Catalytic degradation of toxic organic dye using an aluminum-doped Cu0.4Zn0.6-xAlxFe2O4 (x = 0.0, 0.2, 0.4, 0.6) spinel ferrite in a photo-Fenton system

Catalytic activity of spinel ferrite in breaking down toxic dye materials are promising due to their uniqueness. In this study, aluminum-doped copper zinc ferrite, Cu_0.4Zn_0.6-xAl_xFe₂O₄ (x = 0.0, 0.2, 0.4, 0.6), a catalyst for toxic dye degradation is synthesized through chemical co-precipitation route. The formation of the spinel ferrite catalyst is initially confirmed by Fourier transform infrared spectra, which shows the frequency of metal-oxygen bond vibration at 539 and 427 cm⁻¹ attributed to the tetrahedral and octahedral sites respectively. Higher intensity sharp peak of X-ray diffraction for (311) plane is the evidence for the phase purity and the formation of spinel ferrite. The crystallite size is found to decrease with the increase of Al³⁺ ion. The surface structure of the obtained particles is investigated using a scanning electron microscope. Analyses of the material's magnetic characteristics using a vibrating sample magnetometer (VSM) revealed that it is, in fact, a soft magnet, as evidenced by the loop of its hysteresis, which is narrow. The catalytic degradation of methylene blue dye under the mechanism of the photo-Fenton process is studied with the obtained spinel ferrites and the result is found to be as high as 96.5%. The process follows pseudo-second order kinetics and the Langmuir isotherm.     

碘化阶层孔氧化硅纳米球对水中典型有机氯污染物的吸附性能研究

采用共缩聚法,成功地原位合成了碘改性的阶层多孔氧化硅纳米球（SiO₂-I）,研究了其基于卤键作用对典型有机氯污染物六六六的吸附性能,并考察了改性剂I-硅烷含量和pH值对吸附效果的影响.实验结果表明,该纳米材料对六六六表现出优异的吸附富集性能,吸附速率快,60 min内对六六六的去除率为71.6%,240 min内达到吸附平衡,去除率可达98.3%,最大吸附量为178.6 mg·g^-1;吸附动力学符合拟二级动力学模型;碘物种的加入提高了阶层多孔氧化硅的吸附速率和吸附效率.另外,为了便于纳米吸附材料的分离,本研究对SiO₂-I纳米球进行了磁性化.研究发现,磁性化修饰后,SiO₂-I纳米材料仍然保留对六六六优异的吸附富集性能,240 min时的吸附去除率达90.3%.     

Plasmonic silver/silver oxide nanoparticles anchored bismuth vanadate as a novel visible-light ternary photocatalyst for degrading pharmaceutical micropollutants

The degradation of pharmaceutical micropollutants is an intensifying environmental problem and synthesis of efficient photocatalysts for this purpose is one of the foremost challenges worldwide. Therefore, this study was conducted to develop novel plasmonic Ag/Ag₂O/BiVO₄ nanocomposite photocatalysts by simple precipitation and thermal decomposition methods, which could exhibit higher photocatalytic activity for mineralized pharmaceutical micropollutants. Among the different treatments, the best performance was observed for the Ag/Ag₂O/BiVO₄ nanocomposites (5 wt.%; 10 min's visible light irradiation) which exhibited 6.57 times higher photodegradation rate than the pure BiVO₄. Further, the effects of different influencing factors on the photodegradation system of tetracycline hydrochloride (TC-HCl) were investigated and the feasibility for its practical application was explored through the specific light sources, water source and cycle experiments. The mechanistic study demonstrated that the photogenerated holes (h⁺), superoxide radicals (?O₂^?) and hydroxyl radicals (?OH) participated in TC-HCl removal process, which is different from the pure BiVO₄ reaction system. Hence, the present work can provide a new approach for the formation of novel plasmonic photocatalysts with high photoactivity and can act as effective practical application for environmental remediation.     

保定市大气污染特征和潜在输送源分析

保定市是京津冀地区重要城市之一.为了解保定市大气污染物质量浓度特征和潜在输送源,对保定市国控点2017年1月1日-12月31日PM₁₀、PM_2.5、SO₂、NO₂、O₃、CO等常规大气污染物数据进行分析,并利用TrajStat后向轨迹模型进行区域传输研究.结果表明:①ρ（PM₁₀）、ρ（PM_2.5）、ρ（SO₂）、ρ（NO₂）分别为（138±96）（84±66）（29±23）和（50±24）μg/m3,与2016年相比分别下降5.9%、9.1%、25.5%和13.1%;ρ（CO）较2016年下降了14.0%;ρ（O₃）较2016年增长了25.2%.ρ（PM₁₀）、ρ（PM_2.5）、ρ（NO₂）和ρ（O₃）分别超过GB 3095-2012《环境空气质量标准》二级标准限值的0.97、1.40、0.25和0.34倍,ρ（SO₂）和ρ（CO）未超标.②除ρ（O₃）外,其他污染物质量浓度均呈冬季最高、夏季最低的季节性特征,其中,冬季PM_2.5污染最为严重,春季PM_2.5~10（粗颗粒物）污染严重.③空气质量模型源解析结果显示,保定市ρ（PM_2.5）约60.0%~70.0%来自本地污染源排放.后向轨迹结果表明,在外来区域传输影响中,保定市主要受到西北方向气团（占比为21.7%~60.0%）远距离传输和正南方向气团（占比为34.8%~50.5%）近距离传输的影响.④PSCF（潜在源贡献因子分析法）和CWT（浓度权重轨迹分析法）分析表明,除保定市及周边区县本地污染贡献外,位于太行山东麓沿线西南传输通道的邯郸市、邢台市、石家庄市是影响保定市PM_2.5的主要潜在源区.研究显示,PM_2.5为保定市大气中的主要污染物,并呈冬季高、夏季低的变化特征,其主要来自西北远距离输送和南部近距离传输.      

人工智能技术对长江流域水污染治理的思考

随着经济的快速发展和城市化进程的不断加速,促使水污染严重的长江流域需从污染物去除过程的建模与优化、污水处理过程的优化控制、水污染监测系统的构建开展水污染治理研究.传统的水污染处理技术存在污染物去除效率预测精度较低、污水优化控制成本较高、水污染监测滞后效应严重的问题.人工智能技术能够有效克服上述问题,因此通过梳理国内外学者利用人工智能技术在污水污染物去除过程的建模与优化、污水处理过程的优化控制及水污染监测系统的构建等方面的研究成果,为全面加强长江流域水污染治理能力提供科学可靠的技术指导.结果表明:①利用人工神经网络技术（径向基神经网络、多层前馈网络-人工神经网络、多层感知器神经网络）对污水污染物去除过程进行建模与优化,为精确预测长江流域重金属（Cr、Cu）、营养盐（TN、TP）、持久性有机污染物〔PBDEs（多溴二苯醚）、HCH（六氯环己烷）〕的去除率提供重要参考价值.②采用污水处理的自动控制技术与人工智能技术（递归神经网络、支持向量机、模糊神经网络等）构建污水智能控制系统,为长江流域实现高效节能的污水优化控制提供重要的技术指导.③利用在线监测仪器和人工智能技术（小波神经网络、多元线性回归-人工神经网络、叠层去噪自动编码器等）建立水污染智能监测系统,为解决长江流域水污染监测响应滞后问题提供有力的技术支持.因此,人工智能技术对长江流域提高污水污染物去除率,降低污水优化控制成本,提升水污染监测时效性具有重要的推广价值.      

尕海湿地植被退化过程中有机碳及相关土壤酶活性变化特征

以青藏高原东缘尕海湿地不同植被退化程度样地为研究对象,采用定位研究与室内试验相结合的方法,研究植被退化过程对土壤有机碳和土壤酶活性的影响。结果表明：植被退化显著影响土壤有机碳含量和土壤酶活性,降低了土壤有机碳的含量和酶促反应效率,且这种影响随土层深度变化有所不同。四种植被退化阶段有机碳含量和酶活性均值总体上（0~100 cm）表现为未退化>轻度退化>中度退化>重度退化。不同土层中,20~40 cm土层的有机碳含量中度退化>轻度退化;0~10 cm土层中淀粉酶和纤二糖酶中度退化阶段活性值较高;20~100 cm土层中蔗糖酶重度退化阶段活性值最高,纤二糖酶活性重度退化>中度退化。同一退化程度土壤有机碳含量、蔗糖酶和纤二糖酶活性随土层深度增加显著降低（P<0.05）;淀粉酶和纤维素酶活性在>40 cm土层中活性值有所上升,整体呈波动下降趋势。     

Pt负载暴露(001)晶面Bi2WO6催化剂合成及光催化性能

通过水热法制备了暴露（001）晶面的Bi₂WO₆纳米片,利用光还原法将Pt纳米颗粒负载于其表面.选择苯甲醇氧化和罗丹明B（RhB）降解为探针反应,评价了催化剂的光催化性能.在苯甲醇氧化实验中,Pt负载暴露001晶面的Bi₂WO₆样品的苯甲醇转化率为20.7%,约为未负载样品的2倍.在RhB降解实验中,Pt负载样品在光照40min后对RhB的矿化率可达81.1%,而未负载样品RhB矿化率仅为55.8%,表明Pt负载样品具有更优的降解速率和矿化能力.催化剂性能的提升归因于高能晶面暴露和Pt负载的协同作用.Pt纳米颗粒的负载作为助催化剂增加了催化剂表面的活性位点,同时提高了晶面光生电子空穴对的分离和迁移效率.     

普伐他汀的光催化降解性能及机理研究

通过水热法合成Bi₂WO₆,并利用XPS,XRD,BET,UV-Vis和SEM等方法对样品进行了表征;通过光催化性能实验考察了Bi₂WO₆投加量,溶液pH值对普伐他汀（PR）降解效果的影响;通过自由基捕获实验及中间产物的鉴定探明了Bi₂WO₆光催化降解PR过程的主要活性自由基,中间产物及降解机理,并采用发光细菌急性毒性试验评估了PR降解前后的毒性.结果表明,所制备的Bi₂WO₆是由大量纳米片组成的正交晶系花瓣状微球,各元素物质的量比为Bi：W：O=2.5：1：6.7,比表面积为26.67m²/g,带隙能为2.74eV;光催化性能结果表明,对于10mg/L PR,当溶液pH=6.5,Bi₂WO₆投加量为0.4g/L时降解效果最佳,降解率可达80.6%,矿化度为40.2%;自由基捕获实验结果表明降解过程中h⁺起主要的氧化作用,·OH和·O₂^-的氧化起辅助作用.基于活性自由基和中间产物的鉴别结果提出了Bi₂WO₆光催化降解PR的机理,即以h⁺为主,·OH和·O₂^-为辅联合攻击PR分子中C－C键,C＝C双键,酯键等化学键,进而将其分解为易降解小分子有机物.另外,毒性测试结果表明PR经光催化降解后的小分子产物与PR原液相比毒性并没有增强.     

水稻土中五氯酚的降解转化动态及其对微生物群落的影响

采用室内培养实验,研究厌氧条件下水稻土中五氯酚(Pentachlorophenol,PCP)的还原转化与微生物群落组成变化。结果表明,室内培养实验条件下,PCP在水稻土样品中降解比较迅速,在反应17 d时,实验添加的PCP能够完全被还原转化。高通量测序结果显示PCP的添加明显改变了水稻土壤的微生物群落结构,其微生物多样性显著下降,在PCP降解完之后逐渐恢复。原始土壤以及添加PCP的土壤样品中其优势微生物主要分布在变形菌门(Proteobacteria)。PCP添加刺激了水稻土中伯克氏菌科(Burkholderiaceae)、丛毛单胞菌科(Comamonadaceae)、地杆菌科(Geobacteraceae)、红环菌科(Rhodocyclaceae)和假单胞菌科(Pseudomonadaceae)等脱氯相关的微生物菌群繁殖,成为PCP降解过程中的优势菌群,有利于PCP还原降解。本研究结果可为水稻土中有机氯农药污染物的微生物降解脱毒提供理论依据。     

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.