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

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

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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

Utilisation agricole des résidus organiques dans l'évaluation du cycle de vie: Pratiques actuelles et proposition pour le calcul des émissions sur le terrain et de l'équivalent en engrais azotés minéraux

International audienceAgricultural utilization of organic residues is often included in Life cycle assessment (LCA) studies on livestock and crop production as well as waste and wastewater treatment. A review on the current state-of-the-art practices in agricultural use of organic residues in LCA studies is presented. This reveals that agricultural use of organic residues in LCA studies can be represented in several ways and at different levels of detail. About 100 published references were thoroughly analyzed showing that agricultural use of organic residues usually replaces the use of a mineral fertilizer (substitution of avoided mineral fertilizer). The mineral fertilizer equivalents (MFE) applied are rarely documented, although LCA results can be significantly affected by the way avoided impacts are modeled. Accounting of field emissions from organic residue application varies with the topic of the LCA study. To facilitate nitrogen MFE and field emission calculations, an Excel-tool is proposed for determining the nitrogen MFE of organic residues, direct nitrogen field emissions from organic residue applications, as well as avoided emissions (avoided mineral fertilizers). Computation of the nitrogen MFE of organic residues is based on their nitrogen content and composition, and on nitrogen emissions from field applications of the organic residues. Nitrogen field emissions were estimated using simple models and average climate and soil conditions. A global sensitivity analysis revealed that the choice of the application method, which determines the extent of incorporation into the soil, is the main cause of uncertainty in calculated nitrogen MFE values

ELDAM: A Python software for Life Cycle Inventory data management

International audienceLife Cycle Inventory (LCI) data are the backbone of Life Cycle Analysis (LCA). They reflectmodelling choices made by the LCA practitioner that can strongly influence the results ofenvironmental impact calculations. The high sensitivity of these data are the reason why theymust be accurately documented and tend to the highest possible quality. High quality, welldocumented LCI data can also be shared between LCA practitioners, allowing productivitygains and better research reproducibility, in particular between practitioners who do not workon the same life cycle inventory database. Unfortunately, SimaPro (PRé Sustainability B.V.,2020), one of the most used LCA software solutions, has only limited features to document,review and exchange LCI data.ELDAMhas been developed to fill this ga

Introducing dynamics in (bio)CCS, does it matter ?

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