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

被引：61

作者：

Canonaco, Francesco ^{[1
,2
]}

Tobler, Anna ^{[1
,2
]}

Chen, Gang ^{[2
]}

Sosedova, Yulia ^{[1
]}

Slowik, Jay Gates ^{[2
]}

Bozzetti, Carlo ^{[1
]}

Daellenbach, Kaspar Rudolf ^{[2
,3
]}

El Haddad, Imad ^{[2
]}

Crippa, Monica ^{[4
]}

Huang, Ru-Jin ^{[5
,6
]}

Furger, Markus ^{[2
]}

Baltensperger, Urs ^{[2
]}

Prevot, Andre Stephan Henry ^{[2
]}

机构：

[1] 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

来源：

ATMOSPHERIC MEASUREMENT TECHNIQUES | 2021年 / 14卷 / 02期

基金：

瑞士国家科学基金会; 欧盟地平线“2020”;

关键词：

CHEMICAL SPECIATION MONITOR; POSITIVE MATRIX FACTORIZATION; EESI-TOF-MS; MASS-SPECTROMETER; MULTILINEAR ENGINE; RESOLVED MEASUREMENTS; PARTICULATE MATTER; AIR-POLLUTION; WINTERTIME; COOKING;

D O I：

10.5194/amt-14-923-2021

中图分类号：

P4 [大气科学（气象学）];

学科分类号：

0706 ; 070601 ;

摘要：

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. 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.

引用

页码：923 / 943

页数：21

共 67 条

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

Froehlich, R. ;

Crenn, V. ;

Setyan, A. ;

Belis, C. A. ;

Canonaco, F. ;

Favez, O. ;

Riffault, V. ;

Slowik, J. G. ;

Aas, W. ;

Aijala, M. ;

Alastuey, A. ;

Artinano, B. ;

Bonnaire, N. ;

Bozzetti, C. ;

Bressi, M. ;

Carbone, C. ;

Coz, E. ;

Croteau, P. L. ;

Cubison, M. J. ;

Esser-Gietl, J. K. ;

Green, D. C. ;

Gros, V. ;

Heikkinen, L. ;

Herrmann, H. ;

Jayne, J. T. ;

Lunder, C. R. ;

Minguillon, M. C. ;

Mocnik, G. ;

O'Dowd, C. D. ;

Ovadnevaite, J. ;

Petralia, E. ;

Poulain, L. ;

Priestman, M. ;

Ripoll, A. ;

Sarda-Esteve, R. ;

Wiedensohler, A. ;

Baltensperger, U. ;

Sciare, J. ;

Prevot, A. S. H. .

ATMOSPHERIC MEASUREMENT TECHNIQUES, 2015, 8 (06) :2555-2576

[22] The ToF-ACSM: a portable aerosol chemical speciation monitor with TOFMS detection [J].

Froehlich, R. ;

Cubison, M. J. ;

Slowik, J. G. ;

Bukowiecki, N. ;

Prevot, A. S. H. ;

Baltensperger, U. ;

Schneider, J. ;

Kimmel, J. R. ;

Gonin, M. ;

Rohner, U. ;

Worsnop, D. R. ;

Jayne, J. T. .

ATMOSPHERIC MEASUREMENT TECHNIQUES, 2013, 6 (11) :3225-3241

[23] The formation, properties and impact of secondary organic aerosol: current and emerging issues [J].

Hallquist, M. ;

Wenger, J. C. ;

Baltensperger, U. ;

Rudich, Y. ;

Simpson, D. ;

Claeys, M. ;

Dommen, J. ;

Donahue, N. M. ;

George, C. ;

Goldstein, A. H. ;

Hamilton, J. F. ;

Herrmann, H. ;

Hoffmann, T. ;

Iinuma, Y. ;

Jang, M. ;

Jenkin, M. E. ;

Jimenez, J. L. ;

Kiendler-Scharr, A. ;

Maenhaut, W. ;

McFiggans, G. ;

Mentel, Th. F. ;

Monod, A. ;

Prevot, A. S. H. ;

Seinfeld, J. H. ;

Surratt, J. D. ;

Szmigielski, R. ;

Wildt, J. .

ATMOSPHERIC CHEMISTRY AND PHYSICS, 2009, 9 (14) :5155-5236

[24] An evaluation of some issues regarding the use of aethalometers to measure woodsmoke concentrations [J].

Harrison, Roy M. ;

Beddows, David C. S. ;

Jones, Alan M. ;

Calvo, Ana ;

Alves, Celia ;

Pio, Casimiro .

ATMOSPHERIC ENVIRONMENT, 2013, 80 :540-548

[25] Characterization of high-resolution aerosol mass spectra of primary organic aerosol emissions from Chinese cooking and biomass burning [J].

He, L. -Y. ;

Lin, Y. ;

Huang, X. -F. ;

Guo, S. ;

Xue, L. ;

Su, Q. ;

Hu, M. ;

Luan, S. -J. ;

Zhang, Y. -H. .

ATMOSPHERIC CHEMISTRY AND PHYSICS, 2010, 10 (23) :11535-11543

[26] A 2.5 year's source apportionment study of black carbon from wood burning and fossil fuel combustion at urban and rural sites in Switzerland [J].

Herich, H. ;

Hueglin, C. ;

Buchmann, B. .

ATMOSPHERIC MEASUREMENT TECHNIQUES, 2011, 4 (07) :1409-1420

[27] Investigations of primary and secondary particulate matter of different wood combustion appliances with a high-resolution time-of-flight aerosol mass spectrometer [J].

Heringa, M. F. ;

DeCarlo, P. F. ;

Chirico, R. ;

Tritscher, T. ;

Dommen, J. ;

Weingartner, E. ;

Richter, R. ;

Wehrle, G. ;

Prevot, A. S. H. ;

Baltensperger, U. .

ATMOSPHERIC CHEMISTRY AND PHYSICS, 2011, 11 (12) :5945-5957

[28] Development of an aerosol mass spectrometer for size and composition analysis of submicron particles [J].

Jayne, JT ;

Leard, DC ;

Zhang, XF ;

Davidovits, P ;

Smith, KA ;

Kolb, CE ;

Worsnop, DR .

AEROSOL SCIENCE AND TECHNOLOGY, 2000, 33 (1-2) :49-70

[29] Evolution of Organic Aerosols in the Atmosphere [J].

Jimenez, J. L. ;

Canagaratna, M. R. ;

Donahue, N. M. ;

Prevot, A. S. H. ;

Zhang, Q. ;

Kroll, J. H. ;

DeCarlo, P. F. ;

Allan, J. D. ;

Coe, H. ;

Ng, N. L. ;

Aiken, A. C. ;

Docherty, K. S. ;

Ulbrich, I. M. ;

Grieshop, A. P. ;

Robinson, A. L. ;

Duplissy, J. ;

Smith, J. D. ;

Wilson, K. R. ;

Lanz, V. A. ;

Hueglin, C. ;

Sun, Y. L. ;

Tian, J. ;

Laaksonen, A. ;

Raatikainen, T. ;

Rautiainen, J. ;

Vaattovaara, P. ;

Ehn, M. ;

Kulmala, M. ;

Tomlinson, J. M. ;

Collins, D. R. ;

Cubison, M. J. ;

Dunlea, E. J. ;

Huffman, J. A. ;

Onasch, T. B. ;

Alfarra, M. R. ;

Williams, P. I. ;

Bower, K. ;

Kondo, Y. ;

Schneider, J. ;

Drewnick, F. ;

Borrmann, S. ;

Weimer, S. ;

Demerjian, K. ;

Salcedo, D. ;

Cottrell, L. ;

Griffin, R. ;

Takami, A. ;

Miyoshi, T. ;

Hatakeyama, S. ;

Shimono, A. .

SCIENCE, 2009, 326 (5959) :1525-1529

[30] Characterization of Gas-Phase Organics Using Proton Transfer Reaction Time-of-Flight Mass Spectrometry: Cooking Emissions [J].

Klein, Felix ;

Platt, Stephen M. ;

Farren, Naomi J. ;

Detournay, Anais ;

Bruns, Emily A. ;

Bozzetti, Carlo ;

Daellenbach, Kaspar R. ;

Kilic, Dogushan ;

Kumar, Nivedita K. ;

Pieber, Simone M. ;

Slowik, Jay G. ;

Temime-Roussel, Brice ;

Marchand, Nicolas ;

Hamilton, Jacqueline F. ;

Baltensperger, Urs ;

Prevot, Andre S. H. ;

El Haddad, Imad .

ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2016, 50 (03) :1243-1250

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