A CMBR Measurement Reproduced A Statistical Comparison of MSAM1-94 to MSAM1-92
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REV. DE CIÊNCIA & TECNOLOGIA, Piracicaba, v. 11, n. 21, p. 1-73, jan./jun. 2003.R EVISTA DE C IÊNCIA& T ECNOLOGIA • 211COMISSÃO EDITORIALN IVALDO L EMOS C OPPINI – presidente (Engenharia de Produção) K LAUS S CHÜTZER (Engenharia Mecânica)N ELSON C ARVALHO M AESTRELLI (Gestão da Produção)N IVALDI C ALONEGO JÚNIOR (Ciência da Computação)SÔNIA M ARIA M ALMONGE (Engenharia Química)COMITÊ CIENTÍFICOB ERT L AUWERS (Katholieke Universiteit Leuven –Bélgica)C ARLOS A LBERTO G ASPARETTO (Facens/Unicamp –ESTEVAL project partners.– Feature Based Integrated Design Environment.Identification of interdependencies between manufacturing features.Unsuitable finishing quality (Schützer et al., 1999).After considering several possibilities, which could result in this poor surface quality cutting tool geometry , clamping and balancing of the tool system, technological parameters and machining set-ups, it was realized, that the problem came from the CNC of the machine tool, which was incapable of processing the NC program as fast as the feed rate defined in the NC program. So the machine reaches the point refereed in one line of the program and the information to move the tool to the next point was not processed yet, then the machine had to wait a couple of milliseconds to start moving again.This incompatibility between the feed rate defined in the NC program and the processing time of the CNC results in two different situations according to the CNC used:poor surface quality – the CNC tries to move the machine at the programmed feed rate, but it can-Improper surface finishingThe workpiece used and the finishing tool path.The roughing and semi-finishing operations were accomplished using exactly the same technological parameters and cutting strategies for all workpieces.The comparison analysis was done only during the finishing operation and the same technological para-meters, cutting strategies and tools were used in both cases. The finishing operation was distinguished by the methodology of interpolation. The trajectory of the finishing tool path is shown in figure 3. For this operation it was used a 10 mm ball end mill tool at 10000 rpm and the programmed feed rate was 2000 mm/min.YSISThe results of the machining experiments considering the NC program size, the time required to exe-cute the program and the surface quality in terms of roughness and superficial texture were compared and the conclusions are presented below.NC Program SizesThe table 2 presents the finishing programs sizes calculated for both methods. It proves that less infor-mation are required to describe tool paths by the circular/linear method, thus reducing the program size byMETHOD P ROGRAM SIZE N UMBER586 kbCircular/linear83 kb• V. 11, Nº 21 – pp. 29-36Regions where the feed rate as reduced.2000 mm/min660 mm/min 1500 mm/min700 mm/min2000 mm/minRoughness AnalysisIt was used a digital Surftest Equipment to obtain the Ra and Rz parameters. It was analyzed the same areas for all workpieces. Practically, there are not differences between the roughness parameters for both interpolation methods.Surface T exture AnalysisIt was possible to visually verify the differences between the surfaces of both methods. The circular/ linear workpieces are smoother than the linear ones. The figure 5 shows this texture.Irregular surface texture Regular surface textureBesides those transversal marks at the workpiece machined by the linear interpolation, this method also gives a deficient quality in the longitudinal direction, as it is seen in the next photos taken by a CCD camera connected in an microscopy.Figure 6 was taken using a microscopy with magnification 10x of the linear part. The vertical marks are the cusp heights left by the ball end mill tool. The steps-over of the tool path is on the horizontal direc-Fig. 6. Uneven cusps from a linear workpiece.Fig. 7. Even cusps from a circular/linear workpieceIn linear interpolation method is possible to see uneven cusp heights, what can difficult drastically the hand finishing afterwards. This problem is not seen at the workpieces milled by the circular/linear method, as shown in figure 7. It happens due to the more constant cutting movements.In this method, the cusps height are much more uniform, what can help the manual finishing afterwards, by decreasing this process time, and improving the accuracyCONCLUSIONThe High Speed Cutting T echnology can be attractively applied in die and mould manufacturing, among others applications. However, there are several other technologies in the process chain that must be considered to support an efficient HSC process.R EVISTA DE C IÊNCIA & T ECNOLOGIA • V. 11, Nº 21 – pp. 29-36。
•论著•离子色谱测定唾液葡萄糖含量方法的建立及评估徐春I窦倩2汪诗文2章子锋?戴庆2'解放军总医院第三医学中心内分泌科,北京1()()()39;2国家纳米科学中心,中国科学院卓越中心,中国科学院纳米光子材料与器件重点实验室,北京10()190徐春和窦倩对本文有同等贡献通信作者:戴庆,Email:***************,电话:************【摘要】目的建立用离子色谱测定唾液中葡萄糖浓度的方法。
方法利用热变性法去除唾液中的蛋白质,以CarboPac PA20(3x30mm)作为保护柱.CarboPac PA20(3xl50mm)作为分析柱进行离子色谱分析。
以超纯水(A),250mmol/L NaOH溶液(B),500mmol/L NaAc(C)为淋洗液进行梯度洗脱,采用脉冲安培检测器检测”结果本方法在0.04-0.12mgn.范围内具有较好的线性关系,线性相关系数^.9967:葡萄糖的检出限是0.002mg/L;重复性测量相对标准偏差(RSD)的平均值为0.75%,加标冋收率平均值为103.07%0结论本方法操作简便、灵敏度高、准确性好、结果稳定,可用于唾液中葡萄糖含量的测定。
【关键词】离子色谱;唾液;筍萄糖;糖尿病;无创检测基金项目:中国科学院科技服务网络计划(STS计划)(K町-STS-ZDTP-063);国家重点研发计划(2016YFA0201600)DOI:10.3760/.l15807-20200623-00194Establishment,evaluation,and determination of saliva glucose concentration by ion chromatography XuChun1,Dou Qian2,Wang Shiwen2,Zhang Zifeng2,Dai Qing2'Department of Endocrinology,3rd Medical Center,PLA General Hospital,Beijing100039,China;2CAS Key Laboratory of Nanophotonic Materials(uid Devices,CAS Center for Excellence in Na/ioscience,National Center forNanoscience and Technology,Beijing100190,ChinaXu Chun and Dou Qian contributed equally to this articleCorresponding author:Dili Qing,Email:***************,Tel:************[Abstract]Objective To establish an analytical method for measuring the concentration of glucose insaliva by ion chromatography.Methods The proteins in saliva were removed by thermal denaturation method,CarboPac PA20(3x30mm)was used as a protective column and CarboPac PA20(3x150mm)was used as ananalytical column for ion chromatography analysis.Gradient elution was carried out with A:ultra-pure water,B:250mmol/L NaOH solution and C:500tnmol/L NaAc solution.Pulsed ampere detector was used for detection.Results This method had a good linear relationship in the range of0.04to0.12mg/L,with a linear relation coefficient of0.9967.The detection limit of glucose was2|xg/L,the mean value of the relative standard deviation(RSD)of the repeatability measurement was0.75%,and the average spike recovery was103.07%.Conclusion Thismethod is simple,sensitive,accurate and stable,and can be used for the detennination of glucose concentration insaliva.[Key words]Ion chromatography;Saliva;Glucose;Diabetes;Non-invasive detectionFund program:Science and Technology Service Network Plan of Chinese Academy of Sciences(STS Plan)(KFJ-STS-ZDTP-063);National Key Research and Development Plan(2016YFA0201600)DOI:10.3760/.l15807-20200623-00194唾液由唾液腺(腮腺、颌下腺、舌下腺、小涎腺)分泌,在口腔内起帮助消化、湿润和保护黏膜的作用。
a selection of measurement results -回复选定主题:[一组测量结果]第一步:引言(100-150字)本文将深入探讨一组测量结果,并逐步回答与其相关的问题。
我们将以科学准确性为基础,以合理推理和相关证据为支撑,共同探究这些测量结果的意义和可能的解释。
第二步:概述测量结果(150-200字)首先,让我们简要介绍这组测量结果。
这些结果涉及多个领域,包括物理学、生物学和经济学等。
我们收集了各种实验和调查数据,这些数据对于我们理解和解决当前问题至关重要。
本文将对其中一些测量结果进行详细分析和解释,以便更好地理解它们的含义。
第三步:重要测量结果的详细分析(800-1000字)接下来,我们将深入分析一些关键的测量结果,以便更好地理解它们。
我们将选择几个代表性的结果,涵盖不同领域,以获得更全面的认识。
首先,让我们考虑一项物理学实验的结果。
该实验旨在测量重力加速度,以确定地球表面的重力场强度。
通过精确地测量时间和物体的自由落体运动,我们得出了一个平均值,并计算了其误差范围。
这些结果对于我们理解地球的物理特性和基本常数非常重要,以及对于未来的科学研究和技术应用有着深远的影响。
接着,我们转向生物学领域的一个测量结果。
通过对一群人进行健康调查和评估,我们得出了一个关于肥胖率的统计数据。
这些数据显示了不同年龄组和性别之间的肥胖率差异,并提供了对这一全球问题的见解。
通过这些结果,我们可以加深对肥胖症的根源和其潜在健康影响的理解,并为制定预防措施和干预政策提供依据。
最后,让我们来看一个经济学领域的测量结果。
通过对某个国家的经济指标进行调查和收集,我们得到了一组有关就业率、通货膨胀率和经济增长率的数据。
这些结果对于政策制定者和经济学家来说至关重要,因为它们可以揭示经济的整体健康状况和趋势,进而帮助他们制定适当的政策和措施来促进经济发展。
通过这些详细分析,我们可以更好地理解这组测量结果的重要性和意义。
专利名称:Apparatus and method for determining the amount of entrapped gas in a material发明人:Charles E. Lee,John D. Della-Santina申请号:US09/132630申请日:19980811公开号:US06082174A公开日:20000704专利内容由知识产权出版社提供摘要:An entrapped gas measuring apparatus includes a reservoir housing with a reservoir which is adapted to receive a material sample and to expand according to an expansion of the material sample when a negative pressure is applied externally to the reservoir. A parameter indicating the change in volume of the reservoir during the expansion, such as the actual change of volume of the reservoir or a change in position of a moveable wall which at least in part defines the reservoir, is detected by a detector. A processor coupled to the detector is used to determine the amount of entrapped gas based upon the detected parameter. The amount of entrapped gas determined by the processor may be the percent volume of the entrapped gas in relation to the overall volume of the sample, or may be the actual volume of the entrapped gas in the sample. Based at least in-part upon the measured amount of entrapped gas within the sample, the processor is further adapted to determine at least one of: percent volume of the substrate in the sample in relation to the overall volume of the sample; actual volume of the substrate in the sample; or density of the sample or substrate within the sample. The entrapped gas measuring apparatus may be used to produce a material having a known amount of entrapped gas by: making a first material according to a first method andwhich has a first amount of entrapped gas; applying a negative pressure to the sample such that the sample expands from a first volume to a second volume; detecting a parameter which is indicative of the change of sample volume under the applied negative pressure; comparing the detected parameter with a predetermined range for the parameter; and, if the detected parameter is not within the predetermined range, making a second material according to a second method which has a second amount of entrapped gas that is within the predetermined range.申请人:BENCHTOP MACHINE AND INSTRUMENT, INC.代理人:James C. Peacock III,John P. O'Banion更多信息请下载全文后查看。
fluorescence quantitative analysis -回复"Fluorescence quantitative analysis" refers to a technique used to measure the concentration of a substance by examining its fluorescence properties. This technique is commonly employed in various scientific fields such as chemistry, biology, and environmental science. In this article, we will explore the principles behind fluorescence quantitative analysis, the instruments used, and some applications of this technique.1. Introduction to Fluorescence:Fluorescence is a phenomenon exhibited by certain substances when they absorb light at a specific wavelength and emit light at a longer wavelength. This emission of light is called fluorescence. It occurs due to the excitation of electrons in the atoms or molecules of the substance.2. Principles of Fluorescence Quantitative Analysis: Fluorescence quantitative analysis is based on the principle that the intensity of fluorescence emitted by a substance is directly proportional to its concentration. This principle forms the basis for detecting and measuring the concentration of various substances.3. Instrumentation for Fluorescence Quantitative Analysis:a. Fluorometers: Fluorometers are the primary instruments used for fluorescence quantitative analysis. They consist of a light source, filters to select the excitation and emission wavelengths, and a detector to measure the emitted light.b. Fluorescence Microscopes: Fluorescence microscopes combine traditional microscopy with fluorescence detection. They allow for the visualization and quantification of fluorescently labeled samples.c. Flow Cytometers: Flow cytometers employ fluorescence to analyze individual cells or particles in a solution. They can measure multiple parameters simultaneously, providing detailed information about the sample.4. Process of Fluorescence Quantitative Analysis:a. Selection of fluorophore: The first step involves choosing a suitable fluorophore that exhibits fluorescence properties when bound to the target substance.b. Calibration Curve: A calibration curve is constructed by measuring the fluorescence intensity at different known concentrations of the target substance. This curve establishes therelationship between fluorescence intensity and concentration. c. Sample Preparation: The sample is prepared by incorporating the fluorophore into the solution containing the substance to be quantified.d. Excitation and Emission: The sample is excited with a specific wavelength of light, and the emitted fluorescence is detected and measured.e. Comparison with Calibration Curve: The fluorescence intensity of the sample is compared with the calibration curve to determine its concentration.5. Applications of Fluorescence Quantitative Analysis:a. Biochemical Assays: Fluorescence quantitative analysis is widely used in biochemical assays to determine the concentration of biomolecules such as DNA, proteins, and enzymes.b. Drug Discovery: Researchers use fluorescence quantitative analysis to screen potential drug candidates and study their interactions with target molecules.c. Environmental Monitoring: This technique is utilized to measure the concentration of pollutants in water and air, facilitating environmental monitoring and assessment.d. Medical Research: Fluorescence quantitative analysis helpsdiagnose diseases and monitor their progression by quantifying specific biomarkers in biological samples.In conclusion, fluorescence quantitative analysis is a versatile technique that enables precise and sensitive measurements of substance concentrations. It finds extensive applications in various scientific fields and continues to contribute to advancements in research and analysis.。
msa 计量型计数型英语The MSA (Measurement, Selection, and Analysis) method is a type of statistical analysis that focuses on measuring and analyzing data to make decisions. This method is commonly used in research and business to determine the effectiveness of a particular process or to identify trends and patterns in data. MSA involves the use of various statistical tools and techniques to quantify and evaluate the variability and accuracy of measurements.On the other hand, the Counting method is a type of statistical analysis that focuses on the frequency of occurrences of specific events or items within a given data set. This method is often used to track the number of defects, errors, or occurrences of a particular eventwithin a process. Counting methods can help identify areas for improvement and track the progress of process changes over time.In English:MSA (Measurement, Selection, and Analysis)方法是一种统计分析方法,其重点是测量和分析数据以做出决策。
a r X i v :a s t r o -p h /0701050v 1 2 J a n 2007The Galactic Center MagnetosphereMark MorrisDepartment of Physics &Astronomy,University of California,Los Angeles,CA 90095-1547,USA E-mail:morris@ Abstract.The magnetic field within a few hundred parsecs of the center of the Galaxy is an essential component of any description of that region.The field has several pronounced observational manifestations:1)morphological structures such as nonthermal radio filaments (NTFs)–magnetic flux tubes illuminated by synchrotron emission from relativistic electrons –and a remarkable,large-scale,helically wound structure,2)relatively strong polarization of thermal dust emission from molecular clouds,presumably resulting from magnetic alignment of the rotating dust grains,and 3)synchrotron emission from cosmic rays.Because most of the NTFs are roughly perpendicular to the Galactic plane,the implied large-scale geometry of the magnetic field is dipolar.Estimates of the mean field strength vary from tens of microgauss to ∼a milligauss.The merits and weaknesses of the various estimations are discussed here.If the field strength is comparable to a milligauss,then the magnetic field is able to exert a strong influence on the dynamics of molecular clouds,on the collimation of a Galactic wind,and on the lifetimes and bulk motions of relativistic particles.Related to the question of field strength is the question of whether the field is pervasive throughout the central zone of the Galaxy,or whether its manifestations are predominantly localized phenomena.Current evidence favors the pervasive model.1.Introduction The magnetic field at the center of the Galaxy (hereafter,the ”field”)has been studied with a wide variety of techniques for over 20years,and while there is some consensus that thepredominant,global geometry within the central 200-300parsecs is poloidal,the discussion at this workshop has emphasized that there is no universal agreement on the strength of the field and on the extent to which the field strength varies from one place to another.In this review,I summarize the evidence characterizing the various points of view.Earlier reviews of the Galactic center magnetic field have described many of the central points that have been known for some time [1,2,3,4,5,6],but recent observations have added considerably to the information that can be brought to bear on this discussion.The primary probe of the large-scale field has been radio observations of polarized,filamentary structures which,while typically <0.5pc in width,are tens of parsecs in length.The strong radio polarization,and the occasional filamentary counterpart at X-ray wavelengths [7]indicate that the emission is synchrotron radiation,and the position angle of the polarization,once corrected for Faraday rotation,confirms that the magnetic field lies along the filaments [8,9,10,11].The almost invariant curvature of the filaments,and their absence of distortion in spite of clear interactions with the highly turbulent interstellar medium,led Yusef-Zadeh &Morris (1987[12],see also [5])to note that the implied rigidity of the filaments requires a field strength on theorder of a milligauss,which is surprisingly large,given the scale of these structures.The orientation of the most prominent NTFs is roughly perpendicular to the Galactic plane, as illustrated in Figure1,a schematic diagram depicting allfilaments identified in theλ20-cm VLA survey by Yusef-Zadeh et al.(2004[13]).Because the individualfilaments define the localfield direction,the ensemble offilaments has been interpreted in terms of a predominantly dipolarfield,extending at least200pc along the Galactic plane[14].The deviations from perfect verticality of many of thefilaments can be ascribed to a global divergence of thefield above and below the Galactic plane.The short,nonconformingfilaments are discussed in§2.3(and[15]).Figure1.Schematic map showing the radiofilaments catalogued by Yusef-Zadeh et al.(2004, [13])in the course of theirλ20-cm survey of the Galactic center.Quite a different probe of the magneticfield is provided by mid-and far-IR observations of thermal dust emission from magnetically aligned dust grains.The rotation axes of dust grains align with the magneticfield by dissipative torques[16],leading to a net polarization of the thermal emission such that the E-vector is perpendicular to the magneticfield.This probe, however,is strongly dominated by dense,warm clouds,so it is quite different from the NTFs, which sample thefield in the intercloud medium occupying most of the volume of the Galactic center.The magneticfield implied by the polarized dust emission is parallel to the Galactic plane[17,18,19,20,21],and thus perpendicular to the large-scale intercloudfield revealed by the NTFs.The perhaps surprising orthogonality of these two systems can be understood in terms of the tidal shear suffered by molecular clouds inhabiting the central molecular zone (CMZ).Any portion of a molecular cloud located a distance R gc pc from the Galactic center, and having a density less than104cm−3[75pc/R gc]1.8is subject to such shear[22,23],so cloud envelopes tend to get stretched into tidal streams that may subtend a large angle at the Galactic center(e.g.,[24]).Any magneticfield within the clouds–presumablyflux-frozen to the partially ionized molecular gas–will thus be deformed into an azimuthal configuration,with thefieldlines oriented predominantly along the direction of the shear[17].There is little evidence that the cloud and inter-cloud environments are magnetically coupled to each other in any significant way,as might have been expected if thefield lines were anchored to the cloud layer,and if the rotation of the cloud layer thus imposes a global twist upon the verticalfield[25,26].The most prominent NTFs show very little deformation where they pass through the Galactic plane and interact with gas in the CMZ(e.g.,[12]).Some case can be made that Faraday rotation measurements are consistent with the geometry of a twisted,large-scale field([6],and references therein),but these data remain too sparse to draw anyfirm conclusions.If,as the evidence does indicate,the magneticfield is not anchored in the CMZ,then it is either anchored in the essentially non-rotating Galactic halo or beyond,or it arcs back to the Galactic plane at relatively large radii and is anchored there.In either case,thefield lines do not rotate with the CMZ,and the molecular clouds move through thefield with a large relative velocity.This gives rise to an induced v×B electricfield at cloud surfaces(10−4B(mG)V/cm) which can accelerate particles,drive currents and contribute to the cloud heating[27,28].The residence time of clouds in the Galactic center is a few hundred million years as a result of angular momentum loss resulting from both dynamical friction and magnetic drag[29,2,30], so it is not clear how clouds forming at the outside edge of the CMZ[31]will retain any magnetic contact with their surroundings as they migrate inwards through the verticalfield.Any original connection between the cloud and extra-cloudfields could have pinched offduring the inward migration,leaving the clouds magnetically isolated.If typical cloud lifetimes are less than the inspiral times of clouds,presumably because clouds are sheared in the tidalfield,then the situation is more complex,but these comments can still apply to sheared cloud streams and the new clouds that reform as the streams interact with each other.The remainder of this review focuses on several topics of current interest–both observational and theoretical–and culminates in a description of what I think are some of the most important open questions.2.Uniformity of the Galactic Center Field2.1.Pressure Confinement of Magnetic StructuresRegardless of the magneticfield strength,the pressure of the interstellar medium in the CMZ is very large compared to the Galactic disk[32].A hot diffuse gas(T∼108K,n∼0.04cm−3) that pervades much of the volume of the Galactic center[33,34,35]has a pressure of6x 10−10dynes cm−2,and is in approximate pressure equilibrium with the warm(∼150K,low-density molecular medium[36,37],if the velocity dispersion of∼20km s−1is used to calculate a turbulent pressure.This pressure is at least two orders of magnitude higher than is characteristic of the Galactic disk.The magneticfield,on the other hand,has a pressure of4x10−8B(mG)2 dynes cm−2.Consequently,if the magneticfield strength in observed magneticfield structures is∼a milligauss,then those structures are not confined,and would expand and disappear on a short time scale.This consideration led to the argument that a milligauss magneticfield must be pervasive throughout the CMZ[38];the strong and extended magneticfield would then provide its own support.In this view,the NTFs are then simply illuminated magneticflux tubes into which relativistic electrons have been injected,and along which the electrons are constrained to flow[1].A ring current at the outer edge of the CMZ,or distributed over some range of radii there,is required to generate and confine the overall dipolefield[5].2.2.Models of Localized Magnetic StructuresThe alternative to a strong,pervasivefield is that the NTFs represent localized peaks in the magneticfield strength.A force-free magneticfield configuration might be considered as a way of tying a local current to a local enhancement of the magneticfield strength[39,40],but unless the overall configuration is pressure confined,it will be transient and short-lived.A recent suggestion by Boldyrev&Yusef-Zadeh[41]is that the NTF’s are localized structures of milligaussfield strength confined by the effective pressure of large-scale turbulence in the Galactic center.In their model,the turbulent cells expulse thefield,and concentrate it in regions between the cells.However,while thefield will indeed diffuse out of a zone of strong turbulence,the turbulence itself is generally accompanied by the generation of newfield at a rate at least as fast as the rate of outward diffusion.Consequently,while this mechanism raises the interesting possibility that the geometry of the boundaryfield might be different from that within the turbulent zones because of the interactions of thefield emanating from the different zones,it is not obvious how this mechanism would lead to a relative enhancement of thefield strength at those boundaries.Furthermore,the turbulence in this model must be organized in such a way that the resulting magneticfilaments are predominantly vertical.This places a strong constraint on the overall helicity distribution of plasma motions in the Galactic center. Numerical models that address these concerns are needed to assess this model further.While other models for localized structures have been proposed[42,43,44],they lack the generality needed to account for the population and the orientations of thefilaments.2.3.Significance of the Short Radio Streaks?One relatively recentfinding that has called the notion of a pervasive,uniformfield into question is a population of short radiofilaments,or streaks,that occupy much of the same Galactic longitude range as the prominent NTFs[14,45,15].These structures are largely included in figure1.They differ in three ways from the long-known,prominent NTFs:(i)They are quite short,∼0.1pc.(ii)Their surface brightness is typically about1/4that of the prominent NTFs.(iii)They appear to be more or less randomly oriented,and thus do not conform to the global verticality of the prominent NTFs.This point has been raised as an argument against a globally ordered,dipole magneticfield.Given these pronounced differences,one could argue that the radio streaks represent a different population with a separate origin,such as localized oblique shock structures,or strong local deformations of the large-scalefield as a result of some local,energetic disturbance.It is premature to conclude that they are inconsistent with a predominantly ordered,large-scale dipolefield.Further study of these features is warranted to determine whether they differ systematically from the prominent NTFs in other ways as well,such as in terms of spectral index and polarization properties,and whether they are connected to other interstellar structures in the same way that the prominent NTFs are.2.4.Dynamical ConsequencesAs mentioned above,a pervasive,dipolefield exerts a magnetic drag force on clouds moving through it,enhancing the rate at which they spiral inwards.If sufficiently strong,thefield can also collimate winds and energetic particles that emanate from the center,creating a chimney effect.This is consistent with observations of extended columnar radio features in nearby,radio-bright galactic nuclei[46,47,48],although the extent to which the energetic winds in such galaxies have been collimated by the magneticfield,as opposed to the back pressure of their stratified interstellar gas layers,has not been settled.Recent work by Belmont et al.[34]has shown that at least the hydrogen in the hot,diffuse gas at the Galactic center is unbound,so a thermal galactic wind is implied.A dipole magnetic field can collimate this wind to an extent that depends on thefield strength,so observations of the large-scale morphology of thermal X-ray emission from the hot gas will be a useful probe of both the wind and the magneticfield.Cosmic rays will also be confined by a pervasive,verticalfield.This has two important consequences:first,the residence time for cosmic rays in the Galactic center will be relatively short(a few×105yr)compared to that in the Galactic disk(a few×106yr),because the constraint that cosmic rays diffuse primarily along thefield lines implies,in the Galactic center, that they diffuse directly away from the Galactic plane,whereas in the Galactic disk,they are largely trapped by the azimuthalfield.This relatively short residence time implies a much smaller cosmic ray density than one might infer from the volume rate of supernovae alone.This is consistent with the fact that the high-energyγ-ray emission intensity across the CMZ does not have a peak comparable in its contrast to the peak in the total column density of gas[49,50]. Second,the longitudinal diffusion of cosmic rays,especially electrons,would be suppressed by a pervasive verticalfield.Such diffusion–for protons–is assumed in a recent model for the extended TeV emission observed by HESS invoking a single source of high-energy cosmic rays [51,52];this model is probably inconsistent with the presence of a strong,pervasive,vertical field.ments on Arguments for a Weak Field3.1.The Minimum Energy AssumptionA number of researchers have estimated the strength of the Galactic center magneticfield using the minimum energy assumption,also referred to as”equipartition”,applied to observations of synchrotron emission from relativistic particles(e.g.,[60]).This assumption can be applied to a medium in which energy exchange takes place between particles andfields on time scales much less than the energy loss times of particles or thefield generation time from macroscopic particle dynamics.This can,for example,describe environments characterized by isotropic turbulence and tangledfields,such as the hot spots in the lobes of double radio source galaxies.However,it is quite generally inapplicable to the Galactic center,except perhaps in very local environments in which energetic events have recently occurred.The striking large-scale order of the Galactic center magneticfield implies that its energy content is not responding in any significant way to localfluid motions or relativistic particle dynamics.The relativistic particles are responding to thefield,but the reverse is not true.The energy content of the Galactic centerfield is far greater than that of the emitting particles,and thus thefield strength can be much larger than the equipartition value.3.2.Zeeman MeasuresThe most compelling measure offield strength would be a direct measure via the Zeeman effect. Zeeman measures have indeed been made in Galactic center clouds in lines of both H and OH [53,54,55,56,57],with the result that,where any significant Zeeman signal is seen at all,it implies afield strength on the order of a milligauss or larger.However,there are only a few places where a significant Zeeman signal has been detected.(We do not include in these comments the Zeeman measures deduced from1720-MHz OH masers around Sgr A East and the circumnuclear disk,which givefield strengths of3-5mG[58,59],because such masers presumably arise from locally compressed gas,and may therefore not be representative of the magneticfield on large scales.)One strong selection effect in Zeeman measures is that the extremely broad lines of Galactic center clouds make detection of the Zeeman splitting very difficult unless thefield strength exceeds∼1mG.Two other points must be considered when interpreting Zeeman measurements:first,they apply largely to the magneticfield within clouds or at the surfaces of clouds.As the above discussion indicates,the magneticfield geometry in clouds is not necessarily related to the large-scale intercloudfield.Second,the Zeeman effect measures only the mean line-of-sight component of thefield,so if there arefield reversals along the line of sight,or if thefield direction changes across the radiotelescope beam,then there is significant averaging and dilution of the Zeemansignal.In any case,even if Zeeman measures were able to provide insight into the strength of the intercloudfield,the line-of-sight restriction makes it difficult to draw conclusions about a largely vertical dipolefield.Further Zeeman measurements,not only of H and OH with improved sensitivity and spatial resolution,but also of other molecules that probe denser regions,will be very important for achieving a more complete understanding of the Galactic centerfield.3.3.Synchrotron LifetimesOne argument that has been raised against a pervasivefield of milligauss strength is that the synchrotron lifetime of the electrons responsible for the nonthermal radio emission is relatively short,∼105years for electrons responsible for the330-MHz radio emission arising from the central4◦×2◦diffuse nonthermal source[60].So the supernova rate in the CMZ(or in the Galactic and nuclear bulges above it,since not much less than half of the relativistic electrons created in a supernova will diffuse along thefield lines and reach the Galactic plane)must be somewhat larger than1per105yrs if supernovae alone are to account for the uniformity of the synchrotron emission.The rate of only Type Ia supernovae in the Galactic bulge has been estimated at30per105yrs,[61],and in the nuclear bulge(defined in[62,63])it is about20 per105yrs,so allowing also for core collapse supernovae,the particle production rate seems abundantly sufficient,even if no particles diffuse to the Galactic center from the rest of the Galaxy[64],and if there is no particle reacceleration process operating.The synchrotron lifetimes of electrons responsible for the5-GHz radio emission from the NTFs is only∼104years,so if they diffuse along thefield lines at the Alfv´e n speed,2200km s−1 B(mG)/n(cm−3)1/2,then the net distance they can travel before losing an appreciable amount of energy is∼20pc×B(mG)/n(cm−3)1/2,somewhat shorter than the length of the longest filaments(60pc).(The Alfv´e n speed is assumed because the diffusion is usually limited by scattering of the streaming particles offof Alfv´e n waves propagating along thefield lines).So far,observations indicate that the radio spectral index has no noticeable variation along the length of thefilaments(e.g.,[10]).Consequently,if the relativistic electrons are produced at a specific location along them,then the synchrotron lifetime may present a problem unless thefield strength is substantially less than a milligauss.Two possible alternatives warrant consideration:first that the diffusion along thefield lines is much faster than the relatively slow rate assumed here because the magneticfield is much more rigid and smooth than in most situations where the Alfv´e n speed is invoked.Second,a reacceleration process may take place along thefilaments via shocks,wave dissipation,or reconnection,in analogy with the reacceleration processes needed to account for the persistence of highly relativistic particles in extragalactic jet sources,in spite of their synchrotron and Compton losses.4.The Double Helix NebulaA potential new probe of the Galactic center magneticfield was recently revealed at24µm with the Spitzer Space Telescope[65].At a distance of∼100pc toward positive Galactic latitude from the Galactic center,a nebula having the form of an intertwined double helix extends over at least50pc,with its long axis oriented approximately perpendicular to the Galactic plane (Figure2).This feature was interpreted as a torsional Alfv´e n wave propagating away from the Galactic center along the magneticfield,and driven by the rotation of the circumnuclear gas disk (CND).The few-parsec scale of the CND matches the width of the nebula,and the wavelength of the torsional wave,19pc,corresponds to the∼104-year rotation period of the CND if the Alfv´e n speed is103km s−1.This speed,in turn,constrains the magneticfield to have a strength of0.5n1/2mG in the context of this hypothesis,where n is the hydrogen density in the medium through which the wave propagates.The density is not known,but for values of the magnetic field ranging from0.1to1mG,a plausible density is found:n=0.04-4cm−3.The presence of two strands has been attributed to an apparent”dumbbell”asymmetry of the driving disk(see[65]);the magneticfield threading the disk is concentrated into two diametrically opposed density maxima.A potential weakness of the torsional wave hypothesis is that the wave cannot yet be followed all the way down to its hypothetical source,the CND.However,this also raises the question of why the double helix is visible in thefirst place;its mid-infrared emission is most likely thermal emission from dust,so the visibility of the nebula at its present location presumably requires that the wave has levitated charged dust grains.Because of variable conditions at the base of the wave over the past105years(indeed,the CND is a rather disturbed,non-equilibrated disk [5]),such dust may not have been continuously available to highlight the wave.This may also help explain why a similar nebula is not present on the opposite side of the CND.An alternative scenario for understanding the Double Helix feature is that it be connected in some way with the linear radiofilaments of the Galactic Center Radio Arc.If the Northern extension of the Arc[66]is followed and extrapolated to Galactic latitudes beyond0.5◦(seefig 20b of[13]),then it coincides approximately with the long axis of the Double Helix.However, there is no continuous connection in the radio maps between the linearfilaments and the Double Helix,and the only radio emission associated with the Double Helix lies outside the mid-IR strands(w,personal communication).There is so far no explanation for how a long bundle of linear,nonthermalfilaments could culminate in helically wound,thermal structures. Whether or not the CND hypothesis for the Double Helix is valid,further study of this feature should provide valuable insight into the Galactic center magneticfield.5.Open QuestionsThe questions that seem now to be the most compelling for guiding near-future research on the Galactic center magneticfield,besides those already mentioned above,are the following:•Whether or not the central verticalfield is more or less uniform,how and where does it merge with the azimuthalfield of the Galactic disk?•If the Galactic center magnetosphere is defined as the region in which nonthermal radio filaments are observed,then its outer edge roughly coincides with the edge of the CMZ, with the Galaxy’s inner inner Lindblad resonance,and with the transition from X1to X2 gas orbits in the bar.What is the interplay between these phenomena,at this critical juncture in the Galaxy?•Can high-resolution observations be used to obtain more detail on the points of interaction between cloud and intercloudfields?This may best be done with a combination of radio and far-infrared polarization measurements.•What process produces the relativistic particles that illuminate the NTFs via their synchrotron emission?•At the moment,we lack consensus on the power source for the108K gas occupying much of the volume of the nuclear bulge.Can we appeal to the stirring that takes place as clouds move through thefield,leaving magnetosonic and Alfv´e n waves in their wake?Or can the energy be supplied by magneticfield line annihilation of new verticalfield constantly migrating inwards from the rest of the Galaxy?•What is the origin of the poloidalfield?Dynamo models have been hard-pressed to produce a dipolefield like that observed,and a promising possibility is that the centralfield represents protogalacticfield that has been concentrated over the history of the Galaxy by mass inflow[67].Now is a propitious time to take these models to the next stage of sophistication.AcknowledgmentsI 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Technical NoteGreen manure plants for remediation of soils polluted by metals and metalloids:Ecotoxicity and human bioavailabilityassessmentY.Foucault a ,b ,c ,T.Lévêque a ,b ,d ,T.Xiong a ,b ,E.Schreck e ,A.Austruy a ,b ,M.Shahid f ,C.Dumat a ,b ,⇑aUniversitéde Toulouse,INP-ENSAT,Avenue de l’Agrobiopôle,31326Castanet-Tolosan,FrancebUMR 5245CNRS-INP-UPS,EcoLab (Laboratoire d’écologie fonctionnelle),Avenue de l’Agrobiopôle,BP 32607,31326Castanet-Tolosan,France cSTCM,Sociétéde Traitements Chimiques des Métaux,30Avenue de Fondeyre,31200Toulouse,France dAgence de l’Environnement et de la Maîtrise de l’Energie,27rue Louis Vicat,75737PARIS Cedex 15,France eUMR 5563CNRS/UPS/IRD/CNES GET,14avenue Avenue Edouard Belin,31400Toulouse,France fDepartment of Environmental Sciences,COMSATS Institute of Information Technology,Vehari,Pakistanh i g h l i g h t sGreen manures plants were tested for quality restoration of soils polluted by metal(loid)s. Bioavailability and ecotoxicity of metal(loid)s were measured. Borage and mustard improve polluted soil quality.Phytoremediation decreases ecotoxicity and quantity of bioaccessible metal(loid)s.a r t i c l e i n f o Article history:Received 15April 2013Received in revised form 14July 2013Accepted 16July 2013Available online 19August 2013Keywords:Green manure plants Metal(loid)s Polluted soil EcotoxicityBioaccessibility Phytoremediationa b s t r a c tBorage,white mustard and phacelia,green manure plants currently used in agriculture to improve soil properties were cultivated for 10wk on various polluted soils with metal(loid)concentrations represen-tative of urban brownfields or polluted kitchen gardens.Metal(loid)bioavailability and ecotoxicity were measured in relation to soil characteristics before and after treatment.All the plants efficiently grow on the various polluted soils.But borage and mustard only are able to modify the soil characteristics and metal(loid)impact:soil respiration increased while ecotoxicity,bioaccessible lead and total metal(loid)quantities in soils can be decreased respectively by phytostabilization and phytoextraction mechanisms.These two plants could therefore be used for urban polluted soil refunctionalization.However,plant effi-ciency to improve soil quality strongly depends on soil characteristics.Ó2013Published by Elsevier Ltd.1.IntroductionIn many countries,the regulation was recently reinforced to im-prove the management of (eco)toxicity due to chemicals uses (Sch-reck et al.,2013).Total quantity of lead emitted into the environment strongly decreased last years (Cecchi et al.,2008).But in the world numerous brownfields and kitchen gardens are polluted (Bacigalupo and Hale,2012).However,the recovery of ur-ban brownfields is required and the possibility of healthy soil gar-dening becomes an important issue (Foucault et al.,2012).Phytoremediation techniques modify total and/or bioavailable soil metal(loid)concentrations in relation with compartmentalization and/or speciation (Butcher,2009).Soil (micro)biology is improved (Kidd et al.,2009),large machinery and excavation equipment are not needed and soil erosion is reduced.Green manure undemand-ing plants,usually used in agriculture to improve soil fertility thanks to a high rhizosphere activity (Zotarelli et al.,2012)appear as good candidates for phytoremediation,as certain mustard spe-cies (Kim et al.,2010).Otherwise,in addition to the measure of to-tal metal(loid)soil concentrations,ecotoxicity and availability measures (Denys et al.,2007)are needed.As these parameters de-pend on physicochemical soil properties (Foucault et al.,2013),due to their rhizosphere activity (Vamerali et al.,2010;Shahid et al.,2011,2013),green manure plants could change metal(loid)impact.For the first time to our knowledge,green manure plants were0045-6535/$-see front matter Ó2013Published by Elsevier Ltd./10.1016/j.chemosphere.2013.07.040⇑Corresponding author.Address:EcoLab,INP-ENSAT,Avenue de l’Agrobiopôle,BP 32607,31326Castanet-Tolosan,France.Tel.:+330534323903;fax:+330534323901.E-mail address:camille.dumat@ensat.fr (C.Dumat).therefore tested for phytoremediation on various polluted soils, with both metal(loid)human bioavailability and ecotoxicity assessment in relation with soil characteristics.2.Materials and methods2.1.Soils sampling,preparation and characterizationVarious polluted soils were prepared from a highly contami-nated sandy top soil(call T)collected from a secondary lead smel-ter located in Toulouse with pH=7,CEC(cation exchange capacity)=8.9cmol(+)kgÀ1,CaCO3=8g kgÀ1,metal(loid)concen-trations(mg kgÀ1):Pb39,800±796,As288±6,Cu286±6,Cd 18±1,Zn294±6and Sb2095±42.Soils were cleaned of roots and visible plant materials,dried and sieved under2mm.Accord-ing to Leveque et al.(2013),soil T was mixed with two unpolluted loamy calcic top cambisol profiles,with metal(loid)concentrations close to the natural geochemical background and noticed C1,in or-der to prepare the metal(loid)concentrations ranges: 400mg Pb kgÀ1(C2)and825mg kgÀ1(C3).Soil1has a high OM(Or-ganic Matter)content(44.7g kgÀ1),pH=6.5, CEC=12.3cmol(+)kgÀ1and a low CaCO3content(16.1g kgÀ1); while soil2is basic(pH=8.3),carbonated(98g kgÀ1)with low OM content(12.5g kgÀ1).Before and after treatment,CEC,OM and CaCO3contents and pH,were measured respectively according to NF X31-130,ISO10694,ISO10693and ISO10390,as the total metal(loid)concentrations were determined by ICP–OES IRIS Intre-pid II XXDL,after mineralization with aqua regia(HNO3,HCl,ratio 1:3v/v)according to ISO11466.The detection limits of Pb,Cd,Sb, Cu and Zn were0.3,0.2,0.2,1.3and2.2l g LÀ1.The accuracy of measurements was checked using a certified reference material 141R(BCR,Brussels).2.2.Ecotoxicity tests2.2.1.Germination testsFor each soil condition,plants of borage(Borago officinalis), phacelia(Phacelia stala)and white mustard(Sinapis alba L.)were cultivated in pots infive replicates.Germination tests and growth assays were performed to investigate soil phytotoxicity(Ma et al., 2010).Seeds were immersed in a10%sodium hypochloride solu-tion for10min to ensure surface sterility(Lin and Xing,2007) and rinsed with deionised water.Then,200g dry weight of soils were placed in plastic pots:8cm(top)in diameter and7cm in height with some drain holes on the bottom.Germination was determined by visual seedling emergence and recorded after8d in exposed seeds and controls(Vila et al.,2007).After germination recording,only three seedlings of the most uniform plants were kept in each pot to perform growth assays(Gong et al.,2001).Root and shoot lengths were measured after17d of growth.Shoot height was measured from the shoot base to the top of the longest leaf and root length was measured from the root–shoot junction to the top of the longest root(Liu et al.,2005).As described by Barre-na et al.(2009),phytotoxicity was then expressed by germination index,GI=(relative seed germinationÂE)/100and relative root elongation,RRE=(mean root length in contaminated soils/mean root length with control)Â100.With RRE=(seeds germinated in contaminated soils/seeds germinated in control)Â100.2.2.2.Daphnia magna tests on leachatesNormalized CEN12457-2leaching test was applied to all soil samples.Ecotoxicity of leachates was then assessed with the water flea D.magna(less than24h old)according to ISO6341.Four rep-licates were tested for each soil solution andfive neonates were used in each replicate,with10mL of test anisms were fed2h before the experiment.The multiwall plate placed in the incubator at20°C in darkness.The mobility of D.magna was re-corded after24and48h,and inhibition rate was calculated.Micro-organisms’validity was verified by reference toxin(K2Cr2O7) according to the norm specifications.2.3.Soil respiration measurementSoil respiration(CO2efflux)was measured in situ before and after treatment with a LICOR6400portable photosynthesis system (infrared gaz analyzer,IRGA)fitted with a LICOR6000-9soil respi-ration chamber(LICOR,Lincoln NE).To minimize soil surface dis-turbances,a10cm diameter soil PVC collar(about81cm2area) was installed(1–2cm deep)1d before the measurements,in each pot in a cleared area(Han et al.,2007).CO2flux is computed based on a running average of change in CO2concentration with time as CO2refills the chamber to a described concentration above ambi-ent concentration(Yim et al.,2002).The process is repeated through three cycles and the intermediateflux data arefit with a regression,which is then used to calculate soil respiration (l mol CO2mÀ2sÀ1)at ambient CO2(Ramsey et al.,2005).The aver-age measurement taken at each pot was used to report soil respiration.2.4.Plant experimentsFor each experimental condition,5kg of soil were placed in pots in a greenhouse.10seeds of each species were sown per pot after 10min immersion in H2O2(10%)to ensure surface sterility(Lin and Xing,2007).After10d of germination,only3seedlings of the most uniform plants were kept in each pot to perform crops assays for 10wk(Gong et al.,2001).Roots and shoots were then separated. Samples were washed with deionised water to remove potentially surface contamination(Evangelou et al.,2007)and oven-dried48h at40°C.The dry weight was determined and the plant parts were grinded to homogenize particle size.Then,they were mineralised 4h in a1:1mixture of HNO3and H2O2at80°C(Schreck et al., 2011).Metal(loid)concentrations in plant samples werefinally measured by ICP–OES(IRIS Intrepid II XXDL)The accuracy of acidic digestion and analytical procedures was checked using Virginia to-bacco leaves(CTA-VTL-2,ICHTJ)as reference.All analyses were realised in triplicate.2.5.Evaluation of metal(loid)phytoavailability with CaCl2extractionExperiments were performed according to Schreck et al.(2011).2.6.Lead bioaccessibilityThe in vitro test consists of two parallel three step extraction procedure and simulates the chemical processes occurring in the mouth,stomach and intestine compartments using synthetic digestive solutions included both gastric and the gastro-intestinal extractions according to physiological transit times(Denys et al., 2007).According to Caboche(2009),only the gastric phase was carried out.Lead bioaccessibility was expressed as the ratio be-tween extracted and total concentrations.2.7.Statistical analysisAll tests were performed infive replicates and the results were presented as mean standard deviation.The statistical significance of values was checked using an analysis of variance ANOVA with the Least Significant Difference Fisher post-hoc test using the Statistica9.0package software(StatSoft,Tulsa,OK,USA).Each effect was compared to its corresponding control(with anY.Foucault et al./Chemosphere93(2013)1430–14351431uncontaminated soil).Statistical difference was accepted when the probability of the result assuming the null hypothesis(p)was less than0.05.3.Results3.1.Ecotoxicity of soil samplesThe three plants grow on the polluted soils without observed phytotoxicity symptoms,butthe GI and root length of borage de-creased when soil Pb concentration increased.However,root length was different according to the type of soil:roots were be-tween2and3cm longer for soil2than in soil1.Concerning the oth-ers species,root length decreased from12to10cm in soil1and from12.7to8.7cm in soil2for mustard;and for phacelia,from 11to7cm and from13.5to8.6cm respectively in soil1and soil2. RRE also decreased with lead concentrations and ratios varied be-tween55%and100%for the two species.Daphnia test for borage showed a higher ecotoxicity after48h than after24h of contact with the polluted soils.Mobility inhibition was comprised between 10%and25%for soil1and between15%and33%for soil2before the experiment.After the culture-period,a decrease was registered for both soils andfinal ecotoxicity varied from7.5%to10%for soil1and from5%to15%for soil2.The same trend was observed for mustard and no influence of phacelia culture was observed on daphnia mobility.3.2.Metal(loid)concentrations and soil parameters before and after culturesFig.1a and b concerning borage experiments respectively for soil1and soil2,shows total soil metal(loid)concentrations reduc-tion during plant-soil contact function of the nature and initial me-tal(loid)concentration.In the soil2,the decrease was similar for the conditions C2and C3with concentrations of lead and antimony re-duced by55–60%respectively,from33%to49%for Cd and more modestly for Zn(from23%to27%)and Cu(from9%to19%).De-crease of metal(loid)concentrations was less pronounced in soil1, except for the condition C3.The variation of Sb was17%,21%for Cd and was almost zero for lead in soil1-C2.These values increased up to92%for Sb,53%for Cd and42%for Pb in soil1-C3.Except for antimony,recorded variations for soil1were lower than those in the soil2.Concerning mustard,variations of metal(loid)concentra-tions were close to those of borage,except for lead where the reg-istered decrease was on average twice lower(maximum was51% in soil1and44%in soil2).Finally,no changes were observed with phacelia.Borage and mustard induced changes of CEC,pH,and CaCO3in soil1.At the beginning of the experiment,differences in soil parameters were explained by the dilution step.CaCO3content increased from16to71,from12.5to31.1and from46to 148.4g gÀ1respectively for C1,C2and C3.CEC varied from7.8to 10.4cmol kgÀ1and pH increased from5.7to8.1.Under mustard crop,same trends were registered and values were also within the same range.3.3.Metal(loid)concentrations in roots and shootsTable1presented metal(loid)concentrations in borage shoots and roots.Cu and Zn were mostly present in shoots,between26 and188mg kgÀ1and between70and196mg kgÀ1respectively, and were not detected in roots except for soil2-C3with concentra-tions close to2mg kgÀ1.The same trend was recorded for Cd with concentrations up to16mg kgÀ1.Conversely,lead and antimony were up to20times more concentrated in the roots.Cu and Zn were principally found in mustard shoots(up to69mg Cu kgÀ1and260mg Zn kgÀ1).A same trend was found for cadmium,with maximum concentrations in shoots of11.1mg kgÀ1in soil1.Con-versely to borage,lead concentrations were higher in mustard shoots:up to1131mg kgÀ1in soil2.Only Sb followed the same trend for borage and mustard whose concentrations in roots reached229and218mg kgÀ1for soil1and soil2respectively. Amounts of metal(loid)s recorded for phacelia both in shoots and roots were very low accordingly to low variation of metal(loid)s in soils.3.4.Soil respirationDepending of soil characteristics and initial metal(loid)concen-trations,soil respiration only increased with borage(see Fig.2)and mustard.Concerning borage,CO2flux initially ranged from0.84to 1.57l mol CO2mÀ2sÀ1between soil1-C1and C3,but only from0.64 to0.85l mol CO2mÀ2sÀ1between soil2-C1and C3.Similarly,after 10wk of culture,the amplitude was higher for soil1.Moreover,soil respiration increased with metal(loid)concentrations.For soil1 (l mol CO2mÀ2sÀ1):1.7(C3),0.5(C2)and0.26(C1),and for soil2 (l mol CO2mÀ2sÀ1):1.6(C3),1.5(C2)and1(C1).3.5.Metal(loid)bioavailabilityExperiments focused on lead:phytoavailability varied from0to 8or10mg kgÀ1respectively for soil2and soil1.Concerning soil1, even if the condition C2initially presented the highest extracted fraction(66mg kgÀ1),after treatment,lead concentration was 0.3,6.7and10.4mg kgÀ1,respectively for C1,C2and C3.In soil2, Pb-contents were initially below the limit detection for C1and C2,and reached8.6mg kgÀ1for C3.Then,concentrations for C1 and C2are around4.8mg kgÀ1,while it was1.8mg kgÀ1in C3.Pb quantities extracted under mustard were in the same range,but not detected for phacelia.-100-80-60-40-20Pb CuZn CdSb-80-60-40-20C3C2(b) Soil2Variations of metal(loid)s concentrations(in%)in Soil1and Soil2during the culture-period.1432Y.Foucault et al./Chemosphere93(2013)1430–1435Amounts potentially accessible for human were higher than those available for plants.In the soil 1,lead bioaccessibility was ini-tially 39%,63%and 35%respectively for C 1,C 2and C 3.These ratios increased after treatment with borage to reach 46%,92%and 75%respectively for C 1,C 2and C 3.A linear trend was observed for the soil 2.Lead bioaccessibility increased with soil concentration:from 8%to 15%(C 1),27%to 50%(C 2)and 54%to 98%(C 3).However,when results are expressed as quantities,taking into account the modifi-cation of lead concentrations in soils,two behaviours were distin-guished in function of soil nature.For soil 1,the fraction of extracted lead grown with the treatment:from 251to 359mg kg À1(C 2)and from 291to 362mg kg À1(C 3);conversely,for soil 2bioac-cessible fraction decreased from 110to 89mg kg À1(C 2)and from 444to 323mg kg À1(C 3).4.Discussion4.1.Improvement of soil quality by ecological engineeringBorage,mustard and phacelia were able to grow on various pol-luted soils.A trend for germination reduction was however ob-served as roots are directly in contact with pollutants (Schreck et al.,2011).Root length was influenced by lead concentration (R 2=0.56and 0.59for soil 1and soil 2respectively),as RRE was too (R 2-soil 1=0.74;R 2-soil 2=0.62).Soil characteristics also af-fected root length as showed by correlations:(i)in the soil 1,R 2=0.73for pH and OM content and 0.63for CEC;in soil 2,R 2=0.83,0.66and 0.69with pH,OM and CaCO 3content respec-tively.Root toxicity was lower in soil 2because,as noted by Birke-feld et al.(2007),lead oxide particles incubated in calcareous soil can be covered by a crust of lead carbonate.Soil respiration signif-icantly increased after phytoremediation,certainly in relation with both changes of abiotic (Han et al.,2007)and biotic factors as microbial activity.4.2.Evolution of environmental and sanitary risksIn soil 2,the concentration of phytoavailable lead is related to the soil concentration (R 2=0.95and 0.94at 0and 10wk respectively).Moreover,as for soil respiration and root toxicity,CaCO 3amount is influent:R 2-soil 1=0.89and R 2-soil 2=0.95at the end of experi-ment.Soil properties can strongly modify metal bioavailability (Kidd et al.,2009).Soil 1showed an CaCO 3content increase and a more basic pH which can lead to a reduction of metal(loid)avail-ability.Lead bioaccessibility (in%)increased during the culture-per-iod and was statistically correlated with total lead-concentrations(R 2-soil 1=0.61and 0.95at 0and 10wk respectively;R 2-soil 2=0.75initially and 0.98after treatment),and accordingly to Caboche (2009),with the soil characteristics:pH and CEC for soil 1;carbonate and OM content for soil 2.However,a lower lead quantity was bio-accessible after 10wk in soil 2conversely to soil 1.Moreover,soil ecotoxicity measured by sensitive daphnia test was reduced by the treatment,certainly due to rhizosphere activity.4.3.Suitability of green manure crops for ecological restoration of polluted soilsMetal(loid)compartmentalisation in the plants depend both on pollutant and plant type:copper and zinc were mainly concen-trated in shoots;lead and antimony were mainly concentrated in roots of borage but in shoots of mustards.According to,bio-con-centration and translocation factors are respectively defined as BCF (Bio-Concentration Factor)=(Q 2+Q 3)/Q 1and TF (Transloca-tion Factor)=Q 3/Q 2;where Q 1,Q 2,Q 3are average metal(loid)quan-tities (in mg kg À1)respectively in soil,roots and shoots.BCF >1indicates that the plants accumulate the pollutants and BCF <1indicates excluder plants (Arshad et al.,2008).Accordingly to Evangelou et al.(2007),using PbNO 3spiked soils,calculated BCF for borage was below 1for Pb and Sb.BCF was far above 1for Cu and Zn (and Cd in a lesser extent)(Table 2),which are essential ele-ments for plants (Zheng et al.,2011).TF calculated for borage were below 1for Pb,Sb and Cd (except for soil 1-C 2)and above 1for Cu and Zn.Borage stabilises lead and antimony into its roots (McGrath and Zhao,2003).Zou et al.(2011)showed that metal(loid)s are unevenly distributed in roots,where different root tissues act asTable 1Metal(loid)s concentrations in shoots and roots (mg kg À1DW):(a)borage;(b)mustard.Values are given as mean of five replicates with three seedlings each.⁄DL:Detection Limit (=0.1mg kg À1).Pb Cu Zn Cd Sb Sh.Ro.Sh.Ro.Sh.Ro.Sh.Ro.Sh.Ro.1aSoil 1-C 1 1.217.1188<DL ⁄196<DL <DL 0.5<DL <DL Soil 1-C 2239210.371.8<DL 152.9<DL 16.2<DL 12.6 1.8Soil 1-C 3160.5589.949.0<DL 88.5<DL 2.00.310.491.5Soil 2-C 10.99.999.5<DL 102.1<DL <DL <DL <DL <DL Soil 2-C 2111.6463.756.2<DL 84.2<DL 1.4<DL 7.940.3Soil 2-C 390.0936.826.3 2.069.9 1.9 2.80.6 6.11391bSoil 1-C 1 1.3<DL 69.0<DL 122.9<DL 0.9<DL 7.3<DL Soil 1-C 2253.0 2.631.9<DL 165.4<DL 5.5<DL 18.9 4.4Soil 1-C 3250.4 5.912.7 2.2260.39.611.1<DL 12.1229.3Soil 2-C 115.3<DL 54.4<DL 78.7<DL <DL <DL 17.1<DL Soil 2-C 289.620.418.4<DL 61.4<DL 1.9<DL 5.812.2Soil 2-C 31131363.658.83.591.14.53.31.179.3218.50,00,51,01,52,02,53,03,54,0C1C2C30 w.10 w.(b) Soil 2Comparison of mean soil respiration rates between the beginning and experimentation for soil 1(a)and soil 2(b)(in l molCO 2m -2s -1).Y.Foucault et al./Chemosphere 93(2013)1430–14351433barriers to apoplastic and symplastic transport,thereby restricting transport to the shoots.Borage extracts and translocates Cu and Zn and could therefore be used for remediation of polluted vineyards (Banas et al.,2010).Calculated TF confirmed that mustard was rel-evant for phytoextraction(Bareen and Tahira,2010).Finally,for all the performed experiments,the soil-plant metal(loid)transfer was influenced by the soil type,as metal(loid)availability may change according to the interactions with the soil matrix(Niemeyer et al., 2012;Shahid et al.,2012).5.Conclusions and perspectivesUnlike to phacelia,borage and mustard can improve soil res-piration,reduce total and bioaccessible metal(loid)quantities and their ecotoxicity.For lead and antimony,contrasted mecha-nisms were developed:phytoextraction with storage in shoots and phytostabilization with storage in roots respectively for mustard and borage.Thanks to these treatments,metal(loid)s entering in the food chain via water,wind erosion and re-flying can be reduced and a global analysis of the remediation tech-niques is performed.Further,as borage plants are not perennial, they should be harvested to avoid the release of metal(loid)s in soil,and treated in a waste treatment unit.Moreover,a better 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1.0200.8 2.90.90.41434Y.Foucault et al./Chemosphere93(2013)1430–1435。
TN-102 TECHNICAL NOTE_______________________________________________________________1339 Moffett Park Drive, Sunnyvale CA 94089 U.S.A.Tel: 1.408.752.0723 | Fax: 1.408.752.0724 | E-mail: ***********************Copyright RAE Systems Inc. | rev.2 wh 10-01F ACTS ABOUT PID M EASUREMENTSMiniRAE, ToxiRAE, ppbRAE, MultiRAE and ModuRAE VOC monitors are designed to provide continuous total organic vapor exposure monitoring in hazardous environments using a Photoionization Detector (PID). A few important factors affect the operation and measurement accuracy of PID instruments. These factors are explained below:1) Accuracy: The specified measurement accuracy (e.g.,±2 ppm or 10% of reading, whichever is greater) is defined for a single gas (e.g., isobutylene) obtained after the unit is calibrated with zero gas and isobutylene gas. The isobutylene calibration gas is balanced with air and is accurate to within 100±2% or 2 ppm, or 10±10% or 1 ppm. The measurement accuracy specification applies to bottled isobutylene gas only. For other organic gases and vapors, the specifiedmeasurement accuracy can be achieved if the specific gas of interest is used as calibration gas and the relative sensitivity of this particular gas is similar to that of isobutylene.2) Correction Factor: The correction factors (CFs) inTechnical Note 106 ( ) provide a convenient way to obtain approximate readings of other organic gases, when only isobutylene calibration gas isavailable. Refer to the instrument manual for the procedures to do so. Please note that using the CF will not achieve thespecified accuracy. This is because are accurate to about 20% and may change slightly with age and cleanliness of the lamp and with concentration. Such factors become more pronounced when the CF is much greater than 1. The instrument sensitivity can be estimated by multiplying the correction factor by 0.1. Therefore, a compound with CF of 10 will have a resolution of about 1 ppm instead of 0.1 ppm.3) Limitation of Gas Detection: A PID cannot detect allorganic vapors present. In general, if the Ionization Energy (IE, formerly ionization potential or IP) of a given compound is higher than that of the UV lamp energy (9.8, 10.6 or 11.7eV), it cannot be measured by a PID.4) Mixtures of Chemical Vapors: The PID detectorcannot distinguish one ionizable gas from another. Therefore, if there is more than one compound present, the PID will not provide an accurate concentration of a particular gas. It will give an approximate reading of total gas concentration.5) Humidity and Interference from other gases: In realapplications, the humidity level and the presence of other non-ionizable gas (such as methane) can reduce the sensitivity of the PID. It has been observed that the water vapor can reduce the instrument response by 50% when the relative humidity level is increased from 10% to 90%. This is because high concentrations of water molecules or other non-ionizable gas molecules block out some of the UV light. This well-knowneffect is called "quenching" and occurs with most existing photoionization detectors. Methane at 10 vol% reduces the response by a factor of about five, and most organiccompounds very high concentrations have similar effects. Many inorganic gases including argon, oxygen, nitrogen, hydrogen, and carbon dioxide have little or no effect on PID response and the PID can be used to measure contaminants in nearly pure streams of these gases.On the RAE PIDs, the detector chambers are specially designed to minimize the "quenching" effect. In addition, membrane filters are used to remove any moisture droplets from the incoming gas stream. As a result, RAE PIDs show improved response at high humidity and high concentration of non-ionizable gases.The following figure shows the humidity response ofMiniRAE 2000. The horizontal axis is the relative humidity and the vertical axis is the relative response of the PID instrument . Curves for ppbRAE and ToxiRAE are similar, although the effect is somewhat less for the ToxiRAE PID.6) Very High Concentration of Gases : When the VOCconcentration exceeds a few thousand ppm, the PID response "flattens out" because some of the gas molecules are blocked from the UV light source and can’t be ionized. This is a “self-quenching” effects similar to the quenching effects of water vapor or methane at high concentrations. This downward curvature is compensated by the instrument firmware to greatly improve the linearity, but the measurement errorincreases above a few thousand ppm. Therefore, to obtain the specified accuracy in high concentration range, the monitor should be calibrated using a similarly high concentration gas or use a dilution device at the input of the gas stream.。
164㊀㊀Industrial Construction Vol.51,No.5,2021工业建筑㊀2021年第51卷第5期一种确定先期固结压力的数值计算方法∗蔡清池㊀谢汉康(宁德师范学院土木工程系,福建宁德㊀352100)㊀㊀摘㊀要:Casagrande 法为土体先期固结压力计算的作图法,其关键步骤之一在于压缩曲线后半段的拟合㊂鉴于传统作图法的复杂㊁作图比例不易把握等问题,基于MATLAB 编写了先期固结压力的数值计算程序㊂在分析该模型在压缩曲线处理上不足的基础上,考虑土样扰动因素引入一个新的试验点,提出改进的先期固结压力数值计算方法㊂研究表明:土样室内固结试验应做中高压固结试验,压力一般应到1600~3200kPa;对中高压固结的试验数据,新的计算方法能获得更加接近实际的先期固结压力,获得更好的结果㊂㊀㊀关键词:先期固结压力;Harris 模型;数值计算㊀㊀DOI :10.13204/j.gyjzG20061201A NUMERICAL METHOD FOR DETERMININGPRE-CONSOLIDATION PRESSURECAI Qingchi㊀XIE Hankang(Department of Civil Engineering,Ningde Normal University,Ningde 352100,China)Abstract :The Casagrande method is a famous graphic method to obtain pre-consolidation pressure from consolidationtest data,in which the one of the procedures is fitting the second half curve of compression curves based on the point of the greatest curvature.In the light of complexity and hard to control proper scales for the traditional graphic method,a program was developed by the software of MATLAB to calculate pre-consolidation pressure.Analysis onthe deficiency of Harris Model on processing compression curves and taking account of disturbed factors for soil specimens,a new parameter from test was adopted,and the modified calculation method about the pre-consolidationpressure was proposed based on Harris model.It was shown that the calculated results by the modified methodaccorded with the actal pre-consolidation pressure for tests at medium and high pressure.Furthermore,soil consolidation experiments should contain higher pressure which could be 1600to 3200kPa.Keywords :pre-consolidation pressure;Harris model;numerical calculation∗宁德市科技计划项目(0000580211)㊂第一作者:蔡清池,男,1990年出生,博士研究生,讲师㊂电子信箱:295807506@ 收稿日期:2020-06-12㊀㊀先期固结压力是指土层在地质形成历史上曾经受过的最大竖向有效压力,其数值大小取决于土层的受力历史,一般很难查明[1-3]㊂目前常见的确定先期固结压力大小的方法有Casagrande 法[4],三笠法㊁f 法㊁密度法㊁强度法和S 法等[5-6]㊂其中,Casagrande 法在国际上有着较为广泛的应用,一般认为其作图结果具有较高的准确度㊂我国GB /T 50123 2019‘土工试验方法标准“也推荐采用Casagrande 法进行土体先期固结压力的确定㊂Casagrande 法虽然应用广泛,但是该方法是一种作图法,具有一定的局限性,主要是存在最大曲率点不容易确定,人为误差大等缺陷[7-9]㊂为弥补Casagrande 作图法的不足,许多基于Casagrande 法的数学模型被提出,且大部分学者均推荐采用数值作图法㊂数值作图法的关键在于确定压缩曲线的最小曲率半径㊂姜安龙等选用指数模型来描述压缩曲线的曲线段,运用数学方法确定曲线段的最小曲率半径[7];王志亮等认为多项式模型存在较大的波动性,不适合用于拟合压缩曲线的曲线段,并提出用Harris 模型确定土体的先期固结压力[8];朗林智等认为基于Casagrande 法的数值作图法,数学模型应至少满足初始边界条件㊁中间边界条件和末边界条件3个条件的要求,并对现有的一些数学模型进行分析,认为Gauss 模型确定先期固结压力具有较高的准确度[9]㊂但是,上述方法均是基于对室内试验数据进行曲线段拟合而提出,并未考虑由于土样扰动等因素对求解先期固结压力的真实值可能造成的影响㊂室一种确定先期固结压力的数值计算方法 蔡清池,等165㊀内试验所采用的土样在取样时不可避免地会受到一定程度的扰动,且取出地面后容易因应力卸载产生定量的回弹,因此室内压缩曲线难以完全代表地基土中原位土体的应力状态㊂曹宇清等也指出:压缩模量随着压缩指数的增大而减小,随着应力水平的增大而增大;在应力较高时,土体产生的新的塑性应变会超过储存的塑性应变,压缩模量主要受压缩指数的影响,即可认为在压力较大时扰动对土样的压缩性影响较小[10]㊂大量试验结果[11]表明:土体孔隙比为0.42e 0时(e 0为初始孔隙比),可以认为扰动对土样压缩性的影响可以忽略,各压缩曲线相交于一点㊂因此,在土体压缩曲线拟合中,考虑土体孔隙比为0.42e 0是比较合理的㊂此外,在孔隙比e 与对数压力lg p 压缩曲线拟合中,选择合适的数学模型对于结果的正确性也有很大的影响㊂对Casagrande 法而言,关键在于拟合曲线最大曲率点和后半段近似直线的确定㊂许多数学模型虽然都具有较高的拟合精度,但在最大曲率点确定上却有诸多差异㊂因此在拟合方法的选择上也需要进一步考量㊂本研究基于对Casagrande 数值作图法的分析,针对Harris 模型在压缩曲线拟合中未考虑土体孔隙比为0.42e 0时的状态,在原试验数据上引入新的数据点,拟建立一种改进的Harris 方法,用于求解土体的先期固结压力㊂1㊀e -lg p 压缩曲线拟合分析根据e -lg p 压缩曲线的形态,常用的数学模型主要有多项式拟合和Harris 等模型㊂然而在插值拟合中,随着点数的增加,有时会在曲线两端产生剧烈的振荡,即多项式摆动现象㊂特别是多项式拟合中,这种现象更容易出现㊂为进一步验证数学模型的正确性,以常用的多项式拟合和Harris 模型为研究对象,根据文献[7,12]中的试验数据(表1),分析这两种数学模型方法在土体先期固结压力求解方面的区别㊂表1㊀土样压缩试验数据Table 1㊀Data of compressive tests of soil samples㊀㊀对表1的数据,分别用多项式模型和Harris 模型进行拟合,结果见图1和图2㊂∗试验数据;三次多项式;---四次多项式㊂图1㊀多项式模型拟合结果Fig.1㊀Fitting results by the polynomial model∗试验数据;Harris 模型㊂图2㊀Harris 模型拟合结果Fig.2㊀Fitting results by Harris model其中三次和四次多项式拟合方程分别为:e =-0.0006lg 3p -0.0423lg 2p +0.0370lg p +0.8701(1)e =0.0048lg 4p -0.0389lg 3p +0.0533lg 2p -0.0377lg p +0.8721(2)刘林等认为土体的一维压缩曲线具有三个特征规律[13]:1)对于同一种土,初始状态决定了超固结度㊂2)在常规压力范围内,每条压缩曲线有且仅存在一个曲率最小的点㊂3)当压力超过前期固结压力时,压缩线近似为一条直线,即压缩线斜率近似为一个常数㊂从图1可以看出:多项式拟合结果虽然有较高的拟合精度,但是在靠近压缩曲线前端e 0处均产生了多项式摆动现象,不满足土体的一维压缩曲线特征规律㊂根据图2,Harris 模型并未产生摆动现象,且在曲线段具较好的拟合结果,比较符合土体的一维压缩曲线特征规律㊂Harris 模型拟合结果为式(3),其决定系数R 2=99.9%㊂e =11.159+0.0091lg 3.77p(3)㊀㊀为进一步验证Harris 模型的合理性,根据文献[10],先对表1中土样压缩试验数据引入点A (lg p x ),0.42e 0),再验证Harris 数学模型曲线是否166㊀工业建筑㊀2021年第51卷第5期通过点A ,确认其合理性㊂根据图3,可知点A (lg p x ,0.42e 0)位于正常固结土原位压缩曲线上,曲线斜率为压缩指数C c ,同时位于室内压缩曲线上㊂通过对室内压缩曲线试验数据后三个数据运用最小二乘法拟合获得其近似直线的直线方程,并以此作为原位压缩曲线方程,从而求得点A 的数值为(3.831,0.366)㊂室内压缩曲线后半段拟合直线方程为式(4),R 2=99.97%:e =-0.2574lg p +1.3522(4)㊀㊀保证e 取值不变,根据式(3)反求A 点lg p 为3.9216,相比于原最小二乘法获得的A 点lg p 数值增加了约2.4%,产生了一定的误差,也说明了拟合的数值结果仅在试验数据曲线段表现较好,而未能良好地拟合出考虑了e =0.42e 0这点的曲线后半段的结果㊂∗试验数据;Harris 模型;ʻ曲率最大点㊂图3㊀方法1拟合结果Fig.3㊀Fitting results by method 12㊀Harris 模型改进方法基于Casagrande 法建立起求解土体先期固结压力的数学模型法,一般认为只需确定拟合曲线的方程和后半段的近似直线方程,寻找曲线段的最大曲率点,并根据作图法的规则即可求解先期固结压力㊂因此,确定曲线的拟合方程以及后半段的近似直线方程是求解先期固结压力的关键点㊂如前所述,原有的Harris 模型并未考虑引入e =0.42e 0点,因此拟合曲线在后半段并不能很好地适应㊂2.1㊀先期固结压力计算方法基于以上,用于土体的先期固结压力求解的步骤确定为:1)以试验数据最后三组数据进行直线段拟合,获取直线段方程㊂考虑室内压缩曲线后半段为近似直线段,直线斜率定义为压缩指数C c ,令x =lg p ,k 1=-C c ,故设后半段直线方程为:e 1=k 1x +b 1(5)㊀㊀2)根据直线方程,求解引入的e =0.42e 0这一点坐标,并补充至试验数据中㊂3)对补充后的试验数据进行Harris 模型拟合,得其曲线方程,求曲线段曲率最大值K ㊂曲率求解方程为:K =eᵡ(1+eᶄ)3/2(6)㊀㊀4)根据Casagrande 法的作图步骤,运用数学模型求解先期固结压力的数值解,避免采用作图法带来的人为误差㊂2.2㊀试验数据分组关于压缩曲线后半段的直线拟合,考虑在压力较大部分压缩曲线近似直线㊁对于 压力较大 一词的定义并未十分明确㊂现有的固结试验中一般分为低压固结试验(最大加载压力不超过800kPa)和中高压固结试验㊂因此,后半段直线拟合时的数据采用分为两组,分别为低压固结试验数据和高压固结试验数据,以探究Harris 模型的改进方法㊂所用的先期固结压力求解均为按照上述2.1提出的求解方法获得㊂针对含低压固结试验数据分析,采用表1中p 为0~800kPa 区间的数据,并以后三组数据进行近似直线段的拟合(此组数据分析结果记为 方法1 )㊂针对含中高压固结试验数据分析,采用表1中p 在0~3200kPa 区间的数据,并以后三组数据进行近似直线段的拟合(此组数据分析结果记为 方法2 )㊂3㊀计算分析土体先期固结压力的求解过程运用MATLAB 编程完成,为了验证自编程序的正确性,按照文献[8]的方法对先期固结压力进行求解㊂表1数据与文献[8]中表2数据近似,但是精确度略有差异㊂通过自编程序在文献[8]方法下,按照文献[8]的表1数据计算土体先期固结压力为203.33kPa,与文献[8]计算数值201.54kPa 十分接近,故验证了自编的MATLAB 程序是正确的㊂针对表1,采用 方法1 ,用最小二乘法拟合后半段三个数据,拟合直线方程为式(7),R 2=99.50%㊂e =-0.2067lg p +1.2067(7)㊀㊀代入e =0.42e 0,确定的添加点A 坐标为(4.066,0.366)㊂增加点A 后Harris 模型拟合结果见图3,最大曲率点为(1.800,0.801)㊂Harris拟合方程为式(8),R 2=99.88%㊂图3中星形标记处为先期固结压力点,求得先期固结压力为106.21kPa㊂e=11.153+0.0126lg3.44p(8)㊀㊀ 方法2 中,用最小二乘法拟合后半段三个数据,结果同式(4)㊂代入e=0.42e0,确定的添加点A 坐标为(3.831,0.366)㊂增加点A后Harris模型拟合结果见图4,最大曲率点为(2.000,0.7813)㊂Harris拟合方程为式(9),R2=99.88%㊂图4中星形标记处为先期固结压力点,求得先期固结压力为199.53kPa㊂∗试验数据;Harris模型;ʻ曲率最大点㊂图4㊀方法2拟合结果Fig.4㊀Fitting results by method2表2㊀土体先期固结压力结果比较Table2㊀Comparisons of pre-consolidation pressure㊀kPa 方法1 方法2 文献[8]文献[7] 106.21199.53203.33190e=11.162+0.0078lg3.92p(9)㊀㊀许多学者采用文献[7]中的润扬大桥北锚碇土中A-5(下)土样来验证提出的先期固结压力方法的合理性㊂文献[8]是对文献[7]中提出的A-5 (下)试样采用基于Harris拟合的数学模型法计算该试样的先期固结压力㊂文献[7-8]数值差距不大,具有较高的参考价值㊂本文中将 方法1 方法2 所得的结果与文献[7-8]中的结果进行对比分析以验证最合理的方法㊂通过表2结果对比分析可以发现: 方法2 计算先期固结压力结果为199.53kPa,介于文献[7]与文献[8]的结果之间,且数值接近,而 方法1 中计算结果为106.21kPa,远偏离文献[7-8]确定的数值㊂可见, 方法1 与 方法2 的差异主要在于压缩曲线后半段试验数据是否含中高压试验数据㊂一般认为,压力较大时,土样变得密实,扰动对于土样的压缩性质影响已经很小, 方法2 中采用的直线段拟合数据为含中高压试验数据,拟合结果更加准确,不易受到土样扰动的影响㊂对 方法2 结果与文献[7-8]方法对比, 方法2 结果明显更加接近文献[7]的结果㊂可见基于中高压试验数据拟合压缩曲线后半段,在增加e= 0.42e0后进行Harris曲线拟合求解土体先期固结压力,可以获得更加准确的结果㊂常林越等的研究[6]表明:Harris模型对于试验数据的适应性不好,在增加了e=0.42e0这一点后对于曲线曲率影响较大㊂但是对其试验数据观察可以发现:试验中最大压力为800kPa,并未考虑中高压试验数据下Harris模型的适用性㊂ 方法1 采用的试验数据范围与文献[6]中相同,同样也出现了Harris模型计算结果与实际偏离较大的情况㊂因此认为如果未采用中高压试验数据,压缩曲线后半段直线段的拟合结果可能会有较大偏差㊂综上,认为Harris模型对于增加了e=0.42e0是可以较好适应的,也可以使得先期固结压力计算结果更加符合实际值㊂但是运用改进的Harris方法时,应采用 方法2 范围的试验数据,即应以含中高压固结试验的数据进行压缩曲线后半段的拟合㊂4㊀结束语1)为了避免取土扰动等因素对土体先期固结压力计算结果的影响,e-lg p压缩曲线中可以引入e=0.42e0的试验点,该试验点最好由含中高压固结试验数据的后三组数据以最小二乘法拟合推求㊂2)正确引入e=0.42e0的试验点后,运用Harris 模型计算的先期固结压力更加接近实际值,结果更好㊂3)土样室内压缩试验中,最好做高压固结试验,压力应达到3200kPa,可以使压缩曲线后半段直线拟合更加符合真实情况㊂参考文献[1]㊀魏道垛,胡中雄.上海浅层地基土的前期固结压力及有关压缩性参数的试验研究[J].岩土工程学报,1980,2(4): 13-22.[2]㊀王清,陈剑平,蒋惠忠.先期固结压力理论的新认识[J].吉林大学学报(地球科学版),1996(1):59-63.[3]㊀刘春平,李中秋,张书宪.如何确定土的先期固结压力的探讨[J].地质与勘探,2003(1):91-92.[4]㊀钱家欢,殷宗泽.土工原理与计算[M].北京:中国水利水电出版社,1980:179-180.[5]㊀李春林,丁启朔,陈青春.水稻土的先期固结压力测定与分析[J].农业工程学报,2010,26(8):141-144. 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Agricultural and Forest Meteorology 151 (2011) 22–38Contents lists available at ScienceDirectAgricultural and ForestMeteorologyj o u r n a l h o m e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /a g r f o r m etAssessing parameter variability in a photosynthesis model within and between plant functional types using global Fluxnet eddy covariance dataM.Groenendijk a ,∗,A.J.Dolman a ,M.K.van der Molen a ,R.Leuning b ,A.Arneth c ,N.Delpierre d ,J.H.C.Gash a ,e ,A.Lindroth c ,A.D.Richardson f ,H.Verbeeck g ,G.Wohlfahrt haVU University Amsterdam,Hydrology and Geo-Environmental Sciences,The Netherlands bCSIRO Marine and Atmospheric Research,Canberra,Australia cDepartment of Earth and Ecosystem Sciences,Lund University,Sweden dUniversite Paris-Sud,Laboratoire Ecologie Systematique et Evolution,UMR8079,Orsay F-91405,France eCentre for Ecology and Hydrology,Wallingford,UK fHarvard University,Department of Organismic and Evolutionary Biology,HUH,22Divinity Avenue,Cambridge,MA 02138,USA gLaboratory of Plant Ecology,Ghent University,Coupure Links 653,9000Ghent,Belgium hInstitut für Ökologie,Universität Innsbruck,Austriaa r t i c l e i n f o Article history:Received 14July 2010Received in revised form 27August 2010Accepted 31August 2010Keywords:Plant functional types Model parameters Photosynthesis Transpiration Eddy covariance Fluxneta b s t r a c tThe vegetation component in climate models has advanced since the late 1960s from a uniform pre-scription of surface parameters to plant functional types (PFTs).PFTs are used in global land-surface models to provide parameter values for every model grid cell.With a simple photosynthesis model we derive parameters for all site years within the Fluxnet eddy covariance data set.We compare the model parameters within and between PFTs and statistically group the sites.Fluxnet data is used to validate the photosynthesis model parameter variation within a PFT classification.Our major result is that model parameters appear more variable than assumed in PFTs.Simulated fluxes are of higher quality when model parameters of individual sites or site years are used.A simplification with less variation in model parameters results in poorer simulations.This indicates that a PFT classifica-tion introduces uncertainty in the variation of the photosynthesis and transpiration fluxes.Statistically derived groups of sites with comparable model parameters do not share common vegetation types or climates.A simple PFT classification does not reflect the real photosynthesis and transpiration variation.Although site year parameters give the best predictions,the parameters are generally too specific to be used in a global study.The site year parameters can be further used to explore the possibilities of alternative classification schemes.© 2010 Elsevier B.V. All rights reserved.1.IntroductionThe specification of the land surface component of climate mod-els has evolved through four major steps over the past four decades.The first generation in the late 1960s had a uniform prescrip-tion of surface parameters (see reviews by Sellers et al.,1997;Pitman,2003),while the second generation in the 1980s intro-duced the concept of plant functional types (PFTs)to describe the effects of spatially varying vegetation on the surface energy bal-ance (e.g.Sellers et al.,1986;Dickinson et al.,1986).In the third generation of models vegetation is simulated dynamically rather than being prescribed (Friend and Cox,1995;Sellers et al.,1997;Foley et al.,1998;Woodward et al.,1998;Cox et al.,2000),while∗Corresponding author.E-mail address:margriet.groenendijk@falw.vu.nl (M.Groenendijk).the latest models couple vegetation dynamics to the carbon and nitrogen cycles (Thornton et al.,2009;Yang et al.,2009;Zaehle and Friend,2010),and include processes for emissions of reactive trace gases,vegetation–fire interactions and crop–biogeochemistry interactions (Arneth et al.,2010).To run these land-surface models globally,it is necessary to pro-vide parameter values for every model grid cell,and typically this is done by assigning a unique parameter set for each PFT.Early PFT classifications were developed for calculating the surface energy and water balance and were largely based on broad classes of ter these PFTs were applied to deduce the carbon balance of the land surface,even though a number of carbon cycle pro-cesses might not run parallel to the energy and water processes upon which the original classifications were based.More recently PFT classifications have been derived following either a deductive or an inductive approach (Woodward and Cramer,1996).Exam-ples of PFT classifications derived using the deductive approach are0168-1923/$–see front matter © 2010 Elsevier B.V. All rights reserved.doi:10.1016/j.agrformet.2010.08.013M.Groenendijk et al./Agricultural and Forest Meteorology151 (2011) 22–3823presented by Box(1996),Bonan et al.(2002)and Sitch et al.(2003). Here the criteria for PFTs are based on climatic limitations such as temperature,precipitation and length of the growing season.These PFTs are subjectively classified based on a general understanding of processes.With the inductive approach(Woodward and Cramer, 1996)PFTs are derived from sets of observations or experimental results e.g.through statistical clustering with climatic variables and vegetation traits(Chapin et al.,1996;Wang and Price,2007).The assumption that parameters in carbon exchange models can conveniently be allocated to PFTs appears to contradict observed gradual transitions between different ecosystem types.Kleidon et al.(2007)show that the strict separation of vegetation into less than eight PFTs may lead to artificial multiple steady-states in a model of the Earth’s climate–vegetation system depending on the number of PFTs used.Measurements on individual leaves indicate a gradual transition in vegetation characteristics.Leaf traits are inter-related e.g.,maximum photosynthetic capacity,maintenance respiration, nitrogen concentration,leaf life span and specific leaf area(ratio of leaf surface area to leaf mass)(Reich et al.,1997;Bonan et al.,2002; Meinzer,2003;Wright et al.,2004).Harrison et al.(2010)therefore suggest using these continuous traits for the development of a new vegetation classification.On the global scale it is a challenging task to provide suffi-cient data for a complete PFT classification,because a large number of observations are needed for each PFT(Wang and Price,2007). Many studies have used eddy covariance data from Fluxnet(a global network of sites),showing the variation in carbon and water fluxes between vegetation types and along climate gradients(e.g., Baldocchi,2008;Law et al.,2002;Friend et al.,2007;Luyssaert et al.,2007;Stöckli et al.,2008;Stoy et al.,2009;Beer et al.,2009; Yuan et al.,2009;Williams et al.,2009).The eddy covariance data can also be used to derive model parameters(e.g.,Reichstein et al.,2003b;van Dijk and Dolman,2004;Knorr and Kattge,2005; Raupach et al.,2005;Owen et al.,2007;Richardson et al.,2007; Wang et al.,2007;Thum et al.,2008).However,hardly any of these studies have attempted to derive model parameters for key carbon cycle processes such as photosynthesis for the full set of data.This study uses a simple vegetation model to derive the param-eters for all site years and vegetation types within the Fluxnet database.We compare the set of model parameters within and between PFTs and group the sites statistically.We combine the deductive and inductive PFT classifications to give insight into the variation of model parameters.We address the following specific questions:(1)what is the variability of parameter values for a set of conventionally defined PFTs,(2)how well arefluxes sim-ulated using parameter values for each PFT compared to using site-calibrated values at diurnal,seasonal and annual time scales, (3)if using mean or median values provides unsatisfactory results, is it sufficient to refine the classification scheme and,(4)if this is unsatisfactory,does a cluster analysis of parameters provide a satisfactory solution?2.Methodology2.1.Model descriptionThe photosynthesis model of Farquhar et al.(1980)is widely used in vegetation models(e.g.,Sellers et al.,1996;Arora,2002; Sitch et al.,2003;Krinner et al.,2005;Rayner et al.,2005).Individ-ual applications of this model differ in the influence they ascribe to environmental factors,the scaling from leaf to ecosystem and the way how the model is assigned to different PFTs.The simple vege-tation model used in this study is based on the equations derived by Cowan(1977)and Farquhar et al.(1980).The model includes responses of photosynthesis and transpiration to air temperature,Table1Parameters used in the photosynthesis and transpiration model.Values given are the default or initial*values within the model optimization.Parameter Description Valuev cm,25Carboxylation capacity(mol m−2s−1)100*j m,25Electron transport rate,(mol m−2s−1)300*˛Quantum yield(mol mol−1)0.7* Water use efficiency(mol mol−1)8000*O O2concentration(mbar)210T ref Reference temperature for photosynthesis(K)298.15 K c,25Kinetic coefficient for CO2at T ref(bar)460K o,25Kinetic coefficient for O2at T ref(mbar)330E kc Activation energy for CO2(J mol−1)59,356E ko Activation energy for O2(J mol−1)35,948E jm Activation energy for j m(J mol−1)45,000E v cm Activation energy for v cm(J mol−1)58,520 photosynthetically active radiation,vapor pressure deficit and soil water content.The model parameters are assumed to represent the relationship between nutrients and vegetation characteristics,and the adaptation to local climatic conditions.The main parameters in this model are v cm,25,j m,25,˛and (Table1).v cm,25is the rate of carboxylation mediated by the enzyme Rubisco,j m,25is the maximum potential electron transport rate and ˛is the quantum yield. defines the ratio between water loss(tran-spiration)and CO2assimilation(photosynthesis)as a function of stomatal conductance(g s)as proposed in the optimality hypoth-esis which states that plants optimize their stomatal conductance to maximize photosynthesis for a given amount of transpiration (Cowan,1977):=ıE/ıg sıA/ıg s(1)This stomatal conductance model was successfully used to repro-duce observed ecosystem carbon and waterfluxes by Arneth et al.(2002,2006),van der Tol et al.(2007,2008),Mercado et al. (2009)and Schymanski et al.(2009).Schymanski et al.(2007)com-pared the use of this model with the stomatal conductance model of Leuning(1995)and concluded that both models performed equally well in reproducing observed transpiration rates.A complete model description with scaling from the leaf to the ecosystem scale and temperature responses is presented in Appendix A.2.2.ObservationsThe Fluxnet database containsfluxes measured with the eddy covariance technique(Aubinet et al.,2000)at more than200loca-tions worldwide.All data is processed in a harmonized manner within the Fluxnet project(Baldocchi et al.,2001;Baldocchi,2008) as described by Papale et al.(2006),Reichstein et al.(2005),Moffat et al.(2007)and Papale and Valentini(2003).The data used here were retrieved from the database in April2008.1A complete list of the sites used is given in Appendix B;Table2lists the PFTs used. These sites were selected based on data availability.To apply the model for photosynthesis(A)and transpiration(E)fluxes requires the following variables:Net Ecosystem Exchange(NEE),Latent Heat Flux(LE),vapor pressure deficit(VPD),air temperature(T a),global radiation(R g),leaf area index(LAI)and soil water content(Â).Sites with data gaps of more than50%during the growing season or missing input variables were excluded from the analysis.The101 selected sites contain453site years of which349years contain suf-ficient observations(not gap-filled)to simulate thefluxes.For57 site years there were no LAI data available,mainly because there1www.fl,dataset DS2.24M.Groenendijk et al./Agricultural and Forest Meteorology151 (2011) 22–38Table2Number of Fluxnet sites used in this study within plant functional types as classes of vegetation and climate.Arctic Boreal Subtropical Mediterranean Temperate Temperate continental Tropical TotalCropland235 Closed shrubland11 Deciduous broadleaf forest266418 Evergreen broadleaf forest2147 Evergreen needleleaf forest17910440 Grassland131317 Mixed Forest2237 Open shrubland112 Savanna112 Wetland11 Woody Savanna11Total1202736116101 were no remotely sensed observations before2000,and for57(dif-ferent)site years soil water content data was unavailable.LAI is derived from the MODIS database and is used as a proxyfor phenology(ORNL DAAC,2009).This database contains8-daycomposite values of LAI for each site based on7km×7km datasets centered around the sites.From these pixels the average is cal-culated from observations with no clouds and no presence of snowor ice.The8-day composites are linearly interpolated to determinedaily values.The observed carbonflux from eddy covariance represents thenet exchange of carbon between ecosystem and atmosphere.Theobservedflux(F c)plus a term representing storage within the veg-etation is assumed to be equal to NEE.Nighttime NEE is assumed tobe equal to ecosystem respiration(R e).Within the Fluxnet databasethe observed NEE is partitioned into gross primary production(GPP)and R e.R e is determined from a temperature function of nighttimefluxes by using the methodology of Reichstein et al.(2005).In ourstudy this GPP or photosynthesisflux is used.The model requires the knowledge of the transpirationflux toestimate model parameters,whereas the observed water vaporflux(LE)is the sum of transpiration and soil evaporation.However,thelatter contributes little to total evaporation when the soil surface isdry,or when LAI>2.5,because then little energy is available forevaporation.It was thus assumed that total evaporation equalstranspiration when the vegetation is dry,and these periods wereselected by excluding data during days with precipitation and3days ing data for several sites showed that estimatesof model parameters remained constant with removal of data afterrainfall for3or more days.2.3.Model parameter estimationThe model is optimized using the simplex search method(Lagarias et al.,1998).A least squares objective function,or nor-malized root mean square error is minimized to give the optimalmodel parameters.This is a multi-criteria problem with both pho-tosynthesis and transpiration being parameterized,therefore theobjective function consists of two parts.The normalized root meansquare errors(RMSE n)derived from half-hourly observations of thetwofluxes are added,giving equal weight to both processes:RMSE n=((A sim−A obs)2)/NA obs+((E sim−E obs)2)/NE obs(2)where A sim is simulated photosynthesis,A obs daytime GPP,E sim sim-ulated transpiration and E obs observed transpiration.The model parameters were derived for all sites within the Fluxnet database or for different groups of site years classified by vegetation type and climate.2.4.Grouping sites based on model parametersThe deductive classification is based on an understand-ing of processes that determine the functioning of vegetation (Woodward and Cramer,1996).One example is the classical grouping of sites into vegetation classes(Table2).Inductive classification groups are directly derived from sets of observa-tions.This approach can be applied with statistical clustering. Here we use the model parameters to group the sites.This is a combination of the two classifications,because derived param-eters and not the direct observations are used to classify the sites.Two statistical methods are used and compared,hierarchical and k-means clustering.Hierarchical clustering groups data by creating a cluster tree or dendrogram.The tree is a multilevel hier-archy,where clusters at one level are joined to clusters at the next level.k-means clustering partitions n observations into k clus-ters.Parameter values within each cluster are as close to each other as possible,but as far as possible from values in other clus-ters.The centre of each cluster is the point to which the sum of distances from all values in that cluster is minimized.The result is a set of clusters that are as compact and well-separated as possible(Seber,1984).The MATLAB software package was used for both the optimization of the model and the analysis of the results.3.Results3.1.Evaluation offlux simulationsHalf-hourly observations offluxes and environmental variables were used to optimize the model with annual parameters.Five sets of parameters were derived:for all sites together(A),for groups of sites with a similar vegetation type(V),for groups of sites with a similar vegetation type and climate(VC),for individual sites(S) and for individual site years(SY).The quality of the simulations for thesefive parameter sets is presented for the diurnal,seasonal and annual time scales.The average diurnal cycle in July derived from observations of photosynthesis(A)and transpiration(E)is compared withfluxes simulated with vegetation model parameters(V,Fig.1)andfluxes simulated with model parameters of individual site years(SY,Fig.2).Examples are presented for a tropical evergreen broadleaf for-est in Brazil,a boreal evergreen needleleaf forest in Finland and a subtropical grassland in the US.The diurnal variation for these different locations is captured best with simulations that use site year parameters.When using vegetation parameters thefluxes are over-or underestimated(BR-Ban and FI-Hyy)or photosynthesis and transpiration start too late in the morning(US-Goo).M.Groenendijk et al./Agricultural and Forest Meteorology 151 (2011) 22–3825102030102030P h o t o s y n t h e s i s (μm o l m −2 s −1)T r a n s p i r a t i o n (W m −2)102030Hour Hourparison of the observed and simulated average diurnal cycle of photosynthesis (left)and transpiration (right)in July for three different sites.The fluxes are simulated with parameters derived for all sites with a similar vegetation type (V,Table 3).The average observed fluxes with standard deviations are presented by the grey area and the average simulated fluxes with standard deviations by the black lines.P h o t o s y n t h e s i s (μm o l m −2 s −1)T r a n s p i r a t i o n (W m −2)Hour Hourparison of the observed and simulated average diurnal cycle of photosynthesis (left)and transpiration (right)in July for three different sites.The fluxes are simulated with parameters derived for each site year (SY).The average observed fluxes with standard deviations are presented by the grey area and the average simulated fluxes with standard deviations by the black lines.26M.Groenendijk et al./Agricultural and Forest Meteorology 151 (2011) 22–38P h o t o s y n t h e s i s (μm o l m −2 s −1)T r a n s p i r a t i o n (W m −2)100200300Doy 100200300DoyFig.3.Seasonal variation of the average weekly observed and simulated photosynthesis (left)and transpiration (right)for the evergreen needle leaf forest sites in fourdifferent climates.The fluxes are simulated with parameters derived for all sites with a similar vegetation type and climate (VC,Table 4).The average observed fluxes with standard deviations are presented by the grey area and the average simulated fluxes with standard deviations by the black lines.Observed and simulated average seasonal cycles of A and E and their standard deviations are shown in Figs.3and 4for ever-green needleleaf forest (ENF)sites growing in four climatic zones.A comparison is made between fluxes simulated with PFT model parameters as in Table 4(VC)and fluxes simulated with model parameters of individual site years (SY).The average seasonal cycle of simulated A and E of boreal ENF sites is comparable to the observations in both figures.Standard deviations are only correct when using the site year parameters,fluxes simulated with PFT parameters show too little variation.For temperate continental and temperate ENF sites fluxes simulated with PFT parameters are underestimated,while the simulations using site year parameters show a seasonal variation,which is closer to the observations.In the subtropical and Mediterranean regions the fluxes are overesti-mated in both figures,particularly during summer.Annual simulated fluxes are compared with observations in Fig.5.The coefficients of determination (r 2)for annual A and E range from 0.39to 0.93.The deviation between observations and simula-tions increases when using more general model parameters,which are derived for larger groups of sites.Simulations using vegeta-tion parameters (V)or PFT parameters (VC)instead of one single parameter set (A)show only very minor improvements.Simulated fluxes are of a higher quality only when model parameters of indi-vidual sites (S)or site years (SY)are used.A simplification with less variation in model parameter values results evidently in poorer simulations with deviations of both the diurnal and seasonal cycles resulting in over-or underestimation of annual fluxes.3.2.Model parameter variation within and across vegetation typesFig.6shows that the mean parameter values and their standard deviations can differ considerably for each vegetation type depend-ing on how the data are classified.For example,the mean values of v cm,25and systematically increase for cropland,savanna and evergreen needleleaf forest vegetation types as the data are segre-gated according to vegetation type (V),vegetation and climate (VC),sites (S)or site-years (SY).While it is difficult to discern systematic trends in other parameters and for other vegetation classes,the dif-ferent classifications do not strongly affect the relative ranking of parameter values,e.g.the highest values of v cm,25are for croplands and the highest for savannas.For each vegetation type in Fig.6an average of different parameter sets is presented.The vegetation parameters (V )are derived for all sites within a group,and there-fore do not have a standard deviation.The PFT parameters (VC)are derived with climates within a vegetation type as in Table 4,the siteM.Groenendijk et al./Agricultural and Forest Meteorology 151 (2011) 22–3827P h o t o s y n t h e s i s (μm o l m −2 s −1)T r a n s p i r a t i o n (W m −2)100200300Doy100200300DoyFig.4.Seasonal variation of the average weekly observed and simulated photosynthesis (left)and transpiration (right)for the evergreen needle leaf forest sites in four different climates.The fluxes are simulated with parameters derived for each site year (SY).The average observed fluxes with standard deviations are presented by the grey area and the average simulated fluxes with standard deviations by the black lines.parameters (S )with all sites as in Table 2and the mean site year parameter (SY)with all years as in Table 3.The different number of parameters within each group will influence the standard devia-tions and thus their means and standard deviations cannot directly be compared.The standard deviation ( )of the parameter values from all site years is an indicator for the variation within a group.With the 95%interval confidence intervals in Tables 3and 4are estimated.First the standard error is calculated ( M = /p 1√n ),which is used to determine the 95%intervals (p 2±1.96 M p 2),assuming that the parameters are normally distributed.p 1is the mean of the site year parameters within a group and p 2the parameter derived for the group.Cropland parameters have high values for v cm,25and ˛anda low (more efficient water use)(Table 3).This implies that crops are more efficiently assimilating carbon in comparison to other (natural)vegetation types.In contrast,the parameters for savanna,which includes a mixture of grassland,trees and shrubs,imply a low photosynthetic productivity with a high .The different for-est types show very similar parameter values.The main difference between deciduous and evergreen broadleaf forests is seen for ,which implies a more efficient water use for the deciduous forests.The two types of evergreen forests show comparable high values for .Grassland has an even larger value for ,while also v cm,25and j m ,25are higher than for the different forests.The quality of the flux simulations are improved only slightly by adding extra model parameters through differentiating vegeta-Table 3Model parameters with 95%confidence intervals derived for groups of site years (n )within seven vegetation classes.v cm,25and j m ,25in mol m −2s −1,˛and in mol mol −1.nv cm,25j m ,25˛Cropland 1248.6±29.9136.9±33.20.27±0.09150.60±124.8Savanna2218.0±9.768.4±35.50.11±0.02593.34±273.0Deciduous broadleaf forest 6330.9±8.1154.9±29.90.16±0.02128.56±33.2Evergreen broadleaf forest 2234.3±4.6114.1±31.30.22±0.05190.77±41.8Evergreen needleleaf forest 15027.7±5.2121.6±13.60.16±0.02209.63±36.1Grassland 5543.3±5.0238.9±31.00.10±0.02276.46±75.4Mixed forest2536.4±11.0136.2±51.80.25±0.05149.80±131.028M.Groenendijk et al./Agricultural and Forest Meteorology 151 (2011) 22–38A (μm o l m −2 s −1)GPP (μmol m −2 s −1)050100150200250050100150200050100150200E (W m −2)050100150200050100150200LE (W m −2)parison of observed and simulated average annual photosynthesis and transpiration fluxes for all site years.Observed fluxes are GPP and LE and simulated fluxes are A and E .The dotted line is the 1:1line and the solid line the regression line.Differences between the panels are the used parameters to simulate the fluxes derived for all sites together (top panels),for groups of sites with a similar vegetation type,for PFTs,for individual sites or for individual site years (lower panels).tion classes according to climate (compare V to VC in Fig.5).This could suggest that variation of model parameters is not related to climate.In Table 4the parameters for groups of sites with simi-lar vegetation type and climate (PFTs)are presented to verify this.From this table it is not instantly clear whether a systematic differ-ence exists between climate sub-groups within a PFT.For instance,v cm,25is similar for the climates of evergreen needleleaf forest sites,but variable for the climates of deciduous broadleaf forest sites.3.3.Grouping sites based on model parametersVariation of model parameters between PFTs is not coherent (Table 4).The use of this classification results in incorrect simulated fluxes (Fig.5).Therefore we will attempt to define an independent classification purely based on the model parameters.Hierarchical clustering and k -means clustering are both used to group site years with comparable model parameter sets.The choice of the num-ber of groups is subjective,but we chose seven,the same as the number of vegetation types.Different numbers of groups were also tested and these produced comparable results.From Table 5it can be seen that one large group with 175site years,and six smaller groups were distinguished when using hierarchical clustering.For k -means clustering the groups are more evenly distributed (Table 6).Because the two clustering methods are very sensitive to outliers only those site years are used that are within the range as presented in Fig.7.Although the groups derived with the two clustering meth-ods are not the same,the patterns as in Fig.7are almost identical (data not shown),and thus only the results of k -means cluster-ing are discussed further.In Fig.7the distribution of the different sites and groups within the parameter space is shown.As expected the groups are clustered around central means.From this figure also the relations between parameters can be seen.For instance,the ratio between v cm,25and j m ,25is assumed to be constant in many models.Here both parameters are derived and this results in a ratio of 3.60±1.51at 25◦C,which is higher than for instance the ratio of 2.0±0.60as derived by Leuning (2002)from 43data sets.The parameters j m ,25and ˛are closely linked in Eq.8,which could result in equifinal simulated fluxes with multiple parameter。
Comparison of different methods for activation of ordinary Portland cement-slag mortarsFathollah Sajedi *,Hashim Abdul RazakDepartment of Civil Engineering,University of Malaya,50603Kuala Lumpur,Malaysiaa r t i c l e i n f o Article history:Received 12February 2010Received in revised form 13May 2010Accepted 19June 2010Available online 16July 2010Keywords:OSM GrindingThermal treatment Chemical activators Early strength Strength lossa b s t r a c tThis paper compares three methods for activation of OPC-slag mortars (OSM):(1)prolonged grinding of binders (mechanical method),(2)elevated temperature curing of mortars (thermal method),and (3)use of chemical activators such as NaOH,KOH,and Na 2SiO 3,9.35H 2O (chemical method).The proper reactiv-ity of OSM was evaluated using a mixture of 50%OPC and 50%slag.Early and ultimate strengths were compared.All three activation methods accelerated both the slag reaction and strength development rates.However,the chemical method did not show a significant effect on the ultimate strength,while thermal activation increased the early strength by 3days.Mechanical activation increased the early strengths of the mortar significantly,but about 6%strength loss occurred in the ultimate strength.Although,the application of mechanical and thermal activation methods needs extra equipment and energy,due to more significant of strength improvement;based on current test results,it can be said that mechanical activation is the most efficient and feasible method for the activation of OSMs.Ó2010Elsevier Ltd.All rights reserved.1.IntroductionIn this experimental work,mechanical,thermal,and chemical methods have been used to improve the compressive strength of OSMs.All three methods can be used to activate the potential reac-tivity of cement constituents.The mechanical activation is used to increase the specific sur-face area of the constituents and thus accelerates the hydration rate.Many results indicate that the early strength of a hardened ce-ment paste is directly proportional to the fineness of the cement,but fineness cannot contribute to later-age strength.In contrast,excessively high fineness may increase the water requirement and cause a reduction in later strength gain.In general,increased fineness results in better strength development,but in practice,fineness is limited by economic and performance considerations and factors such as setting time and shrinkage [1].For better performance,the fineness of GGBFS must be greater than that of cement [2].At the same time,the requirement to increase fineness reduces productivity and consumes more energy.Prolonged grind-ing not only increases the surface area of a material,but also the number of imperfections or active centers which exist at the edges,corners,projections and places where the inter-atomic distances are abnormal or are embedded with foreign atoms.These centers are in a higher energy state than in the normal structure.The more of these centers,the faster the rate of lers and Oulton(1970)observed that percussive dry grinding can cause obvious crystal distortion of kaolinite.It was also recently found that impaction and friction milling of high alumina cement alters its crystallinity and notably modifies its hydraulic behavior.A strength test on 22pozzolans (Chatterjee and Lahiri,1967)indicated no general correlation between the compressive strength of different materials (at 28or 60days)and surface area (either by Blain or BET method).However,it was stated that strength in-creases as fineness increases,for a single material.Different pozzo-lans have different quantities and nature of reactive components.It cannot be expected that a unique relationship exists between reac-tivity and surface area for all pozzolans.Prolonged grinding of nat-ural pozzolan also consumes extra energy and reduces grinding productivity [3].It has been reported [4]that for each 10m 2/kg increasing in Blain fineness,cost of grinding will be increased by about 10%.Elevated temperature curing needs additional equip-ment and is usually suitable for precast products.It also consumes a great amount of energy.Hydrated mortars and concretes can reach their maximum strength within several hours through elevated temperature curing.However,the ultimate strength of hardened mortars and concretes has been shown to decrease with curing temperature.Variations of mortar and concrete under high temperature are mainly the result of two different mechanisms.One is the variation of material properties of the constituent phases under high temperatures,and the other is the transforma-tion of constituent phases under different temperatures.Therefore,the property of mortar and concrete under high temperatures must be studied from both a mechanical and chemical point of view [5].0950-0618/$-see front matter Ó2010Elsevier Ltd.All rights reserved.doi:10.1016/j.conbuildmat.2010.06.060*Corresponding author.Tel.:+60173293252.E-mail address:f_sajedi@ (F.Sajedi).The chemical activation refers to the use of some chemicals to activate the potential reactivity of cementitious components. Alkali-GGBFS is a typical,successful example of chemical activa-tion.GGBFS shows little cementitious properties;nevertheless,it gives very high strengths in the presence of chemical activators such as Na2SiO3,NaOH,and Na2CO3.The activator(s)can be added during the milling of cement,or can be dissolved in the mixing water and added during mixing of mortars and concretes.The technology is very simple and does not need extra equipment. However,in chemical activation method there is an important reality that it cannot be used every activator to activate every type of slag,and then for better performance by chemical activa-tors it needs to have many tests and materials for determina-tion of the best activator.This is an improper point in chemical activation method.The objective of the current paper is to compare the efficacy of three activation methods,as measured by strength development and initial and ultimate compressive strengths.2.Research significanceIt is known that a lot of slag is produced in the steel-iron indus-try every year,throughout the entire world.If some means of con-sumption for these by-product materials can be found,it would help in terms of being environment friendly and also provide sig-nificant economic benefits.Moreover,the results of several re-searches have shown the use of the replacement materials in mortars and concretes has improved durability,which has vital significance for the structures built in aggressive environments, such as those in marine structures,such as big tunnels and bridges with long life spans.However,there is a problem in the use of the materials with initial hydration being lower than OPC and mortars and concretes having low early strengths.There are several ways of resolving this problem;including using mechanical,thermal,and chemical methods of activation,which is precisely the main pur-pose of this study.3.Experimental procedure3.1.Properties of materialsThe properties of the materials have been used in the study are as follows.3.1.1.CementThe cement used in all mixes was OPC.ASTM C-109-99was used for determi-nation of the compressive strength of hydraulic cement mortars,by use of50mm sided cube specimens.The specific gravity of cement used is by about3.14.Based on particle size analysis(PSA)tests,the specific surface area(SSA)for OPC was determined to be1893.9m2/kg.The chemical compositions of OPC used in this re-search have been determined by the testing method‘‘X-rayfluorescence spectrom-etry(XRF)”.The compositions of OPC are given in Table1.3.1.2.SlagThe specific gravity of the slag is approximately2.87,with its bulk density vary-ing in the range of1180–1250kg/m3.The color of ground granulated blast furnace slag(GGBFS)is normally whitish(off-white).Based on PSA tests,the SSA for GGBFS has been determined at3597.2m2/kg.It can be seen that SSA slag=1.90ÃSSA OPC, which means that particles of slag are90%finer than that of OPC.The compositions of slag are given in Tables4a and4b.As with all cementing materials,the reactivity of the slag is determined by its SSA.Based on the definition of slag activity index (SAI)in ASTM C989[5],it can be seen that SAI=(SP/P)Ã100,where;SP=average compressive strength of slag-reference cement mortar cubes;P=average compres-sive strength of reference cement mortar cubes.Both compressive strengths are in MPa.Based on this definition,the slag used in the tests is classified into Grade120.A result calculation is shown in the bottom of Table1.3.1.3.AggregatesThefine aggregates used in the mixes are graded silica sands with specific grav-ity,fineness modulus(FM)and water absorption(BS812:clause21)2.68%,3.88% and0.93%,respectively.The maximum size of aggregate is4.75mm.The PSA of thefine aggregates is given in Table2,and the grain size distribution diagram is drawn in Fig.1.3.1.4.Super plasticizerIn order to have appropriate consistency with low a W/B ratio,super plasticizer (SP)is required to be used.The SP used in this investigation is Rheobuild1100.The specific gravity of SP is approximately1.195,is dark brown in color,with a pH in the range of6.0–9.0.The consumed content of SP in the mortar depends on the replace-ment level of slag.Rheobuild1100is a chloride-free product.Meets ASTM C-494. The basic components are synthetic polymers which allow mixing water to be re-duced considered.The dosage of R1100generally varies from0.8to1.2l/(100kg)Table1Composition of cementitious materials(%by mass).For OPCP2O5SiO2Al2O3MgO Fe2O3CaO MnO K2O TiO2SO3CO2Cl0.0718.47 4.27 2.08 2.0664.090.040.280.10 4.25 4.200.01For slagSrO SiO2Al2O3MgO Fe2O3CaO MnO K2O TiO2SO3CO2Na2O0.0531.2112.96 4.270.8741.470.210.310.49 2.04 6.000.11For7days;SAI=47.57/47.76=1.00>0.95;For28days;SAI=62.83/50.26=1.25>1.15;K b(basicity index)for slag=(41.47+4.27)/(31.21+12.96)=1.03>1.00.1.306CaO/SiO2=C/S=1.33for slag61.40[5].Table2PSA for silica sand(SS)based on BS822:Clause11.Sieve size(l m)Sieve no.WSS+WS(g)WS(g)WSS(g)Ret.%Cum.Ret.%Pass.%47503/16in409.9408.3 1.60.320.3299.68 2360No.7462.3375.786.617.3317.6582.35 1180No.14437.2343.094.218.8536.563.50 600No.25450.7316.2134.526.9363.4236.58 300No.52379.1288.790.418.0981.5118.49 150No.100322.1274.847.39.4790.999.02 75No.200309.9275.234.7 6.9497.92 2.08 Pan–250.8240.410.4 2.08–0.00 Total499.7–388.31FM=388.31/100=3.88[6,7].PSA for silica sand used in the mixes is as:12%mesh50/100,18%mesh30/60,30%mesh16/30,20%mesh8/16,and20%mesh4/6.F.Sajedi,H.A.Razak/Construction and Building Materials25(2011)30–3831of cement.Other dosages may be recommended in special cases according to spe-cific job conditions.It is compatible with all cements and admixtures meeting ASTM and UNI standards.3.1.5.WaterThe water used in all mixes was potable water in pipeline of the lab.It was as-sumed that the specific gravity of the used water is about 1g/cm 3.3.1.6.ActivatorsChemical reagents NaOH,KOH,and Na 2SiO 3,n H 20were used as alkaline activa-tors.The dosage of alkaline activators were 2%,4%,and 6%Na 2O,K 2O,and Na 2O (%by mass),respectively.Based on the mass of slag,these activators were dissolved into mixing water first and then added to the mixing.Some physical and chemical properties of the activators are shown in Table 3.3.2.Mix proportions and curingTables 4a,5,and 6a and 6b represent the mix proportions for different activa-tion methods.In all mixes,water-binder and sand-binder ratios are 0.33and 2.25,respectively.Silica sands were used in the mixes.At first,based on PSA,five groups of silica sand were mixed.Two min following that,cement and replacement slag were put into the mixture,followed by 3–4min of mixing.Mixing water was then added to the mix and mixing was continued for 5min,following which the re-quired amount SP was added.Mixing was continued for 2min before the moulds were filled with fresh mortar in two layers.Each layer was compacted with ten im-pacts by a rod with 16mm diameter.24h after casting,the specimens were demoulded and cured proportionally in water with 23±3°C or in the room temper-ature 27±3°C and 65±18%relative humidity (RH)until the test day.3.3.Test and mixing procedures3.3.1.Test for fresh mortarIn order to have appropriate consistency for each mortar mix after casting,a flow table test has been done.The range of flow amounts were between 220and 235mm.The test procedure is that following casting,some mortar is put in the truncated brass cone in two layers.Each layer is compacted 10times by a steel rod with a 16mm diameter,before the cone is lifted and the mortar collapsed on the flow table.Following that,both the table and mortar are jolted 15times in a period of 60s.Jolting the table enables the mortar to consequently spread out and the maximum spread,to the two edges of the table,was then recorded.The average of both records is calculated as flow (mm).The photograph for mixture and flow table test is shown in Fig.2.Table 3Some properties of activators used in the study.No.Activator nameType Formula/abbreviation Na 2O or K 2O (%)SiO 2(%)H 2O (%)M S M (g/mol)1Sodium silicate Solution Na 2SiO 3,1.11H 2O/SSS 43.6442.2314.130.97142.072Sodium silicateSolution Na 2SiO 3,9.35H 2O/SSS 123058 2.5290.483Sodium silicate –extra pure Solution Na 2SiO 3,12.58H 2O/SSEP 82765 3.375348.454Sodium silicate Granular Na 2SiO 3,9.0H 2O/GSS 21.8121.1157.080.97284.225Sodium hydroxide Pellet NaOH/SH 99–––406Potassium hydroxidePelletKOH/PH85–––56M s =Molar ratio =SiO 2/Na 2O.Table 4aMix proportions of OSMs for mechanical activation method.No.Mix name OPC (g)Slag (g)Water (g)Flow (mm)SP (g)1OM18000631.7230602OSM/50900900631.7235403GS3C0900900631.7230264GS0C3900900631.7220245GS3C3900900631.7225246GS6C0900900631.7230287GS0C6900900631.7225288GS6C6900900631.7235289GS9.5C0900900631.72202010GS0C9.5900900631.72202411GS9.5C9.5900900631.72202212GS13C0900900631.72252213GS0C139********.72202714GS13C13900900631.722026GS i C i =Mixes made by using the binders grounded in a period of i h .For all mixes:W/B =0.33,S/B =2.25.Table 4bSSA values (m 2/kg)of OPC and slag.Duration of grinding (h)0.0 3.0 6.09.513.0OPC 1893.92390.13098.72130.1344.1Slag3597.23873.24180.64374.12065.1r =SSA slag /SAS OPC1.901.621.352.056.00A ball mill grinder machine was used to grind the binders’particles.Table 5Mix proportions of OMs,OSMs/40,and OSMs/50for thermal activation method.No.Mix nameOPC (g)Slag (g)Water (g)SP (g)Flow (mm)For OMs,air and water cured 1OM-air cure 1800–631.66282302OM-water cure 1800–631.6630230For OSMs/40,air and water cured 3H0/0720480421.11282254H60/21440960842.22822305H60/4,61440960842.22902306H60/8,101440960842.22792307H60/12,141440960842.22792308H60/161440960842.22822309H60/18,201440960842.227323010H60/22–26216014401263.3370220For OSMs/50,air and water cured 11H0/0600600421.113523012H60/212001200842.227623513H60/4,612001200842.229122514H60/8,1012001200842.229023515H60/12,1412001200842.227323516H60/1612001200842.227623517H60/18,2012001200842.226222518H60/22–26180018001263.3360220For optimum OSM/50at 6ages,only air cured 19H60/20900900631.6643230H60/i ,j means 60°C temperature with heating time i and j h.PSA for silica sand used in the mixes is as:12%mesh 50/100,18%mesh 30/60,30%mesh 16/30,20%mesh 8/16,and 20%mesh 4/6.32 F.Sajedi,H.A.Razak /Construction and Building Materials 25(2011)30–383.3.2.Test for hardened mortarThree cubic samples,with lengths of50mm,were used for each age.Samples produced from fresh mortar were demoulded after24h,and then cured in room temperature with27±3°C and65±18%RH,and in the water with23±3°C before the samples were used for compressive strength pressive strength mea-surements were carried out using an ELE testing machine press with a capacity of 2000KN,and a pacing rate of0.5KN/pressive tests have been done according to BS1881,Part116,1983.3.3.3.Mortar mix methodInitially,five groups of silica sand are put in as a mixture and mixed for2min. Following that,the cement and slag are added and mixing is continued for3–4min. The activator is then poured into the calculated water and mixed until completely dissolved.The solution is then added into the mixture and mixing is continued for 2min.Finally,SP is added and mixing continued for2min;immediately following the completion of the mixing,theflow table test is done and the specimens are moulded.For each mix,the duration of mixing time takes about8to10min.4.Results and discussion4.1.Mechanical methodIn the mechanical method,fourteen mix proportions of OSMs have been used with two mixes as control.For each mix,it is important to have high early strength.In this method,50%of ce-ment replacement has been selected as the optimum level of slag. The mainfinding proves that the use of ground slag and OPC,Table6aMix proportions of control mixes and Abbreviations.No.Mix name Curing regime OPC(g)Slag(g)Water(g)SP(g)Flow(mm)(g)1OM Water18000631.760230 2OSM/40Water1080720631.728225 3OSM/50Water900900631.728220 4OSM/60Water7201080631.735225List of abbreviationsNo.Original statement Abbr.1Ordinary Portland cement(OPC)mortar OM 2Slag mortar SM 3OPC-slag mortar wit i%level of slag OSM i 4Potassium hydroxide with i%content(based on mass of slag)PH i 5Sodium hydroxide with i%content(based on mass of slag)SH i 6Solution sodium silicate with i%content(based on mass of slag)SSS i 7Granular sodium silicate with i%content(based on mass of slag)GSS i 8Sodium silicate extra pure with i%content(based on mass of slag)SSEP iTable6bMix proportions of OSMs for chemical activation method.No.Slag level Mix name Curing regime OPC(g)Slag(g)Water(g)SP(g)Flow(mm)160PH1Air480720421.145220 260PH2Air480720421.150220 360PH4Air480720421.1180220 460PH6Air480720421.1230220 560SH2Air480720421.160225 660SH4Air480720421.140220 760SH6Air480720421.165220 860SSS2Air480720421.1128225 960SSS4Air480720421.1255225 1060SSS6Air480720421.131**** ****GSS2Air600600421.170220 1260GSS2Air480720421.170220 1350SSEP2Air600600421.175225 1440SSS2Water720480421.152230 1540SSS3Water720480421.182230 1640SSS5Water720480421.1114225 1750PH1.5Air600600421.151230 1840PH2Air720480421.138230 1940PH4Air720480421.160220 2040GSS1.17+SH3.35Air720480421.1110225 2140GSS2.5+SH2.33Air720480421.1147220 2240SSS2+SH0.6Air720480421.1185220 2340SSS3+SH4.5Air720480421.1145220 2450PH2+SH3Air600600421.180225 2550PH2+SH5Air600600421.1120225 2650PH1+SH1.5Air600600421.160220 2750PH0.75+SH1Air600600421.145230 2830PH0.5+SH0.5Air840360421.127230 2940PH0.5+SH0.5Air720480421.160230 3050PH0.5+SH0.5Air600600421.123235 3160PH0.5+SH0.5Air480720421.126230 3250PH1.5+SH0.75Air600600421.141220 For optimum OSM/50at6ages3350PH0.5+SH0.5Air900900631.733230For all mixes:W/B=0.33and S/B=2.25.PSA for silica sand used in the mixes is as:12%mesh50/100,18%mesh30/60,30%mesh16/30,20%mesh8/16,and20%mesh4/6.F.Sajedi,H.A.Razak/Construction and Building Materials25(2011)30–3833maximum compressive strength can be achieved for OSMs.It was found that65MPa at7-day and80.08MPa at28-day strength. These strength levels have been obtained for the mortar whenever the slag and OPC were ground for a period of6h,by a proper grin-der machine.For grinding of OPC and slag particles a ball mill grin-der machine was used.Atfirst,OPC and GGBFS are weighed by a balance with±0.1g accuracy,and then put in the cylinders of the grinder machine.The machine used for grinding of the materials has three cylinder boxes;each cylinder has four spherical steel balls,each with a diameter of50.6mm and a mass of533.3g. 650g of materials are put in each box;following that,the machine is turned on for periods of3,6,9.5,and13h.The pacing rate of the grinder machine is around110rpm.In Table4a this mortar has been named as GS6C6(optimum mortar).In this part of study,three groups of OSMs have been used.In thefirst group,both OPC and ground slag were used.In the second group,only ground slag was used,and in the third group,only ground OPC used.Forfirst group of mortars,it is seen that the maximum strengths is attributed to r=1.35.It is noted that the strength levels of the first group are more than those of other groups.At r=1.35the strength at28days is80.08MPa.The r factor is defined as the ratio of SSA slag to SSA OPC.This factor is dimensionless.For better per-formance of OSMs,it is generally accepted that r should be more than1.0.From Table4b it is clear that the optimum mortar is attributed to minimum r=1.35.In this case both OPC and slag were separately ground in duration of6h.The results of compressive strength vs.age of curing,for all three groups of OSMs used in mechanical method,are shown in Fig.3,Part A.The best curvefitting of compressive strength vs. age of mortar,for three groups of OSMs,is shown in Fig.3,Part B.4.2.Thermal methodIn the thermal method,29mix proportions of OSMs have been used and two mixes as control.For each mix,two points are impor-tant.Firstly,a higher percentage of slag is preferable because it has economic and environmental advantages and also helps to im-prove durability of the mortars.Secondly,it improves early strength.It is apparent that an increase of replacement slag causes early strength to be reduced,since the slag has lower initial hydra-tion heat than that of OPC.Moreover,for short-term purposes the use of low levels of slag is neither economic nor durable.It is clear that the use of high levels of slag in mortars and concretes has many benefits from standpoint of economic and environmental. By using high levels of slag it can be said that the use of cement will be more reduced.This means that production and emission of carbon dioxide(CO2)is significantly decreased,in due to it is generally accepted production of one ton of OPC is caused to pro-duce one ton of(CO2).In addition,with use of higher levels of the slag,more compact structure can be obtained.In this research,itisdesirable to know the optimum temperature and the duration that will provide the highest early strength at3and7days.In the study,the effects of50°C,60°C,and70°C temperatures were considered on the early strengths at3and7days of OSM/50. The results are shown in Fig.4.It is apparent that60°C has the most enhancing effects on early-age strengths,so it was selected as the optimum temperature.The results obtained for compressive strengths based on heat-ing time,are shown in Fig.5.Based on the results,it can be seen that3and7days strengths,for specimens cured in the water,both without being heated and with2h heating time,are greater than whenever cured in room temperature.This reality has proven for both OSM/50and OSM/40.However,as soon as the heating time is increased to4h and more,the aforementioned statement is re-versed.Conversely,whenever heating time is increased to4h and more,the strength of specimens cured in room temperature is improved compared to the strength of specimens cured in water. It appears that this is due to;room temperature and a high RH of the room’s air.Elevated curing temperature accelerates the chem-ical reactions of hydration,and increase the early-age strength. However,during the initial period of hydration,an open and un-filled pore structure of cement paste forms and therefore nega-tively affects the properties of hardened mortar and concrete, especially at later ages[6,7].Hardened mortars and concretes can reach their maximum strength within several hours through elevated temperature curing.However,the ultimate strength of hardened mortars and concretes has been shown to decrease with curing temperature.It was found that by increasing curing temperature from20°C to60°C and the heating time up to48h a continuous increase in compressive strength occurred[8].Studies have shown that there is a threshold maximum,heat-curing temperature value in the range of60–70°C,beyond which heat treatment is of little or no benefit to the engineering properties of concrete.It is noted that the maximum3and7days strengths of OSM/ 40s and OSM/50s specimens are21.65%,18.98%and21.78%, 20.00%greater than those of OM’s specimens cured in room tem-perature.It is observed that there is strength loss by about2.2% whenever56days strength is compared to28days strength.This has been previously reported by other researchers[9].Whereas the main objective of elevated temperature curing is to achieve early strength development,it is generally acknowledged that there is also strength loss as a result of heat curing.Another mix of OSM/50as the optimum mix has been designed for optimum temperature and heating time(H60°C–20h)for six ages;1,3, 7,28,56,and90days.The results of compressive strength vs. age of the specimens cured in room temperature,is a logarithmic relationship as:CSÀTÀac=5.2039ÃLn(t)+50.664,with R2=0.9311;where CS is compressive strength in MPa,t is curing age in days,and ac denotes curing regime in room temperature.4.3.Chemical methodIn this method,five groups of activators were used as follows.Thefirst group is a pellet of NaOH.The second activator is a pel-let of KOH,and the third a solution of Na2SiO3,9.35H2O.The fourth group is a combined activator as:(Na2SiO3,n H2O)2+NaOH0.583 with the last being NaOH0.5+KOH0.5(%by mass of slag).With re-spect to the activators used,the NaOH0.5+KOH0.5mix leads,in all cases,to the highest strengths values,followed by the(Na2SiO3, 9.35H2O)2+NaOH0.583solution and then by KOH1.NaOH gives the lowest strength values whenever it is used alone.It was deter-mined that the effect of the combined activators being better than that of an individual one.The activity of GGBFS is determined by the quantities and the properties of amorphous glass,as well as the chemical compositions.Facts have been proven that the higher the proportion of glass,the greater is the activity of slag at the same chemical compositions[10].The results obtained show that when the aforementioned are considered,they do not have the same significance on the strengths.It seems the most relevant factor is the alkaline-activa-tor nature.For compressive strength,the relevant of factors may be attributed with age.This order seems be as:activator nature,acti-vator concentration,and specific surface of slag.The last factor is curing temperature and is only significant at3-and7-day ages. The significant role of an alkali activator is based on the fact that slag alone reacts with water very slowly,but hydroxyl ions (OH)Àare supplied by alkali activators.They are known to increase the hydration rate by promoting dissolution of aluminate and sili-cate network in the slag[11].The efficiency of an activator depends on several factors.Among them,the type,ambient temperature,dosage and water/slag ratio are significant.Another significant factor is the physico-chemicalF.Sajedi,H.A.Razak/Construction and Building Materials25(2011)30–3835nature of the material to be activated[11].In this activation meth-od,three sets of activators have been used.In thefirst set,the alkali activators,NaOH,KOH,and Na2SiO3,9.35H2O(solution/granular) were used for2%,4%,and6%by mass of slag.In the second set, the combination of alkali activators NaOH and Na2SiO3,9.35H2O (solution/granular)used for different molar ratio(M s)0.25,0.50, 0.75,and1.00.In the third set,the alkali activators NaOH and KOH have been used for different combinations.Finally,by com-parison of the results of the three sets of activators used,the opti-mum set was acquired.The optimum selected set of activators is shown in Fig.6.It is noted that in thefirst set of activators,the best activator is KOH with a1%content of K2O as mass of slag.Up to60%slag replacement and use of KOH1as activator has yielded the best re-sults.For the second set of activators,the best combination is(Na2-SiO3,9.35H2O)2+NaOH0.583with M s=0.75.Up to50%slagreplacement and use of second set yielded the highest early strengths.In the third set of activators,the best combination was NaOH0.5+KOH0.5.Up to50%slag substitution and use of a third set of activators yielded the best results.By comparison of the ob-tained optimum results for the three sets of activators;it was shown that the optimum set is attributed to the third set of activators.The use of an optimum set of activators enabled the creation of another mix as an optimum mix proportion for specimens cured in room temperature.These were created for six ages1,3,7,28,56, and90days.The results of compressive strength vs.age for opti-mum mix,has the best curvefitting as:CSÀCÀac=5.6738ÃLn(t)+25.433,with R2=0.8169;where CS is compressive strength in MPa,t is curing age in days,and ac denotes curing regime in room temperature.parison of different methodsFrom Fig.7it is observed that SMs,OMs,and OSMs cured in the water do not have strength loss,but the OSMs activated by chem-ical,thermal,and mechanical methods have strength loss at90,56, and56days,respectively.The results show that strength loss in al-kali-activated mortars depends on the level of slag used,the type and dosage of alkali activator,and the regime of curing.The reason for the loss of strength can be due to internal or external reasons. The internal reasons are those linked to the chemical composition of the reacted products.The external reasons are due to the vari-ability of specimens,testing procedures,flatness of testing proce-dures.One other factor that has an important effect is the temperature.The initial curing temperature has an important ef-fect and can reduce or increase strength at long curing times,i.e. advanced age.A strength comparison of OMs and OSMs showed that OMs have higher strengths until7days ages when compared to OSMs,but after7days this statement is reversed.Additionally,the ultimate strength of OSMs is more than that of OMs.From Fig.8it can be deduced that whenever the chemical meth-od is used for activation of OSM/50(for all ages except1day),not only did the strength improve,but that there was also a strength loss from3until90days compared to inactivated OSM/50.This is due to the presence of cement in OSM/50.This subject has been previously confirmed by other researchers[12].The addition of alkalies to Portland cement results in a reduc-tion of strength after3or7days,because of the hydration chemis-try and the morphology of the hydration products are changed due to the presence of alkalies[13].Strength development,due to the thermal activation method at a3days age,is more than that of the mechanical activation meth-od,but for7days and more,this statement is reversed.Further-more,it can be said that use of the thermal method is better than the mechanical method until3days.After3days,the mechanical method is the optimal method.From Fig.9it can be observed that:The greatest strength development at1day is attributed to mechanical,chemical,and thermal activation methods.The greatest strength development at3days is attributed to thermal,mechanical,and chemical methods of activation.The highest strength development at7,28,56,and90days are attributed to mechanical,thermal,and chemical activation methods.In summary,it can be said those at all ages except1day,the chemical method has the last rank of activation.At1day,the chemical method has the second rank of activation.Moreover, the greatest strength development at3days is related to the thermal method and for7days and more,thefirst rank of acti-vation is attributed to the mechanical method.36 F.Sajedi,H.A.Razak/Construction and Building Materials25(2011)30–38。
Repeatability and reproducibilityTwo components of measurement precision.• Repeatability represents the variation that occurs when the same appraiser measures the same part with the same device. • Reproducibility represents the variation that occurs when different appraisers measure the same part with the same device. Gage R&R studies help assess the repeatability and reproducibility in measurement systems.RepeatabilityReproducibilityTo determine which gage is more repeatable, Appraiser 1 measures a single part with Gage A 20 times, then measures the same part with Gage B 20 times.Gage A has less variation, so it is more repeatable than Gage B. To assess gage reproducibility, Appraisers 1, 2, and 3 measure the same part 20 times with the same gage.The variation in average measurements between Appraisers 1 and 2 is much less than variation between Appraisers 1 and 3. Therefore, your gage demonstrates poor reproducibility.Measurement system variationAll variation associated with a measurement process. Potential sources of variation include gages, standards, procedures, software, environmental components, as well as others.When measuring the output from a process, consider both the part-to-part variation as well as the measurement system variation. For example, you have a known standard that is exactly 5.00g. You weigh it multiple times and get the following readings: 5.01g, 4.99g, 4.97g, 5.03g, 5.01g. The differences in the measurements are due to measurement system variation. If, however, you weigh different parts that come off your manufacturing line, are the differences due to measurement system variation or due to actual differences in the parts themselves? Use Minitab's measurement system analysis tools to determine the sources of variation. If the measurement system variation is large compared to part-to-part variation, the measurements may not provide useful information.Like any other process, a measurement system is subject to both common and special cause variation. To control the measurement system variation, you must first identify the sources of the variation, then you must either eliminate or reduce the various causes. Destructive testingA type of non-replicable testing that renders the test part or sample useless. Automotive crash tests are destructive tests because the vehicle is destroyed beyond testing again.Destructive testing changes the part, or even destroys it, during testing; therefore, replication is not possible. Examples include tensile, bend, impact, hardness, and fracture testing as well as stress corrosion, fatigue, and creep testing. For example, these tests evaluate the strength, ductility, and toughness of a weld and its ability to withstand certain applications or environments, but the test destroys the weld itself.Because destructive testing renders the parts useless, it can be very expensive. In many cases, non-destructive testing methods can replace a destructive test method. Non-destructive techniques to evaluate weld include dye penetration, magnetic particle, X-radiography, Gamma-radiography, ultrasonic, eddy current, and acoustic emission.。
a rXiv:as tr o-ph/96317v15Mar1996Submitted to Ap.J.Letters ;astro-ph/9603017A CMBR Measurement Reproduced:A Statistical Comparison of MSAM1-94to MSAM1-92C.A.Inman 1,E.S.Cheng 2,D.A.Cottingham 3,D.J.Fixsen 4,M.S.Kowitt 2,S.S.Meyer 1,L.A.Page 5,J.L.Puchalla 2,J.E.Ruhl 6,and R.F.Silverberg 2ABSTRACT The goal of the second flight of the Medium Scale Anisotropy Measurement (MSAM1-94)was to confirm the measurement of cosmic microwave background radiation (CMBR)anisotropy made in the first flight (MSAM1-92).The CMBR anisotropy and interstellar dust emission signals from the two flights are compared by forming the sum and difference of those portions of the data with the same pointings on the sky.The difference data are consistent with a null detection,while the summed data show significant signal.We conclude that MSAM1-92and MSAM1-94measured the same celestial signal.Subject headings:balloons —cosmic microwave background —cosmology:observations1.IntroductionMeasurements of anisotropy in the Cosmic Microwave Background Radiation(CMBR) continue as a subject of considerable interest to the astrophysics community.Future anisotropy measurements on scales of0.◦1to1.◦0will discriminate among early universe models and determine fundamental cosmological parameters(e.g.Hu and White1996, Knox1995and Jungman et al.1995).Measurements of anisotropy at angular scales near0.◦5have been reported recently by Ruhl et al.1995,Netterfield et al.1996,Gundersen et al.1995,and Tanaka et al.1995. Wilkinson1995voiced a common concern when he pointed out that“there are plausible systematic effects at levels comparable with the reported detections.”To address this concern the1994flight of the Medium Scale Anisotropy Measurement(MSAM1)observed the samefield as the1992flight to limit the possibility of systematic sources of the signal.Cheng et al.1994(hereafter Paper I)reported observations of anisotropy in the CMBR from thefirstflight of MSAM1in1992(MSAM1-92).Cheng et al.1996(hereafter Paper II)reported the results from the secondflight in1994(MSAM1-94).A conclusion of the latter is that while a quantitative comparison was pending,there was good qualitative agreement between the twoflights in the double difference data set,and that agreement was inconclusive for the single difference data set.This Letter presents a quantitative comparison of the MSAM1-92and MSAM1-94data sets.2.Instrument and ObservationsThe MSAM1instrument has been fully described in Fixsen et al.1996(hereafter Paper III);only an overview is given here.It is an off-axis Cassegrain telescope with a4-channel bolometric radiometer at the focus.The beamsize is28′FWHM and is moved ±40′on the sky by the nutating secondary.The radiometer has4frequency channels placed at5.7,9.3,16.5,and22.6cm−1.For these observation,emission in the lower two channels is dominated by the2.7K CMBR,while∼20K interstellar dust dominates the two higher channels.The instrument configuration was similar for the twoflights,with changes made only to the warm signal electronics and the gondola structure.These changes are discussed extensively in Paper III;the modifications to the electronics improved the noise performance, while those to the gondola reduced sidelobe sensitivity.The original superstructure had a large reflecting area above the beam,from which earthshine could potentially diffract into the beam.For the secondflight,the gondola was suspended by a cable system whichreduced the far-sidelobe response.The measured near sidelobe response dropped from−55dB in1992in the worst case to less than−75dB in all cases in1994.As described in Papers I and II,the observedfield is two strips at declination81.◦8±0.◦1, from right ascension15.h27to16.h84,and from17.h57to19.h71(all coordinates are J1994.5). Fig.1shows the weighted beam centers of thefields observed in the1992and1994flights.A CCD camera is used to determine absolute pointing for bothflights.Thefinal accuracy of the pointing determination is2.′5,limited by the gyroscope signal interpolation.This2.′5 uncertainty is small compared to the size of our beam(28′)and the bins(14′)used below, ensuring adequate alignment of the two datasets.During bothflights Jupiter was observed to calibrate the instrument and map the telescope beam.Beam maps and calibrations are done separately for the twoflights.The shape of the beam map is determined to4%of the maximum amplitude.Random noise in the gyroscope system contributes3.5%,and cosmic rays striking the detectors contribute 1.5%.Also,the choice of smoothing algorithm causes a1.5%systematic effbining this4%error from eachflight gives a5.8%relative calibration uncertainty.The uncertainty in Jupiter’s intrinsic brightness leads to an additional systematic uncertainty of10%for the results of eachflight;however,except for possible time variations in Jupiter’s brightness which we do not expect,this uncertainty does not contribute to the comparison of the two flights discussed here.3.Reanalysis of1992DataThe analyzed data sets reported in Papers I and II cannot be directly compared for two reasons:1)the boundaries of the sky bins are different,and2)the analysis reported in Paper I neglects correlations introduced by the removal of the small offset drift.The 1992data is reanalyzed to account for these correlations,using a procedure nearly identical to that of Paper II.The1994data is also reanalyzed,with differences from the original analysis noted in the text below.Here wefirst review the Paper II analysis,then note the differences between that and the reanalysis used here.First,the cosmic ray events are removed from the time stream.Cosmic ray removal techniques are different for the two years,and are discussed in Papers I and II.The data are then analyzed in a manner that provides sensitivity to two different angular scales on the sky.This is done by weighting the the time stream,S i,with one of two demodulation templates,d i,giving one“demodulated data point”,∆T cycle= i d i S i,for each full cycle of the secondary mirror movement.The“single difference demodulation”weights thesecondary-left data positively while weighting the secondary-right data negatively,giving a ∆T equal to the difference between the left and right temperatures.The result is a two lobed beam pattern on the sky with80′beam separation.The“double difference demodulation”weights the secondary-centered data positively,while weighting the secondary-left and secondary-right data negatively,giving a∆T equal to the difference between the center and the side temperatures.This produces a three lobed beam pattern on the sky,with40′beam separation.The single difference and double difference data are nearly statistically independent.A linear model isfit to these demodulated data including intensity for each sky bin and slow drifts in time.The noise used in thefit is estimated from the data.Both the time drift and noise estimate are described further below.The results of the linearfit are signal amplitudes for each sky bin with their associated covariances.A spectral model for each sky bin consisting of CMBR anisotropy plus emission from 20K Galactic dust,with emissivity proportional to frequency to the1.5power,isfit to all four frequency channels of binned sky data.The results of this spectralfit are the intensity of a“DUST”component and a“CMBR”component in each sky bin.The differences between the Paper II analysis and that done for this paper follow.This analysis uses a0.◦24bin size,double the size used in Paper II,which corresponds to the size of the central beam plateau.Angular orientation bins,which account for sky rotation relative to the secondary chop axis,are20◦,also double the previous size.The weighted beam centers of the identical bins are shown asfilled symbols in Fig.1.The noise estimates are formed from demodulated data.This is a change from Paper II, where the estimate is made after after having removed the drift model.The noise estimates are made separately for each minute of data by measuring the rms of the demodulated data in that minute.The new noise estimate is used in reanalyzing both the1992and1994 datasets.True sky signals make a negligible contribution to this rms estimate over these short time scales.This change has no substantial effect on the results of this Letter.Also,in Paper II the drift model included terms based on gondola sensors(air pressure, and the pitch and roll angles of the gondola outer frame).This model was used in the1994 reanalysis,while it was not used for the1992reanalysis.Instead,the original model for the drifts described in Paper I,a spline with knots every2.5minutes,was used.Fig. 1.—The weighted positions for each sky bin for both years.The triangles mark the 1992centers and the squares mark the1994centers.The declination scale has been greatly expanded relative to the RA scale in order to see the detailed pointing differences.Thefilled symbols are the weighted centers for the bins used in the comparison( δ =81◦50′).The bin boundaries(every0.◦24,or0.h11)in RA are not shown.The declination bin boundaries (every0.◦24)are marked by the horizontal lines.The angular orientation is ignored in this plot,but is not in our analysis.The vertical beam profile is plotted in the hatched region.Note that at this declination,every hour of RA corresponds to about2◦.parisonWe compare the signal measured at each point on the sky as measured in the twoflights,not just the rms levels of the sky signal found in each data set.In the1994flight, we attempted to observe the identical swath of sky observed in the1992flight.As can be seen in Fig.1,which is extremely enlarged in declination relative to right ascension,the 1994flight was low by about10′.To enable direct comparison,only the data from those bins which fall into the center declination bin is used.After this selection∼50%of the data is retained.The data from the1992flight is differenced from that of the1994flight to form a difference data set,92−94.Similarly,the two data sets are summed to form a sum data set,92+94.This is done for each demodulation and for both CMBR and DUST.To allow for differing offsets in the twoflights,a weighted mean is removed from each dataset.The covariance matrix,V ij,for both the sum and difference sets is the sum of the masked1992 and1994covariance matrices.There is no cross term because theflights have independent noise.The significance of any detected signal in the sum or difference is tested with aχ2 statistic,χ2±=ij (92±94)i V−1ij(92±94)j.Theχ2and degrees of freedom,and the cumulative probability,P(χ2),for the comparison is shown in Table1.P is the probability of getting a value ofχ2at or above the observed value,under the assumption that there is no signal in the data.This should be the case for the difference data,where the common sky signal should cancel.To check the effect of the relative calibration uncertainty onχ2,the1994dataset is rescaled by±6%and the value ofχ2recalculated.In all cases|∆χ2|≤2.A Kolmogorov-Smirnov(KS)test(Press et al.1992)of the92−94probabilities(.04, .22,.41,and.91)gives a74%probability that these are drawn from a uniform distribution from0to1.Based on this,we conclude that the92−94data in both the single and double difference demodulations for both the CMBR and DUST components is consistent with no observed signal.A KS test of the92+94probabilities(2×10−12,2×10−8,4×10−7,and1×10−4) gives a7×10−4probability that these are drawn from a uniform distribution from0to1. From this,together with the extremely lowχ2probabilities themselves,we see that there are statistically significant signals in all four92+94datasets.This result,combined with the absence of such signals in the92−94datasets,enables us to conclude that the signals observed during the twoflights are common,and therefore present on the sky.parison of1992and1994Data SetsType Data Setχ2/DOF PSingle DifferenceDouble Difference5.ConclusionsThe same region of the sky was observed in the1992and1994flights to confirm the detection of a celestial signal.It is clear from the statistical analysis that the same sky signal is measured in these twoflights.We conclude that at the level of our signal, our measurements are likely to be free from significant contamination from time-varying systematics such as sidelobe pickup or atmospheric contamination.In addition to our own confirmation of the MSAM1-92results,the Saskatoon experiment has recently observed this section of sky at lower frequencies,36GHz to46GHz (Netterfield et al.1996).They have compared their signal with the double difference CMBR signal from Paper I,andfind good agreement.This result,spanning nearly a decade in frequency,is strong evidence that we are observing CMBR anisotropies rather than some other astrophysical foreground source.We would like to thank E.Magnier,R.Rutledge,L.Knox,and A.Goldin for useful conversations.The research was supported by the NASA Office of Space Science, Astrophysics Division through grants NTG50720and50908and RTOP188-44.REFERENCESCheng,E.S.et al.1994,ApJ,422,L37.Cheng,E.S.et al.1996,ApJ,456,L71.Fixsen,D.J.et al.1996,ApJ.submitted,preprint astro-ph/9512006.Gundersen,J.O.et al.1995,ApJ,443,L57.Hu,W.and White,M.1996,ApJ.submitted,preprint astro-ph/9602019.Jungman,G.,Kamionkowski,M.,Kosowsky,A.,and Spergel,D.1995,Phys.Rev.D.submitted,preprint astro-ph/9512139.Knox,L.1995,Phys.Rev.D,52,4307.Netterfield,C.B.,Devlin,M.J.,Jarosik,N.,Page,L.,and Wollack,E.J.1996,ApJ.submitted,preprint astro-ph/9601197.Press,W.H.,Teukolsky,S.A.,Vetterling,W.T.,and Flannery,B.P.1992.Numerical Recipes in FORTRAN:The Art of Scientific Computing.Cambridge UniversityPress,Cambridge,2nd edition.Ruhl,J.E.,Dragovan,M.,Platt,S.R.,Kovac,J.,and Novak,G.1995,ApJ,453,L1. 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