Characterising uncertainties in human exposure modelling through the Random Sampling-High Dimensional Model Representation (RS-HDMR) methodology. (3rd June 2005)

• Record Type: Journal Article
• Authors: Wang, Sheng-Wei; Georgopoulos, Panos G.; Li, Genyuan; Rabitz, Herschel
• Is Part Of: International journal of risk assessment and management. Volume 5:Number 2/3/4(2004)
• Page Start: 387
• Page End: 406
• Publication Date: 2005-06-03
• Subjects: global uncertainty -- sensitivity analysis -- HDMR -- human exposure assessment -- risk assessment -- microenvironmental models -- model reduction -- Monte Carlo methods -- multimedia exposure -- multipathway exposure -- human exposure modelling -- dose modelling -- pharmacokinetics models -- random sampling -- trichloroethylene -- model uncertainty -- environmental contaminants -- health risks; Risk assessment -- Periodicals; Risk management -- Periodicals; 361.1
• Languages: English
• ISSNs: 1466-8297

Abstract

This paper presents the application of a quantitative model assessment and analysis tool, the Random Sampling-High Dimensional Model Representation (RS-HDMR), in characterising uncertainties of population-based human exposure modelling to trichloroethylene (TCE). The RS-HDMR method is used to construct the Fully Equivalent Operational Model (FEOM) as an "accurate and efficient" approximation of the mechanistic multimedia and multipathway exposure and dose model for calculating internal doses of TCE, so as to reduce "model uncertainty" that may result from simplifying approximations (e.g. steady-state assumptions) of the original mechanistic model in order to obtain computational efficiency. RS-HDMR can also be used to assess the influence of "parameter uncertainty" on model outputs by providing quantitative estimates and qualitative descriptors of independent and cooperative influences of model parameters/inputs on the output through global uncertainty/sensitivity analysis. The outcomes of global uncertainty/sensitivity analysis can be used to direct available resources towards reducing uncertainty where it is most appropriate.