Pétremand Rémy, Suárez Guillaume, Besançon Sophie, Dil J Hugo, Guseva Canu Irina
Department of Occupational and Environmental Health, Center of Primary Care and Public Health (Unisanté), University of Lausanne, Epalinges, 1066 Lausanne, Switzerland;
Régie Automne de Transport Parisien (RATP), 75012 Paris, France;
Sustainability. 2022 May 15;14(10):5999. doi: 10.3390/su14105999. eCollection 2022 May.
We developed a Bayesian spline model for real-time mass concentrations of particulate matter (PM10, PM2.5, PM1, and PM0.3) measured simultaneously in the personal breathing zone of Parisian subway workers. The measurements were performed by GRIMM, a gravimetric method, and DiSCmini during the workers' work shifts over two consecutive weeks. The measured PM concentrations were analyzed with respect to the working environment, the underground station, and any specific events that occurred during the work shift. Overall, PM0.3 concentrations were more than an order of magnitude lower compared to the other PM concentrations and showed the highest temporal variation. The PM2.5 levels raised the highest exposure concern: 15 stations out of 37 had higher mass concentrations compared to the reference. Station PM levels were not correlated with the annual number of passengers entering the station, the year of station opening or renovation, or the number of platforms and tracks. The correlation with the number of station entrances was consistently negative for all PM sizes, whereas the number of correspondence concourses was negatively correlated with PM0.3 and PM10 levels and positively correlated with PM1 and PM2.5 levels. The highest PM10 exposure was observed for the station platform, followed by the subway cabin and train, while ticket counters had the highest PM0.3, PM1, and PM2.5 mass concentrations. We further found that compared to gravimetric and DiSCmini measurements, GRIMM results showed some discrepancies, with an underestimation of exposure levels. Therefore, we suggest using GRIMM, calibrated by gravimetric methods, for PM sizes above 1μm, and DiSCmini for sizes below 700 nm.
我们开发了一种贝叶斯样条模型,用于实时分析巴黎地铁工作人员个人呼吸区内同时测量的颗粒物(PM10、PM2.5、PM1和PM0.3)质量浓度。测量工作由GRIMM(一种重量法)和DiSCmini在工作人员连续两周的工作班次期间进行。针对工作环境、地铁站以及工作班次期间发生的任何特定事件,对测得的PM浓度进行了分析。总体而言,与其他PM浓度相比,PM0.3浓度低了一个数量级以上,并且显示出最高的时间变化。PM2.5水平引发了最高的暴露担忧:37个车站中有15个车站的质量浓度高于参考值。车站的PM水平与进入车站的年客流量、车站开放或翻新年份、站台和轨道数量均无关联。对于所有PM粒径,与车站入口数量的相关性始终为负,而换乘大厅数量与PM0.3和PM10水平呈负相关,与PM1和PM2.5水平呈正相关。在车站站台观察到最高的PM10暴露,其次是地铁车厢和列车,而售票柜台的PM0.3、PM1和PM2.5质量浓度最高。我们进一步发现,与重量法和DiSCmini测量结果相比,GRIMM的结果存在一些差异,低估了暴露水平。因此,我们建议对于粒径大于1μm的PM,使用经重量法校准的GRIMM进行测量;对于粒径小于700nm的PM,使用DiSCmini进行测量。