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Stoffenmanager exposure model: development of a quantitative algorithm.

Erik Tielemans1, Dook Noy, Jody Schinkel

  • 1Business Unit Food & Chemical Risk Analysis, TNO Quality of Life, PO Box 360, 3700 AJ Zeist, The Netherlands. Erik.Tielemans@tno.nl <Erik.Tielemans@tno.nl>

The Annals of Occupational Hygiene
|July 16, 2008
PubMed
Summary
This summary is machine-generated.

The Stoffenmanager tool accurately ranks chemical exposure risks in workplaces, particularly for solids and liquids. Refined models can now estimate worst-case exposure scenarios, enhancing occupational safety.

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Area of Science:

  • Occupational Health and Safety
  • Industrial Hygiene
  • Environmental Science

Background:

  • The Stoffenmanager is a web-based tool developed in The Netherlands to help SMEs manage chemical handling risks.
  • Accurate exposure assessment is crucial for effective risk management in occupational settings.

Purpose of the Study:

  • To evaluate the accuracy of the Stoffenmanager's semi-quantitative exposure algorithm.
  • To compare Stoffenmanager rankings with actual exposure measurements.
  • To develop a quantitative exposure algorithm based on collected data.

Main Methods:

  • Collected exposure data from 378 solid and 320 liquid scenarios across various occupational settings.
  • Validated the Stoffenmanager using seven dedicated surveys and existing datasets.
  • Employed Spearman correlation and mixed-effect regression models for analysis.

Main Results:

  • Good correlations were found between Stoffenmanager scores and exposure measurements for solids (r(s) = 0.80) and liquids (r(s) = 0.83).
  • Lower correlations were observed for volatile (r(s) = 0.56) and non-volatile (r(s) = 0.53) liquids.
  • Mixed-effect models explained significant exposure variability (52% for solids, 76% for liquids).

Conclusions:

  • The Stoffenmanager demonstrates good performance as a generic risk assessment tool.
  • Mixed-effect models can aid in assessing reasonable worst-case exposures.
  • Ongoing evaluation and refinement of the algorithm are planned with new data.