A synergic approach to perform source apportionment of organic aerosol using offline and online measurements in positive matrix factorization
Résumé
Understanding the sources and processes responsible for high-level atmospheric PM concentrations is required to implement effective PM control strategies. During the last decade, the use of AMS and ACSM instrumentations has successfully allowed real time measurements of particulate organic fraction. However, to profoundly understand the sources and formation processes of organic aerosol (OA), a comprehensive source apportionment analysis is still needed. The combination of different datasets from several measurements to refine the apportionment of OA sources, and notably secondary ones, is probably one of the best way to achieve this goal. In this study, we propose a novel approach combining online and offline measurements in Positive Matrix Factorization source-receptor model (PMF). An intensive campaign was performed at the SIRTA atmospheric research observatory, representing the suburban background air quality conditions of the Paris area (about 25 km SW from Paris). PM10 samples were collected every 4 hours over a period of intensive PM pollution events (PM > 50 µg m-3 over several days) on March 6-24 2015, concomitantly with online measurements including ACSM, PTRMS, 7λ Aethalometer, TEOM-FDMS, NOx and O3 analyzers. Regular PMF was first performed on organic matrix obtained from offline measurements using specific primary (e.g. levoglucosan (biomass burning), methane sulphonic acid (MSA) (marine), 1-nitropyrene (traffic)) and secondary organic molecular markers (e.g. hydroxyglutaric acid (α-pinene), 3-methyl,5-nitrocatechol (biomass burning), α-methyl glyceric acid (isoprene)) and on ACSM OA matrix. Results show that PMF performed on individual dataset is not truly viable to procure complete information about the different atmospheric processes involved. Here, the synergic approach proposes to combine traditional off-line PMF factors, such as primary biomass burning, primary biogenics, secondary biogenics, traffic, with OA matrix from ACSM measurements. The unified matrix was again deconvolved with PMF in order to retrieve factors such as HOA, BBOA, OOA, and to explore additional information about OA sources.