Dimensionality-reduction techniques for complex mass spectrometric datasets: application to laboratory atmospheric organic oxidation experiments
<p>Oxidation of organic compounds in the atmosphere produces an immensely complex mixture of product species, posing a challenge for both their measurement in laboratory studies and their inclusion in air quality and climate models. Mass spectrometry techniques can measure thousands of these s...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2020-01-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/20/1021/2020/acp-20-1021-2020.pdf |
| _version_ | 1818476126805164032 |
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| author | A. R. Koss A. R. Koss M. R. Canagaratna A. Zaytsev J. E. Krechmer M. Breitenlechner K. J. Nihill C. Y. Lim J. C. Rowe J. R. Roscioli F. N. Keutsch J. H. Kroll |
| author_facet | A. R. Koss A. R. Koss M. R. Canagaratna A. Zaytsev J. E. Krechmer M. Breitenlechner K. J. Nihill C. Y. Lim J. C. Rowe J. R. Roscioli F. N. Keutsch J. H. Kroll |
| author_sort | A. R. Koss |
| collection | DOAJ |
| description | <p>Oxidation of organic compounds in the atmosphere produces an immensely
complex mixture of product species, posing a challenge for both their
measurement in laboratory studies and their inclusion in air quality and
climate models. Mass spectrometry techniques can measure thousands of these
species, giving insight into these chemical processes, but the datasets
themselves are highly complex. Data reduction techniques that group
compounds in a chemically and kinetically meaningful way provide a route to
simplify the chemistry of these systems but have not been systematically
investigated. Here we evaluate three approaches to reducing the
dimensionality of oxidation systems measured in an environmental chamber:
positive matrix factorization (PMF), hierarchical clustering analysis (HCA),
and a parameterization to describe kinetics in terms of multigenerational
chemistry (gamma kinetics parameterization, GKP). The evaluation is
implemented by means of two datasets: synthetic data consisting of a
three-generation oxidation system with known rate constants, generation
numbers, and chemical pathways; and the measured products of OH-initiated
oxidation of a substituted aromatic compound in a chamber experiment. We
find that PMF accounts for changes in the average composition of all
products during specific periods of time but does not sort compounds into
generations or by another reproducible chemical process. HCA, on the other
hand, can identify major groups of ions and patterns of behavior and
maintains bulk chemical properties like carbon oxidation state that can be
useful for modeling. The continuum of kinetic behavior observed in a typical
chamber experiment can be parameterized by fitting species' time traces to
the GKP, which approximates the chemistry as a linear, first-order kinetic
system. The fitted parameters for each species are the number of reaction steps
with OH needed to produce the species (the generation) and an effective
kinetic rate constant that describes the formation and loss rates of the
species. The thousands of species detected in a typical laboratory chamber
experiment can be organized into a much smaller number (10–30) of groups,
each of which has a characteristic chemical composition and kinetic behavior.
This quantitative relationship between chemical and kinetic characteristics,
and the significant reduction in the complexity of the system, provides an
approach to understanding broad patterns of behavior in oxidation systems
and could be exploited for mechanism development and atmospheric chemistry
modeling.</p> |
| first_indexed | 2024-12-10T09:21:24Z |
| format | Article |
| id | doaj.art-9db5158b49ff495ab8871d96bed5561d |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-12-10T09:21:24Z |
| publishDate | 2020-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-9db5158b49ff495ab8871d96bed5561d2022-12-22T01:54:40ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242020-01-01201021104110.5194/acp-20-1021-2020Dimensionality-reduction techniques for complex mass spectrometric datasets: application to laboratory atmospheric organic oxidation experimentsA. R. Koss0A. R. Koss1M. R. Canagaratna2A. Zaytsev3J. E. Krechmer4M. Breitenlechner5K. J. Nihill6C. Y. Lim7J. C. Rowe8J. R. Roscioli9F. N. Keutsch10J. H. Kroll11Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA, USAnow at: Tofwerk USA, Boulder, CO, USAAerodyne Research Incorporated, Billerica, MA, USAHarvard University, Paulson School of Engineering and Applied Sciences, Cambridge, MA, USAAerodyne Research Incorporated, Billerica, MA, USAHarvard University, Paulson School of Engineering and Applied Sciences, Cambridge, MA, USAMassachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA, USAMassachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA, USAMassachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA, USAAerodyne Research Incorporated, Billerica, MA, USAHarvard University, Paulson School of Engineering and Applied Sciences, Cambridge, MA, USAMassachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA, USA<p>Oxidation of organic compounds in the atmosphere produces an immensely complex mixture of product species, posing a challenge for both their measurement in laboratory studies and their inclusion in air quality and climate models. Mass spectrometry techniques can measure thousands of these species, giving insight into these chemical processes, but the datasets themselves are highly complex. Data reduction techniques that group compounds in a chemically and kinetically meaningful way provide a route to simplify the chemistry of these systems but have not been systematically investigated. Here we evaluate three approaches to reducing the dimensionality of oxidation systems measured in an environmental chamber: positive matrix factorization (PMF), hierarchical clustering analysis (HCA), and a parameterization to describe kinetics in terms of multigenerational chemistry (gamma kinetics parameterization, GKP). The evaluation is implemented by means of two datasets: synthetic data consisting of a three-generation oxidation system with known rate constants, generation numbers, and chemical pathways; and the measured products of OH-initiated oxidation of a substituted aromatic compound in a chamber experiment. We find that PMF accounts for changes in the average composition of all products during specific periods of time but does not sort compounds into generations or by another reproducible chemical process. HCA, on the other hand, can identify major groups of ions and patterns of behavior and maintains bulk chemical properties like carbon oxidation state that can be useful for modeling. The continuum of kinetic behavior observed in a typical chamber experiment can be parameterized by fitting species' time traces to the GKP, which approximates the chemistry as a linear, first-order kinetic system. The fitted parameters for each species are the number of reaction steps with OH needed to produce the species (the generation) and an effective kinetic rate constant that describes the formation and loss rates of the species. The thousands of species detected in a typical laboratory chamber experiment can be organized into a much smaller number (10–30) of groups, each of which has a characteristic chemical composition and kinetic behavior. This quantitative relationship between chemical and kinetic characteristics, and the significant reduction in the complexity of the system, provides an approach to understanding broad patterns of behavior in oxidation systems and could be exploited for mechanism development and atmospheric chemistry modeling.</p>https://www.atmos-chem-phys.net/20/1021/2020/acp-20-1021-2020.pdf |
| spellingShingle | A. R. Koss A. R. Koss M. R. Canagaratna A. Zaytsev J. E. Krechmer M. Breitenlechner K. J. Nihill C. Y. Lim J. C. Rowe J. R. Roscioli F. N. Keutsch J. H. Kroll Dimensionality-reduction techniques for complex mass spectrometric datasets: application to laboratory atmospheric organic oxidation experiments Atmospheric Chemistry and Physics |
| title | Dimensionality-reduction techniques for complex mass spectrometric datasets: application to laboratory atmospheric organic oxidation experiments |
| title_full | Dimensionality-reduction techniques for complex mass spectrometric datasets: application to laboratory atmospheric organic oxidation experiments |
| title_fullStr | Dimensionality-reduction techniques for complex mass spectrometric datasets: application to laboratory atmospheric organic oxidation experiments |
| title_full_unstemmed | Dimensionality-reduction techniques for complex mass spectrometric datasets: application to laboratory atmospheric organic oxidation experiments |
| title_short | Dimensionality-reduction techniques for complex mass spectrometric datasets: application to laboratory atmospheric organic oxidation experiments |
| title_sort | dimensionality reduction techniques for complex mass spectrometric datasets application to laboratory atmospheric organic oxidation experiments |
| url | https://www.atmos-chem-phys.net/20/1021/2020/acp-20-1021-2020.pdf |
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