Chemical speciation and mobilization of copper and zinc in naturally contaminated mine soils with citric and tartaric acids

A one-step extraction procedure and a leaching column experiment were performed to assess the effects of citric and tartaric acids on Cu and Zn mobilization in naturally contaminated mine soils to facilitate assisted phytoextraction. A speciation modeling of the soil solution and the metal fractionation of soils were performed to elucidate the chemical processes that affected metal desorption by organic acids. Different extracting solutions were prepared, all of which contained 0.01 M KNO3 and different concentrations of organic acids: control without organic acids, 0.5 mM citric, 0.5 mM tartaric, 10 mM citric, 10 mM tartaric, and 5 mM citric +5 mM tartaric. The results of the extraction procedure showed that higher concentrations of organic acids increased metal desorption, and citric acid was more effective at facilitating metal desorption than tartaric acid. Metal desorption was mainly influenced by the decreasing pH and the dissolution of Fe and Mn oxides, not by the formation of soluble metal–organic complexes as was predicted by the speciation modeling. The results of the column study reported that low concentrations of organic acids did not significantly increase metal mobilization and that higher doses were also not able to mobilize Zn. However, 5–10 mM citric acid significantly promoted Cu mobilization (from 1 mg kg−1 in the control to 42 mg kg−1 with 10 mM citric acid) and reduced the exchangeable (from 21 to 3 mg kg−1) and the Fe and Mn oxides (from 443 to 277 mg kg−1) fractions. Citric acid could efficiently facilitate assisted phytoextraction techniques.

Emission factors from road dust resuspension in a Mediterranean freeway

Particulate matter emissions from paved roads are currently one of the main challenges for a sustainable transport in Europe. Emissions are scarcely estimated due to the lack of knowledge about the resuspension process severely hampering a reliable simulation of PM and heavy metals concentrations in large cities and evaluation of population exposure. In this study the Emission Factors from road dust resuspension on a Mediterranean freeway were estimated per single vehicle category and PM component (OC, EC, mineral dust and metals) by means of the deployment of vertical profiles of passive samplers and terminal concentration estimate. The estimated PM10 emission factors varied from 12 to 47 mg VKT?1 (VKT: Vehicle Kilometer Traveled) with an average value of 22.7 ? 14.2 mg VKT?1. Emission Factors for heavy and light duty vehicles, passenger cars and motorbikes were estimated, based on average fleet composition and EPA ratios, in 187e733 mg VKT?1, 33e131 VKT?1, 9.4e36.9 VKT?1 and 0.8e3.3 VKT?1, respectively. These range of values are lower than previous estimates in Mediterranean urban roads, probably due to the lower dust reservoir on freeways. PM emitted material was dominated by mineral dust (9e10 mg VKT?1), but also OC and EC were found to be major components and approximately 14 e25% and 2e9% of average PM exhaust emissions from diesel passenger cars on highways respectively.

Detección óptica de harinosidad en manzana mediante una nueva técnica láser.

Mealiness, a textural disorder that produces quality loss, combines softness and absence of juiciness. The only one (destructive) test to measure it, combines information from a mechanical test on fruit probes to classify the samples according to instrumental mealiness. Time-domain laser reflectance spectroscopy (TDRS) is able to assess simultaneously and independently the absorption of the light inside the irradiated body (µa coefficient) and the scattering of the photons across the tissues (µS, transport scattering coeff.) measured at each wavelength. Using VIS&NIR lasers as light sources, TDRS was applied to Golden Delicious and Cox apples (n=90), conforming batches of untreated samples and storage-treated (20°C&95%RH) to induce mealiness development. The collected database was clustered into different groups according to their instrumental mealiness. Optical variables were used to build discriminant functions, achieving classification scores 75-89% of correctly identified mealy apples.

Dopamine receptor 4 promoter polymorphism modulates memory and neuronal responses to salience

Animal models and human functional imaging data implicate the dopamine system in mediating enhanced encoding of novel stimuli into human memory. A separate line of investigation suggests an association between a functional polymorphism in the promoter region for the human dopamine 4 receptor gene (DRD4) and sensitivity to novelty. We demonstrate, in two independent samples, that the -521Cmayor queT DRD4 promoter polymorphism determines the magnitude of human memory enhancement for contextually novel, perceptual oddball stimuli in an allele dose-dependent manner. The genotype-dependent memory enhancement conferred by the C allele is associated with increased neuronal responses during successful encoding of perceptual oddballs in the ventral striatum, an effect which is again allele dose-dependent. Furthermore, with repeated presentations of oddball stimuli, this memory advantage decreases, an effect mirrored by adaptation of activation in the hippocampus and substantia nigra/ventral tegmental area in C carriers only. Thus, a dynamic modulation of human memory enhancement for perceptually salient stimuli is associated with activation of a dopaminergic-hippocampal system, which is critically dependent on a functional polymorphism in the DRD4 promoter region.

Valor documental de los herbarios históricos: aportación de la exsiccata Flora Forestal Española al conocimiento de las cubiertas forestales de las provincias de Huelva y Sevilla (España)

Los herbarios, además de información botánica, pueden contener datos históricos de valor científico para el estudio de los montes y de su historia, como los recogidos en las colecciones de material que fue recolectado para la descripción y cartografía de las cubiertas forestales españolas. En este trabajo se sintetizan los datos obtenidos de la exsiccata Flora Forestal Española creada por investigadores del Instituto Forestal de Investigaciones y Experiencias- IFIE que, junto con información obtenida de documentos inéditos, evidencian la vinculación de esta colección a la realización de los mapas forestales y estudios de flora y vegetación provinciales; en particular a los de las provincias de Huelva y Sevilla, que quedaron inéditos, permitiendo precisar mejor su datación y aportando datos botánicos relevantes e información sobre el paisaje forestal anterior a las grandes repoblaciones con pinos y eucaliptos realizadas en el suroeste peninsular.

Modelling uncertainty of flood quantile estimation at ungauged sites by Bayesian networks

Prediction at ungauged sites is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. Regression models relate physiographic and climatic basin characteristics to flood quantiles, which can be estimated from observed data at gauged sites. However, these models assume linear relationships between variables Prediction intervals are estimated by the variance of the residuals in the estimated model. Furthermore, the effect of the uncertainties in the explanatory variables on the dependent variable cannot be assessed. This paper presents a methodology to propagate the uncertainties that arise in the process of predicting flood quantiles at ungauged basins by a regression model. In addition, Bayesian networks were explored as a feasible tool for predicting flood quantiles at ungauged sites. Bayesian networks benefit from taking into account uncertainties thanks to their probabilistic nature. They are able to capture non-linear relationships between variables and they give a probability distribution of discharges as result. The methodology was applied to a case study in the Tagus basin in Spain.

Management of irrigation frequency and nitrogen fertilization to mitigate GHG and NO emissions from drip-fertigated crops

Drip irrigation combined with split application of fertilizer nitrogen (N) dissolved in the irrigation water (i.e. drip fertigation) is commonly considered best management practice for water and nutrient efficiency. As a consequence, its use is becoming widespread. Some of the main factors (water-filled pore space, NH4+ and NO3−) regulating the emissions of greenhouse gases (i.e. N2O, CO2 and CH4) and NO from agroecosystems can easily be manipulated by drip fertigation without yield penalties. In this study, we tested management options to reduce these emissions in a field experiment with a melon (Cucumis melo L.) crop. Treatments included drip irrigation frequency (weekly/daily) and type of N fertilizer (urea/calcium nitrate) applied by fertigation. Crop yield, environmental parameters, soil mineral N concentrations and fluxes of N2O, NO, CH4 and CO2 were measured during 85 days. Fertigation with urea instead of calcium nitrate increased N2O and NO emissions by a factor of 2.4 and 2.9, respectively (P < 0.005). Daily irrigation reduced NO emissions by 42% (P < 0.005) but increased CO2 emissions by 21% (P < 0.05) compared with weekly irrigation. We found no relation between irrigation frequency and N2O emissions. Based on yield-scaled Global Warming Potential as well as NO cumulative emissions, we conclude that weekly fertigation with a NO3−-based fertilizer is the best option to combine agronomic productivity with environmental sustainability. Our study shows that adequate management of drip fertigation, while contributing to the attainment of water and food security, may provide an opportunity for climate change mitigation.

Assessing the suitability of student interactions from Moodle data logs as predictors of cross-curricular competencies

In the past decades, online learning has transformed the educational landscape with the emergence of new ways to learn. This fact, together with recent changes in educational policy in Europe aiming to facilitate the incorporation of graduate students to the labor market, has provoked a shift on the delivery of instruction and on the role played by teachers and students, stressing the need for development of both basic and cross-curricular competencies. In parallel, the last years have witnessed the emergence of new educational disciplines that can take advantage of the information retrieved by technology-based online education in order to improve instruction, such as learning analytics. This study explores the applicability of learning analytics for prediction of development of two cross-curricular competencies – teamwork and commitment – based on the analysis of Moodle interaction data logs in a Master’s Degree program at Universidad a Distancia de Madrid (UDIMA) where the students were education professionals. The results from the study question the suitability of a general interaction-based approach and show no relation between online activity indicators and teamwork and commitment acquisition. The discussion of results includes multiple recommendations for further research on this topic.