Simultaneous removal of cyanobacterial blooms and production of clean water by coupling flocculation with a rotary drum filter

Environmental Science and Pollution Research - A mechanical harvesting technology based on coupling flocculation with a rotary drum filter (RDF, 35-μm) was applied to remove cyanobacterial...     

三峡工程与长江中游沿江产业带建设

讨论了长江中游沿江产业带发展条件、限制因素和未来发展，指出三峡工程的建设将对长江中游产业的布局和经济发展带来深刻的变化；防洪作用促进中游两岸大型工业项目的兴建，调整现有沿江城镇的布局，使之结构趋于合理化；电力供应经解能源的紧张状态，改善航运条件，发挥长江黄金水道的综合效益。当然，由于坝下河道冲刷，对现有的码头带来一定的影响，由于冬季水位抬升引起两岸低湖田地下水位上升，加剧两岸土壤潜育化和沼泽化。     

水雾荷电净化空气飘尘与细菌的研究

研究一种高压静电水雾场产生的荷电净化空气中飘尘与细菌的方法。实验结果表明:在静电电压15kV、通风量540m~3/h的工况下,实验装置的一次性除尘效率高于80%、除菌效率达到88%以上;实验装置可以连续运行,能耗低、出风无二次污染。     

Algal toxicity induced by effluents from textile-dyeing wastewater treatment plants

This research aimed to evaluate the alga Scenedesmus obliquus toxicity induced by textiledyeing effluents(TDE).The toxicity indicator of TDE in alga at the physiological(algal growyth),biochemical(chlorophyll-a(Chl-a) synthesis and superoxide dismutase(SOD) activity) and structural(cell membrane integrity) level were investigated.Then we further study the relationship among toxicity indicators at physiological and biochemical level,and supplemented by research on algal biomacromolecules.According to the analysis of various endpoints of the alga,the general sensitivity sequence of toxicity endpoints of Scenedesmus obliquus was:SOD activity Chl-a synthesis algal growth.The stimulation rate of SOD activity increased from day 3(57.25%～83.02%) to day 6(57.25%～103.81%),and then decreased on day 15(-4.23%～-32.96%),which indicated that the antioxidant balance system of the algal cells was destroyed.The rate of Chl-a synthesis inhibition increased gradually,reaching19.70%～79.39% on day 15,while the rate of growth inhibition increased from day 3(-12.90%～10.16%) to day 15(-21.27%～72.46%).Moreover,the algal growth inhibition rate was positively correlated with the inhibition rate of SOD activity or Chl-a synthesis,with the correlation coefficients were 0.6713 and 0.5217,respectively.Algal cells would be stimulating to produce excessive reactive oxygen species,which would cause peroxidation in the cells,thereby destroying chloroplasts,inhibiting chlorophyll synthesis and reducing photosynthesis.With increasing exposure time,irreversible damage to algae can lead to death.This study is expected to enhance our understanding of the ecological risks through algal tests caused by TDE.     

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.     

Effect of TiO2 nanoparticle aggregation on marine microalgae Isochrysis galbana

TiO_2 nanoparticles(NPs) could adversely impact aquatic ecosystems. However, the aggregation of these NPs could attenuate this effect. In this work, the biological effects of TiO_2 NPs on a marine microalgae Isochrysis galbana were investigated. The aggregation kinetics of TiO_2 NPs under different conditions was also investigated to determine and understand these effects. Results showed that, though TiO_2 NPs had no obvious impact on the size and reproducibility of algal cells under testing conditions, they caused a negative effect on algal chlorophyll, which led to a reduction in photosynthesis. Furthermore, fast aggregation of TiO_2 NPs occurred under all conditions, especially at the pH close to the p Hzpc. Increasing ionic strength and NP concentration also enhanced the aggregation rate.The aggregation and the following sedimentation of TiO_2 NPs reduced their adverse effects on I. galbana.     

生物活性碳反应器强化生活污水同步硝化反硝化:C/N影响研究

在生物活性碳(BAC)反应器中探究了C/N对实际生活污水同步硝化反硝化的影响。结果表明C/N对化学需氧量(COD)的去除影响不明显,然而对NH+4-N和总氮(TN)的去除影响较显著。随C/N由3.2升高至7.2,NH+4-N的去除效率由81%升高至91%,TN的去除效率也由35%升高至68%。然而继续升高C/N至9.1,NH+4-N和TN的去除效率却分别下降至87%和51%。生物活性碳反应器处置实际污水的最佳C/N为7.2。较传统SBR活性污泥反应器,BAC反应器能够依赖活性碳实现溶解氧区域化,从而有助于反硝化过程。     

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.