Simultaneous Determination of Fluorine,Chlorine, Bromine and Iodine in Six Geochemical Reference Materials Using Pyrohydrolysis,Ion Chromatography and Inductively Coupled Plasma‐Mass Spectrometry

Concentrations of halogens (fluorine, chlorine, bromine and iodine) were determined in six geochemical reference materials (BHVO‐2, GS‐N, JG‐1, JR‐1, JB‐1b, JB‐2). Halogens were first extracted from powdered samples using a pyrohydrolysis technique, then hydrolysis solutions were analysed by ion chromatography for F and Cl and inductively coupled plasma‐mass spectrometry for Br and I. The detection limits in solutions were 100 μg l^?1 for both F and Cl and 10 ng l^?1 for Br and I. Considering the extraction procedure, performed on a maximum of 500 mg of sample and producing 100 ml of pyrohydrolysis solution, detection limits in rock samples were 20 mg kg^?1 for F and Cl and 2 μg kg^?1 for Br and I. The mean analytical errors on the studied composition ranges were estimated at 10 mg kg^?1 for F and Cl, 100 μg kg^?1 for Br and 25 μg kg^?1 for I. The concentration values, based on repeated (generally > 10) sample analysis, were in good agreement generally with published values and narrowed the mean dispersion around mean values. Large dispersions are discussed in terms of samples heterogeneity and contaminations during sample preparation. Basaltic RMs were found to be more suitable for studies of halogen compositions than differentiated rock material, especially granites – the powders of which were heterogeneous in halogens at the 500 mg level.     

Representing geographical objects with scale‐induced indeterminate boundaries: A neural network‐based data model

The degree of uncertainty of many geographical objects has long been known to be in intimate relation with the scale of its observation and representation. Yet, the explicit consideration of scaling operations when modeling uncertainty is rarely found. In this study, a neural network‐based data model was investigated for representing geographical objects with scale‐induced indeterminate boundaries. Two types of neural units, combined with two types of activation function, comprise the processing core of the model, where the activation function can model either hard or soft transition zones. The construction of complex fuzzy regions, as well as lines and points, is discussed and illustrated with examples. It is shown how the level of detail that is apparent in the boundary at a given scale can be controlled through the degree of smoothness of each activation function. Several issues about the practical implementation of the model are discussed and indications on how to perform complex overlay operations of fuzzy maps provided. The model was illustrated through an example of representing multi‐resolution, sub‐pixel maps that are typically derived from remote sensing techniques.     

A comparative analysis of DEM‐based models to estimate the solar radiation in mountainous terrain

Daily solar radiation estimates of four up‐to‐date solar radiation models (Solar Analyst, r.sun, SRAD and Solei‐32), based on a digital elevation model (DEM), have been evaluated and compared in a Mediterranean environment characterized by a complex topography. The models' estimates were evaluated against 40 days of radiometric data collected in 14 stations. Analyzed sky conditions ranged from completely overcast conditions to clear skies. Additionally, the role of the spatial resolution of the DEM has been evaluated through the use of two different resolutions: 20 and 100 m. Results showed that, under clear‐sky conditions, the daily solar radiation variability in the study area may be reasonably estimated with mean bias errors under 10% and root mean square error values of around 15%. On the other hand, results proved that the reliability of the estimates substantially decreases under overcast conditions for some of the solar radiation models. Regarding the role of the DEM spatial resolution, results suggested that the reliability of the estimates for complex topography areas under clear‐sky conditions improves using a higher spatial resolution.