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ACTRIS ACSM intercomparison - Part 2: Intercomparison of ME-2 organic source apportionment results from 15 individual, co-located aerosol mass spectrometers

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TLDR
In this article, the authors report on the results from factor analysis source apportionment by positive matrix factorization (PMF) utilising the multilinear engine 2 (ME-2).
Abstract
. Chemically resolved atmospheric aerosol data sets from the largest intercomparison of the Aerodyne aerosol chemical speciation monitors (ACSMs) performed to date were collected at the French atmospheric supersite SIRTA. In total 13 quadrupole ACSMs (Q-ACSM) from the European ACTRIS ACSM network, one time-of-flight ACSM (ToF-ACSM), and one high-resolution ToF aerosol mass spectrometer (AMS) were operated in parallel for about 3 weeks in November and December~2013. Part 1 of this study reports on the accuracy and precision of the instruments for all the measured species. In this work we report on the intercomparison of organic components and the results from factor analysis source apportionment by positive matrix factorisation (PMF) utilising the multilinear engine 2 (ME-2). Except for the organic contribution of mass-to-charge ratio m/z 44 to the total organics (f44), which varied by factors between 0.6 and 1.3 compared to the mean, the peaks in the organic mass spectra were similar among instruments. The m/z 44 differences in the spectra resulted in a variable f44 in the source profiles extracted by ME-2, but had only a minor influence on the extracted mass contributions of the sources. The presented source apportionment yielded four factors for all 15 instruments: hydrocarbon-like organic aerosol (HOA), cooking-related organic aerosol (COA), biomass burning-related organic aerosol (BBOA) and secondary oxygenated organic aerosol (OOA). ME-2 boundary conditions (profile constraints) were optimised individually by means of correlation to external data in order to achieve equivalent / comparable solutions for all ACSM instruments and the results are discussed together with the investigation of the influence of alternative anchors (reference profiles). A comparison of the ME-2 source apportionment output of all 15 instruments resulted in relative standard deviations (SD) from the mean between 13.7 and 22.7 % of the source's average mass contribution depending on the factors (HOA: 14.3 ± 2.2 %, COA: 15.0 ± 3.4 %, OOA: 41.5 ± 5.7 %, BBOA: 29.3 ± 5.0 %). Factors which tend to be subject to minor factor mixing (in this case COA) have higher relative uncertainties than factors which are recognised more readily like the OOA. Averaged over all factors and instruments the relative first SD from the mean of a source extracted with ME-2 was 17.2 %.

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Journal ArticleDOI

Primary and secondary aerosols in Beijing in winter: sources, variations andprocesses

TL;DR: In this paper, a comprehensive characterization of the sources, variations and processes of submicron aerosols were measured by an Aerodyne high-resolution aerosol mass spectrometer from 17 December 2013 to 17 January 2014 along with offline filter analysis by gas chromatography/mass spectrometry.
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Characterization of a real-time tracer for isoprene epoxydiols-derived secondary organic aerosol (IEPOX-SOA) from aerosol mass spectrometer measurements

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TL;DR: In this article, the authors applied positive matrix factorization (PMF) to aerosol mass spectrometer (AMS) measurements to identify secondary organic aerosol (SOA) formed from isoprene epoxydiols (IEPOX).
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European guide on air pollution source apportionment with receptor models.

TL;DR: In this article, the authors present a guide and a European harmonised protocol for the identification of air pollution sources using receptor models, which aims at disseminating and promoting the best available methodologies for source identification and at harmonising their application across Europe.
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Characterization and source apportionment of organic aerosol using offline aerosol mass spectrometry

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Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values†

TL;DR: In this paper, a new variant of Factor Analysis (PMF) is described, where the problem is solved in the weighted least squares sense: G and F are determined so that the Frobenius norm of E divided (element-by-element) by σ is minimized.
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