Abstract
Atmospheric Particulate Matter (PM10) control is at the moment a great challenge for air quality management, due to the strong non linearities that affect formation and accumulation of this pollutant. This work presents the formalization and application of a two-objective methodology to select effective particulate matter control strategies on a mesoscale domain. The two considered objectives are emission reduction costs and the PM10 exposure index. The decision variables are the precursor emission reductions due to ablation technologies. The nonlinear relationships linking air quality objective and precursor emissions are described by neuro-fuzzy models, identified through the processing of simulations of the TCAM deterministic multiphase modeling system, performed in the framework of the CityDelta-CAFE Project (EU 6th Framework Program). The two-objective problem has been applied to a complex domain in Northern Italy, including the Milan metropolitan area, a region characterized by high emissions and frequent and persistent secondary pollution episodes.
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