基于铁锰泥的除砷吸附剂性能比较及吸附机理

为解决反冲洗铁锰泥粉末吸附剂（BSPA）使用后难以泥水分离问题,将除铁锰水厂生物滤池产生的反冲洗泥制成颗粒吸附剂（GA）和磁性粉末吸附剂（MPA）,并对BSPA、GA和MPA的除砷性能进行了比较,通过SEM、TED、XRD、BET、FTIR等技术对3种材料进行了表征,寻找3种吸附剂之间除砷性能差异的根源.结果表明,BSPA、GA和MPA对As （V）的最大吸附容量分别为40.980,5.048,8.694mg/g,改性后的吸附材料GA和MPA对砷的吸附能力下降.BSPA是一种以纤铁矿为主的无定形结构混合物,并混有针铁矿和结晶度差的水铁矿,GA的XRD图谱中出现石英晶体和少量赤铁矿晶体的衍射峰,而MPA的主要成分为结晶程度较高的磁赤铁矿.3种材料中均存在有利于吸附的含羟基官能团.BSPA、MPA和GA的比表面积分别为253.150,238.660,43.803m²/g.物相改变且结晶程度增加、表面羟基减少和比表面积降低是导致GA和MPA的砷吸附容量比BSPA低的主要因素.     

Long-term effects of sliver nanoparticles on the abundance and activity of soil microbiome

正Silver nanoparticles(Ag NPs)are among the most extensively used engineered nanomaterials because of their wellestablished antimicrobial and unique physicochemical properties(Yin et al.,2015).Applications of AgNPs have now been     

电絮凝净化含聚污水影响因素研究

目的为保障油田正常安全生产,促进油田含聚污水的循环利用。方法采用电絮凝技术单因素研究方法开展净化含聚污水的静态实验研究,探讨外加电流密度、极板间距、初始pH及聚合物浓度等因素对净化效果的影响规律。结果当初始pH为7.0,电流密度为4.0 m A/m2,极板间距为1.0 cm,电解16 min时,综合处理效果最佳。此时,含油量与浊度去除率分别为98.85%,99.93%,处理每克油平均消耗为0.0494 g的Al,处理每立方米含聚污水的能耗为0.2895 k Wh。污水中的聚合物浓度越小,净化效果越好。结论中性处理,极板间距为1.0 cm,外加电流密度为4.0 m A/m2,通电16 min是最佳处理条件。     

环境因素对建筑工程造价的影响模型研究

现代建筑材料多数使用钢材、混凝土、木材等高能耗产品,这些材料在生产和运输的过程中会消耗较大的能量,对建筑绿色度和的影响非常大,同时也不利于降低工程预算。通过对建筑资源能源消耗和造价进行调查分析,并对建筑材料对建筑环境负荷影响和建筑工程造价进行了研究,提出基于环境因素设置的工程造价模型。通过实际操作发现,相对于传统的工程造价模型,该模型对工程造价的变化趋势反应的更为直观准确,可以大幅度提高工程造价工作的效率。     

呼兰河哈尔滨段水环境质量现状及污染源解析

为了解呼兰河哈尔滨段水环境质量现状及污染物来源,在平水期和枯水期,对呼兰河哈尔滨段沿线进行现场勘查及断面水质监测。结果表明,呼兰河哈尔滨段沿岸集中式排污点源、工业点源、农村生活污染、畜禽粪便、农田径流等各类污染源化学需氧量总入河量为7 671.02吨/年,氨氮总入河量为1 195.16吨/年。松北区的主要污染贡献源为肇兰新河支流污染;呼兰区的主要污染贡献源为集中式排污点源和农业面源污染。     

好氧颗粒污泥对有机物吸附类型的研究

为探索好氧颗粒污泥对有机物的去除机制,进行好氧颗粒污泥对有机物的吸附性能及吸附类型研究,在实验室条件下,采用动态吸附方式,利用SBAR反应器分析好氧颗粒污泥对有机物的去除情况。结果表明,好氧颗粒污泥存在初期吸附作用,最佳吸附时间为30~45 min,COD去处率60%;通过对失活前后好氧颗粒污泥对有机物吸附性能测定以及红外光谱分析,探究好氧颗粒污泥对有机物的吸附类型,表明好氧颗粒污泥对有机物的吸附是一个包含生物吸附和化学吸附的复杂过程。     

8-氨基喹啉-5-偶氮-对苯酚荧光光度法测定微量铬(Ⅵ)

在盐酸介质中,铬(Ⅵ)与AQAPOH形成可被氯仿萃取的荧光化合物,其λex/λem=295nm/400nm.线性范围为0-1.0μg Cr(Ⅵ)/25ml(水相),检测限为0.7μg/L.除Mn(Ⅶ)外,常见金属离子无干扰,可在数千倍Cr(Ⅲ)存在下测定废水中微量铬(Ⅵ).初步探讨了荧光反应的机理.     

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.     

Determination of organophosphorus pesticides and related compounds in water samples by membrane extraction and gas chromatography

A membrane extraction-gas chromatography method was developed fordetermination of organophosphorus pesticides and related compounds including methamidophos, DDVP, dimethoate, methyl parathion, parathion, thiophosphoric acid trimethyl ester, and thiophosphoramidic acid dimethyl ester in water samples. In thismethod, surface-modified acetic cellulose membranes were used to extract the target analytes in water samples, the extracted analytes were back-extracted into a small amount of methanol, andgas chromatography with pulsed flame photometric detector (GC-PFPD) was used to determine the concentrations of targetanalytes in the extracts. The recoveries obtained for thetarget analytes spiked into the water samples ranged from 66to 94%. The method detection limit for each target analyte was 0.05 g L^-1. The method developed in this study had shown the advantages of being cheap, simple, fast, and reliable. It had been used successfully to determine the concentrations of target analytes in river water samples.     

对室内装饰装修材料检测中甲醛分析方法的研究

目前室内装饰装修材料中甲醛含量的检测有六个类别,分析方法不统一,且没有一定的质量控制措施.文章对分析方法进行了改进,并且研究用水中的甲醛标样来对检测过程进行质量控制,结果较好.