Bista Sanjeev, Fancello Giovanna, Zeitouni Karine, Annesi-Maesano Isabella, Chaix Basile
Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, Faculté de Médecine Saint-Antoine, Nemesis team, 27 rue Chaligny, Paris, 75012, France.
Centre de Recherche en Santé Publique, Université de Montréal, 7101, Avenue du Parc, Montreal, QC, H3N 1X9, Canada.
Int J Health Geogr. 2025 Mar 27;24(1):5. doi: 10.1186/s12942-025-00393-y.
Past epidemiological studies, using fixed-site outdoor air pollution measurements as a proxy for participants' exposure, might have suffered from exposure misclassification.
In the MobiliSense study, personal exposures to ozone (O), nitrogen dioxide (NO), and particles with aerodynamic diameters below 2.5 μm (PM) 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.
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 NO (median across participants: 0.23), O (median: 0.21), and PM (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 (R < 0.07) for all air pollutants. The R for only a few models was higher than 0.15, namely for O in the active transport microenvironment (R: 0.25) and for PM in active transport (R: 0.16) and in the separated rail transport microenvironment (R: 0.20). Model fit slightly increased with decreasing distance between participants' location and the nearest monitoring station.
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.
过去的流行病学研究使用固定地点的室外空气污染测量值来代表参与者的暴露情况，可能存在暴露误分类的问题。
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微米的颗粒物（PM₂.₅）的暴露情况。利用个人GPS接收器收集的所有空间位置点和出行调查数据，从最近的Airparif监测站获取空气污染物的背景小时浓度。我们对246名参与者的851343分钟级观测数据进行了建模。
包括居住地在内的访问地点贡献了大部分分钟级观测数据，占93.0%，其次是主动出行（3.4%），其余分别来自公路和铁路运输，占2.4%和1.1%。对每个个体的个人暴露与监测站测量浓度进行比较，结果显示二氧化氮（参与者中位数：0.23）、臭氧（中位数：0.21）和细颗粒物（中位数：0.27）的斯皮尔曼相关性较低，且不同微环境下的相关性水平有所不同（根据微环境，范围从0.06到0.35）。混合效应模型的结果表明，对于所有空气污染物，监测站测量的浓度对个人暴露的解释作用非常微弱（R²＜0.07）。只有少数模型的R²高于0.15，即在主动出行微环境中的臭氧（R²：0.25）、主动出行中的细颗粒物（R²：0.16）以及单独的铁路运输微环境中的细颗粒物（R²：0.20）。随着参与者位置与最近监测站之间距离的减小，模型拟合度略有提高。
我们的结果表明个人暴露与监测站测量的空气污染物之间的相关性相对较低，证实了将监测站测量的浓度作为个人暴露的替代指标可能导致暴露误分类。然而，距离和微环境类型会影响误分类的程度。
