Disjunct eddy covariance measurements of volatile organic compound fluxes using proton transfer reaction mass spectrometry

Volatile organic compounds (VOCs) are emitted into the atmosphere from natural and anthropogenic sources, vegetation being the dominant source on a global scale. Some of these reactive compounds are deemed major contributors or inhibitors to aerosol particle formation and growth, thus making VOC measurements essential for current climate change research. This thesis discusses ecosystem scale VOC fluxes measured above a boreal Scots pine dominated forest in southern Finland. The flux measurements were performed using the micrometeorological disjunct eddy covariance (DEC) method combined with proton transfer reaction mass spectrometry (PTR-MS), which is an online technique for measuring VOC concentrations. The measurement, calibration, and calculation procedures developed in this work proved to be well suited to long-term VOC concentration and flux measurements with PTR-MS. A new averaging approach based on running averaged covariance functions improved the determination of the lag time between wind and concentration measurements, which is a common challenge in DEC when measuring fluxes near the detection limit. The ecosystem scale emissions of methanol, acetaldehyde, and acetone were substantial. These three oxygenated VOCs made up about half of the total emissions, with the rest comprised of monoterpenes. Contrary to the traditional assumption that monoterpene emissions from Scots pine originate mainly as evaporation from specialized storage pools, the DEC measurements indicated a significant contribution from de novo biosynthesis to the ecosystem scale monoterpene emissions. This thesis offers practical guidelines for long-term DEC measurements with PTR-MS. In particular, the new averaging approach to the lag time determination seems useful in the automation of DEC flux calculations. Seasonal variation in the monoterpene biosynthesis and the detailed structure of a revised hybrid algorithm, describing both de novo and pool emissions, should be determined in further studies to improve biological realism in the modelling of monoterpene emissions from Scots pine forests. The increasing number of DEC measurements of oxygenated VOCs will probably enable better estimates of the role of these compounds in plant physiology and tropospheric chemistry. Keywords: disjunct eddy covariance, lag time determination, long-term flux measurements, proton transfer reaction mass spectrometry, Scots pine forests, volatile organic compounds

Resistometric studies of solute-vacancy interactions and clustering kinetics in an FCC matrix [AlZn Alloy with Ag, Ce, Dy, Li, Nb, Pt, Sb, Y, or Yb]

Al-4.4 a/oZn and Al-4.4 a/oZn with Ag, Ce, Dy, Li, Nb, Pt, Y, or Yb, alloys have been investigated by resistometry with a view to study the solute-vacancy interactions and clustering kinetics in these alloys. Solute-vacancy binding energies have been evaluated for all these elements by making use of appropriate methods of evaluation. Ag and Dy additions yield some interesting results and these have been discussed in the thesis. Solute-vacancy binding energy values obtained here have been compared with other available values and discussed. A study of the type of interaction between vacancies and solute atoms indicates that the valency effect is more predominant than the elastic effect.

An acid method for the volumetric estimation of water-soluble dithiocarbamates

Dithiocarbamates have been estimated previously by reaction with a strong acid, the carbon disulfide evolved being converted into a xanthate and the latter estimated iodimetrically. In the present method, a water-soluble dithiocarbamate is reacted with a decinormal mineral acid and the excess acid is determined to compute the amount of dithiocarbamate present. This method is applicable for the determination of a dithiocarbamate in a mixture containing thiuram disulfide.

Identification, estimation and preparation of fatty acids by circular paper chromatography

A comparatively simple and rapid method for the identification, estimation and preparation of fatty acids has been developed, using reversed phase circular paper chromatography. The method is also suitable for the analysis of “Critical Pairs” of fatty acids and for the preparation of fatty acids. Further, when used at a higher temperature, the method is more sensitive in revealing the presence of even traces of higher fatty acids in the seeds of Adenanthera pavonina.

Jodimetric estimation of water-soluble dithiocarbamates

Iodimetric estimation of dialkyl dithiocarbamate in alcoholic solution is not accurate. The method has not met with success for the water-soluble dithiocarbamates before. A simple and accurate iodimetric method has been developed for the estimation of water-soluble dithiocarbamates. The success of the method is due to the removal of the oxidation product which interferes during the titration with iodine.

Estimation of the critical viscous sublayer in heat transfer problems

Imagining a disturbance made on a compressible boundary layer with the help of a heat source, the critical viscous sublayer, through which the skin friction at any point on a surface is connected with the heat transferred from a heated element embedded in it, has been estimated. Under similar conditions of external flow (Ray1)) the ratio of the critical viscous sublayer to the undisturbed boundary layer thickness is about one-tenth in the laminar case and one hundredth in the turbulent case. These results are similar to those (cf.1)) found in shock wave boundary layer interaction problems.

Potassium phosphate as a reagent for the gravimetric estimation of lithium

Previous attempts for the quantitative estimation of lithium as orthophosphate, employing an alkali metal phosphate, have not been successful. A method, is described for the estimation of lithium as trilithium phosphate from 60% ethyl alcohol solution at 65° to 70° C., employing potassium phosphate reagent, at pH 9.5. The method is applicable in the presence of varying amounts of sodium and/or potassium cations and chloride, sulfate, nitrate, and phosphate anions.

Effect of matrix cracking and material uncertainty on composite plates

A laminated composite plate model based on first order shear deformation theory is implemented using the finite element method.Matrix cracks are introduced into the finite element model by considering changes in the A, B and D matrices of composites. The effects of different boundary conditions, laminate types and ply angles on the behavior of composite plates with matrix cracks are studied.Finally, the effect of material property uncertainty, which is important for composite material on the composite plate, is investigated using Monte Carlo simulations. Probabilistic estimates of damage detection reliability in composite plates are made for static and dynamic measurements. It is found that the effect of uncertainty must be considered for accurate damage detection in composite structures. The estimates of variance obtained for observable system properties due to uncertainty can be used for developing more robust damage detection algorithms. (C) 2010 Elsevier Ltd. All rights reserved.

On Using Projective Relations for Calibration and Estimation in a Structured-Light Scanner

For structured-light scanners, the projective geometry between a projector-camera pair is identical to that of a camera-camera pair. Consequently, in conjunction with calibration, a variety of geometric relations are available for three-dimensional Euclidean reconstruction. In this paper, we use projector-camera epipolar properties and the projective invariance of the cross-ratio to solve for 3D geometry. A key contribution of our approach is the use of homographies induced by reference planes, along with a calibrated camera, resulting in a simple parametric representation for projector and system calibration. Compared to existing solutions that require an elaborate calibration process, our method is simple while ensuring geometric consistency. Our formulation using the invariance of the cross-ratio is also extensible to multiple estimates of 3D geometry that can be analysed in a statistical sense. The performance of our system is demonstrated on some cultural artifacts and geometric surfaces.

Topics in Spatial Econometrics - With Applications to House Prices

Topics in Spatial Econometrics — With Applications to House Prices Spatial effects in data occur when geographical closeness of observations influences the relation between the observations. When two points on a map are close to each other, the observed values on a variable at those points tend to be similar. The further away the two points are from each other, the less similar the observed values tend to be. Recent technical developments, geographical information systems (GIS) and global positioning systems (GPS) have brought about a renewed interest in spatial matters. For instance, it is possible to observe the exact location of an observation and combine it with other characteristics. Spatial econometrics integrates spatial aspects into econometric models and analysis. The thesis concentrates mainly on methodological issues, but the findings are illustrated by empirical studies on house price data. The thesis consists of an introductory chapter and four essays. The introductory chapter presents an overview of topics and problems in spatial econometrics. It discusses spatial effects, spatial weights matrices, especially k-nearest neighbours weights matrices, and various spatial econometric models, as well as estimation methods and inference. Further, the problem of omitted variables, a few computational and empirical aspects, the bootstrap procedure and the spatial J-test are presented. In addition, a discussion on hedonic house price models is included. In the first essay a comparison is made between spatial econometrics and time series analysis. By restricting the attention to unilateral spatial autoregressive processes, it is shown that a unilateral spatial autoregression, which enjoys similar properties as an autoregression with time series, can be defined. By an empirical study on house price data the second essay shows that it is possible to form coordinate-based, spatially autoregressive variables, which are at least to some extent able to replace the spatial structure in a spatial econometric model. In the third essay a strategy for specifying a k-nearest neighbours weights matrix by applying the spatial J-test is suggested, studied and demonstrated. In the final fourth essay the properties of the asymptotic spatial J-test are further examined. A simulation study shows that the spatial J-test can be used for distinguishing between general spatial models with different k-nearest neighbours weights matrices. A bootstrap spatial J-test is suggested to correct the size of the asymptotic test in small samples.

Effect of matrix strength on the mechanical properties of Al-Zn-Mg/SiCp composites

The mechanical properties of Al-Zn-Mg alloy reinforced with SiCP composites prepared by solidification route were studied by altering the matrix strength with different heat treatments. With respect to the control alloy, the composites have shown similar ageing behaviour in terms of microhardness data at 135 degrees C. It was shown that although composites exhibited enhanced modulus values, the strengthening was found to be dependent on the damage that is occurring during straining. Thus the initial matrix strength plays an important role in determining the strengthening. Consequently, compression data had shown a different trend compared to tension. (C) 2000 Elsevier Science Ltd. All rights reserved.

Estimation of signals in colored non gaussian noise based on Gaussian Mixture models

Non-Gaussianity of signals/noise often results in significant performance degradation for systems, which are designed using the Gaussian assumption. So non-Gaussian signals/noise require a different modelling and processing approach. In this paper, we discuss a new Bayesian estimation technique for non-Gaussian signals corrupted by colored non Gaussian noise. The method is based on using zero mean finite Gaussian Mixture Models (GMMs) for signal and noise. The estimation is done using an adaptive non-causal nonlinear filtering technique. The method involves deriving an estimator in terms of the GMM parameters, which are in turn estimated using the EM algorithm. The proposed filter is of finite length and offers computational feasibility. The simulations show that the proposed method gives a significant improvement compared to the linear filter for a wide variety of noise conditions, including impulsive noise. We also claim that the estimation of signal using the correlation with past and future samples leads to reduced mean squared error as compared to signal estimation based on past samples only.