Application of the Bayesian spline method to analyze real-time measurements of ultrafine particle concentration in the Parisian subway

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License: CC BY-NC-ND 4.0
Serval ID
serval:BIB_692B5B8339BB
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Application of the Bayesian spline method to analyze real-time measurements of ultrafine particle concentration in the Parisian subway
Journal
Environment International
Author(s)
Pétremand Rémy (co-first), Wild Pascal, Crézé Camille, Suarez Guillaume, Besançon Sophie, Jouannique Valérie, Debatisse Amélie, Guseva Canu Irina
ISSN
0160-4120
Publication state
Published
Issued date
11/2021
Volume
156
Pages
106773
Language
english
Abstract
Background
Air pollution in subway environments is a growing concern as it often exceeds WHO recommendations for indoor air quality. Ultrafine particles (UFP), for which there is still no regulation nor a standardized exposure monitoring method, are the strongest contributor to this pollution when the number concentration is used as exposure metric.
Objectives
We aimed to assess the real-time UFP number concentration in the personal breathing zone (PBZ) of three types of underground Parisian subway professionals and analyze it using a novel Bayesian spline approach. Consecutively, we investigated the effect of job, week day, subway station, worker location, and some further events on UFP number concentrations.
Methods
The data collection procedure originated from a longitudinal study and lasted for a total duration of 6 weeks (from October 7 to November 15, 2019, i.e. two weeks per type of subway professionals). Time-series were built from the real-time particle number concentration (PNC) measured in the PBZ of professionals during their work-shifts. Complementarily, contextual information expressed as Station, Environment, and Event variables were extracted from activity logbooks completed for every work-shift. A Bayesian spline approach was applied to model the PNC within a Bayesian framework as a function of the mentioned contextual information.
Results
Overall, the Bayesian spline method suited a real-time personal PNC data modeling approach. The model enabled estimating the differences in UFP exposure between subway professionals, stations, and various locations. Our results suggest a higher PNC closer to the subway tracks, with the highest PNC on subway station platforms. Studied event and week day variables had a lesser influence.
Conclusion
It was shown that the Bayesian spline method is suitable to investigate individual exposure to UFP in underground subway settings. This method is informative for better documenting the magnitude and variability of UFP exposure, and for understanding the determinants in view of further regulation and control of this exposure.
Keywords
Underground, Particle number concentration, Occupational exposure, Indoor air pollution, Bayesian Inference
Research datasets
Open Access
Yes
Funding(s)
Swiss National Science Foundation
Create date
12/08/2021 8:30
Last modification date
22/01/2022 6:33
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