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

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

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