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Limitations of toxicity characterization in life cycle assessment: can adverse outcome pathways provide a new foundation?
Gust, K.A.; Collier, Z.A.; Mayo, M.L.; Stanley, J.K.; Gong, P.; Chappell, M.A. (2016). Limitations of toxicity characterization in life cycle assessment: can adverse outcome pathways provide a new foundation? Integr. Environ. Assess. Manag. 12(3): 580-590. https://dx.doi.org/10.1002/ieam.1708
In: Integrated Environmental Assessment and Management. Wiley: Pensacola. ISSN 1551-3777; e-ISSN 1551-3793
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| Auteurs | | Top |
- Gust, K.A.
- Collier, Z.A.
- Mayo, M.L.
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- Stanley, J.K.
- Gong, P.
- Chappell, M.A.
|
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| Abstract |
Life cycle assessment (LCA) has considerable merit for holistic evaluation of product planning, development, production, and disposal, with the inherent benefit of providing a forecast of potential health and environmental impacts. However, a technical review of current life cycle impact assessment (LCIA) methods revealed limitations within the biological effects assessment protocols, including: simplistic assessment approaches and models; an inability to integrate emerging types of toxicity data; a reliance on linear impact assessment models; a lack of methods to mitigate uncertainty; and no explicit consideration of effects in species of concern. The purpose of the current study is to demonstrate that a new concept in toxicological and regulatory assessment, the adverse outcome pathway (AOP), has many useful attributes of potential use to ameliorate many of these problems, to expand data utility and model robustness, and to enable more accurate and defensible biological effects assessments within LCIA. Background, context, and examples have been provided to demonstrate these potential benefits. We additionally propose that these benefits can be most effectively realized through development of quantitative AOPs (qAOPs) crafted to meet the needs of the LCIA framework. As a means to stimulate qAOP research and development in support of LCIA, we propose 3 conceptual classes of qAOP, each with unique inherent attributes for supporting LCIA: 1) mechanistic, including computational toxicology models; 2) probabilistic, including Bayesian networks and supervised machine learning models; and 3) weight of evidence, including models built using decision-analytic methods. Overall, we have highlighted a number of potential applications of qAOPs that can refine and add value to LCIA. As the AOP concept and support framework matures, we see the potential for qAOPs to serve a foundational role for next-generation effects characterization within LCIA. |
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