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OP16 The effect ofair pollution on individuals’ mental well-being in the United Kingdom: a spatial-temporal longitudinal study
  1. Mary Abed Al Ahad1,
  2. Urška Demšar1,
  3. Frank Sullivan2,
  4. Hill Kulu1
  1. 1School of Geography and Sustainable Development, University of St Andrews, St Andrews, UK
  2. 2School of Medicine, University of St Andrews, St Andrews, UK


Background Recent studies suggest an association between ambient air pollution and mental well-being, though evidence is mostly fragmented and inconclusive and suffers from methodological limitations related to the study design. In this study, we aimed to investigate the effect of air pollution on mental well-being in the United Kingdom (UK) using a spatial-temporal (between-within) longitudinal design.

Methods Data for 60,146 adult individuals (age:16+) with 349,748 repeated responses across 10-data collection waves (11-years: 2009–2019) from the “Understanding Society: The UK Household Longitudinal Study” were linked to annual concentrations of NO2, SO2, PM10, and PM2.5 pollutants using the individuals’ place of residence, given at the Lower Super Output Areas (LSOAs) geography level. Mental well-being was measured through the General Health Questionnaire (GHQ12) scale which is composed of 12 questions about the individuals’ experience of 12 mental well-being symptoms, each assessed on a 4-point Likert scale. The 12 questions are either summed up resulting in a total score between 0 and 36 (GHQ0–36) or dichotomised and then summed up resulting in a total score between 0 and 12 (GHQ0–12). Based on relevant literature, a cut-off of 12 for GHQ0–36 indicates poor mental well-being, while two cut-offs of 4 and 2 indicate poor mental well-being for GHQ0–12. Multilevel mixed-effect logit models were used to analyse the association between air pollution and the three binary mental well-being outcomes (GHQ0–36 ≥12; GHQ0–12 ≥2; GHQ0–12 ≥4).

Results Higher odds of poor mental well-being were observed with every 10µg/m3 increase in NO2 (GHQ0–36 ≥12: OR=1.12, 95% CI=1.09–1.15; GHQ0–12 ≥2: OR=1.14, 95%CI=1.11–1.17; GHQ0–12 ≥4: OR=1.12, 95%CI=1.09–1.16), SO2 (GHQ0–36 ≥12: OR=1.30, 95% CI=1.18–1.44; GHQ0–12 ≥2: OR=1.29, 95%CI=1.17–1.42; GHQ0–12 ≥4: OR=1.31, 95%CI=1.17–1.47), PM10 (GHQ0–36 ≥12: OR=1.22, 95% CI=1.15–1.30; GHQ0–12 ≥2: OR=1.28, 95%CI=1.20–1.36; GHQ0–12 ≥4: OR=1.23, 95%CI=1.15–1.31) and PM2.5 (GHQ0–36 ≥12: OR=1.35, 95% CI=1.24–1.47; GHQ0–12 ≥2: OR=1.44, 95%CI=1.33–1.56; GHQ0–12 ≥4: OR=1.38, 95%CI=1.25–1.51) pollutants. Decomposing air pollution into between (spatial: average 11-years air pollution across LSOAs) and within (temporal: annual deviation in air pollution from the 11-years average within each LSOA) effects showed significant between, but not within effects.

Conclusion Using longitudinal individual-level and contextual-linked pollution data, this study demonstrates the negative effect of air pollution on individuals’ mental well-being which is mainly attributed to residing in more polluted areas rather than the air pollution variation across time within each geographical area. Thus, environmental policies to reduce air pollution emissions can eventually improve the mental well-being of people in the UK.

  • Air pollution
  • mental well-being
  • longitudinal
  • spatial
  • temporal
  • United Kingdom (UK).

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