Kunwar P. Singh

Single- and multi-component adsorption of cadmium and zinc using activated carbon derived from bagasse - an agricultural waste

The use of low-cost activated carbon derived from bagasse, an agricultural waste material, has been investigated as a replacement for the current expensive methods of removing heavy metals from wastewater. With a view to find a suitable application of the material, activated carbon has been derived, characterized and utilized for the removal of cadmium and zinc. The uptake of cadmium was found to be slightly greater than that of zinc and the sorption capacity increases with increase in temperature. The adsorption studies were carried out both in single- and multi-component systems. Adsorption data on derived carbon follows both the Freundlich and Langmuir models. The data are better fitted by the Freundlich isotherm as compared to Langmuir in both the single- and multi-component systems. Isotherms have been used to obtain the thermodynamic parameters. The kinetics of adsorption depends on the adsorbate concentration and the physical and chemical characteristics of the adsorbent. Studies were conducted to delineate the effect of temperature, initial adsorbate concentration, particle size of the adsorbent and solid-to-liquid ratio. On the basis of these studies, various parameters such as mass transfer coefficient, effective diffusion coefficient, activation energy and entropy of activation were evaluated to establish the mechanisms. It was concluded that the adsorption occurs through a film diffusion mechanism at low as well as at higher concentrations.

Optimizing adsorption of crystal violet dye from water by magnetic nanocomposite using response surface modeling approach

A magnetic nanocomposite was developed and characterized. Adsorption of crystal violet (CV) dye from water was studied using the nanocomposite. A four-factor central composite design (CCD) combined with response surface modeling (RSM) was employed for maximizing CV removal from aqueous solution by the nanocomposite based on 30 different experimental data obtained in a batch study. Four independent variables, viz. temperature (10-50°C), pH of solution (2-10), dye concentration (240-400 mg/l), and adsorbent dose (1-5 g/l) were transformed to coded values and a second-order quadratic model was built to predict the responses. The significance of independent variables and their interactions were tested by the analysis of variance (ANOVA) and t-test statistics. Adequacy of the model was tested by the correlation between experimental and predicted values of the response and enumeration of prediction errors. Optimization of the process variables for maximum adsorption of CV by nanocomposite was performed using the quadratic model. The Langmuir adsorption capacity of the adsorbent was determined as 81.70 mg/g. The model predicted maximum adsorption of 113.31 mg/g under the optimum conditions of variables (concentration 240 mg/l; temperature 50°C; pH 8.50; dose 1g/l), which was very close to the experimental value (111.80 mg/g) determined in batch experiment.

Support vector machines in water quality management

Support vector classification (SVC) and regression (SVR) models were constructed and applied to the surface water quality data to optimize the monitoring program. The data set comprised of 1500 water samples representing 10 different sites monitored for 15 years. The objectives of the study were to classify the sampling sites (spatial) and months (temporal) to group the similar ones in terms of water quality with a view to reduce their number; and to develop a suitable SVR model for predicting the biochemical oxygen demand (BOD) of water using a set of variables. The spatial and temporal SVC models rendered grouping of 10 monitoring sites and 12 sampling months into the clusters of 3 each with misclassification rates of 12.39% and 17.61% in training, 17.70% and 26.38% in validation, and 14.86% and 31.41% in test sets, respectively. The SVR model predicted water BOD values in training, validation, and test sets with reasonably high correlation (0.952, 0.909, and 0.907) with the measured values, and low root mean squared errors of 1.53, 1.44, and 1.32, respectively. The values of the performance criteria parameters suggested for the adequacy of the constructed models and their good predictive capabilities. The SVC model achieved a data reduction of 92.5% for redesigning the future monitoring program and the SVR model provided a tool for the prediction of the water BOD using set of a few measurable variables. The performance of the nonlinear models (SVM, KDA, KPLS) was comparable and these performed relatively better than the corresponding linear methods (DA, PLS) of classification and regression modeling.

Nano-QSAR modeling for predicting biological activity of diverse nanomaterials

This study reports robust reliable ensemble learning (EL) approach based nano-QSAR models for predicting the biological effects of diverse nanomaterials (NMs) using simple molecular descriptors. EL based nano-QSAR models implementing stochastic gradient boosting and bagging algorithms were constructed and used to establish statistically significant relationships between measured biological activity profiles of nanoparticles (NPs) and their simple structural properties. To demonstrate the predictive ability of the developed nano-QSAR models, five different representative data sets (case studies) of NMs (NPs with diverse metal cores, NPs with similar core but diverse surface modifiers, metal oxide NPs, surface modified multi-walled carbon nanotubes, and fullerene derivatives) studied recently using in vitro cell based assays were employed. Rigorous validation of the constructed classification and regression nano-QSAR models performed using various statistical parameters suggested robustness of the EL based models for their future use. Proposed nano-QSAR models showed high prediction accuracy (binary classification) of more than 93.18% (case study 1), 97.25% (case study 2), and yielded correlation (R2) of more than 0.851 between experimental and model predicted values of biological activity in complete data of different diverse sets of NPs. Results for all five case studies demonstrated better predictive performance of the proposed nano-QSAR models compared to the previous studies. The proposed models reliably predicted the biological activity of all considered NPs, and the methodology is expected to provide guidance for the future design and manufacturing of NMs ensuring better and safer products.

Mobile and bound forms of trace metals in sediments of the lower ganges

Abstract Mobile and bound trace metals associated with sediment components (viz. exchangeable, carbonate, organic, Fe/Mn oxide and residual fractions) were determined at five locations on the River Ganges in the lower reaches. In the exchangeable phase, 5–22% of Pb, 5–14.4% of Cr, 3–16.4% of Cd, 3–16% of Zn and 1–13.5% of Cu were found, and in the carbonate phase 73–87% of Zn, 38–41% of Cd, 13–27% of Ni and 3–10.1% of Pb were found. The Fe/Mn oxide phase retained about 79–83% of Mn, 30–40% of Cr and Fe, 22–25% of Cu, 14–16% of Ni and 9–11% of Pb. In the organic phase about 36–47% of Cd, 22–28% of Cu and 10–15% of Pb were found. The order of release of metals was Cd > Cr > Pb > Cu > Zn > Ni > Mn > Fe, and the order of adsorption characteristics of most of the mobile metal fractions was Fe/Mn oxide > organic > clay. Correlations of the physico-chemical parameters with adsorption characteristics were also determined and a good correlation ( r = 0.7) of cation exchange capacity with the clay fraction was found. I geo (geoaccumulation indices) of metals in the sediments were also evaluated. Results showed a considerable enrichment of trace metals in the sediment phase at almost all the sites.

Modeling the performance of "up-flow anaerobic sludge blanket" reactor based wastewater treatment plant using linear and nonlinear approaches - a case study.

The paper describes linear and nonlinear modeling of the wastewater data for the performance evaluation of an up-flow anaerobic sludge blanket (UASB) reactor based wastewater treatment plant (WWTP). Partial least squares regression (PLSR), multivariate polynomial regression (MPR) and artificial neural networks (ANNs) modeling methods were applied to predict the levels of biochemical oxygen demand (BOD) and chemical oxygen demand (COD) in the UASB reactor effluents using four input variables measured weekly in the influent wastewater during the peak (morning and evening) and non-peak (noon) hours over a period of 48 weeks. The performance of the models was assessed through the root mean squared error (RMSE), relative error of prediction in percentage (REP), the bias, the standard error of prediction (SEP), the coefficient of determination (R(2)), the Nash-Sutcliffe coefficient of efficiency (E(f)), and the accuracy factor (A(f)), computed from the measured and model predicted values of the dependent variables (BOD, COD) in the WWTP effluents. Goodness of the model fit to the data was also evaluated through the relationship between the residuals and the model predicted values of BOD and COD. Although, the model predicted values of BOD and COD by all the three modeling approaches (PLSR, MPR, ANN) were in good agreement with their respective measured values in the WWTP effluents, the nonlinear models (MPR, ANNs) performed relatively better than the linear ones. These models can be used as a tool for the performance evaluation of the WWTPs.

Linear and nonlinear modeling approaches for urban air quality prediction.

In this study, linear and nonlinear modeling was performed to predict the urban air quality of the Lucknow city (India). Partial least squares regression (PLSR), multivariate polynomial regression (MPR), and artificial neural network (ANN) approach-based models were constructed to predict the respirable suspended particulate matter (RSPM), SO(2), and NO(2) in the air using the meteorological (air temperature, relative humidity, wind speed) and air quality monitoring data (SPM, NO(2), SO(2)) of five years (2005-2009). Three different ANN models, viz. multilayer perceptron network (MLPN), radial-basis function network (RBFN), and generalized regression neural network (GRNN) were developed. All the five different models were compared for their generalization and prediction abilities using statistical criteria parameters, viz. correlation coefficient (R), standard error of prediction (SEP), mean absolute error (MAE), root mean squared error (RMSE), bias, accuracy factor (A(f)), and Nash-Sutcliffe coefficient of efficiency (E(f)). Nonlinear models (MPR, ANNs) performed relatively better than the linear PLSR models, whereas, performance of the ANN models was better than the low-order nonlinear MPR models. Although, performance of all the three ANN models were comparable, the GRNN over performed the other two variants. The optimal GRNN models for RSPM, NO(2), and SO(2) yielded high correlation (between measured and model predicted values) of 0.933, 0.893, and 0.885; 0.833, 0.602, and 0.596; and 0.932, 0.768 and 0.729, respectively for the training, validation and test sets. The sensitivity analysis performed to evaluate the importance of the input variables in optimal GRNN revealed that SO(2) was the most influencing parameter in RSPM model, whereas, SPM was the most important input variable in other two models. The ANN models may be useful tools in the air quality predictions.

Groundwater Contaminated with Hexavalent Chromium [Cr (VI)]: A Health Survey and Clinical Examination of Community Inhabitants (Kanpur, India)

Background We assessed the health effects of hexavalent chromium groundwater contamination (from tanneries and chrome sulfate manufacturing) in Kanpur, India. Methods The health status of residents living in areas with high Cr (VI) groundwater contamination (N = 186) were compared to residents with similar social and demographic features living in communities having no elevated Cr (VI) levels (N = 230). Subjects were recruited at health camps in both the areas. Health status was evaluated with health questionnaires, spirometry and blood hematology measures. Cr (VI) was measured in groundwater samples by diphenylcarbazide reagent method. Results Residents from communities with known Cr (VI) contamination had more self-reports of digestive and dermatological disorders and hematological abnormalities. GI distress was reported in 39.2% vs. 17.2% males (AOR = 3.1) and 39.3% vs. 21% females (AOR = 2.44); skin abnormalities in 24.5% vs. 9.2% males (AOR = 3.48) and 25% vs. 4.9% females (AOR = 6.57). Residents from affected communities had greater RBCs (among 30.7% males and 46.1% females), lower MCVs (among 62.8% males) and less platelets (among 68% males and 72% females) than matched controls. There were no differences in leucocytes count and spirometry parameters. Conclusions Living in communities with Cr (VI) groundwater is associated with gastrointestinal and dermatological complaints and abnormal hematological function. Limitations of this study include small sample size and the lack of long term follow-up.

Multispecies QSAR Modeling for Predicting the Aquatic Toxicity of Diverse Organic Chemicals for Regulatory Toxicology

The research aims to develop multispecies quantitative structure-activity relationships (QSARs) modeling tools capable of predicting the acute toxicity of diverse chemicals in various Organization for Economic Co-operation and Development (OECD) recommended test species of different trophic levels for regulatory toxicology. Accordingly, the ensemble learning (EL) approach based classification and regression QSAR models, such as decision treeboost (DTB) and decision tree forest (DTF) implementing stochastic gradient boosting and bagging algorithms were developed using the algae (P. subcapitata) experimental toxicity data for chemicals. The EL-QSAR models were successfully applied to predict toxicities of wide groups of chemicals in other test species including algae (S. obliguue), daphnia, fish, and bacteria. Structural diversity of the selected chemicals and those of the end-point toxicity data of five different test species were tested using the Tanimoto similarity index and Kruskal-Wallis (K-W) statistics. Predictive and generalization abilities of the constructed QSAR models were compared using statistical parameters. The developed QSAR models (DTB and DTF) yielded a considerably high classification accuracy in complete data of model building (algae) species (97.82%, 99.01%) and ranged between 92.50%-94.26% and 92.14%-94.12% in four test species, respectively, whereas regression QSAR models (DTB and DTF) rendered high correlation (R(2)) between the measured and model predicted toxicity end-point values and low mean-squared error in model building (algae) species (0.918, 0.15; 0.905, 0.21) and ranged between 0.575 and 0.672, 0.18-0.51 and 0.605-0.689 and 0.20-0.45 in four different test species. The developed QSAR models exhibited good predictive and generalization abilities in different test species of varied trophic levels and can be used for predicting the toxicities of new chemicals for screening and prioritization of chemicals for regulation.

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. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models was performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes.