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1.
Atmos Chem Phys ; 20(2): 1021-1041, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33777125

RESUMO

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.

2.
Atmos Meas Tech ; 12(3): 1861-1870, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-32215111

RESUMO

Chemical ionization mass spectrometry (CIMS) instruments routinely detect hundreds of oxidized organic compounds in the atmosphere. A major limitation of these instruments is the uncertainty in their sensitivity to many of the detected ions. We describe the development of a new high-resolution time-of-flight chemical ionization mass spectrometer that operates in one of two ionization modes: using either ammonium ion ligand-switching reactions such as for N H 4 + CIMS or proton transfer reactions such as for protontransfer-reaction mass spectrometer (PTR-MS). Switching between the modes can be done within 2min. The N H 4 + CIMS mode of the new instrument has sensitivities of up to 67 000 dcps ppbv-1 (duty-cycle-corrected ion counts per second per part per billion by volume) and detection limits between 1 and 60 pptv at 2σ for a 1 s integration time for numerous oxygenated volatile organic compounds. We present a mass spectrometric voltage scanning procedure based on collision-induced dissociation that allows us to determine the stability of ammonium-organic ions detected by the N H 4 + CIMS instrument. Using this procedure, we can effectively constrain the sensitivity of the ammonia chemical ionization mass spectrometer to a wide range of detected oxidized volatile organic compounds for which no calibration standards exist. We demonstrate the application of this procedure by quantifying the composition of secondary organic aerosols in a series of laboratory experiments.

3.
Atmos Chem Phys ; 19(23): 15117-15129, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-32256548

RESUMO

Aromatic hydrocarbons make up a large fraction of anthropogenic volatile organic compounds and contribute significantly to the production of tropospheric ozone and secondary organic aerosol (SOA). Four toluene and four 1,2,4-trimethylbenzene (1,2,4-TMB) photooxidation experiments were performed in an environmental chamber under relevant polluted conditions (NO x ~ 10ppb). An extensive suite of instrumentation including two proton-transfer-reaction mass spectrometers (PTR-MS) and two chemical ionisation mass spectrometers ( NH 4 + CIMS and I- CIMS) allowed for quantification of reactive carbon in multiple generations of hydroxyl radical (OH)-initiated oxidation. Oxidation of both species produces ring-retaining products such as cresols, benzaldehydes, and bicyclic intermediate compounds, as well as ring-scission products such as epoxides and dicarbonyls. We show that the oxidation of bicyclic intermediate products leads to the formation of compounds with high oxygen content (an O : C ratio of up to 1.1). These compounds, previously identified as highly oxygenated molecules (HOMs), are produced by more than one pathway with differing numbers of reaction steps with OH, including both auto-oxidation and phenolic pathways. We report the elemental composition of these compounds formed under relevant urban high-NO conditions. We show that ring-retaining products for these two precursors are more diverse and abundant than predicted by current mechanisms. We present the speciated elemental composition of SOA for both precursors and confirm that highly oxygenated products make up a significant fraction of SOA. Ring-scission products are also detected in both the gas and particle phases, and their yields and speciation generally agree with the kinetic model prediction.

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