Positive matrix factorization of organic aerosol: insights from a chemical transport model
<p>Factor analysis of aerosol mass spectrometer measurements (organic aerosol mass spectra) is often used to determine the sources of organic aerosol (OA). In this study we aim to gain insights regarding the ability of positive matrix factorization (PMF) to identify and quantify the OA sources...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2019-01-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/19/973/2019/acp-19-973-2019.pdf |
| _version_ | 1828389255087390720 |
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| author | A. D. Drosatou A. D. Drosatou K. Skyllakou G. N. Theodoritsi G. N. Theodoritsi S. N. Pandis S. N. Pandis S. N. Pandis |
| author_facet | A. D. Drosatou A. D. Drosatou K. Skyllakou G. N. Theodoritsi G. N. Theodoritsi S. N. Pandis S. N. Pandis S. N. Pandis |
| author_sort | A. D. Drosatou |
| collection | DOAJ |
| description | <p>Factor analysis of aerosol mass spectrometer measurements
(organic aerosol mass spectra) is often used to determine the sources of
organic aerosol (OA). In this study we aim to gain insights regarding the
ability of positive matrix factorization (PMF) to identify and quantify the
OA sources accurately. We performed PMF and multilinear engine (ME-2)
analysis on the predictions of a state-of-the-art chemical transport model
(PMCAMx-SR, Particulate Matter Comprehensive Air Quality Model with
extensions – source resolved) during a photochemically active period for
specific sites in Europe in an effort to interpret the diverse factors
usually identified by PMF analysis of field measurements. Our analysis used
the predicted concentrations of 27 OA components, assuming that each of them
is “chemically different” from the others.</p>
<p>The PMF results based on the chemical transport model predictions are quite
consistent (same number of factors and source types) with those of the
analysis of AMS measurements. The estimated uncertainty of the contribution
of fresh biomass burning is less than 30 % and of the other primary
sources less than 40 %, when these sources contribute more than 20 % to
the total OA. The PMF uncertainty increases for smaller source
contributions, reaching a factor of 2 or even 3 for sources which
contribute less than 10 % to the OA.</p>
<p>One of the major questions in PMF analysis of AMS measurements concerns the
sources of the two or more oxygenated OA (OOA) factors often reported in
field studies. Our analysis suggests that these factors include secondary OA
compounds from a variety of anthropogenic and biogenic sources and do not
correspond to specific sources. Their characterization in the literature as
low- and high-volatility factors is probably misleading, because they have
overlapping volatility distributions. However, the average volatility of the
one often characterized as a low-volatility factor is indeed lower than that
of the other (high-volatility factor). Based on the analysis of the
PMCAMx-SR predictions, the first oxygenated OA factor includes mainly
highly aged OA transported from outside Europe, but also highly aged
secondary OA from precursors emitted in Europe. The second oxygenated OA
factor contains fresher secondary organic aerosol from volatile, semivolatile, and intermediate
volatility anthropogenic and biogenic organic compounds. The exact
contribution of these OA components to each OA factor depends on the site
and the prevailing meteorology during the analysis period.</p> |
| first_indexed | 2024-12-10T06:26:44Z |
| format | Article |
| id | doaj.art-666913e9289d46799028fc0b8e093e03 |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-12-10T06:26:44Z |
| publishDate | 2019-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-666913e9289d46799028fc0b8e093e032022-12-22T01:59:11ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242019-01-011997398610.5194/acp-19-973-2019Positive matrix factorization of organic aerosol: insights from a chemical transport modelA. D. Drosatou0A. D. Drosatou1K. Skyllakou2G. N. Theodoritsi3G. N. Theodoritsi4S. N. Pandis5S. N. Pandis6S. N. Pandis7Department of Chemical Engineering, University of Patras, Patras, GreeceInstitute of Chemical Engineering Sciences, Foundation for Research and Technology Hellas (FORTH/ICE-HT), Patras, GreeceInstitute of Chemical Engineering Sciences, Foundation for Research and Technology Hellas (FORTH/ICE-HT), Patras, GreeceDepartment of Chemical Engineering, University of Patras, Patras, GreeceInstitute of Chemical Engineering Sciences, Foundation for Research and Technology Hellas (FORTH/ICE-HT), Patras, GreeceDepartment of Chemical Engineering, University of Patras, Patras, GreeceInstitute of Chemical Engineering Sciences, Foundation for Research and Technology Hellas (FORTH/ICE-HT), Patras, GreeceDepartment of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA<p>Factor analysis of aerosol mass spectrometer measurements (organic aerosol mass spectra) is often used to determine the sources of organic aerosol (OA). In this study we aim to gain insights regarding the ability of positive matrix factorization (PMF) to identify and quantify the OA sources accurately. We performed PMF and multilinear engine (ME-2) analysis on the predictions of a state-of-the-art chemical transport model (PMCAMx-SR, Particulate Matter Comprehensive Air Quality Model with extensions – source resolved) during a photochemically active period for specific sites in Europe in an effort to interpret the diverse factors usually identified by PMF analysis of field measurements. Our analysis used the predicted concentrations of 27 OA components, assuming that each of them is “chemically different” from the others.</p> <p>The PMF results based on the chemical transport model predictions are quite consistent (same number of factors and source types) with those of the analysis of AMS measurements. The estimated uncertainty of the contribution of fresh biomass burning is less than 30 % and of the other primary sources less than 40 %, when these sources contribute more than 20 % to the total OA. The PMF uncertainty increases for smaller source contributions, reaching a factor of 2 or even 3 for sources which contribute less than 10 % to the OA.</p> <p>One of the major questions in PMF analysis of AMS measurements concerns the sources of the two or more oxygenated OA (OOA) factors often reported in field studies. Our analysis suggests that these factors include secondary OA compounds from a variety of anthropogenic and biogenic sources and do not correspond to specific sources. Their characterization in the literature as low- and high-volatility factors is probably misleading, because they have overlapping volatility distributions. However, the average volatility of the one often characterized as a low-volatility factor is indeed lower than that of the other (high-volatility factor). Based on the analysis of the PMCAMx-SR predictions, the first oxygenated OA factor includes mainly highly aged OA transported from outside Europe, but also highly aged secondary OA from precursors emitted in Europe. The second oxygenated OA factor contains fresher secondary organic aerosol from volatile, semivolatile, and intermediate volatility anthropogenic and biogenic organic compounds. The exact contribution of these OA components to each OA factor depends on the site and the prevailing meteorology during the analysis period.</p>https://www.atmos-chem-phys.net/19/973/2019/acp-19-973-2019.pdf |
| spellingShingle | A. D. Drosatou A. D. Drosatou K. Skyllakou G. N. Theodoritsi G. N. Theodoritsi S. N. Pandis S. N. Pandis S. N. Pandis Positive matrix factorization of organic aerosol: insights from a chemical transport model Atmospheric Chemistry and Physics |
| title | Positive matrix factorization of organic aerosol: insights from a chemical transport model |
| title_full | Positive matrix factorization of organic aerosol: insights from a chemical transport model |
| title_fullStr | Positive matrix factorization of organic aerosol: insights from a chemical transport model |
| title_full_unstemmed | Positive matrix factorization of organic aerosol: insights from a chemical transport model |
| title_short | Positive matrix factorization of organic aerosol: insights from a chemical transport model |
| title_sort | positive matrix factorization of organic aerosol insights from a chemical transport model |
| url | https://www.atmos-chem-phys.net/19/973/2019/acp-19-973-2019.pdf |
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