Application of the Bayesian spline method to analyze real-time measurements of ultrafine particle concentration in the Parisian subway. (November 2021)

• Record Type: Journal Article
• Authors: Pétremand, Rémy; Wild, Pascal; Crézé, Camille; Suarez, Guillaume; Besançon, Sophie; Jouannique, Valérie; Debatisse, Amélie; Guseva Canu, Irina
• Is Part Of: Environment international. Volume 156(2021)
• Publication Date: 2021-11
• Subjects: Underground -- Particle number concentration -- Occupational exposure -- Indoor air pollution -- Bayesian Inference; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2021.106773
• Languages: English
• ISSNs: 0160-4120

Abstract

Highlights: Individual ultrafine particle (UFP) number concentration in subway workers over 6 weeks. Application of Bayesian spline method for concentration medelling. Integration of contextual information (Job, Day, Station, Environment, Event). Insights on UFP exposure determinants for workers and passengers. 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.