陶瓷印花废水处理的混凝剂及工艺条件

采用混凝剂聚合氯化铝（PAC）、聚丙烯酰胺（PAM）、聚合硫酸铁（PFS）对陶瓷印花废水进行混凝沉降处理,监测水样的吸光度、浊度、悬浮物,以脱色率、浊度去除率、悬浮物去除率评价混凝处理的效果。结果表明：PAC是陶瓷印花废水沉降处理的理想混凝剂;水样的吸光度、浊度、悬浮物随混凝剂用量增大和沉降时间延长而呈降低趋势,而脱色率、浊度去除率、悬浮物去除率随混凝剂和沉降时间的增大呈增大的趋势;PAC投加量为20mg／L,沉降时间约为24h,水样脱色率达到90．0％,而当PAC投加量达到100mg／L,沉降时间约为4h,陶瓷印花水的脱色率可达到96．0％。证明了药剂用量的增加与沉降时间的延长对混凝过程具有增效作用。     

Ce-PbO_2/C电极的制备及其去除水中酸性红B

采用电沉积法制备铈修饰的PbO2/C电极,通过SEM、XRD、XPS及循环伏安对PbO2/C、Ce-PbO2/C电极进行表征,结果表明,Ce-PbO2/C电极比PbO2/C颗粒细小,表面均匀致密,电化学氧化能力较强,修饰电极中Ce以CeO2的形态存在。以Ce-PbO2/C为工作电极,电解浓度为1 000 mg/L的高盐酸性红B模拟活性染料废水,考察了电压、pH、电解质浓度、极间距对脱色率、氨氮去除率及COD去除率的影响。确定适宜工艺条件为：初始酸性红B溶液浓度为1 000 mg/L,pH值为6,电压10 V,电解时间1 h,电极间距1.5 cm,该条件下脱色率、氨氮去除率和COD去除率分别为99.98%、97.23%和90.17%。通过UV-Vis及GC-MS初步分析了降解过程可能存在的中间产物及降解途径。     

东洞庭湖湿地生态系统健康状态与水位关系研究

基于CBERS卫星遥感影像数据,以东洞庭湖国家级自然保护区为研究区,通过光谱特征分析建立决策树分类模型,提取东洞庭湖湿地的地物类型。以生态系统健康理论为基础,依据东洞庭湖湿地的特点,综合湿地生产力、组织结构、弹性和功能4个方面,建立湿地生态系统健康状态评价指标体系和评价模型,利用GIS技术,分析东洞庭湖湿地在2000～2006年10个时相的生态系统健康状态。结合城陵矶水文站的水位数据,探讨了东洞庭湖湿地健康状态与城陵矶水位之间的相关性,并利用2007年两个时相的数据进行检验。研究结果表明：东洞庭湖湿地健康状态与城陵矶水位之间存在着较大的相关性,当城陵矶水位达到30 m左右时,东洞庭湖湿地健康状态达到最佳值     

肖四海不同渔业发展阶段的水环境特征分析

2006～2008年,对长江中游的一个浅水湖泊--肖四海进行了水质和水生植被研究,并结合历史资料分析了肖四海1987～2008年不同渔业发展阶段的水环境特征。研究结果表明：1987～2008年期间,该湖营养状况经历了中营养→富营养→中营养→富营养→中营养的变化过程,沉水植被经历了消亡→恢复→旺盛→衰退的演替过程,这种变化主要受不同阶段的渔业方式的影响。草食性鱼类和河蟹的过量放养、化肥的大量使用以及饵料的大量投放等不合理的渔业方式导致了水生植被的消亡和水体的富营养化,而合理的渔业方式不会对水体环境产生消极影响,并据此提出了长江中下游湖泊渔业可持续发展的建议     

ABR处理硫酸盐有机废水的BP神经网络建模

通过厌氧折流板反应器(ABR)处理硫酸盐有机废水的实验数据对BP神经网络进行训练,建立了ABR处理硫酸盐有机废水的BPNN模型,通过测试对比,找出了较优训练函数为traingda,较优训练次数为1 900.利用分割连接权值法(PCW)对影响出水SO42-和COD的主要因素进行分析,结果显示进水COD、SO42-、pH、COD/SO42-和HRT对出水SO42-和COD均产生一定影响,其中进水pH对出水SO42-和COD的影响最大,相对重要性(RI)指数分别为30.79％和23.44％;并通过样本试验数据分别建立了对SO42-和COD去除率的限制因子仿真模型,为预测硫酸盐有机废水的厌氧处理过程提供指导.     

响应面优化CCl4增强超声降解左旋氧氟沙星模拟废水

利用响应面优化实验设计方法对CCl₄增强超声降解左旋氧氟沙星模拟废水的影响因素进行探讨和分析,考察了溶液初始pH值、超声功率、左旋氧氟沙星初始浓度的影响。应用Box-Behnken中心组合设计得到一个二次多项式数学模型,确定了US/CCl₄降解左旋氧氟沙星的优化条件:初始pH值6.8,超声功率189 W,左旋氧氟沙星初始浓度为5 mg/L时,左旋氧氟沙星的去除率达到最大(82.99%)。经实验验证, 实际值与模型预测值吻合性良好,偏差仅为0.036%。     

Evaluation of a classification method for biodegradable solid wastes using anaerobic degradation parameters

We studied the biochemical and anaerobic degradation characteristics of 29 types of materials to evaluate the effects of a physical composition classification method for degradable solid waste on the computation of anaerobic degradation parameters, including the methane yield potential (L₀), anaerobic decay rate (k), and carbon sequestration factor (CSF). Biochemical methane potential tests were conducted to determine the anaerobic degradation parameters of each material. The results indicated that the anaerobic degradation parameters of nut waste were quite different from those of other food waste and nut waste was classified separately. Paper was subdivided into two categories according to its lignin content: degradable paper with lignin content of <0.05 g g VS^?1, and refractory paper with lignin content >0.15 g g VS^?1. The L₀, k, and CSF parameters of leaves, a type of garden waste, were similar to those of grass. This classification method for degradable solid waste may provide a theoretical basis that facilitates the more accurate calculation of anaerobic degradation parameters.     

粉煤灰的改性及对刚果红的吸附

分别采用沸水浸泡、酸浸、碱浸和加热的方法对粉煤灰进行改性处理,利用FTIR仪和XRD仪对改性粉煤灰的成分和官能团进行了分析,并利用改性粉煤灰对模拟刚果红废水进行脱色。实验结果表明：碱改性粉煤灰中含有大量官能团,以及NaPl型沸石类物质,能够明显提高粉煤灰对刚果红的吸附性能;与活性炭相比,碱改性粉煤灰具有更高的性价比;在初始刚果红质量浓度为20mg/L、碱改性粉煤灰加入量为50g/L的条件下,废水的脱色率可达87.52%;碱改性粉煤灰对刚果红的吸附过程遵循二级反应动力学,较好地符合Langmuir等温式和Freundlich等温式。     

改性SiO2气凝胶对废水中Fe3+的吸附性能

制备了改性SiO₂气凝胶,考察了经不同类型、不同配比的改性剂改性的SiO₂气凝胶对模拟含Fe³⁺废水的吸附处理效果。实验结果表明：改性SiO₂气凝胶的最佳制备条件为三甲基氯硅烷（TMCS）作改性剂,V（TMCS）#x02236;V（正己烷）=1#x02236;5;当改性SiO₂气凝胶加入量为75g/L、吸附时间为4h、Fe³⁺质量浓度为10mg/L时,模拟含Fe³⁺废水的Fe³⁺去除率为98.32%,剩余Fe³⁺质量浓度为0.168mg/L;采用改性SiO₂气凝胶动态吸附处理流量为420mL/h、Fe³⁺质量浓度为100mg/L的模拟含Fe³⁺废水,吸附后废水中剩余Fe³⁺质量浓度仅为0.196mg/L。     

Occurrence, distribution, and sources of six phenolic endocrine disrupting chemicals in the 22 river estuaries around Dianchi Lake in China

The objectives of this study are to track the occurrence, distribution, and sources of phenolic endocrine disrupting compounds (EDCs) in the 22 rivers around Dianchi Lake in China, to estimate the input and output amounts of phenolic EDCs in the water system, and to provide more comprehensive fundamental data for risk assessment and contamination control of phenolic EDCs in aquatic environment. Six phenolic EDCs were systematically evaluated in water and surface sediment in the estuaries of those rivers. The water and sediment samples were preconcentrated by solid-phase extraction system and microwave-assisted extraction system, respectively. Phenolic EDCs were analyzed by GC-MS (Thermo Fisher Scientific, USA) after derivatization. Phenolic EDCs were found ubiquitously in the aquatic environment. The total concentrations ranged from 248 to 4,650 ng/L in water, and 113 to 3,576 ng/g dry weight in surface sediment. The residue amount of phenolic EDCs in Dianchi Lake was 258 kg/a. Concentrations of the phenolic EDCs in the Lake decreased with increase in distance to the estuaries of those rivers which run through urban and industrial areas. The rivers seriously contaminated by phenolic EDCs were Xin River, Yunliang River, Chuanfang River, Cailian River, Jinjia River, Zhengda River, and Daqing River which run through the old area of Kunming City. Satisfying correlations were observed between the concentrations of the target compounds in water and in surface sediment. NP1EO, NP2EO, and BPA were identified as the three predominant phenolic EDCs. There were significant correlations between phenolic EDCs and many basic water quality parameters. Urban and industrial areas are the major contributors for phenolic EDCs, especially in Kunming City. Compositional profiles of phenolic EDCs in surface sediment were similar to those in river water. The concentrations of phenolic EDCs in the rivers located in the northwest part of the valley were very high, and posed a potential risk to aquatic organisms and even human. The concentrations of NP2EO, NP1EO, and BPA were at moderate levels of other areas. The basic water quality parameters (TOC, TN, DO, and pH) play important roles on the distribution, fate, and behavior of phenolic EDCs in the valley.