Abstract
Urban living is synonymous with a higher exposure to environmental stressors such as air pollution and associated physiological stress; however, the modifying role of greenness has been understudied. We included 190,200 participants from a UK-wide cohort to examine the modifying role of residential greenness on associations between air pollutants and composite physiological stress (CPS) constructed from 13 biomarkers of three physiological functions and two organs. We found that living in areas with higher air pollutants was associated with higher CPS, whereas higher residential greenness was inversely associated with CPS. Relative to participants exposed to low air pollution and high greenness (least-impacted group), those exposed to high air pollution and low greenness (double-impacted group) had higher odds of their CPS being in the highest quartile (22% (95% confidence interval (CI): 12–31%) for PM2.5, 18% (95% CI: 9–28%) for PM10, 17% (95% CI: 7–27%) for PM2.5–10 and 13% (95% CI: 4–23%) for NOx), with evidence of synergistic interactions between the pollutants PM10, PM2.5–10 and NOx and greenness exposures on the risk of high CPS. Considerable between-city variability was observed. The evidence points to the need for nature-based interventions, such as optimizing urban greenness for healthy cities with lower stress levels and related health burdens.
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Data availability
UK Biobank data, including linked environmental exposure metrics, are available from the UK Biobank at https://www.ukbiobank.ac.uk/ for researchers who meet the criteria for access to de-identified data. The built environment metrics of UKBUMP were developed by the authoring team based at The University of Hong Kong and linked to the UK Biobank. Several spatial databases were used in its creation, which were obtained upon request. The Bluesky color infrared data were obtained from LandMap Services of Manchester Information and Associated Services (MIMAS) at the University of Manchester. The urbanicity metrics were created using AddressBase Premium data and Integrated Transport Network Layers, which were obtained from UK Ordnance Survey GB. The greenspace typologies data50 used to generate Extended Data Fig. 5 were obtained by accessing the Green and Blue Infrastructure (England) database under the Open Government Licence at https://www.data.gov.uk/dataset/f335ab3a-f670-467f-bedd-80bdd8f1ace6/green-and-blue-infrastructure-england. The air pollution data43 used to generate Supplementary Fig. 1 were obtained by accessing the Modelled Background Pollution Data under the Open Government Licence at https://uk-air.defra.gov.uk/data/pcm-data.
Code availability
The study was performed as a part of a project approved by UK Biobank under a restricted Material Transfer Agreement, and thus computer codes are not publicly available. Analysis was performed using custom-made scripts in Stata v.17. The codes for exposure metrics used in the models can be requested from the corresponding author upon reasonable request.
References
Abbott, A. Stress and the city: urban decay. Nature 490, 162–164 (2012).
Thomson, E. M. Air pollution, stress, and allostatic load: linking systemic and central nervous system impacts. J. Alzheimers Dis. 69, 597–614 (2019).
Snow, S. J., Henriquez, A. R., Costa, D. L. & Kodavanti, U. P. Neuroendocrine regulation of air pollution health effects: emerging insights. Toxicol. Sci. 164, 9–20 (2018).
Cohen, A. J. et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet 389, 1907–1918 (2017).
World Bank. The Global Health Cost of PM2.5 Air Pollution: A Case for Action Beyond 2021 (World Bank, 2022)
Chrousos, G. P. & Gold, P. W. The concepts of stress and stress system disorders: overview of physical and behavioral homeostasis. JAMA 267, 1244–1252 (1992).
Cohen, S., Kessler, R. C. & Gordon, L. U. (eds) Measuring Stress: A Guide for Health and Social Scientists (Oxford Univ. Press, 1997)
Alderete, T. L. et al. Ambient and traffic-related air pollution exposures as novel risk factors for metabolic dysfunction and type 2 diabetes. Curr. Epidemiol. Rep. 5, 79–91 (2018).
Liu, L. et al. Air pollution, residential greenness, and metabolic dysfunction biomarkers: analyses in the Chinese Longitudinal Healthy Longevity Survey. BMC Public Health 22, 885 (2022).
Hennig, F. et al. Association between source-specific particulate matter air pollution and hs-CRP: local traffic and industrial emissions. Environ. Health Perspect. 122, 703–710 (2014).
Wirsching, J. et al. Exposure to ambient air pollution and elevated blood levels of gamma-glutamyl transferase in a large Austrian cohort. Sci. Total Environ. 883, 163658 (2023).
McEwen, B. S. Stress, adaptation, and disease: allostasis and allostatic load. Ann. N. Y. Acad. Sci. 840, 33–44 (1998).
Seeman, T. E., Singer, B. H., Rowe, J. W., Horwitz, R. I. & McEwen, B. S. Price of adaptation—allostatic load and its health consequences: MacArthur studies of successful aging. Arch. Intern. Med. 157, 2259–2268 (1997).
Open Spaces and Green Areas (UN-Habitat, 2021); https://data.unhabitat.org/pages/open-spaces-and-green-areas
Chen, B. et al. Contrasting inequality in human exposure to greenspace between cities of Global North and Global South. Nat. Commun. 13, 4636 (2022).
Hartig, T., Mitchell, R., Vries, S. D. & Frumkin, H. Nature and health. Annu. Rev. Public Health 35, 207–228 (2014).
Barwise, Y. & Kumar, P. Designing vegetation barriers for urban air pollution abatement: a practical review for appropriate plant species selection. npj Clim. Atmos. Sci. 3, 12 (2020).
Ji, J. S., Zhu, A., Lv, Y. & Shi, X. Interaction between residential greenness and air pollution mortality: analysis of the Chinese Longitudinal Healthy Longevity Survey. Lancet Planet. Health 4, e107–e115 (2020).
Sun, S. et al. Air pollution associated respiratory mortality risk alleviated by residential greenness in the Chinese Elderly Health Service Cohort. Environ. Res. 183, 109139 (2020).
Nowak, D. J., Crane, D. E. & Stevens, J. C. Air pollution removal by urban trees and shrubs in the United States. Urban For. Urban Green. 4, 115–123 (2006).
Yin, S. et al. Quantifying air pollution attenuation within urban parks: an experimental approach in Shanghai, China. Environ. Pollut. 159, 2155–2163 (2011).
van den Bosch, M. & Ode Sang, Å. Urban natural environments as nature-based solutions for improved public health – a systematic review of reviews. Environ. Res. 158, 373–384 (2017).
Sarkar, C. & Lai, K. Y. Urban built environments: interventions for reducing cardiometabolic risks. Nat. Rev. Endocrinol. 19, 315–316 (2023).
Xu, H. et al. Increased allostatic load associated with ambient air pollution acting as a stressor: cross-sectional evidence from the China multi-ethnic cohort study. Sci. Total Environ. 831, 155658 (2022).
Iyer, H. S. et al. Impact of neighborhood socioeconomic status, income segregation, and greenness on blood biomarkers of inflammation. Environ. Int. 162, 107164 (2022).
Egorov, A. I. et al. Greater tree cover near residence is associated with reduced allostatic load in residents of central North Carolina. Environ. Res. 186, 109435 (2020).
Zhou, W. et al. The role of residential greenness levels, green land cover types and diversity in overweight/obesity among older adults: a cohort study. Environ. Res. 217, 114854 (2023).
Lai, K. Y. et al. Calculating a national Anomie Density Ratio: measuring the patterns of loneliness and social isolation across the UK’s residential density gradient using results from the UK Biobank study. Landsc. Urban Plan. 215, 104194 (2021).
Nemitz, E. et al. Potential and limitation of air pollution mitigation by vegetation and uncertainties of deposition-based evaluations. Philos. Trans. R. Soc. A 378, 20190320 (2020).
Selmi, W. et al. Air pollution removal by trees in public green spaces in Strasbourg city, France. Urban For. Urban Green. 17, 192–201 (2016).
Janhäll, S. Review on urban vegetation and particle air pollution – deposition and dispersion. Atmos. Environ. 105, 130–137 (2015).
Ward Thompson, C. 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. 11, 3317 (2019).
Egorov, A. I. et al. Vegetated land cover near residence is associated with reduced allostatic load and improved biomarkers of neuroendocrine, metabolic and immune functions. Environ. Res. 158, 508–521 (2017).
Valdés, S. et al. Association between exposure to air pollution and blood lipids in the general population of Spain. Eur. J. Clin. Invest. 5, e14101 (2023).
Baumgartner, J. et al. Household air pollution and measures of blood pressure, arterial stiffness and central haemodynamics. Heart 104, 1515–1521 (2018).
Bates, J. T. et al. Review of acellular assays of ambient particulate matter oxidative potential: methods and relationships with composition, sources, and health effects. Environ. Sci. Technol. 53, 4003–4019 (2019).
Singh, R. et al. Effects of environmental air pollution on endogenous oxidative DNA damage in humans. Mutat. Res. 620, 71–82 (2007).
Sarkar, C., Webster, C. & Gallacher, J. Residential greenness and prevalence of major depressive disorders: a cross-sectional, observational, associational study of 94 879 adult UK Biobank participants. Lancet Planet. Health 2, e162–e173 (2018).
Jimenez, R. B., Lane, K. J., Hutyra, L. R. & Fabian, M. P. Spatial resolution of Normalized Difference Vegetation Index and greenness exposure misclassification in an urban cohort. J. Expo. Sci. Environ. Epidemiol. 32, 213–222 (2022).
Emissions of Air Pollutants in the UK, 1970 to 2017 (Department for Environment, Food and Rural Affairs, 2019)
Doiron, D. et al. Air pollution, lung function and COPD: results from the population-based UK Biobank study. Eur. Respir. J. 54, 1802140 (2019).
Wang, M. et al. Joint exposure to various ambient air pollutants and incident heart failure: a prospective analysis in UK Biobank. Eur. Heart J. 42, 1582–1591 (2021).
Modelled Background Pollution Data (Department for Environment, Food and Rural Affairs, 2021); https://uk-air.defra.gov.uk/data/pcm-data
Avery, C. L. et al. Estimating error in using residential outdoor PM2.5 concentrations as proxies for personal exposures: a meta-analysis. Environ. Health Perspect. 118, 673–678 (2010).
Chadeau-Hyam, M. et al. Education, biological ageing, all-cause and cause-specific mortality and morbidity: UK Biobank cohort study. eClinicalMedicine 29, 100658 (2020).
Ganster, D. C., Crain, T. L. & Brossoit, R. M. Physiological measurement in the organizational sciences: a review and recommendations for future use. Ann. Rev. Organ. Psychol. Organ. Behav. 5, 267–293 (2018).
Beelen, R. et al. Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe – The ESCAPE project. Atmos. Environ. 72, 10–23 (2013).
Eeftens, M. et al. Development of land use regression models for PM2.5, PM2.5 absorbance, PM10 and PMcoarse in 20 European study areas; results of the ESCAPE project. Environ. Sci. Technol. 46, 11195–11205 (2012).
Sarkar, C., Webster, C. & Gallacher, J. UK Biobank Urban Morphometric Platform (UKBUMP) – a nationwide resource for evidence-based healthy city planning and public health interventions. Ann. GIS 21, 135–148 (2015).
Green and Blue Infrastructure (England) (Natural England, 2023); https://www.data.gov.uk/dataset/f335ab3a-f670-467f-bedd-80bdd8f1ace6/green-and-blue-infrastructure-england
Stata Statistical Software: Release 17 (Stata Corp., 2021).
ArcGIS Desktop (Environmental Systems Research Institute, 2014)
ArcGIS Desktop (Environmental Systems Research Institute, 2023)
Acknowledgements
The research used the UK Biobank resource (approved application number: 11730). C.S. acknowledges a fellowship in Global Health Leadership from the National Academy of Medicine, Washington DC and the University of Hong Kong. J.G. acknowledges funding from the Medical Research Council: MR/T0333771 award for the Dementias Platform UK. The built environment metrics of UKBUMP used in this study were supported by a seed grant from the UK Biobank and the UK Economic and Social Research Council’s Transformative Research grant (ES/L003201/1).
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C.S., K.Y.L., C.W., J.G. and S.K. contributed to concept and design of the study. K.Y.L. and S.K. contributed to the data cleaning. K.Y.L. and C.S. contributed to the data analyses and drafted the manuscript. All authors participated in interpretation of the data. All authors contributed to critical revision of the manuscript. C.S. supervised the study. All authors read and approved the final paper.
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Nature Cities thanks Elisabetta Salvatori and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data
Extended Data Fig. 1 Flowchart for participant selection.
Abbreviation: NDVI, Normalized Difference Vegetation Index.
Extended Data Fig. 2 Associations of NDVI greenness with composite physiological stress allowing for non-linear associations (N = 190,200).
Models were fitted for air pollutants using restricted cubic splines with Harrell’s knots placed at 10th, 50th and 90th percentiles, adjusting for sex, age, education attainment, employment status, household income, healthy lifestyle score (including smoking, alcohol consumption and dietary factors), sleeping duration, sound level of noise pollution, urbanicity, stressful life events, biological age (measured as residual of leucocyte telomere length, adjusted for chronological age and co-morbidities of cancer, cardiovascular diseases, hypertension, psychiatric disorders and respiratory diseases) and PM2.5. The continuous line shows the estimated composite physiological stress and the shaded regions show the corresponding 95% confidence intervals. Abbreviations: NDVI, Normalized Difference Vegetation Index.
Extended Data Fig. 3 Joint associations of air pollutants and NDVI greenness with odds of allostatic load in the 4th quartile (N = 190,990).
The joint associations of PM2.5 and NDVI greenness (a), PM10 and NDVI greenness (b), PM2.5-10 and NDVI greenness (c), and NOx and NDVI greenness (d) with odds of allostatic load in the 4th quartile using logistic regression models. Participants were stratified into 9 groups by air pollutants (Q1, Q2-Q4, Q5) and NDVI greenness (Q1, Q2-Q4, Q5) categories, with participants exposed to low air pollution (Q1) and high NDVI greenness (Q5) (least-impacted group) acting as the reference group. RERI was used to examine additive interaction between air pollution (high (Q5) vs low (Q1)) and NDVI greenness (low (Q1) vs high (Q5)), and additive interaction was statistically significant when confidence intervals did not include 0. P-interaction indicates significance of multiplicative interaction between categories of NDVI greenness and air pollutants. Models adjusted for sex, age, education attainment, employment status, household income, healthy lifestyle score (including smoking, alcohol consumption and dietary factors), sleeping duration, sound level of noise pollution, urbanicity, stressful life events and biological age (measured as residual of leucocyte telomere length, adjusted for chronological age and co-morbidities of cancer, cardiovascular diseases, hypertension, psychiatric disorders and respiratory diseases). The vertical bars show the odds ratios and the error bars show the corresponding 95% confidence intervals. The asterisks represent statistically significant (two-sided p < 0.05) point estimates. The index of allostatic load comprises nine biomarkers of three physiological functions (cardiovascular, metabolic and inflammatory functions). Abbreviations: NDVI, Normalized Difference Vegetation Index; Q, Quintile; RERI, relative excess risk due to interaction.
Extended Data Fig. 4 Joint associations of air pollutants and greenspace categories (by use) with odds of composite physiological stress in the 4th quartile (N = 228,154).
The joint associations of PM2.5 and total green area (a), PM10 and total green area, (b), PM2.5-10 and total green area (c), NOx and total green area (d), PM2.5 and outdoor sports (e), PM10 and outdoor sports (f), PM2.5-10 and outdoor sports (g), NOx and outdoor sports (h), PM2.5 and natural greenspace (i), PM10 and natural greenspace (j), PM2.5-10 and natural greenspace (k), and NOx and natural greenspace (l) with odds of composite physiological stress in the 4th quartile using logistic regression models. Participants were stratified into 9 groups by air pollutants (Q1, Q2-Q4, Q5) and NDVI greenness (Q1, Q2-Q4, Q5) categories, with participants exposed to low air pollution (Q1) and high NDVI greenness (Q5) (least-impacted group) acting as the reference group. Models adjusted for sex, age, education attainment, employment status, household income, healthy lifestyle score (including smoking, alcohol consumption and dietary factors), sleeping duration, sound level of noise pollution, urbanicity, stressful life events and biological age (measured as residual of leucocyte telomere length, adjusted for chronological age and co-morbidities of cancer, cardiovascular diseases, hypertension, psychiatric disorders and respiratory diseases). The vertical bars show the odds ratios and the error bars show the corresponding 95% confidence intervals. The asterisks represent statistically significant (two-sided p < 0.05) point estimates. Abbreviations: Q, Quintile.
Extended Data Fig. 5 Map of England showing greenspace categories (by use).
The English Green and Blue Infrastructure Database was accessed from https://www.data.gov.uk/dataset/f335ab3a-f670-467f-bedd-80bdd8f1ace6/green-and-blue-infrastructure-england. Adapted from ref. 50 under an Open Government Licence v3.0. UK Crown ©2023.
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Supplementary Methods, Tables 1–16 and Fig. 1.
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Lai, K.Y., Kumari, S., Gallacher, J. et al. Nexus between residential air pollution and physiological stress is moderated by greenness. Nat Cities 1, 225–237 (2024). https://doi.org/10.1038/s44284-024-00036-6
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DOI: https://doi.org/10.1038/s44284-024-00036-6