he present study proposes an advanced methodology to refine the source apportionment of organic aerosol (OA). This methodology is based on the combination of offline and online datasets in a single Positive Matrix Factorization (PMF) analysis using the multilinear engine (ME-2) algorithm and a customized time synchronization procedure. It has been applied to data from measurements conducted in the Paris region (France) during a PM pollution event in March 2015. Measurements included OA ACSM (Aerosol Chemical Speciation Monitor) mass spectra and specific primary and secondary organic molecular markers from PM10 filters on their original time resolution (30 min for ACSM and 4 h for PM10 filters). Comparison with the conventional PMF analysis of the ACSM OA dataset (PMF-ACSM) showed very good agreement for the discrimination between primary and secondary OA fractions with about 75% of the OA mass of secondary origin. Furthermore, the use of the combined datasets allowed the deconvolution of 3 primary OA (POA) factors and 7 secondary OA (SOA) factors. A clear identification of the source/origin of 54% of the total SOA mass could be achieved thanks to specific molecular markers. Specifically, 28% of that fraction was linked to combustion sources (biomass burning and traffic emissions). A clear identification of primary traffic OA was also obtained using the PMF-combined analysis while PMF-ACSM only gave a proxy for this OA source in the form of total hydrocarbon-like OA (HOA) mass concentrations. In addition, the primary biomass burning-related OA source was explained by two OA factors, BBOA and OPOA-like BBOA. This new approach has showed undeniable advantages over the conventional approaches by providing valuable insights into the processes involved in SOA formation and their sources. However, the origins of highly oxidized SOA could not be fully identified due to the lack of specific molecular markers for such aged SOA.< Leer menos