Using smartphones to collect time-activity data for long-term personal-level air pollution exposure assessment
Using smartphones to collect time-activity data for long-term personal-level air pollution exposure assessment
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DOI：
10.1038/jes.2014.78
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发表时间：
2016-07-01
影响因子：
4.5
通讯作者：
Mu, Lina
中科院分区：
文献类型：
--
作者：
Glasgow, Mark L.;Rudra, Carole B.;Mu, Lina
由于城市中人和空气污染物的时空变异性，在估计个人空气污染暴露时，必须考虑到一个人随时间的移动。本研究旨在探讨使用智能手机收集个人水平的时间活动数据的可行性。使用Skyhook Wireless的混合地理定位模块，我们开发了"Apolux"（空气、污染、暴露），这是一款Android（TM）智能手机应用程序，旨在以5分钟的间隔跟踪参与者的位置，持续3个月。从42名参与者，我们比较了阿波罗数据与同期数据从两个自我报告，24小时的时间活动日记。大约四分之三的测量值是在5分钟内收集的（平均值= 74.14%），79%的参与者报告不断通电的智能手机（n = 38）的每日平均数据收集频率为o10分钟。Apolux的时间分辨率程度因制造商，移动的网络和数据收集发生的时间而异。日志点和相应的Apolux数据之间的差异为342.3米（欧几里德距离），并在移动的网络中变化。这项研究的高度合规性和数据收集的可行性表明，将基于智能手机的时间活动数据整合到长期和大规模的空气污染暴露研究中的潜力。
Because of the spatiotemporal variability of people and air pollutants within cities, it is important to account for a person's movements over time when estimating personal air pollution exposure. This study aimed to examine the feasibility of using smartphones to collect personal-level time-activity data. Using Skyhook Wireless's hybrid geolocation module, we developed "Apolux" (Air, Pollution, Exposure), an Android (TM) smartphone application designed to track participants' location in 5-min intervals for 3 months. From 42 participants, we compared Apolux data with contemporaneous data from two self-reported, 24-h time-activity diaries. About three-fourths of measurements were collected within 5 min of each other (mean = 74.14%), and 79% of participants reporting constantly powered-on smartphones (n = 38) had a daily average data collection frequency of o10 min. Apolux's degree of temporal resolution varied across manufacturers, mobile networks, and the time of day that data collection occurred. The discrepancy between diary points and corresponding Apolux data was 342.3m (Euclidian distance) and varied across mobile networks. This study's high compliance and feasibility for data collection demonstrates the potential for integrating smartphone-based time-activity data into long-term and large-scale air pollution exposure studies.