Scottish Longitudinal Study
Development & Support Unit
The effect of spatio temporal variability of air pollution and population mobility on public health
Tomas Liska (University of Edinburgh)
Mathew Heal (University of Edinburgh)
Stefan Reis (Centre for Ecology & Hydrology, University of Edinburgh)
1st March 2018
A large number of epidemiological studies have identified air pollution as a major risk to human health. Exposure to pollutants such as nitrogen dioxide, ozone and fine particulate matter (NO2, O3 and PM2.5) causes cardiovascular and respiratory diseases, cancer and other adverse health effects (WHO, 2013). Research has also suggested that more deprived population groups of the society are on average exposed to poorer air quality than the less deprived groups (Mitchell & Dorling, 2003; Pye et al., 2006) and that this ‘environmental injustice’ has been increasing in the UK (Mitchell et al., 2015). To address the issue of poor urban air quality and comply with current (and future) air quality limits many urban authorities are planning to designate low emission zones in their urban centres.
However, a vast majority of air pollution exposure studies ignore the spatio-temporal variability of air pollutants and/or exposure at microenvironments other than at home in their assessments. This is likely to lead to exposure misclassification and, consequently, a bias in the associated health effects. The few studies that considered population mobility in their exposure assessment using high spatial and temporal resolution pollution fields point to an increase in air pollution exposure in their dynamic population scenarios and hence higher impact on public health (Ragettli et al., 2015; Dhondt et al., 2012; Dewulf et al., 2016). In the UK, only Smith et al. (2016) has taken into account exposure in various microenvironments to estimate population exposure for the residents in London.
By considering exposure to ambient air pollution at work (the microenvironment in which people spend most time after home) my study should improve the estimate of population exposure to air pollution in the Central Belt of Scotland and assess more accurately the effect of creating low emission zones in Scotland’s two largest cities. Furthermore, the study should also help to better quantify the exposure inequality among different socio-economic groups.
The aim of the project is to improve the current estimates of air pollution (nitrogen dioxide, particulate matter, ozone) exposure of populations and population subgroups (age, sex, occupation, socio-economic status) living in the Central Belt of Scotland by accounting for exposure to ambient air pollution at both the place of residence and the place of work/study. The EMEP4UK atmospheric chemistry transport model output for 2015 at 1 km ´ 1 km spatial resolution and 1 hour temporal resolution is used to estimate air pollution concentrations in the region. Additionally, the ADMS-Urban dispersion model is used to generate spatially more detailed pollution fields in the Glasgow and Edinburgh urban areas. The modelled pollution concentrations are then spatially allocated to unit post code level.
Using the improved exposure estimates and exposure-response function coefficients from epidemiological studies answers to the following research questions are sought:
- What is the effect of air pollution on public health in the Central Belt of Scotland, and in particular Scotland’s two largest cities?
- Does accounting for exposure at the place of work/study increase or decrease air pollution exposure inequality?
- How effective would the creation of low emission zones in Glasgow and Edinburgh city centres be in decreasing population exposure to air pollution and improving public health?
Dewulf, B., et al. (2016). Dynamic assessment of exposure to air pollution using mobile phone data. International Journal of Health Geographics. [Online]. 15 (1). p. 14. Available from: http://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-016-0042-z.
Dhondt, S., et al. (2012). Health impact assessment of air pollution using a dynamic exposure profile: Implications for exposure and health impact estimates. Environmental Impact Assessment Review. 36. pp. 42–51.
Mitchell, G. & Dorling, D. (2003). An environmental justice analysis of British air quality. Environment and Planning A. 35 (5). pp. 909–929.
Mitchell, G., Norman, P. & Mullin, K. (2015). Who benefits from environmental policy? An environmental justice analysis of air quality change in Britain, 2001–2011. Environmental Research Letters. [Online]. 10 (10). p. 105009. Available from: http://stacks.iop.org/1748-9326/10/i=10/a=105009?key=crossref.806aec5054105fe7054fc629caa0132a.
Pye, S., King, K. & Sturman, G. (2006). Air Quality and Social Deprivation in the UK: an environmental inequalities analysis. London, UK.
Ragettli, M.S., et al. (2015). The relevance of commuter and work/school exposure in an epidemiological study on traffic-related air pollution. Journal of exposure science & environmental epidemiology. [Online]. 25 (5). pp. 474–81. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25492241.
WHO (2013). Review of evidence on health aspects of air pollution – REVIHAAP Project.
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