Rigorous proof of fuzzy error propagation with matrix-based LCI

Background, aim, and scope  

Propagation of parametric uncertainty in life cycle inventory (LCI) models is usually performed based on probabilistic Monte Carlo techniques. However, alternative approaches using interval or fuzzy numbers have been proposed based on the argument that these provide a better reflection of epistemological uncertainties inherent in some process data. Recent progress has been made to integrate fuzzy arithmetic into matrix-based LCI using decomposition into α-cut intervals. However, the proposed technique implicitly assumes that the lower bounds of the technology matrix elements give the highest inventory results, and vice versa, without providing rigorous proof.     

Numerical Approaches to Life Cycle Interpretation - The case of the Ecoinvent’96 database (10 pp)

Goal, Scope and Background To strengthen the evaluative power of LCA, life cycle interpretation should be further developed. A previous contribution (Heijungs & Kleijn 2001) elaborated five examples of concrete methods within the subset of numerical approaches towards interpretation. These methods were: contribution analysis, perturbation analysis, uncertainty analysis, comparative analysis, and discernibility analysis. Developments in software have enabled the possibility to apply the five example methods to explore the much-used Ecoinvent”96 database.Discussion of Methods The numerical approaches implemented in this study include contribution analysis, perturbation analysis, uncertainty analysis, comparative analysis, discernibility analysis and the newly developed key issue analysis. The data used comes from a very large process database: Ecoinvent’96, containing 1163 processes, 1181 economic flows and 571 environmental flows. Conclusions Results are twofold: they serve as a benchmark to the usefulness and feasibility of these numerical approaches, and they shed light on the question of stability and structure in an often-used large system of interconnected processes. Most of the approaches perform quite well: computation time on a moderate PC is between a few seconds a few minutes. Only Monte Carlo analyses may require much longer, but even then it appears that most questions can be answered within a few hours. Moreover, analytical expressions for error propagation are much faster than Monte Carlo analyses, while giving almost identical results. Despite the fact that many processes are connected to each other, leading to the possibility of a very unstable system and very sensitive coefficients, the overall results show that most results are not extremely uncertain. There are, however, some exceptions to this positive message.     

Similarities,Differences and Synergisms Between HERA and LCA—An Analysis at Three Levels

Linkages between Human and Environmental Risk Assessment (HERA) and Life-Cycle Assessment (LCA) can be analyzed at three levels: the basic equations to describe environmental behavior and dose-response relationships of chemicals; the overall model structure of these tools; and the applications of the tools. At level 1 few differences exist: both tools use essentially the same fate and effect models, including their coefficients and data. At level 2 distinctive differences emerge: regional or life-cycle perspective, emission pulses or fluxes, scope of chemicals and types of impacts, use of characterization factors, spatial and temporal detail, aggregation of effects, and the functional unit as basis of the assessment. Although the two tools typically differ in all these aspects, only the functional unit issue renders the tools fundamentally different, expressing itself also in some main characteristics of the modeling structure. This impedes full integration, which is underpinned in mathematical terms. At level 3 the aims of the tools are complementary: quantified risk estimates of chemicals for HERA versus quantified product assessment for LCA. Here, beneficial synergism is possible between the two tools, as illustrated by some cases. These also illustrate that where full integration is suggested, in practice this is not achieved, thus in fact supporting the conclusions.     

Characterization of a cathepsin L-associated protein in Artemia and its relationship to the FAS-I family of cell adhesion proteins.

We reported previously that the major cysteine protease in embryos and larvae of the brine shrimp, Artemia franciscana, is a heterodimeric protein consisting of a catalytic subunit (28.5 kDa) with a high degree of homology with cathepsin L, and a noncatalytic subunit (31.5 kDa) of unknown function. In the study reported here the noncatalytic subunit, or cathepsin L-associated protein (CLAP), was separated from cathepsin L by chromatography on Mono S and found to contain multiple isoforms with pIs ranging from 5.9 to 6.1. Heterodimeric and monomeric cathepsin L showed similar activity between pH 5 and 6.5, while the heterodimer was about twice as active as monomeric cathepsin L below pH 5. The heterodimer was more stable than the monomer between pH 6 and 7.4 and at 30-50 degrees C. Artemia CLAP and cathepsin L are present in nearly equimolar amounts at all stages in the life cycle and most abundant in encysted eggs and embyros. Moreover, CLAP, either free or as a complex with cathepsin L, was resistant to hydrolysis by cathepsin L. Two clones coding for CLAP were isolated from an Artemia embryo cDNA library and sequenced. Both clones have nearly identical open reading frames, but show differences at the 5'- and 3'-termini. Each cDNA clone has an extensive 3'-untranslated region containing 70-72% A+T. The deduced amino acid sequence of CLAP cDNA revealed two domains which were very similar to domains in fasciclin I and other cell adhesion proteins. The nucleotide sequences of clones 1 and 2 have been entered into the NCBI database (AY307377 and AY462276). This study supports the view that the noncatalytic subunit of the heterodimeric cysteine protease in Artemia stabilizes cathepsin L at various pH and temperatures normally inconsistent with cathepsin L from other organisms, and that CLAP serves as a docking mechanism for cathepsin L at nonlysosomal sites in Artemia embryos.     

Sensitivity to weighting in life cycle impact assessment (LCIA)

The International Journal of Life Cycle Assessment - Weighting in life cycle assessment (LCA) incorporates stakeholder preferences in the decision-making process of comparative LCAs. Research...     

Correction to: Abiotic resource depletion potentials (ADPs) for elements revisited—updating ultimate reserve estimates and introducing time series for production data

The International Journal of Life Cycle Assessment - The original version of this article unfortunately contained a mistake which was missed during typesetting. The caption to Fig. 5 was...