Defining a nonlinear control problem to reduce particulate matter population exposure

Carnevale, C., Finzi, G., Pisoni, E., Volta, M., & Wagner, F. ORCID: https://orcid.org/0000-0003-3429-2374 (2012). Defining a nonlinear control problem to reduce particulate matter population exposure. Atmospheric Environment 55 410-416. 10.1016/j.atmosenv.2012.03.033.

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Abstract

In this paper a multi-objective nonlinear approach to control air quality at a regional scale is presented. Both economic and air quality sides of the problem are modeled through artificial neural network models. Simulating the complex nonlinear atmospheric phenomena, they can be used in an optimization routine to identify the efficient solutions of a decision problem for air quality planning. The methodology is applied over Northern Italy, an area in Europe known for its high concentrations of particulate matter. Results illustrate the effectiveness of the approach assessing the nonlinear chemical reactions in an air quality decision problem.

Item Type:	Article
Uncontrolled Keywords:	Multi-objective optimization; Year of Lost Life; Emission control; Neural networks; GAINS model
Research Programs:	Air Quality & Greenhouse Gases (AIR) Mitigation of Air Pollution (MAG)
Bibliographic Reference:	Atmospheric Environment; 55:410-416 (August 2012) (Published online 22 March 2012)
Depositing User:	IIASA Import
Date Deposited:	15 Jan 2016 08:46
Last Modified:	27 Aug 2021 17:22
URI:	https://pure.iiasa.ac.at/9989

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