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EN
Sulphur and nitrogen deposition were calculated with the FRAME model and used to assess the exceedances of the critical loads for acidification and eutrophication of natural ecosystems in Poland. For the first time two tools: the FRAME and SONOX models were used jointly to provide information on ecosystems at risk. The FRAME model obtained close agreement with available sulphur and nitrogen wet deposition measurements. The total mass of sulphur deposited in Poland in year 2008 was estimated as 292 Gg S. Total deposition of nitrogen (oxidized + reduced) is 389 Gg N. 11% of the ecosystems in Poland were calculated to be at risk of acidification due to deposition of sulphur and nitrogen. In the case of eutrophication, over 95% of terrestrial ecosystems are at risk due to the large deposition of nitrogen compounds.
PL
W pracy zastosowano model FRAME do obliczenia informacji o depozycji związków siarki i azotu w Polsce. Na tej podstawie określono przekroczenia ładunków krytycznych dla ekosystemów naturalnych. Po raz pierwszy dwa narzędzia, modele FRAME i SONOX, zostały zastosowane razem w celu określenia zagrożenia dla ekosystemów. Wykazano, że depozycja siarki i azotu, obliczona za pomocą modelu FRAME, jest w dobrej zgodności z dostępnymi pomiarami. Całkowita depozycja siarki, zdeponowanej w Polsce w 2008, została określona na 292 Gg S. Masa zdeponowanych związków azotu (utlenionego i zredukowanego) to 389 Gg N. Wykazano, że 11% ekosystemów w Polsce jest zagrożonych nadmiernym zakwaszeniem w wyniku depozycji atmosferycznej siarki i azotu, a ponad 95% ekosystemów lądowych jest zagrożonych eutrofizacją w wyniku depozycji związków azotu.
EN
The main objective of this paper is to introduce principal component analysis and two robust fuzzy principal component algorithms as useful tools in characterizing and comparing rime samples collected in different locations in Poland (2004–2007). The efficiency of the applied procedures was illustrated on a data set containing 108 rime samples and concentration of anions, cations, HCHO, as well as pH and conductivity. The fuzzy principal component algorithms achieved better results mainly because they are more compressible than classical PCA and very robust to outliers. For example, a three component model, fuzzy principal component analysis-first component (FPCA-1) accounts for 62.37% of the total variance and fuzzy principal component analysis-orthogonal (FPCA-o) 90.11%; PCA accounts only for 58.30%. The first two principal components explain 51.41% of the total variance in the case of FPCA-1 and 79.59% in the case of FPCA-o as compared to only 47.55% for PCA. As a direct consequence, PCA showed only a partial differentiation of rime samples onto the plane or in the space described by different combination of two or three principal components, whereas a much sharper differentiation of the samples, regarding their origin and location, is observed when FPCAs are applied. [...]
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