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Luo J.,CAS Research Center for Eco Environmental Sciences | Luo J.,Beijing Municipal Research Institute of Environmental Protection | Lei B.,CAS Research Center for Eco Environmental Sciences | Ma M.,CAS Research Center for Eco Environmental Sciences | And 2 more authors.
Water Research | Year: 2011

Assignment of ecological impacts of contamination to specific classes of contaminants is a prerequisite for risk assessment and remediation. In this study, the combination of polarity-based fractionation, two-hybrid yeast bioassay, and chemical analysis were used to evaluate and identify estrogen receptor agonists (ER-agonists) in sediments from Wenyu River, Beijing, China. By bioassay, organic raw sediment extracts could induce significant estrogenicity and the bioassay-derived 17β-estradiol equivalents (EEQs) of raw extracts (EEQraws) ranged from 0.8 to 19.8 ng/g dry weight. By polarity-based fractionation, the raw extracts were separated into three fractions, i.e. non-polar, moderately polar, and polar fractions, which were subjected to bioassay and chemical analysis. The highest estrogenicity was observed in the polar fraction, which accounted for more than 78% of the total. The medium polar fraction contains PAHs and OCPs, and the estrogenic activities in this fraction contributed 3%-12% of the total in raw extract. An estrogenic activity of non-polarity fraction was negligible in compare to other two fractions. By chemical analysis and toxic equivalent calculation, major part of the estrogenicity in polar fraction could be attributed to six natural/synthetic estrogens (16%-63%), i.e. 17β-estradiol, estrone, estriol, 17α-ethynylestradiol, diethylstilbestrol, and β-estradiol-17-valerate, and to nonylphenols (26%-55%). The proposed approach has been successfully used for characterization of ER-agonists in this case study. © 2011 Elsevier Ltd. Source

Ji L.,Lanzhou University | Zhou L.,Lanzhou University | Bai X.,Tsinghua University | Shao Y.,Lanzhou University | And 4 more authors.
Journal of Materials Chemistry | Year: 2012

A one-step thermal decomposition strategy, in which a novel reductant participated, was developed to prepare superparamagnetic nearly cubic monodisperse Fe 3O 4 nanoparticles loaded on multiwall carbon nanotubes (MWCNTs/Fe 3O 4). Subsequently, the as-prepared MWCNTs/Fe 3O 4 nanocomposites were modified with 3-aminopropyltriethoxysilane (APTS) (MWCNTs/Fe 3O 4-NH 2). The materials were characterized by transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), vibrating sample magnetometry (VSM) and the BET surface area method. The results indicated that superparamagnetic Fe 3O 4 nanoparticles were successfully loaded onto the surface of the MWCNTs, and APTS was also modified on the MWCNTs/Fe 3O 4 magnetic nanocomposites. The two as-prepared magnetic nanocomposites were used as adsorbents to remove tetrabromobisphenol A (TBBPA) and Pb(ii) from wastewater. The adsorption kinetics and adsorption isotherms of TBBPA and Pb(ii) on the two as-prepared adsorbents were studied at pH 7.0 and 5.3, respectively. It was revealed that MWCNTs/Fe 3O 4-NH 2 performed better than the MWCNTs/Fe 3O 4 nanocomposites for the adsorption properties of TBBPA and Pb(ii). After adsorption, both adsorbents could be conveniently separated from the media by an external magnetic field within several seconds, and regenerated in 0.1 M NaOH solution. © 2012 The Royal Society of Chemistry. Source

Li M.,Water Resources University | Feng C.,Water Resources University | Zhang Z.,University of Tsukuba | Chen R.,University of Tsukuba | And 3 more authors.
Electrochimica Acta | Year: 2010

Electrochemical reduction of nitrate in an undivided cell was studied in the present experiments. The optimization of the influencing factors on electrochemical reduction of nitrate by response surface methodology (RSM) was also studied. An ideal condition of performing both cathodic reduction of nitrate and anodic oxidation of the formed by-product in the presence of NaCl was achieved in the present experiment. The Box-Behnken design can be employed to develop mathematical models for predicting electrochemical nitrate removal geometry. The removal is sensitive to the current density and time in the present study. The value of R2 > 0.99 for the present mathematical model indicates the high correlation between observed and predicted values. The optimal NaCl dosage, current density and electrolysis time for nitrate removal in the present experiment are 0.47 g L-1, 26.06 mA cm-2, and 111.88 min, respectively, at which the nitrate nitrogen (nitrate-N) and ammonia nitrogen (ammonia-N) concentration in the treated solution are 9.80 and 0 mg L-1, respectively, which will meet the standards for drinking water. © 2010 Elsevier Ltd. Source

Yu J.,Beijing Municipal Research Institute of Environmental Protection
Advanced Materials Research | Year: 2011

The problem and several restricted factors of agriculture application of sewage sludge are analyzed at home and abroad. Meanwhile, this paper points out that the several restricted factors of agriculture application include mainly: hazardous chemical matters and heavy metal, receptivity of farmers and consumers to the product of sewage sludge, perfect policy and criterion standards, disposing charge of sewage sludge. The several restricted factors of agriculture application should be resolved if sewage sludge is used safely and environment. © (2011) Trans Tech Publications. Source

Wang D.,Beijing University of Civil Engineering and Architecture | Shi X.X.,Beijing University of Civil Engineering and Architecture | Yin J.Y.,Beijing Municipal Research Institute of Environmental Protection
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | Year: 2015

Support Vector Machine(SVM) is widely used in the pattern classification and regression analysis, as well as SVM is a kind of artificial intelligent method at present in China. In nature SVM is essentially a kernel method. this paper adapts RBF, linear and Sigmoid kernel separately to establish SVM model for training and predicting the air conditioning hourly load of a bank in summer. At the same time the comparisons among them are produced, the results show that the model based on RBF kernel function holds stronger extensive ability and higher learning capacity. ©, 2015, The editorial office of Transaction of China Electrotechnical Society. All right reserved. Source

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