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Organic Aerosol Components across Europe Using 22 ACSM/AMS Year-long Datasets and a Harmonized Source Apportionment Protocol
GANG CHEN, MariCruz Minguillon, André S. H. Prévôt, Team COLOSSAL, Paul Scherrer Institute
Abstract Number: 97
Working Group: Source Apportionment
Abstract
Atmospheric aerosol can indirectly and directly affect climate, reduce visibility, and cause adverse public health issues. Organic aerosol (OA) is a significant air pollutant, representing 20-90% of the total submicron aerosol mass. However, the spatial and temporal variabilities of OA sources remain poorly characterized in Europe. In this study, we collected year-long data using 21 Aerosol Chemical Speciation Monitors (ACSM) and one Aerosol Mass Spectrometer (AMS) (Aerodyne Research Inc., MA, USA) between 2014 and 2019 in Europe. We performed OA source apportionment (SA) applying Positive Matrix Factorization (PMF) analysis using novel techniques within SoFi Pro (Datalystica Ltd., Villigen, Switzerland), including rolling mechanism, a-value approach, bootstrap re-sampling, criteria-based selection, and uncertainty assessments.
Overall, we found that oxygenated OA (OOA) factors dominated at all sites, and their contribution was more significant at non-urban sites than urban environments. On the contrary, hydrocarbon-like OA (surrogate of road traffic emission, HOA) showed higher contributions in urban locations, although identified at most non-urban sites. Biomass burning OA (BBOA) was present at most stations with the larger temporal variation throughout the year. Cooking-like OA (COA) only exited in cities (mostly in southern Europe). The median of normalized diurnals of HOA showed clear patterns with morning and evening rush-hour peaks in urban sites, while it did not show prominent morning peak in non-urban environments. However, both urban and non-urban sites showed similar patterns for BBOA, coal combustion OA (CCOA), and OOA factors.
These high-time resolution SA results from 22 ACSM/AMS long-term European datasets offer a more comprehensive picture of OA sources’ spatial and temporal variabilities. Air quality and climate modellers and policymakers can benefit from our results, as they provide essential knowledge to understand and eventually mitigate OA from different sources.