Fancello Giovanna, Can Arnaud, Aumond Pierre, Bista Sanjeev, Chaix Basile
Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, IPLESP, NEMESIS, F75012, Paris, France.
Unité Mixte de Recherche en Acoustique Environnementale (UMRAE), Université Gustave Eiffel - Cerema, Campus de Nantes, Allée des Ponts et Chaussées - CS 5004, Champs-sur-Marne, France.
J Expo Sci Environ Epidemiol. 2025 Mar 21. doi: 10.1038/s41370-025-00765-3.
The individual exposure to environmental noise in cities is usually assessed at the residential neighbourhood level with static, year-averaged strategic maps. This representation may underestimate noise exposure, given the mobility of individuals within the city and proximate sources of exposure.
Our study employs high-resolution sensor analysis to observe how personal noise exposure differs from modelled noise map metrics, identify socioeconomical and behavioural determinants of exposure, and explore the impact of reallocating certain behaviours to others on daily personal noise exposure (L).
Data on daily activities of 259 participants of the MobiliSense cohort living in the metropolitan area of Paris were collected between 2018 and 2020. Participants were equipped of a personal monitor for sound pressure, and of a GPS receiver and an accelerometer. Modes of transport were collected during a mobility survey.
Results showed that noise exposure based on personal monitoring during space-time behaviours differed from modelled noise levels at residence. Participants were exposed to values below the recommended critical value for health of 55 dB(A) in urban areas in only 36% of the days. Individual socioeconomic characteristics and residential factors explained very little variance in personal noise exposure. Noise exposure varied among performed activities and transport modes, with public transport associated with the highest sound levels. While time spent in the underground public transport was on average 1.4% of the total daily time-budget, it contributes on average to 9.5% of the daily noise dose.
This research reveals that individual mobility behaviours significantly influence daily noise exposure in urban environments. By analysing how people move throughout their day, we found that traditional static assessments, limited to residential noise, underestimate actual exposure. Notably, despite limited time spent in it (1,4%), underground transport contributed substantially to daily noise (9.5%). Furthermore, participants experienced noise levels below recommended health thresholds in only 36% of days. These findings underscore the need for policy changes that prioritize walkable cities and minimize commuting time, alongside the design of "quiet areas" within urban spaces for recovering from the city noise stress.
城市中个人接触环境噪声的情况通常是通过静态的年度平均战略地图在居民区层面进行评估的。鉴于个人在城市中的流动性以及近距离的暴露源,这种表示方式可能会低估噪声暴露情况。
我们的研究采用高分辨率传感器分析,以观察个人噪声暴露与建模噪声地图指标有何不同,确定暴露的社会经济和行为决定因素,并探讨将某些行为重新分配给其他行为对每日个人噪声暴露(L)的影响。
2018年至2020年期间收集了居住在巴黎大都市区的MobiliSense队列中259名参与者的日常活动数据。参与者配备了一个用于声压的个人监测器、一个GPS接收器和一个加速度计。在一次出行调查中收集了交通方式。
结果表明,基于时空行为期间个人监测的噪声暴露与居住处建模的噪声水平不同。参与者在城市地区只有36%的日子里暴露于低于健康推荐临界值55分贝(A)的值。个人社会经济特征和居住因素在个人噪声暴露方面解释的方差非常小。噪声暴露在不同的活动和交通方式中有所不同,公共交通的声音水平最高。虽然在地下公共交通中花费的时间平均占每日总时间预算的1.4%,但它平均占每日噪声剂量的9.5%。
这项研究表明,个人出行行为显著影响城市环境中的每日噪声暴露。通过分析人们一天中的出行方式,我们发现仅限于住宅噪声的传统静态评估低估了实际暴露情况。值得注意的是,尽管在地下交通中花费的时间有限(1.4%),但它对每日噪声的贡献很大(9.5%)。此外,参与者只有36%的日子里经历的噪声水平低于推荐的健康阈值。这些发现强调了政策变革的必要性,即优先建设适宜步行的城市并尽量减少通勤时间,同时在城市空间内设计“安静区域”以从城市噪声压力中恢复。