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

Rémy Pétremand; Pascal Wild; Camille Crézé; Guillaume Suarez; Sophie Besançon; Valérie Jouannique; Amélie Debatisse; Irina Guseva Canu

doi:10.1016/j.envint.2021.106773

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

Environ Int. 2021 Nov:156:106773. doi: 10.1016/j.envint.2021.106773. Epub 2021 Jul 22.

Authors

Rémy Pétremand¹, Pascal Wild², Camille Crézé¹, Guillaume Suarez¹, Sophie Besançon³, Valérie Jouannique³, Amélie Debatisse³, Irina Guseva Canu⁴

Affiliations

¹ Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland.
² Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland; Institut National de Recherche et Sécurité (INRS), Vandoeuvre lès Nancy, France.
³ Autonomous Paris Transport Authority (RATP), Paris, France.
⁴ Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland. Electronic address: irina.guseva-canu@unisante.ch.

PMID: 34425645
DOI: 10.1016/j.envint.2021.106773

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: Bayesian Inference; Indoor air pollution; Occupational exposure; Particle number concentration; Underground.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Air Pollutants* / analysis
Bayes Theorem
Environmental Monitoring
Longitudinal Studies
Particulate Matter / analysis
Railroads*

Substances

Air Pollutants
Particulate Matter