Abstract

Abstract Stoffenmanager® is a well-established and widely accepted tool that is applied for regulatory risk assessments (e.g. REACH). This online-tool enables companies to identify hazardous chemicals, chemical risks and to control exposure to hazardous substances at the workplace. In the current version, however, Stoffenmanager® is not applicable to all areas of activity with solids in which dusty hazardous substances are used or may arise. Therefore, the aim of this project is to expand the applicability domain of Stoffenmanager® by developing three innovative algorithms: 1) respirable dust and quartz for tasks with dusty products, 2) respirable dust for metal-cutting manufacturing and 3) respirable dust and quartz for the mechanical processing of stone. To derive new quantitative regression models calibration and validation measurement datasets on hazardous substances are required. In this project, a total of approximately 6000 data points including comprehensive contextual information were extracted from the IFA Exposure database MEGA and MEGA variables were converted into Stoffenmanager® variables. Subsequently, the variables were divided into classes with scores on a logarithmic scale. Spearman correlation coefficients were calculated, and in case of significant positive relationship between the Stoffenmanager® scores and the measurements statistical regression analyses were performed to calculate the regression equations. After the development of the new algorithms, exposure models were validated against exposure data from the MEGA database. Scatter plots and regression equations will be presented. The new algorithms serve to improve workers’ health by reducing occupational exposure to respirable dust and quartz which are known to be human carcinogens.

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