Centre for Ecology & Hydrology, Bush Estate, Penicuik EH26 0QB, UK; University of Exeter Medical School, Knowledge Spa, Truro TR1 3HD, UK.
Centre for Ecology & Hydrology, Bush Estate, Penicuik EH26 0QB, UK; School of Chemistry, University of Edinburgh, Joseph Black Building, David Brewster Road, Edinburgh EH9 3FJ, UK.
Environ Int. 2018 Dec;121(Pt 1):803-813. doi: 10.1016/j.envint.2018.10.005. Epub 2018 Oct 16.
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 NO, PM and O. 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 NO, with an estimated 0.3 μg m (equivalent to 2%) increase in population-weighted annual exposure to NO across the whole UK population. Population-weighted exposure to PM and O 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 NO 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.
传统的量化人群暴露于空气污染的方法假设研究人群的居住地址处的空气污染物浓度可代表整体暴露情况。这可能会导致在量化人类健康影响时出现偏差。我们的研究结合了新的英国人口普查数据,这些数据包含了工作日人口密度的信息,以及来自 WRF-EMEP4UK 大气化学输送模型的高时空分辨率的空气污染浓度场,以更真实地估计人群对 NO、PM 和 O 的暴露情况。我们明确地为工作日上午 8 点至下午 6 点之间的工作日分配了暴露情况。我们的分析涵盖了整个英国,空间分辨率为 1 公里。考虑工作日地点对潜在暴露于 NO 的影响最大,估计整个英国人口的加权年平均 NO 暴露量增加了 0.3μg/m(相当于 2%)。PM 和 O 的加权暴露量分别增加和减少了 0.3%,这反映了不同的大气过程对这些污染物时空分布的影响。我们还说明了如何利用我们的建模方法来量化由于模型化的时间-活动模式而导致的个体水平暴露变化,这些模式适用于生活和工作在三个示例城市不同地点的若干虚拟个体。当包括他们的工作日地点时,这些个体的年平均 NO 暴露估计值的变化明显高于整个英国人口的平均值。进行这里描述的基于模型的评估可能有助于提高使用小型、便携式、自动传感器来估计个人空气污染暴露情况的研究的代表性。