微波强化CWPO工艺CuO_x-CeO_2/GAC催化剂的制备及其对二甲亚砜的降解

以颗粒活性炭(GAC)为载体、铜为活性组分、铈为助剂组分、草酸钠为沉淀剂,采用浸渍焙烧法制得CuO_x-CeO_2/GAC催化剂。以H_2O_2为氧化剂,微波强化催化湿式过氧化氢氧化(CWPO)处理二甲亚砜(DMSO)初始质量浓度为1 000 mg/L的废水,处理3 min后DMSO去除率达93.8%。催化剂第7次使用时DMSO去除率仍保持在75%以上。初始废水pH在3~9范围内,DMSO去除率均在85%以上。助剂Ce的加入提高了催化剂表面活性组分的分散性和稳定性,使催化剂的活性稳定性和使用寿命显著提高。     

Sustainable Use of Coffee Husks For Reinforcing Polyethylene Composites

The utilization of the coffee husk fiber (CHF) from the coffee industry as a reinforcing filler in the preparation of a cost-effective thermoplastic based composite was explored in this study. The chemical composition and thermal properties of the CHF were investigated and compared with those of wood fiber (WF). CHF proved to be mainly composed of cellulose, hemicellulose and lignin, and exhibited similar thermal behavior to WF. High density polyethylene (HDPE) composites with CHF loadings of from 40 to 70% were prepared using melt processing and extrusion. The processing properties, mechanical behavior, water absorption and thermal performance of these composites were investigated. The effect of maleated polyethylene (MAPE) used as a coupling agent on the composite was explored. The experimental results showed that increasing the CHF loading in the HDPE matrix resulted in an increase in the modulus and thermal properties of the composites, but resulted in poor water resistance. The addition of a 4% MAPE significantly improved the interfacial behavior of the hydrophilic lignocellulosic fiber and the hydrophobic polymer matrix.     

Sb release characteristics of the solid waste produced in antimony mining smelting process

Sb release characteristics of blast furnace slag, mining waste rock and tailing sand were investigated in static immersion and dynamic leaching test. These three kinds of waste samples were collected from the antimony mine in Lengshuijiang, China, produced in mining smelting process. Effects of solid/liquid ratio, sample size and pH of leaching solution on Sb release characteristics were inspected based on the analysis of scanning electron microscope, pH and EC of leachate. The optimal parameters for Sb leaching of each sample were analyzed. For blast furnace slag and mining waste rock, Sb release contents increased along with the decline of solid/liquid ratio. The maximum accumulative release contents were 42.13, 34.26 mg/kg at the solid/liquid ratio of 1:20. While Sb release content for tailing sand decreased first and then increased with the reduction of solid/liquid ratio. When the solid/liquid ratio was 1:5, the accumulative Sb release content reached the most (24.30 mg/kg). Sb release content of mining waste rock increased with the drop of leaching solution pH, with the highest accumulative release content of 26.01 mg/kg at pH 2.0. Sb release contents of blast furnace slag and tailing sand showed positive correlation with the variation of leaching solution pH. The maximum accumulative release contents of these two samples were 215.91 and 147.83 mg/kg, respectively, when leaching solution pH was 7.0. In summary, Sb release capacity of the three samples in descending order was tailing sand, blast furnace slag and mining waste rock. pH and EC of the leachate in dynamic test varied independently with the initial pH of leaching solution while showing close relationship with mineral hydrolysis in the waste.     

Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.     

Effect of alginate on the aggregation kinetics of copper oxide nanoparticles (CuO NPs): bridging interaction and hetero-aggregation induced by Ca<Superscript>2+</Superscript>

The stability of CuO nanoparticles (NPs) is expected to play a key role in the environmental risk assessment of nanotoxicity in aquatic systems. In this study, the effect of alginate (model polysaccharides) on the stability of CuO NPs in various environmentally relevant ionic strength conditions was investigated by using time-resolved dynamic light scattering. Significant aggregation of CuO NPs was observed in the presence of both monovalent and divalent cations. The critical coagulation concentrations (CCC) were 54.5 and 2.9 mM for NaNO₃ and Ca(NO₃)₂, respectively. The presence of alginate slowed nano-CuO aggregation rates over the entire NaNO₃ concentration range due to the combined electrostatic and steric effect. High concentrations of Ca²⁺ (>6 mM) resulted in stronger adsorption of alginate onto CuO NPs; however, enhanced aggregation of CuO NPs occurred simultaneously under the same conditions. Spectroscopic analysis revealed that the bridging interaction of alginate with Ca²⁺ might be an important mechanism for the enhanced aggregation. Furthermore, significant coagulation of the alginate molecules was observed in solutions of high Ca²⁺ concentrations, indicating a hetero-aggregation mechanism between the alginate-covered CuO NPs and the unabsorbed alginate. These results suggested a different aggregation mechanism of NPs might co-exist in aqueous systems enriched with natural organic matter, which should be taken into consideration in future studies.

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Graphical abstract Hetero-aggregation mechanism of CuO nanoparticles and alginate under high concentration of Ca²⁺

     

Rebound effect and its decomposition – an analysis based on energy types in China

Rebound effect derived from energy efficiency improvement has been widely invested. However, most of studies focus on the rebound effect of the energy composite level and neither distinguish nor compare different energy types. We compare the differences in energy saving and energy rebound between primary and secondary energy sources, and further decompose the rebound effect into production rebound part and final demand component. To do so, we add a module for rebound into a comparative state China-CGE model. We design and test two simulation scenarios using the model. In Scenario 1, all production sectors’ energy efficiency of using primary energy increases by 5%. In Scenario 2, all production sectors’ energy efficiency of using secondary energy increases by 5%. The results show that Scenario 2 leads to more GDP growth and more energy saving. Our scenarios show rebound effects range between 9.6% and 27.9%, and in general are higher when energy efficiency of using primary energy sources is improved. Our decomposition analysis shows that improving energy efficiency in production sectors would stimulates energy use of final demand. Indeed, the consumption side has significant contribution to rebound in secondary energy use, especially in crude oil and gas. This study reveals that improving efficiency of using secondary energy is better than improving that of primary energy, both in terms of economic impact and energy rebound. And complementary policies that prevent energy services prices from falling too much can be adopted to reduce rebound. Controlling residential energy use could also be effective in reducing rebound, this has particular implication to economies in which residential energy consumption are far from saturation.     

Plasticity in metamorphic traits of <Emphasis Type="Italic">Rana kukunoris</Emphasis> tadpoles: The interactive effects of food level and rearing temperature

Larval amphibians are particularly likely to encounter variation in rearing temperature and resource availability due to variation in aquatic breeding habitats. In this study, plasticity in growth rates, larval mass, larval period, and size at metamorphosis were examined in Rana kukunoris Nikolskii, 1918 under different combinations of temperature and food level. Larval period and larval body mass was sensitive to food level, and varied with temperature. Tadpoles metamorphosed at an older age at low temperature than those reared at warm temperature. Food level was a significant affect on larval period at low temperature, but not at warm temperature. Mass was heavier for tadpoles reared at low temperatures than those reared at warm temperatures. The effect of food level depended on temperature, because larvae reared at low temperature that were offered a high food level achieved a larger size than larvae offered a low food level, but this did not occur at warm temperature. Therefore, we suggest that high food availability at low temperature prolonged developmental periods, thus larvae are larger as metamorphs than those reared at warm temperatures.     

电化学氧化法处理垃圾渗滤液纳滤浓缩液

实验利用电化学氧化法处理垃圾渗滤液纳滤浓缩液,以提高废水的可生化性。研究考察了水力停留时间、进水流量、循环流量、电流强度和原水氯离子浓度对有机物去除的影响。研究结果表明,电化学氧化法的最佳运行条件如下:水力停留时间为3 h,进水流量为1 m3/h,循环流量为15 m3/h,电流强度为420 A。在上述条件下,原水COD浓度从3 100mg/L降到1 311.3 mg/L,去除率达到57.7%,BOD/COD值由0.03提升至0.31。氯离子对电解有促进作用,但原水氯离子浓度超过5 000 mg/L,不需要外加工业盐。