Klasyfikacja danych monitoringowych frakcji aerozolu o różnych rozmiarach cząstek

The present study deals with the application of self-organizing maps (SOM) of Kohonen for the classification of aerosol monitoring data sets from two sampling points (Arnoldstein and Unterloibach) located close to the border between Austria and Slovenia. The goal of the chemometric data treatment was to find some specific patterns in the classification maps for five different aerosol fractions collected in four different seasons of the year. The results obtained indicated a distinct separation of the ultrafine particles (PM 0.01–PM 0.4) from the other fractions which underlines their specific effect on human health. Seasonal separation but only between summer and winter sampling is also observed.Przedstawiono wyniki badań monitoringowych próbek aerozolu atmosferycznego pobranych z dwóch punktów pomiarowych (Arnoldstein i Unterloibach) z pobliża granicy między Austrią i Słowenią. Dane zinterpretowano z wykorzystaniem samoorganizujących się map (SOM) Kohonena. Celem chemometrycznej interpretacji danych było znalezienie charakterystycznych struktur na mapach klasyfikacji dla pięciu różnych frakcji aerozoli, zebranych w czterech różnych porach roku. Uzyskane wyniki wskazują na wyraźne oddzielenie najdrobniejszych cząstek (PM 0,01 – PM 0,4) od innych frakcji, co wskazuje na ich specyficzne działanie na zdrowie człowieka. Obserwuje się również zmiany sezonowe, ale tylko między próbkami pobranymi latem i zimą

Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets

Three classification techniques (loading and score projections based on principal components analysis (PCA), cluster analysis (CA) and self-organizing maps (SOM)) were applied to a large environmental data set of chemical indicators of river water quality. The study was carried out by using long-term water quality monitoring data. The advantages of SOM algorithm and its classification and visualization ability for large environmental data sets are stressed. The results obtained allowed detecting natural clusters of monitoring locations with similar water quality type and identifying important discriminant variables responsible for the clustering. SOM clustering allows simultaneous observation of both spatial and temporal changes in water quality. The chemometric approach revealed different patterns of monitoring sites conditionally named \u2018\u2018tributary\u2019\u2019, \u2018\u2018urban\u2019\u2019, \u2018\u2018rural\u2019\u2019 or \u2018\u2018background\u2019\u2019. This objective separation could lead to an optimization of river monitoring nets and to a better tracing natural and anthropogenic changes along the river stream

Multivariate Classification and Modelling in surface water pollution estimation

The present study deals with the application of selforganizing maps (SOM) and multiway principal-components analysis to classify, model, and interpret a large monitoring data set for surface water quality. The chemometric methods applied made it possible to reveal specific quality patterns of the chemical and biological parameters used to monitor the water quality (relation between water temperature, turbidity, hardness, colibacteria), seasonal impacts during the long period of observation and the relative independence on the spatial location of the sampling sites (water supply sources for the City of Trieste)

Arctic catchment as a sensitive indicator of the environmental changes: distribution and migration of metals (Svalbard)

Arctic regions experience metal pollution, despite their remote location, and the distribution and migration of those metals determine their potential impact on the local environment. Here, a High-Arctic catchment (Revelva, Svalbard) located remotely from human-induced pollution sources is studied with respect to the distribution and migration of chosen trace elements (Ag, Al, As, B, Ba, Bi, Ca, Cd, Co, Cr, Cu, Cs, Mo, Ni, Pb, Sb, Se, Sr, Tl, U, V and Zn) in surface waters. The metal concentrations fluctuated in 2010–2012 between 0.01 and 354 μg L−1, the highest mean-weighted concentration noted for Sr (42.5 μg L−1). The concentrations in the river water were likely influenced by both natural and human-activity-related processes. These factors can produce substances of the same chemical composition (e.g. carbon dioxide, sulphur dioxide and metals may be emitted both by a volcanic eruption and by industrial sources). Therefore, chemometric techniques were used in the current paper to distinguish the multiple sources of pollution in the Revelva catchment. The authors were seeking to determine whether there is indeed evidence for contamination, sufficient to cause environmental damage in polar region. As a result, it was shown that the long-range transport could play an important role in shaping the metal concentration profile of this Arctic tundra environment, capturing both the influence of volcanic eruptions within the region and the human activity in a range of distances from the study site