Abstract

Abstract. Factor analysis of aerosol mass spectrometer measurements (organic aerosol mass spectra) is often used to determine the sources of organic aerosol (OA). In this study we aim to gain insights regarding the ability of positive matrix factorization (PMF) to identify and quantify the OA sources accurately. We performed PMF and multilinear engine (ME-2) analysis on the predictions of a state-of-the-art chemical transport model (PMCAMx-SR, Particulate Matter Comprehensive Air Quality Model with extensions – source resolved) during a photochemically active period for specific sites in Europe in an effort to interpret the diverse factors usually identified by PMF analysis of field measurements. Our analysis used the predicted concentrations of 27 OA components, assuming that each of them is “chemically different” from the others. The PMF results based on the chemical transport model predictions are quite consistent (same number of factors and source types) with those of the analysis of AMS measurements. The estimated uncertainty of the contribution of fresh biomass burning is less than 30 % and of the other primary sources less than 40 %, when these sources contribute more than 20 % to the total OA. The PMF uncertainty increases for smaller source contributions, reaching a factor of 2 or even 3 for sources which contribute less than 10 % to the OA. One of the major questions in PMF analysis of AMS measurements concerns the sources of the two or more oxygenated OA (OOA) factors often reported in field studies. Our analysis suggests that these factors include secondary OA compounds from a variety of anthropogenic and biogenic sources and do not correspond to specific sources. Their characterization in the literature as low- and high-volatility factors is probably misleading, because they have overlapping volatility distributions. However, the average volatility of the one often characterized as a low-volatility factor is indeed lower than that of the other (high-volatility factor). Based on the analysis of the PMCAMx-SR predictions, the first oxygenated OA factor includes mainly highly aged OA transported from outside Europe, but also highly aged secondary OA from precursors emitted in Europe. The second oxygenated OA factor contains fresher secondary organic aerosol from volatile, semivolatile, and intermediate volatility anthropogenic and biogenic organic compounds. The exact contribution of these OA components to each OA factor depends on the site and the prevailing meteorology during the analysis period.

Highlights

  • Exposure to high levels of fine atmospheric particles results in increased mortality and morbidity (Pope et al, 2009)

  • PMCAMx-SR predicted that long-rangetransported Organic aerosol (OA) contributed 24 %, biogenic SOA 23 %, SOA from semivolatile organic compounds (SVOCs) and intermediate volatility organic compounds (IVOCs) 20 %, anthropogenic SOA 18 %, biomass burning SOA 10 %, POA 3 % and biomass burning POA 2 % to the total OA

  • We analyzed for the first time, to the best of our knowledge, the organic aerosol composition predictions of a chemical transport model (PMCAMx-SR) using positive matrix factorization in an effort to better understand the results of PMF analysis of ambient organic aerosol aerosol mass spectrometer (AMS) measurements

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Summary

Introduction

Exposure to high levels of fine atmospheric particles results in increased mortality and morbidity (Pope et al, 2009). Organic aerosol (OA) represents an important fraction (20 % to 90 %) of fine particulate matter (Kanakidou et al, 2005; Zhang et al, 2007) and is generated from biogenic and anthropogenic sources (de Gouw and Jimenez, 2009). It is usually characterized as primary (POA) when it is emitted directly in the particulate phase and secondary (SOA) when formed during. Drosatou et al.: Positive matrix factorization of organic aerosol the atmospheric oxidation of volatile, intermediate-volatility, and semivolatile organic components

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