U.S. Environmental Protection Agency, Center for Environmental Solutions and Emergency Response, 26 West Martin Luther King Drive, Cincinnati, OH 45268, United States.
Knowledge Evolution, Inc., 1748 Seaton Street NW, Washington, DC 20009, United States.
Sci Total Environ. 2020 Apr 10;712:136263. doi: 10.1016/j.scitotenv.2019.136263. Epub 2019 Dec 27.
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.
在其 2014 年的报告《化学替代品选择框架指南》中，美国国家科学院强调了在化学品的整个生命周期（即从制造到生命周期结束）中进行比较暴露评估的重要性。将整个生命周期包括在内，极大地增加了暴露评估的数据需求，包括数据的数量和类型。高通量暴露估计工具通过需要快速访问数据，增加了这一挑战。在这项工作中，本体建模被用于弥合暴露建模和生命周期清单建模之间的领域，以促进数据共享和集成。暴露本体（ExO）被扩展为描述人类对消费品的暴露，而库存建模本体（LciO）被制定为支持自动数据挖掘。使用桥接本体连接核心本体部分，并通过理论示例进行讨论，以演示如何利用生命周期评估 (LCA) 中的数据来支持生命周期背景下的快速暴露建模。
