Istituto di Scienze Marine     
Sommerfreund J., Arhonditsis J., Diamond M., Frignani M., Capodaglio G., Gerino M., Bellucci L. G., Giuliani S., Mugnai C. Examination of the uncertainty in contaminant fate and transport modeling: A case study in the Venice Lagoon. In: Ecotoxicology and Environmental Safety, vol. 73 (3) pp. 231 - 239. ACADEMIC PRESS INC ELSEVIER SCIENCE, 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA, 2010.
A Monte Carlo analysis is used to quantify environmental parametric uncertainty in a multi-segment, multi-chemical model of the Venice Lagoon. Scientific knowledge, expert judgment and observational data are used to formulate prior probability distributions that characterize the uncertainty pertaining to 43 environmental system parameters. The propagation of this uncertainty through the model is then assessed by a comparative analysis of the moments (central tendency, dispersion) of the model output distributions. We also apply principal component analysis in combination with correlation analysis to identify the most influential parameters, thereby gaining mechanistic insights into the ecosystem functioning. We found that modeled concentrations of Cu, Pb, OCDD/F and PCB-180 varied by LIP to an order of magnitude, exhibiting both contaminant- and site-specific variability. These distributions generally overlapped with the measured concentration ranges. We also found that the uncertainty of the contaminant concentrations in the Venice Lagoon was characterized by two modes of spatial variability, mainly driven by the local hydrodynamic regime, which separate the northern and central parts of the lagoon and the more isolated southern basin. While spatial contaminant gradients in the lagoon were primarily shaped by hydrology, our analysis also shows that the interplay amongst the in-place historical pollution in the central lagoon, the local suspended sediment concentrations and the sediment burial rates exerts significant control on the variability of the contaminant concentrations. We conclude that the probabilistic analysis presented herein is valuable for quantifying uncertainty and probing its cause in over-parameterized models, while some of our results can be used to dictate where additional data collection efforts should focus on and the directions that future model refinement should follow. (C) 2009 Elsevier Inc. All rights reserved.
DOI: 10.1016/j.ecoenv.2009.05.008

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