The site at Orłowo Cliff was used to analyse the stratigraphic position and palaeogeographic interpretations of the properties and depositional conditions of two basal tills from the Late Pleistocene. A multi-proxy approach involved lithofacies, petrographic analysis of the fine gravel fraction, analyses of indicator erratics and till fabric. TL dating of intra-moraine deposits was used to determine depositional time frames of tills. The sediment profile at Orłowo Cliff shows a distinct reduction in number of Pleistocene units. Obtained dating results suggest the presence of Middle and Late Pleistocene fluvial units. The main issue discussed is the stratigraphic position of the older till (Unit O-4). It can be assumed that this till was deposited probably during the Middle Weichselian (MIS4). At Orłowo Horn the till of Unit O-4 reveals incorporation of the erratic material derived from an older till in the surrounded area (according to petrographic composition – probably from MIS 8). The younger till (Unit O-6) was deposited in the Late Weichselian (MIS 2). Moreover, the till of Unit O-6 is characterised by a significant shift towards the south-west in terms of the erratic origin in Unit O-4.
Electrocatalytic gas sensors belong to the family of electrochemical solid state sensors. Their responses are acquired in the form of I-V plots as a result of application of cyclic voltammetry technique. In order to obtain information about the type of measured gas the multivariate data analysis and pattern classification techniques can be employed. However, there is a lack of information in literature about application of such techniques in case of standalone chemical sensors which are able to recognize more than one volatile compound. In this article we present the results of application of these techniques to the determination from a single electrocatalytic gas sensor of single concentrations of nitrogen dioxide, ammonia, sulfur dioxide and hydrogen sulfide. Two types of classifiers were evaluated, i.e. linear Partial Least Squares Discriminant Analysis (PLS-DA) and nonlinear Support Vector Machine (SVM). The efficiency of using PLS-DA and SVM methods are shown on both the raw voltammetric sensor responses and pre-processed responses using normalization and auto-scaling