Zdzislaw Stegowski

A synchrotron radiation micro-X-ray absorption near edge structure study of sulfur speciation in human brain tumors—a methodological approach

Sulfur stands out among non-metallic elements essential to life. In biological systems sulfur exists in various oxidation states, but the most significant role in biochemical processes is played by sulfane sulfur. In this work sulfur speciation in brain tumors was studied using X-ray absorption near edge structure spectroscopy. The comparison of full XANES spectra measured inside the tumor cell, outside the cell and on its periphery with the XANES profiles of reference materials containing sulfur compounds shows that in all the examined tissue sites, sulfur is in the 2− oxidation state. However, special emphasis is placed on the experimental and methodological aspects of two-dimensional imaging of chemical forms of sulfur in tissue sections. It was found that after changing the photon energy of the exciting radiation beam, not only did a permanent shift of its position in the XY plane occur, but also a change in the beam incidence angle on the sample. Therefore procedures to eliminate these effects in imaging the distribution of chemical forms of sulfur, using a synchrotron radiation microprobe, are also shown. The results allowed for the identification of the presence of sulfur in 2−, 4+ and 6+ oxidation states in various tissue structures.

Preliminary PM2.5 and PM10 fractions source apportionment complemented by statistical accuracy determination

Abstract Samples of PM10 and PM2.5 fractions were collected between the years 2010 and 2013 at the urban area of Krakow, Poland. Numerous types of air pollution sources are present at the site; these include steel and cement industries, traffic, municipal emission sources and biomass burning. Energy dispersive X-ray fluorescence was used to determine the concentrations of the following elements: Cl, K, Ca, Ti, Mn, Fe, Ni, Cu, Zn, Br, Rb, Sr, As and Pb within the collected samples. Defining the elements as indicators, airborne particulate matter (APM) source profiles were prepared by applying principal component analysis (PCA), factor analysis (FA) and multiple linear regression (MLR). Four different factors identifying possible air pollution sources for both PM10 and PM2.5 fractions were attributed to municipal emissions, biomass burning, steel industry, traffic, cement and metal industry, Zn and Pb industry and secondary aerosols. The uncertainty associated with each loading was determined by a statistical simulation method that took into account the individual elemental concentrations and their corresponding uncertainties. It will be possible to identify two or more sources of air particulate matter pollution for a single factor in case it is extremely difficult to separate the sources.