Multi-time and multi-size resolution receptor modeling to exploit jointly atmospheric aerosol data measured at different time resolutions and in multiple size classes

In the recent decades, advanced instrumentation has been developed to measure the atmospheric aerosol's physical-chemical properties with increased temporal detail and size resolution. The characterization of the atmospheric aerosol is now provided at a more detailed level. Nevertheless, it is still challenging to maximize the exploitation of such detailed information in receptor models to perform more reliable source apportionment studies. Indeed, detailed time- and size-resolved sampling can, in principle, provide additional information to better identify specific emission sources and/or atmospheric processes, but an associated complete chemical characterization is often lacking, or is provided at low time resolution by PMX samples. To this aim, a completely novel, multi-time, multi-size resolution positive matrix factorization (MTMS-PMF) is presented. This cutting-edge receptor model is an expansion of the widely used PMF and allows the analysis of data measured at different time resolutions in multiple size classes. As output, it provides size-segregated chemical profiles and factor temporal contributions retrieved at the highest temporal resolution available in the dataset. The MTMS-PMF was implemented in a script for the Multilinear Engine ME-2 program and successfully tested on a large dataset collected in the Po Valley (Ferrara, Italy) during years 2008–2018. The dataset included aerosol chemical species measured on multistage impactor samples (8 size classes) at a low time resolution of about 1–3 weeks and daily PM10 samples covering almost the same sampling periods. The outputs retrieved at the higher time and size resolutions greatly strengthened the source-to-factor assignment. Moreover, the possibility to acquire information about the size distributions of atmospheric aerosol emitted by a variety of sources is highly valuable for impact assessment and for developing focused mitigation strategies aimed at addressing specific negative aerosol effects.