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Abstract: The paper describes linear and nonlinear modeling for simultaneous prediction of the dissolved oxygen (DO) and biochemical oxygen demand (BOD) levels in the river water using the set of independent measured variables. Partial least squares (PLS2) regression and feed forward back propagation artificial neural networks (FFBP ANNs) modeling methods were applied to predict the DO and BOD levels using eleven input variables measured monthly in the river water at eight different sites over a period of ten years. The performance of the models was assessed through the root mean squared error (RMSE), 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 (DO, BOD). Goodness of the model fit to the data was also evaluated through the relationship between the residuals and the model predicted values of DO and BOD, respectively. Although, the model predicted values of DO and BOD by both the linear (PLS2) and nonlinear (ANN) models were in good agreement with their respective measured values in the river water, the nonlinear model (ANN) performed relatively better than the linear one. Relative importance and contribution of the input variables to the identified ANN model was evaluated through the partitioning approach. The developed models can be used as tool for the water quality prediction. [ABSTRACT FROM AUTHOR]

Waste Polyethylene terephthalate (PET) bottles were pyrolyzed in the presence of nitrogen and converted into activated carbon (PETAC) by physical activation in carbon dioxide flow. An ex-situ precipitation and external reduction method were applied for the intercalation of ferromagnetic iron oxides onto the PETAC matrix. The characteristic structural and chemical properties of PETAC and magnetic PETAC (M-PETAC) were studied by Brunauer Emmett Teller (BET) surface area analysis, Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), Fourier Transform Infrared (FTIR) analysis, Raman spectroscopy, X-Ray Diffraction (XRD) analysis, Energy Dispersive analysis of X-rays (EDAX), Vibrating Sample Magnetometer (VSM), Thermal gravimetric analysis (TGA) and elemental analysis. Characterization results indicated that PETAC exhibited a relatively smooth and microporous texture with a surface area of 659.6 m 2 g -1 while M-PETAC displayed a rugged morphology with a diminished surface area of 288.8 m 2 g -1 . XRD measurements confirmed the formation of iron oxide nanocrystallites with an average Scherrer crystallite size of 19.2 nm. M-PETAC delivered a quick response to an external magnet and exhibited saturation magnetization value of 35.4 emu g -1 . PETAC and M-PETAC were explored as potential adsorbents for the adsorption of a pharmaceutical (cephalexin) from water. Isotherm analysis revealed that M-PETAC exhibited a superior adsorption capacity (71.42 mg g -1 ) compared to PETAC (21.27 mg g -1 ). FTIR analysis of the adsorbents after CEX adsorption revealed the role of FeO as the nucleation site for enhanced adsorption of cephalexin by M-PETAC., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Volatile organic compounds (VOCs) are among the priority atmospheric pollutants that have high indoor and outdoor exposure potential. The toxicity assessment of VOCs to living ecosystems has received considerable attention in recent years. Development of computational methods for safety assessment of chemicals has been advocated by various regulatory agencies. The paper proposes robust and reliable quantitative structure-activity relationships (QSARs) for estimating the sensory irritation potency and screening of the VOCs. Here, decision tree (DT) based classification and regression QSARs models, such as single DT, decision tree forest (DTF), and decision tree boost (DTB) were developed using the sensory irritation data on VOCs in mice following the OECD principles. Structural diversity and nonlinearity in the data were evaluated through the Euclidean distance and Brock-Dechert-Scheinkman statistics. The constructed QSAR models were validated with external test data and the predictive performance of these models was established through a set of coefficients recommended in QSAR literature. The performance of all three classification and regression QSAR models was satisfactory, but DTF and DTB performed relatively better. The classification and regression QSAR models (DTF, DTB) rendered classification accuracies of 98.59 and 100 %, and yielded correlations (R(2)) of 0.950 and 0.971, respectively in complete data. The lipoaffinity index and SwHBa were identified as the most influential descriptors in proposed QSARs. The developed QSARs performed better than the previous studies. The developed models exhibited high statistical confidence and identified the structural properties of the VOCs responsible for their sensory irritation, and hence could be useful tools in screening of chemicals for regulatory purpose.

High concentrations of pharmacological active compounds (PACs) detected in global drinking water resources and their toxicological implications in aquatic life has become a matter of concern compelling for the development of reliable QSTRs (qualitative/quantitative structure-toxicity relationships) for their risk assessment. Robust QSTRs, such as decision treeboost (DTB) and decision tree forest (DTF) models implementing stochastic gradient boosting and bagging algorithms were established by experimental toxicity data of structurally diverse PACs in daphnia using molecular descriptors for predicting toxicity of new untested compounds in multiple test species. Developed models were rigorously validated using OECD recommended internal and external validation procedures and predictive power tested with external data of different trophic level test species (algae and fish). Classification QSTRs (DTB, DTF) rendered accuracy of 98.73% and 97.47%, respectively in daphnia and 84.38%, 85.94% (algae), 78.46% and 79.23% (fish). On the other hand, the regression QSTRs (DTB, DTF) yielded squared correlation coefficient values of 0.831, 0.852 (daphnia), 0.534, 0.556 (algae) and 0.620, 0.637 (fish). QSTRs developed in this study passed the OECD validation criteria and performed better than reported earlier for predicting toxicity of PACs, and can be used for screening the new untested compounds for regulatory purpose., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

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 < 0.05). A risk assessment based on the calculated risk quotient (RQ) in six different bioassays (bacteria, duckweed, algae, daphnia, rotifers, and fish) showed that the nonsteroidal anti-inflammatory drugs (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.

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.

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.

Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure-toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data, optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R(2)) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R(2) and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals., (Copyright © 2014 Elsevier Inc. All rights reserved.)

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., (© 2013.)

The research aims to develop global modeling tools capable of categorizing structurally diverse chemicals in various toxicity classes according to the EEC and European Community directives, and to predict their acute toxicity in fathead minnow using set of selected molecular descriptors. Accordingly, artificial intelligence approach based classification and regression models, such as probabilistic neural networks (PNN), generalized regression neural networks (GRNN), multilayer perceptron neural network (MLPN), radial basis function neural network (RBFN), support vector machines (SVM), gene expression programming (GEP), and decision tree (DT) were constructed using the experimental toxicity data. Diversity and non-linearity in the chemicals' data were tested using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Predictive and generalization abilities of various models constructed here were compared using several statistical parameters. PNN and GRNN models performed relatively better than MLPN, RBFN, SVM, GEP, and DT. Both in two and four category classifications, PNN yielded a considerably high accuracy of classification in training (95.85 percent and 90.07 percent) and validation data (91.30 percent and 86.96 percent), respectively. GRNN rendered a high correlation between the measured and model predicted -log LC50 values both for the training (0.929) and validation (0.910) data and low prediction errors (RMSE) of 0.52 and 0.49 for two sets. Efficiency of the selected PNN and GRNN models in predicting acute toxicity of new chemicals was adequately validated using external datasets of different fish species (fathead minnow, bluegill, trout, and guppy). The PNN and GRNN models showed good predictive and generalization abilities and can be used as tools for predicting toxicities of structurally diverse chemical compounds., (Copyright © 2013 Elsevier Inc. All rights reserved.)

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.

The present study aims to investigate the EDTA catalyzed reduction of nitrate (NO (3) (-) ) by zero-valent bimetallic (Fe-Ag) 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-Behnken 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 Fe(0) 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.

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-silver 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.

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., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Purpose: The present research aims to optimize the removal of ibuprofen (IBP), a non-steroidal anti-inflammatory, analgesic, and antipyretic drug from the aqueous solution using a synthesized magnetic carbon-iron nanocomposite, and to investigate the individual and combined effects of the independent process variables., Method: Combining the adsorptive capability of carbon and magnetic property of iron, a carbon-iron nanocomposite was synthesized. A four-factor Box-Behnken experimental design-based optimization modeling was performed for maximizing the removal of IBP from water by the nanocomposite using the batch experimental data. A quadratic model was built to predict the responses. Significance of the process variables and their interactions was tested by the analysis of variance and t test statistics., Results: The experimental maximum removals of IBP from the aqueous solution by carbon and magnetic nanocomposite were 14.74% and 60.39%, respectively. The model predicted maximum removal of 65.81% under the optimum conditions of the independent variables (IBP concentration 80 mg/l; temperature 48°C; pH 2.50; dose 0.6 g/l) was very close to the experimental value (65.12 ± 0.92%). pH of the solution exhibited most significant effect on IBP adsorption., Conclusion: The developed magnetic nanocomposite was found superior than its precursor carbon exhibiting higher removal of IBP from the water and can be easily separated from the aqueous phase under temporary external magnetic field. The developed magnetic nanocomposite may be used for an efficient removal of IBP from the water.

Purpose: The present research aims to investigate the individual and interactive effects of chlorine dose/dissolved organic carbon ratio, pH, temperature, bromide concentration, and reaction time on trihalomethanes (THMs) formation in surface water (a drinking water source) during disinfection by chlorination in a prototype laboratory-scale simulation and to develop a model for the prediction and optimization of THMs levels in chlorinated water for their effective control., Methods: A five-factor Box-Behnken experimental design combined with response surface and optimization modeling was used for predicting the THMs levels in chlorinated water. The adequacy of the selected model and statistical significance of the regression coefficients, independent variables, and their interactions were tested by the analysis of variance and t test statistics., Results: The THMs levels predicted by the model were very close to the experimental values (R(2) = 0.95). Optimization modeling predicted maximum (192 μg/l) TMHs formation (highest risk) level in water during chlorination was very close to the experimental value (186.8 ± 1.72 μg/l) determined in laboratory experiments. The pH of water followed by reaction time and temperature were the most significant factors that affect the THMs formation during chlorination., Conclusion: The developed model can be used to determine the optimum characteristics of raw water and chlorination conditions for maintaining the THMs levels within the safe limit.

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., (Copyright © 2011 Elsevier B.V. All rights reserved.)

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., (Copyright © 2010 Elsevier B.V. All rights reserved.)

The spatial and temporal distribution of polycyclic aromatic hydrocarbons (PAHs) was investigated in Gomti River, a major tributary of the Ganga river (India). A total of 96 samples (water and sediments) were collected from eight different sites over a period of 2 years and analysed for 16 PAHs. The total concentrations of 16 PAHs in water and bed sediments ranged between 0.06 and 84.21 μg/L (average (n = 48), 10.33 ± 19.94 μg/L) and 5.24-3,722.87 ng/g dw [average (n = 48): 697.25 ± 1,005.23 ng/g dw], respectively. In water, two- and three-ring PAHs and, in sediments, the three- and four-ring PAHs were the dominant species. The ratios of anthracene (An)/An + phenenthrene and fluoranthene (Fla)/Fla + pyrene were calculated to evaluate the possible sources of PAHs. These ratios reflected a pattern of pyrolytic input as a major source of PAHs in the river. Principal component analysis, further, separated the PAHs sources in the river sediments, suggesting that both the pyrolytic and petrogenic sources are contributing to the PAHs burden. The threat to biota of the river due to PAHs contamination was assessed using effect range low and effect range median values, and the results suggested that sediment at some occasions may pose biological impairment.

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., (Copyright 2009 Elsevier B.V. All rights reserved.)

Biochemical changes in the plants of Pistia stratiotes L., a free floating macrophyte exposed to different concentrations of hexavalent chromium (0, 10, 40, 60, 80 and 160 microM) for 48, 96 and 144 h were studied. Chromium-induced oxidative stress in macrophyte was investigated using the multivariate modeling approaches. Cluster analysis rendered two fairly distinct clusters (roots and shoots) of similar characteristics in terms of their biochemical responses. Discriminant analysis identified ascorbate peroxidase (APX) as discriminating variable between the root and shoot tissues. Principal components analysis results suggested that malondialdehyde (MDA), superoxide dismutase (SOD), APX, non-protein thiols (NP-SH), cysteine, ascorbic acid, and Cr-accumulation are dominant in root tissues, whereas, protein and guaiacol peroxidase (GPX) in shoots of the plant. Discriminant partial least squares analysis results further confirmed that MDA, SOD, NP-SH, cysteine, GPX, APX, ascorbic acid and Cr-accumulation dominated in the root tissues, while protein in the shoot. Three-way analysis helped in visualizing simultaneous influence of metal concentration and exposure duration on biochemical variables in plant tissues. The multivariate approaches, thus, allowed for the interpretation of the induced biochemical changes in the plant tissues exposed to chromium, which otherwise using the conventional approaches is difficult.

Iron-induced oxidative stress in plants of Bacopa monnieri L., a macrophyte with medicinal value, was investigated using the chemometric approach. Cluster analysis (CA) rendered two distinct clusters of roots and shoots. Discriminant analysis (DA) identified discriminating variables (NP-SH and APX) between the root and shoot tissues. Principal component analysis (PCA) results suggested that protein, superoxide dismutase (SOD), ascorbic acid, proline, and Fe uptake are dominant in root tissues, whereas malondialdehyde (MDA), guaiacol peroxidase (POD), cysteine, and non-protein thiol (NP-SH) in shoot of the stress plant. Discriminant partial-least squares (DPLS) results further confirmed that SOD and ascorbic acid contents dominated in root tissues, while NP-SH, cysteine, POD, ascorbate peroxidase (APX), and MDA in shoot. MDA and NP-SH were identified as most pronounced variables in plant during the highest exposure time. The chemometric approach allowed for the interpretation of the induced biochemical changes in plant tissues exposed to iron.

This study reports the concentration levels and distribution pattern of the persistent organochlorine pesticide (OCPs) residues in the water and bed-sediments of the Gomti River collected seasonally over a period of 2 years. The water and bed-sediment samples were collected from eight different sites and analyzed for aldrin, dieldrin, endrin, HCB, HCH isomers, DDT isomers/metabolites, endosulfan isomers (alpha and beta), endosulfan sulfate, heptachlor and its metabolites, alpha-chlordane, gamma-chlordane and methoxychlor. In the river water and sediments SigmaOCPs residues ranged between 2.16 and 567.49 ng l(-1) and 0.92 and 813.59 ng g(-1), respectively. The results, further, suggested that source of DDT contamination is from the aged and weathered agricultural soils with signature of recently used DDT in the river catchments. To assess any adverse effect of OCPs contamination on river's biological component, the threshold effect level (TEL) was used. The results revealed that bed-sediments of the Gomti River are contaminated with lindane, endrin, heptachlor epoxides and DDT, particularly at site-4 and may contribute to sediment toxicity in the freshwater ecosystem of the river.

The paper reports a direct method for the determination of pyridine in water and wastewater samples based on ultraviolet spectrophotometric measurements using multi-way modeling techniques. Parallel factor analysis (PARAFAC) and multi-way partial least squares (N-PLS) regression methods were employed for the decomposition of spectra and quantification of pyridine. The study was carried out in the pH range of 1.0-12.0 and concentration range of 0.67-51.7 microgmL(-1) of pyridine. Both the three-way PARAFAC and tri-PLS1 models successfully predicted the concentration of pyridine in synthetic (spiked) river water and field wastewater samples. The mean recovery obtained from PARAFAC regression model were 97.39% for the spiked and 99.84% for the field wastewater samples, respectively. The sensitivity and precision of the method for pyridine determination were 0.58% and 5.95%, respectively. The N-PLS regression model yielded mean recoveries of 99.29% and 100.18% for the spiked and field wastewater samples, respectively. The prediction accuracy of the methods was evaluated through the root mean square error of prediction (RMSEP). For PARAFAC, it was 0.65 and 0.82 microgmL(-1) for spiked river water and field wastewater samples, respectively, while for N-PLS, it was 0.25 and 0.37 microgmL(-1), respectively. Both the PARAFAC and N-PLS methods, thus, yielded satisfactory results for the prediction of pyridine concentration in water and wastewater samples.

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.

This study reports the levels and distribution patterns of selected polycyclic aromatic hydrocarbons (PAHs) in fish samples of the Gomti river, India, collected from three sites during the pre- and post-monsoon seasons of the years 2004-2005. In the fish muscles, SigmaPAHs ranged between 12.85 and 34.89 ng g(-1) wet wt (mean value: 23.98+/-6.70 ng g(-1)). Naphthalene was the most prevalent compound both in terms of detection as well as levels, while, benzo[k]fluoranthene, benzo(a)pyrene, and indeno(123-cd)pyrene + benzo(ghi)perylene could not be detected in any of the sample. Low-molecular weight PAHs were observed dominating over the high molecular weight PAHs.