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).

     

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.

     

Review of LCA datasets in three emerging economies: a summary of learnings

Purpose

In response to a need for interoperable LCA databases of adequate quality, a review of 40 datasets from emerging economies has been conducted with UNEP support by independent LCA experts, with the purpose to encourage improvements, either prior to publication or as part of a continuous improvement cycle. We discuss the lessons learned during this reviewing process.

Methods

The review effort had to be delivered in a set and limited timeline of 9 months and covered 20 datasets from Malaysia, ten from Brazil, and ten from Thailand. The developed review process consisted in, among others, (i) developing a set of review criteria for this specific effort, (ii) pairing a local sector reviewer with an international LCA expert to enhance capacity development, (iii) establishing a legal relationship via a non-disclosure agreement (NDA) between the data provider and the data reviewers, and (iv) providing a review report for each dataset that assesses the presence and magnitude of gaps or deficiencies relative to the criteria, as well as their implications for qualifying usage.

Results and discussion

The reviews provided solid recommendations for improvement to the majority of the datasets submitted by the three countries. This review exercise has been conducted in the given timeframe for the majority of the datasets. Many challenges have been faced along the way; among them agreeing on an NDA, the need for sufficient information, and the question of the confidentiality of the data. The developed set of criteria for this exercise have not been presented or discussed outside of the narrow review exercise and therefore can be seen as a starting point for further debates and improvements that should involve the broad LCA community.

Conclusions

This hands-on exercise provided some valuable insights into the road towards global consensus and guidance on data review. Beyond this time-constrained effort, a broader discussion and consensus at international level on the review of datasets and a continuous effort in reviewing datasets is desirable. Four key aspects were identified as necessary to consider: review criteria, review process, reviewers’ selection criteria, and development of a legal basis which protects the confidentiality of the data, as well as the position of the reviewers. This last aspect is sensitive as, in LCA datasets reviews, companies and/or countries might disclose some information with high stakes regarding competition and trade.

     

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.

     

The application of life cycle assessment to public policy development

Purpose

Despite the potential value it offers, integration of life cycle assessment (LCA) into the development of environmental public policy has been limited. This paper researches potential barriers that may be limiting the use of LCA in public policy development, and considers process opportunities to increase this application.

Methods

Research presented in this paper is primarily derived from reviews of existing literature and case studies, as well as interviews with key public policy officials with LCA experience. Direct experience of the author in LCA projects with public policy elements has also contributed to approaches and conclusions.

Results and discussion

LCAs have historically been applied within a rational framework, with experts conducting the analysis and presenting results to decision-makers for application to public policy development. This segmented approach has resulted in limited incorporation of LCA results or even a broader approach of life cycle thinking within the public policy development process. Barriers that limit the application of LCA within the public policy development process range from lack of technical knowledge and LCA understanding on the part of policy makers, to a lack of trust in LCA process and results. Many of the identified barriers suggest that the failure of LCAs to contribute positively to public policy development is due to the process within which the LCA is being incorporated, rather than technical problems in the LCA itself. Overcoming the barriers to effective use of LCAs in public policy development will require a more normative approach to the LCA process that incorporates a broad group of stakeholders at all stages of the assessment. Specifically, a set of recommendations have been developed to produce a more inclusive and effective process.

Conclusions

In an effort to effectively incorporate LCA within the overall public policy decision-making process, the decision-making process should incorporate a multi-disciplinary approach that includes a range of stakeholders and public policy decision-makers in a collaborative process. One of the most important aspects of incorporating LCA into public policy decisions is to encourage life cycle thinking among policy makers. Considering the life cycle implications will result in more informed and thoughtful decisions, even if a full LCA is not undertaken.

     

Review: life cycle assessments in Nigeria,Ghana, and Ivory Coast

Purpose

Life cycle assessments (LCAs) are considered common quantitative environmental techniques to analyze the environmental impact of products and/or services throughout their entire life cycle. A few LCA studies have been conducted in West Africa. This study aimed to discuss the availability of LCA (and similar) studies in Nigeria, Ghana, and Ivory Coast.

Methods

An online literature review of reports published between 2000 and 2016 was conducted using the following keywords: “life cycle assessment,” “carbon footprinting,” “water footprinting,” “environmental impact,” “Nigeria,” “Ghana” and “Ivory Coast.”

Results and discussion

A total of 31 LCA and environmental studies in Nigeria, Ghana, and Ivory Coast were found; all but one were conducted after 2008. These were mainly academic and most were publicly available. The industries studied included energy sector, waste management, real estate, food sector, and others such as timber and gold. The minimal number of studies on LCAs and environmental impacts in these West African states could be because companies are failing to promote quantitative environmental studies or studies are kept internally for the use of other assessment techniques. Furthermore, it could be that academic research institutions lack cutting-edge research resources for LCA, environmental impact, carbon, and water footprinting studies.

Conclusions

Further quantitative environmental studies should be conducted in Nigeria, Ghana, and Ivory Coast to increase the understanding of environmental impacts. In these countries, the existence of LCA studies (and by association the localized life cycle inventory (LCI) datasets) is crucial as more companies request this information to feed into background processes.

     

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.

     

A clustering-based preprocessing method for the elimination of unwanted residuals in metabolomic data

Introduction

The metabolome of a biological system is affected by multiple factors including factor of interest (e.g. metabolic perturbation due to disease) and unwanted factors or factors which are not primarily the focus of the study (e.g. batch effect, gender, and level of physical activity). Removal of these unwanted data variations is advantageous, as the unwanted variations may complicate biological interpretation of the data.

Objectives

We aim to develop a new unwanted variations elimination (UVE) method called clustering-based unwanted residuals elimination (CURE) to reduce metabolic variation caused by unwanted/hidden factors in metabolomic data.

Methods

A mean-centered metabolomic dataset can be viewed as a combination of a studied factor matrix and a residual matrix. The CURE method assumes that the residual should be normally distributed if it only contains inter-individual variation. However, if the residual forms multiple clusters in feature subspace of principal components analysis or partial least squares discriminant analysis, the residual may contain variation due to unwanted factors. This unwanted variation is removed by doing K-means data clustering and removal of means for each cluster from the residuals. The process is iterated until the residual no longer forms multiple clusters in feature subspace.

Results

Three simulated datasets and a human metabolomic dataset were used to demonstrate the performance of the proposed CURE method. CURE was found able to remove most of the variations caused by unwanted factors, while preserving inter-individual variation between samples.

Conclusion

The CURE method can effectively remove unwanted data variation, and can serve as an alternative UVE method for metabolomic data.

     

Iridoid and phenylethanoid/phenylpropanoid metabolite profiles of <Emphasis Type="Italic">Scrophularia</Emphasis> and <Emphasis Type="Italic">Verbascum</Emphasis> species used medicinally in North America

Introduction

Botanicals containing iridoid and phenylethanoid/phenylpropanoid glycosides are used worldwide for the treatment of inflammatory musculoskeletal conditions that are primary causes of human years lived with disability, such as arthritis and lower back pain.

Objectives

We report the analysis of candidate anti-inflammatory metabolites of several endemic Scrophularia species and Verbascum thapsus used medicinally by peoples of North America.

Methods

Leaves, stems, and roots were analyzed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) and partial least squares-discriminant analysis (PLS-DA) was performed in MetaboAnalyst 3.0 after processing the datasets in Progenesis QI.

Results

Comparison of the datasets revealed significant and differential accumulation of iridoid and phenylethanoid/phenylpropanoid glycosides in the tissues of the endemic Scrophularia species and Verbascum thapsus.

Conclusions

Our investigation identified several species of pharmacological interest as good sources for harpagoside and other important anti-inflammatory metabolites.

     

NS-kNN: a modified <Emphasis Type="Italic">k</Emphasis>-nearest neighbors approach for imputing metabolomics data

Introduction

A common problem in metabolomics data analysis is the existence of a substantial number of missing values, which can complicate, bias, or even prevent certain downstream analyses. One of the most widely-used solutions to this problem is imputation of missing values using a k-nearest neighbors (kNN) algorithm to estimate missing metabolite abundances. kNN implicitly assumes that missing values are uniformly distributed at random in the dataset, but this is typically not true in metabolomics, where many values are missing because they are below the limit of detection of the analytical instrumentation.

Objectives

Here, we explore the impact of nonuniformly distributed missing values (missing not at random, or MNAR) on imputation performance. We present a new model for generating synthetic missing data and a new algorithm, No-Skip kNN (NS-kNN), that accounts for MNAR values to provide more accurate imputations.

Methods

We compare the imputation errors of the original kNN algorithm using two distance metrics, NS-kNN, and a recently developed algorithm KNN-TN, when applied to multiple experimental datasets with different types and levels of missing data.

Results

Our results show that NS-kNN typically outperforms kNN when at least 20–30% of missing values in a dataset are MNAR. NS-kNN also has lower imputation errors than KNN-TN on realistic datasets when at least 50% of missing values are MNAR.

Conclusion

Accounting for the nonuniform distribution of missing values in metabolomics data can significantly improve the results of imputation algorithms. The NS-kNN method imputes missing metabolomics data more accurately than existing kNN-based approaches when used on realistic datasets.

     

The ecoinvent database version 3 (part I): overview and methodology

Purpose

Good background data are an important requirement in LCA. Practitioners generally make use of LCI databases for such data, and the ecoinvent database is the largest transparent unit-process LCI database worldwide. Since its first release in 2003, it has been continuously updated, and version 3 was published in 2013. The release of version 3 introduced several significant methodological and technological improvements, besides a large number of new and updated datasets. The aim was to expand the content of the database, set the foundation for a truly global database, support regionalized LCIA, offer multiple system models, allow for easier integration of data from different regions, and reduce maintenance efforts. This article describes the methodological developments.

Methods

Modeling choices and raw data were separated in version 3, which enables the application of different sets of modeling choices, or system models, to the same raw data with little effort. This includes one system model for Consequential LCA. Flow properties were added to all exchanges in the database, giving more information on the inventory and allowing a fast calculation of mass and other balances. With version 3.1, the database is generally water-balanced, and water use and consumption can be determined. Consumption mixes called market datasets were consistently added to the database, and global background data was added, often as an extrapolation from regional data.

Results and discussion

In combination with hundreds of new unit processes from regions outside Europe, these changes lead to an improved modeling of global supply chains, and a more realistic distribution of impacts in regionalized LCIA. The new mixes also facilitate further regionalization due to the availability of background data for all regions.

Conclusions

With version 3, the ecoinvent database substantially expands the goals and scopes of LCA studies it can support. The new system models allow new, different studies to be performed. Global supply chains and market datasets significantly increase the relevance of the database outside of Europe, and regionalized LCA is supported by the data. Datasets are more transparent, include more information, and support, e.g., water balances. The developments also support easier collaboration with other database initiatives, as demonstrated by a first successful collaboration with a data project in Québec. Version 3 has set the foundation for expanding ecoinvent from a mostly regional into a truly global database and offers many new insights beyond the thousands of new and updated datasets it also introduced.

     

rDolphin: a GUI R package for proficient automatic profiling of 1D <Superscript>1</Superscript>H-NMR spectra of study datasets

Introduction

Adoption of automatic profiling tools for ¹H-NMR-based metabolomic studies still lags behind other approaches in the absence of the flexibility and interactivity necessary to adapt to the properties of study data sets of complex matrices.

Objectives

To provide an open source tool that fully integrates these needs and enables the reproducibility of the profiling process.

Methods

rDolphin incorporates novel techniques to optimize exploratory analysis, metabolite identification, and validation of profiling output quality.

Results

The information and quality achieved in two public datasets of complex matrices are maximized.

Conclusion

rDolphin is an open-source R package (http://github.com/danielcanueto/rDolphin) able to provide the best balance between accuracy, reproducibility and ease of use.

     

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.

     

Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation,missing value imputation,transformation and scaling

Introduction

The generic metabolomics data processing workflow is constructed with a serial set of processes including peak picking, quality assurance, normalisation, missing value imputation, transformation and scaling. The combination of these processes should present the experimental data in an appropriate structure so to identify the biological changes in a valid and robust manner.

Objectives

Currently, different researchers apply different data processing methods and no assessment of the permutations applied to UHPLC-MS datasets has been published. Here we wish to define the most appropriate data processing workflow.

Methods

We assess the influence of normalisation, missing value imputation, transformation and scaling methods on univariate and multivariate analysis of UHPLC-MS datasets acquired for different mammalian samples.

Results

Our studies have shown that once data are filtered, missing values are not correlated with m/z, retention time or response. Following an exhaustive evaluation, we recommend PQN normalisation with no missing value imputation and no transformation or scaling for univariate analysis. For PCA we recommend applying PQN normalisation with Random Forest missing value imputation, glog transformation and no scaling method. For PLS-DA we recommend PQN normalisation, KNN as the missing value imputation method, generalised logarithm transformation and no scaling. These recommendations are based on searching for the biologically important metabolite features independent of their measured abundance.

Conclusion

The appropriate choice of normalisation, missing value imputation, transformation and scaling methods differs depending on the data analysis method and the choice of method is essential to maximise the biological derivations from UHPLC-MS datasets.

     

Identification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method

Background

Human cancers are complex ecosystems composed of cells with distinct molecular signatures. Such intratumoral heterogeneity poses a major challenge to cancer diagnosis and treatment. Recent advancements of single-cell techniques such as scRNA-seq have brought unprecedented insights into cellular heterogeneity. Subsequently, a challenging computational problem is to cluster high dimensional noisy datasets with substantially fewer cells than the number of genes.

Methods

In this paper, we introduced a consensus clustering framework conCluster, for cancer subtype identification from single-cell RNA-seq data. Using an ensemble strategy, conCluster fuses multiple basic partitions to consensus clusters.

Results

Applied to real cancer scRNA-seq datasets, conCluster can more accurately detect cancer subtypes than the widely used scRNA-seq clustering methods. Further, we conducted co-expression network analysis for the identified melanoma subtypes.

Conclusions

Our analysis demonstrates that these subtypes exhibit distinct gene co-expression networks and significant gene sets with different functional enrichment.

     

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.

     

An oncoplastic technique to reduce the formation of lateral 'dog-ears' after mastectomy

Background

Lateral skin folds or 'dog-ears' are frequent following mastectomy, particularly in patients with large body habitus.

Methods

We describe a method of modifying the mastectomy incision and suturing to eliminate these lateral 'dog-ears'.

Conclusion

This surgical technique, as compared to others described in the literature, is simple, does not require additional incisions and is cosmetically acceptable to the patient.

     

Life cycle assessment to evaluate the environmental performance of new construction material from stainless steel slag

Purpose

Many new opportunities are explored to lower the CO₂ emissions of the cement industry. Academic and industrial researches are currently focused on the possibility of recycling steel production residues in the cement industry, in order to produce new “low-carbon” binders for construction materials. The purpose of this paper is to assess the environmental benefits and costs of steel residue valorisation processes to produce a new binder for construction materials.

Methods

Among other stainless steel slags (SSS), argon oxygen decarburisation (AOD)-slag has the potential to be recovered as a binder during the production of new construction materials. Alkali activation and carbonation processes can, in fact, activate the binding properties of the AOD-slag. However, AOD-slag is today only recycled as low-quality aggregate. For the present study, three different types of construction blocks (called SSS-blocks) were developed starting from the AOD-slag (one block through alkali activation and two blocks through carbonation). The data from the production of the three construction blocks have been collected and used to perform a life cycle assessment (LCA) study, comparing SSS-block production with the production of traditional paver ordinary Portland cement (OPC) concrete.

Results and discussion

The analysis showed that SSS-block production through alkali activation and carbonation has the potential of lowering some of the environmental impacts of OPC-concrete. The LCA results also show that the main bottleneck in the alkali activation process is the production of the alkali activators required in the process, while the use of electricity and of pure CO₂ streams in carbonation lowers the environmental performances of the entire process.

Conclusions

The valorisation of AOD-slag to produce new construction materials is a promising route to lower the environmental impacts of cement and concrete industries. This product-level analysis stresses the need of updating the LCI datasets for alkali activators and boric oxide and of widening the scope of the environmental analysis up to system level, including potential economic interactions and market exchanges between steel and construction sectors.