Cost-Effective Plans to Mitigate Air Quality Effects on Human Health in Northern Italy

Abstract

AbstractThe use of modeling to support environmental authorities to plan air quality control policies is now quite widespread in different parts of Europe. At the sub-national level the most common way to face the problem is through the scenario analysis approach, using Chemical Transport Models to assess the impact of emission reductions on pollution concentrations. In this paper a multi-objective approach to define air quality policies is proposed. The considered objective function is a vector including an Air Quality Index and an Internal Costs Index. In this work the Air Quality Index (AQI) is the yearly mean PM10 concentration over the study domain. It is estimated processing source-receptor models linking emission and concentrations over the domain cells, taking into account nonlinear phenomena and using Artificial Neural Networks (ANNs). The ExternE methodology is then applied to estimate health impacts and external costs of optimal control policies up to 2020. The data used to identify the source-receptor models has been provided by a set of 10 simulations computed through the TCAM (Transport Chemical Aerosol) model. The Internal Cost Index (CI) describes the costs to implement a particular emission reduction policy. This index is computed by means of ANNs linking emission reductions and their relative implementation costs, for different CORINAIR macro sectors. Simulations with IIASA’s GAINS model have been used to calibrate the emission-to-cost model. The solutions of the decision problem represent cost-effective policies at the sectoral level. The methodology is applied to Northern Italy, one of the most polluted areas in Europe, to select optimal control policies up to 2020.KeywordsIntegrated assessment modelsMulti-objective problem