不同来源土样胶体对氯霉素吸附行为的影响

选取不同来源沉积物或土壤样品（沉积物A1和A2,天然土壤B1和B2,市售营养土B3）,采用切向超滤与多种纳米分析技术相结合的方法,分析了不同土样中胶体的理化特征及其对氯霉素（CAP）吸附行为的影响.荧光区域积分法结果显示,土样胶体的荧光组分以色氨酸类蛋白质、类富里酸和类腐殖酸为主,络氨酸类蛋白质和溶解性微生物代谢物质存在较少.吸附实验结果表明,B3胶体对CAP的吸附率最大（36.25%）,在胶体浓缩液和超滤液中的分配比例最高（1.91）,但其有机碳归一化结合系数最低（3.93）.与天然土壤相比,河湖沉积物胶体的吸附率较高,但其与CAP的结合能力却较低.冗余分析结果表明,胶体对CAP的吸附主要与其芳香度、有色溶解性有机质丰度、溶解性有机碳浓度及分子量有关;此外,胶体的腐殖化程度、芳环上羧基、羰基等官能团的比重及荧光物质中溶解性微生物代谢产物和类腐殖酸对CAP的吸附行为也具有较大影响.     

铁锰复合氧化物滤料去除地下水中四环素的效能

以化学挂膜成熟运行5a的铁锰复合氧化物滤料为研究对象,利用静态与连续流柱实验相结合的方式,探究了该滤料去除四环素的效果与机理.结果表明,去除过程符合准二级反应动力学模型;且滤料质量越大,初始浓度越高,对四环素去除的效果越好;当水中共存阳离子浓度为0.01mol/L时,Mg²⁺抑制四环素的去除,而Ca²⁺、K⁺对四环素的去除过程无显著影响.连续流柱实验与发光细菌毒性检测分析,表明,铁锰氧化物滤料对四环素去除效果显著高于石英砂,且铁锰复合氧化物滤料去除四环素后急性毒性降低.此外,机理探究实验结果证实了铁锰复合氧化物滤料去除四环素是吸附与氧化的过程.     

An investigation of changes in water quality throughout the drinking water production/distribution chain using toxicological and fluorescence analyses

Changes in water quality from source water to finished water and tap water at two conventional drinking water treatment plants(DWTPs) were monitored.Beside the routine water quality testing,Caenorhabditis elegans-based toxicity assays and the fluorescence excitation–emission matrices technique were also applied.Both DWTPs supplied drinking water that met government standards.Under current test conditions,both the investigated finished water and tap water samples exhibited stronger lethal,genotoxic and reprotoxic potential than the relative source water sample,and the tap water sample was more lethal but tended to be less genotoxic than the corresponding finished water sample.Meanwhile,the nearly complete removal of tryptophan-like substances and newly generated tyrosine-like substances were observed after the treatment of drinking water,and humic-like substances were identified in the tap water.Based on these findings,toxic pollutants,including genotoxic/reproductive toxicants,are produced in the drinking water treatment and/or distribution processes.Moreover,further studies are needed to clarify the potentially important roles of tyrosine-like and humic-like substances in mediating drinking water toxicity and to identify the potential sources of these contaminants.Additionally,tryptophan-like fluorescence may be adopted as a useful parameter to monitor the treatment performance of DWTPs.Our observations provided insights into the importance of utilizing biotoxicity assays and fluorescence spectroscopy as tools to complement the routine evaluation of drinking water.     

气候变化和人类活动对伊逊河流域径流变化的影响

借助Mann-Kendall趋势检验和突变检验对气象水文序列进行一致性分析,划分基准期（1961—1979年）和影响期（① 1980—1989年、② 1990—1999年、③ 2000—2016年）,利用基准期校准的可变下渗容量（VIC）模型,采用步进式方法,探究气候变化和人类活动对伊逊河流域径流变化的波动影响过程。结果表明:研究区近56年年均气温显著升高,年降水量无明显变化趋势,流域年径流量下降趋势明显,季节尺度上流域非汛期降水量增加显著。气候变化和人类活动均会对径流产生显著影响且作用机理复杂,步进式方法对影响机理的研究较传统方法更能体现其变化过程;在降水丰沛的影响 ② 期,冬季降水量增加会显著增加流域径流量,而在降水略少的影响 ① 期和 ③ 期,蒸发量增加以及土壤含水量降低使得流域径流减少;人类活动耗水在影响 ① 期和 ③ 期引起流域径流减少并且影响作用逐渐增强,影响 ② 期由于城镇化和耕地扩张使得流域产流能力增强导致径流增加。深入研究气候变化和人类活动对径流的影响机制,可为流域水资源管理和规划提供理论依据。     

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.     

垃圾填埋场渗出液中有害成分的研究

研究垃圾填埋场渗出液中的有害成分。渗出液样品用适当的方法处理后 ,用气相色谱 -质谱联用方法和原子发射光谱法进行定性分析。然后用原子吸收分光光度法和反相高效液相色谱法定量测定样品中微量的 Cu、Zn、Pb、Cr、Cd、Hg、苯酚、对甲苯酚、萘、蒽     

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.