Citation:

Meyer, David E., S. Bailin, D. Vallero, Peter P. Egeghy, Shi V. Liu, AND Elaine A. Cohen Hubal. Enhancing life cycle chemical exposure assessment through ontology modeling. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, Netherlands, 712:136263, (2019). https://doi.org/10.1016/j.scitotenv.2019.136263

Impact/Purpose:

The purpose of this manuscript is to improve exposure modeling workflows by introducing non-traditional data from disciplines such as life cycle assessment using ontology modeling and the concepts of linked open data. The approach presented in the manuscript based on ontology bridging is advantageous because it is agile and supports automation.

Description:

In its 2014 report, A Framework Guide for the Selection of Chemical Alternatives, the National Academy of Sciences placed increased emphasis on comparative exposure assessment throughout the life cycle (i.e., from manufacturing to end-of-life) of a chemical. The inclusion of the full life cycle greatly increases the data demands for exposure assessments, including both the quantity and type of data. High throughput tools for exposure estimation add to this challenge by requiring rapid accessibility to data. In this work, ontology modeling was used to bridge the domains of exposure modeling and life cycle inventory modeling to facilitate data sharing and integration. The exposure ontology, ExO, is extended to describe human exposure to consumer products, while an inventory modeling ontology, LciO, is formulated to support automated data mining. The core ontology pieces are connected using a bridging ontology and discussed through a theoretical example to demonstrate how data from LCA can be leveraged to support rapid exposure modeling within a life cycle context.

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