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. All the six drugs were detected in wastewater of Ghaziabad, whereas naproxen was not detected in Lucknow wastewater. Results suggest that levels of these detected drugs were relatively higher in Ghaziabad as compared to those in Lucknow, and diclofenac was the most frequently detected drug in both the study areas. Detection of these drugs in wastewater reflects the importance of wastewater inputs as a source of pharmaceuticals. In terms of the regional distribution of compounds in wastewater of two cities, higher spatial variations (coefficient of variation 112.90–459.44 %) were found in the Lucknow wastewater due to poor water exchange ability. In contrast, lower spatial variation (162.38–303.77 %) was observed in Ghaziabad. Statistical analysis results suggest that both data were highly skewed, and populations in two study areas were significantly different (p?NSAIDs) posed high (RQ >1) risk to all the test species. The present study would contribute to the formulation of guidelines for regulation of such emerging pharmaceutical contaminants in the environment.     

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. Kernel ridge regression, kernel principal component regression, kernel partial least squares regression, and support vector regression models were developed using the Gaussian kernel function and their generalization and predictive abilities were compared in terms of several statistical parameters. Model parameters were optimized using the cross-validation procedure. The proposed kernel regression methods successfully captured the nonlinear features of the original data by transforming it to a high dimensional feature space using the kernel function. Performance of all the kernel-based modeling methods used here were comparable both in terms of predictive and generalization abilities. Values of the performance criteria parameters suggested for the adequacy of the constructed models to fit the nonlinear data and their good predictive capabilities.     

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. Five nonlinear models were constructed to predict the adsorptive removal of CP in aqueous solution by CSC using four variables as input. Performances of the constructed models were evaluated and compared using statistical criteria. BDS statistics revealed strong nonlinearity in experimental data. Performance of all the models constructed here was satisfactory. Radial basis function network (RBFN) and multilayer perceptron network (MLPN) models performed better than generalized regression neural network, support vector machines, and gene expression programming models. Sensitivity analysis revealed that the contact time had highest effect on adsorption followed by the solution pH, temperature, and CP concentration. The study concluded that all the models constructed here were capable of capturing the nonlinearity in data. A better generalization and predictive performance of RBFN and MLPN models suggested that these can be used to predict the adsorption of CP in aqueous solution using CSC.     

In situ phytoremediation of heavy metal–contaminated soil and groundwater: a green inventive approach

Environmental Science and Pollution Research - The heavy metal contamination of soil and groundwater is a serious threat to environment worldwide. The survival of human being primarily relies upon...     

Phytoremediation potential of weed plants’ oxidative biomarker and antioxidant responses

The purpose of this study was to assess the oxidative biomarkers responses, antioxidant potential and metal accumulation tendency of weed plants collected from the control and metal-contaminated site. The metal contamination was found to be higher in soil and plant parts collected from contaminated site and the most serious problem seemed to be metal elevations above than the safe limit for Cd, Pb and Ni in the aerial parts of weed plants. There were variations in metal accumulation in different weed plants that was justified by principal component analysis. The enzymatic and non-enzymatic antioxidants were found to be higher in plant parts collected from contaminated site. Based upon metal accumulation tendency, weed plants showed a translocation ratio (>1) that reflect their potential for metal remediation. The values of metal pollution index also showed higher tendency of metal accumulation in weed plants, particularly in Solanum nigrum, Euphorbia hirta, Amaranthus hybridus and Xanthium strumarium, and they can also flourish at contaminated sites with more production of antioxidants. So, it suggests that studied weed species can be classified as phytoremediation plants and can be used as eco-friendly and economically feasible technique for restoring the land contaminated with toxic metals.     

Removal of Cr(VI) onto Ficus carica biosorbent from water

The utilization of sustainable and biodegradable lignocellulosic fiber to detoxify the noxious Cr(VI) from wastewater is considered a versatile approach to clean up a contaminated aquatic environment. The aim of the present research is to assess the proficiency and mechanism of biosorption on Ficus carica bast fiber via isotherm models (Langmuir, Freundlich, Temkin, Harkin’s–Jura, and Dubinin–Radushkevich), kinetic models, and thermodynamic parameters. The biomass extracted from fig plant was characterized by scanning electron microscopy and Fourier-transform infrared spectroscopy. To optimize the maximum removal efficiency, different parameters like effect of initial concentration, effect of temperature, pH, and contact time were studied by batch method. The equilibrium data were best represented by the Langmuir isotherm model, and the maximum adsorption capacity of Cr(VI) onto biosorbent was found to be 19.68 mg/g. The pseudo-second-order kinetic model adequately described the kinetic data. The calculated values of thermodynamic parameters such as enthalpy change (?H ⁰), entropy change (?S ⁰), and free energy change (?G ⁰) were 21.55 kJ/mol, 76.24 J/mol?K, and ?1.55 kJ/mol, respectively, at 30 °C which accounted for spontaneous and endothermic processes. The study of adsorbent capacity for Cr(VI) removal in the presence of Na⁺, Mg²⁺, Ca²⁺, SO ₄ ^2? , HCO ₃ ^? and Cl^? illustrated that the removal of Cr(VI) increased in the presence of HCO^3? ions; the presence of Na⁺, SO ₄ ^2? or Cl^? showed no significant influence on Cr(VI) adsorption, while Ca²⁺ and Mg²⁺ ions led to an insignificant decrease in Cr(VI) adsorption. Further, the desorption studies illustrated that 31.10 % of metal ions can be removed from an aqueous system, out of which 26.63 % of metal ions can be recovered by desorption in first cycle and the adsorbent can be reused. The results of the scale-up study show that the ecofriendly detoxification of Cr(VI) from aqueous systems was technologically feasible.     

Role of nano-powder of Azadirachta indica leaves to regulate the physiological responses and metal uptake in Triticum aestivum seedlings

In the present study different doses (0.05, 2.0 and 5.0?mg per 30?ml nutrient medium) of nano-powder (Azadirachta indica leaves) were applied in Cd contaminated (6?ppm) hydroponic system to regulate the metal uptake in Triticum aestivum (wheat) seedlings. Other physiological attributes including oxidative biomarkers, antioxidants and photosynthetic responses were also assessed. The level of Cd was maximally reduced at the dose of 2.0?mg nano powder per 30?ml nutrient medium by 45 and 49% in the shoot and root, respectively. With the maximum reduction in the Cd uptake at this dose, the generation of oxidative stress markers such as H₂O₂ (12%), MDA (26%) and SOR (20%) content showed maximum reduction in treated seedlings. At different doses of nano-powder, the activities of antioxidative enzymes were also showed significant variation. Further, the photosynthetic O₂ evolution rate was improved with the treatment of nano-powder and the best response was noted at 2.0?mg per 30?ml nutrient medium with the maximum value of fresh shoot biomass (38%). The overall results suggest that, this technique could be easily applied for reducing the metal content and increasing the quality of agricultural crops.     

Selection of poly(R)-3-hydroxybutyric acid utilising bacteria by enrichment,optimisation and compatibility testing for consortia development

Bacterial strains with poly-3-hydroxybutyrate (PHB) degradation potential were isolated from waste yard soil samples of selected industrial sites in Uttarakhand, India, and two microbial consortia were developed, i.e. Consortium I comprises Pseudomonas sp. strain Rb10, Pseudomonas sp. strain Rb11 and Bacillus sp. strain Rb18, and Consortium II is composed of Lysinibacillus sp. strain Rb1, Pseudomonas sp. strain Rb13 and Pseudomonas sp. strain Rb19. The current study involved enrichment selection via liquid and semi-solid media, followed by isolation and screening of bacterial strains using PHB pellets and films. Furthermore, the identification and characterisation was done by triphasic approach. The utilisation of PHB by the characterised strains was confirmed by Fourier transform infrared spectroscopy and scanning electron microscopy. Moreover, the minimum inhibitory concentration of solubilised PHB was found to be 2.5?mg/mL, which was detected through ‘clear zone assay’. Further, the selection and biocompatibility testing of potential isolates were performed for the formation of bacterial consortia. Thus, the present work would provide direct and standardised protocol for screening and selection of potential microbiomes for biodegradation of polymers by overcoming the negative effect of organic solvents. Moreover, indigenously developed consortia would be evaluated for their in situ biodegradation potential against various bioplastic films.