The EcoSpold 2 format—why a new format?

Purpose

This paper introduces the new EcoSpold data format for life cycle inventory (LCI).

Methods

A short historical retrospect on data formats in the life cycle assessment (LCA) field is given. The guiding principles for the revision and implementation are explained. Some technical basics of the data format are described, and changes to the previous data format are explained.

Results

The EcoSpold 2 data format caters for new requirements that have arisen in the LCA field in recent years.

Conclusions

The new data format is the basis for the Ecoinvent v3 database, but since it is an open data format, it is expected to be adopted by other LCI databases. Several new concepts used in the new EcoSpold 2 data format open the way for new possibilities for the LCA practitioners and to expand the application of the datasets in other fields beyond LCA (e.g., Material Flow Analysis, Energy Balancing).

     

A life cycle assessment framework combining nutritional and environmental health impacts of diet: a case study on milk

Purpose

While there has been considerable effort to understand the environmental impact of a food or diet, nutritional effects are not usually included in food-related life cycle assessment (LCA).

Methods

We developed a novel Combined Nutritional and Environmental Life Cycle Assessment (CONE-LCA) framework that evaluates and compares in parallel the environmental and nutritional effects of foods or diets. We applied this framework to assess human health impacts, expressed in Disability Adjusted Life Years (DALYs), in a proof-of-concept case study that investigated the environmental and nutritional human health effects associated with the addition of one serving of fluid milk to the present average adult US diet. Epidemiology-based nutritional impacts and benefits linked to milk intake, such as colorectal cancer, stroke, and prostate cancer, were compared to selected environmental impacts traditionally considered in LCA (global warming and particulate matter) carried to a human health endpoint.

Results and discussion

Considering potential human health effects related to global warming, particulate matter, and nutrition, within the context of this study, findings suggest that adding one serving of milk to the current average diet could result in a health benefit for American adults, assuming that existing foods associated with substantial health benefits are not substituted, such as fruits and vegetables. The net health benefit is further increased when considering an iso-caloric substitution of less healthy foods (sugar-sweetened beverages). Further studies are needed to test whether this conclusion holds within a more comprehensive assessment of environmental and nutritional health impacts.

Conclusions

This case study provides the first quantitative epidemiology-based estimate of the complements and trade-offs between nutrition and environment human health burden expressed in DALYs, pioneering the infancy of a new approach in LCA. We recommend further testing of this CONE-LCA approach for other food items and diets, especially when making recommendations about sustainable diets and food choices.

     

Bridge processes: a solution for LCI datasets independent of background databases

Introduction

New platforms are emerging that enable more data providers to publish life cycle inventory data.

Background

Providing datasets that are not complete LCA models results in fragments that are difficult for practitioners to integrate and use for LCA modeling. Additionally, when proxies are used to provide a technosphere input to a process that was not originally intended by the process authors, in most LCA software, this requires modifying the original process.

Results

The use of a bridge process, which is a process created to link two existing processes, is proposed as a solution.

Discussion

Benefits to bridge processes include increasing model transparency, facilitating dataset sharing and integration without compromising original dataset integrity and independence, providing a structure with which to make the data quality associated with process linkages explicit, and increasing model flexibility in the case that multiple bridges are provided. A drawback is that they add additional processes to existing LCA models which will increase their size.

Conclusions

Bridge processes can be an enabler in allowing users to integrate new datasets without modifying them to link to background databases or other processes they have available. They may not be the ideal long-term solution but provide a solution that works within the existing LCA data model.

     

Regionalization of agri-food life cycle assessment: a review of studies in Portugal and recommendations for the future

Purpose

One of the main trends in life cycle assessment (LCA) today is towards increased regionalization in inventories and impact assessment methods. LCA studies require the collection of activity data but also of increasingly region-specific background data to accurately depict supply chain processes and enable the application of an increasing number of geographically explicit impact assessment models. This is particularly important for agri-food products. In this review, we assess progress in Portugal towards this goal and provide recommendations for future developments.

Methods

We perform a comprehensive review of available LCA studies conducted for Portuguese agri-food products, in order to evaluate the current state of Portuguese agri-food LCA. Among other issues, we assess availability of data, methods used, level of regionalization, impact assessment model relevance and coherence for inter-product comparability. We also provide conclusions and recommendations based on recent developments in the field.

Results and discussion

We found 22 LCA studies, covering 22 different products. The analysis of these studies reveals limitations in inter-study comparability. The main challenges have to do with a lack of country-specific foreground data sources applied consistently in the studies found, with discrepancies in impact assessment categories, and with the use of simple functional units that may misrepresent the product analyzed.

Conclusions

We conclude that Portuguese agri-food LCA studies do not have a systematic and country-scale approach in order to guarantee regional accuracy and comparability. We propose a research strategy to engage the Portuguese agri-food LCA community in devising a consistent framework before practical application studies are conducted.

     

From behind the curtain: talking about values in LCA

Purpose

Practitioners of life cycle assessment (LCA) acknowledge that more input from social scientists can help advance the cause of life cycle management (LCM). This commentary offers a social science perspective on a long-running question within LCA, namely, how the field should manage not only stakeholders’ values but also those of practitioners themselves.

Methods

More than 60 interviews were conducted with LCA practitioners and their industry clients. Qualitative data were also collected through participant observation at several LCA and LCM conferences, a study of the field’s history, and extensive content and discourse analysis of LCA publications and online forums.

Results and discussion

Results show that LCA practitioners’ values are informed partly by the knowledge acquired through their LCA work. At the same time, LCA standards and professional norms implicitly advise practitioners to keep those values out of their work as much as possible, so as not to compromise its apparent objectivity. By contrast, many social scientists contend openly that value-based judgments, based on “situated knowledge,” can actually enhance the rigor, accountability, and credibility of scientific assessments.

Conclusions

LCA practitioners’ own situated knowledge justifies not only the value choices required by LCA but also their evaluative judgments of contemporary life cycle-based sustainability initiatives. This more critical voice could advance the goals of LCM while also boosting the credibility of LCA more generally.

     

TarMet: a reactive GUI tool for efficient and confident quantification of MS based targeted metabolic and stable isotope tracer analysis

Introduction

Untargeted and targeted analyses are two classes of metabolic study. Both strategies have been advanced by high resolution mass spectrometers coupled with chromatography, which have the advantages of high mass sensitivity and accuracy. State-of-art methods for mass spectrometric data sets do not always quantify metabolites of interest in a targeted assay efficiently and accurately.

Objectives

TarMet can quantify targeted metabolites as well as their isotopologues through a reactive and user-friendly graphical user interface.

Methods

TarMet accepts vendor-neutral data files (NetCDF, mzXML and mzML) as inputs. Then it extracts ion chromatograms, detects peak position and bounds and confirms the metabolites via the isotope patterns. It can integrate peak areas for all isotopologues automatically.

Results

TarMet detects more isotopologues and quantify them better than state-of-art methods, and it can process isotope tracer assay well.

Conclusion

TarMet is a better tool for targeted metabolic and stable isotope tracer analyses.

     

Criteria for the evaluation of life cycle assessment software packages and life cycle inventory data with application to concrete

Purpose

Life cycle assessment (LCA) software packages have proliferated and evolved as LCA has developed and grown. There are now a multitude of LCA software packages that must be critically evaluated by users. Prior to conducting a comparative LCA study on different concrete materials, it is necessary to examine a variety of software packages for this specific purpose. The paper evaluates five LCA tools in the context of the LCA of seven concrete mix designs (conventional concrete, concrete with fly ash, slag, silica fume or limestone as cement replacement, recycled aggregate concrete, and photocatalytic concrete).

Methods

Three key evaluation criteria required to assess the quality of analysis are adequate flexibility, sophistication and complexity of analysis, and usefulness of outputs. The quality of life cycle inventory (LCI) data included in each software package is also assessed for its reliability, completeness, and correlation to the scope of LCA of concrete products in Canada. A questionnaire is developed for evaluating LCA software packages and is applied to five LCA tools.

Results and discussion

The result is the selection of a software package for the specific context of LCA of concrete materials in Canada, which will be used to complete a full LCA study. The software package with the highest score is software package C (SP-C), with 44 out of a possible 48 points. Its main advantage is that it allows for the user to have a high level of control over the system being modeled and the calculation methods used.

Conclusions

This comparative study highlights the importance of selecting a software package that is appropriate for a specific research project. The ability to accurately model the chosen functional unit and system boundary is an important selection criterion. This study demonstrates a method to enable a critical and rigorous comparison without excessive and redundant duplication of efforts.

     

What causes more LCA research—subjective values,objective problems,or scientific traditions?

Purpose

This paper aims to verify whether life cycle assessment (LCA) research can be mainly treated as a kind of pro-environmental behavior due to public environment concerns, or academic and research activities based on scientific traditions.

Methods

This paper uses the international comparisons method for modeling and SPSS 16.0 for data processing. The data in this study were obtained from the Human Development Report by the United Nations Development Programme and the Web of Science by the Institute for Scientific Information.

Results and discussion

Our empirical study shows that the two main factors influencing the outputs per capita of the research articles in LCA in a particular country are the value of Environmental Performance Index, which represents the overall environmental quality, as well as the outputs per capita of the research articles in environmental science and technology. The results of statistical analysis show two J-type curves: with the change of the independent variables, the dependent variable changes in the same direction, but at a rate that is first slow, then fast.

Conclusions

LCA research results from scientific traditions and can only develop based on fundamental research in environmental science and technology. Further, LCA research is a pro-environmental behavior due to actual and objective effects rather than subjective motives as more research on LCA can accompany, even in some degree may lead to better overall environmental qualities. However, although environmental concerns are likely to affect the number of LCA studies as an implicit variable, this has not been empirically confirmed in our optimization model.

     

Developing a systematic framework for consistent allocation in LCA

Purpose

Multifunctionality in life-cycle assessment (LCA) is solved with allocation, for which many different procedures are available. Lack of sufficient guidance and difficulties to identify the correct allocation approach cause a large number of combinations of methods to exist in scientific literature. This paper reviews allocation procedures for recycling situations, with the aim to identify a systematic approach to apply allocation.

Methods

Assumptions and definitions for the most important terms related to multifunctionality and recycling in LCA are given. The most relevant allocation procedures are identified from literature. These procedures are expressed in mathematical formulas and schemes and arranged in a systematic framework based on the underlying objectives and assumptions of the procedures.

Results and discussion

If the LCA goal asks for an attributional approach, multifunctionality can be solved by applying system expansion—i.e. including the co-functions in the functional unit—or partitioning. The cut-off approach is a form of partitioning, attributing all the impacts to the functional unit. If the LCA goal asks for a consequential approach, substitution is applied, for which three methods are identified: the end-of-life recycling method and the waste mining method, which are combined in the 50/50 method. We propose to merge these methods in a new formula: the market price-based substitution method. The inclusion of economic values and maintaining a strict separation between attributional and consequential LCA are considered to increase realism and consistency of the LCA method.

Conclusions and perspectives

We identified the most pertinent allocation procedures—for recycling as well as co-production and energy recovery—and expressed them in mathematical formulas and schemes. Based on the underlying objectives of the allocation procedures, we positioned them in a systematic and consistent framework, relating the procedures to the LCA goal definition and an attributional or consequential approach. We identified a new substitution method that replaces the three existing methods in consequential LCA. Further research should test the validity of the systematic framework and the market price-based substitution method by means of case studies.

     

A generalized computational structure for regional life-cycle assessment

Purpose

Regional life-cycle assessment (LCA) is gaining an increasing attention among LCA scholars and practitioners. Here, we present a generalized computational structure for regional LCA, discuss in-depth the major challenges facing the field, and point to a direction in which we believe regional LCA should be headed.

Methods

Using an example, we first demonstrate that when there is regional heterogeneity (be it due to environmental conditions or technologies), average data would be inadequate for estimating the life-cycle impacts of a product produced in a specific region or even that of an average product produced in many regions. And when there is such regional heterogeneity, an understanding of how regions are connected through commodity flows is important to the accuracy of regional LCA estimates. Then, we present a generalized computational structure for regional LCA that takes into account interregional commodity flows, can evaluate various cases of regional differentiation, and can account for multiple impact categories simultaneously. In so doing, we show what kinds of data are required for this generalized framework of regional LCA.

Results and discussion

We discuss the major challenges facing regional LCA in terms of data requirements and computational complexity, and their implications for the choice of an optimal regional scale (i.e., the number of regions delineated within the geographic boundary studied).

Conclusions

We strongly recommend scholars from LCI and LCIA to work together and choose a spatial scale that not only adequately captures environmental characteristics but also allows inventory data to be reasonably compiled or estimated.

     

Conceptualising attributional LCA is necessary for resolving methodological issues such as the appropriate form of land use baseline

Purpose

The purposes of this commentary are to further an on-going debate concerning the appropriate form of land use baseline for attributional life cycle assessment (LCA) and to respond to a number of arguments advanced by Soimakallio (Int J Life Cycle Assess 20:1364–1375, 2016). The commentary also seeks to clarify the conceptual nature of attributional LCA.

Methods

The overarching approach for resolving the question of the appropriate form of land use baseline for attributional LCA is to clarify what attributional LCA is seeking to represent, i.e. methodological questions can only be resolved if it is clear what the method is seeking to do. An illustrative example is used to explore the different results produced by ‘natural regeneration’ and ‘natural’ baselines.

Results and discussion

It is proposed that attributional LCA should be conceptualised as an inventory of anthropogenic impacts, conceptually akin to other forms of environmental inventory, such as national GHG inventories. The use of natural regeneration baselines is not consistent with this conceptualisation of attributional LCA, and such baselines necessitate further ad hoc or arbitrary adjustments, such as arbitrary temporal windows or the inconsistent treatment of natural emissions.

Conclusions

The use of natural regeneration baselines may be motivated by the impulse to make attributional LCA both an inventory-type method and an assessment of system-wide change. Pulling attributional LCA in two different directions at once results in a conceptually and methodologically incoherent method. The solution is to recognise attributional LCA as an inventory-type method, which therefore has distinct but complementary uses to consequential LCA, which is an assessment of system-wide change.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

Comparing normalization methods and the impact of noise

Introduction

Failure to properly account for normal systematic variations in OMICS datasets may result in misleading biological conclusions. Accordingly, normalization is a necessary step in the proper preprocessing of OMICS datasets. In this regards, an optimal normalization method will effectively reduce unwanted biases and increase the accuracy of downstream quantitative analyses. But, it is currently unclear which normalization method is best since each algorithm addresses systematic noise in different ways.

Objective

Determine an optimal choice of a normalization method for the preprocessing of metabolomics datasets.

Methods

Nine MVAPACK normalization algorithms were compared with simulated and experimental NMR spectra modified with added Gaussian noise and random dilution factors. Methods were evaluated based on an ability to recover the intensities of the true spectral peaks and the reproducibility of true classifying features from orthogonal projections to latent structures—discriminant analysis model (OPLS-DA).

Results

Most normalization methods (except histogram matching) performed equally well at modest levels of signal variance. Only probabilistic quotient (PQ) and constant sum (CS) maintained the highest level of peak recovery (>?67%) and correlation with true loadings (>?0.6) at maximal noise.

Conclusion

PQ and CS performed the best at recovering peak intensities and reproducing the true classifying features for an OPLS-DA model regardless of spectral noise level. Our findings suggest that performance is largely determined by the level of noise in the dataset, while the effect of dilution factors was negligible. A minimal allowable noise level of 20% was also identified for a valid NMR metabolomics dataset.

     

Resolution effects in reconstructing ancestral genomes

Background

The reconstruction of ancestral genomes must deal with the problem of resolution, necessarily involving a trade-off between trying to identify genomic details and being overwhelmed by noise at higher resolutions.

Results

We use the median reconstruction at the synteny block level, of the ancestral genome of the order Gentianales, based on coffee, Rhazya stricta and grape, to exemplify the effects of resolution (granularity) on comparative genomic analyses.

Conclusions

We show how decreased resolution blurs the differences between evolving genomes, with respect to rate, mutational process and other characteristics.

     

Interpretation of comparative LCAs: external normalization and a method of mutual differences

Purpose

Identification of environmentally preferable alternatives in a comparative life cycle assessment (LCA) can be challenging in the presence of multiple incommensurate indicators. To make the problem more manageable, some LCA practitioners apply external normalization to find those indicators that contribute the most to their respective environmental impact categories. However, in some cases, these results can be entirely driven by the normalization reference, rather than the comparative performance of the alternatives. This study evaluates the influence of normalization methods on interpretation of comparative LCA to facilitate the use of LCA in decision-driven applications and inform LCA practitioners of latent systematic biases. An alternative method based on significance of mutual differences is proposed instead.

Methods

This paper performs a systematic evaluation of external normalization and describes an alternative called the overlap area approach for the purpose of identifying relevant issues in a comparative LCA. The overlap area approach utilizes the probability distributions of characterized results to assess significant differences. This study evaluates the effects in three LCIA methods, through application of four comparative studies. For each application, we call attention to the category indicators highlighted by each interpretation approach.

Results and discussion

External normalization in the three LCIA methods suffers from a systematic bias that emphasizes the same impact categories regardless of the application. Consequently, comparative LCA studies that employ external normalization to guide a selection may result in recommendations dominated entirely by the normalization reference and insensitive to data uncertainty. Conversely, evaluation of mutual differences via the overlap area calls attention to the impact categories with the most significant differences between alternatives. The overlap area approach does not show a systematic bias across LCA applications because it does not depend on external references and it is sensitive to changes in uncertainty. Thus, decisions based on the overlap area approach will draw attention to tradeoffs between alternatives, highlight the role of stakeholder weights, and generate assessments that are responsive to uncertainty.

Conclusions

The solution to the issues of external normalization in comparative LCAs proposed in this study call for an entirely different algorithm capable of evaluating mutual differences and integrating uncertainty in the results.

     

Principles for life cycle inventories of land use on a global scale

Purpose

To assess the diverse environmental impacts of land use, a standardization of quantifying land use elementary flows is needed in life cycle assessment (LCA). The purpose of this paper is to propose how to standardize the land use classification and how to regionalize land use elementary flows.

Materials and methods

In life cycle inventories, land occupation and transformation are elementary flows providing relevant information on the type and location of land use for land use impact assessment. To find a suitable land use classification system for LCA, existing global land cover classification systems and global approaches to define biogeographical regions are reviewed.

Results and discussion

A new multi-level classification of land use is presented. It consists of four levels of detail ranging from very general global land cover classes to more refined categories and very specific categories indicating land use intensities. Regionalization is built on five levels, first distinguishing between terrestrial, freshwater, and marine biomes and further specifying climatic regions, specific biomes, ecoregions and finally indicating the exact geo-referenced information of land use. Current land use inventories and impact assessment methods do not always match and hinder a comprehensive assessment of land use impact. A standardized definition of land use types and geographic location helps to overcome this gap and provides the opportunity to test the optimal resolution of land cover types and regionalization for each impact pathway.

Conclusions and recommendation

The presented approach provides the necessary flexibility to providers of inventories and developers of impact assessment methods. To simplify inventories and impact assessment methods of land use, we need to find archetypical situations across impact pathways, land use types and regions, and aggregate inventory entries and methods accordingly.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Uncertainty analysis in LCA using precalculated aggregated datasets

Purpose

Some LCA software tools use precalculated aggregated datasets because they make LCA calculations much quicker. However, these datasets pose problems for uncertainty analysis. Even when aggregated dataset parameters are expressed as probability distributions, each dataset is sampled independently. This paper explores why independent sampling is incorrect and proposes two techniques to account for dependence in uncertainty analysis. The first is based on an analytical approach, while the other uses precalculated results sampled dependently.

Methods

The algorithm for generating arrays of dependently presampled aggregated inventories and their LCA scores is described. These arrays are used to calculate the correlation across all pairs of aggregated datasets in two ecoinvent LCI databases (2.2, 3.3 cutoff). The arrays are also used in the dependently presampled approach. The uncertainty of LCA results is calculated under different assumptions and using four different techniques and compared for two case studies: a simple water bottle LCA and an LCA of burger recipes.

Results and discussion

The meta-analysis of two LCI databases shows that there is no single correct approximation of correlation between aggregated datasets. The case studies show that the uncertainty of single-product LCA using aggregated datasets is usually underestimated when the correlation across datasets is ignored and that the magnitude of the underestimation is dependent on the system being analysed and the LCIA method chosen. Comparative LCA results show that independent sampling of aggregated datasets drastically overestimates the uncertainty of comparative metrics. The approach based on dependently presampled results yields results functionally identical to those obtained by Monte Carlo analysis using unit process datasets with a negligible computation time.

Conclusions

Independent sampling should not be used for comparative LCA. Moreover, the use of a one-size-fits-all correction factor to correct the calculated variability under independent sampling, as proposed elsewhere, is generally inadequate. The proposed approximate analytical approach is useful to estimate the importance of the covariance of aggregated datasets but not for comparative LCA. The approach based on dependently presampled results provides quick and correct results and has been implemented in EcodEX, a streamlined LCA software used by Nestlé. Dependently presampled results can be used for streamlined LCA software tools. Both presampling and analytical solutions require a preliminary one-time calculation of dependent samples for all aggregated datasets, which could be centrally done by database providers. The dependent presampling approach can be applied to other aspects of the LCA calculation chain.

     

Using multitask classification methods to investigate the kinase-specific phosphorylation sites

Background

Identification of phosphorylation sites by computational methods is becoming increasingly important because it reduces labor-intensive and costly experiments and can improve our understanding of the common properties and underlying mechanisms of protein phosphorylation.

Methods

A multitask learning framework for learning four kinase families simultaneously, instead of studying each kinase family of phosphorylation sites separately, is presented in the study. The framework includes two multitask classification methods: the Multi-Task Least Squares Support Vector Machines (MTLS-SVMs) and the Multi-Task Feature Selection (MT-Feat3).

Results

Using the multitask learning framework, we successfully identify 18 common features shared by four kinase families of phosphorylation sites. The reliability of selected features is demonstrated by the consistent performance in two multi-task learning methods.

Conclusions

The selected features can be used to build efficient multitask classifiers with good performance, suggesting they are important to protein phosphorylation across 4 kinase families.

     

Untargeted metabolomics suffers from incomplete raw data processing

Introduction

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.

Objectives

Assessment of the quality of raw data processing in untargeted metabolomics.

Methods

Five published untargeted metabolomics studies, were reanalyzed.

Results

Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.

Conclusion

Incomplete raw data processing shows unexplored potential of current and legacy data.