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Author: Birmili, W. × Author: Spindler, G. × End date: 2019 × Start date: 2019 × Type: Text × Subject: atmospheric chemistry ×

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    A statistical proxy for sulphuric acid concentration
    (München : European Geopyhsical Union, 2011) Mikkonen, S.; Romakkaniemi, S.; Smith, J.N.; Korhonen, H.; Petäjä, T.; Plass-Duelmer, C.; Boy, M.; McMurry, P.H.; Lehtinen, K.E.J.; Joutsensaari, J.; Hamed, A.; Mauldin III, R.L.; Birmili, W.; Spindler, G.; Arnold, F.; Kulmala, M.; Laaksonen, A.
    Gaseous sulphuric acid is a key precursor for new particle formation in the atmosphere. Previous experimental studies have confirmed a strong correlation between the number concentrations of freshly formed particles and the ambient concentrations of sulphuric acid. This study evaluates a body of experimental gas phase sulphuric acid concentrations, as measured by Chemical Ionization Mass Spectrometry (CIMS) during six intensive measurement campaigns and one long-term observational period. The campaign datasets were measured in Hyytiälä, Finland, in 2003 and 2007, in San Pietro Capofiume, Italy, in 2009, in Melpitz, Germany, in 2008, in Atlanta, Georgia, USA, in 2002, and in Niwot Ridge, Colorado, USA, in 2007. The long term data were obtained in Hohenpeissenberg, Germany, during 1998 to 2000. The measured time series were used to construct proximity measures ("proxies") for sulphuric acid concentration by using statistical analysis methods. The objective of this study is to find a proxy for sulfuric acid that is valid in as many different atmospheric environments as possible. Our most accurate and universal formulation of the sulphuric acid concentration proxy uses global solar radiation, SO2 concentration, condensation sink and relative humidity as predictor variables, yielding a correlation measure (R) of 0.87 between observed concentration and the proxy predictions. Interestingly, the role of the condensation sink in the proxy was only minor, since similarly accurate proxies could be constructed with global solar radiation and SO2 concentration alone. This could be attributed to SO2 being an indicator for anthropogenic pollution, including particulate and gaseous emissions which represent sinks for the OH radical that, in turn, is needed for the formation of sulphuric acid.
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    Relating particle hygroscopicity and CCN activity to chemical composition during the HCCT-2010 field campaign
    (München : European Geopyhsical Union, 2013) Wu, Z.J.; Poulain, L.; Henning, S.; Dieckmann, K.; Birmili, W.; Merkel, M.; van Pinxteren, D.; Spindler, G.; Müller, K.; Stratmann, F.; Herrmann, H.; Wiedensohler, A.
    Particle hygroscopic growth at 90% RH (relative humidity), cloud condensation nuclei (CCN) activity, and size-resolved chemical composition were concurrently measured in the Thüringer Wald mid-level mountain range in central Germany in the fall of 2010. The median hygroscopicity parameter values, κ, of 50, 75, 100, 150, 200, and 250 nm particles derived from hygroscopicity measurements are respectively 0.14, 0.14, 0.17, 0.21, 0.24, and 0.28 during the sampling period. The closure between HTDMA (Hygroscopicity Tandem Differential Mobility Analyzers)-measured (κHTDMA) and chemical composition-derived (κchem) hygroscopicity parameters was performed based on the Zdanovskii–Stokes–Robinson (ZSR) mixing rule. Using size-averaged chemical composition, the κ values are substantially overpredicted (30 and 40% for 150 and 100 nm particles). Introducing size-resolved chemical composition substantially improved closure. We found that the evaporation of NH4NO3, which may happen in a HTDMA system, could lead to a discrepancy in predicted and measured particle hygroscopic growth. The hygroscopic parameter of the organic fraction, κorg, is positively correlated with the O : C ratio (κorg = 0.19 × (O : C) − 0.03). Such correlation is helpful to define the κorg value in the closure study. κ derived from CCN measurement was around 30% (varied with particle diameters) higher than that determined from particle hygroscopic growth measurements (here, hydrophilic mode is considered only). This difference might be explained by the surface tension effects, solution non-ideality, gas-particle partitioning of semivolatile compounds, and the partial solubility of constituents or non-dissolved particle matter. Therefore, extrapolating from HTDMA data to properties at the point of activation should be done with great care. Finally, closure study between CCNc (cloud condensation nucleus counter)-measured (κCCN) and chemical composition (κCCN, chem) was performed using CCNc-derived κ values for individual components. The results show that the κCCN can be well predicted using particle size-resolved chemical composition and the ZSR mixing rule.