{"doc_desc":{"title":"Application of the Bayesian spline method to analyze the real-time measurements of ultrafine particle concentration in Parisian subway","idno":"10.16909-DATASET-26","producers":[{"name":"Center for Primary Care and Public Health (Unisant\u00e9), University of Lausanne, Switzerland","abbreviation":"Unisant\u00e9","affiliation":"","role":"Data publisher"}],"version_statement":{"version":"Version 1.0 (December 2020)"}},"study_desc":{"title_statement":{"idno":"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 m\u00e9thode bay\u00e9sienne avec splines pour analyser les mesures en temps r\u00e9el de la concentration de particules ultrafines dans le m\u00e9tro parisien"},"authoring_entity":[{"name":"Guseva Canu, Irina","affiliation":"Center for Primary Care and Public Health (Unisant\u00e9), Department of occupational and environmental health (DSTE)"}],"production_statement":{"producers":[{"name":"P\u00e9tremand, Remy","affiliation":"Center for Primary Care and Public Health (Unisant\u00e9), Department of occupational and environmental health (DSTE)","role":"Investigator, first author"},{"name":"Pascal Wild","affiliation":"Center for Primary Care and Public Health (Unisant\u00e9), Department of occupational and environmental health (DSTE)","role":"Investigator, co-author"},{"name":"Camille Cr\u00e9z\u00e9","affiliation":"Center for Primary Care and Public Health (Unisant\u00e9), Department of occupational and environmental health (DSTE)","role":"Investigator, co-author"},{"name":"Guillaume Suarez","affiliation":"Center for Primary Care and Public Health (Unisant\u00e9), Department of occupational and environmental health (DSTE)","role":"Investigator, co-author"},{"name":"Sophie Besan\u00e7on","affiliation":"R\u00e9gie autonome des transports parisiens (RATP)","role":"Investigator, co-author"},{"name":"Val\u00e9rie Jouannique","affiliation":"R\u00e9gie autonome des transports parisiens (RATP)","role":"Investigator, co-author"},{"name":"Am\u00e9lie Debatisse","affiliation":"R\u00e9gie autonome des transports parisiens (RATP)","role":"Investigator, co-author"}],"copyright":"(c) 2021, Center for Primary Care and Public Health (Unisant\u00e9), University of Lausanne, Switzerland and R\u00e9gie autonome des transports parisiens (RATP)","funding_agencies":[{"name":"Center for Primary Care and Public Health","abbreviation":"Unisant\u00e9","role":""},{"name":"R\u00e9gie autonome des transports parisiens","abbreviation":"RATP","role":""}]},"version_statement":{"version":"Version 1.0","version_date":"2021-04-19"},"study_info":{"keywords":[{"keyword":"Subway","vocab":"","uri":""},{"keyword":"Underground workplace","vocab":"","uri":""},{"keyword":"Exposure","vocab":"","uri":""},{"keyword":"Ultrafine particles","vocab":"","uri":""},{"keyword":"Exposure assessment","vocab":"","uri":""},{"keyword":"Bayesian Inference","vocab":"","uri":""}],"abstract":"Background\nAir 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.\nObjectives \nWe 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. \nMethods \nThe 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. \nResults\nOverall, 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\nConclusion\nThe 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.","coll_dates":[{"start":"2019-09-01","end":"2021-03-30","cycle":""}],"nation":[{"name":"France","abbreviation":"FR"}],"geog_coverage":"Paris, France","analysis_unit":"Particle Number Concentration (#\/cm3)","data_kind":"One script of statistical analysis using R (Suppl_file_1_BUGS_model.R)\nThree csv files:\nThe first one (Suppl_file_2_Summary_airborne_sample.csv) presents the summary statistics of particle number concentration per time-series, sampling type and job. \nThe 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.\nFinally, the third file (Suppl_file_4_Bayesian_Spline_coefficient.csv) presents the Summary statistics for coefficients \u03b4 and \u03c3(Day), Summary statistics for coefficients \u03bc\u03b4(Job) and \u03c3\u03b4(job), Summary statistics for coefficients \u03b1(Station) and Summary statistics for coefficients \u03b2(Environments)."},"data_access":{"dataset_use":{"conf_dec":[{"txt":"","required":"","form_no":"","uri":""}],"contact":[{"name":"Racine, C\u00e9line (Repository manager)","affiliation":"Center for Primary Care and Public Health (Unisant\u00e9), University of Lausanne, Switzerland","email":"dfri.data@unisante.ch","uri":""}],"cit_req":"R\u00e9my P\u00e9tremand, Pascal Wild, Guillaume Suarez, Sophie Besan\u00e7on, Val\u00e9rie Jouannique, Am\u00e9lie 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 (Unisant\u00e9), University of Lausanne, Switzerland. Version 1.0 of the licensed dataset (April 2021), provided by the Unisant\u00e9 Research Data Repository. DOI:10.16909\/DATASET\/26","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."}}},"schematype":"survey"}