Comparison of fine particulate matter level, chemical content and oxidative potential derived from two dissimilar urban environments.

Urban airborne particles contain a wide spectrum of components, known to have harmful effects on human health. This study reports a detailed investigation of fine particulate matter (PM2.5), chemical content and oxidative potential derived from two different urban environments. During summer and winter, 20-day campaigns were conducted at Belgrade city center (urban-background site - UB) and Bor (urban-industrial site - UI). Using various analytical techniques, carbonaceous compounds, water-soluble inorganic ions, major and trace elements were determined, while the oxidative potential of PM2.5 was estimated by dichloro-dihydro-fluorescein diacetate (DCFH-DA) assay (OPDCFH values). The mean PM2.5 concentrations in both urban environments were above the recommended daily value, and the dominant PM2.5 mass contributor was organic matter (29-55%). The OC/EC ratio was significantly higher at UB site during winter, which was an indication of a considerable contribution of secondary organic carbon to the overall organic carbon (OC). Water-soluble organic carbon (WSOC) was also higher at UB than at UI site, and it probably came from the same sources as OC. In general, the different partition of secondary organic aerosol (SOA) in warm and cold periods affected the number of organic components. Sulfates and nitrates were the most abundant ions at both sites and they counted approximately 40% (summer) and 50% (winter) of total ions. Further, the concentrations of the most elements, particularly some potentially carcinogenic elements such as As, Cd and Pb were significantly higher at UI, due to the emissions from the copper smelter complex in the vicinity. The mean OPDCFH values were similar during the summer at both sampling sites, whereas a statistically significant difference between sites was noticed in favor of UB environment in winter.

Interpretative optimization and artificial neural network modeling of the gas chromatographic separation of polycyclic aromatic hydrocarbons.

An interpretative strategy (factorial design experimentation+total resolution analysis+chromatogram simulation) was employed to optimize the separation of 16 polycyclic aromatic hydrocarbons (PAHs) (naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, chrysene, benzo(a)anthracene, benzo(k)fluoranthene, benzo(b)fluoranthene, benzo(a)pyrene, indeno(1,2,3-c,d)pyrene, dibenzo(a,h)anthracene, benzo(g,h,i)perylene) in temperature-programmed gas chromatography (GC). Also, the retention behavior of PAHs in the same GC system was studied by a feed-forward artificial neural network (ANN). GC separation was investigated as a function of one (linear temperature ramp) or two (linear temperature ramp+the final hold temperature) variables. The applied interpretative approach resulted in rather good agreement between the measured and the predicted retention times for PAHs in both one and two variable modeling. The ANN model, strongly affected by the number of input experiments, was shown to be less effective for one variable used, but quite successful when two input variables were used. All PAHs, including difficult to separate peak pairs (benzo(k)fluoranthene/benzo(b)fluoranthene and indeno(1,2,3-c,d)pyrene/dibenzo(a,h)anthracene), were separated in a standard (5% phenyl-95% dimethylpolysiloxane) capillary column at an optimum temperature ramp of 8.0 degrees C/min and final hold temperature in the range of 260-320 degrees C.