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 (TU1/2). 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, TU1/2, respectively) were selected as the most predictive possessing 0.839

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, 17ß-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.

Development of Artificial Neural Network Model for Diesel Fuel Properties Prediction using Vibrational Spectroscopy.

This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement.

Optimization of Polystyrene Biodegradation by Bacillus cereus and Pseudomonas alcaligenes Using Full Factorial Design.

Microplastics (MP) are a global environmental problem because they persist in the environment for long periods of time and negatively impact aquatic organisms. Possible solutions for removing MP from the environment include biological processes such as bioremediation, which uses microorganisms to remove contaminants. This study investigated the biodegradation of polystyrene (PS) by two bacteria, Bacillus cereus and Pseudomonas alcaligenes, isolated from environmental samples in which MPs particles were present. First, determining significant factors affecting the biodegradation of MP-PS was conducted using the Taguchi design. Then, according to preliminary experiments, the optimal conditions for biodegradation were determined by a full factorial design (main experiments). The RSM methodology was applied, and statistical analysis of the obtained models was performed to analyze the influence of the studied factors. The most important factors for MP-PS biodegradation by Bacillus cereus were agitation speed, concentration, and size of PS, while agitation speed, size of PS, and optical density influenced the process by Pseudomonas alcaligenes. However, the optimal conditions for biodegradation of MP-PS by Bacillus cereus were achieved at γMP = 66.20, MP size = 413.29, and agitation speed = 100.45. The best conditions for MP-PS biodegradation by Pseudomonas alcaligenes were 161.08, 334.73, and 0.35, as agitation speed, MP size, and OD, respectively. In order to get a better insight into the process, the following analyzes were carried out. Changes in CFU, TOC, and TIC concentrations were observed during the biodegradation process. The increase in TOC values was explained by the detection of released additives from PS particles by LC-MS analysis. At the end of the process, the toxicity of the filtrate was determined, and the surface area of the particles was characterized by FTIR-ATR spectroscopy. Ecotoxicity results showed that the filtrate was toxic, indicating the presence of decomposition by-products. In both FTIR spectra, a characteristic weak peak at 1715 cm-1 was detected, indicating the formation of carbonyl groups (-C=O), confirming that a biodegradation process had taken place.

Assessment of the Influence of Size and Concentration on the Ecotoxicity of Microplastics to Microalgae Scenedesmus sp., Bacterium Pseudomonas putida and Yeast Saccharomyces cerevisiae.

The harmful effects of microplastics are not yet fully revealed. This study tested harmful effects of polyethylene (PE), polypropylene (PP), polystyrene (PS), polyvinyl chloride (PVC), and polyethylene terephthalate (PET) microplastics were tested. Growth inhibition tests were conducted using three microorganisms with different characteristics: Scenedesmus sp., Pseudomonas putida, and Saccharomyces cerevisiae. The growth inhibition test with Scenedesmus sp. is relatively widely used, while the tests with Pseudomonas putida and Saccharomyces cerevisiae were, to our knowledge, applied to microplastics for the first time. The influence of concentration and size of microplastic particles, in the range of 50-1000 mg/L and 200-600 µm, was tested. Determined inhibitions on all three microorganisms confirmed the hazardous potential of the microplastics used. Modeling of the inhibition surface showed the increase in harmfulness with increasing concentration of the microplastics. Particle size showed no effect for Scenedesmus with PE, PP and PET, Pseudomonas putida with PS, and Saccharomyces cerevisiae with PP. In the remaining cases, higher inhibitions followed a decrease in particle size. The exception was Scenedesmus sp. with PS, where the lowest inhibitions were obtained at 400 µm. Finally, among the applied tests, the test with Saccharomyces cerevisiae proved to be the most sensitive to microplastics.

Novel criteria for fast searching for optimal method in gradient ion chromatography: an integrated approach.

In this article, an integrated approach for prediction and optimization in ion chromatography (IC) was presented. The approach provides a fast and reliable insight in the elution behavior of an IC system. The predictions are based on a mathematical model that predicts ion retentions (for both isocratic and gradient modes) by using an empirical isocratic model. Other chromatographic values significant for the optimal elution conditions (resolution, peak asymmetry) are calculated quickly and easily from the predicted retention values of characteristic points of a chromatographic peak. Every day, IC users might find this approach a suitable tool for finding optimal IC elution conditions in a given system.

Development of an ion chromatographic method for determination of inorganic anions in surface water by using computer-assisted gradient optimization methodology.

This work focuses on computer-assisted gradient elution method development in inorganic anion analysis of surface water using ion chromatography. An integral elution model was applied to model gradient retention behavior based on isocratic experimental information. Applied optimization strategy incorporates in-house developed elimination criteria for optimal condition search routine with gradient retention modeling resulting in baseline separation within satisfactory run time. The reliability of developed method was extensively tested by carrying out a performance characteristics evaluation process. Based on the evaluation results it can be stated that the method developed shows more than satisfactory performance characteristics, proving that the applied computer-assisted method development process is a very useful alternative when surface waters differing in composition significantly have to be analyzed in a limited time frame.

Environmental aspects of UV-C-based processes for the treatment of oxytetracycline in water.

This study is focused on oxytetracycline (OTC) degradation by direct photolysis (UV-C) and photobased advanced oxidation processes (AOPs) (UV-C/H2O2 and UV-C/S2O82-). OTC degradation pathways were revealed by LC-MS/MS and GC-MS/MS analyses. The evolution/degradation profiles of 12 detected byproducts were correlated with changes in biodegradability and toxicity toward Vibrio fischeri recorded during the treatment. Both photobased AOPs yielded higher OTC degradation and mineralization rates than direct photolysis. The OTC degradation pathway was found to be rather specific regarding the main reactive species (HO• or SO4•-)/mechanism, yielding different patterns in toxicity changes, while biodegradability profiles were less affected. Biodegradability was correlated with the observed degradation and mineralization kinetics. The recorded toxicity changes indicate that byproducts formed by initial OTC degradation are more toxic than the parent pollutant. The prolonged treatment resulted in the formation of byproducts that contributed to a decrease in toxicity and an increase in biodegradability, as particularly emphasized in the case of UV-C/S2O82-.

Prediction of the chromatographic signal in gradient elution ion chromatography.

This study describes the development of a signal prediction model in gradient elution ion chromatography. The proposed model is based on a retention model and generalized logistic peak shape function which guarantees simplicity of the model and its easy implementation in method development process. Extensive analysis of the model predictive ability has been performed for ion chromatographic determination of bromate, nitrite, bromide, iodide, and perchlorate, using KOH solutions as eluent. The developed model shows good predictive ability (average relative error of gradient predictions 1.94%). The developed model offers short calculation times as well as low experimental effort (only nine isocratic runs are used for modeling).

Key structural features promoting radical driven degradation of emerging contaminants in water.

Diverse contaminants of emerging concern (CECs) can be found in nowadays aquatic environment, possessing high potential to cause adverse ecological and human health effects. Due to their recalcitrance, conventional water treatment methods are shown to be inadequately effective. Thus, their upgrade by advanced oxidation processes, involving the generation of highly reactive species (HO and SO4-), is highly demanded. In order to assess the susceptibility of CECs by HO and SO4-, as well as to determine the corresponding reaction rate constants kHO and kSO4-, the complex experimental studies has to be maintained. The alternative is the application of modeling approaches which correlate structural characteristics with activities/properties of interest, i.e. quantitative structure activity/property relationship (QSAR/QSPR). In this study kHO and kSO4- of fifteen selected CECs were determined by competitive kinetics, and afterward used to elucidate key structural features promoting their degradation. In that purpose, QSPR models were constructed using multiple linear regression (MLR) combined with genetic algorithm (GA) approach. The models were submitted to the internal and external validation (using additional set of 17 CECs). Selected 3-variable models predicting kHO and kSO4- were characterized with high accuracy and predictivity (R2 = 0.876 and Q2 = 0.847 and R2 = 0.832 and Q2 = 0.778, respectively). Although selected models at the first sight include descriptors derived through complicated calculation procedures, their weighting schemes indicate on their relevance and transparency toward established reaction theories and differences regarding radical type.

Evaluation of separation in gradient elution ion chromatography by combining several retention models and objective functions.

In this work, three different methods for modeling of gradient retention were combined with several optimization objective functions in order to find the most appropriate combination to be applied in ion chromatography method development. The system studied was a set of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) with a KOH eluent. The retention modeling methods tested were multilayer perceptron artificial neural network (MLP-ANN), radial-basis function artificial neural network (RBF-ANN), and retention model based on transfer of data from isocratic to gradient elution mode. It was shown that MLP retention model in combination with the objective function based on normalized retention difference product was the most adequate tool for optimization purposes.

Modeling of policies for reduction of GHG emissions in energy sector using ANN: case study-Croatia (EU).

This study describes the development of tool for testing different policies for reduction of greenhouse gas (GHG) emissions in energy sector using artificial neural networks (ANNs). The case study of Croatia was elaborated. Two different energy consumption scenarios were used as a base for calculations and predictions of GHG emissions: the business as usual (BAU) scenario and sustainable scenario. Both of them are based on predicted energy consumption using different growth rates; the growth rates within the second scenario resulted from the implementation of corresponding energy efficiency measures in final energy consumption and increasing share of renewable energy sources. Both ANN architecture and training methodology were optimized to produce network that was able to successfully describe the existing data and to achieve reliable prediction of emissions in a forward time sense. The BAU scenario was found to produce continuously increasing emissions of all GHGs. The sustainable scenario was found to decrease the GHG emission levels of all gases with respect to BAU. The observed decrease was attributed to the group of measures termed the reduction of final energy consumption through energy efficiency measures.

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.

Optimization of IC Separation Based on Isocratic-to-Gradient Retention Modeling in Combination with Sequential Searching or Evolutionary Algorithm.

GRADIENT ION CHROMATOGRAPHY WAS USED FOR THE SEPARATION OF EIGHT SUGARS: arabitol, cellobiose, fructose, fucose, lactulose, melibiose, N-acetyl-D-glucosamine, and raffinose. The separation method was optimized using a combination of simplex or genetic algorithm with the isocratic-to-gradient retention modeling. Both the simplex and genetic algorithms provided well separated chromatograms in a similar analysis time. However, the simplex methodology showed severe drawbacks when dealing with local minima. Thus the genetic algorithm methodology proved as a method of choice for gradient optimization in this case. All the calculated/predicted chromatograms were compared with the real sample data, showing more than a satisfactory agreement.

Multi-criteria decision making development of ion chromatographic method for determination of inorganic anions in oilfield waters based on artificial neural networks retention model.

This paper describes the development of ad hoc methodology for determination of inorganic anions in oilfield water, since their composition often significantly differs from the average (concentration of components and/or matrix). Therefore, fast and reliable method development has to be performed in order to ensure the monitoring of desired properties under new conditions. The method development was based on computer assisted multi-criteria decision making strategy. The used criteria were: maximal value of objective functions used, maximal robustness of the separation method, minimal analysis time, and maximal retention distance between two nearest components. Artificial neural networks were used for modeling of anion retention. The reliability of developed method was extensively tested by the validation of performance characteristics. Based on validation results, the developed method shows satisfactory performance characteristics, proving the successful application of computer assisted methodology in the described case study.