Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia
<p>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 e...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2019-06-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/19/7279/2019/acp-19-7279-2019.pdf |
| _version_ | 1819060686254243840 |
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| author | A. Vlachou A. Tobler H. Lamkaddam F. Canonaco K. R. Daellenbach K. R. Daellenbach J.-L. Jaffrezo M. C. Minguillón M. Maasikmets E. Teinemaa U. Baltensperger I. El Haddad A. S. H. Prévôt |
| author_facet | A. Vlachou A. Tobler H. Lamkaddam F. Canonaco K. R. Daellenbach K. R. Daellenbach J.-L. Jaffrezo M. C. Minguillón M. Maasikmets E. Teinemaa U. Baltensperger I. El Haddad A. S. H. Prévôt |
| author_sort | A. Vlachou |
| collection | DOAJ |
| description | <p>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-Järve (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 % <span class="inline-formula">±</span> 21 % of the total OA, primary biological OA which was
abundant in Tartu and Tallinn in spring (21 % <span class="inline-formula">±</span> 8 % and
11 % <span class="inline-formula">±</span> 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 % <span class="inline-formula">±</span> 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 % <span class="inline-formula">±</span> 14 % for KJ,
25 % <span class="inline-formula">±</span> 9 % for Tallinn and 13 % <span class="inline-formula">±</span> 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 % <span class="inline-formula">±</span> 5 % in KJ, 41 % <span class="inline-formula">±</span> 7 % in Tallinn
and 35 % <span class="inline-formula">±</span> 7 % in Tartu) and exhibited high correlations with
oxidation products of <span class="inline-formula"><i>a</i></span>-pinene-like pinic acid and 3-methyl-1, 2,
3-butanetricarboxylic acid (MBTCA), suggesting a biogenic
origin.</p> |
| first_indexed | 2024-12-21T14:30:56Z |
| format | Article |
| id | doaj.art-281c9143d36f47099f11ea2125cd4f68 |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-12-21T14:30:56Z |
| publishDate | 2019-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-281c9143d36f47099f11ea2125cd4f682022-12-21T19:00:30ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242019-06-01197279729510.5194/acp-19-7279-2019Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in EstoniaA. Vlachou0A. Tobler1H. Lamkaddam2F. Canonaco3K. R. Daellenbach4K. R. Daellenbach5J.-L. Jaffrezo6M. C. Minguillón7M. Maasikmets8E. Teinemaa9U. Baltensperger10I. El Haddad11A. S. H. Prévôt12Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, 5232 Villigen PSI, SwitzerlandLaboratory of Atmospheric Chemistry, Paul Scherrer Institute, 5232 Villigen PSI, SwitzerlandLaboratory of Atmospheric Chemistry, Paul Scherrer Institute, 5232 Villigen PSI, SwitzerlandLaboratory of Atmospheric Chemistry, Paul Scherrer Institute, 5232 Villigen PSI, SwitzerlandLaboratory of Atmospheric Chemistry, Paul Scherrer Institute, 5232 Villigen PSI, Switzerlandnow at: Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, P.O. Box 64, 00014, Helsinki, FinlandUniversité Grenoble Alpes, CNRS, IRD, G-INP, IGE, 38000 Grenoble, FranceInstitute of Environmental Assessment and Water Research (IDAEA), CSIC, 08034 Barcelona, SpainEstonian Environmental Research Centre, 10617, Tallinn, EstoniaEstonian Environmental Research Centre, 10617, Tallinn, EstoniaLaboratory of Atmospheric Chemistry, Paul Scherrer Institute, 5232 Villigen PSI, SwitzerlandLaboratory of Atmospheric Chemistry, Paul Scherrer Institute, 5232 Villigen PSI, SwitzerlandLaboratory of Atmospheric Chemistry, Paul Scherrer Institute, 5232 Villigen PSI, Switzerland<p>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-Järve (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 % <span class="inline-formula">±</span> 21 % of the total OA, primary biological OA which was abundant in Tartu and Tallinn in spring (21 % <span class="inline-formula">±</span> 8 % and 11 % <span class="inline-formula">±</span> 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 % <span class="inline-formula">±</span> 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 % <span class="inline-formula">±</span> 14 % for KJ, 25 % <span class="inline-formula">±</span> 9 % for Tallinn and 13 % <span class="inline-formula">±</span> 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 % <span class="inline-formula">±</span> 5 % in KJ, 41 % <span class="inline-formula">±</span> 7 % in Tallinn and 35 % <span class="inline-formula">±</span> 7 % in Tartu) and exhibited high correlations with oxidation products of <span class="inline-formula"><i>a</i></span>-pinene-like pinic acid and 3-methyl-1, 2, 3-butanetricarboxylic acid (MBTCA), suggesting a biogenic origin.</p>https://www.atmos-chem-phys.net/19/7279/2019/acp-19-7279-2019.pdf |
| spellingShingle | A. Vlachou A. Tobler H. Lamkaddam F. Canonaco K. R. Daellenbach K. R. Daellenbach J.-L. Jaffrezo M. C. Minguillón M. Maasikmets E. Teinemaa U. Baltensperger I. El Haddad A. S. H. Prévôt Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia Atmospheric Chemistry and Physics |
| title | Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia |
| title_full | Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia |
| title_fullStr | Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia |
| title_full_unstemmed | Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia |
| title_short | Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia |
| title_sort | development of a versatile source apportionment analysis based on positive matrix factorization a case study of the seasonal variation of organic aerosol sources in estonia |
| url | https://www.atmos-chem-phys.net/19/7279/2019/acp-19-7279-2019.pdf |
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