Resource depletion potentials from bottom-up models:Population dynamics and the Hubbert peak theory

Life cycle impact assessment uses so-called characterization factors to address different types of environmental impact (e.g. climate change, particulate matter, land use…). For the topic of resource depletion, a series of proposals was based on heuristic and formal arguments, but without the use of expert-based models from relevant research areas. A recent study in using fish population models has confirmed the original proposal for characterization factors for biotic resources of the nineties. Here we trace the milestones of the arguments and the designs of resource depletion, delivering an ecological-based foundation for the biotic case, and extend it by a novel analysis of the Hubbert peak theory for the abiotic case. We show that the original abiotic depletion potential, used for two decades in life cycle assessment, estimates accurately a marginal depletion characterization factor obtained from a dynamic model of the available reserve. This is illustrated for 29 metal resources using published data

Can we assess the model complexity for a bioprocess ? Theory and example of the anaerobic digestion process

In this paper we propose a methodology to determine the structure of the pseudo-stoichiometric coefficient matrix K in a mass balance based model, i.e. the maximal number of biomasses that must be taken into account to reproduce an available data set. It consists in estimating the number of reactions that must be taken into account to represent the main mass transfer within the bioreactor. This provides the dimension of K. The method is applied to data from an anaerobic digestion process and shows that even a model including a single biomass is sufficient. Then we apply the same method to the “synthetic data” issued from the complex ADM1 model, showing that the main model features can be obtained with 2 biomasses

Machine learning models based on molecular descriptors to predict human and environmental toxicological factors in continental freshwater

It is a real challenge for life cycle assessment practitioners to identify all relevant substances contributing to the ecotoxicity. Once this identification has been made, the lack of corresponding ecotoxicity factors can make the results partial and difficult to interpret. So, it is a real and important challenge to provide ecotoxicity factors for a wide range of compounds. Nevertheless, obtaining such factors using experiments is tedious, time-consuming, and made at a high cost. A modeling method that could predict these factors from easy-to-obtain information on each chemical would be of great value. Here, we present such a method, based on machine learning algorithms, that used molecular descriptors to predict two specific endpoints in continental freshwater for ecotoxicological and human impacts. The method shows good performances on a learning database. Then, predictions were derived from the validated model for compounds with missing toxicity/ecotoxicity factors

A tool to guide the selection of impact categories for LCA studies by using the representativeness index

Understanding the environmental profile of a product computed from the Life Cycle Assessment (LCA) framework is sometimes challenging due to the high number of environmental indicators involved. The objective here, in guiding interpretation of LCA results, is to highlight the importance of each impact category for each product alternative studied. For a given product, the proposed methodology identifies the impact categories that are worth focusing on, relatively to a whole set of products from the same cumulated database. The approach extends the analysis of Representativeness Indices (RI) developed by Esnouf et al. (2018). It proposes a new operational tool for calculating RIs at the level of impact categories for a Life Cycle Inventory (LCI) result. Impact categories and LCI results are defined as vectors within a standardized vector space and a procedure is proposed to treat issues coming from the correlation of impact category vectors belonging to the same Life Cycle Impact Assessment (LCIA) method. From the cumulated ecoinvent database, LCI results of the Chinese and the German electricity mixes illustrate the method. Relevant impact categories of the EU-standardized ILCD method are then identified. RI results from all products of a cumulated LCI database were therefore analysed to assess the main tendencies of the impact categories of the ILCD method. This operational approach can then significantly contribute to the interpretation of the LCA results by pointing to the specificities of the inventories analysed and for identifying the main representative impact categories

Sunflower Associated With Legumes-Based Cover Crop : A Way To Increase Nitrogen Availability For The Following Winter Wheat?

Sunflower is one of the most important crop of organic crops systems in the South of France. In this region, sunflower is mostly cultivated before soft winter wheat, which is very often deficient in nitrogen because of a lack of nitrogen in the soil when the wheat needs it. To increase the soil nitrogen availability, one way is to introduce a legumes-based cover crop before wheat, which is sown just after the previous crop harvest. Thus, the time between sunflower harvest and wheat sowing is often too short to produce enough biomass. An alternative is to sow the cover crop during the sunflower cultivation, so to be intercropped into it. In a trial repeated over 3 years (from 2015 to 2017) in the southwest of France, Terres Inovia tested this practice, by intercropping 3 kinds of legumes-based cover crops into sunflower: alfalfa, purple vetch and legumes mixture. Over the 3 years, the growth of the cover crops was satisfying, and the average amount of nitrogen returned to soil after cover crops destruction was of 40 kg N/ha for purple vetch, 18 kg N/ha for alfalfa and 19.5 kg N/ha for legumes mixture. Nevertheless, cover crops impact severely sunflower performance because of competition for water and poor weed control due to no hoeing. Sunflower yield was reduced on average by 45% over the 3 years. This economic loss was partially compensated by a benefit on wheat yield, which was observed in 2016 and 2018, but only for wheat following sunflower intercropped with alfalfa

Agrégation/abstraction de modèles pour l'analyse et l'organisation de réseaux de flux : Application à la gestion des effluents d'élevage à la Réunion

The development of the intensive livestock production, notably in the island of Reunion, increases the animal waste production that cannot be any more neglected because of environmental and legislation constraints. The key points to evaluate animal waste management scenarios are the modelling of spreading decisions, their causes, and their consequence. This Ph. D. Thesis concerns the dynamic representation of producer's (i. E. , livestocks) and consumer's (i. E. , crop cultures) in the waste network. Consequently, imprecise stock dynamics and decision taking discrete models have to be confronted. Our approach is i) a modelling by the timed automata formalism join to ii) an automated and generic procedure definition to continuous models approximation, with imprecision on initial state and the input variables. The discrete abstraction approach is iIIustrated on a carbon substrate anaerobic digestion process. For each (production or consumption) unit, a model is defined. Then, the spreading decisions are studied by confronting the models via model-checking tools, which allow an automated verification of system 's properties. The Kronos software was used as a tool dedicated to the Timed Computation Tree Logic (TCTL). The model parameters are given for livestocks and crop cultures in the Reunion Island context and the approach was illustrated by the study of the sample farm functioning. This is realized by an iterative procedure between test and result's interpretations in front of agronomie knowledge. It is shown lastly in this study how this approach can be used to represent a farm network and a waste treatment plant stock supply.L'essor de l'élevage intensif, notamment à l'île de la Réunion, induit une forte production d'effluents qui ne peut plus être négligée face aux contraintes environnementales et réglementaires associées. Modéliser les décisions d'épandage, leurs causes et conséquences apparaissent alors comme des points clés pour tester des scénarios de gestion des effluents d'élevages. Cette thèse porte sur la représentation dynamique d'un réseau de producteurs (i. E. , les élevages) et de consommateurs (i. E. , les cultures) d'effluents. Il faut alors confronter des dynamiques de stocks imprécises avec des modèles discrets de prise de décision. Notre approche est (i) une modélisation par le formalisme des automates temporisé accompagnée de (ii) la définition d'une procédure, automatisée et générique, d'approximation des modèles continus, avec prise en compte d'imprécisions SUl les états initiaux et les variables d'entrée. L'approche de discrétisation est notamment illustrée sur un procédé de digestion anaérobie de substrat carboné. Pour chacune des unités (de production ou de consommation) mises en jeu, un modèle est défini. Les décisions d'épandages sont alors étudiées en confrontant les modèles via des outils de model-checking qui permettent une vérification automatisée de propriétés sur les systèmes. Nous utilisons le logiciel Kronos, un outil de vérification dédié à la logique TCTL (Timed Computation Tree Logic). Nous donnons les paramètres des modèles pour les élevages et les cultures réunionnaises et illustrons notre approche par l'étude du fonctionnement d'une exploitation type. Ceci est réalisé par une procédure itérative entre tests et interprétations des résultats face aux connaissances agronomiques. Nous montrons en dernier lieu comment notre approche peut être utilisée pour représenter un réseau d'exploitations et l'approvisionnement d'une unité de transformation d'effluents

Data for Fish Stock Assessment Obtained from the CMSY Algorithm for all Global FAO Datasets

Assessing the state of fish stocks requires the determination of descriptors. They correspond to the absolute and relative (to the carrying capacity of the habitat) fish biomasses in the ecosystem, and the absolute and relative (to the intrinsic growth rate of the population) fishing mortality resulting from catches. This allows, among other things, to compare the catch with the maximum sustainability yield. Some fish stocks are well described and monitored, but for many data-limited stocks, catch time series are remaining the only source of data. Recently, an algorithm (CMSY) has been proposed, allowing an estimation of stock assessment variables from catch and resilience. In this paper, we provide stock reference points for all global fisheries reported by Food and Agriculture Organization (FAO) major fishing area for almost 5000 fish stocks. These data come from the CMSY algorithm for 42% of the stock (75% of the global reported fish catch) and are estimated by aggregated values for the remaining 58%

Life cycle assessment at the scale of France on Human health and aquatic environment of micropollutants released by wastewater treatment plants

International audienc

Life cycle assessment at the scale of France on Human health and aquatic environment of micropollutants released by wastewater treatment plants

International audienc

Optimal integration of microalgae production with photovoltaic panels: environmental impacts and energy balance

International audience13 Background: Microalgae are 10 to 20 times more productive than the current agricultural biodiesel 1