Application of the Bayesian spline method to analyze the real-time measurements of ultrafine particle concentration in Parisian subway
France, 2019 - 2021
Guseva Canu, Irina
Created on
April 19, 2021
Last modified
August 09, 2021
Page views
2222
Documentation in PDF
Metadata
DDI/XML
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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.
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).
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 | |
|---|---|---|
| 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)