An implemented approach for estimating uncertainties for toxicological impact characterisation

Ralph K. Rosenbaum, David W Pennington, Olivier Jolliet

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

One approach accounting for parameter and model uncertainty is implemented in the LCIA (life cycle impact assessment) method IMPACT 2002. The uncertainty is estimated for intermediate results from the chemical fate, human intake fraction, and two toxicological effect modules. Overall uncertainty estimates are then arithmetically calculated. Results are presented for impact contributions in the contexts of aquatic ecosystems and human health. The approach of Hofstetter (1998) was adapted for estimating the uncertainty related to chemical fate and human intake fractions. A fundamental problem when estimating uncertainties for 1000’s of substances consists of the lack of uncertainty distributions for all of the input data and the need to have a practical approach to assign distributions to each chemical. Hofstetter (1998) proposed the use of fixed factors for clusters of substances. The choice of a factor is then dependent on the emission medium, exposure route, and the robustness of the model relative to the chemical being considered. The factors are initially determined for representative substances for each category using evaluation data, expert judgement, or approaches such as Monte Carlo. There is then no need to repeat the Monte Carlo calculations. Multiplying and dividing the geometric mean estimate by a factor provides an estimate of the upper and lower 95th percentile confidence interval bounds. The human health effect factor uncertainty is similarly defined and readily combined through addition with that of the intake fraction. Using expert judgement, three uncertainty classes were proposed to estimate uncertainty related to the human effects input data. These effects data account for both the risk of an effect, as well as the potential consequences of population-based exposures. The uncertainty for ecotoxicological effects is currently related to the number of species tested for aquatic species in the water column. The more species test results available, the more robust the estimate of the ecotoxicological factor is assumed to be. For estimating the ecotoxicological effect factor uncertainty, the combined use of two distinct approaches was suggested, – the higher uncertainty estimate being adopted. The combination of both guaranteed more robust results compared to applying either method – both being based on differing assumptions related to the sample versus the population distribution. The presented approach proved to be very transparent, robust but while reflecting our current level of knowledge, quick to use, and is easily applied in practice to combine the uncertainty of the emissions inventory with those of the impact assessment phase in a life cycle assessment study.
Keyword: Uncertainty; LCIA; Toxicity; Multimedia Modelling
Original languageEnglish
Title of host publicationComplexity and Integrated Resources Management : Transactions of the 2nd Biennial Meeting of the International Environmental Modelling and Software Society, iEMSs
Number of pages6
Place of PublicationManno, Switzerland
PublisherInternational Environmental Modelling and Software Society, iEMSs
Publication date2004
ISBN (Print)88-900787-1-5
Publication statusPublished - 2004
Externally publishedYes
Event2nd Biennial Meeting of the International Environmental Modelling and Software Society, iEMSs - Osnabrück, Germany
Duration: 1 Jan 2004 → …

Conference

Conference2nd Biennial Meeting of the International Environmental Modelling and Software Society, iEMSs
CityOsnabrück, Germany
Period01/01/2004 → …

Cite this

Rosenbaum, R. K., Pennington, D. W., & Jolliet, O. (2004). An implemented approach for estimating uncertainties for toxicological impact characterisation. In Complexity and Integrated Resources Management: Transactions of the 2nd Biennial Meeting of the International Environmental Modelling and Software Society, iEMSs Manno, Switzerland: International Environmental Modelling and Software Society, iEMSs.