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    Particle hygroscopicity during atmospheric new particle formation events: Implications for the chemical species contributing to particle growth
    (Göttingen : Copernicus, 2013) Wu, Z.; Birmili, W.; Poulain, L.; Poulain, L.; Merkel, M.; Fahlbusch, B.; Van Pinxteren, D.; Herrmann, H.; Wiedensohler, A.
    This study examines the hygroscopicity of newly formed particles (diameters range 25-45 nm) during two atmospheric new particle formation (NPF) events in the German mid-level mountains during the Hill Cap Cloud Thuringia 2010 (HCCT-2010) field experiment. At the end of the NPF event involving clear particle growth, we measured an unusually high soluble particle fraction of 58.5% at 45 nm particle size. The particle growth rate contributed through sulfuric acid condensation only accounts for around 6.5% of the observed growth rate. Estimations showed that sulfuric acid condensation explained, however, only around 10% of that soluble particle fraction. Therefore, the formation of additional water-soluble matter appears imperative to explain the missing soluble fraction. Although direct evidence is missing, we consider water-soluble organics as candidates for this mechanism. For the case with clear growth process, the particle growth rate was determined by two alternative methods based on tracking the mode diameter of the nucleation mode. The mean particle growth rate obtained from the inter-site data comparison using Lagrangian consideration is 3.8 (± 2.6) nm h-1. During the same period, the growth rate calculated based on one site data is 5.0 nm h-1 using log-normal distribution function method. In light of the fact that considerable uncertainties could be involved in both methods, we consider both estimated growth rates consistent.
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    Characterization and source apportionment of organic aerosol using offline aerosol mass spectrometry
    (Katlenburg-Lindau : Copernicus, 2016) Daellenbach, K.R.; Bozzetti, C.; Křepelová, A.; Canonaco, F.; Wolf, R.; Zotter, P.; Fermo, P.; Crippa, M.; Slowik, J.G.; Sosedova, Y.; Zhang, Y.; Huang, R.-J.; Poulain, L.; Szidat, S.; Baltensperger, U.; El Haddad, I.; Prévôt, A.S.H.
    Field deployments of the Aerodyne Aerosol Mass Spectrometer (AMS) have significantly advanced real-time measurements and source apportionment of non-refractory particulate matter. However, the cost and complex maintenance requirements of the AMS make its deployment at sufficient sites to determine regional characteristics impractical. Furthermore, the negligible transmission efficiency of the AMS inlet for supermicron particles significantly limits the characterization of their chemical nature and contributing sources. In this study, we utilize the AMS to characterize the water-soluble organic fingerprint of ambient particles collected onto conventional quartz filters, which are routinely sampled at many air quality sites. The method was applied to 256 particulate matter (PM) filter samples (PM1, PM2.5, and PM10, i.e., PM with aerodynamic diameters smaller than 1, 2.5, and 10 µm, respectively), collected at 16 urban and rural sites during summer and winter. We show that the results obtained by the present technique compare well with those from co-located online measurements, e.g., AMS or Aerosol Chemical Speciation Monitor (ACSM). The bulk recoveries of organic aerosol (60–91 %) achieved using this technique, together with low detection limits (0.8 µg of organic aerosol on the analyzed filter fraction) allow its application to environmental samples. We will discuss the recovery variability of individual hydrocarbon ions, ions containing oxygen, and other ions. The performance of such data in source apportionment is assessed in comparison to ACSM data. Recoveries of organic components related to different sources as traffic, wood burning, and secondary organic aerosol are presented. This technique, while subjected to the limitations inherent to filter-based measurements (e.g., filter artifacts and limited time resolution) may be used to enhance the AMS capabilities in measuring size-fractionated, spatially resolved long-term data sets.