A Data Characterization Framework for Material Flow Analysis

The validity of material flow analyses (MFAs) depends on the available information base, that is, the quality and quantity of available data. MFA data are cross‐disciplinary, can have varying formats and qualities, and originate from heterogeneous sources, such as official statistics, scientific models, or expert estimations. Statistical methods for data evaluation are most often inadequate, because MFA data are typically isolated values rather than extensive data sets. In consideration of the properties of MFA data, a data characterization framework for MFA is presented. It consists of an MFA data terminology, a data characterization matrix, and a procedure for database analysis. The framework facilitates systematic data characterization by cell‐level tagging of data with data attributes. Data attributes represent data characteristics and metainformation regarding statistical properties, meaning, origination, and application of the data. The data characterization framework is illustrated in a case study of a national phosphorus budget. This work furthers understanding of the information basis of material flow systems, promotes the transparent documentation and precise communication of MFA input data, and can be the foundation for better data interpretation and comprehensive data quality evaluation.     

A Novel Approach to Characterize Data Uncertainty in Material Flow Analysis and its Application to Plastics Flows in Austria

Material flow analysis (MFA) is widely used to investigate flows and stocks of resources or pollutants in a defined system. Data availability to quantify material flows on a national or global level is often limited owing to data scarcity or lacking data. MFA input data are therefore considered inherently uncertain. In this work, an approach to characterize the uncertainty of MFA input data is presented and applied to a case study on plastics flows in major Austrian consumption sectors in the year 2010. The developed approach consists of data quality assessment as a basis for estimating the uncertainty of input data. Four different implementations of the approach with respect to the translation of indicator scores to uncertainty ranges (linear‐ vs. exponential‐type functions) and underlying probability distributions (normal vs. log‐normal) are examined. The case study results indicate that the way of deriving uncertainty estimates for material flows has a stronger effect on the uncertainty ranges of the resulting plastics flows than the assumptions about the underlying probability distributions. Because these uncertainty estimates originate from data quality evaluation as well as uncertainty characterization, it is crucial to use a well‐defined approach, building on several steps to ensure the consistent translation of the data quality underlying material flow calculations into their associated uncertainties. Although subjectivity is inherent in uncertainty assessment in MFA, the proposed approach is consistent and provides a comprehensive documentation of the choices underlying the uncertainty analysis, which is essential to interpret the results and use MFA as a decision support tool.     

Defects in mitochondrial fatty acid synthesis result in failure of multiple aspects of mitochondrial biogenesis in Saccharomyces cerevisiae

Mitochondrial fatty acid synthesis (mtFAS) shares acetyl‐CoA with the Krebs cycle as a common substrate and is required for the production of octanoic acid (C8) precursors of lipoic acid (LA) in mitochondria. MtFAS is a conserved pathway essential for respiration. In a genetic screen in Saccharomyces cerevisiae designed to further elucidate the physiological role of mtFAS, we isolated mutants with defects in mitochondrial post‐translational gene expression processes, indicating a novel link to mitochondrial gene expression and respiratory chain biogenesis. In our ensuing analysis, we show that mtFAS, but not lipoylation per se, is required for respiratory competence. We demonstrate that mtFAS is required for mRNA splicing, mitochondrial translation and respiratory complex assembly, and provide evidence that not LA per se, but fatty acids longer than C8 play a role in these processes. We also show that mtFAS‐ and LA‐deficient strains suffer from a mild haem deficiency that may contribute to the respiratory complex assembly defect. Based on our data and previously published information, we propose a model implicating mtFAS as a sensor for mitochondrial acetyl‐CoA availability and a co‐ordinator of nuclear and mitochondrial gene expression by adapting the mitochondrial compartment to changes in the metabolic status of the cell.     

Information Content,Complexity, and Uncertainty in Material Flow Analysis

Material Flow Analysis (MFA) is a useful method for modeling, understanding, and optimizing sociometabolic systems. Among others, MFAs can be distinguished by two general system properties: First, they differ in their complexity, which depends on system structure and size. Second, they differ in their inherent uncertainty, which arises from limited data quality. In this article, uncertainty and complexity in MFA are approached from a systems perspective and expressed as formally linked phenomena. MFAs are, in a graph‐theoretical sense, understood as networks. The uncertainty and complexity of these networks are computed by use of information measures from the field of theoretical ecology. The size of a system is formalized as a function of its number of flows. It defines the potential information content of an MFA system and holds as a reference against which complexity and uncertainty are gauged. Integrating data quality measures, the uncertainty of an MFA before and after balancing is determined. The actual information content of an MFA is measured by relating its uncertainty to its potential information content. The complexity of a system is expressed based on the configuration of each individual flow in relation to its neighboring flows. The proposed metrics enable different material flow systems to be compared to one another and the role of individual flows within a system to be assessed. They provide information useful for the design of MFAs and for the communication of MFA results. For exemplification, the regional MFAs of aluminum and plastics in Austria are analyzed in this article.     

A General Nonlinear Least Squares Data Reconciliation and Estimation Method for Material Flow Analysis

The extraction, transformation, use, and disposal of materials can be represented by directed, weighted networks, known in the material flow analysis (MFA) community as Sankey or flow diagrams. However, the construction of such networks is dependent on data that are often scarce, conflicting, or do not directly map onto a Sankey diagram. By formalizing the forms of data entry, a nonlinear constrained optimization program for data estimation and reconciliation can be formulated for reconciling data sets for MFA problems where data are scarce, in conflict, do not directly map onto a Sankey diagram, and are of variable quality. This method is demonstrated by reanalyzing an existing MFA of global steel flows, and the resulting analytical solution measurably improves upon their manual solution.     

Methodology and Indicators of Economy‐wide Material Flow Accounting

This contribution presents the state of the art of economy‐wide material flow accounting. Starting from a brief recollection of the intellectual and policy history of this approach, we outline system definition, key methodological assumptions, and derived indicators. The next section makes an effort to establish data reliability and uncertainty for a number of existing multinational (European and global) material flow accounting (MFA) data compilations and discusses sources of inconsistencies and variations for some indicators and trends. The results show that the methodology has reached a certain maturity: Coefficients of variation between databases lie in the range of 10% to 20%, and correlations between databases across countries amount to an average R² of 0.95. After discussing some of the research frontiers for further methodological development, we conclude that the material flow accounting framework and the data generated have reached a maturity that warrants material flow indicators to complement traditional economic and demographic information in providing a sound basis for discussing national and international policies for sustainable resource use.     

Peptides from aminoacyl-tRNA synthetases can cure the defects due to mutations in mt tRNA genes

Recent results from several laboratories have confirmed that human and yeast leucyl- and valyl-tRNA synthetases can rescue the respiratory defects due to mutations in mitochondrial tRNA genes. In this report we show that this effect cannot be ascribed to the catalytic activity per se and that isolated domains of aminoacyl-tRNA synthetases and even short peptides thereof have suppressing effects.     

Metabolic flux analysis using 13C peptide label measurements

¹³C metabolic flux analysis (MFA) has become the experimental method of choice to investigate the cellular metabolism of microbes, cell cultures and plant seeds. Conventional steady‐state MFA utilizes isotopic labeling measurements of amino acids obtained from protein hydrolysates. To retain spatial information in conventional steady‐state MFA, tissues or subcellular fractions must be dissected or biochemically purified. In contrast, peptides retain their identity in complex protein extracts, and may therefore be associated with a specific time of expression, tissue type and subcellular compartment. To enable ‘single‐sample’ spatially and temporally resolved steady‐state flux analysis, we investigated the suitability of peptide mass distributions (PMDs) as an alternative to amino acid label measurements. PMDs are the discrete convolution of the mass distributions of the constituent amino acids of a peptide. We investigated the requirements for the unique deconvolution of PMDs into amino acid mass distributions (AAMDs), the influence of peptide sequence length on parameter sensitivity, and how AAMD and flux estimates that are determined through deconvolution compare to estimates from a conventional GC–MS measurement‐based approach. Deconvolution of PMDs of the storage protein β–conglycinin of soybean (Glycine max) resulted in good AAMD and flux estimates if fluxes were directly fitted to PMDs. Unconstrained deconvolution resulted in inferior AAMD and flux estimates. PMD measurements do not include amino acid backbone fragments, which increase the information content in GC–MS‐derived analyses. Nonetheless, the resulting flux maps were of comparable quality due to the precision of Orbitrap quantification and the larger number of peptide measurements.     

Approaches for Quantifying the Metabolism of Physical Economies: Part I: Methodological Overview

This article is the first of a two-part series that describes and compares the essential features of nine existing "physical economy" approaches for quantifying the material demands of the human economy upon the natural environment. A range of material flow analysis (MFA) and related techniques is assessed and compared in terms of several major dimensions. These include the system boundary identification for material flow sources, extents, and the key socioinstitutional entities containing relevant driving forces, as well as the nature and detailing of system components and flow interconnections, and the comprehensiveness and types of flows and materials covered.
Shared conceptual themes of a new wave of physical economy approaches are described with a brief overview of the potential applications of this broad family of methodologies. The evolving and somewhat controversial nature of the characteristics and role that define MFA is examined. This review suggests the need to specify whether MFA is a general metabolic flow measurement procedure that can be applied from micro to macrolevels of economic activity, or a more specific methodology aimed primarily at economy-wide analyses that "map" the material relations between society and nature. Some alternative options for classifying MFA are introduced for discussion before a more detailed comparative summary of the key methodological features of each approach in the second part of this two-part article.
The review is presented (1) as a reference and resource for the increasing number of policy makers and practitioners involved in industrial ecology and the evaluation of the material basis of economies and the formulation of eco-efficiency strategies, and (2) to provoke discussion and ongoing dialogue to clarify the many existing areas of discordance in environmental accounting related to material flows, and help consolidate the methodological basis and application of MFA.     

Evaluating the Use of Global Sensitivity Analysis in Dynamic MFA

Dynamic material flow analysis (MFA) provides information about material usage over time and consequent changes in material stocks and flows. In order to understand the effect of limited data quality and model assumptions on MFA results, the use of sensitivity analysis methods in dynamic MFA studies has been on the increase. So far, sensitivity analysis in dynamic MFA has been conducted by means of a one‐at‐a‐time method, which tests parameter perturbations individually and observes the outcomes on output. In contrast to that, variance‐based global sensitivity analysis decomposes the variance of the model output into fractions caused by the uncertainty or variability of input parameters. The present study investigates interaction and time‐delay effects of uncertain parameters on the output of an archetypal input‐driven dynamic material flow model using variance‐based global sensitivity analysis. The results show that determining the main (first‐order) effects of parameter variations is often sufficient in dynamic MFA because substantial effects attributed to the simultaneous variation of several parameters (higher‐order effects) do not appear for classical setups of dynamic material flow models. For models with time‐varying parameters, time‐delay effects of parameter variation on model outputs need to be considered, potentially boosting the computational cost of global sensitivity analysis. Finally, the implications of exploring the sensitivities of model outputs with respect to parameter variations in the archetypical model are used to derive model‐ and goal‐specific recommendations on choosing appropriate sensitivity analysis methods in dynamic MFA.     

Combining Material Flow Analysis,Life Cycle Assessment,and Multiattribute Utility Theory

Three assessment methods, material flow analysis (MFA), life cycle analysis (LCA), and multiattribute utility theory (MAUT) are systematically combined for supporting the choice of best end‐of‐life scenarios for polyethylene terephthalate (PET) waste in a municipality of a developing country. MFA analyzes the material and energy balance of a firm, a region, or a nation, identifying the most relevant processes; LCA evaluates multiple environmental impacts of a product or a service from cradle to grave; and MAUT allows for inclusion of other aspects along with the ecological ones in the assessment. We first systematically coupled MFA and LCA by defining “the service offered by the total PET used during one year in the region” as the functional unit. Inventory and impacts were calculated by multiplying MFA flows with LCA impacts per kilogram. We used MAUT to include social and economic aspects in the assessment. To integrate the subjective point of view of stakeholders in the MAUT, we normalized the environmental, social, and economic variables with respect to the magnitude of overall impacts or benefits in the country. The results show large benefits for recycling scenarios from all points of view and also provide information about waste treatment optimization. The combination of the three assessment methods offers a powerful integrative assessment of impacts and benefits. Further research should focus on data collection methods to easily determine relevant material flows. LCA impact factors specific to Colombia should be developed, as well as more reliable social indicators.     

The derivation of log‐transformed abundance data for the quantitative analysis of macroinvertebrate traits – an addendum to ‘A macroecological perspective of trait patterns in stream communities’ by Heino et al. ()

相似文献   

宁夏银川平原沙化土地不同人工林土壤质量评价

沙化土地土壤贫瘠、生产力低,人工植被恢复是重要的土壤改良措施。为研究宁夏银川平原沙地不同人工林栽植对土壤的影响,以银川腹部沙地银川植物园沙化土壤恢复区的2017年3月种植的文冠果(Xanthoceras sorbifolium)和欧李(Cerasus humilis)人工林地为研究对象,以邻近的长期撂荒地为对照,对土壤质量进行了研究。2020年7—8月采集土样并测定了土壤物理、化学、生物学等20个指标作为土壤质量评价的总数据集,方差分析筛选出差异显著性的9个指标作为重要数据集指标,采用主成分分析法和Norm值建立土壤质量评价指标最小数据集(MDS),利用线性和非线性两种评分方法计算土壤质量指数(SQI)。结果表明：与长期撂荒地相比,银川平原沙化土壤恢复区人工文冠果、欧李林地的土壤理化性质较好,土壤砂粒百分比减少、粉粒和黏粒百分比有所增加,pH降低;有机质、全氮、全磷、全钾等主要养分指标提高,蔗糖酶、脲酶、纤维素酶等主要生物酶活性也高于撂荒地,筛选出的研究区土壤质量评价MDS指标为土壤粉粒百分比和全磷。2种评价方法下,文冠果和欧李人工恢复林地的SQI高于撂荒地,但提高不显著(P>0...     

Expert elicitation and data noise learning for material flow analysis using Bayesian inference

Bayesian inference allows the transparent communication and systematic updating of model uncertainty as new data become available. When applied to material flow analysis (MFA), however, Bayesian inference is undermined by the difficulty of defining proper priors for the MFA parameters and quantifying the noise in the collected data. We start to address these issues by first deriving and implementing an expert elicitation procedure suitable for generating MFA parameter priors. Second, we propose to learn the data noise concurrent with the parametric uncertainty. These methods are demonstrated using a case study on the 2012 US steel flow. Eight experts are interviewed to elicit distributions on steel flow uncertainty from raw materials to intermediate goods. The experts' distributions are combined and weighted according to the expertise demonstrated in response to seeding questions. These aggregated distributions form our model parameters' informative priors. Sensible, weakly informative priors are adopted for learning the data noise. Bayesian inference is then performed to update the parametric and data noise uncertainty given MFA data collected from the United States Geological Survey and the World Steel Association. The results show a reduction in MFA parametric uncertainty when incorporating the collected data. Only a modest reduction in data noise uncertainty was observed using 2012 data; however, greater reductions were achieved when using data from multiple years in the inference. These methods generate transparent MFA and data noise uncertainties learned from data rather than pre-assumed data noise levels, providing a more robust basis for decision-making that affects the system.     

Mutants of TETRAHYMENA THERMOPHILA with Temperature-Sensitive Food Vacuole Formation. I. Isolation and Genetic Characterization

Germ-line mutants have been isolated in Tetrahymena thermophila that have recessive, temperature-sensitive defects in phagocytosis. Nitrosoguanidine-mutagenized cells were induced to undergo cytogamy, and clones were isolated that were unable to form food vacuoles after two days of growth at 39°. Most of the mutants belong to a single complementation group, designated vacA. They have defects in oral development—not in phagocytosis per se—that are undetectable under light microscopy. One fertile mutant, phenotypically indistinguishable from the vacA group, has its vac mutation(s) restricted to the macronucleus, and it is a heterokaryon for two other markers. This clone probably resulted from a failure of the two gametic nuclei to fuse after normal exchange. Two additional mutants were studied, but their sterility prevented a full genetic analysis. One of these clones has a rudimentary oral apparatus and defective contractile vacuole pores; both defects may be determined by the same mutation. The other clone has a structurally normal oral apparatus and may be defective in phagocytosis per se.—The induction and characterization of germ-line mutations that affect oral development open the way for the genetic dissection of the morphogenesis of a complex eukaryotic organelle, and make available additional useful mutants for the study of nutrition and transmembrane active transport.     

Applying Fuzzy and Probabilistic Uncertainty Concepts to the Material Flow Analysis of Palladium in Austria

Material flow analysis (MFA) is a widely applied tool to investigate resource and recycling systems of metals and minerals. Owing to data limitations and restricted system understanding, MFA results are inherently uncertain. To demonstrate the systematic implementation of uncertainty analysis in MFA, two mathematical concepts for the quantification of uncertainties were applied to Austrian palladium (Pd) resource flows and evaluated: (1) uncertainty ranges expressed by fuzzy sets and (2) uncertainty ranges defined by normal distributions given as mean values and standard deviations. Whereas normal distributions represent the traditional approach for quantifying uncertainties in MFA, fuzzy sets may offer additional benefits in relation to uncertainty quantification in cases of scarce information. With respect to the Pd case study, the fuzzy representation of uncertain quantities is more consistent with the actual data availability in cases of incomplete databases, and fuzzy sets serve to highlight the effect of uncertainty on resource efficiency indicators derived from the MFA results. For both approaches, data reconciliation procedures offer the potential to reduce uncertainty and evaluate the plausibility of the model results. With respect to Pd resource management, improved formal collection of end‐of‐life (EOL) consumer products is identified as a key factor in increasing the recycling efficiency. In particular, the partial export of EOL vehicles represents a substantial loss of Pd from the Austrian resource system, whereas approximately 70% of the Pd in the EOL consumer products is recovered in waste management. In conclusion, systematic uncertainty analysis is an integral part of MFA required to provide robust decision support in resource management.     

Unified Materials Information System (UMIS): An Integrated Material Stocks and Flows Data Structure

Modern society depends on the use of many diverse materials. Effectively managing these materials is becoming increasingly important and complex, from the analysis of supply chains, to quantifying their environmental impacts, to understanding future resource availability. Material stocks and flows data enable such analyses, but currently exist mainly as discrete packages, with highly varied type, scope, and structure. These factors constitute a powerful barrier to holistic integration and thus universal analysis of existing and yet to be published material stocks and flows data. We present the Unified Materials Information System (UMIS) to overcome this barrier by enabling material stocks and flows data to be comprehensively integrated across space, time, materials, and data type independent of their disaggregation, without loss of information, and avoiding double counting. UMIS can therefore be applied to structure diverse material stocks and flows data and their metadata across material systems analysis methods such as material flow analysis (MFA), input‐output analysis, and life cycle assessment. UMIS uniquely labels and visualizes processes and flows in UMIS diagrams; therefore, material stocks and flows data visualized in UMIS diagrams can be individually referenced in databases and computational models. Applications of UMIS to restructure existing material stocks and flows data represented by block flow diagrams, system dynamics diagrams, Sankey diagrams, matrices, and derived using the economy‐wide MFA classification system are presented to exemplify use. UMIS advances the capabilities with which complex quantitative material systems analysis, archiving, and computation of material stocks and flows data can be performed.     

Quantitative trait locus (QTL) analysis of wood quality traits in <Emphasis Type="Italic">Eucalyptus nitens</Emphasis>

To identify the chromosomal regions affecting wood quality traits, we conducted a genome-wide quantitative trait locus (QTL) analysis of wood quality traits in Eucalyptus nitens. This information is important to exploit the full potential of the impending Eucalyptus genome sequence. A three generational mapping population consisting of 296 progeny trees was used to identify QTL associated with several wood quality traits in E. nitens. Thirty-six QTL positions for cellulose content, pulp yield, lignin content, density, and microfibril angle (MFA) were identified across different linkage groups. On linkage groups (LG)2 and 8, cellulose QTL cluster with pulp yield and extractives QTL while on LG4 and 10 cellulose and pulp yield QTLs cluster together. Similarly, on LG4, 5, and 6 QTL for lignin traits were clustered together. At two positions, QTL for MFA, a physical trait related to wood stiffness, were clustered with QTL for lignin traits. Several cell wall candidate genes were co-located to QTL positions affecting different traits. Comparative QTL analysis with Eucalyptus globulus revealed two common QTL regions for cellulose and pulp yield. The QTL positions identified in this study provide a resource for identifying wood quality genes using the impending Eucalyptus genome sequence. Candidate genes identified in this study through co-location to QTL regions may be useful in association studies.