Defining a nonlinear control problem to reduce particulate matter population exposure

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.

► A multi-objective nonlinear approach to control PM population exposure is presented.
► Both internal costs and health effects are modeled using artificial neural networks.
► The case study domain is Northern Italy.