McGill University , Department of Epidemiology, Biostatistics and Occupational Health , Montreal , Quebec H3A 1A2 , Canada.
University of Toronto , Department of Civil Engineering , Toronto , Ontario M5S 1A4 , Canada.
Environ Sci Technol. 2018 Sep 18;52(18):10777-10786. doi: 10.1021/acs.est.8b02260. Epub 2018 Aug 29.
Epidemiological studies often assign outdoor air pollution concentrations to residential locations without accounting for mobility patterns. In this study, we examined how neighborhood characteristics may influence differences in exposure assessments between outdoor residential concentrations and mobility-based exposures. To do this, we linked residential location and mobility data to exposure surfaces for NO, PM, and ultrafine particles in Montreal, Canada for 5452 people in 2016. Mobility data were collected using the MTL Trajet smartphone application (mean: 16 days/subject). Generalized additive models were used to identify important neighborhood predictors of differences between residential and mobility-based exposures and included residential distances to highways, traffic counts within 500 m of the residence, neighborhood walkability, median income, and unemployment rate. Final models including these parameters provided unbiased estimates of differences between residential and mobility-based exposures with small root-mean-square error values in 10-fold cross validation samples. In general, our findings suggest that differences between residential and mobility-based exposures are not evenly distributed across cities and are greater for pollutants with higher spatial variability like NO. It may be possible to use neighborhood characteristics to predict the magnitude and direction of this error to better understand its likely impact on risk estimates in epidemiological analyses.
流行病学研究通常将室外空气污染浓度分配给居住地点，而不考虑移动模式。在这项研究中，我们研究了邻里特征如何可能影响户外居住浓度和基于移动性的暴露评估之间的差异。为此，我们将居住地点和移动数据与 2016 年加拿大蒙特利尔的 NO、PM 和超细颗粒暴露表面联系起来，涉及 5452 人。使用 MTL Trajet 智能手机应用程序（平均值：每人 16 天）收集移动数据。广义加性模型用于确定居住和基于移动性的暴露之间差异的重要邻里预测因素，包括居住地点到高速公路的距离、居住地 500 米范围内的交通量、邻里步行能力、中位数收入和失业率。最终模型包含这些参数，在 10 倍交叉验证样本中提供了居住和基于移动性的暴露之间差异的无偏估计，根均方误差值较小。总体而言，我们的研究结果表明，居住和基于移动性的暴露之间的差异在城市之间分布不均，对于像 NO 这样空间变异性较高的污染物差异更大。可能可以使用邻里特征来预测这种误差的大小和方向，以更好地理解其对流行病学分析中风险估计的可能影响。
