Fig 1.
A diagram showing the four data collection sub-samples of the UKHLS main survey.
Fig 2.
A diagram showing the reasons for dropping survey responses from the UKHLS longitudinal panel data.
Fig 3.
A map showing the local authorities in the UK and an enlarged subset of 20 local authorities in the south-east of UK with an example of PM10 concentrations at 1x1 km grid for the year of 2017 for Tower Hamlets local authority and its corresponding LSOAs.
The map was constructed by the authors in ArcGIS Pro software using PM10 air pollution shapefile for the year of 2017 downloaded from the DEFRA online data repository [51], local authorities UK boundaries shapefile downloaded from the Office for National Statistics [54], and LSOAs and data zones UK boundaries also downloaded from the Office for National Statistics, National Records of Scotland, and Northern Ireland Statistics [55]. Both DEFRA and Office for National Statistics shapefiles are governed under the Open Government Licence v.3.0.
Table 1.
The socio-demographics and lifestyle covariates selected for this study.
Table 2.
Description of individual’s socio-demographic and lifestyle factors for each wave of the UKHLS data (N = 349,748 surveys from 60,146 individuals).
Fig 4.
The annual mean of NO2, SO2, PM10, and PM2.5 air pollutants at the LSOAs level in the UK from the year of 2009 to 2019 (N = 42,619 LSOAs).
Table 3.
Correlation matrix of air pollutants at the LSOAs level (N = 42,619 LSOAs).
Fig 5.
The annual mean of NO2, SO2, PM10, and PM2.5 air pollutants at the local authority level in the UK from the year of 2009 to 2019 (N = 391 local authorities).
Table 4.
Correlation matrix of air pollutants at the local authority level (N = 391 local authorities).
Table 5.
Intraclass correlation coefficient for within individual and household clusters.
Table 6.
The association of individuals’ mental well-being with each of NO2, SO2, PM10, and PM2.5 air pollutants linked at the LSOAs level in separate models (N = 349,748 surveys from 60,146 individuals).
Table 7.
The association of individuals’ mental well-being with each of NO2, PM10, and PM2.5 air pollutants linked at the LSOAs level in bi-pollutant models adjusted for SO2 pollutant (N = 349,748 surveys from 60,146 individuals).
Table 8.
The association of individuals’ mental well-being with each of NO2, SO2, PM10, and PM2.5 air pollutants linked at the local authority level in separate models (N = 349,748 surveys from 60,146 individuals).
Table 9.
The association of individuals’ mental well-being with each of NO2, PM10, and PM2.5 air pollutants linked at the local authority level in bi-pollutant models adjusted for SO2 pollutant (N = 349,748 surveys from 60,146 individuals).
Fig 6.
The overall effect of air pollution linked at the LSOAs level on individuals’ mental well-being by ethnicity and country of birth (N = 349,748 surveys from 60,146 individuals).
The dashed line is placed at OR = 1 as a cut-off for statistically insignificant results; The solid line separates between the air pollution-ethnicity interaction models and the air pollution-country of birth interaction models; Air pollution-ethnicity interaction models are adjusted for country of birth, age, gender, marital status, education, occupation, perceived financial situation, smoking status, and year dummies (2009 to 2019); Air pollution-country of birth interaction models are adjusted for ethnicity, age, gender, marital status, education, occupation, perceived financial situation, smoking status, and year dummies (2009 to 2019).
Fig 7.
The overall effect of air pollution linked at the local authority level on individuals’ mental well-being by ethnicity and country of birth (N = 349,748 surveys from 60,146 individuals).
The dashed line is placed at OR = 1 as a cut-off for statistically insignificant results; The solid line separates between the air pollution-ethnicity interaction models and the air pollution-country of birth interaction models; Air pollution-ethnicity interaction models are adjusted for country of birth, age, gender, marital status, education, occupation, perceived financial situation, smoking status, and year dummies (2009 to 2019); Air pollution-country of birth interaction models are adjusted for ethnicity, age, gender, marital status, education, occupation, perceived financial situation, smoking status, and year dummies (2009 to 2019).