Use of the MEGA Exposure Database for the Validation of the Stoffenmanager Model.
Objectives: This paper explores the usefulness of the exposure database MEGA for model validation and evaluates the capability of two Stoffenmanager model equations (i.e. handling of powders/granules and machining) to estimate workers exposure to inhalable dust. Methods: For the task groups, ‘handli...
| Publicado en: | Annals of Occupational Hygiene Vol. 56; no. 4; pp. 426 - 440 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Jun2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=74197150&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 74197150 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00034878 JQ4 jtl: Annals of Occupational Hygiene issn: 00034878 maglogo: N pubinfo: dt: Jun2012 vid: 56 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 74197150 104548250 104548250 10.1093/annhyg/mer097 74197150 ppf: 426 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Use of the MEGA Exposure Database for the Validation of the Stoffenmanager Model. aug: au: Koppisch, Dorothea Schinkel, Jody Gabriel, Stefan Fransman, Wouter Tielemans, Erik affil: Institute for Occupational Safety and Health of the German Social Accident Insurance (IFA), Alte Heerstraße 111, 53757 Sankt Augustin, Germany sug: subj: Environmental Exposure Powders Databases Utilization Risk Assessment Human Work Machinery Validation Studies Models, Statistical Dust Funding Source ab: Objectives: This paper explores the usefulness of the exposure database MEGA for model validation and evaluates the capability of two Stoffenmanager model equations (i.e. handling of powders/granules and machining) to estimate workers exposure to inhalable dust. Methods: For the task groups, ‘handling of powders and granules’ (handling) and ‘machining of wood and stone’ (machining) measurements were selected from MEGA and grouped in scenarios depending on task, product, and control measures. The predictive capability of the model was tested by calculating the relative bias of the single measurements and the correlation between geometric means (GMs) for scenarios. The conservatism of the model was evaluated by checking if the percentage of measurement values above the 90th percentile estimate was ≤10%. Results: From 22 596 personal measurements on inhalable dust within MEGA, 390 could be selected for handling and 1133 for machining. The relative bias for the task groups was −25 and 68%, respectively, the percentage of measurements with a higher result than the estimated 90th percentile 11 and 7%. Correlations on a scenario level were good for both model equations as well for the GM (handling: rs = 0.90, n = 15 scenarios; machining: rs = 0.84, n = 22 scenarios) as for the 90th percentile (handling: rs = 0.79; machining: rs = 0.76). Conclusions: The MEGA database could be used for model validation, although the presented analyses have learned that improvements in the database are necessary for modelling purposes in the future. For a substantial amount of data, contextual information on exposure determinants in addition to basic core information is stored in this database. The relative low bias, the good correlation, and the level of conservatism of the tested model show that the Stoffenmanager can be regarded as a useful Tier 1 model for the Registration, Evaluation, Authorisation and Restriction of Chemicals legislation. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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