Environmetal Chemistry

The quantitative structure-retention relationship (QSRR) was employed to predict the retention time (min) (RT) of pesticides using five molecular descriptors selected by genetic algorithm (GA) as a feature selection technique. Then the data set was randomly divided into training and prediction sets. The selected descriptors were used as inputs of multi-linear regression (MLR), multilayer perceptron neural network (MLP-NN) and generalized regression neural network (GR-NN) modeling techniques to build QSRR models. Both linear and nonlinear models show good predictive ability, of which the GR-NN model demonstrated a better performance than that of the MLR and MLP-NN models. The root mean square error of cross validation of the training and the prediction set for the GR-NN model was 1.245 and 2.210, and the correlation coefficients (R) were 0.975 and 0.937 respectively, while the square correlation coefficient of the cross validation (Q 2 LOO) on the GR-NN model was 0.951, revealing the reliability of this model. The obtained results indicated that GR-NN could be used as predictive tools for prediction of RT (min) values for understudy pesticides.

Thyroid hormone disrupting chemicals (THDCs) interfere with the thyroid hormone system and may induce multiple severe physiological disorders. Indoor dust ingestion is a major route of THDCs exposure in humans, and one of the molecular targets of these chemicals is the hormone transporter transthyretin (TTR). To virtually screen indoor dust contaminants and their metabolites for THDCs targeting TTR, we developed a quantitative structure-activity relationship (QSAR) classification model. The QSAR model was applied to an in-house database including 485 organic dust contaminants reported from literature data and their 433 in silico derived metabolites. The model predicted 37 (7.6%) dust contaminants and 230 (53.1%) metabolites as potential TTR binders. Four new THDCs were identified after testing 23 selected parent dust contaminants in a radio-ligand TTR binding assay; 2,2',4,4'-tetrahydroxybenzophenone, perfluoroheptanesulfonic acid, 3,5,6-trichloro-2-pyridinol, and 2,4,5-tric...

In this work the aqueous solubilities of 145 drug-like compounds were predicted from their theoretical derived molecular descriptors. Descriptors which were selected by stepwise multiple subset selection methods are; 1st-order solvation connectivity index, average span R, overall hydrogen bond basicity, and percent of hydrophilic surface area. These descriptors can encode features of molecules which are effected on dispersion, hydrophobic and steric interactions between solute and solvent molecules. To develop quantitative structureactivity relationship (QSAR) models, the methods of multiple linear regressions, least-squares support vector machine, and artificial neural network (ANN) were used by applying the selected descriptors as their inputs. The obtained statistical parameters of these models revealed that ANN model was superior to other methods. The standard error (SE), average error (AE), and average absolute error (AAE) for ANN model are: SE = 0.714, AE = ¹0.178, and AAE = 0.546, while these values for internal test set are: SE = 0.830, AE = ¹0.056, and AAE = 0.630 and for external test set are: SE = 0.762, AE = ¹0.431, and AAE = 0.626, respectively. Moreover the leave-many-out cross validation test was used to further investigate the prediction power and robustness of model, which lead to R L10O 2 = 0.816 and SPRESS = 0.32 for ANN model, which revealed the reliability of this model.

A set of 69 drug-like compounds with corneal permeability was studied using quantitative and qualitative modeling techniques. Multiple linear regression (MLR) and multilayer perceptron neural network (MLP-NN) were used to develop quantitative relationships between the corneal permeability and seven molecular descriptors selected by stepwise MLR and sensitivity analysis methods. In order to evaluate the models, a leave many out cross-validation test was performed, which produced the statistic Q 2 = 0.584 and SPRESS = 0.378 for MLR and Q 2 = 0.774 and SPRESS = 0.087 for MLP-NN. The obtained results revealed the suitability of MLP-NN for the prediction of corneal permeability. The contribution of each descriptor to MLP-NN model was evaluated. It indicated the importance of the molecular volume and weight. The pattern recognition methods principal component analysis (PCA) and hierarchical clustering analysis (HCA) have been employed in order to investigate the possible qualitative relationships between the molecular descriptors and the corneal permeability. The PCA and HCA results showed that, the data set contains two groups. Then, the same descriptors used in quantitative modeling were considered as inputs of counter propagation neural network (CPNN) to classify the compounds into low permeable (LP) and very low permeable (VLP) categories in supervised manner. The overall classification non error rate was 95.7% and 95.4% for the training and prediction test sets, respectively. The results revealed the ability of CPNN to correctly recognize the compounds belonging to the categories. The proposed models can be successfully used to predict the corneal permeability values and to classify the compounds into LP and VLP ones.

Carbon isotope fractionation factors were determined with the dichloro elimination of γ-hexachlorocyclohexane (γ-HCH) by the sulfate-reducing bacteria Desulfococcus multivorans DSM 2059 and Desulfovibrio gigas DSM 1382. Both strains are known for cometabolic HCH dechlorination. Degradation experiments with γ-HCH in concentrations of 22-25 µM were carried out using benzoate (for D. multivorans) and lactate (for D. gigas) as electron donors, respectively. γ-HCH was dechlorinated by both bacterial strains within four weeks, and the metabolites γ-3,4,5,6-tetrachlorocyclohexene (γ-TCCH), chlorobenzene (CB), and benzene were formed. The carbon isotope fractionation of γ-HCH dechlorination was quantified by the Rayleigh model, using a bulk enrichment factor (εC) of -3.9 ( 0.6 for D. gigas and -3.4 ( 0.5 for D. multivorans, which correspond to apparent kinetic isotope effect (AKIE C ) values of 1.023 ( 0.004 or 1.02 ( 0.003 for stepwise Cl-C bond cleavage. The extent and range of isotope fractionation suggest that γ-HCH dechlorination can be monitored in anoxic environments by compound-specific isotope analysis (CSIA).

• The isotope fractionation of α-HCH during dechlorination by FeS was explored. • Dehydrochlorination is the main degradation pathway of the α-HCH by FeS. • The apparent rate constants during dehalogenation of α-HCH by FeS increased with pH. • Carbon isotope fractionation occurs during dehalogenation of α-HCH by FeS.

The ability of carbon nanotubes (CNTs) to undergo surface modification allows them to form nanocomposites (NCs) with materials such as polymers, metal nanoparticles, biomolecules, and metal oxides. The biocidal nature, protein fouling resistance, and fouling release properties of CNT-NCs render them the perfect material for biofouling prevention. At the same time, the cytotoxicity of CNT-NCs can be reduced before applying them as substrates to promote biofilm formation in environmental biotechnology applications. This paper reviews the potential prospects of CNT-NCs to accomplish two widely varying objectives in environmental engineering applications: (i) preventing biofouling, and (ii) promoting the formation of desirable biofilms on materials surface. This paper addresses practical issues such as costs, risks to human health, and ecological impacts that are associated with the application, development and commercialization of CNT-NC technology.

Cities in developing nations have to deal with two significant sustainability challenges amidst rampant urbanization. First, consumer-generated food waste is increasing monumentally since open dumping is still followed as a predominant practice, the negative environmental externalities associated with food waste disposal are growing beyond manageable proportions. Second, the dependency on conventional fuels like diesel to operate transit buses, which is one of the major causes for deteriorating urban air quality. A nexus established between food waste management and operation of transit buses can improve the sustainable performance of cities in developing nations. In this study, a Life Cycle Assessment (LCA) supported Social Cost-Benefit Analysis (SCBA) is performed by considering a hypothetical scenario of establishing a large food waste treating biomethanation plant in Mumbai, India. The food waste from the city is transported to a biomethanation plant where it is subjected to an ...

The health of key species in the Baltic region has been impaired by exposure to anthropogenic hazardous substances (AHSs), which accumulate in organisms and are transferred through food chains. There is, thus, a need for comprehensive characterization of the occurrence and accumulation of AHSs in the ecosystem. In this study, we use a non-target screening (NTS) approach for this purpose. A major challenge in NTS of biological samples is the removal of matrix components such as lipids that may interfere with the detection and identification of compounds of interest. Here, we combine gel permeation chromatography with Florisil® column fractionation to achieve sufficient lipid removal for gas chromatography–high-resolution mass spectrometry analysis using electron ionization (EI) and electron capture negative ion chemical ionization (ECNI). In addition, we present new data processing workflows designed to systematically find and identify frequently occurring and biomagnifying AHSs, inc...