Kinetics of Limonene Secondary Organic Aerosol Oxidation in the Aqueous Phase.
Bartłomiej WitkowskiMohammed Al-SharafiTomasz GierczakPublished in: Environmental science & technology (2018)
Twenty semivolatile organic compounds that contribute to limonene secondary organic aerosol (SOA) were synthesized in the flow-tube reactor. Kinetics of the aqueous-phase oxidation of the synthesized compounds by hydroxyl radicals (OH) and ozone (O3) were investigated at 298 ± 2 K using the relative rate method. Oxidized organic compounds identified as the major components of limonene SOA were quantified with liquid chromatography coupled to the electrospray ionization and quadrupole tandem mass spectrometry (LC-ESI/MS/MS). The bimolecular rate coefficients measured for the oxidation products of limonene are kOH = 2-5 × 109 M-1 s-1 for saturated and kOH = 1-2 × 1010 M-1 s-1 for unsaturated compounds. Ozonolysis reaction bimolecular rate coefficients obtained for the unsaturated compounds in the aqueous phase are between 2 and 6 × 104 M-1 s-1. The results obtained in this work also indicate that oxidation of limonene carboxylic acids by OH was about a factor of 2 slower for the carboxylate ions than for the protonated acids while the opposite was true for the ozonolysis. The data acquired provided new insights into kinetics of the limonene SOA processing in the aqueous phase. Ozonolysis of limonene SOA also increased the concentration of dimers, most likely due to reactions of the stabilized Criegee intermediates with the other, stable products. These results indicate that aqueous-phase oxidation of limonene SOA by OH and O3 will be relevant in clouds, fogs, and wet aerosols.
Keyphrases
- liquid chromatography
- tandem mass spectrometry
- water soluble
- hydrogen peroxide
- ultra high performance liquid chromatography
- ms ms
- simultaneous determination
- mass spectrometry
- high performance liquid chromatography
- ionic liquid
- electron transfer
- solid phase extraction
- nitric oxide
- high resolution
- machine learning
- quantum dots
- big data
- deep learning
- high speed