IEECAS OpenIR  > 粉尘与环境研究室
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
Canonaco, Francesco1,2; Tobler, Anna1,2; Chen, Gang2; Sosedova, Yulia1; Slowik, Jay Gates2; Bozzetti, Carlo1; Daellenbach, Kaspar Rudolf2,3; El Haddad, Imad2; Crippa, Monica4; Huang, Ru-Jin5,6; Furger, Markus2; Baltensperger, Urs2; Prevot, Andre Stephan Henry2
Corresponding AuthorPrevot, Andre Stephan Henry(andre.prevot@psi.ch)
2021-02-08
Source PublicationATMOSPHERIC MEASUREMENT TECHNIQUES
ISSN1867-1381
Volume14Issue:2Pages:923-943
AbstractA 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. The measured organic aerosol mass spectra were analyzed by PMF using a small (14 d) and rolling PMF window to account for the temporal evolution of the sources. The rotational ambiguity is explored and the uncertainties of the PMF solutions were estimated. Factor-tracer correlations for averaged seasonal results from the rolling window analysis are higher than those retrieved from conventional PMF analyses of individual seasons, highlighting the improved performance of the rolling window algorithm for long-term data. In this study four to five factors were tested for every PMF window. Factor profiles for primary organic aerosol from traffic (HOA), cooking (COA) and biomass burning (BBOA) were constrained. Secondary organic aerosol was represented by either the combination of semi-volatile and low-volatility organic aerosol (SV-OOA and LV-OOA, respectively) or by a single OOA when this separation was not robust. This scheme led to roughly 40 000 PMF runs. Full visual inspection of all these PMF runs is unrealistic and is replaced by predefined user-selected criteria, which allow factor sorting and PMF run acceptance/rejection. The selected criteria for traffic (HOA) and BBOA were the correlation with equivalent black carbon from traffic (eBC(tr)) and the explained variation of m/z 60, respectively. COA was assessed by the prominence of a lunchtime concentration peak within the diurnal cycle. SV-OOA and LV-OOA were evaluated based on the fractions of m/z 43 and 44 in their respective factor profiles. Seasonal pre-tests revealed a noncontinuous separation of OOA into SV-OOA and LV-OOA, in particular during the warm seasons. Therefore, a differentiation between four-factor solutions (HOA, COA, BBOA and OOA) and five-factor solutions (HOA, COA, BBOA, SVOOA and LV-OOA) was also conducted based on the criterion for SV-OOA. HOA and COA contribute between 0.4-0.7 mu g m(-3) (7.8 %-9.0 %) and 0.7-1.2 mu g m(-3) (12.2 %-15.7 %) on average throughout the year, respectively. BBOA shows a strong yearly cycle with the lowest mean concentrations in summer (0.6 mu g m(-3), 12.0 %), slightly higher mean concentrations during spring and fall (1.0 and 1.5 mu g m(-3), or 15.6% and 18.6 %, respectively), and the highest mean concentrations during winter (1.9 mu g m(-3), 25.0 %). In summer, OOA is separated into SV-OOA and LV-OOA, with mean concentrations of 1.4 mu g m(-3) (26.5 %) and 2.2 mu g m(-3) (40.3 %), respectively. For the remaining seasons the seasonal concentrations of SV-OOA, LV-OOA and OOA range from 0.3 to 1.1 mu g m(-3) (3.4 %-15.9 %), from 0.6 to 2.2 mu g m(-3) (7.7 %33.7 %) and from 0.9 to 3.1 mu g m(-3) (13.7 %-39.9 %), respectively. The relative PMF errors modeled for this study for HOA, COA, BBOA, LV-OOA, SV-OOA and OOA are on average +/- 34 %, +/- 27 %, +/- 30 %, +/- 11 %, +/- 25 % and +/- 12 %, respectively.
DOI10.5194/amt-14-923-2021
WOS KeywordCHEMICAL SPECIATION MONITOR ; POSITIVE MATRIX FACTORIZATION ; EESI-TOF-MS ; MASS-SPECTROMETER ; MULTILINEAR ENGINE ; RESOLVED MEASUREMENTS ; PARTICULATE MATTER ; AIR-POLLUTION ; WINTERTIME ; COOKING
Indexed BySCI ; SCI
Language英语
Funding ProjectCOST action Chemical On-Line cOmpoSition and Source Apportionment of fine aerosoLs (COLOSSAL)[CA16109] ; SNF COST project SAMSAM[IZCOZO_177063] ; SNF project Haze pollution in China: Sources and atmospheric evolution of particulate matter (HAZECHINA)[IZLCZ2_169986] ; EU Horizon 2020 Framework Programme via the ERA-PLANET and transnational project SMURBS[689443] ; Swiss State Secretariat for Education, Research and Innovation (SERI)[15.0159-1] ; Swiss State Secretariat for Education, Research and Innovation (SERI)[15.0329-1]
WOS Research AreaMeteorology & Atmospheric Sciences
Funding OrganizationCOST action Chemical On-Line cOmpoSition and Source Apportionment of fine aerosoLs (COLOSSAL) ; SNF COST project SAMSAM ; SNF project Haze pollution in China: Sources and atmospheric evolution of particulate matter (HAZECHINA) ; EU Horizon 2020 Framework Programme via the ERA-PLANET and transnational project SMURBS ; Swiss State Secretariat for Education, Research and Innovation (SERI)
WOS SubjectMeteorology & Atmospheric Sciences
WOS IDWOS:000618561800001
PublisherCOPERNICUS GESELLSCHAFT MBH
Citation statistics
Cited Times:13[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ieecas.cn/handle/361006/16044
Collection粉尘与环境研究室
Corresponding AuthorPrevot, Andre Stephan Henry
Affiliation1.Datalyst Ltd, Pk InnovAARE, CH-5234 Villigen, Switzerland
2.Paul Scherrer Inst, Lab Atmospher Chem, CH-5232 Villigen, Switzerland
3.Inst Atmospher & Earth Syst Res, Helsinki, Finland
4.European Commiss, Joint Res Ctr JRC, Via Fermi 2749, I-21027 Ispra, Italy
5.Chinese Acad Sci, State Key Lab Loess & Quaternary Geol, Ctr Excellence Quaternary Sci & Global Change, Xian 710061, Peoples R China
6.Chinese Acad Sci, Key Lab Aerosol Chem & Phys, Inst Earth Environm, Xian 710061, Peoples R China
Recommended Citation
GB/T 7714
Canonaco, Francesco,Tobler, Anna,Chen, Gang,et al. 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[J]. ATMOSPHERIC MEASUREMENT TECHNIQUES,2021,14(2):923-943.
APA Canonaco, Francesco.,Tobler, Anna.,Chen, Gang.,Sosedova, Yulia.,Slowik, Jay Gates.,...&Prevot, Andre Stephan Henry.(2021).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.ATMOSPHERIC MEASUREMENT TECHNIQUES,14(2),923-943.
MLA Canonaco, Francesco,et al."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".ATMOSPHERIC MEASUREMENT TECHNIQUES 14.2(2021):923-943.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Canonaco, Francesco]'s Articles
[Tobler, Anna]'s Articles
[Chen, Gang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Canonaco, Francesco]'s Articles
[Tobler, Anna]'s Articles
[Chen, Gang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Canonaco, Francesco]'s Articles
[Tobler, Anna]'s Articles
[Chen, Gang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.