Different strategies for recovering metals from CARON process residue

http://dx.doi.org/10.1016/j.jhazmat.2011.03.048The capacity of Acidithiobacillus thiooxidans DMS 11478 to recover the heavy metals contained in the residue obtained from the CARON process has been evaluated. Different bioreactor configurations were studied: a two-stage batch system and two semi-continuous systems (stirred-tank reactor leaching and column leaching). In the two-stage system, 46.8% Co, 36.0% Mg, 26.3% Mn and 22.3% Ni were solubilised after 6 h of contact between the residue and the bacteria-free bioacid. The results obtained with the stirred-tank reactor and the column were similar: 50% of the Mg and Co and 40% of the Mn and Ni were solubilised after thirty one days. The operation in the column reactor allowed the solid–liquid ratio to be increased and the pH to be kept at low values (<1.0). Recirculation of the leachate in the column had a positive effect on metal removal; at sixty five days (optimum time) the solubilisation levels were as follows: 86% Co, 83% Mg, 72% Mn and Ni, 62% Fe and 23% Cr. The results corroborate the feasibility of the systems studied for the leaching of metals from CARON process residue and these methodologies can be considered viable for the recovery of valuable metals

Characterization of Pporous materials as radon source and its radiological implications

In this work, a magnitude is proposed in order to compare the potential radiological risk due to radon exposition generated by different materials, and a method based in the ^^^Rn accumulation technique is presented for its determination.. The obtained results indicate that the proposed magnitude and their corresponding measurement methodology are useful in order to take decisions about the management of different kinds of porous materials

Servidor de datos y página web para el aprendizaje de SIG en la ingeniería forestal

Los Sistemas de Información geográfica (SIG) son una herramienta de trabajo habitual en el ámbito de la ingeniería forestal, tanto en la faceta de redacción de proyectos, como en la investigación sobre el medio ambiente y el territorio. Cada vez hay más información cartográfica disponible desde servidores de diferentes instituciones, por lo que consideramos que es muy útil contar con una herramienta de organización de la información. En el trabajo que se presenta, se pretende proporcionar a los estudiantes e investigadores en materia forestal un portal que contenga información actualizada y ordenada sobre los recursos existentes compatibles con los SIG. Por tanto constituiráuna herramienta de apoyo que facilitarála fase de documentación, búsqueda de datos compatibles y aprendizaje de las herramientas que sirven de base para el desarrollo de cualquier trabajo técnico o de investigación relacionado con el medio ambiente y el territorio que se apoye en los SIG

Siting Background Towers to Characterize Incoming Air for Urban Greenhouse Gas Estimation: A Case Study in the Washington, DC/Baltimore Area

There is increased interest in understanding urban greenhouse gas (GHG) emissions. To accurately estimate city emissions, the influence of extraurban fluxes must first be removed from urban greenhouse gas (GHG) observations. This is especially true for regions, such as the U.S. Northeastern Corridorâ Baltimore/Washington, DC (NECâ B/W), downwind of large fluxes. To help site background towers for the NECâ B/W, we use a coupled Bayesian Information Criteria and geostatistical regression approach to help site four background locations that best explain CO2 variability due to extraurban fluxes modeled at 12 urban towers. The synthetic experiment uses an atmospheric transport and dispersion model coupled with two different flux inventories to create modeled observations and evaluate 15 candidate towers located along the urban domain for February and July 2013. The analysis shows that the average ratios of extraurban inflow to total modeled enhancements at urban towers are 21% to 36% in February and 31% to 43% in July. In July, the incoming air dominates the total variability of synthetic enhancements at the urban towers (R2 = 0.58). Modeled observations from the selected background towers generally capture the variability in the synthetic CO2 enhancements at urban towers (R2 = 0.75, rootâ meanâ square error (RMSE) = 3.64 ppm; R2 = 0.43, RMSE = 4.96 ppm for February and July). However, errors associated with representing background air can be up to 10 ppm for any given observation even with an optimal background tower configuration. More sophisticated methods may be necessary to represent background air to accurately estimate urban GHG emissions.Key PointsFactoring in the variability of greenhouse gas enhancements in incoming air is critical for estimating emissions in an urban domainStatistical methods were used to site four towers sampling background air in the Washington, DC/Baltimore regionOptimal background tower configurations for representing incoming air can still have large errors for any given urban GHG observationPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142902/1/jgrd54353_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142902/2/jgrd54353.pd

Investigating Sources of Variability and Error in Simulations of Carbon Dioxide in an Urban Region

Greenhouse gas (GHG) emissions estimation methods that use atmospheric trace gas observations, including inverse modeling techniques, perform better when carbon dioxide (CO2) fluxes are more accurately transported and dispersed in the atmosphere by a numerical model. In urban areas, transport and dispersion is particularly difficult to simulate using current mesoscale meteorological models due, in part, to added complexity from surface heterogeneity and fine spatial/temporal scales. It is generally assumed that the errors in GHG estimation methods in urban areas are dominated by errors in transport and dispersion. Other significant errors include, but are not limited to, those from assumed emissions magnitude and spatial distribution. To assess the predictability of simulated trace gas mole fractions in urban observing systems using a numerical weather prediction model, we employ an Eulerian model that combines traditional meteorological variables with multiple passive tracers of atmospheric CO2 from anthropogenic inventories and a biospheric model. The predictability of the Eulerian model is assessed by comparing simulated atmospheric CO2 mole fractions to observations from four in situ tower sites (three urban and one rural) in the Washington DC/Baltimore, MD area for February 2016. Four different gridded fossil fuel emissions inventories along with a biospheric flux model are used to create an ensemble of simulated atmospheric CO2 observations within the model. These ensembles help to evaluate whether the modeled observations are impacted more by the underlying emissions or transport. The spread of modeled observations using the four emission fields indicates the model's ability to distinguish between the different inventories under various meteorological conditions. Overall, the Eulerian model performs well; simulated and observed average CO2 mole fractions agree within 1% when averaged at the three urban sites across the month. However, there can be differences greater than 10% at any given hour, which are attributed to complex meteorological conditions rather than differences in the inventories themselves. On average, the mean absolute error of the simulated compared to actual observations is generally twice as large as the standard deviation of the modeled mole fractions across the four emission inventories. This result supports the assumption, in urban domains, that the predicted mole fraction error relative to observations is dominated by errors in model meteorology rather than errors in the underlying fluxes in winter months. As such, minimizing errors associated with atmospheric transport and dispersion may help improve the performance of GHG estimation models more so than improving flux priors in the winter months. We also find that the errors associated with atmospheric transport in urban domains are not restricted to certain times of day. This suggests that atmospheric inversions should use CO2 observations that have been filtered using meteorological observations rather than assuming that meteorological modeling is most accurate at certain times of day (such as using only mid-afternoon observations)

Comparative study of Spanish norms used to quantify gypsum content in civil and building construction

The "Pliego de Prescripciones Técnicas Generales para Obras de Carreteras y Puentes (PG3)" is the Spanish's Government Technical Instruction that stablishes the properties that materials used in road and bridge construction must accomplish, and includes the corresponding standardized norms to test these properties

A new filtering technique for removing anti-Stokes emission background in gated CW-STED microscopy

Stimulated emission depletion (STED) microscopy is a prominent approach of super-resolution optical microscopy, which allows cellular imaging with so far unprecedented unlimited spatial resolution. The introduction of time-gated detection in STED microscopy significantly reduces the (instantaneous) intensity required to obtain sub-diffraction spatial resolution. If the time-gating is combined with a STED beam operating in continuous wave (CW), a cheap and low labour demand implementation is obtained, the so called gated CW-STED microscope. However, time-gating also reduces the fluorescence signal which forms the image. Thereby, background sources such as fluorescence emission excited by the STED laser (anti-Stokes fluorescence) can reduce the effective resolution of the system. We propose a straightforward method for subtraction of anti-Stokes background. The method hinges on the uncorrelated nature of the anti-Stokes emission background with respect to the wanted fluorescence signal. The specific importance of the method towards the combination of two-photon-excitation with gated CW-STED microscopy is demonstrated. © 2014 The Authors. J. Biophotonics

Development of Automatic Speech Recognition for the Documentation of Cook Islands Māori

This paper describes the process of data processing and training of an automatic speech recognition (ASR) system for Cook Islands Māori (CIM), an Indigenous language spoken by approximately 22,000 people in the South Pacific. We transcribed four hours of speech from adults and elderly speakers of the language and prepared two experiments. First, we trained three ASR systems: one statistical, Kaldi; and two based on Deep Learning, DeepSpeech and XLSR-Wav2Vec2. Wav2Vec2 tied with Kaldi for lowest character error rate (CER=6±1) and was slightly behind in word error rate (WER=23±2 versus WER=18±2 for Kaldi). This provides evidence that Deep Learning ASR systems are reaching the performance of statistical methods on small datasets, and that they can work effectively with extremely low-resource Indigenous languages like CIM. In the second experiment we used Wav2Vec2 to train models with held-out speakers. While the performance decreased (CER=15±7, WER=46±16), the system still showed considerable learning. We intend to use ASR to accelerate the documentation of CIM, using newly transcribed texts to improve the ASR and also generate teaching and language revitalization materials. The trained model is available under a license based on the Kaitiakitanga License, which provides for non-commercial use while retaining control of the model by the Indigenous community.falseMarseille, Franc