No abstract

A novel construction strategy of monolithic capillary column for selectively enriching perfluorinated persistent organic pollutants was proposed. The organic-inorganic hybrid fluorous monolithic capillary column was synthesized by a "one-pot" approach via the polycondensation of γ-methacryloxypropyltrimethoxy-silane, then in situ copolymerization of 1H,1H,7H-dodecafluoroheptyl methacrylate and vinyl group on the precondensed siloxanes. The obtained monolithic columns were systematically characterized. The results demonstrated that the optimal column possessed good mechanical stability and high permeability. The adsorption capacities of the optimized monolithic column for perfluorooctanoic acid and perfluorooctane sulfonate were 0.257 and 0.513 μg/mg, respectively. Adsorption capacities of the monoliths were proved to increasing with increasing the amounts of fluorinated monomers in the fluorous monoliths. Sodium 1-octanesulfonate, as a comparison compound, was hardly adsorbed on the fluorous monolith. In addition, the trace amounts of perfluorooctanoic acid and perfluorooctane sulfonate in water samples can be successfully concentrated about 160 times to their original concentrations by this monolithic column. These results demonstrated that the capacity and selectivity of the affinity fluorous column is high and can be applied to the selective enrichment for the perfluorinated persistent organic pollutants from environmental samples.

No abstract

Abstract. Better forecast of carbon emission prices may increase risk control capabilities of emission market stakeholders, and provide decision support for policy makers, financial institutions and enterprises. However, the issue of carbon price forecasting is complicated and several existing prediction models are difficult to achieve satisfactory results. This paper proposes an integration model based on SVR to predict international carbon market price. The model we suggest includes two steps: we respectively establish ARIMA, BP neural network, grey model GM(1,1) and genetic programming to fit the original sequence of the carbon price at the first phase, and get four prediction results. Additionally, SVR is used to integrate these four results and eventually obtain prediction result. For verification and testing, EUA (DEC15) carbon price from December 3, 2012 to April 10, 2015 under the EU ETS was used to examine the forecasting ability of the proposed integration model. The result demonstrates the accuracy of integration model proposed in this paper is superior to the other three basic models.

No abstract