Department of Environmental Health, Harvard School of Public Health, Harvard University, Boston, MA, 02115, USA.
Senseable City Laboratory, Department of Urban Studies & Planning, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
J Expo Sci Environ Epidemiol. 2019 Mar;29(2):238-247. doi: 10.1038/s41370-018-0038-9. Epub 2018 Apr 27.
A critical question in environmental epidemiology is whether air pollution exposures of large populations can be refined using individual mobile-device-based mobility patterns. Cellular network data has become an essential tool for understanding the movements of human populations. As such, through inferring the daily home and work locations of 407,435 mobile phone users whose positions are determined, we assess exposure to PM. Spatiotemporal PM concentrations are predicted using an Aerosol Optical Depth- and Land Use Regression-combined model. Air pollution exposures of subjects are assigned considering modeled PM levels at both their home and work locations. These exposures are then compared to residence-only exposure metric, which does not consider daily mobility. In our study, we demonstrate that individual air pollution exposures can be quantified using mobile device data, for populations of unprecedented size. In examining mean annual PM exposures determined, bias for the residence-based exposures was 0.91, relative to the exposure metric considering the work location. Thus, we find that ignoring daily mobility potentially contributes to misclassification in health effect estimates. Our framework for understanding population exposure to environmental pollution could play a key role in prospective environmental epidemiological studies.
一个关键问题在环境流行病学是空气污染暴露的大人群是否可以使用个人移动设备为基础的移动模式来改进。蜂窝网络数据已成为理解人口流动的重要工具。因此，通过推断 407435 个确定位置的手机用户的每日家庭和工作地点，我们评估 PM 的暴露情况。使用气溶胶光学深度和土地利用回归相结合的模型来预测 PM 的时空浓度。考虑到家庭和工作地点的建模 PM 水平，为主体分配空气污染暴露。然后将这些暴露与仅考虑居住的暴露指标进行比较，该指标不考虑日常活动。在我们的研究中，我们证明可以使用移动设备数据来量化个体空气污染暴露，这是史无前例的大规模人群。在检查确定的平均年度 PM 暴露时，相对于考虑工作地点的暴露指标，基于居住的暴露存在 0.91 的偏差。因此，我们发现忽略日常活动可能会导致健康影响估计的分类错误。我们理解人群对环境污染暴露的框架可以在未来的环境流行病学研究中发挥关键作用。
