Organic aerosol sources in the Milan metropolitan area – Receptor modelling based on field observations and air quality modelling

The Milan metropolitan area in Northern Italy experiences historically severe particulate matter pollution episodes characterized by high organic aerosol (OA) concentrations. However, the main sources of OA, especially atmospherically formed secondary OA (SOA) are not well understood. Here, we investigated the emission sources contributing to the directly emitted OA (Primary – POA) and to the SOA in urban Milan, where such information is particularly lacking. We used advanced mass spectrometric analytical techniques for the characterization of archive samples, as well as statistical receptor modeling (positive matrix factorization, PMF) and air quality modeling, to establish a base case for investigating the time evolution of source contributions. We found that residential heating biomass burning POA (BBOA) were a major detrimental factor for air quality during the winter (37% of OA, under polluted conditions up to 56% of OA). Inefficient combustion conditions identified by high BBOA/K+ ratios contributed to the high relative contribution of BBOA to OA. Long-term tracer analyses suggest that BBOA concentrations remained approximately constant over the last decade (2012–2022), supporting the conclusion that emissions from biomass burning remained a major driver of winter-time OA pollution. Yet assessing changes in the contribution of other OA sources require future research. While POA emissions were the most important contributor to OA during winter (62% of OA), SOA dominated OA during summer (62% of OA). Our combined advanced mass spectral source apportionment and air quality modelling analyses indicated that winter-time SOA were mostly affected by biomass burning related precursor emissions, while summer-time SOA were linked to both the remaining anthropogenic emissions (industry, energy production, shipping, and traffic) and to biogenic emissions. Altogether, this study quantified the major emission sources of OA and thus provides crucial information about OA sources and a baseline for comparison to the present situation which is needed for tackling OA pollution in one of the major pollution hotspots in Europe. Overall, this study presents a transferable framework combining chemical source apportionment with bottom-up air quality OA source analyses in order to better understand the formation of SOA.