Modelling Occupational Inhalation Exposure to Concentration Peaks of Chemicals and Associated Health Risk Assessment

The aims of this study were to estimate inhalation exposure to chemicals and the resulting acute health risks for working scenarios characterized by successive peaks of pollutant concentrations. A stochastic two-zone model combining a time-varying emission function and field-derived probabilistic distributed input parameter was used to predict both instantaneous and 15-min averaged pollutant concentrations during the decanting operations performed in a pathology laboratory. The location of the workers was taken into account in the model for computing probability distributions of inhalation exposures and for subsequently characterizing hazard quotients (HQ) for health risk purposes. The model was assessed by comparison with repeated individual monitoring performed on the workers during the same tasks. Modelled inhalation exposure profiles revealed 15-min average concentrations of 1.7 and 208 mg m(-3) for formaldehyde (FA) and toluene (TOL), respectively. The individual monitoring performed showed similar average concentrations, with 1.2 and 175 mg m(-3) for FA and TOL. No more than three to five successive FA concentration peaks were generally sufficient in the modelling exercise to provide 15-min estimated exposures exceeding short-term exposure limits (STEL). Modelled HQ higher than unity and STEL exceedance probabilities higher than 0.5 were found for FA, whereas estimated TOL health risks were notably lower according to high exposure limits. Estimated inhalation exposure distributions frequently ranged over one order of magnitude for the two pollutants, reflecting both the natural exposure variability and the uncertainty of some of the two-zone model input parameters. These findings indicate that the developed approach may be useful for modelling occupational exposures and acute health risks related to chemicals in situations involving time-varying emission sources. Modelled exposure distributions may also be used within Bayesian decision analysis frameworks for making exposure judgements and refining risk management measures.