Sensitivity-based research prioritization through stochastic characterization modeling

Ben A. Wender; Valentina Prado-Lopez; Peter Fantke; Dwarakanath Ravikumar; Thomas P. Seager

doi:10.1007/s11367-017-1322-y

Sensitivity-based research prioritization through stochastic characterization modeling

Ben A. Wender^*, Valentina Prado-Lopez, Peter Fantke, Dwarakanath Ravikumar, Thomas P. Seager

^*Corresponding author for this work

Research output: Contribution to journal › Journal article › Research › peer-review

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Abstract

Product developers using life cycle toxicity characterization models to understand the potential impacts of chemical emissions face serious challenges related to large data demands and high input data uncertainty. This motivates greater focus on model sensitivity toward input parameter variability to guide research efforts in data refinement and design of experiments for existing and emerging chemicals alike. This study presents a sensitivity-based approach for estimating toxicity characterization factors given high input data uncertainty and using the results to prioritize data collection according to parameter influence on characterization factors (CFs). Proof of concept is illustrated with the UNEP-SETAC scientific consensus model USEtox.

Original language	English
Journal	International Journal of Life Cycle Assessment
Volume	23
Issue number	2
Pages (from-to)	324-332
ISSN	0948-3349
DOIs	https://doi.org/10.1007/s11367-017-1322-y
Publication status	Published - 2018

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abstract = "Product developers using life cycle toxicity characterization models to understand the potential impacts of chemical emissions face serious challenges related to large data demands and high input data uncertainty. This motivates greater focus on model sensitivity toward input parameter variability to guide research efforts in data refinement and design of experiments for existing and emerging chemicals alike. This study presents a sensitivity-based approach for estimating toxicity characterization factors given high input data uncertainty and using the results to prioritize data collection according to parameter influence on characterization factors (CFs). Proof of concept is illustrated with the UNEP-SETAC scientific consensus model USEtox.",

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AU - Ravikumar, Dwarakanath

AU - Seager, Thomas P.

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Y1 - 2018

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AB - Product developers using life cycle toxicity characterization models to understand the potential impacts of chemical emissions face serious challenges related to large data demands and high input data uncertainty. This motivates greater focus on model sensitivity toward input parameter variability to guide research efforts in data refinement and design of experiments for existing and emerging chemicals alike. This study presents a sensitivity-based approach for estimating toxicity characterization factors given high input data uncertainty and using the results to prioritize data collection according to parameter influence on characterization factors (CFs). Proof of concept is illustrated with the UNEP-SETAC scientific consensus model USEtox.

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DO - 10.1007/s11367-017-1322-y

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