Open AccessA Decision Analytic Approach to Exposure-Based Chemical Prioritization
Author(s) -
Jade Mitchell,
Nicolas Pabon,
Zachary A. Collier,
Peter Egeghy,
Elaine Cohen-Hubal,
Igor Linkov,
Daniel A. Vallero
Publication year - 2013
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0070911
Subject(s) - prioritization , context (archaeology) , risk assessment , risk analysis (engineering) , ranking (information retrieval) , computer science , variety (cybernetics) , expert elicitation , hazard , process (computing) , life cycle assessment , management science , business , machine learning , engineering , artificial intelligence , chemistry , paleontology , statistics , computer security , mathematics , organic chemistry , macroeconomics , production (economics) , economics , biology , operating system
The manufacture of novel synthetic chemicals has increased in volume and variety, but often the environmental and health risks are not fully understood in terms of toxicity and, in particular, exposure. While efforts to assess risks have generally been effective when sufficient data are available, the hazard and exposure data necessary to assess risks adequately are unavailable for the vast majority of chemicals in commerce. The US Environmental Protection Agency has initiated the ExpoCast Program to develop tools for rapid chemical evaluation based on potential for exposure. In this context, a model is presented in which chemicals are evaluated based on inherent chemical properties and behaviorally-based usage characteristics over the chemical’s life cycle. These criteria are assessed and integrated within a decision analytic framework, facilitating rapid assessment and prioritization for future targeted testing and systems modeling. A case study outlines the prioritization process using 51 chemicals. The results show a preliminary relative ranking of chemicals based on exposure potential. The strength of this approach is the ability to integrate relevant statistical and mechanistic data with expert judgment, allowing for an initial tier assessment that can further inform targeted testing and risk management strategies.