Recent advancements in atmospheric mass spectrometry provide huge amounts of new information but at the same time present considerable challenges for the data analysts. High-resolution (HR) peak identification and separation can be effort- and time-consuming yet still tricky and inaccurate due to the complexity of overlapping peaks, especially at larger mass-to-charge ratios. This study presents a simple and novel method, mass spectral binning combined with positive matrix factorization (binPMF), to address these problems. Different from unit mass resolution (UMR) analysis or HR peak fitting, which represent the routine data analysis approaches for mass spectrometry datasets, binPMF divides the mass spectra into small bins and takes advantage of the positive matrix factorization's (PMF) strength in separating different sources or processes based on different temporal patterns. In this study, we applied the novel approach to both ambient and synthetic datasets to evaluate its performance. It not only succeeded in separating overlapping ions but was found to be sensitive to subtle variations as well. Being fast and reliable, binPMF has no requirement for a priori peak information and can save much time and effort from conventional HR peak fitting, while still utilizing nearly the full potential of HR mass spectra. In addition, we identify several future improvements and applications for binPMF and believe it will become a powerful approach in the data analysis of mass spectra.

While cloud condensation nuclei (CCN) production associated with atmospheric new particle formation (NPF) is thought to be frequent throughout the continental boundary layers, few studies on this phenomenon in marine air exist. Here, based on simultaneous measurement of particle number size distributions, CCN properties and aerosol chemical composition, we present the first direct evidence on CCN production resulting from NPF in the eastern Mediterranean atmosphere. We show that condensation of both gaseous sulfuric acid and organic compounds from multiple sources leads to the rapid growth of nucleated particles to CCN sizes in this environment during the summertime. Sub-100 nm particles were found to be substantially less hygroscopic than larger particles during the period with active NPF and growth (the value of kappa was lower by 0.2-0.4 for 60 nm particles compared with 120 nm particles), probably due to enrichment of organic material in the sub-100 nm size range. The aerosol hygroscopicity tended to be at minimum just before the noon and at maximum in the afternoon, which was very likely due to the higher sulfate-to-organic ratios and higher degree of oxidation of the organic material during the afternoon. Simultaneous with the formation of new particles during daytime, particles formed during the previous day or even earlier were growing into the size range relevant to cloud droplet activation, and the particles formed in the atmosphere were possibly mixed with long-range-transported particles.

We evaluated a modified VBS (volatility basis set) scheme to treat biomass-burning-like organic aerosol (BBOA) implemented in CAMx (Comprehensive Air Quality Model with extensions). The updated scheme was parameterized with novel wood combustion smog chamber experiments using a hybrid VBS framework which accounts for a mixture of wood burning organic aerosol precursors and their further functionalization and fragmentation in the atmosphere. The new scheme was evaluated for one of the winter EMEP intensive campaigns (February March 2009) against aerosol mass spectrometer (AMS) measurements performed at 11 sites in Europe. We found a considerable improvement for the modelled organic aerosol (OA) mass compared to our previous model application with the mean fractional bias (MFB) reduced from 61 to 29 %. We performed model-based source apportionment studies and compared results against positive matrix factorization (PMF) analysis performed on OA AMS data. Both model and observations suggest that OA was mainly of secondary origin at almost all sites. Modelled secondary organic aerosol (SOA) contributions to total OA varied from 32 to 88 % (with an average contribution of 62 %) and absolute concentrations were generally under-predicted. Modelled primary hydrocarbon-like organic aerosol (HOA) and primary biomass-burning-like aerosol (BBPOA) fractions contributed to a lesser extent (HOA from 3 to 30 %, and BBPOA from 1 to 39 %) with average contributions of 13 and 25 %, respectively. Modelled BBPOA fractions were found to represent 12 to 64 % of the total residential-heating-related OA, with increasing contributions at stations located in the northern part of the domain. Source apportionment studies were performed to assess the contribution of residential and non-residential combustion precursors to the total SOA. Non-residential combustion and road transportation sector contributed about 30-40 % to SOA formation (with increasing contributions at urban and near industrialized sites), whereas residential combustion (mainly related to wood burning) contributed to a larger extent, around 60-70 %. Contributions to OA from residential combustion precursors in different volatility ranges were also assessed: our results indicate that residential combustion gas-phase precursors in the semivolatile range (SVOC) contributed from 6 to 30 %, with higher contributions predicted at stations located in the southern part of the domain On the other hand, the oxidation products of higher-volatility precursors (the sum of intermediate-volatility compounds (IVOCs) and volatile organic compounds (VOCs)) contribute from 15 to 38 % with no specific gradient among the stations. Although the new parameterization leads to a better agreement between model results and observations, it still under predicts the SOA fraction, suggesting that uncertainties in the new scheme and other sources and/or formation mechanisms remain to be elucidated. Moreover, a more detailed characterization of the semivolatile components of the emissions is needed.