Effects of calcination temperature on physicochemical property and activity of CuSO4/TiO2 ammonia-selective catalytic reduction catalysts

CuSO_4/TiO_2 catalysts with high catalytic activity and excellent resistant to SO_2 and H_2 O,were thought to be promising catalysts used in Selective catalytic reduction of nitrogen oxides by NH_3.The performance of catalysts is largely affected by calcination temperature.Here,effects of calcination temperature on physicochemical property and catalytic activity of CuSO_4/TiO_2 catalysts were investigated in depth.Catalyst samples calcined at different temperatures were prepared first and then physicochemical properties of the catalyst were characterized by N2 adsorption-desorption,X-ray diffraction,thermogravimetric analysis,Raman spectra,Fourier-transform infrared spectroscopy,X-ray photoelectron spectroscopy,temperature-pro grammed desorption of NH_3,temperature-programmed reduction of H_2 and in situ diffuse reflectance infrared Fourier transform spectroscopy.Results revealed that high calcination temperature had three main effects on the catalyst.First,sintering and anatase transform into rutile with increase of calcination temperature,causing a decrement of specific surface area.Second,decomposition of CuSO_4 under higher calcination temperature,resulting in disappears of Br(?)nsted acid sites(S-OH),which had an adverse effect on surface acidity.Third,CuO from the decomposition of CuSO_4 changed surface reducibility of the catalyst and favored the process of NH_3 oxidation to nitrogen oxides(NO_x).Thus,catalytic activity of the catalyst calcined under high temperatures(≥600℃) decreased largely.     

UV/H2O2 oxidation of tri(2-chloroethyl) phosphate: Intermediate products, degradation pathway and toxicity evaluation

Tri(2-chloroethyl) phosphate (TCEP) with the initial concentration of 5 mg/L was degraded by UV/H₂O₂ oxidation process. The removal rate of TCEP in the UV/H₂O₂ system was 89.1% with the production of Cl^? and PO₄^3? of 0.23 and 0.64 mg/L. The removal rate of total organic carbon of the reaction was 48.8% and the pH reached 3.3 after the reaction. The oxidative degradation process of TCEP in the UV/H₂O₂ system obeyed the first order kinetic reaction with the apparent rate constant of 0.0025 min^?1 (R²=0.9788). The intermediate products were isolated and identified by gas chromatography-mass spectrometer. The addition reaction of HO? and H₂O and the oxidation reaction with H₂O₂ were found during the degradation pathway of 5 mg/L TCEP in the UV/H₂O₂ system. For the first time, environment risk was estimated via the “ecological structure activity relationships” program and acute and chronic toxicity changes of intermediate products were pointed out. The luminescence inhibition rate of photobacterium was used to evaluate the acute toxicity of intermediate products. The results showed that the toxicity of the intermediate products increased with the increase of reaction time, which may be due to the production of chlorine compounds. Some measures should be introduced to the UV/H₂O₂ system to remove the highly toxic Cl-containing compounds, such as a nanofiltration or reverse osmosis unit.     

国土空间规划新时代旅游规划的定位与转型

我国已经迈入以国土空间规划引领“多规合一”的空间治理新时代,对各专项规划的管控约束性更严、技术性和衔接性要求更高。为此,本文重新审视了旅游规划的性质和地位,并基于产业专项性和空间融合性特征,将旅游规划分为战略规划、协调规划、发展规划和开发规划。以旅游发展规划为示例,提出了六大转型:规划理念从无限思维向边界思维、融合理念和空间思维转变,编制思路从甲方意志向市场导向转变,规划内容从大而全向专而精转变,技术路线从概念逻辑向空间逻辑转变,规划方式从封闭向开放转变,规划技术从缺乏技术到技术融合转变以期推动旅游规划衔接和融入新时代国土空间规划体系,进一步引领中国旅游产业的转型升级和高质量发展。     

Well-to-wheels greenhouse gas and air pollutant emissions from battery electric vehicles in China

Mitigation and Adaptation Strategies for Global Change - Electric vehicles (EVs) play a crucial role in addressing climate change and urban air quality concerns. China has emerged as the global...     

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.     

分光光度法测定染色废水的色度

为消除测定染色废水色度的主观误差，采用分光光度法测定染色废水的色度，与稀释倍数法相比，具有精确，重现性好，适用范围广等特点，ＰＨ值对色度的测定有明显影响，控制ＰＨ值为７．６０，测定色度具有可比性。     

Chemical monitoring and evaluation of the water quality of the Douro River at the Crestuma-Lever dam

Water taken from the Crestuma-Lever dam on the Douro River was evaluated considering the Portuguese Decree-law no. 236/98 of 1st August, 1998. This paper presents the metrological characteristics, validation of analytical methodologies and the data obtained from seasonal samplings. The test parameters selected are: temperature, pH, chloride, conductivity, carbon dioxide, alkalinity, hardness, oxidability, dissolved oxygen, BOD5, total suspended solids, sulfide, phosphate, silica, ammonium, nitrite, nitrate, sulfate, sodium, potassium, calcium, magnesium, iron, manganese, copper and zinc. From data analysis we can conclude that the water presented the following characteristics: a pH situated in the neutral zone; a median mineralization; a medium hardness; well oxygenated water; and a carbonate hardness exclusively due to bicarbonates. The temperature, pH, dissolved oxygen, ammonia, chlorides, sulfates, copper, zinc and BOD5 were compared with the legal limits presented in annexes XXI and I of Decree-law 236/98. The results relating to total hardness, nitrites, calcium, magnesium, sodium, potassium, aluminium, sulfides and oxidability were compared with the legal values in annex VI as there are no limits relating to these parameters in annexes XXI and I.