Abstract
To design air quality plans, regional Authorities need tools to understand both the impact of emission reduction strategies on pollution indexes and the costs of such emission reduction. The problem can be formalized as a multiobjective mathematical program, integrating local pollutant-precursor models and the estimate of emission reduction costs. Both aspects present several complex elements. In particular the source-receptor models, describing transport phenomena and chemical non linear dynamics, require deterministic complex modelling systems with high computational costs. In this paper a neural network approach is proposed to identify local PM10 (particulate matter with size smaller than 10 μm) precursor models based on the simulations of a multi-phase modelling system (GAMES). The methodology has been applied to Lombardia region (Northern Italy) PM10 pollution control. The area, characterized by complex terrain, high urban and industrial emissions and a dense road network, is often affected by severe PM10 pollution episodes exceeding law standards.
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