This study introduces a model predictive control methodology to determine optimal measures for mitigating air pollution, assisting Local Authorities in policy development. Anchored in an auto-regressive model, it analyzes dynamic air quality patterns over a defined timeframe using daily observed pollutant concentration, meteorological variables, and estimated emission data. Employing model predictive control methodology, the approach aims to optimize daily emission reductions. Evaluated in Milan, a heavily polluted European city, the findings highlight the methodology's potential as a robust tool for Local Authorities, enabling informed decisions in crafting efficient air quality management strategies, in the specific context of NO2.
A model predictive control application for air quality management
Sangiorgi, L.;Carnevale, C.;De Nardi, S.;Raccagni, S.
2024-01-01
Abstract
This study introduces a model predictive control methodology to determine optimal measures for mitigating air pollution, assisting Local Authorities in policy development. Anchored in an auto-regressive model, it analyzes dynamic air quality patterns over a defined timeframe using daily observed pollutant concentration, meteorological variables, and estimated emission data. Employing model predictive control methodology, the approach aims to optimize daily emission reductions. Evaluated in Milan, a heavily polluted European city, the findings highlight the methodology's potential as a robust tool for Local Authorities, enabling informed decisions in crafting efficient air quality management strategies, in the specific context of NO2.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
