development of new techniques to monitor atmospheric levels of ammonia, flame retardants, polycyclic aromatic hydrocarbons, bioaerosols, and secondary organic aerosols (SOAs) have received much attention. SOAs are formed through the oxidation of volatile organic compounds and are comprised of thousands of compounds, posing extreme challenges to their characterization. Similarly, methods are being developed for the determination of radical species in the atmosphere, such as the use of fluorescence assay by gas expansion (FAGE) for methyl peroxy (CH O ) radicals,(7) where the reactivity of these species presents significant 3 2 challenges in sample handling and analysis. Developments in the field of atmospheric chemistry monitoring have been facilitated by improvements in the selectivity and sensitivity of analytical methods, while time, labor, and cost savings as well as a reduction in environmental impacts (green chemistry approaches) have also stimulated progress.

Fouquet, T.N.J., 2019. The Kendrick analysis for polymer mass spectrometry. Journal of Mass Spectrometry 54, 933-947.

https://doi.org/10.1002/jms.4480 The mass spectrum of a polymer often displays repetitive patterns with peak series spaced by the repeating unit(s) of the polymeric backbones, sometimes complexified with different adducts, chain terminations, or charge states. Exploring the complex mass spectral data or filtering the unwanted signal is tedious whether performed manually or automatically. In contrast, the now 60-year-old Kendrick (mass defect) analysis, when adapted to polymer ions, produces visual two-dimensional maps with intuitive alignments of the repetitive patterns and favourable deconvolution of features overlaid in the one-dimensional mass spectrum. This special feature article reports on an up-to-date and theoretically sound use of Kendrick plots as a data processing tool. The approach requires no prior knowledge of the sample but offers promising dynamic capabilities for visualizing, filtering, and sometimes assigning congested mass spectra. Examples of applications of the approach to polymers are discussed throughout the text, but the same tools can be readily extended to other applications, including the analysis of polymers present as pollutants/contaminants, and to other analytes incorporating a repetitive moiety, for example, oils or lipids. In each of these instances, data processing can benefit from the application of an updated and interactive Kendrick analysis.

Francis, S., Passow, U., 2020. Transport of dispersed oil compounds to the seafloor by sinking phytoplankton aggregates: A modeling study. Deep Sea Research Part I: Oceanographic Research Papers 156, 103192.

http://www.sciencedirect.com/science/article/pii/S0967063719303383 Up to 20% of the oil spilled as a result of the Deepwater Horizon explosion in the Gulf of Mexico in 2010 was deposited as degraded oil compounds on the seafloor. Much of that deposition was likely due to dispersed oil compounds having been integrated into fast-sinking aggregates, which transported it effectively to depth. Understanding the details of this oil transport mechanism, and predicting its potential magnitude, is important for the management and mitigation of future oil spills. The 1-D model described here simulates a) a diatom bloom and the resulting formation of aggregates via coagulation of cells, b) the scavenging of dispersed oil compounds by these aggregates as they sink through the water column, c) the degradation of diatom carbon and oil carbon during transit, and d) the ultimate deposition of aggregates and oil compounds to the seafloor. The model is parameterized using primarily field- and laboratory-collected data and the model results are compared to sediment trap data. Specifically, one large diatom sedimentation event observed shortly after the Deepwater well was capped was modeled. The comparison of simulation results to field data assesses the model's ability to explain the observed sedimentation event and tests our understanding of the main mechanisms driving such an event. The model results indicate that diatom carbon and oil compounds captured in the trap were linked to a sedimentation event that reached peak intensity just before the trap was deployed. The model simulations predict the measured sedimentation rates of oil compounds and organic carbon reasonably well, indicating that the key mechanisms driving this process were captured by the model. In the baseline case, which simulates the Skeletonema sedimentation of a large bloom in late August 2010, about 10% of the dispersed oil compounds