A new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to 1 year of organic aerosol data

A new methodology for performing long-term source apportionment (SA) using positive matrix factorization (PMF) is presented. The method is implemented within the SoFi Pro software package and uses the multilinear engine (ME-2) as a PMF solver. The technique is applied to a 1-year aerosol chemical sp...

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Bibliographic Details
Published in:Atmospheric measurement techniques 2021-02, Vol.14 (2), p.923-943
Main Authors: Canonaco, Francesco, Tobler, Anna, Chen, Gang, Sosedova, Yulia, Slowik, Jay Gates, Bozzetti, Carlo, Daellenbach, Kaspar Rudolf, El Haddad, Imad, Crippa, Monica, Huang, Ru-Jin, Furger, Markus, Baltensperger, Urs, Prévôt, André Stephan Henry
Format: Article
Language:English
Subjects:
Acceptance criteria
Aerosols
Algorithms
Apportionment
Atmospheric aerosols
Biomass burning
Black carbon
Burning
Chemical speciation
Computer software
Cooking
Correlation
Datasets
Diurnal cycle
Diurnal variations
Inspection
Mass spectra
Methods
Seasonal variations
Seasons
Secondary aerosols
Separation
Speciation
Summer
Tracers
Tracers (Biology)
Trends
Uncertainty
Visual inspection
Volatility
Warm seasons
Winter
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Summary:A new methodology for performing long-term source apportionment (SA) using positive matrix factorization (PMF) is presented. The method is implemented within the SoFi Pro software package and uses the multilinear engine (ME-2) as a PMF solver. The technique is applied to a 1-year aerosol chemical speciation monitor (ACSM) dataset from downtown Zurich, Switzerland.
ISSN:1867-8548
1867-1381
1867-8548
DOI:10.5194/amt-14-923-2021