Enhanced CAMx source apportionment analysis at an urban receptor in Milan based on source categories and emission regions

Source apportionment results from CAMx/PSAT v6.3 model simulation at an urban receptor placed in Milan city centre are presented. CAMx was run over a domain covering the Po valley for the calendar year of 2010. Model simulations considered nitrogen dioxide (NO2), fine particulate matter (PM2.5), and its primary and secondary components, i.e.: elemental carbon (EC) and nitrate (NO3-). Source apportionment results are separately reported with respect to emission regions (e.g.: local, urban, metropolitan areas, counties) and emission categories (e.g.: transport, space heating, industrial activities) and to the combination of emission regions and categories. Five emission regions were considered, starting from a narrow region covering Milan city centre, up to Milan municipality, Milan metropolitan area, Lombardy region, and to the entire Po valley. In terms of emission region contributions, Milan municipality, its metropolitan area, and Lombardy region account for about 60% of PM2.5 total mass at the selected receptor. However, local scale emissions contribute for more than 50% to EC ambient levels at this receptor. Conversely, the sources located in the farthest emission regions (Lombardy and Po valley) and long range transport determine the largest contribution (80%) to NO3- concentration. For NO2, local scale emissions are responsible for more than 60% of the ambient concentration levels in Milan city centre. In terms of source categories, traffic is the main contributor to NO2 and NO3-, biomass burning and traffic to EC and PM2.5 mass. The emission categories contributions to PM2.5 estimated by CAMx/PSAT for the selected receptor show a rather good agreement with Positive Matrix Factorization (PMF) source apportionment results available for Milan area. However, the two approaches provide similar estimations only for biomass burning and traffic contributions (24% and 20%, respectively) whereas CAMx gives remarkably lower estimates for the share of secondary organic aerosol (SOA), likely because of missing formation processes in CAMx chemical module.