PARENT SESSION
IP09 - Uncertainties in Life-Cycle and Comparative Risk Assessment
Chair: Pennington, David1, 1 GECOS-DGR, EPFL, Lausanne, Vaud, Switzerland
Co-chair: McKone, Tom 2, Jones, Kevin3, 2 University of California, Berkeley, CA3 Lancaster University, ON, LA1 4YQ
2:10 PM to 5:30 PM - Tuesday, 19 November 2002
Room Room 151 G
(IP69) Probabilistic versus Stochastic Approaches to Uncertainty Analysis: An Intake-Fraction Case Study .
McKone, Thomas*,1,2, Bodnar, Agnes1,2, Maddalena, Randy1, 1 Lawrence Berkeley National Laboratory, Berkeley, California, USA2 University of California, School of Public Health, Berkeley, California, USA
ABSTRACT- Both life-cycle assessment (LCA) and comparative risk assessment make use of source-to-dose calculations to quantify the potential impacts of industrial emissions. It is recognized that source-to-dose calculations involve model, parameter (data), and scenario uncertainties. The common approach for characterizing uncertainties in source-to-dose estimates is probabilistic modeling--an approach in which uncertainties are propagated through a model using repeated simulations, i.e. Monte Carlo modeling. An alternative approach is stochastic modeling. In contrast to the probabilistic-deterministic approach, we use stochastic analyses to obtain useful information from seemingly random measurements. Stochastic models are developed directly from data to gain insights into the nature of randomness and to establish the relationship between factors such as emissions and intake. In this presentation we will explore the advantages and limitations of these two methods for characterizing uncertainties in source-to-dose relationships for a set of persistent bio-accumulative pollutants. We express source-to-dose relationships using the intake fraction (iF), which is defined as the mass of chemical taken up by the receptor population divided by the mass of chemical released. Using both probabilistic multimedia exposure models and stochastic models developed from emissions data, food surveys, and dose biomarkers, we develop probability distributions to characterize intake fraction for food-chain exposures to air emissions of dioxin compounds and a set of polycyclic aromatic hydrocarbons (PAHs). We then compare the distributions obtained from the two approaches. In contrast to the PAH compounds, we find a better correlation between the multimedia probabilistic model results and the stochastic model results for dioxin-like compounds. This appears to be due in part to the persistence, widespread distribution in the environment, and food-chain dominant exposures of the dioxin compounds.
Key words: uncertainty analysis, intake fraction, stochastic models, life-cycle impact