法国大巴黎固定地点二氧化氮、臭氧和颗粒物测量与个人暴露之间的关系:MobiliSense研究。

IF 3 2区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Sanjeev Bista, Giovanna Fancello, Karine Zeitouni, Isabella Annesi-Maesano, Basile Chaix
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引用次数: 0

摘要

背景:过去的流行病学研究,使用固定地点室外空气污染测量作为参与者暴露的代理,可能会受到暴露错误分类的影响。方法:在MobiliSense研究中,使用个人空气质量监测仪监测个人对臭氧(O3)、二氧化氮(NO2)和空气动力学直径小于2.5 μm的颗粒(PM2.5)的暴露情况。利用个人GPS接收器和移动调查收集的所有空间定位点,从最近的Airparif监测站检索每小时空气污染物的背景浓度。我们对246名参与者的851343个min-level观测数据进行了建模。结果:包括住所在内的访问场所对分钟级观测的贡献最大(93.0%),其次是主动交通(3.4%),其余分别为公路和铁路交通(2.4%和1.1%)。个人暴露与站测浓度的比较表明,NO2(参与者的中位数:0.23)、O3(中位数:0.21)和PM2.5(中位数:0.27)的Spearman相关性较低,微环境的相关性不同(根据微环境的不同,从0.06到0.35不等)。混合效应模型的结果表明,站点测量浓度对个人暴露的解释非常弱(只有少数模型的R2大于0.15,即主动运输微环境中的O3 (R2: 0.25)、主动运输微环境中的PM2.5 (R2: 0.16)和分离轨道交通微环境中的PM2.5 (R2: 0.20)。模型拟合随参与者位置与最近监测站之间距离的减小而略有增加。结论:我们的研究结果表明,个人暴露与站点测量的空气污染物之间的相关性相对较低,证实站点测量的浓度作为个人暴露的代表可能导致暴露错误分类。然而,距离和微环境类型对误分类的程度有影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Relationships between fixed-site ambient measurements of nitrogen dioxide, ozone, and particulate matter and personal exposures in Grand Paris, France: the MobiliSense study.

Background: Past epidemiological studies, using fixed-site outdoor air pollution measurements as a proxy for participants' exposure, might have suffered from exposure misclassification.

Methods: In the MobiliSense study, personal exposures to ozone (O3), nitrogen dioxide (NO2), and particles with aerodynamic diameters below 2.5 μm (PM2.5) were monitored with a personal air quality monitor. All the spatial location points collected with a personal GPS receiver and mobility survey were used to retrieve background hourly concentrations of air pollutants from the nearest Airparif monitoring station. We modeled 851,343 min-level observations from 246 participants.

Results: Visited places including the residence contributed the majority of the minute-level observations, 93.0%, followed by active transport (3.4%), and the rest were from on-road and rail transport, 2.4% and 1.1%, respectively. Comparison of personal exposures and station-measured concentrations for each individual indicated low Spearman correlations for NO2 (median across participants: 0.23), O3 (median: 0.21), and PM2.5 (median: 0.27), with varying levels of correlation by microenvironments (ranging from 0.06 to 0.35 according to the microenvironment). Results from mixed-effect models indicated that personal exposure was very weakly explained by station-measured concentrations (R2 < 0.07) for all air pollutants. The R2 for only a few models was higher than 0.15, namely for O3 in the active transport microenvironment (R2: 0.25) and for PM2.5 in active transport (R2: 0.16) and in the separated rail transport microenvironment (R2: 0.20). Model fit slightly increased with decreasing distance between participants' location and the nearest monitoring station.

Conclusions: Our results demonstrated a relatively low correlation between personal exposure and station-measured air pollutants, confirming that station-measured concentrations as proxies of personal exposures can lead to exposure misclassification. However, distance and the type of microenvironment are shown to affect the extent of misclassification.

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来源期刊
International Journal of Health Geographics
International Journal of Health Geographics PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH -
CiteScore
10.20
自引率
2.00%
发文量
17
审稿时长
12 weeks
期刊介绍: A leader among the field, International Journal of Health Geographics is an interdisciplinary, open access journal publishing internationally significant studies of geospatial information systems and science applications in health and healthcare. With an exceptional author satisfaction rate and a quick time to first decision, the journal caters to readers across an array of healthcare disciplines globally. International Journal of Health Geographics welcomes novel studies in the health and healthcare context spanning from spatial data infrastructure and Web geospatial interoperability research, to research into real-time Geographic Information Systems (GIS)-enabled surveillance services, remote sensing applications, spatial epidemiology, spatio-temporal statistics, internet GIS and cyberspace mapping, participatory GIS and citizen sensing, geospatial big data, healthy smart cities and regions, and geospatial Internet of Things and blockchain.
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