Advances in the Applications of Clinoptilolite-Rich Tuffs.

Adsorptive, catalytic, and antibacterial properties of clinoptilolite-rich tuffs (ZT) are presented here. ZT transformed into Fe-containing ZT (Fe-ZT) removes various organic and inorganic anions from water. Fe-ZT, which contains selenium, is beneficial for growing Pleurotus ostreatus mushrooms. The fungi convert inorganic Se from Fe-ZT into a more useful organically bonded form. ZT and Fe-ZT as supplements retain nitrogen and potassium in sandy, silty loam and silty clay soils. ZT shows an affinity toward toxic metal cations, which are essential for cleaning contaminated water. The adsorption of atenolol, acetylsalicylic, and salicylic acid onto M-ZT (M-Cu2+, Mn2+, Ni2+, or Zn2+) from water solutions suggests that both the natures of M and pharmaceuticals have a significant impact on the adsorption mechanism and determine the adsorption capability of the ZT. ZT is an excellent carrier for ultrafine (2-5 nm) nano oxide particles, which have been shown to have catalytic activity in different chemical processes and photodegradation reactions of organic pollutants. ZT can also be transformed into SO4-SnO2-ZT, which is catalytically active as a solid acid. M-ZT is an effective carrier of valuable bacteria. Ag-ZT possesses beneficial bactericidal activity in disinfecting water and soil remediation.

Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study.

The aims of this study are to create an artificial neural network (ANN) model using non-specific water quality parameters and to examine the accuracy of three different ANN architectures: General Regression Neural Network (GRNN), Backpropagation Neural Network (BPNN) and Recurrent Neural Network (RNN), for prediction of dissolved oxygen (DO) concentration in the Danube River. The neural network model has been developed using measured data collected from the Bezdan monitoring station on the Danube River. The input variables used for the ANN model are water flow, temperature, pH and electrical conductivity. The model was trained and validated using available data from 2004 to 2008 and tested using the data from 2009. The order of performance for the created architectures based on their comparison with the test data is RNN > GRNN > BPNN. The ANN results are compared with multiple linear regression (MLR) model using multiple statistical indicators. The comparison of the RNN model with the MLR model indicates that the RNN model performs much better, since all predictions of the RNN model for the test data were within the error of less than ± 10 %. In case of the MLR, only 55 % of predictions were within the error of less than ± 10 %. The developed RNN model can be used as a tool for the prediction of DO in river waters.

PM(10) emission forecasting using artificial neural networks and genetic algorithm input variable optimization.

This paper describes the development of an artificial neural network (ANN) model for the forecasting of annual PM(10) emissions at the national level, using widely available sustainability and economical/industrial parameters as inputs. The inputs for the model were selected and optimized using a genetic algorithm and the ANN was trained using the following variables: gross domestic product, gross inland energy consumption, incineration of wood, motorization rate, production of paper and paperboard, sawn wood production, production of refined copper, production of aluminum, production of pig iron and production of crude steel. The wide availability of the input parameters used in this model can overcome a lack of data and basic environmental indicators in many countries, which can prevent or seriously impede PM emission forecasting. The model was trained and validated with the data for 26 EU countries for the period from 1999 to 2006. PM(10) emission data, collected through the Convention on Long-range Transboundary Air Pollution - CLRTAP and the EMEP Programme or as emission estimations by the Regional Air Pollution Information and Simulation (RAINS) model, were obtained from Eurostat. The ANN model has shown very good performance and demonstrated that the forecast of PM(10) emission up to two years can be made successfully and accurately. The mean absolute error for two-year PM(10) emission prediction was only 10%, which is more than three times better than the predictions obtained from the conventional multi-linear regression and principal component regression models that were trained and tested using the same datasets and input variables.

Isolation and biological evaluation of jatrophane diterpenoids from Euphorbia dendroides.

From the Montenegrin spurge Euphorbia dendroides, seven new diterpenoids [jatrophanes (1-6) and a tigliane (7)] were isolated and their structures elucidated by spectroscopic techniques. The biological activity of the new compounds was studied against four human cancer cell lines. The most effective jatrophane-type compound (2) and its structurally closely related derivative (1) were evaluated for their interactions with paclitaxel and doxorubicin using a multi-drug-resistant cancer cell line. Both compounds exerted a strong reversal potential resulting from inhibition of P-glycoprotein transport.