The research deals in complex with the issue of landfilling and with a pos-sible use of biological indicators to assess the impact of landfill on its surroundings. The problem is topical as landfilling remains the most spread technology for the disposal of communal waste in the Czech Republic.Assessing the impact of the landfill on its environs, we based our study on the selected bioindicators present in 1995 and in 2007 - 2010. During the period of vegetation biomonitoring, we did not detect any significant impact of the landfills on the biotic composition of the environment and no symptoms of leaf area chlorosis or necrosis that would have indicated the direct impact of sanitary landfills operation on the locality. The Štěpánovice landfill and Kuchyňky landfill have a functional system of drains combined with the system of ground sealing and the system of seepage water drainage pits. It further has a sophisticated system to check fencing, fly-offs and to collect lightweight waste. Both landfills are constructed and operated in compliance with the most modern and strictest requirements and standards. ...
The results of field research at 230 river sections located throughout Poland were used to examine the possibility of predicting values of macrophyte metrics of ecological status. Artificial intelligence methods such as artificial neural networks were used in the modelling. The physicochemical parameters of water (alkalinity, conductivity, nitrate and ammonium nitrogen, reactive and total phosphorus, and biochemical oxygen demand) were used as the explanatory (modelling) variables. The explained (modelled) parameters were the Polish MIR (Macrophyte Index for Rivers), the British MTR (Mean Trophic Rank) and the French IBMR (River Macrophytes Biological Index). The quality of the constructed models was assessed using the normalized root mean square error (NRMSE) and the r-Pearson's linear correlation coefficient between variables modelled by the networks and calculated on the basis of the botanical research. These analyses demonstrated that the network modelling MIR values had the highest accuracy. The lowest prediction accuracy was obtained for MTR and IBMR indices. The differences between particular models are likely to result from better adjustment of the Polish method to local rivers (particularly in terms of indicator species used). ...
