TREXMO plus: an advanced self-learning model for occupational exposure assessment

被引:0
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作者
Nenad Savic
Eun Gyung Lee
Bojan Gasic
David Vernez
机构
[1] University of Lausanne,Center for Primary Care and Public Health (Unisanté)
[2] National Institute for Occupational Safety and Health (NIOSH),Health Effects Laboratory Division (HELD), Exposure Assessment Branch (EAB)
[3] Swiss State Secretariat for Economic Affairs (SECO),Chemicals and Occupational Health Unit
关键词
Exposure assessment; REACH; Advanced REACH tool; Stoffenmanager; ECETOC TRA; TREXMO;
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中图分类号
学科分类号
摘要
In Europe, several occupational exposure models have been developed and are recommended for regulatory exposure assessment. Only some information on the substance of interest (e.g., vapor pressure) and the workplace conditions (e.g., ventilation rate) is required in these models to predict an exposure value that will be later used to characterize the risk. However, it has been shown that models may differ in their predictions and that usually, one of the models best fits a given set of exposure conditions. Unfortunately, there are no clear rules on how to select the best model. In this study, we developed a new modeling approach that together uses the three most popular models, Advanced REACH Tool, Stoffenmanger, and ECETOC TRAv3, to obtain a unique exposure prediction. This approach is an extension of the TREXMO tool, and is called TREXMO+. TREXMO+ applies a machine-learning technique on a set of exposure data with the measured values to split them into smaller subsets, corresponding to exposure conditions sharing similar characteristics. For each subset, TREXMO+ then establishes a regression model with the three REACH tools used as the exposure predictors. The performance of the new model was tested and a comparison was made between the results obtained by TREXMO+ and those obtained by conventional tools. TREXMO+ model was found to be less biased and more accurate than the REACH models. Its prediction differs generally from measurements by a factor of 2–3 from measurements, whereas conventional models were found to differ by a factor 2–14. However, as the available test dataset is limited, its results will need to be confirmed by larger-scale tests.
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页码:554 / 566
页数:12
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