Organochlorines in urban soils from Central India: probabilistic health hazard and risk implications to human population.

This study presents distribution of organochlorines (OCs) including HCH, DDT and PCBs in urban soils, and their environmental and human health risk. Forty-eight soil samples were extracted using ultrasonication, cleaned with modified silica gel chromatography and analyzed by GC-ECD. The observed concentrations of ∑HCH, ∑DDT and ∑PCBs in soils ranged between < 0.

Valorization of Poly (ethylene) terephthalate (PET) wastes into magnetic carbon for adsorption of antibiotic from water: Characterization and application.

Waste Polyethylene terephthalate (PET) bottles were pyrolyzed in the presence of nitrogen and converted into activated carbon (PETAC) by physical activation in carbon dioxide flow. An ex-situ precipitation and external reduction method were applied for the intercalation of ferromagnetic iron oxides onto the PETAC matrix. The characteristic structural and chemical properties of PETAC and magnetic PETAC (M-PETAC) were studied by Brunauer Emmett Teller (BET) surface area analysis, Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), Fourier Transform Infrared (FTIR) analysis, Raman spectroscopy, X-Ray Diffraction (XRD) analysis, Energy Dispersive analysis of X-rays (EDAX), Vibrating Sample Magnetometer (VSM), Thermal gravimetric analysis (TGA) and elemental analysis.

Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches.

Binding affinity of chemical to carbon is an important characteristic as it finds vast industrial applications. Experimental determination of the adsorption capacity of diverse chemicals onto carbon is both time and resource intensive, and development of computational approaches has widely been advocated. In this study, artificial intelligence (AI)-based ten different qualitative and quantitative structure-property relationship (QSPR) models (MLPN, RBFN, PNN/GRNN, CCN, SVM, GEP, GMDH, SDT, DTF, DTB) were established for the prediction of the adsorption capacity of structurally diverse chemicals to activated carbon following the OECD guidelines.

Occurrence of pharmaceuticals in urban wastewater of north Indian cities and risk assessment.

Six pharmaceuticals of different categories, such as nonsteroidal anti-inflammatory drugs (ibuprofen, ketoprofen, naproxen, diclofenac), anti-epileptic (carbamazepine), and anti-microbial (trimethoprim), were investigated in wastewater of the urban areas of Ghaziabad and Lucknow, India. Samples were concentrated by solid phase extraction (SPE) and determined by high-performance liquid chromatography (HPLC) methods. The SPE-HPLC method was validated according to the International Conference on Harmonization guidelines.

Investigating hydrochemistry of groundwater in Indo-Gangetic alluvial plain using multivariate chemometric approaches.

Groundwater hydrochemistry of an urban industrial region in Indo-Gangetic plains of north India was investigated. Groundwater samples were collected both from the industrial and non-industrial areas of Kanpur. The hydrochemical data were analyzed using various water quality indices and nonparametric statistical methods.

Predicting dissolved oxygen concentration using kernel regression modeling approaches with nonlinear hydro-chemical data.

Kernel function-based regression models were constructed and applied to a nonlinear hydro-chemical dataset pertaining to surface water for predicting the dissolved oxygen levels. Initial features were selected using nonlinear approach. Nonlinearity in the data was tested using BDS statistics, which revealed the data with nonlinear structure.

Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches.

Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived.

Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches.

The research aims to develop global modeling tools capable of categorizing structurally diverse chemicals in various toxicity classes according to the EEC and European Community directives, and to predict their acute toxicity in fathead minnow using set of selected molecular descriptors. Accordingly, artificial intelligence approach based classification and regression models, such as probabilistic neural networks (PNN), generalized regression neural networks (GRNN), multilayer perceptron neural network (MLPN), radial basis function neural network (RBFN), support vector machines (SVM), gene expression programming (GEP), and decision tree (DT) were constructed using the experimental toxicity data. Diversity and non-linearity in the chemicals' data were tested using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics.

Predicting adsorptive removal of chlorophenol from aqueous solution using artificial intelligence based modeling approaches.

The research aims to develop artificial intelligence (AI)-based model to predict the adsorptive removal of 2-chlorophenol (CP) in aqueous solution by coconut shell carbon (CSC) using four operational variables (pH of solution, adsorbate concentration, temperature, and contact time), and to investigate their effects on the adsorption process. Accordingly, based on a factorial design, 640 batch experiments were conducted. Nonlinearities in experimental data were checked using Brock-Dechert-Scheimkman (BDS) statistics.

Modeling and optimization of reductive degradation of chloramphenicol in aqueous solution by zero-valent bimetallic nanoparticles.

Purpose: The present study aims to investigate the individual and combined effects of temperature, pH, zero-valent bimetallic nanoparticles (ZVBMNPs) dose, and chloramphenicol (CP) concentration on the reductive degradation of CP using ZVBMNPs in aqueous medium.

Method: Iron-silver ZVBMNPs were synthesized. Batch experimental data were generated using a four-factor statistical experimental design.

Modeling and optimization of trihalomethanes formation potential of surface water (a drinking water source) using Box-Behnken design.

Purpose: The present research aims to investigate the individual and interactive effects of chlorine dose/dissolved organic carbon ratio, pH, temperature, bromide concentration, and reaction time on trihalomethanes (THMs) formation in surface water (a drinking water source) during disinfection by chlorination in a prototype laboratory-scale simulation and to develop a model for the prediction and optimization of THMs levels in chlorinated water for their effective control.

Methods: A five-factor Box-Behnken experimental design combined with response surface and optimization modeling was used for predicting the THMs levels in chlorinated water. The adequacy of the selected model and statistical significance of the regression coefficients, independent variables, and their interactions were tested by the analysis of variance and t test statistics.