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, PM_2.5 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 PM_2.5 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.

量化人口水平空气污染暴露的传统方法是假设研究人群居住地的空气污染物浓度代表总体暴露。这在量化人类健康影响方面引入了潜在的偏见。我们的研究结合了新的英国人口普查数据，其中包括工作日人口密度的信息，以及来自WRF-EMEP4UK大气化学迁移模型的高时空分辨率空气污染浓度场，以得出更实际的人口对NO ₂，PM _2.5和O暴露的估计值。 ₃。我们明确分配了工作日在上午8:00至下午6:00之间的工作时间。我们的分析以1 km的空间分辨率覆盖了整个英国。考虑到工作日的位置，对潜在的NO ₂暴露影响最为明显，整个英国人口中，按人口加权的每年暴露于NO ₂的估计年加权暴露量增加了0.3μgm ^-3（相当于2％）。人群加权暴露于PM _2.5和O ₃分别增加和减少0.3％，反映了不同的大气过程导致了这些污染物的时空分布。我们还将说明我们的建模方法如何用于量化由于在三个示例城市中居住和工作在不同地点的许多虚拟个体的时间活动模式而产生的个体水平的暴露变化。这些人包括工作日位置在内的年均NO ₂暴露估计值的变化大大高于英国总人口的平均水平。如此处所述，进行基于模型的评估可能有助于提高研究的代表性，该研究使用小型，便携式，自动传感器来估计个人对空气污染的暴露。