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Biotechnology has emerged as an affordable, effective, and eco-friendly alternative to treat carbon disulfide ([CS.sub.2]) containing waste gases. Carbon disulfide is a chemical of widespread use both in the past and in the present. Its industry demand has dramatically changed over the last two decades and is expected to grow in future in step with those industries involving fibre, mining, rubber products, and the agro-chemical sector. This compound, classified as a hazardous air pollutant about 20 years ago, has been re-appraised, as increasingly restrictive pollution standards are expected to be introduced. An array of physical-chemical technologies for treating [CS.sub.2] containing air are already in use, but they have a series of drawbacks, such as high energy consumption (incineration, thermal oxidation), the immobilization of the contaminant solely from one phase to another (adsorption), and the generation of secondary by-products that require additional treatment (hydrolysis). Thus, technological research on affordable processing alternatives is of considerable interest for related industries using or producing [CS.sub.2]. The biological technologies whose efficiency is based on the activity of microorganisms to biodegrade air pollutants overcome many of the disadvantages of conventional techniques and are especially useful for the removal of relatively low concentrations of pollutants. In this paper, the main properties, sources, and uses of [CS.sub.2] are summarized, and alternative biotreatments for [CS.sub.2] are reviewed. Several applications of the technical and economical feasibility of biofilters and biotrickling filters are shown, although their widespread industrial application still requires further research. Key words: carbon disulfide, bioreactors, air pollution control, biotechnology, industrial emissions. La biotechnologie devient une alternative abordable, efficace et eecologiquement acceptable pour traiter les deechets gazeux contenant du disulfure de carbone ([CS.sub.2]). Le [CS.sub.2] est une substance chimique d'utilisation tresrepandue dans le passe; aussi bien que presentement. Sa demande industrielle a fortement change; au cours des deux dernieeres decades et l'on s'attend a ce qu'elle prenne de l'expansion dans le future, de concert avec l'engagement de ces industries dans les secteurs de la fibre, des mines, des produits du caoutchouc et du secteur agrochimique. Ce compose, classified comme dangereux polluant de l'air il y a environ 20 ans a ete reeevalue, car on s'attend a l'introduction de nouveaux standards de plus en plus restrictifs de pollution. On utilise deja un ensemble de technologies physico-chimiques pour traiter l'air contenant du [CS.sub.2], mais ils preesentent des probleemes comme une forte consommation d'energie (incineration, oxydation thermique) l'immobilisation du contaminant uniquement d'une phase a une autre (adsorption) et la generation de produits secondaires qui neecessitent un traitement additionnel (hydrolyses). Ainsi, la recherche technologique de procedes alternatifs abordables presente un inteeret considerable pour les industries utilisant ou produisant du disulfure de carbone. Les techniques biologiques, dont l'efficacite est basee sur l'activite de microorganismes degradant les polluants de l'air, evitent plusieurs des desavantages des techniques conventionnelles et deviennent particulieerement utiles pour eeliminer les contaminants a faibles concentrations. Les auteurs passent en revue et reesument les principales proprietees ainsi que les sources et les usages du [CS.sub.2], et examinent les traitements biologiques alternatifs. On presente plusieurs applications de la faisabilite technique et economique des biofiltres et des filtres par biotrickling, bien que leurs applications industrielles generaliseees demandent encore des recherches. Mots-cles: disulfide de carbone, bioreeacteurs, controle de la pollution de l'air, biotechnologie, emissions industrielles. [Traduit par la Redaction], 1. Introduction Carbon disulfide ([CS.sub.2], CAS Reg. No. 75-15-0) is also known as carbon bisulphide or dithiocarbonic anhydride (ATSDR 1996). Carbon disulfide is a colourless to faintly yellow liquid. The [...]

ABSTRACT The question of how to obtain the best inocula for conventional biofilters arises when an acclimation/adaptation procedure is to be applied. Bearing in mind that no standardized procedure for [...]

The ability of two downscaling experiments to correctly simulate thermodynamic conditions over the Iberian Peninsula (IP) is compared in this paper. To do so, three parameters used to evaluate the unstable conditions in the atmosphere are evaluated: the total totals index (TT), convective available potential energy (CAPE), and convective inhibition (CIN). The Weather and Research Forecasting (WRF) model is used for the simulations. The N experiment is driven by ERA-Interim's initial and boundary conditions. The D experiment has the same configuration as N, but the 3DVAR data assimilation step is additionally run at 00:00, 06:00, 12:00, and 18:00 UTC. Eight radiosondes are available over the IP, and the vertical temperature and moisture profiles from the radiosondes provided by the University of Wyoming and the Integrated Global Radiosonde Archive (IGRA) were used to calculate three parameters commonly used to represent atmospheric instability by our own methodology using the R package aiRthermo. According to the validation, the correlation, standard deviation (SD), and root mean squared error (RMSE) obtained by the D experiment for all the variables at most of the stations are better than those for N. The different methods produce small discrepancies between the values for TT, but these are larger for CAPE and CIN due to the dependency of these quantities on the initial conditions assumed for the calculation of a lifted air parcel. Similar results arise from the seasonal analysis concerning both WRF experiments: N tends to overestimate or underestimate (depending on the parameter) the variability of the reference values of the parameters, but D is able to capture it in most of the seasons. In general, D is able to produce more reliable results due to the more realistic values of dew point temperature and virtual temperature profiles over the IP. The heterogeneity of the studied variables is highlighted in the mean maps over the IP. According to those for D, the unstable air masses are found along the entire Atlantic coast during winter, but in summer they are located particularly over the Mediterranean coast. The convective inhibition is more extended towards inland at 00:00 UTC in those areas. However, high values are also observed near the southeastern corner of the IP (near Murcia) at 12:00 UTC. Finally, no linear relationship between TT, CAPE, or CIN was found, and consequently, CAPE and CIN should be preferred for the study of the instability of the atmosphere as more atmospheric layers are employed during their calculation than for the TT index. [ABSTRACT FROM AUTHOR]

A new diagram is proposed for the verification of vector quantities generated by multiple models against a set of observations. It has been designed with the objective, as in the Taylor diagram, of providing a visual diagnostic tool which allows an easy comparison of simulations by multiple models against a reference dataset. However, the Sailor diagram extends this ability to two-dimensional quantities such as currents, wind, horizontal fluxes of water vapour and other geophysical variables by adding features which allow us to evaluate directional properties of the data as well. The diagram is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. This matrix is separated in a part corresponding to the bias and the relative rotation of the two orthogonal directions (empirical orthogonal functions; EOFs) which best describe the vector data. Since there is no truncation of the retained EOFs, these orthogonal directions explain the total variability of the original dataset. We test the performance of this new diagram to identify the differences amongst the reference dataset and a series of model outputs by using some synthetic datasets and real-world examples with time series of variables such as wind, current and vertically integrated moisture transport. An alternative setup for spatially varying time-fixed fields is shown in the last examples, in which the spatial average of surface wind in the Northern and Southern Hemisphere according to different reanalyses and realizations from ensembles of CMIP5 models are compared. The Sailor diagrams presented here show that it is a tool which helps in identifying errors due to the bias or the orientation of the simulated vector time series or fields. The R implementation of the diagram presented together with this paper allows us also to easily retrieve the individual diagnostics of the different components of the mean squared error and additional diagnostics which can be presented in tabular form. [ABSTRACT FROM AUTHOR]

The ability of two downscaling experiments to correctly simulate the instability conditions that can trigger thunderstorms over the Iberian Peninsula is compared in this paper. To do so, three instability indices are evaluated: TT index, CAPE and CIN. The WRF model is used for the simulations. The N experiment is driven by ERA-Interim's initial and boundary conditions; The D experiment has the same configuration as N, but the 3DVAR data assimilation step is additionally run at 00, 06, 12 and 18 UTC. Eight radiosondes are available over the IP, and the values for these indices calculated from the University of Wyoming were chosen as reference in the validation of both simulations. Additionally, measured variables at different pressure levels from the radiosondes provided byWyoming were used to calculate the three instability indices by our own methodology using the R package aiRthermo. According to the validation, the correlation, SD and RMSE obtained by the experiment D for all the indices in most of the stations are better than those for N. The different methodologies produce small discrepancies between the values for TT, but these are larger for CAPE and CIN due to the dependency of these indices on the initial conditions assumed for the calculation of an air parcel's vertical evolution. Similar results arise from the seasonal analysis concerning both WRF experiments: N tends to overestimate or underestimate (depending on the index) the variability of the reference values, but D is able to capture it in most of the seasons. The heterogeneity of the indices is highlighted in the mean maps over the Iberian Peninsula. According to those from D, the ingredients for the development of convective precipitation during winter are found along the entire Atlantic coast, but in summer they are located particularly in the Mediterranean coast. The chances of developing thunderstorms in those areas at 12 UTC is much higher than at 00 UTC; The convective inhibition is more extended towards inland at 00 UTC in those areas, which prevents storms from developing. However, high values are observed near Murcia also at 12 UTC. [ABSTRACT FROM AUTHOR]

A new diagram is proposed for the verification of vector quantities generated by multiple models against a set of observations. It has been designed with the idea of extending the Taylor diagram to two dimensional quantities such as currents, wind, or horizontal fluxes of water vapour, salinity, energy and other geophysical variables. The diagram is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. This matrix is separated in a part corresponding to the bias and the relative rotation of the empirical orthogonal functions of the data. We test the performance of this new diagram to identify the differences amongst the reference dataset and the different model outputs by using examples with wind, current, vertically integrated moisture transport and wave energy flux time series. An alternative setup is shown in the last examples, where the spatial average of surface wind in the Northern and Southern Hemispheres according to different reanalyses and realizations of CMIP5 models are compared. The examples of use of the Sailor diagram presented show that it is a tool which helps in identifying errors due to the bias or the orientation of the simulated vector time series or fields. The R implementation of the diagram presented together with this paper allows also to easily retrieve the individual diagnostics of the different components of the mean squared error. [ABSTRACT FROM AUTHOR]

Abstract: An analysis of the atmospheric branch of the hydrological cycle by means of a 15 km resolution numerical integration performed using Weather Research and Forecasting (WRF) nested in ERA Interim is presented. Two WRF experiments covering the period 2010–2014 were prepared. The first one (N) was configured as in standard numerical downscaling experiments. The second one (D), with the same parameterizations, included a step of 3DVAR data assimilation every 6 h. Apart from comparing our results with ERA Interim data, several observational data sets were used to validate the precipitable water (radiosondes and MODIS data), precipitation (EOBS, ECA&D, TRMM, and GPCP), or evaporation (GLEAM). The verification results showed that the D experiment systematically performs better than N and in many instances, too, better than the forcing reanalysis. According to the results, the leading terms of the water balance are the tendency of the precipitable water, the divergence of moisture flux, evaporation, and precipitation. No spatial patterns were recognizable for the annual accumulated evaporation, but the effect of the Atlantic fronts was detected in the precipitation patterns. The transboundary moisture fluxes through the contour of the Iberian Peninsula behave differently depending on the season during 2010–2014. During winter, they show a net moisture import through the boundaries. During spring, summer, or autumn moisture is exported specially through the Mediterranean coast, and only during midday, this feature is reversed due to sea breezes. [ABSTRACT FROM AUTHOR]

In 2008, the first commercial wave farm came online in Portugal. As with other types of renewable energy, the electricity obtained from waves has the drawback of intermittency. Knowing a few hours ahead how much energy waves will hold can contribute to a better management of the electricity grid. In this work, three types of statistical models have been used to create up to 24-h forecasts of the zonal and meridional components of wave energy flux (WEF) levels at three directional buoys located off the coast in the Bay of Biscay. Each model’s performance has been compared at a 95% confidence level with the simplest prediction (persistence of levels), along with the forecasts provided by the physics-based WAve Modeling (WAM) wave model at the nearest grid point. The results indicate that for forecasting horizons between 3 and roughly 16 h ahead, the statistical models built on random forests (RFs) outperform the rest, including WAM and persistence. [ABSTRACT FROM PUBLISHER]

This study evaluates the performance of different analog-based downscaling models for probabilistic quantitative precipitation forecasts over the metropolitan area of Bilbao, Spain. The analog-based statistical downscaling models used are a probability of the exceedance models in which the probability is derived from the probabilities given by a set of analogs and three Bayesian models. Results show that the differences in the performance of the models are subtle. The simplest model, which makes no use of Bayesian methods, performs better than the other models in the forecast of very low precipitation events, very likely due to the quality of the found analogs, since these categories are very populated in the phase space and forecast is very easy. However, as precipitation rates increase, Bayesian models perform better than the simple one based on probability of exceedance from individual analogs. The Bayesian models involving precipitation in the computation of the likelihood show in general the smallest biases in the forecast versus observed probabilities. [ABSTRACT FROM AUTHOR]

Abstract: In Bilbao (Spain), an air quality network measures sulphur dioxide levels at 4 locations. The objective of this paper is to develop a practical methodology to identify redundant sensors and evaluate a network''s capability to correctly follow and represent SO2 fields in Bilbao, in the frame of a continuous network optimization process. The methodology is developed and tested at this particular location, but it is general enough to be useable at other places as well, since it is not tied neither to the particular geographical characteristics of the place nor to the phenomenology of the air quality over the area. To assess the spatial variability of SO2 measured at 4 locations in the area, three different techniques have been used: Self-Organizing Maps (SOMs), cluster analysis (CA) and Principal Component Analysis (PCA). The results show that the three techniques yield the same results, but the information obtained via PCA can be helpful not only for that purpose but also to throw light on the major mechanisms involved. This might be used in future network optimization stages. The main advantage of cluster analysis and SOMs is that they provide readily interpretable results. All the calculations have been carried out using the freely available software R. [Copyright &y& Elsevier]

In the last eight years, the new faculty of Engineering in Renewable Energies at the University of the Basque Country in Eibar has developed several sustainability goals related to clean energy and climate change, but also in educative terms related to co-operative learning, motivation, and reflective thinking. The case of the laboratory-windpump challenge is paradigmatic in this sense, since it constitutes successful problem-based learning for the students in terms of the activation of heuristic tools (analogies or diagrams), critical discussions combining complex ideas about aerodynamics, mechanics and hydraulics, and a good group atmosphere. The conclusions of this work are supported by qualitative and quantitative results within a theoretical background based on the logic of discovery and its corresponding constructive-learning strategy, rather than on the logic of justification with given and well-known aprioristic assumptions. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of statistical models applied to the output of numerical models for short-term (1–24 h) hourly wind forecasts at three locations in the Basque Country. The target variables are horizontal wind components and the maximum wind gust at 3 h intervals. Statistical approaches such as persistence, analogues, linear regression, and random forest (RF) are used. The verification statistics used are coefficient of determination (R2) and root mean square error (RMSE). Statistical models use three inputs: (1) Local wind observations; (2) extended EOFs (empirical orthogonal functions) derived from past local observations and ERA-Interim variables in a previous 24-h period covering a domain around the area of study; and (3) wind forecasts provided by ERA-Interim. Results indicate that, for horizons less than 1–4 h, persistence is the best model. For longer predictions, RF provides the best forecasts. For horizontal components at 4–24 h horizons, RF slightly outperformed ERA-Interim wind forecasts. For gust, RF performs better than ERA-Interim for all the horizons. Persistence is the most influential factor for 2–5 h. Beyond this horizon, predictors from the ERA-Interim wind forecasts led the contribution. Hybrid numerical–statistical methods can be used to improve short-term wind forecasts. [ABSTRACT FROM AUTHOR]

In this paper, we have estimated the spatiotemporal distribution of moisture recycling over the Iberian Peninsula (IP). The recycling ratio was computed from two simulations over the IP using the Weather Research and Forecasting (WRF) model with a horizontal resolution of 15 km spanning the period 2010–2014. The first simulation (WRF N) was nested inside the ERA-Interim with information passed to the domain through the boundaries. The second run (WRF D) is similar to WRF N, but it also includes 3DVAR data assimilation every six hours (12:00 a.m., 6:00 a.m., 12:00 p.m. and 6:00 p.m. UTC). It was also extended until 2018. The lowest values of moisture recycling (3%) are obtained from November to February, while the highest ones (16%) are observed in spring in both simulations. Moisture recycling is confined to the southeastern corner during winter. During spring and summer, a gradient towards the northeastern corner of the IP is observed in both simulations. The differences between both simulations are associated with the dryness of the soil in the model and are higher during summer and autumn. WRF D presents a lower bias and produces more reliable results because of a better representation of the atmospheric moisture. [ABSTRACT FROM AUTHOR]

The only regional evaluation of Lebanese wind-energy potential (National Wind Atlas) dates back to 2011 and was carried out by a United Nations agency. In this work, data from the most recent reanalysis (ERA5) developed at the European Center for Medium Range Weather Forecast (ECMWF), corresponding to the 2010–2017 period, were used to evaluate Lebanese offshore-wind-energy potential. In the present study, wind power density associated to a SIEMENS 154/6 turbine was calculated with a horizontal resolution of 31 km and 1 hour time steps. This work incorporated the impact of air density changes into the calculations due to the seasonal evolution of pressure, temperature, and humidity. Observed average offshore air density ρ 0 was 1.19 kg / m 3 for the 2010–2017 period, but if instead of ρ 0 , hourly ρ values were used, seasonal oscillations of wind power density (W P D) represented differences in percentage terms ranging from −4% in summer to +3% in winter. ERA5 provides hourly wind, temperature, pressure, and dew-point temperature values that allowed us to calculate the hourly evolution of air density during this period and could also be used to accurately evaluate wind power density off the Lebanese coast. There was a significant gradient in wind power density along the shore, with the northern coastal area exhibiting the highest potential and reaching winter values of around 400 W / m 2 . Finally, this study suggests that the initial results provided by the National Wind Atlas overestimated the true offshore-wind-energy potential, thus highlighting the suitability of ERA5 as an accurate tool for similar tasks globally. [ABSTRACT FROM AUTHOR]

A constant value of air density based on its annual average value at a given location is commonly used for the computation of the annual energy production in wind industry. Thus, the correction required in the estimation of daily, monthly or seasonal wind energy production, due to the use of air density, is ordinarily omitted in existing literature. The general method, based on the implementation of the wind speed's Weibull distribution over the power curve of the turbine, omits it if the power curve is not corrected according to the air density of the site. In this study, the seasonal variation of air density was shown to be highly relevant for the computation of offshore wind energy potential around the Iberian Peninsula. If the temperature, pressure, and moisture are taken into account, the wind power density and turbine capacity factor corrections derived from these variations are also significant. In order to demonstrate this, the advanced Weather Research and Forecasting mesoscale Model (WRF) using data assimilation was executed in the study area to obtain a spatial representation of these corrections. According to the results, the wind power density, estimated by taking into account the air density correction, exhibits a difference of 8% between summer and winter, compared with that estimated without the density correction. This implies that seasonal capacity factor estimation corrections of up to 1% in percentage points are necessary for wind turbines mainly for summer and winter, due to air density changes. [ABSTRACT FROM AUTHOR]

Hywind-Scotland is a wind farm in Scotland that for many reasons is at the leading edge of technology and is located at a paradigmatic study area for offshore wind energy assessment. The objective of this paper is to compute the Capacity Factor ( C F ) changes and instantaneous power generation changes due to seasonal and hourly fluctuations in air density. For that reason, the novel ERA5 reanalysis is used as a source of temperature, pressure, and wind speed data. Seasonal results for winter show that C F values increase by 3% due to low temperatures and denser air, with economical profit consequences of tens of thousands (US$). Hourly results show variations of 7% in air density and of 26% in power generation via FAST simulations, emphasizing the need to include air density in short-term wind energy studying. [ABSTRACT FROM AUTHOR]

In addition to human error, manufacturing tolerances for blades and hubs cause pitch angle misalignment in wind turbines. As a consequence, a significant number of turbines used by existing wind farms experience power production loss and a reduced turbine lifetime. Existing techniques, such as photometric technology and laser-based methods, have been used in the wind industry for on-field pitch measurements. However, in some cases, regular techniques have difficulty achieving good and accurate measurements of pitch angle settings, resulting in pitch angle errors that require cost-effective correction on wind farms. Here, the authors present a novel patented method based on laser scanner measurements. The authors applied this new method and achieved successful improvements in the Annual Energy Production of various wind farms. This technique is a benchmarking-based approach for pitch angle calibration. Two case studies are introduced to demonstrate the effectiveness of the pitch angle calibration method to yield Annual Energy Production increase. [ABSTRACT FROM AUTHOR]

The wave energy resource in the Chilean coast shows particularly profitable characteristics for wave energy production, with relatively high mean wave power and low inter-annual resource variability. This combination is as interesting as unusual, since high energetic locations are usually also highly variable, such as the west coast of Ireland. Long-term wave resource variations are also an important aspect when designing wave energy converters (WECs), which are often neglected in resource assessment. The present paper studies the long-term resource variability of the Chilean coast, dividing the 20th century into five do-decades and analysing the variations between the different do-decades. To that end, the ERA20C reanalysis of the European Centre for Medium-Range Weather Forecasts is calibrated versus the ERA-Interim reanalysis and validated against buoy measurements collected in different points of the Chilean coast. Historical resource variations off the Chilean coast are compared to resource variations off the west coast in Ireland, showing a significantly more consistent wave resource. In addition, the impact of historical wave resource variations on a realistic WEC, similar to the Corpower device, is studied, comparing the results to those obtained off the west coast of Ireland. The annual power production off the Chilean coast is demonstrated to be remarkably more regular over the 20th century, with variations of just 1% between the different do-decades. [ABSTRACT FROM AUTHOR]

The results are at least as good as those obtained with much more sophisticated models. Furthermore, the models can be easily run on a simple PC. This approach may provide a good diagnostic network (as seen in Bilbao) with real-time, short-term prognostic capabilities for a given location. The models have been built for Bilbao and are applicable only in this location. However, using the same methodology, similar results could be obtained in other environments if a good diagnostic network is available. [ABSTRACT FROM AUTHOR]