The objective of this study is to evaluate the accuracy and robustness of three exposure-modelling tools [STOFFENMANAGER® v.6, European Centre for Ecotoxicology and Toxicology of Chemical Target Risk Assessment v.3.1 (ECETOC TRA v.3.1), and Advanced REACH Tool (ART v.1.5)], by comparing available measured data for exposure to organic solvents and pesticides in occupational exposure scenarios (ESs).
Model accuracy was evaluated by comparing the predicted and the measured values, expressed as an underestimation or overestimation factor (PRED/EXP), and by regression analysis. Robustness was quantitatively described by the so-called variable ‘Uncertainty Factor’ (UF), which was attributed to each model’s input: a higher UF score indicates greater model uncertainty and poorer robustness.
ART was the most accurate model, with median PRED/EXP factors of 1.3 and 0.15 for organic solvent and pesticide ESs, respectively, and a significant correlation (P < 0.05) among estimated and measured data. As expected, Tier 1 model ECETOC TRA demonstrated the worst performance in terms of accuracy, with median PRED/EXP factors of 2.0 for organic solvent ESs and 3545 for pesticide ESs. Simultaneously, STOFFENMANAGER® showed a median UF equal to 2.0, resulting in the most robust model.
ECETOC TRA was not considered acceptable in terms of accuracy, confirming that this model is not appropriate for the evaluation of the selected ESs for pesticides. Conversely, STOFFENMANAGER® was the best choice, and ART tended to underestimate the exposure to pesticides. For organic solvent ESs, there were no cases of strong underestimation, and all models presented overall acceptable results; for the selected ESs, ART showed the best accuracy. Stoffenmanager was the most robust model overall, indicating that even with a mistake in ES interpretation, predicted values would remain acceptable.
ART may lead to more accurate results when well-documented ESs are available. In other situations, Stoffenmanager appears to be a safer alternative because of its greater robustness, particularly when entry data uncertainty is difficult to assess. ECETOC TRA cannot be directly compared to higher tiered models because of its simplistic nature: the use of this tool should be limited only to exceptional cases in which a strong conservative and worst-case evaluation is necessary.