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Nexus between residential air pollution and physiological stress is moderated by greenness

Nature Cities volume 1, pages 225–237 (2024)Cite this article

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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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Fig. 1: Illustrations showing the NDVI modeled from color infrared imagery with a spatial resolution 0.5 m and calculation of the individual-level green exposure.
Fig. 2: Associations between air pollutants and CPS enabling non-linear associations (N = 190,200).
Fig. 3: Joint associations of air pollutants and NDVI greenness with the odds of CPS in the fourth quartile (N = 190,200).
Fig. 4: Associations between air pollutants and CPS by UK Biobank city cluster (N = 190,200).
Fig. 5: Joint associations of air pollutants and NDVI greenness with the odds of CPS in the fourth quartile by UK Biobank city cluster (N = 190,200).

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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.

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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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Authors and Affiliations

  1. Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Hong Kong, China

    Ka Yan Lai, Sarika Kumari, Chris Webster & Chinmoy Sarkar

  2. Department of Urban Planning & Design, Faculty of Architecture, The University of Hong Kong, Hong Kong, China

    Ka Yan Lai, Chris Webster & Chinmoy Sarkar

  3. Department of Psychiatry, University of Oxford, Oxford, UK

    John Gallacher & Chinmoy Sarkar

  4. Urban Systems Institute, The University of Hong Kong, Hong Kong, China

    Chris Webster

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Contributions

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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Correspondence to Chinmoy Sarkar.

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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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