Pedde Meredith, Adar Sara D
Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA.
J Expo Sci Environ Epidemiol. 2024 Sep;34(5):821-826. doi: 10.1038/s41370-024-00644-3. Epub 2024 Feb 6.
Air pollution prediction modeling establishes relationships between measurements and geographical and meteorological characteristics to infer concentrations at locations without measurements. Since air pollution monitors are limited in number, predictions may be generated for locations different than those used to train the model. The epidemiologic impacts of this potential mismatch hinge on whether the population resides in areas well-represented by monitoring sites. Here we quantify the fraction of the population with geographical characteristics not reflected by the 2000, 2010, and 2020 EPA PM and PM regulatory sites. We evaluated this measure nationwide, regionally, and by race. Nationally, the networks were very representative of the population experience; however, there was less overlap regionally and in regions stratified by race. This suggests that sub-national exposure modeling should carefully consider the representativeness of monitors for their populations. It also highlights that exposure models often borrow information from distal places to predict full population exposure.
空气污染预测模型建立测量值与地理和气象特征之间的关系,以推断未进行测量地点的污染物浓度。由于空气污染监测器数量有限,可能会针对与用于训练模型的地点不同的地点生成预测。这种潜在不匹配对流行病学的影响取决于人群是否居住在监测站点能充分代表的地区。在此,我们量化了2000年、2010年和2020年美国环境保护局(EPA)细颗粒物(PM)和可吸入颗粒物(PM)监管站点未反映出地理特征的人口比例。我们在全国、区域以及按种族进行了此项评估。在全国范围内,这些监测网络能很好地代表人群的实际情况;然而,在区域层面以及按种族分层的区域中,重叠程度较低。这表明,国家以下层面的暴露建模应仔细考虑监测器对于其所在人群的代表性。这也凸显出,暴露模型常常从较远的地方借用信息来预测整个人口的暴露情况。