TY - JOUR
T1 - Development of a versatile source apportionment analysis based on positive matrix factorization
T2 - a case study of the seasonal variation of organic aerosol sources in Estonia
AU - Vlachou, Athanasia
AU - Tobler, Anna
AU - Lamkaddam, Houssni
AU - Canonaco, Francesco
AU - Dällenbach, Kaspar
AU - Jaffrezo, Jean-Luc
AU - Cruz Minguillon, Maria
AU - Maasikmets, Marek
AU - Teinemaa, Erik
AU - Baltensperger, Urs
AU - El Haddad, Imad
AU - Prevot, Andre S. H.
PY - 2019/6/3
Y1 - 2019/6/3
N2 - Bootstrap analysis is commonly used to capture the uncertainties of a bilinear receptor model such as the positive matrix factorization (PMF) model. This approach can estimate the factor-related uncertainties and partially assess the rotational ambiguity of the model. The selection of the environmentally plausible solutions, though, can be challenging, and a systematic approach to identify and sort the factors is needed. For this, comparison of the factors between each bootstrap run and the initial PMF output, as well as with externally determined markers, is crucial. As a result, certain solutions that exhibit suboptimal factor separation should be discarded. The retained solutions would then be used to test the robustness of the PMF output. Meanwhile, analysis of filter samples with the Aerodyne aerosol mass spectrometer and the application of PMF and bootstrap analysis on the bulk water-soluble organic aerosol mass spectra have provided insight into the source identification and their uncertainties. Here, we investigated a full yearly cycle of the sources of organic aerosol (OA) at three sites in Estonia: Tallinn (urban), Tartu (suburban) and Kohtla-Jarve (KJ; industrial). We identified six OA sources and an inorganic dust factor. The primary OA types included biomass burning, dominant in winter in Tartu and accounting for 73 % +/- 21 % of the total OA, primary biological OA which was abundant in Tartu and Tallinn in spring (21 % +/- 8 % and 11 % +/- 5 %, respectively), and two other primary OA types lower in mass. A sulfur-containing OA was related to road dust and tire abrasion which exhibited a rather stable yearly cycle, and an oil OA was connected to the oil shale industries in KJ prevailing at this site that comprises 36 % +/- 14 % of the total OA in spring. The secondary OA sources were separated based on their seasonal behavior: a winter oxygenated OA dominated in winter (36 % +/- 14 % for KJ, 25 % +/- 9 % for Tallinn and 13 % +/- 5 % for Tartu) and was correlated with benzoic and phthalic acid, implying an anthropogenic origin. A summer oxygenated OA was the main source of OA in summer at all sites (26 % +/- 5 % in KJ, 41 % +/- 7 % in Tallinn and 35 % +/- 7 % in Tartu) and exhibited high correlations with oxidation products of a-pinene-like pinic acid and 3-methyl-1, 2, 3-butanetricarboxylic acid (MBTCA), suggesting a biogenic origin.
AB - Bootstrap analysis is commonly used to capture the uncertainties of a bilinear receptor model such as the positive matrix factorization (PMF) model. This approach can estimate the factor-related uncertainties and partially assess the rotational ambiguity of the model. The selection of the environmentally plausible solutions, though, can be challenging, and a systematic approach to identify and sort the factors is needed. For this, comparison of the factors between each bootstrap run and the initial PMF output, as well as with externally determined markers, is crucial. As a result, certain solutions that exhibit suboptimal factor separation should be discarded. The retained solutions would then be used to test the robustness of the PMF output. Meanwhile, analysis of filter samples with the Aerodyne aerosol mass spectrometer and the application of PMF and bootstrap analysis on the bulk water-soluble organic aerosol mass spectra have provided insight into the source identification and their uncertainties. Here, we investigated a full yearly cycle of the sources of organic aerosol (OA) at three sites in Estonia: Tallinn (urban), Tartu (suburban) and Kohtla-Jarve (KJ; industrial). We identified six OA sources and an inorganic dust factor. The primary OA types included biomass burning, dominant in winter in Tartu and accounting for 73 % +/- 21 % of the total OA, primary biological OA which was abundant in Tartu and Tallinn in spring (21 % +/- 8 % and 11 % +/- 5 %, respectively), and two other primary OA types lower in mass. A sulfur-containing OA was related to road dust and tire abrasion which exhibited a rather stable yearly cycle, and an oil OA was connected to the oil shale industries in KJ prevailing at this site that comprises 36 % +/- 14 % of the total OA in spring. The secondary OA sources were separated based on their seasonal behavior: a winter oxygenated OA dominated in winter (36 % +/- 14 % for KJ, 25 % +/- 9 % for Tallinn and 13 % +/- 5 % for Tartu) and was correlated with benzoic and phthalic acid, implying an anthropogenic origin. A summer oxygenated OA was the main source of OA in summer at all sites (26 % +/- 5 % in KJ, 41 % +/- 7 % in Tallinn and 35 % +/- 7 % in Tartu) and exhibited high correlations with oxidation products of a-pinene-like pinic acid and 3-methyl-1, 2, 3-butanetricarboxylic acid (MBTCA), suggesting a biogenic origin.
KW - MASS-SPECTROMETRY
KW - ELEMENTAL CARBON
KW - OFFLINE-AMS
KW - CHEMICAL-COMPOSITION
KW - MULTILINEAR ENGINE
KW - DICARBOXYLIC-ACIDS
KW - NONFOSSIL SOURCES
KW - 9 SITES
KW - URBAN
KW - QUANTIFICATION
KW - 116 Chemical sciences
KW - 114 Physical sciences
KW - 1172 Environmental sciences
U2 - 10.5194/acp-19-7279-2019
DO - 10.5194/acp-19-7279-2019
M3 - Article
VL - 19
SP - 7279
EP - 7295
JO - Atmospheric Chemistry and Physics
JF - Atmospheric Chemistry and Physics
SN - 1680-7316
IS - 11
ER -