Mechanism and catalytic performance for direct dimethyl ether synthesis by CO2 hydrogenation over CuZnZr/ferrierite hybrid catalyst

Direct synthesis of dimethyl ether(DME) by CO_2 hydrogenation has been investigated over three hybrid catalysts prepared by different methods:co-precipitation,sol-gel,and solid grinding to produce mixed Cu,ZnO,ZrO_2 catalysts that were physically mixed with a commercial ferrierite(FER) zeolite.The catalysts were characterized by N_2 physisorption,X-ray diffraction(XRD),transmission electron microscopy(TEM),X-ray photoelectron spectroscopy(XPS),temperature programmed desorption of CO_2(CO_2-TPD),temperature programmed desorption of NH_3(NH_3-TPD),and temperature programmed H2 reduction(H_2-TPR).The results demonstrate that smaller CuO and Cu crystallite sizes resulting in better dispersion of the active phases,higher surface area,and lower reduction temperature are all favorable for catalytic activity.The reaction mechanism has been studied using in situ diffuse reflectance infrared Fourier transform spectroscopy(DRIFTS).Methanol appears to be formed via the bidentate-formate(b-HCOO) species undergoing stepwise hydrogenation,while DME formation occurs from methanol dehydration and reaction of two surface methoxy groups.     

Advanced molecular-fingerprinting analysis of dissolved organic sulfur by electrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry using optimal spray solvent

Molecular level characterization of dissolved organic sulfur (DOS) by electrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry (ESI-FTICR MS) is necessary for further understanding of the role of DOS in the environment. Here, ESI spray solvent, a key parameter for ion production during ESI process, was investigated for its effect on the molecular characterization of DOS by ESI-FTICR MS. 100% MeOH as spray solvent was found for the first time to remarkably enhance the ionization efficiency of the majority of CHOS-molecules in NOM, which facilitated a total of 1473 CHOS-molecular formulas with one sulfur atom to be detected. The number of CHOS-molecular formulas obtained using 100%MeOH as spray solvent increased notably over 740 in comparison with those using 50% MeOH aqueous solution (731) or 50% ACN aqueous solution (653). Moreover, due to the enhancement of ionization efficiency of DOS during ESI processes, the tandem mass spectra of the NOM CHOS-molecules could be easily obtained using 100% MeOH as spray solvent, which were hardly obtained using 50% MeOH aqueous solution as spray solvent. The results of the tandem mass spectra suggested the first discovery of organosulfates or sulfonic acids in Suwannee River NOM sample. A simple method based on 100% MeOH as ESI spray solvent for advanced molecular characterization of DOS by ESI-FTICR MS was proposed and applied, and the results revealed more molecular information of DOS in sea DOM samples.     

On-going nitrification in chloraminated drinking water distribution system (DWDS) is conditioned by hydraulics and disinfection strategies

Within the drinking water distribution system (DWDS) using chloramine as disinfectant, nitrification caused by nitrifying bacteria is increasingly becoming a concern as it poses a great challenge for maintaining water quality. To investigate efficient control strategies, operational conditions including hydraulic regimes and disinfectant scenarios were controlled within a flow cell experimental facility. Two test phases were conducted to investigate the effects on the extent of nitrification of three flow rates (Q = 2, 6, and 10 L/min) and four disinfection scenarios (total Cl₂=1 mg/L, Cl₂/NH₃-N=3:1; total Cl₂=1 mg/L, Cl₂/NH₃-N=5:1; total Cl₂=5 mg/L, Cl₂/NH₃-N=3:1; and total Cl₂=5 mg/L, Cl₂/NH₃-N=5:1). Physico-chemical parameters and nitrification indicators were monitored during the tests. The characteristics of biofilm extracellular polymetric substance (EPS) were evaluated after the experiment. The main results from the study indicate that nitrification is affected by hydraulic conditions and the process tends to be severe when the fluid flow transforms from laminar to turbulent (2300<Re<4000). Increasing disinfectant concentration and optimizing Cl₂/NH₃-N mass ratio were found to inhibit nitrification to some extend when the system was running at turbulent condition (Q = 10 L/min, Re = 5535). EPS extracted from biofilm that was established at the flow rate of 6 L/min had greater carbohydrate/protein ratio. Furthermore, several nitrification indicators were evaluated for their prediction efficiency and the results suggest that the change of nitrite, together with total organic carbon (TOC) and turbidity can indicate nitrification potential efficiently.     

Concentrations and chemical compositions of PM10 during hazy and non-hazy days in Beijing

In order to study the concentrations of major components,characteristics and comparison in hazy and non-hazy days of PM_(10) in Beijing,aerosol samples were collected at urban site in Beijing from December 29,2014 to January 22,2015.Heavy metals like Zn,Pb,Mn,Cu,As,V,Cr and Cd were deeply studied considering their toxic effects on human being;nine water-soluble inorganic ions(SO_4~(2-),NO_3~-,NH_4~+,Na~+,K~+,Cl~-,Ca~(2+) and Mg~(2+)) and carbon fractions(OC and EC) were also analyzed.The concentrations of heavy metals were 1.03–1.98 times higher in hazy days than those in non-hazy days,mainly due to biomass burning and coal burning.The trends in total heavy metals concentrations were basically consistent with the trends in PM concentrations except for two obvious periods(12.29–12.30;1.14–1.15);but when air masses accumulated locally or around Beijing,trends in PM concentrations and heavy metals were opposite.The proportion for NO_3~-/SO_4~(2-) indicated that mobile sources such as automobiles were important reasons for haze in Beijing.Correlation between OC and EC during non-hazy days was strong(R~2= 0.95) but it was low(R~2= 0.67) during hazy days,and large variations for OC/EC values occurred in hazy days.The calculated mass concentration of SOC is 2.58 μg/m~3,which only accounted for 10.1% of the OC concentration.When air masses from the far north-west,they decreased PM concentration in Beijing and they were relatively clean;however,those from the near east,south-east and south of the mainland increased PM concentration and they were dirty.     

典型西南工业城市春冬季PM2.5来源与潜在源区分析——以柳州市为例

为了揭示柳州城区春冬季PM_2.5的来源及其潜在源区分布和贡献,利用2018年24h自动监测数据和气象数据对柳州市大气污染物浓度变化特征进行了分析,并且使用后向轨迹模型（HYSPLIT）对春冬季柳州市PM_2.5逐日72h气流后向轨迹和前向轨迹进行聚类分析,同时结合潜在源贡献因子分析法（WPSCF）和轨迹浓度权重法（WCWT）对其潜在源区和浓度贡献进行了分析.结果显示,（1）在研究期内,不利的主导风向和工业区布局导致研究区PM_2.5在春冬季污染较严重,且工业源和交通源是其主要本地来源;（2）春冬季PM_2.5高值主要来源于西北和东南方向,其中,西北向PM_2.5主要来源于本地排放,且浓度在空间上呈现西高东低的趋势;（3）春季后向轨迹PM_2.5浓度整体大于冬季,春冬季中对柳州市PM_2.5影响最大轨迹均来自东部的短距离输送,而来自西北的气流轨迹输对PM_2.5贡献最低.春冬季柳州市大气PM_2.5通过气流传输对贵州地区大气环境有较大影响;（4）春季,柳州市PM_2.5的主要潜在源区分布在广西东南部、广东中西部、南海沿岸海域、湖南中部、江西西北部、湖北东部及安徽西北部;冬季,主要分布在广西东南部、广东西南部和南海沿岸海域.     

Heterogeneity in the relationship between carbon emission performance and urbanization: evidence from China

Mitigation and Adaptation Strategies for Global Change - Global change caused by carbon emissions alone has become a common challenge for all countries. However, current debates about urbanization...     

一种模拟动力学边界条件的环境振动试验方法研究

目的提高环境振动试验的可靠性。方法以某典型钛合金蜂窝夹芯壁板试验件为例,提出一种模拟试验件动力学边界条件的试验方法,在试验件周围引入弹性连接件,再通过刚性转接工装与振动台台面连接,以试验件在试验安装状态下的共振频率及振型节线位置为优化目标,以弹性连接件的外形尺寸及厚度为优化变量,对弹性连接件进行优化设计,从而模拟试验件真实的动力学边界条件。结果弹性连接件的优化设计使得试验件在试验安装状态下前两阶共振频率与试验要求相差小于8%,而且振型节线位置与试验要求基本重合,达到了模拟试验件真实动力学边界条件的设计目标。结论对于壁板类试验件,在传统刚性夹具的基础上引入弹性连接件的试验方法,可以较好地模拟试验件真实动力学边界条件,提高环境振动试验的可靠性,并且这种方法对试验费用及试验周期影响较小,具有良好的工程应用前景。     

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.