This work presents the results of 4 years long monitoring of concentrations of SO₂ gas and PM₁₀ in the urban area around the copper smelter in Bor. The contents of heavy metals Pb, Cd, Cu, Ni, and As in PM₁₀ were determined and obtained values were compared to the limit values provided in EU Directives. Manifold excess concentrations of all the components in the atmosphere of the urban area of the townsite Bor were registered. Through application of a multi-criteria analysis by using PROMETHEE/GAIA method, the zones were ranked according to the level of pollution.

This article presents the results of the statistical modeling of the ground-level ozone concentration in the air in the close vicinity of the city of Zrenjanin (Serbia). This study is aimed at defining the dependence of ozone concentration on the following predictors: SO₂, CO, H₂S, NO, NO₂, NOx, PM₁₀, benzene, toluene, m,p-Xylene, o-Xylene and ethylbenzene concentration in the air, as well as on the meteorological parameters (the wind direction, the wind speed, air pressure, air temperature, solar radiation, and RH). Multiple linear regression analysis (MLRA) and artificial neural networks (ANNs) were used as the tools for the mathematical analysis of the indicated occurrence. The results have shown that ANNs provide better estimates of ozone concentration on the monitoring site, whereas the multilinear regression model once again has proven to be less efficient in the accurate prediction of ozone concentration.

The aim of this article is to evaluate the quality of the Danube River in its course through Serbia as well as to demonstrate the possibilities for using three statistical methods: Principal Component Analysis (PCA), Factor Analysis (FA) and Cluster Analysis (CA) in the surface water quality management. Given that the Danube is an important trans-boundary river, thorough water quality monitoring by sampling at different distances during shorter and longer periods of time is not only ecological, but also a political issue. Monitoring was carried out at monthly intervals from January to December 2011, at 17 sampling sites. The obtained data set was treated by multivariate techniques in order, firstly, to identify the similarities and differences between sampling periods and locations, secondly, to recognize variables that affect the temporal and spatial water quality changes and thirdly, to present the anthropogenic impact on water quality parameters.