Valorization of Poly (ethylene) terephthalate (PET) wastes into magnetic carbon for adsorption of antibiotic from water: Characterization and application.

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 m2g-1 while M-PETAC displayed a rugged morphology with a diminished surface area of 288.8 m2g-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.

A three-tier QSAR modeling strategy for estimating eye irritation potential of diverse chemicals in rabbit for regulatory purposes.

Experimental determination of the eye irritation potential (EIP) of chemicals is not only tedious, time and resource intensive, it involves cruelty to test animals. In this study, we have established a three-tier QSAR modeling strategy for estimating the EIP of chemicals for the use of pharmaceutical industry and regulatory agencies. Accordingly, a qualitative (binary classification: irritating, non-irritating), semi-quantitative (four-category classification), and quantitative (regression) QSAR models employing the SDT, DTF, and DTB methods were developed for predicting the EIP of chemicals in accordance with the OECD guidelines. Structural features of chemicals responsible for eye irritation were extracted and used in QSAR analysis. The external predictive power of the developed QSAR models were evaluated through the internal and external validation procedures recommended in QSAR literature. In test data, the two and four category classification QSAR models (DTF, DTB) rendered accuracy of >93%, while the regression QSAR models (DTF, DTB) yielded correlation (R(2)) of >0.92 between the measured and predicted EIPs. Values of various statistical validation coefficients derived for the test data were above their respective threshold limits (except rm(2) in DTF), thus put a high confidence in this analysis. The applicability domain of the constructed QSAR models were defined using the descriptors range and leverage approaches. The QSAR models in this study performed better than any of the previous studies. The results suggest that the developed QSAR models can reliably predict the EIP of diverse chemicals and can be useful tools for screening of candidate molecules in the drug development process.

Predicting Toxicities of Diverse Chemical Pesticides in Multiple Avian Species Using Tree-Based QSAR Approaches for Regulatory Purposes.

A comprehensive safety evaluation of chemicals should require toxicity assessment in both the aquatic and terrestrial test species. Due to the application practices and nature of chemical pesticides, the avian toxicity testing is considered as an essential requirement in the risk assessment process. In this study, tree-based multispecies QSAR (quantitative-structure activity relationship) models were constructed for predicting the avian toxicity of pesticides using a set of nine descriptors derived directly from the chemical structures and following the OECD guidelines. Accordingly, the Bobwhite quail toxicity data was used to construct the QSAR models (SDT, DTF, DTB) and were externally validated using the toxicity data in four other test species (Mallard duck, Ring-necked pheasant, Japanese quail, House sparrow). Prior to the model development, the diversity in the chemical structures and end-point were verified. The external predictive power of the QSAR models was tested through rigorous validation deriving a wide series of statistical checks. Intercorrelation analysis and PCA methods provided information on the association of the molecular descriptors related to MW and topology. The S36 and MW were the most influential descriptors identified by DTF and DTB models. The DTF and DTB performed better than the SDT model and yielded a correlation (R(2)) of 0.945 and 0.966 between the measured and predicted toxicity values in test data array. Both these models also performed well in four other test species (R(2) > 0.918). ChemoTyper was used to identify the substructure alerts responsible for the avian toxicity. The results suggest for the appropriateness of the developed QSAR models to reliably predict the toxicity of pesticides in multiple avian test species and can be useful tools in screening the new chemical pesticides for regulatory purposes.

Estimating sensory irritation potency of volatile organic chemicals using QSARs based on decision tree methods for regulatory purpose.

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.

In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches.

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.

QSTR modeling for qualitative and quantitative toxicity predictions of diverse chemical pesticides in honey bee for regulatory purposes.

Pesticides are designed toxic chemicals for specific purposes and can harm nontarget species as well. The honey bee is considered a nontarget test species for toxicity evaluation of chemicals. Global QSTR (quantitative structure-toxicity relationship) models were established for qualitative and quantitative toxicity prediction of pesticides in honey bee (Apis mellifera) based on the experimental toxicity data of 237 structurally diverse pesticides. Structural diversity of the chemical pesticides and nonlinear dependence in the toxicity data were evaluated using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) QSTR models were constructed for classification (two and four categories) and function optimization problems using the toxicity end point in honey bees. The predictive power of the QSTR models was tested through rigorous validation performed using the internal and external procedures employing a wide series of statistical checks. In complete data, the PNN-QSTR model rendered a classification accuracy of 96.62% (two-category) and 95.57% (four-category), while the GRNN-QSTR model yielded a correlation (R(2)) of 0.841 between the measured and predicted toxicity values with a mean squared error (MSE) of 0.22. The results suggest the appropriateness of the developed QSTR models for reliably predicting qualitative and quantitative toxicities of pesticides in honey bee. Both the PNN and GRNN based QSTR models constructed here can be useful tools in predicting the qualitative and quantitative toxicities of the new chemical pesticides for regulatory purposes.

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 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.

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 < 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.

ANOTHER NOTCH IN THE BELT OF RHEUMATOID ARTHRITIS.

Notch ligands and receptors, including JAG1/2, DLL1/4, and Notch1/3, are enriched on macrophages (MΦs), fibroblast-like synoviocytes (FLS), and/or endothelial cells in rheumatoid arthritis (RA) compared to normal synovial tissues (ST). Power Doppler ultrasound-guided ST studies reveal that the Notch family is highly involved in early active RA, especially during neovascularization. In contrast, the Notch family is not implicated during the erosive stage, evidenced by their lack of correlation with radiographic damage in RA STs. TLRs and TNF are the common inducers of Notch expression in RA MΦs, FLS, and endothelial cells. Among Notch ligands, JAG1 and/or DLL4 are most inducible by inflammatory responses in RA MΦs or endothelial cells and trans-activate their receptors on RA FLS. TNF plays a central role on Notch ligands, as anti-TNF good responders display JAG1/2 and DLL1/4 transcriptional downregulation in RA ST myeloid cells. In in vitro studies, TNF increases Notch3 expression in MΦs, which is further amplified by RA FLS addition. Specific disease-modifying anti-rheumatic drugs (DMARDs) reduced JAG1 and Notch3 expression in MΦ and RA FLS cocultures. Organoids containing FLS and endothelial cells have increased expression of JAG1 and Notch3. Nonetheless, Methotrexate, IL-6R antibodies, and B cell blockers are mostly ineffective at decreasing Notch family expression. NF-κB, MAPK, and AKT pathways are involved in Notch signaling, whereas JAK/STATs are not. Although Notch blockade has been effective in RA preclinical studies, its small molecule inhibitors have failed in phase I and II studies, suggesting that alternative strategies may be required to intercept their function.

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.

Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches.

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.

Distribution of polycyclic aromatic hydrocarbons in water and bed sediments of the Gomti River, India.

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.

Iron-induced oxidative stress in a macrophyte: a chemometric approach.

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.

Multivariate modeling of chromium-induced oxidative stress and biochemical changes in plants of Pistia stratiotes L.

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.

Levels and distribution of persistent organochlorine pesticide residues in water and sediments of Gomti River (India)--a tributary of the Ganges River.

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.

Liquid-phase adsorption of phenols using activated carbons derived from agricultural waste material.

Physical and chemical properties of activated carbons prepared from coconut shells (SAC and ATSAC) were studied. The adsorption equilibria and kinetics of phenol and 2,4-dichlorophenol from aqueous solutions on such carbons were then examined at three different temperatures (10, 25 and 40 degrees C). Adsorption of both phenol and 2,4-dichlorophenol increased with an increase in temperature. The experimental data were analyzed using the Langmuir and Freundlich isotherm models. Both the isotherm models adequately fit the adsorption data for both the phenols. The carbon developed through the acid treatment of coconut shells (ATSAC) exhibited relatively higher monolayer adsorption capacity for phenol (0.53 mmol g(-1)) and 2,4-dichlorophenol (0.31 mmol g(-1)) as compared to that developed by thermal activation (SAC) with adsorption capacity of 0.36 and 0.20 mmol g(-1), for phenol and 2,4-dichlorophenol, respectively. The equilibrium sorption and kinetics model parameters and thermodynamic functions were estimated and discussed. The thermodynamic parameters (free energy, enthalpy and entropy changes) exhibited the feasibility and spontaneous nature of the adsorption process. The sorption kinetics was studied using the pseudo-first-order and second-order kinetics models. The adsorption kinetics data for both the phenol and 2,4-dichlorophenol fitted better to the second-order model. An attempt was also made to identify the rate-limiting step involved in the adsorption process. Results of mass transfer analysis suggested the endothermic nature of the reaction and change in the mechanism with time and initial concentration of the adsorbate. The results of the study show that the activated carbons derived from coconut shells can be used as potential adsorbent for phenols in water/wastewater.

Wastewater treatment using low cost activated carbons derived from agricultural byproducts--a case study.

A variety of low cost activated carbons were developed from agricultural waste materials viz., coconut shell, coconut shell fibers and rice husk. The low cost activated carbons were fully characterized and utilized for the remediation of various pollutants viz., chemical oxygen demand (COD), heavy metals, anions, etc., from industrial wastewater. Sorption studies were carried out at different temperatures and particle sizes to study the effect of temperatures and surface areas. The removal of chloride and fluoride increased with rise in temperature while COD and metal ions removal decreased with increase in temperature, thereby, indicating the processes to be endothermic and exothermic, respectively. The kinetics of COD adsorption was also carried out at different temperatures to establish the sorption mechanism and to determine various kinetic parameters. The COD removal was 47-72% by coconut shell fiber carbon (ATFAC), 50-74% by coconut shell carbon (ATSAC) and 45-73% by rice husk carbon (ATRHC). Furthermore, COD removal kinetics by rice husk carbon, coconut shell carbon and coconut fiber carbon at different temperatures was approximately represented by a first order rate law. Results of this fundamental study demonstrate the effectiveness and feasibility of low cost activated carbons. The parameters obtained in this study can be fully utilized to establish fixed bed reactors on large scale to treat the contaminated water.

Receptor modeling for source apportionment of polycyclic aromatic hydrocarbons in urban atmosphere.

This study reports source apportionment of polycyclic aromatic hydrocarbons (PAHs) in particulate depositions on vegetation foliages near highway in the urban environment of Lucknow city (India) using the principal components analysis/absolute principal components scores (PCA/APCS) receptor modeling approach. The multivariate method enables identification of major PAHs sources along with their quantitative contributions with respect to individual PAH. The PCA identified three major sources of PAHs viz. combustion, vehicular emissions, and diesel based activities. The PCA/APCS receptor modeling approach revealed that the combustion sources (natural gas, wood, coal/coke, biomass) contributed 19-97% of various PAHs, vehicular emissions 0-70%, diesel based sources 0-81% and other miscellaneous sources 0-20% of different PAHs. The contributions of major pyrolytic and petrogenic sources to the total PAHs were 56 and 42%, respectively. Further, the combustion related sources contribute major fraction of the carcinogenic PAHs in the study area. High correlation coefficient (R2 > 0.75 for most PAHs) between the measured and predicted concentrations of PAHs suggests for the applicability of the PCA/APCS receptor modeling approach for estimation of source contribution to the PAHs in particulates.

DNA damage induced in human peripheral blood lymphocytes by industrial solid waste and municipal sludge leachates.

Exposure of humans to toxic compounds occurs mostly in the form of complex mixtures. Leachates, consisting of mixtures of many chemicals, are a potential risk to human health. In the present study, leachates of solid wastes from a polyfiber factory (PFL), an aeronautical plant (AEL), and a municipal sludge leachate (MSL) were assessed for their ability to induce DNA damage in human peripheral blood lymphocytes using the alkaline Comet assay. The leachates also were examined for their physical and chemical properties. Lymphocytes were incubated with 0.5-15.0% concentrations (pH range 7.1-7.4) of the test leachates for 3 hr at 37 degrees C, and treatment with 1 mM ethyl methanesulfonate served as a positive control. All three leachates induced significant (P < 0.05), concentration-dependent increases in DNA damage compared with the negative control, as measured by increases in Olive tail moment (arbitrary units), tail DNA (%), and tail length (mum). A comparison of these variables among the treatment groups indicated that the MSL induced the most DNA damage. Inductively coupled plasma emission spectrometry analysis of the leachates indicated that they contained high concentrations of heavy metals, viz. iron, manganese, nickel, zinc, cadmium, chromium, and lead. The individual, synergistic, or antagonistic effects of these chemicals in the leachates may be responsible for the DNA damage. Our data indicate that the ever-increasing amounts of leachates from waste landfill sites have the potential to induce DNA damage and suggest that the exposure of human populations to these leachates may lead to adverse health effects.