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ER - TY - JOUR T1 - Influential input classification in probabilistic multimedia models JF - Stochastic Environmental Research and Risk Assessment Y1 - 2001/03// SP - 1 EP - 17 A1 - Randy L. Maddalena A1 - Thomas E. McKone A1 - Dennis P.H. Hsieh A1 - Shu Geng KW - Error propagation KW - model development KW - Monte Carlo KW - multimedia mass balance KW - variance AB - Monte Carlo analysis is a statistical simulation method that is often used to assess and quantify the outcome variance in complex environmental fate and effects models. Total outcome variance of these models is a function of (1) the variance (uncertainty and/or variability) associated with each model input and (2) the sensitivity of the model outcome to changes in the inputs. To propagate variance through a model using Monte Carlo techniques, each variable must be assigned a probability distribution. The validity of these distributions directly influences the accuracy and reliability of the model outcome. To efficiently allocate resources for constructing distributions one should first identify the most influential set of variables in the model. Although existing sensitivity and uncertainty analysis methods can provide a relative ranking of the importance of model inputs, they fail to identify the minimum set of stochastic inputs necessary to sufficiently characterize the outcome variance. In this paper, we describe and demonstrate a novel sensitivity/uncertainty analysis method for assessing the importance of each variable in a multimedia environmental fate model. Our analyses show that for a given scenario, a relatively small number of input variables influence the central tendency of the model and an even smaller set determines the spread of the outcome distribution. For each input, the level of influence depends on the scenario under consideration. This information is useful for developing site specific models and improving our understanding of the processes that have the greatest influence on the variance in outcomes from multimedia models. VL - 15 IS - 1 U1 -7.2

DO - 10.1007/PL00009786 ER - TY - JOUR T1 - The Use of the Molecular Connectivity Index for Estimating Biotransfer Factors JF - Environmental Science & Technology Y1 - 1996/02// SP - 984 EP - 989 A1 - Deanna L. Dowdy A1 - Thomas E. McKone A1 - Dennis P.H. Hsieh AB - Biotransfer factors (BTFs) represent the ratio of the concentration of a chemical found in animal tissues such as beef or milk to the animal's daily intake of that chemical. Using currently available citations for BTFs in meat and milk, the use of molecular connectivity indices (MCIs) as a quantitative structureâˆ’activity relationship (QSAR) for predicting the BTFs for organic chemicals is evaluated. Based on a statistical evaluation of correlation, residual error, and cross validation, this evaluation reveals that the MCI provides both higher reliability and a fast and cost-effective method for predicting the potential biotransfer of a chemical from environmental media into food. When compared to the use of Kow as a predictor of BTFs, the analysis here indicates that MCI can substantially increase the reliability with which BTFs can be estimated. VL - 30 IS - 3 U1 -7.1

DO - 10.1021/es950398c ER -