In this paper using Artificial Neural Network (ANN) are presented forecasting results of PM10 concentrations for the city of Sarajevo. Input data of the proposed model are meteorological variables (wind speed, humidity, temperature and pressure) and pollution variable (PM10 concentration) recorded in the Federal Institute for Hydrometeorology from 2010 to 2013. The proposed model is tested on the several cases and the results are satisfactory. The results of the forecasting show the different effects that certain meteorological parameters have on the temporal prediction of concentrations of PM10. It can also be concluded that ANN approach is very useful in terms of the time series forecast the concentrations of PM10 particles with good forecasting results. Also, it is presented the idea of a unified system for air quality improvement, which involves a variety of systemic measures in the areas affected by an increase of PM10 concentrations.

Modern power systems are a very complex dynamical system spreading over a large geographical area. They indicate very complex phenomena in some parts as well as in the whole interconnected power system. In large and complex systems, there are coherent groups of generators. A group of generators in one area swings against a group of generators in another area. In this paper, using the Huang's Empirical Mode Decomposition (EMD) signals of the rotor-angle oscillations are separated into several Intrinsic Mode Functions (IMFs) and, thereafter, the correlations between IMFs are applied as a coherency measure and criterion for identification of coherentgenerator groups in a power system. The applied approach is analysed for the Kundur two area-four machines test system and New England (NE) bus 39 test system. A comparison between the results of NE bus 39 test system with those of other researches shows a high degree of similarity.