Mapping gas-phase organic reactivity and concomitant secondary organic aerosol formation: chemometric dimension reduction techniques for the deconvolution of complex atmospheric data sets
Highly non-linear dynamical systems, such as those found in atmospheric chemistry, necessitate hierarchical approaches to both experiment and modelling in order to ultimately identify and achieve fundamental process-understanding in the full open system. Atmospheric simulation chambers comprise an i...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2015-07-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | http://www.atmos-chem-phys.net/15/8077/2015/acp-15-8077-2015.pdf |
| _version_ | 1819298315538268160 |
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| author | K. P. Wyche P. S. Monks K. L. Smallbone J. F. Hamilton M. R. Alfarra A. R. Rickard G. B. McFiggans M. E. Jenkin W. J. Bloss A. C. Ryan C. N. Hewitt A. R. MacKenzie |
| author_facet | K. P. Wyche P. S. Monks K. L. Smallbone J. F. Hamilton M. R. Alfarra A. R. Rickard G. B. McFiggans M. E. Jenkin W. J. Bloss A. C. Ryan C. N. Hewitt A. R. MacKenzie |
| author_sort | K. P. Wyche |
| collection | DOAJ |
| description | Highly non-linear dynamical systems, such as those found in atmospheric
chemistry, necessitate hierarchical approaches to both experiment and
modelling in order to ultimately identify and achieve fundamental
process-understanding in the full open system. Atmospheric simulation
chambers comprise an intermediate in complexity, between a classical
laboratory experiment and the full, ambient system. As such, they can
generate large volumes of difficult-to-interpret data. Here we describe and
implement a chemometric dimension reduction methodology for the deconvolution
and interpretation of complex gas- and particle-phase composition spectra.
The methodology comprises principal component analysis (PCA), hierarchical
cluster analysis (HCA) and positive least-squares discriminant analysis
(PLS-DA). These methods are, for the first time, applied to simultaneous gas-
and particle-phase composition data obtained from a comprehensive series of
environmental simulation chamber experiments focused on biogenic volatile
organic compound (BVOC) photooxidation and associated secondary organic
aerosol (SOA) formation. We primarily investigated the biogenic SOA
precursors isoprene, α-pinene, limonene, myrcene, linalool and
β-caryophyllene. The chemometric analysis is used to classify the
oxidation systems and resultant SOA according to the controlling chemistry
and the products formed. Results show that "model" biogenic oxidative
systems can be successfully separated and classified according to their
oxidation products. Furthermore, a holistic view of results obtained across
both the gas- and particle-phases shows the different SOA formation
chemistry, initiating in the gas-phase, proceeding to govern the differences
between the various BVOC SOA compositions. The results obtained are used to
describe the particle composition in the context of the oxidised gas-phase
matrix. An extension of the technique, which incorporates into the
statistical models data from anthropogenic (i.e. toluene) oxidation and
"more realistic" plant mesocosm systems, demonstrates that such an ensemble
of chemometric mapping has the potential to be used for the classification of
more complex spectra of unknown origin. More specifically, the addition of
mesocosm data from fig and birch tree experiments shows that isoprene and
monoterpene emitting sources, respectively, can be mapped onto the
statistical model structure and their positional vectors can provide insight
into their biological sources and controlling oxidative chemistry. The
potential to extend the methodology to the analysis of ambient air is
discussed using results obtained from a zero-dimensional box model
incorporating mechanistic data obtained from the Master Chemical Mechanism
(MCMv3.2). Such an extension to analysing ambient air would prove a powerful
asset in assisting with the identification of SOA sources and the elucidation
of the underlying chemical mechanisms involved. |
| first_indexed | 2024-12-24T05:27:57Z |
| format | Article |
| id | doaj.art-a6fffdff8db548398c45436fef5b90ea |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-12-24T05:27:57Z |
| publishDate | 2015-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-a6fffdff8db548398c45436fef5b90ea2022-12-21T17:13:16ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242015-07-0115148077810010.5194/acp-15-8077-2015Mapping gas-phase organic reactivity and concomitant secondary organic aerosol formation: chemometric dimension reduction techniques for the deconvolution of complex atmospheric data setsK. P. Wyche0P. S. Monks1K. L. Smallbone2J. F. Hamilton3M. R. Alfarra4A. R. Rickard5G. B. McFiggans6M. E. Jenkin7W. J. Bloss8A. C. Ryan9C. N. Hewitt10A. R. MacKenzie11Air Environment Research, School of Environment and Technology, University of Brighton, Brighton, BN2 4GJ, United KingdomDepartment of Chemistry, University of Leicester, Leicester, LE1 7RH, United KingdomAir Environment Research, School of Environment and Technology, University of Brighton, Brighton, BN2 4GJ, United KingdomWolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, York, YO10 5DD, United KingdomSchool of Earth, Atmospheric and Environmental Sciences, University of Manchester, M13 9PL, United KingdomWolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, York, YO10 5DD, United KingdomSchool of Earth, Atmospheric and Environmental Sciences, University of Manchester, M13 9PL, United KingdomAtmospheric Chemistry Services, Okehampton, Devon, EX20 1FB, United KingdomSchool of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, United KingdomLancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, United KingdomLancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, United KingdomBirmingham Institute of Forest Research, University of Birmingham, Birmingham, B15 2TT, United KingdomHighly non-linear dynamical systems, such as those found in atmospheric chemistry, necessitate hierarchical approaches to both experiment and modelling in order to ultimately identify and achieve fundamental process-understanding in the full open system. Atmospheric simulation chambers comprise an intermediate in complexity, between a classical laboratory experiment and the full, ambient system. As such, they can generate large volumes of difficult-to-interpret data. Here we describe and implement a chemometric dimension reduction methodology for the deconvolution and interpretation of complex gas- and particle-phase composition spectra. The methodology comprises principal component analysis (PCA), hierarchical cluster analysis (HCA) and positive least-squares discriminant analysis (PLS-DA). These methods are, for the first time, applied to simultaneous gas- and particle-phase composition data obtained from a comprehensive series of environmental simulation chamber experiments focused on biogenic volatile organic compound (BVOC) photooxidation and associated secondary organic aerosol (SOA) formation. We primarily investigated the biogenic SOA precursors isoprene, α-pinene, limonene, myrcene, linalool and β-caryophyllene. The chemometric analysis is used to classify the oxidation systems and resultant SOA according to the controlling chemistry and the products formed. Results show that "model" biogenic oxidative systems can be successfully separated and classified according to their oxidation products. Furthermore, a holistic view of results obtained across both the gas- and particle-phases shows the different SOA formation chemistry, initiating in the gas-phase, proceeding to govern the differences between the various BVOC SOA compositions. The results obtained are used to describe the particle composition in the context of the oxidised gas-phase matrix. An extension of the technique, which incorporates into the statistical models data from anthropogenic (i.e. toluene) oxidation and "more realistic" plant mesocosm systems, demonstrates that such an ensemble of chemometric mapping has the potential to be used for the classification of more complex spectra of unknown origin. More specifically, the addition of mesocosm data from fig and birch tree experiments shows that isoprene and monoterpene emitting sources, respectively, can be mapped onto the statistical model structure and their positional vectors can provide insight into their biological sources and controlling oxidative chemistry. The potential to extend the methodology to the analysis of ambient air is discussed using results obtained from a zero-dimensional box model incorporating mechanistic data obtained from the Master Chemical Mechanism (MCMv3.2). Such an extension to analysing ambient air would prove a powerful asset in assisting with the identification of SOA sources and the elucidation of the underlying chemical mechanisms involved.http://www.atmos-chem-phys.net/15/8077/2015/acp-15-8077-2015.pdf |
| spellingShingle | K. P. Wyche P. S. Monks K. L. Smallbone J. F. Hamilton M. R. Alfarra A. R. Rickard G. B. McFiggans M. E. Jenkin W. J. Bloss A. C. Ryan C. N. Hewitt A. R. MacKenzie Mapping gas-phase organic reactivity and concomitant secondary organic aerosol formation: chemometric dimension reduction techniques for the deconvolution of complex atmospheric data sets Atmospheric Chemistry and Physics |
| title | Mapping gas-phase organic reactivity and concomitant secondary organic aerosol formation: chemometric dimension reduction techniques for the deconvolution of complex atmospheric data sets |
| title_full | Mapping gas-phase organic reactivity and concomitant secondary organic aerosol formation: chemometric dimension reduction techniques for the deconvolution of complex atmospheric data sets |
| title_fullStr | Mapping gas-phase organic reactivity and concomitant secondary organic aerosol formation: chemometric dimension reduction techniques for the deconvolution of complex atmospheric data sets |
| title_full_unstemmed | Mapping gas-phase organic reactivity and concomitant secondary organic aerosol formation: chemometric dimension reduction techniques for the deconvolution of complex atmospheric data sets |
| title_short | Mapping gas-phase organic reactivity and concomitant secondary organic aerosol formation: chemometric dimension reduction techniques for the deconvolution of complex atmospheric data sets |
| title_sort | mapping gas phase organic reactivity and concomitant secondary organic aerosol formation chemometric dimension reduction techniques for the deconvolution of complex atmospheric data sets |
| url | http://www.atmos-chem-phys.net/15/8077/2015/acp-15-8077-2015.pdf |
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