Characterization of positive clusters in the CLOUD nucleation experiments

The mechanism of new particle formation (NPF) events is still poorly understood. The CLOUD experiment is studying at which conditions this process occurs. During the CLOUD 7 campaign (fall 2012) the evolution of the nucleation rate with different reagents (sulphuric acid, ammonia, dimethylamine and α-pinene) was tested. Here we investigate the composition of freshly formed positive ions during the nucleation process, which allows us to understand the formation mechanisms of these clusters. The time evolution of the clusters is shown demonstrating the correlation between the formation of these clusters and the nucleation process

Deconvolution of FIGAERO-CIMS thermal desorption profiles using positive matrix factorisation to identify chemical and physical processes during particle evaporation

he measurements of aerosol particles with a filter inlet for gases and aerosols (FIGAERO) together with a chemical ionisation mass spectrometer (CIMS) yield the overall chemical composition of the particle phase. In addition, the thermal desorption profiles obtained for each detected ion composition contain information about the volatility of the detected compounds, which is an important property for understanding many physical properties like gas–particle partitioning. We coupled this thermal desorption method with isothermal evaporation prior to the sample collection to investigate the chemical composition changes during isothermal particle evaporation and particulate-water-driven chemical reactions in α-pinene secondary organic aerosol (SOA) of three different oxidative states. The thermal desorption profiles of all detected elemental compositions were then analysed with positive matrix factorisation (PMF) to identify the drivers of the chemical composition changes observed during isothermal evaporation. The keys to this analysis were to use the error matrix as a tool to weight the parts of the data carrying most information (i.e. the peak area of each thermogram) and to run PMF on a combined data set of multiple thermograms from different experiments to enable a direct comparison of the individual factors between separate measurements. The PMF was able to identify instrument background factors and separate them from the part of the data containing particle desorption information. Additionally, PMF allowed us to separate the direct desorption of compounds detected at a specific elemental composition from other signals with the same composition that stem from the thermal decomposition of thermally instable compounds with lower volatility. For each SOA type, 7–9 factors were needed to explain the observed thermogram behaviour. The contribution of the factors depended on the prior isothermal evaporation. Decreased contributions from the factors with the lowest desorption temperatures were observed with increasing isothermal evaporation time. Thus, the factors identified by PMF could be interpreted as volatility classes. The composition changes in the particles due to isothermal evaporation could be attributed to the removal of volatile factors with very little change in the desorption profiles of the individual factors (i.e. in the respective temperatures of peak desorption, Tmax). When aqueous-phase reactions took place, PMF was able to identify a new factor that directly identified the ions affected by the chemical processes. We conducted a PMF analysis of the FIGAERO–CIMS thermal desorption data for the first time using laboratory-generated SOA particles. But this method can be applied to, for example, ambient FIGAERO–CIMS measurements as well. There, the PMF analysis of the thermal desorption data identifies organic aerosol (OA) sources (such as biomass burning or oxidation of different precursors) and types, e.g. hydrocarbon-like (HOA) or oxygenated organic aerosol (OOA). This information could also be obtained with the traditional approach, namely the PMF analysis of the mass spectra data integrated for each thermogram. But only our method can also obtain the volatility information for each OA source and type. Additionally, we can identify the contribution of thermal decomposition to the overall signal

Nanoscale regulation of L-type calcium channels differentiates between ischemic and dilated cardiomyopathies.

Background Subcellular localization and function of L-type calcium channels (LTCCs) play an important role in regulating contraction of cardiomyocytes. Understanding how this is affected by the disruption of transverse tubules during heart failure could lead to new insights into the disease. Methods Cardiomyocytes were isolated from healthy donor hearts, as well as from patients with cardiomyopathies and with left ventricular assist devices. Scanning ion conductance and confocal microscopy was used to study membrane structures in the cells. Super-resolution scanning patch-clamp was used to examine LTCC function in different microdomains. Computational modeling predicted the impact of these changes to arrhythmogenesis at the whole-heart level. Findings We showed that loss of structural organization in failing myocytes leads to re-distribution of functional LTCCs from the T-tubules to the sarcolemma. In ischemic cardiomyopathy, the increased LTCC open probability in the T-tubules depends on the phosphorylation by protein kinase A, whereas in dilated cardiomyopathy, the increased LTCC opening probability in the sarcolemma results from enhanced phosphorylation by calcium-calmodulin kinase II. LVAD implantation corrected LTCCs pathophysiological activity, although it did not improve their distribution. Using computational modeling in a 3D anatomically-realistic human ventricular model, we showed how LTCC location and activity can trigger heart rhythm disorders of different severity. Interpretation Our findings demonstrate that LTCC redistribution and function differentiate between disease aetiologies. The subcellular changes observed in specific microdomains could be the consequence of the action of distinct protein kinases. Funding This work was supported by NIH grant (ROI-HL 126802 to NT-JG) and British Heart Foundation (grant RG/17/13/33173 to JG, project grant PG/16/17/32069 to RAC). Funders had no role in study design, data collection, data analysis, interpretation, writing of the repor

How Do Amines Affect the Growth of Recently Formed Aerosol Particles

Growth rates of recently born nanometer-scale particles were measured during the CLOUD experiments at CERN. Combining the data from several recently developed measurement techniques allowed us to follow the growth of the particles starting from molecules to molecular clusters and finally to climatically relevant particles. We studied the binary system with sulphuric acid and water, and the ternary systems with ammonia or dimethylamine added to the chamber, both in purely neutral situation, and with ionization from cosmic rays or the CERN particle beam

Composition and temporal behavior of ambient ions in the boreal forest

A recently developed atmospheric pressure interface mass spectrometer (APi-TOF) measured the negative and positive ambient ion composition at a boreal forest site. As observed in previous studies, the negative ions were dominated by strong organic and inorganic acids (e.g. malonic, nitric and sulfuric acid), whereas the positive ions consisted of strong bases (e.g. alkyl pyridines and quinolines). Several new ions and clusters of ions were identified based on their exact masses, made possible by the high resolution, mass accuracy and sensitivity of the APi-TOF. Time series correlograms aided in peak identification and assigning the atomic compositions to molecules. Quantum chemical calculations of proton affinities and cluster stabilities were also used to confirm the plausibility of the assignments. Acids in the gas phase are predominantly formed by oxidation in the gas phase, and thus the concentrations are expected to vary strongly between day and night. This was also the case in this study, where the negative ions showed strong diurnal behavior, whereas the daily changes in the positive ions were considerably smaller. A special focus in this work was the changes in the ion distributions occurring during new particle formation events. We found that sulfuric acid, together with its clusters, dominated the negative ion spectrum during these events. The monomer (HSO<sub>4</sub><sup>−</sup>) was the largest peak, together with the dimer (H<sub>2</sub>SO<sub>4</sub> · HSO<sub>4</sub><sup>−</sup>) and trimer ((H<sub>2</sub>SO<sub>4</sub>)<sub>2</sub> · HSO<sub>4</sub><sup>−</sup>). SO<sub>5</sub><sup>−</sup> also tracked HSO<sub>4</sub><sup>−</sup> at around 20% of the HSO<sub>4</sub><sup>−</sup> concentration at all times. During the strongest events, the tetramer and a cluster with the tetramer and ammonia were also detected. Quantum chemical calculations predict that sulfuric acid clusters containing ammonia are much more stable when neutral, thus the detection of a single ion cluster implies that ammonia can be an important compound in the nucleation process. We also believe to have made the first observations of an organosulfate (glycolic acid sulfate) in the gas phase. This ion, and its cluster with sulfuric acid, correlates with the HSO<sub>4</sub><sup>−</sup>, but peaks in the early afternoon, some hours later than HSO<sub>4</sub><sup>−</sup> itself. A list of all identified ions is presented in the supplementary material, and also a list of all detected masses not yet identified

Comparing simulated and experimental molecular cluster distributions

Peer reviewe

Evolution of α-Pinene Oxidation Products in the Presence of Varying Oxidizers: Negative APi-TOF Point of View

Laboratory experiments conducted in the frame of the CLOUD project at CERN investigated the oxidation of α-pinene oxidation products in a carefully controlled environment and with different oxidation conditions: 1) pure ozonolysis (with the use of an hydroxyl radical (⋅OH) scavenger), 2) ozonolysis without use of a scavenger, and 3) pure ⋅OH oxidation using nitrous acid (HONO) to produce ⋅OH. The anions and negatively charged clusters present in the chamber were analyzed and their chemical composition compared for the different oxidation pathways

Methods for determining particle size distribution and growth rates between 1 and 3 nm using the Particle Size Magnifier

The most important parameters describing the atmospheric new particle formation process are the particle formation and growth rates. These together determine the amount of cloud condensation nuclei attributed to secondary particle formation. Due to difficulties in detecting small neutral particles, it has previously not been possible to derive these directly from measurements in the size range below about 3 nm. The Airmodus Particle Size Magnifier has been used at the SMEAR II station in Hyytiälä, southern Finland, and during nucleation experiments in the CLOUD chamber at CERN for measuring particles as small as about 1 nm in mobility diameter. We developed several methods to determine the particle size distribution and growth rates in the size range of 1–3 nm from these data sets. Here we introduce the appearance-time method for calculating initial growth rates. The validity of the method was tested by simulations with the Ion-UHMA aerosol dynamic model