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    <citation>
      <titlStmt>
        <titl>Real-time comparison of four particulate matter size fractions in personal breathing zone of Paris subway workers: A six-week prospective study </titl>
<parTitl>
Comparaison en temps réel de quatre fractions de taille de particules dans la zone de respiration personnelle des travailleurs du métro de Paris : Une étude prospective de six semaines</parTitl>
        <IDNo agency="DOI System">
10.16909-DATASET-28</IDNo>
      </titlStmt>
      <rspStmt>
        <othId role="Metadata and DDI creator" affiliation="Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland">
            Racine, Céline
        </othId>
      </rspStmt>
      <prodStmt>
        <producer abbr="Unisanté" role="Data publisher">
Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland</producer>
        <software version="7.5.7">
          Notepad++
        </software>
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        <version>
          Version 1.0 (December 2020)
        </version>
      </verStmt>
    </citation>
  </docDscr>
  <stdyDscr>
    <citation>
      <titlStmt>
        <titl>
Real-time comparison of four particulate matter size fractions in personal breathing zone of Paris subway workers: A six-week prospective study</titl>
<parTitl>
Comparaison en temps réel de quatre fractions de taille de particules dans la zone de respiration personnelle des travailleurs du métro de Paris : Une étude prospective de six semaines</parTitl>

        <IDNo agency="DOI System">
10.16909-DATASET-28</IDNo>
      </titlStmt>
      <rspStmt>
        <AuthEnty affiliation="Center for Primary Care and Public Health (Unisanté), Department of occupational and environmental health (DSTE)">
         Guseva Canu, Irina
        </AuthEnty>

      </rspStmt>
      <prodStmt>
<producer affiliation="Center for Primary Care and Public Health (Unisanté), Department of occupational and environmental health (DSTE)" role="Investigator, first author">
Pétremand, Remy
        </producer>
		<producer affiliation="Center for Primary Care and Public Health (Unisanté), Department of occupational and environmental health (DSTE)" role="Investigator, co-author">
Suarez, Guillaume
        </producer>
		<producer affiliation="Régie autonome des transports parisiens (RATP)" role="Investigator, co-author">
Besançon, Sophie
        </producer>
		<producer affiliation="Ecole polytechnique fédérale de Lausanne (EPFL)" role="Investigator, co-author">
Dil, Hugo
        </producer>
        <copyright>
          (c) 2021, Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland and Régie autonome des transports parisiens (RATP), Paris, France
        </copyright>

        <fundAg abbr="FNS">
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (IZCOZ0_177067)       </fundAg>


      </prodStmt>
      <verStmt>
        <version date="2022-04-19">
          Version 1.1
        </version>
      </verStmt>
    </citation>
    <stdyInfo>
      <subject>
        <keyword>Subway</keyword>
		<keyword>Underground workplace</keyword>
		<keyword>Exposure</keyword>
		<keyword>Particulate matter</keyword>
		<keyword>Ultrafine particles</keyword>
		<keyword>Exposure assessment</keyword>
		<keyword>Bayesian Inference</keyword>    
      </subject>
      <abstract>
        <![CDATA[We developed a Bayesian spline model for the real-time mass concentrations of PM10, PM2.5, PM1, and PM0.3 measured simultaneously in personal breathing zone of Parisian subway workers. The measurements were performed by GRIMM, gravimetric method, and DiSCmini during the workers’ work-shifts over two consecutive weeks. The PM0.3 concentrations were more than an order of magnitude lower compared to the other PM and showed the highest temporal variation, followed by PM1. The PM2.5 raised the highest exposure concern: 15 stations out of 37 had higher mass concentrations compared to the reference. Twelve of these also had significantly higher PM1 mass concentration. Station PM levels were not correlated with the annual number of passengers entering the station, year of station opening or renovation, or the number of platforms and tracks. The correlation with the number of station entrances was con-sistently negative for all PM sizes. The number of correspondence concourses was negatively correlated with PM0.3 and PM10 and positively correlated with PM1 and PM2.5. Almost all studied environments had higher PM exposure compared to the study room, although the highest levels of PM were measured outdoors. The highest PM10 exposure was observed at the station platform, followed by the subway cabin and train, while ticket counters had the highest PM0.3, PM1, and PM2.5 mass concentrations. Compared with gravimetric and DiSCmini meas-urements, GRIMM results showed some discrepancies, with an underestimation of exposure levels. Therefore, we suggest to use GRIMM, calibrated by gravimetric methods, for PM sizes above 1μm, and a DiSCmini for sizes below 700nm.]]>
      </abstract>
      <sumDscr>
        <collDate date="2019-09-01" event="start"/>
        <collDate date="2021-03-30" event="end"/>
        <nation abbr="FR">
          France
        </nation>
		<nation abbr="CHE">
          Switzerland
        </nation>
        <geogCover>
Paris, France</geogCover>
        <anlyUnit>
Particle Mass Concentration (μg/cm3) 
Particle Number Concentration (#/cm3) 
</anlyUnit>
<dataKind><![CDATA[One script of statistical analysis using R (Suppl_file_1_BUGS_model.R)
Four Excel files:
The first one (File1_PM_daily_summary.xlsx) presents the summary statistics of particle mass concentration per day. 
The second file (File2_Calibrated_PM10_&_PM25_Bayesian_Spline_coefficients.xlsx) 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) obtained using the calibrated PM2.5 and PüM10 time-series
The third file (File3_All_PM_Bayesian_Spline_Coefficients.xlsx) presents the same 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) but using row time-series data for PM0.3, PM1, PM2.5, and PM10
Finally, the fourth file (File4_All_PM_Bayesian_Spline_Coefficients.xlsx) regroups the descriptive summary of Particulate Number Concentration (PNC) of PM0.3 for the following variables: days, stations, environments and events.  
Each Excel sheet contains the descriptive summary for one variable and is names accordingly
]]>
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    <dataAccs>
      <useStmt>

		<contact affiliation="Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland" email="dfri.data@unisante.ch">
          Racine, Céline (Repository manager)
        </contact>
        <citReq>
         Pétremand, Rémy, Suarez, Guillaume, Besançon, Sophie, Dil, Hugo ,Guseva Canu, Irina. Real-time comparison of four particulate matter size fractions in personal breathing zone of Paris subway workers: A six-week prospective study Subway particle exposure. Supplementary material files. Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland. Version 1.0 of the licensed dataset (December 2021), provided by the Unisanté Research Data Repository. DOI:10.16909/DATASET/28
        </citReq>
    
        <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.
        </disclaimer>
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    </dataAccs>
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