Abstract: Size-fractionated aerosol samples, collected over the Atlantic Ocean, were analyzed for up to 20 elements by PIXE. Using a sailboat as sampling platform, duplicate samples were taken for two-day periods by means of battery operated 6-stage cascade impactors, positioned about 8 m above the sea surface. In the PIXE analysis of the fine particle stages (stages 3 to 5) a 5 times smaller beam size was used than for stages 1 and 2. This led to significant improvement in the detection limits for the former stages. The results from the duplicate impactor samples were normally in good agreement, indicating that the combined uncertainty of sampling and PIXE analysis was of the order of 20%. The precision of the PIXE analysis alone was investigated by rebombarding some samples six months after the first analysis. The trends with time of the fine particle sulfur and the coarse particle iron concentrations are discussed in some detail.

Abstract: Atmospheric nitrogen dioxide (NO2) and sulfur dioxide (SO2) levels were monitored simultaneously by means of Radiello passive samplers at six sites of Santa Clara city, Cuba, in the cold and the warm seasons in 2010. The dissolved ionic forms of NO2 and SO2 as nitrate and sulfite plus sulfate, respectively, were determined by means of ion chromatography. Analysis of NO2 as nitrite was also performed by UVVis spectrophotometry. For NO2, significant t tests show good agreement between the results of IC and UVVis methods. The NO2 and SO2 concentrations peaked in the cold season, while their minimum levels were experienced in the warm season. The pollutant levels do not exceed the maximum allowable limit of the Cuban Standard 39:1999, i.e., 40 μg/m3 and 50 μg/m3 for NO2 and SO2, respectively. The lowest pollutant concentrations obtained in the warm season can be attributed to an increase in their removal via precipitation (scavenging) while to the decreased traffic density and industrial emission during the summer holidays (e.g., July and August).

Abstract: The most unique and only known 16th century Antwerp majolica tile floor in Belgium is situated in a tower of the Rameyenhof castle (Gestel, Belgium). This exceptional work of art has recently been investigated in situ by using X-ray fluorescence (XRF) and Raman spectroscopy in order to study the material characteristics. This study reports on the result of the analyses based on the novel combination of non-destructive and portable instrumentation, including a handheld XRF spectrometer for obtaining elemental information and a mobile Raman spectrometer for retrieving structural and molecular information on the floor tiles in the Rameyenhof castle and on a second, similar medallion, which is stored in the Rubens House museum in Antwerp (Belgium). The investigated material, majolica, is a type of ceramic, which fascinated many people and potters throughout history by its beauty and colourful appearance. In this study the characteristic major/minor and trace element signature of 16th century Antwerp majolica is determined and the pigments used for the colourful paintings present on the floor are identified. Furthermore, based on the elemental fingerprint of the white glaze, and in particular on the presence of zinc in the tiles – an element that was not used for making 16th century majolica – valuable information about the originality of the chapel floor and the two central medallions is acquired. (C) 2014 Elsevier B.V. All rights reserved.

Abstract: Bridging the gap between complex signal data output and clear interpretation by non-expert end-users is a major challenge many scientists face when converting their scientific technology into a real-life application. Currently, pattern recognition algorithms are the most frequently encountered signal data interpretation algorithms to close this gap, not in the least because of their straight-forward implementation via convenient software packages. Paradoxically, just because their implementation is so straight-forward, it becomes cumbersome to integrate the expert's domain-specific knowledge. In this work, a novel signal data interpretation approach is presented that uses this domain-specific knowledge as its fundament, thereby fully exploiting the unique expertise of the scientist. The new approach applies data preprocessing in an innovative way that transcends its usual purpose and is easy to translate into a software application. Multiple case studies illustrate the straight-forward application of the novel approach. Ultimately, the approach is highly suited for integration in various (bio)analytical applications that require interpretation of signal data.