H15-115: Application of a Lagrangian particle model to the source apportionment for primary macropollutants in Taranto area (South Italy)

Dispersion models based on emission inventories and meteorological fields are the primary tool of control agencies to support air quality assessment and source apportionment in complex industrial areas. In this work, a modelling system has been applied to estimate the annual contribution to the total concentrations of different pollutant sources in Taranto, one of the most industrialized areas in Italy, where typical urban emissions are superimposed on industrial ones located in proximity of the city boundary. Main industrial activities consist of an integrated steel plant (one of the largest in Europe) and an oil refinery, together with other smaller industrial facilities which use the Taranto harbour to unload primar y goods and to deliver final products. Modelling system includes the meteorological models SWIFT-SURFPRO and the Lagrangian particle dispersion model SPRAY. The air emissions inventory is partially established using local activity indicators and emission factors. The resolution level of the data is the municipality. In particular, in this study industrial sources (point sources and fugitive), traffic, domestic heating and harbour emissions have been taken into account. The meteorology in the studied area was reconstructed by the SWIFT model from the tridimensional meteorological products supplied, for the year 2007, by the national MINNI project. The annual simulation led to the identification of the main emitting sources and to the source-apportionment of primary pollutants at selected receptor sites, belonging to the air quality monitoring network. Industrial activities were found to be the principal contributor to SO2 emissions. Industry and traffic emissions were, for the most part, responsible for NOx simulated concentrations, while primary PM10 and PM2.5 simulated concentrations appeared to be linked to industrial emissions. Finally, in order to demonstrate the level of representativeness of the system used in this study, the model predictions were compared with measured air quality data.