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Environ Sci Technol. 2019 Jan 15;53(2):719-732. doi: 10.1021/acs.est.8b04056. Epub 2018 Dec 24.

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

Author information

National Center for Computational Toxicology, Office of Research and Development , United States Environmental Protection Agency , Research Triangle Park , North Carolina 27711 , United States.
Oak Ridge Institute for Science and Education , Oak Ridge , Tennessee 37831 , United States.
ARC Arnot Research and Consulting , 36 Sproat Ave . Toronto , Ontario Canada , M4M 1W4.
Department of Physical & Environmental Sciences , University of Toronto Scarborough 1265 Military Trail , Toronto , Ontario Canada , M1C 1A4.
Department of Pharmacology and Toxicology , University of Toronto , 1 King's College Cir , Toronto , Ontario Canada , M5S 1A8.
Department of Public Health Sciences , University of California , Davis , California 95616 , United States.
National Exposure Research Laboratory, Office of Research and Development , United States Environmental Protection Agency , Research Triangle Park , North Carolina 27711 , United States.
Quantitative Sustainability Assessment Division, Department of Management Engineering , Technical University of Denmark , 2800 Kgs. Lyngby, Denmark.
Department of Environmental Health Sciences, School of Public Health , University of Michigan , Ann Arbor , Michigan 48109 , United States.
Department of Earth and Environmental Sciences , University of Texas , Arlington , Texas 76019 , United States.


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 to 81%, with the error rate for identifying positive chemicals ranging from 17 to 36%. We then used exposure pathways to organize predictions from 13 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 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 1880 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 474572 compounds.

[Available on 2020-01-15]
[Indexed for MEDLINE]

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