. And, previous researches about greenness and lung function mainly focused on children and teenagers [22,23,24,25,26,27,28], and seldom on adults [19, 20]. In addition, few studies focused on multiple parameters of lung function to explore the correlation between greenness exposure and lung function [19]. Therefore, we conducted this study with the goal of providing a rational foundation for public health in Anhui Province, China. We did this by carefully evaluating and exploring the relationships between greenness and lung function in adults.


Study design and participants

Anhui Province, a significant component of the Yangtze River Delta economic region, is situated in the Yangtze River Delta district of East China, between 114°54’- 119°37’East longitude and 29°41’- 34°38’North latitude. The province of Anhui is also in a climate transition zone between a mild temperate zone and a subtropical zone. Due to the rapid development of the economy and different types of geographical climates, there is several levels of greenness exposure in Anhui Province.

The current research was conducted on a COPD monitoring database in Anhui Province, China [29]. Between January 1 and June 30, 2015, we carried out a cross-sectional research in Anhui. Previously, extensive information on the study design and participant recruitment was presented [30]. In brief, multistage probability sampling was used to complete a cross-sectional survey. A total of five disease surveillance points (DSPs) covering around 5% of the population in Anhui were selected (Fig. 1). We next chose three townships or sub-districts at random within each DSP, with a probability proportional to the associated population size. Within each sub-district or township, two additional neighborhood communities or villages were then chosen at random. Then, based on residential proximity, we separated the neighborhood communities or villages into groups with 150–299 households. Then, one group was chosen at random from each neighborhood or hamlet. Finally, 100 households from each of the three groups were randomly picked. Finally, using a Kish selection table, one adult who was 40 years of age or older was randomly chosen from each household [31]. Eight KISH table types were randomly assigned to survey households in proportions of 1/6, 1/12, 1/12, 1/6, 1/6, 1/12, 1/12, and 1/6. One section of persons aged from 40 upwards was lastly drawn at random from 30 neighborhood communities or villages. After removing participants with inaccurate lung function tests, the final analysis included 2768 participants (Fig. 2). The ethics review committees of Bengbu Medical College and Anhui Provincial Center for Disease Control & Prevention both authorized the study. To participate, all individuals provided written informed permission.

Fig. 1
figure 1

Spatial distribution of study area

Fig. 2
figure 2

Flow chart of sample selection process

Greenness assessment

Greenness was assessed by normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). The amount of greenness is measured using the NDVI, a vegetation indicator based on satellite images. Its definition is the ratio of the total of the near-infrared and red spectral bands to their difference. More positive values indicate more greenness, whereas negative numbers indicate more water. Its value spans from − 1 (water) to 1 (thick green plants). NDVI is readily available in different study areas, has been widely used in relevant studies investigating the relationship between greenness and health, and has proven to be a valid and practical indicator [32, 33]. In addition, EVI is mostly used in densely vegetated areas, because this indicator can reduce the influence of water vapor, correct the soil background value, and especially improve the detection ability of sparse vegetation. Previous studies have shown that EVI can reflect more detailed surface vegetation characteristics [34].

In our study, we used NDVI and EVI from the vegetation output of the Moderate-resolution imaging spectroradiometer (MODIS) sensor onboard the National Aeronautics and Space Administration (NASA) Terra satellite, particularly MOD13A3, which is accessible at MOD13A3 product provides vegetation index at 1 km spatial resolution. We downloaded NDVI and EVI data from January 1st to December 31th, 2014. The greenness exposure was represented by assigning the mean values of NDVI and EVI to each local community or village within a 1000-m buffer. Greenness indices were extracted by raster, rgdal and sp packages in R statistical software, Version 4.12 (University of Auckland, New Zealand). We treated negative value to NA (not available) which represented water, ice or bare earth.

Measurement of lung function

The first spirometry was required of participants who qualified for the spirometry test. Each eligible participant received 400 g of salbutamol (Ventolin; GlaxoSmithKline, Middlesex, UK) after removing individuals with a resting heart rate of 100 beats per minute or above or those with a salbutamol allergy. Following the American Thoracic Society’s recommendations, qualified personnel used a spirometer (MasterScreen Pneumo, Jaeger, Germany) to perform post-bronchodilator spirometry after 15 min. There were 12 lung function markers found in all. These indications specifically fell into three groups : forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), forced expiratory volume in 1 s / forced expiratory volume in 3 s (FEV1/FEV3) and FEV1/FVC are all signs of obstructive ventilatory dysfunction [20, 35]; peak expiratory flow (PEF) is the unique measure for large-airway dysfunction [19, 36]; FEV3, forced expiratory volume in 6 s (FEV6), forced expiratory flow at 25%, 50%, 75%, 25–75% of exhaled forced vital capacity (FEF25%, FEF50%, FEF75%, MMEF), and FEV3/FVC are all markers of small-airway dysfunction. [18, 19, 35].

Potential covariates

The covariate selection was based on the following procedure: (1) Covariates reported in previous researches about greenness and lung function [19, 37]. The following covariates were considered: age, sex, educational level, occupation, residence, smoking status, history of tuberculosis, family history of lung disease, indoor air pollution, occupational exposure, fine particulate matter (PM2.5), body mass index (BMI). (2) We used directed acyclic graphs to identify which potential confounders to include in the statistical models (Figure S1) [38].

According to Figure S1, we considered several potential confounders to adjust the models, including sociodemographic characteristics, health status, and environmental exposure. Specifically, sociodemographic characteristics included age (40–49, 50–59, 60–69, >=70 years), sex (male or female), educational level (primary school and lower, secondary school, higher and further education), residence (urban or rural), and occupation (agriculture or non-agriculture or unemployed). Health status included history of tuberculosis, family history of lung disease and BMI. History of tuberculosis was defined as tuberculosis diagnosed by a doctor at a township health center or community health service center or above. Family history of lung disease was defined as parents have suffered from asthma, chronic bronchitis, emphysema, COPD, pulmonale, bronchiectasis, tuberculosis, rhinitis or lung cancer. BMI was calculated as weight divided by the square of height, and classified into four groups: <18.5, 18.5–23.9, 24.0–27.9 and > = 28.0 kg/m2. Environmental exposure included occupational exposure, indoor air pollution, smoking status (past or current smoker or never smoker) and PM2.5. Occupational exposure was defined as exposure to dust or toxic gas for one year or more in related work. Indoor air pollution was defined as current household use of kerosene, paraffin, coal, wood, firewood, crop straw, or animal manure for cooking or heating. PM2.5 data comes from China’s 1 km high-quality PM2.5 dataset [39, 40], we calculated the annual PM2.5 values ​​of each sub-district or township in 2014.

Statistical analysis

Using linear mixed effects models, where people and counties were considered as the first and second level units, it was determined if there was a correlation between the interquartile range (IQR) increase in greenness and lung function metrics following bronchodilator inhalation. The effect estimates (beta) and 95% confidence interval (95%CI) were regarded as differences of lung function metrics related to an IQR increase in greenness after adjusted covariates. Other covariates, including age, sex, educational level, occupation, residence, smoking status, history of tuberculosis, family history of lung disease, indoor air pollution, occupational exposure, PM2.5, BMI were adjusted in the final model.

Stratified analyses were deep used to assess the latent modification effect by sex (males and females), age group (40–59, ≥ 60 years), residence (urban and rural), smoking status (never smoker, past or current smoker) and BMI group (< 24, 24–27.9, ≥ 28.0 kg/m2). We further assessed the association between greenness and lung function in the tertiles of PM2.5 (low, medium, high). For analyses, we used FEV1 and FVC as typical measures of lung function [19]. Mediation analysis was used to test the contribution of PM2.5 as mediation between greenness and lung function. By adopting the yearly maximum of NDVI and the one-year averaged EVI values as the exposure criteria, sensitivity analysis was performed to measure how reliable our analyses were. Linear mixed effects model and mediation analysis were performed using nlme and mediation packages in R statistical software, Version 4.12 (University of Auckland, New Zealand), respectively.


Descriptive statistics

The final study comprised 2768 participants from the COPD surveillance database in Anhui province, China. Table 1 shows detailed data for the study population’s sociodemographic characteristics, health status, environmental exposure, and lung function indicators. There were 49.1% males and 50.9% females among them. The number of samples in the 40–49 age group was 1046 (37.8%). There were 1,667 (60.2%) urban participants and 1,101 (39.8%) rural participants over the age of 40. The proportion of urban participants was higher than that of rural participants. There were 1689 participants (61.0%) with primary school and lower, and education level was generally low. Following the spirometry, the yearly average NDVI and EVI within the 1000-m buffer were 0.48 (Standard Deviation (SD) = 0.12) and 0.31 (SD = 0.09), respectively. The mean FVC and FEV1 were 3.48 L and 2.73 L, respectively. The yearly average PM2.5 concentration was 72.50 g/m3 (SD = 11.89).

Table 1 Descriptive characteristics of participants in this study

Correlations of greenness exposure with lung function

Table 2 displayed the relationships between greenness exposure within 1000-m buffer and lung function indicators. An IQR increase in NDVI was linked to elevated FVC (153.33mL, 95%CI: 44.07mL, 262.59mL), and FEV1 (109.09mL, 95%CI: 30.31mL, 187.88mL) for metrics of obstructive ventilatory dysfunction. However, there were no significant associations with FEV1/FVC (-0.281%, 95% CI: -1.244%, 0.681%), FEV1/FEV6 (-0.194%, 95% CI: -0.948%, 0.561%). For indicators of large airway dysfunction, we did not find association of NDVI with PEF (106.92mL/s, 95%CI: -140.21mL/s, 354.05mL/s). For indicators of small airway dysfunction, an IQR increase in NDVI was related to higher FEV3 (138.04mL, 95%CI: 39.43mL, 236.65mL), FEV6 (145.42mL, 95%CI: 42.36mL, 248.47mL). However, the associations with FEF25% (92.57mL/s, 95%CI: -110.89mL/s, 296.03mL/s), FEF50% (30.13mL/s, 95%CI: -82.53mL/s, 142.80mL/s), FEF75% (27.27mL/s, 95%CI: -33.42mL/s, 87.96mL/s), MMEF (23.13mL/s, 95%CI: -80.98mL/s, 127.25mL/s), FEV3/FVC (-0.087%, 95%CI: -1.012%, 0.839%) didn’t reach significant level.

Table 2 Associations between per IQR increase in NDVI and lung function indicators

Stratified analysis

Figure 3 presented the results of stratified analyses by age, sex, residence, BMI, smoking status, and PM2.5. The findings of the stratified analysis demonstrated that age, sex, residence, BMI, smoking status, and PM2.5 modified the relationships between greenness exposure and lung function indicators. An IQR increment in NDVI was related with improved lung function in subjects with less than 60 years, females, urban populations, nonsmokers, and BMI of less than 28 kg/m2. In the medium and high concentrations of PM2.5, NDVI was strongly correlated with increased FEV1.

Fig. 3
figure 3

Forest plot for subgroup analyses by age, sex, residence, smoking status, BMI, and PM2.5. FVC: forced vital capacity; FEV1forced expiratory volume in 1s

Sensitivity analysis

Table S1 and S2 showed that the correlations between greenness and lung function indicators were consistent with main analysis in general. Firstly, Applying EVI as the criteria of greenness, we found the relationships between greenness and FVC, FEV1, FEV3, and FEV6 were somewhat large, but the connections between greenness and PEF, FEF25%, FEF50%, FEF75%, MMEF, FEV1/FVC, FEV1/FEV6, FEV3/FVC were no significant (Table S1). Secondly, using the annual maximum of NDVI, we found the relationships of exposure to greenness with FVC, FEV1, FEV3, and FEV6 were somewhat smaller, yet the associations with PEF, FEF25%, FEF50%, FEF75%, MMEF, FEV1/FVC, FEV1/FEV6, FEV3/FVC were no significant (Table S2). Moreover, we found that PM2.5 might not play a mediating role in the relationship between greenness and lung function (Table S3).


Based on a representative survey data of Anhui province in China, the study examined at the relationships between lung function and greenness exposure. We discovered that greenness exposure was greatly linked to better FVC, FEV1, FEV3, and FEV6. However, there were no significant associations with PEF, FEF25%, FEF50%, FEF75%, MMEF, FEV1/FVC, FEV1/FEV6, FEV3/FVC. The stratified analysis displayed that greenness was noticeably correlated with better lung function amongst < 60 years, females, urban populations, nonsmokers, areas with medium concentrations of PM2.5, and participants less than 28 kg/m2.

FVC, FEV1 and FEV1/FVC could reflect obstructive ventilatory dysfunction. In addition, FEV1/FEV6 is one of indicators of obstructive ventilatory dysfunction as well, but not used very often. Nevertheless, it is the major superiority, for FEV1/FEV6, to refrain the changeability of the FVC duration inherent in the FEV1/FVC [35]. Greenness was related to greater FVC and FEV1, but we could not notice a link between greenness and FEV1/FVC. Findings were heterogeneous yet. A cross-sectional study of Chinese adults aged 20 and older suggested that greenness was related to improved FVC and FEV1, yet they reported that greenness was linked to higher FEV1/FVC, which contrasted with our study’s findings [19]. The repeated assessments of lung function in relation to greenness at ages 8, 15, and 24 years were examined in the Britain. The authors discovered that increased FEV1 (11.4 mL, 95% CI: 2.6mL, 20.3mL) and FVC (12.2 mL, 1.8mL, 22.7mL), which were lower than our impact estimates, were related with an IQR increase in NDVI inside the 100-m buffer [18]. Furthermore, they did not detecte a relation with FEV1/FVC. Conversely, the adult lung function indicators and greenness were found to have a negative correlation in the RHINESSA research conducted in Norway and Sweden. Additionally, no correlation with FEV1/FVC was established [20].

FEV3, FEV6, FEF25%, FEF50%, FEF75%, MMEF, and FEV3/FVC are markers of small-airway dysfunction, whereas PEF is a marker for large-airway dysfunction. We observed that stronger FEV3 and FEV6 were substantially correlated with greenness. However, there were no significant associations with PEF, FEF25%, FEF50%, FEF75%, MMEF, and FEV3/FVC. Findings were heterogeneous yet. The linkage between greenness and dysfunction of the small airways has only been briefly studied in the past. According to the China study, greenness was linked to improved FEV3, FEV6, FEF50%, FEF25%−75%, and FEF75% but not FEF25% [19]. The ALSPAC research conducted in the UK noticed no associations between greenness and FEF50%, FEF75%, and FEF25%−75% [18]. In accordance with an Italian study, greenness was linked to increased FEF25%−75% but not FEF50% [41]. The effect of greenness on PEF is yet unclear. We discovered that greenness was not significantly related to PEF. In contrast to our conclusion, the China research showed that greenspace was strongly linked to decreased PEF [19]. Furthermore, a research conducted in northeast China indicated that more greenness at children’s eye level was correlated with greater PEF [23].

The stratified analysis findings might be significant. We found the correlation between greenness and lung function among people under 60 years old. No semblable association was found in the elderly, which might be due to ageing of lung tissue [42]. We identified a relation between exposure to greenness and lung function in females for analyses stratified by sex, which might be attributable to higher smoking rates among males. We reported considerably positive impacts of greenness exposure on lung function among non-smokers but not among smokers when analysis was stratified by smoking status. This could be accounted for by the reality that smoking might damage lung health [2]. When stratified by residence, we revealed that a link between exposure to greenness and lung function in the urban populations, which may be because a piece of greenness exposure reflected crops in rural areas. Agricultural land was not an entertainment place, but greenness reflected more public areas in urban regions such as parks, which could increase the number of times contacting with nature for residents. When stratified by BMI, We concluded that greenness exposure had positive benefits on lung function among participants less than 28 kg/m2 but not among 28 kg/m2 or more. This may be explained that obesity can impair lung function [43]. When stratified by PM2.5, we found the relationship between greenness and lung function in areas with medium level of PM2.5, which may be due to an interaction between the associations of air pollution levels and greenness [24]. We have known that greenness to be passively associated with air pollution, in accord with known mode of reducing greenness and raising urbanization. Areas of low level of PM2.5 may be highly green regions, which might increase susceptibility to allergic reactions to pollens [13]. In addition, greenness could improve lung function in areas with high level of PM2.5, but we did not observe a significant association in as much as air pollutants were possibly to induce higher pollen production [44, 45].

There are several pathways by which greenness may improve lung function. Firstly, greenness can enhance air quality [46]. Vegetation could eliminate airborne contaminant directly and effectively, especially for ozone and ambient particulate matter pollution [47]. In our study, we tested the hypothesis for PM2.5 with mediation analysis for lung function. Results showed that PM2.5 mediated 18.83% of the association between greenness and FVC, and 17.53% of the association between greenness and FEV1, but that did not reach the level of significance. Secondly, greenness could facilitate physical activity, which is connected with better lung function [48, 49]. However, owing to deficiency of physical activity data, we cannot prove this hypothesis. Thirdly, greenness may raise the microbial diversity, which is associated with improved human health [50]. Biodiversity theory reported that exposure to a biodiversity environment ameliorated the immune system by regulating species of human microorganisms, and reduced the occurrence risk of disease by diluting pathogens in a large number of animal hosts [50]. As a result of technical limitations, it is hard for us to verify this pathway.

Our study’s highlights were the application of a multistage, probability sampling approach that produced a huge, provincially representative sample and reduced selection bias [37]; using a major of indicators to assess lung function, which made us to understand the relationship between exposure to greenness and lung function comprehensively. In addition, our study was in a position to adjust for important confounding variables though residual confounding cannot be excluded in a measure. However, several limitations should be mentioned in our study. First of all, since the study was cross-sectional in nature, drawing conclusions about causality was challenging. Secondly, there is the possibility for selection bias in the selection of included participants. Thirdly, Neither the NDVI nor the EVI may correctly reflect a participant’s actual exposure to greenness, nor how they will utilize or interpret it. Vegetation diversity and green space structure also had effects on respiratory health [51, 52], but they cannot be reflected by NDVI and EVI. Furthermore, limited by greenness assessment technique, we selected MODIS products at 1 km spatial resolution. Fourth, considering we lacked each participant’s complete residence address, we had to rely on their exposure to surrounding villages or communities within 1000-m buffer.


To sum up the above arguments, this cross-sectional investigation found that exposure to greenness was strongly related with improved lung function. Longitudinal studies are needed in the aftertime to investigate the correlation between exposure to greenness and lung function.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.


  1. Soriano JB, Kendrick PJ, Paulson KR, Gupta V, Abrams EM, Adedoyin RA, et al. Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: a systematic analysis for the global burden of Disease Study 2017. Lancet Respir Med. 2020;8:585–96.

    Article  Google Scholar 

  2. Vogelmeier CF, Criner GJ, Martinez FJ, Anzueto A, Barnes PJ, Bourbeau J, et al. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2017 Report. GOLD Executive Summary. Am J Respir Crit Care Med. 2017 Mar 1;195(5):557–582.

    Google Scholar 

  3. Chronic respiratory diseases. Accessed 13 Feb 2023.

  4. Rojas-Rueda D, Nieuwenhuijsen MJ, Gascon M, Perez-Leon D, Mudu P. Green spaces and mortality: a systematic review and meta-analysis of cohort studies. Lancet Planet Health. 2019;3:e469–77.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Kua KP, Lee S. The influence of residential greenness on mortality in the Asia-Pacific region: a systematic review and meta-analysis. Perspect Public Health. 2021;141:342–53.

    Article  CAS  PubMed  Google Scholar 

  6. Zhan Y, Liu J, Lu Z, Yue H, Zhang J, Jiang Y. Influence of residential greenness on adverse pregnancy outcomes: a systematic review and dose-response meta-analysis. Sci Total Environ. 2020;718:137420.

    Article  CAS  PubMed  Google Scholar 

  7. Lee KJ, Moon H, Yun HR, Park EL, Park AR, Choi H, et al. Greenness, civil environment, and pregnancy outcomes: perspectives with a systematic review and meta-analysis. Environ Health. 2020;19:91.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Luo YN, Huang WZ, Liu XX, Markevych I, Bloom MS, Zhao T, et al. Greenspace with overweight and obesity: a systematic review and meta-analysis of epidemiological studies up to 2020. Obes Rev. 2020;21:e13078.

    Article  PubMed  Google Scholar 

  9. De la Fuente F, Saldias MA, Cubillos C, Mery G, Carvajal D, Bowen M, et al. Green space exposure association with type 2 diabetes Mellitus, Physical Activity, and obesity: a systematic review. Int J Environ Res Public Health. 2020;18:E97.

    Article  Google Scholar 

  10. Markevych I, Schoierer J, Hartig T, Chudnovsky A, Hystad P, Dzhambov AM, et al. Exploring pathways linking greenspace to health: theoretical and methodological guidance. Environ Res. 2017;158:301–17.

    Article  CAS  PubMed  Google Scholar 

  11. Campos-Taberner M, García-Haro FJ, Martínez B, Izquierdo-Verdiguier E, Atzberger C, Camps-Valls G, et al. Understanding deep learning in land use classification based on Sentinel-2 time series. Sci Rep. 2020;10:17188.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Aghazadeh F, Ghasemi M, Kazemi Garajeh M, Feizizadeh B, Karimzadeh S, Morsali R. An integrated approach of deep learning convolutional neural network and google earth engine for salt storm monitoring and mapping. Atmospheric Pollut Res. 2023;:101689.

  13. Sarkar C, Zhang B, Ni M, Kumari S, Bauermeister S, Gallacher J, et al. Environmental correlates of chronic obstructive pulmonary disease in 96 779 participants from the UK Biobank: a cross-sectional, observational study. Lancet Planet Health. 2019;3:e478–90.

    Article  PubMed  Google Scholar 

  14. Roscoe C, Mackay C, Gulliver J, Hodgson S, Cai Y, Vineis P, et al. Associations of private residential gardens versus other greenspace types with cardiovascular and respiratory disease mortality: observational evidence from UK Biobank. Environ Int. 2022;167:107427.

    Article  CAS  PubMed  Google Scholar 

  15. Kasdagli M-I, Katsouyanni K, de Hoogh K, Lagiou P, Samoli E. Investigating the association between long-term exposure to air pollution and greenness with mortality from neurological, cardio-metabolic and chronic obstructive pulmonary diseases in Greece.Environ Pollut. 2022;292 Pt B:118372.

  16. Maas J, Verheij RA, de Vries S, Spreeuwenberg P, Schellevis FG, Groenewegen PP. Morbidity is related to a green living environment. J Epidemiol Community Health. 2009;63:967–73.

    Article  CAS  PubMed  Google Scholar 

  17. Zeng XW, Lowe AJ, Lodge CJ, Heinrich J, Roponen M, Jalava P, et al. Greenness surrounding schools is associated with lower risk of asthma in schoolchildren. Environ Int. 2020;143:105967.

    Article  PubMed  Google Scholar 

  18. Fuertes E, Markevych I, Thomas R, Boyd A, Granell R, Mahmoud O, et al. Residential greenspace and lung function up to 24 years of age: the ALSPAC birth cohort. Environ Int. 2020;140:105749.

    Article  PubMed  Google Scholar 

  19. Xiao Y, Gu X, Niu H, Meng X, Zhang L, Xu J, et al. Associations of residential greenness with lung function and chronic obstructive pulmonary disease in China. Environ Res. 2022;209:112877.

    Article  CAS  PubMed  Google Scholar 

  20. Nordeide Kuiper I, Svanes C, Markevych I, Accordini S, Bertelsen RJ, Bråbäck L, et al. Lifelong exposure to air pollution and greenness in relation to asthma, rhinitis and lung function in adulthood. Environ Int. 2021;146:106219.

    Article  CAS  PubMed  Google Scholar 

  21. Lambert KA, Lodge C, Lowe AJ, Prendergast LA, Thomas PS, Bennett CM, et al. Pollen exposure at birth and adolescent lung function, and modification by residential greenness. Allergy. 2019;74:1977–84.

    Article  PubMed  Google Scholar 

  22. Hartley K, Ryan PH, Gillespie GL, Perazzo J, Wright JM, Rice GE, et al. Residential greenness, asthma, and lung function among children at high risk of allergic sensitization: a prospective cohort study. Environ Health. 2022;21:52.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Yu H, Hu L-W, Zhou Y, Qian Z, Schootman M, LeBaige MH, et al. Association between eye-level greenness and lung function in urban chinese children. Environ Res. 2021;202:111641.

    Article  CAS  PubMed  Google Scholar 

  24. Zhou Y, Bui DS, Perret JL, Lowe AJ, Lodge CJ, Markevych I, et al. Greenness may improve lung health in low-moderate but not high air pollution areas: seven northeastern cities’ study. Thorax. 2021;76:880–6.

    Article  PubMed  Google Scholar 

  25. Cilluffo G, Ferrante G, Fasola S, Drago G, Ruggieri S, Viegi G, et al. Association between greenspace and lung function in italian children-adolescents. Int J Hyg Environ Health. 2022;242:113947.

    Article  PubMed  Google Scholar 

  26. Zhang J, Wang Y, Feng L, Hou C, Gu Q. Effects of air pollution and green spaces on impaired lung function in children: a case-control study. Environ Sci Pollut Res Int. 2022;29:11907–19.

    Article  CAS  PubMed  Google Scholar 

  27. Queiroz Almeida D, Paciência I, Moreira C, Cavaleiro Rufo J, Moreira A, Santos AC, et al. Green and blue spaces and lung function in the Generation XXI cohort: a life-course approach. Eur Respir J. 2022;60:2103024.

    Article  PubMed  Google Scholar 

  28. Ye T, Guo Y, Abramson MJ, Li T, Li S. Greenspace and children’s lung function in China: a cross-sectional study between 2013 and 2015. Sci Total Environ. 2023;858:159952.

    Article  CAS  PubMed  Google Scholar 

  29. Zha Z, Leng R, Xu W, Bao H, Chen Y, Fang L, et al. Prevalence and risk factors of chronic obstructive pulmonary disease in Anhui Province, China: a population-based survey. BMC Pulm Med. 2019;19:102.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Fang LW, Bao HL, Wang BH, Feng YJ, Cong S, Wang N, et al. A summary of item and method of national chronic obstructive pulmonary disease surveillance in China. Chin J Epidemiol. 2018;39:546–50.

    CAS  Google Scholar 

  31. Kish L. A Procedure for Objective Respondent selection within the Household. J Am Stat Assoc. 1949;44:380–7.

    Article  Google Scholar 

  32. Yuan F, Bauer ME. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens Environ. 2007;106:375–86.

    Article  Google Scholar 

  33. Rhew IC, Vander Stoep A, Kearney A, Smith NL, Dunbar MD. Validation of the normalized difference Vegetation Index as a measure of Neighborhood Greenness. Ann Epidemiol. 2011;21:946–52.

    Article  PubMed  PubMed Central  Google Scholar 

  34. C de K, C T, A S-M SSXBAV et al. Green and blue spaces and physical functioning in older adults: Longitudinal analyses of the Whitehall II study. Environ Int. 2019;122.

  35. James E, Hansen MD, Janos Porszasz MD, Richard Casaburi P, William W, Stringer MD. Re-defining lower limit of normal for FEV1/FEV6, FEV1/FVC, FEV3/FEV6 and FEV3/FVC to improve detection of Airway obstruction. Chronic Obstr Pulm Dis. 2015;2:94–102.

    Google Scholar 

  36. Lin Z, Gu Y, Liu C, Song Y, Bai C, Chen R, et al. Effects of ambient temperature on lung function in patients with chronic obstructive pulmonary disease: a time-series panel study. Sci Total Environ. 2018;619–620:360–5.

    Article  PubMed  Google Scholar 

  37. Fan J, Guo Y, Cao Z, Cong S, Wang N, Lin H, et al. Neighborhood greenness associated with chronic obstructive pulmonary disease: a nationwide cross-sectional study in China. Environ Int. 2020;144:106042.

    Article  PubMed  Google Scholar 

  38. Textor J, van der Zander B, Gilthorpe MS, Liskiewicz M, Ellison GT. Robust causal inference using directed acyclic graphs: the R package “dagitty. Int J Epidemiol. 2016;45:1887–94.

    PubMed  Google Scholar 

  39. Wei J, Huang W, Li Z, Xue W, Peng Y, Sun L, et al. Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach. Remote Sens Environ. 2019;231:111221.

    Article  Google Scholar 

  40. Wei J, Wei J, Li Z, Cribb M, Huang W, Xue W, et al. Improved 1-km-resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees. Atmospheric Chem Phys. 2020;20:3273–89.

  41. Squillacioti G, Bellisario V, Levra S, Piccioni P, Bono R. Greenness availability and Respiratory Health in a Population of Urbanised Children in North-Western Italy. Int J Environ Res Public Health. 2019;17:E108.

    Article  Google Scholar 

  42. Jl S, Jh R, C G-A, Cf K, Ah S, Mc H. The aging lung: Physiology, disease, and immunity.Cell. 2021;184.

  43. Dixon AE, Peters U. The effect of obesity on lung function. Expert Rev Respir Med. 2018;12:755–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Sedghy F, Sankian M, Moghadam M, Ghasemi Z, Mahmoudi M, Varasteh A-R. Impact of traffic-related air pollution on the expression of Platanus orientalis pollen allergens. Int J Biometeorol. 2017;61:1–9.

    Article  PubMed  Google Scholar 

  45. Sénéchal H, Visez N, Charpin D, Shahali Y, Peltre G, Biolley J-P, et al. A review of the Effects of Major Atmospheric Pollutants on Pollen grains, Pollen Content, and allergenicity. Sci World J. 2015;2015:940243.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Dadvand P, de NA, Triguero -Mas Margarita, Schembari A, Cirach M, Amoly E, et al. Surrounding Greenness and Exposure to Air Pollution During Pregnancy: An Analysis of Personal Monitoring Data. Environ Health Perspect. 2012;120:1286–90.

  47. Kroeger T, Escobedo FJ, Hernandez JL, Varela S, Delphin S, Fisher JRB, et al. Reforestation as a novel abatement and compliance measure for ground-level ozone. Proc Natl Acad Sci U S A. 2014;111:E4204–4213.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Lu Y, Sarkar C, Xiao Y. The effect of street-level greenery on walking behavior: evidence from Hong Kong. Soc Sci Med. 2018;208:41–9.

    Article  PubMed  Google Scholar 

  49. Roda C, Mahmoud O, Peralta GP, Fuertes E, Granell R, Serra I, et al. Physical-activity trajectories during childhood and lung function at 15 years: findings from the ALSPAC cohort. Int J Epidemiol. 2020;49:131–41.

    Article  PubMed  Google Scholar 

  50. Aerts R, Honnay O, Van Nieuwenhuyse A. Biodiversity and human health: mechanisms and evidence of the positive health effects of diversity in nature and green spaces. Br Med Bull. 2018;127:5–22.

    Article  PubMed  Google Scholar 

  51. Donovan GH, Gatziolis D, Longley I, Douwes J. Vegetation diversity protects against childhood asthma: results from a large New Zealand birth cohort. Nat Plants. 2018;4:358–64.

    Article  PubMed  Google Scholar 

  52. Dong Y, Liu H, Zheng T. Association between Green Space structure and the prevalence of Asthma: a case study of Toronto. Int J Environ Res Public Health. 2021;18:5852.

    Article  PubMed  PubMed Central  Google Scholar 

Download references


We sincerely express our gratitude to all participants.


This work was supported by Health Research Program of Anhui Province (AHWJ2021a032); Key Research and Development Program of Anhui Province (No: 202104j07020013); Key Project of Natural Science of Education Department of Anhui Province (No: KJ2021A0710), and Key Project of Natural Science of Bengbu medical college (No: 2020byzd043). The funding bodies had no role in the design of the study, collection, analysis, and interpretation of data, or in writing the manuscript.

Author information

Authors and Affiliations



Jing Mi ( and Zhenqiu Zha ( are corresponding authors and senior authors who contributed equally to this study. Conceptualization, M.J., Z.-Z.Q,; data management, Z.-Z.Q. and Z.-W.H.; methodology, P.-W.J., and Z.-W.H.; software, P.-W.J. and Z.W.H; formal analysis, P.-W.J. and Z.-W.H.; writing original draft, Z.-W.H., C.-J., J. -Y.H and Z-C; review and editing, P.W.J., and M.J.; supervision, M.J. and Z.-Z.Q; project administration, M.J., J. -Y.H and Z.-Z.Q; funding acquisition: M.J. and Z.-Z.Q.

Corresponding authors

Correspondence to Zhenqiu Zha or Jing Mi.

Ethics declarations

Ethics approval and consent to participate

The study protocol was reviewed and approved by the Ethics Review Committees of Anhui Medical University, Bengbu Medical College (Ref. Number: 2022 − 109) and Anhui Provincial Center for Disease Control and Prevention. A written informed consent with a signature was obtained from all participants in our study. All methods were performed in accordance with the relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, W., Peng, W., Cai, J. et al. Residential surrounding greenness is associated with improved lung function in adults: a cross-sectional study in eastern China. BMC Public Health 23, 632 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: