Comparative Response of Indigenously Developed Bacterial Consortia on Progressive Degradation of Polyhydroxybutyrate Film Composites

The global demand of bioplastics has lead to an exponential increase in their production commercially. Hence, biodegradable nature needs to be evaluated in various ecosystems viz. air, water, soil and other environmental conditions to avoid the polymeric waste accumulation in the nature. In this paper, we investigated the progressive response of two indigenously developed bacterial consortia, i.e., consortium-I (C-I: Pseudomonas sp. strain Rb10, Pseudomonas sp. strain Rb11 and Bacillus sp. strain Rb18), and consortium-II (C-II: Lysinibacillus sp. strain Rb1, Pseudomonas sp. strain Rb13 and Pseudomonas sp. strain Rb19), against biodegradation behavior of polyhydroxybutyrate (PHB) film composites, under natural soil ecosystem (in net house). The biodegraded films recovered after 6 and 9 months of incubation were analyzed through Fourier transform infrared spectroscopy and scanning electron microscopy to determine the variations in chemical and morphological parameters (before and after incubation). Noticeable changes in the bond intensity, surface morphology and conductivity were found when PHB composites were treated with C-II. These changes were drastic in case of blends in comparison to copolymer. The potential isolates not only survived, but, also, there was a significant increase in bacterial diversity during whole period of incubation. To the best of our knowledge, it is the first report which described the biodegradation potential of Lysinibacillus sp. as a part of C-II with Pseudomonas sp. against PHB film composites.     

QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes

The safety assessment process of chemicals requires information on their mutagenic potential. The experimental determination of mutagenicity of a large number of chemicals is tedious and time and cost intensive, thus compelling for alternative methods. We have established local and global QSAR models for discriminating low and high mutagenic compounds and predicting their mutagenic activity in a quantitative manner in Salmonella typhimurium (TA) bacterial strains (TA98 and TA100). The decision treeboost (DTB)-based classification QSAR models discriminated among two categories with accuracies of >96% and the regression QSAR models precisely predicted the mutagenic activity of diverse chemicals yielding high correlations (R ²) between the experimental and model-predicted values in the respective training (>0.96) and test (>0.94) sets. The test set root mean squared error (RMSE) and mean absolute error (MAE) values emphasized the usefulness of the developed models for predicting new compounds. Relevant structural features of diverse chemicals that were responsible and influence the mutagenic activity were identified. The applicability domains of the developed models were defined. The developed models can be used as tools for screening new chemicals for their mutagenicity assessment for regulatory purpose.

     

Effect of dust load on the leaf attributes of the tree species growing along the roadside

Dust is considered as one of the most widespread air pollutants. The objective of the study was to analyse the effect of dust load (DL) on the leaf attributes of the four tree species planted along the roadside at a low pollution Banaras Hindu University (BHU) campus and a highly polluted industrial area (Chunar, Mirzapur) of India. The studied leaf attributes were: leaf area, specific leaf area (SLA), relative water content (RWC), leaf nitrogen content (LNC), leaf phosphorus content (LPC), chlorophyll content (Chl), maximum stomatal conductance (Gs_max), maximum photosynthetic rate (A _max) and intrinsic water-use efficiency (WUEi). Results showed significant effect of sites and species for DL and the leaf attributes. Average DL across the four tree species was greater at Chunar, whereas, the average values of leaf attributes were greater at the BHU campus. Maximum DL was observed for Tectona grandis at Chunar site and minimum for Syzygium cumini at BHU campus. Across the two sites, maximum value of SLA, Chl and Gs_max were exhibited by S. cumini, whereas, the greatest value of RWC, LNC, LPC, A _max and WUEi were observed in Anthocephalus cadamba. A. cadamba and S. cumini exhibited 28 and 27 times more dust accumulation, respectively, at the most polluted Chunar site as compared to the BHU campus. They also exhibited less reduction in A _max due to dust deposition as compared to the other two species. Therefore, both these species may be promoted for plantation along the roadside of the sites having greater dust deposition.     

Physiological and Biochemical Effects of Simulated Acid Rain on Phaseolus Vulgaris Var. HUR-15

Pot experiments were performed to determine the effects of simulated acid rain on Phaseolus vulgaris var. HUR-15. Seeds of Phaseolus vulgaris var. HUR-15 were exposed to simulated acid rain at pH values of 5.1, 4.1, 3.1, 2.1 and 1.1 and a control level of pH 6.8. Plants were harvested at three stages, i.e. pre, peak and post-flowering stages. Seedlings succumbed at pH 1.1. Necrosis developed on the leaves of plants treated with water of pH 2.1, resulting in adverse effect on chlorophyll content. They died after peak-flowering. Root and shoot length and dry weight were reduced with the lowering in pH. Carbohydrate, nitrogen and protein contents decreased with the decrease in pH level, adversely affecting their nutrient value.     

Optimization of nitrate reduction by EDTA catalyzed zero-valent bimetallic nanoparticles in aqueous medium

The present study aims to investigate the EDTA catalyzed reduction of nitrate (NO ₃ ^? ) by zero-valent bimetallic (Fe?CAg) nanoparticles (ZVBMNPs) in aqueous medium and to enumerate the effect of temperature, solution pH, ZVBMNPs dose and EDTA concentration on NO ₃ ^? reduction. Batch experimental data were generated using a four-factor Box?CBehnken design. Optimization modeling was performed using the response surface method for maximizing the reduction of NO ₃ ^? by ZVBMNPs. Significance of the independent variables and their interactions were tested by the analysis of variance and t test statistics. The model predicted maximum reduction capacity (340.15?mg?g^?1 NO ₃ ^? ) under the optimum conditions of temperature, 60?°C; pH?4; dose, 1.0?g?l^?1; and EDTA concentration, 2.0?mmol?l^?1 was very close to the experimental value (338.62?mg?g^?1) and about 16?% higher than the experimentally determined capacity (291.32?mg?g^?1). Study demonstrated that ZVBMNPs had higher reduction efficiency than Fe⁰ nanoparticles for NO ₃ ^? . EDTA significantly enhanced the NO ₃ ^? reduction by ZVBMNPs. The EDTA catalyzed reduction of NO ₃ ^? by ZVBMNPs can be employed for the effective decontamination of water.     

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?Csilver ZVBMNPs were synthesized. Batch experimental data were generated using a four-factor statistical experimental design. CP reduction by ZVBMNPs was optimized using the response surface modeling (RSM) and artificial neural network-genetic algorithm (ANN-GA) approaches. The RSM and ANN methodologies were also compared for their predictive and generalization abilities using the same training and validation data set. Reductive by-products of CP were identified using liquid chromatography-mass spectrometry technique.

Results

The optimized process variables (RSM and ANN-GA approaches) yielded CP reduction capacity of 57.37 and 57.10?mg?g^?1, respectively, as compared to the experimental value of 54.0?mg?g^?1 with un-optimized variables. The ANN-GA and RSM methodologies yielded comparable results and helped to achieve a higher reduction (>6%) of CP by the ZVBMNPs as compared to the experimental value. The root mean squared error, relative standard error of prediction and correlation coefficient between the measured and model-predicted values of response variable were 1.34, 3.79, and 0.964 for RSM and 0.03, 0.07, and 0.999 for ANN models for the training and 1.39, 3.47, and 0.996 for RSM and 1.25, 3.11, and 0.990 for ANN models for the validation set.

Conclusion

Predictive and generalization abilities of both the RSM and ANN models were comparable. The synthesized ZVBMNPs may be used for an efficient reductive removal of CP from the water.     

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. Principal components analysis (PCA) was performed to identify the factors responsible for groundwater contamination. Ensemble learning-based decision treeboost (DTB) models were constructed to develop discriminating and regression functions to differentiate the groundwater hydrochemistry of the three different areas, to identify the responsible factors, and to predict the groundwater quality using selected measured variables. The results indicated non-normal distribution and wide variability of water quality variables in all the study areas, suggesting for nonhomogenous distribution of sources in the region. PCA results showed contaminants of industrial origin dominating in the region. DBT classification model identified pH, redox potential, total-Cr, and λ ₂₅₄ as the discriminating variables in water quality of the three areas with the average accuracy of 99.51 % in complete data. The regression model predicted the groundwater chemical oxygen demand values exhibiting high correlation with measured values (0.962 in training; 0.918 in test) and the respective low root mean-squared error of 2.24 and 2.01 in training and test arrays. The statistical and chemometric approaches used here suggest that groundwater hydrochemistry differs in the three areas and is dominated by different variables. The proposed methods can be used as effective tools in groundwater management.     

Thermal, Mechanical and Rheological Behavior of Poly(lactic acid)/Talc Composites

In the present study, influence of talc on thermal, mechanical and rheological behavior of PLA is investigated and the structure?Cproperty correlation for the PLA/talc composites is established. Poly(lactic acid)/talc composites are prepared by melt mixing of PLA with talc in twin screw extruder followed by blown film processing. Various characterizations techniques are used to evaluate thermal, morphological, mechanical and rheological behavior of PLA/talc composites and its blown film. DSC analysis showed that degree of crystallinity of PLA/talc composites was higher than that of neat PLA because of nucleating ability of talc. Spherulite morphology of PLA/talc composites showed that talc has increased nucleation density of spherulite having smaller radius than that of neat PLA. Talc is effective in enhancing tensile modulus and storage modulus of PLA due to reinforcing ability of talc particles.     

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.