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Seventeen water-soluble substances (of sodium, ammonium, potassium, magnesium, calcium, formate, methanesulfonate, glyoxylate, chloride, nitrite, nitrate, glutarate, succinate, malate, malonate, sulfate and oxalate) in 94 samples of particle matter in the ambient air, collected over ten months, in a suburb of Belgrade (Serbia), were determined by ion chromatography. To apportion the sources of the air pollution, the log-transformed data were processed by applying multivariate techniques. Principal component and factor analysis identified three main factors controlling the data variability: stationary combustion processes with the highest loadings of oxalate, malonate and malate; landfill emission and secondary inorganic aerosol characterized by high levels of ammonium, nitrate and sulfate; a contribution of mineral dust composed of magnesium, calcium and chloride. The hierarchical cluster analysis pointed out a differentiation of the samples into five groups belonging to different variables inputs. For the classification of ambient air samples using nine selected ions, the recognition ability of linear discriminant analysis, k-nearest neighbors, and soft independent modeling of class analogy were 87.0, 94.6, and 97.8 %, respectively. Time-series analysis showed that the traffic emission is more pronounced in winter in contrast to the mineral dust influence, while the effect of waste combustion exhibits no trend.

The two retention models, the linear solvent strength model (LSS) and the quadratic relationship, in addition to artificial neural network (ANN) approach, were compared in their ability to predict the retention behaviour of common cations (Li, Na, NH4, K, Mg, and Ca) in isocratic ion chromatography using the methanesulfonic acid (MSA) eluent. Over wide variations in the MSA concentration, the quadratic model shows a quite good prediction power. LSS can be used only for monovalent cations and in the proximity of the experimental design point. ANN fails to predict the retention for the data not included in the training set. To find the optimal conditions in the experimental design, the normalized resolution product as a chromatographic objective function was employed. The optimum MSA concentration in the eluent on a Dionex CS12 column was found to be 18 mM, with the total analysis time of less than 10 min. [Projekat Ministarstva nauke Republike Srbije, br. III43009]

Interpretive retention modeling was utilized to optimize the isocratic ion chromatographic (IC) separation of the nine anions (formate, fluoride, chloride, nitrite, bromide, nitrate, phosphate, sulfate, oxalate). The carbonate-bicarbonate eluent was used and separation was done on a Dionex AS14 ion-exchange column. The influence of combined effects of two mobile phase factors, the total eluent concentration (2 - 6 mM) and the carbonate/bicaronate ratio from 1:9 to 9:1 (which corespondent to pH range 9.35 - 11.27), on the IC separation was studied. The multiple species analyte/eluent model that takes into account ion-exchange equilibria of the eluent and sample anions was used. In order to estimate the parameters in the model, a non-linear fitting of the retention data, obtained at two-factor three-level experimental design, was applied. To find the optimal conditions in the experimental design, the normalized resolution product as a chromatographic objective function was employed. This criterion includes both the individual peak resolution and the total analysis time. A good agreement between experimental and simulated chromatograms was obtained. [Projekat Ministarstva nauke Republike Srbije, br. III43009]

Arsenic exists in the form of various chemical species differing by their physicochemical behavior, in toxicity, bioavailability and biotransformation. The determination of arsenic species is an important issue for environmental, clinical and food chemistry. However, differentiation of these species is a quite complex analytical task. Numerous speciation procedures have been studied that include electrochemical, chromatographic, spectrometric and hyphenated techniques. This review comprehends the relevant research in the field of arsenic speciation analysis with novel applications and significant advances. Stability of arsenic species and each of the analytical steps (sample collection, storage, preservation, extraction) of the arsenic speciation methods was particularly evaluated. Analytical validation and performance of these methods are also reviewed.

Intrinsic ionization and complexation constants at an alumina/electrolyte interface were studied by the site binding model, while the sorption of alkali cations from aqueous solutions was interpreted by the triple-layer model. The surface properties of alumina were investigated by the potentiometric acid-base titrationmethod. The point of zero charge (pHpzc) of alumina obtained by thismethod was found to be 7.2. The obtained mean values of the intrinsic protonation and ionization constants of the surface hydroxyl groups and the intrinsic surface complexation constant, in different electrolytes, are pKinta1 = 4.4, pKinta2 = 9.6 and pKintM+ = 9.5 respectively.