Testing the capability of the minni atmospheric modeling system to simulate air pollution in Italy

TitleTesting the capability of the minni atmospheric modeling system to simulate air pollution in Italy
Publication TypePresentazione a Congresso
Year of Publication2010
AuthorsBriganti, G., Cappelletti Andrea, Mircea Mihaela, Pederzoli A., Vitali L., Pace G., Marri P., Silibello C., Finardi S., Calori G., and Zanini Gabriele
Conference NameHARMO 2010 - Proceedings of the 13th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes
PublisherARIA Technologies
ISBN Number9782868150622
KeywordsAir pollution, Air quality, Air quality modeling, Atmospheric modeling systems, Atmospheric modelling, Atmospheric movements, Greenhouse gases, Integrated assessment modelling, International negotiation, Meteorological modeling, Model validation, Ozone, Pollution, Statistical indicators, Surface measurement

MINNI is the Italian Integrated Assessment Modelling System for supporting the International Negotiation Process on Air Pollution and assessing Air Quality Policies at national/local level sponsored by the Italian Ministry of the Environment. MINNI system is made up by two components: an Atmospheric Modelling System (AMS) and a Greenhouse Gas Air Pollution Interactions and Synergies model over Italy (GAINS - Italy). This presentation describes the AMS components: the emission processor (EMMA), the meteorological model (RAMS) and the air quality model (FARM), and shows an extensive validation exercise over Italy. The simulations were carried out for a whole year, and the AMS ability to predict ozone formation and destruction under different conditions of sun light and temperature, for different seasons was evaluated. The modelled ozone concentrations were compared to surface measurements and statistical indicators such as mean normalized bias error (MNBE), mean absolute normalised gross error (MANGE) and unpaired peak estimation accuracy (UPA) were calculated for the all stations. The results show that the model is able to reproduce temporal evolution and spatial distribution of ozone concentration. The statistical indicators show that AMS performs generally well, simulating the ozone concentrations better during summer rather than winter, and better at rural stations rather than at urban ones.