Characterizing uncertainties in human exposure modeling through the Random Sampling-High Dimensional Model Representation (RS-HDMR) methodology
S.W. Wang1, P.G. Georgopoulos1, G. Li2, H. Rabitz2
1Environmental and Occupational Health Sciences Institute, UMDNJ - R.W. Johnson Medical School and Rutgers University; 2Department of Chemistry, Princeton University
This study presents the application of a quantitative model assessment and analysis tool, the Random Sampling-High Dimensional Model Representation (RS-HDMR), in characterizing uncertainties of population-based human exposure modeling to a multimedia contaminant. The case study focuses on the distribution of internal doses resulting from multi-route (inhalation, ingestion, and dermal absorption) residential human exposures to trichloroethylene (TCE) present in tap water. The RS-HDMR method is used to construct the Fast 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. The FEOM can generate the distribution of TCE internal doses as accurate as the mechanistic model, while being computationally efficient as those simplified models derived based on steady-state assumptions. These features of the FEOM provide potentially promising solutions for reducing "model uncertainty" that may result from simplifying approximations of the original mechanistic model in order to obtain computational efficiency. Furthermore, RS-HDMR can also be used to assess the influence of "parameter uncertainty" on model outputs by providing quantitative estimation and qualitative description of independent and cooperative influences of model parameters/inputs on the output through global uncertainty analysis. The outcomes of global uncertainty analysis can characterize relative importance of model parameters/inputs that affect the model output the most, so that resources can be focused to reduce uncertainty in a more efficient way.
This work had been funded in part by the US Environmental Protection Agency under Cooperative Agreement # EPAR-827033 to the Environmental and Occupational Health Sciences Institute (EOHSI). The viewpoints expressed here are the responsibility of the authors and do not necessarily reflect the views of the USEPA or its contractors.