University of Vienna

About: University of Vienna is a education organization based out in Vienna, Austria. It is known for research contribution in the topics: Population & Context (language use). The organization has 44686 authors who have published 95840 publications receiving 2907492 citations.

Oxidation products of biogenic emissions contribute to nucleation of atmospheric particles.

Abstract: Atmospheric new-particle formation affects climate and is one of the least understood atmospheric aerosol processes. The complexity and variability of the atmosphere has hindered elucidation of the fundamental mechanism of new-particle formation from gaseous precursors. We show, in experiments performed with the CLOUD (Cosmics Leaving Outdoor Droplets) chamber at CERN, that sulfuric acid and oxidized organic vapors at atmospheric concentrations reproduce particle nucleation rates observed in the lower atmosphere. The experiments reveal a nucleation mechanism involving the formation of clusters containing sulfuric acid and oxidized organic molecules from the very first step. Inclusion of this mechanism in a global aerosol model yields a photochemically and biologically driven seasonal cycle of particle concentrations in the continental boundary layer, in good agreement with observations.

Analysis of engineered nanomaterials in complex matrices (environment and biota): General considerations and conceptual case studies

Abstract: Advances in the study of the environmental fate, transport, and ecotoxicological effects of engineered nanomaterials (ENMs) have been hampered by a lack of adequate techniques for the detection and quantification of ENMs at environmentally relevant concentrations in complex media. Analysis of ENMs differs from traditional chemical analysis because both chemical and physical forms must be considered. Because ENMs are present as colloidal systems, their physicochemical properties are dependent on their surroundings. Therefore, the simple act of trying to isolate, observe, and quantify ENMs may change their physicochemical properties, making analysis extremely susceptible to artifacts. Many analytical techniques applied in materials science and other chemical/biological/physical disciplines may be applied to ENM analysis as well; however, environmental and biological studies may require that methods be adapted to work at low concentrations in complex matrices. The most pressing research needs are the development of techniques for extraction, cleanup, separation, and sample storage that introduce minimal artifacts to increase the speed, sensitivity, and specificity of analytical techniques, as well as the development of techniques that can differentiate between abundant, naturally occurring particles, and manufactured nanoparticles.

Restricted maximum likelihood estimation of covariances in sparse linear models

Abstract: This paper surveys the theoretical and computational development of the restricted maximum likelihood (REML) approach for the estimation of covariance matrices in linear stochastic models. A new derivation of this approach is given, valid under very weak conditions on the noise. Then the calculation of the gradient of restricted loglikelihood functions is dis- cussed, with special emphasis on the case of large and sparse model equations with a large number of unknown covariance components and possibly incomplete data. It turns out that the gradient calculations require hardly any extra storage, and only a small multiple of the number of operations needed to calculate the function values alone. The analytic gradient procedure was integrated into the VCE package for co- variance component estimation in large animal breeding models. It resulted in dramatic improvements of performance over the previous implementation with finite difference gradients. An example with more than 250 000 normal equations and 55 covariance components took hours instead of days of CPU time, and this was not an untypical case.