Uncertainty propagation methods in dioxin/furans emission estimation models

G. Ripamonti (1) , G. Lonati (1) , Piero Baraldi (2) , F. Cadini (2) , Enrico Zio (2, 3)

1 DIIAR-Environmental Section
2 Dipartimento di Energia [Milano]
3 SSEC - Chaire Sciences des Systèmes et Défis Energétiques EDF/ECP/Supélec

In this paper we propose a comparison between two different approaches for uncertainty propa-gation in Environmental Impact Assessment (EIA) procedures. Both a purely Probabilistic (PMC) and a Hy-brid probabilistic-possibilistic Monte Carlo method (HMC) are applied on an estimation model of dio-xin/furans emission from a waste gasification plant. The analysis shows that when input variables affected by scarcity of information are present, HMC seems to be a valid alternative method that properly propagates un-certainty from data to output avoiding arbitrary and subjective assumptions on the input probability distribu-tion functions. HMC could improve the transparency of the EIA procedure with positive effects on the com-municability and credibility of its predictions.

Domains: Other

G. Ripamonti, G. Lonati, Piero Baraldi, F. Cadini, Enrico Zio. Uncertainty propagation methods in dioxin/furans emission estimation models. ESREL 2011, Sep 2011, Troyes, France. pp.2222 - 2229. ⟨hal-00658104⟩