Environmental security is rapidly becoming a significant topic of present interest all over the world, and environmental modelling has a very high priority in various scientific fields, respectively. Different optimizations of the Latin Hypercube Sampling algorithm have been used in our sensitivity studies of the model output results for some air pollutants with respect to the emission levels and some chemical reactions rates.
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Pinus cembra forests are limited to the Alps and Carpathians. Although several studies regarding their structure were carried out in some locations in the Tatra Mts. it required further investigations. Therefore, the aims of this study were to describe the stand and shrub structure of P. cembra forests, compare their structure with the Picea abies forests and analyse differences between silicate and calcicolous P. cembra forests in the Tatra Mts. The data were collected on the 16 sampling plots (500 m2), in the Swiss stone pine and Norway spruce forests. We measured the diameter at breast height (dbh) of each tree and recorded the young trees and shrubs. In order to compare species composition between silicate and calcicolous P. cembra forests, we made 91 relevés in their entire range of distribution (917 ha). Furthermore, we examined the share of main tree species along the altitude and inclination gradients, using the GAM models. The tree density in the P. cembra forests reaches 618 stems per ha, whereas their basal area (BA) 23.17 m2 ha-1. Main tree species are P. cembra and P. abies. P. cembra dominates in the higher thickness classes. The BA and dbh structure varies significantly between P. cembra and P. abies forests. The most abundant juveniles are P. abies and Sorbus aucuparia. The differences between forests growing on different substrate are relatively low. The altitude has a significant impact on the share of P. cembra (increase) and P. abies (decrease). The inclination has a significant impact on the increase of share of P. cembra.
Przedstawiono wyniki modelowania statystycznego pomiarów wskaźników tlenowych wykonanych w punkcie monitoringu reperowego na rzece Redze w latach 1992-97. W części pierwszej badano przydatność do opisu danych doświadczalnych sześciu teoretycznych rozkładów prawdopodobieństwa. Wyboru rozkładu dokonano na podstawie analizy wykresów P-P (prawdopodobieństwo-prawdopodobieństwo) oraz wyników testu Kołmogorowa-Smirnowa. Stwierdzono, że zarówno rozkład logarytmiczno-normalny, jak i rozkład Gamma dają adekwatny opis danych doświadczalnych dla wszystkich analizowanych serii pomiarowych. W części drugiej przeprowadzono szczegółową analizę statystyczną wyników pomiarów na bazie rozkładu logarytmiczno-normalnego. Wyniki tej pracy wykazały, że aproksymacja pomiarów wykonywanych w ramach sieci monitoringu odpowiednim rozkładem prawdopodobieństwa może być użytecznym narzędziem do oceny jakości wód.
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This paper presents the statistical modelling of the monitoring data on oxygen indicators in the Rega River, collected in years of 1992-97. In the first part, the six probability distributions are examined to fit the experimental data. The results indicate that the lognormal and Gamma distributions give an adequate description for all considered data series, as measured by P-P probability plots, and a Kolmogorov-Smirnov goodness of fit test. In the second part, the detailed statistical analysis was performed based on lognormal distribution. This study showed that the approximation of monitoring data with the appropriate probability distribution could be very useful for evaluating of water quality.
Results of surface water and groundwater Pollution sources identification in the Biała Przemsza river basin (Southen Poland), by means of factor analysis are presented. Possible influsnceof the atmospheric deposition of heavy metals on groundwater quality was assessed using relative retention parameters of the metals in the top soil layer. Results of estimation of point and diffuse sources of heavy metals (Cd, Pb, Zn) apportion in pollution of the basin, basing on analysis of load-river discharge and concentration-discharge relationships are presented as well. Furhter analysis of the load versus river descharge regression functions allowed to calculate heavy matels concentrations in runoff alements. The paper contains also environment characteristics (mainly groundwater and surface water environments), and schematic model of heavy metals cycle in the examined basin.
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