Quantifying the impact of daily mobility on errors in air pollution exposure estimation using mobile phone location data
Quantifying the impact of daily mobility on errors in air pollution exposure estimation using mobile phone location data
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DOI：
10.1016/j.envint.2020.105772
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发表时间：
2020-08-01
影响因子：
11.8
通讯作者：
Zheng, Junyu
中科院分区：
文献类型：
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作者：
Yu, Xiaonan;Ivey, Cesunica;Zheng, Junyu
准确估计人类空气污染暴露的不确定性的一个主要来源是人类受试者在时空上移动，而在暴露估计中通常不考虑这种移动性。这种流动性如何影响人群和个人层面的暴露估计，特别是对于具有不同流动性水平的受试者，仍有待研究。此外，过去已使用多种方法来开发用于相关健康研究的空气污染物浓度场。方法的选择如何影响暴露估计的结果，特别是在考虑详细的流动性信息时，仍然很大程度上未知。在这项研究中，通过使用公开的大型手机位置数据集（包含从 310,989 名受试者收集的超过 3500 万条位置记录），我们调查了个别受试者的影响？五种选定的环境污染物（CO、NO2、SO2、O-3 和 PM2.5）的估计暴露量的流动性。我们还分别估计了 10 组具有不同活动水平的受试者的暴露量，以探讨活动性增加如何影响他们的暴露估计。此外，我们应用并比较了两种方法来开发用于暴露估计的浓度场，包括一种基于社区多尺度空气质量（CMAQ）模型输出，另一种基于使用反距离加权（IDW）方法插值观测到的污染物浓度。我们的结果表明，详细的流动性信息对样本人群中的平均人群暴露估计没有显着影响，尽管在个人层面上影响可能很大。此外，对于表现出较高流动性的受试者，由于使用家庭位置数据而导致的暴露分类错误增加。忽略流动性可能会导致低估交通相关污染物的暴露量，特别是在下午高峰时段，并高估臭氧暴露量，特别是在下午三点左右。在 CMAQ 和 IDW 之间，我们发现 IDW 方法生成平滑的浓度场，不适合使用详细的迁移率数据进行暴露估计。因此，在应用详细的流动性数据时，应谨慎选择开发空气污染浓度场的方法。我们的研究结果对未来的空气污染健康研究具有重要意义。
One major source of uncertainty in accurately estimating human exposure to air pollution is that human subjects move spatiotemporally, and such mobility is usually not considered in exposure estimation. How such mobility impacts exposure estimates at the population and individual level, particularly for subjects with different levels of mobility, remains under -investigated. In addition, a wide range of methods have been used in the past to develop air pollutant concentration fields for related health studies. How the choices of methods impact results of exposure estimation, especially when detailed mobility information is considered, is still largely unknown. In this study, by using a publicly available large cell phone location dataset containing over 35 million location records collected from 310,989 subjects, we investigated the impact of individual subjects? mobility on their estimated exposures for five chosen ambient pollutants (CO, NO2, SO2, O-3 and PM2.5). We also estimated ex- posures separately for 10 groups of subjects with different levels of mobility to explore how increased mobility impacted their exposure estimates. Further, we applied and compared two methods to develop concentration fields for exposure estimation, including one based on Community Multiscale Air Quality (CMAQ) model out- puts, and the other based on the interpolated observed pollutant concentrations using the inverse distance weighting (IDW) method. Our results suggest that detailed mobility information does not have a significant influence on mean population exposure estimate in our sample population, although impacts can be substantial at the individual level. Additionally, exposure classification error due to the use of home -location data increased for subjects that exhibited higher levels of mobility. Omitting mobility could result in underestimation of ex- posures to traffic -related pollutants particularly during afternoon rush-hour, and overestimate exposures to ozone especially during mid -afternoon. Between CMAQ and IDW, we found that the IDW method generates smooth concentration fields that were not suitable for exposure estimation with detailed mobility data. Therefore, the method for developing air pollution concentration fields when detailed mobility data were to be applied should be chosen carefully. Our findings have important implications for future air pollution health studies.