Comprehensive utilization of residues of Magnolia officinalis based on fiber characteristics

Journal of Material Cycles and Waste Management - The traditional Chinese medicinal plant Magnolia officinalis has a wide range of applications; including more than 200 kinds of patented Chinese...     

广州轨道交通三号线高速行驶条件下对安全限界的影响研究

给出了轨道交通车辆横向偏移量的计算方法 ,并应用列车 -线路动力耦合模型 ,分析了广州地铁三号线的安全限界变化问题。确定了当前按 80km/h行车速度设计的建筑限界能满足 13 0km/h行车速度的要求 ,限界尺寸不需改动可保证列车运行安全平稳     

Adsorption of 17β-estradiol from aqueous solution by raw and direct/pre/post-KOH treated lotus seedpod biochar

Five biochars derived from lotus seedpod(LSP) were applied to examine and compare the adsorption capacity of 17β-estradiol(E2) from aqueous solution.The effect of KOH activation and the order of activation steps on material properties were discussed.The effect of contact time,initial concentration,p H,ionic strength and humic acid on E2 adsorption were investigated in a batch adsorption process.Experimental results demonstrated that the pseudo second-order model fitted the experimental data best and that adsorption equilibrium was reached within 20 hr.The efficiency of E2 removal increased with increasing E2 concentration and decreased with the increase of ionic strength.E2 adsorption on LSP-derived biochar(BCs) was influenced little by humic acid,and slightly affected by the solution p H when its value ranged from 4.0 to 9.0,but considerably affected at p H 10.0.Low environmental temperature is favorable for E2 adsorption.Chemisorption,π–π interactions,monolayer adsorption and electrostatic interaction are the possible adsorption mechanisms.Comparative studies indicated that KOH activation and the order of activation steps had significant impacts on the material.Post-treated biochar exhibited better adsorption capacity for E2 than direct treated,pretreated,and raw LSP biochar.Pyrolyzed biochar at higher temperature improved E2 removal.The excellent performance of BCs in removing E2 suggested that BCs have potential in E2 treatment and that the biochar directly treated by KOH would be a good choice for the treatment of E2 in aqueous solution,with its advantages of good efficiency and simple technology.     

Influence of environmental factors on arsenic accumulation and biotransformation using the aquatic plant species Hydrilla verticillata

Hydrilla verticillata(waterthyme) has been successfully used for phytoremediation in arsenic(As) contaminated water.To evaluate the effects of environmental factors on phytoremediation,this study conducted a series of orthogonal design experiments to determine optimal conditions,including phosphorus(P),nitrogen(N),and arsenate(As(Ⅴ))concentrations and initial pH levels,for As accumulation and biotransformation using this aquatic plant species,while also analyzing As species transformation in culture media after 96-hr exposure.Analysis of variance and the signal-to-noise ratio were used to identify both the effects of these environmental factors and their optimal conditions for this purpose.Results indicated that both N and P significantly impacted accumulation,and N was essential in As species transformation.High N and intermediate P levels were critical to As accumulation and biotransformation by H.verticillata,while high N and low P levels were beneficial to As species transformation in culture media.The highest total arsenic accumulation was(197.2±17.4) μg/g dry weight when As(V) was at level 3(375μg/L),N at level 2(4 mg/L),P at level 1(0.02 mg/L),and pH at level 2(7).Although H.verticillata is highly efficient in removing As(Ⅴ) from aquatic environments,its use could be potentially harmful to both humans and the natural environment due to its release of highly toxic arsenite.For cost-effective and ecofriendly phytoremediation of As-contaminated water,both N and P are helpful in regulating As accumulation and transformation in plants.     

Plasmonic silver/silver oxide nanoparticles anchored bismuth vanadate as a novel visible-light ternary photocatalyst for degrading pharmaceutical micropollutants

The degradation of pharmaceutical micropollutants is an intensifying environmental problem and synthesis of efficient photocatalysts for this purpose is one of the foremost challenges worldwide. Therefore, this study was conducted to develop novel plasmonic Ag/Ag₂O/BiVO₄ nanocomposite photocatalysts by simple precipitation and thermal decomposition methods, which could exhibit higher photocatalytic activity for mineralized pharmaceutical micropollutants. Among the different treatments, the best performance was observed for the Ag/Ag₂O/BiVO₄ nanocomposites (5 wt.%; 10 min's visible light irradiation) which exhibited 6.57 times higher photodegradation rate than the pure BiVO₄. Further, the effects of different influencing factors on the photodegradation system of tetracycline hydrochloride (TC-HCl) were investigated and the feasibility for its practical application was explored through the specific light sources, water source and cycle experiments. The mechanistic study demonstrated that the photogenerated holes (h⁺), superoxide radicals (?O₂^?) and hydroxyl radicals (?OH) participated in TC-HCl removal process, which is different from the pure BiVO₄ reaction system. Hence, the present work can provide a new approach for the formation of novel plasmonic photocatalysts with high photoactivity and can act as effective practical application for environmental remediation.     

Morphology-dependent interfacial interactions of Fe2O3 with Ag nanoparticles for determining the catalytic reduction of p-nitrophenol

In this work,we fabricated three kinds of Ag/Fe_2O_3 model catalysts with different morphologies to study the interfacial interactions between Ag and Fe_2O_3,and how they affected the catalytic activity in hydrogenation of p-nitrophenol was explored.The hydrothermal method was used to synthesize the metal oxide supported silver catalyst,with various morphologies including nanoplates(NPs),nanospheres(NSs),and nanocubes(NCs).The crystal structure,morphology and surface elements of the composite were investigated by various measurements,such as X-ray diffraction(XRD),scanning electron microscopy(SEM),transmission electron microscopy(TEM) and X-ray photoelectron spectroscopy(XPS).The catalytic activity was also evaluated by the reduction of p-nitrophenol to p-aminophenol.It was found that the activities of the above catalysts varied with the morphology of the support.Among them,Ag/Fe_2O_3 NPs promoted the highest performance,Ag/Fe_2O_3 NSs were slightly inferior,and Ag/Fe_2O_3 NCs were the worst.At last,we ascribed the remarkable activity of Ag/Fe_2O_3 NPs to the strong metal-support interactions between Ag and Fe_2O_3.     

Can a low-carbon development path achieve win-win development: evidence from China’s low-carbon pilot policy

Mitigation and Adaptation Strategies for Global Change - Low-carbon pilot (LCP) policy aims to not only achieve economic development but also address climate change problems in China. With a...     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Reduction of SO2 emissions by ammonia gas during unstaged combustion

The reduction of SO₂ by the addition of ammonia gas has been studied in a 2 m high fluidized bed combustor having a 30 cm static bed height and a freeboard height of 170 cm. Ammonia gas was injected at 52 cm above the distributor where the temperature is ca. 700° C by an uncooled stainless steel tube injector. Experiments were carried out to investigate the effects of amminia gas injection on sulphur dioxide emissions at unstaged conditions of: (i) excess air level, (ii) NH₃:SO₂ molar ratio, (iii) fluidizing velocity and (iv) bed height.A maximum reduction of 75% in SO₂ emissions was found at 40% excess air, at an NH₃:SO₂ molar ratio of 5.4. The onset of SO₂ reduction occurred at an NH₃:SO₂ ratio of 1.5 However, the most effective ratio was found to be between 3 and 5. Fluidizing velocity and bed height were also found to have significant influence on SO₂ reduction.It is difficult to determine how the SO₂ reduction varied with operating conditions. When ammonia is added in the main combustor zone, the temperature is much higher than that required for the occurrence of sulphur dioxide-ammonia and sulphur trioxide-ammonia reactions. However, this paper points out the significance of ammonia addition in the reduction of sulphur dioxide.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.