Toxicity of aromatic pollutants and photooxidative intermediates in water: A QSAR study

Extensive commercial use of aromatic hydrocarbons results with significant amounts of these chemicals and related by-products in waters, causing a severe ecological and health threat, thus requiring an increased attention. This study was aimed at developing models for prediction of the initial toxicity of the aromatic water-pollutants (expressed as EC₅₀ and TU₀) as well as the toxicity of their intermediates at half-life of the parent pollutant (TU_1/2). For that purpose, toxicity toward Vibrio fischery was determined for 36 single-benzene ring compounds (S-BRCs), diversified by the type, number and position of substituents. Quantitative structure-activity relationship (QSAR) methodology paired with genetic algorithm optimization tool and multiple linear regression was applied to obtain the models predicting the targeted toxicity, which are based on pure structural characteristics of the tested pollutants, avoiding thus additional experimentation. Upon derivation of the models and extensive analysis on training and test sets, 4-, 4- and 5-variable models (for EC₅₀ and TU₀, TU_1/2, respectively) were selected as the most predictive possessing 0.839<R²< 0.901 and 0.789<Q²< 0.859. The analysis of the selected descriptors indicated three major structural characteristics influencing the toxicity: electronegativity, geometry and electrotopological states of the molecule. Degradation kinetics determining as well the pathways of intermediates formation, reflected over ionization potential, was found to be an important parameter determining the toxicity in half-life.

Influence of process parameters on the effectiveness of photooxidative treatment of pharmaceuticals

In this study, UV-C/H₂O₂ and UV-C/[Formula: see text] processes as photooxidative Advanced oxidation processes were applied for the treatment of seven pharmaceuticals, either already included in the Directive 2013/39/EU "watch list" (17α- ethynylestradiol, 17β-estradiol) or with potential to be added in the near future due to environmental properties and increasing consumption (azithromycin, carbamazepine, dexamethasone, erythromycin and oxytetracycline). The influence of process parameters (pH, oxidant concentration and type) on the pharmaceuticals degradation was studied through employed response surface modelling approach. It was established that degradation obeys first-order kinetic regime regardless structural differences and over entire range of studied process parameters. The results revealed that the effectiveness of UV-C/H₂O₂ process is highly dependent on both initial pH and oxidant concentration. It was found that UV-C/[Formula: see text] process, exhibiting several times faster degradation of studied pharmaceuticals, is less sensitive to pH changes providing practical benefit to its utilization. The influence of water matrix on degradation kinetics of studied pharmaceuticals was studied through natural organic matter effects on single component and mixture systems.

How High Pressure Unifies Solvation Processes in Liquid Chromatography

A series of core-shell-based stationary phases of varying surface chemistry were subjected to solvent adsorption investigation under ultra-HPLC conditions. Acetonitrile and water excess isotherms were measured using a minor disturbance method. It was observed that adsorption of organic solvent is unified under high pressure. Preferential solvation due to specific interactions between the stationary phases and solvent molecules was limited. The obtained results showed that the solvation process is almost independent of surface chemistry, in contrast to HPLC conditions in which specific interactions differentiate solvation processes.

Prediction of biodegradability of aromatics in water using QSAR modeling

The study was aimed at developing models for predicting the biodegradability of aromatic water pollutants. For that purpose, 36 single-benzene ring compounds, with different type, number and position of substituents, were used. The biodegradability was estimated according to the ratio of the biochemical (BOD₅) and chemical (COD) oxygen demand values determined for parent compounds ((BOD₅/COD)₀), as well as for their reaction mixtures in half-life achieved by UV-C/H₂O₂ process ((BOD₅/COD)_t1/2). The models correlating biodegradability and molecular structure characteristics of studied pollutants were derived using quantitative structure-activity relationship (QSAR) principles and tools. Upon derivation of the models and calibration on the training and subsequent testing on the test set, 3- and 5-variable models were selected as the most predictive for (BOD₅/COD)₀ and (BOD₅/COD)_t1/2, respectively, according to the values of statistical parameters R² and Q². Hence, 3-variable model predicting (BOD₅/COD)₀ possessed R²=0.863 and Q²=0.799 for training set, and R²=0.710 for test set, while 5-variable model predicting (BOD₅/COD)_1/2 possessed R²=0.886 and Q²=0.788 for training set, and R²=0.564 for test set. The selected models are interpretable and transparent, reflecting key structural features that influence targeted biodegradability and can be correlated with the degradation mechanisms of studied compounds by UV-C/H₂O₂.

Development of an ion chromatographic gradient retention model from isocratic elution experiments

When facing separation problems in ion chromatography, chromatographers often lack guidelines to decide a priori if isocratic elution will give enough separation in a reasonable analysis time or a gradient elution will be required. This situation may be solved by the prediction of retention in gradient elution mode by using isocratic experimental data. This work describes the development of an ion chromatographic gradient elution retention model for fluoride, chloride, nitrite, bromide, nitrate, sulfate and phosphate by using isocratic experimental data. The isocratic elution retention model was developed by applying a polynomial relation between the logarithm of the retention factor and logarithm of the concentration of competing ions; the gradient elution retention model was based on the stepwise numerical integration of the corresponding differential equation. It was shown that the developed gradient elution retention model was not significantly affected by transferring data form isocratic experiment. The root mean squared prediction error for gradient elution retention model was between 0.0863 for fluoride and 0.7027 for bromide proving a very good predictive ability of developed gradient elution retention model.

Application of artificial neural networks for gradient elution retention modelling in ion chromatography

Gradient elution in ion chromatography (IC) offers several advantages: total analysis time can be significantly reduced, overall resolution of a mixture can be increased, peak shape can be improved (less tailing) and effective sensitivity can be increased (because there is little variation in peak shape). More importantly, it provides the maximum resolution per time unit. The aim of this work was the development of a suitable artificial neural network (ANN) gradient elution retention model that can be used in a variety of applications for method development and retention modelling of inorganic anions in IC. Multilayer perceptron ANNs were used to model the retention behaviour of fluoride, chloride, nitrite, sulphate, bromide, nitrate and phosphate in relation to the starting time of gradient elution and the slope of the linear gradient elution curve. The advantage of the developed model is the application of an optimized two-phase training algorithm that enables the researcher to make use of the advantages of first- and second-order training algorithms in one training procedure. This results in better predictive ability, with less time required for the calculations. The number of hidden layer neurons and experimental data points used for the training set were optimized in terms of obtaining a precise and accurate retention model with respect to minimization of unnecessary experimentation and time needed for the calculation procedures. This study shows that developed, ANNs are the method of first choice for retention modelling of inorganic anions in IC.

Development of an inorganic cations retention model in ion chromatography by means of artificial neural networks with different two-phase training algorithms

This paper describes development of artificial neural network (ANN) retention model, which can be used for method development in variety of ion chromatographic applications. By using developed retention model it is possible both to improve performance characteristic of developed method and to speed up new method development by reducing unnecessary experimentation. Multilayered feed forward neural network has been used to model retention behaviour of void peak, lithium, sodium, ammonium, potassium, magnesium, calcium, strontium and barium in relation with the eluent flow rate and concentration of methasulphonic acid (MSA) in eluent. The probability of finding the global minimum and fast convergence at the same time were enhanced by applying a two-phase training procedure. The developed two-phase training procedure consists of both first and second order training. Several training algorithms were applied and compared, namely: back propagation (BP), delta-bar-delta, quick propagation, conjugate gradient, quasi Newton and Levenberg-Marquardt. It is shown that the optimized two-phase training procedure enables fast convergence and avoids problems arisen from the fact that every new weight initialization can be regarded as a new starting position and yield irreproducible neural network if only second order training is applied. Activation function, number of hidden layer neurons and number of experimental data points used for training set were optimized in order to insure good predictive ability with respect to speeding up retention modelling procedure by reducing unnecessary experimental work. The predictive ability of optimized neural networks retention model was tested by using several statistical tests. This study shows that developed artificial neural network are very accurate and fast retention modelling tool applied to model varied inherent non-linear relationship of retention behaviour with respect to mobile phase parameters.

Simultaneous determination of six inorganic anions in drinking water by non-suppressed ion chromatography

A non-suppressed ion chromatographic method with conductometric detection is described for the simultaneous determination of six inorganic anions: fluoride, chloride, nitrite, bromide, nitrate and sulphate. The separation was achieved on a low-capacity anion-exchange column Metrohm IC Anion Column Super Sep, with a mobile phase consisting of phtalic acid dissolved in high-purity water, 2-amino-2-hydroxymethyl-1,3-propendiol and acetonitrile. In this work computer optimization procedures, using computer programs to select chromatographic conditions have been used, leading to the achievement of a desired separation. By using the different optimization methods in an integrated manner it is, however, possible to both speed method development, by reducing unnecessary experimentation, and to overcome the many shortcomings of each method, because of the different approaches. The purpose of this work is to improve and characterise the method for simultaneous determination of six inorganic anions in drinking water by non-suppressed ion chromatography, using optimization procedures, in order to be applied to the routine analysis. The proposed method has numerous advantages over the other widely used non-suppressed ion chromatography methods: higher selectivity, shorter analysis time, lower quantitation and detection limits. The performance characteristics of the method were established by determining the following validation parameters: precision and accuracy, linearity, detection limits and quantitation limits.