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Discussion papers
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 21 Jun 2019

Submitted as: research article | 21 Jun 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Dimensionality-reduction techniques for complex mass spectrometric datasets: application to laboratory atmospheric organic oxidation experiments

Abigail R. Koss1, Manjula R. Canagaratna2, Alexander Zaytsev3, Jordan E. Krechmer2, Martin Breitenlechner3, Kevin Nihill1, Christopher Lim1, James C. Rowe1, Joseph R. Roscioli2, Frank N. Keutsch3, and Jesse H. Kroll1 Abigail R. Koss et al.
  • 1Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Cambridge, MA
  • 2Aerodyne Research Incorporated, Billerica, MA
  • 3Harvard University, Paulson School of Engineering and Applied Sciences, Cambridge, MA

Abstract. Oxidation of organic compounds in the atmosphere produces an immensely complex mixture of product species, posing a challenge both for 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 data sets 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 data sets: 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. 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 characteristic chemical composition and kinetic behavior. This quantitative relationship between chemical and kinetic characteristics, and the significant reduction in the complexity of the system, provide an approach to understanding broad patterns of behavior in oxidation systems and could be exploited for mechanism development and atmospheric chemistry modeling.

Abigail R. Koss et al.
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Abigail R. Koss et al.
Abigail R. Koss et al.
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Short summary
Oxidation chemistry of organic compounds in the atmosphere produces a diverse spectrum of products. This diversity is difficult to represent in air quality and climate models, and in laboratory experiments results in large and complex datasets. This work evaluates several methods to simplify the chemistry of oxidation systems in environmental chambers, including positive matrix factorization, hierarchical clustering, and kinetics parameterization.
Oxidation chemistry of organic compounds in the atmosphere produces a diverse spectrum of...