A Model Predictive Control Methodology to Integrate Short and Long Term Air Quality Objectives

Abstract

This study introduces and evaluates a methodology to define optimal integrated short and long-term air pollution control measures, to support policy formulation by Local Authorities. The approach utilized in this methodology is based on a receding horizon strategy. In this approach, an auto-regressive model provides understanding on the dynamic characteristics of air quality within a designated time period. The model is established using daily observed data on pollutant concentration, meteorological variables, and estimated emission data in the study area. At each time step, the resulting optimization problem is addressed using genetic algorithms. The effectiveness of the overall control has been assessed in the context of controlling NO 2 concentrations within the city of Milan. The outcomes of the study demonstrate that this control system can serve as a valuable tool to assist Local Authorities in making informed decisions regarding appropriate air quality management strategies.