Surfactant modified waste ash for the removal of chloro and nitro group substituted benzene from wastewater.

A surfactant-modified coal fly ash was developed as a multifunctional adsorbent for the removal of organic pollutants from wastewater. Sodium dodecyl sulfate (SDS) was used to modify the surface of coal fly ash (CFA). The modified CFA was characterized using scanning electron microscopy (SEM), surface porosity analyzer, thermogravimetric analysis (TGA) and Fourier transform infrared (FTIR) spectroscopy. The results showed that loading CFA with SDS not only improved the functionality and surface morphology of the raw ash for the adsorption of organic pollutants, but also enhanced its thermal stability. The efficiency of the modified fly ash was tested in terms of removal of two non-polar organic pollutants namely chlorobenzene (CB) and nitrobenzene (NB) from aqueous phase. The maximum uptake capacity of chlorobenzene and nitrobenzene with SDS-modified coal fly ash (SCFA) was 225 mg/g and 90 mg/g, respectively. The kinetic analysis was done by controlled kinetic models, i.e., pseudo first and second order kinetic models. The results showed that adsorption of CB and NB onto SCFA followed a pseudo second order kinetic model. The adsorption of chlorobenzene was exothermic over the modified adsorbent while nitrobenzene showed an endothermic behavior. The isotherm analysis depicted the multilayer adsorption of both pollutants onto the surface of the surfactant modified adsorbent. This work has shown that surface modification using surfactants can be a viable option to enhance the adsorption capacity of fly ash for pollutants removal.

An analysis of the environmental impacts of ethnic diversity, financial development, economic growth, urbanization, and energy consumption: fresh evidence from less-developed countries.

Improving the quality of environmental indicators has become a global concern that necessitates the identification of possible channels through which environmental welfare can be enhanced worldwide. Against this backdrop, this current study aims to elucidate the environmental effects of ethnic diversity, controlling for financial development, urbanization, economic growth, and energy consumption in the context of 51 less-developed countries during the period from 1996 to 2016. For measuring the environmental impacts, we use both the ecological footprint and carbon dioxide emission figures of these countries. Overall, the cointegration analysis confirms the existence of long-run relationships among the study variables. Besides, the regression analysis reveals that ethnic diversity deteriorates environmental quality by surging the ecological footprint and carbon dioxide emission levels of the selected nations. Similarly, financial development and energy consumption are found to impose identical adversities on the environment while urbanization is evidenced to ensure environmental welfare. Lastly, for both the environmental indicators considered in this study, the environmental Kuznets curve hypothesis is verified from the findings. Hence, considering these key outcomes, a set of relevant environmental welfare-related policy interventions are recommended in the context of less-developed countries.

Development and evaluation of two-parameter linear free energy models for the prediction of human skin permeability coefficient of neutral organic chemicals.

The experimental values of skin permeability coefficients, required for dermal exposure assessment, are not readily available for many chemicals. The existing estimation approaches are either less accurate or require many parameters that are not readily available. Furthermore, current estimation methods are not easy to apply to complex environmental mixtures. We present two models to estimate the skin permeability coefficients of neutral organic chemicals. The first model, referred to here as the 2-parameter partitioning model (PPM), exploits a linear free energy relationship (LFER) of skin permeability coefficient with a linear combination of partition coefficients for octanol-water and air-water systems. The second model is based on the retention time information of nonpolar analytes on comprehensive two-dimensional gas chromatography (GC × GC). The PPM successfully explained variability in the skin permeability data (n = 175) with R2 = 0.82 and root mean square error (RMSE) = 0.47 log unit. In comparison, the US-EPA's model DERMWIN™ exhibited an RMSE of 0.78 log unit. The Zhang model-a 5-parameter LFER equation based on experimental Abraham solute descriptors (ASDs)-performed slightly better with an RMSE value of 0.44 log unit. However, the Zhang model is limited by the scarcity of experimental ASDs. The GC × GC model successfully explained the variance in skin permeability data of nonpolar chemicals (n = 79) with R2 = 0.90 and RMSE = 0.23 log unit. The PPM can easily be implemented in US-EPA's Estimation Program Interface Suite (EPI Suite™). The GC × GC model can be applied to the complex mixtures of nonpolar chemicals.