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Bridging Life Cycle and Exposure Ontologies to Enable Integration of Data Streams for Rapid Exposure Estimation and Comparative Exposure Assessment
Citation:
Hubal, E., P. Egeghy, D. Meyer, D. Vallero, AND S. Liu. Bridging Life Cycle and Exposure Ontologies to Enable Integration of Data Streams for Rapid Exposure Estimation and Comparative Exposure Assessment. 2018 International Symposium on Alternatives Assessment, Sacramento, CA, November 01 - 02, 2018.
Impact/Purpose:
This research will bridge the exposure science and LCA domains to support rapid exposure estimation across the product life cycle. The resulting bridged ontologies will facilitate use of data streams from both fields for a wider range of chemicals management applications.
Description:
The US EPA asked the NRC to recommend a framework to inform government and industry decisions about the use of chemical alternatives. The resulting NAS 2014 report, A Framework Guide for the Selection of Chemical Alternatives, has several important elements including an increased emphasis on comparative exposure assessment. Tools for more efficient exposure estimation have been advancing, and accessible exposure information has grown significantly in recent years. Data that have been developed and curated for exposure forecasting applications and for life cycle assessments (LCA) provide a rich source of information to support informed substitution. Integrating information from these separate knowledge domains and providing easy access to linked open data (LOD) will greatly enhance efficient comparative exposure assessment for chemicals in consumer products. The core ontology for LCA, called lcaMin, and the ontological framework for exposure science, called ExO, have been developed previously. With the availability of these core ontology pieces, it is possible to bridge the two domains and develop a harmonized framework to manage exposure data in a life cycle context. The objectives of this research are to bridge the exposure and LCA domains to support rapid exposure estimation across the product life cycle and to demonstrate this new ontology bridge using a case study. The resulting bridged ontologies will facilitate use of data streams from both fields for a wider range of chemicals management applications.
