Adsorptive removal of Cd2+ ions using dolochar at an industrial-scale process optimization by response surface methodology

Environmental Science and Pollution Research - In this work, performance of laboratory-synthesized dolochar has been investigated for adsorption of Cd2+ ions in a large-scale process with the...     

Emerging trends in photodegradation of petrochemical wastes: a review

Various human activities like mining and extraction of mineral oils have been used for the modernization of society and well-beings. However, the by-products such as petrochemical wastes generated from such industries are carcinogenic and toxic, which had increased environmental pollution and risks to human health several folds. Various methods such as physical, chemical and biological methods have been used to degrade these pollutants from wastewater. Advance oxidation processes (AOPs) are evolving techniques for efficient sequestration of chemically stable and less biodegradable organic pollutants. In the present review, photocatalytic degradation of petrochemical wastes containing monoaromatic and poly-aromatic hydrocarbons has been studied using various heterogeneous photocatalysts (such as TiO₂, ZnO and CdS. The present article seeks to offer a scientific and technical overview of the current trend in the use of the photocatalyst for remediation and degradation of petrochemical waste depending upon the recent advances in photodegradation of petrochemical research using bibliometric analysis. We further outlined the effect of various heterogeneous catalysts and their ecotoxicity, various degradation pathways of petrochemical wastes, the key regulatory parameters and the reactors used. A critical analysis of the available literature revealed that TiO₂ is widely reported in the degradation processes along with other semiconductors/nanomaterials in visible and UV light irradiation. Further, various degradation studies have been carried out at laboratory scale in the presence of UV light. However, further elaborative research is needed for successful application of the laboratory scale techniques to pilot-scale operation and to develop environmental friendly catalysts which support the sustainable treatment technology with the “zero concept” of industrial wastewater. Nevertheless, there is a need to develop more effective methods which consume less energy and are more efficient in pilot scale for the demineralization of pollutant.     

Ganga River sediments of India predominate with aerobic and chemo-heterotrophic bacteria majorly engaged in the degradation of xenobiotic compounds

Environmental Science and Pollution Research - Sediment provides a stagnant habitat to microbes that accumulate organic matter and other industrial pollutants from the upper layer of the water. The...     

Degradations of endocrine-disrupting chemicals and pharmaceutical compounds in wastewater with carbon-based nanomaterials: a critical review

Environmental Science and Pollution Research - Although water occupies 75% of the earth’s surface, only 0.0067% of the total water is available for human activities. These statistics further...     

Vertical characterization of soil contamination using multi-way modeling – A case study

This study describes application of chemometric multi-way modeling approach to analyze the dataset pertaining to soils of industrial area with a view to assess the soil/sub-soil contamination, accumulation pathways and mobility of contaminants in the soil profiles. The three-way (sampling depths, chemical variables, sampling sites) dataset on heavy metals in soil samples collected from three different sites in an industrial area, up to a depth of 60 m each was analyzed using three-way Tucker3 model validated for stability and goodness of fit. A two component Tucker3 model, explaining 66.6% of data variance, allowed interpretation of the data information in all the three modes. The interpretation of core elements revealing interactions among the components of different modes (depth, variables, sites) allowed inferring more realistic information about the contamination pattern of soils both along the horizontal and vertical coordinates, contamination pathways, and mobility of contaminants through soil profiles, as compared to the traditional data analysis techniques. It concluded that soils at site-1 and site-2 are relatively more contaminated with heavy metals of both the natural as well as anthropogenic origins, as compared to the soil of site-3. Moreover, the accumulation pathways of metals for upper shallow layers and deeper layers of soils in the area were differentiated. The information generated would be helpful in developing strategies for remediation of the contaminated soils for reducing the subsequent risk of ground-water contamination in the study region.     

Free radical biology in neurological manifestations: mechanisms to therapeutics interventions

Environmental Science and Pollution Research - Recent advancements and growing attention about free radicals (ROS) and redox signaling enable the scientific fraternity to consider their involvement...     

Artificial neural network modeling of the river water quality—A case study

The paper describes the training, validation and application of artificial neural network (ANN) models for computing the dissolved oxygen (DO) and biochemical oxygen demand (BOD) levels in the Gomti river (India). Two ANN models were identified, validated and tested for the computation of DO and BOD concentrations in the Gomti river water. Both the models employed eleven input water quality variables measured in river water over a period of 10 years each month at eight different sites. The performance of the ANN models was assessed through the coefficient of determination (R²) (square of the correlation coefficient), root mean square error (RMSE) and bias computed from the measured and model computed values of the dependent variables. Goodness of the model fit to the data was also evaluated through the relationship between the residuals and model computed values of DO and BOD. The model computed values of DO and BOD by both the ANN models were in close agreement with their respective measured values in the river water. Relative importance and contribution of the input variables to the model output was evaluated through the partitioning approach. The identified ANN models can be used as tools for the computation of water quality parameters.