Different models have been developed over the past decade to predict the indoor air concentrations of chemicals originating from underground contaminant sources; however, the uncertainty of these models has not been studied systematically.  The most widely applied model for predicting vapor intrusion potentials is the Johnson & Ettinger (J&E) Model. The purpose of this study is to assess the uncertainty of input parameters for the J&E model using probabilistic methods as opposed to computationally inefficient conventional tools such as Monte Carlo analysis. Specifically, this research incorporated Stochastic Response Surface Method (SRSM) to estimate the uncertainty within the J&E model. This method is based on chaos series expansion and a limited number of model simulations are required to determine the coefficients of chaos polynomial. Since the vapor intrusion models are dealing with random variables, the SRSM method could be employed to efficiently estimate the statistical characteristics of model outputs.

Uncertainty analysis of vapor intrusion models using stochastic response surface method

244th National Meeting (2012)

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