14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper ThA6.6

Carnevale, Claudio (Univ. of Brescia), Finzi, Giovanna (Univ. of Brescia), Pisoni, Enrico (Univ. of Brescia), Volta, Marialuisa (Univ. of Brescia)

Identification of Source-Receptor Models for Secondary Tropospheric Pollution Control

Scheduled for presentation during the Invited Session "Identification of Ecological/Environmental Systems" (ThA6), Thursday, March 30, 2006, 12:10−12:30, Newcastle Room

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 22, 2018

Keywords Nonlinear System Identification, Neural Networks, Other


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 multi-objective 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 cost. In this paper a neural network approach is proposed to identify local PM10 (particulate matter with size smaller than 10 micrometers) 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 a complex terrain, high urban and industrial emissions and a dense road network, is often affected by severe PM10 pollution episodes exceeding law standard.