污泥淤砂分离器对分离分流污泥性质的影响

为解决我国很多城镇污水厂活性污泥的MLVSS/MLSS普遍偏低,污泥中泥沙淤积严重的问题,根据水力旋流器的分离原理,开发了污泥淤砂分离器,实现污泥中生物基质和淤砂的分离分流,研究污泥淤砂分离器主要结构参数排口比(底流口直径/溢流管直径)对分离分流污泥性质的影响。实验结果表明,排口比为0.4时,污泥经过污泥淤砂分离器后,得到的底流污泥MLVSS/MLSS比原污泥减小了34%,溢流污泥MLVSS/MLSS增大了16.8%,污泥中的生物基质富集在溢流污泥中,淤砂富集在底流污泥中;底流污泥浓度MLSS比原污泥增加了2.6倍,底流污泥SVI和CST分别减小了68%和70%,底流污泥浓缩效果明显,沉降性能和脱水性能大幅提高,有利于底流污泥的处理处置;进一步减小排口比,底流污泥浓缩效果、沉降性能和脱水性能均进一步提高。     

乙二胺还原的氧化石墨烯对镉离子的动态吸附

以乙二胺(EDA)还原氧化石墨烯(GO)制得一种吸附剂材料,即还原态氧化石墨烯(RGO)。考察了动态条件下Cd2+溶液的初始浓度、流速及吸附床高度对穿透曲线的影响,同时利用Bed-Depth-Service Time(BDST)模型对吸附床高度与穿透时间的关系进行线性拟合分析,研究了RGO对Cd2+溶液的动态吸附性能。结果表明,RGO可以有效地去除水溶液中的Cd2+,随着吸附床高度的增加,离子的去除率增大,穿透时间延长;当溶液初始浓度增大时穿透时间缩短,离子的去除率减小;而溶液的流速加快,穿透时间和去除率都相应减小。吸附床高度与穿透时间的关系可用BDST模型较好地进行描述,预测新的操作条件下的穿透时间与实验值误差均小于5%。     

Fered-Fenton法处理石化废水反渗透浓水

以Ti金属网为阴极、Ti负载RuO2金属网为阳极,构建Fered-Fenton反应系统,处理石化废水反渗透膜法浓水.考察H2O2浓度、Fe2+浓度、初始pH值和电流密度等因素对废水处理效果的影响,并分析了废水可生化性及污染物降解规律.结果表明,在H2O2浓度为75.0 mmol/L,Fe2+浓度为7.5 mmol/L,初始pH值为3.0,电流密度为5.1 mA/cm2的条件下,反应120 min后废水TOC可由198.2 mg/L降到99.6 mg/L,有机污染物矿化率达到49.7％,BOD5/COD由0.11提高至0.31,废水可生化性明显改善.三维荧光光谱(EEM)分析结果表明,Fered-Fenton法对废水中类蛋白、类富里酸等荧光有机物去除率达到66.7％,该类大分子难降解有机物的氧化降解有利于改善废水可生化性,为进一步的生化处理创造了良好的条件.     

Enhanced-electrokinetic remediation of copper–pyrene co-contaminated soil with different oxidants and pH control

Electrokinetic (EK) remediation has potential to simultaneously remove heavy metals and organic compounds from soil, but the removal percent of these pollutants is very low in general if no enhancing treatment is applied. This study developed a new enhanced-EK remediation technology to decontaminate a heavy metal–organic compound co-contaminated soil by applying different oxidants and pH control. A red soil was used as a model clayed soil, and was spiked with pyrene and Cu at about 500 mg kg^?1 for both to simulate real situation. Bench-scale EK experiments were performed using four oxidants (H₂O₂, NaClO, KMnO₄, and Na₂S₂O₈) and controlling electrolyte pH at 3.5 or 10. After the treatments with 1.0 V cm^?1 of voltage gradient for 335 h, soil pH, electrical conductivity, and the concentrations and chemical fractionations of soil pyrene and Cu were analyzed. The results showed that there was significant migration of pyrene and Cu from the soil, and the removal percent of soil pyrene and Cu varied in the range of 30–52% and 8–94%, respectively. Low pH favoured the migration of soil Cu, while KMnO₄ was the best one for the degradation of pyrene among the tested oxidants, although it unfortunately prevented the migration of soil Cu by forming Cu oxide. Application of Na₂S₂O₈ and to control the catholyte pH at 3.5 were found to be the best operation conditions for decontaminating the Cu-pyrene co-contaminated soil.     

Hydrogeo-chemical impacts of air sparging remediation on a semi-confined aquifer: Evidences from field monitoring and modeling

Air sparging (AS) was explored for remediation of a petroleum contaminated semi-confined groundwater system in NE China. Physical, hydro-chemical and hydraulic behaviors in subsurface environment during AS were investigated with support of modeling to understand the hydrogeo-chemical impacts of AS on the aquifer. The responses of groundwater, dissolved oxygen and temperature indicated that the radius of influence of AS was up to 8–9 m, and a 3D boundary of the zone of influence (ZOI) was accordingly obtained with volume of 362 m³. Water mounding unlike normal observations was featured by continuous up-lift and blocked dissipation. AS induced water displacement was calculated showing no obvious spreading of contaminant plume under this AS condition. Slug tests were employed before and after AS to reveal that the physical perturbation led to sharp increase in permeability and porosity. Modeling indicated that the regional groundwater flow field was not affected by AS except the physical perturbation in ZOI. Hydro-chemically increase of pH and Eh, and reduction of TDS, electrical conductivity and bicarbonate were observed in ZOI during AS. PHREEQC modeling inferred that these chemical phenomena were induced by the inorganic carbon transfer during air mixing.     

西安城北地区秋季PM_(2.5)中的矿尘颗粒污染特征

采用单颗粒气溶胶飞行时间质谱仪(Single Particle Aerosol Mass Spectrometer,SPAMS)对西安市大气矿尘颗粒物进行连续12 d在线分析,共采集到107 425个同时含有正负质谱信息的矿尘颗粒,矿尘颗粒物占PM_(2.5)样本数的8.44%。结果表明,矿尘颗粒物的正离子碎片成分以Na~+、K~+、Al~+、Ca~+、CaO~+、Fe~+为主,同时还含有Pb~+等,负离子碎片成分以NO~-_2和NO~-_3为主,另外还含有HSO~-_4、SiO~-_3、HSiO~-_3、H(NO_3)~-_2等。在西安市大气细颗粒物中,矿尘颗粒物中贡献较大的几类(如含钙、含铁、铁氧颗粒物等)大多是老化的成分。将观测阶段采集到的矿尘颗粒纳入本地污染源谱进行来源分析,其主要来源为扬尘源、工业源、燃煤源和汽车尾气源等。     

Spatiotemporal Modeling of Ambient Sulfur Dioxide Concentrations in Rural Western Canada

The provinces of Saskatchewan, Alberta, and British Columbia are major oil- and gas-producing regions in western Canada. With increasing oil and gas production activities, there has been a growing concern of the effect of oil and gas industry emissions on health. Nevertheless, lack of proper tools to estimate the exposure to these emissions has been a hindrance to epidemiological studies and risk assessment. This paper presents a spatiotemporal modeling approach to estimating ambient sulfur dioxide (SO₂) levels based on environmental monitoring data (N = 10,295), which were collected at rural sites (591 per month on average) of this region from June 1, 2001 to May 31, 2002. Based on the model, illustrative maps consistently revealed high and low SO₂ concentration sub-regions. The sub-regions with elevated SO₂ concentrations had increased levels during the winter months from December 2001 to March 2002 and then decreased during the spring of 2002. This statistical modeling approach may help researchers estimate the SO₂ levels within the study area for their epidemiological studies or risk assessment.     

环境一号卫星多光谱数据在巢湖蓝藻动态监测中的应用

采用环境一号卫星(HJ1A/1B)的多光谱(CCD)数据,通过影像预处理提取巢湖湖域蓝藻的归一化植被指数(NDVI),对巢湖湖域的蓝藻进行动态遥感监测,在GIS平台上计算蓝藻在研究时段内的平均空间分布、模拟其变化趋势及平均改善速度,结果表明,HJ1A/1B-CCD影像数据具有较高时空分辨率,对巢湖湖域蓝藻的识别能力强;巢湖湖域生态环境总体较好,蓝藻只是在局部湖域暴发,其中蓝藻极高密度区、高密度区呈团状、片状,主要集中分布在巢湖西湖区北部沿岸,并且为楔形向中心延伸;巢湖蓝藻暴发的湖域总体呈现改善趋势,但各区改善的程度、速度差异较大,原因复杂,尤其是西湖区北部沿岸不仅改善速度缓、改善程度不佳,甚至出现退化;巢湖西区应以治理为主、监测为重,东区则以着重监测。     

Establishment of turbidity forecasting model and early-warning system for source water turbidity management using back-propagation artificial neural network algorithm and probability analysis

The purpose of this study is to establish a turbidity forecasting model as well as an early-warning system for turbidity management using rainfall records as the input variables. The Taipei Water Source Domain was employed as the study area, and ANOVA analysis showed that the accumulative rainfall records of 1-day Ping-lin, 2-day Ping-lin, 2-day Fei-tsui, 2-day Shi-san-gu, 2-day Tai-pin and 2-day Tong-hou were the six most significant parameters for downstream turbidity development. The artificial neural network model was developed and proven capable of predicting the turbidity concentration in the investigated catchment downstream area. The observed and model-calculated turbidity data were applied to developing the turbidity early-warning system. Using a previously determined turbidity as the threshold, the rainfall criterion, above which the downstream turbidity would possibly exceed this respective threshold turbidity, for the investigated rain gauge stations was determined. An exemplary illustration demonstrated the effectiveness of the proposed turbidity early-warning system as a precautionary alarm of possible significant increase of downstream turbidity. This study is the first report of the establishment of the turbidity early-warning system. Hopefully, this system can be applied to source water turbidity forecasting during storm events and provide a useful reference for subsequent adjustment of drinking water treatment operation.