Combining Chemical Composition Data and Numerical Modelling for the Assessment of Air Quality in a Mediterranean Port City

Abstract

The aim of this study is to characterize the air quality in a Mediterranean port city. The impact of ship emissions on both gaseous and particulate pollutants has been investigated through an integrated methodology which includes atmospheric flow and dispersion numerical modelling as well as chemical composition and statistical analyses. Specifically, chemical compositional data (ionic fraction, carbonaceous compounds, and metals) of PM2.5 were acquired during an experimental field campaign carried out in the port city of Brindisi (Apulia Region, Southern Italy). The sampling site was located on the roof of a building (ASI) within the port area. Given the complexity of the site in which both domestic buildings and a large industrial area are present, analyses were done by selecting different wind sectors to test different techniques to discriminate between sources. Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) were applied to evaluate statistical differences in the composition of PM2.5 sampled within the area when the sampling site was downwind to the port or to the urban-industrial area. Only LDA allowed to discriminate the separation between urban-industrial and port macroareas. Those results were further confirmed in terms of PM2.5 concentrations directly associated to ship emissions using a coupled modelling approach. The mesoscale model BOLCHEM was used to investigate the contribution of ship emissions both on primary and secondary PM2.5 concentration in the area surrounding the port, as well as on PM10, NOX and O3 concentrations. Then, the model was coupled offline with the local dispersion model ADMS-Urban. The adopted approach was crucial to evaluate the spatial distribution of the impact of ship emissions. BOLCHEM results showed that in the cell of the port the average impact of ship emissions on NOX was 37.6%, and −11.7% on O3. The average impact on PM2.5 was 6.1%, distributed between primary (2.7%) and secondary fraction (3.4%). At local scale, the analysis of high-resolution modelling results obtained from ADMS-Urban highlighted that, at ASI position, the impact of ship emissions on PM2.5 was 6.8% when the sampling site was positioned downwind to the port area and reduced to lower than 3.0% at about 2 km from the sources.

Keywords

• mesoscale chemistry model
• local scale model
• chemical characterization
• principal component analysis
• linear discriminant analysis
• ship emissions