Taxonomie des sols

Résumé

La taxonomie des sols est une classification des basée sur leurs relations naturelles. Définition La taxinomie des sols a pour objectif « d'avoir des hiérarchies de classes qui permettent de comprendre les relations entre les sols et également entre les sols et les facteurs responsables de leurs caractères ». La taxinomie est un terme plus étroit que la classification car elle exclut les usages pratiques spécifiques pour ne s'intéresser qu'aux relations naturelles. C'est dans les années 1870 qu'une école russe menée par Vassili Dokoutchaïev a introduit une nouvelle approche des matériaux « sols ». Cette école les conçoit comme des corps naturels indépendants, chacun avec une morphologie unique résultant d'une combinaison unique de facteurs, climat, matières vivantes, matériaux terrestres, âge des reliefs. La morphologie de chaque sol est définie par une coupe verticale donnant les différents horizons des couches reflétant les effets combinés de l'ensemble des facteu

Source officielle

https://fr.wikipedia.org/wiki/Taxonomie_des_sols

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Le cours est une introduction aux Sciences du sol. Il a pour but de présenter les principales caractéristiques, propriétés et fonctions des sols. Il fait appel à des notions théoriques mais également à des cas d'étude pratique à l'aide d'exercices et de travaux pratiques.

Les étudiants comprennent le comportement mécanique de la roche intacte, des joints et des massifs rocheux et savent déterminer les facteurs influençant un projet. Ils savent utiliser les méthodes appropriées pour analyser et dimensionner l'excavation et le soutènement des ouvrages souterrains.

"Hydrology for Engineers" is an introduction to the study of floods, droughts and a fair distribution of water. The course will introduce basic hydrologic concepts and methods: probability and statistics, surface and subsurface hydrological processes

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Estimating mean molecular weight, carbon number, and OM∕OC with mid-infrared spectroscopy in organic particulate matter samples from a monitoring network

Satoshi Takahama, Amir Yazdani

Organic matter (OM) is a major constituent of fine particulate matter, which contributes significantly to degradation of visibility and radiative forcing, and causes adverse health effects. However, due to its sheer compositional complexity, OM is difficult to characterize in its entirety. Mid-infrared spectroscopy has previously proven useful in the study of OM by providing extensive information about functional group composition with high mass recovery. Herein, we introduce a new method for obtaining additional characteristics such as mean carbon number and molecular weight of these complex organic mixtures using the aliphatic C−H absorbance profile in the mid-infrared spectrum. We apply this technique to spectra acquired non-destructively from Teflon filters used for fine particulate matter quantification at selected sites of the Inter-agency Monitoring of PROtected Visual Environments (IMPROVE) network. Since carbon number and molecular weight are important characteristics used by recent conceptual models to describe evolution in OM composition, this technique can provide semi-quantitative, observational constraints of these variables at the scale of the network. For this task, multivariate statistical models are trained on calibration spectra prepared from atmospherically relevant laboratory standards and are applied to ambient samples. Then, the physical basis linking the absorbance profile of this relatively narrow region in the mid-infrared spectrum to the molecular structure is investigated using a classification approach. The multivariate statistical models predict mean carbon number and molecular weight that are consistent with previous values of organic-mass-to-organic-carbon (OM/OC) ratios estimated for the network using different approaches. The results are also consistent with temporal and spatial variations in these quantities associated with aging processes and different source classes (anthropogenic, biogenic, and burning sources). For instance, the statistical models estimate higher mean carbon number for urban samples and smaller, more fragmented molecules for samples in which substantial aging is anticipated.

European Geosciences Union2021

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Surprising events trigger measurable brain activity and influence human behavior by affecting learning, memory, and decision-making. Currently there is, however, no consensus on the definition of surprise. Here we identify 18 mathematical definitions of surprise in a unifying framework. We first propose a technical classification of these definitions into three groups based on their dependence on an agent’s belief, show how they relate to each other, and prove under what conditions they are indistinguishable. Going beyond this technical analysis, we propose a taxonomy of surprise definitions and classify them into four conceptual categories based on the quantity they measure: (i) ‘prediction surprise’ measures a mismatch between a prediction and an observation; (ii) ‘change-point detection surprise’ measures the probability of a change in the environment; (iii) ‘confidence-corrected surprise’ explicitly accounts for the effect of confidence; and (iv) ‘information gain surprise’ measures the belief-update upon a new observation. The taxonomy poses the foundation for principled studies of the functional roles and physiological signatures of surprise in the brain.

2022

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Evaluation of Dissolved Organic Carbon as a Soil Quality Indicator in National Monitoring Schemes

Robert Thomas Edmund Mills

Background: Monitoring the properties of dissolved organic carbon (DOC) in soil water is frequently used to evaluate changes in soil quality and to explain shifts in freshwater ecosystem functioning. Methods: Using >700 individual soils (0-15 cm) collected from a 209,331 km(2) area we evaluated the relationship between soil classification (7 major soil types) or vegetation cover (8 dominant classes, e. g. cropland, grassland, forest) and the absorbance properties (254 and 400 nm), DOC quantity and quality (SUVA, total soluble phenolics) of soil water. Results: Overall, a good correlation (r(2) = 0.58) was apparent between soil water absorbance and DOC concentration across the diverse range of soil types tested. In contrast, both DOC and the absorbance properties of soil water provided a poor predictor of SUVA or soluble phenolics which we used as a measure of humic substance concentration. Significant overlap in the measured ranges for UV absorbance, DOC, phenolic content and especially SUVA of soil water were apparent between the 8 vegetation and 7 soil classes. A number of significant differences, however, were apparent within these populations with total soluble phenolics giving the greatest statistical separation between both soil and vegetation groups. Conclusions: We conclude that the quality of DOC rather than its quantity provides a more useful measure of soil quality in large scale surveys.

Public Library Science2014

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Estimating mean molecular weight, carbon number, and OM∕OC with mid-infrared spectroscopy in organic particulate matter samples from a monitoring network

Satoshi Takahama, Amir Yazdani

European Geosciences Union2021