Melek Türker Saçan

Homogenous and Heterogenous Advanced Oxidation of Two Commercial Reactive Dyes

Two commercial reactive dyes, the azo dye Reactive Black 5 and the copper phythalocyanine dye Reactive Blue 21, have been treated at a concentration of 75 mg l-1 by titanium dioxide mediated photocatalytic (TiO2/UV), dark and UV-light assisted Fenton (Fe2+/H2O2) and Fenton-like (Fe3+/H2O2) processes in acidic medium. For the treatment of Reactive Black 5, all investigated advanced oxidation processes were quite effective in terms of colour, COD as well as TOC removal. Moreover, the relative growth inhibition of the azo dye towards the marine algae Dunaliella tertiolecta that was initially 70%, did not exhibit an increase during the studied advanced oxidation reactions and complete detoxification at the end of the treatment period could be achieved for all investigated treatment processes. However, for Reactive Blue 21, abatement in COD and UV-VIS absorbance values was mainly due to the adsorption of the dye on the photocatalyst surface and/or the coagulative effect of Fe3+/Fe2+ ions. Although only a limited fraction of the copper phythalocyanine dye underwent oxidative degradation, 47% of the total copper in the dye was already released after 1 h photocatalytic treatment.

Single and mixture toxicity of pharmaceuticals and chlorophenols to freshwater algae Chlorella vulgaris

Organisms in the aquatic environment are exposed to a variety of substances of numerous chemical classes. The unintentional co-occurrence of pharmaceuticals and other contaminants of emerging concern may pose risk to non-target organisms. In this study, individual and binary mixture toxicity experiments of selected pharmaceuticals (ibuprofen and ciprofloxacin) and chlorophenols (2.4-dichlorophenol (2,4-DCP) and 3-chlorophenol (3-CP)) have been performed with freshwater algae Chlorella vulgaris. All experiments have been carried out according to the 96-h algal growth inhibition test OECD No. 201. Binary mixture tests were conducted using proportions of the respective IC50s in terms of toxic unit (TU). The mixture concentration-response curve was compared to predicted effects based on both the concentration addition (CA) and the independent action (IA) model. Additionally, the Combination Index (CI)-isobologram equation method was used to assess toxicological interactions of the binary mixtures. All substances individually tested had a significant effect on C. vulgaris population density and revealed IC50 values <100mgL(-1) after exposure period of 96-h. The toxic ranking of these four compounds to C. vulgaris was 2,4-DCP>ciprofloxacin>3-CP>ibuprofen. Generally, it can be concluded from this study that toxic mixture effects of all tested chemicals to C. vulgaris are higher than the individual effect of each mixture component. It could be demonstrated that IC50 values of the tested mixtures predominately lead to additive effects. The CA model is appropriate to estimate mixture toxicity, while the IA model tends to underestimate the joint effect. The CI-isobologram equation method predicted the mixtures accurately and elicited synergism at low effect levels for the majority of tested combinations.

First toxicity data of chlorophenols on marine alga Dunaliella tertiolecta: correlation of marine algal toxicity with hydrophobicity and interspecies toxicity relationships.

The toxicity of phenol and 13 chlorinated phenols to the marine alga Dunaliella tertiolecta is presented for the first time. The newly generated marine algal toxicity data was found to correlate strongly with the widely used hydrophobicity parameter-the logarithm of the n-octanol-water partition coefficient (log K(OW)). Interspecies relationships using the new marine algal toxicity data of chlorophenols with the previously published data on bacterium (Vibrio fischeri), protozoan (Tetrahymena pyriformis), daphnid (Daphnia magna), freshwater alga (Pseudokirchneriella subcapitata), and fish (Pimephales promelas) revealed promising results that could be exploited in extrapolations using freshwater data to predict marine algal toxicity.

Quantitative structure–activity relationships (QSARs) using the novel marine algal toxicity data of phenols

The present study reports for the first time in its entirety the toxicity of 30 phenolic compounds to marine alga Dunaliella tertiolecta. Toxicity of polar narcotics and respiratory uncouplers was strongly correlated to hydrophobicity as described by the logarithm of the octanol/water partition coefficient (Log P). Compounds expected to act by more reactive mechanisms, particularly hydroquinones, were shown to have toxicity in excess of that predicted by Log P. A quality quantitative structure-activity relationship (QSAR) was obtained with Log P and a 2D autocorrelation descriptor weighted by atomic polarizability (MATS3p) only after the removal of hydroquinones from the data set. In an attempt to model the whole data set including hydroquinones, 3D descriptors were included in the modeling process and three quality QSARs were developed using multiple linear regression (MLR). One of the most significant results of the present study was the superior performance of the consensus MLR model, obtained by averaging the predictions from each individual linear model, which provided excellent prediction accuracy for the test set (Q(test)²=0.94). The four-parameter Counter Propagation Artificial Neural Network (CP ANN) model, which was constructed using four out of six descriptors that appeared in the linear models, also provided an excellent external predictivity (Q(test)²=0.93). The proposed algal QSARs were further tested in their predictivity using an external set comprising toxicity data of 44 chemicals on freshwater alga Pseudokirchneriella subcapitata. The two-parameter global model employing a 3D descriptor (Mor24m) and a charge-related descriptor (C(ortho)) not only had high external predictivity (Q(ext)²=0.74), but it also had excellent external data set coverage (%97).

QSTR modelling of the acute toxicity of pharmaceuticals to fish

Extensive use of pharmaceuticals as human and veterinary medication raises concerns for their adverse effects on non-target organisms. The purpose of this study was to employ multiple linear regression (MLR) to predict the toxicities of a diverse set of pharmaceuticals to fish. The descriptor pool consisted of about 1500 descriptors calculated using Dragon 5.4, Spartan 06 and Codessa 2.2 software. Descriptor selection was made by the heuristic method available in Codessa 2.2. The data set was divided into training and test sets using Kohonen networks. The training set contained approximately 65% of the compounds of the full data set (99 compounds). The training set model contained eight descriptors from all dimensions, all of which were obtained from Dragon 5.4. The statistical parameters of the model for the training set are R 2 = 0.664, F = 13.588, and (LOO) = 0.542 while it achieves R 2 = 0.605 for the test set. The training, test and external sets have no response outliers considering the standardized residual greater than three. The external validation of the model was made with a set of pharmaceuticals obtained from several databases. The is 0.777, reflecting a relatively good predictive power for the external set.

Physico-chemical properties of PCDD/PCDFs and phthalate esters.

QSPR models for water solubility (S), n-octanol/water partition coefficient (K OW), and Henrys law constant (H) for polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzo-p-furans (PCDFs) and phthalates have been established based on two different sets of parameters. Those parameters were topology based characteristic root index (CRI) and three semi-empirical molecular descriptors, namely – energies of the highest occupied and the lowest unoccupied molecular orbital (E HOMO and E LUMO), and dipole moment (μ). The best fit equation found by ‘forward multiple linear regression’ showed that the topology based CRI was the most important parameter for the modelling of solubility and n-octanol/water partition coefficient. For n-octanol/water partition coefficient a two-parameter equation including the CRI and E HOMO with a correlation coefficient of r = 0.992 was obtained whereas a three-parameter equation for solubility and Henrys law constant including the CRI, E LUMO and μ with a correlation coefficient of r = 0.986 and r = 0.933 was obtained, respectively. E HOMO and μ didn’t appear in the same model because of the collinearity. The results of modified jackknife tests indicated that the three models were statistically robust. Mean deviation of calculated values from experimental data amounted to 0.27, 0.17, and 0.28 log units for the three properties mentioned. The developed models have been used to predict the S, K OW and H of compounds not included in the training sets.

Prediction of the soil sorption coefficient of organic pollutants by the characteristic root index model

Abstract Quantitative Structure-Property Relationship (QSPR) technique was used to develop a simple predictive model for the soil sorption coefficient (K oc ) of Polychlorinated Biphenyls (PCBs), chlorinated phenols (CPs) and chlorinated benzenes (CBs). Tests performed on 36 such compounds clearly demonstrate that this simple model accurately predicts the soil sorption coefficients of these chemicals. The correlation equation was then used to predict the unknown soil sorption data of chlorinated biphenyls, phenols and benzenes. The result of these tests also demonstrates that the CRI can be an alternative and a very accurate predictive tool for the soil sorption coefficients of structurally similar compounds.

Application of the characteristic root index model to the estimation of N-octanol/water partition coefficients. polychlorinated biphenyls

Abstract The ability of the Characteristic Root Index (CRI) to predict the n-octanol/water partition coefficients (K ow ) of polychlorinated biphenyls (PCBs) has been demonstrated. The CRI for 58 congeners were calculated from the roots of the characteristic polynomial obtained from a square matrix comprising all possible orders of valence connectivity index — except zero order — for a congener as the entry. The correlation proposed for the estimation of Kow of PCBs based on the CRI model contains only one variable and predicts log Kow with high regression coefficient (r=0.998) and may be regarded satisfactory for environmental applications.

Water quality and heavy metal monitoring in water and sediment samples of the Küçükçekmece Lagoon, Turkey (2002-2003).

Physical and chemical parameters have been analyzed in water samples from a brackish water lagoon, Küçükçekmece, located on the western outskirts of Istanbul. Samples were collected every two months for a year from nine sampling stations. Of the parameters measured, temperature, pH, salinity, nitrate and phosphate showed changes when compared with the previously published data. The lagoon was found eutrophic as it was reported previously. Sulphate and COD levels were higher when compared with the standards established by the Turkish Water Pollution and Control Regulation. Additionally, concentrations of heavy metals (Cd, Co, Cr, Cu, Mn, Ni, Pb, Zn) in water and bottom sediments were measured and compared with the standards established by the Turkish Water Pollution and Control Regulation and with the previously published data. The results were analysed statistically with respect to location and any relationships between the concentration of the elements in corresponding water and sediment samples were examined. Principal Component Analysis of water samples allowed us to discriminate three areas affected mainly by heavy metal contamination, possibly due to industrial, commercial and/or urban activities. Generally, the concentrations of the heavy metals were higher at stations near the three estuaries, suggesting a direct influence of the three creeks on the pollution of the Küçükçekmece Lagoon. Although elevated levels of Cd were recorded in several water samples, it was not detected in sediment. On the other hand, a particularly high level of Cr pollution was recorded most of the water and sediment samples.

Assessment and modeling of the novel toxicity data set of phenols to Chlorella vulgaris.

This study provides for the first time the 96 h toxicity of 30 phenols to Chlorella vulgaris. Analysis of the novel data set revealed that the algal toxicity of polar narcotics and respiratory uncouplers was correlated strongly to the pH corrected hydrophobicity parameter, Log D, demonstrating the importance of ionization in the C. vulgaris test system. Compounds expected to act by more reactive mechanisms were shown to have toxicity in excess of that predicted by Log D and were successfully modelled using the activation energy index (AEI). Three global quantitative structure-activity relationships (QSARs) were constructed using the C. vulgaris data set and validated externally using a data set retrieved from literature comprising the toxicity of 58 compounds to freshwater alga Pseudokirchneriella subcapitata. Results revealed that the response-surface model was highly interpretable and provided acceptable predictions for polar narcotics and respiratory uncouplers, though it lacked the reliability to predict the toxicity of reactive phenols. In two other externally validated QSAR models, a WHIM (Weighted Holistic Invariant Molecular) descriptor, namely, Tm (T total size index/weighted by atomic masses), revealed promising results that could be used to predict algal toxicity of compounds other than phenols such as anilines.