Increasing the Prediction Efficiency of Hansen Solubility Parameters in Supercritical Fluids

This work describes a simplified method developed for calculating the Hansen parameters (HSPs) for scCO2-polar modifier solvent mixtures. The method consists in fitting 2nd order equations on the calculated values of HSPs of pure components in function of pressure and temperature. It has been proved that these equations are suitable for the characterization of the above system. The current work also proposes a modified representation method, which eliminates the shortcomings of the original ternary Teas diagram, normally used for the representation of the Hansen parameters. On the one hand, the Teas diagram uses quantities without any physical meaning and, on the other hand, the illustration of the solubility information is distorted because it does not take into account the differences of the Hildebrand parameters of different solvents. The factors we have chosen to represent on the ternary diagram possess physical meaning (cohesion energy density partitions). The distortion was eliminated by extending the Teas diagram to a prismatic three dimensional representation. We proved that the Hansen-ellipsoid from the Cartesian coordinate system (dd = f (δH, dp)) is transformed in an ellipsoid also in the new coordinate system (the transformation is pseudo-isomorphic). Nonetheless, the suggested corrections improve the accuracy of the Hansen method, in some cases the interactions between the solvents and the dissolved materials are still not predicted with sufficient accuracy. Most probably a thermodynamic-based correction of the values of the HSPs of small molecules could lead to a significant improvement of the predictive ability of the newly developed method

Cultivating conditions optimization of the anaerobic digestion of corn ethanol distillery residuals using response surface methodology.

AbstractThis study investigated the individual and interactive effects of three factors — temperature, inoculum/substrate ratio (ISR) and inoculum typology — on the anaerobic digestion of corn ethanol distillery wastewater. Biochemical methane potential assays planned with factorial design with two independent quantitative variables on three levels (ISR: 1:1, 2:1 and 3:1; temperature: 30°C, 33.5°C, 37°C) and one independent qualitative variable (inoculum type: suspended, granular, mixed) have been performed. Response Surface Methodology has been used to study the effect of the factors with the aim of maximizing the specific methane yields (YCH4) obtainable with this substrate. The results show that all three investigated factors influence in a significant matter the YCH4, the ISR having the strongest effect on it. The temperature has significant influence on the YCH4 only in combination with high ISR values. The optimal conditions for the maximum YCH4 (551 mL CH4 g−1 VSadded) have been found at 37°C operating temperature, ISR=3:1 and using granular inoculum. These conditions gave rise to a 4-fold increase of YCH4 with respect to the worst combination of factors (YCH4=129 mL g−1 VSadded for the suspended inoculum type, at 30°C and ISR=1:1). The results improve the knowledge on the digestion of this substrate, providing information for successful process up-scaling

Halobacterial Community Analysis of Mierlei Saline Lake in Transylvania (Romania)

In this study a combination of both molecular and biochemical methods have been used to characterize the bacterial microbiota in water and sediment of a saline lake in Transylvania. The physicochemical characterization of the water samples from different lake depths indicated a stratification of the lake that affects the distribution of resident microorganisms, confirmed also by the significant differences in terms of functional diversity of the microbial communities in different water layers. The superficial bacterial community shows a good oxidative capability, degrading a wide range oforganic substrates, yet the bottom layer community exhibits a major level of specialization. The membrane fatty acid analysis of the sediment bacterial community shows the prevalent presence of Gram negative bacteria, confirmed by the culturing techniques. Among the 24 collected isolates, 16S rRNA gene sequencing analysis permitted to identify 10 different species, belonging to Bacillus, Halomonas, Idiomarina, Marinobacter, Pseudoalteromonas, Salinivibrio, Staphylococcus genera and prevalently classified as halophiles

Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

Abstract. Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R2 0.6), gross primary production (R2> 0.7), latent heat (R2 > 0.7), sensible heat (R2 > 0.7), and net radiation (R2 > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R2 > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R2 < 0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). The evaluated large ensemble of ML-based models will be the basis of new global flux products

Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R20.6), gross primary production (R2>0.7), latent heat (R2>0.7), sensible heat (R2>0.7), and net radiation (R2>0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R2>0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R2<0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). The evaluated large ensemble of ML-based models will be the basis of new global flux productsISSN:1810-6277ISSN:1810-628

Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks

Empirical modeling approaches are frequently used to upscale local eddy covariance observations of carbon, water, and energy fluxes to regional and global scales. The predictive capacity of such models largely depends on the data used for parameterization and identification of input-output relationships, while prediction for conditions outside the training domain is generally uncertain. In this work, artificial neural networks (ANNs) were used for the prediction of gross primary production (GPP) and latent heat flux (LE) on local and European scales with the aim to assess the portion of uncertainties in extrapolation due to sample selection. ANNs were found to be a useful tool for GPP and LE prediction, in particular for extrapolation in time (mean absolute error MAE for GPP between 0.53 and 1.56 gC m-2 d-1). Extrapolation in space in similar climatic and vegetation conditions also gave good results (GPP MAE 0.7-1.41 gC m-2 d-1), while extrapolation in areas with different seasonal cycles and controlling factors (e.g., the tropical regions) showed noticeably higher errors (GPP MAE 0.8-2.09 gC m-2 d-1). The distribution and the number of sites used for ANN training had a remarkable effect on prediction uncertainty in both, regional GPP and LE budgets and their interannual variability. Results obtained show that for ANN upscaling for continents with relatively small networks of sites, the error due to the sampling can be large and needs to be considered and quantified. The analysis of the spatial variability of the uncertainty helped to identify the meteorological drivers driving the uncertainty. Key Points Uncertainty due to spatial sampling is evaluated using ANNs and FLUXNET data GPP and LE budgets and IAV are analyzed with different site networks The uncertainty in upscaling due to spatial sampling is highly heterogeneous</p

Global distribution of groundwater-vegetation spatial covariation

Groundwater is an integral component of the water cycle, and it also influences the carbon cycle by supplying moisture to ecosystems. However, the extent and determinants of groundwater-vegetation interactions are poorly understood at the global scale. Using several high-resolution data products, we show that the spatial patterns of ecosystem gross primary productivity and groundwater table depth are correlated during at least one season in more than two-thirds of the global vegetated area. Positive relationships, i.e., larger productivity under shallower groundwater table, predominate in moisture-limited dry to mesic conditions with herbaceous and shrub vegetation. Negative relationships, i.e., larger productivity under deeper groundwater, predominate in humid climates with forests, possibly, indicating a drawdown of groundwater table due to substantial ecosystem water use. Interestingly, these opposite groundwater-vegetation interactions are primarily associated with differences in vegetation than with climate and surface characteristics. These findings put forth the first evidence, and a need for better representation, of an extensive and non-negligible groundwater-vegetation interactions at the global scale