OMNIITOX - operational life-cycle impact assessment models and information tools for practitioners

  • Sverker Molander
  • Peter Lidholm
  • Diederik Schowanek
  • Mar Recasens
  • Pere Fullana i Palmer
  • Frans M. Christensen
  • Jeroen B. Guinee
  • Michael Hauschild
  • Olivier Jolliet
  • David W. Pennington
  • Raul Carlson
  • Till M. Bachmann
OMNIITOX: Openning -Preamble


This article is the preamble to a set of articles describing initial results from an on-going European Commission funded, 5th Framework project called OMNIITOX, Operational Models aNd Information tools for Industrial applications of eco/TOXicological impact assessments. The different parts of this case study-driven project are briefly presented and put in relation to the aims of contributing to an operational life cycle-impact assessment (LCIA) model for impacts of toxicants. The present situation has been characterised by methodological difficulties, both regarding choice of the characterisation model(s) and limited input data on chemical properties, which often has resulted in the omission of toxicants from the LCIA, or at best focus on well characterised chemicals. The project addresses both problems and integrates models, as well as data, in an information system- the OMNIITOX IS. There is also a need for clarification of the relations between the (environmental) risk assessments of toxicants and LCIA, in addition to investigating the feasibility of introducing LCA into European chemicals legislation, tasks that also were addressed in the project. Keywords: Case studies; characterisation factor; chemicals; environmental risk assessment; hazard assessment; information system; life cycle impact assessment (LCIA); potentially toxic substances; regulation; risk assessment; risk ranking


Life Cycle Assessment Impact Category Life Cycle Impact Assessment Characterisation Factor Life Cycle Impact Assessment Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Ecomed Publishers 2004

Authors and Affiliations

  • Sverker Molander
    • 1
  • Peter Lidholm
    • 2
  • Diederik Schowanek
    • 3
  • Mar Recasens
    • 4
  • Pere Fullana i Palmer
    • 5
  • Frans M. Christensen
    • 6
  • Jeroen B. Guinee
    • 7
  • Michael Hauschild
    • 8
  • Olivier Jolliet
    • 9
  • David W. Pennington
    • 9
  • Raul Carlson
    • 10
  • Till M. Bachmann
    • 11
  1. 1.Environmental Systems AnalysisChalmers University of TechnologyGoteborgSweden
  2. 2.Volvo Technology CorporationGoteborgSweden
  3. 3.Procter & Gamble EurocorStrombeek-BeverBelgium
  4. 4.Antonio Puig S.A.BarcelonaSpain
  5. 5.Randa GroupCardenal Vives i TutoBarcelonaSpain
  6. 6.European Commission, Joint Research Centre, Institute for Health and Consumer ProtectionEuropean Chemicals BureauIspraItaly
  7. 7.Institute of Environmental Sciences (CML)Leiden UniversityThe Netherlands
  8. 8.Department of Manufacturing Engineering and ManagementTechnical University of Denmark (DTU)LyngbyDenmark
  9. 9.Industrial Ecology - Life Cycle Systems, GECOSSwiss Federal Institute of Technology Lausanne (EPFL)LausanneSwitzerland
  10. 10.Industrial Environmental InformaticsChalmers University of TechnologyGöteborgSweden
  11. 11.Institute of Energy Economics and the Rational Use of Energy (IER)University of StuttgartStuttgartGermany

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