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

Abstract

<p><strong>Abstract.</strong> 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.</p>

Mechanistic Study of Formation of Ring-retaining and Ring-opening Products from Oxidation of Aromatic Compounds under Urban Atmospheric Conditions

“…We characterize time series behaviour of products using the gamma kinetics parametrization (GKP), which describes kinetics of an oxidation system in terms of multigenerational chemistry. The detailed description of this parametrization technique is given by Koss et al (2019), https://doi.org/10.5194/acp-2019-666 Preprint. Discussion started: 7 August 2019 c Author(s) 2019.…”

“…. In this case, the time-dependent concentration of a compound Xm can be parametrized by (Koss et al, 2019):…”

Comparison of dimension reduction techniques in the analysis of mass spectrometry data

“…Although PMF has been shown to be a very powerful tool in the analysis of environmental AMS data from field studies (e.g. Ulbrich et al, 2009;Zhang et al, 2011;Hao et al, 2014, Chakraborty et al, 2015, it has not been applied as widely in laboratory and smog chamber research (Corbin et al, 2015;Kortelainen et al, 2015;Tiitta et al, 2016, Koss et al, 2019. Latest studies have applied PMF also to chemical ionization https://doi.org/10.5194/amt-2019-404 Preprint.…”

“…mass spectrometry data (Yan et al, 2016;Massoli et al, 2018;Koss et al, 2019), which is able to resolve more oxidized compounds. The special conditions in lab experiments (sharp change at the beginning of experiments, e.g., switching on UVlights) may present additional challenges, as PMF was originally developed for field measurement data sets where real changes in factors are expected to be much slower than e.g.…”

“…In the study from Äijälä et al, (2017), they combined PMF and k-means clustering to classify and extract the characteristics of organic components. In addition, very recent paper by Koss et al, (2019), also compared the dimension reduction abilities of HCA and gamma kinetics parametrization to PMF when studying mass spectrometric data sets.…”