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.

Abstract: This study investigated concentration profiles of trace, rare earth and platinum group metals in fluvial sediments from the Pangani river basin (43,650 km2), one of the largest river basins in Tanzania, to assess its environmental quality. Sediment samples were collected in two distinct seasons from 12 representative sites of diverse land-use practices and characterised by ICP-MS after optimised microwave digestion. Ecological risks were assessed by evaluation of pollution index and comparison with legislated sediment quality guidelines (SQG). The results revealed contamination by some trace metals (e.g. Pb, V, Cu, Cr, Ni, Cd, As, Co, Mn and Zn) in concentrations ranging from 0.7 to 2940 mg kg−1, and four rare earth elements (Y, Ce, Nd, Yb) in concentrations ranging from 0.9 (Yb) to 500 mg kg−1 dry weight (Ce), which significantly exceeded the estimated background values at some stations. Palladium was the only platinum group element that was detected in quantifiable concentrations (0.33.5 mg kg−1). Concentrations of some trace metals exceeded the SQGs at some localised areas. Principal component analysis and multivariate correlations indicated geochemical characteristics of the area as the major control of metal concentrations and spatial variability. Organic matter and clay contents also played a significant role in metal distributions. Assessment of land-use practices upstream of the sampling locations was used to trace potential anthropogenic sources of metal enrichments, where highest levels were found in areas close to urban centres and agricultural activities. The study provides baseline data for future monitoring programs, and highlights the need for more comprehensive analysis involving a wider spatio-temporal scale and ecotoxicological risk assessment.

Abstract: Energy-dispersive X-ray fluorescence (XRF) is an attractive analytical method to determine the level of air pollution by heavy metals. The concentration of the filter in ng/cm(2) is obtained by direct comparison of the net characteristic line intensity of an element with that of a thin film standard. As the sampled area on the filter and the area of the standard are larger than the area analysed by the instrument, the distribution of the elements on the surface of both samples and standards have to be sufficiently uniform. If this is not the case, biased concentration estimates are obtained. Two scanning macro-XRF setups with a beam diameter of similar to 0.5 mm were used to investigate the distribution of elements in (1) commercially available (Micromatter) standards, (2) in-house quartz filter standards obtained with an aerosol generator and (3) particulatematter (PM10) collected on quartz filters by a Leckel SEQ 47/50 sampler. The uniformity of the Micromatter standards was better than 2%. At least some in-house standards showed a concave distribution with less material at the edges. The maximum bias introduced by this is less than 5%. Because of the limited sensitivity of scanning XRF compared with conventional XRF, the distribution of only a few common elements like Ca and Fe could be determined reliably in aerosol filters. The distribution of some heavy elements could only be measured in filters sampled in polluted regions. In general, the loading of particulate matter over the filters was uniform. Copyright (C) 2017 John Wiley & Sons, Ltd.