Validation of the dermal exposure model in ECETOC TRA

Area of Science:

• Occupational Health and Safety
• Risk Assessment Modeling
• Exposure Science

Background:

• The European Chemical Industry and Environmental Research Association (ECETOC) Technical Risk Assessment (TRA) model is widely used for estimating worker exposure.
• Validation of the ECETOC TRA dermal exposure model against independent measured data has been lacking.
• This study aimed to validate the ECETOC TRA dermal model using real-world exposure data.

Purpose of the Study:

• To validate the ECETOC TRA dermal exposure model (version 3.1) by comparing its estimates with measured exposure levels.
• To identify potential biases and limitations in the model's predictions.
• To provide recommendations for improving the model and future data collection.

Main Methods:

• Literature search and data requests to industry for measured worker dermal exposure levels.
• Clustering data into 'exposure cases' for direct comparison with model inputs.
• Expert elicitation to determine model input parameters based on contextual information.
• Statistical analysis of model estimates versus the 75th percentile of measured values.

Main Results:

• The ECETOC TRA dermal model explained 37% of the variance in measured exposure levels.
• The model overestimated exposure in approximately 80% of cases, showing a bias towards overestimation at low exposure values.
• Underestimation occurred at high exposure values, potentially linked to sampling methods and model parameters for glove use, which showed a greater protective effect than incorporated in the model.

Conclusions:

• The ECETOC TRA dermal model demonstrates a tendency to overestimate exposure, especially at lower levels, and may underestimate at higher levels.
• Discrepancies are partly attributed to the model's assumptions regarding protective glove efficacy and the influence of sampling methodologies.
• Further collection of measured dermal exposure data is recommended, particularly for scenarios currently underrepresented in the dataset, to enhance model accuracy and reliability.