Dust Storms are an Indication of an Unhealthy Environment in East Asia

Dust storms frequently occur in Mongolia and in northern China. Each year there are 30 to 120 dusty days in source regions of Mongolia and 14–20 dusty days on the Korean Peninsula. Intense sand storms and associated dust falls produce environmental impacts in East Asia. This paper discusses the environmental degradation in Mongolia and the social, economic and atmospheric impacts of dust storms in the sink area. The impact of dust storms on environmental compartments as well as their direct and indirect consequences to basic resources like water and energy demand and supply is explained. Governments are encouraged to take appropriate action in specified regions. For monitoring dust storms there is a need for international cooperation to combat growing environmental and human security concerns.     

可张孔曝气器曝气能耗试验研究

通过试验研究，总结出可张孔曝气阻力损失的影响因素，以优化产品的结构设计参数，降低产品在工程应用中的能耗，获取更好的经济效益。     

Effects of calibration on L-THIA GIS runoff and pollutant estimation

Urbanization can result in alteration of a watershed's hydrologic response and water quality. To simulate hydrologic and water quality impacts of land use changes, the Long-Term Hydrologic Impact Assessment (L-THIA) system has been used. The L-THIA system estimates pollutant loading based on direct runoff quantity and land use based pollutant coefficients. The accurate estimation of direct runoff is important in assessing water quality impacts of land use changes. An automated program was developed to calibrate the L-THIA model using the millions of curve number (CN) combinations associated with land uses and hydrologic soil groups. L-THIA calibration for the Little Eagle Creek (LEC) watershed near Indianapolis, Indiana was performed using land use data for 1991 and daily rainfall data for six months of 1991 (January 1-June 30) to minimize errors associated with use of different temporal land use data and rainfall data. For the calibration period, the Nash-Sutcliffe coefficient was 0.60 for estimated and observed direct runoff. The calibrated CN values were used for validation of the model for the same year (July 1-December 31), and the Nash-Sutcliffe coefficient was 0.60 for estimated and observed direct runoff. The Nash-Sutcliffe coefficient was 0.52 for January 1, 1991 to December 31, 1991 using uncalibrated CN values. As shown in this study, the use of better input parameters for the L-THIA model can improve accuracy. The effects on direct runoff and pollutant estimation of the calibrated CN values in the L-THIA model were investigated for the LEC. Following calibration, the estimated average annual direct runoff for the LEC watershed increased by 34%, total nitrogen by 24%, total phosphorus by 22%, and total lead by 43%. This study demonstrates that the L-THIA model should be calibrated and validated prior to application in a particular watershed to more accurately assess the effects of land use changes on hydrology and water quality.     

Gas chromatographic methods for monitoring of wastewater chlorophenol degradation in anaerobic reactors

Wastewater samples from an anaerobic reactor were extracted with hexane and derivatized with diazomethane (method 1) and with acetic anidride (method 2). Gas chromatography with electron-capture detection (ECD) was employed for separating the parent compound and intermediates trichlorophenols (TCP) and dichlorophenols (DCP) which originated from the penta chlorophenol (PCP) degradation process. The relations between concentrations of PCP, TCP and DCP areas were linear in the range of concentrations of 0.2 to 8 mg/L and 0.025 mg/L to 5 mg/L for methods 1 and 2, respectively. The repeatability of the extraction methods was satisfactory, with variation coefficients lower than 11%. For method 1, at the fortification level of 0.2 mg/L, recovery of PCP, TCP, and DCP was 112%, 74% and 45%, respectively. For method 2, the corresponding recovery values at the fortification level of 0.1 mg/L were 91%, 93% and 103%, respectively. Storage of the frozen samples did not alter their PCP determination properties. The chromatographic methods adapted for chlorophenol determination in wastewater were suitable with relatively simple manipulation techniques. The obtained results were reproducible and allowed identification of intermediates formed during the PCP degradation process.     

A directional passive air sampler for monitoring polycyclic aromatic hydrocarbons (PAHs) in air mass

A passive air sampler was developed for collecting polycyclic aromatic hydrocarbons (PAHs) in air mass from various directions. The airflow velocity within the sampler was assessed for its responses to ambient wind speed and direction. The sampler was examined for trapped particles, evaluated quantitatively for influence of airflow velocity and temperature on PAH uptake, examined for PAH uptake kinetics, calibrated against active sampling, and finally tested in the field. The airflow volume passing the sampler was linearly proportional to ambient wind speed and sensitive to wind direction. The uptake rate for an individual PAH was a function of airflow velocity, temperature and the octanol-air partitioning coefficient of the PAH. For all PAHs with more than two rings, the passive sampler operated in a linear uptake phase for three weeks. Different PAH concentrations were obtained in air masses from different directions in the field test.     

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.     

构树修复对重金属污染土壤环境质量的影响

通过温室盆栽试验,研究构树（Broussonetia papyrifera）生长对重金属污染土壤酶活性和微生物群落结构的影响.结果表明,构树修复污染土壤中酶活性和微生物多样性明显提高.经270d培养后,构树生长土壤中蔗糖酶和酸性磷酸酶活性与种植土壤相比分别显著（P<0.05）提高3.12倍和2.29倍;土壤脱氢酶与有效态As、Cd、Pb、Zn和Cu含量,蔗糖酶与有效态Cd含量,以及磷酸酶与有效态Cd和Cu含量之间呈显著负相关（P<0.05）.根据16S和18S rDNA PCR-DGGE分析表明,构树修复可提高污染土壤中细菌和丛枝菌根真菌多样性.上述结果表明,构树修复可有效改善重金属污染土壤的环境质量.然而,污染土壤中重金属有效态含量下降不明显,必须辅助物理和化学措施来强化构树对重金属污染土壤的生态修复潜力.     

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.