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    Home / Central Data Catalog / DSTE / 10.16909-DATASET-26
DSTE

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

France, 2019 - 2021
Department of occupational and environmental health (DSTE)
Guseva Canu, Irina
Created on April 19, 2021 Last modified August 09, 2021 Page views 2034 Documentation in PDF Metadata DDI/XML JSON
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Identification

Survey ID Number
10.16909-DATASET-26
Title
Application of the Bayesian spline method to analyze the real-time measurements of ultrafine particle concentration in Parisian subway
Translated Title
Application de la méthode bayésienne avec splines pour analyser les mesures en temps réel de la concentration de particules ultrafines dans le métro parisien
Country
Name Country code
France FR
Abstract
Background
Air pollution in subway environment is a growing concern as it often exceeds WHO recommendations for indoor air quality. Ultrafine particles (UFP), for which there is still no regulation, neither 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 breath zone (PBZ) of three types of underground Parisian subway professionals and analyze it using a novel Bayesian spline approach. Consecutively, we investigate the effect of jobs, week days, subway stations, worker location and some events on UFP number concentration.
Methods
The data collection procedure, originating from a longitudinal study, lasted for a total duration of 6 weeks from 7 October 2019 to 15 November 2019, 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 by study technicians. Subsequently, the Bayesian spline approach was applied to model PNC within a Bayesian framework as a function of the latter contextual information.
Results
Overall, the Bayesian spline approach seams well suited to model real-time personal PNC data. The model enabled estimating the differences in UFP exposure between subway professionals, between stations, and different locations. Our results suggest that the PNC is higher the closer to the subway tracks with the highest PNC at the subway station platforms. Studied events had a lesser influence, as well as the day of the week
Conclusion
The application of the Bayesian spline method to investigate the individual exposure to UFP in the underground subway setting was shown feasible. This method is informative for better documenting the magnitude and variability of UFP exposure and for understanding its determinants in view of its further regulation and control.
Kind of Data
One script of statistical analysis using R (Suppl_file_1_BUGS_model.R)
Three csv files:
The first one (Suppl_file_2_Summary_airborne_sample.csv) presents the summary statistics of particle number concentration per time-series, sampling type and job.
The second file (Suppl_file_3_Summary_PNC_for_Day_Station_Environment_Event.csv) regroups the descriptive summary of Particulate Number Concentration (PNC) for the following variables: days, stations, environments and events.
Finally, the third file (Suppl_file_4_Bayesian_Spline_coefficient.csv) presents the Summary statistics for coefficients δ and σ(Day), Summary statistics for coefficients μδ(Job) and σδ(job), Summary statistics for coefficients α(Station) and Summary statistics for coefficients β(Environments).
Unit of Analysis
Particle Number Concentration (#/cm3)

Version

Version Description
Version 1.0
Version Date
2021-04-19

Scope

Keywords
Keyword
Subway
Underground workplace
Exposure
Ultrafine particles
Exposure assessment
Bayesian Inference

Coverage

Geographic Coverage
Paris, France

Producers and sponsors

Primary investigators
Name Affiliation
Guseva Canu, Irina Center for Primary Care and Public Health (Unisanté), Department of occupational and environmental health (DSTE)
Producers
Name Affiliation Role
Pétremand, Remy Center for Primary Care and Public Health (Unisanté), Department of occupational and environmental health (DSTE) Investigator, first author
Pascal Wild Center for Primary Care and Public Health (Unisanté), Department of occupational and environmental health (DSTE) Investigator, co-author
Camille Crézé Center for Primary Care and Public Health (Unisanté), Department of occupational and environmental health (DSTE) Investigator, co-author
Guillaume Suarez Center for Primary Care and Public Health (Unisanté), Department of occupational and environmental health (DSTE) Investigator, co-author
Sophie Besançon Régie autonome des transports parisiens (RATP) Investigator, co-author
Valérie Jouannique Régie autonome des transports parisiens (RATP) Investigator, co-author
Amélie Debatisse Régie autonome des transports parisiens (RATP) Investigator, co-author
Funding Agency/Sponsor
Name Abbreviation
Center for Primary Care and Public Health Unisanté
Régie autonome des transports parisiens RATP

Data Collection

Dates of Data Collection
Start End
2019-09-01 2021-03-30

Access policy

Access authority
Name Affiliation Email
Racine, Céline (Repository manager) Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland dfri.data@unisante.ch
Confidentiality
Citation requirements
Rémy Pétremand, Pascal Wild, Guillaume Suarez, Sophie Besançon, Valérie Jouannique, Amélie Debatisse, Guseva Canu, Irina. Application of the Bayesian spline method to analyze the real-time measurements of ultrafine particle concentration in Parisian subway. Supplementary material files. Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland. Version 1.0 of the licensed dataset (April 2021), provided by the Unisanté Research Data Repository. DOI:10.16909/DATASET/26

Disclaimer and copyrights

Disclaimer
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Copyright
(c) 2021, Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland and Régie autonome des transports parisiens (RATP)

Metadata production

DDI Document ID
10.16909-DATASET-26
Producers
Name Abbreviation Role
Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland Unisanté Data publisher
DDI Document version
Version 1.0 (December 2020)
Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland
Route de Berne 113
1010 Lausanne
Switzerland
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