The application of the pedigree approach to the distributions foreseen in ecoinvent v3

Purpose

Data used in life cycle inventories are uncertain (Ciroth et al. Int J Life Cycle Assess 9(4):216–226, 2004). The ecoinvent LCI database considers uncertainty on exchange values. The default approach applied to quantify uncertainty in ecoinvent is a semi-quantitative approach based on the use of a pedigree matrix; it considers two types of uncertainties: the basic uncertainty (the epistemic error) and the additional uncertainty (the uncertainty due to using imperfect data). This approach as implemented in ecoinvent v2 has several weaknesses or limitations, one being that uncertainty is always considered as following a lognormal distribution. The aim of this paper is to show how ecoinvent v3 will apply this approach to all types of distributions allowed by the ecoSpold v2 data format.

Methods

A new methodology was developed to apply the semi-quantitative approach to distributions other than the lognormal. This methodology and the consequent formulas were based on (1) how the basic and the additional uncertainties are combined for the lognormal distribution and on (2) the links between the lognormal and the normal distributions. These two points are summarized in four principles. In order to test the robustness of the proposed approach, the resulting parameters for all probability density functions (PDFs) are tested with those obtained through a Monte Carlo simulation. This comparison will validate the proposed approach.

Results and discussion

In order to combine the basic and the additional uncertainties for the considered distributions, the coefficient of variation (CV) is used as a relative measure of dispersion. Formulas to express the definition parameters for each distribution modeling a flow with its total uncertainty are given. The obtained results are illustrated with default values; they agree with the results obtained through the Monte Carlo simulation. Some limitations of the proposed approach are cited.

Conclusions

Providing formulas to apply the semi-quantitative pedigree approach to distributions other than the lognormal will allow the life cycle assessment (LCA) practitioner to select the appropriate distribution to model a datum with its total uncertainty. These data variability definition technique can be applied on all flow exchanges and also on parameters which play an important role in ecoinvent v3.

     

Giving a scientific basis for uncertainty factors used in global life cycle inventory databases: an algorithm to update factors using new information

Purpose

Life cycle inventory (LCI) databases provide generic data on exchange values associated with unit processes. The “ecoinvent” LCI database estimates the uncertainty of all exchange values through the application of the so-called pedigree approach. In the first release of the database, the used uncertainty factors were based on experts’ judgments. In 2013, Ciroth et al. derived empirically based factors. These, however, assumed that the same uncertainty factors could be used for all industrial sectors and fell short of providing basic uncertainty factors. The work presented here aims to overcome these limitations.

Methods

The proposed methodological framework is based on the assessment of more than 60 data sources (23,200 data points) and the use of Bayesian inference. Using Bayesian inference allows an update of uncertainty factors by systematically combining experts’ judgments and other information we already have about the uncertainty factors with new data.

Results and discussion

The implementation of the methodology over the data sources results in the definition of new uncertainty factors for all additional uncertainty indicators and for some specific industrial sectors. It also results in the definition of some basic uncertainty factors. In general, the factors obtained are higher than the ones obtained in previous work, which suggests that the experts had initially underestimated uncertainty. Furthermore, the presented methodology can be applied to update uncertainty factors as new data become available.

Conclusions

In practice, these uncertainty factors can systematically be incorporated in LCI databases as estimates of exchange value uncertainty where more formal uncertainty information is not available. The use of Bayesian inference is applied here to update uncertainty factors but can also be used in other life cycle assessment developments in order to improve experts’ judgments or to update parameter values when new data can be accessed.

     

Geographic variability of agriculture requires sector-specific uncertainty characterization

Purpose

Regionalization in life cycle assessment (LCA) has focused on spatially differentiated environmental variables for regional impact assessment models. Relatively less attention has been paid to spatial disparities in intermediate flows for life cycle inventory (LCI).

Methods

First, we compiled state-specific LCIs for four major crops in the USA and evaluated their geographic variability in the characterized results due to the differences in intermediate inputs. Second, we evaluated the consequence of choosing average or region-specific LCIs in understanding the life cycle environmental implications of land use change from cotton to corn or soybean. Finally, we analyzed the implications of our findings in characterizing the uncertainties associated with geographic variability under the conventional pedigree approach.

Results and discussion

Our results show that spatial disparities in LCI alone lead to two to fourfold differences in characterized results for most impact categories. The differences, however, increase to over an order of magnitude for freshwater ecotoxicity and human health non-cancer. Among the crops analyzed, winter wheat shows higher variability partly due to a larger difference in yield. As a result, the use of national average data derived from top corn and soybean producing states significantly underestimates the characterized impacts of corn and soybean in the states where land conversion from cotton to corn or soybean actually took place. The results also show that the conventional pedigree approach to uncertainty characterization in LCA substantially underestimates uncertainties arising from geographic variability of agriculture. Compared to the highest geometric standard deviation (GSD) value of 1.11 under the pedigree approach, the GSDs that we derived are as high as 7.1, with the median around 1.9.

Conclusions

The results highlight the importance of building regional life cycle inventory for understanding the environmental impacts of crops at the regional level. The high geographic variability of crops also indicates the need for sector-specific approaches to uncertainty characterization. Our results also suggest that the uncertainty values in the existing LCI databases might have been signficantly underestimated especially for those products with high geographic variability, demanding a cautious interpretation of the results derived from them. 

     

The Quebec Life Cycle Inventory Database Project

Purpose

Life cycle assessment (LCA) in Quebec (Canada) is increasingly important. Yet, studies often still need to rely on foreign life cycle inventory (LCI) data. The Quebec government invested in the creation of a Quebec LCI database. The approach is to work as an ecoinvent “National Database Initiative” (NDI), whereby the Quebec database initiative uses and contributes to the ecoinvent database. The paper clarifies the relationship between ecoinvent and the Quebec NDI and provides details on prioritization and data collection.

Methods

The first steps were to select a partner database provider and to work out the modalities of the partnership. The main criterion for partner selection was database transparency, i.e., availability of unit process data (gate-to-gate), necessary for database adaptation. This and other criteria, such as free access to external reviewers, conservation of dataset copyright, seamless embedding of datasets, and overall database sophistication, pointed to ecoinvent. Once started, the NDI project proceeded as follows: (1) data collection was prioritized based on several criteria; (2) some datasets were “recontextualized,” i.e., existing datasets were duplicated and relocated in Quebec and linked to datasets representing regional suppliers, where relevant; (3) new datasets were created; and (4) Canadian environmentally extended supply-use tables were created for the ecoinvent IO repository.

Results and discussion

Prioritization identified 500 candidate datasets for recontextualization, based on the relative importance of relative contribution of direct electricity consumption to cradle-to-gate impacts, and 12 key sectors from which about 450 data adaptation or collection projects were singled out. Data collection and private sector solicitation are underway. Private sector participation is highly variable. A number of communication tools have been elaborated and a solicitation team formed to palliate this obstacle. The new ecoinvent database protocol (Weidema et al. 2011) increases the amount of information that is required to create a dataset, which can lengthen or, in extreme cases, impede dataset creation. However, this new information is required for the new database functionalities (e.g., providing multiple system models based on the same unit process data and regionalized LCA).

Conclusions

Being an NDI is advantageous for the Quebec LCI database project on multiple levels. By conserving dataset copyright, the NDI remains free to spawn or support other LCI databases. Embedding datasets in ecoinvent enables the generation of LCI results from “day 1.” The costs of IT infrastructure and data review are null. For these reasons, and because every NDI improves the global representativity of ecoinvent, we recommend other regional or national database projects work as NDIs.

     

Life cycle inventories of electricity generation and power supply in version 3 of the ecoinvent database—part II: electricity markets

Purpose

Representative, consistent and up-to-date life cycle inventories (LCI) of electricity supply are key elements of ecoinvent as an LCI background database since these are often among the determining factors with regard to life cycle assessment (LCA) results. ecoinvent version 3 (ev3) offers new LCI data of power supply (electricity markets) in 71 geographies. This article gives an overview of these electricity markets and discusses new ecoinvent features in the context of power supply.

Methods

The annual geography- and technology-specific electricity production for the year 2008 specifies the technology shares on the high-, medium- and low-voltage level electricity markets. Data are based on IEA statistics. Different voltage levels are linked by transformation activities. Region-specific electricity losses due to power transmission and voltage transformation are considered in the market and transformation activities. The majority of the 71 power markets are defined by national boundaries. The attributional ecoinvent system model in ev3 with linking to average current suppliers results in electricity markets supplied by all geography-specific power generation technologies and electricity imports, while the consequential system model generates markets only linked to unconstrained suppliers.

Results and discussion

The availability of LCI data for 71 electricity markets in ev3 covering 50 countries reduces the “Rest-of-the-World” electricity supply not covered by country- or region-specific inventories to 17 % for the year 2008. Specific power supply activities for all countries contributing more than 1 % to global electricity production are available. The electricity markets show large variations concerning contributions from specific technologies and energy carriers. Imports can substantially change the national/regional power mix, especially in small markets. Large differences can also be observed between the electricity markets in the attributional and the consequential database calculation. Region-specific total power losses between production on the high voltage level and consumer on the low voltage level are on the order of 2.5–23 %.

Conclusions

Electricity supply mixes (electricity markets) in the ecoinvent database have been updated and substantially extended for v3. Inventories for electricity supply in all globally important economies are available with geography-specific technology and market datasets which will contribute to increasing quality and reducing uncertainties in LCA studies worldwide and to allow more accurate estimation of environmental burdens from global production chains. Future work should focus on improving the details of country-specific data, implementation of more countries into the database, splitting of large countries into smaller regions and on developing a more sophisticated approach specifying country-specific electricity mixes in consequential system models.

     

Ecoinvent 3: assessing water use in LCA and facilitating water footprinting

Purpose

Water footprinting and the assessment of water use in life cycle assessment have become of major interest in sustainability assessments. Various initiatives for combining water resource issues with consumption of products and services have been initiated in the last decade. However, comprehensive databases fulfilling the requirements for addressing these issues have been lacking and are necessary to facilitate efficient and consistent assessments of products and services. To this purpose, ecoinvent focused on integrating appropriate water use data into version 3, since previously water use data has been inconsistently reported and some essential flows were missing. This paper describes the structure of the water use data in ecoinvent, how the data has been compiled and the way it can be used for water footprinting.

Methods

The main changes required for proper assessment of water use are the addition of environmental and product flows in order to allow a water balance over each process. This is in accordance with the strict paradigm in ecoinvent 3 to focus on mass balances, which requires the inclusion of water contents of all products (also for e.g. waste water flows), as well as emissions of water to soil, air and various water bodies. Water inputs from air (e.g. rainwater harvesting) is introduced but is not yet used by any activity.

Results and discussion

Ecoinvent version 3.1 consistently includes the relevant flows to address water use in life cycle assessment (LCA) and calculate water footprints on the product level for most processes including uncertainty information. Although some problems regarding data quality and spatial resolution remain, this is an important step forward and can limit efforts for detailed data collection to the most sensitive processes in the product system. With the combination of data on water use and emissions to water for each process, concentration and corresponding water classes can also be calculated and assessed with existing impact assessment methods.

Conclusions

This comprehensive collection of water use data on the process level facilitates the proper assessment of water use within an LCA and water footprints beyond agricultural production. Especially in LCA, but also in tools for eco-design and specific water footprint, this data is essential and leads to a cost-efficient way of assessing consumption choices and product design decisions with full transparency. It enhances the effectiveness of investing in data collection by performing sensitivity analyses using ecoinvent data to identify the most relevant flows and processes.

     

Life cycle inventories of electricity generation and power supply in version 3 of the ecoinvent database—part I: electricity generation

Purpose

Life cycle inventories (LCI) of electricity generation and supply are among the main determining factors regarding life cycle assessment (LCA) results. Therefore, consistency and representativeness of these data are crucial. The electricity sector has been updated and substantially extended for ecoinvent version 3 (v3). This article provides an overview of the electricity production datasets and insights into key aspects of these v3 inventories, highlights changes and describes new features.

Methods

Methods involved extraction of data and analysis from several publically accessible databases and statistics, as well as from the LCA literature. Depending on the power generation technology, either plant-specific or region-specific average data have been used for creating the new power generation inventories representing specific geographies. Whenever possible, the parent–child relationship was used between global and local activities. All datasets include a specific technology level in order to support marginal mixes used in the consequential version of ecoinvent. The use of parameters, variables and mathematical relations enhances transparency. The article focuses on documentation of LCI data on the unlinked unit process level and presents direct emission data of the electricity-generating activities.

Results and discussion

Datasets for electricity production in 71 geographic regions (geographies) covering 50 countries are available in ecoinvent v3. The number of geographies exceeds the number of countries due to partitioning of power generation in the USA and Canada into several regions. All important technologies representing fossil, renewable and nuclear power are modelled for all geographies. The new inventory data show significant geography-specific variations: thermal power plant efficiencies, direct air pollutant emissions as well as annual yields of photovoltaic and wind power plants will have significant impacts on cumulative inventories. In general, the power plants operating in the 18 newly implemented countries (compared to ecoinvent v2) are on a lower technology level with lower efficiencies and higher emissions. The importance of local datasets is once more highlighted.

Conclusions

Inventories for average technology-specific electricity production in all globally important economies are now available with geography-specific technology datasets. This improved coverage of power generation representing 83 % of global electricity production in 2008 will increase the quality of and reduce uncertainties in LCA studies worldwide and contribute to a more accurate estimation of environmental burdens from global production chains. Future work on LCI of electricity production should focus on updates of the fuel chain and infrastructure datasets, on including new technologies as well as on refining of the local data.

     

Electric passenger car transport and passenger car life cycle inventories in ecoinvent version 3

Purpose

Due to the large environmental challenges posed by the transport sector, reliable and state-of-the art data for its life cycle assessment is essential for enabling a successful transition towards more sustainable systems. In this paper, the new electric passenger car transport and vehicle datasets, which have been developed for ecoinvent version 3, are presented.

Methods

The new datasets have been developed with a strong modular approach, defining a hierarchy of datasets corresponding to various technical components in the vehicle. A vehicle is therefore modelled by linking together the various component datasets. Also, parameters and mathematical formulas have been introduced in order to define the amount of exchanges in the datasets through common transport and vehicle characteristics. This supports users in the choice of the amount of exchanges and enhances the transparency of the dataset.

Results

The new transport dataset describes the transport over 1 km with a battery electric passenger car taking into account the vehicle production and end of life, the energy consumption due to the use phase, non-exhaust emissions, maintenance and road infrastructure. The dataset has been developed and is suitable for a compact class vehicle.

Conclusions

A new electric passenger car transport dataset has been developed for version 3 of the ecoinvent database which exploits modularisation and parameters with the aim of facilitating users in adapting the data to their specific needs. Apart from the direct use of the transport dataset for background data, the various datasets for the different components can also be used as building blocks for virtual vehicles.

     

Estimation of the size of error introduced into consequential models by using attributional background datasets

Purpose

A systematic comparison is made of attributional and consequential results for the same products using the same unit process database, thus isolating the effect of the two system models. An analysis of this nature has only recently been made possible due to the ecoinvent database version 3 providing an access to both unallocated and unlinked unit process datasets as well as both attributional and consequential models based on these datasets. The analysis is therefore limited to the system models provided by ecoinvent.

Methods

For both system models, the analysis was made on the life cycle inventory analysis (LCIA) results as published by ecoinvent (692 impact categories from different methods, for 11,650 product/activity combinations). The comparison was made on the absolute difference relative to the smallest absolute value.

Results and discussion

The comparison provides quantified results showing that the consequential modelling provides large differences in results when the unconstrained (marginal) suppliers have much more/less impact than the average, when analysing the by-products, and when analysing determining products from activities with important amounts of other coproducts.

Conclusions

The analysis confirms that for consequential studies, attributional background datasets are not appropriate as a substitute for consequential background. The overall error will of course depend on the extent to which attributional modelling is used as part of the overall system model. While the identified causes of differences between the attributional and consequential models are of general nature, the identified sizes of the errors are specific to the way the two models are implemented in ecoinvent.

     

Adaptation of ecoinvent database to Polish conditions

Background, aims and scope

This paper is based on the results of a project sponsored by the Ministry of Science and Higher Education and carried out by the Wood Technology Institute in Poznan, Poland. The main purpose of the project was to assess the environmental impact generated during the entire life cycles of chosen wood products. Most of the data came from the Polish wood industry, but some of it was taken from the ecoinvent database, particularly data relating to forest processes. The data were not used as presented in the database but were properly adapted. The aim of the paper is to show how the ecoinvent’s initial assumptions have been changed and how the data were adapted. The influence of the adaptation on the characterization results is presented as well.

Materials and methods

The adaptation was performed in several steps: obtaining information about the principal assumptions of the ecoinvent database, gathering data appropriate to Polish conditions, changing the principal assumptions to include the Polish situation, calculating the new allocation and correction factors, forming the new inventory tables for round, industrial, and residual wood (softwood and hardwood), and calculating the characterization results to check the influence of the data adaptation on the life cycle assessment (LCA) final results. The following methods have been used: the economic allocation with the allocation correction to include mass, energy, and carbon dioxide uptake from nature and Ecoindicator99 method to assess the environmental impact.

Results

The study shows that differences exist between ecoinvent and Polish data for the wood production. Some of the differences are important and significantly impact the final results. The data differ in relation to many factors, for example: tree species, yield of forest, time from planting trees to final harvesting, length and width of forest roads or total area, and land use. The differences in the initial assumptions and further in the allocation factors result in changes of the inventory points and the characterization results. The relevant differences for round hardwood and residual hardwood are not observed. It is not, however, the case of the industrial hardwood where the visible difference exist. The results for all sorts of softwood are completely different and any similarities cannot be observed.

Discussion

At present, the total area of forest land in Poland is 9.0 million ha. This is equivalent to 28.8% of the country’s area. This means that the forest processes are important for the country. The study could be a part of a more general discussion about the suitability of data recognized by the database’s generators as representative for larger areas (Europe, Asia, the world).

Conclusions

The following conclusions could be formulated based on the results of the project: differences between countries always exist, and they can be a source of uncertainty, particularly if unspecific data are used. On the other hand, it is impossible to collect specific data for all inventory items included in the product system which is why LCA databases are needed. The following questions should be asked: how important is the discrepancy? What is its influence on the final results? Is it acceptable? There are some activities like electricity production where differences between countries can be crucial for the LCA final results.

Recommendations and perspectives

Two main recommendations could be formulated: the data taken from LCA databases should always be critically analyzed and reviewed to check if they are suitable for our case study, and, if possible, the adaptation to local or national conditions should be made.In Poland, the interest in LCA continues to grow; however, it is not easy for LCA practitioners to conduct LCAs because of companies’ skepticism and their reluctance to provide data. LCA databases are important sources of information. A review of the most popular LCA databases could be a good line of further improvement to make LCA studies more relevant and reliable.

     

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.

     

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.

     

A trade-based method for modelling supply markets in consequential LCA exemplified with Portland cement and bananas

Purpose

This study proposes a method based on the analysis of trade networks over time for modelling the marginal supply of products in consequential life cycle assessment (LCA). It aims at increasing the geographical granularity of markets, accuracy of transport distances and modes and material losses during transit by creating country-specific markets, instead of region-based supply-origin markets as currently proposed by ecoinvent. It leads to a better consideration of the environmental weight of trade following a change in demand on a local market and may serve as an inspirational basis for future releases of consequential life cycle inventory (LCI) databases.

Methods

The method uses ecoinvent v.3.3 as a support LCI database and two distinct traded products: bananas and grey Portland cement. Each country involved in the trade of a said product has a corresponding market created in the LCI database. The behavior of market to a marginal change in internal demand is modelled after its marginal trading preferences: it can either affect local production, imports, exports or a mix of the first two. Markets are linked to one another based on the linear regression analysis of their historical trade relations. The inventories that follow an increase in demand of 1000 kg of bananas and grey Portland cement are calculated for each market involved in their trade and are environmentally characterized and compared to the generic region-based market datasets provided by ecoinvent to assess the gains in accuracy through a higher geographical granularity. Furthermore, the characterized inventories of the markets for bananas are compared to a parallel scenario where transport distances are kept to a minimum using the shortest path method. It isolates the environmental burden associated to the utility maximization of the demand.

Results and discussion

When comparing the characterized impacts of country-specific markets with the generic ecoinvent market datasets, disparities in results appear. They highlight the importance of transport induced by demand displacement and losses of material during transport, both being the consequences of the extent a given market decides to be supplied directly from producing markets at the margin. These are aspects that may go unaccounted for when using generic regional markets. Second, optimizing transport distances for each market decreases the environmental impacts for most categories by more than 70%.

Conclusions

This study shows there is a need for modelling and understanding market relations to more accurately define the role of trade, supply chain efficiency and import policies in LCA.

     

Assessing the individual and combined effects of uncertainty and variability sources in comparative LCA of pavements

Purpose

Several efforts have attempted to incorporate the sources of uncertainty and variability into the life cycle assessment (LCA) of pavements. However, no method has been proposed that can simultaneously consider data quality, methodological choices, and variability in inputs and outputs without the need for complementary software. This study aims to develop and implement a flexible method that can be used in the LCA software to assess the effects of these sources on the conclusions.

Methods

A Monte Carlo analysis was conducted and applied in a comparative LCA of pavements to assess the preferred scenario. The uncertainty of the results was first estimated by considering the data quality using the ecoinvent database. Moreover, the variabilities of the materials, construction methods, and repair stages of the pavement life cycle were included in the analysis by assigning continuous uniform probability distributions to each variable. Ultimately, the probability of methodological choices was modeled using uniform distributions and assigning a portion of the area of the distribution to each scenario. The individual and combined effects of these uncertainty and variability sources were assessed in a comparative LCA of asphalt and concrete pavements in a cold region such as Quebec (Canada).

Results and discussion

The results of the Monte Carlo analysis show that the allocation choices can change the environmentally preferred scenario in four midpoint categories. These categories are significantly dominated by the crude oil supply chain. The variability in construction materials and methods can change the preferred scenario in the damage categories, namely, human health and global warming. Additionally, parameter uncertainty has a significant effect on the conclusion of the preferred scenario in ecosystem quality. The worst qualitative scores are given to the geographical uncertainty of the elementary flow that primarily contributes to this category (i.e., zinc). The simultaneous effect of the uncertainty and variability sources prevents the decision-maker from reaching a less uncertain conclusion about ecosystem quality, human health, and global warming effects.

Conclusions

This study demonstrates that it is feasible to assess the cumulative effects of common uncertainty and variability sources using commercial LCA software, including Monte Carlo simulation. Based on the variability and uncertainty of the results, the identification of a certain conclusion is case specific at both the midpoint and endpoint levels. Increasing the quality of the inventory is one solution to decreasing the uncertainties related to human health, ecosystem quality, and global warming regarding pavement LCA. This improvement can be achieved by avoiding the adaptation of a similar process to match the considered process and using practical construction efficiencies and realistic construction materials. The effectiveness of these tasks must be assessed in future studies. It should be noted that these conclusions were determined regardless of the uncertainty in the characterization factors of the impact assessment method.

     

A Boolean network model of human gonadal sex determination

Background

Gonadal sex determination (GSD) in humans is a complex biological process that takes place in early stages of embryonic development when the bipotential gonadal primordium (BGP) differentiates towards testes or ovaries. This decision is directed by one of two distinct pathways embedded in a GSD network activated in a population of coelomic epithelial cells, the Sertoli progenitor cells (SPC) and the granulosa progenitor cells (GPC). In males, the pathway is activated when the Sex-Determining Region Y (SRY) gene starts to be expressed, whereas in females the WNT4/ β-catenin pathway promotes the differentiation of the GPCs towards ovaries. The interactions and dynamics of the elements that constitute the GSD network are poorly understood, thus our group is interested in inferring the general architecture of this network as well as modeling the dynamic behavior of a set of genes associated to this process under wild-type and mutant conditions.

Methods

We reconstructed the regulatory network of GSD with a set of genes directly associated with the process of differentiation from SPC and GPC towards Sertoli and granulosa cells, respectively. These genes are experimentally well-characterized and the effects of their deficiency have been clinically reported. We modeled this GSD network as a synchronous Boolean network model (BNM) and characterized its attractors under wild-type and mutant conditions.

Results

Three attractors with a clear biological meaning were found; one of them corresponding to the currently known gene expression pattern of Sertoli cells, the second correlating to the granulosa cells and, the third resembling a disgenetic gonad.

Conclusions

The BNM of GSD that we present summarizes the experimental data on the pathways for Sertoli and granulosa establishment and sheds light on the overall behavior of a population of cells that differentiate within the developing gonad. With this model we propose a set of regulatory interactions needed to activate either the SRY or the WNT4/ β-catenin pathway as well as their downstream targets, which are critical for further sex differentiation. In addition, we observed a pattern of altered regulatory interactions and their dynamics that lead to some disorders of sex development (DSD).

     

Exclusive breastfeeding: measurement and indicators

Background

Accurate measurement of the duration of exclusive breastfeeding is complicated by factors related to definitions, timing, duration of recall, methods of analysis, and sample biases. Clearly prospective methods are likely to be more accurate but are too expensive to use in most large-scale surveys. Internationally, most surveys use a point-in-time or current status measurement (usually 24-hour recall) and report their findings using an indicator established by the World Health Organisation (WHO) in 1991 that involves combining all babies less than six months old in order to obtain a large enough sample size to result in stable proportions that can be compared over time. However, this indicator is complex to understand and explain and is widely misunderstood, even within the breastfeeding community. It is commonly cited in ways that greatly exaggerate how common exclusive breastfeeding actually is.

Discussion

A life-long or since birth indicator, introduced in 2000, counts infants as no longer exclusively breastfed as soon as anything else is fed to them. This is appropriate to do if for example data are being used to link infant feeding patterns with vertical transmission of HIV or later patterns of infant allergy. However, this indicator underestimates the total extent of exclusive breastfeeding, since some women interrupt but then resume it after a period of supplementation (which could for example only be a small amount of water given a single time).

Summary

Exactly which indicator is best to use depends on the purpose for which the data are being used. However, for surveys, the best approach, rarely used, would be to report indicators based on both point-in-time and life-long data.

     

An adaptive gene-level association test for pedigree data

Background

We propose a gene-level association test that accounts for individual relatedness and population structures in pedigree data in the framework of linear mixed models (LMMs). Our method data-adaptively combines the results across a class of score-based tests, only requiring fitting a single null model (under the null hypothesis) for the whole genome, thereby being computationally efficient.

Results

We applied our approach to test for association with the high-density lipoprotein (HDL) ratio of post- and pretreatments in GAW20 data. Using the LMM similar to that used by Aslibekyan et al. (PLos One, 7:48663, 2012), our method identified 2 nearly significant genes (APOA5 and ZNF259) near rs964184, whereas neither the other gene-level tests nor the standard test on each individual single-nucleotide polymorphism (SNP) detected any significant gene in a genome-wide scan.

Conclusions

Gene-level association testing can be a complementary approach to the SNP-level association testing and our method is adaptive and efficient compared to several other existing gene-level association tests.