- Tytuł:
- Experiencing a probabilistic approach to clarify and disclose uncertainties when setting occupational exposure limits
- Autorzy:
-
Vernez, David
Fraize-Frontier, Sandrine
Vincent, Raymond
Binet, Stéphane
Rousselle, Christophe - Powiązania:
- https://bibliotekanauki.pl/articles/2159984.pdf
- Data publikacji:
- 2018-07-04
- Wydawca:
- Instytut Medycyny Pracy im. prof. dra Jerzego Nofera w Łodzi
- Tematy:
-
risk management
chemical toxicity
assessment factors
uncertainty distributions
probabilistic methods
occupational exposure limits - Opis:
- Objectives Assessment factors (AFs) are commonly used for deriving reference concentrations for chemicals. These factors take into account variabilities as well as uncertainties in the dataset, such as inter-species and intra-species variabilities or exposure duration extrapolation or extrapolation from the lowest-observed-adverse-effect level (LOAEL) to the noobserved- adverse-effect level (NOAEL). In a deterministic approach, the value of an AF is the result of a debate among experts and, often a conservative value is used as a default choice. A probabilistic framework to better take into account uncertainties and/or variability when setting occupational exposure limits (OELs) is presented and discussed in this paper. Material and methods Each AF is considered as a random variable with a probabilistic distribution. A short literature was conducted before setting default distributions ranges and shapes for each AF commonly used. A random sampling, using Monte Carlo techniques, is then used for propagating the identified uncertainties and computing the final OEL distribution. Results Starting from the broad default distributions obtained, experts narrow it to its most likely range, according to the scientific knowledge available for a specific chemical. Introducing distribution rather than single deterministic values allows disclosing and clarifying variability and/or uncertainties inherent to the OEL construction process. Conclusions This probabilistic approach yields quantitative insight into both the possible range and the relative likelihood of values for model outputs. It thereby provides a better support in decision-making and improves transparency. Int J Occup Med Environ Health 2018;31(4):475–489
- Źródło:
-
International Journal of Occupational Medicine and Environmental Health; 2018, 31, 4; 475-489
1232-1087
1896-494X - Pojawia się w:
- International Journal of Occupational Medicine and Environmental Health
- Dostawca treści:
- Biblioteka Nauki
Artykuł