Department of Prevention Cancer Environment, Centre Léon Bérard, Lyon, France; INSERM U1296 Unit "Radiation: Defense, Health, Environment", Centre Léon-Bérard, 69008 Lyon, France; Ecole Centrale de Lyon, CNRS, Universite Claude Bernard Lyon 1, INSA Lyon, LMFA, UMR5509, 69130 Ecully, France.
Department of Civil, Maritime and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, United Kingdom.
Environ Res. 2024 Nov 15;261:119666. doi: 10.1016/j.envres.2024.119666. Epub 2024 Jul 27.
Epidemiological studies on health effects of air pollution usually estimate exposure at the residential address. However, ignoring daily mobility patterns may lead to biased exposure estimates, as documented in previous exposure studies. To improve the reliable integration of exposure related to mobility patterns into epidemiological studies, we conducted a systematic review of studies across all continents that measured air pollution concentrations in various modes of transport using portable sensors. To compare personal exposure across different transport modes, specifically active versus motorized modes, we estimated pairwise exposure ratios using a Bayesian random-effects meta-analysis. Overall, we included measurements of six air pollutants (black carbon (BC), carbon monoxide (CO), nitrogen dioxide (NO), particulate matter (PM, PM) and ultrafine particles (UFP)) for seven modes of transport (i.e., walking, cycling, bus, car, motorcycle, overground, underground) from 52 published studies. Compared to active modes, users of motorized modes were consistently the most exposed to gaseous pollutants (CO and NO). Cycling and walking were the most exposed to UFP compared to other modes. Active vs passive mode contrasts were mostly inconsistent for other particle metrics. Compared to active modes, bus users were consistently more exposed to PM and PM, while car users, on average, were less exposed than pedestrians. Rail modes experienced both some lower exposures (compared to cyclists for PM and pedestrians for UFP) and higher exposures (compared to cyclist for PM and BC). Ratios calculated for motorcycles should be considered carefully due to the small number of studies, mostly conducted in Asia. Computing exposure ratios overcomes the heterogeneity in pollutant levels that may exist between continents and countries. However, formulating ratios on a global scale remains challenging owing to the disparities in available data between countries.
流行病学研究表明,空气污染对健康的影响通常是通过居住地址来评估暴露程度的。然而,忽略日常出行模式可能会导致暴露评估出现偏差,这在之前的暴露研究中已有记载。为了将与出行模式相关的暴露可靠地纳入流行病学研究中,我们对在所有大陆进行的使用便携式传感器测量各种交通方式下空气污染浓度的研究进行了系统综述。为了比较不同交通方式(主动与机动)之间的个人暴露情况,我们使用贝叶斯随机效应荟萃分析估计了成对暴露比。总的来说,我们纳入了 52 项已发表研究中六种空气污染物(黑碳(BC)、一氧化碳(CO)、二氧化氮(NO)、颗粒物(PM)和超细颗粒(UFP))的七种交通模式(步行、骑行、公交车、汽车、摩托车、地上轨道、地下轨道)的测量数据。与主动模式相比,机动模式使用者始终暴露于气态污染物(CO 和 NO)的程度更高。与其他模式相比,骑行和步行时 UFP 的暴露量最高。对于其他颗粒指标,主动与被动模式的对比结果则不一致。与主动模式相比,公交车使用者始终比步行者暴露于更多的 PM 和 PM,而汽车使用者平均暴露水平低于步行者。铁路模式的暴露水平相对较低(与骑自行车者相比,PM 和 UFP;与步行者相比,PM 和 BC),也相对较高(与骑自行车者相比,PM 和 BC)。由于研究数量较少,且主要在亚洲进行,因此应谨慎考虑摩托车的暴露比。计算暴露比可以克服不同大洲和国家之间可能存在的污染物水平的异质性。然而,由于各国之间可用数据的差异,在全球范围内制定比值仍然具有挑战性。