A comparison of eight metamodeling techniques for the simulation of N2O fluxes and N leaching from corn crops

International audienceThe environmental costs of intensive farming activities are often under-estimated or not traded by the market, even though they play an important role in addressing future society's needs. The estimation of nitrogen (N) dynamics is thus an important issue which demands detailed simulation based methods and their integrated use to correctly represent complex and non-linear interactions into cropping systems. To calculate the N2O flux and N leaching from European arable lands, a modeling framework has been developed by linking the CAPRI agro-economic dataset with the DNDC-EUROPE bio-geo-chemical model. But, despite the great power of modern calculators, their use at continental scale is often too computationally costly. By comparing several statistical methods this paper aims to design a metamodel able to approximate the expensive code of the detailed modeling approach, devising the best compromise between estimation performance and simulation speed. We describe the use of two parametric (linear) models and six non-parametric approaches: two methods based on splines (ACOSSO and SDR), one method based on kriging (DACE), a neural networks method (multilayer perceptron, MLP), SVM and a bagging method (random forest, RF). This analysis shows that, as long as few data are available to train the model, splines approaches lead to best results, while when the size of training dataset increases, SVM and RF provide faster and more accurate solutions

A comparison of three learning methods to predict N2O fluxes and N leaching

International audienceThe environmental costs of intensive farming activities are often under-estimated or not included into rural development plans, even though they play an important role in addressing future society's needs. This paper focuses on the use of statistical learning methods to predict N2O emissions and N leaching under several conservative scenarios, in order to provide an alternative approach to deterministic models on a macro-scale. To that aim, three learning methods, namely neural networks (multilayer perceptrons), SVM and random forests, are compared and provide accurate solutions

A Comparison of Three Learning Methods to Predict N2O Fluxes and N Leaching

The environmental costs of intensive farming activities are often under-estimated or not included into the rural development plans, even though they play an important role in addressing future society¿s needs. This paper focus on the use of statistical learning methods to predict the N2O emissions and N leaching under several conservative scenarios, in order to provide an alternative approach to deterministic models at macro-scale. To that aim, three learning methods, namely neural networks (multilayer perceptrons), SVM and random forests, are compared and provide accurate solutions.JRC.DDG.H.2-Climate chang

Pure and binary adsorption of CO2, H2, and N2 on activated carbon

A new developing field of application for pressure swing adsorption (PSA) processes is the capture of CO2 to mitigate climate change, especially the separation of CO2 and H2 in a pre-combustion context. In this process scheme the conditions of the feed to the separation step, namely a pressure of 3.5 to 4.5MPa and a CO2 fraction of around 40% are favorable for an adsorption based separation process and make PSA a promising technology. Among the commercial adsorbent materials, activated carbon is most suitable for this application. To evaluate the potential, to benchmark new materials, and for process development a sound basis of the activated carbon thermodynamic data is required, namely equilibrium adsorption isotherms of the relevant pure components and mixtures, Henry's constants and isosteric heats. In this work pure adsorption equilibria of CO2, H2 and N2 on commercial activated carbon (AP3-60 from Chemviron, Germany) are measured using a Rubotherm Magnetic Suspension Balance (MSB) (Bochum, Germany) in a wide temperature and pressure range. The data is used to fit the temperature dependent parameters of Langmuir and Sips (Langmuir-Freundlich) isotherms and to determine the Henry's constants as well as isosteric heats. Based on this evaluation different methods to evaluate the data are compared and discussed. With the pure isotherm parameters of the Sips isotherm binary adsorption is predicted using an empirical binary Sips equation and ideal adsorbed solution theory (IAST). The results are compared to binary measurements in the same MSB applying a gravimetric-chromatographic metho

Fixed bed adsorption of CO2/H2 mixtures on activated carbon: experiments and modeling

We present breakthrough experiments in a fixed bed adsorber packed with commercial activated carbon involving feed mixtures of carbon dioxide and hydrogen of different compositions. The experiments are carried out at four different temperatures (25°C, 45°C, 65°C and 100°C) and seven different pressures (1bar, 5bar, 10bar, 15bar, 20bar, 25bar and 35bar). The interpretation of the experimental data is done by describing the adsorption process with a detailed one-dimensional model consisting of mass and heat balances and several constitutive equations, such as an adsorption isotherm and an equation of state. The dynamic model parameters, i.e. mass and heat transfer, are fitted to one single experiment (reference experiment) and the model is then further validated by predicting the remaining experiments. Furthermore, the choice of the isotherm model is discussed. The assessment of the model accuracy is carried out by comparing simulation results and experimental data, and by discussing key features and critical aspects of the model. This study is valuable per se and a necessary step toward the design, development and optimization of a pressure swing adsorption process for the separation of CO2 and H2 for example in the context of a pre-combustion CO2 capture process, such as the integrated gasification combined cycle technolog

The cycling of carbon into and out of dust

Observational evidence seems to indicate that the depletion of interstellar carbon into dust shows rather wide variations and that carbon undergoes rather rapid recycling in the interstellar medium (ISM). Small hydrocarbon grains are processed in photo-dissociation regions by UV photons, by ion and electron collisions in interstellar shock waves and by cosmic rays. A significant fraction of hydrocarbon dust must therefore be re-formed by accretion in the dense, molecular ISM. A new dust model (Jones et al., Astron. Astrophys., 2013, 558, A62) shows that variations in the dust observables in the diffuse interstellar medium (nH = 1000 cm^3), can be explained by systematic and environmentally-driven changes in the small hydrocarbon grain population. Here we explore the consequences of gas-phase carbon accretion onto the surfaces of grains in the transition regions between the diffuse ISM and molecular clouds (e.g., Jones, Astron. Astrophys., 2013, 555, A39). We find that significant carbonaceous dust re-processing and/or mantle accretion can occur in the outer regions of molecular clouds and that this dust will have significantly different optical properties from the dust in the adjacent diffuse ISM. We conclude that the (re-)processing and cycling of carbon into and out of dust is perhaps the key to advancing our understanding of dust evolution in the ISM.Comment: 14 pages, 6 figure

Expression of the Inhibitory CD200 Receptor Is Associated with Alternative Macrophage Activation

Classical macrophage activation is inhibited by the CD200 receptor (CD200R). Here, we show that CD200R expression was specifically induced on human in vitro polarized macrophages of the alternatively activated M2a subtype, generated by incubation with IL-4 or IL-13. In mice, peritoneal M2 macrophages, elicited during infection with the parasites Taenia crassiceps or Tryponosoma brucei brucei, expressed increased CD200R levels compared to those derived from uninfected mice. However, in vitro stimulation of mouse peritoneal macrophages and T crassiceps infection in IL-4-/- and IL-4R-/- mice showed that, in contrast to humans, induction of CD200R in mice was not IL-4 or IL-13 dependent. Our data identify CD200R as a suitable marker for alternatively activated macrophages in humans and corroborate observations of distinct species- and/or site-specific mechanisms regulating macrophage polarization in mouse and man. Copyright (C) 2009 S. Karger AG, Base

Tropical deforestation modelling : a comparative analysis of different predictive approaches. The case study of Peten, Guatemala.

The frequent use of predictive models for analysing of complex, natural or artificial, phenomena is changing the traditional approaches to environmental and hazard problems. The continuous improvement of computer performances allows more detailed numerical methods, based on space-time discretisation, to be developed and run for a predictive modeling of complex real systems, reproducing the way their spatial patterns evolve and pointing out the degree of simulation accuracy. In this contribution we present an application of several models (Geomatics, Neural Networks, Land Cover Modeler and Dinamica EGO) in a tropical training area of Peten, Guatemala. During the last decades this region, included into the Biosphere Maya reserve, has known a fast demographic raise and a subsequent uncontrolled pressure on its own geo-resources; the test area can be divided into several sub-regions characterized by different land use dynamics. Understand and quantify these differences permits a better approximation of real system; moreover we have to consider all the physic, socio-economic parameters which will be of use for represent the complex and sometime at random, human impact. Because of the absence of detailed data for our test area, nearly all information were derived from the image processing of 41 ETM+, TM and SPOT scenes; we pointed out the past environmental dynamics and we built the Input layers for the predictive models. The data from 1998 and 2000 were used during the calibration to simulate the Land Cover changes in 2003, selected as reference date for the validation. The basic statistics permit to highlight the qualities or the weaknesses for each model on the different sub-regions

Sensitivity of the process-based model DNDC on microbiological parameters

International audienceProcess-based model such as DNDC rely on a large numbers of parameters which were defined by the model developer on the basis of existing references. Subsequently, some values have been changed to improve model performance for specific applications, often without adequate documentation. Many of these parameters are thus estimates of the real values appropriate for local conditions introducing approximation errors for applications at larger scales. Spatially explicit datasets might be required for some parameters for which model output is highly sensitive. We will present a sensitivity analysis of 38 mainly micro-biological internal parameter of DNDC-EUROPE