Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways

Research output: Contribution to journalJournal article – Annual report year: 2019Researchpeer-review

DOI

  • Author: Ring, Caroline L.

    United States Environmental Protection Agency, United States

  • Author: Arnot, Jon

    York University Toronto, Canada

  • Author: Bennett, Deborah H.

    University of California, United States

  • Author: Egeghy, Peter

    EPA National Exposure Research Laboratory, United States

  • Author: Fantke, Peter

    Quantitative Sustainability Assessment, Sustainability, Department of Technology, Management and Economics, Technical University of Denmark, Produktionstorvet, 2800, Kgs. Lyngby, Denmark

  • Author: Huang, Lei

    University of Michigan, Ann Arbor, United States

  • Author: Isaacs, Kristin K.

    EPA National Exposure Research Laboratory, United States

  • Author: Jolliet, Olivier

  • Author: Phillips, Katherine

    EPA National Exposure Research Laboratory, United States

  • Author: Price, Paul S.

    EPA National Exposure Research Laboratory, United States

  • Author: Shin, Hyeong-Moo

    University of Texas, United States

  • Author: Westgate, John N

    ARC Arnot Research & Consulting Inc., Canada

  • Author: Setzer, R. Woodrow

    United States Environmental Protection Agency, United States

  • Author: Wambaugh, John F.

    United States Environmental Protection Agency, United States

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Prioritizing the potential risk posed to human health by chemicals requires tools that can estimate exposure from limited information. In this study chemical structure and physicochemical properties were used to predict the probability that a chemical might be associated with any of four exposure pathways leading from sources – consumer (near-field), dietary, far-field industrial, and far-field pesticide – to the general population. The balanced accuracies of these source-based exposure pathway models range from 73-81%, with the error rate for identifying positive chemicals ranging from 17-36%. We then used exposure pathways to organize predictions from thirteen different exposure models as well as other predictors of human intake rates. We created a consensus, meta-model using the Systematic Empirical Evaluation of Models (SEEM) framework in which the predictors of exposure were combined by pathway and weighted according to predictive ability for chemical intake rates inferred from human biomonitoring data for 114 chemicals. The consensus model yields an R2 of ~0.8. We extrapolate to predict relevant pathway(s), median intake rate, and credible interval for 479,926 chemicals, mostly with minimal exposure information. This approach identifies 1,880 chemicals for which the median population intake rates may exceed 0.1 mg/kg bodyweight/day, while there is 95% confidence that the median intake rate is below 1 µg/kg BW/day for 478,046 compounds.
Original languageEnglish
JournalEnvironmental Science and Technology
Volume53
Issue number2
Pages (from-to)719-732
Number of pages14
ISSN0013-936X
DOIs
Publication statusPublished - 2019
CitationsWeb of Science® Times Cited: No match on DOI
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