Uncertainty propagation in a model for the estimation of the ground level concentration of dioxin/furans emitted from a waste gasification plant

Abstract In this paper we compare two approaches for uncertainty propagation in a model for Environmental Impact Assessment (EIA). A purely Probabilistic (PMC) and a Hybrid probabilistic–possibilistic Monte Carlo (HMC) method are considered in their application for the estimation of the ground level concentration of dioxin/furans emitted from a waste gasification plant. Under the condition of insufficient information for calibrating the estimation model parameters, HMC is shown to be a valid way for properly propagating parameters uncertainty to the model output, without adopting arbitrary and subjective assumptions on the input probability distribution functions. In this sense, HMC could improve the transparency of the EIA procedures with positive effects on the communicability and credibility of its findings.

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