A non-linear optimization programming model for air quality planning including co-benefits for GHG emissions

Enrico Turrini; Claudio Carnevale; Giovanna Finzi; Marialuisa Volta

doi:10.1016/j.scitotenv.2017.10.129

A non-linear optimization programming model for air quality planning including co-benefits for GHG emissions

Sci Total Environ. 2018 Apr 15:621:980-989. doi: 10.1016/j.scitotenv.2017.10.129. Epub 2017 Oct 28.

Authors

Enrico Turrini¹, Claudio Carnevale², Giovanna Finzi², Marialuisa Volta²

Affiliations

¹ DIMI, University of Brescia, via Branze 38, Brescia 25123, Italy. Electronic address: enrico.turrini@unibs.it.
² DIMI, University of Brescia, via Branze 38, Brescia 25123, Italy.

PMID: 29089126
DOI: 10.1016/j.scitotenv.2017.10.129

Abstract

This paper introduces the MAQ (Multi-dimensional Air Quality) model aimed at defining cost-effective air quality plans at different scales (urban to national) and assessing the co-benefits for GHG emissions. The model implements and solves a non-linear multi-objective, multi-pollutant decision problem where the decision variables are the application levels of emission abatement measures allowing the reduction of energy consumption, end-of pipe technologies and fuel switch options. The objectives of the decision problem are the minimization of tropospheric secondary pollution exposure and of internal costs. The model assesses CO₂ equivalent emissions in order to support decision makers in the selection of win-win policies. The methodology is tested on Lombardy region, a heavily polluted area in northern Italy.

Keywords: Air quality; Control strategy; Decision support systems; Environmental modelling; Integrated assessment modelling; Optimization; Particulate matter.