Abstract
Traditional approaches of quantifying population-level exposure to air pollution assume that concentrations of air pollutants at the residential address of the study population are representative for overall exposure. This introduces potential bias in the quantification of human health effects. Our study combines new UK Census data comprising information on workday population densities, with high spatio-temporal resolution air pollution concentration fields from the WRF-EMEP4UK atmospheric chemistry transport model, to derive more realistic estimates of population exposure to NO2, PM2.5 and O3. We explicitly allocated workday exposures for weekdays between 8:00 am and 6:00 pm. Our analyses covered all of the UK at 1 km spatial resolution. Taking workday location into account had the most pronounced impact on potential exposure to NO2, with an estimated 0.3 μg m−3 (equivalent to 2%) increase in population-weighted annual exposure to NO2 across the whole UK population. Population-weighted exposure to PM2.5 and O3 increased and decreased by 0.3%, respectively, reflecting the different atmospheric processes contributing to the spatio-temporal distributions of these pollutants. We also illustrate how our modelling approach can be utilised to quantify individual-level exposure variations due to modelled time-activity patterns for a number of virtual individuals living and working in different locations in three example cities. Changes in annual-mean estimates of NO2 exposure for these individuals were considerably higher than for the total UK population average when including their workday location. Conducting model-based evaluations as described here may contribute to improving representativeness in studies that use small, portable, automatic sensors to estimate personal exposure to air pollution.
Highlights
Traditional approaches to quantifying population-level exposure to air pollution assume that concentrations of air pollutants at the residential address of the study population are representative for overall exposure
It is not feasible to quantitatively apportion how the spatial and temporal factors contribute to the variations in overall exposure; this could only be done at individual level and would vary with individual
Our study demonstrates the utility of using new United Kingdom (UK) Census products comprising information on workday population densities, in combination with high spatio-temporal resolution atmospheric model output, to derive more realistic estimates of population exposure to air pollution
Summary
Traditional approaches to quantifying population-level exposure to air pollution assume that concentrations of air pollutants at the residential address of the study population are representative for overall exposure. As early as 1982, Ott highlighted that ‘Many previous investigators calculate “exposures” by relying on data from fixed air monitoring stations, and they assume that people are located in the same place, usually their residential address, throughout a 24-h period’ (Ott, 1982). This introduces potential bias in the quantification of human health effects, as the individual and population-level mobility of receptors is not accounted for. While results emerging from these studies are important for understanding the impact of specific mobility patterns (Setton et al, 2008, 2011; Beckx et al, 2009; Dons et al, 2011; Dhondt et al, 2012; Ragettli et al, 2014, 2015; Brokamp et al, 2016; Smith et al, 2016), for exposure in different micro-environments and for the relative contributions of these to overall personal exposure, up-scaling from this individual level to population level exposure is not straightforward
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