An improved approach to resolve sources of organic aerosol by combining offline and online ambient measurements
Résumé
Organic constituents are a major component of ambient particulate matter (PM) and have significant impacts on air quality and climate change. Over the last decade, the use of online instrumentation (i.e. AMS and ACSM) has successfully provided real time measurements of particulate organic fractions as well as information on their sources. However, a full comprehension of organic aerosol (OA) sources is still difficult to achieved due to the complexity and variability of the processes involved. Combining different datasets from several measurement systems to refine the source apportionment of OA, and notably secondary ones (SOA), is probably one of the best way to achieve this goal. In this study, we performed a short term intensive campaign, in March 2015, at the SIRTA atmospheric research observatory, representing the suburban background air quality conditions of the Paris region (25 km SW of Paris) over a period of intense PM pollution events (PM10 > 50 μg m 3 over several days). PM10 samples were collected every 4 hours concomitantly with online measurements including ACSM, 7λ Aethalometer, TEOM FDMS (PM1 and PM10), NOx and O3 analyzers. A novel OA source apportionment approach has been applied by combining online and offline measurements using time synchronization script (positive matrix factorization, PM, using ME 2 engine). The unified matrix, included OA matrix from ACSM and specific primary (e.g., levoglucosan (biomass burning), 1 nitropyrene (traffic)) and secondary organic molecular markers (e.g., 3 methyl,5 nitrocatechol (biomass burning), α methyl glyceric acid (isoprene)…) from PM10 filters with their original time resolution (30 min for ACSM and 4 h for PM10 filters). The results obtained allowed the deconvolution of 10 OA factors including 4 different biomass burning sources (primary and secondary OA). The time synchronization provided more information than the conventional PMF approaches based on ACSM or filter data only and allowed a comprehensive description of the atmospheric processes related to the different OA sources.