Abstract
This paper presents the formalisation and the application of a multiobjective methodology to select effective ozone control strategies on a mesoscale domain. The methodology consists of a two-objective problem assessing emission reduction costs and ozone pollution exposure. The decision variables are the ozone precursor emission reductions due to ablation technologies. The nonlinear relationship linking air quality objective and precursor emissions is based on neural networks, identified processing deterministic Chemical Transport Modelling system simulations. The proposed methodology has been applied to a complex domain in Northern Italy, a region characterized by high ozone levels during summer season. Copyright © 2007 IFAC.
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