Estimating arterial blood gases with the EABC® System (“Earlobe Arterialized Blood Collector”) in critically ill patients. A validation study.

The Earlobe Arterialized Blood Collector® is a minimally invasive system able to perform arterialized capillary blood gas analysis from the earlobe (EL). A prospective validation study was performed in 55 critical ill patients. Sampling failure rate was high (53.6%). Risk factors were age > 65 years, diabetes, vasoactive drug therapy and noradrenaline (NA) doses above 0.22 μg / kg / min. Multivariate analysis showed age > 65 years was the only factor independently associated with failure. Concordance analysis with arterial blood gases and Bland-Altman agreement evaluation were insufficient for validating the new system for all gasometrical variables.

Calorimetry of dehydrogenation and dangling-bond recombination in several hydrogenated amorphous silicon materials

Differential scanning calorimetry (DSC) was used to study the dehydrogenation processes that take place in three hydrogenated amorphous silicon materials: nanoparticles, polymorphous silicon, and conventional device-quality amorphous silicon. Comparison of DSC thermograms with evolved gas analysis (EGA) has led to the identification of four dehydrogenation processes arising from polymeric chains (A), SiH groups at the surfaces of internal voids (A'), SiH groups at interfaces (B), and in the bulk (C). All of them are slightly exothermic with enthalpies below 50 meV/H atoms , indicating that, after dissociation of any SiH group, most dangling bonds recombine. The kinetics of the three low-temperature processes [with DSC peak temperatures at around 320 (A),360 (A'), and 430°C (B)] exhibit a kinetic-compensation effect characterized by a linea relationship between the activation entropy and enthalpy, which constitutes their signature. Their Si-H bond-dissociation energies have been determined to be E (Si-H)0=3.14 (A), 3.19 (A'), and 3.28 eV (B). In these cases it was possible to extract the formation energy E(DB) of the dangling bonds that recombine after Si-H bond breaking [0.97 (A), 1.05 (A'), and 1.12 (B)]. It is concluded that E(DB) increases with the degree of confinement and that E(DB)>1.10 eV for the isolated dangling bond in the bulk. After Si-H dissociation and for the low-temperature processes, hydrogen is transported in molecular form and a low relaxation of the silicon network is promoted. This is in contrast to the high-temperature process for which the diffusion of H in atomic form induces a substantial lattice relaxation that, for the conventional amorphous sample, releases energy of around 600 meV per H atom. It is argued that the density of sites in the Si network for H trapping diminishes during atomic diffusion

Thermally-Induced Structural Transformations on Polymorphous Silicon

Polymorphous Si is a nanostructured form of hydrogenated amorphous Si that contains a small fraction of Si nanocrystals or clusters. Its thermally induced transformations such as relaxation, dehydrogenation, and crystallization have been studied by calorimetry and evolved gas analysis as a complementary technique. The observed behavior has been compared to that of conventional hydrogenated amorphous Si and amorphous Si nanoparticles. In the temperature range of our experiments (650700 C), crystallization takes place at almost the same temperature in polymorphous and in amorphous Si. In contrast, dehydrogenation processes reflect the presence of different hydrogen states. The calorimetry and evolved gas analysis thermograms clearly show that polymorphous Si shares hydrogen states of both amorphous Si and Si nanoparticles. Finally, the total energy of the main SiH group present in polymorphous Si has been quantified.

Thermally-Induced Structural Transformations on Polymorphous Silicon

Polymorphous Si is a nanostructured form of hydrogenated amorphous Si that contains a small fraction of Si nanocrystals or clusters. Its thermally induced transformations such as relaxation, dehydrogenation, and crystallization have been studied by calorimetry and evolved gas analysis as a complementary technique. The observed behavior has been compared to that of conventional hydrogenated amorphous Si and amorphous Si nanoparticles. In the temperature range of our experiments (650700 C), crystallization takes place at almost the same temperature in polymorphous and in amorphous Si. In contrast, dehydrogenation processes reflect the presence of different hydrogen states. The calorimetry and evolved gas analysis thermograms clearly show that polymorphous Si shares hydrogen states of both amorphous Si and Si nanoparticles. Finally, the total energy of the main SiH group present in polymorphous Si has been quantified

Composition of Greenhouse Gas Emissions in Spain: an Input-Output Analysis

Extending the traditional input-output model to account for the environmental impacts of production processes reveals the channels by which environmental burdens are transmitted throughout the economy. In particular, the environmental input-output approach is a useful technique for quantifying the changes in the levels of greenhouse emissions caused by changes in the final demand for production activities. The inputoutput model can also be used to determine the changes in the relative composition of greenhouse gas emissions due to exogenous inflows. In this paper we describe a method for evaluating how the exogenous changes in sectorial demand, such as changes in private consumption, public consumption, investment and exports, affect the relative contribution of the six major greenhouse gases regulated by the Kyoto Protocol to total greenhouse emissions. The empirical application is for Spain, and the economic and environmental data are for the year 2000. Our results show that there are significant differences in the effects of different sectors on the composition of greenhouse emissions. Therefore, the final impact on the relative contribution of pollutants will basically depend on the activity that receives the exogenous shock in final demand, because there are considerable differences in the way, and the extent to which, individual activities affect the relative composition of greenhouse gas emissions. Keywords: Greenhouse emissions, composition of emissions, sectorial demand, exogenous shock.

Economic structure and key sectors analysis of greenhouse gas emissions in Uruguay

This paper identifies the key sectors in greenhouse gas emissions of the Uruguayan economy through input-output analysis. This allows to precisely determine the role played by the different productive sectors and their relationship with other sectors in the relation between the Uruguayan productive structure and atmospheric pollution. In order to guide policy design for GHG reduction, we decompose sectors liability between the pollution generated through their own production processes and the pollution indirectly generated in the production processes of other sectors. The results show that all the key polluting sectors for the different contaminants considered are relevant because of their own emissions, except for the sector Motor vehicles and oil retail trade, which is relevant in CO2 emissions because of its pure, both backward and forward, linkages. Finally, the best policy channels for controlling and reducing GHGs emissions are identified, and compared with the National Climate Change Response Plan (NCCRP) lines of action.

On-line event detection by recursive dynamic principal component analysis and gas sensor arrays under drift conditions

Leakage detection is an important issue in many chemical sensing applications. Leakage detection hy thresholds suffers from important drawbacks when sensors have serious drifts or they are affected by cross-sensitivities. Here we present an adaptive method based in a Dynamic Principal Component Analysis that models the relationships between the sensors in the may. In normal conditions a certain variance distribution characterizes sensor signals. However, in the presence of a new source of variance the PCA decomposition changes drastically. In order to prevent the influence of sensor drifts the model is adaptive and it is calculated in a recursive manner with minimum computational effort. The behavior of this technique is studied with synthetic signals and with real signals arising by oil vapor leakages in an air compressor. Results clearly demonstrate the efficiency of the proposed method.

Drift compensation of gas sensor array data by common principal component analysis

A new drift compensation method based on Common Principal Component Analysis (CPCA) is proposed. The drift variance in data is found as the principal components computed by CPCA. This method finds components that are common for all gasses in feature space. The method is compared in classification task with respect to the other approaches published where the drift direction is estimated through a Principal Component Analysis (PCA) of a reference gas. The proposed new method ¿ employing no specific reference gas, but information from all gases ¿has shown the same performance as the traditional approach with the best-fitted reference gas. Results are shown with data lasting 7-months including three gases at different concentrations for an array of 17 polymeric sensors.

International Equity on Greenhouse Gas Emissions and World Levels: an integrated analysis through distributive welfare indices

Up until now, analyses of the international distribution of pollutant emissions have not paid sufficient attention to the implications that, in terms of social welfare, the combined evolution of the global world average entails. In this context, this paper proposes the use of environmental welfare indices, taken and adapted from the literature on social welfare and inequality, in order to make a comprehensive examination of the international equity factor and the mean factor in this field. The proposed methodology is implemented empirically in order to explore the evolution in distributive-based environmental welfare on a global level for the three main pollutants with greenhouse gas effects: CO2, CH4 and NO, both globally and for selected years during the period of 1990- 2005. The main results found are as follows: firstly, typically, the environmental welfare associated with the overall greenhouse gases decreased significantly over the period, due primarily to the role of CO2; secondly, in contrast, the global welfare associated with CH4 and NO improved; and thirdly, typically, the evolutions can be attributed to a greater extent to the mean component than to the distributive component, although there are exceptions. These results would seem to be relevant in policy terms. JEL codes: D39; Q43; Q56. Keywords: environmental welfare: greenhouse gases; environmental equity.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Structural decomposition analysis and input-output subsystems: An application to Spanish CO2 emissions

Analysis of gas emissions by the input-output subsystem approach provides detailed insight into pollution generation in an economy. Structural decomposition analysis, on the other hand, identifies the factors behind the changes in key variables over time. Extending the input-output subsystem model to account for the changes in these variables reveals the channels by which environmental burdens are caused and transmitted throughout the production system. In this paper we propose a decomposition of the changes in the components of CO2 emissions captured by an input-output subsystems representation. The empirical application is for the Spanish service sector, and the economic and environmental data are for years 1990 and 2000. Our results show that services increased their CO2 emissions mainly because of a rise in emissions generated by non-services to cover the final demand for services. In all service activities, the decomposed effects show an increase in CO2 emissions due to a decrease in emission coefficients (i.e., emissions per unit of output) compensated by an increase in emissions caused both by the input-output coefficients and the rise in demand for services. Finally, large asymmetries exist not only in the quantitative changes in the CO2 emissions of the various services but also in the decomposed effects of these changes. Keywords: structural decomposition analysis, input-output subsystems, CO2 emissions, service sector.

New stakeholders, spaces and instruments of analysis in the context of energy relationships: Case studies from Kazakhstan and Turkmenistan

The disintegration of the USSR brought the emergence of a new geo-energy space in Central Asia. This space arose in the context of a global energy transition, which began in the late 1970s. Therefore, this new space in a changing energy world requires both new conceptual frameworks of analysis and the creation of new analytical tools. Taking into account this fact, our paper attempts to apply the theoretical framework of the Global Commodity Chain (GCC) to the case of natural resources in Central Asia. The aim of the paper is to check if there could be any Central Asia’s geo-energy space, assuming that this space would exist if natural resources were managed with regional criteria. The paper is divided into four sections. First an introduction that describes the new global energy context within natural resources of Central Asia would be integrated. Secondly, the paper justifies why the GCC methodology is suitable for the study of the value chains of energy products. Thirdly, we build up three cases studies (oil and uranium from Kazakhstan and gas from Turkmenistan) which reveal a high degree of uncertainty over the direction these chains will take. Finally, we present the conclusions of this study that state that the most plausible scenario would be the integration of energy resources of these countries in GCC where the core of the decision-making process will be far away from the region of Central Asia. Key words: Energy transition, geo-energy space, Global Commodity Chains, Central Asia

Defect study of SnO2 nanostructures by cathodoluminescence analysis: Application to nanowires

Defects in SnO2 nanowires have been studied by cathodoluminescence, and the obtained spectra have been compared with those measured on SnO2 nanocrystals of different sizes in order to reveal information about point defects not determined by other characterization techniques. Dependence of the luminescence bands on the thermal treatment temperatures and pre-treatment conditions have been determined pointing out their possible relation, due to the used treatment conditions, with the oxygen vacancy concentration. To explain these cathodoluminescence spectra and their behavior, a model based on first-principles calculations of the surface oxygen vacancies in the different crystallographic directions is proposed for corroborating the existence of surface state bands localized at energy values compatible with the found cathodoluminescence bands and with the gas sensing mechanisms. CL bands centered at 1.90 and 2.20 eV are attributed to the surface oxygen vacancies 100° coordinated with tin atoms, whereas CL bands centered at 2.37 and 2.75 eV are related to the surface oxygen vacancies 130° coordinated. This combined process of cathodoluminescence and ab initio calculations is shown to be a powerful tool for nanowire defect analysis.

Nonlinear inverse dynamic models of gas sensing systems based on chemical sensor arrays for quantitative measurements

Gas sensing systems based on low-cost chemical sensor arrays are gaining interest for the analysis of multicomponent gas mixtures. These sensors show different problems, e.g., nonlinearities and slow time-response, which can be partially solved by digital signal processing. Our approach is based on building a nonlinear inverse dynamic system. Results for different identification techniques, including artificial neural networks and Wiener series, are compared in terms of measurement accuracy.