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Ryberg M.,Technical University of Denmark | Vieira M.D.M.,PRe Consultants bv | Zgola M.,Quantis International | Bare J.,Sustainable Development Technology | Rosenbaum R.K.,Technical University of Denmark
Clean Technologies and Environmental Policy | Year: 2014

When LCA practitioners perform LCAs, the interpretation of the results can be difficult without a reference point to benchmark the results. Hence, normalization factors are important for relating results to a common reference. The main purpose of this paper was to update the normalization factors for the US and US-Canadian regions. The normalization factors were used for highlighting the most contributing substances, thereby enabling practitioners to put more focus on important substances, when compiling the inventory, as well as providing them with normalization factors reflecting the actual situation. Normalization factors were calculated using characterization factors from the TRACI 2.1 LCIA model. The inventory was based on US databases on emissions of substances. The Canadian inventory was based on a previous inventory with 2005 as reference, in this inventory the most significant substances were updated to 2008 data. The results showed that impact categories were generally dominated by a small number of substances. The contribution analysis showed that the reporting of substance classes was highly significant for the environmental impacts, although in reality, these substances are nonspecific in composition, so the characterization factors which were selected to represent these categories may be significantly different from the actual identity of these aggregates. Furthermore the contribution highlighted the issue of carefully examining the effects of metals, even though the toxicity based categories have only interim characterization factors calculated with USEtox. A need for improved understanding of the wide range of uncertainties incorporated into studies with reported substance classes was indentified. This was especially important since aggregated substance classes are often used in LCA modeling when information on the particular substance is missing. Given the dominance of metals to the human and ecotoxicity categories, it is imperative to refine the CFs within USEtox. Some of the results within this paper indicate that soil emissions of metals are significantly higher than we expect in actuality. © 2013 Springer-Verlag Berlin Heidelberg. Source

Pfister S.,ETH Zurich | Pfister S.,Aveny GmbH | Vionnet S.,Quantis International | Levova T.,Ecoinvent Center | Humbert S.,Quantis International
International Journal of Life Cycle Assessment | Year: 2015

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. © 2015 Springer-Verlag Berlin Heidelberg Source

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